From 21653999ea2d0ab6891eb0493016ea57c4996e51 Mon Sep 17 00:00:00 2001
From: Kevin Sheppard <kevin.k.sheppard@gmail.com>
Date: Tue, 26 Sep 2023 14:48:47 +0000
Subject: [PATCH] Github Actions doc build after commit 95d1d659

---
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diff --git a/_images/univariate_univariate_forecasting_with_exogenous_variables_17_0.png b/_images/univariate_univariate_forecasting_with_exogenous_variables_17_0.png
index b3fef7bfea84d69a43c7971cdce5174a08ba0251..b18e20e38e3c64f9afde2086f0c4f1d96046793f 100644
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diff --git a/_images/univariate_univariate_using_fixed_variance_6_0.png b/_images/univariate_univariate_using_fixed_variance_6_0.png
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diff --git a/_images/univariate_univariate_volatility_forecasting_23_1.png b/_images/univariate_univariate_volatility_forecasting_23_1.png
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diff --git a/_modules/arch/bootstrap/base.html b/_modules/arch/bootstrap/base.html
index 8c3992b379..4ded26817a 100644
--- a/_modules/arch/bootstrap/base.html
+++ b/_modules/arch/bootstrap/base.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.base - arch 6.1.0</title>
+        <title>arch.bootstrap.base - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -484,7 +484,9 @@ <h1>Source code for arch.bootstrap.base</h1><div class="highlight"><pre>
     <span class="k">return</span> <span class="n">b_sb</span><span class="p">,</span> <span class="n">b_cb</span>
 
 
-<div class="viewcode-block" id="optimal_block_length"><a class="viewcode-back" href="../../../bootstrap/generated/arch.bootstrap.optimal_block_length.html#arch.bootstrap.optimal_block_length">[docs]</a><span class="k">def</span> <span class="nf">optimal_block_length</span><span class="p">(</span><span class="n">x</span><span class="p">:</span> <span class="n">ArrayLike1D</span> <span class="o">|</span> <span class="n">ArrayLike2D</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
+<div class="viewcode-block" id="optimal_block_length">
+<a class="viewcode-back" href="../../../bootstrap/generated/arch.bootstrap.optimal_block_length.html#arch.bootstrap.optimal_block_length">[docs]</a>
+<span class="k">def</span> <span class="nf">optimal_block_length</span><span class="p">(</span><span class="n">x</span><span class="p">:</span> <span class="n">ArrayLike1D</span> <span class="o">|</span> <span class="n">ArrayLike2D</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Estimate optimal window length for time-series bootstraps</span>
 
@@ -563,6 +565,7 @@ <h1>Source code for arch.bootstrap.base</h1><div class="highlight"><pre>
     <span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">opt</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="n">idx</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;stationary&quot;</span><span class="p">,</span> <span class="s2">&quot;circular&quot;</span><span class="p">])</span></div>
 
 
+
 <span class="k">def</span> <span class="nf">_get_acceleration</span><span class="p">(</span><span class="n">jk_params</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Estimates the BCa acceleration parameter using jackknife estimates</span>
@@ -669,7 +672,9 @@ <h1>Source code for arch.bootstrap.base</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="n">kwargs_copy</span>
 
 
-<div class="viewcode-block" id="IIDBootstrap"><a class="viewcode-back" href="../../../bootstrap/generated/arch.bootstrap.IIDBootstrap.html#arch.bootstrap.IIDBootstrap">[docs]</a><span class="k">class</span> <span class="nc">IIDBootstrap</span><span class="p">(</span><span class="n">metaclass</span><span class="o">=</span><span class="n">DocStringInheritor</span><span class="p">):</span>
+<div class="viewcode-block" id="IIDBootstrap">
+<a class="viewcode-back" href="../../../bootstrap/generated/arch.bootstrap.IIDBootstrap.html#arch.bootstrap.IIDBootstrap">[docs]</a>
+<span class="k">class</span> <span class="nc">IIDBootstrap</span><span class="p">(</span><span class="n">metaclass</span><span class="o">=</span><span class="n">DocStringInheritor</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Bootstrap using uniform resampling</span>
 
@@ -935,7 +940,9 @@ <h1>Source code for arch.bootstrap.base</h1><div class="highlight"><pre>
             <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_generator</span><span class="p">,</span> <span class="n">RandomState</span><span class="p">)</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">_generator</span><span class="o">.</span><span class="n">set_state</span><span class="p">(</span><span class="n">cast</span><span class="p">(</span><span class="n">RandomStateState</span><span class="p">,</span> <span class="n">value</span><span class="p">))</span>
 
-<div class="viewcode-block" id="IIDBootstrap.get_state"><a class="viewcode-back" href="../../../bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.get_state.html#arch.bootstrap.IIDBootstrap.get_state">[docs]</a>    <span class="k">def</span> <span class="nf">get_state</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">RandomStateState</span> <span class="o">|</span> <span class="n">Mapping</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">]:</span>
+<div class="viewcode-block" id="IIDBootstrap.get_state">
+<a class="viewcode-back" href="../../../bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.get_state.html#arch.bootstrap.IIDBootstrap.get_state">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_state</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">RandomStateState</span> <span class="o">|</span> <span class="n">Mapping</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">]:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Gets the state of the bootstrap&#39;s random number generator</span>
 
@@ -951,7 +958,10 @@ <h1>Source code for arch.bootstrap.base</h1><div class="highlight"><pre>
         <span class="p">)</span>
         <span class="k">return</span> <span class="n">_get_prng_state</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_generator</span><span class="p">)</span></div>
 
-<div class="viewcode-block" id="IIDBootstrap.set_state"><a class="viewcode-back" href="../../../bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.set_state.html#arch.bootstrap.IIDBootstrap.set_state">[docs]</a>    <span class="k">def</span> <span class="nf">set_state</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">state</span><span class="p">:</span> <span class="n">RandomStateState</span> <span class="o">|</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+
+<div class="viewcode-block" id="IIDBootstrap.set_state">
+<a class="viewcode-back" href="../../../bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.set_state.html#arch.bootstrap.IIDBootstrap.set_state">[docs]</a>
+    <span class="k">def</span> <span class="nf">set_state</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">state</span><span class="p">:</span> <span class="n">RandomStateState</span> <span class="o">|</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Sets the state of the bootstrap&#39;s random number generator</span>
 
@@ -972,7 +982,10 @@ <h1>Source code for arch.bootstrap.base</h1><div class="highlight"><pre>
             <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_generator</span><span class="p">,</span> <span class="n">RandomState</span><span class="p">)</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">_generator</span><span class="o">.</span><span class="n">set_state</span><span class="p">(</span><span class="n">state</span><span class="p">)</span></div>
 
-<div class="viewcode-block" id="IIDBootstrap.seed"><a class="viewcode-back" href="../../../bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.seed.html#arch.bootstrap.IIDBootstrap.seed">[docs]</a>    <span class="k">def</span> <span class="nf">seed</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">:</span> <span class="nb">int</span> <span class="o">|</span> <span class="nb">list</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">|</span> <span class="n">Uint32Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+
+<div class="viewcode-block" id="IIDBootstrap.seed">
+<a class="viewcode-back" href="../../../bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.seed.html#arch.bootstrap.IIDBootstrap.seed">[docs]</a>
+    <span class="k">def</span> <span class="nf">seed</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">:</span> <span class="nb">int</span> <span class="o">|</span> <span class="nb">list</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">|</span> <span class="n">Uint32Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Reseeds the bootstrap&#39;s random number generator</span>
 
@@ -994,7 +1007,10 @@ <h1>Source code for arch.bootstrap.base</h1><div class="highlight"><pre>
             <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_generator</span><span class="p">,</span> <span class="n">RandomState</span><span class="p">)</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">_generator</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="n">value</span><span class="p">)</span></div>
 
-<div class="viewcode-block" id="IIDBootstrap.reset"><a class="viewcode-back" href="../../../bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.reset.html#arch.bootstrap.IIDBootstrap.reset">[docs]</a>    <span class="k">def</span> <span class="nf">reset</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">use_seed</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+
+<div class="viewcode-block" id="IIDBootstrap.reset">
+<a class="viewcode-back" href="../../../bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.reset.html#arch.bootstrap.IIDBootstrap.reset">[docs]</a>
+    <span class="k">def</span> <span class="nf">reset</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">use_seed</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Resets the bootstrap to either its initial state or the last seed.</span>
 
@@ -1009,7 +1025,10 @@ <h1>Source code for arch.bootstrap.base</h1><div class="highlight"><pre>
         <span class="bp">self</span><span class="o">.</span><span class="n">_resample</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">state</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_initial_state</span></div>
 
-<div class="viewcode-block" id="IIDBootstrap.bootstrap"><a class="viewcode-back" href="../../../bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.bootstrap.html#arch.bootstrap.IIDBootstrap.bootstrap">[docs]</a>    <span class="k">def</span> <span class="nf">bootstrap</span><span class="p">(</span>
+
+<div class="viewcode-block" id="IIDBootstrap.bootstrap">
+<a class="viewcode-back" href="../../../bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.bootstrap.html#arch.bootstrap.IIDBootstrap.bootstrap">[docs]</a>
+    <span class="k">def</span> <span class="nf">bootstrap</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span> <span class="n">reps</span><span class="p">:</span> <span class="nb">int</span>
     <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">PyGenerator</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="n">ArrayLike</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">ArrayLike</span><span class="p">]],</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">]:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
@@ -1052,7 +1071,10 @@ <h1>Source code for arch.bootstrap.base</h1><div class="highlight"><pre>
             <span class="bp">self</span><span class="o">.</span><span class="n">_index</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">update_indices</span><span class="p">()</span>
             <span class="k">yield</span> <span class="bp">self</span><span class="o">.</span><span class="n">_resample</span><span class="p">()</span></div>
 
-<div class="viewcode-block" id="IIDBootstrap.conf_int"><a class="viewcode-back" href="../../../bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.conf_int.html#arch.bootstrap.IIDBootstrap.conf_int">[docs]</a>    <span class="k">def</span> <span class="nf">conf_int</span><span class="p">(</span>
+
+<div class="viewcode-block" id="IIDBootstrap.conf_int">
+<a class="viewcode-back" href="../../../bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.conf_int.html#arch.bootstrap.IIDBootstrap.conf_int">[docs]</a>
+    <span class="k">def</span> <span class="nf">conf_int</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">func</span><span class="p">:</span> <span class="n">Callable</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">Float64Array</span><span class="p">],</span>
         <span class="n">reps</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1000</span><span class="p">,</span>
@@ -1292,6 +1314,7 @@ <h1>Source code for arch.bootstrap.base</h1><div class="highlight"><pre>
 
         <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">((</span><span class="n">lower</span><span class="p">,</span> <span class="n">upper</span><span class="p">))</span></div>
 
+
     <span class="k">def</span> <span class="nf">_check_data</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
         <span class="n">supported</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">)</span>
         <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">arg</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_args</span><span class="p">):</span>
@@ -1322,7 +1345,9 @@ <h1>Source code for arch.bootstrap.base</h1><div class="highlight"><pre>
         <span class="n">jk_params</span> <span class="o">=</span> <span class="n">_loo_jackknife</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="n">nobs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_args</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_kwargs</span><span class="p">,</span> <span class="n">extra_kwags</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">_get_acceleration</span><span class="p">(</span><span class="n">jk_params</span><span class="p">)</span>
 
-<div class="viewcode-block" id="IIDBootstrap.clone"><a class="viewcode-back" href="../../../bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.clone.html#arch.bootstrap.IIDBootstrap.clone">[docs]</a>    <span class="k">def</span> <span class="nf">clone</span><span class="p">(</span>
+<div class="viewcode-block" id="IIDBootstrap.clone">
+<a class="viewcode-back" href="../../../bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.clone.html#arch.bootstrap.IIDBootstrap.clone">[docs]</a>
+    <span class="k">def</span> <span class="nf">clone</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="o">*</span><span class="n">args</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">,</span>
         <span class="n">seed</span><span class="p">:</span> <span class="kc">None</span> <span class="o">|</span> <span class="nb">int</span> <span class="o">|</span> <span class="n">Generator</span> <span class="o">|</span> <span class="n">RandomState</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
@@ -1348,7 +1373,10 @@ <h1>Source code for arch.bootstrap.base</h1><div class="highlight"><pre>
         <span class="n">bs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="n">seed</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">bs</span></div>
 
-<div class="viewcode-block" id="IIDBootstrap.apply"><a class="viewcode-back" href="../../../bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.apply.html#arch.bootstrap.IIDBootstrap.apply">[docs]</a>    <span class="k">def</span> <span class="nf">apply</span><span class="p">(</span>
+
+<div class="viewcode-block" id="IIDBootstrap.apply">
+<a class="viewcode-back" href="../../../bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.apply.html#arch.bootstrap.IIDBootstrap.apply">[docs]</a>
+    <span class="k">def</span> <span class="nf">apply</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">func</span><span class="p">:</span> <span class="n">Callable</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">ArrayLike</span><span class="p">],</span>
         <span class="n">reps</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1000</span><span class="p">,</span>
@@ -1409,6 +1437,7 @@ <h1>Source code for arch.bootstrap.base</h1><div class="highlight"><pre>
             <span class="n">count</span> <span class="o">+=</span> <span class="mi">1</span>
         <span class="k">return</span> <span class="n">results</span></div>
 
+
     <span class="k">def</span> <span class="nf">_construct_bootstrap_estimates</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">func</span><span class="p">:</span> <span class="n">Callable</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">ArrayLike</span><span class="p">],</span>
@@ -1471,7 +1500,9 @@ <h1>Source code for arch.bootstrap.base</h1><div class="highlight"><pre>
         <span class="bp">self</span><span class="o">.</span><span class="n">_results</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">results</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_studentized_results</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">studentized_results</span><span class="p">)</span>
 
-<div class="viewcode-block" id="IIDBootstrap.cov"><a class="viewcode-back" href="../../../bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.cov.html#arch.bootstrap.IIDBootstrap.cov">[docs]</a>    <span class="k">def</span> <span class="nf">cov</span><span class="p">(</span>
+<div class="viewcode-block" id="IIDBootstrap.cov">
+<a class="viewcode-back" href="../../../bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.cov.html#arch.bootstrap.IIDBootstrap.cov">[docs]</a>
+    <span class="k">def</span> <span class="nf">cov</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">func</span><span class="p">:</span> <span class="n">Callable</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">ArrayLike</span><span class="p">],</span>
         <span class="n">reps</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1000</span><span class="p">,</span>
@@ -1552,7 +1583,10 @@ <h1>Source code for arch.bootstrap.base</h1><div class="highlight"><pre>
 
         <span class="k">return</span> <span class="n">errors</span><span class="o">.</span><span class="n">T</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">errors</span><span class="p">)</span> <span class="o">/</span> <span class="n">reps</span></div>
 
-<div class="viewcode-block" id="IIDBootstrap.var"><a class="viewcode-back" href="../../../bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.var.html#arch.bootstrap.IIDBootstrap.var">[docs]</a>    <span class="k">def</span> <span class="nf">var</span><span class="p">(</span>
+
+<div class="viewcode-block" id="IIDBootstrap.var">
+<a class="viewcode-back" href="../../../bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.var.html#arch.bootstrap.IIDBootstrap.var">[docs]</a>
+    <span class="k">def</span> <span class="nf">var</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">func</span><span class="p">:</span> <span class="n">Callable</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">ArrayLike</span><span class="p">],</span>
         <span class="n">reps</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1000</span><span class="p">,</span>
@@ -1633,7 +1667,10 @@ <h1>Source code for arch.bootstrap.base</h1><div class="highlight"><pre>
 
         <span class="k">return</span> <span class="p">(</span><span class="n">errors</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">/</span> <span class="n">reps</span></div>
 
-<div class="viewcode-block" id="IIDBootstrap.update_indices"><a class="viewcode-back" href="../../../bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.update_indices.html#arch.bootstrap.IIDBootstrap.update_indices">[docs]</a>    <span class="k">def</span> <span class="nf">update_indices</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">BootstrapIndexT</span><span class="p">:</span>
+
+<div class="viewcode-block" id="IIDBootstrap.update_indices">
+<a class="viewcode-back" href="../../../bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.update_indices.html#arch.bootstrap.IIDBootstrap.update_indices">[docs]</a>
+    <span class="k">def</span> <span class="nf">update_indices</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">BootstrapIndexT</span><span class="p">:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Update indices for the next iteration of the bootstrap.  This must</span>
 <span class="sd">        be overridden when creating new bootstraps.</span>
@@ -1642,6 +1679,7 @@ <h1>Source code for arch.bootstrap.base</h1><div class="highlight"><pre>
             <span class="bp">self</span><span class="o">.</span><span class="n">_generator</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_num_items</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_num_items</span>
         <span class="p">)</span></div>
 
+
     <span class="k">def</span> <span class="nf">_resample</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="n">ArrayLike</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">ArrayLike</span><span class="p">]]:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Resample all data using the values in _index</span>
@@ -1671,7 +1709,10 @@ <h1>Source code for arch.bootstrap.base</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span></div>
 
 
-<div class="viewcode-block" id="IndependentSamplesBootstrap"><a class="viewcode-back" href="../../../bootstrap/generated/arch.bootstrap.IndependentSamplesBootstrap.html#arch.bootstrap.IndependentSamplesBootstrap">[docs]</a><span class="k">class</span> <span class="nc">IndependentSamplesBootstrap</span><span class="p">(</span><span class="n">IIDBootstrap</span><span class="p">):</span>
+
+<div class="viewcode-block" id="IndependentSamplesBootstrap">
+<a class="viewcode-back" href="../../../bootstrap/generated/arch.bootstrap.IndependentSamplesBootstrap.html#arch.bootstrap.IndependentSamplesBootstrap">[docs]</a>
+<span class="k">class</span> <span class="nc">IndependentSamplesBootstrap</span><span class="p">(</span><span class="n">IIDBootstrap</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Bootstrap where each input is independently resampled</span>
 
@@ -1757,7 +1798,9 @@ <h1>Source code for arch.bootstrap.base</h1><div class="highlight"><pre>
         <span class="bp">self</span><span class="o">.</span><span class="n">_num_arg_items</span> <span class="o">=</span> <span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="n">arg</span><span class="p">)</span> <span class="k">for</span> <span class="n">arg</span> <span class="ow">in</span> <span class="n">args</span><span class="p">]</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_num_kw_items</span> <span class="o">=</span> <span class="p">{</span><span class="n">key</span><span class="p">:</span> <span class="nb">len</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="n">key</span><span class="p">])</span> <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_kwargs</span><span class="p">}</span>
 
-<div class="viewcode-block" id="IndependentSamplesBootstrap.update_indices"><a class="viewcode-back" href="../../../bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.update_indices.html#arch.bootstrap.IndependentSamplesBootstrap.update_indices">[docs]</a>    <span class="k">def</span> <span class="nf">update_indices</span><span class="p">(</span>
+<div class="viewcode-block" id="IndependentSamplesBootstrap.update_indices">
+<a class="viewcode-back" href="../../../bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.update_indices.html#arch.bootstrap.IndependentSamplesBootstrap.update_indices">[docs]</a>
+    <span class="k">def</span> <span class="nf">update_indices</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
     <span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">list</span><span class="p">[</span><span class="n">Int64Array</span><span class="p">],</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Int64Array</span><span class="p">]]:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
@@ -1778,6 +1821,7 @@ <h1>Source code for arch.bootstrap.base</h1><div class="highlight"><pre>
         <span class="p">}</span>
         <span class="k">return</span> <span class="n">pos_indices</span><span class="p">,</span> <span class="n">kw_indices</span></div>
 
+
     <span class="nd">@property</span>
     <span class="k">def</span> <span class="nf">index</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">BootstrapIndexT</span><span class="p">:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
@@ -1792,7 +1836,9 @@ <h1>Source code for arch.bootstrap.base</h1><div class="highlight"><pre>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_index</span>
 
-<div class="viewcode-block" id="IndependentSamplesBootstrap.reset"><a class="viewcode-back" href="../../../bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.reset.html#arch.bootstrap.IndependentSamplesBootstrap.reset">[docs]</a>    <span class="k">def</span> <span class="nf">reset</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">use_seed</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+<div class="viewcode-block" id="IndependentSamplesBootstrap.reset">
+<a class="viewcode-back" href="../../../bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.reset.html#arch.bootstrap.IndependentSamplesBootstrap.reset">[docs]</a>
+    <span class="k">def</span> <span class="nf">reset</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">use_seed</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Resets the bootstrap to either its initial state or the last seed.</span>
 
@@ -1809,6 +1855,7 @@ <h1>Source code for arch.bootstrap.base</h1><div class="highlight"><pre>
         <span class="bp">self</span><span class="o">.</span><span class="n">_resample</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">state</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_initial_state</span></div>
 
+
     <span class="k">def</span> <span class="nf">_resample</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="n">ArrayLike</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">ArrayLike</span><span class="p">]]:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Resample all data using the values in _index</span>
@@ -1839,7 +1886,10 @@ <h1>Source code for arch.bootstrap.base</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span></div>
 
 
-<div class="viewcode-block" id="CircularBlockBootstrap"><a class="viewcode-back" href="../../../bootstrap/generated/arch.bootstrap.CircularBlockBootstrap.html#arch.bootstrap.CircularBlockBootstrap">[docs]</a><span class="k">class</span> <span class="nc">CircularBlockBootstrap</span><span class="p">(</span><span class="n">IIDBootstrap</span><span class="p">):</span>
+
+<div class="viewcode-block" id="CircularBlockBootstrap">
+<a class="viewcode-back" href="../../../bootstrap/generated/arch.bootstrap.CircularBlockBootstrap.html#arch.bootstrap.CircularBlockBootstrap">[docs]</a>
+<span class="k">class</span> <span class="nc">CircularBlockBootstrap</span><span class="p">(</span><span class="n">IIDBootstrap</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Bootstrap using blocks of the same length with end-to-start wrap around</span>
 
@@ -1933,7 +1983,9 @@ <h1>Source code for arch.bootstrap.base</h1><div class="highlight"><pre>
         <span class="bp">self</span><span class="o">.</span><span class="n">block_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">block_size</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_parameters</span> <span class="o">=</span> <span class="p">[</span><span class="n">block_size</span><span class="p">]</span>
 
-<div class="viewcode-block" id="CircularBlockBootstrap.clone"><a class="viewcode-back" href="../../../bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.clone.html#arch.bootstrap.CircularBlockBootstrap.clone">[docs]</a>    <span class="k">def</span> <span class="nf">clone</span><span class="p">(</span>
+<div class="viewcode-block" id="CircularBlockBootstrap.clone">
+<a class="viewcode-back" href="../../../bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.clone.html#arch.bootstrap.CircularBlockBootstrap.clone">[docs]</a>
+    <span class="k">def</span> <span class="nf">clone</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="o">*</span><span class="n">args</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">,</span>
         <span class="n">seed</span><span class="p">:</span> <span class="kc">None</span> <span class="o">|</span> <span class="nb">int</span> <span class="o">|</span> <span class="n">Generator</span> <span class="o">|</span> <span class="n">RandomState</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
@@ -1959,6 +2011,7 @@ <h1>Source code for arch.bootstrap.base</h1><div class="highlight"><pre>
         <span class="n">block_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_parameters</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="p">(</span><span class="n">block_size</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="n">seed</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
 
+
     <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
         <span class="n">txt</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_name</span>
         <span class="n">txt</span> <span class="o">+=</span> <span class="s2">&quot;(block size: &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">block_size</span><span class="p">)</span>
@@ -1974,7 +2027,9 @@ <h1>Source code for arch.bootstrap.base</h1><div class="highlight"><pre>
         <span class="n">html</span> <span class="o">+=</span> <span class="s2">&quot;, &lt;strong&gt;ID&lt;/strong&gt;: &quot;</span> <span class="o">+</span> <span class="nb">hex</span><span class="p">(</span><span class="nb">id</span><span class="p">(</span><span class="bp">self</span><span class="p">))</span> <span class="o">+</span> <span class="s2">&quot;)&quot;</span>
         <span class="k">return</span> <span class="n">html</span>
 
-<div class="viewcode-block" id="CircularBlockBootstrap.update_indices"><a class="viewcode-back" href="../../../bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.update_indices.html#arch.bootstrap.CircularBlockBootstrap.update_indices">[docs]</a>    <span class="k">def</span> <span class="nf">update_indices</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Int64Array</span><span class="p">:</span>
+<div class="viewcode-block" id="CircularBlockBootstrap.update_indices">
+<a class="viewcode-back" href="../../../bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.update_indices.html#arch.bootstrap.CircularBlockBootstrap.update_indices">[docs]</a>
+    <span class="k">def</span> <span class="nf">update_indices</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Int64Array</span><span class="p">:</span>
         <span class="n">num_blocks</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_num_items</span> <span class="o">//</span> <span class="bp">self</span><span class="o">.</span><span class="n">block_size</span>
         <span class="k">if</span> <span class="n">num_blocks</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">block_size</span> <span class="o">&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">_num_items</span><span class="p">:</span>
             <span class="n">num_blocks</span> <span class="o">+=</span> <span class="mi">1</span>
@@ -1988,10 +2043,14 @@ <h1>Source code for arch.bootstrap.base</h1><div class="highlight"><pre>
         <span class="k">if</span> <span class="n">indices</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">_num_items</span><span class="p">:</span>
             <span class="k">return</span> <span class="n">indices</span><span class="p">[:</span> <span class="bp">self</span><span class="o">.</span><span class="n">_num_items</span><span class="p">]</span>
         <span class="k">else</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">indices</span></div></div>
+            <span class="k">return</span> <span class="n">indices</span></div>
+</div>
+
 
 
-<div class="viewcode-block" id="StationaryBootstrap"><a class="viewcode-back" href="../../../bootstrap/generated/arch.bootstrap.StationaryBootstrap.html#arch.bootstrap.StationaryBootstrap">[docs]</a><span class="k">class</span> <span class="nc">StationaryBootstrap</span><span class="p">(</span><span class="n">CircularBlockBootstrap</span><span class="p">):</span>
+<div class="viewcode-block" id="StationaryBootstrap">
+<a class="viewcode-back" href="../../../bootstrap/generated/arch.bootstrap.StationaryBootstrap.html#arch.bootstrap.StationaryBootstrap">[docs]</a>
+<span class="k">class</span> <span class="nc">StationaryBootstrap</span><span class="p">(</span><span class="n">CircularBlockBootstrap</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Politis and Romano (1994) bootstrap with expon distributed block sizes</span>
 
@@ -2086,7 +2145,9 @@ <h1>Source code for arch.bootstrap.base</h1><div class="highlight"><pre>
         <span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_p</span> <span class="o">=</span> <span class="mf">1.0</span> <span class="o">/</span> <span class="n">block_size</span>
 
-<div class="viewcode-block" id="StationaryBootstrap.update_indices"><a class="viewcode-back" href="../../../bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.update_indices.html#arch.bootstrap.StationaryBootstrap.update_indices">[docs]</a>    <span class="k">def</span> <span class="nf">update_indices</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Int64Array</span><span class="p">:</span>
+<div class="viewcode-block" id="StationaryBootstrap.update_indices">
+<a class="viewcode-back" href="../../../bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.update_indices.html#arch.bootstrap.StationaryBootstrap.update_indices">[docs]</a>
+    <span class="k">def</span> <span class="nf">update_indices</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Int64Array</span><span class="p">:</span>
         <span class="n">indices</span> <span class="o">=</span> <span class="n">_get_random_integers</span><span class="p">(</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">_generator</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_num_items</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_num_items</span>
         <span class="p">)</span>
@@ -2096,10 +2157,14 @@ <h1>Source code for arch.bootstrap.base</h1><div class="highlight"><pre>
         <span class="k">else</span><span class="p">:</span>
             <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_generator</span><span class="p">,</span> <span class="n">RandomState</span><span class="p">)</span>
             <span class="n">u</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_generator</span><span class="o">.</span><span class="n">random_sample</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_num_items</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">stationary_bootstrap_sample</span><span class="p">(</span><span class="n">indices</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p</span><span class="p">)</span></div></div>
+        <span class="k">return</span> <span class="n">stationary_bootstrap_sample</span><span class="p">(</span><span class="n">indices</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p</span><span class="p">)</span></div>
+</div>
 
 
-<div class="viewcode-block" id="MovingBlockBootstrap"><a class="viewcode-back" href="../../../bootstrap/generated/arch.bootstrap.MovingBlockBootstrap.html#arch.bootstrap.MovingBlockBootstrap">[docs]</a><span class="k">class</span> <span class="nc">MovingBlockBootstrap</span><span class="p">(</span><span class="n">CircularBlockBootstrap</span><span class="p">):</span>
+
+<div class="viewcode-block" id="MovingBlockBootstrap">
+<a class="viewcode-back" href="../../../bootstrap/generated/arch.bootstrap.MovingBlockBootstrap.html#arch.bootstrap.MovingBlockBootstrap">[docs]</a>
+<span class="k">class</span> <span class="nc">MovingBlockBootstrap</span><span class="p">(</span><span class="n">CircularBlockBootstrap</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Bootstrap using blocks of the same length without wrap around</span>
 
@@ -2195,7 +2260,9 @@ <h1>Source code for arch.bootstrap.base</h1><div class="highlight"><pre>
             <span class="n">block_size</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="n">seed</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span>
         <span class="p">)</span>
 
-<div class="viewcode-block" id="MovingBlockBootstrap.update_indices"><a class="viewcode-back" href="../../../bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.update_indices.html#arch.bootstrap.MovingBlockBootstrap.update_indices">[docs]</a>    <span class="k">def</span> <span class="nf">update_indices</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Int64Array</span><span class="p">:</span>
+<div class="viewcode-block" id="MovingBlockBootstrap.update_indices">
+<a class="viewcode-back" href="../../../bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.update_indices.html#arch.bootstrap.MovingBlockBootstrap.update_indices">[docs]</a>
+    <span class="k">def</span> <span class="nf">update_indices</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Int64Array</span><span class="p">:</span>
         <span class="n">num_blocks</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_num_items</span> <span class="o">//</span> <span class="bp">self</span><span class="o">.</span><span class="n">block_size</span>
         <span class="k">if</span> <span class="n">num_blocks</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">block_size</span> <span class="o">&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">_num_items</span><span class="p">:</span>
             <span class="n">num_blocks</span> <span class="o">+=</span> <span class="mi">1</span>
@@ -2207,7 +2274,9 @@ <h1>Source code for arch.bootstrap.base</h1><div class="highlight"><pre>
         <span class="k">if</span> <span class="n">indices</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">_num_items</span><span class="p">:</span>
             <span class="k">return</span> <span class="n">indices</span><span class="p">[:</span> <span class="bp">self</span><span class="o">.</span><span class="n">_num_items</span><span class="p">]</span>
         <span class="k">else</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">indices</span></div></div>
+            <span class="k">return</span> <span class="n">indices</span></div>
+</div>
+
 
 
 <span class="k">class</span> <span class="nc">MOONBootstrap</span><span class="p">(</span><span class="n">IIDBootstrap</span><span class="p">):</span>  <span class="c1"># pragma: no cover</span>
@@ -2259,7 +2328,7 @@ <h1>Source code for arch.bootstrap.base</h1><div class="highlight"><pre>
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -2306,7 +2375,7 @@ <h1>Source code for arch.bootstrap.base</h1><div class="highlight"><pre>
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/_modules/arch/bootstrap/multiple_comparison.html b/_modules/arch/bootstrap/multiple_comparison.html
index d121a01589..63ceecdfab 100644
--- a/_modules/arch/bootstrap/multiple_comparison.html
+++ b/_modules/arch/bootstrap/multiple_comparison.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.multiple_comparison - arch 6.1.0</title>
+        <title>arch.bootstrap.multiple_comparison - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -447,7 +447,9 @@ <h1>Source code for arch.bootstrap.multiple_comparison</h1><div class="highlight
         <span class="bp">self</span><span class="o">.</span><span class="n">bootstrap</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
 
 
-<div class="viewcode-block" id="MCS"><a class="viewcode-back" href="../../../multiple-comparison/generated/arch.bootstrap.MCS.html#arch.bootstrap.MCS">[docs]</a><span class="k">class</span> <span class="nc">MCS</span><span class="p">(</span><span class="n">MultipleComparison</span><span class="p">):</span>
+<div class="viewcode-block" id="MCS">
+<a class="viewcode-back" href="../../../multiple-comparison/generated/arch.bootstrap.MCS.html#arch.bootstrap.MCS">[docs]</a>
+<span class="k">class</span> <span class="nc">MCS</span><span class="p">(</span><span class="n">MultipleComparison</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Model Confidence Set (MCS) of Hansen, Lunde and Nason.</span>
 
@@ -562,7 +564,9 @@ <h1>Source code for arch.bootstrap.multiple_comparison</h1><div class="highlight
         <span class="n">mcs</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Index</span><span class="p">(</span><span class="n">model_index</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">mcs</span>
 
-<div class="viewcode-block" id="MCS.compute"><a class="viewcode-back" href="../../../multiple-comparison/generated/generated/arch.bootstrap.MCS.compute.html#arch.bootstrap.MCS.compute">[docs]</a>    <span class="k">def</span> <span class="nf">compute</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+<div class="viewcode-block" id="MCS.compute">
+<a class="viewcode-back" href="../../../multiple-comparison/generated/generated/arch.bootstrap.MCS.compute.html#arch.bootstrap.MCS.compute">[docs]</a>
+    <span class="k">def</span> <span class="nf">compute</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Compute the set of models in the confidence set.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
@@ -572,6 +576,7 @@ <h1>Source code for arch.bootstrap.multiple_comparison</h1><div class="highlight
             <span class="bp">self</span><span class="o">.</span><span class="n">_compute_max</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_results_computed</span> <span class="o">=</span> <span class="kc">True</span></div>
 
+
     <span class="k">def</span> <span class="nf">_compute_r</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Computes the model confidence set using the R method</span>
@@ -730,7 +735,10 @@ <h1>Source code for arch.bootstrap.multiple_comparison</h1><div class="highlight
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pvalues</span></div>
 
 
-<div class="viewcode-block" id="StepM"><a class="viewcode-back" href="../../../multiple-comparison/generated/arch.bootstrap.StepM.html#arch.bootstrap.StepM">[docs]</a><span class="k">class</span> <span class="nc">StepM</span><span class="p">(</span><span class="n">MultipleComparison</span><span class="p">):</span>
+
+<div class="viewcode-block" id="StepM">
+<a class="viewcode-back" href="../../../multiple-comparison/generated/arch.bootstrap.StepM.html#arch.bootstrap.StepM">[docs]</a>
+<span class="k">class</span> <span class="nc">StepM</span><span class="p">(</span><span class="n">MultipleComparison</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    StepM multiple comparison procedure of Romano and Wolf.</span>
 
@@ -833,7 +841,9 @@ <h1>Source code for arch.bootstrap.multiple_comparison</h1><div class="highlight
             <span class="p">]</span>
         <span class="p">)</span>
 
-<div class="viewcode-block" id="StepM.compute"><a class="viewcode-back" href="../../../multiple-comparison/generated/generated/arch.bootstrap.StepM.compute.html#arch.bootstrap.StepM.compute">[docs]</a>    <span class="k">def</span> <span class="nf">compute</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+<div class="viewcode-block" id="StepM.compute">
+<a class="viewcode-back" href="../../../multiple-comparison/generated/generated/arch.bootstrap.StepM.compute.html#arch.bootstrap.StepM.compute">[docs]</a>
+    <span class="k">def</span> <span class="nf">compute</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Compute the set of superior models.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
@@ -861,6 +871,7 @@ <h1>Source code for arch.bootstrap.multiple_comparison</h1><div class="highlight
         <span class="n">all_better_models</span><span class="o">.</span><span class="n">sort</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_superior_models</span> <span class="o">=</span> <span class="n">all_better_models</span></div>
 
+
     <span class="nd">@property</span>
     <span class="k">def</span> <span class="nf">superior_models</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="n">Hashable</span><span class="p">]:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
@@ -878,7 +889,10 @@ <h1>Source code for arch.bootstrap.multiple_comparison</h1><div class="highlight
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_superior_models</span></div>
 
 
-<div class="viewcode-block" id="SPA"><a class="viewcode-back" href="../../../multiple-comparison/generated/arch.bootstrap.SPA.html#arch.bootstrap.SPA">[docs]</a><span class="k">class</span> <span class="nc">SPA</span><span class="p">(</span><span class="n">MultipleComparison</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">DocStringInheritor</span><span class="p">):</span>
+
+<div class="viewcode-block" id="SPA">
+<a class="viewcode-back" href="../../../multiple-comparison/generated/arch.bootstrap.SPA.html#arch.bootstrap.SPA">[docs]</a>
+<span class="k">class</span> <span class="nc">SPA</span><span class="p">(</span><span class="n">MultipleComparison</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">DocStringInheritor</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Test of Superior Predictive Ability (SPA) of White and Hansen.</span>
 
@@ -994,14 +1008,19 @@ <h1>Source code for arch.bootstrap.multiple_comparison</h1><div class="highlight
             <span class="p">]</span>
         <span class="p">)</span>
 
-<div class="viewcode-block" id="SPA.reset"><a class="viewcode-back" href="../../../multiple-comparison/generated/generated/arch.bootstrap.SPA.reset.html#arch.bootstrap.SPA.reset">[docs]</a>    <span class="k">def</span> <span class="nf">reset</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+<div class="viewcode-block" id="SPA.reset">
+<a class="viewcode-back" href="../../../multiple-comparison/generated/generated/arch.bootstrap.SPA.reset.html#arch.bootstrap.SPA.reset">[docs]</a>
+    <span class="k">def</span> <span class="nf">reset</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Reset the bootstrap to its initial state.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_pvalues</span> <span class="o">=</span> <span class="p">{}</span></div>
 
-<div class="viewcode-block" id="SPA.subset"><a class="viewcode-back" href="../../../multiple-comparison/generated/generated/arch.bootstrap.SPA.subset.html#arch.bootstrap.SPA.subset">[docs]</a>    <span class="k">def</span> <span class="nf">subset</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">selector</span><span class="p">:</span> <span class="n">BoolArray</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+
+<div class="viewcode-block" id="SPA.subset">
+<a class="viewcode-back" href="../../../multiple-comparison/generated/generated/arch.bootstrap.SPA.subset.html#arch.bootstrap.SPA.subset">[docs]</a>
+    <span class="k">def</span> <span class="nf">subset</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">selector</span><span class="p">:</span> <span class="n">BoolArray</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Sets a list of active models to run the SPA on.  Primarily for</span>
 <span class="sd">        internal use.</span>
@@ -1014,7 +1033,10 @@ <h1>Source code for arch.bootstrap.multiple_comparison</h1><div class="highlight
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_selector</span> <span class="o">=</span> <span class="n">selector</span></div>
 
-<div class="viewcode-block" id="SPA.compute"><a class="viewcode-back" href="../../../multiple-comparison/generated/generated/arch.bootstrap.SPA.compute.html#arch.bootstrap.SPA.compute">[docs]</a>    <span class="k">def</span> <span class="nf">compute</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+
+<div class="viewcode-block" id="SPA.compute">
+<a class="viewcode-back" href="../../../multiple-comparison/generated/generated/arch.bootstrap.SPA.compute.html#arch.bootstrap.SPA.compute">[docs]</a>
+    <span class="k">def</span> <span class="nf">compute</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Compute the bootstrap pvalue.</span>
 
@@ -1039,6 +1061,7 @@ <h1>Source code for arch.bootstrap.multiple_comparison</h1><div class="highlight
             <span class="p">[(</span><span class="s2">&quot;lower&quot;</span><span class="p">,</span> <span class="n">pvalues</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="p">(</span><span class="s2">&quot;consistent&quot;</span><span class="p">,</span> <span class="n">pvalues</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="p">(</span><span class="s2">&quot;upper&quot;</span><span class="p">,</span> <span class="n">pvalues</span><span class="p">[</span><span class="mi">2</span><span class="p">])]</span>
         <span class="p">)</span></div>
 
+
     <span class="k">def</span> <span class="nf">_simulate_values</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_compute_variance</span><span class="p">()</span>
         <span class="c1"># 2. Compute invalid columns using criteria for consistent</span>
@@ -1120,7 +1143,9 @@ <h1>Source code for arch.bootstrap.multiple_comparison</h1><div class="highlight
         <span class="bp">self</span><span class="o">.</span><span class="n">_check_compute</span><span class="p">()</span>
         <span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_pvalues</span><span class="o">.</span><span class="n">values</span><span class="p">()),</span> <span class="n">index</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_pvalues</span><span class="o">.</span><span class="n">keys</span><span class="p">()))</span>
 
-<div class="viewcode-block" id="SPA.critical_values"><a class="viewcode-back" href="../../../multiple-comparison/generated/generated/arch.bootstrap.SPA.critical_values.html#arch.bootstrap.SPA.critical_values">[docs]</a>    <span class="k">def</span> <span class="nf">critical_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pvalue</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.05</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">:</span>
+<div class="viewcode-block" id="SPA.critical_values">
+<a class="viewcode-back" href="../../../multiple-comparison/generated/generated/arch.bootstrap.SPA.critical_values.html#arch.bootstrap.SPA.critical_values">[docs]</a>
+    <span class="k">def</span> <span class="nf">critical_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pvalue</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.05</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Returns data-dependent critical values</span>
 
@@ -1145,7 +1170,10 @@ <h1>Source code for arch.bootstrap.multiple_comparison</h1><div class="highlight
         <span class="n">crit_vals</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">max_simulated_values</span><span class="p">,</span> <span class="mf">100.0</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">pvalue</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">crit_vals</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_pvalues</span><span class="o">.</span><span class="n">keys</span><span class="p">()))</span></div>
 
-<div class="viewcode-block" id="SPA.better_models"><a class="viewcode-back" href="../../../multiple-comparison/generated/generated/arch.bootstrap.SPA.better_models.html#arch.bootstrap.SPA.better_models">[docs]</a>    <span class="k">def</span> <span class="nf">better_models</span><span class="p">(</span>
+
+<div class="viewcode-block" id="SPA.better_models">
+<a class="viewcode-back" href="../../../multiple-comparison/generated/generated/arch.bootstrap.SPA.better_models.html#arch.bootstrap.SPA.better_models">[docs]</a>
+    <span class="k">def</span> <span class="nf">better_models</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">pvalue</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.05</span><span class="p">,</span>
         <span class="n">pvalue_type</span><span class="p">:</span> <span class="n">Literal</span><span class="p">[</span><span class="s2">&quot;lower&quot;</span><span class="p">,</span> <span class="s2">&quot;consistent&quot;</span><span class="p">,</span> <span class="s2">&quot;upper&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;consistent&quot;</span><span class="p">,</span>
@@ -1184,6 +1212,7 @@ <h1>Source code for arch.bootstrap.multiple_comparison</h1><div class="highlight
         <span class="k">else</span><span class="p">:</span>
             <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">argwhere</span><span class="p">(</span><span class="n">better_models</span><span class="p">)</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span></div>
 
+
     <span class="k">def</span> <span class="nf">_check_compute</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pvalues</span><span class="p">:</span>
             <span class="k">return</span>
@@ -1191,6 +1220,7 @@ <h1>Source code for arch.bootstrap.multiple_comparison</h1><div class="highlight
         <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="n">msg</span><span class="p">)</span></div>
 
 
+
 <span class="k">class</span> <span class="nc">RealityCheck</span><span class="p">(</span><span class="n">SPA</span><span class="p">):</span>
     <span class="c1"># Shallow clone of SPA</span>
     <span class="k">pass</span>
@@ -1225,7 +1255,7 @@ <h1>Source code for arch.bootstrap.multiple_comparison</h1><div class="highlight
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1272,7 +1302,7 @@ <h1>Source code for arch.bootstrap.multiple_comparison</h1><div class="highlight
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/_modules/arch/covariance/kernel.html b/_modules/arch/covariance/kernel.html
index 4f82f80412..eee4c96bde 100644
--- a/_modules/arch/covariance/kernel.html
+++ b/_modules/arch/covariance/kernel.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel - arch 6.1.0</title>
+        <title>arch.covariance.kernel - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -410,7 +410,9 @@ <h1>Source code for arch.covariance.kernel</h1><div class="highlight"><pre>
 <span class="p">]</span>
 
 
-<div class="viewcode-block" id="CovarianceEstimate"><a class="viewcode-back" href="../../../covariance/generated/arch.covariance.kernel.CovarianceEstimate.html#arch.covariance.kernel.CovarianceEstimate">[docs]</a><span class="k">class</span> <span class="nc">CovarianceEstimate</span><span class="p">:</span>
+<div class="viewcode-block" id="CovarianceEstimate">
+<a class="viewcode-back" href="../../../covariance/generated/arch.covariance.kernel.CovarianceEstimate.html#arch.covariance.kernel.CovarianceEstimate">[docs]</a>
+<span class="k">class</span> <span class="nc">CovarianceEstimate</span><span class="p">:</span>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Covariance estimate using a long-run covariance estimator</span>
 
@@ -502,6 +504,7 @@ <h1>Source code for arch.covariance.kernel</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_wrap</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_oss</span><span class="p">)</span></div>
 
 
+
 <span class="k">class</span> <span class="nc">CovarianceEstimator</span><span class="p">(</span><span class="n">ABC</span><span class="p">):</span>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    %(kernel_name)s kernel covariance estimation.</span>
@@ -784,7 +787,9 @@ <h1>Source code for arch.covariance.kernel</h1><div class="highlight"><pre>
 <span class="s2">&quot;&quot;&quot;</span>
 
 
-<div class="viewcode-block" id="Bartlett"><a class="viewcode-back" href="../../../covariance/generated/arch.covariance.kernel.Bartlett.html#arch.covariance.kernel.Bartlett">[docs]</a><span class="nd">@Substitution</span><span class="p">(</span><span class="n">kernel_name</span><span class="o">=</span><span class="s2">&quot;Bartlett&#39;s (Newey-West)&quot;</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="n">_bartlett_formula</span><span class="p">)</span>
+<div class="viewcode-block" id="Bartlett">
+<a class="viewcode-back" href="../../../covariance/generated/arch.covariance.kernel.Bartlett.html#arch.covariance.kernel.Bartlett">[docs]</a>
+<span class="nd">@Substitution</span><span class="p">(</span><span class="n">kernel_name</span><span class="o">=</span><span class="s2">&quot;Bartlett&#39;s (Newey-West)&quot;</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="n">_bartlett_formula</span><span class="p">)</span>
 <span class="k">class</span> <span class="nc">Bartlett</span><span class="p">(</span><span class="n">CovarianceEstimator</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
     <span class="nd">@property</span>
     <span class="k">def</span> <span class="nf">kernel_const</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
@@ -803,6 +808,7 @@ <h1>Source code for arch.covariance.kernel</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="p">(</span><span class="n">bw</span> <span class="o">+</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">bw</span> <span class="o">+</span> <span class="mi">1</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="s2">&quot;double&quot;</span><span class="p">))</span> <span class="o">/</span> <span class="p">(</span><span class="n">bw</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span></div>
 
 
+
 <span class="n">_parzen_formula</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;</span><span class="se">\</span>
 <span class="s2">w=</span><span class="se">\\</span><span class="s2">begin</span><span class="si">{cases}</span><span class="se">\</span>
 <span class="s2">1-6z^</span><span class="si">{2}</span><span class="se">\\</span><span class="s2">left(1-z</span><span class="se">\\</span><span class="s2">right) &amp; z</span><span class="se">\\</span><span class="s2">leq</span><span class="se">\\</span><span class="s2">frac</span><span class="si">{1}{2}</span><span class="s2"> </span><span class="se">\\\\</span><span class="s2"> </span><span class="se">\</span>
@@ -812,7 +818,9 @@ <h1>Source code for arch.covariance.kernel</h1><div class="highlight"><pre>
 <span class="s2">&quot;&quot;&quot;</span>
 
 
-<div class="viewcode-block" id="Parzen"><a class="viewcode-back" href="../../../covariance/generated/arch.covariance.kernel.Parzen.html#arch.covariance.kernel.Parzen">[docs]</a><span class="nd">@Substitution</span><span class="p">(</span><span class="n">kernel_name</span><span class="o">=</span><span class="s2">&quot;Parzen&#39;s&quot;</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="n">_parzen_formula</span><span class="p">)</span>
+<div class="viewcode-block" id="Parzen">
+<a class="viewcode-back" href="../../../covariance/generated/arch.covariance.kernel.Parzen.html#arch.covariance.kernel.Parzen">[docs]</a>
+<span class="nd">@Substitution</span><span class="p">(</span><span class="n">kernel_name</span><span class="o">=</span><span class="s2">&quot;Parzen&#39;s&quot;</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="n">_parzen_formula</span><span class="p">)</span>
 <span class="k">class</span> <span class="nc">Parzen</span><span class="p">(</span><span class="n">CovarianceEstimator</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
     <span class="nd">@property</span>
     <span class="k">def</span> <span class="nf">kernel_const</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
@@ -836,6 +844,7 @@ <h1>Source code for arch.covariance.kernel</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="n">w</span></div>
 
 
+
 <span class="n">_parzen_reisz_formula</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;</span><span class="se">\</span>
 <span class="s2">w=</span><span class="se">\\</span><span class="s2">begin</span><span class="si">{cases}</span><span class="s2"> </span><span class="se">\</span>
 <span class="s2">1-z^2 &amp; z</span><span class="se">\\</span><span class="s2">leq1 </span><span class="se">\\\\</span><span class="s2"> </span><span class="se">\</span>
@@ -844,7 +853,9 @@ <h1>Source code for arch.covariance.kernel</h1><div class="highlight"><pre>
 <span class="s2">&quot;&quot;&quot;</span>
 
 
-<div class="viewcode-block" id="ParzenRiesz"><a class="viewcode-back" href="../../../covariance/generated/arch.covariance.kernel.ParzenRiesz.html#arch.covariance.kernel.ParzenRiesz">[docs]</a><span class="nd">@Substitution</span><span class="p">(</span><span class="n">kernel_name</span><span class="o">=</span><span class="s2">&quot;Parzen-Reisz&quot;</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="n">_parzen_reisz_formula</span><span class="p">)</span>
+<div class="viewcode-block" id="ParzenRiesz">
+<a class="viewcode-back" href="../../../covariance/generated/arch.covariance.kernel.ParzenRiesz.html#arch.covariance.kernel.ParzenRiesz">[docs]</a>
+<span class="nd">@Substitution</span><span class="p">(</span><span class="n">kernel_name</span><span class="o">=</span><span class="s2">&quot;Parzen-Reisz&quot;</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="n">_parzen_reisz_formula</span><span class="p">)</span>
 <span class="k">class</span> <span class="nc">ParzenRiesz</span><span class="p">(</span><span class="n">CovarianceEstimator</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
     <span class="nd">@property</span>
     <span class="k">def</span> <span class="nf">kernel_const</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
@@ -864,6 +875,7 @@ <h1>Source code for arch.covariance.kernel</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">x</span><span class="o">**</span><span class="mi">2</span></div>
 
 
+
 <span class="n">_parzen_geometric_formula</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;</span><span class="se">\</span>
 <span class="s2">w=</span><span class="se">\\</span><span class="s2">begin</span><span class="si">{cases}</span><span class="s2"> </span><span class="se">\</span>
 <span class="se">\\</span><span class="s2">frac</span><span class="si">{1}</span><span class="s2">{1+z} &amp; z</span><span class="se">\\</span><span class="s2">leq1 </span><span class="se">\\\\</span><span class="s2"> </span><span class="se">\</span>
@@ -872,7 +884,9 @@ <h1>Source code for arch.covariance.kernel</h1><div class="highlight"><pre>
 <span class="s2">&quot;&quot;&quot;</span>
 
 
-<div class="viewcode-block" id="ParzenGeometric"><a class="viewcode-back" href="../../../covariance/generated/arch.covariance.kernel.ParzenGeometric.html#arch.covariance.kernel.ParzenGeometric">[docs]</a><span class="nd">@Substitution</span><span class="p">(</span><span class="n">kernel_name</span><span class="o">=</span><span class="s2">&quot;Parzen&#39;s Geometric&quot;</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="n">_parzen_geometric_formula</span><span class="p">)</span>
+<div class="viewcode-block" id="ParzenGeometric">
+<a class="viewcode-back" href="../../../covariance/generated/arch.covariance.kernel.ParzenGeometric.html#arch.covariance.kernel.ParzenGeometric">[docs]</a>
+<span class="nd">@Substitution</span><span class="p">(</span><span class="n">kernel_name</span><span class="o">=</span><span class="s2">&quot;Parzen&#39;s Geometric&quot;</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="n">_parzen_geometric_formula</span><span class="p">)</span>
 <span class="k">class</span> <span class="nc">ParzenGeometric</span><span class="p">(</span><span class="n">CovarianceEstimator</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
     <span class="nd">@property</span>
     <span class="k">def</span> <span class="nf">kernel_const</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
@@ -892,6 +906,7 @@ <h1>Source code for arch.covariance.kernel</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">x</span><span class="p">)</span></div>
 
 
+
 <span class="n">_parzen_cauchy_formula</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;</span><span class="se">\</span>
 <span class="s2">w=</span><span class="se">\\</span><span class="s2">begin</span><span class="si">{cases}</span><span class="s2"> </span><span class="se">\</span>
 <span class="se">\\</span><span class="s2">frac</span><span class="si">{1}</span><span class="s2">{1+z^2} &amp; z</span><span class="se">\\</span><span class="s2">leq1 </span><span class="se">\\\\</span><span class="s2"> </span><span class="se">\</span>
@@ -900,7 +915,9 @@ <h1>Source code for arch.covariance.kernel</h1><div class="highlight"><pre>
 <span class="s2">&quot;&quot;&quot;</span>
 
 
-<div class="viewcode-block" id="ParzenCauchy"><a class="viewcode-back" href="../../../covariance/generated/arch.covariance.kernel.ParzenCauchy.html#arch.covariance.kernel.ParzenCauchy">[docs]</a><span class="nd">@Substitution</span><span class="p">(</span><span class="n">kernel_name</span><span class="o">=</span><span class="s2">&quot;Parzen&#39;s Cauchy&quot;</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="n">_parzen_cauchy_formula</span><span class="p">)</span>
+<div class="viewcode-block" id="ParzenCauchy">
+<a class="viewcode-back" href="../../../covariance/generated/arch.covariance.kernel.ParzenCauchy.html#arch.covariance.kernel.ParzenCauchy">[docs]</a>
+<span class="nd">@Substitution</span><span class="p">(</span><span class="n">kernel_name</span><span class="o">=</span><span class="s2">&quot;Parzen&#39;s Cauchy&quot;</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="n">_parzen_cauchy_formula</span><span class="p">)</span>
 <span class="k">class</span> <span class="nc">ParzenCauchy</span><span class="p">(</span><span class="n">CovarianceEstimator</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
     <span class="nd">@property</span>
     <span class="k">def</span> <span class="nf">kernel_const</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
@@ -920,6 +937,7 @@ <h1>Source code for arch.covariance.kernel</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">x</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span></div>
 
 
+
 <span class="n">_tukey_hamming_formula</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;</span><span class="se">\</span>
 <span class="s2">w=</span><span class="se">\\</span><span class="s2">begin</span><span class="si">{cases}</span><span class="s2"> </span><span class="se">\</span>
 <span class="s2">0.54 + 0.46 </span><span class="se">\\</span><span class="s2">cos{</span><span class="se">\\</span><span class="s2">pi z} &amp; z</span><span class="se">\\</span><span class="s2">leq1 </span><span class="se">\\\\</span><span class="s2"> </span><span class="se">\</span>
@@ -928,7 +946,9 @@ <h1>Source code for arch.covariance.kernel</h1><div class="highlight"><pre>
 <span class="s2">&quot;&quot;&quot;</span>
 
 
-<div class="viewcode-block" id="TukeyHamming"><a class="viewcode-back" href="../../../covariance/generated/arch.covariance.kernel.TukeyHamming.html#arch.covariance.kernel.TukeyHamming">[docs]</a><span class="nd">@Substitution</span><span class="p">(</span><span class="n">kernel_name</span><span class="o">=</span><span class="s2">&quot;Tukey-Hamming&quot;</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="n">_tukey_hamming_formula</span><span class="p">)</span>
+<div class="viewcode-block" id="TukeyHamming">
+<a class="viewcode-back" href="../../../covariance/generated/arch.covariance.kernel.TukeyHamming.html#arch.covariance.kernel.TukeyHamming">[docs]</a>
+<span class="nd">@Substitution</span><span class="p">(</span><span class="n">kernel_name</span><span class="o">=</span><span class="s2">&quot;Tukey-Hamming&quot;</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="n">_tukey_hamming_formula</span><span class="p">)</span>
 <span class="k">class</span> <span class="nc">TukeyHamming</span><span class="p">(</span><span class="n">CovarianceEstimator</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
     <span class="nd">@property</span>
     <span class="k">def</span> <span class="nf">kernel_const</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
@@ -948,6 +968,7 @@ <h1>Source code for arch.covariance.kernel</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="mf">0.54</span> <span class="o">+</span> <span class="mf">0.46</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">x</span><span class="p">)</span></div>
 
 
+
 <span class="n">_tukey_hanning_formula</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;</span><span class="se">\</span>
 <span class="s2">w=</span><span class="se">\\</span><span class="s2">begin</span><span class="si">{cases}</span><span class="s2"> </span><span class="se">\</span>
 <span class="se">\\</span><span class="s2">frac</span><span class="si">{1}{2}</span><span class="s2"> + </span><span class="se">\\</span><span class="s2">frac</span><span class="si">{1}{2}</span><span class="s2"> </span><span class="se">\\</span><span class="s2">cos{</span><span class="se">\\</span><span class="s2">pi z} &amp; z</span><span class="se">\\</span><span class="s2">leq1 </span><span class="se">\\\\</span><span class="s2"> </span><span class="se">\</span>
@@ -956,7 +977,9 @@ <h1>Source code for arch.covariance.kernel</h1><div class="highlight"><pre>
 <span class="s2">&quot;&quot;&quot;</span>
 
 
-<div class="viewcode-block" id="TukeyHanning"><a class="viewcode-back" href="../../../covariance/generated/arch.covariance.kernel.TukeyHanning.html#arch.covariance.kernel.TukeyHanning">[docs]</a><span class="nd">@Substitution</span><span class="p">(</span><span class="n">kernel_name</span><span class="o">=</span><span class="s2">&quot;Tukey-Hanning&quot;</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="n">_tukey_hanning_formula</span><span class="p">)</span>
+<div class="viewcode-block" id="TukeyHanning">
+<a class="viewcode-back" href="../../../covariance/generated/arch.covariance.kernel.TukeyHanning.html#arch.covariance.kernel.TukeyHanning">[docs]</a>
+<span class="nd">@Substitution</span><span class="p">(</span><span class="n">kernel_name</span><span class="o">=</span><span class="s2">&quot;Tukey-Hanning&quot;</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="n">_tukey_hanning_formula</span><span class="p">)</span>
 <span class="k">class</span> <span class="nc">TukeyHanning</span><span class="p">(</span><span class="n">CovarianceEstimator</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
     <span class="nd">@property</span>
     <span class="k">def</span> <span class="nf">kernel_const</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
@@ -976,6 +999,7 @@ <h1>Source code for arch.covariance.kernel</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="mf">0.5</span> <span class="o">+</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">x</span><span class="p">)</span></div>
 
 
+
 <span class="n">_tukey_parzen_formula</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;</span><span class="se">\</span>
 <span class="s2">w=</span><span class="se">\\</span><span class="s2">begin</span><span class="si">{cases}</span><span class="s2"> </span><span class="se">\</span>
 <span class="s2">0.436 + 0.564 </span><span class="se">\\</span><span class="s2">cos{</span><span class="se">\\</span><span class="s2">pi z} &amp; z</span><span class="se">\\</span><span class="s2">leq1 </span><span class="se">\\\\</span><span class="s2"> </span><span class="se">\</span>
@@ -984,7 +1008,9 @@ <h1>Source code for arch.covariance.kernel</h1><div class="highlight"><pre>
 <span class="s2">&quot;&quot;&quot;</span>
 
 
-<div class="viewcode-block" id="TukeyParzen"><a class="viewcode-back" href="../../../covariance/generated/arch.covariance.kernel.TukeyParzen.html#arch.covariance.kernel.TukeyParzen">[docs]</a><span class="nd">@Substitution</span><span class="p">(</span><span class="n">kernel_name</span><span class="o">=</span><span class="s2">&quot;Tukey-Parzen&quot;</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="n">_tukey_parzen_formula</span><span class="p">)</span>
+<div class="viewcode-block" id="TukeyParzen">
+<a class="viewcode-back" href="../../../covariance/generated/arch.covariance.kernel.TukeyParzen.html#arch.covariance.kernel.TukeyParzen">[docs]</a>
+<span class="nd">@Substitution</span><span class="p">(</span><span class="n">kernel_name</span><span class="o">=</span><span class="s2">&quot;Tukey-Parzen&quot;</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="n">_tukey_parzen_formula</span><span class="p">)</span>
 <span class="k">class</span> <span class="nc">TukeyParzen</span><span class="p">(</span><span class="n">CovarianceEstimator</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
     <span class="nd">@property</span>
     <span class="k">def</span> <span class="nf">kernel_const</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
@@ -1004,6 +1030,7 @@ <h1>Source code for arch.covariance.kernel</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="mf">0.436</span> <span class="o">+</span> <span class="mf">0.564</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">x</span><span class="p">)</span></div>
 
 
+
 <span class="n">_qs_name</span> <span class="o">=</span> <span class="s2">&quot;Quadratic-Spectral (Andrews&#39;)&quot;</span>
 <span class="n">_qs_formula</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;</span><span class="se">\</span>
 <span class="s2">w=</span><span class="se">\\</span><span class="s2">begin</span><span class="si">{cases}</span><span class="s2"> </span><span class="se">\</span>
@@ -1013,7 +1040,9 @@ <h1>Source code for arch.covariance.kernel</h1><div class="highlight"><pre>
 <span class="s2">&quot;&quot;&quot;</span>
 
 
-<div class="viewcode-block" id="QuadraticSpectral"><a class="viewcode-back" href="../../../covariance/generated/arch.covariance.kernel.QuadraticSpectral.html#arch.covariance.kernel.QuadraticSpectral">[docs]</a><span class="nd">@Substitution</span><span class="p">(</span><span class="n">kernel_name</span><span class="o">=</span><span class="n">_qs_name</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="n">_qs_formula</span><span class="p">)</span>
+<div class="viewcode-block" id="QuadraticSpectral">
+<a class="viewcode-back" href="../../../covariance/generated/arch.covariance.kernel.QuadraticSpectral.html#arch.covariance.kernel.QuadraticSpectral">[docs]</a>
+<span class="nd">@Substitution</span><span class="p">(</span><span class="n">kernel_name</span><span class="o">=</span><span class="n">_qs_name</span><span class="p">,</span> <span class="n">formula</span><span class="o">=</span><span class="n">_qs_formula</span><span class="p">)</span>
 <span class="k">class</span> <span class="nc">QuadraticSpectral</span><span class="p">(</span><span class="n">CovarianceEstimator</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
     <span class="nd">@property</span>
     <span class="k">def</span> <span class="nf">kernel_const</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
@@ -1039,7 +1068,10 @@ <h1>Source code for arch.covariance.kernel</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="n">w</span></div>
 
 
-<div class="viewcode-block" id="Gallant"><a class="viewcode-back" href="../../../covariance/generated/arch.covariance.kernel.Gallant.html#arch.covariance.kernel.Gallant">[docs]</a><span class="k">class</span> <span class="nc">Gallant</span><span class="p">(</span><span class="n">Parzen</span><span class="p">):</span>
+
+<div class="viewcode-block" id="Gallant">
+<a class="viewcode-back" href="../../../covariance/generated/arch.covariance.kernel.Gallant.html#arch.covariance.kernel.Gallant">[docs]</a>
+<span class="k">class</span> <span class="nc">Gallant</span><span class="p">(</span><span class="n">Parzen</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Alternative name for Parzen covariance estimator.</span>
 
@@ -1049,7 +1081,10 @@ <h1>Source code for arch.covariance.kernel</h1><div class="highlight"><pre>
 <span class="sd">    &quot;&quot;&quot;</span></div>
 
 
-<div class="viewcode-block" id="Andrews"><a class="viewcode-back" href="../../../covariance/generated/arch.covariance.kernel.Andrews.html#arch.covariance.kernel.Andrews">[docs]</a><span class="k">class</span> <span class="nc">Andrews</span><span class="p">(</span><span class="n">QuadraticSpectral</span><span class="p">):</span>
+
+<div class="viewcode-block" id="Andrews">
+<a class="viewcode-back" href="../../../covariance/generated/arch.covariance.kernel.Andrews.html#arch.covariance.kernel.Andrews">[docs]</a>
+<span class="k">class</span> <span class="nc">Andrews</span><span class="p">(</span><span class="n">QuadraticSpectral</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Alternative name of the QuadraticSpectral covariance estimator.</span>
 
@@ -1059,7 +1094,10 @@ <h1>Source code for arch.covariance.kernel</h1><div class="highlight"><pre>
 <span class="sd">    &quot;&quot;&quot;</span></div>
 
 
-<div class="viewcode-block" id="NeweyWest"><a class="viewcode-back" href="../../../covariance/generated/arch.covariance.kernel.NeweyWest.html#arch.covariance.kernel.NeweyWest">[docs]</a><span class="k">class</span> <span class="nc">NeweyWest</span><span class="p">(</span><span class="n">Bartlett</span><span class="p">):</span>
+
+<div class="viewcode-block" id="NeweyWest">
+<a class="viewcode-back" href="../../../covariance/generated/arch.covariance.kernel.NeweyWest.html#arch.covariance.kernel.NeweyWest">[docs]</a>
+<span class="k">class</span> <span class="nc">NeweyWest</span><span class="p">(</span><span class="n">Bartlett</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Alternative name for Bartlett covariance estimator.</span>
 
@@ -1067,6 +1105,7 @@ <h1>Source code for arch.covariance.kernel</h1><div class="highlight"><pre>
 <span class="sd">    --------</span>
 <span class="sd">    Bartlett</span>
 <span class="sd">    &quot;&quot;&quot;</span></div>
+
 </code></pre></div>
 
 
@@ -1098,7 +1137,7 @@ <h1>Source code for arch.covariance.kernel</h1><div class="highlight"><pre>
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1145,7 +1184,7 @@ <h1>Source code for arch.covariance.kernel</h1><div class="highlight"><pre>
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/_modules/arch/unitroot/_engle_granger.html b/_modules/arch/unitroot/_engle_granger.html
index eb291d5a9e..4b24df7e27 100644
--- a/_modules/arch/unitroot/_engle_granger.html
+++ b/_modules/arch/unitroot/_engle_granger.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot._engle_granger - arch 6.1.0</title>
+        <title>arch.unitroot._engle_granger - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -390,7 +390,9 @@ <h1>Source code for arch.unitroot._engle_granger</h1><div class="highlight"><pre
 <span class="kn">from</span> <span class="nn">arch.unitroot.unitroot</span> <span class="kn">import</span> <span class="n">ADF</span><span class="p">,</span> <span class="n">TREND_DESCRIPTION</span>
 
 
-<div class="viewcode-block" id="engle_granger"><a class="viewcode-back" href="../../../unitroot/generated/arch.unitroot.cointegration.engle_granger.html#arch.unitroot.cointegration.engle_granger">[docs]</a><span class="k">def</span> <span class="nf">engle_granger</span><span class="p">(</span>
+<div class="viewcode-block" id="engle_granger">
+<a class="viewcode-back" href="../../../unitroot/generated/arch.unitroot.cointegration.engle_granger.html#arch.unitroot.cointegration.engle_granger">[docs]</a>
+<span class="k">def</span> <span class="nf">engle_granger</span><span class="p">(</span>
     <span class="n">y</span><span class="p">:</span> <span class="n">ArrayLike1D</span><span class="p">,</span>
     <span class="n">x</span><span class="p">:</span> <span class="n">ArrayLike2D</span><span class="p">,</span>
     <span class="n">trend</span><span class="p">:</span> <span class="n">UnitRootTrend</span> <span class="o">=</span> <span class="s2">&quot;c&quot;</span><span class="p">,</span>
@@ -473,7 +475,10 @@ <h1>Source code for arch.unitroot._engle_granger</h1><div class="highlight"><pre
     <span class="p">)</span></div>
 
 
-<div class="viewcode-block" id="EngleGrangerTestResults"><a class="viewcode-back" href="../../../unitroot/generated/arch.unitroot.cointegration.EngleGrangerTestResults.html#arch.unitroot.cointegration.EngleGrangerTestResults">[docs]</a><span class="k">class</span> <span class="nc">EngleGrangerTestResults</span><span class="p">(</span><span class="n">ResidualCointegrationTestResult</span><span class="p">):</span>
+
+<div class="viewcode-block" id="EngleGrangerTestResults">
+<a class="viewcode-back" href="../../../unitroot/generated/arch.unitroot.cointegration.EngleGrangerTestResults.html#arch.unitroot.cointegration.EngleGrangerTestResults">[docs]</a>
+<span class="k">class</span> <span class="nc">EngleGrangerTestResults</span><span class="p">(</span><span class="n">ResidualCointegrationTestResult</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Results class for Engle-Granger cointegration tests.</span>
 
@@ -560,7 +565,9 @@ <h1>Source code for arch.unitroot._engle_granger</h1><div class="highlight"><pre
 
         <span class="k">return</span> <span class="mi">1</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">_adf</span><span class="o">.</span><span class="n">regression</span><span class="o">.</span><span class="n">params</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
 
-<div class="viewcode-block" id="EngleGrangerTestResults.summary"><a class="viewcode-back" href="../../../unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.summary.html#arch.unitroot.cointegration.EngleGrangerTestResults.summary">[docs]</a>    <span class="k">def</span> <span class="nf">summary</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Summary</span><span class="p">:</span>
+<div class="viewcode-block" id="EngleGrangerTestResults.summary">
+<a class="viewcode-back" href="../../../unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.summary.html#arch.unitroot.cointegration.EngleGrangerTestResults.summary">[docs]</a>
+    <span class="k">def</span> <span class="nf">summary</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Summary</span><span class="p">:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;Summary of test, containing statistic, p-value and critical values&quot;&quot;&quot;</span>
         <span class="n">table_data</span> <span class="o">=</span> <span class="p">[</span>
             <span class="p">(</span><span class="s2">&quot;Test Statistic&quot;</span><span class="p">,</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">stat</span><span class="si">:</span><span class="s2">0.3f</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">),</span>
@@ -600,7 +607,9 @@ <h1>Source code for arch.unitroot._engle_granger</h1><div class="highlight"><pre
         <span class="p">]</span>
 
         <span class="n">smry</span><span class="o">.</span><span class="n">add_extra_txt</span><span class="p">(</span><span class="n">extra_text</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">smry</span></div></div>
+        <span class="k">return</span> <span class="n">smry</span></div>
+</div>
+
 
 
 <span class="k">def</span> <span class="nf">engle_granger_cv</span><span class="p">(</span><span class="n">trend</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">num_x</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">nobs</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">:</span>
@@ -710,7 +719,7 @@ <h1>Source code for arch.unitroot._engle_granger</h1><div class="highlight"><pre
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -757,7 +766,7 @@ <h1>Source code for arch.unitroot._engle_granger</h1><div class="highlight"><pre
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/_modules/arch/unitroot/_phillips_ouliaris.html b/_modules/arch/unitroot/_phillips_ouliaris.html
index 8e4aee3cd7..bce04cb539 100644
--- a/_modules/arch/unitroot/_phillips_ouliaris.html
+++ b/_modules/arch/unitroot/_phillips_ouliaris.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot._phillips_ouliaris - arch 6.1.0</title>
+        <title>arch.unitroot._phillips_ouliaris - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -498,7 +498,9 @@ <h1>Source code for arch.unitroot._phillips_ouliaris</h1><div class="highlight">
     <span class="p">)</span>
 
 
-<div class="viewcode-block" id="phillips_ouliaris"><a class="viewcode-back" href="../../../unitroot/generated/arch.unitroot.cointegration.phillips_ouliaris.html#arch.unitroot.cointegration.phillips_ouliaris">[docs]</a><span class="k">def</span> <span class="nf">phillips_ouliaris</span><span class="p">(</span>
+<div class="viewcode-block" id="phillips_ouliaris">
+<a class="viewcode-back" href="../../../unitroot/generated/arch.unitroot.cointegration.phillips_ouliaris.html#arch.unitroot.cointegration.phillips_ouliaris">[docs]</a>
+<span class="k">def</span> <span class="nf">phillips_ouliaris</span><span class="p">(</span>
     <span class="n">y</span><span class="p">:</span> <span class="n">ArrayLike1D</span><span class="p">,</span>
     <span class="n">x</span><span class="p">:</span> <span class="n">ArrayLike2D</span><span class="p">,</span>
     <span class="n">trend</span><span class="p">:</span> <span class="n">UnitRootTrend</span> <span class="o">=</span> <span class="s2">&quot;c&quot;</span><span class="p">,</span>
@@ -687,7 +689,10 @@ <h1>Source code for arch.unitroot._phillips_ouliaris</h1><div class="highlight">
     <span class="p">)</span></div>
 
 
-<div class="viewcode-block" id="PhillipsOuliarisTestResults"><a class="viewcode-back" href="../../../unitroot/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.html#arch.unitroot.cointegration.PhillipsOuliarisTestResults">[docs]</a><span class="k">class</span> <span class="nc">PhillipsOuliarisTestResults</span><span class="p">(</span><span class="n">ResidualCointegrationTestResult</span><span class="p">):</span>
+
+<div class="viewcode-block" id="PhillipsOuliarisTestResults">
+<a class="viewcode-back" href="../../../unitroot/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.html#arch.unitroot.cointegration.PhillipsOuliarisTestResults">[docs]</a>
+<span class="k">class</span> <span class="nc">PhillipsOuliarisTestResults</span><span class="p">(</span><span class="n">ResidualCointegrationTestResult</span><span class="p">):</span>
     <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">stat</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
@@ -729,7 +734,9 @@ <h1>Source code for arch.unitroot._phillips_ouliaris</h1><div class="highlight">
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;Bandwidth used by the long-run covariance estimator&quot;&quot;&quot;</span>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_kernel_est</span><span class="o">.</span><span class="n">bandwidth</span>
 
-<div class="viewcode-block" id="PhillipsOuliarisTestResults.summary"><a class="viewcode-back" href="../../../unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.summary.html#arch.unitroot.cointegration.PhillipsOuliarisTestResults.summary">[docs]</a>    <span class="k">def</span> <span class="nf">summary</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Summary</span><span class="p">:</span>
+<div class="viewcode-block" id="PhillipsOuliarisTestResults.summary">
+<a class="viewcode-back" href="../../../unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.summary.html#arch.unitroot.cointegration.PhillipsOuliarisTestResults.summary">[docs]</a>
+    <span class="k">def</span> <span class="nf">summary</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Summary</span><span class="p">:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;Summary of test, containing statistic, p-value and critical values&quot;&quot;&quot;</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">bandwidth</span> <span class="o">==</span> <span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">bandwidth</span><span class="p">):</span>
             <span class="n">bw</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">bandwidth</span><span class="p">))</span>
@@ -773,7 +780,9 @@ <h1>Source code for arch.unitroot._phillips_ouliaris</h1><div class="highlight">
         <span class="p">]</span>
 
         <span class="n">smry</span><span class="o">.</span><span class="n">add_extra_txt</span><span class="p">(</span><span class="n">extra_text</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">smry</span></div></div>
+        <span class="k">return</span> <span class="n">smry</span></div>
+</div>
+
 
 
 <span class="k">def</span> <span class="nf">phillips_ouliaris_cv</span><span class="p">(</span>
@@ -922,7 +931,7 @@ <h1>Source code for arch.unitroot._phillips_ouliaris</h1><div class="highlight">
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -969,7 +978,7 @@ <h1>Source code for arch.unitroot._phillips_ouliaris</h1><div class="highlight">
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/_modules/arch/unitroot/_shared.html b/_modules/arch/unitroot/_shared.html
index c587ed3426..472f6d9475 100644
--- a/_modules/arch/unitroot/_shared.html
+++ b/_modules/arch/unitroot/_shared.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot._shared - arch 6.1.0</title>
+        <title>arch.unitroot._shared - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -651,7 +651,7 @@ <h1>Source code for arch.unitroot._shared</h1><div class="highlight"><pre>
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -698,7 +698,7 @@ <h1>Source code for arch.unitroot._shared</h1><div class="highlight"><pre>
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/_modules/arch/unitroot/cointegration.html b/_modules/arch/unitroot/cointegration.html
index fd06acba46..ae112e5332 100644
--- a/_modules/arch/unitroot/cointegration.html
+++ b/_modules/arch/unitroot/cointegration.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -664,7 +664,9 @@ <h1>Source code for arch.unitroot.cointegration</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="n">smry</span>
 
 
-<div class="viewcode-block" id="DynamicOLSResults"><a class="viewcode-back" href="../../../unitroot/generated/arch.unitroot.cointegration.DynamicOLSResults.html#arch.unitroot.cointegration.DynamicOLSResults">[docs]</a><span class="k">class</span> <span class="nc">DynamicOLSResults</span><span class="p">(</span><span class="n">_CommonCointegrationResults</span><span class="p">):</span>
+<div class="viewcode-block" id="DynamicOLSResults">
+<a class="viewcode-back" href="../../../unitroot/generated/arch.unitroot.cointegration.DynamicOLSResults.html#arch.unitroot.cointegration.DynamicOLSResults">[docs]</a>
+<span class="k">class</span> <span class="nc">DynamicOLSResults</span><span class="p">(</span><span class="n">_CommonCointegrationResults</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Estimation results for Dynamic OLS models</span>
 
@@ -757,7 +759,9 @@ <h1>Source code for arch.unitroot.cointegration</h1><div class="highlight"><pre>
         <span class="n">nvar</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">full_params</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
         <span class="k">return</span> <span class="n">nobs</span> <span class="o">/</span> <span class="p">(</span><span class="n">nobs</span> <span class="o">-</span> <span class="n">nvar</span><span class="p">)</span>
 
-<div class="viewcode-block" id="DynamicOLSResults.summary"><a class="viewcode-back" href="../../../unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.summary.html#arch.unitroot.cointegration.DynamicOLSResults.summary">[docs]</a>    <span class="k">def</span> <span class="nf">summary</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">full</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Summary</span><span class="p">:</span>
+<div class="viewcode-block" id="DynamicOLSResults.summary">
+<a class="viewcode-back" href="../../../unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.summary.html#arch.unitroot.cointegration.DynamicOLSResults.summary">[docs]</a>
+    <span class="k">def</span> <span class="nf">summary</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">full</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Summary</span><span class="p">:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Summary of the model, containing estimated parameters and std. errors</span>
 
@@ -813,10 +817,14 @@ <h1>Source code for arch.unitroot.cointegration</h1><div class="highlight"><pre>
         <span class="n">table</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_param_table</span><span class="p">(</span><span class="n">params</span><span class="p">,</span> <span class="n">se</span><span class="p">,</span> <span class="n">tstats</span><span class="p">,</span> <span class="n">pvalues</span><span class="p">,</span> <span class="n">stubs</span><span class="p">,</span> <span class="n">title</span><span class="p">)</span>
         <span class="n">smry</span><span class="o">.</span><span class="n">tables</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">table</span><span class="p">)</span>
 
-        <span class="k">return</span> <span class="n">smry</span></div></div>
+        <span class="k">return</span> <span class="n">smry</span></div>
+</div>
+
 
 
-<div class="viewcode-block" id="DynamicOLS"><a class="viewcode-back" href="../../../unitroot/generated/arch.unitroot.cointegration.DynamicOLS.html#arch.unitroot.cointegration.DynamicOLS">[docs]</a><span class="k">class</span> <span class="nc">DynamicOLS</span><span class="p">:</span>
+<div class="viewcode-block" id="DynamicOLS">
+<a class="viewcode-back" href="../../../unitroot/generated/arch.unitroot.cointegration.DynamicOLS.html#arch.unitroot.cointegration.DynamicOLS">[docs]</a>
+<span class="k">class</span> <span class="nc">DynamicOLS</span><span class="p">:</span>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Dynamic OLS (DOLS) cointegrating vector estimation</span>
 
@@ -1044,7 +1052,9 @@ <h1>Source code for arch.unitroot.cointegration</h1><div class="highlight"><pre>
                     <span class="n">best_leads_and_lags</span> <span class="o">=</span> <span class="p">(</span><span class="n">lead</span><span class="p">,</span> <span class="n">lag</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">best_leads_and_lags</span>
 
-<div class="viewcode-block" id="DynamicOLS.fit"><a class="viewcode-back" href="../../../unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLS.fit.html#arch.unitroot.cointegration.DynamicOLS.fit">[docs]</a>    <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span>
+<div class="viewcode-block" id="DynamicOLS.fit">
+<a class="viewcode-back" href="../../../unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLS.fit.html#arch.unitroot.cointegration.DynamicOLS.fit">[docs]</a>
+    <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">cov_type</span><span class="p">:</span> <span class="n">Literal</span><span class="p">[</span>
             <span class="s2">&quot;unadjusted&quot;</span><span class="p">,</span> <span class="s2">&quot;homoskedastic&quot;</span><span class="p">,</span> <span class="s2">&quot;robust&quot;</span><span class="p">,</span> <span class="s2">&quot;kernel&quot;</span>
@@ -1144,6 +1154,7 @@ <h1>Source code for arch.unitroot.cointegration</h1><div class="highlight"><pre>
             <span class="n">df_adjust</span><span class="p">,</span>
         <span class="p">)</span></div>
 
+
     <span class="nd">@staticmethod</span>
     <span class="k">def</span> <span class="nf">_cov</span><span class="p">(</span>
         <span class="n">cov_type</span><span class="p">:</span> <span class="n">Literal</span><span class="p">[</span><span class="s2">&quot;unadjusted&quot;</span><span class="p">,</span> <span class="s2">&quot;homoskedastic&quot;</span><span class="p">,</span> <span class="s2">&quot;robust&quot;</span><span class="p">,</span> <span class="s2">&quot;kernel&quot;</span><span class="p">],</span>
@@ -1180,7 +1191,10 @@ <h1>Source code for arch.unitroot.cointegration</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="n">cov_df</span><span class="p">,</span> <span class="n">est</span></div>
 
 
-<div class="viewcode-block" id="CointegrationAnalysisResults"><a class="viewcode-back" href="../../../unitroot/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.html#arch.unitroot.cointegration.CointegrationAnalysisResults">[docs]</a><span class="k">class</span> <span class="nc">CointegrationAnalysisResults</span><span class="p">(</span><span class="n">_CommonCointegrationResults</span><span class="p">):</span>
+
+<div class="viewcode-block" id="CointegrationAnalysisResults">
+<a class="viewcode-back" href="../../../unitroot/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.html#arch.unitroot.cointegration.CointegrationAnalysisResults">[docs]</a>
+<span class="k">class</span> <span class="nc">CointegrationAnalysisResults</span><span class="p">(</span><span class="n">_CommonCointegrationResults</span><span class="p">):</span>
     <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">params</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">,</span>
@@ -1217,6 +1231,7 @@ <h1>Source code for arch.unitroot.cointegration</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_omega_112</span></div>
 
 
+
 <span class="n">COMMON_DOCSTRING</span> <span class="o">=</span> <span class="sa">r</span><span class="s2">&quot;&quot;&quot;</span>
 <span class="s2">    </span><span class="si">%(method)s</span><span class="s2"> cointegrating vector estimation.</span>
 
@@ -1341,7 +1356,9 @@ <h1>Source code for arch.unitroot.cointegration</h1><div class="highlight"><pre>
 <span class="s2">&quot;&quot;&quot;</span>
 
 
-<div class="viewcode-block" id="FullyModifiedOLS"><a class="viewcode-back" href="../../../unitroot/generated/arch.unitroot.cointegration.FullyModifiedOLS.html#arch.unitroot.cointegration.FullyModifiedOLS">[docs]</a><span class="nd">@Substitution</span><span class="p">(</span><span class="n">method</span><span class="o">=</span><span class="n">FMOLS_METHOD</span><span class="p">,</span> <span class="n">estimator</span><span class="o">=</span><span class="n">FMOLS_ESTIMATOR</span><span class="p">)</span>
+<div class="viewcode-block" id="FullyModifiedOLS">
+<a class="viewcode-back" href="../../../unitroot/generated/arch.unitroot.cointegration.FullyModifiedOLS.html#arch.unitroot.cointegration.FullyModifiedOLS">[docs]</a>
+<span class="nd">@Substitution</span><span class="p">(</span><span class="n">method</span><span class="o">=</span><span class="n">FMOLS_METHOD</span><span class="p">,</span> <span class="n">estimator</span><span class="o">=</span><span class="n">FMOLS_ESTIMATOR</span><span class="p">)</span>
 <span class="nd">@Appender</span><span class="p">(</span><span class="n">COMMON_DOCSTRING</span><span class="p">)</span>
 <span class="k">class</span> <span class="nc">FullyModifiedOLS</span><span class="p">:</span>
     <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
@@ -1406,7 +1423,9 @@ <h1>Source code for arch.unitroot.cointegration</h1><div class="highlight"><pre>
         <span class="n">r2_adj</span> <span class="o">=</span> <span class="mf">1.0</span> <span class="o">-</span> <span class="p">(</span><span class="n">ssr</span> <span class="o">/</span> <span class="p">(</span><span class="n">nobs</span> <span class="o">-</span> <span class="n">nvar</span><span class="p">))</span> <span class="o">/</span> <span class="p">(</span><span class="n">tss</span> <span class="o">/</span> <span class="p">(</span><span class="n">nobs</span> <span class="o">-</span> <span class="n">tss_df</span><span class="p">))</span>
         <span class="k">return</span> <span class="n">resid</span><span class="p">,</span> <span class="n">r2</span><span class="p">,</span> <span class="n">r2_adj</span>
 
-<div class="viewcode-block" id="FullyModifiedOLS.fit"><a class="viewcode-back" href="../../../unitroot/generated/generated/arch.unitroot.cointegration.FullyModifiedOLS.fit.html#arch.unitroot.cointegration.FullyModifiedOLS.fit">[docs]</a>    <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span>
+<div class="viewcode-block" id="FullyModifiedOLS.fit">
+<a class="viewcode-back" href="../../../unitroot/generated/generated/arch.unitroot.cointegration.FullyModifiedOLS.fit.html#arch.unitroot.cointegration.FullyModifiedOLS.fit">[docs]</a>
+    <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">kernel</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;bartlett&quot;</span><span class="p">,</span>
         <span class="n">bandwidth</span><span class="p">:</span> <span class="nb">float</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
@@ -1496,10 +1515,14 @@ <h1>Source code for arch.unitroot.cointegration</h1><div class="highlight"><pre>
             <span class="n">r2</span><span class="p">,</span>
             <span class="n">r2_adj</span><span class="p">,</span>
             <span class="s2">&quot;Fully Modified OLS&quot;</span><span class="p">,</span>
-        <span class="p">)</span></div></div>
+        <span class="p">)</span></div>
+</div>
+
 
 
-<div class="viewcode-block" id="CanonicalCointegratingReg"><a class="viewcode-back" href="../../../unitroot/generated/arch.unitroot.cointegration.CanonicalCointegratingReg.html#arch.unitroot.cointegration.CanonicalCointegratingReg">[docs]</a><span class="nd">@Substitution</span><span class="p">(</span><span class="n">method</span><span class="o">=</span><span class="n">CCR_METHOD</span><span class="p">,</span> <span class="n">estimator</span><span class="o">=</span><span class="n">CCR_ESTIMATOR</span><span class="p">)</span>
+<div class="viewcode-block" id="CanonicalCointegratingReg">
+<a class="viewcode-back" href="../../../unitroot/generated/arch.unitroot.cointegration.CanonicalCointegratingReg.html#arch.unitroot.cointegration.CanonicalCointegratingReg">[docs]</a>
+<span class="nd">@Substitution</span><span class="p">(</span><span class="n">method</span><span class="o">=</span><span class="n">CCR_METHOD</span><span class="p">,</span> <span class="n">estimator</span><span class="o">=</span><span class="n">CCR_ESTIMATOR</span><span class="p">)</span>
 <span class="nd">@Appender</span><span class="p">(</span><span class="n">COMMON_DOCSTRING</span><span class="p">)</span>
 <span class="k">class</span> <span class="nc">CanonicalCointegratingReg</span><span class="p">(</span><span class="n">FullyModifiedOLS</span><span class="p">):</span>
     <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
@@ -1511,7 +1534,9 @@ <h1>Source code for arch.unitroot.cointegration</h1><div class="highlight"><pre>
     <span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
         <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">trend</span><span class="p">,</span> <span class="n">x_trend</span><span class="p">)</span>
 
-<div class="viewcode-block" id="CanonicalCointegratingReg.fit"><a class="viewcode-back" href="../../../unitroot/generated/generated/arch.unitroot.cointegration.CanonicalCointegratingReg.fit.html#arch.unitroot.cointegration.CanonicalCointegratingReg.fit">[docs]</a>    <span class="nd">@Appender</span><span class="p">(</span><span class="n">FullyModifiedOLS</span><span class="o">.</span><span class="n">fit</span><span class="o">.</span><span class="vm">__doc__</span><span class="p">)</span>
+<div class="viewcode-block" id="CanonicalCointegratingReg.fit">
+<a class="viewcode-back" href="../../../unitroot/generated/generated/arch.unitroot.cointegration.CanonicalCointegratingReg.fit.html#arch.unitroot.cointegration.CanonicalCointegratingReg.fit">[docs]</a>
+    <span class="nd">@Appender</span><span class="p">(</span><span class="n">FullyModifiedOLS</span><span class="o">.</span><span class="n">fit</span><span class="o">.</span><span class="vm">__doc__</span><span class="p">)</span>
     <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">kernel</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;bartlett&quot;</span><span class="p">,</span>
@@ -1568,7 +1593,9 @@ <h1>Source code for arch.unitroot.cointegration</h1><div class="highlight"><pre>
             <span class="n">r2</span><span class="p">,</span>
             <span class="n">r2_adj</span><span class="p">,</span>
             <span class="s2">&quot;Fully Modified OLS&quot;</span><span class="p">,</span>
-        <span class="p">)</span></div></div>
+        <span class="p">)</span></div>
+</div>
+
 </code></pre></div>
 
 
@@ -1600,7 +1627,7 @@ <h1>Source code for arch.unitroot.cointegration</h1><div class="highlight"><pre>
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1647,7 +1674,7 @@ <h1>Source code for arch.unitroot.cointegration</h1><div class="highlight"><pre>
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/_modules/arch/unitroot/unitroot.html b/_modules/arch/unitroot/unitroot.html
index f77f1d585a..37ee5fe467 100644
--- a/_modules/arch/unitroot/unitroot.html
+++ b/_modules/arch/unitroot/unitroot.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.unitroot - arch 6.1.0</title>
+        <title>arch.unitroot.unitroot - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -448,6 +448,7 @@ <h1>Source code for arch.unitroot.unitroot</h1><div class="highlight"><pre>
 <span class="kn">from</span> <span class="nn">arch.utility.exceptions</span> <span class="kn">import</span> <span class="p">(</span>
     <span class="n">InfeasibleTestException</span><span class="p">,</span>
     <span class="n">InvalidLengthWarning</span><span class="p">,</span>
+    <span class="n">PerformanceWarning</span><span class="p">,</span>
     <span class="n">invalid_length_doc</span><span class="p">,</span>
 <span class="p">)</span>
 <span class="kn">from</span> <span class="nn">arch.utility.timeseries</span> <span class="kn">import</span> <span class="n">add_trend</span>
@@ -612,8 +613,8 @@ <h1>Source code for arch.unitroot.unitroot</h1><div class="highlight"><pre>
             <span class="n">trendx</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">ones</span><span class="p">((</span><span class="n">nobs</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span> <span class="o">/</span> <span class="n">sqrt</span><span class="p">(</span><span class="n">nobs</span><span class="p">))</span>
     <span class="n">rhs</span> <span class="o">=</span> <span class="n">hstack</span><span class="p">([</span><span class="n">level</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">],</span> <span class="n">hstack</span><span class="p">(</span><span class="n">trendx</span><span class="p">)])</span>
     <span class="n">m</span> <span class="o">=</span> <span class="n">rhs</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-    <span class="n">xpx</span> <span class="o">=</span> <span class="n">empty</span><span class="p">((</span><span class="n">m</span> <span class="o">+</span> <span class="n">maxlag</span><span class="p">,</span> <span class="n">m</span> <span class="o">+</span> <span class="n">maxlag</span><span class="p">))</span> <span class="o">*</span> <span class="n">nan</span>
-    <span class="n">xpy</span> <span class="o">=</span> <span class="n">empty</span><span class="p">((</span><span class="n">m</span> <span class="o">+</span> <span class="n">maxlag</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span> <span class="o">*</span> <span class="n">nan</span>
+    <span class="n">xpx</span> <span class="o">=</span> <span class="n">full</span><span class="p">((</span><span class="n">m</span> <span class="o">+</span> <span class="n">maxlag</span><span class="p">,</span> <span class="n">m</span> <span class="o">+</span> <span class="n">maxlag</span><span class="p">),</span> <span class="n">nan</span><span class="p">)</span>
+    <span class="n">xpy</span> <span class="o">=</span> <span class="n">full</span><span class="p">((</span><span class="n">m</span> <span class="o">+</span> <span class="n">maxlag</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">nan</span><span class="p">)</span>
     <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">xpx</span><span class="p">,</span> <span class="n">ndarray</span><span class="p">)</span>
     <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">xpy</span><span class="p">,</span> <span class="n">ndarray</span><span class="p">)</span>
     <span class="n">xpy</span><span class="p">[:</span><span class="n">m</span><span class="p">]</span> <span class="o">=</span> <span class="n">rhs</span><span class="o">.</span><span class="n">T</span> <span class="o">@</span> <span class="n">lhs</span>
@@ -768,6 +769,14 @@ <h1>Source code for arch.unitroot.unitroot</h1><div class="highlight"><pre>
     <span class="k">if</span> <span class="n">max_lags</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
         <span class="n">max_lags</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">ceil</span><span class="p">(</span><span class="mf">12.0</span> <span class="o">*</span> <span class="n">power</span><span class="p">(</span><span class="n">nobs</span> <span class="o">/</span> <span class="mf">100.0</span><span class="p">,</span> <span class="mi">1</span> <span class="o">/</span> <span class="mf">4.0</span><span class="p">)))</span>
         <span class="n">max_lags</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="nb">min</span><span class="p">(</span><span class="n">max_lags</span><span class="p">,</span> <span class="n">max_max_lags</span><span class="p">),</span> <span class="mi">0</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">max_lags</span> <span class="o">&gt;</span> <span class="mi">119</span><span class="p">:</span>
+            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
+                <span class="s2">&quot;The value of max_lags was not specified and has been calculated as &quot;</span>
+                <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">max_lags</span><span class="si">}</span><span class="s2">. Searching over a large lag length with a sample size &quot;</span>
+                <span class="sa">f</span><span class="s2">&quot;of </span><span class="si">{</span><span class="n">nobs</span><span class="si">}</span><span class="s2"> is likely to be slow. Consider directly setting &quot;</span>
+                <span class="s2">&quot;``max_lags`` to a small value to avoid this performance issue.&quot;</span><span class="p">,</span>
+                <span class="n">PerformanceWarning</span><span class="p">,</span>
+            <span class="p">)</span>
     <span class="k">assert</span> <span class="n">max_lags</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
     <span class="k">if</span> <span class="n">low_memory</span><span class="p">:</span>
         <span class="n">out</span> <span class="o">=</span> <span class="n">_autolag_ols_low_memory</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">max_lags</span><span class="p">,</span> <span class="n">trend</span><span class="p">,</span> <span class="n">method</span><span class="p">)</span>
@@ -1004,7 +1013,9 @@ <h1>Source code for arch.unitroot.unitroot</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_trend</span>
 
 
-<div class="viewcode-block" id="ADF"><a class="viewcode-back" href="../../../unitroot/generated/arch.unitroot.ADF.html#arch.unitroot.ADF">[docs]</a><span class="k">class</span> <span class="nc">ADF</span><span class="p">(</span><span class="n">UnitRootTest</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
+<div class="viewcode-block" id="ADF">
+<a class="viewcode-back" href="../../../unitroot/generated/arch.unitroot.ADF.html#arch.unitroot.ADF">[docs]</a>
+<span class="k">class</span> <span class="nc">ADF</span><span class="p">(</span><span class="n">UnitRootTest</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Augmented Dickey-Fuller unit root test</span>
 
@@ -1170,7 +1181,10 @@ <h1>Source code for arch.unitroot.unitroot</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_max_lags</span></div>
 
 
-<div class="viewcode-block" id="DFGLS"><a class="viewcode-back" href="../../../unitroot/generated/arch.unitroot.DFGLS.html#arch.unitroot.DFGLS">[docs]</a><span class="k">class</span> <span class="nc">DFGLS</span><span class="p">(</span><span class="n">UnitRootTest</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
+
+<div class="viewcode-block" id="DFGLS">
+<a class="viewcode-back" href="../../../unitroot/generated/arch.unitroot.DFGLS.html#arch.unitroot.DFGLS">[docs]</a>
+<span class="k">class</span> <span class="nc">DFGLS</span><span class="p">(</span><span class="n">UnitRootTest</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Elliott, Rothenberg and Stock&#39;s ([1]_) GLS detrended Dickey-Fuller</span>
 
@@ -1342,7 +1356,10 @@ <h1>Source code for arch.unitroot.unitroot</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_max_lags</span></div>
 
 
-<div class="viewcode-block" id="PhillipsPerron"><a class="viewcode-back" href="../../../unitroot/generated/arch.unitroot.PhillipsPerron.html#arch.unitroot.PhillipsPerron">[docs]</a><span class="k">class</span> <span class="nc">PhillipsPerron</span><span class="p">(</span><span class="n">UnitRootTest</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
+
+<div class="viewcode-block" id="PhillipsPerron">
+<a class="viewcode-back" href="../../../unitroot/generated/arch.unitroot.PhillipsPerron.html#arch.unitroot.PhillipsPerron">[docs]</a>
+<span class="k">class</span> <span class="nc">PhillipsPerron</span><span class="p">(</span><span class="n">UnitRootTest</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Phillips-Perron unit root test</span>
 
@@ -1543,7 +1560,10 @@ <h1>Source code for arch.unitroot.unitroot</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_regression</span></div>
 
 
-<div class="viewcode-block" id="KPSS"><a class="viewcode-back" href="../../../unitroot/generated/arch.unitroot.KPSS.html#arch.unitroot.KPSS">[docs]</a><span class="k">class</span> <span class="nc">KPSS</span><span class="p">(</span><span class="n">UnitRootTest</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
+
+<div class="viewcode-block" id="KPSS">
+<a class="viewcode-back" href="../../../unitroot/generated/arch.unitroot.KPSS.html#arch.unitroot.KPSS">[docs]</a>
+<span class="k">class</span> <span class="nc">KPSS</span><span class="p">(</span><span class="n">UnitRootTest</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Kwiatkowski, Phillips, Schmidt and Shin (KPSS) stationarity test</span>
 
@@ -1709,7 +1729,10 @@ <h1>Source code for arch.unitroot.unitroot</h1><div class="highlight"><pre>
         <span class="bp">self</span><span class="o">.</span><span class="n">_lags</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">autolags</span><span class="p">)</span></div>
 
 
-<div class="viewcode-block" id="ZivotAndrews"><a class="viewcode-back" href="../../../unitroot/generated/arch.unitroot.ZivotAndrews.html#arch.unitroot.ZivotAndrews">[docs]</a><span class="k">class</span> <span class="nc">ZivotAndrews</span><span class="p">(</span><span class="n">UnitRootTest</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
+
+<div class="viewcode-block" id="ZivotAndrews">
+<a class="viewcode-back" href="../../../unitroot/generated/arch.unitroot.ZivotAndrews.html#arch.unitroot.ZivotAndrews">[docs]</a>
+<span class="k">class</span> <span class="nc">ZivotAndrews</span><span class="p">(</span><span class="n">UnitRootTest</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Zivot-Andrews structural-break unit-root test</span>
 
@@ -1922,7 +1945,10 @@ <h1>Source code for arch.unitroot.unitroot</h1><div class="highlight"><pre>
         <span class="p">}</span></div>
 
 
-<div class="viewcode-block" id="VarianceRatio"><a class="viewcode-back" href="../../../unitroot/generated/arch.unitroot.VarianceRatio.html#arch.unitroot.VarianceRatio">[docs]</a><span class="k">class</span> <span class="nc">VarianceRatio</span><span class="p">(</span><span class="n">UnitRootTest</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
+
+<div class="viewcode-block" id="VarianceRatio">
+<a class="viewcode-back" href="../../../unitroot/generated/arch.unitroot.VarianceRatio.html#arch.unitroot.VarianceRatio">[docs]</a>
+<span class="k">class</span> <span class="nc">VarianceRatio</span><span class="p">(</span><span class="n">UnitRootTest</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Variance Ratio test of a random walk.</span>
 
@@ -2094,6 +2120,7 @@ <h1>Source code for arch.unitroot.unitroot</h1><div class="highlight"><pre>
         <span class="bp">self</span><span class="o">.</span><span class="n">_pvalue</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">-</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">norm</span><span class="o">.</span><span class="n">cdf</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">abs_stat</span><span class="p">))</span></div>
 
 
+
 <span class="k">def</span> <span class="nf">mackinnonp</span><span class="p">(</span>
     <span class="n">stat</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
     <span class="n">regression</span><span class="p">:</span> <span class="n">Literal</span><span class="p">[</span><span class="s2">&quot;c&quot;</span><span class="p">,</span> <span class="s2">&quot;n&quot;</span><span class="p">,</span> <span class="s2">&quot;ct&quot;</span><span class="p">,</span> <span class="s2">&quot;ctt&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;c&quot;</span><span class="p">,</span>
@@ -2293,7 +2320,9 @@ <h1>Source code for arch.unitroot.unitroot</h1><div class="highlight"><pre>
     <span class="k">return</span> <span class="n">pvalue</span><span class="p">,</span> <span class="n">crit_value</span>
 
 
-<div class="viewcode-block" id="auto_bandwidth"><a class="viewcode-back" href="../../../unitroot/generated/arch.unitroot.auto_bandwidth.html#arch.unitroot.auto_bandwidth">[docs]</a><span class="k">def</span> <span class="nf">auto_bandwidth</span><span class="p">(</span>
+<div class="viewcode-block" id="auto_bandwidth">
+<a class="viewcode-back" href="../../../unitroot/generated/arch.unitroot.auto_bandwidth.html#arch.unitroot.auto_bandwidth">[docs]</a>
+<span class="k">def</span> <span class="nf">auto_bandwidth</span><span class="p">(</span>
     <span class="n">y</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">float</span> <span class="o">|</span> <span class="nb">int</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike1D</span><span class="p">,</span>
     <span class="n">kernel</span><span class="p">:</span> <span class="n">Literal</span><span class="p">[</span>
         <span class="s2">&quot;ba&quot;</span><span class="p">,</span> <span class="s2">&quot;bartlett&quot;</span><span class="p">,</span> <span class="s2">&quot;nw&quot;</span><span class="p">,</span> <span class="s2">&quot;pa&quot;</span><span class="p">,</span> <span class="s2">&quot;parzen&quot;</span><span class="p">,</span> <span class="s2">&quot;gallant&quot;</span><span class="p">,</span> <span class="s2">&quot;qs&quot;</span><span class="p">,</span> <span class="s2">&quot;andrews&quot;</span>
@@ -2369,6 +2398,7 @@ <h1>Source code for arch.unitroot.unitroot</h1><div class="highlight"><pre>
     <span class="n">bandwidth</span> <span class="o">=</span> <span class="n">gamma</span> <span class="o">*</span> <span class="n">power</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">y</span><span class="p">),</span> <span class="n">t_power</span><span class="p">)</span>
 
     <span class="k">return</span> <span class="n">bandwidth</span></div>
+
 </code></pre></div>
 
 
@@ -2400,7 +2430,7 @@ <h1>Source code for arch.unitroot.unitroot</h1><div class="highlight"><pre>
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -2447,7 +2477,7 @@ <h1>Source code for arch.unitroot.unitroot</h1><div class="highlight"><pre>
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/_modules/arch/univariate/base.html b/_modules/arch/univariate/base.html
index 291cfc34bd..8d024a07db 100644
--- a/_modules/arch/univariate/base.html
+++ b/_modules/arch/univariate/base.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base - arch 6.1.0</title>
+        <title>arch.univariate.base - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -389,7 +389,6 @@ <h1>Source code for arch.univariate.base</h1><div class="highlight"><pre>
 <span class="kn">from</span> <span class="nn">statsmodels.tools.tools</span> <span class="kn">import</span> <span class="n">add_constant</span>
 <span class="kn">from</span> <span class="nn">statsmodels.tsa.tsatools</span> <span class="kn">import</span> <span class="n">lagmat</span>
 
-<span class="kn">from</span> <span class="nn">arch.__future__._utility</span> <span class="kn">import</span> <span class="n">check_reindex</span>
 <span class="kn">from</span> <span class="nn">arch.typing</span> <span class="kn">import</span> <span class="p">(</span>
     <span class="n">ArrayLike</span><span class="p">,</span>
     <span class="n">ArrayLike1D</span><span class="p">,</span>
@@ -535,7 +534,9 @@ <h1>Source code for arch.univariate.base</h1><div class="highlight"><pre>
     <span class="k">return</span> <span class="n">rank</span> <span class="o">==</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
 
 
-<div class="viewcode-block" id="ARCHModel"><a class="viewcode-back" href="../../../univariate/generated/arch.univariate.base.ARCHModel.html#arch.univariate.base.ARCHModel">[docs]</a><span class="k">class</span> <span class="nc">ARCHModel</span><span class="p">(</span><span class="n">metaclass</span><span class="o">=</span><span class="n">ABCMeta</span><span class="p">):</span>
+<div class="viewcode-block" id="ARCHModel">
+<a class="viewcode-back" href="../../../univariate/generated/arch.univariate.base.ARCHModel.html#arch.univariate.base.ARCHModel">[docs]</a>
+<span class="k">class</span> <span class="nc">ARCHModel</span><span class="p">(</span><span class="n">metaclass</span><span class="o">=</span><span class="n">ABCMeta</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Abstract base class for mean models in ARCH processes.  Specifies the</span>
 <span class="sd">    conditional mean process.</span>
@@ -597,7 +598,9 @@ <h1>Source code for arch.univariate.base</h1><div class="highlight"><pre>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;The name of the model.&quot;&quot;&quot;</span>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_name</span>
 
-<div class="viewcode-block" id="ARCHModel.constraints"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.base.ARCHModel.constraints.html#arch.univariate.base.ARCHModel.constraints">[docs]</a>    <span class="k">def</span> <span class="nf">constraints</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">Float64Array</span><span class="p">,</span> <span class="n">Float64Array</span><span class="p">]:</span>
+<div class="viewcode-block" id="ARCHModel.constraints">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.base.ARCHModel.constraints.html#arch.univariate.base.ARCHModel.constraints">[docs]</a>
+    <span class="k">def</span> <span class="nf">constraints</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">Float64Array</span><span class="p">,</span> <span class="n">Float64Array</span><span class="p">]:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Construct linear constraint arrays  for use in non-linear optimization</span>
 
@@ -614,7 +617,10 @@ <h1>Source code for arch.univariate.base</h1><div class="highlight"><pre>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_params</span><span class="p">)),</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span></div>
 
-<div class="viewcode-block" id="ARCHModel.bounds"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.base.ARCHModel.bounds.html#arch.univariate.base.ARCHModel.bounds">[docs]</a>    <span class="k">def</span> <span class="nf">bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]]:</span>
+
+<div class="viewcode-block" id="ARCHModel.bounds">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.base.ARCHModel.bounds.html#arch.univariate.base.ARCHModel.bounds">[docs]</a>
+    <span class="k">def</span> <span class="nf">bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]]:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Construct bounds for parameters to use in non-linear optimization</span>
 
@@ -626,6 +632,7 @@ <h1>Source code for arch.univariate.base</h1><div class="highlight"><pre>
         <span class="n">num_params</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_params</span>
         <span class="k">return</span> <span class="p">[(</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">)]</span> <span class="o">*</span> <span class="n">num_params</span></div>
 
+
     <span class="nd">@property</span>
     <span class="k">def</span> <span class="nf">y</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">ArrayLike</span> <span class="o">|</span> <span class="kc">None</span><span class="p">:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the dependent variable&quot;&quot;&quot;</span>
@@ -837,7 +844,9 @@ <h1>Source code for arch.univariate.base</h1><div class="highlight"><pre>
         <span class="n">km</span><span class="p">,</span> <span class="n">kv</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_params</span><span class="p">),</span> <span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">volatility</span><span class="o">.</span><span class="n">num_params</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">x</span><span class="p">[:</span><span class="n">km</span><span class="p">],</span> <span class="n">x</span><span class="p">[</span><span class="n">km</span> <span class="p">:</span> <span class="n">km</span> <span class="o">+</span> <span class="n">kv</span><span class="p">],</span> <span class="n">x</span><span class="p">[</span><span class="n">km</span> <span class="o">+</span> <span class="n">kv</span> <span class="p">:]</span>
 
-<div class="viewcode-block" id="ARCHModel.fix"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.base.ARCHModel.fix.html#arch.univariate.base.ARCHModel.fix">[docs]</a>    <span class="k">def</span> <span class="nf">fix</span><span class="p">(</span>
+<div class="viewcode-block" id="ARCHModel.fix">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.base.ARCHModel.fix.html#arch.univariate.base.ARCHModel.fix">[docs]</a>
+    <span class="k">def</span> <span class="nf">fix</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">params</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike1D</span><span class="p">,</span>
         <span class="n">first_obs</span><span class="p">:</span> <span class="nb">int</span> <span class="o">|</span> <span class="n">DateLike</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
@@ -909,6 +918,7 @@ <h1>Source code for arch.univariate.base</h1><div class="highlight"><pre>
             <span class="n">model_copy</span><span class="p">,</span>
         <span class="p">)</span></div>
 
+
     <span class="nd">@abstractmethod</span>
     <span class="k">def</span> <span class="nf">_adjust_sample</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span> <span class="n">first_obs</span><span class="p">:</span> <span class="nb">int</span> <span class="o">|</span> <span class="n">DateLike</span> <span class="o">|</span> <span class="kc">None</span><span class="p">,</span> <span class="n">last_obs</span><span class="p">:</span> <span class="nb">int</span> <span class="o">|</span> <span class="n">DateLike</span> <span class="o">|</span> <span class="kc">None</span>
@@ -928,7 +938,9 @@ <h1>Source code for arch.univariate.base</h1><div class="highlight"><pre>
 <span class="sd">        Adjusted sample must follow Python semantics of first_obs:last_obs</span>
 <span class="sd">        &quot;&quot;&quot;</span>
 
-<div class="viewcode-block" id="ARCHModel.fit"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.base.ARCHModel.fit.html#arch.univariate.base.ARCHModel.fit">[docs]</a>    <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span>
+<div class="viewcode-block" id="ARCHModel.fit">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.base.ARCHModel.fit.html#arch.univariate.base.ARCHModel.fit">[docs]</a>
+    <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">update_freq</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
         <span class="n">disp</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">|</span> <span class="n">Literal</span><span class="p">[</span><span class="s2">&quot;off&quot;</span><span class="p">,</span> <span class="s2">&quot;final&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;final&quot;</span><span class="p">,</span>
@@ -1180,7 +1192,10 @@ <h1>Source code for arch.univariate.base</h1><div class="highlight"><pre>
             <span class="n">model_copy</span><span class="p">,</span>
         <span class="p">)</span></div>
 
-<div class="viewcode-block" id="ARCHModel.parameter_names"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.base.ARCHModel.parameter_names.html#arch.univariate.base.ARCHModel.parameter_names">[docs]</a>    <span class="nd">@abstractmethod</span>
+
+<div class="viewcode-block" id="ARCHModel.parameter_names">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.base.ARCHModel.parameter_names.html#arch.univariate.base.ARCHModel.parameter_names">[docs]</a>
+    <span class="nd">@abstractmethod</span>
     <span class="k">def</span> <span class="nf">parameter_names</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;List of parameters names</span>
 
@@ -1190,7 +1205,10 @@ <h1>Source code for arch.univariate.base</h1><div class="highlight"><pre>
 <span class="sd">            List of variable names for the mean model</span>
 <span class="sd">        &quot;&quot;&quot;</span></div>
 
-<div class="viewcode-block" id="ARCHModel.starting_values"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.base.ARCHModel.starting_values.html#arch.univariate.base.ARCHModel.starting_values">[docs]</a>    <span class="k">def</span> <span class="nf">starting_values</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
+
+<div class="viewcode-block" id="ARCHModel.starting_values">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.base.ARCHModel.starting_values.html#arch.univariate.base.ARCHModel.starting_values">[docs]</a>
+    <span class="k">def</span> <span class="nf">starting_values</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Returns starting values for the mean model, often the same as the</span>
 <span class="sd">        values returned from fit</span>
@@ -1202,6 +1220,7 @@ <h1>Source code for arch.univariate.base</h1><div class="highlight"><pre>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_fit_no_arch_normal_errors_params</span><span class="p">()</span></div>
 
+
     <span class="nd">@cached_property</span>
     <span class="nd">@abstractmethod</span>
     <span class="k">def</span> <span class="nf">num_params</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
@@ -1209,7 +1228,9 @@ <h1>Source code for arch.univariate.base</h1><div class="highlight"><pre>
 <span class="sd">        Number of parameters in the model</span>
 <span class="sd">        &quot;&quot;&quot;</span>
 
-<div class="viewcode-block" id="ARCHModel.simulate"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.base.ARCHModel.simulate.html#arch.univariate.base.ARCHModel.simulate">[docs]</a>    <span class="nd">@abstractmethod</span>
+<div class="viewcode-block" id="ARCHModel.simulate">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.base.ARCHModel.simulate.html#arch.univariate.base.ARCHModel.simulate">[docs]</a>
+    <span class="nd">@abstractmethod</span>
     <span class="k">def</span> <span class="nf">simulate</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">params</span><span class="p">:</span> <span class="n">ArrayLike1D</span> <span class="o">|</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">float</span><span class="p">],</span>
@@ -1221,7 +1242,10 @@ <h1>Source code for arch.univariate.base</h1><div class="highlight"><pre>
     <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
         <span class="k">pass</span></div>
 
-<div class="viewcode-block" id="ARCHModel.resids"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.base.ARCHModel.resids.html#arch.univariate.base.ARCHModel.resids">[docs]</a>    <span class="nd">@abstractmethod</span>
+
+<div class="viewcode-block" id="ARCHModel.resids">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.base.ARCHModel.resids.html#arch.univariate.base.ARCHModel.resids">[docs]</a>
+    <span class="nd">@abstractmethod</span>
     <span class="k">def</span> <span class="nf">resids</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">params</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">,</span>
@@ -1246,7 +1270,10 @@ <h1>Source code for arch.univariate.base</h1><div class="highlight"><pre>
 <span class="sd">            Model residuals</span>
 <span class="sd">        &quot;&quot;&quot;</span></div>
 
-<div class="viewcode-block" id="ARCHModel.compute_param_cov"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.base.ARCHModel.compute_param_cov.html#arch.univariate.base.ARCHModel.compute_param_cov">[docs]</a>    <span class="k">def</span> <span class="nf">compute_param_cov</span><span class="p">(</span>
+
+<div class="viewcode-block" id="ARCHModel.compute_param_cov">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.base.ARCHModel.compute_param_cov.html#arch.univariate.base.ARCHModel.compute_param_cov">[docs]</a>
+    <span class="k">def</span> <span class="nf">compute_param_cov</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">params</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">,</span>
         <span class="n">backcast</span><span class="p">:</span> <span class="kc">None</span> <span class="o">|</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">Float64Array</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
@@ -1295,7 +1322,10 @@ <h1>Source code for arch.univariate.base</h1><div class="highlight"><pre>
         <span class="k">else</span><span class="p">:</span>
             <span class="k">return</span> <span class="n">inv_hess</span> <span class="o">/</span> <span class="n">nobs</span></div>
 
-<div class="viewcode-block" id="ARCHModel.forecast"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.base.ARCHModel.forecast.html#arch.univariate.base.ARCHModel.forecast">[docs]</a>    <span class="nd">@abstractmethod</span>
+
+<div class="viewcode-block" id="ARCHModel.forecast">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.base.ARCHModel.forecast.html#arch.univariate.base.ARCHModel.forecast">[docs]</a>
+    <span class="nd">@abstractmethod</span>
     <span class="k">def</span> <span class="nf">forecast</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">params</span><span class="p">:</span> <span class="n">ArrayLike1D</span><span class="p">,</span>
@@ -1307,7 +1337,7 @@ <h1>Source code for arch.univariate.base</h1><div class="highlight"><pre>
         <span class="n">rng</span><span class="p">:</span> <span class="n">Callable</span><span class="p">[[</span><span class="nb">int</span> <span class="o">|</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">]],</span> <span class="n">Float64Array</span><span class="p">]</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
         <span class="n">random_state</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
         <span class="o">*</span><span class="p">,</span>
-        <span class="n">reindex</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">reindex</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
         <span class="n">x</span><span class="p">:</span> <span class="kc">None</span> <span class="o">|</span> <span class="nb">dict</span><span class="p">[</span><span class="n">Label</span><span class="p">,</span> <span class="n">ArrayLike</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
     <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">ARCHModelForecast</span><span class="p">:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
@@ -1419,7 +1449,9 @@ <h1>Source code for arch.univariate.base</h1><div class="highlight"><pre>
 <span class="sd">        If `align` is &#39;target&#39;, then the same forecast is in location</span>
 <span class="sd">        [102, 2], so that it is aligned with the observation to use when</span>
 <span class="sd">        evaluating, but still in the same column.</span>
-<span class="sd">        &quot;&quot;&quot;</span></div></div>
+<span class="sd">        &quot;&quot;&quot;</span></div>
+</div>
+
 
 
 <span class="k">class</span> <span class="nc">_SummaryRepr</span><span class="p">:</span>
@@ -1438,7 +1470,9 @@ <h1>Source code for arch.univariate.base</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span><span class="o">.</span><span class="n">as_text</span><span class="p">()</span>
 
 
-<div class="viewcode-block" id="ARCHModelFixedResult"><a class="viewcode-back" href="../../../univariate/generated/arch.univariate.base.ARCHModelFixedResult.html#arch.univariate.base.ARCHModelFixedResult">[docs]</a><span class="k">class</span> <span class="nc">ARCHModelFixedResult</span><span class="p">(</span><span class="n">_SummaryRepr</span><span class="p">):</span>
+<div class="viewcode-block" id="ARCHModelFixedResult">
+<a class="viewcode-back" href="../../../univariate/generated/arch.univariate.base.ARCHModelFixedResult.html#arch.univariate.base.ARCHModelFixedResult">[docs]</a>
+<span class="k">class</span> <span class="nc">ARCHModelFixedResult</span><span class="p">(</span><span class="n">_SummaryRepr</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Results for fixed parameters for an ARCHModel model</span>
 
@@ -1487,7 +1521,9 @@ <h1>Source code for arch.univariate.base</h1><div class="highlight"><pre>
         <span class="bp">self</span><span class="o">.</span><span class="n">_index</span> <span class="o">=</span> <span class="n">dep_var</span><span class="o">.</span><span class="n">index</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_volatility</span> <span class="o">=</span> <span class="n">volatility</span>
 
-<div class="viewcode-block" id="ARCHModelFixedResult.summary"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.summary.html#arch.univariate.base.ARCHModelFixedResult.summary">[docs]</a>    <span class="k">def</span> <span class="nf">summary</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Summary</span><span class="p">:</span>
+<div class="viewcode-block" id="ARCHModelFixedResult.summary">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.summary.html#arch.univariate.base.ARCHModelFixedResult.summary">[docs]</a>
+    <span class="k">def</span> <span class="nf">summary</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Summary</span><span class="p">:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Constructs a summary of the results from a fit model.</span>
 
@@ -1596,6 +1632,7 @@ <h1>Source code for arch.univariate.base</h1><div class="highlight"><pre>
         <span class="n">smry</span><span class="o">.</span><span class="n">add_extra_txt</span><span class="p">(</span><span class="n">extra_text</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">smry</span></div>
 
+
     <span class="nd">@cached_property</span>
     <span class="k">def</span> <span class="nf">model</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">ARCHModel</span><span class="p">:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
@@ -1679,7 +1716,9 @@ <h1>Source code for arch.univariate.base</h1><div class="highlight"><pre>
             <span class="n">std_res</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;std_resid&quot;</span>
         <span class="k">return</span> <span class="n">std_res</span>
 
-<div class="viewcode-block" id="ARCHModelFixedResult.plot"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.plot.html#arch.univariate.base.ARCHModelFixedResult.plot">[docs]</a>    <span class="k">def</span> <span class="nf">plot</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">annualize</span><span class="p">:</span> <span class="nb">str</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">scale</span><span class="p">:</span> <span class="nb">float</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Figure</span><span class="p">:</span>
+<div class="viewcode-block" id="ARCHModelFixedResult.plot">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.plot.html#arch.univariate.base.ARCHModelFixedResult.plot">[docs]</a>
+    <span class="k">def</span> <span class="nf">plot</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">annualize</span><span class="p">:</span> <span class="nb">str</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">scale</span><span class="p">:</span> <span class="nb">float</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Figure</span><span class="p">:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Plot standardized residuals and conditional volatility</span>
 
@@ -1754,7 +1793,10 @@ <h1>Source code for arch.univariate.base</h1><div class="highlight"><pre>
 
         <span class="k">return</span> <span class="n">fig</span></div>
 
-<div class="viewcode-block" id="ARCHModelFixedResult.forecast"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.forecast.html#arch.univariate.base.ARCHModelFixedResult.forecast">[docs]</a>    <span class="k">def</span> <span class="nf">forecast</span><span class="p">(</span>
+
+<div class="viewcode-block" id="ARCHModelFixedResult.forecast">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.forecast.html#arch.univariate.base.ARCHModelFixedResult.forecast">[docs]</a>
+    <span class="k">def</span> <span class="nf">forecast</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">params</span><span class="p">:</span> <span class="n">ArrayLike1D</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
         <span class="n">horizon</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
@@ -1765,7 +1807,7 @@ <h1>Source code for arch.univariate.base</h1><div class="highlight"><pre>
         <span class="n">rng</span><span class="p">:</span> <span class="n">Callable</span><span class="p">[[</span><span class="nb">int</span> <span class="o">|</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">]],</span> <span class="n">Float64Array</span><span class="p">]</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
         <span class="n">random_state</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
         <span class="o">*</span><span class="p">,</span>
-        <span class="n">reindex</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">reindex</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
         <span class="n">x</span><span class="p">:</span> <span class="kc">None</span> <span class="o">|</span> <span class="nb">dict</span><span class="p">[</span><span class="n">Label</span><span class="p">,</span> <span class="n">ArrayLike</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
     <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">ARCHModelForecast</span><span class="p">:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
@@ -1808,11 +1850,11 @@ <h1>Source code for arch.univariate.base</h1><div class="highlight"><pre>
 <span class="sd">            NumPy RandomState instance to use when method is &#39;bootstrap&#39;</span>
 <span class="sd">        reindex : bool, optional</span>
 <span class="sd">            Whether to reindex the forecasts to have the same dimension as the series</span>
-<span class="sd">            being forecast. Prior to 4.18 this was the default. As of 4.19 this is</span>
-<span class="sd">            now optional. If not provided, a warning is raised about the future</span>
-<span class="sd">            change in the default which will occur after September 2021.</span>
+<span class="sd">            being forecast.</span>
 
-<span class="sd">            .. versionadded:: 4.19</span>
+<span class="sd">            .. versionchanged:: 6.2</span>
+
+<span class="sd">               The default has been changed to False.</span>
 
 <span class="sd">        x : {dict[label, array_like], array_like}</span>
 <span class="sd">            Values to use for exogenous regressors if any are included in the</span>
@@ -1868,7 +1910,6 @@ <h1>Source code for arch.univariate.base</h1><div class="highlight"><pre>
 <span class="sd">        [102, 2], so that it is aligned with the observation to use when</span>
 <span class="sd">        evaluating, but still in the same column.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">reindex</span> <span class="o">=</span> <span class="n">check_reindex</span><span class="p">(</span><span class="n">reindex</span><span class="p">)</span>
         <span class="k">if</span> <span class="n">params</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">params</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_params</span>
         <span class="k">else</span><span class="p">:</span>
@@ -1892,7 +1933,10 @@ <h1>Source code for arch.univariate.base</h1><div class="highlight"><pre>
             <span class="n">x</span><span class="o">=</span><span class="n">x</span><span class="p">,</span>
         <span class="p">)</span></div>
 
-<div class="viewcode-block" id="ARCHModelFixedResult.hedgehog_plot"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.html#arch.univariate.base.ARCHModelFixedResult.hedgehog_plot">[docs]</a>    <span class="nd">@deprecate_kwarg</span><span class="p">(</span><span class="s2">&quot;type&quot;</span><span class="p">,</span> <span class="s2">&quot;plot_type&quot;</span><span class="p">)</span>
+
+<div class="viewcode-block" id="ARCHModelFixedResult.hedgehog_plot">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.html#arch.univariate.base.ARCHModelFixedResult.hedgehog_plot">[docs]</a>
+    <span class="nd">@deprecate_kwarg</span><span class="p">(</span><span class="s2">&quot;type&quot;</span><span class="p">,</span> <span class="s2">&quot;plot_type&quot;</span><span class="p">)</span>
     <span class="k">def</span> <span class="nf">hedgehog_plot</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">params</span><span class="p">:</span> <span class="n">ArrayLike1D</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
@@ -1964,7 +2008,6 @@ <h1>Source code for arch.univariate.base</h1><div class="highlight"><pre>
                         <span class="n">start</span><span class="p">,</span>
                         <span class="n">method</span><span class="o">=</span><span class="n">method</span><span class="p">,</span>
                         <span class="n">simulations</span><span class="o">=</span><span class="n">simulations</span><span class="p">,</span>
-                        <span class="n">reindex</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
                     <span class="p">)</span>
                     <span class="n">invalid_start</span> <span class="o">=</span> <span class="kc">False</span>
                 <span class="k">except</span> <span class="ne">ValueError</span><span class="p">:</span>
@@ -1976,7 +2019,6 @@ <h1>Source code for arch.univariate.base</h1><div class="highlight"><pre>
                 <span class="n">start</span><span class="p">,</span>
                 <span class="n">method</span><span class="o">=</span><span class="n">method</span><span class="p">,</span>
                 <span class="n">simulations</span><span class="o">=</span><span class="n">simulations</span><span class="p">,</span>
-                <span class="n">reindex</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
             <span class="p">)</span>
 
         <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
@@ -2013,7 +2055,10 @@ <h1>Source code for arch.univariate.base</h1><div class="highlight"><pre>
 
         <span class="k">return</span> <span class="n">fig</span></div>
 
-<div class="viewcode-block" id="ARCHModelFixedResult.arch_lm_test"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.arch_lm_test.html#arch.univariate.base.ARCHModelFixedResult.arch_lm_test">[docs]</a>    <span class="k">def</span> <span class="nf">arch_lm_test</span><span class="p">(</span>
+
+<div class="viewcode-block" id="ARCHModelFixedResult.arch_lm_test">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.arch_lm_test.html#arch.univariate.base.ARCHModelFixedResult.arch_lm_test">[docs]</a>
+    <span class="k">def</span> <span class="nf">arch_lm_test</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span> <span class="n">lags</span><span class="p">:</span> <span class="nb">int</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">standardized</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span>
     <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">WaldTestStatistic</span><span class="p">:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
@@ -2058,10 +2103,14 @@ <h1>Source code for arch.univariate.base</h1><div class="highlight"><pre>
         <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">lags</span><span class="p">,</span> <span class="nb">int</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">WaldTestStatistic</span><span class="p">(</span>
             <span class="n">stat</span><span class="p">,</span> <span class="n">df</span><span class="o">=</span><span class="n">lags</span><span class="p">,</span> <span class="n">null</span><span class="o">=</span><span class="n">null</span><span class="p">,</span> <span class="n">alternative</span><span class="o">=</span><span class="n">alt</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;ARCH-LM Test&quot;</span>
-        <span class="p">)</span></div></div>
+        <span class="p">)</span></div>
+</div>
+
 
 
-<div class="viewcode-block" id="ARCHModelResult"><a class="viewcode-back" href="../../../univariate/generated/arch.univariate.base.ARCHModelResult.html#arch.univariate.base.ARCHModelResult">[docs]</a><span class="k">class</span> <span class="nc">ARCHModelResult</span><span class="p">(</span><span class="n">ARCHModelFixedResult</span><span class="p">):</span>
+<div class="viewcode-block" id="ARCHModelResult">
+<a class="viewcode-back" href="../../../univariate/generated/arch.univariate.base.ARCHModelResult.html#arch.univariate.base.ARCHModelResult">[docs]</a>
+<span class="k">class</span> <span class="nc">ARCHModelResult</span><span class="p">(</span><span class="n">ARCHModelFixedResult</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Results from estimation of an ARCHModel model</span>
 
@@ -2138,7 +2187,9 @@ <h1>Source code for arch.univariate.base</h1><div class="highlight"><pre>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">scale</span>
 
-<div class="viewcode-block" id="ARCHModelResult.conf_int"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.base.ARCHModelResult.conf_int.html#arch.univariate.base.ARCHModelResult.conf_int">[docs]</a>    <span class="k">def</span> <span class="nf">conf_int</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">alpha</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.05</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
+<div class="viewcode-block" id="ARCHModelResult.conf_int">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.base.ARCHModelResult.conf_int.html#arch.univariate.base.ARCHModelResult.conf_int">[docs]</a>
+    <span class="k">def</span> <span class="nf">conf_int</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">alpha</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.05</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Parameter confidence intervals</span>
 
@@ -2163,7 +2214,10 @@ <h1>Source code for arch.univariate.base</h1><div class="highlight"><pre>
             <span class="n">index</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_names</span><span class="p">,</span>
         <span class="p">)</span></div>
 
-<div class="viewcode-block" id="ARCHModelResult.summary"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.base.ARCHModelResult.summary.html#arch.univariate.base.ARCHModelResult.summary">[docs]</a>    <span class="k">def</span> <span class="nf">summary</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Summary</span><span class="p">:</span>
+
+<div class="viewcode-block" id="ARCHModelResult.summary">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.base.ARCHModelResult.summary.html#arch.univariate.base.ARCHModelResult.summary">[docs]</a>
+    <span class="k">def</span> <span class="nf">summary</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Summary</span><span class="p">:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Constructs a summary of the results from a fit model.</span>
 
@@ -2297,6 +2351,7 @@ <h1>Source code for arch.univariate.base</h1><div class="highlight"><pre>
         <span class="n">smry</span><span class="o">.</span><span class="n">add_extra_txt</span><span class="p">(</span><span class="n">extra_text</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">smry</span></div>
 
+
     <span class="nd">@cached_property</span>
     <span class="k">def</span> <span class="nf">param_cov</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;Parameter covariance&quot;&quot;&quot;</span>
@@ -2381,6 +2436,7 @@ <h1>Source code for arch.univariate.base</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_optim_output</span></div>
 
 
+
 <span class="k">def</span> <span class="nf">_align_forecast</span><span class="p">(</span>
     <span class="n">f</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span> <span class="n">align</span><span class="p">:</span> <span class="n">Literal</span><span class="p">[</span><span class="s2">&quot;origin&quot;</span><span class="p">,</span> <span class="s2">&quot;target&quot;</span><span class="p">]</span>
 <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
@@ -2406,7 +2462,9 @@ <h1>Source code for arch.univariate.base</h1><div class="highlight"><pre>
     <span class="k">return</span> <span class="n">forecasts</span>
 
 
-<div class="viewcode-block" id="ARCHModelForecastSimulation"><a class="viewcode-back" href="../../../univariate/generated/arch.univariate.base.ARCHModelForecastSimulation.html#arch.univariate.base.ARCHModelForecastSimulation">[docs]</a><span class="k">class</span> <span class="nc">ARCHModelForecastSimulation</span><span class="p">:</span>
+<div class="viewcode-block" id="ARCHModelForecastSimulation">
+<a class="viewcode-back" href="../../../univariate/generated/arch.univariate.base.ARCHModelForecastSimulation.html#arch.univariate.base.ARCHModelForecastSimulation">[docs]</a>
+<span class="k">class</span> <span class="nc">ARCHModelForecastSimulation</span><span class="p">:</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Container for a simulation or bootstrap-based forecasts from an ARCH Model</span>
 
@@ -2459,6 +2517,7 @@ <h1>Source code for arch.univariate.base</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_residual_variances</span></div>
 
 
+
 <span class="k">def</span> <span class="nf">_reindex</span><span class="p">(</span>
     <span class="n">a</span><span class="p">:</span> <span class="n">Float64Array</span> <span class="o">|</span> <span class="kc">None</span><span class="p">,</span> <span class="n">idx</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Label</span><span class="p">]</span> <span class="o">|</span> <span class="n">pd</span><span class="o">.</span><span class="n">Index</span>
 <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span> <span class="o">|</span> <span class="kc">None</span><span class="p">:</span>
@@ -2473,7 +2532,9 @@ <h1>Source code for arch.univariate.base</h1><div class="highlight"><pre>
     <span class="k">return</span> <span class="n">a</span>
 
 
-<div class="viewcode-block" id="ARCHModelForecast"><a class="viewcode-back" href="../../../univariate/generated/arch.univariate.base.ARCHModelForecast.html#arch.univariate.base.ARCHModelForecast">[docs]</a><span class="k">class</span> <span class="nc">ARCHModelForecast</span><span class="p">:</span>
+<div class="viewcode-block" id="ARCHModelForecast">
+<a class="viewcode-back" href="../../../univariate/generated/arch.univariate.base.ARCHModelForecast.html#arch.univariate.base.ARCHModelForecast">[docs]</a>
+<span class="k">class</span> <span class="nc">ARCHModelForecast</span><span class="p">:</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Container for forecasts from an ARCH Model</span>
 
@@ -2559,6 +2620,7 @@ <h1>Source code for arch.univariate.base</h1><div class="highlight"><pre>
 <span class="sd">            Container for simulation results</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sim</span></div>
+
 </code></pre></div>
 
 
@@ -2590,7 +2652,7 @@ <h1>Source code for arch.univariate.base</h1><div class="highlight"><pre>
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -2637,7 +2699,7 @@ <h1>Source code for arch.univariate.base</h1><div class="highlight"><pre>
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/_modules/arch/univariate/distribution.html b/_modules/arch/univariate/distribution.html
index 2c18a51587..cc3c156525 100644
--- a/_modules/arch/univariate/distribution.html
+++ b/_modules/arch/univariate/distribution.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.distribution - arch 6.1.0</title>
+        <title>arch.univariate.distribution - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -403,7 +403,9 @@ <h1>Source code for arch.univariate.distribution</h1><div class="highlight"><pre
 <span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;Distribution&quot;</span><span class="p">,</span> <span class="s2">&quot;Normal&quot;</span><span class="p">,</span> <span class="s2">&quot;StudentsT&quot;</span><span class="p">,</span> <span class="s2">&quot;SkewStudent&quot;</span><span class="p">,</span> <span class="s2">&quot;GeneralizedError&quot;</span><span class="p">]</span>
 
 
-<div class="viewcode-block" id="Distribution"><a class="viewcode-back" href="../../../univariate/generated/arch.univariate.distribution.Distribution.html#arch.univariate.distribution.Distribution">[docs]</a><span class="k">class</span> <span class="nc">Distribution</span><span class="p">(</span><span class="n">metaclass</span><span class="o">=</span><span class="n">ABCMeta</span><span class="p">):</span>
+<div class="viewcode-block" id="Distribution">
+<a class="viewcode-back" href="../../../univariate/generated/arch.univariate.distribution.Distribution.html#arch.univariate.distribution.Distribution">[docs]</a>
+<span class="k">class</span> <span class="nc">Distribution</span><span class="p">(</span><span class="n">metaclass</span><span class="o">=</span><span class="n">ABCMeta</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Template for subclassing only</span>
 <span class="sd">    &quot;&quot;&quot;</span>
@@ -514,7 +516,9 @@ <h1>Source code for arch.univariate.distribution</h1><div class="highlight"><pre
 <span class="sd">        Must call `simulate` before using `_simulator`</span>
 <span class="sd">        &quot;&quot;&quot;</span>
 
-<div class="viewcode-block" id="Distribution.simulate"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.distribution.Distribution.simulate.html#arch.univariate.distribution.Distribution.simulate">[docs]</a>    <span class="nd">@abstractmethod</span>
+<div class="viewcode-block" id="Distribution.simulate">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.distribution.Distribution.simulate.html#arch.univariate.distribution.Distribution.simulate">[docs]</a>
+    <span class="nd">@abstractmethod</span>
     <span class="k">def</span> <span class="nf">simulate</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span> <span class="n">parameters</span><span class="p">:</span> <span class="nb">int</span> <span class="o">|</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">float</span> <span class="o">|</span> <span class="nb">int</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike1D</span>
     <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Callable</span><span class="p">[[</span><span class="nb">int</span> <span class="o">|</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">]],</span> <span class="n">Float64Array</span><span class="p">]:</span>
@@ -533,7 +537,10 @@ <h1>Source code for arch.univariate.distribution</h1><div class="highlight"><pre
 <span class="sd">            draws from the distribution</span>
 <span class="sd">        &quot;&quot;&quot;</span></div>
 
-<div class="viewcode-block" id="Distribution.constraints"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.distribution.Distribution.constraints.html#arch.univariate.distribution.Distribution.constraints">[docs]</a>    <span class="nd">@abstractmethod</span>
+
+<div class="viewcode-block" id="Distribution.constraints">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.distribution.Distribution.constraints.html#arch.univariate.distribution.Distribution.constraints">[docs]</a>
+    <span class="nd">@abstractmethod</span>
     <span class="k">def</span> <span class="nf">constraints</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">Float64Array</span><span class="p">,</span> <span class="n">Float64Array</span><span class="p">]:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Construct arrays to use in constrained optimization.</span>
@@ -550,7 +557,10 @@ <h1>Source code for arch.univariate.distribution</h1><div class="highlight"><pre
 <span class="sd">        Parameters satisfy the constraints A.dot(parameters)-b &gt;= 0</span>
 <span class="sd">        &quot;&quot;&quot;</span></div>
 
-<div class="viewcode-block" id="Distribution.bounds"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.distribution.Distribution.bounds.html#arch.univariate.distribution.Distribution.bounds">[docs]</a>    <span class="nd">@abstractmethod</span>
+
+<div class="viewcode-block" id="Distribution.bounds">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.distribution.Distribution.bounds.html#arch.univariate.distribution.Distribution.bounds">[docs]</a>
+    <span class="nd">@abstractmethod</span>
     <span class="k">def</span> <span class="nf">bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]]:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Parameter bounds for use in optimization.</span>
@@ -566,7 +576,10 @@ <h1>Source code for arch.univariate.distribution</h1><div class="highlight"><pre
 <span class="sd">            List containing a single tuple with (lower, upper) bounds</span>
 <span class="sd">        &quot;&quot;&quot;</span></div>
 
-<div class="viewcode-block" id="Distribution.loglikelihood"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.distribution.Distribution.loglikelihood.html#arch.univariate.distribution.Distribution.loglikelihood">[docs]</a>    <span class="nd">@abstractmethod</span>
+
+<div class="viewcode-block" id="Distribution.loglikelihood">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.distribution.Distribution.loglikelihood.html#arch.univariate.distribution.Distribution.loglikelihood">[docs]</a>
+    <span class="nd">@abstractmethod</span>
     <span class="k">def</span> <span class="nf">loglikelihood</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike1D</span><span class="p">,</span>
@@ -595,7 +608,10 @@ <h1>Source code for arch.univariate.distribution</h1><div class="highlight"><pre
 <span class="sd">        and parameters and sigma2 are inputs.</span>
 <span class="sd">        &quot;&quot;&quot;</span></div>
 
-<div class="viewcode-block" id="Distribution.starting_values"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.distribution.Distribution.starting_values.html#arch.univariate.distribution.Distribution.starting_values">[docs]</a>    <span class="nd">@abstractmethod</span>
+
+<div class="viewcode-block" id="Distribution.starting_values">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.distribution.Distribution.starting_values.html#arch.univariate.distribution.Distribution.starting_values">[docs]</a>
+    <span class="nd">@abstractmethod</span>
     <span class="k">def</span> <span class="nf">starting_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">std_resid</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Construct starting values for use in optimization.</span>
@@ -616,7 +632,10 @@ <h1>Source code for arch.univariate.distribution</h1><div class="highlight"><pre
 <span class="sd">        Size of sv depends on the distribution</span>
 <span class="sd">        &quot;&quot;&quot;</span></div>
 
-<div class="viewcode-block" id="Distribution.parameter_names"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.distribution.Distribution.parameter_names.html#arch.univariate.distribution.Distribution.parameter_names">[docs]</a>    <span class="nd">@abstractmethod</span>
+
+<div class="viewcode-block" id="Distribution.parameter_names">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.distribution.Distribution.parameter_names.html#arch.univariate.distribution.Distribution.parameter_names">[docs]</a>
+    <span class="nd">@abstractmethod</span>
     <span class="k">def</span> <span class="nf">parameter_names</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Names of distribution shape parameters</span>
@@ -627,7 +646,10 @@ <h1>Source code for arch.univariate.distribution</h1><div class="highlight"><pre
 <span class="sd">            Parameter names</span>
 <span class="sd">        &quot;&quot;&quot;</span></div>
 
-<div class="viewcode-block" id="Distribution.ppf"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.distribution.Distribution.ppf.html#arch.univariate.distribution.Distribution.ppf">[docs]</a>    <span class="nd">@abstractmethod</span>
+
+<div class="viewcode-block" id="Distribution.ppf">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.distribution.Distribution.ppf.html#arch.univariate.distribution.Distribution.ppf">[docs]</a>
+    <span class="nd">@abstractmethod</span>
     <span class="k">def</span> <span class="nf">ppf</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">pits</span><span class="p">:</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike1D</span><span class="p">,</span>
@@ -650,7 +672,10 @@ <h1>Source code for arch.univariate.distribution</h1><div class="highlight"><pre
 <span class="sd">            Inverse CDF values</span>
 <span class="sd">        &quot;&quot;&quot;</span></div>
 
-<div class="viewcode-block" id="Distribution.cdf"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.distribution.Distribution.cdf.html#arch.univariate.distribution.Distribution.cdf">[docs]</a>    <span class="nd">@abstractmethod</span>
+
+<div class="viewcode-block" id="Distribution.cdf">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.distribution.Distribution.cdf.html#arch.univariate.distribution.Distribution.cdf">[docs]</a>
+    <span class="nd">@abstractmethod</span>
     <span class="k">def</span> <span class="nf">cdf</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">resids</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike1D</span><span class="p">,</span>
@@ -673,7 +698,10 @@ <h1>Source code for arch.univariate.distribution</h1><div class="highlight"><pre
 <span class="sd">            CDF values</span>
 <span class="sd">        &quot;&quot;&quot;</span></div>
 
-<div class="viewcode-block" id="Distribution.moment"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.distribution.Distribution.moment.html#arch.univariate.distribution.Distribution.moment">[docs]</a>    <span class="nd">@abstractmethod</span>
+
+<div class="viewcode-block" id="Distribution.moment">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.distribution.Distribution.moment.html#arch.univariate.distribution.Distribution.moment">[docs]</a>
+    <span class="nd">@abstractmethod</span>
     <span class="k">def</span> <span class="nf">moment</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span> <span class="n">n</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">parameters</span><span class="p">:</span> <span class="kc">None</span> <span class="o">|</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike1D</span> <span class="o">=</span> <span class="kc">None</span>
     <span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
@@ -693,7 +721,10 @@ <h1>Source code for arch.univariate.distribution</h1><div class="highlight"><pre
 <span class="sd">            Calculated moment</span>
 <span class="sd">        &quot;&quot;&quot;</span></div>
 
-<div class="viewcode-block" id="Distribution.partial_moment"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.distribution.Distribution.partial_moment.html#arch.univariate.distribution.Distribution.partial_moment">[docs]</a>    <span class="nd">@abstractmethod</span>
+
+<div class="viewcode-block" id="Distribution.partial_moment">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.distribution.Distribution.partial_moment.html#arch.univariate.distribution.Distribution.partial_moment">[docs]</a>
+    <span class="nd">@abstractmethod</span>
     <span class="k">def</span> <span class="nf">partial_moment</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">n</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
@@ -733,6 +764,7 @@ <h1>Source code for arch.univariate.distribution</h1><div class="highlight"><pre
 <span class="sd">        See [1]_ for more details.</span>
 <span class="sd">        &quot;&quot;&quot;</span></div>
 
+
     <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_description</span><span class="p">()</span>
 
@@ -743,7 +775,10 @@ <h1>Source code for arch.univariate.distribution</h1><div class="highlight"><pre
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">+</span> <span class="s2">&quot; distribution&quot;</span></div>
 
 
-<div class="viewcode-block" id="Normal"><a class="viewcode-back" href="../../../univariate/generated/arch.univariate.Normal.html#arch.univariate.distribution.Normal">[docs]</a><span class="k">class</span> <span class="nc">Normal</span><span class="p">(</span><span class="n">Distribution</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
+
+<div class="viewcode-block" id="Normal">
+<a class="viewcode-back" href="../../../univariate/generated/arch.univariate.Normal.html#arch.univariate.distribution.Normal">[docs]</a>
+<span class="k">class</span> <span class="nc">Normal</span><span class="p">(</span><span class="n">Distribution</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Standard normal distribution for use with ARCH models</span>
 
@@ -770,13 +805,21 @@ <h1>Source code for arch.univariate.distribution</h1><div class="highlight"><pre
         <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="n">seed</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_name</span> <span class="o">=</span> <span class="s2">&quot;Normal&quot;</span>
 
-<div class="viewcode-block" id="Normal.constraints"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.Normal.constraints.html#arch.univariate.distribution.Normal.constraints">[docs]</a>    <span class="k">def</span> <span class="nf">constraints</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">Float64Array</span><span class="p">,</span> <span class="n">Float64Array</span><span class="p">]:</span>
+<div class="viewcode-block" id="Normal.constraints">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.Normal.constraints.html#arch.univariate.distribution.Normal.constraints">[docs]</a>
+    <span class="k">def</span> <span class="nf">constraints</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">Float64Array</span><span class="p">,</span> <span class="n">Float64Array</span><span class="p">]:</span>
         <span class="k">return</span> <span class="n">empty</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">empty</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span></div>
 
-<div class="viewcode-block" id="Normal.bounds"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.Normal.bounds.html#arch.univariate.distribution.Normal.bounds">[docs]</a>    <span class="k">def</span> <span class="nf">bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]]:</span>
+
+<div class="viewcode-block" id="Normal.bounds">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.Normal.bounds.html#arch.univariate.distribution.Normal.bounds">[docs]</a>
+    <span class="k">def</span> <span class="nf">bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]]:</span>
         <span class="k">return</span> <span class="p">[]</span></div>
 
-<div class="viewcode-block" id="Normal.loglikelihood"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.Normal.loglikelihood.html#arch.univariate.distribution.Normal.loglikelihood">[docs]</a>    <span class="k">def</span> <span class="nf">loglikelihood</span><span class="p">(</span>
+
+<div class="viewcode-block" id="Normal.loglikelihood">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.Normal.loglikelihood.html#arch.univariate.distribution.Normal.loglikelihood">[docs]</a>
+    <span class="k">def</span> <span class="nf">loglikelihood</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike1D</span><span class="p">,</span>
         <span class="n">resids</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">,</span>
@@ -820,21 +863,33 @@ <h1>Source code for arch.univariate.distribution</h1><div class="highlight"><pre
         <span class="k">else</span><span class="p">:</span>
             <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span><span class="n">lls</span><span class="p">)</span></div>
 
-<div class="viewcode-block" id="Normal.starting_values"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.Normal.starting_values.html#arch.univariate.distribution.Normal.starting_values">[docs]</a>    <span class="k">def</span> <span class="nf">starting_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">std_resid</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
+
+<div class="viewcode-block" id="Normal.starting_values">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.Normal.starting_values.html#arch.univariate.distribution.Normal.starting_values">[docs]</a>
+    <span class="k">def</span> <span class="nf">starting_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">std_resid</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
         <span class="k">return</span> <span class="n">empty</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span></div>
 
+
     <span class="k">def</span> <span class="nf">_simulator</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">|</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_generator</span><span class="o">.</span><span class="n">standard_normal</span><span class="p">(</span><span class="n">size</span><span class="p">)</span>
 
-<div class="viewcode-block" id="Normal.simulate"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.Normal.simulate.html#arch.univariate.distribution.Normal.simulate">[docs]</a>    <span class="k">def</span> <span class="nf">simulate</span><span class="p">(</span>
+<div class="viewcode-block" id="Normal.simulate">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.Normal.simulate.html#arch.univariate.distribution.Normal.simulate">[docs]</a>
+    <span class="k">def</span> <span class="nf">simulate</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span> <span class="n">parameters</span><span class="p">:</span> <span class="nb">int</span> <span class="o">|</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">float</span> <span class="o">|</span> <span class="nb">int</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike1D</span>
     <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Callable</span><span class="p">[[</span><span class="nb">int</span> <span class="o">|</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">]],</span> <span class="n">Float64Array</span><span class="p">]:</span>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_simulator</span></div>
 
-<div class="viewcode-block" id="Normal.parameter_names"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.Normal.parameter_names.html#arch.univariate.distribution.Normal.parameter_names">[docs]</a>    <span class="k">def</span> <span class="nf">parameter_names</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
+
+<div class="viewcode-block" id="Normal.parameter_names">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.Normal.parameter_names.html#arch.univariate.distribution.Normal.parameter_names">[docs]</a>
+    <span class="k">def</span> <span class="nf">parameter_names</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
         <span class="k">return</span> <span class="p">[]</span></div>
 
-<div class="viewcode-block" id="Normal.cdf"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.Normal.cdf.html#arch.univariate.distribution.Normal.cdf">[docs]</a>    <span class="k">def</span> <span class="nf">cdf</span><span class="p">(</span>
+
+<div class="viewcode-block" id="Normal.cdf">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.Normal.cdf.html#arch.univariate.distribution.Normal.cdf">[docs]</a>
+    <span class="k">def</span> <span class="nf">cdf</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">resids</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike1D</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="kc">None</span> <span class="o">|</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike1D</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
@@ -842,7 +897,10 @@ <h1>Source code for arch.univariate.distribution</h1><div class="highlight"><pre
         <span class="bp">self</span><span class="o">.</span><span class="n">_check_constraints</span><span class="p">(</span><span class="n">parameters</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">stats</span><span class="o">.</span><span class="n">norm</span><span class="o">.</span><span class="n">cdf</span><span class="p">(</span><span class="n">asarray</span><span class="p">(</span><span class="n">resids</span><span class="p">))</span></div>
 
-<div class="viewcode-block" id="Normal.ppf"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.Normal.ppf.html#arch.univariate.distribution.Normal.ppf">[docs]</a>    <span class="k">def</span> <span class="nf">ppf</span><span class="p">(</span>
+
+<div class="viewcode-block" id="Normal.ppf">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.Normal.ppf.html#arch.univariate.distribution.Normal.ppf">[docs]</a>
+    <span class="k">def</span> <span class="nf">ppf</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">pits</span><span class="p">:</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike1D</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="kc">None</span> <span class="o">|</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike1D</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
@@ -859,7 +917,10 @@ <h1>Source code for arch.univariate.distribution</h1><div class="highlight"><pre
         <span class="k">else</span><span class="p">:</span>
             <span class="k">return</span> <span class="n">ppf</span></div>
 
-<div class="viewcode-block" id="Normal.moment"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.Normal.moment.html#arch.univariate.distribution.Normal.moment">[docs]</a>    <span class="k">def</span> <span class="nf">moment</span><span class="p">(</span>
+
+<div class="viewcode-block" id="Normal.moment">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.Normal.moment.html#arch.univariate.distribution.Normal.moment">[docs]</a>
+    <span class="k">def</span> <span class="nf">moment</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span> <span class="n">n</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">parameters</span><span class="p">:</span> <span class="kc">None</span> <span class="o">|</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike1D</span> <span class="o">=</span> <span class="kc">None</span>
     <span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
         <span class="k">if</span> <span class="n">n</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
@@ -867,7 +928,10 @@ <h1>Source code for arch.univariate.distribution</h1><div class="highlight"><pre
 
         <span class="k">return</span> <span class="n">stats</span><span class="o">.</span><span class="n">norm</span><span class="o">.</span><span class="n">moment</span><span class="p">(</span><span class="n">n</span><span class="p">)</span></div>
 
-<div class="viewcode-block" id="Normal.partial_moment"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.Normal.partial_moment.html#arch.univariate.distribution.Normal.partial_moment">[docs]</a>    <span class="k">def</span> <span class="nf">partial_moment</span><span class="p">(</span>
+
+<div class="viewcode-block" id="Normal.partial_moment">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.Normal.partial_moment.html#arch.univariate.distribution.Normal.partial_moment">[docs]</a>
+    <span class="k">def</span> <span class="nf">partial_moment</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">n</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
         <span class="n">z</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.0</span><span class="p">,</span>
@@ -882,10 +946,14 @@ <h1>Source code for arch.univariate.distribution</h1><div class="highlight"><pre
         <span class="k">else</span><span class="p">:</span>
             <span class="k">return</span> <span class="o">-</span><span class="p">(</span><span class="n">z</span> <span class="o">**</span> <span class="p">(</span><span class="n">n</span> <span class="o">-</span> <span class="mi">1</span><span class="p">))</span> <span class="o">*</span> <span class="n">stats</span><span class="o">.</span><span class="n">norm</span><span class="o">.</span><span class="n">pdf</span><span class="p">(</span><span class="n">z</span><span class="p">)</span> <span class="o">+</span> <span class="p">(</span><span class="n">n</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">partial_moment</span><span class="p">(</span>
                 <span class="n">n</span> <span class="o">-</span> <span class="mi">2</span><span class="p">,</span> <span class="n">z</span><span class="p">,</span> <span class="n">parameters</span>
-            <span class="p">)</span></div></div>
+            <span class="p">)</span></div>
+</div>
+
 
 
-<div class="viewcode-block" id="StudentsT"><a class="viewcode-back" href="../../../univariate/generated/arch.univariate.StudentsT.html#arch.univariate.distribution.StudentsT">[docs]</a><span class="k">class</span> <span class="nc">StudentsT</span><span class="p">(</span><span class="n">Distribution</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
+<div class="viewcode-block" id="StudentsT">
+<a class="viewcode-back" href="../../../univariate/generated/arch.univariate.StudentsT.html#arch.univariate.distribution.StudentsT">[docs]</a>
+<span class="k">class</span> <span class="nc">StudentsT</span><span class="p">(</span><span class="n">Distribution</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Standardized Student&#39;s distribution for use with ARCH models</span>
 
@@ -913,13 +981,21 @@ <h1>Source code for arch.univariate.distribution</h1><div class="highlight"><pre
         <span class="bp">self</span><span class="o">.</span><span class="n">_name</span> <span class="o">=</span> <span class="s2">&quot;Standardized Student&#39;s t&quot;</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">num_params</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span>
 
-<div class="viewcode-block" id="StudentsT.constraints"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.StudentsT.constraints.html#arch.univariate.distribution.StudentsT.constraints">[docs]</a>    <span class="k">def</span> <span class="nf">constraints</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">Float64Array</span><span class="p">,</span> <span class="n">Float64Array</span><span class="p">]:</span>
+<div class="viewcode-block" id="StudentsT.constraints">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.StudentsT.constraints.html#arch.univariate.distribution.StudentsT.constraints">[docs]</a>
+    <span class="k">def</span> <span class="nf">constraints</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">Float64Array</span><span class="p">,</span> <span class="n">Float64Array</span><span class="p">]:</span>
         <span class="k">return</span> <span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]),</span> <span class="n">array</span><span class="p">([</span><span class="mf">2.05</span><span class="p">,</span> <span class="o">-</span><span class="mf">500.0</span><span class="p">])</span></div>
 
-<div class="viewcode-block" id="StudentsT.bounds"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.StudentsT.bounds.html#arch.univariate.distribution.StudentsT.bounds">[docs]</a>    <span class="k">def</span> <span class="nf">bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]]:</span>
+
+<div class="viewcode-block" id="StudentsT.bounds">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.StudentsT.bounds.html#arch.univariate.distribution.StudentsT.bounds">[docs]</a>
+    <span class="k">def</span> <span class="nf">bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]]:</span>
         <span class="k">return</span> <span class="p">[(</span><span class="mf">2.05</span><span class="p">,</span> <span class="mf">500.0</span><span class="p">)]</span></div>
 
-<div class="viewcode-block" id="StudentsT.loglikelihood"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.StudentsT.loglikelihood.html#arch.univariate.distribution.StudentsT.loglikelihood">[docs]</a>    <span class="k">def</span> <span class="nf">loglikelihood</span><span class="p">(</span>
+
+<div class="viewcode-block" id="StudentsT.loglikelihood">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.StudentsT.loglikelihood.html#arch.univariate.distribution.StudentsT.loglikelihood">[docs]</a>
+    <span class="k">def</span> <span class="nf">loglikelihood</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike1D</span><span class="p">,</span>
         <span class="n">resids</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">,</span>
@@ -970,7 +1046,10 @@ <h1>Source code for arch.univariate.distribution</h1><div class="highlight"><pre
         <span class="k">else</span><span class="p">:</span>
             <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span><span class="n">lls</span><span class="p">)</span></div>
 
-<div class="viewcode-block" id="StudentsT.starting_values"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.StudentsT.starting_values.html#arch.univariate.distribution.StudentsT.starting_values">[docs]</a>    <span class="k">def</span> <span class="nf">starting_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">std_resid</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
+
+<div class="viewcode-block" id="StudentsT.starting_values">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.StudentsT.starting_values.html#arch.univariate.distribution.StudentsT.starting_values">[docs]</a>
+    <span class="k">def</span> <span class="nf">starting_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">std_resid</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Construct starting values for use in optimization.</span>
 
@@ -994,13 +1073,16 @@ <h1>Source code for arch.univariate.distribution</h1><div class="highlight"><pre
         <span class="n">sv</span> <span class="o">=</span> <span class="nb">max</span><span class="p">((</span><span class="mf">4.0</span> <span class="o">*</span> <span class="n">k</span> <span class="o">-</span> <span class="mf">6.0</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">k</span> <span class="o">-</span> <span class="mf">3.0</span><span class="p">)</span> <span class="k">if</span> <span class="n">k</span> <span class="o">&gt;</span> <span class="mf">3.75</span> <span class="k">else</span> <span class="mf">12.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">array</span><span class="p">([</span><span class="n">sv</span><span class="p">])</span></div>
 
+
     <span class="k">def</span> <span class="nf">_simulator</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">|</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
         <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">_parameters</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
         <span class="n">parameters</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_parameters</span>
         <span class="n">std_dev</span> <span class="o">=</span> <span class="n">sqrt</span><span class="p">(</span><span class="n">parameters</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">/</span> <span class="p">(</span><span class="n">parameters</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="mi">2</span><span class="p">))</span>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_generator</span><span class="o">.</span><span class="n">standard_t</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_parameters</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">size</span><span class="o">=</span><span class="n">size</span><span class="p">)</span> <span class="o">/</span> <span class="n">std_dev</span>
 
-<div class="viewcode-block" id="StudentsT.simulate"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.StudentsT.simulate.html#arch.univariate.distribution.StudentsT.simulate">[docs]</a>    <span class="k">def</span> <span class="nf">simulate</span><span class="p">(</span>
+<div class="viewcode-block" id="StudentsT.simulate">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.StudentsT.simulate.html#arch.univariate.distribution.StudentsT.simulate">[docs]</a>
+    <span class="k">def</span> <span class="nf">simulate</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span> <span class="n">parameters</span><span class="p">:</span> <span class="nb">int</span> <span class="o">|</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">float</span> <span class="o">|</span> <span class="nb">int</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike1D</span>
     <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Callable</span><span class="p">[[</span><span class="nb">int</span> <span class="o">|</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">]],</span> <span class="n">Float64Array</span><span class="p">]:</span>
         <span class="n">parameters</span> <span class="o">=</span> <span class="n">ensure1d</span><span class="p">(</span><span class="n">parameters</span><span class="p">,</span> <span class="s2">&quot;parameters&quot;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
@@ -1009,10 +1091,16 @@ <h1>Source code for arch.univariate.distribution</h1><div class="highlight"><pre
         <span class="bp">self</span><span class="o">.</span><span class="n">_parameters</span> <span class="o">=</span> <span class="n">asarray</span><span class="p">(</span><span class="n">parameters</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_simulator</span></div>
 
-<div class="viewcode-block" id="StudentsT.parameter_names"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.StudentsT.parameter_names.html#arch.univariate.distribution.StudentsT.parameter_names">[docs]</a>    <span class="k">def</span> <span class="nf">parameter_names</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
+
+<div class="viewcode-block" id="StudentsT.parameter_names">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.StudentsT.parameter_names.html#arch.univariate.distribution.StudentsT.parameter_names">[docs]</a>
+    <span class="k">def</span> <span class="nf">parameter_names</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
         <span class="k">return</span> <span class="p">[</span><span class="s2">&quot;nu&quot;</span><span class="p">]</span></div>
 
-<div class="viewcode-block" id="StudentsT.cdf"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.StudentsT.cdf.html#arch.univariate.distribution.StudentsT.cdf">[docs]</a>    <span class="k">def</span> <span class="nf">cdf</span><span class="p">(</span>
+
+<div class="viewcode-block" id="StudentsT.cdf">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.StudentsT.cdf.html#arch.univariate.distribution.StudentsT.cdf">[docs]</a>
+    <span class="k">def</span> <span class="nf">cdf</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">resids</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike1D</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="kc">None</span> <span class="o">|</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike1D</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
@@ -1022,7 +1110,10 @@ <h1>Source code for arch.univariate.distribution</h1><div class="highlight"><pre
         <span class="n">var</span> <span class="o">=</span> <span class="n">nu</span> <span class="o">/</span> <span class="p">(</span><span class="n">nu</span> <span class="o">-</span> <span class="mi">2</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">stats</span><span class="o">.</span><span class="n">t</span><span class="p">(</span><span class="n">nu</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">1.0</span> <span class="o">/</span> <span class="n">sqrt</span><span class="p">(</span><span class="n">var</span><span class="p">))</span><span class="o">.</span><span class="n">cdf</span><span class="p">(</span><span class="n">asarray</span><span class="p">(</span><span class="n">resids</span><span class="p">))</span></div>
 
-<div class="viewcode-block" id="StudentsT.ppf"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.StudentsT.ppf.html#arch.univariate.distribution.StudentsT.ppf">[docs]</a>    <span class="k">def</span> <span class="nf">ppf</span><span class="p">(</span>
+
+<div class="viewcode-block" id="StudentsT.ppf">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.StudentsT.ppf.html#arch.univariate.distribution.StudentsT.ppf">[docs]</a>
+    <span class="k">def</span> <span class="nf">ppf</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">pits</span><span class="p">:</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike1D</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="kc">None</span> <span class="o">|</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike1D</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
@@ -1033,7 +1124,10 @@ <h1>Source code for arch.univariate.distribution</h1><div class="highlight"><pre
         <span class="n">var</span> <span class="o">=</span> <span class="n">nu</span> <span class="o">/</span> <span class="p">(</span><span class="n">nu</span> <span class="o">-</span> <span class="mi">2</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">stats</span><span class="o">.</span><span class="n">t</span><span class="p">(</span><span class="n">nu</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">1.0</span> <span class="o">/</span> <span class="n">sqrt</span><span class="p">(</span><span class="n">var</span><span class="p">))</span><span class="o">.</span><span class="n">ppf</span><span class="p">(</span><span class="n">pits</span><span class="p">)</span></div>
 
-<div class="viewcode-block" id="StudentsT.moment"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.StudentsT.moment.html#arch.univariate.distribution.StudentsT.moment">[docs]</a>    <span class="k">def</span> <span class="nf">moment</span><span class="p">(</span>
+
+<div class="viewcode-block" id="StudentsT.moment">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.StudentsT.moment.html#arch.univariate.distribution.StudentsT.moment">[docs]</a>
+    <span class="k">def</span> <span class="nf">moment</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span> <span class="n">n</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">parameters</span><span class="p">:</span> <span class="kc">None</span> <span class="o">|</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike1D</span> <span class="o">=</span> <span class="kc">None</span>
     <span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
         <span class="k">if</span> <span class="n">n</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
@@ -1043,7 +1137,10 @@ <h1>Source code for arch.univariate.distribution</h1><div class="highlight"><pre
         <span class="n">var</span> <span class="o">=</span> <span class="n">nu</span> <span class="o">/</span> <span class="p">(</span><span class="n">nu</span> <span class="o">-</span> <span class="mi">2</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">stats</span><span class="o">.</span><span class="n">t</span><span class="o">.</span><span class="n">moment</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">nu</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">1.0</span> <span class="o">/</span> <span class="n">sqrt</span><span class="p">(</span><span class="n">var</span><span class="p">))</span></div>
 
-<div class="viewcode-block" id="StudentsT.partial_moment"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.StudentsT.partial_moment.html#arch.univariate.distribution.StudentsT.partial_moment">[docs]</a>    <span class="k">def</span> <span class="nf">partial_moment</span><span class="p">(</span>
+
+<div class="viewcode-block" id="StudentsT.partial_moment">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.StudentsT.partial_moment.html#arch.univariate.distribution.StudentsT.partial_moment">[docs]</a>
+    <span class="k">def</span> <span class="nf">partial_moment</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">n</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
         <span class="n">z</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.0</span><span class="p">,</span>
@@ -1056,6 +1153,7 @@ <h1>Source code for arch.univariate.distribution</h1><div class="highlight"><pre
         <span class="n">moment</span> <span class="o">=</span> <span class="p">(</span><span class="n">scale</span> <span class="o">**</span> <span class="n">n</span><span class="p">)</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">_ord_t_partial_moment</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">z</span> <span class="o">/</span> <span class="n">scale</span><span class="p">,</span> <span class="n">nu</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">moment</span></div>
 
+
     <span class="nd">@staticmethod</span>
     <span class="k">def</span> <span class="nf">_ord_t_partial_moment</span><span class="p">(</span><span class="n">n</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">z</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span> <span class="n">nu</span><span class="p">:</span> <span class="nb">float</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
 <span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
@@ -1105,7 +1203,10 @@ <h1>Source code for arch.univariate.distribution</h1><div class="highlight"><pre
         <span class="k">return</span> <span class="n">moment</span></div>
 
 
-<div class="viewcode-block" id="SkewStudent"><a class="viewcode-back" href="../../../univariate/generated/arch.univariate.SkewStudent.html#arch.univariate.distribution.SkewStudent">[docs]</a><span class="k">class</span> <span class="nc">SkewStudent</span><span class="p">(</span><span class="n">Distribution</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
+
+<div class="viewcode-block" id="SkewStudent">
+<a class="viewcode-back" href="../../../univariate/generated/arch.univariate.SkewStudent.html#arch.univariate.distribution.SkewStudent">[docs]</a>
+<span class="k">class</span> <span class="nc">SkewStudent</span><span class="p">(</span><span class="n">Distribution</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Standardized Skewed Student&#39;s distribution for use with ARCH models</span>
 
@@ -1148,13 +1249,21 @@ <h1>Source code for arch.univariate.distribution</h1><div class="highlight"><pre
         <span class="bp">self</span><span class="o">.</span><span class="n">_name</span> <span class="o">=</span> <span class="s2">&quot;Standardized Skew Student&#39;s t&quot;</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">num_params</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2</span>
 
-<div class="viewcode-block" id="SkewStudent.constraints"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.SkewStudent.constraints.html#arch.univariate.distribution.SkewStudent.constraints">[docs]</a>    <span class="k">def</span> <span class="nf">constraints</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">Float64Array</span><span class="p">,</span> <span class="n">Float64Array</span><span class="p">]:</span>
+<div class="viewcode-block" id="SkewStudent.constraints">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.SkewStudent.constraints.html#arch.univariate.distribution.SkewStudent.constraints">[docs]</a>
+    <span class="k">def</span> <span class="nf">constraints</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">Float64Array</span><span class="p">,</span> <span class="n">Float64Array</span><span class="p">]:</span>
         <span class="k">return</span> <span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]]),</span> <span class="n">array</span><span class="p">([</span><span class="mf">2.05</span><span class="p">,</span> <span class="o">-</span><span class="mf">300.0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">])</span></div>
 
-<div class="viewcode-block" id="SkewStudent.bounds"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.SkewStudent.bounds.html#arch.univariate.distribution.SkewStudent.bounds">[docs]</a>    <span class="k">def</span> <span class="nf">bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]]:</span>
+
+<div class="viewcode-block" id="SkewStudent.bounds">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.SkewStudent.bounds.html#arch.univariate.distribution.SkewStudent.bounds">[docs]</a>
+    <span class="k">def</span> <span class="nf">bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]]:</span>
         <span class="k">return</span> <span class="p">[(</span><span class="mf">2.05</span><span class="p">,</span> <span class="mf">300.0</span><span class="p">),</span> <span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)]</span></div>
 
-<div class="viewcode-block" id="SkewStudent.loglikelihood"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.SkewStudent.loglikelihood.html#arch.univariate.distribution.SkewStudent.loglikelihood">[docs]</a>    <span class="k">def</span> <span class="nf">loglikelihood</span><span class="p">(</span>
+
+<div class="viewcode-block" id="SkewStudent.loglikelihood">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.SkewStudent.loglikelihood.html#arch.univariate.distribution.SkewStudent.loglikelihood">[docs]</a>
+    <span class="k">def</span> <span class="nf">loglikelihood</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike1D</span><span class="p">,</span>
         <span class="n">resids</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">,</span>
@@ -1228,7 +1337,10 @@ <h1>Source code for arch.univariate.distribution</h1><div class="highlight"><pre
         <span class="k">else</span><span class="p">:</span>
             <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span><span class="n">lls</span><span class="p">)</span></div>
 
-<div class="viewcode-block" id="SkewStudent.starting_values"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.SkewStudent.starting_values.html#arch.univariate.distribution.SkewStudent.starting_values">[docs]</a>    <span class="k">def</span> <span class="nf">starting_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">std_resid</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
+
+<div class="viewcode-block" id="SkewStudent.starting_values">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.SkewStudent.starting_values.html#arch.univariate.distribution.SkewStudent.starting_values">[docs]</a>
+    <span class="k">def</span> <span class="nf">starting_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">std_resid</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Construct starting values for use in optimization.</span>
 
@@ -1252,6 +1364,7 @@ <h1>Source code for arch.univariate.distribution</h1><div class="highlight"><pre
         <span class="n">sv</span> <span class="o">=</span> <span class="nb">max</span><span class="p">((</span><span class="mf">4.0</span> <span class="o">*</span> <span class="n">k</span> <span class="o">-</span> <span class="mf">6.0</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">k</span> <span class="o">-</span> <span class="mf">3.0</span><span class="p">)</span> <span class="k">if</span> <span class="n">k</span> <span class="o">&gt;</span> <span class="mf">3.75</span> <span class="k">else</span> <span class="mf">12.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">array</span><span class="p">([</span><span class="n">sv</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">])</span></div>
 
+
     <span class="k">def</span> <span class="nf">_simulator</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">|</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
         <span class="c1"># No need to normalize since it is already done in parameterization</span>
         <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">_parameters</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
@@ -1263,7 +1376,9 @@ <h1>Source code for arch.univariate.distribution</h1><div class="highlight"><pre
         <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">ppf</span><span class="p">,</span> <span class="n">ndarray</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">ppf</span>
 
-<div class="viewcode-block" id="SkewStudent.simulate"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.SkewStudent.simulate.html#arch.univariate.distribution.SkewStudent.simulate">[docs]</a>    <span class="k">def</span> <span class="nf">simulate</span><span class="p">(</span>
+<div class="viewcode-block" id="SkewStudent.simulate">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.SkewStudent.simulate.html#arch.univariate.distribution.SkewStudent.simulate">[docs]</a>
+    <span class="k">def</span> <span class="nf">simulate</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span> <span class="n">parameters</span><span class="p">:</span> <span class="nb">int</span> <span class="o">|</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">float</span> <span class="o">|</span> <span class="nb">int</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike1D</span>
     <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Callable</span><span class="p">[[</span><span class="nb">int</span> <span class="o">|</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">]],</span> <span class="n">Float64Array</span><span class="p">]:</span>
         <span class="n">parameters</span> <span class="o">=</span> <span class="n">ensure1d</span><span class="p">(</span><span class="n">parameters</span><span class="p">,</span> <span class="s2">&quot;parameters&quot;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
@@ -1276,9 +1391,13 @@ <h1>Source code for arch.univariate.distribution</h1><div class="highlight"><pre
         <span class="bp">self</span><span class="o">.</span><span class="n">_parameters</span> <span class="o">=</span> <span class="n">asarray</span><span class="p">(</span><span class="n">parameters</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_simulator</span></div>
 
-<div class="viewcode-block" id="SkewStudent.parameter_names"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.SkewStudent.parameter_names.html#arch.univariate.distribution.SkewStudent.parameter_names">[docs]</a>    <span class="k">def</span> <span class="nf">parameter_names</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
+
+<div class="viewcode-block" id="SkewStudent.parameter_names">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.SkewStudent.parameter_names.html#arch.univariate.distribution.SkewStudent.parameter_names">[docs]</a>
+    <span class="k">def</span> <span class="nf">parameter_names</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
         <span class="k">return</span> <span class="p">[</span><span class="s2">&quot;eta&quot;</span><span class="p">,</span> <span class="s2">&quot;lambda&quot;</span><span class="p">]</span></div>
 
+
     <span class="k">def</span> <span class="nf">__const_a</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">parameters</span><span class="p">:</span> <span class="n">Float64Array</span> <span class="o">|</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">float</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Compute a constant.</span>
@@ -1337,7 +1456,9 @@ <h1>Source code for arch.univariate.distribution</h1><div class="highlight"><pre
             <span class="n">gammaln</span><span class="p">((</span><span class="n">eta</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span> <span class="o">-</span> <span class="n">gammaln</span><span class="p">(</span><span class="n">eta</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span> <span class="o">-</span> <span class="n">log</span><span class="p">(</span><span class="n">pi</span> <span class="o">*</span> <span class="p">(</span><span class="n">eta</span> <span class="o">-</span> <span class="mi">2</span><span class="p">))</span> <span class="o">/</span> <span class="mi">2</span>
         <span class="p">)</span>
 
-<div class="viewcode-block" id="SkewStudent.cdf"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.SkewStudent.cdf.html#arch.univariate.distribution.SkewStudent.cdf">[docs]</a>    <span class="k">def</span> <span class="nf">cdf</span><span class="p">(</span>
+<div class="viewcode-block" id="SkewStudent.cdf">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.SkewStudent.cdf.html#arch.univariate.distribution.SkewStudent.cdf">[docs]</a>
+    <span class="k">def</span> <span class="nf">cdf</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">resids</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike1D</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="kc">None</span> <span class="o">|</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike1D</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
@@ -1363,7 +1484,10 @@ <h1>Source code for arch.univariate.distribution</h1><div class="highlight"><pre
             <span class="n">p</span> <span class="o">=</span> <span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
         <span class="k">return</span> <span class="n">p</span></div>
 
-<div class="viewcode-block" id="SkewStudent.ppf"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.SkewStudent.ppf.html#arch.univariate.distribution.SkewStudent.ppf">[docs]</a>    <span class="k">def</span> <span class="nf">ppf</span><span class="p">(</span>
+
+<div class="viewcode-block" id="SkewStudent.ppf">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.SkewStudent.ppf.html#arch.univariate.distribution.SkewStudent.ppf">[docs]</a>
+    <span class="k">def</span> <span class="nf">ppf</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">pits</span><span class="p">:</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike1D</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="kc">None</span> <span class="o">|</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike1D</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
@@ -1394,7 +1518,10 @@ <h1>Source code for arch.univariate.distribution</h1><div class="highlight"><pre
         <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">icdf</span><span class="p">,</span> <span class="n">ndarray</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">icdf</span></div>
 
-<div class="viewcode-block" id="SkewStudent.moment"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.SkewStudent.moment.html#arch.univariate.distribution.SkewStudent.moment">[docs]</a>    <span class="k">def</span> <span class="nf">moment</span><span class="p">(</span>
+
+<div class="viewcode-block" id="SkewStudent.moment">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.SkewStudent.moment.html#arch.univariate.distribution.SkewStudent.moment">[docs]</a>
+    <span class="k">def</span> <span class="nf">moment</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span> <span class="n">n</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">parameters</span><span class="p">:</span> <span class="kc">None</span> <span class="o">|</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike1D</span> <span class="o">=</span> <span class="kc">None</span>
     <span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
         <span class="n">parameters</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_check_constraints</span><span class="p">(</span><span class="n">parameters</span><span class="p">)</span>
@@ -1426,7 +1553,10 @@ <h1>Source code for arch.univariate.distribution</h1><div class="highlight"><pre
 
         <span class="k">return</span> <span class="n">moment</span></div>
 
-<div class="viewcode-block" id="SkewStudent.partial_moment"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.SkewStudent.partial_moment.html#arch.univariate.distribution.SkewStudent.partial_moment">[docs]</a>    <span class="k">def</span> <span class="nf">partial_moment</span><span class="p">(</span>
+
+<div class="viewcode-block" id="SkewStudent.partial_moment">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.SkewStudent.partial_moment.html#arch.univariate.distribution.SkewStudent.partial_moment">[docs]</a>
+    <span class="k">def</span> <span class="nf">partial_moment</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">n</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
         <span class="n">z</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.0</span><span class="p">,</span>
@@ -1470,10 +1600,14 @@ <h1>Source code for arch.univariate.distribution</h1><div class="highlight"><pre
 
             <span class="n">moment</span> <span class="o">+=</span> <span class="n">comb</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">k</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">lhs</span> <span class="o">+</span> <span class="n">rhs</span><span class="p">)</span>
 
-        <span class="k">return</span> <span class="n">moment</span></div></div>
+        <span class="k">return</span> <span class="n">moment</span></div>
+</div>
 
 
-<div class="viewcode-block" id="GeneralizedError"><a class="viewcode-back" href="../../../univariate/generated/arch.univariate.GeneralizedError.html#arch.univariate.distribution.GeneralizedError">[docs]</a><span class="k">class</span> <span class="nc">GeneralizedError</span><span class="p">(</span><span class="n">Distribution</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
+
+<div class="viewcode-block" id="GeneralizedError">
+<a class="viewcode-back" href="../../../univariate/generated/arch.univariate.GeneralizedError.html#arch.univariate.distribution.GeneralizedError">[docs]</a>
+<span class="k">class</span> <span class="nc">GeneralizedError</span><span class="p">(</span><span class="n">Distribution</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Generalized Error distribution for use with ARCH models</span>
 
@@ -1501,13 +1635,21 @@ <h1>Source code for arch.univariate.distribution</h1><div class="highlight"><pre
         <span class="bp">self</span><span class="o">.</span><span class="n">_name</span> <span class="o">=</span> <span class="s2">&quot;Generalized Error Distribution&quot;</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">num_params</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span>
 
-<div class="viewcode-block" id="GeneralizedError.constraints"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.GeneralizedError.constraints.html#arch.univariate.distribution.GeneralizedError.constraints">[docs]</a>    <span class="k">def</span> <span class="nf">constraints</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">Float64Array</span><span class="p">,</span> <span class="n">Float64Array</span><span class="p">]:</span>
+<div class="viewcode-block" id="GeneralizedError.constraints">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.GeneralizedError.constraints.html#arch.univariate.distribution.GeneralizedError.constraints">[docs]</a>
+    <span class="k">def</span> <span class="nf">constraints</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">Float64Array</span><span class="p">,</span> <span class="n">Float64Array</span><span class="p">]:</span>
         <span class="k">return</span> <span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]),</span> <span class="n">array</span><span class="p">([</span><span class="mf">1.01</span><span class="p">,</span> <span class="o">-</span><span class="mf">500.0</span><span class="p">])</span></div>
 
-<div class="viewcode-block" id="GeneralizedError.bounds"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.GeneralizedError.bounds.html#arch.univariate.distribution.GeneralizedError.bounds">[docs]</a>    <span class="k">def</span> <span class="nf">bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]]:</span>
+
+<div class="viewcode-block" id="GeneralizedError.bounds">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.GeneralizedError.bounds.html#arch.univariate.distribution.GeneralizedError.bounds">[docs]</a>
+    <span class="k">def</span> <span class="nf">bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]]:</span>
         <span class="k">return</span> <span class="p">[(</span><span class="mf">1.01</span><span class="p">,</span> <span class="mf">500.0</span><span class="p">)]</span></div>
 
-<div class="viewcode-block" id="GeneralizedError.loglikelihood"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.GeneralizedError.loglikelihood.html#arch.univariate.distribution.GeneralizedError.loglikelihood">[docs]</a>    <span class="k">def</span> <span class="nf">loglikelihood</span><span class="p">(</span>
+
+<div class="viewcode-block" id="GeneralizedError.loglikelihood">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.GeneralizedError.loglikelihood.html#arch.univariate.distribution.GeneralizedError.loglikelihood">[docs]</a>
+    <span class="k">def</span> <span class="nf">loglikelihood</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike1D</span><span class="p">,</span>
         <span class="n">resids</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">,</span>
@@ -1564,7 +1706,10 @@ <h1>Source code for arch.univariate.distribution</h1><div class="highlight"><pre
         <span class="k">else</span><span class="p">:</span>
             <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span><span class="n">lls</span><span class="p">)</span></div>
 
-<div class="viewcode-block" id="GeneralizedError.starting_values"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.GeneralizedError.starting_values.html#arch.univariate.distribution.GeneralizedError.starting_values">[docs]</a>    <span class="k">def</span> <span class="nf">starting_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">std_resid</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
+
+<div class="viewcode-block" id="GeneralizedError.starting_values">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.GeneralizedError.starting_values.html#arch.univariate.distribution.GeneralizedError.starting_values">[docs]</a>
+    <span class="k">def</span> <span class="nf">starting_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">std_resid</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Construct starting values for use in optimization.</span>
 
@@ -1585,6 +1730,7 @@ <h1>Source code for arch.univariate.distribution</h1><div class="highlight"><pre
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">return</span> <span class="n">array</span><span class="p">([</span><span class="mf">1.5</span><span class="p">])</span></div>
 
+
     <span class="k">def</span> <span class="nf">_simulator</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">|</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
         <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">_parameters</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
         <span class="n">parameters</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_parameters</span>
@@ -1599,7 +1745,9 @@ <h1>Source code for arch.univariate.distribution</h1><div class="highlight"><pre
 
         <span class="k">return</span> <span class="n">randoms</span> <span class="o">/</span> <span class="n">scale</span>
 
-<div class="viewcode-block" id="GeneralizedError.simulate"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.GeneralizedError.simulate.html#arch.univariate.distribution.GeneralizedError.simulate">[docs]</a>    <span class="k">def</span> <span class="nf">simulate</span><span class="p">(</span>
+<div class="viewcode-block" id="GeneralizedError.simulate">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.GeneralizedError.simulate.html#arch.univariate.distribution.GeneralizedError.simulate">[docs]</a>
+    <span class="k">def</span> <span class="nf">simulate</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span> <span class="n">parameters</span><span class="p">:</span> <span class="nb">int</span> <span class="o">|</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">float</span> <span class="o">|</span> <span class="nb">int</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike1D</span>
     <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Callable</span><span class="p">[[</span><span class="nb">int</span> <span class="o">|</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">]],</span> <span class="n">Float64Array</span><span class="p">]:</span>
         <span class="n">parameters</span> <span class="o">=</span> <span class="n">ensure1d</span><span class="p">(</span><span class="n">parameters</span><span class="p">,</span> <span class="s2">&quot;parameters&quot;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
@@ -1608,10 +1756,16 @@ <h1>Source code for arch.univariate.distribution</h1><div class="highlight"><pre
         <span class="bp">self</span><span class="o">.</span><span class="n">_parameters</span> <span class="o">=</span> <span class="n">asarray</span><span class="p">(</span><span class="n">parameters</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_simulator</span></div>
 
-<div class="viewcode-block" id="GeneralizedError.parameter_names"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.GeneralizedError.parameter_names.html#arch.univariate.distribution.GeneralizedError.parameter_names">[docs]</a>    <span class="k">def</span> <span class="nf">parameter_names</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
+
+<div class="viewcode-block" id="GeneralizedError.parameter_names">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.GeneralizedError.parameter_names.html#arch.univariate.distribution.GeneralizedError.parameter_names">[docs]</a>
+    <span class="k">def</span> <span class="nf">parameter_names</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
         <span class="k">return</span> <span class="p">[</span><span class="s2">&quot;nu&quot;</span><span class="p">]</span></div>
 
-<div class="viewcode-block" id="GeneralizedError.ppf"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.GeneralizedError.ppf.html#arch.univariate.distribution.GeneralizedError.ppf">[docs]</a>    <span class="k">def</span> <span class="nf">ppf</span><span class="p">(</span>
+
+<div class="viewcode-block" id="GeneralizedError.ppf">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.GeneralizedError.ppf.html#arch.univariate.distribution.GeneralizedError.ppf">[docs]</a>
+    <span class="k">def</span> <span class="nf">ppf</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">pits</span><span class="p">:</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike1D</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="kc">None</span> <span class="o">|</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike1D</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
@@ -1622,7 +1776,10 @@ <h1>Source code for arch.univariate.distribution</h1><div class="highlight"><pre
         <span class="n">var</span> <span class="o">=</span> <span class="n">stats</span><span class="o">.</span><span class="n">gennorm</span><span class="p">(</span><span class="n">nu</span><span class="p">)</span><span class="o">.</span><span class="n">var</span><span class="p">()</span>
         <span class="k">return</span> <span class="n">stats</span><span class="o">.</span><span class="n">gennorm</span><span class="p">(</span><span class="n">nu</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">1.0</span> <span class="o">/</span> <span class="n">sqrt</span><span class="p">(</span><span class="n">var</span><span class="p">))</span><span class="o">.</span><span class="n">ppf</span><span class="p">(</span><span class="n">pits</span><span class="p">)</span></div>
 
-<div class="viewcode-block" id="GeneralizedError.cdf"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.GeneralizedError.cdf.html#arch.univariate.distribution.GeneralizedError.cdf">[docs]</a>    <span class="k">def</span> <span class="nf">cdf</span><span class="p">(</span>
+
+<div class="viewcode-block" id="GeneralizedError.cdf">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.GeneralizedError.cdf.html#arch.univariate.distribution.GeneralizedError.cdf">[docs]</a>
+    <span class="k">def</span> <span class="nf">cdf</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">resids</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike1D</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="kc">None</span> <span class="o">|</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike1D</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
@@ -1633,7 +1790,10 @@ <h1>Source code for arch.univariate.distribution</h1><div class="highlight"><pre
         <span class="n">resids</span> <span class="o">=</span> <span class="n">asarray</span><span class="p">(</span><span class="n">resids</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">stats</span><span class="o">.</span><span class="n">gennorm</span><span class="p">(</span><span class="n">nu</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">1.0</span> <span class="o">/</span> <span class="n">sqrt</span><span class="p">(</span><span class="n">var</span><span class="p">))</span><span class="o">.</span><span class="n">cdf</span><span class="p">(</span><span class="n">resids</span><span class="p">)</span></div>
 
-<div class="viewcode-block" id="GeneralizedError.moment"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.GeneralizedError.moment.html#arch.univariate.distribution.GeneralizedError.moment">[docs]</a>    <span class="k">def</span> <span class="nf">moment</span><span class="p">(</span>
+
+<div class="viewcode-block" id="GeneralizedError.moment">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.GeneralizedError.moment.html#arch.univariate.distribution.GeneralizedError.moment">[docs]</a>
+    <span class="k">def</span> <span class="nf">moment</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span> <span class="n">n</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">parameters</span><span class="p">:</span> <span class="kc">None</span> <span class="o">|</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike1D</span> <span class="o">=</span> <span class="kc">None</span>
     <span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
         <span class="k">if</span> <span class="n">n</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
@@ -1644,7 +1804,10 @@ <h1>Source code for arch.univariate.distribution</h1><div class="highlight"><pre
         <span class="n">var</span> <span class="o">=</span> <span class="n">stats</span><span class="o">.</span><span class="n">gennorm</span><span class="p">(</span><span class="n">nu</span><span class="p">)</span><span class="o">.</span><span class="n">var</span><span class="p">()</span>
         <span class="k">return</span> <span class="n">stats</span><span class="o">.</span><span class="n">gennorm</span><span class="o">.</span><span class="n">moment</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">nu</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">1.0</span> <span class="o">/</span> <span class="n">sqrt</span><span class="p">(</span><span class="n">var</span><span class="p">))</span></div>
 
-<div class="viewcode-block" id="GeneralizedError.partial_moment"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.GeneralizedError.partial_moment.html#arch.univariate.distribution.GeneralizedError.partial_moment">[docs]</a>    <span class="k">def</span> <span class="nf">partial_moment</span><span class="p">(</span>
+
+<div class="viewcode-block" id="GeneralizedError.partial_moment">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.GeneralizedError.partial_moment.html#arch.univariate.distribution.GeneralizedError.partial_moment">[docs]</a>
+    <span class="k">def</span> <span class="nf">partial_moment</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">n</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
         <span class="n">z</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.0</span><span class="p">,</span>
@@ -1656,6 +1819,7 @@ <h1>Source code for arch.univariate.distribution</h1><div class="highlight"><pre
         <span class="n">moment</span> <span class="o">=</span> <span class="p">(</span><span class="n">scale</span> <span class="o">**</span> <span class="n">n</span><span class="p">)</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">_ord_gennorm_partial_moment</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">z</span> <span class="o">/</span> <span class="n">scale</span><span class="p">,</span> <span class="n">nu</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">moment</span></div>
 
+
     <span class="nd">@staticmethod</span>
     <span class="k">def</span> <span class="nf">_ord_gennorm_partial_moment</span><span class="p">(</span><span class="n">n</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">z</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">beta</span><span class="p">:</span> <span class="nb">float</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
 <span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
@@ -1705,6 +1869,7 @@ <h1>Source code for arch.univariate.distribution</h1><div class="highlight"><pre
         <span class="n">moment</span> <span class="o">=</span> <span class="n">lterm</span> <span class="o">+</span> <span class="n">rterm</span>
 
         <span class="k">return</span> <span class="n">moment</span></div>
+
 </code></pre></div>
 
 
@@ -1736,7 +1901,7 @@ <h1>Source code for arch.univariate.distribution</h1><div class="highlight"><pre
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1783,7 +1948,7 @@ <h1>Source code for arch.univariate.distribution</h1><div class="highlight"><pre
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/_modules/arch/univariate/mean.html b/_modules/arch/univariate/mean.html
index a5de11d2a9..f2a79cd24e 100644
--- a/_modules/arch/univariate/mean.html
+++ b/_modules/arch/univariate/mean.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.mean - arch 6.1.0</title>
+        <title>arch.univariate.mean - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -379,7 +379,6 @@ <h1>Source code for arch.univariate.mean</h1><div class="highlight"><pre>
 <span class="kn">from</span> <span class="nn">scipy.optimize</span> <span class="kn">import</span> <span class="n">OptimizeResult</span>
 <span class="kn">from</span> <span class="nn">statsmodels.tsa.tsatools</span> <span class="kn">import</span> <span class="n">lagmat</span>
 
-<span class="kn">from</span> <span class="nn">arch.__future__._utility</span> <span class="kn">import</span> <span class="n">check_reindex</span>
 <span class="kn">from</span> <span class="nn">arch.typing</span> <span class="kn">import</span> <span class="p">(</span>
     <span class="n">ArrayLike</span><span class="p">,</span>
     <span class="n">ArrayLike1D</span><span class="p">,</span>
@@ -515,7 +514,9 @@ <h1>Source code for arch.univariate.mean</h1><div class="highlight"><pre>
     <span class="k">return</span> <span class="n">impulse</span>
 
 
-<div class="viewcode-block" id="HARX"><a class="viewcode-back" href="../../../univariate/generated/arch.univariate.HARX.html#arch.univariate.HARX">[docs]</a><span class="k">class</span> <span class="nc">HARX</span><span class="p">(</span><span class="n">ARCHModel</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
+<div class="viewcode-block" id="HARX">
+<a class="viewcode-back" href="../../../univariate/generated/arch.univariate.HARX.html#arch.univariate.HARX">[docs]</a>
+<span class="k">class</span> <span class="nc">HARX</span><span class="p">(</span><span class="n">ARCHModel</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Heterogeneous Autoregression (HAR), with optional exogenous regressors,</span>
 <span class="sd">    model estimation and simulation</span>
@@ -688,9 +689,12 @@ <h1>Source code for arch.univariate.mean</h1><div class="highlight"><pre>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;Gets the value of the exogenous regressors in the model&quot;&quot;&quot;</span>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_x</span>
 
-<div class="viewcode-block" id="HARX.parameter_names"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.HARX.parameter_names.html#arch.univariate.HARX.parameter_names">[docs]</a>    <span class="k">def</span> <span class="nf">parameter_names</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
+<div class="viewcode-block" id="HARX.parameter_names">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.HARX.parameter_names.html#arch.univariate.HARX.parameter_names">[docs]</a>
+    <span class="k">def</span> <span class="nf">parameter_names</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_generate_variable_names</span><span class="p">()</span></div>
 
+
     <span class="k">def</span> <span class="nf">_model_description</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">include_lags</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">str</span><span class="p">]:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;Generates the model description for use by __str__ and related</span>
 <span class="sd">        functions&quot;&quot;&quot;</span>
@@ -734,7 +738,9 @@ <h1>Source code for arch.univariate.mean</h1><div class="highlight"><pre>
         <span class="n">html</span> <span class="o">+=</span> <span class="s2">&quot;&lt;strong&gt;ID: &lt;/strong&gt; &quot;</span> <span class="o">+</span> <span class="nb">hex</span><span class="p">(</span><span class="nb">id</span><span class="p">(</span><span class="bp">self</span><span class="p">))</span> <span class="o">+</span> <span class="s2">&quot;)&quot;</span>
         <span class="k">return</span> <span class="n">html</span>
 
-<div class="viewcode-block" id="HARX.resids"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.HARX.resids.html#arch.univariate.HARX.resids">[docs]</a>    <span class="k">def</span> <span class="nf">resids</span><span class="p">(</span>
+<div class="viewcode-block" id="HARX.resids">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.HARX.resids.html#arch.univariate.HARX.resids">[docs]</a>
+    <span class="k">def</span> <span class="nf">resids</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">params</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">,</span>
         <span class="n">y</span><span class="p">:</span> <span class="n">ArrayLike1D</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
@@ -745,6 +751,7 @@ <h1>Source code for arch.univariate.mean</h1><div class="highlight"><pre>
         <span class="k">assert</span> <span class="n">regressors</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
         <span class="k">return</span> <span class="n">y</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">regressors</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">params</span><span class="p">)</span></div>
 
+
     <span class="nd">@cached_property</span>
     <span class="k">def</span> <span class="nf">num_params</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
@@ -792,7 +799,9 @@ <h1>Source code for arch.univariate.mean</h1><div class="highlight"><pre>
 
         <span class="k">return</span> <span class="n">y</span>
 
-<div class="viewcode-block" id="HARX.simulate"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.HARX.simulate.html#arch.univariate.HARX.simulate">[docs]</a>    <span class="k">def</span> <span class="nf">simulate</span><span class="p">(</span>
+<div class="viewcode-block" id="HARX.simulate">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.HARX.simulate.html#arch.univariate.HARX.simulate">[docs]</a>
+    <span class="k">def</span> <span class="nf">simulate</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">params</span><span class="p">:</span> <span class="n">ArrayLike1D</span> <span class="o">|</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">float</span><span class="p">],</span>
         <span class="n">nobs</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
@@ -894,6 +903,7 @@ <h1>Source code for arch.univariate.mean</h1><div class="highlight"><pre>
         <span class="n">df</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">y</span><span class="p">[</span><span class="n">burn</span><span class="p">:],</span> <span class="n">volatility</span><span class="o">=</span><span class="n">vol</span><span class="p">[</span><span class="n">burn</span><span class="p">:],</span> <span class="n">errors</span><span class="o">=</span><span class="n">errors</span><span class="p">[</span><span class="n">burn</span><span class="p">:])</span>
         <span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">df</span><span class="p">)</span></div>
 
+
     <span class="k">def</span> <span class="nf">_generate_variable_names</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;Generates variable names or use in summaries&quot;&quot;&quot;</span>
         <span class="n">variable_names</span> <span class="o">=</span> <span class="p">[]</span>
@@ -1299,7 +1309,9 @@ <h1>Source code for arch.univariate.mean</h1><div class="highlight"><pre>
             <span class="n">arr</span> <span class="o">=</span> <span class="n">arr</span><span class="p">[:,</span> <span class="n">start</span><span class="p">:]</span>
         <span class="k">return</span> <span class="n">arr</span>
 
-<div class="viewcode-block" id="HARX.forecast"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.HARX.forecast.html#arch.univariate.HARX.forecast">[docs]</a>    <span class="k">def</span> <span class="nf">forecast</span><span class="p">(</span>
+<div class="viewcode-block" id="HARX.forecast">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.HARX.forecast.html#arch.univariate.HARX.forecast">[docs]</a>
+    <span class="k">def</span> <span class="nf">forecast</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">params</span><span class="p">:</span> <span class="n">ArrayLike1D</span><span class="p">,</span>
         <span class="n">horizon</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
@@ -1310,10 +1322,9 @@ <h1>Source code for arch.univariate.mean</h1><div class="highlight"><pre>
         <span class="n">rng</span><span class="p">:</span> <span class="n">Callable</span><span class="p">[[</span><span class="nb">int</span> <span class="o">|</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">]],</span> <span class="n">Float64Array</span><span class="p">]</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
         <span class="n">random_state</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
         <span class="o">*</span><span class="p">,</span>
-        <span class="n">reindex</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">reindex</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
         <span class="n">x</span><span class="p">:</span> <span class="kc">None</span> <span class="o">|</span> <span class="nb">dict</span><span class="p">[</span><span class="n">Label</span><span class="p">,</span> <span class="n">ArrayLike</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
     <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">ARCHModelForecast</span><span class="p">:</span>
-        <span class="n">reindex</span> <span class="o">=</span> <span class="n">check_reindex</span><span class="p">(</span><span class="n">reindex</span><span class="p">)</span>
         <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">horizon</span><span class="p">,</span> <span class="p">(</span><span class="nb">int</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">integer</span><span class="p">))</span> <span class="ow">or</span> <span class="n">horizon</span> <span class="o">&lt;</span> <span class="mi">1</span><span class="p">:</span>
             <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;horizon must be an integer &gt;= 1.&quot;</span><span class="p">)</span>
         <span class="c1"># Check start</span>
@@ -1431,10 +1442,14 @@ <h1>Source code for arch.univariate.mean</h1><div class="highlight"><pre>
             <span class="n">simulated_variances</span><span class="o">=</span><span class="n">long_run_variance_paths</span><span class="p">,</span>
             <span class="n">simulated_residual_variances</span><span class="o">=</span><span class="n">variance_paths</span><span class="p">,</span>
             <span class="n">reindex</span><span class="o">=</span><span class="n">reindex</span><span class="p">,</span>
-        <span class="p">)</span></div></div>
+        <span class="p">)</span></div>
+</div>
 
 
-<div class="viewcode-block" id="ConstantMean"><a class="viewcode-back" href="../../../univariate/generated/arch.univariate.ConstantMean.html#arch.univariate.ConstantMean">[docs]</a><span class="k">class</span> <span class="nc">ConstantMean</span><span class="p">(</span><span class="n">HARX</span><span class="p">):</span>
+
+<div class="viewcode-block" id="ConstantMean">
+<a class="viewcode-back" href="../../../univariate/generated/arch.univariate.ConstantMean.html#arch.univariate.ConstantMean">[docs]</a>
+<span class="k">class</span> <span class="nc">ConstantMean</span><span class="p">(</span><span class="n">HARX</span><span class="p">):</span>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Constant mean model estimation and simulation.</span>
 
@@ -1490,9 +1505,12 @@ <h1>Source code for arch.univariate.mean</h1><div class="highlight"><pre>
         <span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_name</span> <span class="o">=</span> <span class="s2">&quot;Constant Mean&quot;</span>
 
-<div class="viewcode-block" id="ConstantMean.parameter_names"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.ConstantMean.parameter_names.html#arch.univariate.ConstantMean.parameter_names">[docs]</a>    <span class="k">def</span> <span class="nf">parameter_names</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
+<div class="viewcode-block" id="ConstantMean.parameter_names">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.ConstantMean.parameter_names.html#arch.univariate.ConstantMean.parameter_names">[docs]</a>
+    <span class="k">def</span> <span class="nf">parameter_names</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
         <span class="k">return</span> <span class="p">[</span><span class="s2">&quot;mu&quot;</span><span class="p">]</span></div>
 
+
     <span class="nd">@cached_property</span>
     <span class="k">def</span> <span class="nf">num_params</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
         <span class="k">return</span> <span class="mi">1</span>
@@ -1500,7 +1518,9 @@ <h1>Source code for arch.univariate.mean</h1><div class="highlight"><pre>
     <span class="k">def</span> <span class="nf">_model_description</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">include_lags</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">str</span><span class="p">]:</span>
         <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">_model_description</span><span class="p">(</span><span class="n">include_lags</span><span class="p">)</span>
 
-<div class="viewcode-block" id="ConstantMean.simulate"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.ConstantMean.simulate.html#arch.univariate.ConstantMean.simulate">[docs]</a>    <span class="k">def</span> <span class="nf">simulate</span><span class="p">(</span>
+<div class="viewcode-block" id="ConstantMean.simulate">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.ConstantMean.simulate.html#arch.univariate.ConstantMean.simulate">[docs]</a>
+    <span class="k">def</span> <span class="nf">simulate</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">params</span><span class="p">:</span> <span class="n">ArrayLike1D</span> <span class="o">|</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">float</span><span class="p">],</span>
         <span class="n">nobs</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
@@ -1569,17 +1589,24 @@ <h1>Source code for arch.univariate.mean</h1><div class="highlight"><pre>
 
         <span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">df</span><span class="p">)</span></div>
 
-<div class="viewcode-block" id="ConstantMean.resids"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.ConstantMean.resids.html#arch.univariate.ConstantMean.resids">[docs]</a>    <span class="k">def</span> <span class="nf">resids</span><span class="p">(</span>
+
+<div class="viewcode-block" id="ConstantMean.resids">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.ConstantMean.resids.html#arch.univariate.ConstantMean.resids">[docs]</a>
+    <span class="k">def</span> <span class="nf">resids</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">params</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">,</span>
         <span class="n">y</span><span class="p">:</span> <span class="n">ArrayLike1D</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
         <span class="n">regressors</span><span class="p">:</span> <span class="n">ArrayLike2D</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
     <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">ArrayLike1D</span><span class="p">:</span>
         <span class="n">y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_fit_y</span> <span class="k">if</span> <span class="n">y</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">y</span> <span class="o">-</span> <span class="n">params</span></div></div>
+        <span class="k">return</span> <span class="n">y</span> <span class="o">-</span> <span class="n">params</span></div>
+</div>
+
 
 
-<div class="viewcode-block" id="ZeroMean"><a class="viewcode-back" href="../../../univariate/generated/arch.univariate.ZeroMean.html#arch.univariate.ZeroMean">[docs]</a><span class="k">class</span> <span class="nc">ZeroMean</span><span class="p">(</span><span class="n">HARX</span><span class="p">):</span>
+<div class="viewcode-block" id="ZeroMean">
+<a class="viewcode-back" href="../../../univariate/generated/arch.univariate.ZeroMean.html#arch.univariate.ZeroMean">[docs]</a>
+<span class="k">class</span> <span class="nc">ZeroMean</span><span class="p">(</span><span class="n">HARX</span><span class="p">):</span>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Model with zero conditional mean estimation and simulation</span>
 
@@ -1638,9 +1665,12 @@ <h1>Source code for arch.univariate.mean</h1><div class="highlight"><pre>
         <span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_name</span> <span class="o">=</span> <span class="s2">&quot;Zero Mean&quot;</span>
 
-<div class="viewcode-block" id="ZeroMean.parameter_names"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.ZeroMean.parameter_names.html#arch.univariate.ZeroMean.parameter_names">[docs]</a>    <span class="k">def</span> <span class="nf">parameter_names</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
+<div class="viewcode-block" id="ZeroMean.parameter_names">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.ZeroMean.parameter_names.html#arch.univariate.ZeroMean.parameter_names">[docs]</a>
+    <span class="k">def</span> <span class="nf">parameter_names</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
         <span class="k">return</span> <span class="p">[]</span></div>
 
+
     <span class="nd">@cached_property</span>
     <span class="k">def</span> <span class="nf">num_params</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
         <span class="k">return</span> <span class="mi">0</span>
@@ -1648,7 +1678,9 @@ <h1>Source code for arch.univariate.mean</h1><div class="highlight"><pre>
     <span class="k">def</span> <span class="nf">_model_description</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">include_lags</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">str</span><span class="p">]:</span>
         <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">_model_description</span><span class="p">(</span><span class="n">include_lags</span><span class="p">)</span>
 
-<div class="viewcode-block" id="ZeroMean.simulate"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.ZeroMean.simulate.html#arch.univariate.ZeroMean.simulate">[docs]</a>    <span class="k">def</span> <span class="nf">simulate</span><span class="p">(</span>
+<div class="viewcode-block" id="ZeroMean.simulate">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.ZeroMean.simulate.html#arch.univariate.ZeroMean.simulate">[docs]</a>
+    <span class="k">def</span> <span class="nf">simulate</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">params</span><span class="p">:</span> <span class="n">ArrayLike1D</span> <span class="o">|</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">float</span><span class="p">],</span>
         <span class="n">nobs</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
@@ -1720,7 +1752,10 @@ <h1>Source code for arch.univariate.mean</h1><div class="highlight"><pre>
 
         <span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">df</span><span class="p">)</span></div>
 
-<div class="viewcode-block" id="ZeroMean.resids"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.ZeroMean.resids.html#arch.univariate.ZeroMean.resids">[docs]</a>    <span class="k">def</span> <span class="nf">resids</span><span class="p">(</span>
+
+<div class="viewcode-block" id="ZeroMean.resids">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.ZeroMean.resids.html#arch.univariate.ZeroMean.resids">[docs]</a>
+    <span class="k">def</span> <span class="nf">resids</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">params</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">,</span>
         <span class="n">y</span><span class="p">:</span> <span class="n">ArrayLike1D</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
@@ -1729,10 +1764,14 @@ <h1>Source code for arch.univariate.mean</h1><div class="highlight"><pre>
         <span class="k">if</span> <span class="n">y</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="k">return</span> <span class="n">y</span>
         <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">_fit_y</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_fit_y</span></div></div>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_fit_y</span></div>
+</div>
 
 
-<div class="viewcode-block" id="ARX"><a class="viewcode-back" href="../../../univariate/generated/arch.univariate.ARX.html#arch.univariate.ARX">[docs]</a><span class="k">class</span> <span class="nc">ARX</span><span class="p">(</span><span class="n">HARX</span><span class="p">):</span>
+
+<div class="viewcode-block" id="ARX">
+<a class="viewcode-back" href="../../../univariate/generated/arch.univariate.ARX.html#arch.univariate.ARX">[docs]</a>
+<span class="k">class</span> <span class="nc">ARX</span><span class="p">(</span><span class="n">HARX</span><span class="p">):</span>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Autoregressive model with optional exogenous regressors estimation and</span>
 <span class="sd">    simulation</span>
@@ -1863,7 +1902,10 @@ <h1>Source code for arch.univariate.mean</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="n">names</span></div>
 
 
-<div class="viewcode-block" id="LS"><a class="viewcode-back" href="../../../univariate/generated/arch.univariate.LS.html#arch.univariate.LS">[docs]</a><span class="k">class</span> <span class="nc">LS</span><span class="p">(</span><span class="n">HARX</span><span class="p">):</span>
+
+<div class="viewcode-block" id="LS">
+<a class="viewcode-back" href="../../../univariate/generated/arch.univariate.LS.html#arch.univariate.LS">[docs]</a>
+<span class="k">class</span> <span class="nc">LS</span><span class="p">(</span><span class="n">HARX</span><span class="p">):</span>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Least squares model estimation and simulation</span>
 
@@ -1933,7 +1975,10 @@ <h1>Source code for arch.univariate.mean</h1><div class="highlight"><pre>
         <span class="bp">self</span><span class="o">.</span><span class="n">_name</span> <span class="o">=</span> <span class="s2">&quot;Least Squares&quot;</span></div>
 
 
-<div class="viewcode-block" id="ARCHInMean"><a class="viewcode-back" href="../../../univariate/generated/arch.univariate.ARCHInMean.html#arch.univariate.ARCHInMean">[docs]</a><span class="k">class</span> <span class="nc">ARCHInMean</span><span class="p">(</span><span class="n">ARX</span><span class="p">):</span>
+
+<div class="viewcode-block" id="ARCHInMean">
+<a class="viewcode-back" href="../../../univariate/generated/arch.univariate.ARCHInMean.html#arch.univariate.ARCHInMean">[docs]</a>
+<span class="k">class</span> <span class="nc">ARCHInMean</span><span class="p">(</span><span class="n">ARX</span><span class="p">):</span>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    (G)ARCH-in-mean model and simulation</span>
 
@@ -2070,7 +2115,9 @@ <h1>Source code for arch.univariate.mean</h1><div class="highlight"><pre>
         <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">regressors</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
         <span class="k">return</span> <span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">regressors</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="o">+</span> <span class="mi">1</span>
 
-<div class="viewcode-block" id="ARCHInMean.forecast"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.ARCHInMean.forecast.html#arch.univariate.ARCHInMean.forecast">[docs]</a>    <span class="k">def</span> <span class="nf">forecast</span><span class="p">(</span>
+<div class="viewcode-block" id="ARCHInMean.forecast">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.ARCHInMean.forecast.html#arch.univariate.ARCHInMean.forecast">[docs]</a>
+    <span class="k">def</span> <span class="nf">forecast</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">params</span><span class="p">:</span> <span class="n">ArrayLike1D</span><span class="p">,</span>
         <span class="n">horizon</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
@@ -2088,7 +2135,10 @@ <h1>Source code for arch.univariate.mean</h1><div class="highlight"><pre>
             <span class="s2">&quot;forecasts are not implemented for (G)ARCH-in-mean models&quot;</span>
         <span class="p">)</span></div>
 
-<div class="viewcode-block" id="ARCHInMean.resids"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.ARCHInMean.resids.html#arch.univariate.ARCHInMean.resids">[docs]</a>    <span class="k">def</span> <span class="nf">resids</span><span class="p">(</span>
+
+<div class="viewcode-block" id="ARCHInMean.resids">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.ARCHInMean.resids.html#arch.univariate.ARCHInMean.resids">[docs]</a>
+    <span class="k">def</span> <span class="nf">resids</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">params</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">,</span>
         <span class="n">y</span><span class="p">:</span> <span class="n">ArrayLike1D</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
@@ -2096,9 +2146,13 @@ <h1>Source code for arch.univariate.mean</h1><div class="highlight"><pre>
     <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">ArrayLike1D</span><span class="p">:</span>
         <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">resids</span><span class="p">(</span><span class="n">params</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">y</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">regressors</span><span class="o">=</span><span class="n">regressors</span><span class="p">)</span></div>
 
-<div class="viewcode-block" id="ARCHInMean.starting_values"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.ARCHInMean.starting_values.html#arch.univariate.ARCHInMean.starting_values">[docs]</a>    <span class="k">def</span> <span class="nf">starting_values</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
+
+<div class="viewcode-block" id="ARCHInMean.starting_values">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.ARCHInMean.starting_values.html#arch.univariate.ARCHInMean.starting_values">[docs]</a>
+    <span class="k">def</span> <span class="nf">starting_values</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
         <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">starting_values</span><span class="p">(),</span> <span class="mf">0.0</span><span class="p">]</span></div>
 
+
     <span class="k">def</span> <span class="nf">_loglikelihood</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">,</span>
@@ -2195,7 +2249,10 @@ <h1>Source code for arch.univariate.mean</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="n">y</span></div>
 
 
-<div class="viewcode-block" id="arch_model"><a class="viewcode-back" href="../../../univariate/introduction.html#arch.univariate.arch_model">[docs]</a><span class="k">def</span> <span class="nf">arch_model</span><span class="p">(</span>
+
+<div class="viewcode-block" id="arch_model">
+<a class="viewcode-back" href="../../../univariate/introduction.html#arch.univariate.arch_model">[docs]</a>
+<span class="k">def</span> <span class="nf">arch_model</span><span class="p">(</span>
     <span class="n">y</span><span class="p">:</span> <span class="n">ArrayLike</span> <span class="o">|</span> <span class="kc">None</span><span class="p">,</span>
     <span class="n">x</span><span class="p">:</span> <span class="n">ArrayLike2D</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
     <span class="n">mean</span><span class="p">:</span> <span class="n">Literal</span><span class="p">[</span>
@@ -2227,15 +2284,15 @@ <h1>Source code for arch.univariate.mean</h1><div class="highlight"><pre>
 
 <span class="sd">    Parameters</span>
 <span class="sd">    ----------</span>
-<span class="sd">    y : {ndarray, Series, None}</span>
+<span class="sd">    y : ndarray, Series, None</span>
 <span class="sd">        The dependent variable</span>
-<span class="sd">    x : {np.array, DataFrame}, optional</span>
+<span class="sd">    x : ndarray, DataFrame, optional</span>
 <span class="sd">        Exogenous regressors.  Ignored if model does not permit exogenous</span>
 <span class="sd">        regressors.</span>
 <span class="sd">    mean : str, optional</span>
 <span class="sd">        Name of the mean model.  Currently supported options are: &#39;Constant&#39;,</span>
 <span class="sd">        &#39;Zero&#39;, &#39;LS&#39;, &#39;AR&#39;, &#39;ARX&#39;, &#39;HAR&#39; and  &#39;HARX&#39;</span>
-<span class="sd">    lags : int or list (int), optional</span>
+<span class="sd">    lags : int or list[int], optional</span>
 <span class="sd">        Either a scalar integer value indicating lag length or a list of</span>
 <span class="sd">        integers specifying lag locations.</span>
 <span class="sd">    vol : str, optional</span>
@@ -2388,6 +2445,7 @@ <h1>Source code for arch.univariate.mean</h1><div class="highlight"><pre>
     <span class="n">am</span><span class="o">.</span><span class="n">distribution</span> <span class="o">=</span> <span class="n">d</span>
 
     <span class="k">return</span> <span class="n">am</span></div>
+
 </code></pre></div>
 
 
@@ -2419,7 +2477,7 @@ <h1>Source code for arch.univariate.mean</h1><div class="highlight"><pre>
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -2466,7 +2524,7 @@ <h1>Source code for arch.univariate.mean</h1><div class="highlight"><pre>
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/_modules/arch/univariate/recursions_python.html b/_modules/arch/univariate/recursions_python.html
index 4aada911de..676c89f1fd 100644
--- a/_modules/arch/univariate/recursions_python.html
+++ b/_modules/arch/univariate/recursions_python.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.recursions_python - arch 6.1.0</title>
+        <title>arch.univariate.recursions_python - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1002,7 +1002,9 @@ <h1>Source code for arch.univariate.recursions_python</h1><div class="highlight"
 <span class="n">aparch_recursion</span> <span class="o">=</span> <span class="n">jit</span><span class="p">(</span><span class="n">aparch_recursion_python</span><span class="p">,</span> <span class="n">nopython</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 
 
-<div class="viewcode-block" id="VolatilityUpdater"><a class="viewcode-back" href="../../../univariate/generated/arch.univariate.recursions_python.VolatilityUpdater.html#arch.univariate.recursions_python.VolatilityUpdater">[docs]</a><span class="k">class</span> <span class="nc">VolatilityUpdater</span><span class="p">(</span><span class="n">metaclass</span><span class="o">=</span><span class="n">ABCMeta</span><span class="p">):</span>
+<div class="viewcode-block" id="VolatilityUpdater">
+<a class="viewcode-back" href="../../../univariate/generated/arch.univariate.recursions_python.VolatilityUpdater.html#arch.univariate.recursions_python.VolatilityUpdater">[docs]</a>
+<span class="k">class</span> <span class="nc">VolatilityUpdater</span><span class="p">(</span><span class="n">metaclass</span><span class="o">=</span><span class="n">ABCMeta</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Base class that all volatility updaters must inherit from.</span>
 
@@ -1024,7 +1026,9 @@ <h1>Source code for arch.univariate.recursions_python</h1><div class="highlight"
     <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
         <span class="k">pass</span>
 
-<div class="viewcode-block" id="VolatilityUpdater.initialize_update"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.recursions_python.VolatilityUpdater.initialize_update.html#arch.univariate.recursions_python.VolatilityUpdater.initialize_update">[docs]</a>    <span class="nd">@abstractmethod</span>
+<div class="viewcode-block" id="VolatilityUpdater.initialize_update">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.recursions_python.VolatilityUpdater.initialize_update.html#arch.univariate.recursions_python.VolatilityUpdater.initialize_update">[docs]</a>
+    <span class="nd">@abstractmethod</span>
     <span class="k">def</span> <span class="nf">initialize_update</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span> <span class="n">parameters</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">,</span> <span class="n">backcast</span><span class="p">:</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">Float64Array</span><span class="p">,</span> <span class="n">nobs</span><span class="p">:</span> <span class="nb">int</span>
     <span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
@@ -1048,7 +1052,10 @@ <h1>Source code for arch.univariate.recursions_python</h1><div class="highlight"
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">pass</span></div>
 
-<div class="viewcode-block" id="VolatilityUpdater.update"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.recursions_python.VolatilityUpdater.update.html#arch.univariate.recursions_python.VolatilityUpdater.update">[docs]</a>    <span class="nd">@abstractmethod</span>
+
+<div class="viewcode-block" id="VolatilityUpdater.update">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.recursions_python.VolatilityUpdater.update.html#arch.univariate.recursions_python.VolatilityUpdater.update">[docs]</a>
+    <span class="nd">@abstractmethod</span>
     <span class="k">def</span> <span class="nf">update</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">t</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
@@ -1081,6 +1088,7 @@ <h1>Source code for arch.univariate.recursions_python</h1><div class="highlight"
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">pass</span></div>
 
+
     <span class="k">def</span> <span class="nf">_update_tester</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">t</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
@@ -1110,6 +1118,7 @@ <h1>Source code for arch.univariate.recursions_python</h1><div class="highlight"
         <span class="bp">self</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">parameters</span><span class="p">,</span> <span class="n">resids</span><span class="p">,</span> <span class="n">sigma2</span><span class="p">,</span> <span class="n">var_bounds</span><span class="p">)</span></div>
 
 
+
 <span class="k">class</span> <span class="nc">GARCHUpdater</span><span class="p">(</span><span class="n">VolatilityUpdater</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
     <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">p</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">o</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">q</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">power</span><span class="p">:</span> <span class="nb">float</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
         <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
@@ -1509,7 +1518,7 @@ <h1>Source code for arch.univariate.recursions_python</h1><div class="highlight"
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1556,7 +1565,7 @@ <h1>Source code for arch.univariate.recursions_python</h1><div class="highlight"
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/_modules/arch/univariate/volatility.html b/_modules/arch/univariate/volatility.html
index b999f78dc6..83cf32a019 100644
--- a/_modules/arch/univariate/volatility.html
+++ b/_modules/arch/univariate/volatility.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.volatility - arch 6.1.0</title>
+        <title>arch.univariate.volatility - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -539,7 +539,9 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_shocks</span>
 
 
-<div class="viewcode-block" id="VolatilityProcess"><a class="viewcode-back" href="../../../univariate/generated/arch.univariate.volatility.VolatilityProcess.html#arch.univariate.VolatilityProcess">[docs]</a><span class="k">class</span> <span class="nc">VolatilityProcess</span><span class="p">(</span><span class="n">metaclass</span><span class="o">=</span><span class="n">ABCMeta</span><span class="p">):</span>
+<div class="viewcode-block" id="VolatilityProcess">
+<a class="viewcode-back" href="../../../univariate/generated/arch.univariate.volatility.VolatilityProcess.html#arch.univariate.VolatilityProcess">[docs]</a>
+<span class="k">class</span> <span class="nc">VolatilityProcess</span><span class="p">(</span><span class="n">metaclass</span><span class="o">=</span><span class="n">ABCMeta</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Abstract base class for ARCH models.  Allows the conditional mean model to be</span>
 <span class="sd">    specified separately from the conditional variance, even though parameters</span>
@@ -617,7 +619,9 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
         <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">_volatility_updater</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_volatility_updater</span>
 
-<div class="viewcode-block" id="VolatilityProcess.update"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.update.html#arch.univariate.VolatilityProcess.update">[docs]</a>    <span class="k">def</span> <span class="nf">update</span><span class="p">(</span>
+<div class="viewcode-block" id="VolatilityProcess.update">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.update.html#arch.univariate.VolatilityProcess.update">[docs]</a>
+    <span class="k">def</span> <span class="nf">update</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">index</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">,</span>
@@ -654,6 +658,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;Subclasses may optionally implement&quot;</span><span class="p">)</span></div>
 
+
     <span class="nd">@abstractmethod</span>
     <span class="k">def</span> <span class="nf">_check_forecasting_method</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span> <span class="n">method</span><span class="p">:</span> <span class="n">ForecastingMethod</span><span class="p">,</span> <span class="n">horizon</span><span class="p">:</span> <span class="nb">int</span>
@@ -862,7 +867,9 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
             <span class="n">parameters</span><span class="p">,</span> <span class="n">resids</span><span class="p">,</span> <span class="n">backcast</span><span class="p">,</span> <span class="n">var_bounds</span><span class="p">,</span> <span class="n">start</span><span class="p">,</span> <span class="n">horizon</span><span class="p">,</span> <span class="n">simulations</span><span class="p">,</span> <span class="n">rng</span>
         <span class="p">)</span>
 
-<div class="viewcode-block" id="VolatilityProcess.variance_bounds"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.variance_bounds.html#arch.univariate.VolatilityProcess.variance_bounds">[docs]</a>    <span class="k">def</span> <span class="nf">variance_bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">,</span> <span class="n">power</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">2.0</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
+<div class="viewcode-block" id="VolatilityProcess.variance_bounds">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.variance_bounds.html#arch.univariate.VolatilityProcess.variance_bounds">[docs]</a>
+    <span class="k">def</span> <span class="nf">variance_bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">,</span> <span class="n">power</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">2.0</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Construct loose bounds for conditional variances.</span>
 
@@ -906,7 +913,10 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
         <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">ascontiguousarray</span><span class="p">(</span><span class="n">var_bounds</span><span class="p">)</span></div>
 
-<div class="viewcode-block" id="VolatilityProcess.starting_values"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.starting_values.html#arch.univariate.VolatilityProcess.starting_values">[docs]</a>    <span class="nd">@abstractmethod</span>
+
+<div class="viewcode-block" id="VolatilityProcess.starting_values">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.starting_values.html#arch.univariate.VolatilityProcess.starting_values">[docs]</a>
+    <span class="nd">@abstractmethod</span>
     <span class="k">def</span> <span class="nf">starting_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Returns starting values for the ARCH model</span>
@@ -923,7 +933,10 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 <span class="sd">            Array of starting values</span>
 <span class="sd">        &quot;&quot;&quot;</span></div>
 
-<div class="viewcode-block" id="VolatilityProcess.backcast"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.backcast.html#arch.univariate.VolatilityProcess.backcast">[docs]</a>    <span class="k">def</span> <span class="nf">backcast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">Float64Array</span><span class="p">:</span>
+
+<div class="viewcode-block" id="VolatilityProcess.backcast">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.backcast.html#arch.univariate.VolatilityProcess.backcast">[docs]</a>
+    <span class="k">def</span> <span class="nf">backcast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">Float64Array</span><span class="p">:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Construct values for backcasting to start the recursion</span>
 
@@ -943,7 +956,10 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
         <span class="k">return</span> <span class="nb">float</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">resids</span><span class="p">[:</span><span class="n">tau</span><span class="p">]</span> <span class="o">**</span> <span class="mf">2.0</span><span class="p">)</span> <span class="o">*</span> <span class="n">w</span><span class="p">))</span></div>
 
-<div class="viewcode-block" id="VolatilityProcess.backcast_transform"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.backcast_transform.html#arch.univariate.VolatilityProcess.backcast_transform">[docs]</a>    <span class="k">def</span> <span class="nf">backcast_transform</span><span class="p">(</span>
+
+<div class="viewcode-block" id="VolatilityProcess.backcast_transform">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.backcast_transform.html#arch.univariate.VolatilityProcess.backcast_transform">[docs]</a>
+    <span class="k">def</span> <span class="nf">backcast_transform</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span> <span class="n">backcast</span><span class="p">:</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">Float64Array</span>
     <span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">Float64Array</span><span class="p">:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
@@ -963,7 +979,10 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
             <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;User backcast value must be strictly positive.&quot;</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">backcast</span></div>
 
-<div class="viewcode-block" id="VolatilityProcess.bounds"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.bounds.html#arch.univariate.VolatilityProcess.bounds">[docs]</a>    <span class="nd">@abstractmethod</span>
+
+<div class="viewcode-block" id="VolatilityProcess.bounds">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.bounds.html#arch.univariate.VolatilityProcess.bounds">[docs]</a>
+    <span class="nd">@abstractmethod</span>
     <span class="k">def</span> <span class="nf">bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]]:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Returns bounds for parameters</span>
@@ -979,7 +998,10 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 <span class="sd">            List of bounds where each element is (lower, upper).</span>
 <span class="sd">        &quot;&quot;&quot;</span></div>
 
-<div class="viewcode-block" id="VolatilityProcess.compute_variance"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.compute_variance.html#arch.univariate.VolatilityProcess.compute_variance">[docs]</a>    <span class="nd">@abstractmethod</span>
+
+<div class="viewcode-block" id="VolatilityProcess.compute_variance">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.compute_variance.html#arch.univariate.VolatilityProcess.compute_variance">[docs]</a>
+    <span class="nd">@abstractmethod</span>
     <span class="k">def</span> <span class="nf">compute_variance</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">,</span>
@@ -1006,7 +1028,10 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 <span class="sd">            Array containing columns of lower and upper bounds</span>
 <span class="sd">        &quot;&quot;&quot;</span></div>
 
-<div class="viewcode-block" id="VolatilityProcess.constraints"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.constraints.html#arch.univariate.VolatilityProcess.constraints">[docs]</a>    <span class="nd">@abstractmethod</span>
+
+<div class="viewcode-block" id="VolatilityProcess.constraints">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.constraints.html#arch.univariate.VolatilityProcess.constraints">[docs]</a>
+    <span class="nd">@abstractmethod</span>
     <span class="k">def</span> <span class="nf">constraints</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">Float64Array</span><span class="p">,</span> <span class="n">Float64Array</span><span class="p">]:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Construct parameter constraints arrays for parameter estimation</span>
@@ -1025,7 +1050,10 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 <span class="sd">        constraints of the form A.dot(parameters) - b &gt;= 0</span>
 <span class="sd">        &quot;&quot;&quot;</span></div>
 
-<div class="viewcode-block" id="VolatilityProcess.forecast"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.forecast.html#arch.univariate.VolatilityProcess.forecast">[docs]</a>    <span class="k">def</span> <span class="nf">forecast</span><span class="p">(</span>
+
+<div class="viewcode-block" id="VolatilityProcess.forecast">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.forecast.html#arch.univariate.VolatilityProcess.forecast">[docs]</a>
+    <span class="k">def</span> <span class="nf">forecast</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="n">ArrayLike1D</span><span class="p">,</span>
         <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">,</span>
@@ -1132,7 +1160,10 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
                 <span class="n">random_state</span><span class="p">,</span>
             <span class="p">)</span></div>
 
-<div class="viewcode-block" id="VolatilityProcess.simulate"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.simulate.html#arch.univariate.VolatilityProcess.simulate">[docs]</a>    <span class="nd">@abstractmethod</span>
+
+<div class="viewcode-block" id="VolatilityProcess.simulate">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.simulate.html#arch.univariate.VolatilityProcess.simulate">[docs]</a>
+    <span class="nd">@abstractmethod</span>
     <span class="k">def</span> <span class="nf">simulate</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span> <span class="o">|</span> <span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike1D</span><span class="p">,</span>
@@ -1168,6 +1199,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 <span class="sd">            The simulated variance</span>
 <span class="sd">        &quot;&quot;&quot;</span></div>
 
+
     <span class="k">def</span> <span class="nf">_gaussian_loglikelihood</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">,</span>
@@ -1184,7 +1216,9 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
         <span class="bp">self</span><span class="o">.</span><span class="n">compute_variance</span><span class="p">(</span><span class="n">parameters</span><span class="p">,</span> <span class="n">resids</span><span class="p">,</span> <span class="n">sigma2</span><span class="p">,</span> <span class="n">backcast</span><span class="p">,</span> <span class="n">var_bounds</span><span class="p">)</span>
         <span class="k">return</span> <span class="nb">float</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_normal</span><span class="o">.</span><span class="n">loglikelihood</span><span class="p">([],</span> <span class="n">resids</span><span class="p">,</span> <span class="n">sigma2</span><span class="p">))</span>
 
-<div class="viewcode-block" id="VolatilityProcess.parameter_names"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.parameter_names.html#arch.univariate.VolatilityProcess.parameter_names">[docs]</a>    <span class="nd">@abstractmethod</span>
+<div class="viewcode-block" id="VolatilityProcess.parameter_names">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.parameter_names.html#arch.univariate.VolatilityProcess.parameter_names">[docs]</a>
+    <span class="nd">@abstractmethod</span>
     <span class="k">def</span> <span class="nf">parameter_names</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Names of model parameters</span>
@@ -1193,10 +1227,14 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 <span class="sd">        -------</span>
 <span class="sd">        names : list (str)</span>
 <span class="sd">            Variables names</span>
-<span class="sd">        &quot;&quot;&quot;</span></div></div>
+<span class="sd">        &quot;&quot;&quot;</span></div>
+</div>
+
 
 
-<div class="viewcode-block" id="ConstantVariance"><a class="viewcode-back" href="../../../univariate/generated/arch.univariate.ConstantVariance.html#arch.univariate.ConstantVariance">[docs]</a><span class="k">class</span> <span class="nc">ConstantVariance</span><span class="p">(</span><span class="n">VolatilityProcess</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
+<div class="viewcode-block" id="ConstantVariance">
+<a class="viewcode-back" href="../../../univariate/generated/arch.univariate.ConstantVariance.html#arch.univariate.ConstantVariance">[docs]</a>
+<span class="k">class</span> <span class="nc">ConstantVariance</span><span class="p">(</span><span class="n">VolatilityProcess</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Constant volatility process</span>
 
@@ -1211,7 +1249,9 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
         <span class="bp">self</span><span class="o">.</span><span class="n">_name</span> <span class="o">=</span> <span class="s2">&quot;Constant Variance&quot;</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">closed_form</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span>
 
-<div class="viewcode-block" id="ConstantVariance.compute_variance"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.ConstantVariance.compute_variance.html#arch.univariate.ConstantVariance.compute_variance">[docs]</a>    <span class="k">def</span> <span class="nf">compute_variance</span><span class="p">(</span>
+<div class="viewcode-block" id="ConstantVariance.compute_variance">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.ConstantVariance.compute_variance.html#arch.univariate.ConstantVariance.compute_variance">[docs]</a>
+    <span class="k">def</span> <span class="nf">compute_variance</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">,</span>
         <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">,</span>
@@ -1222,10 +1262,16 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
         <span class="n">sigma2</span><span class="p">[:]</span> <span class="o">=</span> <span class="n">parameters</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
         <span class="k">return</span> <span class="n">sigma2</span></div>
 
-<div class="viewcode-block" id="ConstantVariance.starting_values"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.ConstantVariance.starting_values.html#arch.univariate.ConstantVariance.starting_values">[docs]</a>    <span class="k">def</span> <span class="nf">starting_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
+
+<div class="viewcode-block" id="ConstantVariance.starting_values">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.ConstantVariance.starting_values.html#arch.univariate.ConstantVariance.starting_values">[docs]</a>
+    <span class="k">def</span> <span class="nf">starting_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
         <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">resids</span><span class="o">.</span><span class="n">var</span><span class="p">()])</span></div>
 
-<div class="viewcode-block" id="ConstantVariance.simulate"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.ConstantVariance.simulate.html#arch.univariate.ConstantVariance.simulate">[docs]</a>    <span class="k">def</span> <span class="nf">simulate</span><span class="p">(</span>
+
+<div class="viewcode-block" id="ConstantVariance.simulate">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.ConstantVariance.simulate.html#arch.univariate.ConstantVariance.simulate">[docs]</a>
+    <span class="k">def</span> <span class="nf">simulate</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span> <span class="o">|</span> <span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike1D</span><span class="p">,</span>
         <span class="n">nobs</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
@@ -1239,25 +1285,41 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
         <span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">sigma2</span><span class="p">)</span> <span class="o">*</span> <span class="n">errors</span>
         <span class="k">return</span> <span class="n">data</span><span class="p">[</span><span class="n">burn</span><span class="p">:],</span> <span class="n">sigma2</span><span class="p">[</span><span class="n">burn</span><span class="p">:]</span></div>
 
-<div class="viewcode-block" id="ConstantVariance.constraints"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.ConstantVariance.constraints.html#arch.univariate.ConstantVariance.constraints">[docs]</a>    <span class="k">def</span> <span class="nf">constraints</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">Float64Array</span><span class="p">,</span> <span class="n">Float64Array</span><span class="p">]:</span>
+
+<div class="viewcode-block" id="ConstantVariance.constraints">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.ConstantVariance.constraints.html#arch.univariate.ConstantVariance.constraints">[docs]</a>
+    <span class="k">def</span> <span class="nf">constraints</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">Float64Array</span><span class="p">,</span> <span class="n">Float64Array</span><span class="p">]:</span>
         <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span></div>
 
-<div class="viewcode-block" id="ConstantVariance.backcast_transform"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.ConstantVariance.backcast_transform.html#arch.univariate.ConstantVariance.backcast_transform">[docs]</a>    <span class="k">def</span> <span class="nf">backcast_transform</span><span class="p">(</span>
+
+<div class="viewcode-block" id="ConstantVariance.backcast_transform">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.ConstantVariance.backcast_transform.html#arch.univariate.ConstantVariance.backcast_transform">[docs]</a>
+    <span class="k">def</span> <span class="nf">backcast_transform</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span> <span class="n">backcast</span><span class="p">:</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">Float64Array</span>
     <span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">Float64Array</span><span class="p">:</span>
         <span class="n">backcast</span> <span class="o">=</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">backcast_transform</span><span class="p">(</span><span class="n">backcast</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">backcast</span></div>
 
-<div class="viewcode-block" id="ConstantVariance.backcast"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.ConstantVariance.backcast.html#arch.univariate.ConstantVariance.backcast">[docs]</a>    <span class="k">def</span> <span class="nf">backcast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">Float64Array</span><span class="p">:</span>
+
+<div class="viewcode-block" id="ConstantVariance.backcast">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.ConstantVariance.backcast.html#arch.univariate.ConstantVariance.backcast">[docs]</a>
+    <span class="k">def</span> <span class="nf">backcast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">Float64Array</span><span class="p">:</span>
         <span class="k">return</span> <span class="nb">float</span><span class="p">(</span><span class="n">resids</span><span class="o">.</span><span class="n">var</span><span class="p">())</span></div>
 
-<div class="viewcode-block" id="ConstantVariance.bounds"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.ConstantVariance.bounds.html#arch.univariate.ConstantVariance.bounds">[docs]</a>    <span class="k">def</span> <span class="nf">bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]]:</span>
+
+<div class="viewcode-block" id="ConstantVariance.bounds">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.ConstantVariance.bounds.html#arch.univariate.ConstantVariance.bounds">[docs]</a>
+    <span class="k">def</span> <span class="nf">bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]]:</span>
         <span class="n">v</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">resids</span><span class="o">.</span><span class="n">var</span><span class="p">())</span>
         <span class="k">return</span> <span class="p">[(</span><span class="n">v</span> <span class="o">/</span> <span class="mf">100000.0</span><span class="p">,</span> <span class="mf">10.0</span> <span class="o">*</span> <span class="p">(</span><span class="n">v</span> <span class="o">+</span> <span class="nb">float</span><span class="p">(</span><span class="n">resids</span><span class="o">.</span><span class="n">mean</span><span class="p">())</span> <span class="o">**</span> <span class="mf">2.0</span><span class="p">))]</span></div>
 
-<div class="viewcode-block" id="ConstantVariance.parameter_names"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.ConstantVariance.parameter_names.html#arch.univariate.ConstantVariance.parameter_names">[docs]</a>    <span class="k">def</span> <span class="nf">parameter_names</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
+
+<div class="viewcode-block" id="ConstantVariance.parameter_names">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.ConstantVariance.parameter_names.html#arch.univariate.ConstantVariance.parameter_names">[docs]</a>
+    <span class="k">def</span> <span class="nf">parameter_names</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
         <span class="k">return</span> <span class="p">[</span><span class="s2">&quot;sigma2&quot;</span><span class="p">]</span></div>
 
+
     <span class="k">def</span> <span class="nf">_check_forecasting_method</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span> <span class="n">method</span><span class="p">:</span> <span class="n">ForecastingMethod</span><span class="p">,</span> <span class="n">horizon</span><span class="p">:</span> <span class="nb">int</span>
     <span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
@@ -1304,7 +1366,10 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="n">VarianceForecast</span><span class="p">(</span><span class="n">forecasts</span><span class="p">,</span> <span class="n">forecast_paths</span><span class="p">,</span> <span class="n">shocks</span><span class="p">)</span></div>
 
 
-<div class="viewcode-block" id="GARCH"><a class="viewcode-back" href="../../../univariate/generated/arch.univariate.GARCH.html#arch.univariate.GARCH">[docs]</a><span class="k">class</span> <span class="nc">GARCH</span><span class="p">(</span><span class="n">VolatilityProcess</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
+
+<div class="viewcode-block" id="GARCH">
+<a class="viewcode-back" href="../../../univariate/generated/arch.univariate.GARCH.html#arch.univariate.GARCH">[docs]</a>
+<span class="k">class</span> <span class="nc">GARCH</span><span class="p">(</span><span class="n">VolatilityProcess</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    GARCH and related model estimation</span>
 
@@ -1394,9 +1459,12 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
         <span class="n">descr</span> <span class="o">=</span> <span class="n">descr</span><span class="p">[:</span><span class="o">-</span><span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="s2">&quot;)&quot;</span>
         <span class="k">return</span> <span class="n">descr</span>
 
-<div class="viewcode-block" id="GARCH.variance_bounds"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.GARCH.variance_bounds.html#arch.univariate.GARCH.variance_bounds">[docs]</a>    <span class="k">def</span> <span class="nf">variance_bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">,</span> <span class="n">power</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">2.0</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
+<div class="viewcode-block" id="GARCH.variance_bounds">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.GARCH.variance_bounds.html#arch.univariate.GARCH.variance_bounds">[docs]</a>
+    <span class="k">def</span> <span class="nf">variance_bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">,</span> <span class="n">power</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">2.0</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
         <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">variance_bounds</span><span class="p">(</span><span class="n">resids</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">power</span><span class="p">)</span></div>
 
+
     <span class="k">def</span> <span class="nf">_generate_name</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
         <span class="n">p</span><span class="p">,</span> <span class="n">o</span><span class="p">,</span> <span class="n">q</span><span class="p">,</span> <span class="n">power</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">o</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">q</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">power</span>  <span class="c1"># noqa: F841</span>
         <span class="k">if</span> <span class="n">power</span> <span class="o">==</span> <span class="mf">2.0</span><span class="p">:</span>
@@ -1421,7 +1489,9 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
             <span class="k">else</span><span class="p">:</span>
                 <span class="k">return</span> <span class="sa">f</span><span class="s2">&quot;Asym. Power GARCH (power: </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">power</span><span class="si">:</span><span class="s2">0.1f</span><span class="si">}</span><span class="s2">)&quot;</span>
 
-<div class="viewcode-block" id="GARCH.bounds"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.GARCH.bounds.html#arch.univariate.GARCH.bounds">[docs]</a>    <span class="k">def</span> <span class="nf">bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]]:</span>
+<div class="viewcode-block" id="GARCH.bounds">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.GARCH.bounds.html#arch.univariate.GARCH.bounds">[docs]</a>
+    <span class="k">def</span> <span class="nf">bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]]:</span>
         <span class="n">v</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">resids</span><span class="p">)</span> <span class="o">**</span> <span class="bp">self</span><span class="o">.</span><span class="n">power</span><span class="p">))</span>
 
         <span class="n">bounds</span> <span class="o">=</span> <span class="p">[(</span><span class="mf">1e-8</span> <span class="o">*</span> <span class="n">v</span><span class="p">,</span> <span class="mf">10.0</span> <span class="o">*</span> <span class="nb">float</span><span class="p">(</span><span class="n">v</span><span class="p">))]</span>
@@ -1436,7 +1506,10 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
         <span class="k">return</span> <span class="n">bounds</span></div>
 
-<div class="viewcode-block" id="GARCH.constraints"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.GARCH.constraints.html#arch.univariate.GARCH.constraints">[docs]</a>    <span class="k">def</span> <span class="nf">constraints</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">Float64Array</span><span class="p">,</span> <span class="n">Float64Array</span><span class="p">]:</span>
+
+<div class="viewcode-block" id="GARCH.constraints">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.GARCH.constraints.html#arch.univariate.GARCH.constraints">[docs]</a>
+    <span class="k">def</span> <span class="nf">constraints</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">Float64Array</span><span class="p">,</span> <span class="n">Float64Array</span><span class="p">]:</span>
         <span class="n">p</span><span class="p">,</span> <span class="n">o</span><span class="p">,</span> <span class="n">q</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">o</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">q</span>
         <span class="n">k_arch</span> <span class="o">=</span> <span class="n">p</span> <span class="o">+</span> <span class="n">o</span> <span class="o">+</span> <span class="n">q</span>
         <span class="c1"># alpha[i] &gt;0</span>
@@ -1456,7 +1529,10 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
         <span class="n">b</span><span class="p">[</span><span class="n">k_arch</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mf">1.0</span>
         <span class="k">return</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span></div>
 
-<div class="viewcode-block" id="GARCH.compute_variance"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.GARCH.compute_variance.html#arch.univariate.GARCH.compute_variance">[docs]</a>    <span class="k">def</span> <span class="nf">compute_variance</span><span class="p">(</span>
+
+<div class="viewcode-block" id="GARCH.compute_variance">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.GARCH.compute_variance.html#arch.univariate.GARCH.compute_variance">[docs]</a>
+    <span class="k">def</span> <span class="nf">compute_variance</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">,</span>
         <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">,</span>
@@ -1481,13 +1557,19 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
         <span class="k">return</span> <span class="n">sigma2</span></div>
 
-<div class="viewcode-block" id="GARCH.backcast_transform"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.GARCH.backcast_transform.html#arch.univariate.GARCH.backcast_transform">[docs]</a>    <span class="k">def</span> <span class="nf">backcast_transform</span><span class="p">(</span>
+
+<div class="viewcode-block" id="GARCH.backcast_transform">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.GARCH.backcast_transform.html#arch.univariate.GARCH.backcast_transform">[docs]</a>
+    <span class="k">def</span> <span class="nf">backcast_transform</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span> <span class="n">backcast</span><span class="p">:</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">Float64Array</span>
     <span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">Float64Array</span><span class="p">:</span>
         <span class="n">backcast</span> <span class="o">=</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">backcast_transform</span><span class="p">(</span><span class="n">backcast</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">backcast</span><span class="p">)</span> <span class="o">**</span> <span class="bp">self</span><span class="o">.</span><span class="n">power</span></div>
 
-<div class="viewcode-block" id="GARCH.backcast"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.GARCH.backcast.html#arch.univariate.GARCH.backcast">[docs]</a>    <span class="k">def</span> <span class="nf">backcast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">Float64Array</span><span class="p">:</span>
+
+<div class="viewcode-block" id="GARCH.backcast">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.GARCH.backcast.html#arch.univariate.GARCH.backcast">[docs]</a>
+    <span class="k">def</span> <span class="nf">backcast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">Float64Array</span><span class="p">:</span>
         <span class="n">power</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">power</span>
         <span class="n">tau</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="mi">75</span><span class="p">,</span> <span class="n">resids</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
         <span class="n">w</span> <span class="o">=</span> <span class="mf">0.94</span> <span class="o">**</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">tau</span><span class="p">)</span>
@@ -1496,7 +1578,10 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
         <span class="k">return</span> <span class="nb">float</span><span class="p">(</span><span class="n">backcast</span><span class="p">)</span></div>
 
-<div class="viewcode-block" id="GARCH.simulate"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.GARCH.simulate.html#arch.univariate.GARCH.simulate">[docs]</a>    <span class="k">def</span> <span class="nf">simulate</span><span class="p">(</span>
+
+<div class="viewcode-block" id="GARCH.simulate">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.GARCH.simulate.html#arch.univariate.GARCH.simulate">[docs]</a>
+    <span class="k">def</span> <span class="nf">simulate</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span> <span class="o">|</span> <span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike1D</span><span class="p">,</span>
         <span class="n">nobs</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
@@ -1550,7 +1635,10 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
         <span class="k">return</span> <span class="n">data</span><span class="p">[</span><span class="n">burn</span><span class="p">:],</span> <span class="n">sigma2</span><span class="p">[</span><span class="n">burn</span><span class="p">:]</span></div>
 
-<div class="viewcode-block" id="GARCH.starting_values"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.GARCH.starting_values.html#arch.univariate.GARCH.starting_values">[docs]</a>    <span class="k">def</span> <span class="nf">starting_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
+
+<div class="viewcode-block" id="GARCH.starting_values">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.GARCH.starting_values.html#arch.univariate.GARCH.starting_values">[docs]</a>
+    <span class="k">def</span> <span class="nf">starting_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
         <span class="n">p</span><span class="p">,</span> <span class="n">o</span><span class="p">,</span> <span class="n">q</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">o</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">q</span>
         <span class="n">power</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">power</span>
         <span class="n">alphas</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.01</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">]</span>
@@ -1583,9 +1671,13 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
         <span class="k">return</span> <span class="n">svs</span><span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">loc</span><span class="p">)]</span></div>
 
-<div class="viewcode-block" id="GARCH.parameter_names"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.GARCH.parameter_names.html#arch.univariate.GARCH.parameter_names">[docs]</a>    <span class="k">def</span> <span class="nf">parameter_names</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
+
+<div class="viewcode-block" id="GARCH.parameter_names">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.GARCH.parameter_names.html#arch.univariate.GARCH.parameter_names">[docs]</a>
+    <span class="k">def</span> <span class="nf">parameter_names</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
         <span class="k">return</span> <span class="n">_common_names</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">o</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">q</span><span class="p">)</span></div>
 
+
     <span class="k">def</span> <span class="nf">_check_forecasting_method</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span> <span class="n">method</span><span class="p">:</span> <span class="n">ForecastingMethod</span><span class="p">,</span> <span class="n">horizon</span><span class="p">:</span> <span class="nb">int</span>
     <span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
@@ -1768,7 +1860,10 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="n">VarianceForecast</span><span class="p">(</span><span class="n">forecasts</span><span class="p">,</span> <span class="n">paths</span><span class="p">,</span> <span class="n">shocks</span><span class="p">)</span></div>
 
 
-<div class="viewcode-block" id="HARCH"><a class="viewcode-back" href="../../../univariate/generated/arch.univariate.HARCH.html#arch.univariate.HARCH">[docs]</a><span class="k">class</span> <span class="nc">HARCH</span><span class="p">(</span><span class="n">VolatilityProcess</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
+
+<div class="viewcode-block" id="HARCH">
+<a class="viewcode-back" href="../../../univariate/generated/arch.univariate.HARCH.html#arch.univariate.HARCH">[docs]</a>
+<span class="k">class</span> <span class="nc">HARCH</span><span class="p">(</span><span class="n">VolatilityProcess</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Heterogeneous ARCH process</span>
 
@@ -1830,7 +1925,9 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
         <span class="k">return</span> <span class="n">descr</span>
 
-<div class="viewcode-block" id="HARCH.bounds"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.HARCH.bounds.html#arch.univariate.HARCH.bounds">[docs]</a>    <span class="k">def</span> <span class="nf">bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]]:</span>
+<div class="viewcode-block" id="HARCH.bounds">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.HARCH.bounds.html#arch.univariate.HARCH.bounds">[docs]</a>
+    <span class="k">def</span> <span class="nf">bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]]:</span>
         <span class="n">lags</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lags</span>
         <span class="n">k_arch</span> <span class="o">=</span> <span class="n">lags</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
 
@@ -1839,7 +1936,10 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
         <span class="k">return</span> <span class="n">bounds</span></div>
 
-<div class="viewcode-block" id="HARCH.constraints"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.HARCH.constraints.html#arch.univariate.HARCH.constraints">[docs]</a>    <span class="k">def</span> <span class="nf">constraints</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">Float64Array</span><span class="p">,</span> <span class="n">Float64Array</span><span class="p">]:</span>
+
+<div class="viewcode-block" id="HARCH.constraints">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.HARCH.constraints.html#arch.univariate.HARCH.constraints">[docs]</a>
+    <span class="k">def</span> <span class="nf">constraints</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">Float64Array</span><span class="p">,</span> <span class="n">Float64Array</span><span class="p">]:</span>
         <span class="n">k_arch</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_num_lags</span>
         <span class="n">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">k_arch</span> <span class="o">+</span> <span class="mi">2</span><span class="p">,</span> <span class="n">k_arch</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
         <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">k_arch</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
@@ -1849,7 +1949,10 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
         <span class="n">b</span><span class="p">[</span><span class="n">k_arch</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mf">1.0</span>
         <span class="k">return</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span></div>
 
-<div class="viewcode-block" id="HARCH.compute_variance"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.HARCH.compute_variance.html#arch.univariate.HARCH.compute_variance">[docs]</a>    <span class="k">def</span> <span class="nf">compute_variance</span><span class="p">(</span>
+
+<div class="viewcode-block" id="HARCH.compute_variance">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.HARCH.compute_variance.html#arch.univariate.HARCH.compute_variance">[docs]</a>
+    <span class="k">def</span> <span class="nf">compute_variance</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">,</span>
         <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">,</span>
@@ -1865,7 +1968,10 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
         <span class="p">)</span>
         <span class="k">return</span> <span class="n">sigma2</span></div>
 
-<div class="viewcode-block" id="HARCH.simulate"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.HARCH.simulate.html#arch.univariate.HARCH.simulate">[docs]</a>    <span class="k">def</span> <span class="nf">simulate</span><span class="p">(</span>
+
+<div class="viewcode-block" id="HARCH.simulate">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.HARCH.simulate.html#arch.univariate.HARCH.simulate">[docs]</a>
+    <span class="k">def</span> <span class="nf">simulate</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span> <span class="o">|</span> <span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike1D</span><span class="p">,</span>
         <span class="n">nobs</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
@@ -1899,7 +2005,10 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
         <span class="k">return</span> <span class="n">data</span><span class="p">[</span><span class="n">burn</span><span class="p">:],</span> <span class="n">sigma2</span><span class="p">[</span><span class="n">burn</span><span class="p">:]</span></div>
 
-<div class="viewcode-block" id="HARCH.starting_values"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.HARCH.starting_values.html#arch.univariate.HARCH.starting_values">[docs]</a>    <span class="k">def</span> <span class="nf">starting_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
+
+<div class="viewcode-block" id="HARCH.starting_values">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.HARCH.starting_values.html#arch.univariate.HARCH.starting_values">[docs]</a>
+    <span class="k">def</span> <span class="nf">starting_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
         <span class="n">k_arch</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_num_lags</span>
 
         <span class="n">alpha</span> <span class="o">=</span> <span class="mf">0.9</span>
@@ -1908,12 +2017,16 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
         <span class="k">return</span> <span class="n">sv</span></div>
 
-<div class="viewcode-block" id="HARCH.parameter_names"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.HARCH.parameter_names.html#arch.univariate.HARCH.parameter_names">[docs]</a>    <span class="k">def</span> <span class="nf">parameter_names</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
+
+<div class="viewcode-block" id="HARCH.parameter_names">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.HARCH.parameter_names.html#arch.univariate.HARCH.parameter_names">[docs]</a>
+    <span class="k">def</span> <span class="nf">parameter_names</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
         <span class="n">names</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;omega&quot;</span><span class="p">]</span>
         <span class="n">lags</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lags</span>
         <span class="n">names</span><span class="o">.</span><span class="n">extend</span><span class="p">([</span><span class="s2">&quot;alpha[&quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">lags</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="o">+</span> <span class="s2">&quot;]&quot;</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_num_lags</span><span class="p">)])</span>
         <span class="k">return</span> <span class="n">names</span></div>
 
+
     <span class="k">def</span> <span class="nf">_harch_to_arch</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">params</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
         <span class="n">arch_params</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lags</span><span class="o">.</span><span class="n">max</span><span class="p">()))</span>
         <span class="n">arch_params</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">params</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
@@ -2004,7 +2117,10 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="n">VarianceForecast</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">paths</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="mi">1</span><span class="p">)),</span> <span class="n">paths</span><span class="p">,</span> <span class="n">shocks</span><span class="p">)</span></div>
 
 
-<div class="viewcode-block" id="MIDASHyperbolic"><a class="viewcode-back" href="../../../univariate/generated/arch.univariate.MIDASHyperbolic.html#arch.univariate.MIDASHyperbolic">[docs]</a><span class="k">class</span> <span class="nc">MIDASHyperbolic</span><span class="p">(</span><span class="n">VolatilityProcess</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
+
+<div class="viewcode-block" id="MIDASHyperbolic">
+<a class="viewcode-back" href="../../../univariate/generated/arch.univariate.MIDASHyperbolic.html#arch.univariate.MIDASHyperbolic">[docs]</a>
+<span class="k">class</span> <span class="nc">MIDASHyperbolic</span><span class="p">(</span><span class="n">VolatilityProcess</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    MIDAS Hyperbolic ARCH process</span>
 
@@ -2071,7 +2187,9 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
         <span class="k">return</span> <span class="n">descr</span>
 
-<div class="viewcode-block" id="MIDASHyperbolic.bounds"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.MIDASHyperbolic.bounds.html#arch.univariate.MIDASHyperbolic.bounds">[docs]</a>    <span class="k">def</span> <span class="nf">bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]]:</span>
+<div class="viewcode-block" id="MIDASHyperbolic.bounds">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.MIDASHyperbolic.bounds.html#arch.univariate.MIDASHyperbolic.bounds">[docs]</a>
+    <span class="k">def</span> <span class="nf">bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]]:</span>
         <span class="n">bounds</span> <span class="o">=</span> <span class="p">[(</span><span class="mf">0.0</span><span class="p">,</span> <span class="mi">10</span> <span class="o">*</span> <span class="nb">float</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">resids</span><span class="o">**</span><span class="mf">2.0</span><span class="p">)))]</span>  <span class="c1"># omega</span>
         <span class="n">bounds</span><span class="o">.</span><span class="n">extend</span><span class="p">([(</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)])</span>  <span class="c1"># 0 &lt;= alpha &lt; 1</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_asym</span><span class="p">:</span>
@@ -2080,7 +2198,10 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
         <span class="k">return</span> <span class="n">bounds</span></div>
 
-<div class="viewcode-block" id="MIDASHyperbolic.constraints"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.MIDASHyperbolic.constraints.html#arch.univariate.MIDASHyperbolic.constraints">[docs]</a>    <span class="k">def</span> <span class="nf">constraints</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">Float64Array</span><span class="p">,</span> <span class="n">Float64Array</span><span class="p">]:</span>
+
+<div class="viewcode-block" id="MIDASHyperbolic.constraints">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.MIDASHyperbolic.constraints.html#arch.univariate.MIDASHyperbolic.constraints">[docs]</a>
+    <span class="k">def</span> <span class="nf">constraints</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">Float64Array</span><span class="p">,</span> <span class="n">Float64Array</span><span class="p">]:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Constraints</span>
 
@@ -2118,7 +2239,10 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
         <span class="k">return</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span></div>
 
-<div class="viewcode-block" id="MIDASHyperbolic.compute_variance"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.MIDASHyperbolic.compute_variance.html#arch.univariate.MIDASHyperbolic.compute_variance">[docs]</a>    <span class="k">def</span> <span class="nf">compute_variance</span><span class="p">(</span>
+
+<div class="viewcode-block" id="MIDASHyperbolic.compute_variance">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.MIDASHyperbolic.compute_variance.html#arch.univariate.MIDASHyperbolic.compute_variance">[docs]</a>
+    <span class="k">def</span> <span class="nf">compute_variance</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">,</span>
         <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">,</span>
@@ -2137,7 +2261,10 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
         <span class="n">rec</span><span class="o">.</span><span class="n">midas_recursion</span><span class="p">(</span><span class="n">params</span><span class="p">,</span> <span class="n">weights</span><span class="p">,</span> <span class="n">resids</span><span class="p">,</span> <span class="n">sigma2</span><span class="p">,</span> <span class="n">nobs</span><span class="p">,</span> <span class="n">backcast</span><span class="p">,</span> <span class="n">var_bounds</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">sigma2</span></div>
 
-<div class="viewcode-block" id="MIDASHyperbolic.simulate"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.MIDASHyperbolic.simulate.html#arch.univariate.MIDASHyperbolic.simulate">[docs]</a>    <span class="k">def</span> <span class="nf">simulate</span><span class="p">(</span>
+
+<div class="viewcode-block" id="MIDASHyperbolic.simulate">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.MIDASHyperbolic.simulate.html#arch.univariate.MIDASHyperbolic.simulate">[docs]</a>
+    <span class="k">def</span> <span class="nf">simulate</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span> <span class="o">|</span> <span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike1D</span><span class="p">,</span>
         <span class="n">nobs</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
@@ -2183,7 +2310,10 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
         <span class="k">return</span> <span class="n">data</span><span class="p">[</span><span class="n">burn</span><span class="p">:],</span> <span class="n">sigma2</span><span class="p">[</span><span class="n">burn</span><span class="p">:]</span></div>
 
-<div class="viewcode-block" id="MIDASHyperbolic.starting_values"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.MIDASHyperbolic.starting_values.html#arch.univariate.MIDASHyperbolic.starting_values">[docs]</a>    <span class="k">def</span> <span class="nf">starting_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
+
+<div class="viewcode-block" id="MIDASHyperbolic.starting_values">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.MIDASHyperbolic.starting_values.html#arch.univariate.MIDASHyperbolic.starting_values">[docs]</a>
+    <span class="k">def</span> <span class="nf">starting_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
         <span class="n">theta</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">,</span> <span class="mf">0.9</span><span class="p">]</span>
         <span class="n">alpha</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.8</span><span class="p">,</span> <span class="mf">0.9</span><span class="p">,</span> <span class="mf">0.95</span><span class="p">,</span> <span class="mf">0.98</span><span class="p">]</span>
         <span class="n">var</span> <span class="o">=</span> <span class="p">(</span><span class="n">resids</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
@@ -2211,13 +2341,17 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
         <span class="k">return</span> <span class="n">svs</span><span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">loc</span><span class="p">)]</span></div>
 
-<div class="viewcode-block" id="MIDASHyperbolic.parameter_names"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.MIDASHyperbolic.parameter_names.html#arch.univariate.MIDASHyperbolic.parameter_names">[docs]</a>    <span class="k">def</span> <span class="nf">parameter_names</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
+
+<div class="viewcode-block" id="MIDASHyperbolic.parameter_names">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.MIDASHyperbolic.parameter_names.html#arch.univariate.MIDASHyperbolic.parameter_names">[docs]</a>
+    <span class="k">def</span> <span class="nf">parameter_names</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
         <span class="n">names</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;omega&quot;</span><span class="p">,</span> <span class="s2">&quot;alpha&quot;</span><span class="p">,</span> <span class="s2">&quot;theta&quot;</span><span class="p">]</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_asym</span><span class="p">:</span>
             <span class="n">names</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="s2">&quot;gamma&quot;</span><span class="p">)</span>
 
         <span class="k">return</span> <span class="n">names</span></div>
 
+
     <span class="k">def</span> <span class="nf">_weights</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">params</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
         <span class="n">m</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">m</span>
         <span class="c1"># Prevent 0</span>
@@ -2335,7 +2469,10 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="n">VarianceForecast</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">paths</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="mi">1</span><span class="p">)),</span> <span class="n">paths</span><span class="p">,</span> <span class="n">shocks</span><span class="p">)</span></div>
 
 
-<div class="viewcode-block" id="ARCH"><a class="viewcode-back" href="../../../univariate/generated/arch.univariate.ARCH.html#arch.univariate.ARCH">[docs]</a><span class="k">class</span> <span class="nc">ARCH</span><span class="p">(</span><span class="n">GARCH</span><span class="p">):</span>
+
+<div class="viewcode-block" id="ARCH">
+<a class="viewcode-back" href="../../../univariate/generated/arch.univariate.ARCH.html#arch.univariate.ARCH">[docs]</a>
+<span class="k">class</span> <span class="nc">ARCH</span><span class="p">(</span><span class="n">GARCH</span><span class="p">):</span>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    ARCH process</span>
 
@@ -2368,7 +2505,9 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
         <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_num_params</span> <span class="o">=</span> <span class="n">p</span> <span class="o">+</span> <span class="mi">1</span>
 
-<div class="viewcode-block" id="ARCH.starting_values"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.ARCH.starting_values.html#arch.univariate.ARCH.starting_values">[docs]</a>    <span class="k">def</span> <span class="nf">starting_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
+<div class="viewcode-block" id="ARCH.starting_values">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.ARCH.starting_values.html#arch.univariate.ARCH.starting_values">[docs]</a>
+    <span class="k">def</span> <span class="nf">starting_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
         <span class="n">p</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">p</span>
 
         <span class="n">alphas</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.95</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">)</span>
@@ -2382,10 +2521,14 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
             <span class="n">svs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">sv</span><span class="p">)</span>
             <span class="n">llfs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_gaussian_loglikelihood</span><span class="p">(</span><span class="n">sv</span><span class="p">,</span> <span class="n">resids</span><span class="p">,</span> <span class="n">backcast</span><span class="p">,</span> <span class="n">var_bounds</span><span class="p">)</span>
         <span class="n">loc</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">llfs</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">svs</span><span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">loc</span><span class="p">)]</span></div></div>
+        <span class="k">return</span> <span class="n">svs</span><span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">loc</span><span class="p">)]</span></div>
+</div>
 
 
-<div class="viewcode-block" id="EWMAVariance"><a class="viewcode-back" href="../../../univariate/generated/arch.univariate.EWMAVariance.html#arch.univariate.EWMAVariance">[docs]</a><span class="k">class</span> <span class="nc">EWMAVariance</span><span class="p">(</span><span class="n">VolatilityProcess</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
+
+<div class="viewcode-block" id="EWMAVariance">
+<a class="viewcode-back" href="../../../univariate/generated/arch.univariate.EWMAVariance.html#arch.univariate.EWMAVariance">[docs]</a>
+<span class="k">class</span> <span class="nc">EWMAVariance</span><span class="p">(</span><span class="n">VolatilityProcess</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Exponentially Weighted Moving-Average (RiskMetrics) Variance process</span>
 
@@ -2433,22 +2576,33 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
             <span class="n">descr</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">+</span> <span class="s2">&quot;(lam: &quot;</span> <span class="o">+</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">lam</span><span class="si">:</span><span class="s2">0.2f</span><span class="si">}</span><span class="s2">&quot;</span> <span class="o">+</span> <span class="s2">&quot;)&quot;</span>
         <span class="k">return</span> <span class="n">descr</span>
 
-<div class="viewcode-block" id="EWMAVariance.starting_values"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EWMAVariance.starting_values.html#arch.univariate.EWMAVariance.starting_values">[docs]</a>    <span class="k">def</span> <span class="nf">starting_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
+<div class="viewcode-block" id="EWMAVariance.starting_values">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EWMAVariance.starting_values.html#arch.univariate.EWMAVariance.starting_values">[docs]</a>
+    <span class="k">def</span> <span class="nf">starting_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_estimate_lam</span><span class="p">:</span>
             <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.94</span><span class="p">])</span>
         <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([])</span></div>
 
-<div class="viewcode-block" id="EWMAVariance.parameter_names"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EWMAVariance.parameter_names.html#arch.univariate.EWMAVariance.parameter_names">[docs]</a>    <span class="k">def</span> <span class="nf">parameter_names</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
+
+<div class="viewcode-block" id="EWMAVariance.parameter_names">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EWMAVariance.parameter_names.html#arch.univariate.EWMAVariance.parameter_names">[docs]</a>
+    <span class="k">def</span> <span class="nf">parameter_names</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_estimate_lam</span><span class="p">:</span>
             <span class="k">return</span> <span class="p">[</span><span class="s2">&quot;lam&quot;</span><span class="p">]</span>
         <span class="k">return</span> <span class="p">[]</span></div>
 
-<div class="viewcode-block" id="EWMAVariance.bounds"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EWMAVariance.bounds.html#arch.univariate.EWMAVariance.bounds">[docs]</a>    <span class="k">def</span> <span class="nf">bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]]:</span>
+
+<div class="viewcode-block" id="EWMAVariance.bounds">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EWMAVariance.bounds.html#arch.univariate.EWMAVariance.bounds">[docs]</a>
+    <span class="k">def</span> <span class="nf">bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]]:</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_estimate_lam</span><span class="p">:</span>
             <span class="k">return</span> <span class="p">[(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)]</span>
         <span class="k">return</span> <span class="p">[]</span></div>
 
-<div class="viewcode-block" id="EWMAVariance.compute_variance"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EWMAVariance.compute_variance.html#arch.univariate.EWMAVariance.compute_variance">[docs]</a>    <span class="k">def</span> <span class="nf">compute_variance</span><span class="p">(</span>
+
+<div class="viewcode-block" id="EWMAVariance.compute_variance">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EWMAVariance.compute_variance.html#arch.univariate.EWMAVariance.compute_variance">[docs]</a>
+    <span class="k">def</span> <span class="nf">compute_variance</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">,</span>
         <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">,</span>
@@ -2459,14 +2613,20 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
         <span class="n">lam</span> <span class="o">=</span> <span class="n">parameters</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_estimate_lam</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">lam</span>
         <span class="k">return</span> <span class="n">ewma_recursion</span><span class="p">(</span><span class="n">lam</span><span class="p">,</span> <span class="n">resids</span><span class="p">,</span> <span class="n">sigma2</span><span class="p">,</span> <span class="n">resids</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="nb">float</span><span class="p">(</span><span class="n">backcast</span><span class="p">))</span></div>
 
-<div class="viewcode-block" id="EWMAVariance.constraints"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EWMAVariance.constraints.html#arch.univariate.EWMAVariance.constraints">[docs]</a>    <span class="k">def</span> <span class="nf">constraints</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">Float64Array</span><span class="p">,</span> <span class="n">Float64Array</span><span class="p">]:</span>
+
+<div class="viewcode-block" id="EWMAVariance.constraints">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EWMAVariance.constraints.html#arch.univariate.EWMAVariance.constraints">[docs]</a>
+    <span class="k">def</span> <span class="nf">constraints</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">Float64Array</span><span class="p">,</span> <span class="n">Float64Array</span><span class="p">]:</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_estimate_lam</span><span class="p">:</span>
             <span class="n">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
             <span class="n">b</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">1</span><span class="p">,))</span>
             <span class="k">return</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span>
         <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="mi">0</span><span class="p">,))</span></div>
 
-<div class="viewcode-block" id="EWMAVariance.simulate"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EWMAVariance.simulate.html#arch.univariate.EWMAVariance.simulate">[docs]</a>    <span class="k">def</span> <span class="nf">simulate</span><span class="p">(</span>
+
+<div class="viewcode-block" id="EWMAVariance.simulate">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EWMAVariance.simulate.html#arch.univariate.EWMAVariance.simulate">[docs]</a>
+    <span class="k">def</span> <span class="nf">simulate</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span> <span class="o">|</span> <span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike1D</span><span class="p">,</span>
         <span class="n">nobs</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
@@ -2496,6 +2656,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
         <span class="k">return</span> <span class="n">data</span><span class="p">[</span><span class="n">burn</span><span class="p">:],</span> <span class="n">sigma2</span><span class="p">[</span><span class="n">burn</span><span class="p">:]</span></div>
 
+
     <span class="k">def</span> <span class="nf">_check_forecasting_method</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span> <span class="n">method</span><span class="p">:</span> <span class="n">ForecastingMethod</span><span class="p">,</span> <span class="n">horizon</span><span class="p">:</span> <span class="nb">int</span>
     <span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
@@ -2557,7 +2718,10 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="n">VarianceForecast</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">paths</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="mi">1</span><span class="p">)),</span> <span class="n">paths</span><span class="p">,</span> <span class="n">shocks</span><span class="p">)</span></div>
 
 
-<div class="viewcode-block" id="RiskMetrics2006"><a class="viewcode-back" href="../../../univariate/generated/arch.univariate.RiskMetrics2006.html#arch.univariate.RiskMetrics2006">[docs]</a><span class="k">class</span> <span class="nc">RiskMetrics2006</span><span class="p">(</span><span class="n">VolatilityProcess</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
+
+<div class="viewcode-block" id="RiskMetrics2006">
+<a class="viewcode-back" href="../../../univariate/generated/arch.univariate.RiskMetrics2006.html#arch.univariate.RiskMetrics2006">[docs]</a>
+<span class="k">class</span> <span class="nc">RiskMetrics2006</span><span class="p">(</span><span class="n">VolatilityProcess</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    RiskMetrics 2006 Variance process</span>
 
@@ -2646,7 +2810,9 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
         <span class="n">mus</span> <span class="o">=</span> <span class="n">cast</span><span class="p">(</span><span class="n">Float64Array</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="mf">1.0</span> <span class="o">/</span> <span class="n">taus</span><span class="p">))</span>
         <span class="k">return</span> <span class="n">mus</span>
 
-<div class="viewcode-block" id="RiskMetrics2006.backcast"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.RiskMetrics2006.backcast.html#arch.univariate.RiskMetrics2006.backcast">[docs]</a>    <span class="k">def</span> <span class="nf">backcast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">Float64Array</span><span class="p">:</span>
+<div class="viewcode-block" id="RiskMetrics2006.backcast">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.RiskMetrics2006.backcast.html#arch.univariate.RiskMetrics2006.backcast">[docs]</a>
+    <span class="k">def</span> <span class="nf">backcast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">Float64Array</span><span class="p">:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Construct values for backcasting to start the recursion</span>
 
@@ -2675,7 +2841,10 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
         <span class="k">return</span> <span class="n">backcast</span></div>
 
-<div class="viewcode-block" id="RiskMetrics2006.backcast_transform"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.RiskMetrics2006.backcast_transform.html#arch.univariate.RiskMetrics2006.backcast_transform">[docs]</a>    <span class="k">def</span> <span class="nf">backcast_transform</span><span class="p">(</span>
+
+<div class="viewcode-block" id="RiskMetrics2006.backcast_transform">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.RiskMetrics2006.backcast_transform.html#arch.univariate.RiskMetrics2006.backcast_transform">[docs]</a>
+    <span class="k">def</span> <span class="nf">backcast_transform</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span> <span class="n">backcast</span><span class="p">:</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">Float64Array</span>
     <span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">Float64Array</span><span class="p">:</span>
         <span class="n">backcast</span> <span class="o">=</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">backcast_transform</span><span class="p">(</span><span class="n">backcast</span><span class="p">)</span>
@@ -2691,24 +2860,42 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
         <span class="k">return</span> <span class="n">backcast_arr</span></div>
 
-<div class="viewcode-block" id="RiskMetrics2006.starting_values"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.RiskMetrics2006.starting_values.html#arch.univariate.RiskMetrics2006.starting_values">[docs]</a>    <span class="k">def</span> <span class="nf">starting_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
+
+<div class="viewcode-block" id="RiskMetrics2006.starting_values">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.RiskMetrics2006.starting_values.html#arch.univariate.RiskMetrics2006.starting_values">[docs]</a>
+    <span class="k">def</span> <span class="nf">starting_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
         <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="mi">0</span><span class="p">,))</span></div>
 
-<div class="viewcode-block" id="RiskMetrics2006.parameter_names"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.RiskMetrics2006.parameter_names.html#arch.univariate.RiskMetrics2006.parameter_names">[docs]</a>    <span class="k">def</span> <span class="nf">parameter_names</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
+
+<div class="viewcode-block" id="RiskMetrics2006.parameter_names">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.RiskMetrics2006.parameter_names.html#arch.univariate.RiskMetrics2006.parameter_names">[docs]</a>
+    <span class="k">def</span> <span class="nf">parameter_names</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
         <span class="k">return</span> <span class="p">[]</span></div>
 
-<div class="viewcode-block" id="RiskMetrics2006.variance_bounds"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.RiskMetrics2006.variance_bounds.html#arch.univariate.RiskMetrics2006.variance_bounds">[docs]</a>    <span class="k">def</span> <span class="nf">variance_bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">,</span> <span class="n">power</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">2.0</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
+
+<div class="viewcode-block" id="RiskMetrics2006.variance_bounds">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.RiskMetrics2006.variance_bounds.html#arch.univariate.RiskMetrics2006.variance_bounds">[docs]</a>
+    <span class="k">def</span> <span class="nf">variance_bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">,</span> <span class="n">power</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">2.0</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
         <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">resids</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">))</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span>
             <span class="p">[</span><span class="o">-</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">finfo</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span><span class="o">.</span><span class="n">max</span><span class="p">]</span>
         <span class="p">)</span></div>
 
-<div class="viewcode-block" id="RiskMetrics2006.bounds"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.RiskMetrics2006.bounds.html#arch.univariate.RiskMetrics2006.bounds">[docs]</a>    <span class="k">def</span> <span class="nf">bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]]:</span>
+
+<div class="viewcode-block" id="RiskMetrics2006.bounds">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.RiskMetrics2006.bounds.html#arch.univariate.RiskMetrics2006.bounds">[docs]</a>
+    <span class="k">def</span> <span class="nf">bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]]:</span>
         <span class="k">return</span> <span class="p">[]</span></div>
 
-<div class="viewcode-block" id="RiskMetrics2006.constraints"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.RiskMetrics2006.constraints.html#arch.univariate.RiskMetrics2006.constraints">[docs]</a>    <span class="k">def</span> <span class="nf">constraints</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">Float64Array</span><span class="p">,</span> <span class="n">Float64Array</span><span class="p">]:</span>
+
+<div class="viewcode-block" id="RiskMetrics2006.constraints">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.RiskMetrics2006.constraints.html#arch.univariate.RiskMetrics2006.constraints">[docs]</a>
+    <span class="k">def</span> <span class="nf">constraints</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">Float64Array</span><span class="p">,</span> <span class="n">Float64Array</span><span class="p">]:</span>
         <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="mi">0</span><span class="p">,))</span></div>
 
-<div class="viewcode-block" id="RiskMetrics2006.compute_variance"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.RiskMetrics2006.compute_variance.html#arch.univariate.RiskMetrics2006.compute_variance">[docs]</a>    <span class="k">def</span> <span class="nf">compute_variance</span><span class="p">(</span>
+
+<div class="viewcode-block" id="RiskMetrics2006.compute_variance">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.RiskMetrics2006.compute_variance.html#arch.univariate.RiskMetrics2006.compute_variance">[docs]</a>
+    <span class="k">def</span> <span class="nf">compute_variance</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">,</span>
         <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">,</span>
@@ -2733,7 +2920,10 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
         <span class="k">return</span> <span class="n">sigma2</span></div>
 
-<div class="viewcode-block" id="RiskMetrics2006.simulate"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.RiskMetrics2006.simulate.html#arch.univariate.RiskMetrics2006.simulate">[docs]</a>    <span class="k">def</span> <span class="nf">simulate</span><span class="p">(</span>
+
+<div class="viewcode-block" id="RiskMetrics2006.simulate">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.RiskMetrics2006.simulate.html#arch.univariate.RiskMetrics2006.simulate">[docs]</a>
+    <span class="k">def</span> <span class="nf">simulate</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span> <span class="o">|</span> <span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike1D</span><span class="p">,</span>
         <span class="n">nobs</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
@@ -2762,6 +2952,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
         <span class="k">return</span> <span class="n">data</span><span class="p">[</span><span class="n">burn</span><span class="p">:],</span> <span class="n">sigma2</span><span class="p">[</span><span class="n">burn</span><span class="p">:]</span></div>
 
+
     <span class="k">def</span> <span class="nf">_check_forecasting_method</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span> <span class="n">method</span><span class="p">:</span> <span class="n">ForecastingMethod</span><span class="p">,</span> <span class="n">horizon</span><span class="p">:</span> <span class="nb">int</span>
     <span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
@@ -2835,7 +3026,10 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="n">VarianceForecast</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">paths</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="mi">1</span><span class="p">)),</span> <span class="n">paths</span><span class="p">,</span> <span class="n">shocks</span><span class="p">)</span></div>
 
 
-<div class="viewcode-block" id="EGARCH"><a class="viewcode-back" href="../../../univariate/generated/arch.univariate.EGARCH.html#arch.univariate.EGARCH">[docs]</a><span class="k">class</span> <span class="nc">EGARCH</span><span class="p">(</span><span class="n">VolatilityProcess</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
+
+<div class="viewcode-block" id="EGARCH">
+<a class="viewcode-back" href="../../../univariate/generated/arch.univariate.EGARCH.html#arch.univariate.EGARCH">[docs]</a>
+<span class="k">class</span> <span class="nc">EGARCH</span><span class="p">(</span><span class="n">VolatilityProcess</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    EGARCH model estimation</span>
 
@@ -2902,10 +3096,15 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
         <span class="n">descr</span> <span class="o">=</span> <span class="n">descr</span><span class="p">[:</span><span class="o">-</span><span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="s2">&quot;)&quot;</span>
         <span class="k">return</span> <span class="n">descr</span>
 
-<div class="viewcode-block" id="EGARCH.variance_bounds"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EGARCH.variance_bounds.html#arch.univariate.EGARCH.variance_bounds">[docs]</a>    <span class="k">def</span> <span class="nf">variance_bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">,</span> <span class="n">power</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">2.0</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
+<div class="viewcode-block" id="EGARCH.variance_bounds">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EGARCH.variance_bounds.html#arch.univariate.EGARCH.variance_bounds">[docs]</a>
+    <span class="k">def</span> <span class="nf">variance_bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">,</span> <span class="n">power</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">2.0</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
         <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">variance_bounds</span><span class="p">(</span><span class="n">resids</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">)</span></div>
 
-<div class="viewcode-block" id="EGARCH.bounds"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EGARCH.bounds.html#arch.univariate.EGARCH.bounds">[docs]</a>    <span class="k">def</span> <span class="nf">bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]]:</span>
+
+<div class="viewcode-block" id="EGARCH.bounds">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EGARCH.bounds.html#arch.univariate.EGARCH.bounds">[docs]</a>
+    <span class="k">def</span> <span class="nf">bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]]:</span>
         <span class="n">v</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">resids</span><span class="o">**</span><span class="mf">2.0</span><span class="p">)</span>
         <span class="n">log_const</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="mf">10000.0</span><span class="p">)</span>
         <span class="n">lnv</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">v</span><span class="p">)</span>
@@ -2915,7 +3114,10 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
         <span class="k">return</span> <span class="n">bounds</span></div>
 
-<div class="viewcode-block" id="EGARCH.constraints"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EGARCH.constraints.html#arch.univariate.EGARCH.constraints">[docs]</a>    <span class="k">def</span> <span class="nf">constraints</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">Float64Array</span><span class="p">,</span> <span class="n">Float64Array</span><span class="p">]:</span>
+
+<div class="viewcode-block" id="EGARCH.constraints">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EGARCH.constraints.html#arch.univariate.EGARCH.constraints">[docs]</a>
+    <span class="k">def</span> <span class="nf">constraints</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">Float64Array</span><span class="p">,</span> <span class="n">Float64Array</span><span class="p">]:</span>
         <span class="n">p</span><span class="p">,</span> <span class="n">o</span><span class="p">,</span> <span class="n">q</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">o</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">q</span>
         <span class="n">k_arch</span> <span class="o">=</span> <span class="n">p</span> <span class="o">+</span> <span class="n">o</span> <span class="o">+</span> <span class="n">q</span>
         <span class="n">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="n">k_arch</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
@@ -2924,7 +3126,10 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
         <span class="n">b</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mf">1.0</span>
         <span class="k">return</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span></div>
 
-<div class="viewcode-block" id="EGARCH.compute_variance"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EGARCH.compute_variance.html#arch.univariate.EGARCH.compute_variance">[docs]</a>    <span class="k">def</span> <span class="nf">compute_variance</span><span class="p">(</span>
+
+<div class="viewcode-block" id="EGARCH.compute_variance">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EGARCH.compute_variance.html#arch.univariate.EGARCH.compute_variance">[docs]</a>
+    <span class="k">def</span> <span class="nf">compute_variance</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">,</span>
         <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">,</span>
@@ -2959,16 +3164,25 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
         <span class="k">return</span> <span class="n">sigma2</span></div>
 
-<div class="viewcode-block" id="EGARCH.backcast_transform"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EGARCH.backcast_transform.html#arch.univariate.EGARCH.backcast_transform">[docs]</a>    <span class="k">def</span> <span class="nf">backcast_transform</span><span class="p">(</span>
+
+<div class="viewcode-block" id="EGARCH.backcast_transform">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EGARCH.backcast_transform.html#arch.univariate.EGARCH.backcast_transform">[docs]</a>
+    <span class="k">def</span> <span class="nf">backcast_transform</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span> <span class="n">backcast</span><span class="p">:</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">Float64Array</span>
     <span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">Float64Array</span><span class="p">:</span>
         <span class="n">backcast</span> <span class="o">=</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">backcast_transform</span><span class="p">(</span><span class="n">backcast</span><span class="p">)</span>
         <span class="k">return</span> <span class="nb">float</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">backcast</span><span class="p">))</span></div>
 
-<div class="viewcode-block" id="EGARCH.backcast"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EGARCH.backcast.html#arch.univariate.EGARCH.backcast">[docs]</a>    <span class="k">def</span> <span class="nf">backcast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">Float64Array</span><span class="p">:</span>
+
+<div class="viewcode-block" id="EGARCH.backcast">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EGARCH.backcast.html#arch.univariate.EGARCH.backcast">[docs]</a>
+    <span class="k">def</span> <span class="nf">backcast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">Float64Array</span><span class="p">:</span>
         <span class="k">return</span> <span class="nb">float</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">backcast</span><span class="p">(</span><span class="n">resids</span><span class="p">)))</span></div>
 
-<div class="viewcode-block" id="EGARCH.simulate"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EGARCH.simulate.html#arch.univariate.EGARCH.simulate">[docs]</a>    <span class="k">def</span> <span class="nf">simulate</span><span class="p">(</span>
+
+<div class="viewcode-block" id="EGARCH.simulate">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EGARCH.simulate.html#arch.univariate.EGARCH.simulate">[docs]</a>
+    <span class="k">def</span> <span class="nf">simulate</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span> <span class="o">|</span> <span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike1D</span><span class="p">,</span>
         <span class="n">nobs</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
@@ -3022,7 +3236,10 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
         <span class="k">return</span> <span class="n">data</span><span class="p">[</span><span class="n">burn</span><span class="p">:],</span> <span class="n">sigma2</span><span class="p">[</span><span class="n">burn</span><span class="p">:]</span></div>
 
-<div class="viewcode-block" id="EGARCH.starting_values"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EGARCH.starting_values.html#arch.univariate.EGARCH.starting_values">[docs]</a>    <span class="k">def</span> <span class="nf">starting_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
+
+<div class="viewcode-block" id="EGARCH.starting_values">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EGARCH.starting_values.html#arch.univariate.EGARCH.starting_values">[docs]</a>
+    <span class="k">def</span> <span class="nf">starting_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
         <span class="n">p</span><span class="p">,</span> <span class="n">o</span><span class="p">,</span> <span class="n">q</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">o</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">q</span>
         <span class="n">alphas</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.01</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">]</span>
         <span class="n">gammas</span> <span class="o">=</span> <span class="p">[</span><span class="o">-</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">]</span>
@@ -3050,9 +3267,13 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
         <span class="k">return</span> <span class="n">svs</span><span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">loc</span><span class="p">)]</span></div>
 
-<div class="viewcode-block" id="EGARCH.parameter_names"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EGARCH.parameter_names.html#arch.univariate.EGARCH.parameter_names">[docs]</a>    <span class="k">def</span> <span class="nf">parameter_names</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
+
+<div class="viewcode-block" id="EGARCH.parameter_names">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EGARCH.parameter_names.html#arch.univariate.EGARCH.parameter_names">[docs]</a>
+    <span class="k">def</span> <span class="nf">parameter_names</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
         <span class="k">return</span> <span class="n">_common_names</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">o</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">q</span><span class="p">)</span></div>
 
+
     <span class="k">def</span> <span class="nf">_check_forecasting_method</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span> <span class="n">method</span><span class="p">:</span> <span class="n">ForecastingMethod</span><span class="p">,</span> <span class="n">horizon</span><span class="p">:</span> <span class="nb">int</span>
     <span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
@@ -3143,7 +3364,10 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="n">VarianceForecast</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">paths</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="mi">1</span><span class="p">)),</span> <span class="n">paths</span><span class="p">,</span> <span class="n">shocks</span><span class="p">)</span></div>
 
 
-<div class="viewcode-block" id="FixedVariance"><a class="viewcode-back" href="../../../univariate/generated/arch.univariate.FixedVariance.html#arch.univariate.FixedVariance">[docs]</a><span class="k">class</span> <span class="nc">FixedVariance</span><span class="p">(</span><span class="n">VolatilityProcess</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
+
+<div class="viewcode-block" id="FixedVariance">
+<a class="viewcode-back" href="../../../univariate/generated/arch.univariate.FixedVariance.html#arch.univariate.FixedVariance">[docs]</a>
+<span class="k">class</span> <span class="nc">FixedVariance</span><span class="p">(</span><span class="n">VolatilityProcess</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Fixed volatility process</span>
 
@@ -3172,7 +3396,9 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
         <span class="bp">self</span><span class="o">.</span><span class="n">_variance_series</span> <span class="o">=</span> <span class="n">ensure1d</span><span class="p">(</span><span class="n">variance</span><span class="p">,</span> <span class="s2">&quot;variance&quot;</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_variance</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">variance</span><span class="p">)</span>
 
-<div class="viewcode-block" id="FixedVariance.compute_variance"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FixedVariance.compute_variance.html#arch.univariate.FixedVariance.compute_variance">[docs]</a>    <span class="k">def</span> <span class="nf">compute_variance</span><span class="p">(</span>
+<div class="viewcode-block" id="FixedVariance.compute_variance">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FixedVariance.compute_variance.html#arch.univariate.FixedVariance.compute_variance">[docs]</a>
+    <span class="k">def</span> <span class="nf">compute_variance</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">,</span>
         <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">,</span>
@@ -3187,13 +3413,19 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
             <span class="n">sigma2</span> <span class="o">*=</span> <span class="n">parameters</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
         <span class="k">return</span> <span class="n">sigma2</span></div>
 
-<div class="viewcode-block" id="FixedVariance.starting_values"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FixedVariance.starting_values.html#arch.univariate.FixedVariance.starting_values">[docs]</a>    <span class="k">def</span> <span class="nf">starting_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
+
+<div class="viewcode-block" id="FixedVariance.starting_values">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FixedVariance.starting_values.html#arch.univariate.FixedVariance.starting_values">[docs]</a>
+    <span class="k">def</span> <span class="nf">starting_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
         <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_unit_scale</span><span class="p">:</span>
             <span class="n">_resids</span> <span class="o">=</span> <span class="n">resids</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_variance</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_start</span> <span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">_stop</span><span class="p">])</span>
             <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">_resids</span><span class="o">.</span><span class="n">var</span><span class="p">()])</span>
         <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span></div>
 
-<div class="viewcode-block" id="FixedVariance.simulate"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FixedVariance.simulate.html#arch.univariate.FixedVariance.simulate">[docs]</a>    <span class="k">def</span> <span class="nf">simulate</span><span class="p">(</span>
+
+<div class="viewcode-block" id="FixedVariance.simulate">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FixedVariance.simulate.html#arch.univariate.FixedVariance.simulate">[docs]</a>
+    <span class="k">def</span> <span class="nf">simulate</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span> <span class="o">|</span> <span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike1D</span><span class="p">,</span>
         <span class="n">nobs</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
@@ -3203,16 +3435,25 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
     <span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">Float64Array</span><span class="p">,</span> <span class="n">Float64Array</span><span class="p">]:</span>
         <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;Fixed Variance processes do not support simulation&quot;</span><span class="p">)</span></div>
 
-<div class="viewcode-block" id="FixedVariance.constraints"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FixedVariance.constraints.html#arch.univariate.FixedVariance.constraints">[docs]</a>    <span class="k">def</span> <span class="nf">constraints</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">Float64Array</span><span class="p">,</span> <span class="n">Float64Array</span><span class="p">]:</span>
+
+<div class="viewcode-block" id="FixedVariance.constraints">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FixedVariance.constraints.html#arch.univariate.FixedVariance.constraints">[docs]</a>
+    <span class="k">def</span> <span class="nf">constraints</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">Float64Array</span><span class="p">,</span> <span class="n">Float64Array</span><span class="p">]:</span>
         <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_unit_scale</span><span class="p">:</span>
             <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span></div>
 
-<div class="viewcode-block" id="FixedVariance.backcast"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FixedVariance.backcast.html#arch.univariate.FixedVariance.backcast">[docs]</a>    <span class="k">def</span> <span class="nf">backcast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">Float64Array</span><span class="p">:</span>
+
+<div class="viewcode-block" id="FixedVariance.backcast">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FixedVariance.backcast.html#arch.univariate.FixedVariance.backcast">[docs]</a>
+    <span class="k">def</span> <span class="nf">backcast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">Float64Array</span><span class="p">:</span>
         <span class="k">return</span> <span class="mf">1.0</span></div>
 
-<div class="viewcode-block" id="FixedVariance.bounds"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FixedVariance.bounds.html#arch.univariate.FixedVariance.bounds">[docs]</a>    <span class="k">def</span> <span class="nf">bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]]:</span>
+
+<div class="viewcode-block" id="FixedVariance.bounds">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FixedVariance.bounds.html#arch.univariate.FixedVariance.bounds">[docs]</a>
+    <span class="k">def</span> <span class="nf">bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]]:</span>
         <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_unit_scale</span><span class="p">:</span>
             <span class="n">v</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">starting_values</span><span class="p">(</span><span class="n">resids</span><span class="p">)))</span>
             <span class="n">_resids</span> <span class="o">=</span> <span class="n">resids</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_variance</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_start</span> <span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">_stop</span><span class="p">])</span>
@@ -3220,11 +3461,15 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
             <span class="k">return</span> <span class="p">[(</span><span class="n">v</span> <span class="o">/</span> <span class="mf">100000.0</span><span class="p">,</span> <span class="mf">10.0</span> <span class="o">*</span> <span class="p">(</span><span class="n">v</span> <span class="o">+</span> <span class="n">mu</span><span class="o">**</span><span class="mf">2.0</span><span class="p">))]</span>
         <span class="k">return</span> <span class="p">[]</span></div>
 
-<div class="viewcode-block" id="FixedVariance.parameter_names"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FixedVariance.parameter_names.html#arch.univariate.FixedVariance.parameter_names">[docs]</a>    <span class="k">def</span> <span class="nf">parameter_names</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
+
+<div class="viewcode-block" id="FixedVariance.parameter_names">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FixedVariance.parameter_names.html#arch.univariate.FixedVariance.parameter_names">[docs]</a>
+    <span class="k">def</span> <span class="nf">parameter_names</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
         <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_unit_scale</span><span class="p">:</span>
             <span class="k">return</span> <span class="p">[</span><span class="s2">&quot;scale&quot;</span><span class="p">]</span>
         <span class="k">return</span> <span class="p">[]</span></div>
 
+
     <span class="k">def</span> <span class="nf">_check_forecasting_method</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span> <span class="n">method</span><span class="p">:</span> <span class="n">ForecastingMethod</span><span class="p">,</span> <span class="n">horizon</span><span class="p">:</span> <span class="nb">int</span>
     <span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
@@ -3264,7 +3509,10 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="n">VarianceForecast</span><span class="p">(</span><span class="n">forecasts</span><span class="p">,</span> <span class="n">forecast_paths</span><span class="p">,</span> <span class="n">shocks</span><span class="p">)</span></div>
 
 
-<div class="viewcode-block" id="FIGARCH"><a class="viewcode-back" href="../../../univariate/generated/arch.univariate.FIGARCH.html#arch.univariate.FIGARCH">[docs]</a><span class="k">class</span> <span class="nc">FIGARCH</span><span class="p">(</span><span class="n">VolatilityProcess</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
+
+<div class="viewcode-block" id="FIGARCH">
+<a class="viewcode-back" href="../../../univariate/generated/arch.univariate.FIGARCH.html#arch.univariate.FIGARCH">[docs]</a>
+<span class="k">class</span> <span class="nc">FIGARCH</span><span class="p">(</span><span class="n">VolatilityProcess</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    FIGARCH model</span>
 
@@ -3372,9 +3620,12 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
         <span class="k">return</span> <span class="n">descr</span>
 
-<div class="viewcode-block" id="FIGARCH.variance_bounds"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FIGARCH.variance_bounds.html#arch.univariate.FIGARCH.variance_bounds">[docs]</a>    <span class="k">def</span> <span class="nf">variance_bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">,</span> <span class="n">power</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">2.0</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
+<div class="viewcode-block" id="FIGARCH.variance_bounds">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FIGARCH.variance_bounds.html#arch.univariate.FIGARCH.variance_bounds">[docs]</a>
+    <span class="k">def</span> <span class="nf">variance_bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">,</span> <span class="n">power</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">2.0</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
         <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">variance_bounds</span><span class="p">(</span><span class="n">resids</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">power</span><span class="p">)</span></div>
 
+
     <span class="k">def</span> <span class="nf">_generate_name</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
         <span class="n">q</span><span class="p">,</span> <span class="n">power</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">q</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">power</span>
         <span class="k">if</span> <span class="n">power</span> <span class="o">==</span> <span class="mf">2.0</span><span class="p">:</span>
@@ -3393,7 +3644,9 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
             <span class="k">else</span><span class="p">:</span>
                 <span class="k">return</span> <span class="sa">f</span><span class="s2">&quot;Power FIGARCH (power: </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">power</span><span class="si">:</span><span class="s2">0.1f</span><span class="si">}</span><span class="s2">)&quot;</span>
 
-<div class="viewcode-block" id="FIGARCH.bounds"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FIGARCH.bounds.html#arch.univariate.FIGARCH.bounds">[docs]</a>    <span class="k">def</span> <span class="nf">bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]]:</span>
+<div class="viewcode-block" id="FIGARCH.bounds">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FIGARCH.bounds.html#arch.univariate.FIGARCH.bounds">[docs]</a>
+    <span class="k">def</span> <span class="nf">bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]]:</span>
         <span class="n">eps_half</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">finfo</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span><span class="o">.</span><span class="n">eps</span><span class="p">)</span>
         <span class="n">v</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">resids</span><span class="p">)</span> <span class="o">**</span> <span class="bp">self</span><span class="o">.</span><span class="n">power</span><span class="p">)</span>
 
@@ -3404,7 +3657,10 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
         <span class="k">return</span> <span class="n">bounds</span></div>
 
-<div class="viewcode-block" id="FIGARCH.constraints"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FIGARCH.constraints.html#arch.univariate.FIGARCH.constraints">[docs]</a>    <span class="k">def</span> <span class="nf">constraints</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">Float64Array</span><span class="p">,</span> <span class="n">Float64Array</span><span class="p">]:</span>
+
+<div class="viewcode-block" id="FIGARCH.constraints">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FIGARCH.constraints.html#arch.univariate.FIGARCH.constraints">[docs]</a>
+    <span class="k">def</span> <span class="nf">constraints</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">Float64Array</span><span class="p">,</span> <span class="n">Float64Array</span><span class="p">]:</span>
         <span class="c1"># omega &gt; 0 &lt;- 1</span>
         <span class="c1"># 0 &lt;= d &lt;= 1 &lt;- 2</span>
         <span class="c1"># 0 &lt;= phi &lt;= (1 - d) / 2 &lt;- 2</span>
@@ -3432,7 +3688,10 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
         <span class="k">return</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span></div>
 
-<div class="viewcode-block" id="FIGARCH.compute_variance"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FIGARCH.compute_variance.html#arch.univariate.FIGARCH.compute_variance">[docs]</a>    <span class="k">def</span> <span class="nf">compute_variance</span><span class="p">(</span>
+
+<div class="viewcode-block" id="FIGARCH.compute_variance">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FIGARCH.compute_variance.html#arch.univariate.FIGARCH.compute_variance">[docs]</a>
+    <span class="k">def</span> <span class="nf">compute_variance</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">,</span>
         <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">,</span>
@@ -3455,13 +3714,19 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
         <span class="k">return</span> <span class="n">sigma2</span></div>
 
-<div class="viewcode-block" id="FIGARCH.backcast_transform"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FIGARCH.backcast_transform.html#arch.univariate.FIGARCH.backcast_transform">[docs]</a>    <span class="k">def</span> <span class="nf">backcast_transform</span><span class="p">(</span>
+
+<div class="viewcode-block" id="FIGARCH.backcast_transform">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FIGARCH.backcast_transform.html#arch.univariate.FIGARCH.backcast_transform">[docs]</a>
+    <span class="k">def</span> <span class="nf">backcast_transform</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span> <span class="n">backcast</span><span class="p">:</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">Float64Array</span>
     <span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">Float64Array</span><span class="p">:</span>
         <span class="n">backcast</span> <span class="o">=</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">backcast_transform</span><span class="p">(</span><span class="n">backcast</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">backcast</span><span class="p">)</span> <span class="o">**</span> <span class="bp">self</span><span class="o">.</span><span class="n">power</span></div>
 
-<div class="viewcode-block" id="FIGARCH.backcast"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FIGARCH.backcast.html#arch.univariate.FIGARCH.backcast">[docs]</a>    <span class="k">def</span> <span class="nf">backcast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">Float64Array</span><span class="p">:</span>
+
+<div class="viewcode-block" id="FIGARCH.backcast">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FIGARCH.backcast.html#arch.univariate.FIGARCH.backcast">[docs]</a>
+    <span class="k">def</span> <span class="nf">backcast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">Float64Array</span><span class="p">:</span>
         <span class="n">power</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">power</span>
         <span class="n">tau</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="mi">75</span><span class="p">,</span> <span class="n">resids</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
         <span class="n">w</span> <span class="o">=</span> <span class="mf">0.94</span> <span class="o">**</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">tau</span><span class="p">)</span>
@@ -3470,7 +3735,10 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
         <span class="k">return</span> <span class="n">backcast</span></div>
 
-<div class="viewcode-block" id="FIGARCH.simulate"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FIGARCH.simulate.html#arch.univariate.FIGARCH.simulate">[docs]</a>    <span class="k">def</span> <span class="nf">simulate</span><span class="p">(</span>
+
+<div class="viewcode-block" id="FIGARCH.simulate">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FIGARCH.simulate.html#arch.univariate.FIGARCH.simulate">[docs]</a>
+    <span class="k">def</span> <span class="nf">simulate</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span> <span class="o">|</span> <span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike1D</span><span class="p">,</span>
         <span class="n">nobs</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
@@ -3517,7 +3785,10 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
         <span class="k">return</span> <span class="n">data</span><span class="p">[</span><span class="n">truncation</span> <span class="o">+</span> <span class="n">burn</span> <span class="p">:],</span> <span class="n">sigma2</span><span class="p">[</span><span class="n">truncation</span> <span class="o">+</span> <span class="n">burn</span> <span class="p">:]</span></div>
 
-<div class="viewcode-block" id="FIGARCH.starting_values"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FIGARCH.starting_values.html#arch.univariate.FIGARCH.starting_values">[docs]</a>    <span class="k">def</span> <span class="nf">starting_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
+
+<div class="viewcode-block" id="FIGARCH.starting_values">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FIGARCH.starting_values.html#arch.univariate.FIGARCH.starting_values">[docs]</a>
+    <span class="k">def</span> <span class="nf">starting_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
         <span class="n">truncation</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">truncation</span>
         <span class="n">ds</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">]</span>
         <span class="n">phi_ratio</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">]</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">p</span> <span class="k">else</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span>
@@ -3554,7 +3825,10 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
         <span class="k">return</span> <span class="n">starting_vals</span><span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">loc</span><span class="p">)]</span></div>
 
-<div class="viewcode-block" id="FIGARCH.parameter_names"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FIGARCH.parameter_names.html#arch.univariate.FIGARCH.parameter_names">[docs]</a>    <span class="k">def</span> <span class="nf">parameter_names</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
+
+<div class="viewcode-block" id="FIGARCH.parameter_names">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FIGARCH.parameter_names.html#arch.univariate.FIGARCH.parameter_names">[docs]</a>
+    <span class="k">def</span> <span class="nf">parameter_names</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
         <span class="n">names</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;omega&quot;</span><span class="p">]</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="p">:</span>
             <span class="n">names</span> <span class="o">+=</span> <span class="p">[</span><span class="s2">&quot;phi&quot;</span><span class="p">]</span>
@@ -3563,6 +3837,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
             <span class="n">names</span> <span class="o">+=</span> <span class="p">[</span><span class="s2">&quot;beta&quot;</span><span class="p">]</span>
         <span class="k">return</span> <span class="n">names</span></div>
 
+
     <span class="k">def</span> <span class="nf">_check_forecasting_method</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span> <span class="n">method</span><span class="p">:</span> <span class="n">ForecastingMethod</span><span class="p">,</span> <span class="n">horizon</span><span class="p">:</span> <span class="nb">int</span>
     <span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
@@ -3601,6 +3876,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
         <span class="n">temp_forecasts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">truncation</span> <span class="o">+</span> <span class="n">horizon</span><span class="p">)</span>
         <span class="n">resids2</span> <span class="o">=</span> <span class="n">resids</span><span class="o">**</span><span class="mi">2</span>
         <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">start</span><span class="p">,</span> <span class="n">t</span><span class="p">):</span>
+            <span class="n">fcast_loc</span> <span class="o">=</span> <span class="n">i</span> <span class="o">-</span> <span class="n">start</span>
             <span class="n">available</span> <span class="o">=</span> <span class="n">i</span> <span class="o">+</span> <span class="mi">1</span> <span class="o">-</span> <span class="nb">max</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">i</span> <span class="o">-</span> <span class="n">truncation</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
             <span class="n">temp_forecasts</span><span class="p">[</span><span class="n">truncation</span> <span class="o">-</span> <span class="n">available</span> <span class="p">:</span> <span class="n">truncation</span><span class="p">]</span> <span class="o">=</span> <span class="n">resids2</span><span class="p">[</span>
                 <span class="nb">max</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">i</span> <span class="o">-</span> <span class="n">truncation</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="p">:</span> <span class="n">i</span> <span class="o">+</span> <span class="mi">1</span>
@@ -3612,9 +3888,8 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
                 <span class="n">temp_forecasts</span><span class="p">[</span><span class="n">truncation</span> <span class="o">+</span> <span class="n">h</span><span class="p">]</span> <span class="o">=</span> <span class="n">omega_tilde</span> <span class="o">+</span> <span class="n">lam_rev</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span>
                     <span class="n">lagged_forecasts</span>
                 <span class="p">)</span>
-            <span class="n">forecasts</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="n">temp_forecasts</span><span class="p">[</span><span class="n">truncation</span><span class="p">:]</span>
+            <span class="n">forecasts</span><span class="p">[</span><span class="n">fcast_loc</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="n">temp_forecasts</span><span class="p">[</span><span class="n">truncation</span><span class="p">:]</span>
 
-        <span class="n">forecasts</span><span class="p">[:</span><span class="n">start</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
         <span class="k">return</span> <span class="n">VarianceForecast</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span>
 
     <span class="k">def</span> <span class="nf">_simulation_forecast</span><span class="p">(</span>
@@ -3673,7 +3948,10 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="n">VarianceForecast</span><span class="p">(</span><span class="n">forecasts</span><span class="p">,</span> <span class="n">paths</span><span class="p">,</span> <span class="n">shocks</span><span class="p">)</span></div>
 
 
-<div class="viewcode-block" id="APARCH"><a class="viewcode-back" href="../../../univariate/generated/arch.univariate.APARCH.html#arch.univariate.APARCH">[docs]</a><span class="k">class</span> <span class="nc">APARCH</span><span class="p">(</span><span class="n">VolatilityProcess</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
+
+<div class="viewcode-block" id="APARCH">
+<a class="viewcode-back" href="../../../univariate/generated/arch.univariate.APARCH.html#arch.univariate.APARCH">[docs]</a>
+<span class="k">class</span> <span class="nc">APARCH</span><span class="p">(</span><span class="n">VolatilityProcess</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Asymmetric Power ARCH (APARCH) volatility process</span>
 
@@ -3787,7 +4065,9 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
         <span class="n">_parameters</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">parameters</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_est_delta</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">delta</span>
         <span class="k">return</span> <span class="n">_parameters</span>
 
-<div class="viewcode-block" id="APARCH.compute_variance"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.APARCH.compute_variance.html#arch.univariate.APARCH.compute_variance">[docs]</a>    <span class="k">def</span> <span class="nf">compute_variance</span><span class="p">(</span>
+<div class="viewcode-block" id="APARCH.compute_variance">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.APARCH.compute_variance.html#arch.univariate.APARCH.compute_variance">[docs]</a>
+    <span class="k">def</span> <span class="nf">compute_variance</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">,</span>
         <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">,</span>
@@ -3818,7 +4098,10 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
         <span class="k">return</span> <span class="n">sigma2</span></div>
 
-<div class="viewcode-block" id="APARCH.bounds"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.APARCH.bounds.html#arch.univariate.APARCH.bounds">[docs]</a>    <span class="k">def</span> <span class="nf">bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]]:</span>
+
+<div class="viewcode-block" id="APARCH.bounds">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.APARCH.bounds.html#arch.univariate.APARCH.bounds">[docs]</a>
+    <span class="k">def</span> <span class="nf">bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]]:</span>
         <span class="n">v</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">resids</span><span class="p">)</span> <span class="o">**</span> <span class="mf">0.5</span><span class="p">)),</span> <span class="nb">float</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">resids</span><span class="o">**</span><span class="mi">2</span><span class="p">)))</span>
 
         <span class="n">bounds</span> <span class="o">=</span> <span class="p">[(</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">10.0</span> <span class="o">*</span> <span class="nb">float</span><span class="p">(</span><span class="n">v</span><span class="p">))]</span>
@@ -3831,7 +4114,10 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
         <span class="k">return</span> <span class="n">bounds</span></div>
 
-<div class="viewcode-block" id="APARCH.starting_values"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.APARCH.starting_values.html#arch.univariate.APARCH.starting_values">[docs]</a>    <span class="k">def</span> <span class="nf">starting_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
+
+<div class="viewcode-block" id="APARCH.starting_values">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.APARCH.starting_values.html#arch.univariate.APARCH.starting_values">[docs]</a>
+    <span class="k">def</span> <span class="nf">starting_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
         <span class="n">p</span><span class="p">,</span> <span class="n">o</span><span class="p">,</span> <span class="n">q</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">o</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">q</span>
         <span class="n">alphas</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.03</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">,</span> <span class="mf">0.08</span><span class="p">,</span> <span class="mf">0.15</span><span class="p">]</span>
         <span class="n">alpha_beta</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.8</span><span class="p">,</span> <span class="mf">0.9</span><span class="p">,</span> <span class="mf">0.95</span><span class="p">,</span> <span class="mf">0.975</span><span class="p">,</span> <span class="mf">0.99</span><span class="p">]</span>
@@ -3868,7 +4154,10 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
             <span class="n">sv</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="n">sv</span><span class="p">[:</span> <span class="n">p</span> <span class="o">+</span> <span class="mi">1</span> <span class="o">+</span> <span class="p">(</span><span class="n">o</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">)],</span> <span class="n">sv</span><span class="p">[</span><span class="n">p</span> <span class="o">+</span> <span class="n">o</span> <span class="o">+</span> <span class="mi">1</span> <span class="p">:]]</span>
         <span class="k">return</span> <span class="n">sv</span></div>
 
-<div class="viewcode-block" id="APARCH.constraints"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.APARCH.constraints.html#arch.univariate.APARCH.constraints">[docs]</a>    <span class="k">def</span> <span class="nf">constraints</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">Float64Array</span><span class="p">,</span> <span class="n">Float64Array</span><span class="p">]:</span>
+
+<div class="viewcode-block" id="APARCH.constraints">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.APARCH.constraints.html#arch.univariate.APARCH.constraints">[docs]</a>
+    <span class="k">def</span> <span class="nf">constraints</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">Float64Array</span><span class="p">,</span> <span class="n">Float64Array</span><span class="p">]:</span>
         <span class="n">p</span><span class="p">,</span> <span class="n">o</span><span class="p">,</span> <span class="n">q</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">o</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">q</span>
         <span class="n">o</span> <span class="o">=</span> <span class="mi">1</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_common_asym</span> <span class="k">else</span> <span class="n">o</span>
         <span class="n">k_arch</span> <span class="o">=</span> <span class="n">p</span> <span class="o">+</span> <span class="n">o</span> <span class="o">+</span> <span class="n">q</span>
@@ -3901,7 +4190,10 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
         <span class="n">b</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mf">1.0</span>  <span class="c1"># sum &lt; 1</span>
         <span class="k">return</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span></div>
 
-<div class="viewcode-block" id="APARCH.parameter_names"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.APARCH.parameter_names.html#arch.univariate.APARCH.parameter_names">[docs]</a>    <span class="k">def</span> <span class="nf">parameter_names</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
+
+<div class="viewcode-block" id="APARCH.parameter_names">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.APARCH.parameter_names.html#arch.univariate.APARCH.parameter_names">[docs]</a>
+    <span class="k">def</span> <span class="nf">parameter_names</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
         <span class="n">names</span> <span class="o">=</span> <span class="n">_common_names</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">o</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">q</span><span class="p">)</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_common_asym</span><span class="p">:</span>
             <span class="n">names</span> <span class="o">=</span> <span class="n">names</span><span class="p">[:</span> <span class="bp">self</span><span class="o">.</span><span class="n">p</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="p">[</span><span class="s2">&quot;gamma&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="n">names</span><span class="p">[</span><span class="mi">1</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">p</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">o</span> <span class="p">:]</span>
@@ -3909,7 +4201,10 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
             <span class="n">names</span> <span class="o">+=</span> <span class="p">[</span><span class="s2">&quot;delta&quot;</span><span class="p">]</span>
         <span class="k">return</span> <span class="n">names</span></div>
 
-<div class="viewcode-block" id="APARCH.simulate"><a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.APARCH.simulate.html#arch.univariate.APARCH.simulate">[docs]</a>    <span class="k">def</span> <span class="nf">simulate</span><span class="p">(</span>
+
+<div class="viewcode-block" id="APARCH.simulate">
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.APARCH.simulate.html#arch.univariate.APARCH.simulate">[docs]</a>
+    <span class="k">def</span> <span class="nf">simulate</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span> <span class="o">|</span> <span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike1D</span><span class="p">,</span>
         <span class="n">nobs</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
@@ -3959,6 +4254,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
         <span class="k">return</span> <span class="n">data</span><span class="p">[</span><span class="n">burn</span><span class="p">:],</span> <span class="n">sigma2</span><span class="p">[</span><span class="n">burn</span><span class="p">:]</span></div>
 
+
     <span class="k">def</span> <span class="nf">_simulate_paths</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">m</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
@@ -4091,6 +4387,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
         <span class="n">descr</span> <span class="o">=</span> <span class="n">descr</span><span class="p">[:</span><span class="o">-</span><span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="s2">&quot;)&quot;</span>
 
         <span class="k">return</span> <span class="n">descr</span></div>
+
 </code></pre></div>
 
 
@@ -4122,7 +4419,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -4169,7 +4466,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/_modules/arch/utility/testing.html b/_modules/arch/utility/testing.html
index 5950ef238e..a48e6813c1 100644
--- a/_modules/arch/utility/testing.html
+++ b/_modules/arch/utility/testing.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.utility.testing - arch 6.1.0</title>
+        <title>arch.utility.testing - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -373,7 +373,9 @@ <h1>Source code for arch.utility.testing</h1><div class="highlight"><pre>
 <span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;WaldTestStatistic&quot;</span><span class="p">]</span>
 
 
-<div class="viewcode-block" id="WaldTestStatistic"><a class="viewcode-back" href="../../../univariate/utility.html#arch.utility.testing.WaldTestStatistic">[docs]</a><span class="k">class</span> <span class="nc">WaldTestStatistic</span><span class="p">:</span>
+<div class="viewcode-block" id="WaldTestStatistic">
+<a class="viewcode-back" href="../../../univariate/utility.html#arch.utility.testing.WaldTestStatistic">[docs]</a>
+<span class="k">class</span> <span class="nc">WaldTestStatistic</span><span class="p">:</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Test statistic holder for Wald-type tests</span>
 
@@ -450,6 +452,7 @@ <h1>Source code for arch.utility.testing</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="p">(</span>
             <span class="bp">self</span><span class="o">.</span><span class="fm">__str__</span><span class="p">()</span> <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span> <span class="o">+</span> <span class="sa">f</span><span class="s2">&quot;, id: </span><span class="si">{</span><span class="nb">hex</span><span class="p">(</span><span class="nb">id</span><span class="p">(</span><span class="bp">self</span><span class="p">))</span><span class="si">}</span><span class="s2">&quot;</span>
         <span class="p">)</span></div>
+
 </code></pre></div>
 
 
@@ -481,7 +484,7 @@ <h1>Source code for arch.utility.testing</h1><div class="highlight"><pre>
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -528,7 +531,7 @@ <h1>Source code for arch.utility.testing</h1><div class="highlight"><pre>
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/_modules/functools.html b/_modules/functools.html
index 4ab1206be9..e42be02d1e 100644
--- a/_modules/functools.html
+++ b/_modules/functools.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>functools - arch 6.1.0</title>
+        <title>functools - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1387,7 +1387,7 @@ <h1>Source code for functools</h1><div class="highlight"><pre>
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1434,7 +1434,7 @@ <h1>Source code for functools</h1><div class="highlight"><pre>
     <script id="__config" type="application/json">{"base": "..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/_modules/index.html b/_modules/index.html
index 9deb5ca09d..e4782e24e7 100644
--- a/_modules/index.html
+++ b/_modules/index.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Overview: module code - arch 6.1.0</title>
+        <title>Overview: module code - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -369,6 +369,7 @@ <h1>All modules for which code is available</h1>
 <li><a href="arch/covariance/kernel.html">arch.covariance.kernel</a></li>
 <li><a href="arch/unitroot/_engle_granger.html">arch.unitroot._engle_granger</a></li>
 <li><a href="arch/unitroot/_phillips_ouliaris.html">arch.unitroot._phillips_ouliaris</a></li>
+<li><a href="arch/unitroot/_shared.html">arch.unitroot._shared</a></li>
 <li><a href="arch/unitroot/cointegration.html">arch.unitroot.cointegration</a></li>
 <li><a href="arch/unitroot/unitroot.html">arch.unitroot.unitroot</a></li>
 <li><a href="arch/univariate/base.html">arch.univariate.base</a></li>
@@ -377,6 +378,7 @@ <h1>All modules for which code is available</h1>
 <li><a href="arch/univariate/recursions_python.html">arch.univariate.recursions_python</a></li>
 <li><a href="arch/univariate/volatility.html">arch.univariate.volatility</a></li>
 <li><a href="arch/utility/testing.html">arch.utility.testing</a></li>
+<li><a href="functools.html">functools</a></li>
 </ul>
 
 
@@ -408,7 +410,7 @@ <h1>All modules for which code is available</h1>
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -455,7 +457,7 @@ <h1>All modules for which code is available</h1>
     <script id="__config" type="application/json">{"base": "..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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diff --git a/_sources/changes/6.0.rst.txt b/_sources/changes/6.0.rst.txt
index c6a5489d44..96de9ab77f 100644
--- a/_sources/changes/6.0.rst.txt
+++ b/_sources/changes/6.0.rst.txt
@@ -2,6 +2,19 @@
 Version 6
 =========
 
+Release 6.2
+===========
+- Fixed a bug that affected forecasting from  :class:`~arch.univariate.volatility.FIGARCH` models (:issue:`606`).
+- Added a performance warning when testing for unit roots in large series using a lag-length
+  search with no-max-lag specified.
+- Fixed a bug that affected forecasting from :class:`arch.univariate.volatility.FIGARCH'
+  models (:issue:`606`).
+- Changed the default value of ``reindex`` to ``False`` so that forecasts
+  will not match the input by default.  Set ``reindex`` to ``True`` if this is
+  required.
+- Made ``from __future__ import reindex`` a no-op.
+- Updated notebooks to reflect best practices
+
 Release 6.1
 ===========
 - Pushed back the adoption of Cython 3 until a later date
diff --git a/_sources/univariate/univariate_forecasting_with_exogenous_variables.ipynb.txt b/_sources/univariate/univariate_forecasting_with_exogenous_variables.ipynb.txt
index 213d3e89e9..5c98e7a4a6 100644
--- a/_sources/univariate/univariate_forecasting_with_exogenous_variables.ipynb.txt
+++ b/_sources/univariate/univariate_forecasting_with_exogenous_variables.ipynb.txt
@@ -1,1009 +1,988 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "# Forecasting with Exogenous Regressors\n",
-    "\n",
-    "This notebook provides examples of the accepted data structures for passing the expected value of exogenous variables when these are included in the mean.  For example, consider an AR(1) with 2 exogenous variables. The mean dynamics are\n",
-    "\n",
-    "$$ Y_t = \\phi_0 + \\phi_1 Y_{t-1} + \\beta_0 X_{0,t} + \\beta_1 X_{1,t} + \\epsilon_t. $$\n",
-    "\n",
-    "The $h$-step forecast, $E_{T}[Y_{t+h}]$, depends on the conditional expectation of $X_{0,T+h}$ and $X_{1,T+h}$,\n",
-    "\n",
-    "$$ E_{T}[Y_{T+h}] = \\phi_0 + \\phi_1 E_{T}[Y_{T+h-1}] + \\beta_0 E_{T}[X_{0,T+h}] +\\beta_1 E_{T}[X_{1,T+h}] $$\n",
-    "\n",
-    "where $E_{T}[Y_{T+h-1}]$ has been recursively computed.\n",
-    "\n",
-    "In order to construct forecasts up to some horizon $h$, it is necessary to pass $2\\times h$ values ($h$ for each series).  If using the features of `forecast` that allow many forecast to be specified, it necessary to supply $n \\times 2 \\times h$ values.\n",
-    "\n",
-    "There are two general purpose data structures that can be used for any number of exogenous variables and any number steps ahead:\n",
-    "\n",
-    "* `dict` - The values can be pass using a `dict` where the keys are the variable names and the values are 2-dimensional arrays. This is the most natural generalization of a pandas `DataFrame` to 3-dimensions.\n",
-    "* `array` - The vales can alternatively be passed as a 3-d NumPy `array` where dimension 0 tracks the regressor index, dimension 1 is the time period and dimension 2 is the horizon.\n",
-    "\n",
-    "When a model contains a single exogenous regressor it is possible to use a 2-d array or `DataFrame` where dim0 tracks the time period where the forecast is generated and dimension 1 tracks the horizon.\n",
-    "\n",
-    "In the special case where a model contains a single regressor _and_ the horizon is 1, then a 1-d array of pandas Series can be used."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    },
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# initial setup\n",
-    "%matplotlib inline\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import seaborn\n",
-    "from arch.__future__ import reindexing\n",
-    "\n",
-    "seaborn.set_style(\"darkgrid\")\n",
-    "plt.rc(\"figure\", figsize=(16, 6))\n",
-    "plt.rc(\"savefig\", dpi=90)\n",
-    "plt.rc(\"font\", family=\"sans-serif\")\n",
-    "plt.rc(\"font\", size=14)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "## Simulating data\n",
-    "\n",
-    "Two $X$ variables are simulated and are assumed to follow independent AR(1) processes. The data is then assumed to follow an ARX(1) with 2 exogenous regressors and GARCH(1,1) errors."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    },
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from arch.univariate import ARX, GARCH, ZeroMean, arch_model\n",
-    "\n",
-    "burn = 250\n",
-    "\n",
-    "x_mod = ARX(None, lags=1)\n",
-    "x0 = x_mod.simulate([1, 0.8, 1], nobs=1000 + burn).data\n",
-    "x1 = x_mod.simulate([2.5, 0.5, 1], nobs=1000 + burn).data\n",
-    "\n",
-    "resid_mod = ZeroMean(volatility=GARCH())\n",
-    "resids = resid_mod.simulate([0.1, 0.1, 0.8], nobs=1000 + burn).data\n",
-    "\n",
-    "phi1 = 0.7\n",
-    "phi0 = 3\n",
-    "y = 10 + resids.copy()\n",
-    "for i in range(1, y.shape[0]):\n",
-    "    y[i] = phi0 + phi1 * y[i - 1] + 2 * x0[i] - 2 * x1[i] + resids[i]\n",
-    "\n",
-    "x0 = x0.iloc[-1000:]\n",
-    "x1 = x1.iloc[-1000:]\n",
-    "y = y.iloc[-1000:]\n",
-    "y.index = x0.index = x1.index = np.arange(1000)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "## Plotting the data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    },
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1152x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax = pd.DataFrame({\"ARX\": y}).plot(legend=False)\n",
-    "ax.legend(frameon=False)\n",
-    "_ = ax.set_xlim(0, 999)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "## Forecasting the X values\n",
-    "\n",
-    "The forecasts of $Y$ depend on forecasts of $X_0$ and $X_1$.  Both of these follow simple AR(1), and so we can construct the forecasts for all time horizons.  Note that the value in position `[i,j]` is the time-`i` forecast for horizon `j+1`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    },
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([5.32610653, 5.16305327, 5.08152663, 5.04076332, 5.02038166,\n",
-       "       5.01019083, 5.00509541, 5.00254771, 5.00127385, 5.00063693])"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x0_oos = np.empty((1000, 10))\n",
-    "x1_oos = np.empty((1000, 10))\n",
-    "for i in range(10):\n",
-    "    if i == 0:\n",
-    "        last = x0\n",
-    "    else:\n",
-    "        last = x0_oos[:, i - 1]\n",
-    "    x0_oos[:, i] = 1 + 0.8 * last\n",
-    "    if i == 0:\n",
-    "        last = x1\n",
-    "    else:\n",
-    "        last = x1_oos[:, i - 1]\n",
-    "    x1_oos[:, i] = 2.5 + 0.5 * last\n",
-    "\n",
-    "x1_oos[-1]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "## Fitting the model\n",
-    "\n",
-    "Next, the most is fit. The parameters are accurately estimated."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    },
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                          AR-X - GARCH Model Results                          \n",
-      "==============================================================================\n",
-      "Dep. Variable:                   data   R-squared:                       0.993\n",
-      "Mean Model:                      AR-X   Adj. R-squared:                  0.993\n",
-      "Vol Model:                      GARCH   Log-Likelihood:               -1302.15\n",
-      "Distribution:                  Normal   AIC:                           2618.31\n",
-      "Method:            Maximum Likelihood   BIC:                           2652.65\n",
-      "                                        No. Observations:                  999\n",
-      "Date:                Mon, May 17 2021   Df Residuals:                      995\n",
-      "Time:                        16:04:11   Df Model:                            4\n",
-      "                               Mean Model                               \n",
-      "========================================================================\n",
-      "                 coef    std err          t      P>|t|  95.0% Conf. Int.\n",
-      "------------------------------------------------------------------------\n",
-      "Const          2.9480      0.149     19.818  2.088e-87 [  2.656,  3.240]\n",
-      "data[1]        0.6971  3.421e-03    203.759      0.000 [  0.690,  0.704]\n",
-      "x0             2.0186  2.063e-02     97.827      0.000 [  1.978,  2.059]\n",
-      "x1            -2.0115  2.483e-02    -81.001      0.000 [ -2.060, -1.963]\n",
-      "                              Volatility Model                             \n",
-      "===========================================================================\n",
-      "                 coef    std err          t      P>|t|     95.0% Conf. Int.\n",
-      "---------------------------------------------------------------------------\n",
-      "omega          0.1144  5.879e-02      1.946  5.162e-02 [-8.041e-04,  0.230]\n",
-      "alpha[1]       0.0633  2.982e-02      2.123  3.374e-02  [4.866e-03,  0.122]\n",
-      "beta[1]        0.7940  9.035e-02      8.788  1.522e-18    [  0.617,  0.971]\n",
-      "===========================================================================\n",
-      "\n",
-      "Covariance estimator: robust\n"
-     ]
-    }
-   ],
-   "source": [
-    "exog = pd.DataFrame({\"x0\": x0, \"x1\": x1})\n",
-    "mod = arch_model(y, x=exog, mean=\"ARX\", lags=1)\n",
-    "res = mod.fit(disp=\"off\")\n",
-    "print(res.summary())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "## Using a `dict`\n",
-    "\n",
-    "The first approach uses a dict to pass the two variables. The key consideration here is the the keys of the dictionary must **exactly** match the variable names (`x0` and `x1` here).  The dictionary here contains only the final row of the forecast values since `forecast` will only make forecasts beginning from the final in-sample observation by default.\n",
-    "\n",
-    "### Using `DataFrame`\n",
-    "\n",
-    "While these examples make use of NumPy arrays, these can be `DataFrames`. This allows the index to be used to track the forecast origination point, which can be a helpful device."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    },
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1152x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "exog_fcast = {\"x0\": x0_oos[-1:], \"x1\": x1_oos[-1:]}\n",
-    "forecasts = res.forecast(horizon=10, x=exog_fcast)\n",
-    "forecasts.mean.T.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Using an `array`\n",
-    "\n",
-    "An array can alternatively be used.  This frees the restriction on matching the variable names although the order must match instead. The forecast values are 2 (variables) by 1 (forecast) by 10 (horizon)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    },
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The shape is (2, 1, 10)\n",
-      "     h.01  h.02  h.03  h.04  h.05  h.06  h.07  h.08  h.09  h.10\n",
-      "999   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "exog_fcast = np.array([x0_oos[-1:], x1_oos[-1:]])\n",
-    "print(f\"The shape is {exog_fcast.shape}\")\n",
-    "array_forecasts = res.forecast(horizon=10, x=exog_fcast)\n",
-    "print(array_forecasts.mean - forecasts.mean)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Producing multiple forecasts\n",
-    "\n",
-    "`forecast` can produce multiple forecasts using the same fit model.  Here the model is fit to the first 500 observations and then forecasting for the remaining values are produced. It must be the case that the `x` values passed for `forecast` have the same number of rows as the table of forecasts produced.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    },
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>h.01</th>\n",
-       "      <th>h.02</th>\n",
-       "      <th>h.03</th>\n",
-       "      <th>h.04</th>\n",
-       "      <th>h.05</th>\n",
-       "      <th>h.06</th>\n",
-       "      <th>h.07</th>\n",
-       "      <th>h.08</th>\n",
-       "      <th>h.09</th>\n",
-       "      <th>h.10</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>990</th>\n",
-       "      <td>0.197255</td>\n",
-       "      <td>0.766334</td>\n",
-       "      <td>1.577107</td>\n",
-       "      <td>2.505601</td>\n",
-       "      <td>3.459461</td>\n",
-       "      <td>4.377620</td>\n",
-       "      <td>5.224185</td>\n",
-       "      <td>5.981486</td>\n",
-       "      <td>6.644090</td>\n",
-       "      <td>7.214210</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>991</th>\n",
-       "      <td>-1.890108</td>\n",
-       "      <td>-1.296009</td>\n",
-       "      <td>-0.321045</td>\n",
-       "      <td>0.826688</td>\n",
-       "      <td>2.010886</td>\n",
-       "      <td>3.148451</td>\n",
-       "      <td>4.193448</td>\n",
-       "      <td>5.124665</td>\n",
-       "      <td>5.936575</td>\n",
-       "      <td>6.633031</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>992</th>\n",
-       "      <td>-8.507089</td>\n",
-       "      <td>-7.755860</td>\n",
-       "      <td>-6.044790</td>\n",
-       "      <td>-4.011246</td>\n",
-       "      <td>-1.975703</td>\n",
-       "      <td>-0.089727</td>\n",
-       "      <td>1.584868</td>\n",
-       "      <td>3.033252</td>\n",
-       "      <td>4.264283</td>\n",
-       "      <td>5.297785</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>993</th>\n",
-       "      <td>-12.095946</td>\n",
-       "      <td>-10.869621</td>\n",
-       "      <td>-8.890602</td>\n",
-       "      <td>-6.627904</td>\n",
-       "      <td>-4.352285</td>\n",
-       "      <td>-2.211499</td>\n",
-       "      <td>-0.277740</td>\n",
-       "      <td>1.422307</td>\n",
-       "      <td>2.888448</td>\n",
-       "      <td>4.135017</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>994</th>\n",
-       "      <td>-10.415586</td>\n",
-       "      <td>-8.143577</td>\n",
-       "      <td>-5.654791</td>\n",
-       "      <td>-3.258179</td>\n",
-       "      <td>-1.089347</td>\n",
-       "      <td>0.805444</td>\n",
-       "      <td>2.424787</td>\n",
-       "      <td>3.788488</td>\n",
-       "      <td>4.925033</td>\n",
-       "      <td>5.865051</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>995</th>\n",
-       "      <td>-1.921109</td>\n",
-       "      <td>1.075917</td>\n",
-       "      <td>3.299730</td>\n",
-       "      <td>4.950490</td>\n",
-       "      <td>6.178127</td>\n",
-       "      <td>7.093677</td>\n",
-       "      <td>7.778853</td>\n",
-       "      <td>8.293623</td>\n",
-       "      <td>8.681971</td>\n",
-       "      <td>8.976192</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>996</th>\n",
-       "      <td>-0.935776</td>\n",
-       "      <td>1.954178</td>\n",
-       "      <td>4.178160</td>\n",
-       "      <td>5.832262</td>\n",
-       "      <td>7.036522</td>\n",
-       "      <td>7.901080</td>\n",
-       "      <td>8.515904</td>\n",
-       "      <td>8.950270</td>\n",
-       "      <td>9.255725</td>\n",
-       "      <td>9.469814</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>997</th>\n",
-       "      <td>2.416878</td>\n",
-       "      <td>4.689798</td>\n",
-       "      <td>6.409599</td>\n",
-       "      <td>7.650164</td>\n",
-       "      <td>8.515074</td>\n",
-       "      <td>9.101508</td>\n",
-       "      <td>9.488851</td>\n",
-       "      <td>9.737583</td>\n",
-       "      <td>9.891902</td>\n",
-       "      <td>9.983206</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>998</th>\n",
-       "      <td>4.633381</td>\n",
-       "      <td>6.555164</td>\n",
-       "      <td>8.072909</td>\n",
-       "      <td>9.153335</td>\n",
-       "      <td>9.864477</td>\n",
-       "      <td>10.296675</td>\n",
-       "      <td>10.532205</td>\n",
-       "      <td>10.636352</td>\n",
-       "      <td>10.657221</td>\n",
-       "      <td>10.628546</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>999</th>\n",
-       "      <td>10.573900</td>\n",
-       "      <td>12.317818</td>\n",
-       "      <td>13.245648</td>\n",
-       "      <td>13.612879</td>\n",
-       "      <td>13.620680</td>\n",
-       "      <td>13.415346</td>\n",
-       "      <td>13.097526</td>\n",
-       "      <td>12.733225</td>\n",
-       "      <td>12.363590</td>\n",
-       "      <td>12.012647</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          h.01       h.02       h.03       h.04       h.05       h.06  \\\n",
-       "990   0.197255   0.766334   1.577107   2.505601   3.459461   4.377620   \n",
-       "991  -1.890108  -1.296009  -0.321045   0.826688   2.010886   3.148451   \n",
-       "992  -8.507089  -7.755860  -6.044790  -4.011246  -1.975703  -0.089727   \n",
-       "993 -12.095946 -10.869621  -8.890602  -6.627904  -4.352285  -2.211499   \n",
-       "994 -10.415586  -8.143577  -5.654791  -3.258179  -1.089347   0.805444   \n",
-       "995  -1.921109   1.075917   3.299730   4.950490   6.178127   7.093677   \n",
-       "996  -0.935776   1.954178   4.178160   5.832262   7.036522   7.901080   \n",
-       "997   2.416878   4.689798   6.409599   7.650164   8.515074   9.101508   \n",
-       "998   4.633381   6.555164   8.072909   9.153335   9.864477  10.296675   \n",
-       "999  10.573900  12.317818  13.245648  13.612879  13.620680  13.415346   \n",
-       "\n",
-       "          h.07       h.08       h.09       h.10  \n",
-       "990   5.224185   5.981486   6.644090   7.214210  \n",
-       "991   4.193448   5.124665   5.936575   6.633031  \n",
-       "992   1.584868   3.033252   4.264283   5.297785  \n",
-       "993  -0.277740   1.422307   2.888448   4.135017  \n",
-       "994   2.424787   3.788488   4.925033   5.865051  \n",
-       "995   7.778853   8.293623   8.681971   8.976192  \n",
-       "996   8.515904   8.950270   9.255725   9.469814  \n",
-       "997   9.488851   9.737583   9.891902   9.983206  \n",
-       "998  10.532205  10.636352  10.657221  10.628546  \n",
-       "999  13.097526  12.733225  12.363590  12.012647  "
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "res = mod.fit(disp=\"off\", last_obs=500)\n",
-    "exog_fcast = {\"x0\": x0_oos[-500:], \"x1\": x1_oos[-500:]}\n",
-    "multi_forecasts = res.forecast(start=500, horizon=10, x=exog_fcast)\n",
-    "multi_forecasts.mean.tail(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The plot of the final 5 forecast paths shows the the mean reversion of the process."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    },
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1152x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_ = multi_forecasts.mean.tail().T.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The previous example made use of dictionaries where each of the values was a 500 (number of forecasts) by 10 (horizon) array.  The alternative format can be used where `x` is a 3-d array with shape 2 (variables) by 500 (forecasts) by 10 (horizon)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    },
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(2, 500, 10)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "h.01    0.0\n",
-       "h.02    0.0\n",
-       "h.03    0.0\n",
-       "h.04    0.0\n",
-       "h.05    0.0\n",
-       "h.06    0.0\n",
-       "h.07    0.0\n",
-       "h.08    0.0\n",
-       "h.09    0.0\n",
-       "h.10    0.0\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "exog_fcast = np.array([x0_oos[-500:], x1_oos[-500:]])\n",
-    "print(exog_fcast.shape)\n",
-    "array_multi_forecasts = res.forecast(start=500, horizon=10, x=exog_fcast)\n",
-    "np.max(np.abs(array_multi_forecasts.mean - multi_forecasts.mean))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## `x` input array sizes\n",
-    "\n",
-    "While the natural shape of the `x` data is the number of forecasts, it is also possible to pass an `x` that has the same shape as the `y` used to construct the model.  The may simplify tracking the origin points of the forecast.  Values are are not needed are ignored. In this example, the out-of-sample values are 2 by 1000 (original number of observations) by 10.  Only the final 500 are used. \n",
-    "\n",
-    "<div class=\"alert alert-warning\">\n",
-    "    <h3><b>WARNING</b></h3>\n",
-    "    Other sizes are <b>not</b> allowed. The size of the out-of-sample data must either match the original data size or the number of forecasts.\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    },
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(2, 1000, 10)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "h.01    0.0\n",
-       "h.02    0.0\n",
-       "h.03    0.0\n",
-       "h.04    0.0\n",
-       "h.05    0.0\n",
-       "h.06    0.0\n",
-       "h.07    0.0\n",
-       "h.08    0.0\n",
-       "h.09    0.0\n",
-       "h.10    0.0\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "exog_fcast = np.array([x0_oos, x1_oos])\n",
-    "print(exog_fcast.shape)\n",
-    "array_multi_forecasts = res.forecast(start=500, horizon=10, x=exog_fcast)\n",
-    "np.max(np.abs(array_multi_forecasts.mean - multi_forecasts.mean))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "## Special Cases with a single `x` variable\n",
-    "\n",
-    "When a model consists of a single exogenous regressor, then `x` can be a 1-d or 2-d array (or `Series` or `DataFrame`)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    },
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                          AR-X - GARCH Model Results                          \n",
-      "==============================================================================\n",
-      "Dep. Variable:                   data   R-squared:                       0.949\n",
-      "Mean Model:                      AR-X   Adj. R-squared:                  0.949\n",
-      "Vol Model:                      GARCH   Log-Likelihood:               -2310.31\n",
-      "Distribution:                  Normal   AIC:                           4632.63\n",
-      "Method:            Maximum Likelihood   BIC:                           4662.07\n",
-      "                                        No. Observations:                  999\n",
-      "Date:                Mon, May 17 2021   Df Residuals:                      996\n",
-      "Time:                        16:04:11   Df Model:                            3\n",
-      "                               Mean Model                               \n",
-      "========================================================================\n",
-      "                 coef    std err          t      P>|t|  95.0% Conf. Int.\n",
-      "------------------------------------------------------------------------\n",
-      "Const         -6.3468      0.283    -22.464 9.346e-112 [ -6.901, -5.793]\n",
-      "data[1]        0.7555  9.631e-03     78.446      0.000 [  0.737,  0.774]\n",
-      "x0             1.7840  6.195e-02     28.799 2.192e-182 [  1.663,  1.905]\n",
-      "                             Volatility Model                             \n",
-      "==========================================================================\n",
-      "                 coef    std err          t      P>|t|    95.0% Conf. Int.\n",
-      "--------------------------------------------------------------------------\n",
-      "omega          3.0493      0.753      4.052  5.085e-05   [  1.574,  4.524]\n",
-      "alpha[1]       0.1626  3.741e-02      4.346  1.385e-05 [8.926e-02,  0.236]\n",
-      "beta[1]        0.3401      0.129      2.631  8.523e-03 [8.670e-02,  0.593]\n",
-      "==========================================================================\n",
-      "\n",
-      "Covariance estimator: robust\n"
-     ]
-    }
-   ],
-   "source": [
-    "mod = arch_model(y, x=exog.iloc[:, :1], mean=\"ARX\", lags=1)\n",
-    "res = mod.fit(disp=\"off\")\n",
-    "print(res.summary())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "These two examples show that both formats can be used."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    },
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     h.01  h.02  h.03  h.04  h.05  h.06  h.07  h.08  h.09  h.10\n",
-      "999   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "forecast_1d = res.forecast(horizon=10, x=x0_oos[-1])\n",
-    "forecast_2d = res.forecast(horizon=10, x=x0_oos[-1:])\n",
-    "print(forecast_1d.mean - forecast_2d.mean)\n",
-    "\n",
-    "## Simulation-forecasting\n",
-    "\n",
-    "mod = arch_model(y, x=exog, mean=\"ARX\", lags=1, power=1.0)\n",
-    "res = mod.fit(disp=\"off\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Simulation\n",
-    "\n",
-    "`forecast` supports simulating paths. When forecasting a model with exogenous variables, the same value is used to in all mean paths.  If you wish to also simulate the paths of the `x` variables, these need to generated and then passed inside a loop. \n",
-    "\n",
-    "### Static out-of-sample `x`\n",
-    "This first example shows that variance of the paths when the same `x` values are used in the forecast. There is a sense the out-of-sample `x` are treated as deterministic."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    },
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[0.94510757, 0.97700028, 1.13035701, 1.10824845, 1.26825095,\n",
-       "        1.26590793, 1.23342443, 1.18098812, 1.28265677, 1.2315994 ]])"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x = {\"x0\": x0_oos[-1], \"x1\": x1_oos[-1]}\n",
-    "sim_fixedx = res.forecast(horizon=10, x=x, method=\"simulation\", simulations=100)\n",
-    "sim_fixedx.simulations.values.std(1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Simulating the out-of-sample `x`\n",
-    "\n",
-    "This example simulates distinct paths for the two exogenous variables and then simulates a single path.  This is then repeated 100 times.  We see that variance is much higher when we account for variation in the x data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    },
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from numpy.random import RandomState\n",
-    "\n",
-    "\n",
-    "def sim_ar1(params: np.ndarray, initial: float, horizon: int, rng: RandomState):\n",
-    "    out = np.zeros(horizon)\n",
-    "    shocks = rng.standard_normal(horizon)\n",
-    "    out[0] = params[0] + params[1] * initial + shocks[0]\n",
-    "    for i in range(1, horizon):\n",
-    "        out[i] = params[0] + params[1] * out[i - 1] + shocks[i]\n",
-    "    return out\n",
-    "\n",
-    "\n",
-    "simulations = []\n",
-    "rng = RandomState(20210301)\n",
-    "for i in range(100):\n",
-    "    x0_sim = sim_ar1(np.array([1, 0.8]), x0.iloc[-1], 10, rng)\n",
-    "    x1_sim = sim_ar1(np.array([2.5, 0.5]), x1.iloc[-1], 10, rng)\n",
-    "    x = {\"x0\": x0_sim, \"x1\": x1_sim}\n",
-    "    fcast = res.forecast(horizon=10, x=x, method=\"simulation\", simulations=1)\n",
-    "    simulations.append(fcast.simulations.values)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Finally the standard deviation is quite a bit larger. This is a most accurate value fo the long-run variance of the forecast residuals which should account for dynamics in the model and any exogenous regressors."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    },
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[3.21343234, 4.99297815, 6.29477322, 7.49884207, 8.47501636,\n",
-       "        8.64628226, 8.72478144, 8.78606273, 9.03824963, 9.30368669]])"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "joined = np.concatenate(simulations, 1)\n",
-    "joined.std(1)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
\ No newline at end of file
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "# Forecasting with Exogenous Regressors\n",
+    "\n",
+    "This notebook provides examples of the accepted data structures for passing the expected value of exogenous variables when these are included in the mean.  For example, consider an AR(1) with 2 exogenous variables. The mean dynamics are\n",
+    "\n",
+    "$$ Y_t = \\phi_0 + \\phi_1 Y_{t-1} + \\beta_0 X_{0,t} + \\beta_1 X_{1,t} + \\epsilon_t. $$\n",
+    "\n",
+    "The $h$-step forecast, $E_{T}[Y_{t+h}]$, depends on the conditional expectation of $X_{0,T+h}$ and $X_{1,T+h}$,\n",
+    "\n",
+    "$$ E_{T}[Y_{T+h}] = \\phi_0 + \\phi_1 E_{T}[Y_{T+h-1}] + \\beta_0 E_{T}[X_{0,T+h}] +\\beta_1 E_{T}[X_{1,T+h}] $$\n",
+    "\n",
+    "where $E_{T}[Y_{T+h-1}]$ has been recursively computed.\n",
+    "\n",
+    "In order to construct forecasts up to some horizon $h$, it is necessary to pass $2\\times h$ values ($h$ for each series).  If using the features of `forecast` that allow many forecast to be specified, it necessary to supply $n \\times 2 \\times h$ values.\n",
+    "\n",
+    "There are two general purpose data structures that can be used for any number of exogenous variables and any number steps ahead:\n",
+    "\n",
+    "* `dict` - The values can be pass using a `dict` where the keys are the variable names and the values are 2-dimensional arrays. This is the most natural generalization of a pandas `DataFrame` to 3-dimensions.\n",
+    "* `array` - The vales can alternatively be passed as a 3-d NumPy `array` where dimension 0 tracks the regressor index, dimension 1 is the time period and dimension 2 is the horizon.\n",
+    "\n",
+    "When a model contains a single exogenous regressor it is possible to use a 2-d array or `DataFrame` where dim0 tracks the time period where the forecast is generated and dimension 1 tracks the horizon.\n",
+    "\n",
+    "In the special case where a model contains a single regressor _and_ the horizon is 1, then a 1-d array of pandas Series can be used."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# initial setup\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import seaborn\n",
+    "\n",
+    "seaborn.set_style(\"darkgrid\")\n",
+    "plt.rc(\"figure\", figsize=(16, 6))\n",
+    "plt.rc(\"savefig\", dpi=90)\n",
+    "plt.rc(\"font\", family=\"sans-serif\")\n",
+    "plt.rc(\"font\", size=14)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Simulating data\n",
+    "\n",
+    "Two $X$ variables are simulated and are assumed to follow independent AR(1) processes. The data is then assumed to follow an ARX(1) with 2 exogenous regressors and GARCH(1,1) errors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from arch.univariate import ARX, GARCH, ZeroMean, arch_model\n",
+    "\n",
+    "burn = 250\n",
+    "\n",
+    "x_mod = ARX(None, lags=1)\n",
+    "x0 = x_mod.simulate([1, 0.8, 1], nobs=1000 + burn).data\n",
+    "x1 = x_mod.simulate([2.5, 0.5, 1], nobs=1000 + burn).data\n",
+    "\n",
+    "resid_mod = ZeroMean(volatility=GARCH())\n",
+    "resids = resid_mod.simulate([0.1, 0.1, 0.8], nobs=1000 + burn).data\n",
+    "\n",
+    "phi1 = 0.7\n",
+    "phi0 = 3\n",
+    "y = 10 + resids.copy()\n",
+    "for i in range(1, y.shape[0]):\n",
+    "    y[i] = phi0 + phi1 * y[i - 1] + 2 * x0[i] - 2 * x1[i] + resids[i]\n",
+    "\n",
+    "x0 = x0.iloc[-1000:]\n",
+    "x1 = x1.iloc[-1000:]\n",
+    "y = y.iloc[-1000:]\n",
+    "y.index = x0.index = x1.index = np.arange(1000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Plotting the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = pd.DataFrame({\"ARX\": y}).plot(legend=False)\n",
+    "ax.legend(frameon=False)\n",
+    "_ = ax.set_xlim(0, 999)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Forecasting the X values\n",
+    "\n",
+    "The forecasts of $Y$ depend on forecasts of $X_0$ and $X_1$.  Both of these follow simple AR(1), and so we can construct the forecasts for all time horizons.  Note that the value in position `[i,j]` is the time-`i` forecast for horizon `j+1`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([5.65805541, 5.3290277 , 5.16451385, 5.08225693, 5.04112846,\n",
+       "       5.02056423, 5.01028212, 5.00514106, 5.00257053, 5.00128526])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x0_oos = np.empty((1000, 10))\n",
+    "x1_oos = np.empty((1000, 10))\n",
+    "for i in range(10):\n",
+    "    if i == 0:\n",
+    "        last = x0\n",
+    "    else:\n",
+    "        last = x0_oos[:, i - 1]\n",
+    "    x0_oos[:, i] = 1 + 0.8 * last\n",
+    "    if i == 0:\n",
+    "        last = x1\n",
+    "    else:\n",
+    "        last = x1_oos[:, i - 1]\n",
+    "    x1_oos[:, i] = 2.5 + 0.5 * last\n",
+    "\n",
+    "x1_oos[-1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Fitting the model\n",
+    "\n",
+    "Next, the most is fit. The parameters are accurately estimated."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                          AR-X - GARCH Model Results                          \n",
+      "==============================================================================\n",
+      "Dep. Variable:                   data   R-squared:                       0.992\n",
+      "Mean Model:                      AR-X   Adj. R-squared:                  0.992\n",
+      "Vol Model:                      GARCH   Log-Likelihood:               -1391.04\n",
+      "Distribution:                  Normal   AIC:                           2796.08\n",
+      "Method:            Maximum Likelihood   BIC:                           2830.43\n",
+      "                                        No. Observations:                  999\n",
+      "Date:                Tue, May 30 2023   Df Residuals:                      995\n",
+      "Time:                        10:55:41   Df Model:                            4\n",
+      "                               Mean Model                               \n",
+      "========================================================================\n",
+      "                 coef    std err          t      P>|t|  95.0% Conf. Int.\n",
+      "------------------------------------------------------------------------\n",
+      "Const          2.9447      0.151     19.443  3.308e-84 [  2.648,  3.242]\n",
+      "data[1]        0.6947  3.807e-03    182.482      0.000 [  0.687,  0.702]\n",
+      "x0             1.9813  2.175e-02     91.078      0.000 [  1.939,  2.024]\n",
+      "x1            -1.9620  2.564e-02    -76.519      0.000 [ -2.012, -1.912]\n",
+      "                             Volatility Model                             \n",
+      "==========================================================================\n",
+      "                 coef    std err          t      P>|t|    95.0% Conf. Int.\n",
+      "--------------------------------------------------------------------------\n",
+      "omega          0.0946  3.606e-02      2.622  8.733e-03 [2.389e-02,  0.165]\n",
+      "alpha[1]       0.0974  2.496e-02      3.903  9.516e-05 [4.849e-02,  0.146]\n",
+      "beta[1]        0.8083  5.059e-02     15.977  1.839e-57   [  0.709,  0.907]\n",
+      "==========================================================================\n",
+      "\n",
+      "Covariance estimator: robust\n"
+     ]
+    }
+   ],
+   "source": [
+    "exog = pd.DataFrame({\"x0\": x0, \"x1\": x1})\n",
+    "mod = arch_model(y, x=exog, mean=\"ARX\", lags=1)\n",
+    "res = mod.fit(disp=\"off\")\n",
+    "print(res.summary())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Using a `dict`\n",
+    "\n",
+    "The first approach uses a dict to pass the two variables. The key consideration here is the the keys of the dictionary must **exactly** match the variable names (`x0` and `x1` here).  The dictionary here contains only the final row of the forecast values since `forecast` will only make forecasts beginning from the final in-sample observation by default.\n",
+    "\n",
+    "### Using `DataFrame`\n",
+    "\n",
+    "While these examples make use of NumPy arrays, these can be `DataFrames`. This allows the index to be used to track the forecast origination point, which can be a helpful device."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "exog_fcast = {\"x0\": x0_oos[-1:], \"x1\": x1_oos[-1:]}\n",
+    "forecasts = res.forecast(horizon=10, x=exog_fcast)\n",
+    "forecasts.mean.T.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Using an `array`\n",
+    "\n",
+    "An array can alternatively be used.  This frees the restriction on matching the variable names although the order must match instead. The forecast values are 2 (variables) by 1 (forecast) by 10 (horizon)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The shape is (2, 1, 10)\n",
+      "     h.01  h.02  h.03  h.04  h.05  h.06  h.07  h.08  h.09  h.10\n",
+      "999   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "exog_fcast = np.array([x0_oos[-1:], x1_oos[-1:]])\n",
+    "print(f\"The shape is {exog_fcast.shape}\")\n",
+    "array_forecasts = res.forecast(horizon=10, x=exog_fcast)\n",
+    "print(array_forecasts.mean - forecasts.mean)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Producing multiple forecasts\n",
+    "\n",
+    "`forecast` can produce multiple forecasts using the same fit model.  Here the model is fit to the first 500 observations and then forecasting for the remaining values are produced. It must be the case that the `x` values passed for `forecast` have the same number of rows as the table of forecasts produced.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>h.01</th>\n",
+       "      <th>h.02</th>\n",
+       "      <th>h.03</th>\n",
+       "      <th>h.04</th>\n",
+       "      <th>h.05</th>\n",
+       "      <th>h.06</th>\n",
+       "      <th>h.07</th>\n",
+       "      <th>h.08</th>\n",
+       "      <th>h.09</th>\n",
+       "      <th>h.10</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>990</th>\n",
+       "      <td>17.638678</td>\n",
+       "      <td>19.057943</td>\n",
+       "      <td>19.305712</td>\n",
+       "      <td>18.876070</td>\n",
+       "      <td>18.090794</td>\n",
+       "      <td>17.153279</td>\n",
+       "      <td>16.187233</td>\n",
+       "      <td>15.263806</td>\n",
+       "      <td>14.420245</td>\n",
+       "      <td>13.672511</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>991</th>\n",
+       "      <td>20.307394</td>\n",
+       "      <td>20.095492</td>\n",
+       "      <td>19.176459</td>\n",
+       "      <td>18.008276</td>\n",
+       "      <td>16.816890</td>\n",
+       "      <td>15.707369</td>\n",
+       "      <td>14.722133</td>\n",
+       "      <td>13.871672</td>\n",
+       "      <td>13.150830</td>\n",
+       "      <td>12.547444</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>992</th>\n",
+       "      <td>18.286380</td>\n",
+       "      <td>16.896235</td>\n",
+       "      <td>15.586099</td>\n",
+       "      <td>14.454254</td>\n",
+       "      <td>13.517502</td>\n",
+       "      <td>12.759810</td>\n",
+       "      <td>12.154606</td>\n",
+       "      <td>11.674450</td>\n",
+       "      <td>11.294780</td>\n",
+       "      <td>10.994971</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>993</th>\n",
+       "      <td>24.177049</td>\n",
+       "      <td>22.846561</td>\n",
+       "      <td>20.903832</td>\n",
+       "      <td>18.944579</td>\n",
+       "      <td>17.198290</td>\n",
+       "      <td>15.728217</td>\n",
+       "      <td>14.527010</td>\n",
+       "      <td>13.561360</td>\n",
+       "      <td>12.791967</td>\n",
+       "      <td>12.181810</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>994</th>\n",
+       "      <td>21.476395</td>\n",
+       "      <td>19.945167</td>\n",
+       "      <td>18.325031</td>\n",
+       "      <td>16.830889</td>\n",
+       "      <td>15.536242</td>\n",
+       "      <td>14.450638</td>\n",
+       "      <td>13.557059</td>\n",
+       "      <td>12.829543</td>\n",
+       "      <td>12.241130</td>\n",
+       "      <td>11.767149</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>995</th>\n",
+       "      <td>13.887372</td>\n",
+       "      <td>11.827018</td>\n",
+       "      <td>10.504285</td>\n",
+       "      <td>9.701208</td>\n",
+       "      <td>9.250338</td>\n",
+       "      <td>9.029847</td>\n",
+       "      <td>8.954356</td>\n",
+       "      <td>8.965773</td>\n",
+       "      <td>9.025626</td>\n",
+       "      <td>9.109140</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>996</th>\n",
+       "      <td>12.091224</td>\n",
+       "      <td>10.738291</td>\n",
+       "      <td>9.893515</td>\n",
+       "      <td>9.402421</td>\n",
+       "      <td>9.147641</td>\n",
+       "      <td>9.044580</td>\n",
+       "      <td>9.034509</td>\n",
+       "      <td>9.077894</td>\n",
+       "      <td>9.148903</td>\n",
+       "      <td>9.231197</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>997</th>\n",
+       "      <td>14.752402</td>\n",
+       "      <td>14.165799</td>\n",
+       "      <td>13.584948</td>\n",
+       "      <td>13.038435</td>\n",
+       "      <td>12.542245</td>\n",
+       "      <td>12.103274</td>\n",
+       "      <td>11.722401</td>\n",
+       "      <td>11.396828</td>\n",
+       "      <td>11.121754</td>\n",
+       "      <td>10.891488</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>998</th>\n",
+       "      <td>15.530046</td>\n",
+       "      <td>14.262848</td>\n",
+       "      <td>13.244627</td>\n",
+       "      <td>12.449407</td>\n",
+       "      <td>11.837882</td>\n",
+       "      <td>11.371370</td>\n",
+       "      <td>11.016716</td>\n",
+       "      <td>10.747264</td>\n",
+       "      <td>10.542302</td>\n",
+       "      <td>10.386034</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999</th>\n",
+       "      <td>11.481761</td>\n",
+       "      <td>10.462828</td>\n",
+       "      <td>10.056773</td>\n",
+       "      <td>9.917344</td>\n",
+       "      <td>9.885242</td>\n",
+       "      <td>9.890079</td>\n",
+       "      <td>9.902827</td>\n",
+       "      <td>9.913039</td>\n",
+       "      <td>9.918147</td>\n",
+       "      <td>9.918574</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          h.01       h.02       h.03       h.04       h.05       h.06   \n",
+       "990  17.638678  19.057943  19.305712  18.876070  18.090794  17.153279  \\\n",
+       "991  20.307394  20.095492  19.176459  18.008276  16.816890  15.707369   \n",
+       "992  18.286380  16.896235  15.586099  14.454254  13.517502  12.759810   \n",
+       "993  24.177049  22.846561  20.903832  18.944579  17.198290  15.728217   \n",
+       "994  21.476395  19.945167  18.325031  16.830889  15.536242  14.450638   \n",
+       "995  13.887372  11.827018  10.504285   9.701208   9.250338   9.029847   \n",
+       "996  12.091224  10.738291   9.893515   9.402421   9.147641   9.044580   \n",
+       "997  14.752402  14.165799  13.584948  13.038435  12.542245  12.103274   \n",
+       "998  15.530046  14.262848  13.244627  12.449407  11.837882  11.371370   \n",
+       "999  11.481761  10.462828  10.056773   9.917344   9.885242   9.890079   \n",
+       "\n",
+       "          h.07       h.08       h.09       h.10  \n",
+       "990  16.187233  15.263806  14.420245  13.672511  \n",
+       "991  14.722133  13.871672  13.150830  12.547444  \n",
+       "992  12.154606  11.674450  11.294780  10.994971  \n",
+       "993  14.527010  13.561360  12.791967  12.181810  \n",
+       "994  13.557059  12.829543  12.241130  11.767149  \n",
+       "995   8.954356   8.965773   9.025626   9.109140  \n",
+       "996   9.034509   9.077894   9.148903   9.231197  \n",
+       "997  11.722401  11.396828  11.121754  10.891488  \n",
+       "998  11.016716  10.747264  10.542302  10.386034  \n",
+       "999   9.902827   9.913039   9.918147   9.918574  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res = mod.fit(disp=\"off\", last_obs=500)\n",
+    "exog_fcast = {\"x0\": x0_oos[-500:], \"x1\": x1_oos[-500:]}\n",
+    "multi_forecasts = res.forecast(start=500, horizon=10, x=exog_fcast)\n",
+    "multi_forecasts.mean.tail(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The plot of the final 5 forecast paths shows the the mean reversion of the process."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Tpg307Nk70CgEMJlMjB59Ax6PhxkzprFo0XxcLhcjR44KNAoB4uLiGDnySvbu3c2yZUsAWLNmFVu3buaKK64ONAoBbDYbd9xxL1deeS1lZWUNd5M1jnvNwp07d3LHHXfg8/l+9vimTZvIzs7mmmuuqXOsbdu6z2dLaDNZLMSOuhr70NMo/9tTeJYtwfnay7i/m0nCw3/C2q5DsEsUERERERERETkpcnMPAdDuZ/ofzZu3AKofU46LizvCec0D5w0YkMPy5UsByMkZDFTPUqysdBIXF8/gwUNrNSUb0nE1C2fOnMkjjzxCSUnJzx4/cOAAJSUl5OTkMGzYsBMqUEKL5ZSmJP39FVwzplPxyj/wbtpI8c3XE3v1aOKuvwmTwxHsEkVERERERESkoRkGeCuDXcUvs8ZWP4t7nGJjq5uATmdFnWPFxcUAFBTkH/V5ALt27QQgLi6eZ575P779diaVlU7S09MZNeparrrqWkwnUPPxOuZm4dixY5k1axbt2rVj6NChTJ8+vc45mzZtAqB9+/YnXqGEHJPJRMyFF2MfkEP5P57HPes7Kt+diHvW9yQ89Ai2Hj2DXaKIiIiIiIiINBTDIOWTS7EdXBrsSn6Rp0k/ii/95Lgbhq1atSYxMYkFC+ZRXl5OQkJC4Nh3380EwOVy0aOmJzJz5pecf/5Ftd7j+++/CZwHUFZWCsAjjzxIQkIi99//eywWC599NoV//eslCgryuOuu+4+r3hNxzGsWbt++nfvvv58pU6bQqlWrnz1n48aNwOFmodPpxK8ddCOOOSODpCeeIfGJZzGlpePbvYuSO8ZS/uLf8FeUB7s8EREREREREWkoQZgB15CsVitjxtxEUVEh9913BytXLmffvr189NEkJk4cT3x8PFarlY4dOzFkyKksXryQp556nC1bNrNjx3ZefPFZ1q1bE3gvALfbA0BMTCz/+td4zj//Is4553z++c/X6dy5K5MnT2L37l0Nf6/H+gdmzJiB3X7kXW9+bBZ+9NFHzJgxg/z8fOLi4jj77LN56KGHSE9PP+ZCI/lr7sd7C9d7jDn9DOy9+1Dxr39SNf0zqqZ8hHveHBIefBhHzXP3ItEu3HMuIkdHWReJfMq5SHRQ1us64t+FyVQ9ay+CH0MGGDXqGtxuNxMnjufOO8cCkJGRyaOP/pVXXnmJpKRkAB599Amee+4pvvhiOjNmTAOgc+cuPP7409x7728D58XGxgAwfPhlWCyWwHXMZjMXXzycDRvWsWzZElq0aHlU9ZlMv3yLx3Lrx9ws/LVGIRx+DHnjxo38/ve/x+FwMG/ePCZPnsyKFSuYPHkyKSkpx3Td9PSj2945nIX1PWYkwvPPUnHZcA48+hiePXsoffA+ki6+mEZ//APW1NRgVygSEsI65yJy1JR1kcinnItEB2X9sKqqKgoLzVgsJqzWX3hQ1Zbw869HkBtuuIlRo65i69YtOBwO2rVrj9/v589/fpju3btjtZpJSkrg//7vKe655z727t1DWloGLVq0YNmy6se0W7RojtVqpnHjJgBkZmbU+TvNyMgAoLKy4pf/vmv4/SbMZjOpqfHExMSc8D0e927IR3L11VdTWVnJLbfcEliI8bzzzqNNmzY888wzjBs3jgcffPCY3rOgoAzDOBnVBp/JVD0ARcQ9tu9G8tvvUTH+DSonT6J02jTK5swh4Z4HcJx1TlAW5hQJBRGVcxH5Rcq6SORTzkWig7Jel8fjxu/34/MZeL3RudTcd999g81mZejQ0+nSpTtQva/LwoUL8Hg8ZGf3pri4lNmzv6d16zZ07tyVlJTqp2u9Xj/z5s0BoEeP3ni9fjp37grA5s2bGThwSK1r/fj4caNGTX7179vnM/D7/RQVVWCzeX72nB+/po/GSWkWjh49+mdfv+aaa3juueeYO3fuMTcLDYOID2jE3GNMLPF33ov9N2dT/uwT+LZvo+zxP1P19ZckPPAwlkaNgl2hSNBETM5F5IiUdZHIp5yLRAdl/TD9PcCUKZPZsmUzH37YK/AosdNZwVtvvUlGRiZnn30uAC+88AydO3fl5ZffCEya2rlzB599NoWhQ0+nadNmAAwdejpJScl8/PEHXHzxcFJT02re08nHH08mPj6enJwhP1PJz6uvr9dj3uDkRNjtdpKSkqioqLt9tEQeW5eupIz/N3E33QpWK54F8yi+7koqp3yEoQ1vRERERERERCSMXH/9TVRWOrnzzrF89NEkJk+exG233cj27Vv5wx8exeGIweGI4eqrr2PlyuU8/PD9TJ36CRMnjueOO24mPj6Be+55IPB+cXFxPPTQnyguLubmm6/j3XcnMmnSe4wdez0HD+7n/vsfIi4ursHvs96bhRs2bODCCy/kqaeeqnOsoKCAoqIiWrY8uoUZJfyZbDbixtxEytvvYe3WHcNZQcWLf6Pk7tvwBmFHHxERERERERGR49G3b3+ef/6fJCUlM37867zzzngaNz6FV18dx4ABOYHzbrjhFh544GEOHNjPyy+/yGefTeH008/k9dffDqxT+KPTTjuDV18dR6tWbXjvvQm89dabpKSk8sILL3PuuRc09C0CJ+Ex5FatWnHo0CGmTJnCjTfeSOPGjQEwDIMXXngBgBEjRtT3ZSXEWVu3IfmVN6ma8hEVb/4L76qVFN9wDXFjbib2qmsxWU/KE/EiIiIiIiIiIvWmX78B9Os34IjnmM1mLr10JJdeOvKo3rNbtx688MI/66O8elHvMwtjY2P505/+RFlZGVdccQWvvfYa7777LmPGjOHjjz/m4osv5sILL6zvy0oYMFksxI4cReq/J2HrPxDcbpxv/ovisWPwbtoY7PJERERERERERKLeSZnONXz4cLKysnjjjTd488038fl8tGnThkcffZSrrrrqZFxSwoilcROSnn8J11dfUPHyi/i2bKb41huIHXU1cTfegslx4tt8i4iIiIiIiIjIsTMZRnjsZ5OfH7nblZtMkJGRGNH3+Ev8hQWUv/Qi7u9mAmBu1pzE3z+CrVfvIFcmUr+iOeci0URZF4l8yrlIdFDW6/J43BQUHCA9vQk2mz3Y5chPHM2/zY9f00ejQXdDFvlf5rR0kh5/ksSnn8eckYl/7x5K7r6N8uefxl9eHuzyRERERERERESiipqFEhIcQ04l5d0PiLnkUgCqpk6hePQoXHNnB7kyEREREREREZHooWahhAxzQgIJD/6B5H++jrlZc/z5eZT94XeUPvYI/qLCYJcnIiIiIiIiIhLx1CyUkGPr1ZvUif8h9urRYLHg/m4mRddeQdWXMwiTJTZFRERERERERMKSmoUSkkyOGOJvv4uUN97G0q49Rmkp5U/+hdLf3YPv4IFglyciIiIiIiIiEpHULAwBfsOPz+8LdhkhydqxMynj3iFu7G/BbsezeCFF111J5UcfYPj9wS5PRERERERERCSiqFkYZKXuUkZ8cxGnfnAqT6z4C9/tn0mZpzTYZYUUk9VK3OgxpEz4D9Ye2VBZScVLL1Byxy14d2wPdnkiIiIiIiIiIhHDGuwCop3VbCHRlsjeij18u/9rvt3/NWaThR6p2QzMGkRO1hCaJ7QIdpkhwdqiJckvv0HV1E9wvvYK3rVrKL5pNHHX3UjsNddhstmCXaKIiIiIiIiISFgzGWGyY0R+fhnhUemx8+Njn387X26ZyfxD89hVvqPW8WZxzclpNJicrCF0S+2B1awer+/QIcpfeAbPgnkAWNq0I+HhP2Hr3CXIlYn8PJMJMjISI3osExFlXSQaKOci0UFZr8vjcVNQcID09CbYbPZglyM/cTT/Nj9+TR8NNQtDwP8OQvud+1iYO48FufNYVbACr+ENnJtgTaRf5gAGZQ2hX+ZAkuxJQaw8uAzDwPXNV1S89CJGSTGYzcRecRVxN92KKSYm2OWJ1KIPGyLRQVkXiXzKuUh0UNbrUrMwdKlZGIGONAhVeCpYmr+IhbnzWZg3nxJ3ceCY2WShW2p3BmYNZlDWYJrHt8RkMjVs8SHAX1RExcsv4pr5FQDmU5qS8Ps/Yu/TL8iViRymDxsi0UFZF4l8yrlIdFDW61KzsNr27dsYN+5frFixDMMwaN++I9dccx05OUNqnTd37mzefXcCW7duJi4unl69+jBmzM20adM2cM6TT/6FL76YfsTr/fGPj3HBBRcf8Rw1CyPQ0Q5CPsPHxuL1LMidx4JDc9lRXntzj1PimpKTNYScrMF0T8vGZo6uNfzc8+dS/sIz+HNzAXBceAnxd9yNOTF6Z19K6NCHDZHooKyLRD7lXCQ6KOt1qVkIGzeu5667bsPjcTN8+GU0b96SefNms3jxQu6++wGuuOIqAD799COef/4ZUlPTGDHicmJiYpk+/VPy8vJ47rmXyM7uCcDatavZt29vneuUl5fz8ssvEhcXz1tvvUuTJqccsS41CyPQ8Q5CB50HWJA7j4W581hZuByP3xM4Fm+Np1/mQAZmDWJA5iCS7cknofLQ468ox/nGv6ia8hEAprR0Eu7/PY7TzghyZRLt9GFDJDoo6yKRTzkXiQ7Kel1qFsJNN41m06YNPPvs3xk8eChQvUTaY4/9kTlzfuCddyaRmJjEyJEXYbc7mDjxfRo1agyA0+lkzJirMJstvPfeh1itv7wfxWOP/ZHvvpvJc8+9xMCBg361rvpuFmqnjDDWOK4Jl7YayaWtRuL0VrAsfykLcueyKHc+Re4ifjjwLT8c+BYzZrqmdg/srtwyoVXEPq5sjk+obg6eeTblzz6Jb89uyv70EK7TfkPCfb/DnJ4R7BJFREREREREJMwcOnSQTZs20LNn70CjEMBkMjF69A18991MZsyYRqtWrXG5XFx99XWBRiFAXFwcI0deyT//+QLLli1hwICcn73O7Nk/8O23X3PhhZccVaPwZFCzMELEWeMZ2vg0hjY+Db/hZ1PJBuYfmsvC3PlsK9vCmqJVrClaxbhNr9Ek9hRyGg1mYOZgstN7ReTjyrbsXqRM+A/Od96i8v13cc/6jqJlS4i/814cF1wUsc1SERERERERkWAwDIMqX1Wwy/hFMZaYE+oF5OYeAqBduw51jjVv3gKofkw5Li7uCOc1D5z3c81Cj8fDv/71EomJSdx++93HXeuJUrMwAplNZjqndKVzSldu6ngrhyoPsjB3Pgty57GiYBkHKvfzyc7JfLJzMnHWOPpm9CcnawgDMnNIcaQGu/x6Y3I4iB/7WxxnnEXZM0/g27yR8mf+D9c3X5Hw4B+wnNI02CWKiIiIiIiIhD3DMLh74W2sK1oT7FJ+UbfUHrw08LXjbhjGxlY3AZ3OijrHiouLASgoyD/q837O559PZe/ePdx6652kpKQcV531Qc3CKNAotjHDWo5gWMsRVHorWV6whAWH5rEgdx5F7kJmH/yB2Qd/wISJzildGZQ1hIFZg2md2CYiZuBZ23cg5Y23qfzwvzjfehPP0sUUXX8V8TffRszIUZgslmCXKCIiIiIiIhLWTIR//+BIWrVqTWJiEgsWzKO8vJyEhITAse++mwmAy+WiR4+eAMyc+SXnn39Rrff4/vtvAuf9L8Mw+PDD/5KQkMCIESNP0l0cHTULo0ysNZbBjU5lcKNT8Rt+NpdsrN5dOXceW0s3s754LeuL1zJ+8+s0im3MwKzBDMoaTHZab+yW8F3A1GS1Enf1aBxDT6Psb0/hXbmcilf+geu7b0h46BGsP9m6XERERERERESOnslk4qWBr0X0Y8hWq5UxY27i5Zf/zn333cEdd9xDZmYWCxbMZeLE8cTHx2O1WunYsRNDhpzK3Lmzeeqpx7n88quwWq1MmTKZdevWBN7rfy1Zsojdu3dx9dXXER+fUOd4Q1KzMIqZTWY6pXShU0oXbuhwC3mVuSzMm8+CQ3NZXrCUQ5UHmbrrY6bu+pgYS2zN48qDGZA1iDRHWrDLPy6W5i1IfulfuKZPpeJf/8S7fi3FN40m9toxxI0eg8kevg1RERERERERkWAxmUzEWmODXcZJNWrUNbjdbiZOHM+dd44FICMjk0cf/SuvvPISSUnJADz66BM899xTfPHFdGbMmAZA585dePzxp7n33t8Gzvup77//FoBzzjm/ge7ml5kMIzw2AY/k7cpDcUv2Kl8Vy2t2V16YO58C1+Hn6U2Y6JTShZysweRkDaZNYruwfFzZl5dLxYt/wz13NgCWVq1JePhP2Lp2D3JlEolCMeciUv+UdZHIp5yLRAdlvS6Px01BwQHS05tgs0X3RBun08m2bVtwOBy0bdsev9/POeecym9+czZ//vNfA+fl5+ezb98e0tLSad68BcuXL+Xuu2/j4Yf/zEUXDQuc5/f7GT78fJKSknjvvcnHXM/R/Nv8+DV9NDSzUH5WjCWGQY2GMKjREAzDYEvppurHlQ/NY3PpRjYUr2ND8Tre3vwmWTGNGJg1iJysIfRK743d4gh2+UfFkplF4lPP4f7+G8r/8QK+nTsouf1mYkaOIv7m2zDV7GAkIiIiIiIiIvLdd99gs1kZOvR0unfPDry+ePECPB4PvXr1oby8nNmzv6d16zZ07tyVjIyMwHkLF84DoFevPrXed9eunRQWFnD22ec2zI38CjUL5VeZTCY6JHeiQ3Inrm9/E/lVeYHdlZfnLyG36hCf7Z7CZ7unEGOJoU9GP3KyhjAwaxBpjvRgl39EJpMJx2/OxtanX/Uahl/OoGryJNxzZpHw4B+w9x8Y7BJFREREREREJARMmTKZLVs28+GHvQKPEjudFbz11ptkZGQGmn0vvPAMnTt35eWX3wg8iblz5w4++2wKQ4eeTtOmzWq978aN6wHo3LlrA97NL1OzUI5ZRkwmF7UYxkUthuHyuVhRsJQFufNZkDuX/Ko85h2aw7xDcwDomNy5ZnflQbRL6hCyjyubk1NIfOQvOM46l/LnnsZ/8AClD9yN47wLib/rXsw/s56AiIiIiIiIiESP66+/iQceuIs77xzLJZdcimHAtGlT2Lt3D08//QIORwwAV199HRMmjOPhh+9n0KChFBUVMnnyf4mPT+Ceex6o8767d+8CoHHjUxr0fn6J1iwMAZGyFoJhGGwt3czC3PnMz53LppINtY5nxGSSkzmYnEaD6ZXeF0eIPq5sOJ1UjHuNqo8/BMPAlJpGwn2/w376mSHb7JTQFyk5F5EjU9ZFIp9yLhIdlPW6tGZhtSVLFvHOO2+xdetmrFYrXbp054Ybbq41K9Dv9zN16idMmTKZ/fv3kZSUTE7OYMaMuZnMzKw67/nss08ybdoU/vOfj2jZstUx11TfaxaqWRgCInUQKnQVBB5XXpa/uNYW6g6zg94Z/cjJGsTArMFkxGQGsdKf51m7mvJnnsS3awcA9iGnEv/AQ1gyQq9WCX2RmnMRqU1ZF4l8yrlIdFDW61KzMHSpWRiBomEQcvtcrChYzsLceSzInUdu1aFaxzskdWJg1iAGNRpCu6QOmE3mIFVam+F243x3IpXvTgCfD1NCAvG3343j4mGaZSjHJBpyLiLKukg0UM5FooOyXpeahaFLzcIIFG2DkGEYbC/bxoLcuSzIncfG4vUYHL7xdEdGYHfl3hl9ibHEBLHaat5tWyl/9gm8G6oXHbX17kPCg3/E0qx5kCuTcBFtOReJVsq6SORTzkWig7Jel5qFoUvNwggU7YNQoauQRbnzWZg7n6X5i6n0OQPH7GY7vdP7MjBrMDlZg8mMrftsf0MxfD6qJk+iYvzr4HKB3UHczbcSe/mVmKzaK0iOLNpzLhItlHWRyKeci0QHZb0uNQtDl5qFEUiD0GFun5tVhStYkDuPBblzOVR5sNbxdkntyckaQk7WYDokdwrK48q+/fsof/ZJPMuXAmDt2ImEh/+EtV2HBq9FwodyLhIdlHWRyKeci0QHZb0uNQtDl5qFEUiD0M8zDIOd5dtZcGgeC/Lmsb5oba3HldMc6QzMHMTArEH0yehPrDW2QWtzzZhGxSv/wCgvB4uF2KtHE3f9TZgcobnLswSXci4SHZR1kcinnItEB2W9LjULQ5eahRFIg9DRKXYVsShvAQty57E0fxFO7+HHlW1mO73Se5OTNZiBWYNpFNu4QWry5+dT/o/ncM/6HgBLi5YkPPQIth49G+T6Ej6Uc5HooKyLRD7lXCQ6KOt1qVkYutQsjEAahI6dx+9hdeHK6k1SDs3jQOX+WsfbJLYjp2adw04pXU7648quWd9T/uLfMAoLAIgZcTlxt/4Wc1z8Sb2uhA/lXCQ6KOsikU85F4kOynpdahaGLjULI5AGoRNjGAa7yneyIHcuC3Pns65oDX78geOp9lQGZA1iYNZg+mb0I856chp4/rJSKl59Cdfn0wAwZzUi4XcPY88ZfFKuJ+FFOReJDsq6SORTzkWig7Jel5qFoUvNwgikQah+lbiLWZy3kAW581iSt5AKb0XgmM1sIzutV2CTlMZxTer9+u6liyn/21P4D1TPdnScfR7xd9+POSWl3q8l4UM5F4kOyrpI5FPORaKDsl6XmoWhS83CCKRB6OTx+r01jytX766837mv1vHWCW3IaTSEgVmD6ZzSBYvJUi/XNSorqXjrDaomTwK/H1NyCgn3PoD9zHMwmUz1cg0JL8q5SHRQ1kUin3IuEh2U9brULAxdahZGIA1CDcMwDPZU7GJB7nwW5M5lbdEa/IYvcDzZnsKAzBxysgbTN2MA8bYTf1zZs34d5c8+gW/7NgBsg4aQcP9DWBo1OuH3lvCinItEB2VdJPIp5yLRQVmvS83C0KVmYQTSIBQcpe5SluQtZH7uXBbnLaTCWx44ZjVZqx9XblS9u/IpcU2P+zqGx0Ple+/g/Pfb4PViiosn7rY7iBk2ApP55G68IqFDOReJDsq6SORTzkWig7Jel5qFoUvNwgikQSj4vH4va4tW1zyuPI+9FbtrHW+Z0JqcrEHkZA2hS2q343pc2btjO+V/exLv2jUAWLtnE3/bndh6ZNfLPUhoU85FooOyLhL5lHOR6KCs16VmYehSszACaRAKPXvKd7OwpnG4umhVrceVk2xJ9M/MYVCjIfTNGECCLeGo39fw+aia8hEVb/4LKisBsA0cRPzNt2Ht2Kne70NCh3IuEh2UdZHIp5yLRAdlvS41C6tt376NceP+xYoVyzAMg/btO3LNNdeRkzOk1nlz587m3XcnsHXrZuLi4unVqw9jxtxMmzZta51nGAaffPIhn376MXv37iEmJpZevfpw66130LJlq6OqSc3CCKRBKLSVe8oCuysvzltAmacscMxistAjrScDswYzKGsITeObHdV7+g4dwjlxPK4vpoOvuhFpP+03xN00FmvrNiflPiS4lHOR6KCsi0Q+5VwkOijrdalZCBs3rueuu27D43EzfPhlNG/eknnzZrN48ULuvvsBrrjiKgA+/fQjnn/+GVJT0xgx4nJiYmKZPv1T8vLyeO65l8jO7hl4z5dffpEPPnifzp27ct55F1BcXMyHH76PyWTizTcn0qJFq1+tS83CCKRBKHz4/F7WFq9hYe58Fhyay+6KXbWOt4hvycCsweQ0Gky3lO5YzNYjv9+e3TgnjMP1zddgGGA24zj7XOJuHIvllONfJ1FCj3IuEh2UdZHIp5yLRAdlvS41C+Gmm0azadMGnn327wwePBSonhn42GN/ZM6cH3jnnUkkJiYxcuRF2O0OJk58n0aNGgPgdDoZM+YqzGYL7733IVarlaKiQoYNO48WLVoxYcJ/sNlsACxfvpS7776NM888h8cff+pX66rvZqF2VxA5BhZz9cYnt3a6g4mn/Zd3T/uQ33a+h97pfbGYLOyu2MWHO97nvoV3MOLbC3ly5V/4bv9MyjylP/9+zVuQ+Oj/kTLxfeynng5+P66vvqDo6pGUP/80vtxDDXuDIiIiIiIiIlLHoUMH2bRpAz179g40CgFMJhOjR9+Ax+NhxoxpLFo0H5fLxciRowKNQoC4uDhGjrySvXt3s2zZEgD27duL3++nf/8BgUYhQO/efYmPj2fLlk0Nd4M/ceRpTyJyRE3jmzGy9ShGth5FuaecpfmLWJA7j0W5Cyj1lPDt/q/5dv/XmE0WeqRmM7Bmk5TmCS1qvY+1TVuSnvwbno3rcY57Hc/ihVRNnULVF58TM/wy4q69HnNqWpDuUkREREREROTIDMOAqqpgl/HLYmIwmUzH/cdzaybztGvXoc6x5s2rf8bfuHE9cXFxRziveeC8AQNyaNq0OVarlR07ttc6r6AgH6fTSadOjY673hOhZqFIPUmwJXB6kzM5vcmZ+AwfG4rWMT93Lgty57GrfAcrC5ezsnA5r298hWZxzclpNJiBWYPpnpqNteZxZVunLiS/8E88K5dTMf51vKtWUvXhf6ma9imxI0cRe9W1mBOTgnynIiIiIiIiIocZhkHJb2/Bu3Z1sEv5Rdbu2SS/+uZxNwxjY6ubgE5nRZ1jxcXFQHWT72jPA0hNTeX22+/i1Vdf4s03/8WFF15CSUkxL7/8Ilarleuuu+G4aj1RahaKnAQWk4VuaT3oltaDsZ1+y37nvsDuyqsKVrDXuYfJOyYxecck4q3x9MscyMDMQfTPHEiKIxVbz94kv/wGnsULcY57De+mjVS+O5GqKR8Te9U1xI68ElPNbytEREREREREgu4EZu2Fg1atWpOYmMSCBfMoLy8nISEhcOy772YC4HK56NGjJwAzZ37J+edfVOs9vv/+m8B5Pzr77PNYvXol//732/z7328DYLFYeOyxJ+nTp9/JvKVfpGahSAM4Ja4pI1pdwYhWV1DhqWBp/iIW5s5nUd58it3F/HDgW3448C0mTHRO6UpOVvWswzb9B5LcfyDuOT/gHP8Gvh3bcY57ncrJHxB37fXEDL8Mk8MR7NsTERERERGRKGYymUh+9c2IfgzZarUyZsxNvPzy37nvvju44457yMzMYsGCuUycOJ74+HisVisdO3ZiyJBTmTt3Nk899TiXX34VVquVKVMms27dmsB7ARQWFjB27BgOHTrIhRdewsCBg3A6nUyb9il/+csfKSjI5/LLr6yX2z8W2g05BGiXpejlN/xsKtnAgtx5LMydx9bSLbWOZ8U0YkDWIHKyBtEzpRf8MBvn22/i37cXAHNmFrHX30jMhZdgsqr3H8qUc5HooKyLRD7lXCQ6KOt1aTfkau++O4GJE8cHZgdmZGTyu989zCuvvERKSgqvv/42TqeT5557im+++Yof226dO3fh1lvv5N57f8vo0Tdw66138MYbr/LuuxO4+ebbGDPm5sA1/H4/d999G6tXr+SddybRunWbI9ZU37shq7sgEkRmk5nOKV3pnNKVGzuMJa8yl0V581mQO5/l+UvIrTrEtN1TmLZ7Cg6zg14ZfRn01DUMWFGB5f0P8efmUvH8M1S+/y5xN9yC4+xzMVkswb4tERERERERkYg0evQNXHbZKLZt24LD4aBt2/b4/X7+/OeH6dq1G1C98/Fjjz3BHXfcy759e0hLS6d58xYsX74UgKZNmwGwbVv1hKGLLx5e6xpms5lLLrmUlSuXs3Tp4l9tFtY3NQtFQkhmbBYXtRjORS2G4/K5WFGwjIW581iYO5/cqkM1/38exELHO9pyxYbWdP1iA+zfR/mTf6HyvXeIu2ks9tPOwGQ2B/t2RERERERERCLGd999g81mZejQ0+nePTvw+uLFC/B4PPTq1Yfy8nJmz/6e1q3b0LlzVzIyMgLnLVw4D4BevfoAYLdXzwL0+/11ruXz+QAwjLrHTrYT6iasWrWKLl26MH/+/COe53Q6Offcczn11FNP5HIiUcVhcTAwaxD3dnuQ/57xCeOHvMvNHW6ja2p3TJjYVLWN/2u9jOtvruCTM+Nwxdnw7dpB2aN/oPjm63EvmEeYrDIgIiIiIiIiEvKmTJnMk08+TmlpSeA1p7OCt956k4yMTM4++1xsNisvvPAMr776Uq2fyXfu3MFnn01h6NDTAzMLc3KGADBp0nu1ruP1epky5SNMJhN9+w5ogDur7bhnFu7cuZM77rgj0Ok8kieeeIKdO3fSqFGj472cSFQzmUy0SWpLm6S2XN3uOkrcxSzKW8Ci3PkszlvEpP7lfNbD4KLFJi5cYhC7ZROlv78Pf5dOpNx2D/aa31qIiIiIiIiIyPG5/vqbeOCBu7jzzrFccsmlGAZMmzaFvXv38PTTL+BwxABw9dXXMWHCOB5++H4GDRpKUVEhkyf/l/j4BO6554HA+5133oV89903fPDB++zevYvBg4fidDr54ovp7Nixneuuu5E2bdo2+H0e1wYnM2fO5JFHHqGkpLqTOmHCBAYNGvSz53799dfcfffdWK1W0tLSmD179nEVGsmLimrhVDkRXr+XtUWrA5ukFOftYvgCP+cuN7B7q8852LkxjLmOjgOHYTPbgltwlFLORaKDsi4S+ZRzkeigrNelDU6qLVmyiHfeeYutWzdjtVrp0qU7N9xwM507dw2c4/f7mTr1E6ZMmcz+/ftISkomJ2cwY8bcTGZmVq3383q9fPjh+3zxxXT27duLxWKhXbsOjBx5JWeeefZR1VTfG5wcc7Nw7NixzJo1i3bt2tGpUyemT5/+i83CQ4cOcckll3DhhRfyww8/4PV61Sz8GRqEpD7tq9jLwtx5rN3yAx0+X8kZK31Ya5Y4WNHByqbh/ejQ6zz6Zw4kxZEa3GKjiHIuEh2UdZHIp5yLRAdlvS41C0NX0HdD3r59O/fffz833HADb7zxxi+eZxgGDz/8MGlpafz+97/nhx9+ONZLichxaBrfjMtaj+Ky1qOoOKOC1eu/xHj3fdot3kOvzV6y/7aABZ0XcudQCyltuzIwazA5WYNpk9gOk8kU7PJFREREREREJIiOuVk4Y8aMwG4tRzJhwgSWLFnCpEmTiImJOa7ifiqSexg/3lsk36MER4I9nkE9L4Oel+HZuZ1Db7yIY85iBm8wGLjRy6zuq/loyFreTn6TzJgscrIGMzBrEL0y+hBjOfHcymHKuUh0UNZFIp9yLhIdlPW69HcR+kymX/53OpZ/v2NuFh5No3Djxo28+OKL/Pa3v6Vbt27HeomflZ5+dFMlw1k03KMEUUY2Tfq+Q9WGDeS99E/Kf/iB36w2OG2dn+96WZmcc4jPqqbw2e4pOCwOBjQZwGnNTuPUZqfSOL5xsKuPGMq5SHRQ1kUin3IuEh2U9cOqqqooLDRjsZiwWs3BLkd+wu83YTabSU2Nr5cJe8e9G/IvcblcPPDAA3Tr1o1bb7213t63oCBy1wkwmaoHoEi+Rwkhmc2IfeJvWNeupmLc67BsKWcv9XDmahsbT2vDO31K2OHLZ/be2czeW73GaJvEduRkDSKn0WA6pXTBYrIE+SbCj3IuEh2UdZHIp5yLRAdlvS6Px43f78fnM/B6/cEuR37C5zPw+/0UFVVgs3l+9pwfv6aPRr03C//2t7+xe/du3nvvvcBuyVC9E4zf76ewsBCbzUZi4rF15w2DiA9oNNyjhA5r1x4k/+NfuJctwTnuNbzr1tJl5ib+Ni+OquHDmDs0nXllS1lftJbtZVvZXraV/2z7N8n2FAZk5jAwazB9M/qTYEsI9q2EFeVcJDoo6yKRTzkXiQ7K+mH6ewh99fX1Wu/Nwu+//x63280VV1zxs8dzcnLo378/7777bn1fWkSOg71PP2y9++KZP5eK8a/j27oFx/sfc9b0JC6++jrcF/+FJWUrWZg7j8V5iyhxF/P1vi/4et8XWEwWuqdlk5M5mIFZg2me0CLYtyMiIiIiIiIiJ6Dem4XPPfccLperzusPPvggPp+PF198kaSkpPq+rIicAJPJhH3wUGw5g3H/8C3Ot97Et3sXztdfwfThfxly3Q2cdfGf8VnNrCtaw4LceSzMncfuil2sLFjOyoLlvLbxZZrFNWdg1iAGZg2me1o2NrMt2LcmIiIiIiIiIseg3puFffr0+dnXHQ4HXq+XQYMG1fclRaSemMxmHL85G/upZ+D6+kucE8fhP3CAin88T+V/3yPuhpvpce4FZKf34rbOd7KvYi8Lc+exMHc+qwpXsNe5h492fsBHOz8gzhpH34wB5GQNpn/mQFIdacG+PRERERERERH5FfXeLJRj4/H5+eP0DXRvkcp1vZoA2otcgs9ktRJzwUU4zj6XqulTqXznbfyHDlL+zBNU/uffxN04FvtvzqJpfDMuaz2Ky1qPwumtYGn+EhbmzmNR7nyK3EXMPvg9sw9+jwkTnVK6MDBrEDlZg2mb2B7TsezbLiIiIiIiIiINQs3CICtzeZmzrYAfthaQX1zJfae3URNFQobJZiP20pHEXHARlVM+pvK9ifj27Kbs8T9heW8icTffhn3wUEwmE3HWeE5tfDqnNj4dv+FnU8lGFuXOZ0HuPLaUbmJD8To2FK9jwuZxZMRkBtY57JXRhxjLiW/tLiIiIiIiIiInzmQY4bGfTX5+5G5XPn3dQR7/cjMANw5swe2DWwW3IJFf4HdWUPXhJConvYdRUQGAtXNX4sbejq1Pv19sdOdV5bEodz4Lc+exvGApVb6qwDG72U6v9D4MzBrMwKxBNIpt3CD30tBMJsjISIzosUxElHWRaKCci0QHZb0uj8dNQcEB0tObYLPZg12O/MTR/Nv8+DV9NNQsDAEmE8zYUsCjU9cBcOfQ1lzfv3mQqxL5Zf7SEirff4/Kjz+AqurGn7Vnb+JvuR1bj+wj/lm3z8XKwhWBTVIOVR6sdbxNYrvAJimdU7pgMVlO2n00JH3YEIkOyrpI5FPORaKDsl6XmoWhS83CCPTjP9jzn6/nlTk7AHjwN+24otcpQa5M5Mj8Bfk433uHqqmfgMcDgG3gIOJvvg1rx06/+ucNw2Bn+XYW5M5jUe4C1hWtwY8/cDzJlsyArBxysgbTN6M/CbajG9hCkT5siEQHZV0k8innItFBWa9LzcJq27dvY9y4f7FixTIMw6B9+45cc8115OQMqXXe3LmzeffdCWzdupm4uHh69erDmDE306ZN21rn+Xw+PvjgfaZP/5SDBw+QlpbO6aefyXXX3UBSUvJR1aRmYQT66SD0r7k7eXvhbgAePbcDF3eLzEcyJbL4Dh3EOfEtXF9MB58PAPtpvyHuprFYW7c56vcpcZewJG8hC3LnsSRvEeXessAxi8lC99TswKzD5vEtwmp9T33YEIkOyrpI5FPORaKDsl6XmoWwceN67rrrNjweN8OHX0bz5i2ZN282ixcv5O67H+CKK64C4NNPP+L5558hNTWNESMuJyYmlunTPyUvL4/nnnuJ7Oyegfd87LE/8u23X9OhQ0fOPfcCysrK+OijSaSlpfPqq+NITU371brULIxAPx2E/H6Dv/+wnf8u34fZBE9e2JmzOmYGu0SRo+LbsxvnhHG4vvkaDAPMZhxnn0vcjWOxnNL0mN7L6/eyrmgNC/Oq1zrcVb6z1vGmcc0C6xz2SOuJzWyrxzupf/qwIRIdlHWRyKeci0QHZb0uNQvhpptGs2nTBp599u8MHjwUqH5i7rHH/sicOT/wzjuTSExMYuTIi7DbHUyc+D6NGlVPAnM6nYwZcxVms4X33vsQq9XKvHlzeOih++jePZuXXnoNu73673Xr1i3cdNO1nHPO+TzyyF9+ta76bhaaj+osaTAmk4n7Tm/DsO6N8Rvwpxkbmbu9INhliRwVS/MWJD76f6RMfB/7qaeD34/rqy8ounok5c8/jS/30FG/l9VsJTu9F7d2uoMJp77Pu6d9yJ1d7qVPRj+sJiv7nHv5eOcHPLj4Hi795nz+svyPfLn3c4pchSfvBkVERERERCQqHTp0kE2bNtCzZ+9AoxCq+zijR9+Ax+NhxoxpLFo0H5fLxciRowKNQoC4uDhGjrySvXt3s2zZEgBmz/4egFtuuT3QKARo1649gwYNZebML3E6KxroDg+zNvgV5VeZTCb+cFZ7qjw+vtqYx0OfrecfI7rRr0VqsEsTOSrWNm1JevJveDauxznudTyLF1I1dQpVX3xOzPDLiLv2esxHMZX6p5rGN2NE/BWMaHUFTm8Fy/KXsjB3Hgtz51PkLmT2wR+YffAHTJjolNKFgZmDGJg1iHZJHcLqcWUREREREREJPbk1k1/atetQ51jz5i2A6seU4+LijnBe88B5Awbk/Mp7NmfOHC9bt26hR4+e9XIPR0vNwhBlMZv4y3kdqfL4mbWtgAc+XccrI3vQ45SkYJcmctRsnbqQ/MI/8axcTsX41/GuWknVh/+latqnxF5+JbFXXos58dg3LYmzxjO08WkMbXwafsPP5pKNLMydz8Lc+Wwu3ciG4nVsKF7HhC3jyIjJrGkcDqZ3Rl9iLDEn4U5FRERERESim2EY+Dz+Xz8xSCw28wlNJImNrW4C/txMv+LiYgAKCvKP+rz/fc+kpKQjntuQ1CwMYVaLmacu6sz9n65l0a5i7vlkDa9fnk3HRgnBLk3kmNh69ib55TfwLF6Ic9xreDdtpPLfE6j65CNir7qW2JGjMNX89uVYmU1mOqV0oVNKF8Z0uJn8qjwW5S1gYe48luUvIb8qj+l7pjJ9z1TsZjs90/uQkzWIAVmDaBzbpJ7vVEREREREJPoYhsF34zdSsLs82KX8oowWCZxxc6fjbhi2atWaxMQkFiyYR3l5OQkJh3sz3303EwCXyxWYBThz5pecf/5Ftd7j+++/CZwH0KNHNrNnf8/MmV8yevQNgfOcTieLFs2vdW5D0pqFIc5uNfPcsK70bJpEucvHnR+vYXtBwz+vLnKiTCYT9gE5JI97h8Qnn8XSug1GeRnOca9ROOpSKj94H6MeBsGMmEwubH4J/9fnWT496wue6fciw1uOpFFsY9x+N4vzFvDSuhe4+vvLuHnOaMZvep21havxGb56uEsREREREZHoFOmLP1mtVsaMuYmiokLuu+8OVq5czr59e/noo0lMnDie+Ph4rFYrHTt2YsiQU1m8eCFPPfU4W7ZsZseO7bz44rOsW7cm8F4AF100nKysRrz99pv85z/vsHfvHtauXc3vf38vXq+31rkNSbshh4Cj2WWp3OXlt5NXs+FQORnxdsZdmU2zlNiGLVSkHhk+H65vZ+J8+038+/YCYM7MIvb6G4m58BJM9TwgGobBzvIdgXUO1xWtwc/hKfJJtmT6Zw4kJ2sw/TIHkGA79sejj0S7qYlEB2VdJPIp5yLRQVmv69d23I30x5B/9O67E5g4cXxgxl9GRia/+93DvPLKS6SkpPD662/jdDp57rmn+Oabr/ix7da5cxduvfVO7r33t4wefQO33noHAHv27Obxx//Exo3rATCbzZxxxln06tWb559/hhdffIX+/Qcesab63g1ZzcIQcLSDUHGlh9s+XMW2fCdNkhy8OSqbxklaf03Cm+H14poxHec74/Hn5gJgPqUpcTfcguPsczFZLCfluiXuEpbmLWJB7jwW5y2k3FsWOGY2WeiRms2ArEHkZA2ieXzLE/6mog8bItFBWReJfMq5SHRQ1us6moZUtHA6nWzbtgWHw0Hbtu3x+/2cc86p/OY3Z/PnP/81cF5+fj779u0hLS2d5s1bsHz5Uu6++zYefvjPXHTRsFrvuXv3ToqKimnWrBnp6Rm89dYbTJgwjg8++JSmTZsdsZ76bhZqzcIwkhJr45WRPRg7aSV7iqu446M1vDkqm/T46A6phDeT1UrMJcNxnHs+VZ9NwfnuRPz791H+5F+o/M87xN10K/bTzqj3HY2T7cmc2fQczmx6Dj6/l7XFa2o2SZnHrvKdrCxczsrC5byx8RVOiWvKwKzB5GQNpkdaT2xmW73WIiIiIiIiIqHvu+++wWazMnTo6XTvnh14ffHiBXg8Hnr16kN5eTmzZ39P69Zt6Ny5KxkZGYHzFi6cB0CvXn2A6lmFK1YsY9CgobRo0YoWLah1bqNGjX+1UXgyaM3CMJMRb+dfl/egcaKD3UWV3PXxGkoqPcEuS+SEmRwOYi+/krQPphB36x2YEpPw7dxB2Z8fpvjm63EvnM/JmghtMVvJTuvFrZ3uYMKp7/Pe6ZO5s8t99M3oj81sY79zH5/s/JAHF9/D8Jnn89iyP/LFnukUugpPSj0iIiIiIiISeqZMmcyTTz5OaWlJ4DWns4K33nqTjIxMzj77XGw2Ky+88AyvvvpSrZ9hd+7cwWefTWHo0NMDDcD8/Dz+9rcn+fTTj2pdZ+bML9mwYT2jRl3TMDf2P/QYcgg4nunNe4oqueWDVRRUuOnaOJFXRnYnwaGJohI5/GVlVH7wPlUf/hej0gmAtXs28bfcjq1X7warw+mtYFn+Uhblzmdh3nwKXQW1jndK7kJO1mAGZg2iXVKHX5wBqccYRKKDsi4S+ZRzkeigrNelx5Bh6dLFPPDAXbRs2YpLLrkUw4Bp06awd+8enn76BQYMyAEIPEI8ePBQBg0aSlFRIZMn/5eYmFhefXUcjRs3AcDv9wc2Sxk+/DLat+/A1q1bmDLlI3r37svf/vYPbLZff7JNaxZGoOMdhLbmV3DbB6soqfLSq1ky/xzRjRjbyVnfTSRY/EVFVL7/byo/+Qjc1QvI2voNIO7m27B16dqwtRh+tpRsYkHNJimbSzfWOp7uyGBg1iAGZg2md3pfYq2HNyHShw2R6KCsi0Q+5VwkOijrdalZWG3JkkW8885bbN26GavVSpcu3bnhhpvp3Pnwz6d+v5+pUz9hypTJ7N+/j6SkZHJyBjNmzM1kZmbVer/y8nLeeectfvjhOwoL82ncuAnnnXchV1xxNQ6H46hqUrMwAp3IILThUBm3f7iaCrePnFapPD+sK3arni6XyOPLy6Xy3xOomvYp+HwA2IecStxNt2Jt1z4oNRVU5bMobwELcuexLH8JVb7KwDGb2U6v9D4MzBzEwEaDaBLXRB82RKKAfrAQiXzKuUh0UNbrUrMwdKlZGIFOdBBata+EOz9aQ5XXz+nt0nn64i5YzfW7GYRIqPDt34dzwnhcX38Bfj+YTNh/czbxN96CpUXLoNXl9rlYVbiCBTWbpBysPFDreOvENpzR8nS6J/SmS0p3bZIiEqH0g4VI5FPORaKDsl6XmoWhS83CCFQfg9CiXUXcN2UtHp/B+Z2z+Mv5HTHX8+6xIqHEu3MHzrffxP39t9UvWCw4zruAuDE3Y6lZ/yFYDMNgV/lOFubOY0HuPNYVrcGPP3A81hJHr/Te9MscQL/MgZwS1zSI1YpIfdIPFiKRTzkXiQ7Kel1qFoYuNQsjUH0NQrO3FfD7z9bj8xuM6NGEh89q94ubLYhECu/mTVSMfx3Pguot6LHZiLl4OHHX3YA5PePIf7iBlLpLWZK/kJWlS5i3dx7F7uJax5vGNaNf5kD6ZQygZ3ovYq1xwSlURE6YfrAQiXzKuUh0UNbrUrMwdKlZGIHqcxD6emMuf/p8IwZwdZ+m3HtaGzUMJSp41q7GOe41PMuXVb/gcBB72RXEXj0ac3JKUGuDwznPzSthS8lmluQtYkn+ItYVrcFn+ALnWU1Wuqdl0y9jAP0yB9AmUU1/kXCiHyxEIp9yLhIdlPW61CwMXWoWRqD6HoQ+W3OQ//t6MwC35LRg7KBWJ/6mImHCvWwJznGv4V23FgBTfDyxo64m5oqrMMcnBK2uX8p5haeCFQVLWZK/mCV5C+usdZjuyKBvRn/6Zvanb0Z/ku0pDVu4iBwT/WAhEvmUc5HooKzXpWZh6FKzMAKdjEFo0vJ9vPD9NgDuPrU1o/s1r583FgkDhmHgmT+XivGv49u6BQBTcjKxV19H7IjLMcXENHhNR5NzwzDYW7GHJfmLWJK3kFWFK6jyVR1+D0x0SO5Ev8wB9M8YSOeULljM1ga6AxE5GvrBQiTyKeci0UFZr0vNwtClZmEEOlmD0IRFu/nX3J0APHRmO0b2PKX+3lwkDBh+P+4fvsX51pv4du8CwJSWTtz1NxJz0TBM9ob7Bnc8OXf73KwtWs3ivIUszV/E9rJttY7HWxPond63ZqOUATSKbXwSKheRY6EfLEQin3IuEh2U9brULAxdahZGoJM5CL06ZwcTF+8B4PHzO3JBl0b1ewGRMGB4vbi+/hLnxHH4D1Q/5mtu3IS4MTfhOPcCTNaTPzuvPnKeV5XH0rxFLM1fxLL8JZR6SmsdbxHfsnqjlMwBZKf1wmFx1EPlInIs9IOFSORTzkWig7Je1+GGVGNsNv2sEUrcbheFhQfVLIwkJ3MQMgyDF77fxgcr9mM2wdMXdeY3HTLr9yIiYcLweKiaPpXKd97GX5APgKV5C+JuHIv9N2dhMptP2rXrO+c+w8fmko0syVvE4ryFbCxejx9/4LjNbCc7rWfNRikDaZnQShuliDQA/WAhEvmUc5HooKzX5ff7yM3dR0JCMgkJycEuR36ivLyE8vISsrKaYjZbfvYcNQvDzMkehPyGwRNfbWbaukNYzSaeH96Vwa3T6v9CImHCqKqicspHVP7nHYySEgAsbdsRd/Nt2AcPPSlNtZOd8zJPKcvzlwZ2Wc6ryq11PDMmK7DDcu+MviTakuq/CBHRDxYiUUA5F4kOyvrPKykpoLKynJiYeGJi4jCbLZqUECSGYeD3+6iqclJVVUFsbALJyem/eL6ahWGmIQYhn9/gzzM2MnNTHg6rmZdGdKNP85STczGRMOGvKKdq8gdUTnoPo6ICAGuXbsTdchu2Pv3q9ZteQ37YMAyDXeU7f7JRyko8fnfguBkznVK60L/mkeUOyZ2wmH7+t08icmz0g4VI5FPORaKDsv7zDMOgsrKC8vJi/H5fsMsRwGy2kJCQQmxs/BF/hlWzMMw01CDk9fl58LP1zN1eSJzNwquXd6dbE80uEvGXllD5/ntUfvwBVFXvPmzr1Ye4W27H1r1HvVwjmB82XD4XqwtXsLhmvcNd5TtrHU+yJdEnox99a2YeZsRoqQKR46UfLEQin3IuEh2U9SOrntXmV8MwyMxmC2az+agmuqhZGGYachByef3cO2UtS3cXk+iw8voVPeiQlXByLyoSJvwF+Tjfe4eqqZ+AxwOAbeBg4m+5DWuHjif03qH0YeNQ5UGWBDZKWUqFt7zW8TaJbembMYD+mQPpltoDu0U7nYkcrVDKuoicHMq5SHRQ1iXSqFkYZhp6EHK6fdz18RpW7y8lNdbGm6OyaZUed/IvLBImfIcO4pz4Fq4vpoOv+jdl9tN/Q9xNt2Jt1fq43jNUP2z4/F42FK9nSX71RimbSzZicLjAGEsM2Wm9qndZzhhAs/jmWpNE5AhCNesiUn+Uc5HooKxLpFGzMMwEYxAqq/Jy++TVbMotJyvBzptXZtM0ObZhLi4SJnx7duOcMA7XN1+DYYDZjOOc84i74RYspzQ9pvcKlw8bJe5iluUvCWyUUugqqHW8cWyTmsZhf3ql9yXeFh+kSkVCU7hkXUSOn3IuEh2UdYk0ahaGmWANQsVOD2M/XMWOAienJMcwblQ2WYmOhitAJEx4t2/D+dYbuGf/UP2CxULMRZcQe/1NWDKzjuo9wvHDhmEYbC/bxpK8hSzJX8SawlV4DW/guMVkoWtq98Auy+2SOmA2mYNYsUjwhWPWReTYKOci0UFZl0ijZmGYCeYglFfuYuwHq9hbXEWrtFjeGJVNWpzWJxP5OZ6N63GOex3P4oXVL9jtxAy/jLhrx2BOTT3in42EDxuVXicrC1YEdlne59xb63iKPYW+Gf3pmzmAvhkDSHOkBalSkeCJhKyLyJEp5yLRQVmXSKNmYZgJ9iB0oLSKm/+7ktxyNx0y43ntih4kxdgavhCRMOFZuZyK8a/jXbWy+oXYWGIvv5LYK6/FnPjzg2+wc34y7Hfuq35cOW8hKwqWU+lz1jreLqkD/Wo2SumS2g2bWeOKRL5IzLqI1Kaci0QHZV0ijZqFYSYUBqFdhU7GfrCKQqeH7k0SeXlkd+Lt1uAUIxIGDMPAs3ghznGv4d20EQBTQiKxV11L7MhRmOJqbxoUCjk/mTx+D+uL1rK45pHlraWbax2PtcTRK7139XqHmQM4Je7Y1nwUCReRnnURUc5FooWyLpFGzcIwEyqD0Na8Cm79cBWlVV76Nk/m75d2I8ZmCV5BImHAMAzcc37AOf4NfDu2A2BKTSPu2uuJGTYCk6N6HdBQyXlDKXQVsjR/EUvzFrE0fzHF7uJax5vGNQvssNwzvRexVu3ILpEh2rIuEo2Uc5HooKxLpFGzMMyE0iC07mAZd0xeTYXbx+DWaTw3rAs2izYsEPk1hs+H69uZON9+E/++6rX8zFlZxF1/E44LLsZss4ZMzhua3/CztXRzYIfldUVr8Bm+wHGb2Ua31B6BjVLaJLbDZDIFsWKR4xdK39NF5ORQzkWig7IukUbNwjATaoPQ8r3F3P3xWlxeP2d2yOCJCztjNesHd5GjYXi9uGZMx/nOePy5uQCYmzYj/sZbaHrlZRQUOUMi58FU4algRcHSQPPwYOWBWsfTHRn0zehPv8wB9MnoT7I9OUiVihy7UPueLiL1TzkXiQ7KukQaNQvDTCgOQgt2FvLAp+vw+Awu7NqIR8/tgFkzfUSOmuFyUfXZFJzvTsQoKgTA3rYt9ksvx372uZjj4oNcYWgwDIO9FXtYkr+QJXmLWFW4gipfVeC4CRMdkjvRv+aR5c4pXbCYtZ6qhK5Q/J4uIvVLOReJDsq6RBo1C8NMqA5CP2zJ5+Fp6/EZMDK7Cb8/U48Gihwro7KSyo8/pPL9dzHKSgEwxcbhOOdcYoZdhrV9hyBXGFrcPhdrilazJG8RS/MXsb1sW63j8dYE+mT0pW/NI8uNYhsHqVKRnxeq39NFpP4o5yLRQVmXSKNmYZgJ5UHoiw2HeGzGJgzgun7NuHNoazUMRY6DUV6GdfZM8v/zX3y7dwVet3bpRsywETh+cxammJggVhia8qryajZJqd4opcxTVut4y4RWgcZhdlovHBZHkCoVqRbK39NFpH4o5yLRQVmXSKNmYZgJ9UFoyuoDPDVzCwC3DW7JTQNbBrkikfDzY87z8kpxr1hO1aef4J79PXi91ccTEnGcdwExl1yKtXWbIFcbmnyGj80lG1mSt4jFeQvZWLweP/7AcbvZTo+0noFdllsmtNIvN6TBhfr3dBE5ccq5SHRQ1iXSqFkYZsJhEHp/2V7+/sN2AO47vQ1X92kW5IpEwsvP5dxfVEjVjGlUTZ2C/8D+wLnW7F7EDh+B/dQzMNntQao49JV5SlmWv5SlNRul5FXl1jqeGZNFv8wB9MsYQO+MviTakoJUqUSTcPieLiInRjkXiQ7KukQaNQvDTLgMQuMX7OKN+dWPT/7x7PZc2qNJkCsSCR9Hyrnh9+NZsoiqz6bgnjcHfL7qP5OcQswFFxFzyaVYmjUPQtXhwzAMdpXvZEneQpbkL2JV4Uo8fnfguBkznVO70q/mkeUOyZ2wmCxBrFgiVbh8TxeR46eci0QHZV0ijZqFYSZcBiHDMHh59g7eXboXE/DXCzpxXuesYJclEhaONue+vFxc0z+javqn+HMPz5Sz9RtAzLAR2AcPxWTVbsC/pspXxerClSzJW8SSvIXsrthV63iSLYk+Gf0C6x1mxGQGqVKJNOHyPV1Ejp9yLhIdlHWJNGoWhplwGoQMw+DZb7fy8aoDWEzwzMVdOL19RrDLEgl5x5pzw+vFvXA+VVM/wbNoAT/+IXN6Bo6LhhFz8TAsjbQT8NE6VHmwpnG4iOUFS6jwVtQ63iaxLX0zBtA/cyDdUntgt+jxbzk+4fQ9XUSOj3IuEh2UdYk0ahaGmXAbhPyGwV+/3MTn63OxWUy8OLwrA1ulBbsskZB2Ijn3HdhP1bRPqfr8M4zCwuoXzWZsAwcRO2wEtgE5mCx6pPZo+fxeNhSvZ3H+QpbkLWJzyUYMDv+jxFhiyE7rFdgopVl8c22UIkct3L6ni8ixU85FooOyLpFGzcIwE46DkNdv8Mj0DXy3JR+H1czLl3WnV7PkYJclErLqI+eGx4N77iyqPv0Ez/KlgdfNjRoTc8lwYi64BHOGZvoeqxJ3MUvzF7M0bzFL8hdR6CqodbxxbJOaxmF/eqX3Jd4WH6RKJRyE4/d0ETk2yrlIdFDWJdKoWRhmwnUQ8vj8/G7qOubvKCLebuHVy3vQtfHRfeGJRJv6zrlv967q2YYzpmGUlla/aLFgH3IaMcNHYOvdF5PZfOIXijKGYbC9bCtL8haxOH8hawtX4zW8geMWk4Wuqd1rNkoZSLuk9phN+nuWw8L1e7qIHD3lXCQ6KOsSaRqsWbhq1Squuuoqxo8fz6BBg2od27BhAy+99BKrVq2iqqqKbt26MXbsWIYOHXpc14rkgIbzIFTl8XHvlLUs21NCUoyVN67Ipl2mZt2I/K+TlXPD5cL1w3dUTf0E75pVgdfNzZoTc8mlxJx/EeaUlPq7YJSp9DpZWbCCxfkLWZq3iH3OvbWOp9hT6JvRn36ZA+mb0Z9Uh5ZkiHbh/D1dRI6Oci4SHZR1iTQN0izcuXMn1157LXl5eUyYMKFWs3DNmjVcc801pKSkcO211xITE8PkyZPZvHkzzz33HJdccskxXy+SAxrug1CF28udH61h7YEy0uJsvDkqm5ZpccEuSySkNETOvdu3UTX1E1xfzcCoqNnAw2bDcfpviBk2AmuPnlp77wTtq9jL0vzqjVJWFCyn0uesdbxdUgf616x12CW1GzazLUiVSrCE+/d0Efl1yrlIdFDWJdKc9GbhzJkzeeSRRygpKQGo0yy88sor2bhxI9OmTaN58+YAlJeXc/755wMwa9YszMf4eFwkBzQSBqHSKg+3fbiaLXkVZCXYGXdlT05Jjgl2WSIhoyFzblRW4vr26+rZhhs3BF63tGpNzLAROM69AHOilgw4UR6/h3VFa6p3Wc5fxNbSzbWOx1ri6JXem94Z/eiZ1ptWia31yHIUiITv6SJyZMq5SHRQ1iXSnNRm4dixY5k1axbt2rWjU6dOTJ8+vVaz0OVycdddd9G4cWP++te/1vqzd999N1999RWzZ8+mUaNGx3LZiA5opAxChU43t36wip2FlTRLiWHcqGwyEhzBLkskJAQr595NG6icOgXXzC+hqqr6RYcDx5nnVM827NxFsw3rSaGroGajlEUsyV9Mibu41vFkewrZab3old6bnul9aBHfUn/3EShSvqeLyC9TzkWig7IukeakNgvPOussLr/8cm644QbeeOMNXnnllTozC3+O1+vlwgsvJDc3l0WLFmG324/lshEd0EgahHLLXNzywSr2l1TROj2ON6/IJiVOj+GJBDvn/vJyXDO/pOrTT/Bt3xp43dK+I7HDR2A/6xzMcVpvtL74DT9bSzezNG8xKwqXsbZwNS6/q9Y5qfY0eqb3omd6H3qm9aZZfHM1DyNAsLMuIiefci4SHZR1iTQntVnodrsDjb6XX375V5uFRUVFbNu2jXHjxvHDDz/w0EMPceONNx7LJQEoKIjcgJpMkJ6eGDH3uK+4klsmrSK33E3HrARev6IHiTHWYJclElShknPDMPCuXUPl1E9wffcNuN3V9cXG4Tj3PGKHjcDavkPwCoxQHr+HjcXrWVmwnBUFy1lXtAa3313rnAxHBtnpvWtmHvbmlLimah6GoVDJuoicPMq5SHRQ1iXS/Pg1fVTnnshuyEfTLLz44ovZvLl6HaczzzyTv/3tbyQkJBzvJSVMbM0tZ9QbCyiocNOnZSrv3tSfOLsahiKhxFtURMnUqRRP+gD3zp2B12Ozs0m58kqSzj8Pc4zWHj0ZXD4Xq/NWs+TgEpYcXMKqvFV4/J5a5zSOb0y/Rv3o17gf/Zv0p2lC0yBVKyIiIiIi0eSkNws///xz7HY7ixYt4v3336dVq1b85z//ITU19ZiuFcnd/Ej9jcXm3HJu/WA1ZS4v/Vuk8PcR3XBYtbi/RKdQzrlhGHhWLK/eSXnW9+D1AmBKSCTm/AuJGXYp1latg1xlZHP5XKwrWsPKguWsLFjBhuJ1eA1vrXMaxzahZ82sw17pvcmKPba1f6VhhHLWRaR+KOci0UFZl0gTUjMLf+qdd97hqaee4vbbb+fee+89pmtF8joBkbwWwpr9pdz50RqcHh9D2qTx3CVdsFrUMJToEy459xcWUDVjOlWffYL/wIHA69aevYkdNgL7qadjOsY1Z+XYVXorq5uHhctZWbCcTSUb8Bm+WuecEte0unmYVt1AzIjJDFK18lPhknUROX7KuUh0UNYl0hzLmoUN+lzosGHDeOqpp1i7dm1DXlaCqPspSbx4aVfu+WQtc7cX8ugXm/i/CzphMWsdLpFQZE5LJ+7a64m9ejSeJYuo+vRj3PPn4l25nLKVyzGlpBJz4cXEXHIpllP0WOzJEmuNpW9mf/pm9geg0utkTdFqVhYsY2XBCjaXbGS/cx/7nfuYsWcaAM3iW9CrpnGYnd6bNEdaMG9BRERERETCVL03C9evX89dd93F+eefz+9+97taxyoqKgCIjY2t78tKCOvTPIVnL+nC7z5dx8xNecRYzfzp3A6YtXC/SMgymc3YB+RgH5CDL/cQrs8/o2raVPx5uVT+599U/uff2PoNIGb4COyDhmKyak3SkynWGkf/zIH0zxwIQIWngjVFK1lRUD3zcGvpZvZW7GZvxW6m7fkUgJYJrQKzDrPTepHiOLblP0REREREJDrV+093rVu3prS0lI8//pgxY8aQkZEROPbGG28AcNZZZ9X3ZSXEDW6dxpMXduIP0zcwbd0h4uwWHjijrXb6FAkDlqxGxN1wC7Gjb8C9YB5VUz/Bs3ghniWL8CxZhDkjE8eFlxBz8XAsjbSOXkOIt8UzMGswA7MGA1DuKWNV4cqaNQ+Xs61sC7vKd7KrfCdTd38CQOuENjVrHvahR1pPku3JwbwFEREREREJUSdlzcJp06bx4IMPcsopp3DllVcSGxvLt99+y4IFCzj33HN56aWXjrlJFMnrBETTWggz1h/isS82ATCmf3PuGKpNEyQ6RFrOffv3UTVtKlWff4ZRVFj9otmMPWcwMcNGYOs/EJPFEtwio1iJu4TVhStrHltezo7y7bWOmzDRJrFdYMOU7LSeJNiObv0SObJIy7qI1KWci0QHZV0izbGsWXjSNjhZsGABr7/+OqtXr8br9dK2bVsuv/xyrrrqKszmY9/gIpIDGm2D0Ecr9/Pst1sB+O2QVtwwoEWQKxI5+SI154bHg3vOD9WzDZcvC7xubtyEmIuHE3PhxZjTM375DaRBFLuKWFW4onrmYeFydpXvrHXchIl2SR0COy13T+1JvC0+OMWGuUjNuogcppyLRAdlXSJNgzULG1IkBzQaB6F3l+zhn7N3APDAGW25src2SpDIFg059+7eRdVnU3DNmI5RVlr9osWCfehp1bMNe/fFdBy/LJL6V+gqZFXNI8srCpezt2J3reNmk4UOSR3pmd6Lnul96J7ag1hrXJCqDS/RkHWRaKeci0QHZV0ijZqFYSZaB6E35+9k3ILqH1D/fE4HLuneOMgViZw80ZRzw1WF6/vvqJr6Cd61qwOvm5s1J2bYCGLOuxBzSkrwCpQ68qryWF2wghWF1Y8t73fuq3XcYrLQMblz9WPLab3pltaDGEtMkKoNbdGUdZFopZyLRAdlXSKNmoVhJloHIcMw+Mes7by/bB8m4IkLO3FOp6xglyVyUkRrzr3btlbPNvxyBoazovpFmw3H6WcSM3wE1u7Z2ugoBOVWHmJl4fLAhikHKw/UOm41Wemc0jWw03LX1O44LI4gVRtaojXrItFEOReJDsq6RBo1C8NMNA9ChmHwzDdb+WT1ASxmE89e3IXT2qUHuyyRehfNOQcwnE5c335dPdtw08bA65bWbYgZNgLHOedjTtQGG6HqoPMAKwqWBdY9zK06VOu4zWynS03zsGdabzqndMVusQep2uCK9qyLRAPlXCQ6KOsSadQsDDPRPgj5DYPHvtjElxtysVlM/P3SbgxomRrsskTqVbTn/Kc8G9dTNXUKrm++gqqq6hcdDhxnnUPMsMuwduqs2YYhzDAM9jv3sbJwOasKlrOiYDkFrvxa59jNdrqmdq/eMCWtDx1TOmMz24JUccNS1kUin3IuEh2UdYk0ahaGGQ1C4PUb/GHaen7YWkCM1cwrI7uT3TQ52GWJ1BvlvC5/eTmur7+gauon+LZvC7xu6dCJ2GEjcJx1DqY4baoR6gzDYG/FnlqPLRe5C2udE2OJoWtqd3ql9aFnem86JHfCarYGqeKTS1kXiXzKuUh0UNYl0qhZGGY0CFVze/08MHUdC3cWEW+38NoVPejcSI8lSmRQzn+ZYRh416ym6rNPcH3/LbjdAJji4nGcez4xl1yKtV37IFcpR8swDHZX7GJlwTJWFqxgVeFyit3Ftc6JtcTRPa0HPdN60zO9N+2TOmCJkOahsi4S+ZRzkeigrEukUbMwzGgQOqzK4+PuT9ayYm8JyTFW3hiVTduM+GCXJXLClPOj4y8pxvXF51RO/QT/3j2B163dulevbXjGmZgc2oU3nBiGwc7y7awoWM6qmuZhqae01jnx1ni6p2ZXr3mY3oe2Se2wmCxBqvjEKOsikU85F4kOyrpEGjULw4wGodrKXV7u+GgN6w+WkR5vZ9yobJqnxga7LJETopwfG8Mw8CxfStXUT3DP/gF8PgBMiUk4zruAmGEjsLZsFdQa5fj4DT87yrZVNw8Ll7OqYCXl3rJa5yRYE8lO70l2Wm96pfemdWJbzCZzkCo+Nsq6SORTzkWig7IukUbNwjCjQaiukkoPt324mq35FTROdDDuymwaJ2k2kYQv5fz4+QvyqZoxnarPpuA/eCDwuq1XH2KGjcB+6umYbNGxeUYk8hk+tpVurXlseTmri1bi9DprnZNkSyY7rRfZ6b3old6bVgltQnYTHGVdJPIp5yLRQVmXSKNmYZjRIPTzCircjP1gFbuLKmmeEsObV/YkI94e7LJEjotyfuIMnw/PkkXVsw3nzwW/HwBTahoxF1xEzCWXYjmlaZCrlBPl83vZUrq5erOUwuWsLlxFla+y1jkp9hSy03rTM70XPdP70CK+Zcg0D5V1kcinnItEB2VdIo2ahWFGg9AvO1haxdgPVnGg1EXbjDhevyKblFjNIJLwo5zXL9+hQ1R9PhXXtKn48/MCr9v6D6yebThoCCZrZGyYEe28fi+bSzaysmA5KwqXsa5oDVW+qlrnpDnSyU6rnnWYndabZvHNg9Y8VNZFIp9yLhIdlHWJNGoWhhkNQke2t7iSWyatIr/CTedGCfzr8h4kONQEkPCinJ8chteLe/5cqqZ+gmfxwsDr5oxMHBcPI+aiYViyGgWxQqlvHr+HTcUbWFFY/djyuqI1uP3uWudkxGTSM6161mF2Wi9OiWvaYM1DZV0k8innItFBWZdIo2ZhmNEg9Ou2F1Rw6werKa700LNpEv+8rDuxtvDcKVOik3J+8vn276Pqs0+p+vwzjOKi6hfNZuyDhhAzbAS2fgMwWTRuRBq3z82GknXVjy0XLGd98Vo8fk+tc7JiGtXstFz9X+PYJietHmVdJPIp5yLRQVmXSKNmYZjRIHR0Nh4q4/bJqyl3+RjQMoUXh3fDbg2P3TFFlPOGY3g8uGf/UD3bcMWywOvmJk2IufhSYi68GHNaehArlJPJ5XOxvnhtoHm4oXgdXsNb65zGsU0CjcNeaX3IjM2qt+sr6yKRTzkXiQ7KukQaNQvDjAaho7dqXwl3fbyGSo+f09qm88zFnbFa1DCU0KecB4d3106qPpuC64vPMcpKq1+0WLCfenr1bMPefUNmYww5OSq9lTXNw2WsKFjOppIN+AxfrXOaxjWr3mk5rQ8903uTHpNx3NdT1kUin3IuEh2UdYk0ahaGGQ1Cx2bJ7iLu/WQtbp/BuZ0yefz8TljM+mFfQptyHlyGqwrX999SNfUTvGvXBF63NG9BzCWX4jj/QszJKcErUBpMpdfJ2qLVrKiZebi5ZCN+/LXOaR7fgp5p1TMPs9N7k+ZIO+r3V9ZFIp9yLhIdlHWJNGoWhhkNQsduzrYCHvxsPT6/wbDujXnk7PaaHSQhTTkPHd6tW6pnG371BYazovpFux3H6WcSM2wE1u49NJ5EkQpPBWuKVrKyYAUrC5azpXQTBrVD2jKhdc2GKdUNxGR7yi++n7IuEvmUc5HooKxLpFGzMMxoEDo+Mzfl8afPN+A34KreTbnv9Db6AV9ClnIeegynE9c3X1P56cf4tmwKvG5p05aYYSNwnHM+5oSEIFYowVDuKWNV4UpWFixnVeFytpVurdM8bJPYtnrWYVpvstN6kWRPChxT1kUin3IuEh2UdYk0ahaGGQ1Cx2/a2oP89avNANw0sAW3DW4V3IJEfoFyHroMw8C7cQNVUz/G9c3X4HJVH4iJwXHmOcQMH4GtU5fgFilBU+ouZVXhClYWLGNV4Qq2l22rddyEibZJ7eiZVv3Ics/0nrRqcoqyLhLB9D1dJDoo6xJp1CwMMxqETsyHK/bx3HfVP7zdObQ11/dvHuSKROpSzsODv6wM19dfUDX1E3w7tgdet3bsVD3b8MxzMMXFBbFCCbZiV1FN83A5KwtXsKt8R63jZsx0SOtAh4TOdE3tTpeUbpwS11Qz30UiiL6ni0QHZV0ijZqFYUaD0Il7Z/EeXplT/QPbg79pxxW9TglyRSK1KefhxTAMvGtWUTV1Cq7vvwGPBwBTXDyOc8+vXtuwbbsgVymhoNBVyKqazVJWFi5nT8XuOuek2lPpnNK1unmY2o2OyZ2JscQEoVoRqQ/6ni4SHZR1iTRqFoYZDUL147W5O3h70R4AHjuvAxd1bRzkikQOU87Dl7+4mKovP6dq6if49+4JvG7t1qN6tuEZv8HkUONHqhW68tnt28rCXUtYW7SGLaWb8Pg9tc6xmCy0S2pPl5RudEntRtfU7jSKaazZhyJhQt/TRaKDsi6RRs3CMKNBqH4YhsGLP2xn0vJ9mE3w5IWdOatjZrDLEgGU80hg+P14li+lauonuOfMAp8PAFNSEo7zLyLmkkuxtmgZ5Col2P43626fm62lm1lXtIZ1xWtZV7SGAld+nT+X7sigS0o3uqZ2o0tqdzokdcBucQThDkTk1+h7ukh0UNYl0qhZGGY0CNUfwzB48ustTF17EIvZxPPDujCkTXqwyxJRziOMvyCfqs+nUTXtU/wHDwRet/XuQ8ywEdiHno7JZgtihRIsv5Z1wzDIrTrE+qK1gebh1tLN+AxfrfNsZhvtkjrQNaW6edg1pRuZsVkNdBciciT6ni4SHZR1iTRqFoYZDUL1y+c3eHTGRr7elIfdYuIfI7rRr0VqsMuSKKecRybD58OzeGH1bMMF88DvB8CUmkbMhZcQc/EwLKc0DXKV0pCOJ+sun4vNJRsDsw/XF62lyF1Y57ysmEZ0Se1WMwOxO+2S2mMzqykt0tD0PV0kOijrEmnULAwzGoTqn9fn56FpG5i9rYBYm5lXRvagxylJwS5LophyHvl8hw5RNX0qrmmf4i+oeczUZMLarQf2oafhGHIqluYtgluknHT1kXXDMDhYeaBW83Bb2Vb8/zP70G620yG5U2DX5a6p3UhzaDa9yMmm7+ki0UFZl0ijZmGY0SB0cri8fu6fspbFu4tJcFh4/fJsOjZKCHZZEqWU8+hheL2458+h6tNP8CxZVOuYpVVr7ENPwz70dKwdO2Eym4NUpZwsJyvrld5KNpVsYH3RWtYWr2F90VpKPSV1zmsc26RW87BNYjusZmv9FSIi+p4uEiWUdYk0ahaGGQ1CJ0+lx8ddH61h1f5SUmJtvDkqm9bpccEuS6KQch6dfLmHcM+bg3vOLDzLlwY2RQEwZ2ZhH3Iq9qGnYevZW2scRoiGyrphGOxz7mVd0ZrA+oc7yrZhUPuiMZYYOiZ3Djy63CWlKykOLc0hciL0PV0kOijrEmnULAwzGoROrnKXl99OXs2GQ+VkxNsZd2U2zVJig12WRBnlXPxlZbgXzqtuHC5cgFHpDBwzxcdjzxlc3TgckIM5XrOgw1Uws17hqWBjyfpA83B90VrKvWV1zmsa14wuqd3omtKdrqndaJXYBovJ0rDFioQxfU8XiQ7KukQaNQvDjAahk6+40sOtH6xie4GTJkkOxl3Zk0aJjmCXJVFEOZefMtxuPMuX4p4zC9fcWRiFP9nMwmbD1qcf9iGn4hh8KuaMjOAVKscslLLuN/zsqdhd3TysWf9wV/mOOufFWuLonNIlsHlKl5RuJNm1zq/ILwmlnIvIyaOsS6RRszDMaBBqGPnlLsZ+sIo9xVW0SI3lzVHZpMfbg12WRAnlXH6J4ffjXb8W99zZuGf/gG/P7lrHrV27HV7nsEXLIFUpRyvUs17mKWVD8Y+zD9ewoXgdTq+zznkt4ltWzz6sWf+wZUIrzCatsSkCoZ9zEakfyrpEGjULw4wGoYZzsLSKWyat4mCZi/aZ8bx2eQ+SY7VOmJx8yrkcLe+unbjnzMI9Zxbe9WtrHbO0bBVY59Dauas2SAlB4ZZ1n+FjV9lO1hevYV3N48t7K3bXOS/emkDnlC6B5mHnlK4k2PS4vESncMu5iBwfZV0ijZqFYUaDUMPaXVTJLZNWUuj00LVxIq9e3p14u3aKlJNLOZfj4cvPO7xByrIl4PUGjpnS0nEMPRX70NOx9eqDya6Z0qEgErJe4i5hQ/HhR5c3Fm+gyldZ6xwTJloltA48utw1tTvN41tgMpmCVLVIw4mEnIvIr1PWJdKoWRhmNAg1vK35Fdz2wSpKqrz0apbMP0d0I8amxd3l5FHO5UT5K8rxLFyAa84sPAvnYVRUBI6Z4uKxDczBMfQ0bAMHY07QjK9gicSs+/xetpdtY33x2prZh2s44Nxf57wkWxKdU7r+ZPZhF2KtcUGoWOTkisSci0hdyrpEGjULw4wGoeDYcKiM2z9cTYXbR06rVJ4f1hW7VY/0ycmhnEt9MjwePCuWVT+uPHc2/vy8wwetVmy9+lSvczjkVCyZWcErNApFS9YLXYWHZx8WrWVTyQbcfnetc8yYaZ3Ylq6p3QK7L58S11SzDyXsRUvORaKdsi6RRs3CMKNBKHhW7i3hzo/X4PL6Ob1dOk9f3AWrWT/ESP1TzuVkMfx+vBs31DQOZ+HbWXu3W2vnroF1Di2tWqtRc5JFa9a9fi/bSrewrmbtw/XFazlUebDOeSn2lOodl2s2T+mY3JkYS0wQKhY5ftGac5Foo6xLpFGzMMxoEAquRTuLuO/TtXh8Bud3zuIv53fErB+mpZ4p59JQfLt34Zo7u3qDlHVr+OkXnLlZcxw/7qzcpSsmi5ZfqG/K+mH5VXk1uy5Xz0DcUroJj99T6xyLyULbxPY1zcPq2YeNYhurqS0hTTkXiQ7KukQaNQvDjAah4Ju1tYCHPluHz4ARPZrw8Fnt9IOK1CvlXILBX5CPe95cXHNn4Vm6GDyHGzWmtDTsg4ZWr3PYpx8mhyOIlUYOZf2XuX1utpZuDmycsq5oDQWu/DrnpTnS6ZrSvebR5W50SO6I3aKvTwkdyrlIdFDWJdKoWRhmNAiFhq825PLnGRsxgGv6NOOe0/S4ntQf5VyCze+swLNoYfXjygvmYpSXHz4YG4t9QA72oadjzxmEOTEpeIWGOWX96BmGQW7VocDsw/VFa9lSugmf4at1ntVkpX1yx8Cuy11TupEZq7U4JXiUc5HooKxLpFGzMMxoEAodU9cc4ImvtwAwNqcltwxqGeSKJFIo5xJKDK8Xz8rlgXUO/bm5hw9aLNh69q7ZIOU0LI0aBa/QMKSsnxiXz8Xmko2BmYfri9ZS5C6sc15mTNbh5mFqN9oldcBmtgWhYolGyrlIdFDWJdKoWRhmNAiFlv8u38eL328D4O5TWzO6X/MgVySRQDmXUGUYBr7NG3HNmYV7zix827fVOm7t2Olw47BNW824/hXKev0yDIODlQcCjy6vL1rLtrKt+P9n9qHNbKdjcqfDm6ekdCM9JiNIVUukU85FooOyLpFGzcIwo0Eo9Ly9cDevzdsJwMNnteOy7FOCW5CEPeVcwoVv757qDVLmzsK7elXtDVJOaYp96Gk4hp6GtVsPbZDyM5T1k6/SW8mmkg2sL1rL2uLq2YelnpI65zWObRJoHnZL7U6bxHZYzdYgVCyRRjkXiQ7KukQaNQvDjAah0GMYBq/O3ck7i/dgAv5yfkcu6KJH8eT4KecSjvxFhbjnz61+XHnJInC7A8dMySnYhwzFPuQ07P36Y3LEBLHS0KGsNzzDMNjn3Bt4bHld8Vp2lm3Hj7/WeQ6zg44pnWttnpLiSA1S1RLOlHOR6KCsS6RRszDMaBAKTYZh8Px32/hw5X7MJnj6os78pkNmsMuSMKWcS7gznE7cSxbhnjsL97y5GGWlhw/GxGDvP7D6ceWcwZiTU4JWZ7Ap66GhwlPBxpL1tTZPKfeW1TmvaVwzuqR2q1n/sButE9pg0exD+RXKuUh0UNYl0qhZGGY0CIUuv2Hwf19tZvq6Q1jNJp4f3pXBrdOCXZaEIeVcIonh9eJZvbJ6xuGcWfgPHTx80GLBlt2zesbhkFOxNImuZRyU9dDkN/zsqdhd3TysWf9wV/mOOufFWuLolNI5sHlKl5RuJNm1O7jUppyLRAdlXSKNmoVhRoNQaPP5Df70+Ua+2ZyHw2rmpRHd6NM8JdhlSZhRziVSGYaBb8tmXHNrNkjZuqXWcUv7DjiGnoZ96GlY2raP+A1SlPXwUeYpZUPxj7MP17CheB1Or7POec3jWwQah11Tu9EyoTVmkzkIFUuoUM5FooOyLpFGzcIwo0Eo9Hl8fn7/2Xrmbi8kzmbh1cu7062JZhrI0VPOJVr49u/DPXc2rjmz8K5eCf7D68aZGzepflR56GnYumdjskbe457KevjyGT52le1kffEa1tU8vry3Yned8+Kt8XRO6RpoHnZO6UqC7eg+eEtkUM5FooOyLpFGzcIwY/JVktEok/yC8oi9x0hQ5fFx36frWLq7mESHldev6EGHrIRglyVhQh82JBr5i4txL6jZIGXxQnC5AsdMSUnYBw2tbh72H4gpJjI2SFHWI0uJu4QNxYcfXd5YvIEqX2Wtc0yYaJHQkvZJHWif1JH2yR1pl9ReDcQIppyLRAdlXSJNgzULV61axVVXXcX48eMZNGhQrWNLly7l9ddfZ9WqVVRWVtK0aVMuuOACbr/9dux2+zFfK1IDaqoqJu29IZgTMik+4wU8jXoHuyQ5Aqfbx50frWHNgVJSY228OSqbVulxwS5LwoA+bEi0M6qqqjdImTML9/w5GCUlhw/aHdj7D6he53DQEMyp4btDrbIe2Xx+L9vLtrG+eG3N7MM1HHDu/9lzm8SdQvukjnRI6ki75A60T+pAqkPrHkcC5VwkOijrEmkapFm4c+dOrr32WvLy8pgwYUKtZuHixYsZM2YMGRkZjBo1itTUVObPn8/MmTMZMGAAEyZMwGKxHNP1Ijag7grSPjgHS+kuDJMFZ587cfa9Fyy2YFcmv6Csysvtk1ezKbecrAQ7b16ZTdPk2GCXJSFOHzZEDjO8XrxrV+P6cYOUAz9ptpjNWLtnH17n8JSmwSv0OCjr0afQVcjmko1sKd3ElpLNbC3dzMHKAz97bkZMZvXsw6QOtE+unomYGZMV8Wt5RhrlXCQ6KOsSaU56s3DmzJk88sgjlNTMCvjfZuG5555LcXExM2bMID09PfD6008/zcSJE3nhhRe46KKLjumakRxQs6uY9EWPw5rJAHgyu1N21kv40joEuTL5JUVON7d+sJodhU5OSY5h3KhsshIdwS5LQpg+bIj8PMMw8G3binvOLFxzZ+PbvLHWcUubdtiHnoZj6GlYOnQM+aaKsi4Ape5StpZuZnPpJraWbGZL6Sb2VuzBoO4XRbI9hXZJ7atnISZ3pF1SB06Ja6pNVEKYci4SHZR1iTQntVk4duxYZs2aRbt27ejUqRPTp0+v1Szcs2cPZ511FiNHjuTJJ5+s9Wc3bNjA8OHDGTVqFH/961+P5bIRHdAf/8FKF7xPwg9/wOwqxrA4qMj5A5U9bgR9WAxJeeUubpm0in0lVbRKi+XNUdmkxh37I/YSHfRhQ+To+A4ewD13Nu45s/CsWgE+X+CYOavR4Q1SsnuF5AYpyrr8kkqvk22lW9lcuoktJZvYUrqZXeU78Bm+OufGWeNo9+MaiDWzEFvEt8RiDr2v+WiknItEB2VdIs2xNAuP+RPH9u3buf/++7nhhht444036hxv3LgxX331FQ5H3VlW+fn5AMf8CHK0cLe/mKIm/Uj47kEcu78nYe5fsO/4mrIz/44/Mbwew4oGmQkO/nV5D26ZtJKdhZXc+dEaXruiB0kxeoRcROR4WRo3IXbkKGJHjsJfWoJ7/rzq5uGi+fhzD1H18YdUffwhpoRE7IOGHN4gJU7rx0poi7XG0S2tB93SegRec/tc7CjbHniEeUvpZraVbcXpdbK6cCWrC1cGzrWb7bRJbEf75OoGYofkjrRKaIPdol9UioiISP065mbhjBkzjrhBic1mo1WrVj97bPz48QAMGDDgWC8bNfzxjSm96N/ErPsPCfMex75vPqmTzqJ86F9xdRxZ3QqWkHFKcgyvXt6DWz9Yxea8Cu79ZC0vj+xOvF2/+RcROVHmpGRizruAmPMuwHBV4Vm6pHqdw3lzMIqLcH39Ba6vvwC7HVvf/tXrHA4eijlVm0hIeLBbHHRM6UzHlM6B17x+L7vLdwUeY95SsomtpVuo9DnZWLKejSXrA+daTBZaJbQJrH/YPqkDbZPaEWtV81xERESO3wnthvzyyy/zyiuv1Fmz8Ejndu3alY8++giz+dgerS0oiNypvyYTpKcn1rlHc/EOEr+5F9vBZQC42pxH+RnPYsSm/8I7SbBsySvn1g9WU1rlpW/zZP4xohsxNs2glcN+KecicuwMnw/vurW45szCNfsH/Pv2Hj5oMmHt1gPH0NNwnHoalmbNG7Q2ZV1OBr/hZ79zH5tLfmwebmZzyWZKPSV1zjVhonl8C9old6BDUsfqRmJyBxJtSUGoPDIp5yLRQVmXSPPj1/RRndsQzcJXXnmFl19+mYyMDCZNmkTz5g37wT2s+bww/yX4/mnweyA+Ey55GTqeH+zK5H+s3FPMNeMWUuH2cUbHTN4Y3Re7VetNioicTIZh4N66lbJvv6Xsm2+pWru21nFH+3YknHkmiWeeSUy3biG/QYrI0TIMg4MVB9lQuKH6v4Lq/3Irc3/2/KYJTemc1pnO6Z0D/5sRm9HAVYuIiEg4OKnNQo/Hw1/+8hc++ugjGjVqxIQJE2jbtu1xXSuSu/lH8xsLS946EmfejbVwEwBVXa6kYshfMOwJDVip/Jrle4q56+O1uLx+zuyQwZMXdcZq1g+mot9MijQUX+4h3HPn4JozC8/ypbU3SMnMwj7kVBynnoatZ29MtvpfY1ZZl2ArdBWyteSnjzBvZr9z/8+em+7IqHmEuUNgLcRGsY3VVP8VyrlIdFDWJdKExMzC8vJy7rnnHubOnUvbtm0ZN24cTZse/yYdkbwD0VHvsuStIn7Rc8SufBMTBr7E5pSd9Q88p2gNyFCyYGch909Zh9dvcGHXRjx6bgfM+tAd9bSbmkjD85eV4V44r3pn5YULMCqdgWOmhATsAwdV76w8IAdzfP388k1Zl1BU5illa+kWtpZsrt5MpXQzu8t3YVD3izTRlli9/mFN87BdUgeaxTfHbNLTEj9SzkWig7IukeZYdkM+Kc1Cp9PJjTfeyIoVK+jfvz+vvvoqSUkntk5KJAf0WAch2/6FJH5zH5ayPRiYqOx1KxUDHgRL3R2oJTi+35LPH6atx2fAyOwm/P7MdvotfZTThw2R4DJcLjwrluGeMwvX3FkYhYWHD9ps2Pr0O7xBSvrxP5qprEu4qPRWsr1sa80uzNUNxJ1l2/Ea3jrnxlriaJfUnnZJ7WuaiB1pmdAKqzk6N3RTzkWig7IukSbozcL777+fzz//nDPOOIN//vOfR9w9+WhFckCPZxAyucuIn/s4sRsmAeBN60jp2S/jy+hyEiuVYzFj/SH+8sUmDOC6fs24c2hrNQyjmD5siIQOw+/Hu34t7jmzcM+ZhW/P7sMHTSasXbpiH3oa9qGnY23R8pjeW1mXcOb2udlZvoMtpZsCsxC3lW7F5XfVOddmttMmsU2tWYhtEttij4JfXivnItFBWZdIcyzNwnr/deDSpUv5/PPPsdvtnHrqqXzxxRd1zmnevDm9e/eu70tHFcOeSPlvnsfd+hwSv/891sJNpE6+kIr+D1DZ63YwayfeYLugSyOqvH6enrmFfy/ZS5zdwk0Dj+2HThERqX8msxlbtx7YuvUg/va78O7aGWgcetevxbuu+j/n669iadkK+5DTsA89DWvnLpjMehRTIpfdYqdDckc6JHeEmv0IfX4vuyt2s7V0M1tKqmcgbi3dTIW3gk0lG9lUshH2VJ9rNlloldCK9kkdA7MQ2yW1J84aH7ybEhERkWNW783C77//HgC3283jjz/+s+dcfPHFahbWE3frcyhs3IfE73+PY8dXJCx8BsfObyg96x/4k1sFu7yoN6JHEyrdPv4xazuvz9tFrM3C1X2aBbssERH5CWvLVlhbtiLu2uvx5efhnju7ep3D5Uvx7dpJ5a6dVP7nHczpGdiHDMU+9HRsvfpgqocnJ0RCncVspXViG1ontuHspucB4Df8HHDuZ0tNA3FrafWGKiXuYraXbWN72Ta+2lf9502YaBrfnPZJ7QOzENsldSDZnhzEuxIREZEjOaHHkBtSJE/9rZfpzYaBY9NHJMz+M2ZPOYY1jvIhj1LV5ZrqC0hQjVuwizfn7wLgj2e359IeTYJckTQ0PcYgEn785eV4Fi2o3ll5wTwMZ0XgmCkuHtvAQTiGnoZt4CDMCdUbpCjrEq0MwyC/Ko/NpZtqzULMq8r92fOzYhrRPrkjHZKqm4ftkzuQ7sgIiyVblHOR6KCsS6RpsDULG1IkB7Q+ByFz6V4Sv7sP+74FALha/obyM57DH9+oHiqV42UYBi/P3sG7S/diAv56QSfO65wV7LKkAenDhkh4M9zuwAYp7rmz8RfkHz5otWLr1Qf70NNwDD2VRp3bKusiNYpdRdUzEEs3saWk+hHmfc69P3tuqj2tZv3Dw7MQG8c2CbkGor6ni0QHZV0ijZqFYabeByHDT+yqt4hf+Awmnwt/TCplpz2Nu91F9fDmcrwMw+DZb7fy8aoDWEzwzMVdOL398e+4KeFFHzZEIofh9+PduOHwBim7dtQ67ujYEVPXHth69MSa3RNLRmaQKhUJTeWecraVbvlJE3ETu8t34cdf59wEayLtkmuah0kdaJ/ckWbxzbGYgrc+t76ni0QHZV0ijZqFYeZkDUKWgk0kfnMPtvy1AFR1uJTyU5/AcGiNmGDxGwaPf7mJGetzsZjg+gEtuHlgC2wWLZgf6fRhQyRy+XbvwlWzzqF33Rr+N+TmU5pi69ETW3ZPrD16YmneIuRmSokEW5Wvih1l29hcsqlmN+Yt7CjfhsfvqXNujCWGtkntaZfUgQ5JHWmf3IGWCa2xmW0NUqu+p4tEB2VdIo2ahWHmpA5CPjdxS1/i/9u77/hGzjp/4J+ZUZcl9+7t3r5rbxISSEjfAAkQCAkcpFASOAgcB8fB5SgHhF6PXC4cBxwk8AtwR0gCoSWQHlIJKfb2XmzLvapLM/P8/hhJlizZa+/aKqPP+/VyJI9Go2c2/o7kj5/ieuE2SEKHVtEM/8XfQXzZeYv8QjRfqi7w5T/vxx92DQIA1ta7cfOl67GuoaLALaOlxA8bROVBTIzBcWgvxp58BrGul6EdOgDomb2lpOoaWDs6UwGismYtJMuirzlHVPLiehzHAkdwYHJ/qhfioakDiGiRrH2tshUrK1ZjbeW6VC/E1d52OBTHoreL7+lE5YG1TmbDsLDE5OMiZBl4AZ6H/gmWSWOoVGjr9Qie/WnA6lyaF6QTemjfML7+0AFMRlQosoT3vWo53nPWMljYy9CU+GGDqDzMrHU9GIC6cwfi3S8j3vUy1D27gFgs8zlOFyxbtsLauQ3WjtNg2bQJkn3xAw4iM9CEhr5gDw5MGiswJ3shBlR/1r4yZCyvWJFagXmddz3WeNeiwnpqf6DlezpReWCtk9kwLCwxebsIxUOoePorcO78KQBArVoD/yW3Qm3ctoQvSnMZDcbw9YcO4LGDowCAjY0V+Pyl67Gmzl3gltFi44cNovJwoloXsRjUvXumw8OdXRCBQOZOFgssGzaleh9aOjohe7z5OQGiEiSEwEC4P7UC84Gp/TgwuRfjsfGc+7e4WhMLqEz3QqyyV8/79fieTlQeWOtkNgwLS0y+L0LW44/B88jHoQQHISQFoVd8BKEzPgIo+ZnnhTIJIfCnvcP41iMHMRVRYVUkfOCclbjuFW1QZM5pZRb8sEFUHhZa60LToB05hHh3lxEedr8MfWQ466DKqjWJnoeJRVPqG5bmBIhMQgiB0egIDk7tn+6FOLkPQ5HBnPvXOxpSC6gkeyHWOepzzi/K93Si8sBaJ7NhWFhiCnERkiLjqHji3+A4cB8AIN7QCf8lt0Krbs9PAyjLcCCKrz54AE8eHgMAbG324HOXrsfKGleBW0aLgR82iMrDqda6EAJ6vw/xrpdS4aHWczxrP7m5JS08PI2LphDN02RsMhEgTvdC7A1m1xgAVNmq0O5N9D6sNHogtrhaIcsS39OJygA/v5PZMCwsMYW8CNkP3IeKxz8NOToJodgRPPtTCHfcAEicN68QhBD43a5BfOfRQwjGNNgtMj507kq84/RWyPwlsKTxwwZReViKWtfHRo2eh91GeKge2J+9aEpV9fSw5c5tsLSv46IpRPMUjAdxyH8g1QvxwNQ+HA0chS60rH3dFjfaveuwtXEzmixtWFGxCisqVp3yPIhEVHz4+Z3MhmFhiSn0RUgO9MPz6CdgO/44ACDWdi78F38Huqcl/40hAMDAVARf+fMBPHvMmGvntFYvPnfperRVcUGaUlXoOiei/MhHreuhYOaiKbt3AbFoZjuSi6YkwkPrxs2QHFw0hWi+oloUR/yHEvMfGr0QD/sPIa7Hcu5f56jHioqVWFmx2rj1rMbKipWosM7vlzIiKj78/E5mw7CwxBTFRUgIOHbdiYqnvgRJDUO3eRE4/4uIrrvKaCDlnRACv+7ux388fhjhuA6HRcZHLliNqzqb2cuwBBVFnRPRkitErYtYDOq+vYh3J4Yu7+iGCMxYGdZigWX9htSwZevWDsjeyvw0kMgkVF3FscBRHPTvR1/sKPYO78fRwBGMRIZnfU6tvQ4rK1ZhhWcVVibDRM9KeKxctIio2PHzO5kNw8ISU0wXIWXiMDwPfRTWwZcAANE1r4f/gq9DOGsK27Ay1jcZxpf+tB8v9EwCAF6xvAqfe906NHvZQ6SUFFOdE9HSKYZaF7oO7cjh6Z6H3S9DHx7K2k9ZvQbWjm2wdm6DpWMblIbGArSWqPTMrPNA3I9jgaM4GjiCY/4jxm3gKIYj2XWXVGuvS/RANIYxr0wMZ/baGCISFYtieE8nWkwMC0tM0V2EdBWuF78H1/PfgaSr0J318F/8bcRWbi90y8qWLgR+9ZIPt/3lCKKqDrdNwT9dsBpv3trECe1LRNHVOREtiWKs9dSiKWnhoXb8WNZ+cnOzER52JMLDFSv5HkOUw3zrPBAP4HgyRAwcwVG/ESLOtiIzANTYaxPDmRMhYiJMrLSxJzBRvhXjezrRqWBYWGKK9SJkGd4Bz4MfhWV8PwAgvOkaBF79ecDmLnDLylfPeBhfeGAfunxTAICzV1bjM69dh0aPvcAtoxMp1jonosVVKrWuj49lL5qiZS7mIFVWpS2achosa7loChFw6nUejAdxPHgUR1O9EI0gca4QsdpWgxWe9DkRjd6Ilbaqkz8RIppTqbynE80Xw8ISU9QXITUC97PfhLPrfyBBQPOuwNT2W6C2nFXolpUtTRf43xf78N9PHkFME6iwK/jERe14/aYG9gApYkVd50S0aEq11vVQEOqunameh/FdO7MWTYHTCevmtEVTNm3hoilUlpaqzkNqEMcCx3DUf3h6WHPgCAbDA7M+p9pWbazInD4nYsVKVNmrF69hRGWqVN/TiWbDsLDElMJFyNr3NDwP/zMUfy8EJIRP/yCCZ30cUNijrVCOjoZw8wP7sGvAmMT+vNU1+PRr16HObStwyyiXUqhzIjp1Zql1EY9D3bcH8a6Xjd6HO7oh/FOZOykKLOs3ToeHHZ1cNIXKQr7rPKQGcTxwLDUXYjJMHAj3z/qcKltVxlyIxnDmlai2cx5yovkyy3s6URLDwhJTKhchKeZHxV8+D8feuwAAau1GTF1yK7S6TQVuWflSdYE7n+/BD58+BlUXqHRYcNP2drxmfT17GRaZUqlzIjo1Zq11oevQjh5BvOslo+dh98vQh3IsmrJqdVp4uA1KY1MBWku0tIqlzsNqKC1EnJ4TsT/sm/U5lbaqnHMiVtuq+dmRaIZiqXWixcKwsMSU2kXIdvgBeB77V8jhUQjZhuArP4Hwtg8AslLoppWtg8NB3PzAPuwbCgAALl5bh09e0o5qF3sZFotSq3MiOjnlUutCCOgD/dM9D7tfhnbsaNZ+cmNTZni4chUDCSp5xV7nYTWMnuCx1JyIyTBxINQPgdwN9lorE3Mhrk6FiSs9q1Btq2HNUtkq9lonWiiGhSWmFC9CUmgEnkdvgv3onwEA8eazMLX9FuiVKwrcsvKlajrueK4HP37uODRdoNppxSdfsxYXr60rdNMIpVnnRLRw5Vzr+vg44jsSKy7v6IK6f1+ORVMqYd3aCUvnaUaIuG49F02hklOqdR7RIomeiIdxzD89J2J/yDdHiOidHs6ctsBKjb2WISKZXqnWOtFsGBaWmJK9CAkB+967UPGXz0OOB6Bb3Qie+3lENl5tnBQVxN5BP77wwH4cHAkCAF63oR7/cnE7Kp3WAresvJVsnRPRgrDWp4lQCPHdyUVTXjIWTYnOWDTF4YB181ZYEqsuWzdvheR0FqbBRPNktjqPaBH05JgT0RfqmzVE9Fg9aXMiTvdIrLXXMUQk0zBbrRMxLCwxpX4Rkqd64Hn4n2DzPQcAiK68BP4Lvwnhbihwy8pXTNXxo2eP4ad/7YEugFq3DZ95zVqct6a20E0rW6Ve50Q0P6z12Yl4HOr+vameh/HulyGmciyasm4DrJ3bYOlILJpSWVWQ9hLNplzqPKpFU8OZjyWHM/uNEFGHnvM5FRZPogfiqrQeiatQxxCRSlC51DqVD4aFJcYUFyGhw/ny/8D97Dcg6THojmr4L/w6YmveUOiWlbVd/VO4+YF9ODoWBgC8YXMjPn7hGngcHPKVb6aocyI6Idb6/Aldh3bsSKLnoTF8WR8azNpPWbkqER6eBmsnF02hwiv3Oo9qUfQGj8+YE/EofMHeWUNEt6Ui0QNxOkRcWbEKdQ4uykfFq9xrncyHYWGJMdNFSBndC89DH4V1ZBcAILL+KgTO+yKEvbLALStfUVXH9586ip//rRcCQEOFDf/2unU4e2VNoZtWVsxU50Q0O9b6qdESi6Ykw0Pt2JGsfeSGxumeh53boKxYBUmWC9BaKles89xiWhQ9wR5jTsTA0VSPxL5QH3Sh5XyO2+JOLKiyOiNMrHc0MESkgmOtk9kwLCwxsbCKxiYvJqZC5jhHLQbX87fA9eJ/QRI6tIoW+C/+DuLLzi10y8paV98kvvDAPvRMRAAAV2xtwj9duBpuG3sZ5gM/bBCVB9b64tInJhDf0ZUKD9X9e7MXTfF6jcVSOowVly3rN3DRFFpSrPOFiWkx9OYIEXtDvbOGiC6LK+eciA2ORoaIlDesdTIbhoUlJBqM4w/f6YZilbH+3Ca0n9UAi00pdLMWhaX/b/A+9FEoU8cAAKGO9yJ49icBCycuL5RIXMN3/3IEv3zJBwBo9trx2detw5nLqwvcMvPjhw2i8sBaX1oiHEZ81w7Eu1+G2vUy4rt3ApFI5k52O6ybt6SGLVs3bYHkchWmwWRKrPPFEdNi6Av1JMLDo6kwsSfYM2uI6FRcWcOZV3hWotHRxBCRFh1rncyGYWEJUWMaHv3xXoz7QgAAR4UVGy9oxupX1EOxmGBITSyIiqe/DOeuOwEAanU7/JfcCrWhs8ANK28v9Ezgi3/aD9+k8QvW27a14B/PXwWn1RxBdTHihw2i8sBazy+hqlD37TXCw+6XEd/RBTE5mbmTosCybn2q56G1YxvkqqqCtJfMgXW+tOJ6HL3BHmNRFf/0nIi9wePQ5ggRl1esSC2okpwTscHZCFkywe9UVBCsdTIbhoUlRgiBscNBPHvfYQTHowAAV6UNmy5qwcptdZCV0v8rme3YI6h45F+ghAYhZAtCr/goQqd/GFCshW5a2QrFNPznE4dxT1c/AKCtyoHPv249trVxfsmlwA8bROWBtV5YxqIpR6d7Hna/DH1wIGs/ZUVi0ZStnbB2boPc1MxeSTRvrPPCiOtx9AV7jUVV/IkhzYEjc4aIDsWJFRUrsuZEbHQ2MUSkE2Ktk9kwLCwxyf9hgwOTOPzCCPY85kPYHwcAeGod2HxxC5ZtqYEkl/aHWCkyjorHPw3Hwd8BAOINnfBf8p/QqtcUuGXl7bmj4/jSn/dj0B+FBODqM1rxwVevhIO9DBcVP2wQlQfWevHRBgcSi6a8ZCyacjTXoikN0/Medp4GZSUXTaHZsc6Li6qr0z0RE70Qj/oPozfYA1WoOZ/jUBxY7k6Gh4kFVjwr0eRsZohIKax1MhuGhSVm5kVIjes49Nch7H2iH9GQ8QZX2ejElu2taNlQVdp/+RYC9gO/QcUT/wY5OglhcSBw9mcQ2fpugG/MBROIqrjlsUP47c5BAMCKaiduvmw9tjR7C9wy8+CHDaLywFovfvrkBOI7ulM9D9V9e3IvmrK1MzF0uROW9RshWTkaggys89Kg6ir6Qr04lhrKbAxr7gkenzVEtMt2LK+YESJWrESzq4UhYhlirZPZMCwsMbNdhOJRDfufHsT+pwYQjxofYmva3Nh6SSsaVntLOjSUAz54HvkEbD1PAABibefBv/3foVe0FLhl5e3Jw6P4yp8PYCQYgywB7zxzGd5/9grYzDB/ZoHxwwZReWCtlx4RDiO+e2dqxeX4rh25F03ZtCXR83AbrJu3ctGUMsY6L21aIkQ8GjiaEST2BI8jrsdzPscu27GsYgWWuZehxdWGVncbWhO31baakv69jGbHWiezYVhYYk50EYqGVOx7agAHnhmEFtcBAPWrPNh6SSvqls/vf3RREjocO/8fKp7+MiQ1At3mReD8LyO67i3GPwoVxFQkjm8/cgj37xkCAKyudeHmy9ZjY2MJ/6wVAX7YICoPrPXSJ1QV6v59RnjY/TLi3V0QkxOZOykKLGvXZYSHcm1dQdpL+cc6NycjROybXpnZb8yJ2BM8NmuICBjzIra62tDiakWruzUjTKxz1LNHYgljrZPZMCwsMfO9CEUCcex53IdDzw9D14wdm9dVYsv2VlS3uPPU2sWnjB+C56GPwjr0MgAgsuaNCFz4NQhHdWEbVuYeOzCCrz10AGOhOBQJuP6Vy3HDq5bDqvADz8nghw2i8sBaNx8hBLRjR6d7Hna/DH2gP2s/qboGlva1sKxph7KmHZb2tVCWr4RksxWg1bSUWOflRdNV+EI+HAscgS/Uh75QH3zBXvSFejEUHoQOfdbnWmWbESK6WtHqbjOCRFcbWtytaHQ0QpEteTwTWijWOpkNw8ISs9CLUHAiit2P+XD0pRGIxHtT2+ZqbLm4Fd4G59I2dqnoKlwvfBeuv/0HJF2F5mpA4OJvI7bi4kK3rKxNhOL4xsMH8dD+YQDAuno3br5sPdbWVxS4ZaWHHzaIygNrvTxog4OId7+UmvdQO3oEOf+HKwqUFSthWbN2OkBc0w65to7DFksY65ySYloMA+F++EK96Av2GkFi4v5AuH/WVZoBQJEUNDtb0OJuywoTm1zNsMqcJ7XQWOtkNgwLS8zJXoT8oxHseqQPx3eMAcI4zvLOWmy+qAUVNY6la/ASsgx1wfPQP8EyfgAAEN58HQLnfBawlW7PSTN4cN8wvvHQAUxGVFhkCX9/9gq866xlsJT4Ct35xA8bROWBtV6eRCQC9chhaAf3Qz10EOqhg9AOHoAI+HPuL1VWpfVAXGfcX7kKkt2e55bTyWCd03xouorByCD6gr1ZYaIv1Dfn0GYZMhqdTWhxt6YNcTbCxBZXK+wKrxX5wFons2FYWGJO9SI0ORjCzof70LdnwjieLGH1GXXYeGELXN4SHPqihuF+9htwdf0IAKB5V2DqkluhNr+iwA0rb6PBGL7+0AE8dnAUALCxsQI3X7Yeq2sZ5M4HP2wQlQfWOiUJIaAPDUE7dADqwQNGgHjoILSeY4CeY9iiokBZthzKGmMos2VNO5T2tZDrG9gLsciwzulUaULDSGQ4ESQmeyP2oS9kBIsRLTLn8+sdDanhzK2u6cVWWlytcFq4+NJiYa2T2TAsLDGLdREa6w1gx8N9GDw4BQBQLBLWnNWADec3w+EuvW7s1t6n4Hn4Y1ACPghJRvi0DyF41j8DSgkGoCYhhMD9e4bw7UcOwR9VYVMk3PjqlbjmjDYo7GU4J37YICoPrHU6ERGNQDt6BOrBg1APHUiFiWJqKuf+ksebGsJs9EZcC8uq1ZAcpTmKxAxY57SUhBAYi46iL9Eb0RdK9EgM9qEv1IOgGpzz+dW2mtQCKzPDxAorFyxcCNY6mQ3DwhKz2Beh4aN+7HioFyPHAgAAi03G2rMbsf7VTbA5S2sSXSk6hYq/fA6OfXcDANTaTZh6zX9Cq91Q4JaVt+FAFF/58wE8dWQMALC12YvPX7oOK2r4l8zZ8MMGUXlgrdPJEEJAHx2BdvBAIkA8CPXgAWjHjwFajjnPZBlK2zIjRFzTDiUxlFlubGIvxDxgnVOhCCEwFZ9MDGnODBP7gr2Yik/O+XyvtTIRJBqrNqeHiZW2Kl4/ZmCtk9kwLCwxS3EREkJg4OAUdj7Ui3FfCABgcypY/+omrD27ERabsjgvlCe2Q3+E57FPQo6MQcg2BF91E8Kdfw/IpXUeZiKEwO92DuI7jx1CMKbBbpHxD+etwttPa4HMDxpZ+GGDqDyw1mkxiVjMWIk50fswGSKKifGc+0sVFYkAMa0X4uo1kJwlugBekWKdU7Hyx6fgC/YlVm3uTQ1z7gv1Yiw6Oudz3Ra3scBKWpiY7KFYY68tyyCRtU5mw7CwxCzlRUgIgb7d49j5cB+mho25L+xuCzZe0Iw1r2iAYpUX9wWXkBQcguexm2A/+hAAINbySvi33wLdu7zALStvA1MRfOlP+/HX4xMAgNPaKvG5161DWxV/MUnHDxtE5YG1Tvmgj45kLKSiHjoA7dhRQFWzd5YkyK1tiXkQp1dllpuaIcml8zmwmLDOqRSF1ZARHCZ7JYZ6U8HiUGRwzuc6FAdaMgLE6fv1jgbIkjmvJax1MhuGhSUmHxchXRfo6R7Fzkd8CI5HAQBOrxWbLmrFqtNqISslcoEXAo49/wf3kzdDjgehW90InnszIhvfYfxDUkEIIXBvdz9uffwwwnEdTquMj5y/Gld2NrOXYQI/bBCVB9Y6FYqIx6EdP5bogWgsqKIePAAxlrs3keRyQ1m9JrWQihEkroHs4sJlJ8I6J7OJalH0h3yJADEzTBwMD0BHjkWZEqyyDS2uFiM8TK3abISJTc4mKHJpTYOVjrVOZsOwsMTk8yKkazqOvDiC3Y/5EJ6KAwAqauzYfHErlm2tgVwii1TIk8fgffhjsPb/FQAQXfka+C/6JoSrvsAtK2+9E2F86U/78WKvMV/Kmcur8LnXrUOTl5Ow88MGUXlgrVOx0cfH0nogJnojHj0MxOM595ebW2BpXzu9KnP7WsgtreyFmIZ1TuUkrscxEOpPDWf2JYY394X60B/qgyZyzKuaoEgKmpzNiQAxPUxsQ5OzGbYiX7iStU5mw7CwxBTiIqTFdRx6fgh7nuhHNGgMWfE2OLFleytaN5bI5La6BufLP4T7uW9B0mPQHTXwX/QNxFZfVuiWlTVdCNz1kg/f/csRRFUdbpuCj124Gm/aUt6TrvPDBlF5YK1TKRCqCq3nWGIOxIOpRVX04aHcT3A6YVm1JhEiJhZVWd0O2VOeK6uyzokMmq5iKDKUGtrsC/Wmhjr7Qn2I6bFZnytDRoOzES2u1sTKzdNhYrOrFQ6l8J0NWOtkNgwLS0whL0LxqIYDzw5i35MDiEeMvwpVt7qwdXsbGtu9JRHuKCO74X3oo7CM7gEARDa8DYFzvwBh9xa4ZeXt2FgIX/zTfnT7pgAA56yqxmdesw4NHnuBW1YY/LBBVB5Y61TK9MkJo+fhoUSAePAA1CNHgFg05/5yU3NiIZX2VG9EpbUNkmLuBehY50Qnpgsdo5GR1JDm1MrNQaOHYkQLz/n8Okd9aqXm5KrNLYkw0WXJz3QJrHUyG4aFJaYYLkKxsIp9Tw3gwDODUGPGnBR1Kyqw9ZI21K8sgb8aa1G4//odOF/6b0hCh1bRCv/27yDe9upCt6ysabrAL17oxfefOoqYJuCxW/CJi9fgso0NJRFEL6ZiqHMiWnqsdTIboarQ+nqNeRATQ5m1QwehDw7kfoLdDsvqNVBWt2f0RJS9lflt+BJinROdGiEExmNj8CWCw/QwsTfYi6AamPP51bbqRE/EzDCx1d0Gj3XxOoyw1slsGBaWmGK6CEUCcez9Sz8O/nUIumo0pqndiy2XtKGmtfgnvLb0Pw/vQ/8EZeoYACDU+fcIvuomwMKVeQvpyGgINz+wD7sH/ACAC9bU4pOvWYs6d3HPU7KYiqnOiWjpsNapXOj+qUQPxIOJRVUOQj18EIjO0guxoQHK6rWwtK9N9UZUli2HZCm9xQ9Y50RLRwiBqfhU2tyIvalhzb5QLyZiE3M+32v1oiWtF2L6EOcqW/WCOiyw1slsGBaWmGK8CIUmY9j9uA9HXhiB0I1GtW6swpbtbahsLPLgLRZExVNfhHP3zwEAavU6+F9zK9T6rQVuWHlTdYE7n+/BD58+BlUXqHRYcNP2drx2Q0Ohm5YXxVjnRLT4WOtUzoSmQe/rnV5I5dABqIcOQO/vz/0Emw3KytXGQippqzLLVVV5bfdCsc6JCicQD6SCw5lh4mh0ZM7nuiyuxEIrbWlBotErsdZelxUkstbJbBgWlphivggFxiLY9YgPx7pHAQFAApZvrcHmi1vhqS38pLNzsR19GJ5HPgE5PAwhWxA682MInf4PgFx6f8E2k4PDQdz8wD7sGzKGF1yyrg43bW9HtcvcvQyLuc6JaPGw1omy6YEAtMOHEgupTK/KjHDuOcvk2rpEcNhurMrcvhbK8hVF0wuRdU5UnMJqGP0hX2posy8tTByODEFg9oK1y3a0uFqnhze7ja9Nre2whN2wSNY8ngnR0mBYWGJK4QPH5FAYux7uQ+/ucQCAJAOrTq/Hpgub4aos3gUrpPAYPI9/CvZDfwAAxBtPg/+SW6FVrS5wy8qbqum4/bnjuP25Hmi6QLXTik+9Zi0uWltX6KYtmVKocyI6dax1ovkRug693wc1MRdickiz3teb+wkWC5SVq1ILqSR7I8o1tfltOFjnRKUopkXRH+6fXrk50RuxL9SLgfAAdKHN+lwJEmrstWhwNqLB0YgGZyMaU/eb0OhshNdaWXZzslPpYVhYYkrpA8dYXxA7H+7DwIFJAIBskbDmzAZsPL8Zjooi/WuLELDvvxcVT3wWcmwKwuJA4JzPIrLlXcY/PhXM3kE/bn5gHw6NhAAAl25swCcuWoNKZ5H+LJ2CUqpzIjp5rHWiU6OHgoleiIlVmRNBoggFc+4v1dTAsia9F2I7lOUrIdmWbsQC65zIXFRdxWB4INULMb1X4lBkEFEt91ys6RyKIxUkNjgb0ehoSt1vcDSi3tEAm2LukVRU/PIeFnZ1deHqq6/Gj370I5xzzjlZj8fjcbztbW9De3s7vv3tb5/Ua5j5zbgUP3AMH/Nj50N9GD5qLFihWGWsPbsRG85tgs1ZHENEZpL9Pnge+WfYep8EAMSWXQD/xd+GXtFc4JaVt5iq43+eOYb/93wPdAHUuW34zGvX4tzV+e8psJRKsc6JaOFY60SLTwgBfaA/rQeicav19iBnoSkKlBUrYVmTWI05MReiVFu7KD1/WOdE5UGSgNraChzy9WAwPIjB8CCGwgMYiiTvD2IoMoix6Oi8jldjr521Z2Kjs4m9E2nJ5TUsPHr0KK677joMDw/jjjvuyAoLNU3DJz7xCfzxj3/E5ZdfzrAwh1L9wCGEwOChKex8qA9jfcZfe60OBetf3YS1ZzfCalcK3MIchA5n9x1wP/NVSFoUur0SgQu+iujaNxe6ZWVvZ/8Ubr5/H46NG/MXXb65Ef980RpU2IszfF6oUq1zIloY1jpR/ohwGOqRw8Y8iAcPpHojioA/5/5SZVXGQiqWNe1QVq6CZF/YlDqsc6LyMN9aj2lRDEeGMZgIEocSQeJgZCB1P6qfuHeiXbbP2jOx0dmEekc9bErxTgFGxS9vYeGDDz6Iz3zmM5icNIakzgwLfT4fbrrpJjz//PMAwLBwFqX+gUMIAd/eCex8qA+TQ0bQY3dZsOH8Zqw5qwEWq1zgFmZTxg/C89BHYR3qAgBE2t+EwAVfgXBUF7hl5S0S1/D9p47hFy/0QgBoqLDhs69bh1etrCl0005Zqdc5Ec0Pa52osIQQ0IcGoR1MLqSS6IXYcxzQ9ewnKAqUZcun50FsN3ojyvUNs/bwYZ0TlYfFqnUhBKbik7P2TBwMD8y7d2K1rSarZ2KDsxFNziY0OBpRaati70SaVV7Cwve///14/PHH0d7ejg0bNuD3v/99Rlj4m9/8Bp/73OcAANdffz2+//3vMyychVk+cAhdoGfnGHY+0ofAqPGXE6fXio0XtGDV6XVQLEUWGmpxuF64Da6/3QpJaNBcjfBf/G3EV1xU6JaVvZd7J/GFP+1D70QEAHBlRzM+csEquG2l28vQLHVORHNjrRMVJxGNQDt6JK0HotEbUUxN5dxf8nqnhzEn50NctRqSw8E6JyoT+az1mBbDcGRo1p6Jg+GBefVOtMk2Y2hz2vyJyZ6Jxv0G9k4sY3kJCy+55BK87W1vw/XXX48f/OAH+O53v5sRFn7961+Hz+fDxz/+cSiKgu3btzMsnIXZPnDomsDRl0ew+1EfQpMxAIC72o7NF7VgeWctZLm4/tJhGXwZnoc+CsvEIQBAeMu7EDjn3wCrq8AtK2/huIb/+ssR/PIlHwCgxWvHZ1+3Hq9YXlXYhp0ks9U5EeXGWicqHUII6CPDaQupJILE48cALcfKqLIMpW0ZLGva4dm6CdGaRsgtbVBaWyF7K/N/AkS0pIrpPd3onTiV0TNxMDyQ6p04FB7EaHRkXseqtlUngsPpoc6NaQFjla2avRNNKi9hYSwWgy2xythtt92WFRamP97b28uwcA7FdBFaTJqq49Dzw9j7hA+RgAoA8NY7sPniVrRtqoZUTKGhGob7ma/B1X278W3lSvgvuRVq0xkFbhj97fgEvvSnffBNGX9Je/tpLfiH81bBaS3COTHnYNY6J6JMrHWi0ieiUWjHjkJNhocHE70QJydmfY5U4YHS1pYKD5XWNigtbZBbWyHX1UOSi2yEDRGdUKm9p8e0GEaiwzl7JiaHO0e0yAmPY5Vtc/RMNLbZ2TuxJC0kLDzpMX3JIPBkH18oMwfbyXMz2zlarDLWn9OINa+ow4Fnh7D3L/2YGo7gmV8eQlWzC1svaUXzuiJZ8cnqROj8LyK+6rWoePhjsEweRdW9b0H4jA8jdOY/AVzmvmDOXFGF/33PGbj18SO4t6sfv3zJh6ePjOHzl63HttbS+Su+WeuciDKx1olKn+SwQ16/Htb161PbhBAQY6OpYcwWXw9Ch49A6+uDPjoCEfBD3bsH2Lsn+4A2O5SWFiNATASJcksblLY2KE3NkKzWPJ4dEc1Xqb2n2y02tFpa0epuzfm4EAL+uB9D4QEMRgYxGEqfP3Eg0TtxFHE9ht5QD3pDPbO+VpWtKmO4czJMbHQa8yhW2aogS/wjSbFZyM/yKa+GDOTuWZhuMXoWUumLhlW8/NBxdD3Ug3jUGNrRvKYSr3zTarSuL6KFRcITwP03Ad2/NL5v6gCu/CHQsLGgzSLgif3D+Nd7utE/GYEkAe87dxU+/tr1cJRYL0MiIiIyDz0UQqynF/Ge44gd70Gs5zjix3sQO34ccZ8v95DmJFmGtbkZ1uXLYFu2HLbly2Bdvhy25cthW7YMstudvxMhorIX1+IYDA2iP9iPgeAA+oP9qa+BwAB8QR/CaviEx7HJNjS5m9DsbjZuK5qn7ydunRZnHs6ITlbJhIWjo6XR9fdkSBJQW+sx9TmmiwTj2PtEPw4+NwRNNU64cY0XW1/Titq2igK3bprt4O9R8dinIEfGIRQ7Qq+6CeFtfw/wLyQF5Y+o+M5jh/C7nYMAgJU1Ttx82XpsafYWuGVzK7c6JypXrHUi81tInQtVhT44AK2vN/HVB62vJ3HbC0TnXrBAqq6ZHtac+jK+l6o4rxjRUuJ7ejYhBAKqP2POxPSeiYORQYxGRiBw4n+wSltVYohz2ryJiZ6JjY5GVNmr2TtxkSV/puejZJYWFQKmL9ByOEcAsLus6Lx0Odad04Tdj/tw5IURDB6awuChKbRsqMKW7a2oair84iLRNW9EvOlMVDz6L7AfewTup74E65EH4d9+C3TvskI3r2xV2C343OvW46L2OnzlwQM4OhbGDb94Ge86cxn+/uwVsBXbqtszlEudE5U71jqR+c2rzhUL5BZj2LH1zJnPFxCjo9B8vakwUU8Gir5eiMlJiPExqONjUHfuyDq05HRBbm2F0rosbXhz4rahEZLCkRdEi4Hv6ekkVFi8qPB4scazNuceqq5iJDKcOWdiIkgcCg9gMDyIsBbCZGwCk7EJHJjal/M4VtmKekeDESSmLcaSDBjrHY3snbiESiYsJPNxem044/KVWH9uM3Y/2odjL4/Ct3cCvn0TWLalBlsuboWnzlHQNuruRky94adw7P4FKp78Amy+Z1H9f69B4LwvILrh70pnAgsTOm9NLX7Z4sW3Hz2EB/YM4Sd/7cFfDo/i5kvXY0Pj/P5aQkRERFQokiRBqquDXFcHa8e2rMf1QCARHk73RNR9idvhIYhwCNrBA9AOHsg+uMUCpbkFcmsblGSAmOyZ2NwCyc7FCYhoaVhkC5pczWhyNed8XAiBoBrAYCI4NBZgSdxPruwcGUFcj8MX6oMv1Dfra3mtlam5EhudTYkFWBI9FB2NqLbXsHfiSWJYSAVXUW3HWVeuxoZzm7HzkT707hpHz44x9O4aw8rT6rDpwha4qwr4gUaSENl8LWJtr4b34Y/B2v88vI98HNEjf4b/wm9AuOoK17YyV+m04kuv34CL19bhaw8ewKGREN7zi5dxwyuX4fpXLodV4RsDERERlSa5ogLy+g2wrN+Q9ZiIRqH1+4wwMREgan190Pt6oPX7AFWF1nMcWs9xxGc+WZIg19dPr9ycXGwlsfCK7OEfXYlo6UiShAqrBxVWD9Z45+idmLayc3rPxORKzyE1hKn4JKbikzg4tT/ncSySBfXOhpw9ExsS4SJ7J+bGsJCKhrfBiXPe0Y5xXxA7H+5D//5JHHlhBMdeHsXqM+ux8fwWOD2FWy1Or1yJiSvuhvPl78P93LdhP/InWAf+Bv+F30Rs9esK1i4CLlpbh22tXnzj4YN4eP8I/ueZ43j84Chuvmw91tYXzzyYRERERItBstthWbkKWLkq6zGhadCHhxLDmo0hzVpvLzSf8b0IBaEPDUEfGoL68ovZx/Z6M1dsToSKcmsb5No6zpNIREvOIlvQ5GxGk/NEvRNn9ExM6504EhmGKlT0h3zoD/lmfS2v1Tu9orOjEZ21p+P8pguX6MxKB8NCKjrVLW6c9851GDkewM6HejF0xI+Dzw7hyAsjWPuqBqw/txl2V4F+dGUF4dP/AbHlF8H70EdgGd2Lyvvfi/CGtyN43s0QNv4ltlCqXTZ8/fJN+PPeIXzz4YPYPxzEu372Ev7+7BV411nLYJH5wZaIiIjMT1IUKE3NUJqagTMyJ0oUQkBMTKQCRN2XtvCKrxdibAxiagrq1G5gz+7sg9vt2cOak983NUOy8NdLIlp6mb0T23Puo+kqRqIjaT0Tp+dQHAwPpPVOnMJUfAoHp4wpHX597G787jUPwm0t79XoF2U15HwYGTHvCkSSBNTVeUx9jqdi8NAUdjzUi7HeIADAalew7pxGrDunCVZHASdu1qJwP/dtOF/6PiQIaJ42+Lffgnjr2YVrEwEARoIxfP3BA3j80CgAYFOTBzdfuh6ragu3cA7rnKg8sNaJzM/Mda6Hgol5EZNzJPZO3x8cAHR99icrCuTGplSAKLemDXNubYPk5FA/Ki1mrnUyBOKBjJ6Jg+EBtLha8MblVxS6aUsi+TM9r30ZFhYeL0InJoRA/75J7HioF5ODYQCAzWXBhvOa0P7KRlishZubzup7Dp6HPwZl6jgEJIS3vR/BV/4LYCns4izlTgiB+/cM4duPHII/qsKmSLjx1StxzRltUArQy5B1TlQeWOtE5leudS7icegD/WlzJCYWXOntgebzAbHonM+XamqNIDERIKZ6Jra2Qaqs5PBmKjrlWutkXgwLSwwvQvMndIGeXWPY9bAP/tEIAMDhsWLTBS1YdUYdFEthQkMpFoD7qS/Auft/AQBqzXr4L7kVav2WgrSHpg35o/jKg/vx9JFxAEBHixefv3Q9llfn96/brHOi8sBaJzI/1nk2oevQR0cSqzf3pVZwTvZMFP6pOZ8vud3TC660LkvNkai0tkGub4Akc9E6yj/WOpkNw8ISw4vQwumawLGuEex61IfQRAwA4KqyYfNFLVjRWQdZKcxfJm1HHoTn0X+BHB6BkK0InfnPCJ3+QUDm/C2FJITAb3cO4JbHDiMY02C3yPjweavwd6e1QM7TX7FZ50TlgbVOZH6s84XT/VNGT8TEQivTw5z7oA8Pzf1kqxVKc0tGT8TUMOfmFkg2W35OgsoOa53MhmFhieFF6ORpqo7DLwxjz2P9iATiAABPnQNbLm5F2+ZqSIUYbhoeheexT8J++H4AQLzpDExt/w/oVdmr1VF+DUxF8KU/7cdfj08AAE5vq8RnX7cObVVL38uQdU5UHljrRObHOl9cIhpJDG3uM3ompg9z7vcBmjb7kyUJckOj0ROxpS1rmLNcUZG/EyHTYa2T2TAsLDG8CJ06Nabh4F+HsPeJfsTCxgeKqiYntlzShuZ1BZgDRQjY992Dir98FnLMD2FxIvDqzyGy+TrjfzgVjBAC93T14z+fOIxwXIfTKuOjF6zGlR3NS/pzwjonKg+sdSLzY53nj1BV6EODqQBR7+tNBYmarw8Ih+d8vlRZlQoQ5ZY2KG1tiQVXWiHV1HKeRJoTa53MhmFhieFFaPHEIxr2Pz2AfU8PQI0aq7XVLnNjyyVtaFztzXt7ZH8fPA9/DLa+pwEAseUXwn/xt6G7m/LeFsrUOxHGF/+0Hy/1TgIAzlpehc++bh2avEuzMA3rnKg8sNaJzI91XhyEEBDjY6khzakwMTlP4sT43AdwOqE0t6bmSZRbW1PDnOWGRkgWTiNU7ljrZDYMC0sML0KLLxpSsfcv/Tj43BC0uBEaNqz2YOslbahdlufhCEKHs/t2uJ/5GiQtCt1ehcAFX0N07eX5bQdl0YXAL1/y4b/+cgRRVYfbpuCfL1yDy7c0LvpfmlnnROWBtU5kfqzz0qAHA9CTQWIiQEwOc9YHBzDn/zxFgdzUDKWlFUrbsuk5EhsaIdfXQ6qq5qIrZYC1TmbDsLDE8CK0dML+GPY83o/DfxuGrhn/uM3rK7Flexuqm115bYsyth+eh/4J1uFuAEB0zesROuMfodZvzWs7KNuxsRC+8MB+7Og3Vup79aoafOa1a1FfYV+012CdE5UH1jqR+bHOS5+IxaAN9E8Pa04f5tzvA2KxuQ+gKJBr6yDX10Oua4Bcl3a/vh5yXT2UunpIrvz+vkGLi7VOZsOwsMTwIrT0ghNR7H7Uh6MvjaT+jZdtqcbmi1vhrV/6xS1StDhcf/sPuF74LiRhzK0Ybz4LoY4bEFt9KVdNLiBNF/jFC734/lNHEdMEPHYLPnHxGly2sWFRehmyzonKA2udyPxY5+YmdB368DA0X3KOxD5ofT3QfX3Qhochxsfm7pWYRnK7ZwkT66bvV9dwyHORYq2T2TAsLDG8COWPfySCnY/0oWfHGADj337FtjpsvqgF7urF60V2IpbhnXC+9N+wH/oDJF0FAGgVzQhveTcim66BcNbkrS2U6fBoEDffvw97BgMAgAvba/HJS9ai1m07peOyzonKA2udyPxY5+VNqCr00VHoI0PQR0agDw9BHxmGPjxs3Cbui3BofgeUZcg1tYkAsR5yfYNxW1ef6qUo1zdAcru5IEuesdbJbBgWlhhehPJvYiCEnQ/3wbd3AgAgKxJWnVGPTRc0w+k9tVBoIeTgABw774Rz188gh0cBAEKxI7LuLQh33ACtblPe2kLTVF3g//21B//zzDGoukClw4J/vWQtXrO+/qSPyTonKg+sdSLzY53TfOjBwJxhoj4yDH1sFNC0+R3Q4TCGN6fCxLrpYDEZKtbWQbJal/bEyghrncyGYWGJ4UWocEZ7Atj5cB8GDxlz1SkWCe2vbMSG85pgd+fxjVaNwH7wd3B2/RjWkZ2pzbHWsxHuuAGxla8FZCV/7SEAwP6hAG5+YB8ODAcBAJesq8e/bm9HlWvhPxusc6LywFonMj/WOS0WoWkQ4+PQRoZyh4nJXooB/7yPKVXXpMJEJT1MrG9IzbMoeSvZS3EeWOtkNgwLSwwvQoU3dGQKOx7qw+hxY+ipxS5j3dlNWPfqRtgceZxDRAhY+p+Hs/t22A/fn5rXUPO0Ibz1PYhsfAeEoyp/7SHENR23P3scdzx3HJoAalxWfOqStbhwbd2CjsM6JyoPrHUi82OdU76JcDgRHo7MGSxCVed3QJs9Mex5eu5EJX3ocyJYlOz5m6apGLHWyWwYFpYYXoSKgxACAwcmseOhPkz0G3OM2JwK1p/bjLWvaoDFlt+efbLfB+fO/wfH7p9DjowbbbQ4EVl/FcJbr4dWuz6v7Sl3ewb9uPn+fTg8avxsXLaxAZ+4eA28jvn1MmSdE5UH1jqR+bHOqRgJXYeYnMgY+qxlhIrGNjE5Oe9jSl5vxgrPyTBRSc2tWAepqhqSLC/hmRUOa53MhmFhieFFqLgIXaB39zh2PtwH/0gEAOCosGDjBS1Y/Yp6KJY8vxmqYTj2/wbO7tthGd2T2hxrO88YorziYg5RzpOYquOHzxzDnc/3QBdAnduGf3vtOrx69YkXpGGdE5UH1jqR+bHOqZSJaBT66EhWr0QjWEwu2jIMxKLzO6DFkhrenAoUk2FifT2UWmNuRcnpXNoTWwKsdTIbhoUlhheh4qTrAse7RrHrUR+C48abpavShk0XtWDltjrISp7n+RACVt+zcHb/GLYjf4YkdACA5l2BcMf1iGz4Owi7N79tKlM7fFP4wgP7cGw8DAB405ZGfOzCNaiwzz5knXVOVB5Y60TmxzonsxNCQPincgx3NsLEZLAoxscx3yKQKipmhInp8ygmei9W10BSiqcTBGudzIZhYYnhRai4aaqOIy+OYM9jPoT9cQBARa0dWy5uxbItNZDk/E8OLE/1wLnzp3Ds/l/IUWMogbC4ENn4NoS33gCtek3e21RuInEN//3UUfzvC30QABo9dnz2tevwypXVOfdnnROVB9Y6kfmxzokMQlWhj44aIWKuYHF4GNrIMBAOz++Asgy5pna6l2LaoizpKz9LLndeFmhhrZPZMCwsMbwIlQY1ruPQX4ew94l+REPG5MGVjU5s2d6Klg1VhVlRLB6CY9+9xhDl8f2pzbHlFxpDlJdfCEjmnEOkWLzUO4kv/mkfeieMIetXdTbjI+evhmvGHJesc6LywFonMj/WOdHC6MFA7kVZEvMo6iMj0EdHAF2f3wGdzsS8ifXGnIp1dRlholxXbyzQYjm1hSpZ62Q2DAtLDC9CpSUe1XDgmUHse2oA8YixWnFNmxtbtreicY23MKGhELD2PmUMUT76ECQYP0hq1WqEt16P6Ia3Qdgq8t+uMhGOa/juE0dw18s+AECL147PXboeZyyrSu3DOicqD6x1IvNjnRMtPqFp0MfHZoSKiR6Lo9OLtohAYH4HlCRI1dWpMFFJBoszFmuRPLP//sZaJ7NhWFhieBEqTdGQin1PDeDAM4PQ4sZfwepXerD1klbUrZhfAS4FefIonDt+Csee/4Mc8wMAdGsFIhvfjvDW90CvWlWwtpnd88fH8aU/7Uf/lDHH5dtPa8GHz1sFh1VhnROVCdY6kfmxzokKR4TDxoIsyR6Kw0MZYWIyXISqzu+ANnuiZ6IRJir19al5FJX6etSuWYYJYQPcFYXpFEK0iBgWlhh+4ChtkUAce57ox6G/DkHXjP+BTWsrsfWSVlS3uAvWLikWgH3f3XB23wHLxCEAgICE2IqLEe58L+Jt5xk/fLSogjEVtz5+GL/uHgAALKty4POXrse2tkrWOVEZ4Hs6kfmxzomKm9B1iMmJjDBRG04f9mx8icnJ+R9UUSBVVkGuMr6kyirIlVWQqqohV1Ya26qqE9sSj9lsS3eSRCeBYWGJ4QcOcwhNRrH7sX4ceXEYiYWK0bapGpu3t6KywVm4hgkd1p4n4Oz6MezHH01tVqvXGqsor38rYHUVrn0m9czRMXz5T/sxFIhBAnDdK9rwmTdvQWAyxDonMjG+pxOZH+ucyBxENGr0Skzrpagl51BMLNCCiXHoodBJHV9yuozgsKoKUmU15KpKyFXViaCxMhUuGkFjFaQKDySZ883T0mFYWGL4gcNc/KMR7HqkD8d3jAHC+P+7vKMWmy9uQUWNo6BtUyYOw9F9Bxx774IcDwIAdHslIhvfgfDWd0P3Li9o+8zGH1HxnccO4fe7BgEAa+rdeOcZrdi+rh4Oq3KCZxNRKeJ7OpH5sc6JykOy1of7RqBNTEBMTkKfnICYGIc+MQF9YsLowTg5ATFh3OoT40aPRU1b+AsqCiRv5YwgsTKrx2KqR2NVJSR7YX+/pNLCsLDE8AOHOU0OhrDz4T707ZkAAEiyhFVn1GHTBS1wVRa2S7oUnYJj711wdt8BZeoYAEBIMmIrX4Nwxw2It57DIcqL6IlDo/jqgwcwGowBALwOC964uRFXdjRjRQ17dRKZCd/TicyPdU5UHk621oUQEIGAESpOpoWKE9mhYjJ8FMHgyTXS6UwEiDlCxaq0IdHJxzweSAo7LZQrhoUlhh84zG2sN4AdD/dh8OAUAEC2SGg/qwEbzmuGo8Ja2MYJHbZjj8LZfTtsPY+nNqs16xHuuAGRdVcC1gIOoTYRfzSO+w+M4WfPHE0tgAIAZy6vwls7m3H+mlpYFA47ICp1fE8nMj/WOVF5yGeti3g8o4di6jYRMhpB43hi2yT0ifH5L+KSTpYhebyZQWJyLsZUj8UqSJXGkGm5sgqSk78PmgXDwhLDDxzlYfioHzse6sXIsUBqm6fWgepWF2ra3KhpdaOq2Q2LtTCBkTK2H84dP4Fj768gqWEAgG6vQmTzNQhveTd0T2tB2mUWyTofHJrC00fGcE9XP546PIZkyde5bbhiaxOu6GhGo8de0LYS0cnjezqR+bHOicpDMde6EAIiFDRCxbSei6lh0mmhYrJHowj4T+7F7PbMUDG1mEtlYltaj8aqKkgeLySLZXFPmBYFw8ISU8wXIVpcQggMHJzCrkf6MNab3dVckoHKBieqW43wsKbVjcpGJ+Q89jiTIhNw7PklnDt+AsXfY7RbUhBbfakxRLn5LA5RPgm56tw3GcFvdvTjvh0DGAvFAQCKBJy3phZXdjbjlSuqIfPfmqik8D2dyPxY50TlwWy1LlQVYmoyM1ScmA4VMwLHxH3EYgt/IUmC5PFkrBadDBWTgWJ6+ChVVRmLwfD3niXHsLDEmO0iRPMTCcYx3hfEWOJrvC+ISCC7K7likVDV7MoIED21DkjyEl9MdQ22ow8ZQ5T7nkptjtdtRrjjBkTXvhmwcELd+ZqrzuOajkcPjOCern682DuZ2t5W5cCVHc24fHMTqlwFHrJORPPC93Qi82OdE5WHcq91IQQQDhu9EycnEwHieKo3Y3qomHpsaurkXsxmSwSIaStGp4ZGJ1eUTvZiNMJH9l5cOIaFJabcL0JkEEIgPBXDWG8yQAxh3BdEPJK9kpbVrqC6JS1AbHPDVWlbsr/GKKN74Oy+A45990DSjPn2dEcNwpuvQ2TLO6FXNC/J65rJfOv88GgQ93b14/e7BhGMGf/vrYqE7evq8dbOZnS0ePlXN6Iixvd0IvNjnROVB9b6wglVhfD7Z+2paASNmeEjotETHzgHqcKTu6dieqhYVZUKHyW3u+x/j2JYWGJ4EaLZCF0gMBZN9T4c6wtioj8ELa5n7Wt3W1DT6s7ogbjYC6hIkXE4dv8Czh0/hRLwGW2ULYiueQPCHTdAbTydQ5RnsdA6D8c1/HnvEO7p6seewel5Ltvr3LiqsxmXbWqA28a/phEVG76nE5kf65yoPLDW80NEIpnhYrKn4uSM1aMnEqtHT00CevbvwydksWQNfzaCxMrE6tHGfcuatZCrqxf/RIsAw8ISw4sQLYSuCUwNhTMCxMnBMISe/cPjqrKlgsOaVjeqW9ywOpRFaIQK25E/wdl1O2z9z6U2xxs6Ee64HtH2ywGFi3SkO5U63zXgxz0v+/DnfcOIqsYbo8uq4LJNDbiqsxlr6yuWoMVEdDL4nk5kfqxzovLAWi9OQtMgAv6MhV0yQsXJCaPnYmKhF31iAgiH5318yeVGza//AMnlWrqTKBCGhSWGFyE6VWpcx+RAyAgPE8OY/aMRYObPk2SswJwculzT6kZVkwvKKazAbBneCUf3HXAc+M30EGVnPcJbrkN48zsh3A2ncGbmsRh1PhWJ4w+7h3DPyz4cG59+w9va7MVbtzVj+7p62C2FWU2biAx8TycyP9Y5UXlgrZuHiEYSAWJ6qDiZCBqnQ0UxOQFl2XJ4vvg1SMoidLIpMgwLSwwvQrQU4hEN475gRg/E0ET2alaSLKGy0ZnRA9Hb4ISsLGw4sRQehXPXL+DY+RMowUEAgJCtiLa/EeGO90Jt3LYYp1WyFrPOhRB4oWcS93T58OjBUWiJXqWVDgsu39KEKzuasazauQitJqKF4ns6kfmxzonKA2udzIZhYYnhRYjyJRKIGwFi73SAGA3mWIHZKqOq2ZURIFbU2Oe3ArMWh/3w/XB23w7rwN9Sm+ONpxurKK95A6CU38q+S1XnI4Eo7ts5gF93D2DQPz058KtWVOPKzmact6YWlqVeOZuIUvieTmR+rHOi8sBaJ7NhWFhieBGiQhFCIDQZw3ha78PxvhDi0RwrMDsUVLe4UdPqSg1jdnrnXoHZMvgynDvugP3AbyHpcQCA5m5EZMu7EN50LYSrbsnOrdgsdZ1rusBTR8ZwT5cPzxwZT41Ab6iw4YqtzXjz1iY0eDiPJNFS43s6kfmxzonKA2udzIZhYYnhRYiKidAF/KORjABxoj8ETc3+4XRUWDJWX65pdcPuzu41KAWH4Nz9czh23gklNGS8jmxDdN2bjSHK9VuW/LwKLZ913jsRxq+7B/DbnQOYCBshrSIB57fX4aqOZpy5ogoyV60mWhJ8TycyP9Y5UXlgrZPZMCwsMbwIUbHTNR2TQ5FEz0NjGPPkUAgix4r17iqbESC2pa3AbE9MDqvFYD/4ezi7fwzrUFfqOfHmsxDquAGx1ZcCsiVPZ5VfhajzmKrjkQMjuLfLh5f6plLbl1c78ZaOZrxxcyOqnOU3JJxoKfE9ncj8WOdE5YG1TmbDsLDE8CJEpUiN65joD00HiH1B+Eci2TtKgLfOkdEDsarJBfvoS3B23w77oT9A0o15E7WKZoS3vBuRTddAOGvyfEZLq9B1fnAkiHu7+vHH3YMIxoxh5jZFwmvW1+OqzhZsafbMOaSciOan0LVOREuPdU5UHljrZDYMC0sML0JkFrGIinFfCGO90wFiaDJ7BWZZMVZgrm51o7ZORXPwz2g+/j+wRIYBAEKxI7LuLQh33ACtblO+T2NJFEudh2IaHtg7hHte9mH/cDC1fV29G1d1NuPSjY1w2ZTCNZCoxBVLrRPR0mGdE5UH1jqZDcPCEsOLEJlZJBBPWzxlrhWYJdRUhdGov4hm9Vk0WA+iUulHvO1shDtuQGzlawG5dEOsYqtzIQR2Dfhxd1c/Hto3jKhqjCl32xRctrEBV21rQXudu8CtJCo9xVbrRLT4WOdE5YG1TmbDsLDE8CJE5SS5AvNYb1qA6AtCjWZPgGiXAqi3HkSD9SDqvePwdL4S8ulXAs7qArT81BRznU+G4/j9rkHc292P4+Ph1PZtrV5c1dmCi9fWwWaRC9hCotJRzLVORIuDdU5UHljrZDYMC0sML0JU7pIrMKf3QBzvD0HPsQKzSx5HbU0YVWtXoWrtCmMFZlfxL4pSCnWuC4Hnj0/g3q5+PH5wBFqinVVOK960pRFv6WhGW5WzsI0kKnKlUOtEdGpY50TlgbVOZsOwsMTwIkSUTdd0TA6GjQCxZwoTRwYwMWGBQPZQZHe1zVh5uTXHCsxFotTqfDgQxW92DOA33f0YChjzTkoAXrWyGld1tuDc1TVQZC6IQjRTqdU6ES0c65yoPLDWyWwYFpYYXoSI5keNqgjsfB5TLz+H0QEVw/F2TGitWftJEuCpd6Km1ZUKEKuaXFAKOJS2VOtc1QWePDSKe7r68eyx8dT2Ro8dV2xtwhVbm1BXYS9gC4mKS6nWOhHNH+ucqDyw1slsGBaWGF6EiBZOnuqBc+dPIe28DyOBegzF2zGobsCQ2Ixg1JW9f2IF5mQPxNo2Nzz1Tsh56h1nhjrvGQ/j1939+O3OAUxGjEVqFFnChe21uKqzGa9YVgVJYm9DKm9mqHUimhvrnKg8sNbJbBgWlhhehIhOQTwEx7574ey+HZbx/QCAoFYFX9Vb0F/xOgz76zHmCyEWyl6B2WKTUdXsQk2i92FNmxvuavuSBF5mqvOoquPh/cO4p6sf3b6p1PYV1U5c2dmMN25uhNdhLWALiQrHTLVORLmxzonKA2udzIZhYYnhRYhoEQgBa++TcHbfDtvRhyDBKCa1ajVCW67HWNObMTaE6UVUfEGosewVmG1OJTV0Ofnl9NpOuXlmrfMDwwHc09WP+3cPIRTXAAB2i4zXrK/HWzubsanJw96GVFbMWutENI11TlQeWOtkNgwLSwwvQkSLS548CueOn8Kx5/8gx/wAAN1agcjGtyO89T3Qq1ZB1wX8IxGMJ8LDsb4gJvpD0LXsInR6rNMBYpuxgMpCV2A2e50HYyoe2DOEe7r6cWA4mNq+oaECV3U243UbG+C0FteiM0RLwey1TkSsc6JywVons2FYWGJ4ESJaGlIsAPu+u40hyhOHAQACEmIrtyPccQPibecZBZigqdMrMCdDxKmhcM66rKixZ/RArG5xwWKbPQwrlzoXQqDbN4V7uvrx8P5hxBLhq9um4A2bGnHVtmasrnUXuJVES6dcap2onLHOicoDa53MJm9hYVdXF66++mr86Ec/wjnnnJPxmM/nw3/8x3/g6aefht/vx/r16/GBD3wA27dvP6nXMnOB8iJEtMSEDuvxx+Hsvh3244+mNqvVaxHuuB6R9W8FrNmLogCAGtMw3h8yAsReI0AMjEWz9pMkwFvvRHXbdIBY2ehMrcBcjnU+EYrjd7sGcG93P3onIqntp7VV4q2dzbhobR2sSuFWqCZaCuVY60TlhnVOVB5Y62Q2eQkLjx49iuuuuw7Dw8O44447MsLC4eFhvP3tb8fExATe+c53orGxEXfffTd27dqFb3/727j88ssX/HpmLlBehIjyR5k4DEf3HXDsvQty3Bguq9srEdn4DoS3vhu6d/kJjxELq9NzHyZuw1PxrP1kRUJVkyu1+vKqTXUIx2OwOiypELEc6ELgr8fGcU9XP544NAo9cZ2rcVnxpi1NeEtHM1oqHYVtJNEi4Xs6kfmxzonKA2udzGbJw8IHH3wQn/nMZzA5OQkAWWHh5z//efzf//0ffvGLX+CMM84AAEQiEbztbW/D8PAwHnnkEbhcuXvxzMbMBcqLEFH+SdEpOPbeBWf3HVCmjgEAhCQjtvI1xhDl1nMyhiifSNgfw1hvZoAYC2uz7q9YZdhdFticCmxOi/GV/N5lmd7mVBL7GV+KtbRDxkF/FL/p7sdvdgxgJBgDAEgAzllVg6s6m3HOqhooMhdEodLF93Qi82OdE5UH1jqZzZKGhe9///vx+OOPo729HRs2bMDvf//7jLBQ0zS84hWvwNq1a3HXXXdlPPeuu+7CZz/7Wdxyyy14/etfv5CXNXWB8iJEVEBCh+3YI8Yqyj1PpDartRsQ7rgBkbVvAazOhR9WCATHo2k9EEPwj0QQDcZPqc4VqzwdMLqmA0Wb05IWKqYFjolbS5GFjKqm44nDY7jnZR/+enwitb3Za8dbOprxpi1NqHWf+irURPnG93Qi82OdE5UH1jqZzULCwoUt5wng8OHD+Od//mdcf/31+MEPfpD1+IEDBxAKhdDZ2Zn1WEdHBwBjrsOFhoVEREtCkhFbeQliKy+BMrYfzh0/gWPvr2AZ3QvPozfB/fRXEdl8DcJb3g3d0zr/w0oSKmocqKhxYPnW2tSFeXhoCrGohlhIRSysIRZWU/ejIdX4PvWV3M/4EjqgxXWE43rOYc9zUSzS3D0YXRbYM3o5KqmejNICeljOl0WRcfHaOly8tg7Hx8O4t6sfv981gP6pKL735FH88OljuGhtHa7qbMbpbZVL0gYiIiIiIiLKtuCw8I9//CNsttl7ewwODgIAmpubsx5ramoCAPT29i70ZRcyGrDkJM/NzOdIVAr02nUIXvhVhF51Exx7fglH90+g+HvgevF7cL70A8RWX4pw5w1Qm89acMEmd5cVCXanBXbnwi6/QgioUR2xsJoZKoa0GdumA8ZoIoQUuoCmCoT9cYT9CwsZ5bSQ0Z7VY3FGj8a0bRbb/EPGFTVOfOyi1fjguSvw0P4R3POyDzv6/Xhw3zAe3DeMVTUuXLWtGW/Y1AiPY8FvW0R5xfd0IvNjnROVB9Y6mc1CfpYX/FvXXEEhAPj9fgDIOSeh02kM5QuHwwt9WdTWzq+rZCkrh3MkKg0eoO0TwPaPAfsfAJ79b0hH/wL7oT/AfugPQFMH8MobgS1XAdaFLcyR7zoXQiAe1RAJxhENqogE48b9UNr9YByRoIpoyLhNbtM1AV0ViPjjiJxEyOhwWWF3W+FwW2B3WeGosMLhsiS2WWF3WRLbrLC7LfBWO/Hu8yvxngvascs3iZ89exz3vdyHI2MhfPuRQ/juX47gTZ0tuO5VK9DRVrU0/2BEi4Tv6UTmxzonKg+sdSpHi95FY64pEJOPncxwstFR884TIEnGBcjM50hUsurOB954PpSRPXB23wH7vnsgDXQD930I+p/+DZEt1yGy5Z3QK7J7U6crhjqXXIDTZYWz3nrCfYUQUGP6LL0VZwyRDqmIpn2fDBlDUzGEpmILaqOsSKneiducFpxeWYehmIoDkyEM+OPY/1Q//uXpfjTWOHDexnpcsKkOHo8dFvvSDJcmWqhiqHUiWlqsc6LywFons0n+TM/HooeFbrcbgLH68UzJHoVer3fBxxUCpi/QcjhHolKl1m6E/6JvInD2p+DY/Qs4d/wUSsAH19/+E84Xv4fomjcg3HED1MbT5+zfXTp1LsFiU2CxKXBVzv9ZQghocT01RDqampNx5hyNM0LIsApdFdA1gUhARSSgZhx3LYC1SAs5QzpivYN48MHBZHNzDoeee+EXBVa7wpCRlkTp1DoRnSzWOVF5YK1TOVr0sLCtrQ0AMDAwkPVYcj7D5NyFRESlRjiqET79HxDe9gHYjvwJzq7bYet/Do4D98Fx4D7EGzoR7rgB0fY3Aoq90M3NO0k61ZBRS5uPMbPHYnJbKBjH6HgUkVAcVg2wQgIEEA2qiAbVE79YentlwObIXNRl5iIwdmd22Gi1K5BkhoxERERERGQ+ix4Wrl69Gh6PB93d3VmPdXV1AQBOP/30xX5ZIqL8ki2IrXkDYmveAMvwTji674DjwG9gHeqC9aGPQn/qywhvuQ7hze+EcDcUurVFLzNknHtu3CRdCDx7dBz3vuTDS4fHYNMlOISEeqsFr2qpxNbaCtgFMleaTuvVqMV1CB2Ihozh1RhdSHsBqzPHIi/JHo1p38sWGbIsQZKRuDW+ZFmCpKTdlyXIMqYfTzwmSSc3fQcREREtPiEEkOhlJlL/Me5k9D7L2Gf6gZn7iIwdc+0j0l9ixj7Tz83cR8zyWtkvkLVP+oZFO0+R4zjp+8z2b3qi9izFeWZ2IZzwhOH3h4ujZ+EitUEs3oGKymL9P/I2OFHdnL0GR7lZ9LDQYrHg9a9/Pe666y68+OKLqWAwEongZz/7Gerq6nD++ecv9ssSERWMWr8Fge3/juA5n4Zz18/h2PlTKMFBuJ+/Ba4Xvoto++WIdN4A1J1X6KaaiixJOGdVDc5ZVYOBqQh+vWMA9+0YwEvBGF7qG4bsG8arV9Xgqm0teNWKaigzegKqcR3xcOaQ6Gg4PVTMMWw6rEKN6RACxvaQCiC65OeaChIVKTtwTN0iI2TM3C87hJx+HLM8R8p43ezXmz5uKticz3GVHG3K8VwGpETmIUQiXEj+gi6mfylP3578RS+1j8B0UDLbvvpJHE+cRLvStkESGKsIwj8VgdBFKrNIhjep5yLz9dIDisxzSwsuss47+7gZ5zTjNTKCrJM9Tq72nPR5IfWkrP8f8znOjHOZvp8IPHIcd+5/n1M5DoioDMiKhDd/8jRYHUqhm1JQkphrRZITuO222/Dd734Xd9xxB84555zU9uHhYbzlLW9BOBzG9ddfj5qaGtx9993YvXs3vvOd7+D1r3/9gl9rZMS8k4pKElBX5zH1ORKVFS0O++H74ey+HdaBv01vbzkdwbYLEW85G/Gm08tymPJSUzUdjx8axd1d/fjb8YnU9havHW/paMabtjahxjW/nouz0eI6YhFjTsbk8OjoHPMy6powfpnUBfTkbWobprfpgr+IpMnZEzMjmMQsIWRacJozwJRn7eWZ9dyscDbHcWe8rqxIqKpyY2IimPMXzFl7MqTupP0Sm2u/OXpJzHn8GR8wTqZny4wmznjoFNs96+uLGfvN3CfH+Z5EuzN7opzg+FkvkhY6pIUQuQOvzO3T93MEG4nt8wm8Mo6HGfukb9fnsc8itis9lCKiRSYBUtr95HcZf2vL2Gf6gex9pLTj5NpHmvFaM/eZfiEpa5/0b9L3mb09xo00Z3tyv1aO9pziuVqtCtS4dsqXsqL7I+giNWfRzqqIDlTV7ETn65YV3/+zRZDMnua171KEhQDQ09ODf//3f8fTTz+NeDyO9evX44Mf/CAuuOCCk3otMwdpDAuJzMsy+DKcO+6A/cBvIenx1Hah2BFvOgPx1rONr8bTGB4usqNjIdzb1Y/f7xqEP2rMZWiRJWxfV4crO5txWmtl0X0ISA8UZwaJYkbQqOvIDCG1WZ6rzTwGoOvGEOycQWZin9zPTbyuNnNbejuQ43izP5fve0Q0G0mC8cu9JE3/7i9JGdulVCCQ2EdK2wdp+6Q/D9n7SonUQZLn2mc6jLHaLFBVLfEimY9lPR8z24TpQCJ9qomsc80+LpB+HtnnihO8xgmPk9WemfvOfV5Z5zLvf5PMxzLPZbH+TXIc5yT+H6Vu8hV2pR+zyD63mB1/TyezyVtYmE9mLlBehIjMTw4NoXboMUT3Pw5r79OQw8MZjzM8XDqRuIY/7xvGvV392DXgT21fXevCVZ3NeP2mRlTYF31WDponIzBMBpPIHUJqaQHmrAFq9nNz9uA8UUCacz/MeK0Z4euMbbIiQ9f06ZNM/pKcuJ/avNBfHJPHmscvpbmOP/NQxub0b9JuZux8Mj1VUu2a9Zzmalf2i82n/Tn/vebxvNTNKZx3VliVatP09syQY0Y4k7XPjOOlvc6CwjJ5IYHazEAlM2jLCo5yHC9zn+wASsr1vDn+rYoRP7sTlQfWOpkNw8ISw4sQkfll1LkuoEwcgrXvGVj7noGt75lZwsNXpIWH2xgeLoI9g37c09WPP+0ZQkQ1whynVcbrNjTgrZ0tWN9YUeAWUqnjezqR+bHOicoDa53MhmFhieFFiMj85qxzwfAw3/wRFX/cPYh7uvpxZCyU2r6l2YMrO5rxmvX1cFjLe1JjOjl8TycyP9Y5UXlgrZPZMCwsMbwIEZnfguo8FR4+nRYejmTuYnGkwsNYy9lQGzsZHp4EIQRe7J3EvV39eOTACFTd+J/jdVjwxs2NuLKjGStqXAVuJZUSvqcTmR/rnKg8sNbJbBgWlhhehIjM75TqXAgo4wdh9T2zgPBwG6Cc2qq/5WY0GMNvdw7g19396J+KprafubwKb+1sxvlramFR5AK2kEoB39OJzI91TlQeWOtkNgwLSwwvQkTmt6h1vtDwsPUcqA2dDA/nSdMFnj06jru7fHjq8BiS/7vq3Da8eWsTrtjahCavo6BtpOLF93Qi82OdE5UH1jqZDcPCEsOLEJH5LWmdCwFl/MD0nIe+ZyCHRzN3sTgQbzozER6ezfBwnvqnIvh1dz/u2zGAsVAcACBLwHmra3HVtma8ckU15CJesZPyj+/pRObHOicqD6x1MhuGhSWGFyEi88trnS84PDwHakMHw8M5xDUdjx4YwT1d/XixdzK1vbXSgSs7mvGmLU2oclkL2EIqFnxPJzI/1jlReWCtk9kwLCwxvAgRmV9B61wIKGP7YfUZQ5atfc9Ajoxl7mJxIt58JuItyZ6HDA9nc2Q0hHu6fPjD7kEEohoAwKpI2L6uHm/tbEZHixcSexuWLb6nE5kf65yoPLDWyWwYFpYYXoSIzK+o6pzh4aIIxzX8ee8Q7unqx57BQGp7e50bV3U249KNDaiwWwrYQiqEoqp1IloSrHOi8sBaJ7NhWFhieBEiMr+irnOhG+FhYsiyte/ZWcLDsxBrPRvx1rOh1ncACofdJu0a8OOel334875hRFUdAOCyKrh0YwPOX1OLrS0eeB389yoHRV3rRLQoWOdE5YG1TmbDsLDE8CJEZH4lVedZ4eEzkCPjmbtYXIg3n8nwcIapSBx/2D2Ee7t8ODoWznhsda0LHS1edLZ60dlSibYqB4crm1BJ1ToRnRTWOVF5YK2T2TAsLDG8CBGZX0nXeSo8fNoYtux7Nnd42HImYi0MDwFACIEXeibxx92D6PJN4fh4OGufGpc1ER5WorPFiw2NFbAqcgFaS4uppGudiOaFdU5UHljrZDYMC0sML0JE5meqOhc6lLF9Rs9DhofzMhaKobtvCl2+KXT1TWHvkB9xLfMHwaZI2NTkQUdLJTpbveho8aLKWb7/ZqXKVLVORDmxzonKA2udzIZhYYnhRYjI/Exd5zPDw75nIEcnMnfJCA/PgVq/tazDw6iqY++gH12pAHESkxE1a7+VNU50tlSiIxEerqh2cuhykTN1rRMRANY5UblgrZPZMCwsMbwIEZlfWdW50KGM7k30OkwsmDIjPNStbqjJOQ9bEqsty+W7crAQAsfGw+jum0K3bwpdvsmsOQ8BoMqZGLqcmPtwQ6MHdguHLheTsqp1ojLFOicqD6x1MhuGhSWGFyEi8yvrOmd4eFImQnF09xvDlrt9k9g94EdsxtBlqyJhY6MnI0CsdtkK1GICyrzWicoE65yoPLDWyWwYFpYYXoSIzI91niYVHj4Na3LOw+hkxi661Y1481mIp1Zb3lr24WFM1bFvKJAattztm8JYKJ613/JqZ1p4WIkVNU7IHLqcN6x1IvNjnROVB9Y6mQ3DwhLDixCR+bHO58Dw8KQIIdA7EUGXbzLR+3AKh0dDWftVOizY2uJNrLzsxaZGDxxWpQAtLg+sdSLzY50TlQfWOpkNw8ISw4sQkfmxzhdgXuFhBeItZyGeWm15S9mHhwAwFYljh8+fChB3DfgRVfWMfSyyhA2NFanehx2tlahzc+jyYmGtE5kf65yoPLDWyWwYFpYYXoSIzI91fgp0zQgPfc8wPFwgVZseutztM+Y/HAnGsvZrrXSgs3U6PFxd6+LQ5ZPEWicyP9Y5UXlgrZPZMCwsMbwIEZkf63wRJcPDZM/D/ufmDg/bzoFat5nhIYyhy76pSGrYcrdvCgeHg5j5I+mxW7C1JblwSiU2N3vg5NDleWGtE5kf65yoPLDWyWwYFpYYXoSIzI91voR0DZbRPUZwOFt4aPNkznnI8DAlEFWxI7HqcpdvCrv6pxCOZw5dViRgXUMFOlsrU6su11fYC9Ti4sZaJzI/1jlReWCtk9kwLCwxvAgRmR/rPI9mhoe+ZyHHpjJ3yQgPkz0P2XMOAFRd4MBwINX7sKtvEkOB7KHLzV57YtEUI0BcU+eGInPoMmudyPxY50TlgbVOZsOwsMTwIkRkfqzzAtI1WEZ3p4WHz+UOD1teOT3nIcPDDAOJoctdifDw4EgQ+oyfY7dNwdZmY9XljlYvtjR74LaVX+9N1jqR+bHOicoDa53MhmFhieFFiMj8WOdFhOHhKQvGVOzs96O7bwpdvkns7PcjGNMy9pElYG19RWrYckeLF01eR4FanD+sdSLzY50TlQfWOpkNw8ISw4sQkfmxzouYrsEysisRHCbDQ3/mLjavER4m5zys3cTwMI2mCxwcCSaGLk+i2zeF/qlo1n6NnsTQ5USA2F5fAYvJhi6z1onMj3VOVB5Y62Q2DAtLDC9CRObHOi8hDA8XxaA/mprzsNs3hf1DAWgzfvZdVgWbmz2p8HBLsxcV9tIeusxaJzI/1jlReWCtk9kwLCwxvAgRmR/rvISlwsOnE6st/zU7PLRXIt6cHh5uZHg4QyimYfeAH12+SXT1TWFH/xQC0cyhyxKA9np3YuEULzpbKtHstUOSSqf3IWudyPxY50TlgbVOZsOwsMTwIkRkfqxzE9HV6Z6HyTkP44HMXVLh4aug1qyD7l0OzdMKKPYCNbr46ELg8EgoFR52+6bQNxnJ2q++wobOFi+2JlZeXl/vhkWRC9Di+WGtE5kf65yoPLDWyWwYFpYYXoSIzI91bmLzCA8BQECCXtEEzbMcuncZNO8yaN4Vqfu6uwmQijcEy4eRQGLosm8KXX1T2DsUgDZj2WWHRU4NXe5orURHsxceR/EMXWatE5kf65yoPLDWyWwYFpYYXoSIzI91XkZ0FZbhnUZwOPA3KJNHoUwdh6SG53yakG3QvG1GeOhZDs273AgRE7fCXmX8IJWRSFzDrgE/un1Tqa+piJqxjwRgdZ0rsXBKJTpbvWitdBRs6DJrncj8WOdE5YG1TmbDsLDE8CJEZH6s8zInBKTwKJSp41D8PVAmj0P2H4cy1QNl6jhkfx8koc15CN3mTYSHiTCxcjl0z7JEqNgGWJx5OpnC0YXA0bEQuvqM3ofdfZPomcgeulzjsqKztTK1cMr6hgpY8zR0mbVOZH6sc6LywFons2FYWGJ4ESIyP9Y5zUlXIQf6jTBx6jjkRIiYChbDwyc8hOZqTBvebPRMTAaLekWzaRdcGQ3GsCNt6PKeQT/UGUOX7RYZmxor0JEIEDtavKh0WpekPax1IvNjnROVB9Y6mQ3DwhLDixCR+bHO6ZTEw0ZwONUDeepYqkei8f3xnHMkphOyFXpFS2posxEkTt8XjhrTDHGOqjr2DPgT4eEkun1TmJwxdBkAVtUYQ5c7Wr3obPFiebVzUYYus9aJzI91TlQeWOtkNgwLSwwvQkTmxzqnJSMEpOiEER5mDG9OBIv+Pkh6fM5D6FZ32lyJyXkSp8NEWF15OpnFJ4TAsfEwuvumUisvHxvPnj+y2mk15j1sNXoebmz0wGZZ+NBl1jqR+bHOicoDa53MhmFhieFFiMj8WOdUMLoGOTgIxX8c8uTx6eHNyTAxOHjiQzjrcg5v1rzLoFe0AMrSDOldKhOhuDHnoW8K3b5J7B7wI6ZlFqZVkbCx0ZOa97CjxYtql+2Ex2atE5kf65yoPLDWyWwYFpYYXoSIzI91TkVLjUDx90FOzJeYDBOT8ybK0ck5ny4kJTHEeVmiV+KK6WDRswzCVV/0Q5xjqo69Q4HUsOWuvimMh7N7Yy6vdqaFh5VYWZM9dJm1TmR+rHOi8sBaJ7NhWFhieBEiMj/WOZUqKTqZmhsxfZ7EZO9ESYvO+XxhcULzLJteyTktTNS9yyBs8/vAkk9CCPRORFLDlrt8UzgyGsrar9JhwdYWbyJArMTGxgo4bQprncjk+J5OVB5Y62Q2DAtLDC9CRObHOidTEjrk0FCiF+KxtLkSE6s6B/ohYe4feN1RneqFqFcuT5s3cRk0TxugnHjobz5MhuPY0W/0Ouz2TWHXgB9RVc/YxyJL2NhYgc1tVai0ymjw2NFYYUejx45Grx1OqzlXpCYqN3xPJyoPrHUyG4aFJYYXISLzY51TWdJikP19Ri/EyePGvImplZyPQ46Mz/l0AQl6RXP2oiue5dArl0N3NQDSwhchWQxxTcf+oUBi1WWj9+FoMDbnc7wOCxo9djQkA8TEV4PHhkaPAw0VNjgYKBIVPb6nE5UH1rqJCB2IhyGpIUjxECQ1nPMWaghadTviy84vdIuXBMPCEsOLEJH5sc6JskmxQKIXYmJ+xLThzcrUMUhqZM7nC8UOzdM2Y3jzdLAo7JV5OhNj6LJvKoIdvimMxQSODE5hwB/FkD+KQX8UwZg2r+NUOa2JQNE2HSh67amQsaHCflKrNBPR4uF7OlF5YK3nkRCAGskR5oUgxcNAxve5wz6k7S/N3P8E0+ZkNEWSMfq+XUU5Vc6pWkhYaFnithARERHlJGwV0Oo2QavblONBASk8kponMRUmTvUYC7D4+yBpUVgmDgETh3IeX7dXGsObZ67k7F1uDHG2OBbtXCRJQmulE21Vzpy/WASiKgYTwWEyQJz5FVF1TITjmAjHsW9o9teqcVkzeiZm9lK0o95tg0VhoEhERESLRAhAi06HcMmgLhHGGWFerp57s4V9kazwL2+nYnFCWF3GrcUFYc38Xm3cZsqgcKEYFhIREVHxkSQIVz1UVz3UpjOyH9dVyAFfqhfi9PDmRLAYHoEcnTRWcx7ZmfMlNHej0QvRMzNMXAHd3QjIizckuMJuQYXdgjV17pyPCyHgTwsUc4WKQ4EYoqqOsVAcY6E49gwGch5LloBa93TPxJnDnhs9dtS6bVDk4l6lmoiIiOZJCECPZwR0mb3tkgFeZqA3Z2+8RKAHNfFcoZ+4HYtxKoo9Ed4lgrzELVIhnyst3EtsS3yP5GNWZ1oYmP64o2BT2JQahoVERERUemQLdO9y6N7liOPV2Y/HQ6leiMrkMcip4c1GD0U5HoQSHIQSHIS1//mspwvZCs3TmjFXop5YfEWrXAFhrzLGciwSSZLgdVjhdVixtr4i5z5CCEyGjUBxIBUgRrMCRlUXGA7EMByIYWe/P+exFAmoSwsRGyqmhzsnv2pcVsiLeI5ERERlTVczwjjk6F2XOX/e3L3xsnrziflNeXKqhGzL6o2HXAGdJW2b1ZUI8pyZYZ81c39YHIv6x1o6eQwLiYiIyHysLmi166HVrs9+TAhIkfFUT0Q5uZKzP7GSs78Xkh6HZfIoMHk05+F1a0XG8ObkXIm6dxngXAVoApDti3pKkiShymVFlcuK9Y25A0VdCIyH4llDnNN7KQ4HotAEUt/PxiJLqbkTGzx2NHocaPTYMgLFKqcVEgNFIiIqdUIAWsQI5NSIEdhpESAswzoykuiBNz0EF2oYmUNyM3vuIUf4J+nx/JyKbMkI6jCjd13u3ngzh+Zm7o/0/WXGSOWAC5wUAU6cSmR+rHOiEqJrkIMDafMkps2VOHkcSmhwXocRsg3C5oaweSCsbghbBXRrBYQt8WWdcZt2P2M/WwWgLF7wqOkCY6HYnIHiSDAGfR7XKpsiJYJE+6zDnr0OCwNFMhW+pxPliRY3hsqqEUhaWoinRhKLYaR9r0WMoC6xH9Iek7QZ36uRRNgXST1+okXVFpOQ5FQghxy96zJ74zlzbs98vgvp8/BBseXtXKi0cDXkEsMPHETmxzonMhE1DMXfl7noytRxY95Ef48xT+IiE7I1K1hMBYrW9EDSkxZQpu83vQ8U+wmHUKu6wEgg90IsQwEjaBwNxubVdodFzgoUp3sr2tHksaPCzl4KVDr4nk5lS+jZIV0ybEsEdTMfzwjmtNxBHdJCvlQ4qEbyNqw26zRlC4TiACwOyHY3VNmR1RvPGHqbqzeeE5lDc7MX0oBsW9SpTIjmi6shExERES0VixNadTu06vashyQJqKtxYbR/AIgGIMUDkGJ+SPGgcRsLQk5+Hw9AiqV9xTNv5VjAGMoEQNLjkCLjQGT8lJsvZGuOYNENPa0HpLBWwGOrwMpkQFnvgWh1Q7d6IGzVEDYPYrITQ2FgMBDDkD+Ws5fieDiOiKrj+HgYx8dnX+nQbVOM8LBiZqBoSwx/tsNl4xxGREQZhAD02IygLj2kO1HvuszHM4K69H2SQZ42+9QVS36qFieExWF8KQ6jR13y+8TCFcnHhNUJKI6Mx4XFCP+SjyfDwPTjJh+HYgUwHaxM8A8DJUUIgZgmEI5riMQ1hOM6wnEt8b1xP6Ia2yOJ7cl9InENa+rceOeZywp9GgXHsJCIiIhoMckKhN0LYfOe+rF0LTtYnHFfztoWzB1QpgeP0QkgOnHKzWuWLbmHUddXQLRWQLW4ERAOTOoOjKt2jMRtGIzZ0B+xoi9sQU9IwUDEikDMiSOjKo6MhmZ9LY/dkhYg5h767LAyUCSiAtPV3EFdRu+5HMNg4+GsOfNyDrWd2asPhUmxhGzLDupmhG4ZIV16aKfkCPFmhoGJQM/Y/8Q94qm0aHoizFNzB3YzA75kuDfz8Zn7RlXj/nymUpmNBOAtHc1lP+phyc/+wIEDuOWWW/DXv/4VQghs2rQJ73vf+3DBBRcs9UsTERERlbZk8Ghf7OBxRqCYuJVjQUhx/4zHcwSU8SAAQNLVEwaPHgDNc7XLkWieZEFccSEsuxCCE37hxKRux5hqx6TmQEBzIjDhQGDciSCc8AkH9sOJoHAiACcCcEC2e+CuqESVx4NGryNnqGizyKf+b0lEhaVrRo86XTXmtdNjgKYmbuNGYJe4L+lxQI9D0hK3enzG9pixvxYH9OT9WGJfFdBiiX3VHHPmpfXSS/TMk3S1IP8kxjx4zvn1nrOkBXEzH097bu4w0GmEd1yx1tTm7J03I+CbLezL1XsveT+m5SfktioSnFYFDosMp1VJfMlwWBU4EveNx6fvb2isKPugEFjisHDHjh1417vehXg8jne84x1YtWoVHnnkEbz//e/Hpz/9abz73e9eypcnIiIioqTFDB6FntFzUcoxtFrOMbQ653DrRPAoCxV2dQp2TKEq/bUkLOwTawCI+xUEfQ4jRBROBOHAlHDCBwfiihuwVUCye2BxeGFzeeF0V8LtqYS3shpeTxVkhzdtcRkHe7SQeQkBCG06cJstKNOmQ7bpwC0R1qUHbunb00K26fvJ0C49zFON4E/LDPXmDPiEXuh/uXkRij13UJej95zx+PRjGd/nejwjDHQAspXXqjKj6WLO4bSp+yfovTdbuHcqvfPmSwKMsC4R4KXCO6sCZ1rA50htT3vcKsNpyXx8ZhhokVkTJ2tJw8LPf/7zCIVC+P73v4+LLroIAHDNNdfgYx/7GL71rW/h/PPPx6pVq5ayCURERES02CQ5Mefh/CbJnlMqeMwdJiaHVhtzPZ5gnsdE8GiVNFQhiCoEjd9EZoolvvwnbp4GBXHFDc3qgrB5INsroDg8gM2TWmQGFgcACUKSACnZczFxX5Km7yf3AVLfQ5Iy76f2mfn89Fs5bZ+0x2Y8RyTbkv4aOY4rZrZj5nElCQKztCWt7dP7zHV+SLRr+lyQ41xE1vOz/02n90l7bJbXzAshEiFXeuA2sxdbjvAra1tmT7fM0O5ke8+l304fX9Lnt1BRsROSAihWCNkKyFYIJXErWwHFBiFbjDAteV+xQsg2QLZAKLbEvpbE49aM7dPHs8zoXZdrKG3mUFuGd+UtV+88I5Cbvcdd6v4cvfeKoXfedLiXu3eeIy20m7nNaVXgtBjHsFtkSKyTorRkYWF/fz927dqFs846KxUUAoAkSbjxxhtx//33495778XHP/7xpWoCERERERW7RQ8eQ1lzNkpxP6RoAJHQJEKBSUSCk4iH/dCifiBqhJBWNQi7HoIbYbgRgUcyFmRRoEHRpgBtCogMnHobKe9ERsCZHihOh6xi1mB2RtCb3CbLqFFj2b3qTCAZrBlhmSUrcJvebjMWgki7nxm4zQztZoZ5ybDOuC8US+I27ZizhXlK5vGnA2WiE9OFQEzVEdN0xLTEfVVHVNPTthtf1t4pDI8FETpR771ZgsB89c7L6nE3S++8nOGdZWbvvcx92DuvPC1pWAgA69evz3psxYoVAICdO3cu1csTERERUbmR5Onhw7keBuBOfOWiC4HxUBz7A1EMToYxNjmByclxBPwTCAUnEU2EjC4RgluKwIMw3FIYDsRh9M8zfiuUoScjKMip7cn7ie2SSD1HkQCrDCgSYJEBiwQoyVtJwJJ4LPPLeJ4sAYqkT9+HgJzabrRFlpKRmG70foMAksM4RfK+gCQEkNoHqe3pz5FyPpZ+3ORxRI7np++TeCy5PcdzFmvhBin12jCG3ObcZ+HmE0+J+QRiyZAttd2aGYilB205ArfZesql7qc/5wRhHoey0lITQiCuielATtURVdPuazriqsgI7qKqjrg2Yz818xjJ/WLJfTWRtT25fzxPPfLSzeydl9nzbrbeeTkeZ+88ypMlCwtdLhcAIBgMZj02Pj4OABgeHp738cz8s58arWHicyQqd6xzovLAWi9tiiShrsKGugobNjV5ADRk7aPpAmOhGAb90dRXXziOaFxPzQtl3OqIqtP3I6qWcavmo7tJGgmA3ZKYx8kip34JdVjkGdunb+0Weca2Ez/HpkiL+0treoiYFSjCCDAzQse0x2aEo1KO0HLmPhBIO17ucFWCjqpKFyYCcQgpV+85I7SDbCmZi0FptJJOlRACmi6yArn0EC49tMsZ3s3sgacavfPmOkZyn2RwV0wkALbENc2mTN/aLDJsigSPywYLBOxpIV6uOfKSQd5s29k7j4rBQt6SliwsbG9vR2VlJR5//HH4/X54PNNDS+6//34AQCQSmffxamsXYWhKkSuHcyQqd6xzovLAWje3RgAbT/EYqmaEi+GYNj20LaalhrZNb0sb7pa+b9q25LC3aI7jJHvQCMAILpf4F3VJgtEzxpb2C3XqvpLWS8bYx2HN3C/78WTPGiuc9unjFENPmqqCvjqVIk0XiKpaKqCLJv6oEFXTbzO3p/ZVtcR2PfMYqlH70/umHSfxnJg2fcw8/53ihOwWORHWGXVttxr3kwGePfmYdcb3yftWxQj5rJmP2bKel77deJ7dIsMiL/IfOIhMQhJCLNnl4ic/+Qm+9rWvoaOjAzfddBOamprw2GOP4ZZbboEkSaivr8cDDzwwr2ONjvqxdC0tLEkyfqkw8zkSlTvWOVF5YK1TsUmGktEZvR7TezpGZ+n9GEmEELP1joyqxdlTMnevyFw9Ik/8nFw9JUuhzoUQRv9IMX1fT95PdLzUE43Xk9sEICCM/TC97/R+mcfVE/eRdj/jGBmvPeMYaW1IPsc4pnHA7OdP75PZptznCTHjPLLO8wTnn9Gm6bbHcwyJjaoibehrrp56ItXLTiuypM6qSEYvOkXO6F2X7FVns8iwJ79PPpbsfWeRUvetyoyeecnnzzzujO+ti90TeZGVQq0TLUTyZ3o+lnQ15Pe85z2IRqP43ve+h+uuuw4A0NDQgG9961v45je/iaqqqnkfKzWViYmVwzkSlTvWOVF5YK1TsVBkGW6bDLdtaV8nGUpGEr2ZIjOGZadCx8S2XOHlnKHljFAybz0lgcScYJkBo8tugarqqaDpRIFaekAmZgZZM58/6/bsUMt4TGSHZVT0FFmCPRGY2dPCuGT4lhHUJfaxzhgma1dkWC0y7IlgLzOsy9wvPQBMbpeLIKgrhfdKvqdTOVrSsBAAPvCBD+C6667Dvn374HA4sH79emiaho9+9KPo7Oxc6pcnIiIiIqIlZlFkVCgyKuxL+zrpoWQqiMwVSuZ47MQ9Kqf31dJCSWO10+KaZ22pSTAWyYEkTS+QI0mJ7VJqMWkJifuJx5PTsqX2SdueOoaEVEglpx8jY7uU2D/H80/QNkgzjpvR5rTnI/H8mdslCTJmbjeeb031rJPSeuDJOXrgSVnBXXrYZ1VkzmFHREVtScPC+++/H1arFZdccglOP/301PYnn3wS8XgcZ5111lK+PBERERERmUhBQ8m0ANLusiPgDwOYLaiaPVzLCtTSgqr04Czj+cgMzjKej1kCOeQIzqT0480evBERUXlb0rDwF7/4Bfbu3YtXvOIVqSHHgUAA//mf/4mGhga88Y1vXMqXJyIiIiIiWrC5QklJAurqPBgZ4TxmRERkTksaFn7oQx/Ce9/7Xrzzne/E3/3d30EIgV/96lc4evQovve978HhcCzlyxMREREREREREdECyEt58LPPPhs/+tGPUFlZiVtvvRXf+9730Nraip///Oc477zzlvKliYiIiIiIiIiIaIGWfIGTc845B+ecc85SvwwRERERERERERGdoiXtWUhERERERERERESlg2EhERERERERERERAWBYSERERERERERERAkMC4mIiIiIiIiIiAgAw0IiIiIiIiIiIiJKYFhIREREREREREREABgWEhERERERERERUQLDQiIiIiIiIiIiIgLAsJCIiIiIiIiIiIgSGBYSERERERERERERAIaFRERERERERERElMCwkIiIiIiIiIiIiAAwLCQiIiIiIiIiIqIEhoVEREREREREREQEALAUugHzJUmFbsHSSZ6bmc+RqNyxzonKA2udyPxY50TlgbVOZrOQn2VJCCGWrilERERERERERERUKjgMmYiIiIiIiIiIiAAwLCQiIiIiIiIiIqIEhoVEREREREREREQEgGEhERERERERERERJTAsJCIiIiIiIiIiIgAMC4mIiIiIiIiIiCiBYSEREREREREREREBYFhIRERERERERERECQwLiYiIiIiIiIiICADDQiIiIiIiIiIiIkpgWHiK3vnOd2L9+vVQVfWkj/HrX/8aV1xxBbZt24Zzzz0XX/jCFzA5OTnnc3p7e3HaaafhV7/61Um/LhHNXz5rfWRkBJ/73Odw8cUXo6OjA5dffjl+9rOfQdf1UzkFIjqBfNb5d7/7Xaxfvz7n12233XYqp0FEJ5CvWp+txtO/iGhp5PM93efz4aabbsKrXvUqnHbaaXjrW9+K++6771SaT1RwlkI3oNz94Ac/wHe+8x2cffbZ+Jd/+Rf09vbizjvvxIsvvohf/vKXcDgcWc8ZHR3FBz7wAYRCoQK0mIhOxnxrfWpqCtdccw0GBgZw7bXXYvny5Xj88cfxpS99Cfv27cOXvvSlAp8JEc1mIe/pe/fuRUVFBT73uc9lHYcBAlFxm2+tf/Ob38z5/CeffBK//e1v8drXvjafzSaiBZhvnff19eHv/u7vMDo6iiuuuAJbt27F888/j5tuugm7du3Cpz/96QKfCdHJYVhYQP39/bjttttw3nnn4Yc//CFk2ejouWHDBtx0002488478fd///cZz/nb3/6GT3ziE+jv7y9Ek4noJCyk1n/84x/j2LFjuOWWW/D6178eAHD11VfjxhtvxF133YX3vOc9WLNmTcHOhYhyW+h7+t69e7F27Vq8+c1vLlSTiegkLKTWc9X34OAgvvrVr2LlypX42te+lte2E9H8LKTOv/GNb6RGBV177bUAgGuvvRYtLS348Y9/jO3bt+OVr3xlwc6F6GRxGHIB/f73v0c8Hse73vWu1AUIAN70pjehsbER9957b8b+X/jCF3DttddCCIG3v/3t+W4uEZ2khdR6PB5HZ2cnLr300oxjnHvuuQCA3bt356fRRLQgC6nzYDCI3t5erFu3rhBNJaJTsNDP7zN94QtfwMTEBL785S+joqJiqZtLRCdhvnUei8Xw2GOPoa2tDVdffXXGMW688UYAwC9/+cv8NZxoETEsXCQHDhzAjTfeiNNPPx2nnXYarr/+euzcuXPO53R1dQEAtm3blrFdkiR0dHTg8OHD8Pv9qe179+7Fe9/7XvzhD3/Ieg4R5cdS1/pNN92Eu+66K+ODCQDs2rULANDa2rpIZ0JEs1nqOt+3bx+EEFi7di0AIBqNIh6PL/6JENGc8vH5Pd0zzzyDhx9+GG9605tw5plnLso5ENHclrLOx8fHEY1GsW7duqzP7l6vFzU1NSd8LaJixbBwkVxzzTXweDz45Cc/iXe84x14/vnn8e53vxvDw8OzPmdgYAAulwterzfrsaamJgDGHAhJP/3pT3HTTTfxr5BEBZSPWk8KhULYt28fvvGNb+Dee+/FRRddhNNPP33xToaIclrqOt+7dy8AYOfOnbjsssvQ2dmJzs5OvOtd70r9YYCIll4+39MB4JZbboHVasXHPvaxxTkBIjqhpaxzl8sFAAgEAln7aZoGv98/5+sQFTPOWbhI3ve+9+Ef/uEfUt87nU7813/9Fx577DG87W1vy/kcv98Pt9ud87HkhKnpi5jYbLZFbDERnYx81HrSd77zHdx5550AgBUrVuAzn/nMqTafiOZhqet83759AIAXXngB119/PZqamrBnzx7cfvvtuPrqq3HnnXeis7NzMU+JiHLI53v6iy++iK6uLlx55ZVoaWlZhNYT0XwsZZ17PB6sW7cOXV1d6OnpwbJly1L7Pfjgg4jH49A0bRHPhih/2LNwkVxxxRUZ3yc/5A8NDZ3U8YQQAJDVnZmICiuftb59+3Z873vfw8c//nFMTEzgiiuuSA2LIKKls9R1fv755+PGG2/E3XffjWuvvRbbt2/Hhz/8YfzkJz+Bqqr48pe/fPKNJ6J5y+d7+s9//nMAwHvf+96TOjYRnZylrvN//Md/RDQaxfve9z48/vjj6O3txX333YfPf/7zqKqqgsXC/llUmphELZL6+vqM75N/cYjFYrM+x+12IxwO53wsEokAADwezyK1kIgWQz5r/eyzz8b27dvx/ve/H7fffjvC4TC+8Y1vnGzTiWielrrOt2/fjo997GOoqqrK2K+jowOnnXYauru7MTU1dbLNJ6J5ytd7eiwWw6OPPopNmzahvb39VJpMRAu01HX+2te+Fl/96lcxNjaG97///di+fTu+8pWv4BOf+AQ2b96MysrKxTgNorxjWLhITqYHYFtbGwKBQM45DgYGBiDLMhobGxejeUS0SApV61u2bMGaNWs4STJRHhTyPb22thaAsWIyES2tfNX6M888g2AwiNe//vUn3VYiOjn5qPOrrroKTz75JO666y786le/wpNPPom3ve1tOHr0KFasWHFK7ScqFIaFBdTR0QEA2LFjR8Z2IQS6u7uxdu1aLmZCZALzrXVd1/GmN70J11xzTc7jBINBOJ3OJW8vES3cQur8yiuvxDve8Y6cxzl06BBcLldWTwgiKg4n8/n9ueeeAwCcd955+WkkEZ2ShdT5E088gd/+9rew2+3o7OxER0cHbDYbDh48iL6+Ppx11ll5bz/RYmBYWECXXXYZrFYrfvzjH6fmPgCA++67D0NDQ7jyyisL2DoiWizzrXVZllFfX48XXngBzzzzTMYxfve736Gvrw+XXHJJXttORPOzkDqvrKzESy+9hMceeyzjGL/5zW9w8OBBXH755ZzjiKhInczn9507d8LpdGLt2rX5bCoRnaSF1Pn999+Pf/3Xf8XRo0dT2+LxOL71rW/B6XTi6quvzmfTiRYNP4nmSU9PD1588UUsX74cp512GgCgtbUVN954I2677TbccMMNuOyyy3DkyBHceeed2Lp166y9DoioeJ1qrX/qU5/CNddcgw996EO45pprsGzZMnR1deE3v/kNVq1ahY9//OOFOjUiSlisOv/oRz+Kt7/97Vi1ahW6u7vx61//GuvWrWOdExWJxfr8fuTIETQ1NUFRlHyfAhGdwKnW+Q033IAHHngAN9xwA6699lo4nU787ne/w0svvYQvf/nLaGhoKNSpEZ0ShoV58vzzz+NTn/oU3vKWt6QuQgDw4Q9/GLW1tfjZz36GL37xi6irq8Pb3/52fOQjH0lNvkpEpeNUa729vR133303br31Vtxzzz3w+/1oamrCe97zHnzoQx/iokdEReBU63zdunW4++67cdttt+F3v/sd/H4/GhsbccMNN+CDH/wg65yoSCzW5/fx8XE0Nzfns+lENE+npCM12gAAANVJREFUWudr167FnXfeiVtvvRU//OEPoes6Nm7ciB//+Md49atfXYhTIloUkkjvV0tERERERERERERli3MWEhEREREREREREQCGhURERERERERERJTAsJCIiIiIiIiIiIgAMCwkIiIiIiIiIiKiBIaFREREREREREREBIBhIRERERERERERESUwLCQiIiIiIiIiIiIADAuJiIiIiIiIiIgogWEhERERERERERERAWBYSERERERERERERAkMC4mIiIiIiIiIiAgAw0IiIiIiIiIiIiJK+P+0djJgurpUtwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "_ = multi_forecasts.mean.tail().T.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The previous example made use of dictionaries where each of the values was a 500 (number of forecasts) by 10 (horizon) array.  The alternative format can be used where `x` is a 3-d array with shape 2 (variables) by 500 (forecasts) by 10 (horizon)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(2, 500, 10)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0.0"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "exog_fcast = np.array([x0_oos[-500:], x1_oos[-500:]])\n",
+    "print(exog_fcast.shape)\n",
+    "array_multi_forecasts = res.forecast(start=500, horizon=10, x=exog_fcast)\n",
+    "np.max(np.abs(array_multi_forecasts.mean - multi_forecasts.mean))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## `x` input array sizes\n",
+    "\n",
+    "While the natural shape of the `x` data is the number of forecasts, it is also possible to pass an `x` that has the same shape as the `y` used to construct the model.  The may simplify tracking the origin points of the forecast.  Values are are not needed are ignored. In this example, the out-of-sample values are 2 by 1000 (original number of observations) by 10.  Only the final 500 are used. \n",
+    "\n",
+    "<div class=\"alert alert-warning\">\n",
+    "    <h3><b>WARNING</b></h3>\n",
+    "    Other sizes are <b>not</b> allowed. The size of the out-of-sample data must either match the original data size or the number of forecasts.\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(2, 1000, 10)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0.0"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "exog_fcast = np.array([x0_oos, x1_oos])\n",
+    "print(exog_fcast.shape)\n",
+    "array_multi_forecasts = res.forecast(start=500, horizon=10, x=exog_fcast)\n",
+    "np.max(np.abs(array_multi_forecasts.mean - multi_forecasts.mean))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Special Cases with a single `x` variable\n",
+    "\n",
+    "When a model consists of a single exogenous regressor, then `x` can be a 1-d or 2-d array (or `Series` or `DataFrame`)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                          AR-X - GARCH Model Results                          \n",
+      "==============================================================================\n",
+      "Dep. Variable:                   data   R-squared:                       0.955\n",
+      "Mean Model:                      AR-X   Adj. R-squared:                  0.955\n",
+      "Vol Model:                      GARCH   Log-Likelihood:               -2280.96\n",
+      "Distribution:                  Normal   AIC:                           4573.92\n",
+      "Method:            Maximum Likelihood   BIC:                           4603.36\n",
+      "                                        No. Observations:                  999\n",
+      "Date:                Tue, May 30 2023   Df Residuals:                      996\n",
+      "Time:                        10:55:42   Df Model:                            3\n",
+      "                               Mean Model                               \n",
+      "========================================================================\n",
+      "                 coef    std err          t      P>|t|  95.0% Conf. Int.\n",
+      "------------------------------------------------------------------------\n",
+      "Const         -6.4609      0.226    -28.561 2.064e-179 [ -6.904, -6.018]\n",
+      "data[1]        0.7695  8.995e-03     85.552      0.000 [  0.752,  0.787]\n",
+      "x0             1.7394  5.329e-02     32.643 1.021e-233 [  1.635,  1.844]\n",
+      "                               Volatility Model                              \n",
+      "=============================================================================\n",
+      "                 coef    std err          t      P>|t|       95.0% Conf. Int.\n",
+      "-----------------------------------------------------------------------------\n",
+      "omega          0.1073  7.460e-02      1.438      0.150   [-3.895e-02,  0.253]\n",
+      "alpha[1]   8.6689e-03  1.513e-02      0.573      0.567 [-2.099e-02,3.832e-02]\n",
+      "beta[1]        0.9717  2.616e-02     37.149 4.549e-302      [  0.920,  1.023]\n",
+      "=============================================================================\n",
+      "\n",
+      "Covariance estimator: robust\n"
+     ]
+    }
+   ],
+   "source": [
+    "mod = arch_model(y, x=exog.iloc[:, :1], mean=\"ARX\", lags=1)\n",
+    "res = mod.fit(disp=\"off\")\n",
+    "print(res.summary())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These two examples show that both formats can be used."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     h.01  h.02  h.03  h.04  h.05  h.06  h.07  h.08  h.09  h.10\n",
+      "999   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "forecast_1d = res.forecast(horizon=10, x=x0_oos[-1])\n",
+    "forecast_2d = res.forecast(horizon=10, x=x0_oos[-1:])\n",
+    "print(forecast_1d.mean - forecast_2d.mean)\n",
+    "\n",
+    "## Simulation-forecasting\n",
+    "\n",
+    "mod = arch_model(y, x=exog, mean=\"ARX\", lags=1, power=1.0)\n",
+    "res = mod.fit(disp=\"off\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Simulation\n",
+    "\n",
+    "`forecast` supports simulating paths. When forecasting a model with exogenous variables, the same value is used to in all mean paths.  If you wish to also simulate the paths of the `x` variables, these need to generated and then passed inside a loop. \n",
+    "\n",
+    "### Static out-of-sample `x`\n",
+    "This first example shows that variance of the paths when the same `x` values are used in the forecast. There is a sense the out-of-sample `x` are treated as deterministic."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1.04095499, 1.27798546, 1.30149937, 1.53498654, 1.60825322,\n",
+       "        1.53831759, 1.55948399, 1.49811376, 1.74794163, 1.54496175]])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = {\"x0\": x0_oos[-1], \"x1\": x1_oos[-1]}\n",
+    "sim_fixedx = res.forecast(horizon=10, x=x, method=\"simulation\", simulations=100)\n",
+    "sim_fixedx.simulations.values.std(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Simulating the out-of-sample `x`\n",
+    "\n",
+    "This example simulates distinct paths for the two exogenous variables and then simulates a single path.  This is then repeated 100 times.  We see that variance is much higher when we account for variation in the x data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from numpy.random import RandomState\n",
+    "\n",
+    "\n",
+    "def sim_ar1(params: np.ndarray, initial: float, horizon: int, rng: RandomState):\n",
+    "    out = np.zeros(horizon)\n",
+    "    shocks = rng.standard_normal(horizon)\n",
+    "    out[0] = params[0] + params[1] * initial + shocks[0]\n",
+    "    for i in range(1, horizon):\n",
+    "        out[i] = params[0] + params[1] * out[i - 1] + shocks[i]\n",
+    "    return out\n",
+    "\n",
+    "\n",
+    "simulations = []\n",
+    "rng = RandomState(20210301)\n",
+    "for i in range(100):\n",
+    "    x0_sim = sim_ar1(np.array([1, 0.8]), x0.iloc[-1], 10, rng)\n",
+    "    x1_sim = sim_ar1(np.array([2.5, 0.5]), x1.iloc[-1], 10, rng)\n",
+    "    x = {\"x0\": x0_sim, \"x1\": x1_sim}\n",
+    "    fcast = res.forecast(horizon=10, x=x, method=\"simulation\", simulations=1)\n",
+    "    simulations.append(fcast.simulations.values)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally the standard deviation is quite a bit larger. This is a most accurate value fo the long-run variance of the forecast residuals which should account for dynamics in the model and any exogenous regressors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[2.95980851, 4.9043695 , 6.49587772, 7.50716786, 8.27211937,\n",
+       "        8.71428305, 8.71321666, 8.67711577, 8.80764753, 9.17554847]])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "joined = np.concatenate(simulations, 1)\n",
+    "joined.std(1)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/_sources/univariate/univariate_volatility_forecasting.ipynb.txt b/_sources/univariate/univariate_volatility_forecasting.ipynb.txt
index 626d5e99ca..f215beac3c 100644
--- a/_sources/univariate/univariate_volatility_forecasting.ipynb.txt
+++ b/_sources/univariate/univariate_volatility_forecasting.ipynb.txt
@@ -31,24 +31,6 @@
     "plt.rc(\"font\", size=14)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Future Forecast Shape Changes\n",
-    "\n",
-    "<div class=\"alert alert-warning\">\n",
-    "    <h3><b>WARNING</b></h3>\n",
-    "    The future behavior of `forecast` is changing.\n",
-    "</div>\n",
-    "\n",
-    "Versions of `arch` before 4.19 defaulted to returning forecast values with the same shape as the data used to fit the model.  While this is convenient it is also computationally wasteful. This is especially true when using `method` is `\"simulation\"` or `\"bootstrap\"`. In future version of `arch`, the default behavior will change to only returning the minimal `DataFrame` that is needed to contain the forecast results. In the current version of `arch`, calling `forecast()` without the `reindex` keyword argument produces a `FutureWarning`.  You can silence the future warning by:\n",
-    "\n",
-    "* Using `reindex=False` which uses the future behavior. \n",
-    "* Using `reindex=True` which uses the legacy behavior.\n",
-    "* Importing `from arch.__future__ import reindexing` which will set the default to `False` and silence the warning."
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -101,10 +83,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Iteration:      5,   Func. Count:     35,   Neg. LLF: 6970.282172935112\n",
-      "Iteration:     10,   Func. Count:     63,   Neg. LLF: 6936.718477483884\n",
+      "Iteration:      5,   Func. Count:     35,   Neg. LLF: 6970.2779565773335\n",
+      "Iteration:     10,   Func. Count:     63,   Neg. LLF: 6936.718477482076\n",
       "Optimization terminated successfully    (Exit mode 0)\n",
-      "            Current function value: 6936.718476988963\n",
+      "            Current function value: 6936.718476988951\n",
       "            Iterations: 11\n",
       "            Function evaluations: 68\n",
       "            Gradient evaluations: 11\n"
@@ -122,7 +104,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "forecasts = res.forecast(reindex=False)"
+    "forecasts = res.forecast()"
    ]
   },
   {
@@ -184,75 +166,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "                h.1       h.2       h.3       h.4     h.5\n",
-      "Date                                                     \n",
-      "2018-12-31  3.59647  3.568502  3.540887  3.513621  3.4867\n"
-     ]
-    }
-   ],
-   "source": [
-    "forecasts = res.forecast(horizon=5, reindex=False)\n",
-    "print(forecasts.residual_variance.iloc[-3:])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you fail to set `reindex` you will see a warning."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\git\\arch\\arch\\__future__\\_utility.py:11: FutureWarning: \n",
-      "The default for reindex is True. After September 2021 this will change to\n",
-      "False. Set reindex to True or False to silence this message. Alternatively,\n",
-      "you can use the import comment\n",
-      "\n",
-      "from arch.__future__ import reindexing\n",
-      "\n",
-      "to globally set reindex to True and silence this warning.\n",
-      "\n",
-      "  warnings.warn(\n"
-     ]
-    }
-   ],
-   "source": [
-    "forecasts = res.forecast(horizon=5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "When not specified, or if `reindex` is `True`, then values that are not computed are ``nan``-filled."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                h.1       h.2       h.3       h.4     h.5\n",
-      "Date                                                     \n",
-      "2018-12-27      NaN       NaN       NaN       NaN     NaN\n",
-      "2018-12-28      NaN       NaN       NaN       NaN     NaN\n",
-      "2018-12-31  3.59647  3.568502  3.540887  3.513621  3.4867\n"
+      "                h.1       h.2       h.3       h.4       h.5\n",
+      "Date                                                       \n",
+      "2018-12-31  3.59647  3.568502  3.540887  3.513621  3.486701\n"
      ]
     }
    ],
    "source": [
+    "forecasts = res.forecast(horizon=5)\n",
     "print(forecasts.residual_variance.iloc[-3:])"
    ]
   },
@@ -276,7 +197,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -302,7 +223,7 @@
    ],
    "source": [
     "res = am.fit(last_obs=\"2011-1-1\", update_freq=5)\n",
-    "forecasts = res.forecast(horizon=5, reindex=False)\n",
+    "forecasts = res.forecast(horizon=5)\n",
     "print(forecasts.variance.dropna().head())"
    ]
   },
@@ -317,7 +238,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -358,7 +279,7 @@
     "    sys.stdout.write(\".\")\n",
     "    sys.stdout.flush()\n",
     "    res = am.fit(first_obs=i, last_obs=i + end_loc, disp=\"off\")\n",
-    "    temp = res.forecast(horizon=3, reindex=False).variance\n",
+    "    temp = res.forecast(horizon=3).variance\n",
     "    fcast = temp.iloc[0]\n",
     "    forecasts[fcast.name] = fcast\n",
     "print()\n",
@@ -376,7 +297,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -420,7 +341,7 @@
     "    sys.stdout.write(\".\")\n",
     "    sys.stdout.flush()\n",
     "    res = am.fit(last_obs=i + end_loc, disp=\"off\")\n",
-    "    temp = res.forecast(horizon=3, reindex=False).variance\n",
+    "    temp = res.forecast(horizon=3).variance\n",
     "    fcast = temp.iloc[0]\n",
     "    forecasts[fcast.name] = fcast\n",
     "print()\n",
@@ -445,17 +366,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Iteration:      5,   Func. Count:     40,   Neg. LLF: 6846.496665348549\n",
-      "Iteration:     10,   Func. Count:     75,   Neg. LLF: 6822.883179474602\n",
+      "Iteration:      5,   Func. Count:     40,   Neg. LLF: 6846.48928243015\n",
+      "Iteration:     10,   Func. Count:     75,   Neg. LLF: 6822.883188650879\n",
       "Optimization terminated successfully    (Exit mode 0)\n",
-      "            Current function value: 6822.882823372691\n",
+      "            Current function value: 6822.882823441358\n",
       "            Iterations: 13\n",
       "            Function evaluations: 93\n",
       "            Gradient evaluations: 13\n",
@@ -468,7 +389,7 @@
     "# TARCH specification\n",
     "am = arch_model(returns, vol=\"GARCH\", power=2.0, p=1, o=1, q=1)\n",
     "res = am.fit(update_freq=5)\n",
-    "forecasts = res.forecast(reindex=False)\n",
+    "forecasts = res.forecast()\n",
     "print(forecasts.variance.iloc[-1])"
    ]
   },
@@ -483,7 +404,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -492,15 +413,15 @@
        "(16804.0, 17165.0)"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 1152x432 with 1 Axes>"
+       "<Figure size 1600x600 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -518,14 +439,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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jZWG4GM5YbA4XPX/pP26MJqL1YoA4BFKpFD772asxMjKKJz/5LxNDrdHRUezcuQvz850vCVmtsCIxKZibnV3+en/ykx/j5ptvxGMe8zj88z+/peHB6mc/u3PF6x4fn0Amk0WxWMBDH/qwlst/8IP/hqbpsCwLe/ceCwC49957Wj5u3757V7yukFIK+/b9oWXZyb33/h7j4+MYGxtb9/dFRERERETdp5TCwfkqbNeHoWvYM5XddLME4+FiJuHyMFxsmbvIjdFEtAarnwxLA0fTNDzpSU/B7OwM3v3uK+B5rdV+P/7xHbj77t/g7LPbt/eu19jYOEzTxG9+8yv4/tKD0B/+cC/+939/tuznFgoLAIATTzyp4cFoYWEBX/7ydQAaqxjDWYxh5aGu6zjrrEfh3nt/jxtv/GrD1/7Nb36N//N//gbveMflMAwD97nPfXHsscfhpptuwMGDB6KPq9Vq+MIXPreq7/nqqz/R8O9bbrkJf/jDvTjnnMdDCLHu74uIiIiIiLpvbtHBYsWtL03JJLYPb3a6JpC2dOSzJqbyKeyczODY7TmctGsU992Txwk7R7BnOovt42lMjFjIpQ1Y9Y3PUgZLXRarLuYWbRyar+IPR8v47cFF/Gr/In53qIR9MxUcWahhvuSgVHXhuH7XxlsR0eBhBeKQePnLX4W77/4trr32M/je976Ls89+LHbv3g3bdvDTn/4E3/jGzbjPfe6Hl7zk5T27DalUCo95zGNx44034NJL/xqPeczjcPToEXzxi9di9+49uOee37X93Ac/+FSMjY3hqqs+hlqthr1792L//v346levQ6lUAgAsLpaij5+cnAQAfPrTn8QZZ/wZzjrr0Xj5y1+F//mfO/DmN78JP/rRD/CABzwQR44cxn/+5+eh6zouvfSy6PNf+9p/xN/+7SV4yUtegGc+8znIZDL48pe/hEqlsqrv+ZZbvo5CoYA//dMzce+99+CLX7wWe/bsxYte9LKufV9ERERERNQ9paqLo4VgacqO8TSyqa13WrzixuiWZS5q1Rujm6sXuTGaaHPbekfKIZXJZPDOd74fX/vaV3DrrV/HV75yHYrFAlKpFI477gS88pWvxtOe9iyYptnT23HppZdhdDSPb3zjFlxxxVtx7LHH4ZWvfDXm5+eX3W48Pj6O//iP9+ADH3g3rrvui3BdB9u2bcejH30O/uqvzsd55z0D3/ved3DBBS8EADz96c/BnXf+BF/5ynX44Q9/gLPOejSmp7fhwx/+JD7+8Q/jv/7r2/j617+GsbFx/MmfnIYXvOBFuO997x9d3ymn/Ane974P48or34drrrkKQLBB+cwzH4E3vOGypJuY6N///e341Kc+gXe/+x3I5/N46lOfjhe96KXI5/Nd+76IiIiIiKg7bNfHwbkqAGBixMJ4H5amDDohBCxTh9UmXOx0Y3Q54WuHQWJLuMiN0UQDT6hNUmM8O7uI5bo6XdfB7OxBTE3tgmnyQYB6681vfhNuuOHLuOaaLyZuc6YA75fJhACmp0cxM7OIzXEEJiJaHo9rRLQZ+FLh90fKcFwf2ZSBY7Zl287y43Ft9TrZGL2cho3RCUtdiGh9NA2Ymhpd8+ezApGIiIiIiIiGmlIKB2YrcFwfpqFh91SGi0C6rNON0VGwWG+L5sZoos2BASIRERERERENtaMFG+WaB00T2DOVDUIu2jANG6NTrZe3hIuxt57PjdFEg4ABIhEREREREQ2tYiXYJAwAOycySFuts/2ov3RNQLf0xN+NlAqu3y5cVMHGaJm81EUTIpqxGJ+3aBkaTIaLRKvCGYhE1DO8XybjTB0iGjY8rhHRoKo5Pu49UoZUClP5FLaNpTv6PB7XNod2G6PDfy8Xdyy7MdrQoDFcpCHDGYhERERERERETTxfYv9sBVIpjGRMTOcTemdpU+vVxuiw5do0NFgJy100LnWhLYgBIhEREREREQ2VYGlKFa4nkTJ17Jrk0pStRggB0whCwGZhuOj67TdGu15Q3VhJ+NqGntASzY3RNOQYIBIREREREdFQObxQQ8X2oGsCu6cyDHWoQUO4uIqN0Y4n4cc2RqPNxuiklmhujKbNjgEiERERERERDY2FkoOFkgMhBHZNZpBKaG8laqcbG6Orjo9qm43RLeGizo3RtDkwQCQiIiIiIqKhULE9HF6oAQCm8ymMZMw+3yIaNittjA5nLLbbGF1zfNSSwkVujKYBxwCRiIiIiIiINj3XkzgwW4VSCvmsiclRq983ibYYTRNIWzrSSAgX6xuj48FifGO0VAq268N2W8NFboymQcAAkYiIiIiIiDY1KRX2z1bg+RJpS8fOCS5NocGiCYGUqSe21Cul4PqNrdHx/5eqw43RDVWL3BhN3cUAkYiIiIiIiDYtpRQOzVdRc3wYuoY9U1mGJrSpCCFgGcF8xFzTZdHGaE/C8VvDxdVsjG5+y+VCtBoMEIfEhz/8AXz0ox9a8eOe9azn4tWvvnQDblH3+L6PQ4cOYs+evV37mp7n4dGP/lOceuppePe7P9i1r7seBw8ewLOe9RQ8/vFPwBvf+K/9vjlERERERJvC3KKDYsWFEMHGZdPQ+n2TiLomvjE623RZ4sboWGv0WjZGx8NFVvFSHAPEIfOUpzwNp5zyJ20vP+64Ezbw1qzfgQP78fd//7c4++xz8KIXvbTfN6enxscn8IY3/At2797T75tCRERERLQplKouZopBMrJjPI1siqe4tHVs9MZoSw/ecmP01sSj65B54AMfjD//8yf2+2Z0zf79+3DPPXcDOKffN6XnMpnMUP3uiIiIiIh6yXZ9HJwLlqaMj1gYH+HSFKK4TjdGNweMa9kYHS11Ybg4tBggEhERERER0abiS4UDs1X4UiGbMrBjPN3vm0S0qax2Y3R8c/SKG6PrQSI3Rg8XBohb1E9+8mO86lUvxa5de/Dxj1+NVCp4wD106CBe+MLnIpPJ4qMfvRrj4+N4xCNOx7nnPhUPechD8fGPfxgHDuzH9u078KQnPQXnnfd86HrjAeemm76Ga6+9Bnff/RsIIXDf+94fz33uBXjEI85q+DjP83DNNVfhpptuwP79+zA6mscpp/wJXvSil+LYY49rmOv40Y9+CB/96Ifwuc9dh127dq/qeubn53Hlle/D7bd/C6XSIh7wgAfiFa941Yo/o/n5eTztaU/Agx98Kt75zvc3XOb7Pp7+9CdibGwcn/jEZwAAf/jDvfjUpz6OH/3oB5iZOQpd13HMMcfi3HP/Es94xrOjz33zm9+EW265CW9+8+V429v+DfPzc3jkIx+Nl73sksQZiD/60Q/wuc99Gj//+c9QLBaQyWRxv/vdH+ef/0I89KEPiz7umc98Mo499ng8//kX4sor349f/vIX0DQNp532ULz85Zfg2GOPb/gefvSjH+Dqqz+JX/zi5/B9D8cddwKe85zn4ZxzHrem3ycRERER0UZQSuHgXBW268M0NOye4sZlom5a7cbo+OZoqRQc14fjou3G6HigyI3Rm8eWCRCVUqjU3H7fjLayabMrD3rVagULCwttLx8bG4MQAqecciouuOBCfOxjV+JDH3o/Lrnkb+D7Pv75n1+ParWKf//3KzA+Ph593g9/+H3ccMOX8eQnPw1Pe9ozcfvt38IHPvAe/PrXv8K//Mu/RR/33ve+E1df/Qk89KEPw0tfegkcx8bNN9+Iyy57DV71qtfg2c8+DwAgpcTf/d2r8MMffh9nnvlIPPWpz0ChsIBrr/0M7rjjh/jQhz6ORz3qMXBdF1dd9TGcddbZeNSjzsb4+MSqrqdSKePlL78IBw8ewJOf/DSceOJJ+NGPvo9Xv/oVK/4sJyYm8PCHPxLf/vY3ceTIYWzfviO67Pvf/y5mZ2dx3nnPBxDManzJS56PVCqNpz716di+fTtmZmZw/fX/iSuueCt0Xcdf/uUzos/3PA///M//iGc+8znI58ewffv2xNtw22234A1vuAz3uc/9cP75L0A2m8PvfvdbXH/9f+Lv/u5V+OhHr8aJJ54Uffzvfvdb/N3fvQp//udPxOMf/wT86le/xJe+9Hn8+te/xGc+859R2HvDDV/GW97yz9i5cxee/eznIp8fw403fhX/9E//gGKxgKc97Zmr+jkTEREREW2UmaKNUtWFJgT2TGVh6FyaQrRRVrsxOh4yytjG6CTcGD3YtkSAqJTCua98L37ws9/3+6a0dcaDjsf17375ukPEK664HFdccXnby2+44RsYHR0FALzwhS/GD37wPXz2s1fj7LMfi//+7//CT396J17ykpe3LGI5dOgg3vjGf8XjH/8EAMDTn/5svP71f49bb/06nvKUp+H008/A//7vz3D11Z/A0572LFx66Wujz33Oc56H17zmErzvfe/Cox99DrZv34GbbroBP/zh9/G8570AL3/5X0cfe9ppp+OSSy7GZz97NV71qksxPz+Hq676GE466eRoPuBqrufTn74K+/b9Aa9//ZvwhCecW7/tz8J73vP/8OlPf3LFn+e55z4V3/zmN3DTTTfg/PNfGPs5fgWGYeDxjw9u0+c//xmUSiW84x3vw/3v/0fRxz3qUY/BBRc8G9/5zrcbAkQpJZ75zOfgxS9+WfS+gwcPtFz/Jz7xEUxOTuI97/kQMplM9P69e4/Ff/zHv+O///s7DQHi0aNH8MY3/l88/vF/Eb3PcWx89avX4447foCHPvRPUa1WccUVl2Pv3mNw5ZWfQC43En2vF130PHzkIx/Ek5/8l/jVr+7q+OdMRERERLQRihUXs/WlKTsnM4mz3YioP9a0Mbr+1pdq+Y3R8XBRF9wY3QdbIkAEsGX+mM477wI89KF/2vbyeAhlGAbe+MZ/xYUXPg9vetPrceTIITz0oQ/DBRdc2PJ5xx13fBQeAsHP8/zzX4BvfesbuO22W3H66Wfg5ptvBACcc87jWqogH/OYx+GOO35YD9KeidtuuwUA8NznXtDwcaeeeho+9KGPL7uJeDXX861v3YZ8fqxlOcl55z2/owDxjDP+DNu2bceNN341ChBLpRJuv/1bePjDH4mJiaAi8pJL/hbPe94LMDk5FX2ulBK+H8yEKJebi7eB008/Y8Xr/+AHP45SqdTwe3McJyrtrlQav65lWXjMYx7b8L773e+P8NWvXo/Z2VkAwPe//9+oVMq48MKXROFh+Ln/3//3HwAAXddX9XMmIiIiIuq1muPj0HwVADA5mkI+a/b5FhFRpzrZGB1vhW7ZGO1LVH2JalK4qInWqkVujO66LREgCiFw/btfviVamI8//sSGuXgr2bNnL175ylfj8svfAstK4Q1v+BdoWmsLwAknnNTyvuOOOx4AcODAPgDAvfcGFZ6XXHJx2+s7ePBg/XP2Y3x8vKFNOvRHf/THy97m1V3PPhx33Akt39PExETUDr0cXdfxF3/xJHzykx/Fr351F+573/vjlltuguPYeNKTnhp9nBACnufhIx/5IH75y1/g4MED2L9/HxzHARCEic0mJydXvH7DMHDo0EF87GNX4p577sahQwdx8OCB6Os1f93R0TwMo/FubVlWw8ceOLAfwNLvL27v3mOi/1/Nz5mIiIiIqJc8X2L/bAVSKuTSBraNJSQQRLRp6ZpAxtKRWcPGaF8q+O02RmsimrHYvNCFG6NXZ0sEiEAQ8OQyVr9vxkC6444fAAhaXb/5zW9E8+/iwhAqLqyuCwMrKRUA4M1vvhzZbHPBcmDHjp0AghmAwNruqKu5HiD4vpIolTx3odmTnvQUfPKTH8WNN34V973v/fG1r30F09Pb8LCH/Vn0Mbff/k288Y3/AMtK4fTTH4qzzjobJ5xwEh784FPwtKc9MfHratrK7Rbvf/+7cdVVH8OuXXtwyimn4iEPOQMnn3wyfN/HZZddmvA1V57/EvzsgZWOk6v9ORMRERER9YJSwcZl15OwTB27p7I86SfaQta1MVoq2HL5jdFW09bo8H08zjTaMgEiJbvhhi/jllu+jr/8y2fiZz+7E+9+9xU47bTTW6rT/vCH1vmRv//9PQCAY445DgCwe3ewHXlqahoPfOCDGj52374/4Pe/vycKonbt2oM//OFeFIsF5PNjDR97+eVvweTkFF70opcm3ubVXM/evcdg374/wHGchhC0WCyiUCi0/bnE7d17DE499TTceuvNePrTn42f/vQnOP/8FzZsn37Xu66AaZq46qrPYXp6Onr/0aNHOrqOJIcOHcKnPvVxPOhBp+Cd73w/THOpReOmm25Y89cNf3733vt7/NmfPaLhsptvvhHf+9538aIXvXRVP2ciIiIiol45slBDxfagawJ7pjJcpkBEkU42Rie1RjdujE4OF+Mbo5vbo7fixmiuq9rC9u/fFy3TuOSSv8HrXvfG+nbg18N1G9u9f/GL/8Udd/ww+reUEp/85EchhMDjHvfnAIBHP/ocAMBHPvLBqMoNCCre3vKWf8ZrX/u3UaB21lmPhlIKn/vcNQ3X87Of/RRf+tIXMD8/DwBRSKeUij5mNdfzmMc8HtVqFZ/97NUN19PJ/MO4Jz3pKTh69Aje857/F/07rlBYwPj4BKamphre/6lPfRzAUrXmaiwuFqCUwrHHHtcQHtZqtejntpav+9CH/ikymQyuv/4/Ua1Wo/e7rotPfvJj+Pa3b8PU1PSqfs5ERERERL2wUHIwX3IghMCuyUxiSEBElCTYGK1hJG1gYsTC9vE09k5nccLOEdxnzyhO2jWKY7blsHMig8nRFEazJtKWDk0TQfjoSZRrHhZKDo4s1LB/poLfHSrhV/uL+M2BRdx7pIyDc1XMFm0UKy5qjg9fqpVv2CbFCsQh87Of3dlQGdfMMEycc87j4Ps+/vVf34harYp/+Id/Qjqdxn3ve388//kX4aMf/RA++MH34pWvfHX0eZaVwt///d/g6U9/Nnbs2IFvfOMW/PjHd+A5zzkP97//AwAES0HOPfep+PKXv4SXvvRCnHPO42FZJm688Qb84hc/x9Oe9qxovuGTnvQU3HLLTfjoRz+E3/3ut3jIQ87A7OwMPv/5z2L79h248MIXAwAmJoI5gd/+9jexc+dOnHXW2au6nuc85zzcdtsteP/7343f//4ePOABD8Sdd/4Yt9/+TaTT6Y5/rmef/Vi84x2X41vf+gYe/OBTccwxxzZc/ohHPApf+9pXcNllr8GZZz4Stl3Dbbfdip/+9CewLAul0mLH1xU64YSTsHfvMfja176CTCaDk0++D2ZmZuoLUWYAYE1fN5/P41WvuhRvfeubcdFFz8OTnvQUZDIZ3HjjDfjtb3+NN77xX2Ga5qp+zkRERERE3VaxPRxeqAEApvMpjGS4NIWIuiO+MbpZuDG6pSV6i2+M7nmA+Otf/xpXXHEFvv/970MphQc84AF48YtfjEc96lG9vuot6brrvojrrvti28tHRkZwzjmPw8c+diV+9rM78ZznnIdTTjk1uvwFL3gRvv3tb+Kaa67Cn/7pmXjIQx4KALjf/e6Ppz3tWfjwh9+Po0eP4thjj8Nll70B55771Iav/9rX/iP++I8fhOuu+wI+8pEPQNd1HHPMcbjssn9sWDpiGAbe9rZ34qqrPoabbvoa/uu/vo3JySmcddaj8eIXvwxTU0Eb8AknnIjnPOc8fOUr1+Ed73gbdu7cjdNPP6Pj67EsC+961wfwkY98ELfe+nXccstNOOmk++Btb3sX3vSm13X8c02n0zjnnMfjuuu+2FJ9CACvec1rMTY2httuuxU/+MH3MD4+gRNPPAnvfOf7cd11X8TNN9+IAwf2L7tduplhGHj729+F973vnbjllq/j+uv/E9PT23DKKX+CCy98CV75ypfg+9//HpRSqz4APfnJf4lt27bjU5/6OD7+8Q9D13WcfPJ98fa3v6thtmOnP2ciIiIiom5yPYkDs1UopZDPmpgc5Tx7ItoY8Y3RWGFjdHO4uKaN0fW3gx4uChXvDe2yn/70p3j+858P13XxV3/1VzjhhBNw66234vbbb8frXvc6vOAFL+j4a83OLiJhkW3EdR3Mzh7E1NQumCYfXLrpEY84HQ960Cl43/s+3O+bQpsM75fJhACmp0cxM7OI3h2BiYg2Do9rRNRNUirce7SMmuMjZeo4bntuw+eN8bhGRGvhyzZLXfzg/5fTbmO0ZWgwurAxWtOAqanRNX9+TysQ/+mf/gmVSgXvf//7cfbZZwMAzjvvPPzt3/4tLr/8cpx11lk44YQTenkTiIiIiIiIaJNQSuHQQg01x4eua9g7nd2SywqIaHPSNQHd0pG2EjZGSxUFiWvZGB1tiU5Y7rIRlYs9CxAPHjyIn//85zjjjDOi8BAIvumXvexluOGGG/CFL3wBl156aa9uAhEREREREW0i8yUHxXKwNGX3ZCZxPhkR0WakaQIpbfUbox1PQikF220fLpr1WYu93Bjd0wARAO53v/u1XHbccccBAH72s5/16uqJiIiIiIhoEynVPBwtBEPDto+nkUtz5ycRbQ1BhWFQWdhMKQXPb527GLZGS6ngeMHlScx6kJhO6YPZwpzNZgEA5XK55bL5+XkAwNGjRzv+ekIE/y13OfXG7bf/sN83gTa5le6/W034s+DPhIiGBY9rRLRejitxcK4CQGF8xMLEiNnXYwqPa0Q0KIQQsDQBy+x8Y7RT/0/GNkY7Xmv14mr0LEA8+eSTMTY2hm9+85tYXFzE6OhSynnDDTcAAGq1Wsdfb3Jy+ZS0Vqthbk6DrgsYLHMnGghSCmiahomJHNLpdL9vzsBZz6s/RESDiMc1IloL35f45b0LSKct5DIm7rN3bGDmHvK4RkSblVIKvr/U+uwktD+vRs8CRMMw8IpXvAL/9m//hosuugh///d/j507d+K2227De97zHoyMjMAwOr/6ubmVtzBLKeH7Ct4Km22IaGP4voKUEvPzZZim2++bMzCECJ6Mzs5yqx8RDQce14horZRS2D9bRanqwtA17Bq3MDdX6vfN4nGNiIaOtc5au54OlXjhC18I27bx3ve+F+effz4AYPv27bj88svx1re+FePj4x1/LaWw7IF76TIe3YkGharfMVe6/25V/LkQ0bDhcY2IVmumYGOx4kITArunsjA0baCOIzyuEdGwWO+xrOdTaV/60pfi/PPPxy9/+Uuk02nc7373g+/7ePWrX41TTjmla9ejaUGU6vs+TLNrX5aI1kHKoERa01q3TBERERHR1rZYcTFTDJam7JhII2PxOSMR0aDqaYB4ww03wDRNPPaxj8Vpp50Wvf/222+H67o444wzunZdum7AMCxUKiWkUhkITrsl6rtarQJN06OAn4iIiIgIAGqOj4PzVQDA5GgKYzmrz7eIiIiW09MA8eqrr8Zdd92F008/PWpXLpVKeOc734nt27fj3HPP7er15XJ5FAozmJ8/imw2B103ADBIJNpoSik4Tg21Whn5/BQDfSIiIiKKeL7E/tkKpFTIpQ1sG0v1+yYREdEKehogvuIVr8CLXvQiXHDBBXj2s58NpRQ+97nP4Z577sF73/verm9lzWRyAIByuYiFhZmufm0iWi2BTGYkul8SERERESmlcGCuCteTsAwNu6eyfLGZiGgTEEr1diTsd77zHbz3ve/FXXfdBcMwcOqpp+IVr3gFHvzgB6/q68zOLr+FuZnve5Cr+QQi6ipd1zn7sA0hgOnpUczMcKsfEQ0HHteIqFOH56uYLznQNIHjtueQMgfz+SKPa0Q0bDQt2C6/Vj1fonLmmWfizDPP7PXVtNB1A/pgPhYRERERERFtOYWyg/mSAwDYNZkZ2PCQiIhacbMBERERERER9VTV9nBovgYAmM6nMJox+3yLiIhoNRggEhERERERUc8ES1OqUEphNGNiKs+lKUREmw0DRCIiIiIiIuoJqRT2z1Tg+RIpU8fOyQyXphARbUIMEImIiIiIiKjrlFI4PF9D1fGhawJ7prPQNYaHRESbEQNEIiIiIiIi6rr5koNC2YEQArunsrAMnn4SEW1WPIITERERERFRV5VrHo4WbADAtrEUcmmjz7eIiIjWgwEiERERERERdY3jSRyYrUAphbGchYkRq983iYiI1okBIhEREREREXWFlMHSFF8qZCwdOybSXJpCRDQEGCASERERERHRuimlcHCuCtv1Yegadk9loTE8JCIaCgwQiYiIiIiIaN1mFx0sVl0IIbBnKgOTS1OIiIYGj+hERERERES0LotVFzOFGgBg50QamRSXphARDRMGiERERERERLRmtuvj4FwVADAxYmEsx6UpRETDhgEiERERERERrYlfX5oipUI2bWD7eLrfN4mIiHqAASIRERERERGtmlIKB2YrcDwJ09CwezLDjctEREOKASIRERERERGt2tGCjXLNg6YJ7JnKwtB5eklENKx4hCciIiIiIqJVKZQdzC3aAIBdExmkLb3Pt4iIiHqJASIRERERERF1rOr4ODwfbFyeyqcwmjX7fIuIiKjXGCASERERERFRRzxfBktTlMJIxsR0PtXvm0RERBuAASIRERERERGtSCqF/bNVeL5EytSxi0tTiIi2DAaIREREREREtCylFI7M11C1PeiawJ6pDHSN4SER0VbBAJGIiIiIiIiWtVB2sVB2IITArqksLJNLU4iIthIGiERERERERNRWpebhyEKwNGXbWAojaaPPt4iIiDYaA0QiIiIiIiJK5HoS++eqUEohn7MwMWL1+yYREVEfMEAkIiIiIiKiFlIq7J+twPcl0paOneNpLk0hItqiGCASERERERFRA6UUDs1XUXN8GLqGPVNZaFyaQkS0ZTFAJCIiIiIiogZziw6KFRdCCOyeysA0eOpIRLSV8VGAiIiIiIiIIqWqi5miDQDYMZ5GNsWlKUREWx0DRCIiIiIiIgIA2K6Pg/WlKRMjFsa5NIWIiMAAkYiIiIiIiAD4UmH/bBW+VMimDGwfT/f7JhER0YBggEhERERERLTFKaVwYLYCx/VhGhp2T2W4cZmIiCIMEImIiIiIiLa4owUb5ZoHTRPYM5WFofNUkYiIlvBRgYiIiIiIaAsrVlzMLQZLU3ZOZJC29D7fIiIiGjQMEImIiIiIiLaomuPj0FwVADCVTyGfNft8i4iIaBAxQCQiIiIiItqCPF9i/2wFUimMZExM51P9vklERDSgGCASERERERFtMcHSlCpcT8Iydeya5NIUIiJqjwEiERERERHRFnN4oYaK7UHXBPZMZaBrDA+JiKg9BohERERERERbyELJwULJgRACuyYzSJlcmkJERMtjgEhERERERLRFVGwPhxdqAIDpfAojGS5NISKilfU8QPzd736HV7/61XjYwx6GBz7wgXjCE56Aj33sY5BS9vqqiYiIiIiIqM71JA7MVqGUQj5rYnLU6vdNIiKiTcLo5Rfft28f/uqv/gqVSgXnn38+jjnmGHz961/Hv/3bv+Huu+/Gv/zLv/Ty6omIiIiIiAiAlAr7ZyvwfIm0pWPnBJemEBFR53oaIH7kIx/BwsICLr/8cjzlKU8BADz3uc/F85//fHzmM5/BC17wApx00km9vAlERERERERbmlIKh+arqDk+dF3DnqksNC5NISKiVehpC/Pvf/97AMDZZ58dvU8IgXPOOQcAcNddd/Xy6omIiIiIiLa8uZKDYsWFEAJ7JjMwDY7CJyKi1enpI8eJJ54IAPj1r3/d8P577rkHALBjx45eXj0REREREdGWVqq6mCnYAIDt42lk0z1tQiMioiHV0wDx4osvxkknnYTXve51+O53v4t9+/bhqquuwuc+9zmceeaZeMhDHtLLqyciIiIiItqyHNfHwblgacr4iIXxHDcuExHR2gillOrlFdx444143eteh1KpFL3vtNNOw5VXXolcLtfx15mbWwQXNxPRMBACmJoaxezsInp7BCYi2hhVx4MwTFTKNRiaBtMQMHQNhi64pIGoT3yp8PvDZTiej0zKwLHbsrw/rgKfrxHRsNE0YHJydM2f39MA8YMf/CDe/va3Y+/evbjooouwY8cO3Hnnnfj4xz+OY489Fh/+8Iexffv2Xl09EREREfWQlAoHZso4Ml9NvFwIwDR0pEwtemuZOixTg2XoME0NGgMNoq5TSuHu/UUUyg5MQ8P9j5vg3EMiIlqXngWIpVIJD3/4wzEyMoLrr78ek5OT0WXf+c53cNFFF+GJT3wi/uM//qOjr8cKRCIaFnxFm4iGQdUJWiMd1wcA7N6RR7lUg+NJeJ6E5ysorHSQEzB0AVNfqlo0DQ2mLupvNW6KJVqDo4UaZos2hBA4dnsOGUvv903adPh8jYiGzXorEHs2Qfd3v/sdarUanvrUpzaEhwBw5pln4thjj8Xtt9/e8ddTCjxwE9FQ4XGNiDYjpRRmFx3MFm0opWDoGnZNpnH87jxmZkR0XFNKwfMVXF/CrQeKrieDf/sKnichlYLrBe+HnXx9ul4PFOvhohELF01DgybAtkyimGJlaWnKzok00qbO5xvrwOdrRDQs1nss61mAmEqlAACyTdmg7/ttLyMiIiKiwWPXFzLUnKDqcDRrYsd4OrE1UggB0wjCPqRav5ZSCr5UcOvBolcPGuP/9qWC70v4PlCDn3ibNG0pXDT11ipGXeMcRto6ao6PQ/WRApOjKYzlrD7fIiIiGhY9CxBPPvlk7NmzBzfeeCNe/vKXY8+ePdFlt9xyC/bt24cnPOEJvbp6IiIiIuoSpRTmSw5mCjakUtA1gR0TGYxmjDWHc0IE7cuGjrbtlb5MCBf9oEXa9RU8X0JKBVv6sN3kgFEIEQSLsarFsKLRqP8/A0YaBp4vsX+2AikVcmkD28YSknsiIqI16ukSle9+97u4+OKLMTIyguc85znYtWsXfv7zn+Pzn/88Jicn8ZnPfAa7d+/u6GvNznIGIhENByGA6elRzMxwpg4RDT7Xkzg4X0Wl5gEAcmkDOycyDVWH/TquSVkPFBPao8P3r/RUNwwyTb2xPdqIBY2cw0iDTimFPxytoGJ7sAwNx+0Ygc6/23Xh8zUiGjaaFsx2XaueBogAcNddd+G9730vvv/972NxcRHbtm3Dox71KFxyySXYtm1bx1+HASIRDQs+ISWizUAphWLFxZGFGnypoGkC28bSGM+ZLRV7g3pci+YwJoSL8TmMK4nmMIZVjE0VjZzDSP12eL6K+ZIDTRM4bnsOKZNLU9ZrUI9rRETthONhvHqXhucreHLp/xUUTvujnWv++j1rYQ7d//73xzvf+c5eXw0RERERdYnnSxyer2Gx6gIAMikDuyYzsBJmHQ6yhjmMCaI5jAnt0YlzGJ3kNmlda5y9aMSCRs5hpF5bKDuYLzkAgN2TGYaHRERDJjEY9GU9HFx6ny+X77ww9PU9F+l5gEhEREREm8di1cXh+Ro8X0IIgel8CpOj1lAGYEtzGDVkEi5XSkEqRBWMXixoDDdLRyHjMnMYtbBNOqE9OtwsPYw/X+q9qu3h8HwNALBtLI2RjNnnW0RERJ3qVjAYJ4Sov7AZPL8xdAEjfKHTZIBIREREROvkS4UjCzUUykElU8rUsWsyg3SbBSdbgRACugB0S0cayT+HcA5jQ3t0PVwM5zBKpeB4Co6XPI+nYQ5jwpIXzmGkJK4nsX+2CqUURrMmJke5cZmIaBCEI1S8ehdDu5bibgWD8fct1/WgrbORhAEiERER0RZXrnk4NF+F6wVVh5OjFqbyKWisiluRpgmkNB2pNoVfSqmW2YvNm6WVUnC94P2wk7+O0Tx7sV7RGLZOc2HG1iKlwv7ZCjxfBmH/RIZVrEREPRYPBqMQcACCwY3CAJGIiIhoi5JS4WihFs1PswwNOyczyKb4FLFbhBCwDNF2fmTLHMYwXIwtf5HRSQkAzmHc8pRSOLRQQ83xoWsCe6azrFAlIlqHKBhs0z681mAwDAP1pjAwHhBupsdnPjskIiIi2oKqjo9Dc9Vobt/4iIXtY2kGERtstXMYo/boWEWj3+kcxoTZi9FcRs5h3DTmSw6KZQdCCOyeym665UZERBtluWCwOSDsVDwYjELATR4MdooBIhEREdEWopTCbNHG7KIDpRQMXcPOCS5fGFSrmsMYm70YVjR68TmMrg/HbX89SXMYo6CRcxgHQrnm4Wgh6HPfPpZCLs3TOSLaeqRS8FcIBsP5g52Kv6BnNLUU60MeDHaKjzhEREREW4Tt+jg4V0Wt3gabz5rYMZHh/LxNbmkOY3LAGM5hbJ69GN8svZo5jPHZi6a+1DrNv6PecjyJA7MVKKUwlrMwPsKlKUQ0XBqCwaa5gj0JBmPv28rBYKcYIBIRERENOaUU5ksOZgo2pFLQNYEdExnks6w63ArWMocx3CCdNIex2uEcxuagkSdna+dLhf0zFfhSIZMysGMizZ8lEW0ast5K3LCROKFy0JedzRcEGAz2AwNEIiIioiHmehIH56qo2B4AYCRjYudEGobOuWkU6GQOo18/0VvXHEZNRPMW47MXOYdxeUqpaF6poWvYM5XhhnQiGghSJmwk7mYwmLCRWGcw2DcMEImIiIiGkFIKhYqLIws1SKmgaQLbx9IYy5l8wk2rsnQyh47mMIazF+MVjb5UkFLBlj7sDuYwNi95CTdLb8XgbLZoY7HqQgiBPVMZhv9E1HPNwWDSRuLVBoNa/RiftHAk/lYT4POUAcUAkYiIiGjIeL7EofkaStUgqcmmDOyczHBbK/XMhs5hbJq9OMxzGBerLmaKwQ9j50QamRRP34ho7doFg+H7/HUEg0kLRxgMDhc+AhERERENkcWKi0MLNfi+hBAC02MpTI5YfNJOfdXJHMawRbqhPTq2WXppDqNEtc316Fps9mK8PXoTzmGsOcHSIwCYHE1hLMelKUSULDo+NrcPrycY1BLahxMqBxkMbh0MEImIiIiGgC8VDi/UUCw7AIC0pWPnRAZpK7kijGiQCCFgGkHgl2TFOYxeOINRwXd8AJt7DqPnBxuXpVTIpQ1sG0v1+yYRUR9I2RoGJrUUMxikjcAAkYiIiGiTK9c8HJqvwvWCqsPJUQvT+RSf+NPQ6GQOY3giHZ+96PmN/7/aOYzh7MX4XMZez2FUSuHAXBWOJ2EZGnZNZnhfJhoyvoxtJG7TUuyvJRhM2kjMYJC6hAEiERER0SYlpcLRQg3zpaDq0DJ17OKcNNqidE1AX2YOowzbpJvnMMaCxvXMYQxbp9c7h/FowUal5kHTBHZPZbk0hWiTUEpBKrRdOBIPC2UXgsH4RuJuHHuIVsJnl0RERESbUNX2cLBepQQAEyMWto2lofEEgiiRtoo5jPHZi2udw9jcHh1WMC43h7FQdjC3GCSXuyY5goBoEDQHg+GsQd9XLe9bTTCoa/WNxAnBYPg+ncEgDRAGiERERESbiFIKM0Ubc4sOlFIwDQ07JzLIpfm0jmg9GuYwJowcbJjDmLDkpXkOY20VcxhNQ4u2pwsobBtLYzRj9vg7JtraehkMtpsrGA8L+YIfbTZ8pklERES0SdQcH4fmq6g5QTCRz1nYMZ5mdQLRBmiYw9imMtCXybMXV5rDKJXCQsmFVAppU4dluqjYfkt7dPhvzi8jai8xGExqKV5PMLhM5SCDQRpWDBCJiIiIBpxSCnMlBzMFG0op6LqGneNpjGZZoUQ0SHRNQLd0oM2iF6kUvHD2oi/heRK2J3FkoQYhAEPTkEsbQYVjmzmMQohYm3QsXIxVNDLAoGHULhhMqhyUavXBYNgyzGCQKBkDRCIiIqIB5ngSh+aqqNgeAGAkY2LnRJqLFYg2IU0IWKYOq579K6VwaL6GjKVjJG3g2O05aEK0n8PoyWgZTKdzGMPZi/G5jMvNYSTaaCsFg+H/++sIBtttJGYwSNQ5BohEREREA0gphULZxZFCDVIqaJrAjvE08lmTJ/5EQ2Kh5KBQdiBEsHE53CC90hxG1w8rGZe2SXtrmMMYhYtNm6UNtklTF8Tnhia1D3czGDTChSQMBol6hgEiERER0YAJlymUqsGQtGzKwM7JTNvtsUS0+ZRrHo4Ugh7lbWOpjhYhxecwYrVzGGNB49IcxuSAUQgRhIlhFSPnMFJMPBgMWoebKgdjYaHqQjDY0F6sC2j82yPqCwaIRERERAOkWHFxeL4KXypoQmB6LIWJEYsn60RDxPEkDsxWoJTCWM7CxIjVta+92jmM8fZoNxb6OJ6C48nEr7EUZDbOXjQ4h3FT61kwuMxcwfD/dQaDRAOPASIRERHRAPClwuH5KoqVoOowbenYNZmJWhqJaDhIqbB/pgJfKmQsHTvG0xv6AkHzHMZmqj5jMSlcDFunpVJwveD9becwhuFiwhxG09CgCfCFkQ3SEAwmLByJtxR3JRiMVQ4yGCQaHgwQiYiIiPqsVPNwaK4Kz5cQQmBq1MJUPsWTa6Iho5TCwfkqbNeHoWvYPZUduEo9IQRMQ3Q0h7G5PTr8t1+vXvN9dDSHMZy9GN8szUUvK0sKBuNzBdcaDLZdONLUUszfD9HWwgCRiIiIqE+kVDhSqGGh5AAAUqaOnZMZZNrMNiOizW120cFixYUQAnumMkFIt8nE5zC2O1Y1zGGMtUt765nDaCxVNBpDPoexJRhsah8OQ9q1BoNh+7DepqV4WH+uRLQ+DBCJiIiI+qBiB1WH4YyxydEUpvOpgatGIqLuWKy6mCnUAAA7JtLIpIb3VGzFOYxSRfMWm9ujVzuHsXn2ohELGgfteJoYDDZVDoYbizsNBoUIKwUbQ8DmjcQMBolovYb3UYuIiIhoAEmlMFu0MbfoQCkF09CwcyLT0QZWItqcbNfHwblgWuDEiIXxXPeWpmxGmiaQ0nSkVprDmBAuJs1hbCeawxjbJm30YA7jRgaDDRuJGQwS0QbiM1UiIiKiDVJzghAhbNkby1nYPp6GPmBVMkTUPX59aYqUCtmUge3j6X7fpIHXMIcxQTSHMaE9OnEOo5PcJh3O+IvmMMaCRtPU6iGl7G0w2DRXMD5/kMEgEQ0SBohEREREPaaUwtyig5miDaUUDF3Djok0RjNtym+IaCgopXBgtgLHkzANDbunMgyFumBpDqOGTMLlSilIhaiC0YsFjfFA0JcKfps5jEIABxccVCo2OskGGQwS0bBjgEhERETUQ47r4+B8DVXbAwCMZkzsmEjD0Dff8gQiWp2jBRvlmgdNE9gzleX9foMIIaALQLd0pFeYw5jUHh3OYQQAAQHDSNhI3NxSzGCQiIYcA0QiIiKiHlBKYaHs4mihBikVdE1g+3ga+azJk0yiLaBQdjC3aAMAdk5kkOZ29YGy0hxGQGFiYgTz8yUAPGYTETFAJCIiIuoy15M4NF9FuRZUHWbTBnZNZNrO8yKi4VJzfByeDzYuT+VTyGc5rmCzESKYiSiE6KiFmYho2DFAJCIiIuoSpRQWqx4Oz1fhSwVNCGwbS2F8xGLVIdEW4fkS+2crkEphJGNiOp/q900iIiJaNwaIRERERF3g+RJHFmooVlwAQNrSsWsyg5TJtkWirUIqhf2zVbieRMoMjgF88YCIiIYBA0QiIiKidSpVXRyar8HzJYQQmMqnMDXKqkOirUQphSP1hUm6JrBnKgNd4zGAiIiGAwNEIiIiojXypcLRhRoWyg4ARBVHXJZAtPUslF0slB0IIbBrKguL1cdERDREGCASERERrUHF9nBwLmhVFEJgYsTCdD4FjRVHRFtOpebhyEKwNGV6LIWRNE+ziIhouPCRjYiIiGgVpFKYKdiYLzlQSsE0NOyayCDLwIBoS3I9if1zVSilkM+amByx+n2TiIiIuq5nz3T37duHc845Z9mP2bNnD2699dZe3QQiIiKirqo5Pg7OVWG7PgBgLGdh+3iac86ItigpFfbPVuD7EmlLx84JLk0hIqLh1LMAcXJyEm9961sTL7v++uvx7W9/G49//ON7dfVEREREXaOUwtyig5miDaUUDF3Dzok0RjJmv28aEfWJUgqH5quoOT4MXcOeqSxHGBAR0dDqWYCYzWbx1Kc+teX9d911F/7xH/8Rp59+Ov7u7/6uV1dPRERE1BW26+PQXBVVJ6g6HM2Y2DGRhqFrfb5lRNRPc4sOihUXQgjsnsrANHhMICKi4bWhw3qklPiHf/gHAMBb3vIWGAZnBREREdFgUkphoeTgaNGGlAq6JrB9PI181mSLItEWV6q6mCnaAIAd42lkUzyvISKi4bahj3Rf+MIX8L//+794+ctfjuOOO24jr5qIiIioY64ncWi+inLNAwDk0gZ2TrDCiIiCquSD9aUp4yMWxrk0hYiItoANCxBd18W73vUujI+P4yUvecmqP1+I4D8ios0uPJbxmEY0eJRSKFZcHF6oQUoFTRPYPpbG+AirDpfD4xptFb5UODBbhVQK2bSBnRNp/t0PKR7XiGjYrPd4tmEB4te+9jUcOnQIf/3Xf41cLrfqz5+cHO3BrSIi6p+pKR7XiAaJ60n84XAJhZpEOm0hlzZw3K5RpC22Jq7EcX3MFWsw0ymkTB2mqUHjWTcNGaUUfru/CMMykM1auN9xE6xK3gL4fI2IKLBhz4g/9alPIZVK4fzzz1/T58/NLULKLt8oIqI+ECJ4Mjo7uwil+n1riAgAFqsuDs3V4EsJAYGpsRQmszpKxSpK/b5xA8zzJWaLNhbKLjIZC5WKXb9EwNAFTEODqWswjfj/azB1wYpO2nSOLNQwt2hDCIHt23MoLJT7fZOoh/h8jYiGjaatrzhvQwLEw4cP48c//jEe+9jHYnx8fE1fQynwwE1EQ4XHNaL+86XCkYUaCmUHAJAydeyazCBt6QB4H23HlwrzizbmSg6kVHA8CSk82K6EUgoCAromYLt+YiWiEPWAMRYoxgNGgwEjDZhixcVsfWnKrskMUqbO48MWwedrRDQs1nss25AA8eabb4ZSCk984hM34uqIiIiIVlSpeTg4X4XrSQghMDFiYXosxdbbZcj6ZurZRQe+L6FU0PottGDRjJA+pFRQCvCVguspKAACAhAA1NK/dU3A1toHjKYuYDRVLYYhIwNG2kg1x8eh+SoAYCqfQj5r9vkWERERbbwNCRC///3vQwiBRz7ykRtxdURERERtSakwU7QxtxhUE1mGhp2TGWRTnHXYjlIKhXoFlusFM2U0TcDzFUxDQNM0bJ/IYNEAHE/B8yRcX8LzFVTTy91K1QNG2RQwBpdCKUATAq4moLsyceB3GDCahtbSGm0aGnSNASN1h+dL7J+tQEqFkYyJ6Xyq3zeJiIioLzbkmfLPfvYzHH/88Rgd5QBaIiIi6p+a4+PgXBW26wMAxkcsbBtLQ9cYNiVRSmGx6mG2aEc/M0PXYBkCFduHJgDT0LF7KoO920cwo6mG9hipFDxfwa0Hiq5X/6/+Ps9vHXCtlIJUiFqjg4SxKWDUBFyvfcCoCRG1QjcEjPWQkQEjdUKpYOOy60lY9fEG/LshIqKtqucBouM42LdvHx7xiEf0+qqIiIiIEimlMFu0MbvoQCkFQ9ewcyKNkQxbEZMopVCxfRwt1FBzguBQ1zWM50zUHB/lmgcAGM2Y2DGRbruJVhMCliFgtblcqrBasSlk7DBgtN3g8iDTEdHlmibgeLIeFCbcLm35+YsMGAkIlqZUbA+6JrBnKsMXGoiIaEvreYA4Pz8PAMjn872+KiIiIqIWthtUHYZBWD5rYvt4GoaeHGptdVXbw9GijUo9JNQ0gckRCylTx+GFGjxfQhMC28bTGM+Z6wraNCFgmTqsNjmulCoIFf2ltuiuBYyifcCoawJGc8AYCxkZJA2/hZKD+ZIDIUS0NIWIiGgr63mAuGPHDvzyl7/s9dUQERERNVBKYb7kYKZgQyoFXRPYMZHhAoQ2bNfHTMHGYtUFgGixzMSIiYWyiwNzVSilWjZV95KmCaQ0HamVAsamUDH893IBoy8VvKaAMZzXqGkCuiahOe0DxqTZiwYDxqFQsT0cXqgBAKbzKVYqExERYYNmIBIRERFtJNeTODhXRcUOquhyaQM7JzJtW223MteTmCnaKFZcKKUghEA+u7QsIv5zHM9Z2D6ehjYgAdlSwJgcZiYFjPF/++0CRqmCJS9S1tNFFS6Rjq7X8YJKzGUDxjZbpAfl50etXE/iwGwQluezJiZHrX7fJCIiooHAAJGIiIiGhlIKxYqLIws1+DJoVd0+lsbYOltth5HnS8wWbSyU3ajybrQeHKZMHYtVF4fmqvDl5q3eXClg9KVqqFhcWvISvPWlavkcpYJwMQoYUW+OFp0HjIbeWrUYhYwMGPtGSoX9sxV4vkTa0rFzgktTtiKlgvEHxYqD2bKHWtWBobcuZdIE+PdBRFsKA0QiIiIaCp4vcWi+hlK9BTeTMrBrMtN2gcdW5UuF+UUbcyUHsh6Q5dIGpsfSyFg6pFQ4NF/FQskBAGQsHbumskP5c9Q1AX2FgLG5LXqlgFGGFYy+gqNkPVwUEFCxgDGYv6hpAknxQ8v8xdi/DUODxtCi65QK/u5rjg9d17BnKssgd4vxfIlixUWh7MJ2fQgBZLMClarbsF0+lDgrlcuYiGiIMUAkIiKiTW+x6uLQfA2+LyGEwHQ+hclRiydvMVIqLJQdzC46UetuxtIxPZZGLh08JbRdHwdmq7DdYOHMVD6F6Xxqy/4cdU1At3QAyweMYaDorSpglHA8RAFjPKHQNQnbTQ4YhRBBJZSuwWgTMm7V39d6zJUcFCsuhBDYM8lxB1uFUgqlmodi2UWp5i3NQRUCo1kDe3eNYnZWg5MwXzWoRPaj42Wz+Lb38D4bDxsZMBLRZsMAkYiIiDYtXyocWaihUA6q5TZywcdmoZRCoeJitmjD9YLgMGXqmB5LYSRtQIhgeUih7OJIoQYpFQxdw67JTBQsUrIwYEwnBIzhspaGuYtNVYyyKWBUSkEhCHs9X0K6AMI2yfplQgC6EPVFL63hQzxgbFcZxdCiUanmYaZgAwC2j6eR5d/90Ks5PooVF8WK27BsKWPpyOcs5LMmDF1gMp+GdForEDvZEC+lgr1cwCjE0osA8aCxfn/lfZWIBg0fHYmIiGhTKtc8HJqvwvWCqsPJUQtT+RTbO+uUUlisepgt2tEJrGlomM6nkM8uzYT0pcLh+SqKlaD1O5cOWr8NnRVY6yGEgC7QWcCYEEC0DRgV4KsgYIwqGMPLAWgiCCZWDBjbLHjZaqGF4/o4OFuBUgrjOQvjuc0155M65/kSixUXhYqLmrMU6hm6hnzWxFjObDvOoNmKG+KVaqlIjv/b8xWkUnBcH46b/DWEENHYAlNvup+y2piI+oABIhEREW0qUiocLdQwX5/RZxlBtVwmxac1QBAyVWwfRwu16CRZ1zVMjVoYH7EaAtaq7eHA3FIIOz2WwuQIW783QkPAmFAxGy5r8XwFJwwd1hswKkDTgoAxnMGYdLuSqhat+qKXYWq79KXC/tlgUVAmZWD7RHpovjcKKKVQrnkoNLUoCyEwkjEwljWRq1did5MmBCxDtJ0dqxICRtdXDfdzpRQcL7j/J1l2nAHnpRJRD/CZNhEREW0aVcfHwbkqnHpF3cSIhW1jaS47qKvaHo4WbVRqHoCgxXZixMLEaKqhGk0phblFBzNFG0qpIISdyiLD1u+BEYYDho5lA8bmxS7xSiep2gSM9fZL6dWvK7o8CBidZQLGhrbLhCrGzRIwKqVwcC6Y92kaGvZMZRi2DBHb9VEot7Yopy0dYzkLoxmjr1XWQgiYhmg7azMKGOPVi/EZjPX7t+sF93XYydeTtOQl3jbNx04iWg0GiERERDTwlFKYLdqYXXSgVDCjb+dkBiOcVQYgOFmeKdhYrG+gFiIIDidHrZaTZM+XODhXRbkeMuazJnZMZBLbXWlwxQPGpOA3ChgTtkfHA4jmz5Fqab6brFcwxj9KE4DmiWgOY7P44oh4+6U1YJtpZ4o2SlUXmhDYPZVly/4Q8KWqb1F2EluU81lz08zHbQgYU62XJ92/Pb+1Qtnzg/dX21yPoTfeT6P7rb5UcUxEFOKzbiIiIhpothtUHYYnhAy8lriexEzRRrHiQikFIQTyWRPT+VRiZUupvq3a8yU0TWDHeLphHiINj6WAUUMm4fKVAkbXk1G7Z/xzwoDR8SWUCpa6RJcjCBhtrfOA0TS0hkpGLVwa00PF+lIhANgxkWbl7SYWtShXXJSqTS3KaQNjud60KPdbp/fvxDbpetjoRwEjACd50YuutY4z2Oj7KxENDgaIRERENJCUUpgvOZgp2JBKQdcEdkxkkM9yyYHnS8wWbSyU3eiEebQeHCYtAVBK4WjBxtxiEJqkrWBbdacLA2j4dBJAtLRQNgUR7QJGvx4wynrAKIBoi62mCTiaD00TiS3DuhbcpnDmYnNgsd4XDmqOj0PzQT3W5GgKYzlrXV+P+mPZFuWsidGsuaWrSlcagQAg9gJC8n3cl0EI6Ts+amgfMC7XJj0oFcdE1B0MEImIiGjgOJ7EobkqKnbQZjuSMbFzIr2lTwiB4IRvftHGXMmJFmjk0gamx9pXUTmexIHZSlTBOTFiYdt4mvPeaFmdtFA2z2WLNswuFzDK+pIXN6hgbF4jHWyP9lFdJmBM3CBdf99yM908X2L/bAVSKuTSBraNJXxjNLDCFuVi2UE1VjGn6xrGNlmL8iDQNdF2SzyAqEKxpU06HIPghyGjD9tNDhjDiuN2m98ZMBJtLgwQiYiIaGAopVCouDiyUIOUCpomsH0sjbHc1m6zlVJhoexgtmjDrweHGUvH9FgauWXmQBbKDg7Xf5a6JrBrMoORDCs4af1WHTA2h4xtA0YFXyEKGIWoz2CMVTDq7jIVjPWZbsHMxfhMN4HD8zW4noRlaNg9ld3Sx5TNQimFsu2jWHZQqnrR3M6wRTmfMzEyhC3Kg0DXBHRNb1upHs5KDWeqNr+Y4Mvg/mxLH7abfB3BcWRpqUtz1bGhM2AkGiQMEImIiGggeL7EofkaSvVFINmUgZ2TGVhttlRuBWGgOlu0g02bAFKmjumx1LInzVIqHF6ooVB2AAQ/y12TmbYbP4m6reOAMSF4WC5gDEIJwI21rQYXAhCAJmR9gzQaAsZyzUPV8aFrAtvH0zg8X21ot7TqiyRYmTsYbNevL0RpbFFOmTrGckG14VavSO83TRNIaTpSbV6Tkk33ca/NfdxxfTjLBYz68m3SDBiJNg4DRCIiIuq7xYqLQws1+L6EEALbxlKYGLG27ImBUgqLVQ8zRRtOvTXMNDRM51MrLj2pOT4OzFXhuD6EEJjKpzA1unV/ljSYGgLGBEqpxLbJ5QJGWa9glFLC9YO5iwKA40tUbA9CAemsiaodtFwmhYUtQUVT2yXvR73jS4XFiotCxUW1Pr4CCKpK8xkDYzkLKVPj72CT0ISAZYi2LwK2vIjQZgyC4yk4ngTs1q8RznqMB4rRfZYvChB1HQNEIiIi6htfKhyer6JYCcoPtvpyD6UUKraPo4VaNLNQ1zVMjVoYH7GWPREKl84cLdhQSsE0NOyazCCb4tM92nxEB+FD0vZorylg9HyJii1h6hoylo6UqcGXEq4HKCgIBFtewi3muiahOeHyh9bbZOitW6QNBoxrFh7zCgktyrnYFmWGQMOnkxcRWhY5Nd3XpVJwveA4kBQwAgkvCsTDxhXmphJRIz6jJCIior4o1zwcmq/C9YKqw8lRC9P51JY9Aa/aHo4W7GhxjK4JTIwGlZgrbZ5tbv8ezZjYOZlZ98ZaokG1UsAolULV9vH7wyXk0gZSpo58zowqnrymFmilFJQKPi8IGFV97KIItkgjDBiD2YurCRg5z62V4/ooVIItyuF4BoAtyrSkkzEIS5ukY9XKsRcWZH0RjOdLVNtcjxGrVjQb5qZ2Z/M70TBhgEhEREQbSkqFo4Ua5kvBfD7L1LFrIo3MFq2Us10fMwUbi/XwTxMC4yMWJketjk6gyzUPB+eq8HwJTQhsG09jfIsvnSECgJlCDZomMJazcNz2XEOlkVSqZSZbcxARFwaMflPAKOpbXoL/x4oBYxhUWFtwYYQvFRarLoplN3qhBAh+ZvmsyRZlWpUwsDd0DZmEy8OAsd2s1XCLdBAwAnCSN0k3b35v3iitCfBvlraMrflMnYiIiPqiagdhl1OvOJkYsbBtLL0lW4hcT2KmaKNYcaP2yXzWxHQ+1dGyE6UUZoo25hYdKKWQMoP277S1Ndu/iUJKKRyer0VLU/ZMZ1uOMZoQsEwdVrsFEE0bZp2mWYxtA0YZBIyOF69fDAJGTQD2SgFj8xy32L+Dz9lcx8qoRbniolR1IWVji3I+a2IkwxZl6r6lgBFtHxeXKhhjbdKxfwf3ZwXf8VFD+4BxuTbpzXi/JWqHASIRERH1XHPYZRoadk5kkEtvvacini8xW7SxUHajJRCj9eCw09mPridxcK4aVfGM5yxsH9+aQSxRs/mSg0LZgRACu6eya9rkvuKGWakSK5qc+r/9hIBRquDzlgLG5lATsN2lNunmzEETorECqilkHKSgwvEkimUHhYQW5XzOxBhblGkA6JqAbulIo33A2LzEqfmFhCBkDBYzJdE0EVUYD8sLA7R1bb1n7URERLShao6PQ/PVaCnIWD3s2mpzhXypMLdoY77kRFU4ubSB6bE0MquoGlysujg0V4UvFXRNYMdEBvlsm5RjAyilsFjxUPEXUSnZUUtZGHSwsog2UrkWzBIFgG1jqZ69SLEUMCbfd5MCxvi/lwsY3fpyiKB+MVj1AgQBo7ZcwKglzF8M/70BrZa+VChVXRQSWpRH6y3KabYo0yaiawJ6J/fz2AKn+H3el8FmeFv6sN3k62ioPN6Cow1oc2GASERERD2hlMLcooOZYrAVWNc17BxPY7SPYVc/SKmwUHYwW7Th14PDjKVjeiy9qnBDSoUjhRoW6rMjMykDuyYza6qu6gbPl1goOVgou/ClRDabQqVio15UGTHCyov4iRHbu6gHHE/iwGwFSimM5SxMjFh9uy0rBYxh62RzdVO8dTKuXcAIhB8ngoBRax8wJrZaxkLGtbyoo+rLagoVF4tNLcrZlI6xnMUWZRpaK1Yqq8YZjM1zV8Nt8Y7rw1kuYNRb77vc/k79wACRiIiIus7xJA7FWmxHMiZ2TqS3VMuaUgqFsovZRTtq4UuZOqbHUhhJG6t6wm+7Pg7MVqMWqal8qi8bq5VSqDk+5ksOFqte1IJtGhq2T2QwL9TSHKmGDZhoOz+q+eSoOWA0dY2t2bQiXyrsn6nAlwqZlIEdE+mBPqkOWyexTOvkSrPZ4pRSkDJY8uJ6wX1vKUEMPjacvajV/2v+6TQvi2gOKuIBY7sWZcvUMZYNtih3MsuVaJhpK2yLV00Bo5sQNiql4HgqmB1tt36N+PZ3o7n6uL60iQE+dQsDRCIiIuqaMDQ7UqhB1ltst4+nkc9una3ASiksVj3MFG049cDPNDRM51Or/jk0/zwNXcOuyY2fHSmVwmLFxXzJiVrRgaAKcmLEQj5rYNu2EaQ1FVUgxjdgen7T/Cg/DBZXPjkCWiunWsJGVmBsaUopHJoLAnZD17BnKrPpT5g7mc3W0BadsF02LggYFXyFKIAUqwwYNU3AlwqOG1xHWO1o6gJjIxYmchbSls77IlGHgvZl0TZsjwLG+AsJ8RmM9Wpk1wvu++0eQxOXvMT+ny/SUacYIBIREVFXeH6w2KNcC6oOs/UW261ShaKUQtn2MVOoRSGbrmuYGrUwPmKtOtDwpcLh+SqKlaCvKZcOfp4bWcXpehILZQeFshttndVEMM9sYsSKNlsmfWvxDZjtqqziJ0deU8AYD0JWGlLfUIGhJ4eNvZ7/Rv0zW7SxWHUhhMCeqY29j/TLcgFj2O7cLmAMq4ObPyeY1xZUFwYvBChAAV7Ybi3DoDG4L1mGBs3UAQgUyy4qNb+lNTpeEbXZQ12ijdYQMKZaLw/vt9H9O6FNeqkTQKLa5noMPXnJS1L1MW1eridRc31MTa39azBAJCIionUrVlwcng8We2hCYHoshYkRa8sENlU7WNwQtmzrmsDEaPAzWMsT76rt4cBcFa4nIYTAtg38eSqlUHV8LCS0KY/nLIzlurc9daXqC6BxC2a7sFHFKzDaaN6Eaeit1YwMODafxaqLmWJQdrNzIo1Miqc3QgjoAssGjGF1cPNyl3j7ZM2VsD0fvgzuYwLBiwWmrkHXNehacP+sOj4AFc1eDCsTmyVVQYVhI+9/RKu39EKdhkzC5Svd18MX6aJRI07yi3TN4w2aw0a+QDd4lFJwfYWq7aFi+6jYHlxPwtAFTjx27V+Xj7BERES0Zp4vcWShFlXJpS0duyYzbRcHDBvb9TFTCKqfgKA6b3zEwuSotaaQrXnxjGVo2DWVXdWW5rWSUmGx2tqmnK23KY9kVje3sVtW2oLZXIHh+Y3z4sIW6pU2YQJYChXjLV5c+DKwao6Pg3NBTc3kaApjuf4tTdlM4tXB6dixJTwGFMoOyr6CaQT3PQUgbegwTQ0CQUVicwWjrLdIS6nguApSqXplsgjCR4EoYAzbpJNuU/z+ZnFRBNG6tLuvx8XHITRXL4Yb432p4Dt+21nGSQua4v/mY2fvBeNgJKr1sLDq+C0vqgohkFrn80kGiERERLQmpaqLQ/M1eH5QJTc1amGqD4s9+sH1JGaKNooVt35yLDCWNTGVT625Zbu5BTyfNbFjItPz1qGwTXmh7MKPtSnnsybGY23Kg2qlCgwAUQtXGDC2hI1NC1/QpgpDC6+raUB9GDxyltTG8fxg47KUCtm0gW1jCf19tKKw4rhYDrYoh7MThQDGchbGsiZGM2bD33VL22TCshcZW8euVDAbVYbVUPWAMb5EWggBXatvkRbLB4xJVYycg0q0NivNW5VSJVYuxh9HVxoz0twB0Hw/ZsC4emFgWLH9qMowHDUTEkIgY+nIpHRkUgYylg7TWN/PmQEiERERrYqUCkcKNSyUHADBZuGdk5kNqZLrN8+XmC3aWCi7UWvvaNbEdD61rqrLUtXFwfka/Ppigh09XjyjlELVDrYpl2pNbcojQWgwTHPkNE3A0nRYZvLlDW1esSqM5oUvMr7wpY2w1au5mjFsl2bQsX5KKRycq8LxJCxDw+7JDH+mq+R6EsWKi0LFjZY9AYBlaMjXg8N2L4Z02jaZFDCG/1YJAaNfr2L0pIQM88V6yNhJwNhwf0tYGMG/EaLV0zSB1DJdAFHAWF/q4ia8SLdSB0B4/23XJs37b3CctF0ZVBfaPiqOH73oG9KEQNrSkY0Fht1+UZMBIhEREXWsYns4VD9xB4K2wel8auirrnypMLdoY77kRK17ubSBbWPpdVXoKaVwtGBjbjGY4dbrFnApFYr1bcrxSoFsut6mnO5Pm3K/NbR5dbDwpd08xmjhi+MDbVq9mhe+JM1j5Dyp5R0t2CjXPGiawO6p7FCF3b0kpUKp5qFQdlCx/SjE0zSB0YyJsZyJTBe2KK8pYGwKHhIDxnqLtCvDJS9BpaRSqM9eXDlgjC94YQUU0fotBYzJl4ez+JorF+NjRtQKL861q0Ae5hEHSinUYoFh1faiCvGQpgUVhtmUgUxKR9rSez5LlgEiERERrUgqhZlCEKAppWAaGnZOZJBLD/dTCSkVFsoOZot29MQtY+mYHkuv+3t3XB8H5qrRvMGJEQvbxtM9efLneBILJQeFshN9H5oWtClPjFhbZmbleqy0DRNIXvgSr8oIT5RWu/AlaR7jsJ0sdapQdqLAfddEZuBb7PtNKYWa46NQcbFYcRtOQLMpA2O51hblXuskYGwJ6z0JJ1Yd3BwwSoVoBqMbVjCG6SIQW+4S3L+aj7OaEC1VT5apIV11UXP8+gKZ4GuEy2S26n2QaDWCje3BTNMk0f09NnexeSZjw+OmnXwdzY+RDdukN8EW+HCcRHyGYfOsWV0TUWVhth4YbvRxaLif9RMREdG6hYsKwoq1sZyF7ePpns/m6yelFAplF7OLdhT0pEwd02OprlTpFcoODi/UIGWwuXTXZAYjmTYv36+RUgqVeptyOdambIVtyrm1bYim9tay8KU5bPQ7WPgi6osoWha+xP5/GCuqqraHw/M1AMB0PoXRbHfvM8MkbFEuVtyGamPT0DCWNZHPWW1P6PttpbC+OWBMmsPYLmD0fAXpBRWMsRGMiQGjEMBC1UelYkOp1tsRhola/b4WVg4LgejzV3ob/7zmt80fRzSMGu7vCeL39+Zt0vGZq8sFjEDrFvhodnH9cXOjO2mkUqjVW5ErtofaMoFhNhVUGaZMre/HAgaIRERElKh5I7Cha9gxkcZol4OuQaKUwmLVw0zRjuaCmYaG6XyqKzMJpVQ4vFBDoRzMj8ymDOyazKx58Uq76yhUXCw0tSnn6m3KuS3apjwI1rvwJaxmlPUTqhUXvhhLlYtJ26U30+gBz5fYP1uFVAojmWBhETWSSqFUbd+inM+ayKY2vmKl2zoOGBNmL8YrgeMfnxQwAoCnBFzHB4SAhqDyMAwYw5P95rbCXoiCx6gCsl4N2cWQkmElDaJOAsaGkQgJ26SXlqRJVNtcj94UKDa3Sa/3BVcpgwrDqCXZ8RuOQ0AQcmZSOrJWMMNwEALDZgwQiYiIqIXj+jg4X0PVDjYCj2ZM7JhID+2sMaUUyraPmUItaik2dA2ToxbGR6yutL7UnKBl2XH9YGt1PoWpUatrTw7btSmP1bcps015c1jvwhfXC2YxSqXguD6cNlWMQOyEqR4qDurCF6kU9s9W4fkSKTOYEzoIt2sQhHOyimUHxTYtyiMZc0tVG3cSOHQyk00IIJu1UBEqChSVUlD1t2ElsK5r0DVA1zTo9ZAvqgCud1Gr+oIYpQAJQNXvo9Flbd6GpFKA2tiwMjF4DANMLXjfWkNKhpXULZ3OXG2oWm7qAvBlUP3v+4ieAzYLF6QlLXlJml3sS4VqvRW5YvuotQkMw4Un2ZQOyxi8wLAZA0QiIiKKKKWwUHZxtLDUXru9xxuB+61qezhasFGph6W6JjAxmsLESHdafJVSmC85OFqwo/mRuyYzyKbW/zQsDD4X+tym7HgSs4UaSlUXusZFIL3U6cKX5tmLiQtfwhOmTha+GEvBYnzhS6//vpRSOFx/MUPXBPZMZ7dUGNaO50sUKi6K5dYW5XzWxNgAtyj3Wycz2cJwITeawcwMgvbI+n0ovP8AqC9OCn/+rfcjXW+835hG4zxTQ2+dxxi/HUohChqb3y4XPIYf1/BvqZb92FAYVi41ePdOR8Gj1lj9Gb+s05CSj0dbVyePmWEFY9ILCuFokXBB2nKPl+ELBV59oZoWvqBQ/1u0zKWwMGvpMDdBYNiMASIREREBCGZmHZqvolwLgrRs2sCuie621w4S2/UxU7CxWA1KtDQhMD5iYXLU6lqlpedLHJoPgjUgqOTcOZlZdwDiS4Vi2cF82Y1arYGNb1N2PInZoo3FiotM1mmYFRZfBBIPneJBFE/qemOlgARoOmFqapFOXPjSZq6Uri2FiQ3bpbu08CWsqhUi2Li8lUMxqRTKVQ+FitvwgoEmBEayJsaGpEW538L7T8rUMD2WBly3ZQZitDCpfp+J34/CwF7GQvp2W9kBRNW+zZXA8ff1+nfaLqzsJKRc9m09tFTAUgVmU/u438ewsqPgMQwwY7Mxw8uWq8CkzUPXBHRLB9oEjFK2zly1XR+VWlBhWHN9+L5q+SvWtXCZi4BlBBuSfV+i5gC+rzblJngGiERERFucUgrFiosjC7X6K6YC28ZSGB/pXnvtIHE9iZmijWLFjdrQxrLBTLVuhqXlmoeDc0HbpSYEto2nMZ5bXyWn4wZLUeKtinpsm7K1QW3KYXC49DMMAmff9ZZaaFdYBAK0CRljw80NvfcVblvVSidM4Ry5qDW63i7dvPAlrMCKV8HFRQtfmisYO1j4Uq55OFIIksttY6mh3/qeRCkF25UoJLQoZ2JblHk/2VhLC5OSLw+DtjCkb6gCDisZo3mmEt4KlcBJS5MaXphZZ8gYhWno/d9Rc/i41pAyml2Z9HXkUmgZv95+hJVrmk/ZZkFP0tvw+qh3NE1AVwK2Cp6HVeylx7y0pSNlakHXTvhimha0+yuFhhfkbHf55WjxGYxGLFzcqBcSOrH1HoWJiIgo4vkShxdqWKwEz2gylo6dk5mhnJfn+UHotVB2o8qd0ayJ6Xyqq9+vUgozRRtziw6UUtHMtrS1tutQSqFc86JtyqGUqWN8xEI+u3HhgeP6mF10ouAQCKoet42lcMyeCczMLEZVJ0kVOc0ttJ2EjGGFW3OFDkPG3orPkVtu4UtDKBIfYt+y8KX98Ppw4Us8YIQQOLxQA6TC2KiFiRGrV9/qQPL8YItyoV2LctbcsBcMaPWEENAFVgzpw9lsSZXA4f8vhflouzQpPgcuHirGN7QPSoVTP8LKpQrItVVSLhdyxr9O+Li40WHlmkLK5o8PQ8t623gYYMY/VmDrhJWuJ6OFJxXHb+j2CKVMvWGGYbvulU7nrjqeguPJxK8RHynSsk26C9X+nWKASEREtEWVqi4Ozdfg+bInSz0GhS8V5hZtzJecqBohCL3Saw712nE9iQNz1Wj5zHjOwvbx9Jq23fpSoVB2sFByoieUQoioTXkjWxXtenC4GAsORzImpkYtZFIGmm/GSotAgMbwabmQcanCrf3Xahcyxit1GDJ2n6YJpFaowmoeXp+0/KV54YtSQKHswJMqmBGnCfzG9hsWvpixEHmQwpH1UEqhVPNQKCe0KGcMjOU29n5PvRWfzdZJyNh8fIy/Lz5uoF1Q3zDTdJXVwJtVGHwB2JB5rcGCHTRUQK62orLTmZbxsFIpQG5gWNlJC/eqKzAbqi43NqwMA76q7aFiB5uS3aYgT4hgrEE2ZSCT0pGx2geGzTqZu9qwOT4eLnpLLyQsN1IkaWZxPGw0DK0rCwF7HiBKKXH11Vfjs5/9LO655x5MTEzgzDPPxN/8zd9gx44dvb56IiIiauJLhaMLNSyUHQBYd4XcoJJSYaHsYLZoR21/GUvH9Fi6J62QixUXh+ar8OvLZ3ZMZJDPLpOgtWG7wVKUQsWNAk9dExjLBRuhN3IGnO36wYzDqtcYHOZTyKzz72Wl8AloDRkbKhrXGzIarZU6awl6qb1OhtdHFYrhiZMXVEUHIbSI7kNrWfjSHDYOaohcc/ygRbnqwfeXTlozKQNjWROjG1hlTIOl06VJvlxa/BIG9c3HzIYAYpnrS5pXGz9WDlvI2C1CBAEYxMaGlVErdz20jDZ8o3Hu5HrbxaPrrVdWtp/q2R3NYWVXQsrw4xEsOqk5ElXHQ83x4fmq5frTphZVF2ZSRs9+r51sjm9+EaHhRbl6tf9KM4sNXUPa0jE1Nbrm29rzAPGyyy7Dl770JZxzzjk477zz8Lvf/Q6f+tSn8KMf/QjXXnst8vl8r28CERER1VVqHg7OV+F6QdXhxIiF6bFUV16VHBRKKRTKLmYX7ehEKWXqmB5LYaQHy0WkVDhSqGGhFASymZSBXZOZVQV9YeXRQkKb8kS9TXkjw612weF0PrWhQfN6Qsb4SfSaQsamNmmGjL2hNVVmzBbtKDA/ZlsWGUtvmCXX/PvtysKXPsyYCluUixUXtVhbqqFrGMuZyGfNoRwlQd3XaSVj4gsxsUDCl01tlG3uR1q8XbrpvhS+j4tEeiseVqLHreDhc4DE4FEqSDTOnQwCzLXPtOxVWBkuDguCN9WwzAcI/q5NQ4NlaLDM4C2EQM314XjB1vt2IeVqKzFXe9/oJGBc+v6aRorUv+dwtEzNWV+lak8DxK9//ev40pe+hPPOOw//9E//FL3//ve/Py677DJcc801uPjii3t5E4iIiAjBE7SZQtDGq1Sw+W3XRAbZIVpKoJTCYtXDTNGOZtWYhobpfAr57PqWl7Rjuz4OzFajGWVT+RSm86mOrytsU54vOVHYKYTASNrA+Aa3KQNBJdTcYmNwOFqvOBzUCtVOQsZwY6rXJnxaS8gYnjgzZOyeUtXF0UINALBjPI1sKjg+dTJLLmnhSzxsbPz9dj5HrpstnuELBcWyi1KsRVkIgdGMgXzW3LAN6rS1dDLTtLnKqd2xUnYSMmoJ96OmF2cYMm4O4e9I34CwEui8hVsqtRRgysbLfKXguLK+tETC9iSkL6OqTSBYGBgs+AoDcK1hHEvSzMNuaZ47mdTCvZaQUtMEMrqGrNAbPj4+CsGX7SuQO9HTs4ZPf/rTyOVyuPTSSxve/6QnPQl33303jj/++F5ePRERESEIhQ7OLYVc4zkL28bTQ9MSp5RC2fYxU6hFlTyGrmFyNGj57UV1ZVjleKRQg5QKhq5h12Sm49bomuNjob5VtblNeWLE6uo26E5vz+yiHS3TAQY/OFyNlTamAo0hY/PG4eSQsf3JRbtKt/iJNEPGRrYbHKcAYGIkuO92at0LXxJmTLVd+BLbHJ40jzHpd1tz/Kja0Iu3KFs68rmNXYRE1M5KVU7A0qy45lESLSGjVHDk0lzTJPH7UlJYP8hjB6h3wgVEqwkrlVKouTKaYVi1vWh0TcrUkDKD43LG0qMZhmlLhwBWVR0ZhpZJ7eGdtImHpAo2+4S3sZeag0fD0HD8Or5ezwJE3/fxwx/+EA972MMwMjICAKjVatA0DZZltYSKRERE1F1KKcwuBjMAlQpCrp0TaYxkVj+Xb1BVbQ9HCzYq9aUluiYwMZrCxIjVsxMPXyocmq9GYVsuHbQsrzRMO6w+ml90otsL1NuURy3kMxvbpgzUg8OijcVqLDjMmpgaHY7gcDVWEzJGJ9BeazugZMi4ar5U2D9TgS8VsikD28fTXb+OThe+hBtwk8LGMBhZaXN4WF3l+RK2G3wdTasvFzJ0jOdMjI9YbFGmTWelZRBA61zTpPESvlQd3Zc4VoKSKKVQc/xo4UnV8aMXY0O6JpBJGfXQsB4YJrygvJGVlcuFlMsGkGFoWW8bX26reLuwMtwI3ty6vVo9CxD37dsH27axd+9e3HTTTXjXu96FX/3qV9B1HX/2Z3+G17/+9TjxxBM7/npBatqrW0tEtHHCYxmPadRLYTVPWJGXz5rYMZHueGPcoLNdH0cLNkr14EvTgnmOk6NWT7/Hqu3hwFwwQ1ITAtvGg7ByuTYsz5colF0slBy49eqjcKvqxKiFTJsntb1Uc3zMFJd+fkIIjGYNTOdTawo1tspxLQj4dCwXb0UhoxeeNMfbAZdaAIM2QB+O10HIqGswjOEMGZVSODhXgetLWIaGPdOZvnxPYfuXaQBILbPwxQsrrRp/x+GsKdvxsVh1g/bO8DxNAJahIW3qQfVyxUXZ9qK2OdNorWZk5VX/bZXjWrfp9dbQlLlMyNj0YsxScL90n5IyfpzEyrNN68fJxtZpLdrkTpuXVAo1uzEwVE1BmKGLhoUnaVMbqBb5qBW8z2Hlen8kQjX/5LvkzjvvxLOe9SyccMIJOHDgAC688EI86EEPwl133YUrr7wSqVQK1157LY455pheXD0REdGWpFSwYXn/0TKUCrYBH7NjBBOjnc/lG2S24+PgbBlzxaUziamxNHZNZWH1sJpHKYXDc1UcnC1DKcAydZywaxS5Zao5KzUPRxeqmKtXgAJBZdL0WBrT4+me3t52yjUXh2YqKNQ3cAPAxGgKO6eyyKSGZx7mIIsPO3dcCdcLBrQH//aDt55sqaZoJ6xitAwNpqnBMvTg37H/H+ST5/1HSzg8V4UmBO577PimnMtatT3MFmqYK9bguGGru4RpaMimgmUoUik49WH8ndA1AcvUG36v0e/UDBdVDO7vlWi9fKkajomu23qs7LQF1NCXjpHB8TK4P5lG/T424MfJrcaXCuWqi1LVRaniolxz0ZxaGbqGkYyJkayJkYyJzAbPjN6qehYg/vCHP8Tznvc8AMB73vMePPaxj40u+8Y3voGXvexlePKTn4y3ve1tHX29ublFrHPeIxHRQBACmJoaxezsYsuDIdF6uJ7Ewblq1B6bSxvYOZHZ8Hl6veD5ErNFGwtlt2G5x/TY2irmVsP1JQ7OLv1cg2rOTGKFULjIZb7koBprU05bwTbl0azZl5P+qh1UHJZrYa+YQD5rYirfnTZKHte6K2xDSpovFrTUBm87fRqva/HqtuYZfkHVTj/+LgtlJ5p7uHsqi3x284xX8H2FYsVFoeI0bFHWtaUtykljABoWVdR/j/H/D2dtrkxEv0PTSK5UXc/CF+JxbTNoV/G9luOkkVDh3Xyf4v2pN3ypYvMLfdQcHwrNFYZaVF2YTQXBL38fq6dpwOTk6Jo/v2cv8WWzWQDAjh07GsJDADj77LOxfft2fOc73+n46wX94F29iUREfeF6ErOFGooVN2jzqLcqcRsdrZVSwYnskYUafKmgaQLbxtIYz5n17Wv9voVr50uFucVge3RYkZVLG9g2lo5Oznv5/ZWqLg7O1+D7EpomsGM8HW10jl9v1KZcbtymPJoxMDFiNcze2cjfR9X26sGhF92mfNbE5OhScNjN28Pna90SPCakTL1twLtcyBhvDQxbBT0fDUFXs6ST5yhkrAeP3QwZa46PQ3M1KBVsLx/NmAP/t6OUQrnmoVhxGzaVh5vTx3KNW5STv5+lTc9osyemYdZmfB5j/XfdvPBluU24ZtPvMQpGevA7HVY8rg0uTQRzRa02qcaKL8bU72NSBfcl1wOA9lvadU0kLnuJHzP5XH5l8cCwYnuw3dag1zS0KCzMWkE1ffPPlvfL1Vvvz6xnAeKuXbsAANPT04mXT09P4ze/+U2vrp6IaOC4nsTsoo1i2UUm66BSsRsO4poQ0OsVA+H8I10L3mdore9nqwUBQXB1eL4WLcHIpAzsmuhPe2w3SamwUA4WwITVOBlLx/RYuuNNx+u6fqUwU7AxtxicmactHbsmMy1hTs3xMV8KtinH25TDJQn9mjnZLjicGrU2/d8GBcJNlbqlA1h9yNi8MTUMGdudPAPLhIz1uX2dBlKeL7F/tgKpFEYyJqbzqbX9EDaI7foolFu3KKctHflsUG3Yzfv6Sgt92i58aVpa0enCl6VKxtawkVWMtJl1epwMQvvkuYzx0D74GABtXowRQrR/IaZ+rNyK9ynPl6g6Piq1YH5hUmBoxQLDTMqAyTB2IPXsGfjExASOPfZY3HPPPbBtG6nU0hMD3/exb98+7N27t1dXT0Q0MMLgsFBvvRQCyKYNKM8PTtjqm+ikUpCeQvA8v/0JXEjT6sFiPVQ0tKUAMh42so1peC1WXRyer8HzJYQQmM6nMDm6/EKPQaeUQqHsYnbRjir5UqaO6bEURmKVPb3kuD4OxBbQTIxY2DaejoKRdm3KGUvHeB/blAGgYnuYTQoO86llt2bScFrLyfNyFTqrCRkbqnOMpf/XNeDAbLCIKGUGwfwgHrN8qbBYcVEoO6jGW5R1DWPZ9i3KG2EppADQ5jaEm3DjVYvN26UbqlOXqbpqrlqMB4zcgkubXcP9qYPjZPNm9vixMl4ZXF3h+loqGGNVjZv9ebvny3o7clBlaLutx5eUqSOT0utbko2hGLezFfT0JfxnPOMZuOKKK3DllVfila98ZfT+a665BsViES984Qt7efVERH3lehJzi40z27JpA9vGUjh2zwRmZpZm6khZf2IiFfz6/KPg/1XUyuTXP8b362GjVHCkAlYxkF3XtaiaMapsjFU1GrEAcjM/cRl2vlQ4slCLFmGEJ+H9OpnthjCUmynacOpPNE1Dw3Q+FbUMb4RC2cHhhRqkVNB1Dbsm0hipL0rxfImFkoOF8lIVUrC92MREzuzrEpJKveKwEgsOx7ImJjcgOPR8GVSLVnxUKzYMbanqIv6WW2UH02pPnuMVOvGAyqs/Nq0USJVqHhxXwtAFto/rmCnUGk6c+zlrTCmFsu2jWHZQqnqQsRblXL1FeaNeyFivoLVTtL3/J/1OG8LGWCDieCpY/rLMFtywCjUxbGQlEW1y8eNkeoXjZHNYn3SfisYPLHN9UQVjfPRA7HF1kJ6ru54MtiPbPiqOHz2Pi0uZesMMw351aND69GyJCgA4joPnP//5+J//+R+ce+65OOOMM/Dzn/8cn/vc53Df+94Xn/nMZ5BOpzv6WrOzXKJCRJtDu+BwOp9CNmVACGB6erQhQFyNsCXNr4eN8aAxHjZ6sfet9lAfznlpCRbDALKp0pHzGzdOuebh0HxQvSOEwMSIhemx1KadYxWesM8UalHFn6FrmBq1MDZibdj31RzKZlMGdk1mYOgialOOzzwzdA3jIxbGc91tXVytSq0eHNqx4DBnYmo01fNX8+Pt24BCNptqGc0QF5/HlhQwsl1yc+ukDXCx6mGx6kIAGM2abcOt8DGooUqnhyGj7fooVoIW5fhJfcrUo4UoW/Fkt2HhS1PAGP5/JwtfEiuumuYxDuLziPU+XyNqttQGnVzBGG6W7uR5uxaGmgnHxl7er5QKQtL4DMPmMFQIgZSpIZsyoirDrXgMHSRSKdhOsMn85OOn1vx1ehogAkC1WsWHPvQhXH/99Th48CCmp6fxF3/xF7jkkkswMjLS8ddhgEhEgy7cElsou1HVQjZVDw5jM9s2+glpGDhGVYx+Y6VjPGwM26nXGji2BI1NMxzD94sBPFEYdFIqzBSXZvJZhoadkxlk+1j1tl5V28PRwlL4pWsCE6MpTIxYG1qtVnOClmXH9SGEwFQ+hYkRE6V6m3J88UQmFSxFGc30rwpJKYWK7WO2D8FhuERivuREbdJA8CLJcXvGMT9frgcLSydGnW+VbZwfZWiiIWSMnxixZXLzqdge7j1ShpQq2ki+3KyxlTQsNEiYxbhSyBi1KFfchlEEuha0/Y/lLKRMbvlcydIW3NYgZDW/z4aFL80LKvq08IUBIvVDS3Bfb5lunl3bieYX7pKOlyuFjEEVsoyqC6ttAsO02TjDkF0H/eX5we+s6gT/1RwfSikYusCfnbJnzV+35wFitzBAJKJB5fkSc4sOFkpOQ3A4lU8hm9JbHpQH/QlpvIpkqW26XukYDyBjIeRqtVsYE2+hjt7PLZGoOj4OzVWjGTLjIxa2jaU37ZMz2/VxtGCjVF/8ogmB8RELk6Mbu3REKYX5koOjBRtKKZiGhm35FGwv2Kgcb1POZ81om3K/tAsOx3MmJnscHMr6pu/5RSf6O4y2TI8Gx7rljmtSNp74NL91/dVVS4fHiKSTokFs79rqXE/i90fK8HyJfNZcdu5hy6yxLoeMnlSo2R5qjgQEoAlA07SgRTlrIpcxtvxjTjdFbZ3NM+OaWjw7kTQrLt463e37/KA/X6OtK6wCbB4l4TY9tnaieVu7rgf3oWArtYTtBfNS44QQSFtLLckZS9+0z0mHgVIKtitRtb0oMExqkTd0DbmMgQffd/uar2vzli0QEfVZUnCYCSsOE4LDzaJxHtbKpIrNamxT2RgPINeyMEaPVTA2L4xJCiA368++mVIKs0Ubs4tO/VVDDTtjM/k2G8cLqnTDjcXhnL6pfO/bbZt5vsSh+VoUYlqmBkvXcHC+FgUTpqFhPGdhrM9tymFwOFO0o0qpjQoOg/mGLhZKTnQyomsCYzkLEyNWx9etaQKpZbbKArHWLtkaLsb/Hc6M9VfYLpvUNpn0lmFRb0mpsH+2As+XSFs6dk4svzRlNbPGkire4n834d+U7SrYbrD5M/6il6HVT4LTGnypUKy6qDp+YjA9LI8rGy1eVZxp8zHhMpeoIjVhQUXDpvA2G3CbWzpbwkZWL9OQECvMOAWWFim1m8cYtktLqVB2vYblMLIpMdfrLcmZemCYSxmwzKWKb96rNpbnS9TqQWHV9lFz/cSQN2VqSFtBC3m42ToMiNeKASIR0SpFwWHZiQ7WwxAcrpUmBDRDoNNIK3FhzDIBZHii6K9yYUy8gnEpdEzeWD2IvzPb9XEwtgk4nzWxfTy9KWfIhO398bmgo1kT0/kUUubGV/SVax4OzlXh1p9AG7oI5sKI4O8rmzKCbcp9bFMGluZDzsaCQ63eqtzr4NB26/MNYyMZTEPDxIiFsVxvWsyFEDCjY0n74CgcyZC0NTj8d1jNuNKgeiDYqttQwagFoUPYRj2o89k2A6UUDs0HxzFD17BnKtuVAKeTkNHzJQplBwslF47r1k+4g/uMZWqwDB2AglLB41K8jbn99SXMGWPIuG6aJmBpOqw2TySW24AbDxplfOFLGy0LX+JhI3+PNETaLVJSSqFWr1Yr1zyUqsHjvACg1//+FQBdA3RNg6YFlWvhvaJWb4dtFj73bl6g1M8xBMMgrC6MAsM2S2r0+gtiYUVoukdVoQwQiYg6lBgcWjqmxtLIbcHgcK00TQTtEgDahQShaGFMUwt1w7KYpiUyS4HjypWNQNPCmNi8xnYBZK+DhLCtdqZgQyoFXRPYMZFBPrv5qg59qTC3aGO+tHSfyaUNbBtL96UVWKlgjuTRgo2a48H1FXIpPQjB623K431uUw5vZ7nmBcFh/Un6RrR5K6VQtX3MlZyoMhMA0paOydFU3wNVoH5/FYDeaTVjQgWj1xQ4+L6E7wPLVUNr9WNAc/Vic3Vjv38+g2ZuMViyI4TA7qlMzyuNw2rdYsXFYtWNjjspy0AubSCfNTESa1Fu2JraFEzFg+lOt6a2a6lnOLU+nValhr+zdvMYo7Esjo929/d45bJpCNhKQ7lkw9B4X6fNSSmFmuNHC0+qTmO1mhACGUurtyQHMwzT1tJ5Tfic24tVLja/cBfdt6QfjTlJEn/Brt382q0eMvoy+H2F7cg1x08cF5Uy9XpgGFQYWsbGzOxlgEhEtALPl5gvOQ0hSNrSMZ1PIZfu/wn1MIuHBVYHHx+vVkya1eglBJBLQQOAZdoh47cpPqsxvjAm6f2rqbZxPYmDc9Vovl0uHW4C3lxVh1IqzJcdzBXt6ElPxtIxPZZGLt2fpx6O6+P3R8pYKDtwXImUqSOfMWCZ+kC0KQNLweFM0Y5e3dc0gfFc74PDxaqHucWl6xVCIJc2MDlqIWNtvhdIompGo33bZPPWYE+qaFC9V5/ZFlZFS6ngyOUrm+LLpNrNZQxfmNgKSlUXM8Vg6dOO8XRPFz45nkSx7KCQsEU5nzMx1maLcuPIjuXbpd2mk+flQsZqm9vZLmRkEL1+8fs8Uskfk7TwJZytmfR7FA7godKyXb75vt7wNvZCw6B2ONBwk0qhFlt40hwYAkG1WrjwJJsyll0YFTyvXf5Fu+i+1TSrtnkMQScv2LUcFxO2TA/L/So43siosrBqB1uSm2f8amF1YawduV/PJRggEhG1weBw82k4GeygYC9pYUx8W3VSW3V0ctHhbdK01hbq5o3VugDKto+5YlB1qGkC28fSGMuZm+rvTCmFQtnF7KIdncSnTB3TYymM9Ok+I6XCwbkq9s1U4PoSmgBG6os/Jkasvt2uOKUUSvWKw40MDn2psFAO5riGvy+tvoF2csSC1Yf28o3USXgExOZItVn+4sWCh+jFiBWqGcNB9UnzGXuxDGKjhSMYlFIYH7EwPtLJS0CrI6XCYtVFoexGL7oAwcnuaDYIDdNdCL9XHTK2mTfWrZDRNLRN//fRL0tByAq/Ry9c7CSRyaYxp9Z4XxdtQsZwy7yx+hcaiZpJqVB16tWFdlCx1jLDUNeQjS096faG+ZVCxrCjKD6r1m1663my/ngbzDqtLVMlHC3Hat7YPuAvxMiwujD2n5+w7MYyNKTrrcgZS+/672s9GCASETUJ2y4XSk5UPcXgcDh1a2FMuwBSqvrSGNl+YYyUQXgUVjalDA1jOROFiotyzWuY1TioC2PCCraZoh3NZTENDdP5FPLZ/oSgricxu2jjwGwFFTu4TZahYfdUBtP5/rRQN+tXcOh6wYsj8XEMhh7ON+x/JeagaTdHKq6lmjFhLmN4bJBSwZb+shXP0bEpoW06fpI0iMGDLxX2z1bhS4VsysCO8XTXvnbYZl9oalEWQiCb0jGWC14U2OifS8NjSZtjSzycStww3Y2QsYcbibeC5oUvwRbmEWR0FVUgJt3Xo7exKizflx3NZARat8oP44sK1D1+PTCs2h4q9cCwuWLN0LUoLMymNq69tZ2oo8jSsdILMS3LXpqqGhsC/DYLlZZ9IcbYmPtUeDursUpQ202oLqxvtI63Iw/y8zAGiEREdb5UmK/Pa4sHh1P5/lVP0WBZzcIYpYITjoagsamysVR1Uay48PxgI3G2PoNGKiw70D8u3kIdn+HY/P5eLIwJl3zMFGpRAGboGqZGLYyNWBs+xyacf7ZQD8cWKy48qWBoAtvH0zh2e24gnpQppVCqB67hrCBNE5gYCTYb9+o2Vm0P8yUHi1UvegKbMnVMjloYzZpbfu7QenRczShbA6OGt7HlUW4Hm+qTgofmqsaNDB6UCip+HdeHWQ/su3HdrhcsRClW3IYwxjJ1jGVN5LPmhm9yX634CW078dmd3QoZW6pzGDKuy2oql/3l5rDK1W+VD188XG6zPBc+DR+/vuQpnGGYFEKZhrbUkmzpMPscGK7Faqq9kxYqrXWkRNIx0lzD46dUCnasFbnq+PASqgtNQ4uWnIRvN9PvigEiEW15ScFhr9oufalQrDgo2BKVst1y8scn9MNDCAEhAEsTQNOJrS8VjizUIKVCPmsiZerYNZmGaegtC2PaBZDRwphonkxnt6mlhbppYUw8gFzuRKRqezhasKPWQV0TUVvwRs9lkVKhUHGxUHJQc4In1+WaB9PQMJk1cez2HEYy/V9CE1ZqzsaCQ10LlqP0KjgMqxznF52GNs9cOmjj5gKojbXStlkgYbFH08mRH58n1WHwsNJcxm4Nrp8p2ihVXWhCYM9Udl1/01GLcsVFpda7FuVB0jDHr412C4KaK15Xs/ilWyfQtCR6wXGFOaxJW+XbvagQVl21a+0E2i986sX9nXrD84OZeJVa+6o1KxYYZlIGzAFt2e22ThcqrXakxHLXFz1uGo0LYADA9SUcV6JW35Lc/HsSQiBtBr+rMDAc9Be7VsIAkYi2LF8G227nF+3G4DCfwkiXt42G8xQXSg6kUshmFSoVB6p1qRaHcw+5cs3DoflqMKBdCEyOWpjKp6In891YGNMcNCYtjFkucAg1L4wxdC06qbddP/pbnRixsC2fgrnBM/McT2Kh5KBQDsJ/Wa9AFADGRyzks+ZALKFZLjicHE31JHANQ9X5RTuq2BL1TdMTA7BpmtrrtMKpeTNm0glSw9zWZSoxgKVq5qANVjTMaOvkcadYcTFbX5qyczKzpr8xpYJ2r0I5uUU5nzUxmjEHsnV7o3S6IIgh4+BbzVb5pNbOdiMSVlr4BCzd35eraOTvdWN4vkTFXmpJTtpinDKX2luzKWPTh1C91OlIieWOka639LzZ8RRs1295YS88dxQIgvtwvnGmPmsymzaQSxtImfpQVQYL1RyTDqjZ2UXI5Y+DREQd8aXCQsnBXFNwOJVPYbTLwaHt+pgvOSiU3YaWweP2jGF+obK08bPpVeZOxKtKlpuVNUwPWpuZlApHCzXMlxwAwavHuyYzyPRwM2lc88KYaFZjmwCyeWOfLxUqtgfHlVAInjCFT2jDACxpYcxyG6vX8ncZtinPlxyUa17D/cX1JQwtmAu3bSyohuzn33674HBixMJEj4LD+IsV4fGt11WOQDgrbBQzM4uJL4xQfyzXEht/2zxwv512SyGkVDi8UAMATOdT2DHRLtpK5noSxYqLQtlpbFE2NIzlrE3RorzZLHsCHS0R6ew5SUOVThgyGo0t9ZsxjNpsxzUpl1qj21Y0ruL+3vA8s90L21tos3y3uJ6MFp5UHD+aHR2XMvWGGYb9fiF0q/F8iartoVTzUa65qNp+9NxYSgW/PqZICDRU97c75wrDxeVm127U/UjTgKmp0TV/PisQiWjL2MjgsGp7mFt0UIoFHJmUEcwayxjYNpmFLv2WJ6SdDOcO39dJ5QDQYTsLtwD2VNX2cHC+Fj1JnBixsG0svaE/81UvjKkHirbrY6Zoo1h260+OgmHPuXTwFGI1C2Oa6fGgMTarMWljNQAsVj0slJyGV+ezKR2aEEHLsq4FwexUFpk+VtgppVCsuJhbdDYsOKw5QaharCy9WGEZGiZGUxjLbu2Kra2s05bYaDtm+DjjLb2otdJSCFnfvu5LBau+IXix6jU8ziRVOKHeXl+suKjYS61fmiYwmjExljORGbIW5UGymkrG5uDZjS00iFfpLFf11q4VcLOHjIMkGJGw8sKnsG06vF8n/Y6bq5dXut5ws3z4nDJpXMJW/N0qFVSthdWFVdtruZ8IIZAyNWRTxqZYojFslFKw6y3I4Wbk5lDXNDSkNdHQipy2gsrCaNlLrCikuUo4XJ62XPdPfKxV9JgZq/Q2B2RxGgNEIhp6st6qPFdy4NeH2fYiOFRKoVwLgsP4rLGRjInJUQvZeqXZclfXaetaQ1VJu8Bxte0sSSFjwvu24hPAtVJKYaZoY27RgVIKhq5h52QGI+nBf/hVABbKDuZLwaZey9SQSxvYNta6wTg8IQkrGFsWxsSqHeMnJuH8tuX4vkLNDWYAAYAmgr/VkYyBbNpAqRo8GdeEwGjOxM7xNCxDg1Jqw/9Ww+BwdtGJnnz2cjZkuMRmftFGOTYjLpsyMDFqcfkTdaRxO2Z7UrU+1riexOH5GjQhoBtBizwQznQDkl5I8PxgXlQYSmhCQNOCv9t8fSGKZeowBuBEaatbzUxG12+sYGwIn5tDRjv5azVUuDbNG2NbbXespm066cXrtW6WB5D8HHMVYxI2g+DvXEbVhVXbawlg43PxwipDVnFunHCLda2+Gbnm+FFhSVzKbNyM3G6TtWXUQ/tU8vWFj53hCy8tC7K8WEdQhyFjNGokYct0r0PGwT+DISJaIykV5ssO5haXgkPL1DE1GrRDdesJiqyHBvOxaiMhBMayJiZGLaR6MBeukyf1QAftLKvcAgg0PQFs09Ky2Z8AdoPt+jg4V422E+dzFnaMpwf+SWJ0vykuVepmUgam86mo6rDZ0glJ68KYJPEKCD8eNIYt1PV5QItVLxpKrRB8/ZSlI2VqcH2F/TMVyHoLSS5loGZ7+P2R8tJtaloYEy2LiS+Mqb9vPa3+UXBYXJo3qGsCk6MpjPcgOJRKYbGpwlEIgdFMsBiln9WXNLw0IZZOlOoOz1dhmRoyKR3Hbs/BMrTEKvqaE9yfSzUPrucjPFfT6zMX02Zw0lOueQ1hePSiWpsq+qhtbMCPq8NsvSFj/ERaMmQcGNHvFUAnm+Wj32X9MT1p3uZyLyzEr7f5/q2HFVmx55yDcp8PA8P4DMPmzbui3rkRhoUZSx/454LDIvz91GLbkR2vdSmNpomosjBjdTfUTXrsbBbej1y/8W08eGwMGdvfh8LH1aQOANPQYJnr+74YIBLR0JFSYaEeHIYP4pahYSqf6mpwGLZEz5eWrmcjZo2txmrbWdpt/1vzE8CEk7744O5hfJKvVFDxerRgQykFXRPYMZGJKnMGlaq3Ic4u2tGr5b3aRh6vgIjzpUKhHMw29KVCNhW0SofLE8LgcLZoY6HkIF1v8xmr36/jG6tXuzBGE0st1Mttpg7/X9NEcnCoa5gcsXoSHHq+xEI52DYdP+aM5YJjDmfE0UYKH/+EENg1mYleLAur6KXUUKp5qFb9YLmRAEYzBjTNRC5tIJcyYBqifj9NHtcRtVACWO4xJ6mCvrltehgfbzaLTkPGpFmM6woZY+1/qwkZw1m7xYqDQ0UXtaoNXWvfmbGV/7ZWs1l+peeYzeN5ll361MFonl50zYTtrvEZhn5TYKglBIaDEngOOylVQytyNeH3AwTnhen67yZTf2G6n/fhTu5HfnNYn3B8jEJGx0e7x0xDF9g2nV/zbWWASERDY6OCQ9eT9cUoS0sKTEPDxIiFsVz3Q4NeW+sWwHat0w1zcwB0+kpzeMKnJzxB3yw/U8eTODRXjVrYRzImdk6kByJMbidc9DFTtKO2W9PQMN3l+81ybNcPtilXlrauhsHY+IgVBeCO62O2WEPN8ZG29GCW5Hg62mAd/57Cv8V2LdTNC2OkUpDR3+zywhPdoDpyaYj2WM7EWNaEoQvUHL+h+nE9P0fH9TFXn28oh+CYQ5tfxfYalqaMZIIHD6WC+0Wh4mKx4ja0hWVTBsZynW9RDh9z2m2cDU+cOq2gT6psSgofmo8ntDFEJ1U6zUuB2rQEJs3qbKYlzBsTCObJlm0Pvq+gaUAup9WP9cs/lwmP98t1aWzVoHFN43nilVhNnTSruc/Hfy8NAXIHiy/C21RzZcMMw+Z2V00TUdVaNhXOxtt6v+eNFv69VOut4lUnGHfTUl1YD3Tj7ciD/Ly8neAF7uXP1cLnvK6v6gFj65Ks9WKASESb3kYFh7brY26xcUlBytQx2eWW6EG1mieA7RbBRE/w6+HNqhbBrLAFsJ+LYMLqvSOFGqRU0DSB7ePpqDJuEIXz82YKtajN2tA1TI1aGBuxev7kN5wZGm5TDqXMIBjMNy3+KJQdHF4Ifr66rmHXRDoKLZo1tl6tLDwZiVcwJs1w9HyJcs1HJXYCoQlRH6atwfUkZorJJTHNFYzNlY3NG6sBoGoHwWF823Ta0jE52v3FT0Sdcj2JA7NVKKWQzwYzfj1folB2Uay4Da1VphFUCOdz1rLBUJJOH3N8mRwwuu1e2FqpsilcAmG0f8zZqkFQv3XUChiGUJ5snDOWsNTAkT4cF1GLo+tJqOi6guNtNhtcL8JftwCglro3FAAlJZQSHXVnJAaNTS+cbtW/r07H87S7zydVMIedCMv9XuLLBnVNtMysU0BDyBgGhtlYYLgVf18bTSoFO9aKXHVa28WB4HEnEwWGBtJ9ri7cSCuFjM3h6lowQCSiTUsqhULJwWwsODTrwWG3ghulVHQSX6ouvcyZrW9UznFJQYtOT/qkCgKaldpZlhbB+HBWuO5lW1pil3Xzd+b5Eofma9HfRzZlYOdkZtUnyxupans4WrCjSsleLvpoFrYpL5ScqDpECIGRtIHxEQvZVOMTcV8qHFmooVAOfvvZlIFdk5mututq9fC53awnqRSK9fbulKnBMixomkA+GyxyURIt1Y7xSsf4wpiV/obD6kbblfB9CRFbMDGWs5BL6RACqEYVjstXTxB1k5QK+2cr8HwJy9CQSxnYP1ttCLnDLcr5rNlyf+6FTqoy2lY2NT3+yPBFBB/LzphqmMPXZi4jl471R6chY6W+gKpQduH5MngMqM8NMw0BQ9OgaUGFuVQK7c67BQCI4HcdzxmBIFzEGoPG+OxeBo2NOr3PJ72Y7SaEjbbjoxRdLlt+11q9yyBtBhVs4fgUWW9n9qXa8r+TXvB8GQWF4bKT5gAsvpAmnGHIcS7thc8p14MBIhFtOjKc1Va0G4PD0RTGct0LDktVD3OLNqrO0pKCkYyBSS4p6ApNCGidLoJJeGW5+a1Ua2tpWc9Mo2LFxeH5KnwZbPzdNhaEcIP65NF2fRwt2FHYqYlgZufkaO9ndtru/8/en8TKsnRpoeBnZt5Eu9tzu//nzyyg4IGqnpAo9BATQHoIiUHxCsQIwQDRiEkOmMGAESCmCCkRSMwYIhDjHCAhBiBEMgAhUlVIvExe3vac3UTvjZnVYNkyN/dwj25H7O7Eku49u4kd4RFubm72ra/RuG/IcLf5961yjW/vlsgLDSEEbi9S3I6f7/PlueYu8IWMlMTNOMHVMNlLhlnJptuZjkVpMMtKLFYVu1EASGOJfkKsiNmyqDUyuMIxvc3D8amBMef6fMtai+/ul5guCxSaxuh39xWXjyXKo3786mT1u3rwsR9wl2y60HTt7iKRBdZTZ5u+jOcGwPOWcWu7h3mOhWO+R0qglxDr/XIYey9PbnKOL/v46afZWihI2DACaPxsIvfUgEb60pcN/p6BxsKK3W1gmOF+Bhp9dTWzjakkr4tMwzB4DEBJ8syEAFnqeGVLdW61sTXVROfrbvFoPF/39WJvSZYiLx0juFmRcuxCJ0XuxWd/yeeuM4B4rnOd681U22b+2MChMRaPiwL30yoUQQqBy2GM63H6qlll77V2NejuDII5UNKyaREohMD9LMNiRcEAvUTVQgReW+WlwadJ5uX3nBJ+e5GetFNrrcVsVeJ+mnu2I+BkyuMEFx1eaM0gmjiS+Oamj0H6PMuWLuDwdkyeg/ssVuubmPViT9Ws0EgjidTJx0f9yEu02auxDYBsBsbsUmFgTGsydePn58X5uUpt8H99XOD7O2qYcChUHEkHvOwvUX5ttasfcNOHr8uqY9fQMdm4x8R83+EUzROFQXxOtco1HufUwPINGiEw7DnQu0VNIoWAigXGgwTZMO4EB32Ts8l2Y4BRtwCNALALoxHHBxo51fhzARp1AzBsY7GlcRV4MkgVkqgued22xiwCUHlXD/CaNU9gl9BsMry388FV6noy8qrQvrnMJYRwTdRKjhy/48/krdQZQDzXuc716qstHTaOJG4ccHgMr7audNNrl6b6Fs12P6d6ziCYvDSYLUsYayFArJtYCfxwv/KLPgZiwoXgS4AwpSbg8GFe+XZeOODwlGAn+6E9zHN/zTKD93qYoL9B1tiUhI/7Mb6+6T8Lo8k4eXXTFuGYcw3XMif53HRZST89sDrY/bWYJdP0cKzJqAMAcj0wZjvqWAGMzqtRridWR0ogL3ljdl7cv4cy1mK+LKmpNssxmeewoGvy9pI8Xp9DovzaaheJ7E7yyaZFxw7M+U3hL/EL3Wdea2ljMV0UeJznXkkCOF/OYYLLQXyUBppvcm55nDHtKoqmn98+QKNFC9BoASsCoNEC1h4GNHLYyFsCGrWxHixcZGVrqEYckeR14JKS42izR94+a8xtaplWa56ONHFgncX8FlPAmV0YpiPnLRYRSgqfWs2hJ6+NzX6uM4B4rnOd6xVXG3AYKedxeKTNfFEa3E2zWvprHEncjPZnGp3r9dfeSYDBIjAvNe4mOVa5hhQkdRn0FGIlUWiLQndLWoDKn9F3/juM1I8xrrWxuJtmuJ/lflwPexG+uOyhd0L5/SonmfJ0UcC4BbtSElfDGFfDdplyWPNVie/uluRHJQS+uOrh6kjs4k3VFsR0CuCwi5E57EW4HqfO33C/1/JWADs+vjUwZgMAGUqwsUGuKQTw47TAYpHXLQKY2djY/Khz2u2rLN7ohWwtAmJyRErgw0UPv/zV8NxU21I7ewEzqMSBH+7rZnNrV+Z8lw9w/IbAhqcUp4A/zAtMl9W6TgiBcZ98ZF8K9CagcTPwDKwDjbqj2UnWKR1AY/A1PcaFfzikUYCARRwRaAybp8851kptsMw1FqvuFN6EAcP0tCy2eoDblut+gzXPvizmMGHenxfPagzSpp9pT8Osz5WTI69yvZZcDVDTNJQjN5mf53qddQYQz3Wuc726spZkxJ8mLfLBI6XDrnKNuwb755xuei6u5iJwmZW4n2YQghh816MEX1z2AIGNkulQusT+jNjAMgGqBNBNadNdshZjLO7nOe4mmV+s9dMIHy5SDHunueVbazFdlniY1UGxXkJpyuMOmXLzOT5OMtxNc1hrkcYkCT8l2Al0A4e34xQXRwQO26wRhBB+LJ36fYa1LTAmLJZtNSXUofdXoQ1WbHJerlDmJZl0C3ot/zUHDASfaRPs6PImPTdyTl+lNpgsCjzO6ynKSgoUpcHlIMZokOAXXwzOwO8RaxdQKWQzNn34moDSrj7ATeaiakon35h1Qdf4TWOFyyEF+rwV0HtnoHGXIDrXFOJL1pAGunqSBtBobMVmFA0mucXxgEbPbAy8e3ddd5faYJFVkuS2sKOEJckuKfm1hWrsas2zi2Km1KaWML+pThE0aK31KeYsR87LdRCXk6s56KSfRmd24RutM4B4rnOd69VUF3C4T2DBtudfZAQchgbIwx4Fo3yOUqxzbS5jLT4FwFYcSXx93a+BcdukbAC8jLQIFoJrC38OgvEJoN3P1wyCURLIikpaLYVAL1H48irFuH8aBl+XTHncpzTlfrLb9VSUBt/eLbF04OPVKMGXl72Tbl43AYfH8lMF6DO6n1HiNAO6SlJwzfUbsEYIZVuhPK/UBrNVifnSycMA2hD0E8zngDZ07RhrYbRFYUmuZQHPlBEOVJTS/Rt83QQaa15RHUyXsyn9/sWM2Md5UU9RdnYDF4MY927sRkri57f9M3j4AhWyGXsbQP+mFHarR9seYENb+MtLsxl5Tfc4z2vNYE4BvxrG6O14H3qLtXMQXQg0brBtIeY//Q15LLZrp5tAYxNqtHCp08ZCCLsf0NjSQLUWKEqNXBO7tG3cNj0MX/u9dZfaWzHTAJJ5vdnWyN47aNCxGJWSkABKY5AXBquC2J9ar5+TJJLoOTlyP1FI4zO78L3UGUA817nO9eJlrcXEAYf5iYDDqUtUXgWJyuNBjJtnZv8AxH786WGJ6aKoFuhvrNv/OdQq1/jubum725fDBF9e9Q7qmJJX3BODYILFPy8YC5cCush0DaDqJwpFCXx3t8JPKtsomd4XeGGZMgeyAHS9Xg0pSGGfTv90UeB7l2KtpMBX130fznCKYobmfRM4vEhxOTgecNj2GSWRxPWYXuetXevWWqwKg/mywGxV+nmUK1ISo36Eb766wN3dDEXZ7evFZZxsjj0Ztdvw8M/Zx0sAFYNRVmxG2ZImzeEwbeM9BEPes4Rzl1rlmthai6K28eunES4HMcYDSlH++LjCfFVCCoGffxi8i035ey6+z+zk0db0ZaxJp423OtgGNsimdLIjdfqY11tRGjw6b8MQTOonCpfDxI/fc1HtCjTWxkYDaAznc20sJNinEWiDGgUAIQV42peCf2o93GhhYQxcsJsDGkvjX6soCfgO7x0Cjr0XS/STCP1UYZgqpLHyjEZ+L5/LHL9LwjywucHQFTRobbmWTK81nW8+F3zf7cUK/ZQYn8OeQi+Jznubd1pnAPFc5zrXi9WpgUNmGd3PqkWmlAJXQ2L/PKekwVgy836YF1jlJQaDAotFVlOScEjBJpYNhxZ8LgujlyhrLe6mOT5OKAFYKYmvr3sY908HbAH7mnQbTBclfpqsYC18Z3eQRkgiAW2wl6xFynaJkfewk8Ayp03bMpAp9xOFqxFt2PZhJhlj8ePjCg+znJ4njfDNTf9kKa7aWDzMctzNcg+YJA44vDgScGitxTyjYJSQ4TxII1yPk9aUz9dc2ljMV6X/rwyAJiEEerHEsE/ppWlMMuMPlz2gKFrTSttYMLpl88IMKS5rHZvREMBu3Pg3tvLu4mpjM0pBG1mBCmxsZVh0eJS+pXO2qVjiOVkUNQA4UtJLPMNgpemiwMcJOft/dd1D/5kbbec6Te0CNoTNrEI32PONf421yF0ja1Nt82Xc1sjqYssqKXwK+HM3g99b7QpENT2idQcQ1QY00v8DQFDQeiArLErjlBgeMCSYSqAK8kpiiSRSXpKd5RpZvs5s3MRoPFQ6/dZrlwaDNgaLlfb3/UWuUZQaxqkKSD1A99Xm/VMIAoAfyxyP8/rrbmswfE7n4T3UGUA817nO9ezlgcNp7lO4jgkctskGIyV9ovJzdqbzgsy8H+fVsUjHfrRa+45/2O3Ptzxn6CnTmo73Dje/z1V5afBdIKcd9WN8fd17VcybVa7x02Pm/QY5gOO6MbY3sQna0wDXN4HGGdKvcuOk0YCUEoNehMt+hEEvgjGU1BouCjeNu6zQ+PYTMTuFELgZJ/hwkZ5krHrgcFp5Qh4bOOTmwN0092xVlnJfjxL007ex1GIfo/mqxGxVYpnpGpCnpMCgF2HUizDsRXtfE/uwYJq+T20bVO3Yiv7vQJI548APYywKY/zGJ5ROM3OiklFv8GlsY++2gI2vkWVhLYHAj3NijvL55PF5MYgxbAG2V7nGd/dLAHBBQtvyZc/1nmrXZhY3BTb5MhZ7BEHUrAoiAhitsVgWGosVzUd8nQ16Ea6G1Jh5jdfee65dg0LaksgLTV55i0xjlZdY5YaaneHzg9bsccRAs6x7OLoHuSgYAKLWTKK1SsVo3PZePmegsdQGy6xKRl7l9ft+rASSKEYvphAa9i9USnQrZYJ7965M5k1emWebktdXb2NVe65znetdVBtwqBxweH0E4DAvNO5mOSbzKgE2iRVuRgkunlE2aK3FbFniYZ7XmEhxJHE1THA5ivHNV5f4+HHqzKiDoIKWDm7TDNuzygBsWowD7ZvfWjfQgZCfu9yHE79/fFzBODntl1e9o4FMx6isIOBwtqQzLwV56d2M2730dmUTNBMfS22wWJV4XBDTQxsLC/ZVlOglClIIv+BsKx53cVRfgC+dtFeAroef3Q5OEu6ijcX9LMd9CBzGCrfj5GjnlD0g72eVHPqlGM6HlrEWy0xjtqRz3QSQ01hh6ADD5/KI3cf3aU3y3wAbw7CXsIjRWDEbmdFojPXJpO0+jXAy6nX5dC1lvbEJCtmOz7EBYonyZFHUmKM9J/G82CDxLLXBb39awBjrktvTkx7rud5u7dIUaAORmr6M9UaWRuZDGSiZOny9XkKSVWuocbPMylag4XNf07yGIqY3Bc3lJQFVi8AvL41JeixA9+c0kogjmkOrdUk7oxE1j0ZujNB3SjjpNDeMxDrQSFkynxfQaK1FVhgsXTLyssNPMlKylozci1Xr/mmb//cutjxF05cVwM5Nhg5Go1Li7NV74joDiOc617lOXuxB+GmSeYaOkgI3jjX1VGBvmZW4m+Y1hkU/jXDzzLLBojR4mOd4nFebNiEEhr0IV8OK6dE8nFpQwRaVbKd0JJQEBj55uwRyAOsS1ppf2BtZHB1aRWnw/f3Sg70DJ6d9LQBQXhp8nGSYOj89IQQuBzFuL9KjHCMnPkZKYLooaFOWay8N66fV+G3dCDZ+Vh93dL1bC8xWBbKikg+P+zG+u1u2ytoOHXPaWNxPMx/8ANAm5eaIwKFvVCwKL7WKI2I4Xw6fl+F8SBWOZThflZhnZSAXg5PBK2IZ9uOTScqPUbuypIB1kFy3jFsGG2t/FwCNBIQAxsk2DT808GkUDTZjq09jMNeuWVQ8Ya4lWwPyNWxKlEniWZcot5W1Ft/eLVGUBokD+N/bfH+u561dGwLGkET5YZrhcUm+a5ESkFIhjiSSqAIFLOCaV91AQ5dsMpRNv8f1zEsX++VyQvIyK9caOJzGy4En3JTc5bm7Uomba2EZcBorRl0FNAr3hXFII63NTws0hs2k5xh7pa4nI68KXbvf8/GmsazSkdMI8ZHUS/vY8uyaMs9Nhm1KrbYm9msKgHrrdQYQz3Wuc52sNgGHT5USszTrbpp7KSdAktOb8e4JsE8tPo6Hhi8P+0pd7Rkqsa0OkY40za+bUkCzQcLa9vpdLMamtO8tdAAniwI/uBAPKQQ+XBKo/RoWFaU2+DTJ8DCvgjguHHC4DQjY93UeZjkeGsD3xSDGdSNkKFLYeSHIjNn5qsSPD+TVGCuJfqo8MLWLrK22IO+QuAgh8DjL8TCvA4e3FynG/ac3Eay1WOYad9O8dp33EoWb8XFe41RlnQx95kDDrgCUYS/CMH2fckAGybcBomGDRreA5M0GTfV3XT6N1jNhqmOp2IzSbWD816j7NG6yquD09VWuMVmWmC3rEuVRnwJR2iTKXfXjwwqLVQkpKTTltYPh53r7pQ0pUx7nuZ+bYiUwTGPPluU1VBNA6vJl3Cdtto29FDdk1OfqLr6/LDKNRUb3ly7AcBAAhofcL/dhp4fjZK3xGayDpaz/nfvKvV470Mi/OxrQ2LGu2cfznNmFHjDMtVd6haWkQN8lI/fcfy89z++aMt+mlmn717Q0sTe/bvc5OLOZ2+sMIJ7rXOc6ep0aOJy0+I1dDAg4PCawsqlYvvjQSAFkX56XBhTqC63NtQ87Zy/5dAezxgdzvKCnSakNfnxYYbKgd9JLFL656T/b+NlU2ljcORYdd4tJStg7mkk8A2IPsxzTAHjwMvth3CqL3lbhuKMwGo3pskQvUbgYxPjZ7QC9RG2UtTUXiJuCYCqPRg0LAmaSiBKhB6kiVlboz7inV11bgjvgGhWjBP1nkvXuW2EAymxV1pJ2hZMBspchB/Cc60BvrxawMWTDhIwPDzTaKn1Uhz6N/qG2M3VaCgFtDPKSNozMSpaSQPNRL8LYBaJYEMC4S1Pn0QWOAXg1c+G53mfx/edxTox3EwDf436Ey2HSapmwD5txmy9jTTa5AewRAvhukiNbFbSm6VJq7AH2vOUyAWDIUtgmq41BqkFKjLbeM99jDgIam/P4BqCR/hZoAo1WkGcnW76sv97xgEYh4JPTi9IgKzQapwEA3ROI7UlgYRK93fv9Lo3A5jntAhl3vf6Buj1JvKWR/bnUGUA817nOdbRi77+PDeCwLeBh39LGJSpPK78xJQUun9FvzFqLRabxOK+DLnwcu0jEXmPty85pYzHqxs2ab+LbGAD8+p2BMI2vj3GDni0LfH+/QqkNhBC4HSe4PVGIxz5ljMX9PMfdpPLt66cRPlykR/MJ5MCP+1leA8QGaURpykcCvktt8O3dEgsnC78YJvjqqufngH0W+G2S6awwmLjrUBvyr4uk8OzGQlsPhjRrUyJguDifuM/JJ7gLgYshsTJf23XOASizVYn5kjZ1zQAU9jI8JADlXPXatUETekA158imDcWaT2PAZjTGoig1stIiLzS0rh4rJW1q4khCAMQCKvRGn8ZmU6fUFj8+rCAE8MXl6RPnz/V5VqkNHhcFHudFjR2VxsongR9jbpJSIN1BNlljGLtAuyar0bqgmLzQrenyYe2i0FAHNLJesoyxTgJbuuATXQuvAkguOnAg1SB9O02pfebxNkVPm4zabBi+a0CjtRCOeV5/vXWgcZXrinVbGudNHTw3AKUEerHyQSeDXoQkkrV1znuvfc7priGDvJfBFjZz6HXcxWx8L02GM4B4rnOd68llLXnXfHxsAIejBNfj9EnAYVFSonKYYhwpiWuX2Pwc1HJtLB7nJPEMF73sDTfuP19Ay0vWPuycfcINWD6NHTqxtRt0wGJsLtTbur/GWPz4uMKDA5bSmFiHx2L1HVoc4PJxknlwPI0VPlymR/PwZH/Oh3nh2WicBt6UKT+1ZssC392voLWBlAJfPSGMphkEwwnri4xklgza34zpPXSyGgM22CZgmzdLNI8RqytWEuNBjEvH6ipKA+v8uV5yMWisxSJgGTa76ByAMupFr5Yp+d4r9IDCNp9Ga9d8vIpSY74i+XlWGMDZAMSK2MK9WEEpwFrhfRqNoeuEAUjOhKEwAZZPV8EwsMBkVcBaII0kPokVHub5xiCYcyLmuXatriRwKQUu+uTNeaik9Sm1S8iYtQTSXFwO8ONP0zV2mm58va9Co6nM8P54L+g7rVsAQ9sADCMlPbtwkL5tVtsudUjDqA2Iqslst2B5WlOAUF5WTXkhBIQFrKBZXQr4cZO4ABqurpA7KdbXzZ9j2vE+IYNFEzAu272/6XtgYwhMk1Ea/utsE56jwRA2IQ+pM4B4rnOd6+Bi4PDTpJL3KcnJsE8DDrOC/MYmi8r/jYGC8SA+ub8e+7o8NCQ2UpJc+mp4XNDlPdWh4QZNhk7Tu3HXGzSwnojKDFbjvA6vxwm+uuojekF/I5bHfnxcee/JOJL4cJEeJfCDZWL3s7zmj/ZUmXJXGWvx8THD3TQDcFxZeKkN7qa5P4f8/LcXu4Osmxb3i0xjssixyEiOZQFEkl4jjRWxQ1sYjaFP3Zp/UWDifazFOAegzFYlFo0AFCmIgTnqk+/daw5AOdd6hYm2WaGxygtMFiVKbRAr4YGWi36MQU8BEDWwMQQzwk0NV92nka4FAihLlJqYMEoCk0VZT51uyKdFMJZrm9EQCHmnrItz7V5FafA4z/G4KGrNjX4a4dI1Xl+7txj7k/ZTYm5vYiB2KTSaIKP3aHONrG1hEM3Gqf+6hdl4yOepja0FnqycLUJYcSQJLEwUBimF2pyv5/XaJzSE1yJFaYLQmdJ5F7rmemBrQcCT8+qM6o1yC8C6BqkQjtHoTg8rrY2jNRppYXaQTjPQ1eW/+7kAjbuymbdZ8hTuHm2sRV5u95vfqJThsMED7qvchJ8uC3z55cVefxvWGUA817nOtXdxR/ljABxKxzh8CnAYBhXMlhU9aOASlfcxgz+0tCGJ58O8LvHsJYq8DQevf9H7lmof+XQzfU+33KR1Y3Fuc1t10UE35VEvwmxZYractm6Au6TUxxp71lrMM42Pjys/xiIlcTtOcDlKngyOG2MxWRa4D3xCAbqOrk+UTJ4XGt/eLf37uRmn+HCZPvm9HAM45Gou7rkBMl+VyAqNNCawcJAqXA4SJLGkMdexMKx56ADYt+vcBFnazLp5TpyvKCSjaQgeR9KzDAfvNADlcym+9zwuCiyDYDClJC4HJO9cb1pt9/fqmjeZ3R9FEnEEnyDe9Gk01nrWrQ+FceK5JpuRvxaiLp9uY493zbXveTP63ss4G5vHOTVj3ovNyy61r0KjCfi3hX7s2zjtSngPvwYIvFjlBous9D6qYSUMGKbHTeU9V923eZkRS5CVJwBIghwrxzCXSGOFOFaI2POwY+3bBUcxmKiEqIBG2w40WuuCZKyFkQLaAGJHoLFrTfM5AI07e7MGKoPWACg+l1uUMuHr8n013gA2SkHri/tZ7r3Vn0qeOAOI5zrXuXauTcDh9Sg5mM3EG/m7ae43TkIQ0HMzTtBPTz9VEduQGI8MVLDE8+qFJDbnqmofzzzu7M5XJX64X0FK4dPmmEXg5dPWwuxgohzeqJv+Rn7htIO30TIr8dNj5pPDj+URCrTL/Z+DMfs4z/HDwwrGWCgl8c11D6Mneqh1AYfsB/mUa9EYi8dFgftp5rvAXanTmypM7O1MBTT7dZ2J6SW8bCb3/ovM9iLp2NixDN+K19S52iv01Z0ty1qYxLBHTK1DAf9wzmxjT9xNMyxzjX4a4Wc3faSxrPt5NYFzN5b9saPajBrLbHIDU1aBMKF82jMY5XowTM2nsRm41Zxz35iH3OdQWUGBKI+LojZGaAwnGPWjk6tG3kqFjaxky21yU5pwMyjCBHYw4X3GuCYXNw1KY33auxSuiRtJ9FOFfkLNqF6iXjTk7j1VUdaTkdsk4UII9GICbdm/cF9fd28L1LUmcfN3FwTN8zSEgAV5NBoAfBQ8BEJ/RmstpAMat65txJY5/TMYb6HKoN/xmE1KmaZnY9hcWHWcWWMsMheyI5wtTy9WuBymT3ovZwDxXOc619Zi4PDTJPOeGscADo21mMwL3M1y7y3IQQU3owTJiTvVxlhMlwUe5nXGBxt6Xx7ZY5Fl0ZNFgZ9mJZaLrJLavgLvm/dQQghIVGE+QgDjfoyvrteDAdo8x7qk1HUWwOZq8zbS1mK2KLDKNaQU3iP09iJ9koyYAYiHWV7zl4ojietRcvQxHJY2FLzwOCcR1iCN8M1N/0mBRh44nOUeTOk7xuFTgUOWbjzMKoCVLReuhvsHMe3KOOnsOvMYcxKmLNfO78jUzNGloPOZRBIKNIcUpcFkUWz20jmzRl5t5YXG46LApCHvPHaYRFfNV9TIAIAvLlOMBzSKky1/12Y8r1tM6HnO5ArZjMSuAAprPADpYFMIYI3NyLJp6cIGeEw3751dG9P3vCF9yepaP0VK+vXT2UbhabVr4xSo7GBWWWVxschIDusZxdaFbUiWwUrEEV1r1sL9TVl73qZKY82eIPj55w4SW0sBb5xOvXT36mZFSqKfKPRSAgt7sXpyQ4RSwHfw2jUta5AWhnon0CiY1UhAI1u+CAAC1s+1xwIaPzf/3X1k8GFDoclozAuDybLAIiu9DD5SQBopREpgviq7n3yHOgOI5zrXuTqLpZafJplfIEopcDVMcDM+HDjUxuLBUanDROWrJwKSu5bvlgdMLSEExn3qlg+OHDhQlJQ8OJnnyEsDIYCBFbWJva2Y9dbqfxOCjvIMNnLlhcZ39ys/Xhk8bBtTYTdwU21iAdTkJI2Qjhw01hdZibwwfpGVxpRW+OBM5dtkRk0wuXlujbE+JTiUtQ57Ea5Hp5f7r3KNbz8t3HgWuL1IcTtODn7NUht8mmR4nFd+o/00IuDwidfjyvlAhn6qSSRxPU5xOTh9AFJb19m4cTF34SdSAGlCIKGxFpGSSGPp/BNFzVcrlOjvKm/ZZJh+njuepzolyo4lfDlMnoVRmpcG335awFqLyyHdc3etXY3nt82ZoWejaQEabSChLq2pfu6hxm42Y5tPY6c1xblht1dZa7EqyNtwuihq66eRY8w+h9XMuaispdTo0MMwBGiSSDprDulTeXtujqn5NTZksbWQux1VGsC693QbqP+e1qulDtiFmcaq0DVfYoCuDf78e8nLS8LJNmg7+zX0J2+T2bYDjdV7qgGNLuSLGY0CNqA0VmFFlDq9I9Ao19czbRLq9ww0tjUXuAm/zLUHp+OYbFCSSPp7cT3De/86A4jnOte51opZTR+PDByyxDKUJTJT6mqYnHQjz4EVD/Mci6DzcqpACW0sZssCj/Oi1tFlSekvvrnAp7uZuzGvB4Y0WW+bgAKgupmssTJktXHi373HG6q1Fg/zAj89OimtFPjyCem/YR0in85yjU9TkhNLIZDGColbRALYyz8PqGR9EECWV56OUlSMj9txenK5v7Xko/LTYwZrLeJI4pubPgYHvm5RGtxN14HDDxfpk4B8nsPuplmt09p3fqqn8IHcVkVpvOfiYlXWgBMphA8/GfWiToBmq7wlmEd29c6qxve6dPRzWIifsrxEeVFgtqzsMbxEeRBj+IzyTm0sfvvjAtpY9BOFr657Jzmnh8yZ2xo0OtiY1wNhKp9G/tqyoZdYZzNKAQc8rvs0dllUNJt2n9t1oI3FZJ7jYV7UGlZJJL234akbv+dygGFJoV8Ll5TcBPYYsBoEHoZtKoRticLAukqjqdgI2ceV56oG9lyvdoXCvCarAmYXhnLkvFi/ryop0E8jDxj2EvUmfdN39SdvAxrbAkV0bZiK2pcVa1FUTFlrIVlX7Xx3nT0jLCykEdDiDDRy7Wv7I584XZ8cQPxbf+tv4V/+y3/Z+ru///f/Pv7sn/2zpz6Ec53rXDsWb3Y+TSqPNik4Vflw4HCVa9zNqGPNDKBeonAzTjHun3YjX5QGD/Mcj/PCsx1583Z15G75ps3iIFW4GLjkQSVwNU5RZnknA7HG4DAVW6OtY7wvGNUlSXnNi7dNVZQG398vPVA07EX4+vppUtpDSggB2IoZaIz1votfXPZq3nrbFubhIsxakiWxHIaHjHLejlIIFwpTri2Wap3/J7JtSm3w/f3KBxyN+zG+vukftDhm4PBhXs0JA8c4fApwaCyxvO6CABlmF1+PnsdPlWuXAJRRj9I9dw1AOVTe0mR/tQbBHLAQD4HHOBI1kOdzrtwl0LZJlC+GMS5PLFFuK2stvr9bIis0IiXxs9vBi0sOdx3PQH2jumm+DOXT1gOKlYS6NMSoYgASbt6W0kmlG2zGpk9jpx9uSzjMS3++T6kuf04pBEaDGFeDGP0jqzXOVS9ryc+Q2YWLrB64ATj/PJeOzMDVsQCrfVQanY2AYP3KX+/VPG2xhHkOyx9t6P7Na69Vrlvvb2lMMuR+SmBh8pklVB8CNLbN3+1AoyuiMgY+jHWg0TPOP0OgsQ04PJbtz6Y6+Ur6N37jN/CLX/wCv/Irv7L2uz/4B//gqV/+XOc6147FHofHAg67GEDDXoSb8dNAgl1ee74q8TAvMA984ZipdYjf2abKCvI1bG4Wk1j59MxD/NUqBsfmYqZRF7hYM1DeU5LSmerXWMC9hP+NdWDdjw8raEMbwC8uUlyNDpfSHlrGWNzPc9xNMr/IZCbdsLd+q91lYa6NxeM8x/2UpP68OOWk4EjJ2jnvMlFvq01sm+aiSQjyS/nubolSG0gh8MVVD1fD/dmdRWnwyTEOjwkcltrgcV7UbBGYNX01ej4vLg7v4f/CDYcQAn0HKI/60Uk3GvswwLYGwfC8sWVsCQF8PymwWua1Td9bb1DsWl2sc5YoXwwTLx98ifo0zTFdFhBC4Oe3z99geWrtulFdux9uYU1xhWxGtqIoPMuRH9f0aXTejOHXaPFpbGP4yqqR95o2pTyXPjrLFa5eonA5THAxiN8ko+otVOihxyxD3UBUZAtg+NLz6D6NgNbrswVkbNp15LscQwvI2HbvaY5fBmrDZOS8XE+nlpLu4eRfeFyw9r3XzkBjhzf5pkAvX11AowGEsF5SLeDsMAL59FsDGlv9wtMIt+PTWxgBJwYQy7LEf//v/x1/8k/+Sfwf/8f/ccqXOte5znVgLVyqcggcXg7jg8MdWCp8N62SmpkBdDNOT5YEC1QL34d5XgPHiG1ISYDHmlRLbTBdlpjMcx8sAwSbxcHzJTfvGuYAVF1ArdeZb1rXf7cPIAW0m9q3MTWO0SkutcEPDytMF9TD7icKX9/0kZ44eKdZ1lo8zgt8nGQeuEpjhQ+X6cEy2byoPPsYgEpjYrBejZLO9+jNsbu8xw5kAKxyjVVBPn1JJPHFZc9f57tugruAww8XKQYtAOuuxZ/VY5CeHkfSB6OcenHPG775iozrmwmLSgoM+7FnGr62zcbOQTBdfkgtC3pjLXRpgT0aFG8VbORG2WRRYNrCOn8tCbTTZYGPjysAwNfXvWdl4j537TqmQ5ZuE2xsyqhDX7MwEKZiNgY+jZbHgAv1CtmMoh4M430aNzDHn8M7rqvpqqTAeEBN13Pi+/GL7x+LwMOwyXLzoFUaYeCCN156PnlK7XN9MqsxDLZr81ZdD7rbvKYRArDuGubnAADRCHCiZm0lRz5fA6evfVivXcqK5jhpKwFv0hhYYtSBRvdKgPs98PJAYydweAS/8H3qpCuI//E//gfyPMfv/b2/95Qvc65zneuAWmQOOHTsQCEEroYxbsbpQcwE49hSd7MKvJNS4HIQ43qcnowB5FNoncwmXPheDhNcDeOjpTlbazFblZi48Itqo/AyflaHFHcBsaMkJWRreGPtlsVczf9mSzXDYZoylG0bptmywPf3K5SaAjw+XKS4eUKAxyHFANrHx5VfSMSRxIeL9CDfRd7A3c/yGmM3jRWuRruxPrw59g6vtdb1bwGU84KSfgu3AKMFtMJ0SWBJ2+s3WYwWwHxJyY4sDxz2SdJ9qG8iS4Pvp/Xk6V6icO0+q1OOBQ5AYT/DJpM3jRVGffIyfK4mwqlrF/aAEMD19RA//DhFEcwTeocGxVsDG/PSYDIn4HpNonwg6/xUlRUa390tAcAns59rd5Z/7V7YZOs25tBun0Z6jkIbkk8bVtuFPo0NNmOHfLpLLt0EIHeZd1hqH1q8ANTcuRyS5cprBO7fallrsco1sQuzslUWy4Ahexi+l3vIvhWyGrclC28MbdLGexeuCmIWar0eISEAf/3ESkAKiVLTumyV63Vm4ytmEX8OtU+gV5vKQjd+FlpfNF6J5ugtQKOFhTgS0NjWRNKaMgSO5RduWt/r7nVSAPE3fuM3AAC/5/f8HgDAcrlEkiRQ6nkZIuc617mqWmQkVZ4fCTgstfGJyrwQUoqDUU7n81RqAjce5kXNyLifkrfheBAfBcjjDvHjPMdkWdZo872k2iy+NwPxcPG2CyAV+t9os76I6+oU7xIOwws2Kcnrb5WX5PsTK3x1RQw2YwEJe/IFHAN9HycVwzZSErfjBJejZO8xxzLlcBx7j85RcpKO4i4MgOmiwHd3C+dRCs8ebqY11qTxAVtVG4tlppEV2i/Uk0iinyqscoNvPy1bF+FNKXX4ebaxmwFg1I9xM0pO6seVl45luCywzHQ9AEUKDNLIswxfC3D0EqWUROLSozeVB2UaTYnXDDYaYzHtkCiPB+Rr+No2+xyaYozFoBfhy6veSx/Sm6vavXCbT2OH/G7TZtVvSgNmozYWBQOPxsIKQaECMvBpDNiMUlAwDMun1xLYQ6BDUhDXfEWACo/WSEmXBh4/O5P/vZYJAMOl89JrpvRy6AZLkl/S5uCtVtgMMFY6YFaj1BbLnEBEvk/3EzovUgokSiKKJJQElBDQjXXsPmvVLm/xpgf1S9j9fM61D+O1tbH+RKARDmhksHFf6TR7ceaF8XN/PyFiwbCnoA2RaJSsAGwfxsnrqcaaShvyjfziw8XBn+uzAIj/5t/8G/ydv/N38O233yKOY/zRP/pH8Tf/5t/EL/3SL+38XET1P9WRnutc77+WjnHIwKGUwie3HrLhzQuDu1ldkpjEEjfjFJeD03StuXt7PyMGlGcbKpINXw2To0mki5IAysdFHaCMI1mTKB9SPJe9lzlNCAEJINqhOdQlG6u8Gtlg2UIbA4B+t8pJMk6m9yRZFsLix8cV8FgdRw2M8os49qCirxmM3LeWWYkfH6tkcqUEbsYpbkb7J4hnLFMOuolKOc++YYIkfhkQyhj6TB9m5Dg06sf42Q5+acadv2VW4tM0w2RekAQoITBpkFBICEmFmK2KtWCRZkkpoIRAXhosM02m2I6ZczmIcXuRYNCLj979t5YA0JmTJjfTFpOYA1BiDHpvW1J2rNpnXqvmDEoM3VQVA4zmhzY2dPg766SlhbaePbup/MZP8kYv9MoCytJinlFytnfCEwLDnsLlwEmUXyFLy1qL7+4WKLRBEkv8/Lb/Ko/zPZVywN22+dta29qMqQIG6uOZ/mY9eZp9Gq0DG12GQI25yCCjtUBeahSlJbaM2/CmscSoFyOJKQRpsigQqTJo6Bx+z3wPtc+8Ztxmf5GV3kuvCTZESjqwkFiGZ1ns06oojQ85WWYEird5F/ZiYnP2U/Iw3KXJ1W5xQGvTMrhOjaFrapNkNqy2YJhdFTjnOk0J4VQWALYCjc3mkK6PB/4aazxXoEqgdkCjsdBBGAz/Ths3nnPj9wiREohihbzU+PFhGXj21q0zaP6vWO1N6wwpBeInkl6EbYdRj1J/5a/8Ffzbf/tv8ft+3+/DX/gLfwFXV1f4T//pP+Gf/bN/htFohH/+z/85fvGLX5zq5c91rnMBmC8LfPdpgcmcAAEhgNuLHr66HRzUZZ4vC/x4v8T9NPM/G/QifHUzOFl4hdaU2PrxYeXBG4DYhl9c9XB90TuKt5gxFg+zDJ8eM0wXlWWzEBQoc3vZw/jE8shzVWWsRZ5r/PZPM/x4T0EpSgrcXvYQRxKFNihLg6KsS8d2KekWanEkESuJKKIbahxJ//PI/S4rNL79OMfjjK8hChH5+maAaA/wnfwSc/x0v6qNr16i8MV1HzdHGseH1jIr8T++m2CVEVj29c0AX3/YLal1lZf4/tMCd5NqXrgYJvj6doBRv6LtELBD54zPXVEady4tilIT8FMalCUlGC8CmbKUAoNehEGvLummLnPjfEYKsap+zudzk0/jZJ77/+oBKMCwH1OIgGtUnOeB11fWASylG0OFk7D58cZzhrYoS9Mp4yk1BRkss3qIQRwpjAcxLkYJ+knk543Ij7vq35f2u/y/fpzhx/slpBD4X3756l37Hr7XYhCD50key+G8yWOb5ysG3I2hcb7KSixWGkWpHbhezZdprPxYlRJQUvqNpnLs3FogTDDH+vk2uGfG7rk+l7lRu9CsmfNAXawKNKeUOJIY9WOMBjFG/dfHUn5LZSw1KefLEvNlgfmyaGVuxZHEsEeWQtTkO22TxxhbW48WjXtO+PU+qIsQqK1T/Tqmcb+JI/nZAvyvtZjRWJTaz9cU1FNilRnkBSl08kJX4ZaG1iWrXCMrTI0kE7tAQyHEejPDAgYETTLwrJSsg4eSmrZspWEt8L//od9x8Ps7KYD4r/7Vv8Jv/dZv4a//9b+OJKlEcL/2a7+GX/mVX8Gf+lN/Cv/gH/yDnZ7r7m5KVNBznetcO9Uy045xSLx7AYELF46yrx8hyzbvpnlNujXsxbgdn046uMo1HlyYhPHdlZBt+PTOLTONHhsm+AABlJcDkkMfczMoBHB7O8anT9O9FhOfWy1z8u5i9tflMMGXV+0gW1M6FrI4uhgd20o7r7u8tJCgG/CoTxL5XhK1plK3jUetLR4XJPOv/NLoua5HyUkTyXcpay0e5pRmba1FpCS+uem3pkc3Kys0Pk3oGuVu67AX48NFcjBgscw17iYrTBYltDEwlqSxozRCmkgPEIXd/90r9Np0rJyCFnaFNkHHlhi1nJj8GgNQXlu9tXmtyWwsCoPJssBkUcnUjSW5URxJJA4o2fVS3cRsrDxfT8Puepzn3vfwZ7cDXAy2aG/P9ebLA+ea5MmP8xzTZentJawlBksSS8SKRHUebAyZjYFno3Xj3/s0yhb5dJdPo2T/rqf5NL5ENUN1xhcDzKYLAAJFqZGXFqu8RF4Q8ywsYhiSJHmQEtPttb7P116lJnYhszmbIWVUAr1EErvQhc3EHWuxly4OXapYaqHVT5sCZ/ci247gmuv8+nV+Nm+xml7x3VYsdD7biA7c1Cm0cQxa438mJQgoVpLkziCrCvbIbbIN6fmcv6EQYHfGoiRroaI00H5NI/Cn/+jvPvi9nxRA3FR//I//ccxmM/zH//gfd3r8p09nAPFc59qlVjkBhzMXcsCA26HA4WRR4G6ae6khP9/16Hhy4bDYZ+phVk83TmOFyyExgI6xke8ywU9YojxMThb8IgTw4cMYHz++jY32c5e1Fp+mOT5NMg9ofX3dqzHZnlJhomzzhl9qi7zQmCwLzFfVYjWNSYar1OaxF4IDxtL1uMxLCNANP1ISVyPyHH0NPlPaWHx/v/Rp1qN+jK+ve1s9PQk4zDANgotGfZpn+gfMCxxQdL/WpCCQdbgh0bqZLL7Jd4yZAgwaNhlokaKgEPpPeUbNMVP03mu9xXmNA3kmc2og8QKfU5QvBhQkIQT29mzcp47p2bjMNf7nj3MYa3F7keKLy7Pv4edQ7Av9OC9q1hC8dmKv5maoQKtno7HYJp/mr5n1yyO+C2jkzW6nT2OHZ1xXY+6QaoZtFG2J8kEYjnb+YSpSmM6zWvgGvQ9qLvRiJ41NI6QBE/Pse7d7sd84rZnIM7KNXciekZyM3E/Uu2TftXrydXy9TyBG87rzX7fcbz7HpimTEWpe7mb9Z805ctdqhkjSGoQUD9yoGaYRbi9TXAQhVrW5a4tPY6mNBwyL0tRaHEIQKNlLJP73/+2XD/6cXkzPcHt7ix9//PGlXv5c53p3dUzgkIMd7huJylfDBNej5CQhAVmh8Tgv8BhIB4UQGPcjXA2Pw3LUxmK6IIbJmgl+P8bFMHbeep/fTfO1VFZofH+39ODxeBDjq6vtgNY+1ZUoq43F3TTDIivJLydWPpQnVnItebrp4WitRVlqLEqDVW5q/muRFOglCpGyeJwXmCzKKoW6wcpoJv2dCqBaZiW+vVuiKCnN+ovLFNdbbAi6gMMPF+lBDQVjLB4XBe5neS1EZp8mxbZk8bw0mLvk6PlKQwryMIwcGJwEGz4AVWiHtVs9Gvl4WwNhWhbp57nldZT3uJ3ntU1qEkmSqbekKFOQxuYkauBlA2IELL67W8FYi/GArstzvd+ylgz0mW0YWj2M+xSI0lzT7BMq0LlhDf0bA8DcAt6PkdmMtUAYx6wBULFoAkaNlHV2TQg0doGNPN8qB/J3AYOF3qxAMNbWHq8NhR9IKdCTkgBASQwgnttjVUlHS03ry2nH59kEFCMZ3O8b9/7PoSnF4RArFy7TlkgNEADed96FvUQh+UwYnftcpzzuw6bpLiGGwHYP6tp11vL1a2c18jwWfgabwkX2tUMC6rLh5j1Z+ft13deyKA0+TascgV6iMOhxqvI6PBcGBbW9x2WuMVtS0J82BBIqSR7TUgi/zpVSHPQem3UyAPHjx4/4S3/pL+F3/s7fiX/4D/9h7XdFUeA3f/M3z/6H5zrXEWqV84a+ARyOEyR7MpxKFxP/ECQqR5yoPDoO8y8sTlV9mNVZR3EkcTVMcHmEFGeWX08WBWbLsiaFHqQKl8MEoxP7o5xre1lr8TDL8dNjBmPJ6/Cr675LAT7tuTHG4n6e426S+XHfTyN8cUEJz7tUUVIoyv0shxQkqUkteUz1UgUlAG0qRkN9Ebe5eOHggcYGSBUuVnbZeFhrcTfN8dExPJNI4me3g41g3SrXuJvWgcOxYxweAhz6uWZeeI85Jclr9Gr4tCYFb6rnqxLzVVkDAYUABmnkpMkx+ul6AErXgrxtI83nsihtjcncVps2wr7zv2cy8Ll2L2OI5fo4z7HI9E5gyyFVpfY+L9hoAUzmBQptaAwJgf/ft9Ojp1Gf6+WLAfCHeV6bd3qJwtUwOYrtyqYNa1jhfLkeOFEHG8ON6zqb0aIsWW5Nv6PjcGw/CEA4PbULjOHQGGZCik5Qsh4ooKRwT1NnEgkhkEYS/YQ2+fy8/X6EYULhJ0mk4JelTiZoUUnIK7CizuSkn2vk2F6S7xNd124LIPGay1rr/N8qOXJemjUwV0pKmO0nCj3HMvwcWXD7VJgSjy0inSYDt+vr5r2mKyG4dgyNtU1nCvwRwPFQddLFEAyvxUNYgmGQDb+fNeD/AHZxXhrcTVZ4XFRBoMNehNsO4LCrSue/yv+Fc6uSEqOe8vY7baC7EE8DEU8GIN7e3iLPc/zrf/2v8d/+23/D7//9v9//7p/8k3+C6XSKv/bX/tqpXv5c53r31QYcjh1wuK80Mis07qd5bUJLY4XrMTExji29yEuDx3mOx3nhE8so1TKiaPojsA1XucbEsQ3DVLQ0VrgYxrgcPB2cPNdxqigNvrtfYuESwoe9CF9fb0/+fWpRqEmBj5PMj5E0VvhwmWK0QTIbVptPZ8+xFruAsC5mx3oqdQOgArBrx7i56eCuMazFx0mOVa4hBAWdfNXhK8nvL5xnALh55jDgMCs07qb0efn09oiaFJfD/dOsuXgxNXOhK/UAFIF+qlxqcvtiKqx9FuRN781St0ipg00kg8ZZsfl5OeSnjcUYfv0WNo8vWdZarHLncbsoauNi0HMet4FM6LnrWGBjoQ3uJjkAi1hRE1EIHI3ZeAYbX77Y5uFxXmAeBEspSef7cngaW5ltFc6X6bb50tTveYWT2uWFQVZoFzhBPxcOwGBggMBCYjBaVN5h4TY4lEwrJXwqNoQArIG1AtoYyqBmgDHwvU1j8tITUtCcrQ2UpECuRV7icW4AtE/eTRZSL1H+WpGMgloCQI2twI2uJoExu93zQ0lqU8HQ9v1z3C84jTqUI7exnpJI1uTI50Tq01a9KbB5rmiz+mlrEjQb4lvXNkKsNcCVZxvDXasAwI2JddbgvhYhQGUvFF4LTTB+n0b8vpWXBp8mWW3tuw9wyCD8bEmAYTPhXUmBYT/269xtwPuTG6Wn9ED8d//u3+Gv/tW/in6/jz//5/88vvzyS/z7f//v8Wu/9mv4w3/4D+Of/tN/WgtX2VRnD8RznYuqFTjs0yS0L3C4yCgYZRaAA4M0ws14s+fYIcVMwIfG4jdSElfO2/CpgBF7AE0WBVaBf6JSEhf9CJfD5FUsUN6iV9gpij02f3yghGUpBb647OFqeNqka2a+fnxc+c5qHEl8uEjdxns7g6+NOdtLFK5HyVEBCQ9QtQGNHTKyrsoLg9mqgCF/ZVz0KZ0wbsqnlYDWBpNFiWVW+iTOcT/CzQHAITMC76YZ5qt6ivrNKMHoAJYp+yXNloVfTIUVKUksw16EwSsIQNkkB2zr/O9aVWDBelBBU0p96nnvNc1rXqK8KLw0HqDr/PLEHrcvUfezHD/cLyGEwM8/DDBM1ZvxbDzX5soLAsDDhitAa7XL4csC4JuqAhVcQ6VslxZ3+bcZFzjBDEWA2YjE+bMQPlhASvq5sQbawqXe1mXLTR8wBvxI6gdIKaENAZg2CCHoJQq3132sllXCsoWtmI/umaUI/R03fzZdDKdIVc8RvBKpF4JrN2ww7lPCMTLXmosdDMddyAPWWhSa0pEZMAwTZLlImeG8Ih1geG7iv/0K1zY8Likx3iJ3gUOcTF1qE/imwlkaVGzjZjGLuM3mQElOfhc+aKTrmnrJRmsXcPjhIt0aNGhYTbMsMFuVayqXNK5YhruoJxiEZAbw/+P//sXB7+vkISr/5b/8F/zqr/4qfv3Xfx3L5RK/+MUv8Kf/9J/GX/7Lf3ln8BA4A4jnOldWkMchhx0cChxyF/tumpNpq3uuUY+Aw0OTU7uq1AYPztswnPyYbbgr06urjLWYL0s8LurAJL+ny2F8dDD0qfWaNtovVaU2+OF+5YHwfhrhm+ve3rL7fYpB7I+TzAPMkZK4HSe4HCVbF8ulNnic12Vj3qdzlLy4f2ZbmAizk+6n5JFlrIUUAqOegmpZvJfaYJFpD6wKkF/gqBetJ0+3pspWCzVjyROqGcI06hNwuO9cw6nY3IENN9MAbfRGvQjDfozeK2gUHFradLMYm1LqfSo8T20SIw4u2Lf7zhvIrNAYjnqYTZc+MOgQic+htVWiPIiP4qX72mqxKvE/Py5grcWXVz3cjPfzPTxERr1PbWNFn8HG9eIwucd53a85UpLC5AbxSe+VW4+PwcGSxkShDX3dYCrtusVUipKhw3Cq5vdt81JRam9XschKLDPtQYoQgARQAxNC38K8IMCr1JSo7LBDREoiUQJRJNHvJSjywoODzFwUklj9EAKWwU7HMgzLsyfpH/9e6LlQe96uz6fJnqo+E2Zh0rHUruUnSjlrLC332tLZsRSGQxpoTmgeehzJmhz5Ld+Tz3WccBHL4GHgkVolvgM8kvkalLXrTUDI4NoLxtKazc8raGgdChyWmliGMzenhfdbKQQGjmE46kVbSTfGWKyKyjYgZAJHSuCP/IGfH/z+XiyFed86A4jn+lyrLbSAw1H2AQ6NY3vdNzbzl4MY1wfInjcVM44eZjlmodRGEfvjavQ09scmWVo/UbgYJhj3o1fb3fzcAcTpssAP9yuUmsI7PlykuBlvDu94ai2zEj89Zn4zpqTAzZhCQ7YtKFY5+RtOA5kyb+Se6td36soLjW/vlh4wvRmnuL1IAIgaSLVYFbib5VistDe6TyKJJN4viU84FkZeGqwKDfjNmMDFgEKY+qna2bspLzRmzuMlBIUAAiaGaeQ7sK/1ej9VtaY0dkip91nqdRmn8wKdvMqITZSVBpkzvxcCGAxSLBbZ2rzW6SW0wWR818+gU6LsGFqj/tP94F5rFaXB//njHFobXAwTfHPdOzl7+ww2nq5WOQWiTBb1RPCha4Y+teG6rUI/w9YgktLs1byoBUy50BGlhGe98893aTBoQ9f6qiBp7CrXrb6zUgikiULPyZH7iUIcSRq3rqE2XVKjYb7SHsSwlu5TcUTgpRDC+zSmvQiLRe7ZUzbwXfTvFQR/1ADGgDEl3M8AeIDTNlhYIRuTvxTB34pAel37ecB6DK+hmlzTyUOFO9gKzKlkom0J3GuelrrO5uT3HkeSJOAxAYZpJNfn+Tfk2/jeKwwX8fN2CASeKFykzduzOSaavqq1Jmq43jmkkdpoeteYih3N8H0qLzQ+NWx6Rn2yF2sDDq21WBXGswxXLWqaUd+padLNfv2lNg4o1N5CoIsJPOpH+F9+14e93x/XGUA817leabUBh5yquA/Ypw2FU9zPcs/Y4bCC61Fy1E03S4gfZvVkSy+1eaKf4iZZ2sUgxsUgPioQeqr6XAFEbSx+fFjhcU424mms8M1N/6S+Tc10cilo7N+MN499linfzyqmLhDIlE/gDXrsepzn+OFhBWMslJL45rqHUb9uUrV0lgizDi/VtgS7Np9GkqsYLDIKLeFxrYQLlEnWw0qaXWNaRMI/16ow0KWGkJWsK4krL8O2AJRzrVfzHK7J3xsbhbDoPPMi3qI0ptrYgjfIJCdKY4mriz6Wy8x7kxljwbZGuy7Gm55ebewbAJhnJebLsnavea8S5bYyxuI3f5wjKzR6icIvfTF8VaDaGWzcrbQhpvbDPK9tHjlMri0R/JAKpYZFExgMvt/1PISNhsiBbpEkxh4zCA/dhLNFBW+CVx2hG0KQdygDhZt89Nh7e7Io1oJnOHmdGyShNYg2FsNRD3f3c5SlDZhX7RLiEJirwEbrfRvhQMDmEVpUks0au9F5JprweQPmFoOfIeBYAYzdYCOzuqJI+MRWKQWsY5QWJbHKvf8kqtcXcKxId5z7stZfo2/je6i3HC5yaLU2Uju+7rJLaKtwjNYUGy3MRiUF7dmnRDSoAYcXKfqNPc4mNY1wAB+zDLvmMy9HDtiFbWE3kZLeNqAfMIGlBG5vxzt/HmvPe/Bfnutc5zpJtQKHB6SdFiWnnOZ+QRgHYQXHYmRwfPzDvKhNnGzsfTV6GrvRS3kWBZYtsrSLQYzBO5Slvbear0p8f79EURLr8Gac4PYiPdkCIy+Nl/xzuuLlkMI/Nm3GWHL/EADuLFO+HpFJ/Wsfa9pY/PCwwsQBtYM0wjc39VCaZUZSbvYk5PT2mwYbOTTdbjPI5+v/fkqf1yBV6MXE+Bj1IqSJhDFYA63CcJjMLYSKwiDXpgaqM7MhiSUGCXkZFqXBdFlgmZVnz7Udats5BGiezQqSAy6zEgu3adfGwhpOKKV53doQPBBQUjqPRdRkbALV4421kKh+71kwAJydGL0OACksrBVoJpRboApbKI3f1CpJyZ1Dt9gujcXjPH+ziaW7lLUW398vkRUakZL4+e3g1Y37l0qjbqvXBjbyvPk4LzBdFoHHH91rLofJXusa3kQ3gcEmk3BnSXHAOvZswUj6zyyOjhc0ECb0+v9aPPQAuhf0HFDYjxXSLSm9XVJwVsNcDOK1dbUUAjIS/n5JDd8hEtTvTcDuss4wbGJTVWAgPQe/vgA11yiJWvAHV/d1dHR/G0hDee41FrCBtDsEOEM2ZGUBRM/IwKOSQKwk4lhikNI8m0SSxkYkIenhteKwi2bzsRm6sa1O4dv4VqrNX7DZ0H1yuEgLYPuc4SJPLSEE+SAC2BQME7Iaw1CYpj1MCK5WY7R7oGpNzW7e20hJa+7rUYJBL6Lf53SNrTKNebauplGSpMncHG8jN4TBRAvnN9psYHBDJQQMmU197DoDiOc61yupvND42OheHAIcstwypE+nscKNS1Q+1kSijfVswyxgA/YShatRgosnGHuzBHqyqC+ugSo58z3L0t5TGWPxcZLhbpoBIFns1zf9nVLHDqlSk+/Iw7wa/xeOubvJM2oZyJSbAT9XR2bqnrJWuca3nxbIy3Z5eBdweDtO9vLU8gzNaVYLMGGZ8qbNL2+eJ3PyZcwzA1hAKYGeVB7wipUkRoQ7H8baGhu0q7okuE2ZynvbbOxa2oGFK2d238XuSSL6zEgOqHxCKYNBbQvvwSjFvV1nqKrG5xzYHQFwbEYpKiDRgf78OquiRJYzaGgBS+ePJXNSCmhjMVtuHh/MKtjEdgkZj68NlOPiJHMhBH52e/rE+lPX5wI2skrjcV7U1k1prHA5JECrea9pk5C2SYx3KR7/TX/Bpu/gKcd94ewtQsCwjcmnpKgxC3cN3fC2BvMCkwY4O3Trx2E/Osrc78FGOuKtx+UBjJZx6kHHDezGWgXHz4xvAQGpCOwLGzrCvT6F2RBgm5cGhSbgUQoBK2leReg5F3xEpbEoM5JH1j+DOquxAh3dWIuqcTVIo4BVSEcmROVX2cWS09pCa3QFYNeqi7XO12845z83IObnrgD0a0qFy8Y42LekrIN+Ten458ruDNPisSUtvhZ61wQZ3Tlb5SUmyxJZrv1SJomomamkwOM8x6dJ5q81bWyl2hDUDBk6WfKwpxBHlNpOdjDWA5PLrOwOJnJz5MD5jPa3NFSOWWcA8VzneuFqM1od9Qnw2BU47Eo5HfQoNXV4RIbeKidvw3BhxmxAZmgdWlmhfYpyKDFJYuW7xW99o/Q51TLX+P5u6TdKV6MEX172TrI50cbibprhflYxboe9CF9c9jrHJId8PMzyGgjWd93D8QHpwC9V1lrcz3L89JjBWos4kvgmAGoXWYlPbcDhRbqX1FM7dtf9rAqSkULgYkjXfxfbWBsKsOH/wg1vEpPZ+rAft0o2KhbDevL0JrCgTc7RrOaCuk1K9ZYX2qU2HiRk0LDrc4mU9CBhz7F7NnWvmdXIG2e2ZhioSk4H7Mmi0BZGum59oZHlGqWpd+rTWCGNVY3daKx17JwGEYbZjY6NI6TbqAqBbIfNaBuYtGlz9hxjZLYs8HFCDZmvrnona8a81nprYKOUAnlhMFkWmAXKEukArWEvgpIUTEFWM3VgcFfvMSHagcGm3+BzzmPet9D9t8x1K9gppSDvvAAs3Jc5U2qDx0WBSQOcTSLpJcovuX6sARg71P6hFe4PLWBA47g5lkyjaaMkEEcR+R1HNLcqJdYbPtZCt3hjNtN0dcB83Pp5IJBVN64jHse9WEBJahCxl6QU1Gjq8m2kz0Qj3+EzlqKdxbjP/H6McJGNn9OGplcoHebj38cu5FztFao2mo0BVglaUFDnMI0w6EW4GEQQEJitSsyWBZY5p05zMBOdK77WeB+0yCgspWoMObm1sQ3/VOHXyoNUYdgj9V0UyRdphH9eq45znesV1bGAw+myxN20SpVlCczNeD/m4qZiGUgTaEljhash+U0d2vVgJuNkUdTYRUqSF9ulk5icb4hvp6y1+DTJ8Gmaw1qLSEl83eK/d4wyxuJ+nuNukvmNVj+N8MVFikGv/RZXaoOHWY6HeVGTKV8Mng6Cv0SV2uD7+5X3MRz3Y3x904eSAgvHOFwEwOHlIMbNnsAhWyI8zvMgxa2bocmytPmK0uSWB0g2uHjzsG3zt9bdD2UqDaCgudnYVmuL+IbZdgg0vgTYyHKbrCD5X+bCBtpCBgAnBXQgYS+WSGN1MLigDYcrGOBhicm8IDaKpEUvL4K7vHzC9zBdFHhYENASKQHlwLE0kei7FHDeGPOmTGzaj4lqowo4aR/cz1CXXPMxEOBYfW+M2AlM2nWj59kvB9wzs0Lju7slrLW4HiW4GiV7P8fnVC8JNhYsyy0MtDGecasUbSAjKTBbFfWk0Q5WVDjHdAGDL93kMNbSvJNrLJnZXKzPrUJUzGEGC7fNDV1lLSevF5gHgX0+eX0Yo/9G14/7sBuNoUYRJ7cuVhp5EYIX5FOrAOdZ2x5gk5cG6CBx89w2SJWby9w4FXASZnoeAUAbA23oGiictF4H/zKz0FgLbYh1TquXHTTNwbE0772hsoClzwIWFqICOB3A5xOAy/pr13wrAwm4o3jWGlR862FgkyWs2wCdbeEi58CZ11dt9mKjXoTxIEapLSaLshZaMkgVlIr9GneQ0jUc+oUvVsQuXOaamqeBvQCvT2h8E4uXlRbTZYnpsr5f7gq9O9Xa9Awgnutcz1xdwGGb0WpXGWPxuChwP808m0RKTlTeDxjYVFnh2IaNRMBxP8LVKDl4YWYtsZEe50UtpZklJheDGKMjSUzO9bzFG1wGtC8GMb667h+dVm+txeOcmDgMAqaxwofLtDWpkmVN97O8tgCIFPuCrkvH3kLNVyW+u1ui1AZSCHx51cPlMMYyo/AY9n3a1QOyWaucmM3hZ5bGFCRzMajbFBjHhJ4vaTPXZLqlsfKMm1P4loYMj22wSjPlr8lkaPOuYj+cbcy1LvNtv5hjQOlATyFrLSUgB4mkWWE6ZYxJXIGEvFnfdazz50QbQfL54TTWQhvkDqQsXMBKFM2R56X37WIZHadzKykgnfl+KCHn92CcdFkIoJ8oXIxijAaxXzw3QZauJMk20GcXJhcfJ6eUGjIk88cZnioGGXnRL4U4mN0Ybn5bZdVSwFjgtz8tSSqeRvjyqrfTOTzXbrUr2GiMRVFq5C5kIi/Zl9MiL0osMoNlQUw79rMDKhBZCDhQpWIiMuig3BhXLg04cf/Fgf/gawiIafoWdknsAPiQE/+fsxx4SmWFkygvitq81+fAvnducRP6oXGAQji/CQFvN9F30sYQqK2x5trksy2sOZ/Eu0czJXI2E4NeVDHlZAW2UbG02iAP7i1lSfeUUoc+f/VQMKrNoCM3iyAAKaWXbIdgoPfiZeAQFZDo7TY2DCcB955k1QggqwCJKBJIVHUdJ7F0aeSft5XKWyleA08WdFO31iKJFJJYIisNZnfL2uPTWPnUZCYjlJr2vG1zpZLEZPSWDewxqiQM2w7oevO7sxG+w9qU1xRprM4hKuc611uowgGHjwFwOOxF+HCRtka7txUzp+7nhffGUEri+og+bcaSj9TDLK+ZTieRxOUowWWLR88uxal6zDYMF329RPkU5bcI4pyrktB+fMxgrIWSAl9d93ExOC7rkFm3Hx9XHqCKI4kPF2mrxyfLlO9n9YTLQUog+FuSKYdlLXtLEsszjRW+vu7BWOB//rR4EnDIAP/dtD4HDHoRbkYJhgFAWziW4XxVYp6VNb9SIYitMOpFGPbjV5WKG4KNXcEiXG0S3DVGUvD1rubbfBxdyX4EDtCmpkqmJllvGxgWJpKyFJmlvru8txAkLNz3TQll6KNFUhuSxRH4AcTus2TQlTZg65I2fh7uuvMRSrfpipX0YTniXvgAFgYXBRqsDQ++SQ8w8gYu/B6oB7YwC0UbtG6cN7EbedoguV+VlMqvIoSobTz5vm8dBZI28YDYYUMOALMlSf/ZO+mnx6xTxvZavRtfc4VgOV/jJQPmWyTF7FfFIDiNSwJtBr0Io1RBKQ7lqJhaFk5S32A2chWl6WQRc506IIbnM978rlzjoi2wIVJNsHD3ZsW24rTqx4ZaJVISFwNiGz4lsO+1FjeMllk7AMElBQMQlW9k12dfZzduf/02Zv+mpN99QlKAOiMvdmNItYxrZhPmhUaWG6wceJ8XBnmpqZFVclPQOEl1xSzfpSppdQXsCwEoIaEkqAkgq6aPkHQ/gWtoMeOT71Ww8D54G99/i28jrwle0rfxc61VrvFpSkGMrLJgeX/hpMYAjY+6mkZ478L7WY5F1m7bkETS+xb2D2RhNxvhtSZ4x9q0cIzbXX1zu+oMIJ7rXCeuojT4NM3wOD8cOMxLg/spgY+8aEsiietxisvB4WElzdd4mOV4XFTgpBDUGbkcJQf7KHb50vCiry0F71xvq4rS4Lu7pQebRv0YX1/3jgoGM6j1cVLJ9SMlcTtOcDlK1jq4RWnwMCeZsg5u9OM3KlMOqygNvr1b+k3U5TDGqBfjp8c64/BqGONmD+DQODuBu1nupWfCfWY37jNjJufMgYYhKAvQOSHAkLxh3gOQUffD2VybzLe70ih5QWdtub74MxX45mWOgjvIEqlLI+2l5IuTRKrGaKCNXAUIErOj/v2m5EZjK38t494bMwlpk0fMkn4aYdBT+PrLC9zdzaBN5V3Jf1eWhhiqGRmPq0Dq5UNznG7Hy8gMYK1ZZ4R0nquK+cgbLZYCheBjyIyU0oHJAciSRBK9mB5rBXziqQ1ZKo1/vTaajqT2D48jAICqM10EKv/G8M1V79limRFoI6XAKFYetO+qppF+G7vxczLS5+vS+7eVdG1VgMM6eLep+PMlFi79vZICoz75yV2Nko2esJuOcxcZNc8fp/BshKCk+5BhuItvYT853AZh0+exzDQeF+tp1aMesQ2HLYqDt1zGBmBt1u0bGUfSgw/MWjrF57APsx+oAOcudmObbyHdowCbae9pG8qH2WvRWrKggHWyYSlq98VYCSRR5L6vjomfw/2pZwbXXscQOOmNc4O/s3CP0XDMYd4jwb8Wg45CEIIo+MXcZyhcirZ1z8/HEd6fZHCfaobYcB3Dt/Fc3bXKNT4+rnA/z10Dx0JKoJ8QOAhUASjk2a2QFXSdfne3bG2uCCHQiys2cD/dLRBqW+3TCGfWMV+TO97qOusMIJ7rXCeqYwCHbfLBXqJwM06Pwpxi/5iHWV7bjMSRxOUgxuUwOch02hj2pclrcfVS0AL7YvD+Fn2fY1lLgNOPDytoQ4a/X16ShPaY53aRlfgYgGNKCtyMU1yPkhpAxem+97O8ZlYfRxJXw7crUw5ruijw/T3JGKUALocJVoXBb39aAKBr7HJP4LDU5G8Ygq1KClyNElwN6TOer6ibOluVtWRA4VgP3H091SbmrdQm8+2wWIJGBtoaq7xEVlRyaWsIWpKCgjFDgCsEfYyxmK4KPC7yCrSzFejIW6IunzX+N1IkrxKoAIxC03FwoieXUtKl/lH6X5oo//zX4xQ6y2uMw1VO4ViLTEMI8g0a9WghfTGIMezHEKg2cdpWzEAGSPiYtDG1tE6WLmsbMh4DsNFWoIw1pvb7rgX0NsCRPs/13wPWSeQcO9ExEPllqmOi98bjpXq9OgBKsjtBITiFhrXAIIloTDCz0b+HwKcLFsYKaA0UQmAXFmzFAFr3amxuVvlzeC3FIDezMEK/tfDnu4YXqCCNOEwnpvdPYSfTBVlhwFIzl+0Zrp4IaJ3Cs3ET2GgtvEw0tHOgY6nAESmrgKVBwHKLXHLosRtFRclp1XmNubUprfqtVlGamhw59FHj4vssA4a9RL3aQEEhBOJIQFkLpS20FA5gr9iKyv1MiqrBpQ03jQLQMAQOG3M2AX80F1sJGGEhLQF4PkVaOlasv46DJOYG4MZD2Nh6QIufV0JmvjH+eP39xVb3LG78hMfrmY0Bq5HvJcYdtwZbY1hYK2r3jurzraddt4GO1T2r3ZO3xnD8jBpJm2qxKvHt3RIPswx5SecviSVG/cgzq4cprXeYcf7TY1YjxnAp11QNr9eXbqY3PVXlE6ePM4B4rnMduYrS4G6a4SEADgcOONwlLdFai7lLVF4EoN7QJSofwzusKA0e58Q2DOUxwx7JOts85HY57mWuMZlTpziU+fTTCJeDGOPB+/al+ZyqGdwxSCN8fdM/qkx1lZOPH7+GFARq3Yzrcn1jLCYu5KcpU74eHzaeX1sZY/Hj4woPM5Is88LvfkZZg4cAh1mhcTfNa36szJ7pJwoL11Fd5usBKOxluC0A5Vy0wc8Cr0JOQm5uEtOYFvep8wkjDyta2OcFSyS1+48YQkVpPMuPN19NmEQgDDQBlLX+a20trBBYOVm04d95dodAHAsMejGGPYVBSgyrbddTqY2XG4bX5KnkhlUCKALQsfEz/p4ByVCC5za2VYhKBUBWnlju8w1k2byBbIOmmuCjbLAhCV10jBSgsVmkx5aGGFgWQBIJ2rhmdg3QrMBM9xzOX4+DDkTwCtYZPPKdX7CMVoitHkrAOruxyXhpblIPnXvb5FlhAqwHHnaUYlXgfrVpjiJZDyPpAMM47O3jvH6P4YTfl2hO7Qs2ajeW5lmJZUbm/Vkezh914DDyQCpZAwhRNYdnDfbrMWTUbJ/TbDy/p0A9ay1WhcEqq8IT2iTqkZKeqcQMz5f2ydvFb7bJOty1pBCQKmxkdISLCHh/WouK5b6J3QjAzRcaqz2aKSHoVvNxdMchBAF8XSFLupGqGzLWw6CWCnwM/nWNPy+lFoCQwrPVrQMYAYsyYOmHidjWbgAY135ezY/k2yjrjaQWxuNLj8djVO726z/erzBdFt5xJIklRr0E40GMRAkItwZ7mBcoJ9na8ySx8tdr33mPvuV5apc6A4jnOteRqtTkcVgDDtMIHy53Bw4niwJ309x3NJrywacUA5OPjkXkF2eK2IZXo+Qg8CcvDSZzAiHCTjGzGC8GMZJ36EvzOdd0UeD7hxW0NhBC4MNliptRcrQbZl4afJyQ94i15CXW5uPXlgwsJaUpXw3ftkw5rKzQ+PbTEqu8JF82J5PJje0EVbvKWgo6uZtmNdYxsxvYy/LHjgCUUS9C/wQBKO+lSm08SMigYZf3EW9MqlReAI79kBUas2VBLMAO5hQHLNQW/m7TQ+mTvOmokpJZCrvMNXInFwtLAFX4C+h5jQVmLhhHybw13Y8DYYqPc3z7wwyLrNqocfDWKZnn/Bk8pXjTxYBKyHxsY0OGLMmmF1gbwLgGSvqNZItpP+jn7HWmJHkvlaVFCP2B4UdmnAbgJn/MlbQOdbDRA460EV7baAJOdlc9RsBCGgEtdmc3enCpKa0DnDy8DgZ4T84t8vqwZAgOus1v7ADCEDDcZ+yxfPZhUWC2KPy1wkqKy2FykjCoYxT75rEEuc23UArhpXS9gCnDLPLnklGHkl0OI5KuSXU9SjDuH8em5yWq1AG7MGv3juRUav78+2mE+MhS8K4ypg74tfkZhuDcrixerjC0YVsq/bFBqTXvxgbI12UpsmtYGtAdhDWI5Bq7kRmTzWup7bNu3hPC+wUzzfleFZ4SIQUiMNCI6iZAn0jwuViUDbZneGZrDHvRUCy4pliYBh9H0jVj2lKxDwuLO0Xx+nfulHcPAcNZgJKTL4cJYiXpsasSs01sYGZif4ZN9DOAeK5zPbEYOHycVwvMQeoYh73tl5gxFg/zHPez3HcipRS4GpKHzlNlCuxB+DirS0E4ROKQtGOKkSdfwzBkQUqBcZ86xWeQ4f2VNhY/PKwwmRPrrZcofH3dPxpQ1wbCXwxifLhIPQjNC4CHBhAeR5ymnLwblqu1lDT9w/0SWWGQlcaZ0gs/R+wDHDYbFMaCpHlSeL8rLt5cjvoE+rymAJTXULzRyApNzEC3QW9jkxhjaWEvqyRhCMBo45g23V52QJ0ZELtFeuyAEf6+ba7lzesyq0vjOPXPpgRGJ5FEEisf4MIAWShpbDPG5yAU9gkqjUEcKRQFMebiSGKYRhik0t0zSK7dBJJ4o/XSmwwPnj1x/tiVDVl5RAZ+kQGLhgLX6HhG/YgAN1sHCf2m0r0uLMnhjDX+98ZaaBiH1gHwPIuKWQmwZLqSTvOjGWgkkNGjkgQmSkEbSgc0Cn5e97oUJMObU+FDQzpZm1j3NYvceI8VJZiSZJjGbAiaH2vslNrgcU7s2TyQp6WxwtUwxsUrvMeU2gRAIV33bcxMZjgzWNWLu30LTymjXmmDfFFgVWiUus5uT2OJVCqsco0fHlb4NMlOFhBzzGLAP0xGbmsehfJGBiKOdeyhB2+Tkbd2bszunp/N428DAtv8+F5yTj/Eu7ENVOz6LHkO3xU472I3UlK28p+jlBQaZoDWc+aPZ4Nvawg2roGQIVvd/ct7V7P2NvhvAe0sQNpUDv49oo3tWI2ZSAoPNPo53aVTx1HlzXvMMVPqIOhvVSIrDBbu2rSWwNJeonwzqBlcxWzgpmXL515nAPFc5zqwSm1wN83xMMtrwOHtxW4yY+87NqvYU5EiEORq9LQFKsuJH2Z5zT9RMTvrQHPveaYxmZO/XOjhNEjJy+otd4qfq8j7rET5aYHJNKsM/2UgVRDtPmUvWfNVie/vlyhKYh3ejBN8uEiPclzaWNxNM9zPcr8YGvYifHHZ8+AkB3zcz/Ka5wizFN6bp6Y2Ft/dLXA3ybDItWe9REruBRxqYym5fZbX5DRK0EKf02eBujH04J0EoByjmM3DICFLkdnYPmSjaVP5L0FUmxhmAXalJzNbqgYScmd/BzYKhbEY56voGIYt3jyRkhikym9gd/WsDCWQ01WJ+bLAIuMUZguwlDJW/tiZEbgtgZKLzeHXZGz++/pm9TXMi211DDbk93cLvxH7+YeBB3bZ43KNDdnyMx6XbQxIC95cAsYYzwCsAZwWsMxQ8cb/lXSuxnzkA7fWw5P+R6ifp3DjKuCAH0E+n1IKCANIxfdF+hvaNGtkpa7Jwmmctft6NUNjNo0XDul6nBe1xhQz2i+HCXqvxN/VGOvnodUGGSwb94epyMeW1u0jozbGYLbSeJhlmCxKCAH0YgUbwQdCKSlqY/fYATHHBBu1sS7opPQNmrb5PY3rycj7nIO9WHRmdzl/WM1QjlNZErz22jcszc+zXenUjfNyLHZjEtXnPG7uNOXca8FtO46PJtsxVDLUGkLG+RVb8krWzaaW5fsPP/M2tjqDjsIzxkM/Wl4XRU55wf81xyQD+XNntcCN06I0mK1K5IXxxMxeQk1OFayl04YcOf4M5MiHlLD78pFfqD59mrYg4+c61/OXBw7nFdjRZ8bhDsBhVlDIwyRgLCaxws0owcUwflJnQxuLiUueDUGWfqJwdaAUJCs0HucFJouilgSXxsqnKL9WM+fXUOx5s3Ddr2WuAVgMBikWiwy7zsBkCO28yYIOXxvQKJuPk6L+N7xR2wGcNMbip8eV99pLIolvbvo7BwFtKmNILns3zfzCu59G+CJg7/p08IZM+fJAIPwt1HxV4Dd/nGOyKKC1xaBHicb7SJU5uf1+XiDLNfJSQ2vrN2q8UDsHoNTLWuvZmMQmIdZcUZrAQ68CbYhVAN9db5NhMUAYBwBhyKo6JLnUBrK/5YakznDzOkijvV9LG0tBL27+agKBSgoM0soP82ffXOKnnybkqRdsppryuBqb40AmTBujo8mO8eDjCZgNp6qHWY7v75cQQuDnt32M+lviFTdUuNHtYj5SOqNBYUAJ3S4koBmsEf494HJIDWBQbTgB1MNpLPt58TYXxF5kADFkw1R/0giSqf6tuJCBnyQo5INBxfbQG7IIqOTMDuAW8P6ilXwWfr596aZok9XW5Z8K0LUegoVpLF8FUyZ3ntuThud2L1HeG7t5TzuE2bhP7Qo2KkGpu2k/waf7ObGTnBqg0KYCVVxxOj0xlqhJ0yQDcBpqCOq0yVmbPn67Vlso0ibGIF8n5zpddbEbW3+mq5CtXWtjEFbwPVub1K4t086y3Hfs2Wp6r1jkEDCwgGOdU/PLXQPG+AY2KxwOWAb4vQ57Y2pj/b3BsmTcWFhroISk9Hi3HoojgX4S+TVSP41eHbv8VCUlcHs7PvjvzwDiuc61Yz0VOFxmJe6mddllP41wc4SQh5VjG06WhT82KQUu+gSy7Csx7TLAZwbjxSvqxr/GKkrqfi0y2nQ3u9JJLPHVhxEmk6W/mfJmjm+iYRfw1MVAYhNoLLTxIJYQwKgf43oUO/ZPAFJuATabxdLcj5PMgx5prPDhMsXIAYeLjID2eXC9cMDHe5Iph2WMwW9/WuK3Py1daqHA5TDBF5eUOL0NOCR5d4mfHjLcz3PkjiWnlPDGzpGS5wAUV8zUXqxKzDP6lyV1IVjCjCpaiDvJpFuUs79cU5bju+UHAoTNYrbLMis7vbSkA4SfshhufibNRFAhaCwRqK1qoQZCAB8+jPHx43TnxghX3f9ufXPl/wt+t291bbLW2I7q5QDHRVbif/60gLUWX1z2cHuRPvk5K+ZLlVIcfs//btq0hrJpvl+o2v2CwUCH6FmCDY07nwz2WIOKQWkZjDdVgEBD3syvW0+zBgLOI+BTsCsAEXDUGet+Z23AooFvAuhwbEMgigRiKQM2IwWINH0/GfjnOZXZMuF98JCmALOIMydDzvJ1n1KA2OK9BmD4mu6JxpDNzWPD5obXkJdH9Cg+BdiojfVhVdZaxHGEPC/XpJscNMPSer4nMHrhLQLYZiBoPO1TW8NFGk2U89r87VYXu/EprMJmNYOwuhjbUoqABbsd8N6nmOzADGYOFrMQ/joJg8+0Qe39Fy4hu2puVSx7oG7zwfOxFAJpJCg9vqHy8KFRkUQcMCDDBqSU7+caOwOI5zrXiasVOEwUbi97GG4BDlkWczfNa4uoUT/GzZiSTg+dhLqSZ9OY2IYXeyYem0DCE4I2QpBf1sUwfhdptqcokvY5j41sXToYsnQGjum1y0a76V1Sl5XVU9ua4GPr40z9b7qmfwYROP1TCYFh/zAfvGbaW14SG5O7hJEL2xn2IkAI/zmSVLqSyLNMmdMgfUjEOxiP1pIP6v/4vgqf6KcRfsdtH7cX6VaQT2uDnyYZfnxY1QDrJCLvlmEvwth9xk+Zc95iGUMLzaw0WDpQnwA4SjRulxSjtrBOIpIBsk9P04vwFItJBhKWGTEg21hHKkjqPNSbh0GLuTMWX2brDY80Jn+gYS/qBCWJaa3RG/QwnSycLK6SXh2b5dLG4qlAxvaf71vNAJAmeyfcWBxD3leUBr/54xylNrgYxPjmpr/1+TgEoSxNKzBYBKb8uxSDYU2gLPz5oUBVeK+qya4DhqQ2BADl7j3RptW9H2bnmCo12JhqowjAy6p9srUNXtttQvl+yrTHSjrn2IsAIKy/lrwdgXswv3sPWqLanApZNdDCDbpvJgQbUwJ2DfKS5qiiMA5cqjMzOeSE/3utpv3Min5cFJguCn/N8T38cniY5/axj7ELbMzLuuwxZNv20gh5rv15oSdDZQ2w53GwtyfPHQReSAKw3f2FJZr8/dlS5FxttQu7MQT9jspuDH7GbG/t59rNfpL7M2srRmERrN+0ofcGS01eQKAsNbSpgEO2WCBAdLdrlpjtdT9eKeAJE35el9W9MQQYm4Ajf91FrHjuOgOI5zrXiYo9CkNftl6i8OEi3eq3ZqzFtCVR+WJAwOFTZJdZ4diGjQXaeBDjahjvBRCwvJZTlMNN1iZ5yedevFBm1tJyD5YO/f5wps6xqvLFQrAhA5Z5iR/vV9R5h8UgjXA5SsjUuQFYrgGbDZAyfK1CEzuODdM5pKMXSxgLn1rLQ1CKSpLVtWFdS4cLmI/7yLfX/uZABskh52C6LPHtpwXupuSlqqTANzd9/OKL4caNelESO/SnxxUeZjktoECLnl6icHOR4mpI7Ob3bDHAAGFRElhSlAa5SxleujFVJeOulxTE5vG+N84sO4mUBwlP3W1mmaJnGHZ4mpHZeiW3OdTPTBtqFhHLsFx7LSWFb3YM0+7xYyylFE6XtOk2xnRaM+zib3hKufHGcIEAdAx/fkjVNlaN9xX+vAk4GmPxWz/Nsco1eonCLz4MACE8CFhqg7JkJh+N832Os/L3qjNjm/++Bsnrpgqbany+2PTef1bh56QNllmJVUGfn3EIo4Db+DI7uAE68v3RMNLoWY8Bs8w92KAuwa4BjgE7svKgtC4jlagxEiFDJkg3DRoWvDnl+5SUFXsn9E/mn4cqgJApeop5rHRKhceGdU4SSVwOk1dtc8NN/vtZjsd5UZsThIBnmfb7KfIsR3immw1eD/wC7vy7c+z+hoFuPhf7VhOcaAMpms2N1wJWnOt11E7sxoC1y8SEfarZPAnvd3U/Wno8y5jXQEZtkGuDVaaxcGzsOvhYzXlKCsdUF34dzWqbi0GENFaNebnuJ0z3Cm6+GW/x0ZaCbQM2PJdvIkls3G+EzaW2a3ftd43HH3MOPwOI5zrXkespwKE2LlF5mntpppICV6NkJwliVzEg2ZSDsKTzYk+Qj8GHyaK+4IuUdPKS+F16yz2lmDnH0uR1WbLCMCXQcLBFOvgaAMRmWWtxN8vx8TGDtRZKSXx91cN4cJj/Fi9U5kuSKi8yTeIEQf6FwzTCqtCYLGhM8005ksRW6CUkZa6BlKb6+tQlGosABhprC4M2kDLYzHWBlIDFbKXx0XlLLnMNKShx+nd/M271l2RW6GxZus1aXgNcYyVxM07w5SWds/fCVmgDCMvg+7zQNVkNLz7DEoJYtCz766fKM4J7CW3On3OTZUzlX7jIylbzfSEojXTgfLT66eGsI2sp/GSeVSmEaw2PVGGYbvfCZPBxuiSmeigHjJTAl86aodoE7M944GOqb4qDTUljk8y/Pyb41QY4hhLq2sbrgI0Wy3fdq/nzIoTAuB8FktztYVpSdjMGT5FS/NrLM+HmRc3WBYAPiUoi6RPE83Izc9PUNo71oAAGEb1UlY6glsCtm4yXxrVXsQ3pHId2kQjOfQ0kbAKEAWAkZfs80bQq8fekFgBStf0++F4AnaEz436My0GM/g7WPs9ZfE2vcmJbP8xzTBfk68pzlJIuBTquGpdJLHF7PcRykTVYx/uHi/AxVIzb5vzS8rMDQByutXk0nC/dfNoGTL6m83aul6tNjbc1dqPZ34u0yfKPlIA2BBrOnbez1savc4VrsChZ2QZYC6wyjdwF1VhbeT/vypZvMir5Xhv+Nc/x2hgPOBal8cfXBjaaYO73zHcg2D+0gY4Bk71xz29ez+uAIzoByuY1fQYQz3WuI5U2lAQbpiLvChwWpXEdzOpv44gSlZ/i1+YDJBaF97lgSfHVKNnJe5GLPWkIsNG1Bd+oF+FyuN/zvffSZofwAMfQGe7J8nptAGJeGnx/t/Tg9Kgf4+vr3pOYp6tc4+Mkw2xJcXNSEJB+NYwxW5V4mBde6i0EMZ6uRslWWwCgfmPmjVpNvm3qN/JQKrdR8m33XwDtUxzQschoUZSVBrA0V1wOY1wNk1q6LD+e039LbSj9t7SAqBJ1v7zq4XacIIrUm2McMIOobICEBW/i3bxX9+ExtQVts7Mr3Sawn0ROti299PYlPptSm1rYSdNXEKB5mH0LmQl5KAjM44aaHQRSNsd1GpMkeeDA1E2vVWpDLMNl/d4B0Ngd9SKM+jGGPYUvvriozWu8qK4SRE1tY1wD6J4gNwbqPmHNzXLT7/DY7BzeaBXuGs0Ljby0yEuNoqzGtPcbDBixpZNPCtB5Cc9FOLYZKIwDdloSeG22MR3fSyNhl+pkwsUKl0MCtfa5p4VgT9tmmecsDjnJC00slgDwqa4DlkrXWWrh71gSW+GHVQqqrxa2ZPWv9YnXAqBNqPtr2qQ6j0ZJIKN0x8NfN8Nnuj7jrDDOUgH+8WlUzSdRJNsZkMEGd5tP8lOKzxtfh1lpkBcGi1xjuSroHhrMMVLQdTdyrGtOSk5j+lcp8aLrtRprLJgrWwHI4L+nrGU659IOcOLYDZxzvb2q3eu3sBtDYFwbg1VuvOdoqU2N4SdFJUHuxdKHE1kAi1VJYUYOeLscxrgZJYhjCQTXzKaU7CczLF3Th+Z0ERDVrQMc61LucF+ywxbrrQABAABJREFUBjRarIGRfFXVCQntoCMzIduKr1kp6DWkAP63//Vn+55mX2cA8VyffWljKa20ARzeXqRbPf+yQuNuSvJfvpTSWOFmTKzAQxZG1loCWFyABFfs5CCXe8hBKFSBWF7TRid+kEa4HMYY9ffzSnyvtWt4AEv7nhIi81oARA4z+fFxBWMspBT48qqHywPHLkBg5MdJhqm7JoS7qY/7EWbLEo+LahwqSSEhV6PkIH/FU1R4424CjV0gZe3rELj0CyQ3tjLtfJcIHKNFkcC4nyCJ6f2zPDF3oBr9PfzCQ0la0PccKNb2uXXKt2ULq7KFQdlkvIQMyn3HhZcYapYZ1r9vA4v4c2aPKv5cBQKGjDtmJQXShKTwLHlPY/litgthEMLCAYZNT1SgAn8ZNHxqAnapCZhmhnRTlhwp6S0VBhtkyVx5ob00eRmw3gG32e5HGPfj2nEXpUF/1MPkceE9vnhs7VPb2H9tvztkKcuy3nV2Tru8Gqg2A5sCSXYtJQXyUmOyKCEAXAzJpzTkPTz1PbbJybpA1beYyMprnMd5jukyYMIJsnW53NPWZVsVpcGq0J5BnAXs4fA+wWMrdimoSgq/meQxVLf5CP4+YDn65hc3wNyDLUJJNKqkUwYA3fP6990GOvJjAA9oCrcT5o2odL/ksW2M9T6QUhA4y/Mt/U3dv3GXz72N4biNAcn3JvKRZDYQgfaht61vwhXGz4kMxI/7kW/cNYH7sF7Lem3f6gIY137eaOocWl0S63COCdlmpwKQz/V6i6/X6bLA/TTH4yLHItN+Dc1zlJISaey8p5WElNW8VbgmfMGkGnQzDsN7eRWMFYTGcLMe8GvtepOo3jg6hGGpgtfnhg39Dr57FM7/XSnY7WBjdS/gQws/Abb6qP2tW0zHSuD//Ud/917vJ6x1ndS5zvWZVBtwGCbBdt3UWA52N8s9uwogQO5mnGxlK3ZVURo8znM8OB8WIGBm8cZix+fNCzKyniyK2kYyiSQunCfNawFsXqrYl4+9wNpkybuEB7zVKrXB9/crP4YHaYSvb/oHj4tSG3yaZHiYV2D6uB9jkCrMVpQsysVBP5evUGrrpct4+nFZazFZFPg0yVxap8Ay0wAI8OqnEW4vUuSFwcyxXUtt/M3dWoFCA5EC0iSCEgJpGmHgfCGbwCZ/7oZWYk/aDHRVCDpyx9UaC4P6Rpc3KryZdg+t2DGiYtpwOiotioTbpNIiMonChNUq0IQXl6HU7CWK5ZIhw7ANROIFLgOG8ROTmY27DzFgGAZpAZXHKLOktwGU7Ic7WxaYLcsagwugUJ9RP8K4FyFx9hahrJ5Y2hqDQb7mgdjcRG6TJkvBPnC7fRYVO9UETMeAPdaySeaxWZQWeY2pzI2A+ljmBfuapFg2wHi3YYgjiSSu/OvafAfz0uC3fpzjekRWJF9d9TauO/g9tjE5mu+RNx98TdIsvw5kN2ujV2VLiMxLAQClNnick5VDqA7oJcr77j11XmC5K//XdW1LKdYSkbdd311jto0hs2vSavic3MgCRMCGDABKE/g7BnwfvmVUID4dCz9CQkApeGm8EHDeYRrV2xVwjmT15oEAhAWsEB4NWLsnBPdfZjey39n6tV1JG93T+2MImTwMbMURNW0uBjGGPWrES0cYyktTY0y+B1CLm237mNCErNsueXUbMLk+1+xWrRLrtqZOADq+l/PzORSvKxarAtMlWR6scvJCD1enaawwGsS4GsS4HiVr9gfsVfrjY4ZSG/JPt8opKOixrexGbaC33/YA1O99zOJP4zqzn+cYXi90hcTwnM2AaamBbIcLg++tsZJIo/o6u5qpQx9gp2BwDZLSmNq1yp9xyIi3oHn4qdfQmYF4rs+utLHkcTjN6sDhRYpRfzNwOFuWuJtmWOaV9HLUj3AzSlp9y7aVtRbzTONxltd8ZCJFksbL4e7MLG2cT+KiqDFGlKRO/MXguJ34t1jaWB8csOiQJe8SHvCUIuDSYDjq4zFg6jznomiyKPDD/dLJPwU+XKa4HiUHvT5L/0PP0H4aIY2ET48FKjD8ek/p/VusEDjk929dZ5FT4gYpyY1XeeW/VP29e2ywYL4cko9q15j0PiumId82LYnd1rqE7vXE7jr7MlgUaVtL12MD6l1WEASq0L/88BpL0/2s2gDSRi5MokwCOVkXM5IBHAZ1ThGKw2zSpU9z1mtdaSkEpaUGgOFTwQxrSZK3cD6Gy0yvjZteUnk77iKBZvbWbEk+ZmGzSQgao+N+jFE/8oxObWghz2mlIbAhhcDtzQCT6apawB7IDNy2oYwaQGTXe60W8EEYiSZWUu7CN/LSwJjKx8iYxrUSdPd5XHWyoxpjrYsBKAD89LhCaSwGqcIvvhhSUMaRxmlN9tjB3CxbQNV9i89VyORsC5Hh9/+U98ebyIc5+W/y2FJSOO/mBL3kMO9mYy1ZReQay0CS3PZ+uXnRSxR6joH3HIFbTSncJnn1LufSArX7gBA0H+eF8fMasVtoXMcRn1vG/1wTC3U2JN+LPIuxJVhAuHlauEfS5piuPXo/9bHJ0wgzJYNsEn9/ERAwoHsVUIGRDAT0kt3n4ZoEWwlcXPSxnGetgSUhk/e1NUSfo7rmmubcEqYAH+qNC1T3+k3y6rZ7x3tec76W0sb6ILjFqsRkWSDLndqEmwYgv2RSwSUbiTe8Rvk0ybzFkhACV8MYN+N043q4OT/6OZMbNcGceci9L7zXtbEbpW+Si9bjaQKPu6yV1sB839iEn0vpM6IpUonqmFiBxABopAT+yB/4+d7vm+sMIJ7rsyltLB5mOe4awOHtRepMy9tvLsYQGHA3rcAAKQQunNdCckDYSFf3fJCSD9ym4wmLF9STBbFG+IbMYM3FIMaot9nf6j0XM2TYC2yTLHlbeMBTjoE3/kuXJtaVVrrRd6ajK7vP8Wpj8cP9EpMFtcJ6icI3N/2DAnOMA+LD6yl2LLusMK9apnyqstbicVHgLgAOeXExWzqzdmOJoRJ8FnEkkUYShaFQEB4Tx/BR3Xa82tia72DZ8CHk88ibQuv+x19b1BlmlRy62gwWugJrvJE0M2HcmieSzM4SUO7rY1ZXKE4TfGzKvLWxyF1YC/vawYUBMeCppMCgR6EBg0QhTdRR/KBKTT6GnPbeZD9xyqD3HttBtm2MxTwrMXOehiFLlf1wyc+w2mwXpfEgY9MDUUmBYT92fxfhqy8rD8RWllWDJfAUCV3FGqyD0Czp8d12oAYktwHKIRDWDCJh0IQB8C7fxjYGRPtxU/p6XhooQRYPfI+ugacd3o1NwO5Y9yxTex/tTMenGOZvfH8NpmM4pxTa4nFepeRysQ3LuL8fk51lrcwqXOV6LViIixnPIWD4FtZTZsN5XGPLOAbLqqD0d38tCfI27CXSzQXCs3OZjRtuWoFKYm0sIEQFDvLvKqYaKraMH0/BGxChDNvZDQRjn69LASDXBP6ypJE31LHzB6W5WFTPGTAgw2o2sqp7BjAc9rBaZrX32ladMl4GGs7Alq+NHo7aQAeM6/Cxh1bYkO06P+vr67PEuqtCuxZWYLB9Cq/3iIVMa5UkopBO2uNutuI6FDg8tPaZLw9pjLaxG9vu4wI0v+ZlpWypPJVNxfZtvLwQcHOLrDXtwnWFtw9w0m2lBP7X3/vVwZ/ZGUA817uvQ4FD/rv7WT1R+XpEYMi+HlssfX5oePUwwLJP8vEq1z5FOVxQp84sfN9U5vdSvDEIvcCeEh5wSBnrJv6sO2FVSoHb6xMwdbqARiUp1ORxBW0I/LgdJ7i9SPdeHLFv4sdJVht7UgpoZ2YM0Od87RLC38KG6ynFn8mnaVb5LAlKiZvMiV1sLJBGEsN+BCUl+onCoEfyW5aAcvVTYjVvYkTvelzaMBjY4kUYAISbKlLSb8boX4mIv1cSFvBBLz5QoGzfkCvJnoWVX2GshO/Ssmy0K33bMGuxyaoMurFNVuU+VbHVqqCWtvfAIFMcyQZwGoCUMkg35d81HxcAloBFlhssncdaXhiEp19K6tqzrUIS7dbwYCBytiT2dT05WXrwb9CLCCRzc9jM/U1Tzpy4wIFRL6pJjYR4ulcYsVOM9y3LC96MUBhJ3vAcZGB7UwmETMFKZkzjWiCOFJJIIAoW320bS7nHZrIpOQ43JXfTDI/zAhbA9TCGkOJgQA6oZE9tvo2nZEmFm/o2CXUbyLprhd51pbb+s48jArnH/Rg9x+yNGu+3KX0rtfVA4SpvZw0DdC3UwcKX81J9juJ15MM8Rxk0dxJn3ZLGyrPFmud4WzEbnhpUBsYz2EHM9eA5rEP8BOAlxJGqs815zPJcz/ez0lAQGTFwBOKYmnFkfSH90exzHw2bH9Yl26S9GItF3vo+q/AbW2tUMfjEl9umcINWEKvZQJBPZ/G+9WqVWHckVzMoxOuFQyoMZ9skrQ7P33s9P8zSDu1ailLXfbuNrdZHERE0Lge0lt1lv8XA4cdJ5pV0PhzlBMDhIdXKJgxCY3ZlNzaZu2GTlebCZkozzQdJwnOc9OtxIQBt2KN5O+DJc9wf+0O/OPhzOAOI53q35RlSs9zLrHYBDovS0AI/CHuII4kbxwTad/GtjcXEeRuGG7F+onwnZpfn7EoYjBR1dS4G8bNIaV5bcXgAS5Ob4QFKSQxTBg2PL0s2ppI0MsuxCVooKShd1TGU+mk9rbTTd6ZtYbTnZoxYqrRpAmi8XA1j9JyP3tYurFvEM2vm4+PKJzCWxtQlSYI2d20eJu+xjLWYBMAh37DjSKLUxrOCBYDxIHY2CeQLOV+VuJvm/lo+xA6hMpBfDycptPWbwk0Vmv1HDZAwVsInanJxCiexdrQHC9uKN+RpLEnqt4M32LGrDWgMwR3vX+gCETidswKm6DqII4nYXQ9CCA9sPvXYGODNS7uWPihAXeI0UkgShTSiDnMIPspgkxoG4BhDIOAi0y7t2/pNdhJLjPsxxgOaE4UQNWbivMF2ZKb2qE+gYRfrfhcA0UuK3UKXGa9lAxjcFfgVjt1QgbQVa4j5iNaC2Cx7ACBtteYPuGHu7LKlmCwKfPuJ/GC/uenjcpj43zUZOU2JcdPz8NCNcZOF0yY5roC54wEWIRjV9f4YuA49iQUcSzuWW0HzZqOBgqgoeTj0r4yUQD+pGom9RDlPv/d9z9LGunVkXvNN9bY5g3irqqYJKpYOPFjlJVYM+hcauQvBahuhAqiNt3Ad0WQCslS6NARgVLYo9LhYSaSJQhrJ1rU0cySbzRshKpl1+Hz0HhE0kgyiKMIqKzxYWb2PitnYZD5XX9vQZrL1PTYBx01BNLtYPJyl1VW1NXN2WW8fCo/U2F6yhfV4BDXRc1SpjQcLufnCDGTPMtQWiteQbt3IqohRf/cmJ9t5fWoBDm8v0lfbyGl6C/LP/PeW7kFZabDMaI25LA3ynBmFJrAaqts/hE1PJaS7hwHMgubmBQT7tpJkub4ehHekNcY15XKDrNSQUuD/88fOISrnOpcvYyzu5znuphVwmMQKt1uSkVe5xt00q7EDe4nCzXgz4NhWbBz7MMtr6cdSklfP1Y5ePcaS7+JkUff8YbDhcrBfuMp7KGZyzp0XWFN6xN5d7AV2bFBVG+vBQmY1NBcakZIBWLh+DM3D4Q3NrqEBwLonUhvIuMw07mc5Cm0gwB5p9CLN0IVNr8MgKS2qaDOuBMm7pCSQ6bIf4XqcoOdCLd7zmDTMOHxcYeGYYqWxSGOJNBJYOQ8YpQTGaYxf+mKAi2ECY4GHWY7/82HlARopBS4HMa7H6ZrEOwQI2xKMix18U0KAMGQRhqzCLu+ZUpNnKLF2DDIncWurOKpAwp7zCHsN40AIkmsAghZy2vjuefPaTROFviSwjL0LN3kJ8kKx5jFpGoBlg0FZlMHrF0H6oLV+wRhFDtBtbPxKF1bQVdqQf1nmWAFhRUo4P0lF7Pp5TsC3tijDx7sFp+TPwc1jSkkYYzFblRCZbvWblBJY5ZwCbb0kvgYY7uj1Q8dclxK3S4v3H1+1hk1j/lwDtdxjfLBKuV9IQLihN4bYhwBw5YDD2bKob/rd9fmU99EWGsP3iUPCDpqARS25ekfgFHBjJFoPddDG+TfndK9KY4k0TnxSLjNjw3sbA1eUiFzNTdwAqB0/CIiPlIQMAKusoCbIZFF0SqibXptvEWRkVs/jPF+zutlnHUn+zWTnkJUEFGZOWldnNQtEKsLAfR87pi99htKDuaYxNttAca0tspKY7QwoSykg4SxTHANcgNYlxs1jDNoZywAdIMw6OyxkSobMNUMIoG/S9ISEcWAoAYDWibONb5bwHF9jMLYwpBkDYNAxxBfpvQfBN/z48O8bgGP4fRg+E9472pod3pfVMR0jt5aL3DjntNi3XrwGUHL3BTbf2zc18LtYj5YB9h3DO7jCcyQDEHhTuvWxgGFmfIfsQvaA5XtGXrp7COAaMAojd4wMGg57USfY1wTb6DMmgsOnaeb3JNxwvxzGiKT0DWuA/679ucLvYe36Y5u/3+Vvt33fWMusM7YtmmFPYXEjJU4klKysUoTg+YTnkrrSJrSS2HBSa2OYE5u5KXGI/Vrt2M8MxHO9lzJuU3Q3rSTHSSRxe5F2Aoe8sLqbZjUJ4bAX4Wac7h32wH6JD43uLqfO7pIMyPKxx0WB6aLuUdVPI882PGTj9BaLb2zsY7hRltyjhNpjdlu5C7dYld6PojltxhEl6jK7cFvXbRemzlPKWItPkwx30xzWEiPu6+s++onauMlsbkRXzj+yKLX7GTN+hAem2kzkmx3YbZ3ytyL7MNbi42OGH+6XTr5Bku1+SqAZhEDuxkcSSdxcpPjysofSUOJ7yGqOlMTVKMaoFztJlmn1IjwUIAwZhbtuDFmGzJ5gbYmjADVkerH0UuT0FUr9av48GXvJrK/oI0XJnP0OsP+pxQ0Hnr+ax8Cy5GEvwjAlb0zhGL/hhrQpy6bFpHGMLZqfCse24mVirIhtmETK+zkWThbaBjIqKXxozSFAiRBo9XZdf5zYCgxG3rvsddQmue4u7EBuOmhjvRdUW7Wxi7p8EEOG+C7VFXaw5nPY+P0h1Xa8IfAoBVAaAlBDX00hBMZ9MtcP11/sJbxqzFHhZ8zXTMTSLgZClIRtgFVPYRm1AzGvj/2VlwaTeY7HRVFr/KSxwtUwxrjD6qYGFBZsJ7AOFIYlBM0daUyBV6lLId+VgRS+dl5S850CAbUPMgKq15DCAYY7jE9OSw3ZzezDyGCqhx0teztWwGOkFPr9GFlWVGCh+x8DlZ4xKOqS5VpIjAMNEczRXYDjJnDABr/j+b76PvAqDorZjEDFbGQQM2RgCv4ClRUHpzkz8E7jGvWf1Vjg9c+w9vyNn9e+Do5n3799LcVjq9TtadVtgCSf92ZZ/786i7UCm1E7P80wL/4dg8n0M4KtrQWy0iBz82gYmgR3PKUx4DHKjDhm3EZSII2d9YcK1izBsfnvG0AdUM0xi6z06xAhgF6sdgqAe+laZ9PXgcLw+gLcmsfdG2JV2afwZ8djmR/rvw/HOdav1XBtaIxxZA86n8tcEwOfGyJwYGXk1nmxxB/7f/3SwZ/BGUA815uvQ4HDqUtU9l0Pt3C9Gad7J/mtcuruTgLATwpKP2a56LabXFEaPC4KTBrBKnEUSpSf1jF4K8WyZPYxbLKeIiVdUrLa2PE6pBh04NCTNtAhYcAwJdBhX1nmKQHEVa7x3d3SH/flMMGXV729AOdVTh4kk3nuN2uRohuOgPBMRiVFtSENNp2H1MbwgLaN8zNtzKwl1tWPD6taqrISAr1U4aJPoRMAPECopMBXVz0oJfBxQvMCJxorKdGLqeuvTfvCsfm5sJS4FSTcc+zxBo034CxFbjtvvCn0MuSEfLFeY/OCWd+cjsyLp2alcT0d+diSam4AcfDJsiW4qZcoDFOFgUtL3qtJZYPk5GVdaiyFwKAXYdyvmADG0CJ9tqTUZH48H1Hq/M6IZSgaXe/1xG4bdMFrwKbriA8GKfJV4RlfcQMYjJ85cf6lqu6TZPDbH5dYZCWUFPjisgeLde+jQyXxGyXJHY2bXT//5kapecz7Sv+MISAwK3SNLRgriUFPYZi6+7mAf26WksJtmEOwJlLkJxt6F+46P61Zh7R4/e0ajLOpas20hly8LUTmKfc1YyymywKPiwKLoCmuZJVUzU2SJlBIrMLdgcI0Vh4k3BcobBbfZydzCmsKAeVNgYChz6K/n+UaWam9f2rIvm17Vwy0KCmcBJDHmAPSJNDvJViucjAkGLKQPMOxAYLV3p/7nw1QoBrwUwN5GKyp5mmaY80aK864xq6xAXvSNIAc1NnyXmLp5+76Y4AmwEjf7AI81kLKBDxDXdR+XpdpP6WsrQLN+H/r4I3/ZQXSoPqlsBbBE/gHCFv/UeMRndL8rhkinDpCoJfvsewRyvNS2EBsWrF0jeWusrYCzEPAsg5aObBb0HsI2awC1DiO3bV+6BqQGrsWi5yAQx5HvCZSbi/XCpgJsTb+No3Hnb7fMJYFKtVIJdkm5jWvl8Nhw88TK4FeEiHhOdKBdadqiGpDSqGZ+0/ruv3MIFUUjJcSMOtVFbD4nb+4Ofh1zwDiud5sHQIcGkNpfnezvAo7kCQh3Neg1Vgnu5kXPiWKj4HZhtuALW0sZsv155BSYNwn0HBfFuRbLGOtDx2ZO8lkWFIQy4t9DI/FEuIFtJckZ2Wrnxtvshk0fCpgeQoA0VqLu2mOj5MM1looJfH1dQ/jfjvTpa3y0uDjJMP9NMMyo40EgaXKB/RcDZOt6WldG8su9s6xNs5rQQItG+hdxg2HTkyXlKg8W1W+lkpSkNIXlymGvQgWwA/3KzzMcwceEsi3WFH4BL8zBuKagBUvOOIAIIyeABCGxawd9ipkGXLb5y2E8CAhS5G7PKVeQ2lTeY+yHLn5voSoZLgMGJ4C/MxL4z1YF6vKu40riVzDw81f+x6DNtaFoJCVRfj8SlIK8tg9v5TC+2/OVnQ8oZ+glLQhH22RGx1Sp2yMvOX64X6J+1kOKQV++cthZyOwC6xbnz+fBmi1MsRbQMYQiNyV6bMGyhmSGs8zjemi8CE+PCbZbB+WDeBtpwemFKgxONJY+a/bvBsPZWpuem/Goi5z7/B0DEGrfUt2nIMmi5Pfo4BFVtLaNlSt8OaR/bEJLAyAwqLbI5dDwAgsJKAwdczOY61HudnCgYDhvNZLlG+c8xzFthp5kPIafr3ps5ZSVGMlWAtIB5YYU5dSh81QITYzq8NmirVAqDtmMIIBR/63KT1uAx2bxcfb5VXNclcp6q9rbBWu4NNmDdbnGW28zJGtNXTQNAqbR9V7rq6LOnDHrEuSejOwFr5FD9QIerRofmaw/rmqstX/K1Lem6itgKbw8OfazxnYY+DVWuvGrnDIpPCsVmsNtEEtZT0ElfkpmZkIoHXcCnfN0DqU5umKgSq8SiCUWtfnJ7IscK+AeaZxPyV1Ht9PrkcJbsb7h5Ieu7RZt2bYpMIB4IBB5b15GSx8jjVzURoXcldn8AO0Jhz1qeEy6HWvN6UEbm/HBx/DGUA815srYy0eZzk+BcBh7IDDyw7gsNQG97McD7PcLwoiJXE9ovTjfSavvNB4mJMJdbhQG/UjXDVkN23l/WgWBWaBPyJLIS8H8c7BKm+1Qlny3LF0mpt/YrnRJvdYlHZ+XQYLF9k6S4mBlIELPeknTwcMm3XsjXZeGnx3t/Tmw+N+jK+uezsfd6kNPj5m+PFxhWWmvQ/VIIkw7FMoyngQn6yD1ir1OMHGGUBt41X9RwlmWeHYCwUlvq5KDa2tZ11cDCOM0ggWAoUL4JguCs+kUVL4xTRAi7BeojAexOgnqgYSMmh4LDYWMXscSBgkIrd9TlKS/KQXsApfewBTJUfuthJQUnjfwn5KbKRTjFltrG92LFbrTQclK1nywKUl71shCBj63wLw3nAcyAMAWWGIldjSgIkjTlqO0U9P85kAZwCxrR7mOb6/WwIAfseHgWcrH6OeU5LcGSATyngbQGSpLR5dYAc3bK0lC4zUMTJWzrvQNBiuANxm1MnZ3f3fPOE9bJKBt8nEebN+aLWdn/DcrDXT9ggPAlwqqgstMcYCDqzh+T2JKN1eG+MBKgasuDxQGMiOjw0UNisvjQcN86IZCOjSWoVYAwqLLaFgUgjPkIpZQu2UE4fcZxmw1MZgMOrj46eZS1mtUk4rQG79/FVfVlJOfl7P/GMwzoNs9JV1rDgGdvh74ehh/q0ElDfb8roMPNVl2XUwKgyWoddkICpgu9l1oLTJhttHWh0y6CpgrZ0F5r8PGHFKsP9uyJITEOwhB2aWygDUrL9e53nv+hkDbOHJRN2vkh9ja4+Bl/7W3y82/n7tuDvGr7Uc+MMy/XXPPQH4ta4FAoCxmpctnGRfUkOG3yqzbP2xbQM6g2MXAt4/0RgLIencMauY18DsuUlAJJ03f+7DcRB8vekz6SpjrG+gZIGHa5e3N8ABXqrOvH7m5jqTAbrWeEkkCTTs765qOQOI5/psygcXTLI6cDhOcTlsBw5zl6g8mRf+xp5EEjdjYinuOgFYS2EmD/O85pUYRxJXw91ASDbrnjT8aJKYQEOeTN9rMauLpclN4C5y6V2DI8qSedJlOXKY7MhVYyk58OHUEs1jbbSttXiYF/jpceWls19e9TaGBYWljcVPjyt8f7fEwnkNJRGdh+tRgutRspP8/rmra+PcxXzkBVXdgwXIS15EUBpaaOoOVB3aXkzAn5TS+xtpQ5JZXiR69qAkc+LrEaXHpfHxPz9tbA0k5CTkttu5ksJ7FbIU+alys1MXX7ecjrxssTEAKisBvn5P9b6std4HdZ6th6/wHMK2CodeM3lpvDS5KX1OYkWgYY8SYy3gZSvkU1qXrfQS5VmGzwUOnwHEei2zEr/10wLWWnxx2cPtRfrSh9SQVm9u2hwCanGTjtgbtgYqRIrZKgFwIQElCbTiezDPU11AN7+HTcd/DK9D9upsA0272JtPBRytxRqwWAMdtcF8VWC6pHmRpY7WWggp1oKXqvdSAaiRkn4znLhArRrbsREi81QgFXBBOcsCE6e2YcmttfASaADeP7SruJnHQGH49TEDbnjOpyZOgTiJt3q78hgLWXmt0lMG4ZykdKeR2QLAeTRLMNQT/BsyHYEaGFVTQPifVT9hMNH76cm6/yGDQEpJqACI5OPrAhzbwsY4hTYEHP2/huBU35TlzyoArfi168Aoj1lUbOvAb5ctYTZd07syrjefMsunrgawdn4dnOPa3zZ+XmhbNbsduB4+ns8nvVcJIamJUDhJv3HgpoOXg4C1jhTzDcBx/d/6udYO1PShTaiC0TZ9tlWzo/3f8Fyz5QODxSIY5/U9QMUQr4GeAfNVKYkkEkgiavankUAcKUSKwdKGT+cGcPOpQCdQKfMYNGyu8fqJwtCtCfdd+7Kq5Xf/8uES5nMK87lefTFweDfN/AW0DThcukTlWZCo3HeJyqM9EpWL0uBxnuNhXnjAiz1Zrobbk+tKbTBdlpjMcyyDjgH70VwMdvNHfIvFyb3sY7gmS5ZuAnSb3GNs/o0lSQyDhW3MxipdlEJPem/AsLetitLg+/ulB7QHvQjfXPd3AqGNsfj+YYXv7pZYZSUsgFgJXA1SfHmZ4mr08pKCTSWEcKl6Atjwfn04iVtkzVYlZosc8xWBb6HPDHvPAPWQmEgJRFL6RTQEkOUGuQuWEaD5yLrXk25DsyoMvr9ftbJ12iRpXWO/DL2dnBS5TWZPxx34FToZ8rE9/k5Rxlgyfc4qwLAN6E9jWV27R7AS6Cry6SHZ5XxVth4P2xoMe9HB0uiwqzxdlmt+q/1E+a5yGivfhLmb5l4KyiWlwDCNMOofX5p8rv2rKA1++9MS1lqMBzFuxslLHxIABPMakO5AhuTNY5f9BIOMq7zExIEtZWm91DGU45YGKI1G6YCuSElEbqNVaINySeqMTazA8L9dfaV8w6nDu7HLVoP9uvZJq94mDa9Jkhuyat6UJu6+xmBsXhjMCrK64Qa0BV3z7K8VK+mBKC9lDRhaCJpuXFWq++a42E5/4g3sUyEEilLjcU6hglPn10qfbcVC5fVK3tgcUwCUqv5lsPCE9zNjnZdYwy9WCCBO6gDCGjtuw/f8nvz34dc1cMiB4w48M6iANvah1WYd0K+DgBUzrAk8eXDJHU9TGcxQ5lZ5tSVpszbVuKmNEV7XbGAsM8OX103hdde8DsPvOWm9dIFhNSCWgStjUATAVls1QSoh6mAkM5/DkC8OpuuyFWiOy5BRWT9Tu1fYTF3lpQ/v4wpBdF4b9WIZeCXTukI6MHGASpEwdAoG4V9rR6DTPbgNAOXXfZhlzkuVHtNPaD2K4HzznGxs9bOQmcqgsUbwui1Vhb+4McBvJpTFi4rlqYSAioQP2VJujc+PZRbnMgOw8+y/uVrBx5avve1QWVlMMNApJdBP6so4AWokL4WozzUtz28tsMg15ssC80wjkjgDiOd6n2UdcPipARzeOOCwuXC01vrNVegnOOrT4n1XWi8/z8O8qEnHIiV39oDbZAZ9OYgx7EevKmHyGBWy/WjTrdcWOr2k8jE8hpSOQUqWI6/y9df0skYXFPDWAVtrKQDoh/sltLGQQuDDZYrrUbL1fRlj8O2nJb67XyIr6JqKpMD1OMXX17szF19LMSOjKOsJxvx9WWr/87ysB7wQk4E2WMZavyhMY5pjrkcJlKIVLi/cZ8sCv/1p6bvmvVg5v0KFXiKdhJkBqN03nkC1eA0XU7yA9rIcZgYI4ReMxC6UPjzgLZw/TjZnwLDJ6AMCoD+pWEmnZAZzcBN7GTYZj0pJH3wyTKOD2eKWu8rOY7PZVWbD61EvQqSEAxhLfH+/WvucYscWZq+b93ZPeatljMVvf1qg1AZprPDNdf9NXJdt5TfXUoDwRtoEltpgnpW4n5I1DLHKaJOWxhVLrPJ1dQE6yslqOwA7ArWw875tY4OmBcDo79io3Ji6rdt/Z6373WY8rlahZ51x95qKCU8bWb538QZYCoGBs8YY9iIvO2bAbdP7C302295XJcetfr7pvDC4TP8Z72FJQI+pCHJCOHa+RD+RiCKSWLOVRsJhAyeWTzeLmTjM8gmbMuwlNu5H+OXfcY27u9mrYVZ3Jvy2AfxmdyYxA0LGwIOX/JsAn6SfWP6LHUDHlmqCjs3rNYlkJzOQWcgbAUfr/Eq1QVG668lUzY01xpyhNTK9o/aLOARlmow4BhOjSCGSlbd1mLjbnI+an5E2TIIovQKjzds5bKayxdPcKRI+Pq7W1rq7sNWeCnTSa2fICo04UkiTSsm0S0MzbFY1/yt11URn2wYONSEgnt6AEICLYamdFykFlCAQjtmy/DfWEvNZAhCSz6ljNQIQUtRBdwdOdoGqbddZCOA3oVBtqkZRqU3tt6EXbawI5Fy5NfOuVThFQF7qGqDeS57WZD4DiOd6ddUGHEbKeRx2AIeTRYG7ae4ZHEIQw+9mnOycXFxq47ul4aZu0CNvw/EG5iKDZ4/zHJNlPQWpzQz6vRQzYjhxtClLjiNZeYEdgTGkTRB40gE8REp6sLB/xMCV11ClNvjxYYXJglbxvUThm5v+1jFelBrfflri+4eVH9ssd/7mpo9B+jpvBb7j3AESNllhAPutONahJu8nZoWkkUS/F2HYU4AVNcB5UwCT1sT2/P5+hVIbREriZhzjepzixsm8gYa0umsxryvQcxUsGrqCAwBUC1O3SRayChHJS4PFhg1zyBLhxfdzFktuwnTktmTzSEkMnPz3mEFJm45rmWnMXcOj6anInrTD9OkyYE5Cni7XU/JC5uCoH0MIYJk5Bv1qHcjsJRXA+J7mtvdS1hK7e5UTk+7nHwZvkuEeFm9q+b+pA1vyotqQCBAr92IQ42pIygr2fd0XsAvnz21pz/y1O9Ktr7MGWrTNmwFokUQSvR3OX6c0PLgHcIJnVhh/X/PvpXqiWlALb4SVEIjjSrWRRMSGspZCaqyhebbJimqCFFIIyIjB4O3FbGxeb7FvWFaSJJLtQUpjUJZ1ySLg7l1RI/DGbeitrTbDSpZr7EzPbgx+vo2xv0uxv+y0JYAg9ItlP3MG0F9TMcN0H0fVvUFHs1vAXcU+q0BHGrmm5u3Hj30K6MjS6laWshsrSVz/WXjuwuNsAx75uis0Xa9lyaC4qSTY/B7tOmuOqr370QQceW3KYTD8/FLAMzTp+nFqB6e86KcRhIBX1Tzc5WuWJ0q6sLQ+NRlO1Xxle6+Pk8yv65QUuNoDOOQSDgnk5OPQqzCUaRMphGxrAPq8WGpczRfMbm0HJLlhteGN0TgB0KSxSgdE8tzWvJ8QaEmS6maoEcv3V7lbey5J3aKEcIAeAYVMeomVQBOsBOrAZfNr4wgMDEJX90cCtdPY+cNGu2EjXfU6d43n+izLWjLc/jRpAIfjBJejZA041IZS5+6DRGUlBS6HNHHtwhCxjm796CQW4QR16bwNN4EzRWAGna2ZQVcS5fdSxlgs2AtstS65k5LCA1ja91RZMtHIKzlyW3BC7HzQBif2QTtGcRdtVWjMCov5bOVTzNZuQrKSVwDAbFl4AEsIgdtxgtuLdCOovcyIufTTY4ZCV9fU19c9/Oy2/+QbyFOKF3IhIFjuABA2iz8jzQs+Yz3rdCRFxdJyMtDJosD9NPPPncQKt+OkFTgstcGnxxV+6+PSj/V+ovDz2wFuL9ZT22vSagCA8jI03nzlpmKURFIgShWMlV7CFLlNViQriZvFut9jjRWyY3UGIbSwdzi4YF9Wm7XkDRkChm1JdmlcT0c+tdSazwP7sC4aEmA+Jm52DNLoSZtGZqzOluWa3JiZLaM+sRmNY72zJUFNmiwEBo5lOOqfpcmvve5nOSbzHEII/Ox2cFCAzksWB3OEgCHNVwR+rQqNUltvyt9PFa5HCT5cpFstXTbVvoBIKEuuM1U2AyQHzZst82Ubm8iDXkqgKGm+0ZrYLXmwCaY5XsFa5RhQFWhIYQMGSRR5uV3KQGxwTBwwsks1QdE10NT5HIYewGHCcdv8HUkBlUjkJVCUFkJIpJHyG2sOSUhjVTtPzRAZ3sjT+dn9pGyTUDfByFKTB+NsWa41ntNY+QZO7x03ZV496GhtJTUFPPBonZSVwztCRcauoOO2dU/sGgVtoCMdQ50dZ4x1Mm7rGY6FYwwXOvDc0yzXDQJ3mAlpsbaf4eNlkIr+LfA4l2Ck0QSfX+hVmSYRRikxk0f9yPubnsoXeros8akFOLwZp1sBy9DLMUw97vLy5uevhT258L9DGgpd94+2hn+zaWWshdmTKQ9UhIjCeQPz3oX2zeRZPe7HSJ3aZt/3lZcGU4dHlNqQAsABnGOHR4RKTCGeRqc+A4jnevFiBuGnSeYXRMTwSXA1TNYm8lIb3E3zWgpypCSu3eN36bQw+Pg4rwN//ZS8DTelIBvjzKAX9e6lFJTEfDFMMNySxPxWipmV84zSRpuyZKbSD52sr/dEWXJRGg8WLrN2plISKw8WDtLoqMbZxywPFuaUjLsKgBQhgEEJLBb5RkkMdyuXTp4tBb3/DxcJhBSYLIq1zY0QwGxZ4oeHFe6nuQcO00jiq+s+fn7bh3oGECI0uvey4oBFWDEsNpdSVWIxG2BLKXyHcslJ2oLA5Bhwco2KpWUscD/N8P3d0r9mGivcdACHWaFxP83x8XGFybKAccDez24H+PntoHOO8f4lRXXes2JdggJU107PBZykCS2MdgGtOkMEuhb2jhVirIXZU1q9CWRUrrVaaovCsVKyQq+NaZbQMGDYO0GyeVuxLJnT3tuCmzj45Bi+gQX7bG5gtoz7MXqJRKGpc38/XayxByIlPWD4VCDzXM9X81WJnx4zAMCXlwSoveYKmxvhvaoy5Kc5hv1ilRQObKHwuOtR4llaz11Vs2b3JljnvNkyZ/JjfOCDWZ83LRBsMCuwQFvijNQDAOjeTN5/yv1HzaHcrQ+SKMIgJaBn3I9xOaxv+JpgTtk87oa0ugnOZV72WWflcKBJ9dnWLTO8J6GXF7NPGFl/pDEd3yCNcOHWz7synkLmZpeEupkqvivgWGqLPPQSC85HzzWLhv0qOE+4vyH20HnOBV4v6EjjoAIdGXa0Ts7KklIAPqxpL9CxDWxsWQPFsYRKozU/UyBUTFVNSz5ullEbw69V+WQaW80/pbMFyLMS2lQ+lfyOGbCXUqDQBRENHlbu9xXjOvLNDVk1qf26WtTsJmKXiNx+DtqBw+tRgusW4JCBwpBJyKBhl+KGvV0ZIOSvj7nXO/T+wYD3LuO6cPuTVaG9b61/j7xfcYFW1gLTJalUwmqOPVZTeU92WCxzg4Uj9IT3m1E/8jYXbfvyp36W5xTmc71Y7QscZoXG3TTHZFH4BS6DAOPBurS57fVWucbDvMB0UaUySymc7CbpZAuy3O1xQRN0uNg6ZNH0motBPGbqtMmShyl5bj1FlszSmEUQetKWtMphBacOTnhKVczCyqcjfC9h9xJuEdvvJ1iu8soOgyno9Af0vIZuQMbQ4pfkne0bNg6iWKw0cq1hCFNDHCvcjGLcjFMksWpfFMn95a28iA8BwabMeJdFYRQAhFGwgOGbK0mdKvZYW0KtkhVLKwSCtLG4n2a4n1XNhjRWuL1I1ywJmI18P8sxXeReJh8779Pf+dUQaVIBAsZQ93RVmFoicmtHWdKGhQFDXhg918a72T3fFIbQ7LY2y7hzTp122jSHj2JmUuK8rXpOlhy3Mnakl+oc47PYGtzEsmTnw3qMc5AVLqlzWdSCsoCK2TLux0gigWVuvN9Wkz3kU5PfMAtGiM83hTkvDX7zhxm0sbgcJvj6uveqzmEIBq6ct1ZWkzdVxR5jvOmJ3OY4jRUuh+/TjqWtPEteG6zcRnCVV+mnzJK3FjUmIcAyxCrVmFlyUhIbjhk3YUIpMaAVhr24nv68Y2gDH7NnDzrGaMjuCdleTR84a+EBiZCJpaRw9ztTO2aAHsu2E0kka8e377Hvek667luFU6zMVyWWeYmirOSlAqglN28DCPl9syTy+qqP2XTlf8fgTE2CG/wrgdrP1h6D57cTec3VCTpuAGh2BR3r49zJQEOmIz+W/wCHgY78GlUDwjSAeLZqiZzXcYR+Um8QchOaPTmZtMFhMQCQxNKxhi20gWc6VqBj1Tw45DYc2jywEkZJkgOvXINYCiCKHPt8nCCOSGqbh2Chayp3nSfv7+dYhWm8m4/ray3epzTXgzzmlBLoJxHSRCFRom531DLWu18HHogNgUme40L7kDZlmwe/I4nf8bOrg9/vGUA817PXPsChtdYlKueYLav+7yCNcDNOdpLMaGMxXZC3YbihTGOa/MaDbuAvLw0m8xyPi7rZfRI5ifIweXMSpWaxPxczdbpkyRwgcKhEmG+MYehJE5ykzrD0YOFzMZX2qVKbCjTKq+4SV9gh5UUHd0mVS/oSAhgMUiwWWetG21pLScHOo49ljEkkEVrKWMvHox3D0YVuCFDimtuM7AJs17xlXEeUPTtoYcTm2nWAadtYaAMFI/5edS/kOc1tvqQwoybgwnJTlpyGx6GNxd00w8OOwOFkUeB+RvOD1mQGLQXQSxW+vOzh5iJF4TZjfO67pBZKChdsonwi8muW1XcVb9SWucZ8WZJ1QVaicL5nPM5ZRsMyOF607FM1X7IOX6OmVFCKKrFuW3AT+7Cy2fhTP5cVL/AbyckM8o+dHE5JgZkbv/NVPcm5GZhyaCjLa6rPFUDUxuK3fpwjKzT6aYRffDF48UAbbSrfQvava5Oihs0NC5B8OSv9pkRKgQvHhnvrAWTbiv1zs0J7AC4rtJectRWzMtnonmSDjjFoK4ZiXhonoS08CMdPmTo53j7zJs8l/BwskTQ19mMAXkjX3FGyFlYSfi0FRWXwRjYrNCaLEpNlgSzXHiARQiBx9/N9G4/h59ac22vAY/C7Tf692/xlB84zspeowAqkztSssR1bzvW29dqhFYKK3ERbAx2BzY9BA7jsAi2BtZ+99XpJ0NHCOtuc6rUYqA7X6JHiNa/wTdSwuHmvtUVhLAo3N4TXbi9RGA+oETlIVSc7sDr+Sp5LDX3tAkfYf7sKlPGs3w7gKmRtG0ssByGq8CcLai7wm+dj9kw4dy0nsUIvkkgThUEqffhLpNgn8G2ORyYedDWG+2llP7PPPmCtUaINZiuNybLAYlVS496dZ8XscFUlSe9SkRL4I3/g5/u83VqdAcRzPVt54HCaI2fqswMOr1uAw5lLVF66RGUhBEY9Ag77O4Q+ENuQGIt805CCvACuNiyGGXAkiXJFJ1aSZCUXDVnJWysvS3Zpo8uG1I5BvEGv2nQ/BTBkcHKZlWs3p1DaOHiGpNV9q9Sm7gfVBhYaQPPnJwRgKDU3kvXJXAhnyJ5QYu9XX1zg093Mg358o1jlFKDAAFXiGJjhDdZaICvpmJj1pzV5aigl3GsoKLcIYSN23hW6rYeXMvnFEiysqdhq224OvCGJVJU4l0TKbUgE0ki1mwx3nOPCsQx5bIaLPQ+4OJZWG3BfaoP7Wb4TcKiNxcOMPFR5Y83hK8yQGPcj//O2ipT0ICEzC99q9xSgxfIqSEfuumabCYAMfjXlaM3FexvzcZcFfe0Y3Wbcb/bAi23hAUxueAx70cF+MmHxIpFZAc3kZE5CHvUjGGOdjHmdKRupyo9z+A6lyZ8jgGitxbeflpguC0RK4v/21fDZm16eDZ1rLN39Ki/W5Z012wTX5JACmCyKtSZpP41w+Y6UFWE1gUJmy+wKFHLi8Tb/rdBje7YsK7ahIHnZpZOXtbLDdXWMmQMzvT/whuOk5yfvRN9s8WyiSr7Iv6uzA6uUz/mqzuDm9ffFsC6JC1Odm+BN2XEvOGTbGfrYCcCDLdzAkbJieI39Z9ttRdRVYQPY36usxXjcx/3DwjG76g00h6sETM6Wx6CS4L6GWgMd0QE8tjAqm0Bm7XtseUzwPC9RreN1B9CRZcXs1V1Ly/VMv3CNS4QBKduZjoYVHI5BGAKPQgokqgL4m2N4k6VM23W9z9pHu+Na5Rr3Mwoz9eQES0nFcHsJ0xjfAvXxU42j9vMtg7Eh3dwUyqw5dCkOCAfN9/wSY0mbKrm92Rg+pmc1qyYniwLTZd2Ch0lM40GV01Cbuxpjug1sFwD+0P/zm4OP79kARK01/uJf/Iv49V//dfzX//pfEUX7ecOcAcS3W62eCUrixqU0hZOjsRaTeYG7WQUySiFwMYxxM0qQbEmbZX/Ch3nhgUeAfOOuhjEuOzwSifFVYrIo1hZ6g1Thcphg1Hu7G76irHwM55mudWkB+KAJliYfsmEIwQf2MWyCA1IKDzwM3CbmtXymIWODGX1NsLDq0Am+h0J2gIVpLL23Xc8xDPi9tm20rbW4m+b4OMlARusUdjLsRf4GsCoMHmbk/1mUxIjLHeDFYBazrNi3STMzIQAL+flYsuFV1FUjkY4TtJhRojJqlsFNWynhQUpeOO5SvKGRQjhJbGXWzr4uAgRIso/HuBd1ejcycHg/y/2YS2OFDxcpRg3gMC8N7qcZHuY5ST7cwqhwnVljLWK3CQnHZhzVzyeD3W8VLAQq6f2mVHN/zbrr9tggf1f6Kn/NsjQ+vqaXjBCoefdsOiedi+4W1qMQAsusxMyxYMNFopJVwuEgVcgL533okmnD8gb9jgXzlsfLtvocAcSPjyt8nGQQQuCXvhjs1Nx8SoW+hWEybttSvmpYBYChFDCW/Dcf53nNq1PJ3cLj3kodBBQqWTPpT5y0bp+5vigNeWw3QNmmBJzXFBxWUjigMN9iAWIt0YDCOYtZPHyIzEAK59WuYnk7f07hQ5NI+lCpOJKbw1h2ACs2SZBbm0zs39v4nMJ3o6SosT+Fayh1gSrNZOeQ1d5Wx5rXeMyFwGMTdDSoA5BrjwHa/64JWjaeh1/ztdS+bMna9+gGO5vP0wZk8ut3Fc+xy2A/w2uj8LNmb9AoomtCuHV3W2OUrzEev6U7SRb0XFJWcmF6Llrr8lHyWh1oAHRyt7X3JsAR4BAni7LUmK3IrosBKyng7yHeEzVy86QP6qDXKQxQFDoIkzEoS7quQ9ZjyOzcdVQ2wWnejxDwyM0R4ROZ2TM9fN/V3mM/4LEoK1l509/62I3hvNA+nDUkLyglcdGPfDjrU9eSUgK3t+OD//7ZHJ7/8T/+x/j1X//153q5c72C6jJbvRmnuBrVgbw2NpCSu0fB5wV5G4bBKswguhx2G30zuj8JJkuAFnoXwxiXb9Trh2Ud7GPYlCUr6dKSXYBAfIDEkv3GWI7Mctvm63AcPQOGr2ED7cHCQnspct4CFpaaJbrVjTuJVA0slEJUIKHbpCWx3IuSnxUa39+vPOg97sf46rrnNxmZAw5nqxLGGJfWaBw4QWw8lkyyF2FuKkmFFAKQ9F6MpfNCi9F6R1gAQfKbqF2jbcULKW14QVWBq4BvVPrPlBZB8AuJNTAIdDNmBqMQ8IzEH4JjUsGxLRwwHp4LTlWO3Li2lljFPz5m/lovHWMzjgTysurIccOgnyh/PtNYvsl5ICw2sw7TkdtCiiIlfdjJsXwCN1XTpD1kSGel9gxp3iACND/3EoU0osV7yODp2owC/Jhu431jaUxmju0DVAtV/lyGvQi9WKHQBj8+LB14E3TT5Xam7LneR00XBT5OKDTl6+ve0cFD3nQyUMj3rDZgiRtIFVi4PmdlhcbjhNiGYRNx2KN10qjfbrb+2ssY61h6daP+Nk9lLgYK2XfrEKCweQyz1TooKwVcoyECQHPwjw8rD4ZtYmDzRr1NcrxvoEAb82qVa0wXOaZLTYCldUw/WQEEysmxd0195jlw3YZiXZ4cKYle3M0iyguS7M0WpEhguSPfj9NYIo6IRdsETPna2Sdtu4vZFSkBRBEe5zkY0mEgi99z+LPwe/599VgGtNx9xT/29NddCNisMSg72JJPBTubrxEei+YF4TNXmw+ldmElLPm1NpQj04MT30Smf2PHDuQ1dghkArTO5fXrIiNWsRDUjI5ASeYxX8sANkwFvkJptbYUiuePld5c9WBb2QxJIQChK6sDU4GGDOaFUmUmD/Qjhb5b87DVQprIdU9rZ2GzyzgOJdYMNBaamiccusgsT+/v2GT+GrPTyPGgo6wDr8xo9qEyfC4iidiRJGiOpGZdoY3/W+D4jeFSG49FhMxvKR3z27HVX8P+metZAMT//J//M/7RP/pHSJIEeZ4/x0ue6wVrH+CwKJ3kcF4xh+JI4nqUdLIFm6/zMM89gMB/f+W66G2b/q4LldH9y2HyrAEHxyj25Vq4m1VTOsfeXOxjeIgsWRvrwcIuthJvsgeOrfQaPkdeKPtk3BawkLtiAN1/ra0kSzWwUIqat10vUU/yt7PW4n5W4KfHFYyhlMsvr3q4GMQwxuLj4wqfpjlWeenk1HRTlYz6lQaxAxlDj1CuSgqw7kMYt2xC1ijwwYJ8k9/MNhkqMclsjf3BC1kJII6rY+JL3rrNlrWofFfcAoXHOxs603sVGCQRjDH44WGF3/60cKwKSmvWbgEgIBBFAqkDdpa5QeQYZT+76eN6nL5pGTJXaCHAnfQ2D7Q0rtKR+2n0Iu99J4b0E4KbNrFeskJTV3lVem8y4zY/ntniGgJ5aTC7X9XlS6gS9RLn8ZOXhgK3VuVGudFTAwXO9XK1yjW+u18CAG7GKS6HyZOfsxnGtc23kBnn5BXcPo66VBmRC4e6fEM+zp6ttwdQyA0pBgqZXXiMhhDPsY9zss3IHWtQG+vvZ4Dw6822YhZT3AQJj2yHIYWAjASEJmbVZJ57k/9eojCUUc2iB8C6lNdUIEMXa9wyULkHcMebefa1Zf/JshFC0U9pI305iGqBZs0KwZAuZnvzdzWFSWM8CQHMC3t0D8T6a7QDkx6EbDzG/979svb7xmPC5117jrbXFiELEBUbrnlsba/dOLbwe+8liMr/cxNb0n+PCtDcCGyi5WfB91zaECuucABV2QiC4/cRSVnJaJ2831gK1Fxm6wOcxxCFGNnOdULq5iGlKg9RAUCAmvpVs52+D9l6YVM+PH/h+bSoUp4JfDPglPi14euAXF7DC0FgGntHs1KI5jrtvFzX3rr/252aB7wecnPkYEfQMbx2+bwVjjBRYzzqan0XjhVtLNk0dVzD/Fj+7Lyvo5Nm05wu0HNzjzYEEJO0et2mqfl9W0r3bMl2adV+mm1xxi6A77Wo9Jp1cgnzfD7Hn/kzfwa/63f9Lsznc/yH//AfzhLmd1pdwOH1OMV1Azhc5Rp3sxzTlkTli0G8cdFUlAYP8xyP84o1yBfc1bAdpTfWYr4s8bggz4LwQmVfl9ErQ/e3lfeLcxvvpkQliSofQ74R7FOlkw4yANEmk4oj6eXI/fRpYNoxShvrE3F5IxZKCqsbqq1JAyJZ+f9wKXcTZbZTL5YHMTXbSghgdNHHf/n//uQTwWMlMR7EKJzZ+nylg2RHC2sryr5yLCeWWEUBKFgLK3kmMIi7mgzMFLoCs+cuXZtu4jRPSMmLMrFTeAQvDLWmxduq1ODUaiUIEBQAdCDT5hQ8f61D+GsiUgJZQQsqCCCNJIYB+6Ypf2ouBvyiSNQXRy899lfMCHbgQxPY5UZC36V5v1RIETcjNjGk+y745FCGdFfxpn/mPGyaSc29hFi9/USi1CT1nLrgg+pahGfpxJH07IFDl1ObQMY2n7J95TfPVUJ8HhLmUhv81o9z5KXBsBfhd3wYHMTgXwVNrWZji4vtMPoBu3DbfZYbLI9zWmOFqoxRj7wNXxubISxtqNnEsmPeuG4FChv+hMTUO978xuDAMiv9GjQrtLvncNOxPRAljuoMQgYLqWl22vNgrLPomVNTo7lRvRjER7HoqTVqNsmSg8cw65sZjuE6ViBITm6oOjrnzKYdhfv9pjmzxohqaTJpYzEcpphMl24N4/4OFUDF38NWDD5+biba8ePPRbUJuASeBpryY2AtSgNkOQXxZWyVYwWdF/cYKSXSSHjgPnLgP58418f2smP+GUnrtU8oL7V/pGtC0jr3ENbwtrIWnqXH4FkFiNePl4cdf05ElBA1YFUKbmQI/97DsRt+tvQ2HMLmvhRevVSx/Bj8bwLaCJ5v25pnn2s5rJD0EM5BnvXoGhUsUffhVvzhgr0sq8CYtuJxyMfl/5X177lpTGOG9klwDFEhyHd43I9x0Y+QuHuI3OP97luvXsL89/7e38N0OsXf/bt/F3/jb/yNU7/cuV6giP1U4mMTOBwluB6nfiFlLRlK300zzAPG4LAX4WacdsqM+W/nqxIP8zoAyF30q2GyBkSwAenjoqgtogGgnyhcDBOMn2hy+pwVbrrn2bpJOsuSh72oSuzdo4qyzlZqkzcmsfJgIXvivFTVNmAtybgeLNTBnRMEFqaNxSjLYUKvqGPd7LmjzonCzODMzR3yrAQEMEgUChh8d7f0sl7rZARKSiTOkDmOKHToZpwgiY53jE8tKQS068ySR0gVgJLGCv2EZOyjPm1ck0jW2I7G3eBNx0ajKIk1PFsRE9MYWsCwfwvLlFi2xNd6JAWkpNAaCgwQKA2Bm8axGgdxhCSWNFZgazf0fRMLW4HGpszDdWgP9WLhKsoq0bwL4PcWAu6aZQ+05y6ei+eZxuJEDOltr7/MNWbL9aQ8IcjjcdSnRktRErh4P8trTMie+wxHzvuwOb9uYu92barNFuZLV+286A78yvYZx+fqLmstvr1bIi8Nkkjim5v+1s+VQeswFbkrxT1hZiEHMyVqZ4BJG4vJPMeDA7b8c0bSexu+pvUOA4UcErIrUMjgYJh+fKz3xV57IahFX2vf7MiLilkkACTO75iaqKpiErpji9V6EMKpa9P6t5coXAwqL8ZjlRDC+ZAB3pOipYyxmGclpgtKpWZZMq8n2TM6bthTHHPO7Ep8VlIgjmXNE7FqjMgnN0b4mm8DFSvQpwFCNn4Wfr/2HI2f1QCgFnAzfN2Nz3uEY+v8LPyvnw6uslULM9FKXff0BODW1cI13gUipfw6zI+rontc1fw4tamNCQa9GfgO97/190rvl22E+DvC5ARCumG1rjA+dJGvBfo7/wSeMSdlZQ8jhYDi9bIgO4IsNzCwPsU7jihkCQhl57YaR83PsGU6axsT9U+GDlAIC1j3vqVLEpcC0h034KTC7r1UYHH1uzqTsWoa1INYqtCovhKQKTGrc7fGY0Ym7/sAUiIOErr38vzj06oD1mPpgOjStLNhjTEodLUuNCYgNYC3o2wXIdyeqMQyK/HxUaAORFaBMm3Mxk7W44Y1X6kNlkuN29vWX+9UJwUQf+3Xfg3/4l/8C/zqr/4qPnz48KTnYsT8XK+nWoFDRcDhTQM4nC5L3Lk0J4AG9cUgwvU49XKJtiq1IW/DWU5ovathP8KVAwCbF0jh5GOTeYE8MECJXWrRWzEI95vulcY8K7HKNMIpWQqBXqowTCMMe/v5C1rX9V1kHKBQri3ahSDwZ5C60JMDpIPHqjpbw4WHFAZ86+QbaqkNfFtRwFHx64t37xUVeBYeA4jzflUZhSnMVwTurArtb/phxbHydHjtbkwc1JHE/DlbfwO5Gae4aYQOvWQxKDN3ibNNwJkDUDicp3ncQlCyX6TW3w8zSWerAo8TupY5qa6XSPSTgFHrFhnEWLFI4wp47TlWLN/AF87/TwiBJKJU9ebrh4sAbS2sAeg8IFjqVZ8B/dbJfQxQMuiI3QAbAVFLymz7T0r2+bJuo11WwHhQSSw9WDt4YUZw7hjSi5ZEbT7WpwY3bSpjLRarEtMljU8dSBikFN70Oo0kVrnBbFX4ACMupYQHDIdbjnF9PG+/x2ySVje/JxkSzyP8+90/j9oGei00piHBUbwA3e+cVCyDvf7sTdWPDxmWGUnTf/5h0Nq4LMrKY7fL7gNwwUyBDPmQgCJuzD7Oc0yXVXNVSprfroYx+huas89RHih0LB1m7LTJswEnH/SMQscmdF+rlvvFvhWy3sLgEg7yqh27prVHVpA8WUpivHMT+nqQ+PvMa7g356XBZF7gcZHX1nQvvf7V2mK2ojTRkAQAVH5i4z7dCzbNO2tzZmO+rKTJpsZyPGTOrHzSBB5WBstFBumAmLb7Nf9s07XW/ruXHzenrMPATfqlBzf5+9pjrLN9MH5/QI0ZB4q5c0fe5dLJh+tKoubrhIw9/t5YWp8zSy307ma/cAaBSJEgsAaf7XDeec3J+4FwXDexVQZBw3HoQfFGIBCDgauCVGXaWGLWCYFeTFZTEGIN5LS2AjWZmWcMfR7WAZvW0GMqyTi8/LfpiR8evRDrQVb8eQdYYz1EJvjEPLAYPGaN3cj/cx8fH6N/bkcSiBRZGqWJQmItFq7hx+xD9pCkPTHtKyQTDBwIDAYKg88hK0hhM18VKEq6d1irQAGRorbv9NY5tc+3ft65CVwxHddZjuG/MpiXBCoGPQPscSzxu37ppuMcba+TAYg//PAD/vbf/tv4c3/uz+FP/Ik/8eTnu7k5nGZ5ruOWtRaPsxzffVpgmWmoOMI4jfHFdR9fXvc9yKSNxd3jCj/cL5EXGjKKMIpj3F728OV1H2kHcMh+bj89rPA4y2EtEKcxekrg9qKHD1c970HApY3FwzTD3WSFqfOaiZIISRrjapzg5qKH8RZp9GuoLNeYLHJM5wWmiyAQRin0B8RGGA9iXAwSjPboHjMYOXMd39kyTAcUiJMYSUoU6lE/xmgQY9R/GbYCAz2LVYnFqiB6ufMbsdZ13yygAVjr+jhSopf8/9n7l55Lli5NCHzM3H3v/V4i4pzzfflVkgXZgJSg7q5WSlWNlAy6JWghRsUEpG51U0jMkGqEEP8BiR4wQWq1hGAAA0aIlmDEZYBaSgkmLVBJDKq7KFVeKvM750TEe9t7u7tZD9Z6li0z973fS7xxTnyZYTon3n3x7W5ubrZsrWc9ay1WAyvPeDN0mjdNFNOLV2JNTlPC3WHEza0U7rl9GHG3H3Ec0yJ/G6BV2rqA3bbX3JAdQpTzcA+NMeD6YsA4lfwqIQT86tsd/sq3lxpS8fO2aU74eHfEh9sjPt4fLW9kN/S4HKRoxLurDd5ebZ5stDIk7H4/2d/7vVRDfziIoRFiwNV2g2/ebPDN9RZXF5KnaZqlovLtQ2E0X18O+NW3F3h3tbHr748T/n9/9BE3DyO+ycC7qw1+69sLCXWeSliD5E5Rz7XmjHlK895H5k8RJXZFcXQKaoB4XkOQanoIAUm94MfjjFGN7eMKuzDEoKwXzQ2467Hb9OZx7Zq/nzv0dZoTbu9Hk18GKIeI3U5SWLy52pj8OiX/P7UPnJ8fLK9uwHYnBYbeMbduF3F7L2v3463Ondjh4kJk7LurDd5db3B1MXxxRSWSGk1zw7IQj3jzGfM7rSjyE5S8k8B/Fs1YjD7ZeOPZ5/fMAwR8WljMl9x+/WGPQ9rj8nKLf/J33uKbN1txxO3HApbvx0puhE7mVReDphQZbL1uPgHIOU4zfvhwwPcf97rWIi4uNrjY9vjlux2+fbv9yffvaU7YH2bsj5OlU2Dl9KrFDptthw0IoorTY7fpsNNK75/a95QVtFSjf3+c7HUbvQEEoOuw6aRPMQbMs0Q1zEi43vT4pgvYDj1++c0Ov3j3+gVzPqVNc1L992C5kIfNgO1OChH+XPrvcZzx4faI97fSr5wBBJmnm6HDN9cbvLve4nqFCPCaLaV1ueiLNIzu9dSwygDgIwuoZKi4JBKxbGRA2R68+t4xxH/CdDO/yS1nddA/TBLN8zDiOAEIEcMmYtgI7XXTR1xdiA1zdSE697Nzvs8JN9QR7gSM3+7K95daeOrd9Wb1/BVzs2Joio54OApxY38gMWLC4ZgqYFX+CIgUjXndaYqGgGHo0MV4+loKHH68G/HDzR49Et70PbouSNGsqw0juFcA21yDvf7zlWvxt+0YMDWAT3PEvzlLHkEBJYvu7EOmqS/DruP6swL2+rFnoZiUE4Lvm0UXBXQdkCbZT2/29b5A/Z1ApA/fZpQS8yPyMxJJGE7uwU2y0plP02Ol0Y0jX+eUAEjIs4HbgYxHuffAPPFg/kz5seV6dKCm9RNSMXzXUnOf2T5LDsScM/71f/1fxz/4B/8A//l//p/j6uoKAPC3/tbfenEOxB9++JoD8eduOUuVue8/FiZhDAHfvtngu+utGQ/TnLSi8misjy5GfHs94JszFZVZ3OT9bc0cvNj0+OZ6WCQTLZ53AcS8t+NyK3l+WJn2S21zysbQudsvWYAxBmUYPi8sWQDDhIejFj05LHMkMmzwUsORX8J++NSWUtbiJsWDeFBmITefYgRDBD8YJhPcfBDv306p59tBmIWfylZIiaGXhUn1cBBQZ80rCAhriUnQN33AoOERGZBKh/ocLi+3uL8/YNN3uNp1GDXfGjSo4d3VgF+83f6sYeIMvbvbC6tSgM1y06wQdqX/nzP6uLFa5WsN6fOMDwnTF+YOPYNX2x6/+maLd9cStp1Sxsf7ET/cHE1OBAS8OcFo/ng/4k9/fDD2yG9/e4G3l2fiq5o+z43S8yhjLK0DNuvnL6H2JRl0CcEuDuoMZIYwwDzdFpqKJ7Id10Jf1UPJ9bT2/dq5PQP1fj9rQn4HnODlDOnntGlOyjKsE1EDwmK6vhCWY0YWNreGwbtRcaHJ/W8EO/05jcp2yQPk2DkLlqMydlJeMCie0kIIuL7e4rAf0QU/nz6NtfMltIfDhL/3Z3eYpiRg/dBZdca2vSRv4VMadbAPdyPuHiZ7RjEGZZdtsPsJCpfNc8ZhKmAcQ5BPMQqBEnrMvL0savIpOgfDFY/qaPFhx9N0fg7HGOpchFqMjGkWij4ZtEr18EVVqWZanw/34mzLVX8lRPnnSMB/GGfLG9vmly1Mw+GLKLB3qhXgI2OaZX7vLnb48PG+/r7RCx4rJvdYOycfT73/TZCdL23UBx+OkvZkf5gXrLaAgO0mWhG4Cw09fUk7TknJANNCl4hB5MCVVt59ioODkV4s5OeLP53SEWMMNeta87k+9znnnPH+bsQPHw+2R/WdpED65urzRDItw90V4LPX8mWujqm/T8YeltDhpPrKlPkapr8kZRQnDQtmmDCrOOtpwXRQDOemPVYDj64fDpDzLEj2NYRgwC7P44kDbAQVqQO1oKMd8wgDmaf0QG47jjklBUuhaR4K8Gr98eHgkCi4/8u/8HuPPNXT7bO40P7D//A/xB/+4R/i3//3/30cDgccDgcAwDiKZ+z9+/cYhgHv3r178jkLQv61/dSNisqvPXAYNcehAwQPo7CBPtwVMG/TR3z7Zot3l0WR8c+RzLj3d6MVk+D531EhdoCA0IJnq2rnAbfN0OGd5nXxG8iXNG9odLPaaBvaxHxcBAxbY+DUvaTMAgql6EmrzMQYtHhC8fa3yvDnHKuU6zxQJdwgF2ahbgAGFgaGHJechSEQLCxhyGtJy59zPwKUzUo3nyxP20ErAa6dp9NQJubqGTR8IGdYfpKUgENqvVoBm0HWxdttxMNhxo+3pZjQ28sBv3y7NXbKTz1/yQC93a+D2jQCrlZy1XkP4TjX1a/Phax1MZjxt+mjAjoCoDI36jQn/Pn7Pd674kldFE/qt9clByr7kFLGn33Y4/3tEYCwa3/nuwvLa/K0Vjb/57Q1sJGfTbPc58NR8pjux4RxmotigKLoSJhDKYhj58/ZKS1Q72LWHss/5VnwV7nK6fgcNiLzqQDCWmOoX86oQiY4N65VfrVK9mvN5aMWGrp9mBYylH3YbTplCs/449uHyvDoNIR5Dfj+kvaL12lBQ0Ifz03G5pkDVXjgaoh1OQaQ74+uOvpT21MN5jbnz+cCdOg8IUj+x9/fY5ySAffHsRRws/1Iw5FPgSMvnVvHSQqi+MJxQHGULp2rL7tO2yirngMUWjVyV8jkHFD4WF/peKrzEZa/55w1kjuq5CL0VY5pkE+zpLz54ea4mjfyS9InOScZru6fw3bo8PZqwNuLn7a/Ft2yksrE55ddzxv7efv2nObz2vkCPhKumnF5yLi/P1qfSw5jyR237Uv+Nl/YwwMpvM4a+AhAq1c/f1BeAjx+Ttn5kkad8cHlYV9b313UQnDM7bxZ5nV+jt7P3Mh3+2Uank0fLX3JxbZrwoKXfbc0DZxDWpRjrQlQ6Ao+aWXmc0zUp9xXyhkf7kb8cHMw3X0NOPw8a482mgsf/ozh+ZSHN/dHfLSIpaIfdzFYntwuhoVOc/7cRQ8qxSzrceP+OM0JOZY133fBwF8yFhGChnsDQM20ZPobRAH5OJ0L+1FCpZkbMwRhJc45W/qNaW6YitrHqKHMBCoJLKaMk3vyU9tnYSCSaXiu/dW/+lfx3/w3/82Tz/m1CvNP3wgcfv/xoOwSmYjfXEkRBxpd+6MURvH5d3abDt+92a7mKAQ02ff9iPe3tdK223SS2/CyZg7y+I/3Ix4OJVyxiwFvLge8uxw+G8PlUxo3FoJS9ytMQOYZvNIQ26cs6pQUiNScbvvj0jPXxWC5Cy+2SzDyc7akgs3AwrEUeSCri0yrlKHeIdnoWGACKEwOFjbZbmSTfan3zCsptw/yPJgYfS3ZMuByZAwudC9GIGA11IWNhmWb8D0GYD/OmEOHP/6HN0JfDwHXFz1+692uAsx/qsbCEZynbWU2VvO+3i1DwHMWxZsgIUHDtQ3aG9v0qt5pnjrKjsttj1++3eJyJ/6twzjjx5sjPjoHw9BHfMtw1JW5cBhn/PH3DziMku/wuzcb/PLt9meRD1RyfIGiNQN84/JfboYOIWDBblhjQD6V9eAVIv4FsMhXZyEtqCtrTwwvQTGQYoCxd9r0AcwN5HPqnfv/HBPNFEUFDVtFn06XoY82l1v2y6aPmvdwOFuw62t7WZM5lfHumyv8+a9vLO/rGojevv+U5g355xjQPr0AmSIPx7ooFw2Hj/cjxjmjjwG/eLc1xv5ueFnewqe0lIWR/v7uiHtXdK7vXj+X3TRrEZOGJfMYUNiyCTeNDHhqo07gcxF6oPB0Li1fObSucjycMcYrJud+mTfy3dXw6oWcPqWxiNjH+7o4DufC28ufltHH6J/bh3EBZAZlapHR/SUV7WnBaAKFjxXuiTHg3dsL3NzsnxVhsNbaKIBSZdXgF/a2hDhm5khrdIKMT+rL2QIM/n1YOm4+tZWcfI/rRQQLd2ecM09t0ywRNfzf7z8e8Gaxv9pBLvPnsFL46ZQeRqYz9f+tvn7tkPVTwOEv3oie/CXkZ32NlrIWa3yQoop+3RoBR53Y51j/T8o/veIkJaP0OM2VvdgpIMz8mjXoKCs5mEehAHwBy6KNp4o3puQilVIJS2Y4dt8VktCVEpCYDzMEXq0wGREy/rHf+fbFz+KzAIj/0//0P+Hjx4+Lz/+df+ffwf/8P//P+A/+g/8AFxcX+Bt/4288+ZxfAcSfrjGM4/uPBwPrWuCQx/xwc6iU2+uLAd9dn85/JmxDBQN09cUgIOA3VzUIaCEadyIoOFWpoLy9/LJCStgeC0sm++VSCwg8hW4vVP4SjrxfyYnWd9HAwnMsiNduNO4JFBJIasFCVqsKyJYTxoOFUb1Fu6GEIX/KPUxz0qrAI+73M+41z8g45VWDJAZWOg7K+KLwhVXrWmseKKSSwEfKIjX3h9mANc6HYegwjjP6Tqpnb4buZILuxxS9566BxzyvQx8tNPnSFUCxZ+3CkA/jku3KcTkF/h6nhB8+HvDhvjAvL3cKHG57M1B+vD1aTidAgKJvzzgmsipRf/Z+j5Qz+k6qpF7tfrp8VXMiG1gV4xU2MNMHXGgKgU/J+eVzy8xz1iTc54GbU0ZQhuTlHM2zWo7j4Z3muyPj1hBI9XqKqpQXCZ153+caj+00XyOLLxw1T6UpWDHiaiuK4hAjjnPC7Yohu9uU0OSfs6DMX9RG+T7Okkt0SgnX1xe4uXkocqoxPlvFmOc4CzbmJas3nZjDT2mpvabKd85XKuVdBEZlBW03Hf6J377G9e7z5mvbHyXC4sOdy4EcAi634lx9ab44jjPBQc+WWebtLe21gEIDCVcrHJ82wAGXP2oFKHyOIX4YJeXNx/uayXnhmJxfSsqbOUku8A/3Ix5cOCWdjW8vhRn1kzmFk+jjN3vJP+eBF+q0Tyk29VM0OrDXgMJz86zv3LzqA5BL9dvtxRYP9wdLF2IhiACY6czLoxbsew3gkfqh6X2V7ueAMLjQRn39WrITKMVlnuO0ASQXNQt57Ff0Rq8XSUjyp+dCpYObuu5DE7HQddEAb85dAwq9Q0VBw5NAoUYWVezrocPwmXNbngUOrzdfnH38kkZZuAb6Ms0XQd/XdljQ5vmo+b3JKs1ZdOHdRuztGHy17DYy49z5W4ZjXdWZznvmVAQKsZOFgoY+Gns1xvX7zznbXMiA6PbI+Of/md998dh8Fqvqr/21v7b6OUOW/+AP/uDZORC/ts/faLT/+hHg8MPdsQr3CEHy73x7vVllT6WUcfMw4v1dzR7cDh3eXQ0Vi4jhEPS2eiVvt+nM2/qleTUtF9gjYclXu/5JoBjBL7KWDiuA4dBHC0e+1Lwfn1uZ9AAS2YUeLCzl7gWkC4Aljr50zEFSy+lR/JQcUXPKOBwn3O6l2hWfAZXFVnwHvT6ZU71WJA26AfiWUrbyAqYgaKgBgUJhriTcHSb8eHvUSpPrDDy2vgu4uhwQU6eVudTA0g3ouW2NhdMqcxl1CDmfT1DvFecowZac1eC6HxUsXJ+HvP5uKM/yFPh7nBK+f7/HRwccXu16DVXuTb78eHusKrZf73p8+2ZzlhUyp4w//eEBNwo4Xl8M+O1vd59dVkxzMmD/1FolG5hK8W5Yht28tMUQEJ8RngoUJUUqGM6OjTsZs8HPJ4LpnTOczp/fhX8kKXpEA4d5WPyxAL2pAXNKGCfJPZWyCwcLwkboY0BKCb8+TPiTHx8qL2wXA7abDlfqSBGZAivkIGzIaCHZXwHF843FB1i1jxX8Rle0JVV7HXB7zLi/P5xlZj9mcEZltu6az1vgkQr7OfBx1GIYB8cWWWVHQxnwnTiS+k7ClO8Ps1Z/7/BHv74XQ36t3+HxMOxzsuvmYcSHRkca+mipXJ6a16sFCg8aSvcYUFjy9ta5Cp8rpzzo3wKF5/Y2bwwZUKh9+BQjfE7ZiiE8HJfsvZ+rKvFao2P+oxa782DFz5HXm2ytm4dlZALzyzJdxU8NVKyxVgkWjicKRwFLMHo7lPnFSIr9ccaH+7KPhwBMiHjYT08O/fQMfO5DrIZaybFyQwDoECxODg/4vVQ/9DJoGOJJhpP1J+cKePTylEzzNJ3vB9O1yF4ujCneO/fergvG4r7YdrgYOvTqnOhULxbH4fP26qS27N0KSw1waXi2YjcdJ2EV/vmHvYGFp8a5iixSPdczz36qlrIUM/3+5mh28tBHfPdmi3dXX17xt+c25qO8fViCvpXscQSH12ynWN9DH/HmQnCIpxSKXGU60tnffEZ9nESTyRFXfDRPr/pJF6GFU8T2Ps4JKc2V/u3zXfvPAWD4RNvoK4r3tRlw+P3HA+4JHAat3qbA4Zwyfrg54Mfbowljgos+/5hv9Pa2nvQ3Fz2+aaq0soDKh7vTIRo/R2jnWqNHy/LlHaaFV2o7aB5DLVDymIAbpzrEsWWEAVpRWDfay1eqJnyuESw8NGAhy817z0hKKHm2YsTltgYLrbCJ5i18yWabslQuk3EfjdnHzX4NKCSbadOzWqjMbWFCuntFYVhVIQd9xKYX5fIwCpvs7mHEr4+PJ0OmQbQdVDlSttlWw1PfvrvExw/3jl7OcV96i33IyponOaX1UNZpzmbETXOqxigGmVMSNhs0B+SEP/0xW/gh81B6pY80+d2mNwD4MfD3OM74XsOQPXD4y7dbXGx7zCnj+48iX6gMMQ/qt2+2jxYPuj9M+JMfHjBOCSEE/Na7Lb693ry6Qse1/3BQRuthwnEl9GnQsBuu1y+J/UajkPKrzaPVd1HZ0d1KfsD1EGrm/DwVAnKO7ZCzVMBrGSJZkcEhAn2U0IvjmHA/C68iK/eDACcNwhiDKl/jyWsCMCCoj0vAZy28mkVmftMVc7aWPcjE46N7fS6ElY0yQZ5BwLffXOBHrMsvczTNGdNyi3u0nQUdFXic1IFVQmDlQtLHHpc5a599AQ1RzFMqBvO9RhD0yq7abrpiyM8Z8wv77/uakqSzsBQxoTDM3l1t8EZDQE+xrQ0obELqzhm/QxdK3q3+ZUAhQ7mODUB4zvDm9fuuLl7i/76WjKRO++F+xK1LfUFn1Lurn5a991g75TQ/ldf7c7ZRDfebFcOdOeGuL5b5jz9X45571PntgcJzc62LwQDxTV/nwUwZWpRDwNG1IoPeCQ4AsWOBP6wWLfPAG7AMR3xqa5mGmz4uALQQPPAof8haamUuUHTG8zvisrUgaGQhiOb++YwOU8KozuY51WwqoOQk7TXKh0D4NCfc3CfcnOjhWth3+76KhDLnc7mP7UYqGPed5Ip7OMx4f3s8Kys5b6pchT+zLpeSONn/ogGH53KpAi6/9sXw2dJxsSDfzf1oWAigBXQc6/s5Y1z0o+V3nLMs0jkn0Wcvtz3SxuVx3HQa8RMt7+I4laKLo7PvzB7M64Vd4GTSp5IrvgKIf8kbcxx64JDVX/tOJuufayECCtq+k/xj31wv84/lnHHzMOH97bFagEMf8c3VBu+uCnuQzMQPd8eq4tXPFaJxrpFlxNDkRVhyF83gfgzcY76le1f0pD0fNy+GJF++ApX/XFuAhS4HVFKDb1YvIsFCGu6XLm+jFDzpqtDV5+b6KADthLv9jPv9iIejVuw9UdAkBgEZJO9RVEZhYS7V52fOxZLEeDNEDBGYc8DhOOHhKJXE90cxzM4Z0qKoSv46hl7stlRUUCm+398ckVJGCMDl/bzK1GnZhHwvFXgLQ4d/EUoNr2nOuNMCKPfqec0522ZBD1bQcZ50Dc53p8MyWPG6UxBFjGuZI93DSoi1U/ZSyvigmzGVzuuLAhwep4R/+OMDPriUBufky9pc+eHmiF9/PCDnjE0f8Tu/uHw1Z4MvTvRwPF3NnBVXP7UK4OdozJdKZ0ebGzAGqUJM2XWOIX1OGTrVLBTDGTWs1Hm7H7E/JsyJoTcBUZnBkoNRKpcfXWJ5hlBv+g5DT+OhhILMU2IcNeDq2yksaUmmvXF3jpHRtnNgY/VdF92a+OnZjqfYg3zdsgdPNTP6upq17V97WfvLX17jossLubY2D1qWzZrjxOdJbA1heu1ZefFUqok+Mrysw3YTdV+Ki2c29PJ3ViPtzUWPb663+O1vd2Dict/nx9iPa/0f1dA+HGdMTpZ0Udg4wxBwGBP+7P0ef/a+AOkgo1cZSnLOJn8SCjC+1VyzmxcChb6oRFu45DFg2QD9YZmX8HMauOMkBVE+3h0rxw6jXb6k6BU6zT/e19WKuxgqp/lPEVXCEM+1ysklDcTnzbPoDePDWAPTjzlp14BCMn6Za29/nHHzIDrdsQEnuEZDKDnCuhgw6DlCAHbbHml+rDiUXI8O6ZwzRnU6EHjzBHyeyp+zZek9Z7i97rjVfNvg3gaGW5fm+0nAYcE0XHH20CbgPtI6p6lfU+7slNHcdfXNsD/mwM9LJ/kppxP11yKTst2UL8bJ6CKgTk/B/Z7RSJJmJ+Ji01txxi8JjEsp471G/3ng8Bdvtnj7GwocpqT2ioaXtyloLrdF9nwunTolyYf78b7OhxtUL353+Xqs75zFYct6CHeHuYoKEIdF0Og2sdVyhv5mRspTdb4YA7qckbuAjCAFmILoMbLmmfKnOEzFIUk582n381lyIH6O9jUH4uu2e62qfAo4ZOECn6NsM3T47o1UpWuF1TglvG+qBDJX4TfO28tw3493owAXToG+2PZ4dzksCqj8HC1n8VAxj2EblkjhcrV9PCyZ4BwBiPuVZMEhBOyGWBU9+VxjQIWxFDgpeexKJWQxxpIaL70KIAJJANymW/IWPkc5p7eUybjvFUzdH08XNKFiIowRzfkQgBCi5V1rm/dE8zlNczKgUMKxzydrD2AuqGgV4CRsQZTCKaEysh5jYWz6iG++ucDHjw/G2vKe2ue0uVGi/MYQm+dGJZEbSDVOXQEKGMLH81cG/RP6Oc8CkB/HoliyuvKmFybPXsPc2N9tL0be9eUgm+cKMOOf7zgl/MkPDybD3l5t8Fe+2X3SumEIAcPgWwYGx3S3IRu4/2xFFF7aWq/mw2FZYGnnEi231QU/V5/OGaksJCVhZDL+sxYoIoAiDOaIbd8BoTYyKKvmJ4JixVNbgK22QEx7PBSIXxghJ37jW8ugWIKN64DkuT3ltdmDA//29eungp8EnK7fXuD9+zurJkjW5nOB2vrcAircM7eors/JDMxSebGMdZ2r66ktZ+DD3RFTyhi6gG+vt8Y+PRuiHOrP/X2mJHP/x7sjbh+mipXLnF8xaIVzX9hBHXenthPuh50ayn4eCQhxPi9ZCMtcbQLIOoP8RPMMzpZR+FMm7GexmdYR3UUtsHe1+UkLyJ1rNFbbvpIZ+fZq+Ow5NoHC9rl5EGejB9SYzuRU5eSnnt9e6z+UuaNnEo4zjpprbp6THbyIJgnqsOgjNl0pkGORLHo9AlveGd5Wws2AGe0hBPuua5SiGBV4V1DyF99d4ebmAW3n7P4c+NU6Rp4zbl6WZR0369qKN5zgm8jXen96amtZj15WA8HAXbIMp2kG4b+cgwGk3Muon79kFtcs7WBh3whATjJXaLuMU8JMxlViXMKyGnZ77t7kZLT7XYzHqox/eZqKl7a/aMDhpPmqSXLwa7OLP00uVTLUP94LDuHXKFOlvXkl0JLko7t9KQA4J1gVZXFWRJlb3ek5JHtzNgcF7ThPNIlByExMC/BYerAQMn75y7cvvrevAOJfsnZ/UOBQC5+EEPDN1YDv3mwx9BEPhwk/3ByroiUX2x7fvdksFJucJany+6aSXd9FfHNV5+05TgkftXiK9wwzx8/bywGbnzEPDQ1chvadDUveSWjiKSWZHk9f9KQFlGIIlvfjUllLn0PpbsFC5i+kUUJWISsiF8ZZDRaWyrlFMD0HLJxTxsNxwp16mgSgkYIm84oIolHu2W+dKhLxhIAlULhVAzhlYErJin0wP905w5qeWxPAKnx7Pd+nhmoxqXKMAb/85Rv8+tc35n0+ldOrDmEWg/LhKPNrf5yFeZP8pvJ40QoqUWJ0RmNvxkpZWoYIetajdhpASR58OErhkztdPzmXuRMjcDjKcxh9mFYfn8zaYz8k5GVC1hDW795s8VYdD2vG/NraojfQVwFcSx3Qd9FyF/7U1cyf2qyyoLKk2znOfKlMq/BTsHAeM1Lp5QdgoUevkedmrWDMqQrWbSqAx+9pmfA6K9tROXg8svzbsB09u+TkdfQ+/HUJYhLwWUsv0AKaJ9mDXUT/Cbnl5pQtVw/Z6rKvZ1xebh/NgcjxsP6rXIkhIEQBBMgqHJ1TxgBcwDzrF5u+Wp/+ntaYNQt52gDRkn91AiC60Uv35RiEzTROyQoGcA5QJm76iAQUwDevM+YznMz2zMlQJ25vGUQ2BiljTljc91qOYKDdf8se5sOe/X7xczBt98cZH1Sn9Hvx5a7H24seby7k2XkAC2jALXfzXF9rrLD2u+p87uC1a+UMC82jXsnvfaobn+C+ulbbl1PXWumH++OYeBMORzo5FcCE5MZj7uLo1k99jhPXavpS5mGydWXzEqfbOWb3WssZFQN5jVXNZ+BVltaJEVBydQ9ujdn3AU+SawThYrMmuEH43SFkAbxyBpL269Qafqy1rEfuTcYMt39QPSe+8PsSUMCKUZ0Ja6puHwP63rObBZCLep+ZchpYPHPub20xq7U2zWWvOU6lgAXZWjEW2dhFYVcNBJljAZ7bSCgPxHDsnwv4tq0FYs9Wtl44c+o5fg44fHc1fHE66Knmnce3D2OVhxYoOQUpBz/XfVH+3aykihh6X83+ZTiEpRQ5zrjZT1WBSr/XBkCds2ElgqPIjJwhBRF1XgZgsb8y9/xLawnECPziF29edL/AVwDxL027P0io8t0KcNh3Abf7CT/e1GHHby4GfPtmg8ttHek+zQnvNbehD7292vX45roAjXOS8MiPd00+gRjw5mLAuycmIf1crXgGptUw4nO5wHzzYY7MY9huQmLodFb0ZPcZ6PEeLPRVkQkWklU4pRYsLAoT2XHGKlSG4VM9QcxTeKvVsiT0Uxic54yVrgsYYik5T6XgFFDIasddCJiSMDWOrAI9poXXuW1DV9iEu02HjQnymk34GIuHRjnDZnxOp3PPNwQsAMRzzQCivWzCLARA4BcoDMKgGhunoAcLY5SCG2iU1U9twiwWQ5/AhiR932DbRzwcZ2EczwnUpK8very9GND3cTVkcS0EkJ5DKiF9F/BmNyxCY9rGOSWgeZY5MzG0qAZ3dsqGu9SiRz93zpu1xhxtlF8t8Bmj5FG5VNn1U90Dn8+NJr9eC0npNSzj4TgvZO5PFS631m86VE4BjKc+f/zctVFnRpP/XI0pJs9v2ZCeHOONQlM4Y/FEW5XsngWiSuqCczkfz+XZ83sKDbm11ncRv/j2Eh9vHty9FBDr1PjMqYRXe5nmmzfyeV99s+5pwLWsRwKU/nt7zeNiwIe7ET/eHBBjwD/6iwtsN131/BZzwYGT/FvY7TOOk+Yg0r2PrEiPEub2HmMBeWlkbIYCGvI5B3+fgO3x45RwnBOOU8Y4irOGOZE8UyqjAEQeUCmgLiC8/uZ5nXghxhHHnGPt+ykXiuE8mH4SLENxUj0cSxQHwVUBv0RX+RLMmjlJqobDWEckSF+j9fVztpw1hco0a4RF+S4GqL5Co/P552fEigfgCVCfaraOnVyiIzMWIefkXfktgXYyrwWQzHZegmjBf4B6fgdoeG0T9sx15IE+L3/fvr3Aj+/vKzDUA2Cf0lrGW2zWdjV4vC2ChimvOhGe2ua5sJ0tDYTzigUdgD7WLGdzWDXr+anTqL1nZLLqZ+wPyYr2eWczsoAeMcbF/mXzKD5vLrfRAbwnX+zNphKB/Vz2ttfSo20cgjhUHw6Tgd1DL2l93l0OVWqUz8F4fI1GPdDbK74Jw3mwgo2f8x6MvPRQO7G7Loqj6XJ4Uj5Xi/rgWtFoj+M44/6Y8LCfVnPNA7D9fOOiaAhwWzSNyrVTleI/FSxca18BxK/tbHtQxqEHDt9dDfjFmy26LuDj/Ygfm4rKUrhgUyHxFAjvNQTHwkQ6YRB+cyUMwpy1ipwe5w2hy22hBv+UIS5sKWcD+e72p3OBCWh4OiyZOcXuXShVqzB1MVThyK/NWmIoCHO70LCb1Fggq5Ae2VNgIRVZFjfZPrE6bM5SafFOKx/fHWfsNS/evCJA2xCHPmiIQ9C8ZytjY8wHBfZoYJJReFBmyqkWFQylwGX4S9/XbMJz56j68YqhWo8BiAxDZWXOu/2kyh2r2YUm+TzseTL35HYjbMzH+rhIvL1iFJ9i7hxHZe75fFMaqhxCsHnJ/TAGWP+8Eb/w2vu/esw4Jfz5x4MlMn97KSkPTHlu+j3Nsj7GaTZvejvW9AaSodXOxXMe5XPe5NcutmFhyZqD9eGwrPS+G6LJrt1PEJbMxrC82weZp62xzBDvlLFgY8cQcLkTluHV7vMXhnrtljLnWlkfY0oYx2TGz3FMxuSQkNQnhFmr5UBwhQaNB8Q6BWQyLeYVNslT2MhsPC5nSK5bBaRyXjIdY4ApwzvNGTX0kjN5d7nDh4/3FsLcjpWFF2rxj+OUkVS5I9uOOci6jmFmQAzRzlNAvRMpFXL1ZzG07YEZ4ligY2KNYW/PABDD2sazMFh8CgsfnlqYSP58yzxnNNZXGxkzubCq/FiQ3QB4Jqq8JyDhjezOsfu9rGgB77wY8+Z7149PaX4sWkAi5WK4BTu+7HcvkRtr7KxQvlwAWC2o5L/j5ykVfew4Zfs+hoDdVhxTNPpWr6X/PHqt5nf+Nylly699cLppCAJOX+7EkX256RDi+X4AMh9oLFNfKrkJ/aDV49Q7x+qmj9hsOuwcE+wsAzuLTH04lgJ+ZPH6JkzsYOIvpYwutuNWchQz/cxzmddP0ddW9STTl9ZZwmThfUpr0xQUZ0m5f9/PozrbDyp7mWPSr/cYJAy5NyfUcrxOsR4pf2zsUMth7gOZ+lqWPXHUYoweFJbfy30NLGZCvVejeUxe2A/avMfSyYQl2Pop8Mca6LsqS9gPt7clp6t657ikzSnkhy4K8eScM/W5TMfPoZ8CMqZ3J/RAr+ddX5wm47xWkwglYRp6xmOMmipipb7CKYDQv2c+TjlOIyScXRFQcn9KxE+nznA6zrNzxKZVmcZ+vjZYuNa+Aohf22o7BxzGGPD+9lhVPO2iVF3+9npTLW4men5/N1bovbCLJF9hDMEqLrfU4O3QGTX4pzYOfVjy3X6dGVjlAjsRRjynXIUj78dlUmcpJlKKKLwme4ahlnunTBEs9Cy0aZbNuDsDFlbg0vB4QnMClfcswqDhxwdlNizsNv2AVUv7GBGiAIZk+LXjQkWz6wJyyphUMDPRPNl2p1pPNuEQMQwdNl3Z0MkmHOfzykKnQn/wXmlVVD6FJTAnzX15TNhryDEZAbHrMU2TPi/JFzOqN9hyUqqCHgH0PRPj99j09ebC0KOfMnzs+48HyR+ixiMdBBJ6MeJ2X8KYhREnY5tRvLZPbYdx1rQKYmhe7wZNcu08tyggM8OZkGnEiRrbRWHXbhyrJzuF8jVZBW0xnKeCkDSkx6nkMWyTLQPilb7aakqFnygsmW2ak1XLW8tjw+T/05wW+WP7LgpgeCFg58/hTHppo/Low2st1MsVKXnsHAIO1mGfMcAMegEjnhbiVZ97HeSxRmM7JwlpVTYu2YLEA7zxGQDH8BAwD4EV3iVMkXtQzhl935UcYyhGHPtGsMNAlFAKcvnci8+Vux5Ia18T5BOAMldAJRQwvVfGx9AFDH1nx/kLCEjhwUs+H1RrwLMbW/Zjy7prAUQCdsiw/F45AUkt0Aw/rvKOGDKBQjJS6YDxgKcHnU73t0QBeIAiOHnrj9VJ5Yq8FGCxrQhpzFT3ftEfEJAlwOzlh7IN1aAiMLomZ30u3dg171dAkZe0lDPuHiZ8WEnCf6XG6vXF8yp3PreNUzLW96nKyW8u+pNFWQjY+QrHvojJqcaIlarKsepQz1m/ZDnzf88wtT5CAKCg5+XabfePjilHWNDuFdIDPQYgfkpLjd5x3nFbA5GPme9ZnUCF3V2zUAm+MT+4FDyRYidFbwKCQnoGhK2AYO1e40FVslMnu5/k9C7KO1cQKpZ5tR0iYowmx0sKi6UD5rHm2Ya8Du+ykovOAcXrsp+voRt63S8AVjCSe1bfBU3d0gEohX28rrrqOHtGe6ozvEoH1DjYWbH9Vu3CVs+7UsDwp9DzWJT14/24yC97ue1E/m17pFSK/xhI6ADCtTa731jEVyx75NBL5Mybyx5XuwFDF1ZTh/2cYOFa+wogfm1Ve1DD/vZB6hSSUfjd2y0CgB9vj3h/d7SJbNToq1LxVMIRZwEB7sfKG/KWSak3naH8H05VkfsZklcz1JP06VYJoVC7PBOWzNBmgobHFcHCnGIXWymmMLzSgl+AhaN4kT1YODmjz8JTXeGLGINVPnsOuDSrEXX7ICynvYYft6Ev7Ccg86tX5bwLJXzOG0hsNBBjCJhzMcKPykgZpyVzkS14JccBe30ngNRj1fqAWilp2YQvBV9SSprvUsOnj/K8DgyBPnFPOWd0fYfDYbJnyyTQds9Y8XLq8x5cGANB4l4TVw8KoFoxBA2zltyLnwYyeeAQ2r83F5IjdZozfrg9Wn5VQBwN373ZrFZTpxF5zms/TsnOmXO29RtCvakzD2TbWmCCIY/VuIZ1ZSoGmucujM8xbk6FuL5UsUu5gOdUtNtqgZebEl5NltS5PDqv2Y5UFleMVIIuIWDV6NwOJSn/l5hDkq2uLtlULtawuU+qXPwJuQcXBlljnJ0LsybIx72jrVbsQ0VpMERlsaSsQCPDiw30yQvGXAAQu4B5Os+sqQCu6HLbKrrI/IjRrUcDfxxrzr4PZe36fIrslB/plnmWUsYff3+Pcc642HT47W93mDPTWMw4jtkAFcoYX1TAojGigCiXClxwbXpA0BirrdxgWGQ+z+QjmLtm8AU1fD14vDaHDASA7jfZMd/MOIcxXlvAt2U2+bDq5zC1qSN4Bvw4Z3y8P0raG5UxHA/qMjGGJwMo51obuvhUFg/t4IejJuFvcjDuNp0VA/xcTh0yWcj8Xquc/GYlRDDlXAqYNGDhOVCk6wr7ywOFL5VjBxc583Bc17FTxmKOtHrla7ALn9I+J4D40rYG4h00IuThMOFhLAVkqLOw79QXZV96Wfi6Z7JlZCRHZBh1vxHHhmhRSfNhjnPCnIM5dylbJMQzYNN3i3yF/p4J6nlWtAGdK/fR+M8WTOfn3q/JACcPSw/gmIby1uu5Xk7kDJv/3sa+3HYn8/DVTmmAbM+1uzDAF3Te4FXkpj1j3Q/9eDKnK8FPH269Rh751JazsB5ZDIWRICln9FHsxb6Ptrc8dt9BiS4xBMs3P+o5+fyBOkWQgLzQyIp0ki3N3/1cYOFa+wogfm0ARBD9ugEO315KVeWUJDH4R1dReesqKnPyzikL2/D2WOXU2m06fHMlxyIAd/vJwio9kPRTVpFjYzgx8xguwpKjUMCvzuQzG6dk4chSOXZZSGE7aJJ2ZSy+BpuSYOHhWMAnViCemKvQwATJn1SKm5QiJ9tnCiRWwBWqeal8vMb0owIfdMPqumiVyAjitddibgeyC5hvjsy6s2zCKCXsN0PEJorwb9mEZw3TEFzBkhoofAnAQmX34ajh6low4DDOOM4J0wlDOTvlJjebfAYQQ0bXdRZixGO7wLCRiK5T8Eo3fjP+XthopHujiAVUDHzUZ8z8W2QDTXPCj7djJV/eXPT49nqDw5jw422dBkFAxS12m5cXRtofZ/zx9/eWP+XN5YBtH7EfE+73o4XnF2YRrHAN10kIoTbUP1F54v2tGZWVZzuo2qwPPas25/PmzBpizTCtdm4HvR8PQD02f6s+nTGIW4N/TbkzI1U9zK1s5ThnZMtx6sfocqt5br6Q0OTXYA+yLaoVO6CecvnnUArJ+KTjaa/ODBoNHlwKKPsJsqQAOE6zKcLMB1mtllyHoxnQBCAEdUoOPdIsc5ksjxgE5AsRDAJGc1r56wzEc80bLSEERNRGYSDgSMeB2zv7LpacqCnjT3/c42E/IURxtp4C8SSMNiuLJ9sevB06/OLNpioa58OhPJOrDXG2e82FGYkgTo2+j+aU65ycSQglZyafZ6qf7XNlXAsynApRttfuoRUQwLMqCwBpe58HJ4NjOQaBogWsTdVzvNyqTnnRm9PMQItcG8UnHToroPpL9wBJ2yE5Jr2Dp+8jrrcC2jFn9Jrc/RSZQMc+ixH4vGIhuMrJO2H7rAGF54zo4HUnVzDnUxysgMgk7nF0tLbGNd8RnJ2TAE9rTujdK7MLn9K+VACRufIeFIhaY4sOPYvAlegoAKtr51zKGs/Ymp0jarFHrPVVz0HHLR089iybCAr1hVT3mkOoQpDTM9Yyj1mTaUG9J6fAx5xrR67fe57SWtYjADzsJ9wepmofvtrJWABwIOnrgH6Vztr2P9Qh36EBaT07+DCu6ajFqXCOddyG2j/FaeN108KSng2DuHnwBIys5JnT+WUJEA5Ofxt65tJXAF7vtf0d5+nQRyBnHKf8GwUWrrWvAOJf8nYKOPzuzQazMoL4HbDOCNofJbfhx/tSzpyFTr653mDbBxymbFWUf2pvq2/cNJnH8OGwzD+427CynTAE6/w+AmQRLHxYKZ7ivZo8x6feG4WfBwqtgm7DDJlzDRYyWbyUZ6+rIZ/ztspYzbjdz7jfj7jfa0GTKdd5ChujrQjzwqpYy4HSK6Mtm2dRDaZH6ODibRSgkEBVFwvoSI/PqUY24lpOwqeALO0YjVOyKrz744z9NON4LIr3OYCwCvvjdyhGUgBDeJf5tHbbAXlOuNzJGvrmWgoWnfK80ovmAQ/OHzLw2kI5puC9EHxMqeSmoTG47SX3WcoSMgyIF7KPEir1zfUGu02EL+TQPYP5mHPG9x8P+JMfHwRUS7Km24IJMXoFVJTjx8KmfBjIWsjQY3kgP3W7TLlUPh8txLqwvQZnIG1pHDlw4ZyS/ymNCmYMEjY5qvE5p1L5VpwGAswAAdOcKyW609Dk6wuRvc8JYXuNtmAPNq9fiz14KmfrT9m4nxy4nzgW91qj4b3ppe+j5pK1XGneMGiAGZNn/BuDGhmhApUAOWYYOqSUzGBgUzutGIfuUVSgJGS+i/MkK88x23375PbLgVkCnHXuKZHWlANk+oYAMySCAp3Ml9uFYEwfM35jxPVOAKNhiOIo0xBBhoKeW5M0ZE4x4j8VaPIGMoFGDyKzsEvyxzlQsv3eA5X1tZ4COp7Op2hhjW5dMl/zoAzONTBSDM1Yhym7Yhx0innQmMA++13J0JW9gP9P6mBmNIqfW9s+Wn6rpzyz5zAdaTyLw2xaFqUCsNE8x30XMCcYUHhu7nVxWUCErz/VuKVz2ucuPCWTCMoQ/G4v/VOxC5/SvgQAcU6aE/Iw4V7Htl2PfsyoEz3XeUdiw1GLmBzIUjU2o1vXKHYDo4syBFyhjmrrH7ly8FvBv2e01nlLMMyrGgvwEUu25nOKnpT7lTQS2ck4n4aD1+KAEJz0/WP+X16ajMNTa691SgfdhA37s2s6xmGmbosX66zMOXucilOV92nA2ErO/Jx9H56Xa9I/ozY1SAbUliGztTgXulgKf/qCln4/9bYIwUipYzDjfr+MqAGgDpQOUZ2ex+nLDEP+lPYVQPxL2tZCCd9eDvjuesBhyvjx5mDJQ40t9GZrHg7mC3h/e6ySjG6HDt9cSfhxSsJI/Hg/VozEvosayvzykufPaVaBVhd7yxJhOPHVSi6wwiArIcnt74PzhDGP4acYv6tg4ZgwjnPFKiTdurAkaoXXCpto3sJztH7mS7t7mLSgyYT9sakIZQq8vBXQx4fFduj7pXFM4EZCr4BpmjGqJ/5RRbWnId6ht5xKwiZ8zMCid8g2AlV6n6tEjnOyatAlxHgZllYGVJQdM5pysVdDUAah9hFBdswFQIg63MDyNlnl54i/8qu3ONwfPjsYYeBjUkBl8jlxJNyEIYoEHIWKP1WMBTIWyZ7jqFmBHtlpFy3oMVUOKp3vZLWGICEu45Tx/k7YjFENtDdXG71GNC/g5yhM9JRxXDM8z+UrGqfCBluba1FBw+EJ+TbXkqWbgunYGqFSZos3+5SBbMriuAKYZ2FahSDKc7sH9wriUnFiOP2jRnIDLj1l7P+iswdPNTq9fBVkFqtaa1Sm+xgwzpJDbn8QZXk/Si5NshtoEHJampEQBDiLHQ2XYN+vsQS6Ltg6v7jc4O7ugDlJioeUaVjUAFMBmgq49NTxkL9+jhfWIPNrVeCVTw2RA3KAsS4DakONACXnnOGq7v4JZPEzn5crqJEVAami3Mva2KjBxfQiQ98tWMxf0rxbay2LtQUlq9yHJ75/OM64e5is+BzlTR8LYNsCjck9z08xWHxOylYueV0oOkbKcUy2JkIAdoPku2KhMAvBdgDFpzh4ivHu2Kr2AIpj2VcZ93/J8vJ57TxQ+FryjXKJYZhkKK2ZlAZ+ZCjDd51dWMDCUmjtS2g/NYBIEM/YhSdSKXW6/15ue7Nhnjpm3gnF/YWvTznwY2CEUKfguez5x3HG3UraKNpWpUhanXN6VXdaAfpew3nbplpQP6gx4qnbewDS71PPZTDzmCkVBh8dcvLcCiDuyQf8bct2tDX0lHtt5FwFOrpz2P6ZmTpL7MZWD6Qtt+nFifqUbnj7h1OS4zgp6aRK35EK0zKDDPOi96WcK0fSpu9kT+09M92z4GtHTM6wtDR0BJUoBtFlh05y8QNBiROnwcJiy/1mgIVr7SuA+JesreYguxzw7fWA/THhx5uDLY4YpHDKt2+22KgX6jDOeH9bMwkJMArbMOJWcwr4RKSsXvTuaoPL7eMlzz+lsYrcvRY/8eAl+8L8A21Ycs5Zc9KVoidLo52bbvHSvVRR8WAhqwPvNQxaQMJUJQ9mInorcKIshNZ7cYrxOM3JxuVO75Fhz8ntegYUZkk63TlhSuZfaIRtALRSmQKcVmXqTNgLUBnhfUfDMyKlZZGVtg39OgPjOQJ5nh2DcCyKLEOiphWAkF4tAddgzBiqEAHCtBGG2DqD0CcXHjpWGZS5RQ8/w39aYCgE4LtfXOP7X9/gNJ3mp29t8SUAuN51GPoONw+SZyQl2Fxmhd1WEZgtV9q6wVcS6Iun1Bh2cwY5R73zGjJXD8F2MfjqsMQaCGLOx58WDDLPpjo89s6zScBja0CC9H1NoX6NnIq+tQVdEBTcPE7KLFDFOQAxA7EXbTqlYvAldXHL8wiW3+bFfXLrJ2o+n+yulb03WgePwM2pZ1qxB12+wS+JPXiuMUeZ309YUGnB7AIdQHVeIjpHJvebFmQreQbl9z1ZDirnYvBAK8EvsrxjAx5KztucgXGecXl1gdubB2WQwQBoub8VABEFrJOwaZHpzLXIfL+z/p0S81PWzH0vazItI9fMWFATcZxE1vB+cgamNEsur1yYNQUkLACrtSwf8P48ANke6+etBxu5DxPIJKsudlJ8rOsDuqBGUhc0hUg054sH6Xleb3j63IVtdWOvA3zOdqrI3sW2twiWGFAxUPz657wxVqDlW0vLvaeVpbmc71zzrFTuQ2zGinQs1cq4XwXV+WzqXGs+nJLvpVBdwv1hxPGYjJUpBqwLY+dD0xYCltc1ozlURvRz2I9rYPY012Dh/rjOdDTnkMryeWXcW6bcY9E0P3f73ABiztnGlrrsmkNs00ezWWgvPDZmXDcGEDpW4Slgm1E+BkDrXz6joxa1u1spntHFYCmjTuWZf257tvP2lfWndm3EUINyNbNerjXPGTd7SRNF0DHGiIvN4/ZNxfJ0DjjextpPueUFL5fcnrf2G+agt2gTlN8PXXEOD+rkbyMIsnqoEzRayQqNlIravvDMY08hoBS24ZixP10oYLmf92002DyXa5ZKyjXLnb+hXM6B+mW9Lxq7UYk8Fxr9tybnfxMcgG37CiD+JWn744zvbw64ua9zkH1zvcH9YcaPt0djJHQx4NvrDb7RisopZ9w+THh/e8T9oQADmz7i3dUGby97HKdsiUg96s5qy9cXw2cLSSNLkBVH2w2JysbVTqo57VxYcuLGe5BcfmuVlpkHkeHIu033YiOSShRZhQ+HSYSvYxWSYr0GFpJ9QFYhPXhto6f+5mE0mjXzFPpQMwKF9Fh1agRS2d30dUEThoAlZCQFNiey0c54ySX0wIUCheLZecy53ncNODiUEIanALe+UEnxdGuVwLFhWcJvvoBnOIhhx9dACBHQ8NsWIKSSQKVh2wtAaM9PGSTDCijh2VKWz3KuWX9TStjtNri/Pziv2tLwW7wHXvVYtrWq7W80AbvPfxeCOBK+fbPBxYmKjr7NWmzg5mHC3X60NSoGf3k+osAAAZrHRAtyfMrmRGOaDCmyTDqXE63r6nDVPhZ22lPAR7IwWLRpzWGxHTpzdryE3dyG39Te+hOg44ry7BPotwWLQgCiBNwXZpc2shx2Q2chUa2313VW2Vsw2TTPBfA5KmPQDHsFkJ8SWgwoM06ZrASJhyFi48CuKmfcilH9JbSUNJWG5lZ9UJnGXJJrie+RZW0wtywLUc1Jwk3pENFDHWMOmksvVuCvsEm6SkHeDeLM2vRxMf+pkB/HYuSyAMJhFIM3ZwlhHse5Bt4aIKOLK8WfYkDv9phBwzJnHatRWTJrTBwDSgl8unMLsJYxzcBhmrE/iC41Thks5sJUEDTUABjYTsahZ4B4INCMqSygE9kTHiTVpwIY+xEge/0pM59X8yCmNwpL/4Ldk/zl/t+Ah3yPAn6B+wIa8NEd234m47Q8nrd4v59wu59wOM5mfPoIlt2mf9Qp8KmtAIMl3cc0ayTIXIqR3B9YMK6sORqIcj+OjZqXcvJUq8YTcI7LbDqB3zI4zk9hc8tcCNW6L/0vjB345wos5vFaa/ecOeXFXGJkAMP8yGg+yS7UPOJfErvwKe21AcQ51exCsnHra9Y5Ai/OEAuAGig8jlrIbzyfd9wDhWQVUj/3zzDnbDrO7X5a5InfDIVl+LkJJs9t1J/Og4yn09k8/3qwaKesgN7QSxXgy21ntsbaCPk9nKH9z6n8TIZ2DT7WLHqzR6YlwaPvgzH7+ljL+fUQ46K/GbiJUilbBA01gZID0zuzKZ8YBdPqpkMXjcF/zm4nwDwlyU0/K2AkulIS73iQUYghIiramt2+3IVSlHSN+HGu0a56qrPGf/ZzrZevAOJf8HYYhXH4sQEO314OkkjU5S0c+ojv3mzx7nKQJMpTwoe7Iz7cFa8vAYB31xsMEfj4IGxDn6dk00e81aIpZC6+dmPhEnqw1sKSr7Y9Lpuw5DkJYMiiJ2u5C7oYcKEMxU8JdTwFFlo1SvWA51yqvXrhsx26yihjcm3fhDEpibE5FvvDhIMPd/RAYSrU9qjK7aYPi9AUn/S8qsA5n650zLETA1PAz45e4RWlsPpdt8w7wb+PCeGUkoWZ+2S9BAlJX/dj5jfIkgzZzLkSYgwRkvTGs9n4qWHtPa0X+qx85V4PwPpQygocdHPjMUAkBODycov7+8Nn8Wg/pYUQrOK4lw9k5NJDTSXiatcLqDh0J5kuMZRE8xJqKSGU5knUa/ddtJxGwkqR77+53uBX73aQSpvJJe7W8U01EEvgvmWgPMXbeXZscAJ81DVWQisSctLcaLGEjkkuwOHVCi69tB2OEz7cT7h5GPGgSbtpvJqs0DETGVM8s6UgzXkFJ+s5imJZs4j4JAJKKIsZsAji/VVeGEOICd4HoiW81icullOFZM4WnDnjXfbGSWHQLQ2UNkRsnE6nfxDnBR1DxfCxokFu4Mu7YLK77wM2yhocOkn9sBu6ynmzlvOMoZMM8WGC/r0Cm+zzqbVFMOrqYsDDfnoy8wvO0Gn/Evloi6TECAunstQQQ4ddHxFiGbecYSx6jt/HuxHHaUaAGHbjpGBnkDDai60Yw9uhQ+yiFvlYB/Cfym4pwE7tOEkZNfiYfRXljKxoIwEhAyv1nH4O1PNi/fm0IXv8uwAfgzjVToKPLpSsXRcEeg/T7IxK0ekY7rXWFvtJ88wtjBhLILM9vjpXc3wIUozh/jDh5mHCw3Ey1mikfn21wZULUV4DILjeqQO0KRb4l+zZtvK2nzYZxWnZqYMWjTFK+bm4b3evRU7J2mlBBO+Y4Lws95csUoZV1glaw41htD57vavMpxhKuhY6yjd97dj5kp07a+1TAEQyTL3TpY2qAordQsDwHMjKKvAGFiqr8FRaCwKFFVi4AhS217hjtNO+doyGEMwper0TnfAvYrP0BEmYtCejRJKsexac4/ru9ZmycM1zGvXPAjIV5wJQtKJW7jMEmH2j02/NcRxDiQLrIpBzcIx+Ad6oz8ne4+ywlf4GNIzoRp9r9ShfZMyzDX0ucJ/73PY9FDtDCsXNRtzxwKvcow9ZL7KqkHsUNO1dzl23JBixYPfsxtnvC5/SXgI6Pkdm0mFte5PL7/+//9/+Iy/u91cA8QttBA5vHkql47eXUo3u9mGqPt9tOnz3Zos3Fz0A4HZPtmEB1/ou4purAdeXA/aHGR/uxaBk66KEQr+9HJ7ELHpuS6qw0Yt1Kiz5atvh0oUl01PHcOT9So6VvotVOPJTaP1tM7BQq+zSg1SYZOLROAUW+sIm68llZQH7DflBgTLLJ0egMBVPEoWvZxT6RLATjyWgQTBlTrUUbJoxdUxhBUI8H97XxbAMNaZyeAYk5L2Lx7Xk8WKY8Sl6uYUuNQZrAaJo6KwBhBo6FCUH1aYPWvyjKLZtzjOGo1tuQGcc+PdPFZmL8Fr3ftMH/OpXb/HrX9+URMGt1xDrnsRTSes9AHTuWIAAvhTxkZEURSIAOM7FMxkVUNxtupPTiUoKx2gNGCEbyMKL+4BxSrh9mNR4knQLF9t+NdzulOHEudCG73l2Gw0jAo5zEuZHW0nwHPjY5uKp5iMKGG1AYlgqUVVV2K7kWxm02vhGQ7A/xZgim/vmYcTtQy1nGUITgOU9hLqK52boTKaQ6cuUAMdJqpBOY6oYNMZeJDDSNL8mPbursKXqe67ChqJTAg0Cae4dTrFrFfwnAD1rYUM+XLjCMp0S2YIYBjZlVMBf2wIUNBDNHRlBj0/mmPL3xBAbW0u9pk7oOwy95FRqiyL4CqqUb60S6Vnd7ZitP0PmdGVYjzD7L1VOUK4xlIjs03Ga8XAU1iWZhMcxWa6hZSVgzs0CrhDkym78WzlXINViLPAZTU4+bYaIQZ1kEmmxwZvLvoxb5Fo8zdRaZQfnfNrQbIzQ54GPzf2i7A/gPEnZrUOCiwpCkhqM8l0FYjVdKePoADj9gKNBeQtI5VxWqCbwL8XSRCciCAZlxmWcd0q+dstZ5D5BfH+/nuXSx1jvLw6E9HuPD6lmGDX3k2zDLjoZ11vKbj6iXkNdrIFq6bMHBaBgs5O1KLLB5gOBP3kEti/Kp2R+ExwRB6496xAMlOYz5HuCIlk7wuvZvmvgswOXtQN8XYEgHtxFmyO5sMzrSB553Raa+dzz6DkAokVFHYXt/HA8EY481OzCNTBPGIWzOWTJKjyV71fAl1CFHXNPeCzqyiLBlGW4b8gZUilY9IPL3U9fJO1LbXPK+OHmgPe3R1u326HDt2/ECbHYH1b2g/bzT4VlCGhSz+UOkHNJ80J9ipEMk7vuKT2Oum6tyxX7S6V6ZZPU8kdSRBy14JvkNQQQRH8uttlSB86VLM0lGi8z73E2/cgDlyEsC2pF3QpfwjD1bS3lQ7FPtd/6j5fjzBX8qddvHdyt7ezTeLTpSwBx2P+zv/9XX3z9rwDiF9bWgENWtSTwxHa16/Hdmy0utx2mOePD/YgPd8eKTXi1kxDkAODmQSq5WUhnEFbR28sB17v+VUMK/GZEsKwNS94NEZdKeydoSUYUQcM1Tx2LptBoeW7VwmlOBhTuHVhIIUpWE3INFnadMDt2m848q5LvrmzOWQ2m+8OMW4YfK/A5TS1QWAR0UIEsIZTBiirEEGwzEMEgCh8BEXUBr45/1MrGzKdEYKILeRGyy0YG1VpOwlPABg1ThmSwtD2NxJZtUwk5b9xTAANO0QxgbQ5P/bdNTIGYzRANuGWYJUMaGfbXhhSvgYRPEYfcpCpQ0BVh8EnOT5/j56nqd7cf8esPB3Eu6Ja+7WVz3x/Lhjz0EW8ve1zseoQMq7Y3p8KkOhzr8BhvvLRVMLm5yXEZd3uZI4BU7/ucKRJ8a9kta8BkSlJJ/KDz+KhV87L7X4Drkncs55IrsF2SNKKe2uit9UVmurhUgnyF9mnOOBylOuPkkk6lXEIMTVnT5hkPQx/NwJ7UU8n18RTvapt7sPU8B6ewPRZG9KneXCqyQR+0YX6mWGXkVIqI8JoVGJ/d+1MXyvVvks1/ZzgDCrBkC4WUZwEDHYxVmGtAx+89lMGX2+4kUAiUXIiUu5LjSIpGHdSQPQeWh4CSL1cdHcyDdLnpsVVD9xQrNQTgu++u8cd/+mERSrcWeswmaQsKM6bvBGwaFbR+GGdJ7q7n8ywDsvUEFCHwVSdcT7nk9A2AhXATlDh1LwENC0CdbtQHhgbgqCvPn2cJcN48Cjx6QyD5+fo08HHhgHJAv42XzXVhPfJ1gcSL/DZjCDDQzM8jju/QGHEeyKUMtjyBoQBIlH10aAZIHmdjMOtn7FM+cZ802I5jKqkz3F7VxaCpSE47QBcM40ZmnWq+mAznEh0lWw0R3PSnnXJVH/yYVwAwFjKI42HODt2XWpnrnxXBTo61YobIyQPERZ6ZU9T0K9izWHNY1jLytPPzqa0FI0857KpUCQ6QLOGJJT8r93EAFvVwdAUXxilhd7HBw/2xKk4huR7hWKdZc9DniqXbxVJw4VLTKPlw5FlBFR92fDgDFAKio20dk5Bg4XPsOBI7mM+wrZi92xSW4e4zEEt+k9s0J/x4e1wAh798u8X1Rf9JTuBz+8La6yllKxRKUorXXyhzH3NOi0wm6aGA9pZqRATwAiDza3sNBE2p5OD0qbICshI8Sh5iH7mSsxY+USekl19ZQbHiYBYbsSVrrMl2hlIXx6TcyNrQ8P6g/eF+/CnQmbFLQ3GmnOKo8l5zor0KI2oUduzjkR60WSgThz7iYoj4g68A4m9+O4wzvr854uZ+LMChhtHcHeocZG8uB3x3vcF2iLg/SFGU230BHLsu4p0yCR+O8yJx9Xbo8O5K2IavkeCWjZWAWUCgpdMPfcljSA8WQ5kfDjPutfhI27ZD8dI9NyRwmkv1SoKFzMHmE7ADJd+NpzWT0UiGoWc3zinjYT/iZj/jfj/i/ihGzjjNGprUMOlozMeg+Z6kgpTcjxwjid1pMBQhQS+Nbwzx6p3SZEJCjZq15sMZWqDwlDE1zwn3ukGVEGMJUxp9oRIV/CkDOZUwLQJTwZRU9T5H9VzjNEDIvCzMP7jbdJZ/cOiC22TO5Bw8o4i1Y1OYcvVGtBbS/NL2UwKIOUv+mu8/HiwHaghSxYzsUDbvlPAM4HO5eliV7MKxgNc27ZzFM/9H3z/YOn93tcG3bzYAThRYOAHoPOvYJwBS5rGdEo7T0jhnDtDHcneycJAoGcnYpWRvSGRYAZy4LgiEmPHnDDa2dsrRw9gahDSIompF0Sl9vA8azfRGkj1yal6vVSse3OvXDEGrmD0EMlOuqi2PUylOtQDFnqBMnWqUPZ4xSkUz5aDjLQU+ZkXVKZup9KY5yfPPZMA6wCd7oF2vGZzcVsVy0wdsht6e1UbBtU4NVmO+ubFiwS7/mQdH2/vj9XoNLb1Q2UpW+TljlE6jozOw5fWMzXbA3d16aoYuBnd+kacBAnIIS7Gc7xxAxj1szdHVdwKWPxxm/HhzwPu7EfvjbDrS1U50ny4CkxVnKfkkKzD5ZdOoYu350CNjVdEg6yIGdRJYwagnsh8Ly3rFuMxL0PG5IP2aLM36nITFN1fPmBWMuyj6jCFYOZT57sDHx8ePjp2lk8czcbtYOy56nbN7BQ7HKdk5+i7g3dUG7642ltomZzHACNzY34m5eZP1G27t5gx7ppJyw+cWU2tUdR6mSKHuegr4/NTGaADvAMrURYHqeREaJmN77bmQUVgzegpASSAPcHLMGeeIAZ37DgQx5AfyXMC9sBRKIujpmeRr4/WSEavYnP4vDCovbOdQ+r7bDRhH0Z9YIIJriuud42pAhuqoVnGdjjS3dictAEWwsXVsEhw0sFBzFb6U8DFOhWV4f5gqWcBIsFI1+edLv/KlNgKHP94ebey2Q4dfvtvievdy4PCx1kYQMNLkTp8j934AQIBGthQ7UOZ90TWqnL56jU8lEfmokgBIUUtWEFenHgIq8o2sf9HZDmMBQQ9Tqh2soENP9oSuY5GxTt7zfvSm7Dk4ocdXp/aUp9y9Bx5NBvpBdBfj9ajfz+m8nF/u6cscmF4n8NcNCOYUIQbQxaC2tRzR7jNfGYi/4e04zvh1AxxScHuPUIwB31xt8O31BiFA2Ia3x8r4v9z2uL7okXPGx/s6fI2Jq99eDi8K8V1rBBeYu69lC3a6GV3uJDS57wLGORtY+HBYeryobPmiJ08FOeeUXc5CZf4d5sIqnIsiUjF61Khh3hGGuTKkIOWM/WHGrVbUuncgmhiFrbcVoiiFmiG36aVMAfMx0VvDMC/zgPhno0ZiUHaiD9sgy2uNTRjCSrhxEwJdj13S3JKzMa/IIDlOLneiAztowPqQ62I4AYD38jiAMNShcJtew4oJEm66VVZNCwqOmnPwqTTwwjgp1Vl9OOlrAyHn2k8BIK4Bh4Aw/lIu9PkQAt5qJfcYYwUYrjGAmRD94hk5RnPOeH834s/f75GyVD79R767wNWuf92bPnFtb3zkLAn1Hw6lWjLvk+BOQDH6mFAaaIBLlNww50DMp7Z18FGNKv0rRQBKOgX+34It2f5xho0ZfuVZ8VVhyHj2Y7AQWMuL2gUMXYe+k7A/yra+i5VS6kMbOTcqNk8D+i3+r8Ccp4+hfxa04U2kriiIfI5chGrzLxxM3Dfa55XU6rbcj7kOA/KtAIWi5DGtwjDImFJ5Px0OK3MAQLPf1CGrJYedY+aoI4Z5eVkUaucqKxfQubRp9uDgMuy4bSFIbtf9/qh5hRQk1OfgGZLH6Ty79bl7GPT8HzUSg07XlDJuHiYMfcBvvdvhH/3l5Vl5ZfN0rgFrgtWW844RC26+8pl9CogNFABtLQcS2Y+9MxBfwn58CstRvpNndq+Gqtc5yeJjZcpy/nXwscxXfhYqcEvWegEdCXLZOfli5b78PXnA3JyCfdB8Wv63hcVo4Jc3KPUzRjjQkczidA/HZIXuvKwfep8H92nMrbV9ysYM9Weyh+US7cF0OKmEZmfdBAi28jPPECoAmvwosrq3k+V+jzm1x32qCuMN+ha4MydXCCVdDYqszrnk3GV/plkrts8FkOQ9e5m52tye4ZnkXZRiSxnQWnzBplINnBZgps01CQSEkKvzcl8iAO+BcDIkPTDTpvRYpPoI9fuAUijozhXEY+NclQIorxuJ9heprQGHu02HX7x9HeDQAEKfq45RUs55eqp4ZFTb+XIrz9KTQryz97F0HKdYjk+JGskZ2j+ChtICxG7YbqIVSOSY7o8CFo6TEHvsN6EAhn0XrXjlpu/MbqMOnpwMeMo4r8sxLJz2QO1UsX15BYB8rPnImDlDHc1L3cH52mxMycYMiiu0qX06JxtPtZIuqACeXQz4G/+b336886fu6SuA+PO045SsOAofwcW2R98F3LuEtX0X8e31Bu+uBhynhPe3xyq8uYsB15cDhi5if5T8gvwuhIDrix7vLgdcvZKA249JjO5TYcmbzvIY7oaI46Q5DBWQaJlg/M2FUvufWqV0bpQnshjXjL4WLGQ5dlYWZc4FADhMkgj3bi8A58NBrjGrhFkYbijA1NBLNVAR0lBDv8lFkFwxCfOQuISvzqNNkNCzhdqxY6GDNTaGP56FSu71fgiAWnLdmeGXtVD1ihlQAw5GA0edh9ArU+yTrz69HUqVzMKeWWcNPhkcjEvWYPv+pwIHn9I+J4CYc8adAofMc5qzeOy8wd7FgMtdj20XcWyKqfjGXD2X2xIe/pxxnFPGn/7wgJsHKQR1fTHgt7/dvSr7+bGWc13p/eGwZFLu1Gkh1ZJfXqm9ve5zwMbyXtYEZfrdQdjZxlxwihaNauY/K6G7hWEYAFf9lX1zfXz2jVV/HCNFXnjPthY7rxSuAnCJ0doaa0BtwHkgzLMDO6ZpYLhKV9jdj613Ag6UhayIzNw6fBasUE+mtQFGNEKb8SMrh9WPe3MgMf+O3G9Rvutci/zX5osvuqHFDdqwUDbuRWsJuB8Dk8iS9Uo5cl711PP5MZSarNwQgDdvLvD+/R2Oyig8J8OZs2stt+5aHqRT/X84zHh/P+L2fix7aZAULXf7EXPKuNj2+N3fuvrsBjJZBMz9OM1JGLS+0JbOoekLZj8CGQ9HKcTndU5AomOuLgbshmhGp8+3dMro5L2dazVwthISmzKyokdZ07mMGp7uwchi6HFO67VPPH6GSBfjDJpvrxSU8QBcykBEtlDr7RBxfTFYSh4/9o+twcca96+60NzSuUdmLnVoFkZaW4MsarNTh/1mEKDTnFcojqyM4jARvdCn1MmWVudUqDdll4XbOZnWAp4F1OTN13KRr3ImwFz06ByybnTleK9r+72FeyTIjIK7Pmpgm3q+ZwF1MQARCJlj7Zx6dh/NuIcSiu91ZcrYchyM2RRcbsllnskmNYDOgQzH5tZUR16OM9XP5U4KfPRxCUr4Krlfit78c7RpTvjh5oj3d58GHJ4ECB1jeE02etBwTrl6PjvNW/32aoPr7fN189doOWfc7Se8vxtxc3+U+9DPpViiOCxTAh7GGXcPk7AMNW96FY0BmN1Ihyftt1PN73NcC60emt0/pmcRMM1PC0n2Dp5Wd+dFqNfRqTHnot/BH4tav/XN55fkfVVpgQwApPQq12c/4fp5bs/9ykD8DWtrwOFuI8aqDw/cDB2+u97gSoumvL89VgoDQbc5Z9w91BWyLra9sQ0/Na8Y6e40vFtFZNPXeQzHKRlY+HBYHk+h54uePKbQz6nkI9sfhTV0f5wtVyGVb2AJFu42hSVF73EX5Zx3+8IqfDhOOByZvH49f0t055YE9gHIQY2DVAmNnGHhGdRgKViWHkVYOO4am5Deo82wBAkJcqSUqrFn2DZDjEdSwR14kZzgFGMRlfcVQT0bYQUgVO+oVIAWlpaEwXUYBq3eiFAS9BIcfEalYjafs8HAAseMYlLc3yQl53MAiNzIv785GnCYkhg43FwIEIqisabfFkCfaQM+Bei7P0z4kx8eME4JIQT81rutsqg//7PyVQRPVXonYHj5DKbza7Sc1/MNHsck7Om9VL9lPsM1xgQVCQI5V8p4ud5JiH/f1TkaW3mWUACy4ygFpMbRVRCdi/Fp1TmzA5o86FX+eRT48EoTDZ7KUIpSYKDNNdUaN4+FXXuDCSCrTNibLIxFQEF8wxJ+fJyX4SPmlGsUPgnvg4WYXW877DYRfS+RAGSx0ZCbdDyNLXVir5H+F1CjNhaXOb6ocBPsQABC1mIVOgHoqGH+HOYE5Dicem70bnNM/RwmoMLvYgA2mw7TlEqfUSo9SvhdKEzIpmjFc9o0J3y4G/HhfqxSn2yHDt9cDXhzOeDP3u/x8X5E30X8r3519cWH5H0J7EdkZTuruKRRNvSSc/py1ykLpHbWPYX96B2TNbh0Cniqj+U6lDzWM/aHhMnALBFGMQTEjmsaFRAk91OiIiwULCgg1fSZwOOUEuYpoTVBSr7QYCFtHsgxRwrXbWCImeZ/tN8DMTAsVZ5fBTQ4No8BboABP0TZqA+3YByCY7fFYHsD5fjP2XzRJG+YzwpMzhz/uTi025xfBBMLDijPIgRoLsJc9hn/nJyMos7rn53ijAgx4GI34HAYq9DjNSan6dI854pjrLQKAjD/nu0zHvDW5yjzlOBnPY7JO6R4rkDHUsmv3rkK6+Vea2aV9TCQ2ViDGk9lRL6GI/bnaKeAw1++3a4Scl4KEPoWgjh6kuqHI/NphjKmzEl5fTH8bPsZHRof78dFirS+k8jDzRAxz8L+v9uP2B+XrMkAYDtEXFEPvxiw7UuE1GMFZz5VdrVFUM6GMeuaogzyz9b0xVSHXPN3AJo9qHYcdK4ffvEZjhC8Q8zrteebd1LAyzqIiPzKQPwNaWvA4WboEPQ7z0L87s0GfQz4cDfi48Nowov5KWIA9se5CiWRogcMUe5e3M85abVkx3rxjWHJV7seu22HNGcLR344zouQpBhDFY5MsPTc9T1YeHeY8bCfMKUSgrwACzWEh4AkQ5B3GxmH+8OEuwcFEtR7yzw13nij8uJDXYdeFrYoNNK/nErID0INFHo6dAwwxVJyTUkRFjPyXJPKvOs5naJe/zgJKEhW5H6ccTy6QiUtQJjcfakXWQxLMdYJPngDUTztJZeC5OLqzODbKYuG6BMV1s9RqdjAwc/MGPmpGpVigqfv3l3iw4f7KmfQS1gKBhx+POBBw1NGDYknU2qck4D3g1Rs5TW6GIx5x7XzGuOdc8YPN0f8+uMBOWds+ojf+cWlrcnP0ZgQ/LFK75fbzkI8PgeQ6Z9zG37iQTm/Rpir9ejYIoVlLgCLTwotcrjDm4sB1xeSlgJAZXAT9HssXPhTipYUIzwjZwJ1WcDJVMKvi2GTqvxRC+Aqo3pPQ9j+dcZz2wfK4Bh4vMionGlKFw91mmekHKCkJceyXh8LKmIESAZNg9Drei0giKacgDMeUfqQldljsy4URmYIwuamJRyjVyyLoikMk5Lbs1yGsh+rz3jtzmgsk23F8TGzNru9Tj/LKVdFlVi8hDZv10WklBYgL89rzwH8Tbm31kBlGhCvYE9ztsJkHLc+Bry9GvDN9dZSKnz/8YA//7BHCAH/2G9d4nL7+dMl/NytYj/SQaEgI2VRDTwWveowsuDZMvSMz+Oxxnm/xn4k2E32Y6+haadyPwJQvUdyLt4fJny4HfFxP2JPBrnOO2Ekxwpr8QARQKOtOAvk/OV4rzt5nYZzP5k8lsnbKUKVVd6FSpaVaxAHOmmZ5rLWvJFpZzIQsrBsTE4w1LsCKymjxNG66QL6vjttGK+0CkBrXrNPHnirQLgTv7fP3W+QC9veWLkOKE9u4Owcrp/Mz9jHgE7DyglaeQdHCzw8pxGw211scX9/qFnCWfrVKWGhBs1yBYSwecC0OPMKaymrzLUc4kmBSSfDA2i3BNtT13JTF52SoGQBIIEgodhnJgaBCrLLuxCUcdsAGv6ZN+CGB79OOgVD+76AOz81MeAk4/DNBtuhq/IQvgQgJOPeO11igDGM2+gYAw2V3fyppKBPacdJQMOb+xGHUSIQOfc2ShwZp6whyUnXsPyWe8l2iHhzIVjFN9cDhv5lNsEpZ9SC9b6y/rl+z7Vz503Z6Z0LuVSiBwtrUHVFzfefEZ4EgGYnK3zxM/Z/jcEoP4TpY23kiO/zVwbiF95GBQ4/KHBIWm9QRZjt+mLAN1cDRvWq+zwVBJLmOVvlUkAELBfiU/OstC3nbEDd/YmwZFLdd2qkUsi1xwIFYLzYCihxLt9iStmAwoejAIX3WimSDBGvtPkqanZ+DUPe9BFHCz+ezMA4jnO14XugUAwPglgK6mVoWFExxqmgESjMQCWA6M0jQ3GjwGMLEnZRE78PHTZdSSbPEOVxTjqummPxWCoZHx3oQEXDswgrjyOclyIWtk5gX/Uzhj+X5Mx1gQiCpj9XpeIvufE5FGOsBmk82O2/YwtBcoXd3y+LDdQsq6VnlwpXCJIo/uOdsJNTztgfyzxRfBsbDVGyylubwv59rXyovo1Twp/88GB5F99dbfCrb3avrvhYWPJTKr2rTHqNez3FHhyn51UuZi7DacoAslt3MEW870Qm0Y7a9pIGYOijMoVqoOil23nLaqtAnC5UIU5PCYt9Sst0QKh8W6YwyMYyrxhXpsSJ5e3De/maRrkPtePYrg9A0cO4L5BZNGjF3tgVo5THFWCwgGqZ1zcUznmQ9TU1uACsjus5wMaHiMoYcWyKsu7jWtxLIOifgr1UhxPUq1g1/thQSDEeaOSxKWcDVP14FrDQXSgvr95OJ98fArSZCwQ+ly5fR4Qo4PV+nBEAXF/0lhqlzhlUh3t7IJdGcDjx3h/ngZUvuXnGMHXRh2PCh/sjbpVFQuB/MwhLdOgk9Gyck65FVEWLUqqNquTmjV4VK28XYLZfV/7YlBN8iCqAYhhFzcPaM2w3VqFeawwhAoQG4KDoUJNW3qXR6/vTuf139Vk34B8dH+xySmU9yBjXuuiiBTFQz42Zb35u96bj1WAune3MZeudtAUAK8UEX4tBRmaWL7ZUwmsf3ytb57r/+xKdIucVYMHvpbr/HN2ePs0Jm02PNM0W+s20PJshOr16XRYUxnmTazQv11CbDsCDnillHLQy/XHKbk2LhPb5KwG4OVaASIBgYkZV4MgL9/KJ8yZhAUD6TWRtLzEAMoRFKLYHHv1c9YA00OTyrXRgn25gBYQ88zzW2jjN+PMPB/x4cxTGa8rotXp2jCh7z5kWAsHBOu9gm4PQ6663D6M5/tmGXkA2Rsf8nPvLNCfcPEz4cHfE3UNN5vGhw7Kus+39tPE2ei/vrgZcfkbH/XMaZdJhbPL9jxKBMmp+5lOpNAAs5qStvUdkpo/iWytkg+AXHEwHS3QiZFS25Kn7A+qc7Z7dzaQNXR/wf/pnfvdFYwh8BRA/WxunhO9vDvhwV4BDhpMZIh+keMHVrsPDQaoll3AgqZKKIIVWvNFyse3w7nLAm4vhRWyh41TyGN6vhBlvBsljSLbQYUxSfXdMCwHad9HCkS+33UnhQLDwoGAhDX4LkXOeigos7OX8BCO3gxjOZBndHyc8HGXxs3R9m+yZ5xTlUscRobBjsih0pPhGXb8CFBZWonnPXL7DrmETWl6oFTYhsuSQfGjzEKpCwHwQHhSkwLB7cQZLpFETGfZXA4RyXcn5uOkjBlUMSWf2VUy/xErFP2Wjglf970BAFuLx3z2nuANbAWqA6zc73N7sV73Uj/X1qKkFjq4ardjVGcyxtuljxcplcZqTnmA3x1/67G4fRvzJj3vMc0KMAX/lmx3eXW1edK61VlV6X0mpwErvZBo+NyyZCgK9p/Qyn2MPnmq++EgXIzJEltM5UHk25wJAUaRnOKcD85o+4R7IyvDsjFWQyrEZP+WZ/1SNLE0JXSyFfgS8LUbVZPkJRWC2z4oAB0FaA/Zika0nn24uqh2fkw9dNNZgKGy+GIC+k9QOO8vxJ0Znp1SiNoR5yqXqNOci5TRDyGHMRvZNFUPbI8q9EjSqQpCdE8p7umlQJkVEEs/hFGk/lgYUclyi03FQvmwNzKgf1iGEkJxiQa9tinPtaKecArwBXC7AUCmy30o7DVKSCbkACbFe5AHumK41fv28qsDGcg0PIrdjRWM+lM6hUgA4t4EKvMr+Nb9zz1+eqxivh6MUmGPrYjBQ5CV65cK5yWq6maGVfn/1URLFsJH+VY/SJlkBJIo+1j5PjrHlijMDD+hCdDlURW+rdJ8MxC6Y8SsFAEW3jQEWxUGnsjmYc51ryif1Z1oKc8K6Iie+8Z70kQkrL2UzYv14yHwUQIZAKR0C1fMoDwaLSe+ageNYguMcQwLtbdi6hGGrrCEIl4vuRODw3H5JfXIVJHzlCBSyc6cV3df+ruhiIZx2+Lb34vWqtRDfNry31cfaNBGHUYgRNw8lyovrPAYYAWDQyKBzACSwZECa/Eczf7NzyqVs8sQGpPWSrchiflv+uuN5n7l6WTWzcYJb0yaDWwdPDUrynH58M/UBt2Yn1Sn3x9n62neSH3zTpJVaAwj9+3NO1ZylqOHtw4jb/bKA6MWmkxQ0Fz8/yDbPCe/vRvxwc8DNw1SY6blEYQDBHADFBhS5QLYkiUc/x73QpvO5QEfnvHjM3uWz3rjnywJulNXnwquXTopPi/Zhn4q+TkcAAAe46803Dq0aV2D7ykD8wloLHAoar4qiE2hvtfDJrYJ4bNxcWqG/GQQ0fHv5/LwHDEu+24vhfWwEVxeDYxgGTFpQYS1JM0EJgoZric5TUmTfgYXMQTbNNVOCYGGnnuSrbW9gYYyhyut3f5hwOIrB6EE1r4D2XakiR5aDPy7lVlmrF1pROtFsDA4k9IKlLxWW+yisoodjwv44SXUpLVLiK1e2i9kbcLWizI0zWKESK9HeAJV9FzDEKAqw7tGzA0KeGrbxJVUq/pTmw0hbJuBa7qiXsrdCEEPEjHaUdU5liWdtDQ0qpFSGvOLuzy/KDnAcJ9wdRJkctdCDN9S5uZCdu9E8h8+/p3XG4zklOATgx9sjPt6NCEFCPn7nuwtsPiGdAlBXej+XUoGV3h9LIv2p7EHP7LR7d+tTnno2RZVpB8i6oBI+O4O5GMPSWJmc69uHcK6Bf09lrv2mtMo7fCz7yF7Z6a1CWOR3tnXgmxWQ4HhGGeMAYRDO2YMBauSnZt8gUEA0bmWITxngNHL8cT48SzCiYowVD7djX+hr3qU/Nw0/OsHYuXI/VGadJz2VPGLtfXqDkjLNwC4HXpmx5gy7GAO2Q4/DOJVz8S/KdeoH7v7o+TkG/lAD9yD54qRjuQKDgWCGWQnLXgLCXrEuQAv/5PVjF+dYGsstKMg3/n3wx4SVMa5O6H8bqnMFex2qsaExUTMkhUEjIczJziMVhguTquxFJdzK9jcHKNn52Q83PzKAeZG3MZveFpr7IviEwKrfMg8JsnYByoaUObe2hz8196N3FLZh/VYMz0ILqUtGB6AtC7VR/woQpuZ+TNhrhA5zahuoqyB+38memqBRHqoj+vugA4TAHfWvVr6vVRMt4bvcc5ycyAUI9fPQj90a+LgWTkdDtcaEig7jUy/QCbbpo843rQY/lCrwFcvsGcpLSicAweRyhj5DDwaweN7ffXuJH9/fL8Mj3d7xGk3WqhYGUj3Py/vt0EnOuIseu01XF0BZ2W+AMjdOgYs+tPO1AUjawZyboYEU5ab1da7XpD9qHYCUfVNkSGFjJq3u7g/nCSm7uBf5e+tiwLaPGLSwTNcpSMgUC+oMPhVu7Z9DF0WHIGjYOrxjELtb8hn2P2kObt+oEz8cJry/O+LDXd3XnEsORoJWBAspGxmlePnEugav3XcPDD6H3Uy55J30Q1eAwte2c8/m++XrjMW6/JToIjZPEKHzs+sC/vr/+msOxJ+9jVPCDzcHvFfgsBVKgHg131z0yABuHibMioDnLA8S+jtOWgKNby8H7DZPpzFLaMpseQz3q9WSI3aDlFOf54wHpfC2bTt0BkiwAqtvBhYqS/FWvWQl5KUodQWckkV7rQKn7wJmDRm4P8ymfFEAVEChGkE+hI7DQoXQG2B+xLjREZTrQunLoAAgvU0hhAWLkPL9OJEJkzTMeLYwY+99t9ABt9FSWfNetBogFKFMQHXD8BIyifR33Oj/IlcqZvPKzwIIbEDAtXDhp1+nGEM2dxrPjlespUrhy8RnCOc92mQvjHPGOM5S0ZQANJhnLWDoJP/Odujw5oKbd6yM08V9uvul4ucBlOe2ec642Y+WkuFiI30h2/Vk2IkHJWMwJsNxKvLrsUrvF04uLtiDjr3Vsge9Ubc0wJrKs3ZxVOyWk+ORsjkNLGk0gRM9x6ByMARhCV5uVJG87LXQ05e5Fl+7EWRlFeS7B3E2iRIoe4CwbIs89a1ao4EOKZFlNE5PgcoCfikzwRnVDMNdXTs5C6BrIEtx2GQUVncJva7Dsn16gQq8qi6ysHn4cQ1cG2jGA4PlRgsLoE72JDJUDCAMZNjV7DuuUwsfHCJ2fYet6g3bQRxMOWVMBEAmKfqz3Q34eLNXlnZxmrCi65zleRbFuS3UA2dICiBPYKKAIetyStYzwTEUGejGKfi/QQbLmIEeGA3exA21NZsSaKPaw4FhwMXoPSNOnflcdc4byS3YWPXf6T5r8zUEzYc5U+6V78gq61lwx127AvisP2WuMb8bB4TrhmPvc4C2IKOwOpjmRQzzKQPjOGOcy++6LuKtVhe9VIP0MccQ9/+jOoiYn/nhIEUAH44JRy1UaPpYcCGWYQkSn7nggqVlTEm3b8RYmEoiN2RQZz1eQHeY84+FvVjIbKNO/VNhr+fAn6cYnW0Bk+R0qlFzEBIkyw5wXT9vAaxlPDOCd642z8+P05rDheCtsW103sUQzEFHuZLds1wD0HwjMNhR7401s5KfeRAkhPNF73yYYAssejDuVKjyOIstUTka3bUJajwlbNtYSk7feooT2DP1TgGQzw2/9u+BBsR+LgDJvQEZOYfqN1x+OWeTnhl5KV8pXXm8ji9twbYoW+D4cW75195WcPNYQC0Wv4LuX3J830WrCcAImbU0Ra+VPqBtBNzomN0fi81+UAeGTydBBzZTRLAa8naQQqgEDV87PZFvZBFSFxz9/4+k1PLRch4k/JQUCD9HKw6hx/eBJfPxlLz+ykD82ds0S45DAocMxSVYBihrb9Nh1OTfbHPO4oXMJRwqBEmY+u5ywNVF/yQBknPW0D6tULwWlqzGVNTrHcZ5QaEOIWA7xKroifeKMPRlf5yt0AqNPamSVhQMDxZueilawByKY8o4jmSVCEuvBQqTxk1xMwwAs49Wm4UZ+IFjUZT2QIOS4bUuVw7v11OT6eGeVGAdRumbhRlruOhagRJfeUkMUypHfsPWcLa+c/kcY5VslZvSc8NkqRTRS0bGzZdYqfjRcOG26MMLwoWp0JdwsmII+blCk4FG60vH51wes7XP+y7it371Bn/2Zx8x6YbO6uIM7ZfK2gLQT3MBvMiOJRNW8ht+GthEVklHbR1ZwYjKirYm6yzh4Zhwo4WeQhAG8WZ4PkO6KATJAAAqZ30voAUdGGIYF1khlW01N5cqkPL8C4usVVR9CgAC98VDtwxz9K2EoZfwuJSA4zRbpbkQADLpoNchMAiIInl90ZsT5TedNfiUlrIUyPKVsR+OUwkpmRJmVZK8EsvmDUNjC/VSfZhgROf2Kx7jxzbr3kLl61Tj2toOzok0lBQAbfOhMkfnUCJobdfOGfOMEpKs89QMJxRlrwU1awMexmb1nwH83A9cAxCBhlFJ/i8KtTDRPHjB8N8M2BrzYf1tC+FpoX4cjyllHI4Jh2l29xckR7Cya3QxVfv+mjF+GGdME6vMS/qXNjQ7q9W4BkBaOBD8eOk3obzn+AE0vGGhnN5QD5UsyRZCx30oa2dm6xPc58vnKf0v64J7loWYu357Jp/pJQpEFF2gvnuOil1XZbzlg3YyN58YQz+WlufMG9z6PSsbt3nZKIt7HT8//gZ204HTgH+U5RybcZoxTgIq8lwBktLlYitOqK060Q1UyEU2+Dl2nDL244TjmMWxMRWAzkAMQKvHy2AHFFB1zVlAoM10BGX0lL3lcfYjo0TWHE7e6CypMmTtHpjvy8mpUXPOJr9WmuaBp/JcA1VzmzdkPpa5RAa3zu9Gj+eazpqTT2TBSnoCN3YesCxzgf3jGAUMPXOOFxnesh1LKo91Bv9jAOJzW86i893uBcBhtBfXVt9FTTsj+1sF3lWvazn4GuZ8qMaxcfSGM5Ep7vuXAJDmeF2kj1G7OpW1JuesZWNFaJQb8R1ASQsAm6x0pISQoe43rEk2gpJiU7hPNXeA3ZvqF4ECJawziKvwazfmlb0STo/v2dyPDvScEgpYOJbUOdwvD2Mdyis6U7R0YXyOfRctH+Pltn/V6s+0Bz046CNMHgs1pq7Whhvz/9/UPPuv1dbYx3wNAL/3T/zixef+CiC+sBE4/HA3gpXsCARycW0HMewJAACa/FJsBsubA0jIH9mGT6Eyzykrw1CMsRYMpDezU/tfckMtAUMaCwxJpnGUcsbRgYW3Gv5cMSn0PEuwUAxl8awJg2qv5yrhJqhCm6hMUXByWhY2SGj3AzkmiMAs+Rc6BdEKm9ALlxhgoNQ0JxwUKKTRx6p7Ft7lgM0KIAwFdLEE7JGerFg8nLHOQxaURfEcSvKXXqlYjOIlE3BKNQjoWSZPvXcP/gBLVoMdp/8UgGjd6/1YW4JD6wAgv6NCEFAENJmRzG15mGYcxix52iYq8Qmx6zBqqJ/ldIlAh4wcIlKit13ufzdE7LaS2P6bqw2+uZIKZotwlEbBXGMoeMXzJS3lrJWORaZs+qiyy3lNg5nKZpxnAHmWKm0HBVs8YDTNEl4XQQNSjAoqmxUQAG9kl/sIKAow1yUVLT43ryT7Su4Wuu+eLUEWr0QDwMNBDIAbDSn34RRAyV/ItbnbdBquMnyW4jVfUhtZ0Epz1T5ojqHjNGNOtTLjjYJ2bUd9LixMRTkuz7EY0lSYfWEVCZs8L2s8w5wFrrYnwldSdiDhlDA6A7zdW1tmEI1cb1T4ucQ1W8KNHaM6C8tmnp2hDRrfzjECw9rKWIbCGGtznPJYv6ZOsSONoecMHRozdFxtegmH/cW3l9jfH2Q99aILDJpLKug43j6M+Hg/4TjNdv7NEPHN1YB3V5tnh3N9vB/xR7++QwbwV765wNWur4xUzwhikYRxrtMYcO9vU4uYrgIPSJ6DzwoYScDRmHwORLPxRG7Y2hKiTbkV7Vl61ikhlGxsLGExKRjLOQBY0YeNM/rMCAdcQY/W0C/OHJ6MhjSvTuCrrUAbQtFxeD7mAzSmi3TEAB8Cgza2Tud6SmvzTbluV/v4Y3LX+pydczgXnYLPMgboc4OCw5I2ZlYGWcpZmMABCDkjaF7UqAIuApXDoDzVore0XfUgt2+2JmMo+TjtBLL/ppzE8AgZUZl9dM4zTB2oK2Obc1vPLc+mybOXat3Pqlfnei2V6BwnYwwEKs/c5/uGfmbODwcQWcoQp2dk07BXBs+N8RKw4XumvGC1cJVvMeC7765wf7sXYkIbXRGKE5Lna9ucRGe6Zf5mt2eEIGGgVxrO+pIceAuQ7pRO2Oh/PmTyteCANVYj1wjtKoL1fg6F5jn7e5NpUGQkwfZih8HAPC/3mS+cOQ5zllRXJddrcHOxyPvWBkzcdLUnNoYqJ6jfhixyOer6oR5s+7UtYVmL9kPeK2Dz2ewCr88GmEPERw6Ys0D7PivmYE6sACu6lvXcqkCos1R0nxCk31e73vIYfmpORjr6PTBYHBdPCDXuanDQRwf+ZYjUea1GG+VI8Dhl/LXf+60Xn+8rgPjMZiXeb4+YE8NWYWwIgOwgWJWslDLGlDVMNVQsFB+ifK4xLJl5DNuCJiHAvLZAwDTNaNdkVMCQYCFzFeRcMwtvH0ZhFjqleh0sFKETIAJbWHsClkxzydFmQGFOTjFygTq6OXAjqO9bjvEFAQZnTNLQYKhVjDAv+WS5zoq3xYdFe3o8DSnpXMmTSAMsRPUIB6Dve/MOW36y4HJUPXFFWdiKBwX7n7dScRUu7JiArxEu3DIYDODzOrE3GvQgr+w9pz0FAOwUIIrOOEpJwYdpxn6SsKfjVJgH05QwqkE/m5G6NC4fNTIdUO4NEx9ey+OoILWsVb8eOwcoS5hYp+ukk6qymjevBeo9sPgUxXN/nPH+9ijgDICLTaxCiYFiFCUFP8ikPDYyxUK/UIwjPgsaP95i4pzxXtqog9dRsWrX7srfx+aUBxipCIcg8n+vRrCwJLgeaKwXpiFz3FxpnpvX9Np+CY0MywfnYJJ8tcKoKIA6zGgEvDFenqPN477kHNoMEbtNbwxDYfQHu3ZKGUdVhs7JoBhDBRKSheLB3faeCAzSsdSGy1BZ9wo7UGaqGZRuv58pVyw0EMqsAFIulV9X2UqcrzpmwkIWluC213HS6IGdq65ORf0wzjgcEx7GCeOYcZgT5qmEKBqAlLIULXEGPu8rOODDADEUIIqt66IZx1zHNJoY7mngZpRK6dc7Cd0na75XWUdHKD/zCe3Z9scZf//P7pByxi/ebvFb73Yn58K5thqG2MpAHS8y/EqoWgFOeI4S1psXex+N1VPNuHcETIL7PJTn4a/Fz5gWYTd02A0dYlccyVw3nv1D5/epxrm85lSrw/2WDKWcim45pWwgzdBFvFHd93LbG7BGMJTbAsPeU5IK0XMqn01JZM+Dypw5eWZkwKYPFu0hz7d5FqnM/ZG6zMzv1wH0oqPm8+sVLudUPL3XFHkiN1wqucs1PJBqurLNn+b3uh7rfrdvioPG3xP3uoHOMrIeDUgroLaxSw37CPJMskuP4PrJfIl+fRXdpt4H4Oa9Of+acavXkTsn50uGFbzJzbUrULL8URtExq/oHv47kWvTlBZ9btMMkBVLAMg7goAC9nZRCRxbIXFs+050uiiALc/t1/XqZ6j78SltkRfRrZPKGbMiFyen29XsSCcPH7m+6Lk699SR63PT0S46G56t83mac5VWLGdx4r67EgduDbitsyNr53txgBzVScM1iRCqSDIC4X5v9bqud8YsWzZSgb61eUvdydZWs9YQiuywvReUUfV+TYc65ThtauZ1pd5rBfka2U+Zwc+Ra7ZznUboPDjtowGN8NOV178pocZfSqMey/RqjKZkmjW2ryHMP1Ez4PDuiFmrQU4pm3GDLHkM/eZGIRMVJKI34Vpzu1ydKc+ecy7VkrWAiH/wOSvI1QlitpZAmhsUw5F3GwH7WMmSJeTvD7PljPGGWAyF/TZoeAWQrTiHJLVfA+OKt6i6PRVwxTuKxUZujL2uJKgme6FTIwJwlHHNOXVUlqMBhM5r1AI7npnhPbcxECBkDrLi5bLNG1gYnWvN5174OSsVL8KFz4UKqzHxmEjgWCb/F2rUALbZ2eNVpX4NxHlKa1mBSwDQeZOlh8hqFJjgHOnxyiXknuORS/4/P08eAwBtPOwf9nfZf34eUOZf30UkRZ5EmVFljSCL/r7NT1ade/FiZfwaZZfguHjayzgyBLSsPWEU9xHo+g7IGR/vR/yoClkXA7692iDGYM4D5v4ju5CJzE+FpBa2YulrqbbXAsAlPMaet5tPwnDK6p0N1XjXqporqmDPWo0N5rMqX1v6AqkmWUJVcqrzqdJ42GiqhO2mq7zkC6XLKWVr+SFfyyj41EYv/mGcNZ/YrIZ7wkFDkMksnp28ZWuflXcEbXR/utL9yUKOVaYYmKc50p7MJnQgYStn25AZzlm+5/NfhHw0hq+XRVTqS8GIuTKwDNzwc8v1fQGmxoC+l4IkF0PEditg0HbT4VJD+W1ujnRwOMCTIL3JM2/QQ3WGpWzhmoH2R/bSpPlePTBGA1LPhfLcaTTDPltj8BdmMGUAt9XWIKchxP26c+BGCMDdfkIGsBs6fPtmUxX7Kntv0WPWQMjXapxba6CjDzWkccViTnTW+qIgnlHk91wPcPo9yu8tNoLBgUz8x74vv4tORpX0KjLGsSt7RlC0gqAFsKxyzugVOmztOgHmdF4rvrcWOtnKzgxo+hvRP3leOiCuLwa8uRDWzJrjdZpTVZSJUTFt67qAbS/yKGdxkO8PE/bjbKl6vK5QjZsSCDJg+4l3mBVduf7LPcgAQ3fs3B7H/Ypj7/7wOQcA2c8FVSr841rVcRqd/NHm9m6/53ae+d8F59wshQo2fUDXRwWACguSIAjlRlIlk7qRZ4cme1+A8FPbZqu7Zj2nZ3xPCUizFNOac0aaddkEZ0M098+K8qSiSb9rvcPGxRid3hZa6nXeUdrmivTgYW3DOMcn7RYs5btfy8tK8etgJYBqjBh5V2wJD2jL8+Pz8FFErQ4XnTx6DTBiThkPB9kHeT9bjfBbFJ1p5YwfO0i++9sHiTKpKmCrvc89edPXgOQaw9Ov8XoeLoHw3Owb01z2leWaLTOSY5p07mZNP8FnEgMstUapVF/SbRUOb67mgc2/yGcbKp2iXRcy7m6Oq77EFBrMTb1VHW07xIrgQJ3qS9B/v+RGfcPbuT6yqxxXOzUJCm/6gD/4CiB+vuaBQyofc5JwQioXMZa6UmQqBOSKIXe57fH2asCbi+Ekms7qxwxN9mHJnAAlxLgo3GxdDAYWShXWILlbjpOChZMxCwU4KY+eYGEMrPQLIANjKqDLIqktDQZVHOqNNaNUSuQ9ZDUEYCwqyeMiIWrMfxNiNIFqCZ0VJJQ8hKlSvsywoxAzoccNqrxnyFWnirFtGHoPXFjnWmF9/fSVitfChSsg0AOEzsg4d76cl3+R15UwPuNqbKMHas63U0wGUXqiKWnIwJS1Yu44mYE8KvPDh8kY8ObmAY3kx4SbGVWuUe0rt+ONMgfEAUDUcFsqhGiNOHnf9iNAcvvtdZ2bcgCCZ9mUba55b4gbGJD98w3lvk8oGlKp1a9S7WcWY4Mftv1tvZycA2JMUu3geGYbP2M5BGFMbfuI3a7DbuixG4StV7zLygjT59eGHi7y5jgj/dVCb4KEpvjcUNmxK1LOCmKwqJKCYYPkWeri8lm/tBE4X+S+Ca0CjFVlmGv0Kc08lgShxtmAwvsDQao6/Dg388QUzeAAas2n48OzWDgLEPk+Noy/J7MJHUi4xiacUx1yLGHHuoeYgt4AhbmwuXwIPMN9KFtlP0R1Hr/e2vlIhpCBqT7dRQzoegFrOv0uIkiOuFwKs5jsV2OZcgBQ4KGdP6gNwCpcTz87aWmvtKD/1ECBfFiMWmC7GXB3f1QAaXaAcg34eyAEQJlPKmNaoMN3lUb3rBaYMCjiEoQM9fEEwmwf6phzro4o8CxvX1ih76OF636O/d30nsYITRl4OEy4UWN20jksKR/IMlGjvgGry55Bae//ujFagBihAhv9ce1nERkI0YwUHhdjwBADthuRAzFEm4PlOueBA4L9VVEqbZ2TB56dRCBlzpobVwFbhklGlLVQHO1FXz9qkbzjlBZrWVizXcUee2kFVTpnJLy7yUs4r4MOXu52sYBRlr8vyPOYc7Bwfa8P+ggS05tQ1iMdvgY8oHzm508bAfWS5p0FIo88WEZZIe+7zu8tzskYgsuXyU5LhevoJnAIhQnvWXPekWx5WJ1cYv+6Lpa9yb5agsm5UrEU5IteXyrfsOgSC22wn0VmlyJHtX1Vj3vwb9x3Xv8qeunSoUztLcPl7KR95fXrx54nWgdAybNLncmeqd2nB4uK8LdxVYWUuV5r9qnonmSF3x9myycJCNP5YqvpGxqdfK35CAQ64fzYbYeIi63oMLsK9Fo6PrycOyXXvR5Lp9LeF+a0FD9OnmtfKyY4xHm5VxliDjwUvaPNZdmOQXb9TCnBcpAmuDy7xdaw/cPPTo4Vsq3LzuunMVQ6ouklcHPRfd+mx1gFfVv92B3zF6lNM9mvjCiRvJbjVPAYw2eS2FB+r+vkjdhXuq5jBP7Ff/Yff3GfvgKIJ5oHDsm2m1M2lsScsiZMFoNTQraAoQsWPsS8YG+vNlX+GbacxVPCPIYHF5ZMIeMXQbvo+y66cGQ5/17DnG/3AkSODmjx5xFPRDTBwjBrMZgLGDOnwl4oTbX7UDb5+r6ccHOex00X0cWMGLsK6KAn6zgnHMdsYQg+BKgGJoo3xCuCIkSAvusagVWMhr4rXs61ZmEcK6Dga1cqfnK48FxXqVw/VwsCNoAglkpezkUZW3gEH7m/lhUYA4SpwHNDqz/O2UDo2fJO1YqbV+ieCgDmxYvzfQW8Aew2KcDCXm0TAwCHfUdqW/6cgFMQSo4/KnJB1we3V3a1Al67gA+3B4yjMNuieqZZuEHSC9RGgw+BSy5ReTEQ0Rjg3kuIai3bb/h7dw47Ji+H2Cuza8PvjUsqVpXSaHOs5A1iEYKuK2FUvTMMfVhDxxAWpwitsXxWw1GyAyOdl7ew0ArTjeE3OedKMQmo86p6hxCVJ68UmQHjB8ePa2PAeVn9KW2N0RNCCSsnAHGcpLgJE5dPSQvT5Gwhdf5B188wmBHPynzXFz3eXA642vXoozixjuOs4GQqRYEqwI3DIjOr73xqB1eF3sB0XQ+pOBS8c8Hveym5MCqTi3I1Gqs5J2QdnALQaUQByhox+7QZa66HImOCGcM0HksOwjIJrGhALvuBB9XOPl8sjcIaYC7PCVQko5ufNGapzLtny9fwr+H2BWdHVHIGQDd0uL0tRVQ2WgBpu9GqzWqsl5C3UiW4DfHNuRivFg6mRj6Z5AEwACc3z8cvpVMgZC3LV5iQbv1W0Qux6BSFvVeiFphWotNwbEZxiFMIqouc32ePU8IPNwf8eHO0MN1Z718S3XeV/GHz7Euy42LngcZisEpKDq1crMyrmm1arx17r7qhzdkKUKrZUPU4+r0SBmaLjqb7HmQNzlq1m7toCEAfIzaD5Nv0uflSdszOuS7q146Nj6ihE5LgZNEp5T6GLkr18U2vKQJizSznHnfCaPVsZ5/7ay13atvoMPF5v3rVV5BRrSP7m0rRiac0RhN4AN3vd52b70ABmGZlnY3zvCh4YdXo3Z5cGFGeAbjso5ezq/1t3nu9Tl6W+VXshlrPKWgcbRn5y/UNFDkoTL6MvusxzXNZB5RH7T1kJ0dt019WBC5dKPppDVo6R7STT+36Cabfl76bjRc8uFqDREubwfWtsRmKTuciJjxwFkTu+XXjderP2cg4PIyzjV6v7MC+i2Vfdfus12uZsmhqbGX2nwBo75zGZY648QnBjZO3SYMRVnq3NyNLQVGCh8cp2bljMw83vRR/YlhvH4HjDLy/lRDtmwfJqcnnGyByg+lMmKbJsyMnszFlHZf0DYDfz9vxsnmFE886AxKzoMSqDKZehVENzG7i6JHzWCyLokMt9xOvp1RAZHM87XXevyevrDnjfRTQz8V+HKckRWoPChIeZ+ynGdMKUMjRMn0Podgc2ZEscqkzQWZyDMBmiPjn/5nffXFfvwKITZvmhB9vj/jx9miVglMCLrZSrS0lZbJ1wbyFklswmrL25mLA26thNS8YE7reN2HJKWeMU7ZNAcAizGPTRwMLmY/j7iBg4YMymVgFmq0o/8EUYyqP3piqGFz8oTsPjRAKCLZOw1yKp16UVYJTZBLNczKA0oyFVCulZj42won9No9WDOj6zpJFm7KugvrUuqey/7kqFVfhws7r2wKEXngvmCooAtvCXlrjCmVDt1YJzpoJFM7ck9/koglNmYcEMllh294392IsnpzrzeYEAPiYgrjsJI3zojh5o7kK9ZCbglRVK2NQTYoTiqKNrJ/7rTJpv5Ndkc/LHys9y+VeeUK3IacMxC5iGmfshoiLXYd3V1tcbjvkXKpoTlw7czagfUoZyc2fOkSqHvfs1ngd/lR6l92E8gpQtvmnwICGJHkwJaMe2jqpsyoG7ngEmDK/ZLy0L9B8H6xvnlUcafyY00IN9liDjxaSrbLiOGc87Om8mTUVQglVpjwj+LkdSsVfrksPSL4W8AegWovRzXs3jKZAVGBLJjgIZZ/IWh3nku4hZ1nTOSm74Ew/zEgBx7qEgjJMe9DchAbiZgn7mjX0tQJL/T06Za39v903CQ5UslUNWB/uWbN0yyLm+uZcb/MxVXP0zGAU2VOU1K4r+YQEpCnPzoBBlPWUtfJsUfR0DamS7UGtArAHYxsZ68Ezv+23cPdfgzqf0jxolKrXNTiLDAxDh3lOqq902PTOoefuqXbwoQgSJ04zCsPE2Btzwsf7Ee9vR2QAl9vi1F3KuTUQsgkd02sSVG5ByOX8Le9kfZwCIdcNTQ/6Mv1G2aPkYh4Mh/vN0Evyfxbo2wydvT+X2/N5z7pmd7c5cY/jjNuHCbf7EQdlu3A90SDjHlSFmGa4fNi5gPMo87QFI1sjstMCM8ZKym4Pc8+Ae0QMUNZotPUizDM6Y4uxBf1dH2WveGoOas8u8rKngN0shrDUWWJACenrooW38nvuMQbKzYVd9JRmbNrGAd5+9lOzdciSOc5JiytkK6jggW3RPbFgUBbbocyn5OcWanm+Jttr/a/I49geJMYMuhAKwOTkagYKCzLLemVUWgnr8DqX16HWnGgrncVJtajuJ1zl6lz/KKPsEZUu4fU3lHkZ/J7cRVvbXveiI4rHMz+uv3asvvc6eQ1GVY4GJ1P97XgAcJyTpeCirN/04sRkig9PpPDNnMaMMnHfxQCrcG75U3FKly5yzvac1u7hMc3eubaKAwq71juBOF4pA5MWY5zmQjiSMZVIj+uLHte7Dp061aYkv2FROR+15PUe7pUhQCMhSmqeIr/Ks2jzkNY2au3w8+vyMUkjfVPnquo12ebBOgBZzlz+envRE4lKtNiS0OD3Zp8GhY6tRaohA9KXjMhWh/W2PwFjptE4aOjxqHbIqca+cXv3dizQsLP9PccCqPrUcNtNh3/yd7975Imcbl8BRG1zkoSrP9wcLOlzyhkXm84WWaeK0UELp2wH2fi7KEzAd1cbXO/6SnkjrVpAwxKW7A0gCmNWk2TbDuLtHDT/2GEslS3pffdPrw0pyAl2TAa0stTS20Th4Se9p5NTySl0YiCEaEwtATyKB4fARiVQnFAtCxd6fpFaEag2LKMwGzB5WjH+HJWK23DhRc7AR8KFy/07JafZ1PhsfM+88lszA8PZcGH+TkLFpQPzrEAgFTEU8Mnn5loDAF/sHXY3ZL0MznjS9wXs0+/0e2FEunnCm9M/wRL7ZFM0yuYhr21L4ZzOXseSF/UQlq2xbKj6iZvD9KKbAWPHQBWFItBzLudj7/hoAPFs5pzNcKjykTlFyz4zpar2+JXfoHqWND7shpyBCgV8ZB4WhgdUmc4JeNB8TylnY/vC3ZtnuK0B71S9Qqg9kexPZcRTMXPaIseLx5YJBmRxZz4bhAQVHSpL7vmFgKri5NAF7LadME8GLUbjHCYtOG+MYgV7LTG3hdp7RuQ6IFOMiDIBs+tjNT/td8nCr+SayZ0HNraPNY6FV6osj1X0bIo1QNotGNdnW9MBFrbLsBZRljlx5Xifny9llVULRTVXc2MhbDhOuXpb9etcI8vUFPnOFRPQsaAM8XLd5wasZHqop6MHmbwX3Cuja8UDXqMVRqL89bK+YputzMticNttOfkLk0NXl1uMxxGnF+HzWqUMRzHif7w9IiBLNfrrTWElINRjD7+foQIhS/6ubHtkZey4uZ0zDaFk3v85F7nvgfISsu3mmzuPzMcTzjX3Oz+unZ8bAS6PVTFeOjc/ix7lHLzOIPUg0hpo37aUMm4eRny8p8Ge7dlcX/R4q4xjnuMpjPB5dg5wjZZJqehFzJEZcvm9D/lrx9EzRr1cp3zxMmkBboQyPiw641kqfCJehzPnUatPr7SAApLrH+uYrbm83NNtj0GtB3TRFTzo6sqkpehQeLbO+yW2nEsqisOUcNBijw/7GYdpVrsDypyt2VJumE0O296RC7DjlcEFuMJzUbdWYcjnuLSb3LX9m9zKA3lRtjHqXsn0Yvmu6Bc8tJIj7rzVNDw3J5s9qRqrxecFyKl+6/bbSnRwvsJFYjjgsZ3LHMc11qKtB+d8ymoLj1Oya+0GKcrEwqC+8IfY7QnjjMLsYpizPsshihP/ctthO/SVAMnakzX9o7WXSASS3MQKCjnSTJuSwMtyjoM/t5ebAhiWzzlWlFEEwE0fs0fk7SjKIpENlg+yK/tFRNkPiv2UtW9FZtkU82Pi32e4Akqwzc3mqtPTVj/LxfFKW4F2X9E6GrUPXCcKQPqDcvPbxVx3i1TnFdwYVwAkx8XprpWuwbkL50RTZxLlPcewzPeyx8iz0foL0T3nyOuv64i2L7hiOBK5FTQ3bzme7NO/+o980w7Ek9tfegCRwOGPNwep5qaJmbdDJ/lSIEwU5kUo1OiA3UbyGr69GKzCZspCp77XsGQmembepCmRSgrLb0YvzkYZMiGIp4DnsSpG2mcLMeZ7LtTE90tvnG9FgRGvBT+0cJyQIQUJOLETcg6qcDthyGu5zdiu4TcG+UANAF6nKyHOVult6Qk1ZcmDgi+oVHwyXLgB0yRBcFF8Ob6nwL/2b84ZmfeOogQaCHgmXLg1jM3gMQVcQECCEWa4Ns977XnkxYuVMVr5fqkYFOW32vjhPZLZGBgIBJ4KcMiTyXsRmqS3VxeqL11d1x9tm5b9LWyYag3kMrbJnbdVFGvz7fRHdjscF7hNxb6rDWy44zabDrPzfnJjD67SYaVo+nBH3YxEkfDK1lJBM8+TbSo0OKRK87aX6qfIkorh1x8P+Ifv9xgnCQ/cbXsM6s3cDnXOOc7ROTFcvRiOVKjq9eOUKH6GbLI91/8AKAmdfSMgZmvUKSoEs3iaBLdhL5518yz9czohUtrx5mfemChKB8EhoENhHHWaSsGzTbIqs/UYSYhYKc5RZFddJAOu8uTjrazdwj7udS/qgo5nCJL/xsk+akxea7ClYVO/BrVbTyz3jezXZ7tO7fjlA/IKY3B/0byufhbq52sAqYL3G5/jrot2bJtj1bOtHhvfqI4wC2t1IA4ZHZXh1MhRwMvUMpZrxlZrjHFPKo6uVIUOfwqbyTMj1gqE/epXb/Hnf/7Rqvu2+Uv5mXd0eEPJg09tSynj/d2IlDO2Q8Sbi+FJfffOjpYBaWBRKOCqtZwru8L25FxASEs5kjPGaa7Y+qZXZO8gcGtXZV/Z98t89Y5DLxKXe1W91wQ3f/x+UN6XManWQixMC7IoMmDMMDIfYgy42HR4czng3dVGqh2rw9dfe62llHG3n3Czn3D3MFbPuIsBl+qwiQHYjwkPh8nSB7VyQ+ah7JkM2z0em/yBqOUKmvHwxrYHaPz+4cf8nJZJXY96Cv0MAPXpmi3ijXMKqHrfWDJJPANsvQ/NnHYOirUwvU/Jm/vajaww5n/bH2c8HMQmayN4Kp2pAcwFVNXUIsHp/rOEVdKG8WHW01TCP8vg6nPR+dN1HXKawTyeCF7/QLNvcU4t75Pru/3OAx9yuqLHeN3WZuGKbcd5l5ERIfaWxuJUt1V0Zw/wuPduBKqNdlX6PN6eDELyWlynKCAkAItoY/N7ElDkHX/gcw36RxuCMI37vqSoWgCbwTEydX1UchZSXIlRWpOyZhs82vbooRfG+EYZx5s+mqwAgJyT1iyQiMK95UQtEYPUr2KEFT00MC+XZ5ehqSEi/0bHgNYOLvSspf7l9xOObaWTuP1jQXAIBeDy68ETZxa2gJ+Drc1gfVt32NTyuuy1xS4o94Tm+dSSPdW2FoDgbfNq7EpH2v1D/tjCWbUHl7aayvmu6CMy78prDwpuhk7nkoSqU+7xfMSdqoKB+nqaBc/6WoX5BW1OGT8q4/DuMCvQlzF0nSxUfZCjov67QR/Q0OHtRY93VxtsB024rGHJd/vJKjWRkcffF49h8fQy6TcNw/2RRUKK4kOPK3KuKh3K5hGqEOhKvtO4gBMAFIYho1eaVwxAUsFuSnzOlQEr520AQi47vUhUwUSFZVBmplV2jcv8g4HAxglD5FylYk8JbvOtnAII1wGN039tY4VT5mO90L3SyLEWAylpaK8D+FA+916Jcr3CXvHjvbJvl3FYvGhaqEWjbaAofQZKheQqH6B+K17scqKi3jgtHN54F6HZ0qt9pzNgoSAUzJXg5Ubhjkf1ur3h9kLlew/4cAxMmAfX51AfA6zl13DgkFe+Q22cejq7/A9j1UqlWeDbb6/w/fe3BrzNc7Zqs8ep5Afj+CzygxlglGHJxM3QWA9f94oCGWvTnDBN2YAqGnUxSnL5rQKNZEh7Ja2MV9H+gtt4azZyrRzYveSyNnKGMXzaxM1+jvjP7Gnz82qsanDN5kHw86BhV7Zzzg3e2jILzYvQfhOWs9PGz8lpPhM+UyQgh6IM1spec+9nGue6yKwiv/xvU5b5RAag3bI/j19HTt6137cdqhVF8O4W6/1co2ypnqFdd/m67HUseOOrgDLVhd6E7qWZewRkPwyxBoS98hej27tUmRtoHPTy+nMwCBl+aeCVOryqHGjPBAZjdEVCWmDQA57x8fsJAfjlL9/g17++efSZPtY8s5P7+j/4/h7744yhj/jVu53sI2mZSsCH3L6miuvzoNKYLboILA8VR6p9zf0g56y5QadKz+q7AjpLDs5G5ufCZJhn50QEAAPZytpKa2t5IdvkHee435N9435H0A5rxk8s6STEKSHnTEn2mGnOIm8D9T/JLzjo8eMkYHer83W6F11sJFQ7AxjHUk25ne85Z3F0bXpJtaBpa5im4jBKFI+whGYzqjzgsNAH3SBWjnkdz3OgHseX4wUnnwgiFoBPWJAxAl2IxYgMTm+LRRaV8+fFuD23rQHtbWjeKfBxLXSvbVwrR1cM4P4gdtNhTBXo7uduQKm+Xqo6R2PV8HkwL2MtA0qqHSCv6GY1AzeGYMURTb73Ab/1yzf49fe3FbO22BjJnAYp1fYIdA0zty7nVu1AcSwrN1YrT6iyNao16J4hx8zkj5s71fNYfaNRI1mJAAFgOW/TU/R47u3sb2s7+TVTNvB1/aJtOZs4KnMhS1ci6kglNzxCsIH8rUYuiCNP9O9QAZP8rY3hmtLm5IAPQ/a6MJ9D5/ZPRq60ayMlcXSQTXlkUaNEp2Vhogag5GHswmKdtms16kM3QgnK/sGx5TOr9DP9TnCFSvs78cTWP/f6WnGMFvsEcPoVnGyLCho7/Zinrmx3FNuhtudrG6MCIO3Zld8tw7Drva8CBfl3Rb+1UTg1oamXuoNNp6wWqp9Hbo9wjiDv6DEVVjtiz1KffQBcqptoF+J9bYb4tYjKcxqBw+8/HnCn3k1kWfApZ2PkTSljozHimz5aXsPrXY9ZvagMTZ5c3o5xEiHQaY6ooXNeBhVuk6LCpfqqTO6prSaLZkK3kxvF+DTAQ5VTob4KHZkTrMrD5ATGumFcM8ZYbCJGuOrJmg+LyZcdc4NAyVMrFSe34TLv28lw4bkoemug3zo7UMc0LIE/Yx7o+CEXBtvolclUGH/C8myEWfIeoHK9tV1yfeOu3/rxD+4zKqt+wyJAwL9emSgMp+Y6Jg3lrOV67qJJFH7+dgFU82WuX1Pscgy8jOSmy1nX7tMLhheFKd87RciWlt1382ydsmvVCtXIEXCh8faHwsIimMpNCYnGqs5HSG6RlCUfS55FSfTh7LObMxVjLAOxi0hMoh7cvdiYyBvmO+tCYc6IkVaMOu+tBhpmF8prrh8CDWWDTWDRdxqJwjJ8WXVJm5th3Su5ZFXAWM+8V/6+yqN3QuGZJjEE98cJ4yghvUwOzXuKqpwgCKDbm/xh0n73rMtk1GsXhSrn0h9efzW8xT0Qf+zSgP98jTmJ/FrSj6rnUhkmMZTiQlwXumJjiBpRJethVlnJNZH1L0DAuL7fSrZ5pSkXuZbd2Fq1YHhZVxQsy8+koeeFjZ61x2q4qbwmEwHWn/UnIfOwDg+lAr9W7ZUKHmWOze1ARbgI0bUriqzQvJXZ7XfG+lfj+KnqWi59MvnXFQaSzxnKdXZOFVx8k098jow3b3a4uz0gNOucY1N52pvPToEPOWf8yQ8P+Hg/ousi/vFfXVnUx9lhcDrAIpyWxrv/zAEOHH8y8Lw+co6FyrmzBCggziHNG0h5OHRRKpTvemyGzphrpxynCKHSRdaYkCyisTToaxBS1kRS0IOvy7OtdAzoPDn5/KlDlH5Tj1qMkdNbOGZFTtW5mrouiIMbMACGMoogW9cHXAwdLndSHbXXHGhWUXWSAlnHyYdJ5yaVRNFI6GwpSeeL7uL3VFXqAZR8yNJPp3fqQJ2OWjkth8qcCpUc5GemW+Vsa0n6HB2DUXQIGuiWOqY61+k599zGuc5743qic9RHz9Ae4WXJgiVTX+ZBKRRkTGr9XZsTUdXVat3FZg2yMBcJHYxoYij4Wjh/CI87RlLWgkROZpDxcxyZa2/FgerWJHVdPpTSCwWHOeVOrGd5XUJH4eYW5+2nPmFvIiya65/0NzXzKlR2BTKQ3d5TIqiKsyw5gWO6uXs+1VpaWUcGtjgdx3QIHVPTgZDr8c25cqpSZhkIFuq0W73us6EsTJGx1JNYaCiVVBi8u8pGRdlDrAhZzyJHxZHPrp0Cvj6l+fm1xgZcgnDlu6ekdXhq83oqn2VL7Cj7UbluCw56MgNTKXlmpOmodt/yMMp9+TVF/T5U68v6wunpFV77vLFf4bZMPYntNW6j9ftu22ffFuMe6ut41uOm7/B//hd+79TQP9r+0gCIc5K8Ob9+v8ftfsL+OAEoClGMopTFEKTi2tDhatfj7dUGV9sO45wtj+HDYa6qvU0pF1CtU6M3Z+QcSq6dTM9CspCoyW2krXHRTo7Q/AVgFH2/13Eh27lyI1K5iXCmByBkz9YoyUM3fY9eCxL0KiChY3WuUnGnLL0lKFiHCzOUisr5WXZgqt/znpYCBablpeBYCcgK8KwIxVSEo9+C1lZGO5Zn3i6eFz+oFDenSHtlXTYg0vfDSp+ynSCnVEBq+5aTgEqHeCVq8IIC8okiICzvh8K9vrcCPreKb+uNEqMyrobVeBadZ3nyutyo7BkiI83KAM3MC+e80dzwkvMIuznH8fVgXLWW4Db5JzR/3IpMt3uw99F9HkIZ61D/zr8q49yMv1dEQWVbXpX5VTYUGrRdBHabHkNHP6DOkeougsoVN6cq+VXyjOTqDv3Y5OZ9M072s5I0ud1sU0ZxNCjzJ+WiiBPQAbwhGOz6Fdjn1r4xO/09LdbNGfnQ3ssX2PzcMrnj5k8NfumxuXg2CaCUcQuLPczPb1OeTozNmt3qFZ0KdHJAsHZrdW/w12zPX5TPpTxyK2tFgD+zZa44WUW5/srNrQK+tMpe24eAlTBH/zqeD3N8YrfrMc0N8IHl9/w7DB3Gca7kfRXytPJczfHTGPwEcO8OE27vR4QY8Ku3W+y2/QJ8XAMnPYh5Dpz0hn55XZhtvF9vKBHoasGKGLgHiXzajzPu93WV0ADx/u+G7klAaNsWoaqhjFsBkuRKBHOoGylkgzlJfu7b/YSHw1TlH7SUMnKxxb3LfuJASAW3FwBrdvMcK+tc1+gaO4rzsFqKeg8tA4OrK5UFVeklpi4B6givowOY41R0YHGcCXvI575dL0hybl7RcF0FsA2kLrYEIw9oV5TKqIWNu1iL9kzPzBfu981zqFlYLpJBx8Xmlds/yz4B64NF/uQsebdn1b9cP0tf0JzHs6bKc+caa8GR8humhtDnp+DNVteUzxFpIGH/skKJIQDffXeNP/2HH6qCL6MDDB+rqA0U22kBXvas0r6cT+08qhjX+bQThDn2/bPxui7tTq5Pv6+b8wGFZe2fwedsfq1Ar9+bXRqtH1wbdYEUZbBFp/1Wups/cqnXe73PmhPaZe5yT+EVi2KT+ZNcwEev35Y1WOQTAUPmXN5p/YPNSiTDWtqBlhHsbSbbGxGM4cde1A5v7StQzRMv808d53VkO5Z7Qa6dNGRYeplmEXk5W4q2U86vtdfnWjve3nFJXbICpMG55e+7OJzgzkc9tMjDjKCGXKVjZ1gIfwhLm8LLyTWdi8fbDeX6re/74+MA69/QR/zf/sV/+uz4nWt/4QHEOWW8vz3iH/74IDkJx9keiC/isOlFmbvcdXh7OWC36TApaHi3n3RjL5XxhEIvAi3lhDkFt2CyeXLFG1wWT22INhNDW6XwAfXG6g9UObD2BA1IcwoWKxhLCFfAZugt71MXCyuhqlSs4TRwCjlDH+tQ4WRVnlrvd/u3/cz6jHLPUAGRAOQZSFCPzcKA9QssO+HtxuqRGf6UBVAJA/71yq1Xzmz8Ne9HcMmXebDfSVwfgna+yvrmdjqbLyf0n9C8Ce13jbJe50ryhp4DNB2IVxlnOjFJjBZlPzvFRL5n6D1yRsqk1edaSKJJUsz7bTYnKgvnpJbfrJ/b1obVD3d7zoATj8MbLWH5FXQc04kb4ZpdAw/sdWheu/v2m+xao3LhPYVe+eDVpK9nJlzT/HayJtvko1rZ8j9oD8/uXzl/OW5NMWzHx8/5NUyoHsMatOV6aO9vocCsKNer467PiPK9vsfzrazF+h5y+2hWxqQ6DwfEZu1yPS3k5wvbQmai3IP66BFiMcyryqcEYmgAmbHtjBnUSi5USWPoRpV2wCvTOjHCmTlNZbAC9Ow7lx/HZBj3s9YrT7jmaeuH4SZRO+uZCC2jZi1lAY1/z9zkc6yKozjnpTGCks8x9fSn3+45u90G+4djYSyg6D2roP2ZuRYgOtRxFOVvpxEhfo+qwN8zgCVQDOOqqFEq7Ptq3wMQNA3LZhAGyKaPUo19iBpOtg5IHMcZH+5HfLgbDVjIOWM7SO7Aq11v/ZlTY0g1n7W5Ij9FZc+qjx4UKAVKjrGtMvhYxdSDsO4M9m/Ocp93hxl3+xEPhwlzQp0Gw+kP1Gk5XwN1uUxmmfxlPwE/T9oewL4/2Vq5AzcvUDNYfD+7an0t110FGq+E/hsrOqAC81/C7sv5BPDI+ZBKxA7BRyM2zAU4WujjKVcpkSrHy0ofaie2yAfKveq5ZL9HOZCFMikSbglFJqjD75Qd48HGApKIvcLiGr64Qc1oBoxgAJT80o284LV8+HEpsiR2TterYwT1b2tnuOSx32jY86bvLN/dKXbjT9VsLlHW+Hl0QtYUEkgqobVOdzeZ7mS9B7m5B6Scqn2Sv/Uj0YaaAzBALUJ0Zfa33JN/xmWulAOgcyvbnlgD204T0hdB76mQN5wW6nRsnFgzxXZZfmfql3vvZZDIGbGoSPIRWSUd80QLmW8+t+Fy31sdEywBxxIFVOcIXkvP9Cm5UjkHC2N+6TBh6jdPCsjJPy+HJSSnYwV9fhpuz+dq46IPeEyzVnbnnuPnc2MzUe4s5As0CkUZ8LE4NKQGQKwwgQxYmoNpaiIGbM2dsY04oRp5Bc6fpr+8ZjlXuS9PLBn6iP/zv/BPPfs52uX+ogKIDFX+hz8+4OZh0srJ8ni44bAgysWux/XFgKET1tztgwCNFroyk52jE9N5gCRfoW7uc9nsW4MSKA80oJ4c7YTgscFNolNr1Su+XdAqyQMwBPFe+JxPBAV9rpKoK0zXHlKWambjJMxAKiJmGKWicCyAQcCEakrOaAANFLnz4tlK1TitGbFr7SUTtjVyQvPd2iZi3zcHm9GeF7a7HhKa96c7c7ZflbAIVV8qhdidqD5ed1fXkZxREsJCbjjpF1kfRg7uOelOzwS+1fPifeflZ19KWxv7tWcb3AcB7jOnDPOBh/YBNg+6zKd6pVcsAN2AxinVIMiZVivwaNaXew68BpWjUP4vvdJwHz2VZ3tW19DxWHu+ublvlJ9U8JT/6wfI98d/5YfNy4851bLCFAT9jTcyRAErOT/KeinKFX9H4KUe3wJqU5n2CmPOHojySmxA1wf0ARg6CUdMgDHW2xy3fixDENZk15dcT2LUqALOddqOP8chFvADqFl6AMMds8nzIss5zq/f/N7mx4nfyfwsIfh+PZJlYq8DmTFirG+6YGDObtPr6w6bTYchBlM+W8PonEE+zwkjGVWO4VDy1hY2/KPj5dZQlUYhuFx6agzTm82ICG+Q2Tr3e22uQ4fW2PuVjqFzvjKcOWex/IxzcQGKeDAloDImuB6/+fYK73+8MwA262ZO/cHGvXkejNSYNbUCjfmbewEjN32Hi008Mz7FuPWFb7yz0c+9IovLM/HAEMGiKlzq1DzX75jXjUVHqDde7Xpc7XpsGaJMYyzU4d01KLw01M6ykdKS6ca5fVQW5MNRomc4N7qoa6iPFmHyWDuMM/ajVMNlFE1WXciKWChYIoBrh+2mQx+F9ch834zkESNuRsqh9E2fbaWXYSmjvJpDmZ6Rm71u8bRq2etfVJtWtv2vYjvyWo0uZvuM7QXF8CwApAvTjcFypXURhd3IlEArBv5jbEe/rxiIk0palTYMn/NomtKChTvOEnKcWAyJOqJ7Hu0YNqrAk5r/DfdzAiotGMg1Ig+hrsZaPYuVcfLMu3PMJv8s/Tn7vkNKyTGYShoZyVtZP3MvYwj0rAExa7m0Vz9zcujnagvWWOvsoL505jOZe8X2m+aEh6MUDOEzohyZk9qhU2Eamo4bILkQPVJT9bUAl1wLp+7Jr+d2z5JXPK62C+S7Wi9rddgQg8gz7leueeDzCaNPra5cByjAmdNpSz8K45Dj1IKQAnoVmfYUENKDaXQIcR8GCru1YARFhwIKKeqx+15z0rTpZNocpikl7I8J94dJ2fWSJ3c/zpbio7W5svbJ79kEBXsNGfeAKu/Rcofnej0QJPWRb37PqfQQ8PmV5yIFfooTYtsHRN2fg4g+ZxsFI6wtnAI5r+hBRf51MeBv/h//ybPP4Fz7CwcgpiRVlf/kxz1u7o84jLPlweKk2A4dtkPA5bbH0EdMc8Y9w5KVaZgzEGJRVOc5uaTZJb9WUXae3taUmtC+8MaUW6xd0LAK9Yp3MVguwj62lfAI5hXjcTR2YG2EeICwGJcq8FUzyY1Q4D1QcfHj8HNOKi+STsmn9hkEN+4tPlQsjWaf0oW8+M6fnBque7/oUtMZW5GqxNrGyX/8OJsg+fLbYuzWvl+M5XK8Csiwrjicu14dbk2jvZyrVR7tell/G+q8bASgrD+6kUYAiO1crDu73Q44HEZ74DkE86hRecnw7Cb9m4BpnjU8JZX1B5gh5xUBfz+21lMxzgg8VXOISlozfpysfiyLsgF7Xp7Js3wqbj6jHOcBzMrwsU1aflUM/Wi5Goc+oldGAs/nAQwCgWROM/fXnBniU3IzUdFLttby4vnxfn1oPUM7fSggx9yPIZWVYhx6JcUxyUKtYPq8YAMVqa4w9Y7TjP1RlPFSwc8pTWldTlQawJkl5Z8l5xk/F8YcrBhNcMdkPk8nqMolc3UcZ4j1w82j8vmyk/zMK4E+9K6E46kxx3OasVfmSmuP0Hggm8Kz6CpAzvcXdFaa8FgCfG7fbZmMNmbJcdFbpXPxZmV8VY+p5xRW55e/b84JH0JTjUkz130HNpsB0zjZOWksMw2KZ3GdM5RzBv7khwccxxm7bY+/8u3O2O7jlHDwYIfmt6PSbs8sOT0FcKGYDUhXPWtn7GX3mRuFApfK86Sj1R6J6mk7ZSCFZuyf0/wzWuSUbO8nwMDJnEWvvd+POEw1oHl90eP6oseuj0CIS0CXumDK2I8zbh5GieA5ONBQ75WyuHVQp5wxTpqDUFlclhuwTKRqnvi1RAdCYSgrk6sL2AzRKpnHzjlbVL5PqVQbJ4Nqco6bshbX9N/6Oz7j7HSvasbrMbXeVr92ake9jlfl7VLPsPA7t78tQimDz5OsctDmd5l/AJyRywJqSffaomPkVpioUJf7LB0v8ssBvtXYrNxe+/mJJVHLulPjpvIiJfnc9JIFvAjghBOBekhs2Fk2v4Cryy0e9seFEU7ZYK9zM8/Q6GR2XQ/UoNnr6udlbEd9nr1z5pAFfYpR9iUCkSlnPBwmfP/xgJuHyezOoRfn33FKLlow614r656VZrnvpCQOp3FOmCcpSDiZEyrZ9fh8/ATNboqfh0KomyqI5/pVLfBcnp/IjWwAI8A6C5IqKGrNAIP42C+usxBs88koUQ/W6ac8v3bjPgFCgvOQxXLcb7Q761W0ddxC5L2479z68/KPcqvM32C1FKyuguq4JRrFE5/kHNMsTqz7MeHhMOFwFMfWcUwnweKAUuBGwsQ7XG5LBWPK1wQYXjLNpWbFqOzIaUrGVPagoZEN7DFRJhb9qo0o4Z5pBXKwvjZXWaPROXIrfbfIAHNWAzXYn8RF90/8Y9+dmUDn218YADGljO9vDvij7+9xez/hMIqiEwIsDwaFz9B3JeR2Em8xFQSrnquS39OwX2ugVhcVJ1RAVXxEFDNRvqm2EsSzsGg4YICGt+UVrOPqW3Yk5V87C76USdGuIy9j28/9hybczp28laL+YKcceYXI61Rfyhi9pK1uPSvK7KlxrcCUkyfk83MKW3WdUK0B2OvQbDjlN8EdvJrnKyh4hxJ6ELgR64NcKre6jk6s8VQtluX17IVbRAGwRL2m4AKFVRxEWZq0cklozhV4Qf2CSv2sypK/DllMpYInZUWRYRlAmjP2Y8Ik1gFYWAFmMBUlKjc98XPD+tpaC258CQCYwgAYoMpwIoRi9CJoxfJUGM8EDgEYo0Uq3hZjMsYyAZJW78yAVRSeZ/Fql8qOzgmUs4VyMbyBeSg7pb7FKGzCTlkiAHCcE8Yx15U73dgBbipwPobCMAvBGTHNBDbwawE4FAXUvJrJhV+gzJNzrVbwylqp2YtlX+C7knuQx3M+FM5Gy8xr55LvQ9AJYgUKQnv9YPuc3/OyLqJizBcjjf9kVYLhgYontDadgwFMqph1BoIF84JLYZKoid2V/ZpN72/mXG1YVkwYLAFFD7gWQ3OdJVAZm3Yv3O9DAW0aL7SEhaHyopf+1wyMIh8dWNI8WCrIJcys2j0XBrQBYwEF7NBjDpqDMAapwGsiVk9XWNM1CBqjGCBb5kFjlWxWbNW5l+GZe21V1CVz1beUMw7HhIfjVPIkqkzldSkDwbHkuKPdR2liNMMVyrP0z9rfa9tyzsqCZNGG8hQGDb+WEPAif6pnEIJEoIziiHg4iH7MNci52HcR277Dppd+zEnDop1xlWit5+qW7BxmSAYIcBMaRhcNn3prdXOyTE47P8cKZby8vPPH2z1V27uuF91nPeBpjnd/zVz6wTXlT0tgrXpG5WJL+dQOlL3J5WMPjIXmUKztKcLEgtkP7UX9b9d0Mb1+gOXwAnSPbI7tdJ0x1H83SCTUphd7hvussXdysVuqNEhK5JiTRESZce72cV+A0jt3zjW/zwTbx3TO5AzJO657f7MvboYe8zyvsge5T6/ln10DFZPfuxy49RgQuXpP4DOpAcl6P1t+xzB7FhL071mtuM7tumRJ+us8tR3G2YDDnAWc4TXadrHpcH0x4PpCqqkDkiJhP854OEx4OCbsD5MwZTOLjZVaA16fkT297M1+jAEs7k+eqaYhmwoBhwV8vAyI0e+5ZW5y7QbQeUkGbd3sSLdAs70vn3M+IBcZavPACUqCfdWcyaIT83xra4Uysf2dXKDo9OWi5YWtKz///B6GwigO4TwTktei/KSTf6r25NI57xTguEhRpA5DX/bkjTHtRU/0tsHc7vH8PGerrs73HIfaeVfmDvfaraY+YSHZGIOt/7Zo27nUJa/R1sLShyHif/d7v3rxOT87gPj3//7fx7/37/17+O//+/8eNzc3+L3f+z38a//av4a/+Tf/5rPOcwpATCnj1x/3+AffP+Dj3dEq3PEhDh0QQ6laxCpZTCwrDw+1UvEKrQj18jrGIJTrjhVANcxNYfUs1n6lrMyJCn6u2EYUgF55+qwP8oXteVtL3b7E+/ncbXW8wsp3K4ojUAvS+jj3L8/nThCq3wjI4TeLqD+UuZfAauVZXVJLj7KZCk5ZXlGk+Te7e1R9tWy/5Reh/Z29qI+0r5qTtEeUjXtxA/VJPmUin2gxRvOQtqyqtdxENB4AVMprm5KgZnQoM2PO2E+z/jbg+mLA5aZzymPt4aOSkzOkOA0NKQMaYE6KhRcuZwPpzq3hnBulgCCLPhRTOkzRKLnPCjhVFIHyQ8rGLPPTjSNzk3hFJqIAsH3XWS4TQPpFg5w5pdIJK4zPoncsmY2eD6iZpJ59yHuZE5DmGeMsoZDHecY8iQHlAafq6q4bnMtlsmCRvrJe817Bc+EoBuio0gGZA0CU65tyqjIiFMeWzVVvqLUyybrt2Cx+lS+MqWzzsDbcNVUGapae8WDcIPnrZL13G0knG9alSNNvyqpTMkFPQllmf72hqe/JGOo6Lfrj1iErxSML4DzlGvCqko8nMi68YZ4WAKD7c1IurwEQ5bvyYfBGA5g6QOb/pKkZvBHMZmocr+8NFn3hnbYrdqX1067t569nmcblmPM9vf4+fx0T9dfnk9/mDDwcJ9zcT3g4zjJuOSPGaEyGvpPqvz6SowUnfTsHhhl7MmerQOx1P65FURuVJZGy5pSTDvt75HP1Rirl9pQ0L9VchxfW413WdQV0uOdWDnaGY4CC7yx64YAJnUBL2QB34lBVQqVsAFze5PUp8qLmAaFaLtZjIAYyw2gVeMpCWqBsSiq3k9oaOZfibfKsi6PP1kkmaOn2ueT2Mg4FX4P7Sq4+X47lieb1S05Dty+4Q1zahcKIlxx/UvRh6DtzrDBHfBe1ME3wYNQ6mJJSXhQnOU6zMllnYZE2gCHnA/Pn2VjYuMDWnSdQUJ+p5/L6QPF5r33rderqeFDGK9ss0CGpMqrr0IUMX0TQcv7R2NeHkHMBHEpBztr59BQg8iVYRAVA6s15R6cx/BsGFNllDNdPKeFuP+PuMBnJJUZxDg0s0BkCLrUi/dWuAxCwH2dNeSCA4XGci22cim4aQimG5Nlc5T6CRauw4vZgKQRKypNxSrjfT7g7SHGp45QW82PQSt59J/e1H0VH9CBUDAGbXlJEMEVOG1pKvc5yRFpUY3aVgmEyImXKdlTr1n4DVLJkMR/0uHYerM1h+ahRZtz1uA+0c8OdsTTXX7+xtPuM3GcpnPSYQkaQl3Ox7zQKKTOFESod1+4psCvOSeqvY3qF6Gamq+ma7JTIsNsIe5FOEjosnxtlcK4R5DyXB3duv2v0j1MwX98F/LO//1df3LfPCiD+0R/9Ef7lf/lfxjzP+Ft/62/hF7/4Bf7L//K/xP/wP/wP+Df/zX8T/8a/8W88+VwtgJhSxp+9f8Df//N7fLwfrXBHFaeuM9ry68wZKxjkJzXTp3UiI1gUoynVlfe5UQAAvHqfvrbP07wsOyceFoajfm4C0f8+NzIynDi3W6UsXtL2q1yn/v7cEve/ay5TvanPn5fHrJwku46thqzkE69X+vDZhNSntrD6EsByLFvDPAZRHnyN4+pRNQ8ncrOklxsoYJibONzQqSyMKvt47G7T6WaoGyvBNBQDKTYGYMty6hhy2xrausEGKj8oSntKCccpWyV75pllaLFnH/G6HA9L3O3GxitJ4Gs/ZIGjUd5LUQoCkRnMp8RCWMY88eddebY+XHDw4CvRQn1eDO+TZMtB127WkOPUgLCqYFaTfsku9GrcMjcXzODgmNgoBLhjlREJKfSUaWA1lqdf635uBo6tU96CTiAbAeqeei3odYL2r4AinpFXGzxUiNG8P7NMfPeXbeXg6r5O/W6teQW+aNTlq7A8/FE5pnP+VD9ag34trB7tOuJ3XJRomd/yJobCdo2hYRfxGQMFlOP53QILAdjtBjxYEZWi5CZkTV5ePO9c+wDsxsngAGDshODHJch5CwgMVEvmkbY2X3ivHLMynrpeabzl8gwYlt13JUdSDOKQMGathvV74I23aj3JgLFq9bu6wMU6IMC0NIXtwmclrGmCPVxLyRnbc0puvS3HjeNMxs6pgQ3unxiUvaQgiA8XPxU2ynFuz87HXQUAAG3zSURBVFUfh+a4oH0jC6oUgkEg2Osqa4aif7R7so2rTjCqVp8a3un3Q3OAOTC6Bk29QV0mOp1VcwKmScLzRq0OXqo1Z3XwLeeKnXnl2a0+b75unseC9Yz2dbmfdh/ysoeGPq/G9Uzth/e+2NO0D7JasgGYBCRrUHf92XmgeZE+AmV9ADUz3BvhsYsYx6lyBPI+KiA751PLpR7zpp9h8aKMNWWzBxm7UMITewUtejqfOpdjM5bfZbiULTkjzUkcU/MSkHgtIDKlUrWa52F6Ajp0OyX80P3HaJucXRETJ/+t2GdTvKjvhGUmgI6k+Rr0M+9Eaft3f5xxv59wf5hwcLkY+Zx2mw5XWykydbHpMKeMm/sRH+9HcSZR9wwBlxs5bjfE4kDw+51zAD4F6PHzcJpnSbszKTMuA5mEqMxx4oqC6Uqhes9rUC8ra4f3u5iLtk7kDeeB6TsB9ZzP9UtbF9V5yv01P1m0NWm83DfKC/+ZaT3cZ+2YutgeZU7sOhsPyuzCxtW51tXOj7X0AaeYu1UOVIdXfY6UAmR2j3O2dC/ikJE6Fyln/B/++j/64vN/VgDx3/q3/i38F//Ff4H/9D/9T/H7v//7AIB5nvGv/Cv/Cv7u3/27+O/+u/8O7969e9K5CCCmlPHHP9zhf/mH97h9GDUfix6U3WT92r42be2y/JT58ZpL3BsyL+nT64ubT2vPlX+14H/Csc+6bjhpgKyeMyyvscacar9fhHK7F/br1XOLkrfXypWJQArgJkOtTJeNuJynVgUa6DhLxTlqXpYbqbqv4mxpFXDPUCtdanNWFSCSQGbZjOU1cy0xSXtWxguyeN+9UmwholGUmBZs8PLeck8G84Oi0mjcMzH2R2JIc/EGy/eLx1ueVfPc+JkP0aibY4KoNykYKHDeeFv7whtm1bVdf3xeIwEqC8gZQzD2noCLSwXTK5beo1zdo7vX9j4WoXsOGOQUfI6RZXt6KPeftV+czyHAwnRkPJw52o5zJqMnVSMeAGSyiKpz2Qid6WN2r90X2o1qfP0Zn6Awr7WTcqj50uaEvvZrLEZU+TMHzV03aHoXz9Ao4T7lJucMq5o4p1xFdExadK0betzeHTBO2SoPSvXsctcLY4NyJ2ccJ1kwgxqERA/9PGIeV9539byzDEhJIVE/p+q5uc/btmbYtOvg3LMo+0FroNWy1QM00a/bWAwSGh1AtnDhUZNw8xYpNyl/WDGVRZh88SbP8AylmyYvjK2oxwcUmULZP2iCd4ZrVYAOgsu/xzHz99wMltvJWoCnZut8Xu2+hB0WaeIzhtns9baGyk4DRyinnAytn/9pbYbMqmmS/LyTFt2aPNCAIktrQMBdS29AwBr5TgCksksKMFUYcMEAWTiQ2sltvVmf5iBBD9KxqZhSgP3OQM1KmTjdCoDoHKVhqVvYPbvvBdgvuVZZSJJgvxWu0RNzjmaEOjfk7FNBZWy34hiB3YIDOt2tUb+YUxJgDuI4SbkUOijrMDuZVKqSt3vy+bFazqfFnoAyNxahjF3AECWEmXuDsEYDutih1xzNfV/ASD72Ksdogr2e54z7vRSx2B9nmw+UYeyLOBFLmgCgls9rc4CO27AyN3zUjZejLYCfVBfk2gpAlUJjN3S42Ha42kpV+q6T4l0P+xE3WpjDj//ltsPbywFvLobVUOy2kVXmKxCz/gILmI7qJLDIw0bOUH5X+R39OLncx9T7ZXycE5eRC1l0NotoyI6di7L/elmXUrLnl1FsGJON7Kf9bl0nOtVs32h0QRMjjT5qv+Fnj5zXA9JFr9f5E1HS+3hZXp2HTsI6D6GAhyVaytZeWCdJsLXgYpu3tAUnVS2yKNqkYftedjEd1Kn2qQzE/sW/fEKLMeKf++f+OQMPAaDrOvzBH/wB/s7f+Tv4u3/37+Kv//W//qRzzSnh//vHt/hf/uwOd/v52cr31/aXt73mXHnOucLixYnvz7w+e34aT+1vVk7QfvTo9SojKVTXC/WXzgApn4fyojnG9z9U39n3IVjoZQCcgenPnU2xsL+L5qoxqrD1G4G8JyjhbkaPW9MDgr//lTHgmwXj0t0vj7nYDXh4GMFb7GMwKjyTRLchvtwUaJib55KGRSqpD6bZGQ+xhOFV1ezIf6ax3Cqji827+O9W/hgIQA+rKRLL4RBlVhOfV9cLhEOp+NaDHBebMj2C3JyFUZgydKzqEIC1/dTPT28U2IcGJpSboZLl1+FCOZrxaDs1NqbgAJbTxq6VHVvGziP9nOWfxZk9wChGFqziZx8D+p4eVh/C6frpjHoaWWPKmBVE8oW3UtCk3y5EzDMMJflUtqTdZKcUJpHOj+AArGZq2vN291WHTq3/73Or8XnNmmeLBtE4ZUyaj3PSPJcM85znpOyNXBs++k+u1tfKM26em3/e9nkjWzIcG4wndLLKX4jnsSnQ5H6x85/qX9PsUPcb30fKYGSynKONgc8HFkMBlYxBFIDYAQERKQs7ue9kXl5sJYRNjCWZIKdCVtfkf27eZDdoHhwU9m0BqVhcid9T1hCY59VSyoClSJCPK9Cx6aR9lyFzP9EIkuu2cqT8Lpe+us/LelE5P8oEyaExjuBkcBbj0Rc7KfnHZGwC0BicPvQvmqwtzJrSxxLCl58k+9aaMa50jgxdRBxcCDFCde/eWRRCcaQxBLgujOJyGeaSjoP7Zht61moo9n4x4coH2V0ru3lVjOkSbjYnKWIyJYJVHhwok8mrJ3Z53r9+JhVpaVyWvZEAmjxDZWb1LFAQl2KEezgyklbInpUxfxwV5FBWyzQnzPAARA0alD8CXLCvawZ/0TFcy9mKSUy5roJbAMSsLN+i7xx08lXykhc8JexMngX9STkwhIDtMGKc5gqoCTgV2u7nasCmB0Lo6+fXjHvFmIU4u7LO1zkX52dhES/BpLK/1rIyEW1+pD2PFVnYjrJe5btpTDimOqdcVJnDHMEcL7aSYqYwCQn62rE6WDV7z69n52SgHgRUn1uYezMWHmjrYsD9fkK4lWvTcT3POob6nIdO83xuOuSc8XCY8efv905/lb/GQtQ++sJjweaRvPC6J+V033VVKopFVWL3mvpaWwirqsy7wnxc/yxV3zGvpNgkovgaq0/30Er2NdPN1q5X6by+p8+3ksGNqM1Yme+p7J/+kibXnIALeqGAYABkkRFG4UCeaRnldXGP5rMA+BN60BHwzo76t6sgJGWJHsy9tTj9vLPKBrLMpcY+CgE2d4aO+RilwvNu+DQI8LMCiP/uv/vvrn7+d/7O30GMEb/zO7/z5HP9P/6z/w+OU6q8DkFHpx4sZaKE5WdeaEX+1g04QlgOPoKFjEZuDAj1uVuj+2v7S9eK0HefnZkWT5kxp455dL69YDo+5SfFOFqx4PSbcsJXWhOP6zwLZeDnbe19n+6bKO/Zwn4ZWluFCqLeCALo6YrYdYAV5MhyHI3wwzGh72BV57m5V9UmQWUGatQUwM8Mbm7KzkhkPiozFStD4enPIoMMyWb0nOYRVLEIqmVww6chJhtpAtXFnNv+LJtfqxzTIssFtMo5E+NyoB0MPLNx4nco5zFjncqCM1688dEa+bUhXPfV30+l9ABaBAaq65RQ9RAYsl1AMyrkMURYWHHWaqmjACc5K3idV4xf1xG7fdd/Mph4rdjHAlJUuc+CgqC1sQt7HmUAAqgs1eFMVMp9XjmfLmScE46TV2gZQuTYII1Cn9391a2u3tnZWDr2WKW8yZGyjlvPc5kjNLzJRjHjXSdQVqW25PbyBpH0iwaTL/DgH9XqeniJyGx/c+qEczp1UD2vQ/2XTIYQgLkLGOdcAdld7DScSD8DdcCiqPtrWA+cdVGF5KGA4eOchKWbMrq+QwdvzMpzbvc/Y1WBYHlhWWbIeX3obJFb+gztWetzc1XSW+Pr1HZbVsTJo+rx1+NCyhjhZI1bd5QfzCc29FGY35puopt1DXfLQgsBBSgmsFPy+dUhoS27vMxtAiaftq+TUefzyzHcs/1M5BYNTIAggMjaUryqAh1PgJN8PSur6DgnHMcJhylr0a0GKPSytXpWOpEjXNX4sifEJl0GQ9Z97s9OfzCnbEVxfLO9CnVf7F5R5jRQHAO+H3b9juuv5PsrTh2yW43aY4XOfCik9Quwsc7VnClsrFlzdUrRy0fyvbr71cEtG5j+XdNY/V6711BVAwhsHIpd6M+zrgLXu7dTIar95Tz4IBOhO8N088fWeyaBNrIdnUOm0ekyXKhyqseyZnE/xVOQgbHpI/sZSrqXPkbMgWCfy1MbJe8cWel9BwxDZ8Wxhi4ghGjA13GccXuY8bCfcX+cLB2AD7+WaBfmP/TrV/JOk11PmZR1PGPIQAyqX8z4eD/qfF2u43PPlthBF4vORDvAnLoEUrtS+CqnjAkyRiQJeGamz/crAG9AH6Jjl0P3M3EAHKaMw3GSPI5HKVzGcFfKKpPRajfQhuBckSdc0jN0dt+FWVdCgFFVPKadk7MwdadcalTQcZvm4v6D9p26Uo5l328d0DbojWwo81xecR144ouJh9ysIwAhZyS4FCqqhDMVUC0Lzu9jVUTQyuwJ5WXzTfnO2LYaWt3HsnYIyM6a3/g4JZsffTfinzrbu/PtswKIvt3c3ODv/b2/h//4P/6P8Yd/+If4V//VfxW//du//eTf/9//n/8Zbu72n7GHn94q0FEXUgmzWwKO/nhOAiBo0v0l+FmDpf435TN+b99Vvynng1tsTzmfXd8ZQQwbq4BVq2rmPgv1+WhotvdjRleoP2vHxwuKEFc+WxvjlfFZAsG8/rnz+b45YWXPyYE9i/Gpx/ocsL02Pov78ePyjPHx8zL6vjwyPs9rz1f+n3uFL63JED3tvgNEAS4oEgDM1aYX3Ii0m0g5rmwyBBrkY2FhhZBxnORcZJV0EYhDreQHFIMqICMFiBcuZ62CWIqmeEOK3kgCkR6wsLXh+sxp5A3k7D6vf+OqCgc0QNHzZ5j1I7i+yMCZQhQCMFEhnlZ+7DodAgqI6CwB2nU+pJuFGcp6yub19vcR2J9Q7r3OzeS7Ui5KmcOx8QybeU5IE4qBmIuBgJVxXF/qoRo3ymUqgSwI4wHC2NwzgXGCJ72xmepKjz6ME24c4OZXSkW5HLUaNkN/1vIK1ezTXM3TYgyVe6zktQ4K76Vr+8l1E4qCTuWNbMgi56mA8paKt39WJtKYMuZ5xjhB8x0BGckBF8UQOgVCLOaUm6ZVcwZ1qN+Wv2vzwYzOletFWPV0fmfnPqXIN+dmcZy6LQ1Vu7eVv/75VYVrDBAsQI+Fe6uXXqq+F92DgKG0WP2WIYsIxRAlsJj4q3acQtCqr/XeWgCcenxMLvB9Ow4ro7XWPEBgv63mQHkiB5ede3X/afeoplOmT0H0EAJbZtxqfsgYmQajXkNe9wvaNylUwr3GyTIDBnJ1b8v+rHy+eMG3Xra2qSHYVyjgoEU+pozjOOsa1oqdDeOJ4apsVtALPtwStm/YFMzFMWS7Pr9AkY3mYNCwUn5X5Oe67Kj2Bets6SodTmUPKA4je10xV2HyW/oR0AUB1SMCEIEO6pAMQMrCuivMqaLTiDzXwkxYsuvWwMO2Uc/IXrTkemzWGp8U9Z5yIc4pjv+pcTtz4rym+9RrymRQe+K1i6HWs1pAxWwOk23RftO2aly9PpFzCcV+rhLm+ijnle7PEIDjaDJ+rg48OYYnTu4jmKh/1Cy+UtREsChZE6MW7Ck5IKV/ZE9av9l3yifVaSq5an1f6hX+eaYUMCMhz5TFzT7XnKfSI51eYTasgmvUteisKMVYawa2XcJNs4VOiHIA2aTdUOe6HfqI3SbiYtNbUbGNprYwFu0sxWaO0yx/NRXHWEWxyFry4Co6FHvF/V8Ye9K/Vr81XThlF26dAc2tmYG6OJnefZn3S115EZatb0rYdz2CrWyyb6Qby+YOqPRFr8fx+erxSfUOJHF6HexxhWoduOkEABh6Rwd9QfvJAMR/+9/+t/Hf/rf/LQDg93//9/G3//bfftbvf/HNGwzDAHm4teKQ9bPqATfGl/ci8FjSul8qBNtmeUmepMp9bV/bb1ajkK6ZuNy4TgDAcJsdBZg7vgYtTwOnFUi6ct5HwdS1vjTXWetPABomYA2yeiZzDcqe6Btg4WK1gbTex/I6mgFFxbyA/TLuNrYQb20Jy3CGmQO1pA9FwTRlqBk3MQy0Sl4EUuONDgjWHyYc3g4Rfd9LgRHNo9b1DL3odPNXACmWKoPjpODJLMVfknmOWa2yGS+bkzpvVsawBcs5h/09AueND/+534B5ysDckrl8kTlPULy2EQTSouVELACCKH5dF63gCouvUFmiwjJr+Oys4WQMU8kzQ+fkujZOpgTFYngDaDe/YqB65ayMb3v/fmiopE6TfBpiKsaMU2rtWs24m0KXff9KP/lZ2WdRGwnuPec655fPgdTHiM0mau62DttBFF2yEbxiTgWT4U/jJCGH4zxrQQMqwDPGMSmYrkUqLM+m1z30nvx7b3rWC0v/FM2x3Kso1wiOtRwcoNl4/UPIBlSQzSmVH4sxaIYhFW4q3znbmjclOefS62bNZE70lTmy+lku0zC4CcJpc+ocAWRhli/D4jiG+bRq/fJcIfjrF8Wb31cH658q7ysZYCEgNPfv5VU0mRGMgTqlhHEsBZWCUZ9LmJIvnsbfR71hzx6Xir3uuJWb5T7iDRO+LCkulPXbTM7M/73AXLc4F/LFbVXVvJYxWn6+kLVunrfz3dY9n4V7L+Mn6wC5yB5h7yrDkHLRWITJbmHWKsriSJuRE6sp1/tG+7rtv/1VB1I7v1IG0pww2z4mSLLXndrm7R2+97nJCsAYquOsL16WBiDkYPoN+1Zka/NQssuzjIw8lT4Awfaa0rfyQz9WITgu7aPmU5a+NHuAH2+u5WA6T5EnnB/lWpSFfg6qkW5d8uPrn7OX3blIGztgTSK5IQz159wH7Us3SMGudmJN5VztXTIuuu8mO4GtC67klLWQWpacCtzzfP9Ntvg5767P8e4iC5co0zdGk5GiD5X50IbKzinbXmvgVq7lEftRpeFp50urO9j4ZcA5Rco36433FAMQUtEZrTBeF0oKGOd4aqve11EDlC3tZ469TAdobvGLNuUCDFRPzbk86PVYq+RuKPOpAifdd1I5PiBB10eQ93nOGPcJN/vDQlfz9xz0omXeFJ29c5WMpRiOpF1gsRw+88ImLfpJ8u91jMie9Dpjq2MgFLnEMZNxD/bbqrpxYk5nyW+aycxMQO7cjOM/qkhW22gjQ20P1gnt9U+PMhoPMxcpUYOb5TzBCUm5zzIZyh77hAlypn3WIiq+/df/9X+NnDP+x//xf8R/9B/9R3j79i3+k//kP8Hv/u7vPun3P/xwg//s//0PPlv/PPhnSYId4FiDjuWz3Py2oNkr50PxRla/aa4FXQBoru+FQvmsbMQeEK29iyfuB6cB1tK3+nznxmdxPwvBL/dW3cdK32ys2j5Cx6S972ZMymfLsbXXa/eduChLWFxin933Z8fv1PVc3071p7q/leuVz07MxRPP5Cda4l/b1/Yb3VoQuQZpl4Dj6ZQWAOCA9grUPH3cKoNYNbjqsxbQtu8a4NeA4+jYp7EClwioMfSlgFTKTHCWTmu85gzLTwXATBtTOPUEtcKo4ykDWPXZxtl/pv33Y+YZLoX9pyHTDKkMEbEr1a9lLJgjyG4ShQ2UkdVwZr5Jmhy87QJ4MpdTtufjnx+vEUJ93wVoAgKiu78y1wrgnUtYqD6/yHN0QRg8wYPiGh4YhN2TTXlsWBHV7XPMTywIU3plbGpF1RvOTjnWcWKoYTkkV/OkmkP8oNxK9f3J1tjm7jSLz37utrivJxwr89vn5xLQOAQN20zZGJCqWlTAGudHcH957lPPDij6Qn1MMcB4Lf95CP7Y5bn4u8fu/eR3jz3IUP15tC3mPMWAtuSZe6/U/HQ918+T69GdpD2mWQrlGl7eui+4/sneCSZPnGwIdDZIM+1Yxya5Z110fc6P8h6o54s/rm3t+KyN/8nhcXPAP85qD0M2IVhkIV+XXwV/HX3TdRGJhYvUruI+WdaB7Ic8tz+FWwnyXyhjWfYiFHsj1z9cG4uc15+9//5kO/PDp8573bokVFb34xCj3YO/SO3kc+CUySafT7LMyQAZbxbwMmatsyV5n+VqukgyLBd3QJl3NYy4vCdgfY35F4t52B7v9n6m3KidmVpEEEF1AjdGmj7H37elfEgl/ZC378x+bLul+oTXvcp6bMetltm1TuiPDHaszVX7W/dgIYdCHRFQR+bYyjzZan2xmUv6YXDHtc/Td7FK6ZTXUmvUdvZTGseJ1/PjXskFv76DeyYrY0hZUOtUsDXAY+RZpOW6DzqfQjk2QBiI/9d/8Z9+0n2t3utPBSD69l/9V/8V/vbf/tv4l/6lf+lknsS2sQrza7X/1x9+PjDya/syW/B/Q/2awqdscPKifF+EmglkeKU8uFw1YXFunxjWC7mqYwhKhW/6G+Rqa4pj8/Pqs7I5SCfp3RMhpZ4rlHAxAyCtKloNBrceswJQSqBWMhC0AK8eiPUgeM6ovI1AA7TrrmTMihY49b+BB3KLclcpGAZCs8/lvuR3K/2TTtTnhgPL3XhU46f9pbp8CmTu+ohxnF3F2jbPUjavZM4l548xJDSEk+MYgpw3+bHjfaw+T9izARj+nO0crWPAn5fjn91fAxiyv28/Z5rzpfXnvnwuri8o4149i6/ta/vaVpsHmOH2otAAuMF9ZsdRwYd7HWpw3AOfAnrF+rohVCCp74OIRpdDrwKS4YyBcj35xRLQr/56A9TA23Vw/hR7GWicAOfOpazyc0zokwz26l54H82YPeNcdWi9+x0EjPbOAgO7feqZrGkY9No5KGDN+8x16pMyl8o5AqAOApRzhMLKLv1ZeR1CxbYEHgFCvrDm9TZ7T/1S/7JS68JZpQxJ2ctro9I0Cmf4r133/9/e28fadtV1v98x59rnpZzy4jnl8aD1xgsUJLVog09rRBNSyr03wQt/gNQWjIQKBAS1wQdfkGhsExNTL6mUWBoqiaA21obEpAklgRiM6YsSVFSaVER5eR70HJ+2pz177b3WnOP+McbvN35jzDHXXvtlnb3P7veTnLPnmnPMMcf7HOM7f2MM+eEc4uYi+RRx6Y9qWauIDioKSH8GNg/SaLZmSeXNPdZ5EsKifbx5aCx22gf3kmhA/Jgz7PzqwLmSFuF6PtrebhEq+9Viva3e2k72yD2Da3nHPLtf6paGW8YZceCf+R0HCVY8st5ZUcGyqmpUlj176GPgbD2wDlyeTSmvtMyPPK/wS9oRmGvO/BWjyt74IXWxbZ2O3XSNb1uWNUxStv0gnGFssjiddpP+ZXzkvRrCH9aBnDRhg6R2EmaxSPuvy//E5LEGSkDaZGMS12FvXYjjrPPYlF2huzD7QsclNlKmHArycReQadfyvggFwnspylHYr1UopLR18m6V8mTqpXVo64CE065NW2urbBpL30HaqfR+zpdrSO8umPdvGe5ynGXK0hg+j0P667Py5vXCsMJlz/A2VfI0nbQN3nL9SxeFZiEXbAqz5brrrsOJEyfwla98ZT8eDwD4f6/93j3zi2LkxYG3f33lwugdCx3tGNvRHHQ6TQOps6Gs5VEMkZ2iE/xMHXF5SPacbGDjsBZfGDD+ZsdOTKFjjzY+WDpTyX+5s2jQst/pOOtcazjza3q96BiMjSb86I+R3CteOCOXFz8nOzHuFzDsMB89uob16aYuJh5eHPYFm76CybG8/MI6LaEr1Liw/kialqJP0Z3xbCdIXqadLwYJXqZqJm+0U++gnZVJnAomHV4RFdMaK/nzdootNbJGWVaH40H+oi4spB2SgGo652GaXzju1ZQiCrFIIrVzppMRF0ruzZesJGRCRU75SuwBI3D3KXzxeaVID8izPOAd+r5PYUHe4RGBXjqtSWAtxX0jwJt8tmI5fP2cCstSRrwNn+RBFHvLToqJTzplnoW6sGyn5dg45NOaTKcQXj8O6McIn+LaV55pw1umE3z9nOajvddYiO/aP5P2VmQfaeq2RSpfwbMdbo5LyAWlKuIi9V3soC2zxi7FSB24LmMVLgNep/2Opf0qBfnsvH1Wcd6I9mW4ckvS3O+w/mCj6zKKe12Gwom/to+Y0giZ6Avjzoj3OpjO0wQuCC5oUrgaE3YRRPVakQa63q1ZQy2smWvWlI2WWs4BTtbTlWeLABv9TPenjWPakEBo0GjfJUYkiBDyN8bZx75l6NsGP0KzGZ7VNo1u7KMCQpPSDEh9AWFwLO/F+J/XH4Bu/S45Je8I60YeVXkxyCxleZYdQ4zdKtGvvWac8W8QhMJfve5zh7afNojHLsjSDWl4In06Z5bZsZay6j7+bVxYimDWDQWghXGErBZrA5HWRLXu9+IdPvArSwCPxTaVGjwdr0l/Xuu/Wbql1KNKwUzus3XVrkVol6bJ+qKQ42Rtq31I80vDqscO3kkf3Ovu8GIA401e54YG8X4dF9kRhYwnnY4tWrN+a/oYGp4v7c1Ymo7b4TkTjrieraRzjHlex5zJ23ytV0mn1EdPvkhc1B/tf4t3RR7Ea5O2Hq9lWZmAeObMGdx000248sorcfvtt2fXZrMZNjY2cPz48VU9/oJCMZLshNToVU4uvmPPwjDWfDj7HjHH9iUjLyLtdGbnTEdZ/HDFzqt63nZio1ikHWp5ARi/AcjaXyFMoYtrO/fil7wrGg2HfYkATuTZ6DbrNMReie18p3uLZt857RDWOh72FuncXXLJUaCPX/a6tBZZWI+smAJoeg7zea8Wh5O2xdG10HkONziItiSbnHRyb9FblJePLMArzmSnXll0+sgkrg2H0Nmax/DOZl1cY8S8qHZBg3E/tux8msFPW7u8+NaA6XQ7IH71N4J8ke1l/bBlWDpegJlGKOnqik5XCzTGxCDbqdRM2ZFpFV3xNbXsGMBk924o06wcw2z7xmKkUtai2r21666WD6YNKfMhCI7p4dKh0gYGeQcwdbqKrm2RoH5wkAe6UmSSE1OgQvgLi3bTSXcuTh/uPcRcQ0RxxIFvuXRK73sxZlIxMhMuRXTte3gEsdr3aU2f8FHDTJnyaffTINz2+mEjE4J7D7hUVqFhKgVhJKtyQAVZIPcTUVC1YbZiO4w7CY/4J6+AVgf5KbNUoI1hkDWMurg7d9fHTWs6u65SEo2teJ3F36f4Diz1Yx7IhzebVumeMi/Le+W4WCPMl/cUHxAKv7K0U3dlHOx5ZOWnjCtQrlm2e/Fb8qjbrUeErBgVUM0/3RXbCqmxgyF91fKeQd+5vC7rnmLcj8y6Vc4Zf3Or6vx+mWIr7gEb9iYXp0v/jFs9lvAWz8nd52mUhHgjTJv0tOmUhT0+Jwn88bfJBxXYy/SR5V4ap340PoSlDHuWTzDxcflzfAx7ayzryzJjfsXf0WAjjimyjQPln/Tvttkspj5N+NvZK91ynjn71w03dLEb4MmOwCHNw8yE1oWlAKy7sKai13UdO9PPTf2PfG1IfX8vEd5YDOpj00FfLa6UGd9lnUc408UrRT4WXdo8vTPxb/uUomStb58+ykuAbP8mdXNLjUF6SfPugG6icurUKTjn8LnPfQ6PP/44XvKSl+i1e+65B7PZDK997WtX9fiLFoqR5EIy3vgtclS7azWd/PK1qy8wl5+0LzYnHQfkLwjYDpz6Y4RM81usA7RjADM9XV+cRrQorEP1uZn/0rEL1+ZdD+eAtbbBpElfhdJgEGGXsyYIeLN5h3PrcxyZhIHnc45O0DROd6HtzIvEOQ/n4xp38ZkiAva9x8a8VyEyjmdjfMJUhkbD2GFzNo/WhfkUjUU5Li9uK6Y4k8beiJbyYuyL+/e6RNl89uYhGs4YVn2uh1olGP025l+KWLBmSHHTzhG8dl6dc1Fs6DH3ziwTYAfj8rU89TpsGpRdl8blJ33hSiZhOOTCiiBr6JQdDBUkF6TlwrwZbVSWOOWXvC75EMOv09xqd8U81ENIKnkE4S46iwdN0xjxLvoP84HCx46vcwh7XYTUkum4paCbi0i2YxhDYQpjufZaKWymZHBwWYlI++eGsy36rLefH7bF38LZEGljs7bW5deAOAhr0HVxAwoVu5JXW03zKh6bDiSvzXXN9jIMph5KvSzDmtI43efM/ZmlV+PiBlBpACmWD33fo2kayKY0m3OPeRc+lLVtmCZ0fK3FiUsmODJpsDn3WN+YY2PWY30zbMYjH2UkPlLW1ibBunwSK7sv24wivTLLDnPdru9s21yt57523j6gGJjYNPXFQM4+dyBmWhE5HTuYZU8kDpmFr9e2UUTbFJcYi7iLL0zYU7MQBeUmWtKL4IDU9krCN6aVDBblaXMb/cCnmyXZBfbTuSQ455bm8D6Jz75InzgSVGE+c5PcZlPhKsJvqG/exM0Py02R1gMhuVir3Vv/MHQ79qzwXvexfA0F9TJu9TAG/8esv30lLIN12s0yMX7B+Z3iPaLQvQtPyKEmEzzNkhG6uaHL3QAw16zQakQwc39tY8BF9+l4RQTWOLBJs9rcMMzGPw3fSBhDH7UeNzvOypdAMe/t6GfbODQubrjonE7ZnugmNnbzmvC7cWHsFFpz6bukNIk5Eup8g9Cna8o8coC3on8hXsfOhLPXZTzoglAaNr9M8cyXcknuJVy2vyjYtjS9r8v3c3Rba37Kc9oHGresXJaVroH48MMP4+abb8aJEydw00034QUveAEefvhhfPazn8XVV1+NT37ykzh69OhSfu31Gohke1CMJM8mlm1WS/HB298+nUfhDggvGHivAmEbdxpba9MmF86FncDWNzpMZx28D4Ljc461QeiLYkcacMbdyybhhQsA83mPJ8/P8MTTM6xvzIO1o+mwty7skCwvJbWGNJ3qXLwo4iMDcHPNzG6u3rcoPfWFb05KHMVNKdSGe5I6kO6XwaDLwidvYW9+5rs6RlXKAc5nd0bBLQ5YYQYjMo4tE8PEaRC3sXSwwqBJXxVxnPErG9QHB1nnwhUKoQlaKabIbUXwB6SBeXH/AWRReks5FXcpDeO5kM3h2CRWWSQqfbl0T0xQ76MR4TbCfiHS1el/5jegHxYAU/dcXg/lILlzmEzaMNWv0rXs+x7zLsTrSAvdoR3akfaQDzxhUxipZ0kY6XqvYpt0X6vtsKkXEp+F6VC2Lw5wpsZKB1/Twdxry5Askt/GnTllfcd5l6w4OxWloIOdSdvg2JEWz7tkDSeOT3BsrY0CttO2OQhtaap/2BU4CVxdF4SsuVhU9uVGK0kwk3artJQM+eSTUASoiCOiaSlgevu/l7bWvD8q+bEqbL7Y8motqyYxTyYx3Y+a3VPbtsHEhUGfjw2Cj2ndxXWHRSAXkiAVBDOv80ml3ErZhVq2dj5ZmpfiWfAz/t3jtLH13em7bviOlXo48MNU/FobkIv8aUCt07HFjQzoEeNqGh6dyhctsz08GtdkZdJHt3nbm79hHWA2xfLaT8jLbcJ7I4g6hK9s8f05iWtWdz6cl127+y6sTd13HXqPsC617+PsARGW8+U6AFvnvMbFtmMhD3y0uA8h77s++i/1vc8ss3VXXsS2oEsW5CJoa6/HJ3/CbJX4EcOLpVdM2xhescDWZ0kboEuOGLG8twJ6r9ZjVTFc2yVJG8mHci3u0qrb6w7OpYV4bokt90q/bRiWWti8t2uQD0VvQvaSqshqz6kI6TLhVS2Hy+NoMNJEATYTa6NYeuklR/HQvf9j52FepYAIAP/4j/+Ij370o3j00UcxnU5x+eWX4yd/8idx880348iRI0v7QwHx8EAxkpCdEwbXUIsKGfDaqRk6BUA7xMnyRzqnzqXOdSkgAfmAOxNVjF9tk8Q3u9D3oIPu8/uTSIdBp9kONtS9R672GC9LkcaG3cqHwV8rDuQqUOp4OjNtsRgsy+CmCMhwIDKI9oFlkbDpBgey8QJGB5iloDTwQxMzWqICcD78hVk/M/7MwniYKAUoV7km9c7obOo4LSbu0kg5+pbLanLaj9fzsuIaX3I/Bs4GkVJLa/0tcXG6bqssj6DTzGDEOhHuonAmU7nmfVg+wQYlWPomQcEbparWs3XmvxQuI5zBA3FN2rItUT9iOyN+eZ+3jxo+bwSsoo2QNs4Z8XCtMVYWRnSx0+saBNFVRMl8Sl4ZABH/Qpp1fRAdw1TtMNgOIlYSDMUq2a45mrWDyMVHaz0tKS95IPFPIm+mLKm/Nr1S7mHrsjZCaVQxljfyVz7GjD2slq7Zu2kgnkl93Dqs8k4xvqXaK+U/1n2dbCbvx/hDBpn2/dZquTOFsMjXLB3if1JWbJk3VSoF0wbZJKJesukjTl1+Jk87V82nUtRvzKA4f5+n+jx4l3lpGkOF0OVeiv5CTJGsvZIwOOdw7NgE0/VNdL1TUU4FYSsQpkeZeu+R8spYwDqgRbLusululzLpfRIhg1exrHjZXTfMOJH3qe/Trrswz2s13RpN+z62A97LOtcNjh9pcMmxNay10A/Z4kcf+0B2E0QRyOddj81ZWJZncx6XuUBqJ4DwLrNrt8u9SczL3WdW1ZJXZXtalhcpF3rO6wcg/SDsrH95Za1POU7+BavpZPHcxy8G3ssazsGqXJYR0rzscxE0be5RF1e1jSzdZIJscu+QL2kBn9beTu20ubcHPHrz3MrzzQcoEWl7IxJL2KTMos/D7n1ftRge9b+WLrJ0iBz7Mv5Q4br0K3PjR+4tfg/ujXXKPiMTtyV9TdguBi59zjE89Tf/347v35ddmHcCBURSg2Ik2UtqYwUyZNBhMxfyncrSbmwNfBAN4iYsKkBJf07EBC/+mMGBDMKloyCD477POnteNpxB+DvvPEKHSax2bKchDtZ8EiCSR2lwW3tD5kO+cZ6N5SeOi3ScYwd03puL9p6RhLSnkzixmMOS5q744WrXBuJFSu9cVHI6cEUUojIhRESUWBHkY0DcNyiKGqaeSefZhlHqbZOv3STX5tEq0TmZJj4UNJ0zax2a82W+y0Dc2RNAmiIkbuLgvJHF9eOUIvEzX1cpDf5LrBAW7k0LwUt62DzIzg08C38ac1wcDoKgqW/zbBBGI3jIb5eeE/fGTh+d5HzcvKJtnVpPijBZljtpO8MavSLmRI/sIBJpbVxJY7Hc61SwtHltBQOJj8QzCS8yENY12mIepxRJg2hvyk46zsvsQRj92LqaRDFgMmlVaF9rHSaTIDIfWWvi1PYWR1uHtbXWrFkHKawqdAPQQbFMsVZBXo67dC2zmIMVOpJ1pbX6AgqxTN/RJk9h0roow774m/9I7sJfl92LooyU55Xon9YL+Hw9QvNw7V8U/YOmcZjNuhRfEyfbDjkH3fxFlj9o1fo1tHumlQCcC9Z/pn7M43T4rNz6lCxBbEyfMn0lLE6fLeKXeQcgWXOn+hatpicObVxjXNvQ6nF4ZtendbE7G8jo50Ti3zgVtvK23WdpWcYXPoW165Mjj2S93cp7J2tzpIzmbbeUTy0a1b5IfJ/E/yTNtW2FlCcX/YhtnpQXj0xEGgqSLvSBYdIkuncAevHb+7SWsbyTe6j4DKQPJt6UXY2b/ofiZH66TIOtmsUyNiGOxfWsPxKPjNBf3ifpXqaxprKp3/nHqLydgc/zwX74iq6ztii/tnUc/ci1ASIswu46b8wPvE/v5iLtnGwa4wGHXvsbaRmNPlmwRytcu4yG92lTSx8Toia2Hllr8OvvvG6Z2FShgEhIhGIk2WtGX0Le/I3ntWN1CNEBEtJUsnIHt2B2D1USWodkdo/U0ZAXvn79tl91ES6WX7TzDkUpBuR5oPj892HNm4sFN3Js+5I6uJEyE0/KuXRvvTMbr8SvyX0mOkjZgRxHxwepjGzVsS3HMa44nw2+7eDcuJMOdNM06szHSuQANG0bhH0ZtDtgc+7jpiEORyYupZ2I/brmGnIxzwRF8zUO+PWDQxHuWl1Og8i0jmCuysWYOrMIvLGS6XyyJJF2ROLW2AIYvQuzIH0aQWkaJz8zkULSA3WqA8H6qXoZcAuuIU8/e7asV+JYBnH6gSgLS3ixiRVnZhGD+oC9aUI6hk0cwsmwiUEzsCgMfiQBsvMwFiul+LD4Q9DA31oymPM7qd+uOFg2D6pWUBgOtEXIUMFKPro1iJaIQYTUZVJkDbEmP982cs3FnYxtByXVKqmjIjhmU+hlGr1Pf9NGRLJpk1gShU0KwmA21YOBGFBYRfamIIVyYD4wAsjWnZCg+zwPbV5m75Xstrxey4cRB3m/hDWOG60H0Z39oIm8r6L9kfLDxUjBsvlrBRlbn1K5cJlgWZYfaWvSOp0pjLJmbP5GTH0kK/bZdmtMeMna4+I9m72Tkdr1pjgHQEWSLE2clNUwpV2s0rqyH5fVe582xTP+ywT5LA7mcRJnTT8j9qV0GLbPZXaWbcfC97QzaWjd23LshvmUfTCwV4r2b6/6KGUcNI4j7VzZtmo+OFMm9Job9WcsEKleFn0FpPQqxUqd2BH7NfrhRKwNkS/lJNagg9+SN6ZMZMEcZtfiyBWJJemUBEdZ5iXUBTj5+Bfrknw4gHy8cHCNj93eRutu0wLX/cj/sSCBF0MBkZAVQDGSkMPFWB9gu35cFC/cA8jS4ocbuk0dODfq12BgjtQR1WPYTmrIzcwCVv4fDC5yS4jS4q38Sq7+LRCVLhTjaTV041wQfcIaYQHfp6noaqwGLBWxRsQlnXrn0mDajljSyDqko079ywe6WXzcUCyUvOz6aFFjrICkU5/iKquzxXhWhDGHuBslzKBPBkreB+sWb+51pSCZSx72/7BGW0zPWNC8K8qez8NvrbeXFSRXSRrsxd92AFiEyQ6AZQfrUhUqRQBnrmWDVLNuh4jB3o5+ZVBuApoGtyEP7DIbYg0mVlrpeVCJQp6rkkXWRvhMpLF4G0crstkvas4kjkY5lc7tiZFQMUr/msFpmEWQxMhJGyzWwvqRadO3iREmG5uQABw8eud0uuO889ic95h1HvOuw3zuMYtWeXOzll+nVjiy9l3ZjgK+KMW27JdpVHsj69Rxl4tObRsERBEIbVnLy2hI/XLqbQpDWbJjHsT80A+rpl5YEdLGy7lg7SgfYQd1xTy891Dr0hRX6LTwlE7hRp/5U38/WX9su5hilaI7EBJN8dX0EYHVxFXCpR8HxWenqZDVTbiiHSl+Z30A++qQeIrYbcJh21INbl6czIV6Gu2EVbXHrjjOhDwUywkMqog3qS5l3KUEsWp+rEvlOzjzrfZuRmras3Z9JA4wbvPrru44D8HAwcBvW27cMP1q/qu4KE+ovH/1te8wKO82dIvCloexjo2hDe+RSYsb/68rxm/cgpXtwkzIsxnupk3I4WKvOnN23LsK/7d6tg1DGQiH8UHCTtmruObDLWgkbIdLBxvGff7s+pNT58sXv3MhIPVHh4MQWHfRj0YHOcCkaTLxUYUTHVj5NHgFtOMJD12wXzqffRw9++yaGewYQcIh3/m4lr+j+VFLRD/0ww4Cna67NPR3q86x4JCmafUe8J0MVHzKgyLPNb2Mx5qPMSFkkK3Tvjww9wBkjTHjVxke+ZsG9aVUkcpDY/NXcjeGwcX70jjLFWXITMeM9wf/GlOmwhQ4HxNIrCWCVVcfzwHeNbo2mggAduppA5fWwdXwyE7BMJumZMm6K2yWqN9lxi2DH/4dHVxBBnGy5mpagy5trgKtm1lYSzHNybIauXjhMb6Wm1pBGfFQHuDEF1MmbXuSt2+mJJpKqHXApWshX2utX2obVPAyDaa0GXl7u7UYGUKXyrJuXpKJN+n+sh1UCzpn1v1EsKSctA7NpIlWcsYzeb7ki4bdo4/T/sSaSKe3qhVgmBrYdV3Mu2QZqRZ2WcKFNe7mdokFm7ZGyAR8NZ/FM/0Y4pJgmLctstlOssCUdGuawlI05pisJZzy2EHWt5ON8yQwbYuwkVAhhqp41od1oENR8rpbq0OycJxEAVnX4JWM0A2EUnyt0Cu7jKf1Hc3UzLJOuzSNOHsXeZ9qnhuuWynp33ukRt147WJcU+qlHXAlzluh7QUQN6xIZV+CoR+nWoc2vnik/dYqLGXNtBm2rNr2Q9Y5zGJUvAc1jcbw5rq+iGzE8jjKX7H0Teke/pPPI7LxUVhr0mfXtD5kAc+neWdBLOqN/YiSxRN5XpUCe544i/DZxyYTweSvs1ORaz4McYPrTi2VkxtXvUFTzby/U7udwpLe6+PhGJyPP+b9lgmzEFogEvIsgmIkIQcH21exfXC9pp20+s218/mQ1sVhq/G86IiXHnrbEyl+e+u1YWEnonRrwj0YrO6gN2I7lDJwHXu+7StnH8wXPL70Lu9vh9CX093K++qD51w4SAJGulvdZeJFshrJpn6ZQTTi4N2Ox/IFzNNgWXbcrK0blUTLZNk3SKtCeLDaRm/SuAGMSDVMbGsFZRFBJRtb+h0VlR1TK6+2vqaNAYwlo4dZkqHwUDr+QFp/zYyS0oA+DRDKgXjm3YLEKK3iUjo7zc9siYcijLXEEAFW10QLI0cAaXpVj7QeYd8nvyX9tiVkj8VtG/eU60zBpbSxgo7uZAlT32waajo6DbOk56ANjwFMbYYNcWqbVePw+ZT+an1EcFfWgyRSJmrDu6zuFYPOrGzV8l/cVxJ+r+qjbQNced5ct2kd6mBj2sewUYjdedtO7xbLZsl7eXE45NMRdZoxPNbWJtiYzlN76T1kwzW76YMzIZayoW0CoMJzZonk40Ympj22CZssxOQdbRIm/tDmI94jf8r3nL4r4jXtEvgkBWW9AvNBo4keWLHFtofex81KTPvmreBaiN5W+NCwAcXSNSGQabppeqgKroh5KSfNdZkCbS1W802EJNY2PaWAJctizQv5CuXiNHbnsmV47AZXGgh5h3qndTd7J5uss30F8V/a0dQ2pZ10JT/LpTSsACmW9Mmi0hf5YsKDlE5ZGYVpc3xqaarvHslHKS82bs6UI6QyDXWfyhJi/cxt783rUsKP3GI167OaQNkN00IdzMujRaxR7fXha9zUBY2jM3HxqY2peFDrj9q0zdr3WiAH+IG7Wnsuf4+0Dd76/7xsK09HoQUiIc8iaBlJyMGhOlgbG7VF7FjUdm6TeGTWaXKAbBwhwlTtg7M3/pkRMaTTJv5D/DGdIekkSifTDqB63yfBQOIZ75VOKpB3XGvpomH05YPNTZWky8dh5sHxfhl4iLNG3BWdPQ+YhfYxyKzM4i+6KQwfqiG0QkR2svLTDqzVCk3ud6YcwB67bOAp1xo4uDYMeIJlpLGIcW7Q0ZfIybRZWeNP1hf1vQxSPCaTFhubHaabM3gv65ymxfa1LEi8XB63fNCSfm/Vdy4798KWfe4Rf1wZHuNf6abrowiXLX4+vK8MzDJhq8XLppfLfrvBNZ/MXHRdQm8TeIlwuMFxiJ1MyQrtRvClSzWtEDqgdQ5I+a6bsOg5GXiLHy73zFZhJwPyeDJLrDykeboVqerNDrfwQFfG2+amhC/57+IajlbcD1ZbPk5HddFKKbc+C5tV+BjvJguWfjAA4r0hDYLAFWUeHyztALEaDTvDznXn11DvMiuvWFa95KAKAiE2o4PqIi/HyozLnW0bW09G769eyC1MnGRiDNSwDIcDFcOdiDBJAJIPA03r0DSyW3EoyBO7rIJzaJqw2c3xIy2OHWlw/MgER9daXdOz63vM5h6zedileLrR4enpHNPNDhuzLlgKO7HCc+aDRLIGViHZmXentO32fWqSKJRNSYOYv6YtSHlp6giMuGHeuZ23Po/0XwZ1LacUnMT6V4S3VqwdVXiyOygj/0DobVsjFo5GJPPpvIYx/iehTPH1KsbJM4H83Wg/xoSINEmk8x5zAJj3xfN8Snt5lkk7a68H53WZDvlw0TZNXEJAxO/KB+WYLuldEN65E5iPWpClQFK6Sm/NR/E6bxNEdISKrt7nG2Dp+znWC4mTTfdaHsgux9B88tWyoWjcSut8qDgvaSL1RhPZ2fR22ftFClMqsRIup3VMypJuZhNvcMYqtny3+5im0LgPP8xEw+IQRPtO0//Mb+NOvfGFW41r+jDlrVcxneQW54C1SWEOuU1ogUgI2XcoRhJyYbGdUJnOFzoWLopH5uu27UC7NPXJLnyuHRTTaSt7umUnOlDpVstAwQxM7BfhPvbkdMqpdlZTRzTrxCHr36l/y1IYhGjAtvRhcF/Adu6sFUXpvnHDe8dIHUWX/y7dacc770yWFlalmzQIzzvxsl6XDLyPHmnxxLkNzOZh+lzYHTPlqwwWZaAo+aTWPD5Z4tjUkAEktMylspcsoqwlZZ4IMkCqpocOwFG1rhretNjNXnaqq8LH4Ecqo3v57Kz8FR6Xzxkrb6V4AyC3YIpthuYDTLkz94oQnTbeSgvHt42Lu203ScCPo1cd6CK1C31sQMQyUnYctuJCJmQbqyF4a+0yHPj6wcEQX/4wXyeyuuZDwmSDwDi4rQ0Q6wXc1OciM7SNLptgV/gLIM1JRrb2ZhispjqulplZmvjsGXbHWW3fkf/Q2/PXglZs+16wA/Bl2DKPBu8tDJ4x/EgjXsZGJMuPlFO1j01S5psmrWkIl3YXlnNq3WY2SRCDuNncY31zjlmX0vbIWoOjkwa9B2Zzs65r8f5qVKACxEKy78UaNm38om11kXY2f9MHrTSFVTaNkeuSnjIVWj+iFe9UeY7tS0h+6+ZJURCXqdHpvsKSMv5n25MsvCZs2fMh+e7zfoXmqtP426pigjx4fr5MQiwozqUPOxl5+crqfVGX81fesEHQ/ptdIkDf+bk1o2vSe1aXeIjvewlPG8wgQwpk75/wPlfxzAdrd1luRcXI2LZma176YudseS9rmbX5ULiLAVjGOhIwZdGed/a8tVC2ZSu+R3yyuK2Fzaa/jZ/+jm2K5p2rv2tLP2x+jmHbb+3fx7xyHnBNg7W2wZtf+9JxT7aAAiIh5FBBMZKQ/SF1zNLg33bQMys3EQOcnWYmgw/TeUPeUXLxhO34wYhJei0fK+u9qYMrU2yHU3Zl4ORhLBq8NxZcPuvUAqN9voTt2OpvZAOdUoiwg4LQ4ZQhS647DL44x7G+Hb9q5zR2eEf7nkUHW9ItP5BQ2HwGXAN47zDdCHZokyZZVU3MDrDOIZ8+7VMYbblpG4e1SZM2a2jCZ/8eQN+FjRZmXbDqmfd9njc6uMjXENLwSwHq02Bg0EGP7rLz9rev57tPTpcjS/O6kzSQOpjUwl8OcsN1VQKyumrrsBW5y4G/3CoFXux3VJQyHzHk8VbsSUJl8MCZBwQLp2RtIhJIZqlnRsq9MUuJf/IyIYNhIzAMP4yod0MW5PdQMCguIs8T26bm7XBof1uHYOmk1ndON09p4m7YSdiN9+iOvzGvRDQSUdgcexghysvU9rScgt1RWyyUrHhQHoe0swP0kFi6eZPJr1Gh14vV5vh1SUjnnFnCwUz0LfKo2uZW8tHmjXyAsW2frRdJeEIu6EQPxMpU3qVH1hocaRv0CNbR5Y7Rk4nD0UmLI2txN2Mf1kObdX1cX1dEkFw0k+Sw1oJhk50Gzggsm/MO6xsdNmZBtLT2fmuT0JY3ZjfYLF+lrMj7V969vWwEU7dYk7pQflySup9Z30n9102NxLrOCDyZr8N88w5xmQOTZ+bDR4am4bAPkPoklXt03T2nZcl5n9atFacjZW2hZV98cPIXKI29y8Clt6mkX3AiVtfahts8WPI4tMWNCpjeSeBCwML041hXY/9BLXG9WNTGslv0qcqyLB+B7CY5YoEuQrdUdfsReitGBb2YX9ou+/QeKPO/cWY6vbmo5U7Pp7t6Y83YOtPfyZ2l++O5tUmDt+1iCjMFREIIGYFiJCEHA2f+agfUdmBdmr6Svpg77egO/MusAORcOpNEBxgBIw0QdCFrh7hpRbJEsJaR2tlF6rylNeeS9Fd+aS9FAJj7k3gSz5te4UCQ8Gn4lvnkU7zGxhf2uWO/dYztUi/Ze58W2o/Cn4+Dv2SN4uL96elOrGqAuONJmr7mRXiAV3G3DHEoD14z0oz/AaSBr0OY+mmtHKU8TZoGbRvKUO8Rhc4+s8JJCRx/i0gi5zQIWbc/SzuxggtJkUQMeaZHPV8OGpWx8qi72rWmLOcjgzC5P/3NhrLJdxEJdkxR2jXgPjuXC+jOnM9uyrxJgY/u/bAc20eN1kvrvVG4tTXRup3iMXiOL8NnR6vJT4mUbQcHuzQ7Yx0qoiTydlksSMPvZjDYTpuJyKYdRRssoYvB1MF/nz5C2GmWNTGyL/6WAln5MUeSoPcebdOg63vNPxdH/zYN2iaFrevTc2xc27aJgm2KnBfhyiUxqybcSfsiFrNW8Er5avM0FTdnUjF79zn7N33Qa/SjjcNa20Thr8WRSdiJe9I6NG2DSRSb4TzWN3usb8yxOevQNA3gPdrW4ZJja7j0+BqOrTUx3D4TBmedx8asC0tfzHpszDpMNzvMddq9TB/P42bLR7b+bJEe2XTaBa2q1F0RddMaqLW/blCGbTqrMCbte3znWuE3xUH88lk+hfC6VH5Nv2GwLmqBfDwL5cX8jgJk1sbYdsj0F8YE+7I5zBLQNtJYcKyHWeFOwmQm9mYHWV/Q2/Ze8znWZekH9PYTqxEH5f/aZlNZunoNYtY0ZlfLe/KwZu4q2O6CTcbMQdGfyfoilZfesPeRn59MGrz1/6aASAghBxqKkYQcPDKdJx6mjqu1WEoDlUxAKO9f0JHbUtyIo/9MBJSBJJCEAu2Qh6/ydjRQdmZ9rVdbe7R0tvX+irixlD8hbdrWmQGODA5SnJLAawexcRVKM+iTqajehFH8Ej/amDlpsw5vwiM7X3q1etTFzyNNNhiUgYoPu9jaXrhLz5XF7BHXGJOdOG0qyqA1WVmmAU4QVPt43a7dlcSDi42xActO/Rnza9Fz5JpdlkEuOCQLlvIZqWzmF4vs3xVWOIATMS2EKwlxTstXK+W7bSDrJ4rYIG1RsrINOwsjlictRz5sZ2PLV1gmwEc9QdqQ1FbY9iZL48Hg3/y2Yrz+byqNrfgwQkoRHyvciFhjhUsgWX7BnJP42rBIysgOsRIOZ8yB0pTcZI0oVqO2HWrbFn3X6TvBri3nAXRxenBfpJndWEOWbUAtbbPUk11hJZLRUq7vMffA5qxD1/ls13gpU5rM4pttYKUdNXkiG8fYsp1bnheqTRlSFbOM05hvulNz08SyHO6Saau9iqxJ4LPtunNQK8fWObi2wZFJo2tThqg6/RgTo5i1sfZYNlTRNUVNROz7XdPS5H8SdevWsGM5ad9zmh+Vl8ogeU16qoVqcdlUp6xNkfdW/k6LdVvaC++zNXHLWRXDeh/qh1jqZcKiPTbnYNKvrG+lKF5LR9mUyLa94izrIyFvYmptda1fVpJlTfEc689W/SARjr35LWGz/ppquZBafMZuyetxOmnLtpw/Mmnxlut3PoWZm6gQQsgFwG5g4xxw6tSlOHPm3FKD8jEeeeSbeKIHpnsQPkKejQw6dmMXhi6WJhcxzPna2GxMZExjyaw3LaKEuGmMe9vZtSJJEFLC2fqwX4eZWXpoh7eWBl6CHwYnXadPUmFEBoMyJVKCnqYP2c6003EPzGByEq1emja4n816bMapd2EKdYxjFEf63mMuU3xUiKwPFjVu3qZDPN/ncc4HMt6MEsx1ayHiy20edo7LMgZA8XvR4GIgNOSaRkpzuQHI3lFWA7FuqtRGPuZZ+uwF7mp+6u7eY25cTOs+P2dHdUUUTTCGpTsXY+Kxjv1tOZVzwwg5JOGiNjVbBtV9Vy8lwwF9CpQNnwj4VoRpnM93JG4cnA/1Rep5PniOgmTXo4PZRTuKPh7WAkqOnQpUKrioCpPqZbAEKWsXliq/VYqyX2trbdxcpa3M1oMtpmMCwZqybRx857TudH0frAxFvLJijmswaYFJ22heD0qECGA+prZRGUKaisjj0Xc95r1H15mlFrQNK9ayMyKBbrrhvFp9qigLD9mMR/PL+/h8p8KbWL3BCIUh/9MHIptxziFsqgWP2TxvO3bS1XUmnlJWc8EvlXNZ9qKNS2VMmjBlem2txVrThKndk3StcQ6udWg8ABc3JPJpYzARN3WqPaAWkTrtvk9/xeq16+NGKrrhWEgPK6hZERLF79qYQM6NCWGu/OErx+qBz/oNLsbfGyW5bJ/SlO/kr7SVdtpt+J4ZW1IHpN294/UiPh6A712yrtS/YV3OFHGbGDAdnaKrtsW70YYifYiVGi+WiumiVEvb72oauS21G+qnpJUJs3WXLbtg0iK14dGtzimPL5fanOcYOFkXVPz08T9vHuRhrscXaLdLqzwKiIQQcpHy3//73u2qbXn44W/iOzvp7RFCBtQ6cvmFLU8u4zuAxaJKcl57xlaqzMht5WP6NG1PrFsapOmEMrLKLA9Mf1utjFzcBbVJmwkAwHzeYdqnAXy5ZpXzGFjEqN+NQxv/+ugWyDvwYRBs/BwdAKeOvu9Dp1zXtys67DulOvZBPigXXUzELWev6YDHDYSFXGhKg3ERfQYDPclUI2D4pJ1oSDPLEDNAzsOfW6GUg2JfxrvwombRkSXWguItbgfF3Msu7CNVwBXlRAdsPooOKeAyoBT3ElcdZpvnNw7wDeC8EZoysUQS2CXPZJDuzFTaWpglajZP5EAsksz1IjmqwqL9rYKZ5HMmOJhBtzNikwx64TQ9ymzL4iJhd8gG8b68CSl55Lw47407a3HXqX8mgTJRIBw0Dmg6j77vsw0e1IlL01pb52L7Asy7UCBcli5hfTetH7KUAUS8QrKY82ndSEsbLfsmk/i3aVQglnbSTrFN6ZmneFlXgVRfM+HPB4FMdpAWUajXsu7UnUwlNzrS4NVSq555ObPPtn/HWlNfvXf0YfFZ5S1jU7tTXZV3U0pfsQ5szTlNe588a/SB+WYpyfoX2l7adfdSu5J/7Msm5Zr3zSC+pv12xWlnGjR9F8S0AWrlp8gnI/DJ+RCmWM7kgx2kXDnIeo72vYMWcEYRHJQTJy2PNKipIVRrYlNn0rTv/Ok2nQabAdmI+fQetIGx1s62KU5pkaeDnLZpGVd4jP2RuLwD4jtal3EIN0oZyt7tMdG0zS3C6x2SdbnZOE4so32f3leTdtFbY2soIBJCCMm45pq9FyYfeeSb+M7ITqiEkL1htH4tW/Eq7rbVzTQDPhEnuq0e7ms/9qClKL2QKWzFM0oBKxs4OdFAcoubfGfefBBeko0li/PWcqC07rMbEMElSyMJYxq8mjW5vIdd0F+TwoRXDRoyBxKmsHaUkwjFcZQMnmGn/9kBpHOa5+F54f6w8L0MjGO5cIAuiD8MQtglEmaAlB6j7mqCg/XNoZ4dqhe5wmFFUKjfbONXx5YLGy47yE8DOySVoFTAsqPaA7euI4OB7rJUxAP7PBnHu/Jy8UBnlBQHU5YdADRozAVrOQnv0+7u5iGh7jZIO7wCfRwsAx6+jyVH2p94LOKMDWs0OEu/Ac1Ye74HgK7Ls0bbhegmWqTNHAamxnmdyM+NldMaDjLlPcRkNgdmUWbKxCFpAyR8qcJqWwaYtRCjIGHXsXUI1qbzvsd83hvrwxomzaTKxufaHXyBJHBKoGwbmIlySAK6uBQrSS/tgTdLXMS2z8s5yfe4TXVmTV5EQj+IiCJaNiRFPBfVJRGMrDsVgBCtgAEt58lSFJB1NF3TRjEYKkw2cYFNmT0ga/RKuHKLxmGb2/s+1Yk+F5/gUt6lD3I9+i69KcUaU9JL3u02XfJ0cBredFLSL/9wFdznb0jJ++C1XRt0mEflWyT/wJbeIS6KdZD3mxb/scbfmfsLS2/YPLRhtl7JTtRIZTcPKsQCeN4DrvfIvgAOeg7DY/uhNOyeHeucT7M28vROx2utMePcARQQCSGErJxVWEs++ug3caYDZnvuMyFE2GqAa/vedtZReW0Zvy4EZRgGYlA1kDsPeXmnq1zLBidOpuGFQUvY6bbRwXUSAZCNKzIxRNe8go5srIAzzBcZODoVGJOFks8Hn3agWQozW8W/GHSmAbc31hdpGKlThKu+pamWKSwmDUzC6MRZIxB5pDFqKY7sVTndlj8rqBy++LvdG235lLptLUDN+DVLTxdVX7sCmqS5zaQuZu5gOf6yTGd02S+jj+Xl2ghToWQj7WAb3TbmplQ29ITxy2XCXFaO/DBuiwTmSjSXpvdA3wG+0ydWcYuuVcSScXFweWwbZsVfXWPWWls52VwHKs40jRU48rUxYY6t6JiExmEYgCRIpnVujdUaYhvhQ7p23utmXbLbc9cnK9A+iqq9XbvW+1wc8sMyntLVJ9EvdzR2KqWtaRgz8apIE137Naa1ikbRmt95D7fmonFdE4RFuLTrNVLZdWKSjfgBKVr+aTz1o5QV5KQ+mUpm67KPU2eLNmRsx+hBcvi8/GbH9pwHXPFFLhf+kvsmrjHqtJyIJbGst+p0gzdrFejjNWg6DAOs7y/TaIq1oKa1Jri53/ajVHhskAuSyD8Eajk17WkhaNoHrLVl4m4PCoiEEEIuSn7kR/ZelPybv/kmzvfAE9y0i5ClsH3mbgUiyCJKUcxa9AGIlknFuQsVuArlsF+nUUdhQwYZXQ9g7k3cOjPFzExHzn5nqzjFByAuqu8zS4t8+mI+lVxVIfvXemkH72bxvxQevTrIl1yASYOZZNWSD8RlgGXGUincEGuT4eAzC7IO3LJkMeljT+TX1fvsROUZhxw7zl1KHMszaguf94Zy7U540TL8IH/FmUeyxNPVD6XMmt2gJ22DruvrQkG13ji9FDQGscb1sLszS7rKbd7ck+pNJfBI4pf+NvEX26lU19Kz+j0suJl1aKXe+yjMSYidQ6b/iigbg50JMzJl0xotqgWZafvUitW2QYiCmfilolFuxV2G1wqVTePQtva80zjLruFJ9AypLWVD108UUSfmeVgnsTfrKnr4Lu0k7r1Ys4b8FaHItl+64YlpK80fFMViKM4bNOWtuGbFtCYtJxLWjYzT5WVNVTiEWDgz5R7ouj6IrTBTi32IRROn+AchX4R9eS+YgFlhzUTQFbu421fV4L1ViHKakh7wVhBV//K0KvS5BWmYO7brIGrxNeXNfv9Dg8zwMLzHiweHhsz0d0KZ06ndRkz0kDrh9XkpKcKv0O7RApEQQgjZE171qtWsK/noo9/E/+y2dkfIXlL2nw+b6DEY0B/ACFoBLRvc2YGRN3EBtlBpdhfJUmC059RQAjpmSWHPBtkO8H0a8MPBNXEX7T4OV3yvhc7HEaPmVSHK6TNrhdQhWVwuJN1YxtHInMZBCkAp1MggTQdtIlDApEdUH5P8Ib6lCJXCaD6d1hkRIKhI3pQFidIBLtoHCj84qJ8b3rhEXZsNvyi6ynGwVAr3ZUJAzF8Rp9oGakUmwooIC5PGYS0s/ArdIKEoByogxb9iDae7bZvzOyk3eTkPNKaOePP/8NAPRKzBbxTuUdRZ63qkTagcDtw768Y0clZYlDbO1vOyXVT5pWYlbcNSEyXlvDzX3B+mJzu0RlF24sibnbZ9spC0u4SLciRlSNsYpLIBNEV7Uipr6XnwiBuaOHTw8PPol94bvdlSTcvTvtxZXURY2dBm0jqsTRpMmgaTtsHaWlzfs22wNnFomyZY4nsP1zj0cLqhTefDhk+9D8e+T5ajnZzvva6ZrDtJm/TtrRV71mZbETddyz5qSTx9es/0+gKQ/5aRIk1WwLwLizKVpW3tmu/RO+gHSflIkgnRHlhkpbwMFBAJIYSQFbMKa8lHH/0mznbA5p77TA4LfuR4O2R6yRYOG6TpojIgyyw+fNrkxAE6wF3aYOkiwQ7PbPyy9Csie6HiXg7ss3O1QFQDVp70yKeW7iw2ZflS6yNfERlkkFTeVCY+4k6hLpW7NLBK+eNkECn+9WndrzJGteHgUJh1dQfGIqscCcoUxCEi2ThzfxJAUzI4/Z0Hxg/z2A+SiWyDatvaj7mo/c7PyYDeWtFZF1ao8uZC2XaOlk1nxAnjp4tizsQFQceur6fCkc/LlBWpFsbQl2WxEn75z+UXs/JaVqVCA8vKcWX2SP5hYmel3sY1EyQrv23C2/enzcw87bZfA7NNXzAUfCVMsoGKc06FrbTjtmn/YPNicd6Oxrc4nU6IuGnXvvXAfOwme3sh2rpwTneVd9YiNFiFNi5sbNO2QXyctA7H1hq0rbhv4r3GMt+0hyIyyqZJ6a/ZlTsKlbIuYxIZfSY4qmDpRKBM6S7iZW094AYw68zayuGyolTeJ/mZ11ev8StnaewUCoiEEELIRciqRMlzHfD0nvtMLlbsuGthp9NXx247xgqQaiBRGzDu8hl6UPFsp/6P3UehZjFl+myx1NsufF4N+Th4/JlD3dOPXsuFATtS99lpkRiH1lM+/21Mg0qvayFeNFhV78oBa8X9Mmz1gaKu1hYPrAkphVi68DmFlyqW+OG1vUTDt8vGY5FgV6aB/GvggkjS9dmaejKVF0hijaxJKGvsSUFyUXjKUtfn8bLrqeracZkYmTbMGMYheVbGJz3LD64nv/JrZXo5ETtdfkEFtvJ8/GuWt9uGRukHR+W7qBR9Bz5UEklSv16GFhcgDUcMSNOk4Aw2CnFmCvMgz5OglpVpkz96Liaatw+3bVlxzzIMP+TU3OTqbyaow0x5N9aqIlSKNWXrEHdOjnVIlzzI1+Vs40FmxWjKflrWIE73BtL+MRDrSJ+nmSaPKdMpyaLbPF9CmMy5Xa43QwGREEIIIQBWI0oCnMJNto8O/lao/aTBY/08IcuySNOqMXZ9aX+sCCJihjkvYoieM2JEKYrptMnK87NNLOz9JsBqDRUHrWlAW7Nck+OhiCKe+0rcavGtskRd3k79LgWnw4JH0BA6D8z67X36GRO4rXCSiSrxhIgxIlCm7SFiGVM30RrXI+38Xmz7XQprVpjMfmcxNrJ6tMzT94yD7uatU4f1by5q5sJlpRxbEcmbv/BZKLIPYqbs1wShGsuWbRWL40FK57Q5lxWHRS6sTaOV9R7T9O/kSAWtQRzGEm8oXKq/kCx3RVthpxW7aOGXloJIwlxK7VxUSwJePcF2VtlLi9BcmDR1QIXXZGGfb9YSrjdO1gfN1yr0Eg8PeBQ7PUsKafr0g3ogU7N326hRQCSEEELISlmVteR3ur21eiPPTg6hPkAuMHtVhnYznq3dOzpQ9iPHW/u6hZtdpgQr40JGjKqCIDciOLniwGF3m6mMicKDi/32hYqaOGnDL8fJ4qt+T2mBlVswlvJjEX43FI4zHVCv+eyaCoNRxHEY+mNv9eZevyBIJdZirna+eo8JYFMsytt7h95+fUB5jDyjXVGmfLl7dr5xTeNMPjlXhF82o7G7ZSdBbmz528G6hEbkTcJsWls0TTUOFSVf99BMU45Rl41f+phGskN3Jkoacbnc1CZPxdU2aq48dumv/DE5XhzsDAqIhBBCCLnoWJUo+VQHPLPnPhNCCCG7Y2zcv8gqU628fP5bGAhSbuhWBMoaNWvAsXBspZNZN7sXJ4fXnT0/IsCVomRyEKas+jin2UULOLERk/VTww7DSSCUSDlzbK169dlmscYk0LkiXNZKcBgHGz2fDnKx0rgJe155k99WaK2Jpnmc7AVriJwFa6BemaDmOm92nzd+ljiZO25EssIrveCKE7IvcbmBjz7Pm7OZei2CpBGmJa5l/agImxpf5GlYmw5v/mzJQPb1lQsFbpdf3ikgEkIIIYRgNaLk449/E9/538BZTuEmhBCyT4yKj/J3kfCwC4ulXCSLwplPglvN720Y5G0LP/pjGeusBRZ6kVKMzSzB4pqSuhtxI9OHZW09H3cpdskqES5Yv4ntm4fushvEoiSD9fBxGna0iPNOhdggDBrLyJjArjGx8UmgzOIk1no2U4ybcm1iK1guSCrjwU6u7bR0LKGuAXGqtvld+JFZ+mX3Dc5Ud+oePjD+idaM5UPtR4DSWnUs/eV5NcvIRdaqy0ABkRBCCCFkRbzkJd+Ll6zAX64rSQgh5KBTE+1qosZhYCDG2pO5rLZnOGwxndlaudmLCyxF9dZSrBoLwODG3ECxMBjcEYNnmzjtdcqW1q9Vv5c/uWPKqcj6CJO+dv1IsVe1U8EzI0oEMflIm6+tuF0oIBJCCCGEXGSsagr3f3XAxp77TAghFwZX/NXzxUA8bFqQTupgvTebXTjANQ26rtc7dTMGiHUXdFCvGyMUFlpimdT3xcYy+l/OIdTWyIqQcrjQwaLfW3hem46smqGZfmud6NTkoTFdclcIm4NgWku6xUHM/a2c2w+WEUuXCWeZ/mocKWK8EXBlcnbvk1go0+OtP12zuxSigEgIIYQQQnYlSjoHnDp1Kc6cOTcYzHz729/G/97w+K9zHk+vA7OD0LsnhBxKRicplie6qqsKsmDYFm7jAF7EwzSdMdhHOQBtm0TLcgqvj2pit+BxsmvuYNqihJRtK9ljRi1I7emtpr1vc1p8KQJut1hvJd6NWUOOTjE2Yaq68bmf3tyUWQ4WAax9QCjF19railkQigzqK3nkioeUU7S3CwVEQgghhBCyMl70ohfhRSv0/z//8z/xv57u8MTTc0w3gOn8YFggEPJsxk6dW7pC2oHznodo9fTeinhjMdh5zLynSEgOP9sp4jVLYzutV9ykY6dWxE1U+62Ql9Yb9NnUaO/tDtbpHh8foD754dTvakDlVDn9XMTFLaaajzHYvdynTyBZ2HcBBURCCCGEEHLRctlll+Gyy1bn/5kzZ/Cdp3v817lNTDeAjfmwQ04IydlSR1t4U6BqJWQujBkMFUY8+TV/cYqTBwE7PbxmiVVOOR21IivW1duraadlkJjPh5NF5UWm7wJ1R07mZfu0QYxu7mP8l2m/Iga6BvlGOHGDFKcb4gRhUnbxDgKj2cjGe/i4+40ug+DDNOM+qpDBXQq8FSl9ZoroTDr4retP0eattbvbRoUCIiGEEEIIISOcOnUKp06tzv8zZ87gW0/1OHd+E+fXgc2OAiUhwIKZj0sIjb64aDcdGGxAAJfWC1PHIiA49D5cE6GgfI7vPXorCnjkg//F0bxoyKaHbyNSrjyOU7GdEWnCTsQAnIfzyYxMpl+KECM/xPLLF/Nns3UpEYWbQrAcxGubGXRY8vNiZcvZ0QsyKCu7W6luI+eWld+qHztqHz9im9SEhVm13WmiqllaNKbdlYMDjZOLZb0QDL1MWRa9dNFc7SWggEgIIYQQQsg+cSEEym8+6XHu/AYtKMmhZBkdoOpmoZmkiAVJNOh7pF0MzGJkmRVdYShkraFqsxqdS+6sXZEr5jc65Ju7IAbFu1xcKwWS8plllPfSAnCMgeDjZSr2mBS0u9DkU1aRHTRIaegxFGf08Sahq/lrxEtN+z1KxO16Yy0+q9LQFhlcWo7uJAzPJpZNm6rQvvDm8GZeZn3GrN0womP2Nx7DObSNQxNF+qNr3IWZEEIIIYQQUmGVAuXZs2ex7j3OPOnx5DMb2NgENilQkkPEwOpuj3QuK3K11gIPDk00OxILpAaAa8yUSpk+KT45mZrpgqgY7SnF+k7+9vob6GEs87zPrg3i73ORaVG0D4LwlFkr2pM78mmcciq35klxzvuUboOdv2GFv3CUBMtkUTZ48IggbU8u1Mcrpw9C3pElrSoXfhHJL5aC5NqEAiIhhBBCCCHkAnPy5EkAwPeuUKB8cu7xxFM9zk03MV0HZj0FSnLxY0WuPtsRevsyjhWituO+XPsNLq315hAETFnzzSGs9YZ4Pk09FvFTpTEVzUp67+H7Hh7BArHzHr4HfC/ipkcPJNETqE8/3mJK8oWiKlTKhYV3LX89JnnVSfW0FS+3eFJNhDwobBX2qnhaOU/2XiimgEgIIYQQQgg5cJw8eRInAeC/rcZ/ESjPPjHH09M5NjeAzZ6DUHJxsd3yaoU4P3YBfmAhV06j9DUha8Eab8kMz41a6zVmXqaThxiHzngka7k5sdeLIqQNX4iJT8cS58q040UWexdiqneNXh6+TarTYDGc5tw24+5rCqW9X9OxcrNarnrzexus2j3ZORQQCSGEEEIIIc86RKD8P1coUJ6dejz59Bzn1oNAOTvAVj+EWMrZ2mNr/NVFyEXndjIJeuta42rHhUWed7m7xiWfGxUkR/x2MNPCw29n6nN1+ndxfKFEyEXZYK91uzDnVovTsk0zkVThuSYsY5hGDuPlrHTDdnR/oIBICCGEEEIIIXuMWlCuiLNnz+I/nvF48pkZzm922JgCcw6sybOUmmC3vfmb26w5C5yPipAj13YYgn1ldNJ9adVZW8dxCcbSyYqxjUyjrz9+MLXZj5yXCfgiUPbwulbGTuTuwwwFREIIIYQQQgi5yDh58iROrlChPHv2LP7XUx5PrW9ifbPHxhSYre5xhBwatr00IhmwTHp1OxQnd/Y0AlBAJIQQQgghhBBScCEEyv/5lMcT5zewseGxuUmBkhBCDjIUEAkhhBBCCCGEXFBWKVCePXsWGwD+84mwg/f61GM2A+areRwhZEVk61lyiYYBY1O9BxsZubCr96TZ3fMoIBJCCCGEEEIIOTScjMrki3YhUDoHnDp1Kc6cOVfd2OH8+fM433k8dW6G8xtzPDPtsLE5x8bMY95xPUpC9oJFu2PvBaVAucJHrYSlth2KU707QNd23CkUEAkhhBBCCCGEkG1wySWX4BIApy5d/bPW19exvuHxxPlNrG92WN/sMd3ssDnrMOuCYNlTsCRk2+yVQGk3ZhlYBRa7UF/Mu0hTQCSEEEIIIYQQQg4ox48fx/HjwHc9/5KVP2s6neLp8z2emobNc56ZdticzbE577E59+j6IIDs0pCJkENFdRfw0RPDKcb2XLlb9HYQQ8pViZQrFRAfe+wx3HnnnXjkkUfw9NNP47LLLsN1112H97///Xjuc5+7ykcTQgghhBBCCCFkGxw7dgzHjgGnsFqxcjqdYgpg+kyHZzZnOD/tMd3ssb4xx3weLSt7WlaSw8NoOR6ZOr2dcl+Kj4hrHso1H5XF9qCugfi1r30NN9xwAyaTCW688UacPn0aX/7yl/HpT38aDz30EO6991485znPWdXjCSGEEEIIIYQQcgA5duwYjgEI/62W6XSKaQc8fX6O9dkM61OP6WaHjVmHzXmP2bzHvL+4p5aSZzc1q8fBOogAuoO6BuKtt96K2WyG++67Dy9+8YsBADfccANe8YpX4LbbbsOnPvUpvOtd71rV4wkhhBBCCCGEEPIsR8TK518A+6XpdIrpBvDUdBPnNztMN8J6lRvzHrPZHPMemHe6rwUhFxUrERA3Njbw6KOP4lWvepWKh8Ib3/hG3HbbbXjkkUcoIBJCCCGEEEIIIeRQIFPAn/+81ZtWTqdTPLPucW66ielmj/ObHTY2O2x0HvP5POwGTstKsoesREBcW1vDAw88gL4f2keeOXMGANC27SoeTQghhBBCCCGEEHKoEbHyJI6v9DnT6RQbAKbne5zX9So7TGdhJ/B512Pehemx3FzncLMSAbFpGlx++eXVa3fffTcA4JprrtmWn86Ff4QQcrEjbRnbNELIYYHtGiHksMF2jZDA8ePHgkS5Wp0SQL5e5cbmHM9Me6xvzrEx77E569D3nmLlPrLSXZhL7r//ftx///04ffo03vKWt2zr3u/6rktXFCpCCNkfTp5ku0YIOVywXSOEHDbYrhFyIblw9W06neLcFHjy3DN4er3D+ekmnp7OsTENG+zM5j26Pu4G3nMaOHABBcT77rsPH/7wh3HJJZfgjjvuwIkTJ7Z1/3/91zlUZkQTQshFh3OhM3r27Dl4vokIIYcAtmuEkMMG2zVCDj8OwPOPH8HzV2xiOZ1O8fS6xzMbM5zfiFaVsrnOvFOrSu8PtmXlBREQ77jjDtx55504ceIE7rrrLlx11VXb9sN7sOEmhBwq2K4RQg4bbNcIIYcNtmuEkN1y9OgxHD164darPH++x/rmDNONHuenc2zOe0w357tekmGlAuJsNsOHPvQhfOYzn8ELX/hC3H333Xj5y1++ykcSQgghhBBCCCGEEPKs4tixYzgGYGwT8KbZnf8rExC7rsMtt9yCBx98EFdccQU+/vGP4/Tp06t6HCGEEEIIIYQQQgghZAWsTED8yEc+ggcffBBXXXUVPvGJT+C5z33uqh5FCCGEEEIIIYQQQghZESsREL/xjW/gnnvugXMO119/Pb7whS8M3Jw8eRKvfvWrV/F4QgghhBBCCCGEEELIHrESAfGLX/wi5vM5AOD222+vurn66qspIBJCCCGEEEIIIYQQcsBx3l8ce0qdPXsO/UHez5oQQpbEOeDUqUtx5sw57upHCDkUsF0jhBw22K4RQg4bTQOcPHnpzu/fw7AQQgghhBBCCCGEEEIOGRQQCSGEEEIIIYQQQggho1BAJIQQQgghhBBCCCGEjEIBkRBCCCGEEEIIIYQQMgoFREIIIYQQQgghhBBCyCgUEAkhhBBCCCGEEEIIIaNQQCSEEEIIIYQQQgghhIxCAZEQQgghhBBCCCGEEDIKBURCCCGEEEIIIYQQQsgoFBAJIYQQQgghhBBCCCGjUEAkhBBCCCGEEEIIIYSMQgGREEIIIYQQQgghhBAyymS/A7AszoV/hBBysSNtGds0Qshhge0aIeSwwXaNEHLY2G175rz3fm+CQgghhBBCCCGEEEIIOWxwCjMhhBBCCCGEEEIIIWQUCoiEEEIIIYQQQgghhJBRKCASQgghhBBCCCGEEEJGoYBICCGEEEIIIYQQQggZhQIiIYQQQgghhBBCCCFkFAqIhBBCCCGEEEIIIYSQUSggEkIIIYQQQgghhBBCRqGASAghhBBCCCGEEEIIGYUCIiGEEEIIIYQQQgghZBQKiIQQQgghhBBCCCGEkFEuCgHx7/7u7/CKV7wCf/3Xf73fQSGEkB3z2GOP4f3vfz+uvfZaXHnllXjNa16DW2+9FU899dR+B40QQnbMv//7v+OWW27Bj//4j+OHfuiH8OY3vxl/8Rd/sd/BIoSQXdN1HW688Ua87GUvw3w+3+/gEELIjvnVX/1VvOxlL6v+u//++5fyY7LiMO6ar3/963jve9+Lruv2OyiEELJjvva1r+GGG27AZDLBjTfeiNOnT+PLX/4yPv3pT+Ohhx7Cvffei+c85zn7HUxCCNkW3/rWt/BTP/VT6LoOb3vb23Dy5Ek88MAD+MAHPoBvfetbePe7373fQSSEkB3zB3/wB/jbv/3b/Q4GIYTsmq9+9au4/PLL8b73vW9w7eqrr17KjwMtIH7uc5/Dr//6r+PJJ5/c76AQQsiuuPXWWzGbzXDffffhxS9+MQDghhtuwCte8Qrcdttt+NSnPoV3vetd+xxKQgjZHr/3e7+HJ554Avfeey9e+cpXAght25ve9CZ87GMfw0//9E/jec973j6HkhBCts/f//3f42Mf+xiOHDmCzc3N/Q4OIYTsmPl8jscffxyve93r8IY3vGHH/hzYKczvfOc78fM///O47LLL8PrXv36/g0MIITtmY2MDjz76KF71qlepeCi88Y1vBAA88sgj+xAyQgjZHU3T4DWveY2KhwDQti2uvfZabGxs4F/+5V/2MXSEELIznnnmGXzgAx/QpRkIIeRi5l//9V+xubmJK664Ylf+HFgLxK997Wu45ZZb8Pa3vx133XXXfgeHEEJ2zNraGh544AH0fT+4dubMGQBhwE0IIRcbv/u7v1s9/0//9E9omgYvetGLLnCICCFk99x22204d+4cbr31VvzSL/3SfgeHEEJ2xVe/+lUAwEtf+lIAwPr6Oo4cObLtMeiBFRAfeOABHDlyZL+DQQghu6ZpGlx++eXVa3fffTcA4JprrrmQQSKEkD3n3Llz+PrXv45PfepTeOihh/DWt74V3/3d373fwSKEkG3x4IMP4s///M9x55134tSpU/sdHEII2TUiIP7lX/4lfvu3fxvf/va3sba2hp/4iZ/Ar/zKr+D7vu/7lvLnwAqIFA8JIYed+++/H/fffz9Onz6Nt7zlLfsdHEII2RW//Mu/jC984QsAgFe+8pV473vfu88hIoSQ7fGd73wHv/Ebv4E3velNeO1rX7vfwSGEkD3hscceAwB8+ctfxnve8x48//nPx5e+9CX80R/9Eb70pS/hz/7sz0YNXiwHVkAkhJDDzH333YcPf/jDuOSSS3DHHXfgxIkT+x0kQgjZFW9+85vxpje9Cf/wD/+AT37yk3jDG96AT3/600t/1SaEkP3Ee48PfvCDuPTSS/Frv/Zr+x0cQgjZM17/+tfjqquuwrvf/W411rv++uvxwz/8w3jf+96H22+/HR/5yEe29IcCIiGEXGDuuOMO3HnnnThx4gTuuusuXHXVVfsdJEII2TXXXXcdAOC1r30tfvAHfxDvfe978fu///uj6yQSQshB4g//8A/x0EMP4c4778TGxgY2NjYAALPZDADwxBNPYG1tjTvLE0IuOmTjzpLXve51OH36NP7qr/5qKX8oIBJCyAViNpvhQx/6ED7zmc/ghS98Ie6++268/OUv3+9gEULInnPdddfhxIkT+MpXvrLfQSGEkKX4whe+AO893vOe91Sv/9iP/Ri+53u+B5///OcvcMgIIWR1nDx5Ev/xH/+xlFsKiIQQcgHoug633HILHnzwQVxxxRX4+Mc/jtOnT+93sAghZMecOXMGN910E6688krcfvvt2bXZbIaNjQ0cP358n0JHCCHb44Mf/CCeeuqpwfnf+Z3fwWOPPYZPfOITbNMIIRcdZ86cwdvf/nZ8//d/P+64447s2mw2w7/9278ttf4hQAGREEIuCB/5yEfw4IMP4qqrrsInPvEJPPe5z93vIBFCyK44deoUnHP43Oc+h8cffxwveclL9No999yD2WzGTQgIIRcNV155ZfW8TFm+9tprMZlw+EwIubg4efIkNjc38fnPfx7//M//jB/4gR/Qa3fddRfOnTuHd77znUv5xRaQEEJWzDe+8Q3cc889cM7h+uuv111KLSdPnsSrX/3qfQgdIYTsnN/6rd/CzTffjLe97W246aab8IIXvAAPP/wwPvvZz+Lqq6/GO97xjv0OIiGEEELIsxbnHH7zN38TP/dzP4ef+ZmfwY033ogXvvCFeOihh/Dggw/immuuwc/+7M8u5RcFREIIWTFf/OIXMZ/PAWAwzU+4+uqrKSASQi46rrnmGvzpn/4pPvrRj+KTn/wkptMpLr/8cvzCL/wCbr75Zt3pjxBCCCGE7A8/+qM/ij/5kz/BnXfeiT/+4z/G+vo6Lr/8cvziL/4i3vGOdyzdX3Pee7/isBJCCCGEEEIIIYQQQi5Smv0OACGEEEIIIYQQQggh5OBCAZEQQgghhBBCCCGEEDIKBURCCCGEEEIIIYQQQsgoFBAJIYQQQgghhBBCCCGjUEAkhBBCCCGEEEIIIYSMQgGREEIIIYQQQgghhBAyCgVEQgghhBBCCCGEEELIKBQQCSGEEEIIIYQQQggho1BAJIQQQgghhBBCCCGEjEIBkRBCCCGEEEIIIYQQMgoFREIIIYQQQgghhBBCyCgUEAkhhBBCCCGEEEIIIaP8/3BOunE1unILAAAAAElFTkSuQmCC",
       "text/plain": [
-       "<Figure size 1152x432 with 1 Axes>"
+       "<Figure size 1600x600 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -533,7 +454,7 @@
     }
    ],
    "source": [
-    "forecasts = res.forecast(horizon=5, method=\"simulation\", reindex=False)\n",
+    "forecasts = res.forecast(horizon=5, method=\"simulation\")\n",
     "sims = forecasts.simulations\n",
     "\n",
     "x = np.arange(1, 6)\n",
@@ -548,24 +469,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<AxesSubplot:>"
+       "<Axes: >"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 1152x432 with 1 Axes>"
+       "<Figure size 1600x600 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -589,14 +510,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 1152x432 with 1 Axes>"
+       "<Figure size 1600x600 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -604,7 +525,7 @@
     }
    ],
    "source": [
-    "forecasts = res.forecast(horizon=5, method=\"bootstrap\", reindex=False)\n",
+    "forecasts = res.forecast(horizon=5, method=\"bootstrap\")\n",
     "sims = forecasts.simulations\n",
     "\n",
     "lines = plt.plot(x, sims.residual_variances[-1, ::5].T, color=\"#9cb2d6\", alpha=0.5)\n",
@@ -632,7 +553,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -645,12 +566,14 @@
    "metadata": {},
    "source": [
     "## Parametric VaR\n",
-    "First, we use the model to estimate the VaR.  The quantiles can be computed using the `ppf` method of the distribution attached to the model. The quantiles are printed below."
+    "First, we use the model to estimate the VaR.  The quantiles can be computed using the `ppf` method of the distribution attached to the model. The quantiles are printed below.\n",
+    "\n",
+    "**Note**: `forecast` is called with `align=\"target\"` so that the forecasts are already aligned with the target and so do not need further shifting."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -662,9 +585,9 @@
     }
    ],
    "source": [
-    "forecasts = res.forecast(start=\"2018-1-1\", reindex=False)\n",
-    "cond_mean = forecasts.mean[\"2018\":]\n",
-    "cond_var = forecasts.variance[\"2018\":]\n",
+    "forecasts = res.forecast(align=\"target\")\n",
+    "cond_mean = forecasts.mean[\"2018\":].dropna()\n",
+    "cond_var = forecasts.variance[\"2018\":].dropna()\n",
     "q = am.distribution.ppf([0.01, 0.05], res.params[-2:])\n",
     "print(q)"
    ]
@@ -678,14 +601,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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1g81Z5ad9XBGICoIgCIIgCIIgCMeVWW6tGQ09amRiEL5aFQsaMT1XBKKCIAiCIAiCIAjCcWUdKd3yT1qVgnHJISw7WEy51XlaxxWBqCAIgiAIgiAIglCHxeGm2Owg9l+BKMCknhG4PDLXfbuVHTkVDT62CEQFQRAEQRAEQRCEOrLKaycq+qfkMCP/u6oXCkni1h938NnaDFwNSF4kAlFBEARBEARBEAShjsyy2qVb/q17hB/fTuvLBV1C+XRdBrf/uIO8Slu9ji0CUUEQBEEQBEEQBKGOf5duOR6jVsXzF3TmxQs7k1psZuo3W/h7f+Epjy0CUUEQBEEQBEEQBKGOzHIrwT4aDBrlKfcd3yWUb6f1JSHQwFML9/OfP1NOur8IRAVBEARBEARBEIQ6ssqsx01UdCLRJj2fXtmLmwbHsnDvyUu7qE76qCAIgiAIgiAIgnBWyiqzMjIpqEHPUSkV3D4sngu7hp10PzEiKgiCIAiCIAiCINRSbXdRZnUSe5L1oSdzqpFUEYgKgiAIgiAIgiAItZwqY25jiUBUEARBEARBEARBqCVLBKJCY2zJKmfuzjxkuf7FZQVBEARBEARBOLtllnsD0Wh/XbMcXyQrOsN9vTGLdell7C+s5uExSagUUms3SRAEQRAEQRCENi6rzEqYrxad+tSlW06HGBE9wxVW2/HVqvhlRx6P/ranZohdEARBEARBEAThRLLKrc02LRfEiOgZr7DKwfguoSQEGXhzaSqrDpWSHGpkXHII53YKJvo0s2AJgiAIgiAIgnDmyiqzcm6nkGY7vghEz2BWp5squ4tQo4YpvSMZmRjEkgNFLEop4oNVh/lg1WG6hvsytlMw45JDCPdrnvnfgiAIgiAIgiC0HxVWJxU2lxgRFU5PYZUdgFBfLQBhvlqm9otmar9ocitsNUHpeysP897Kw9wyJJZbh8a3YosFQRAEQRAEQWhtWUcSFcU04+xJsUb0DFZU7QAg1Kit81ikv45pA2L45tq+zLlpACM6BPLNpmzKLI6WbqYgCIIgCIIgCG3I0Rqisc04IioC0TNYYbV3RDTEqDnpftEmPfeM7IDd5eHnHXkt0TRBEARBEARBENqorDIrCgmimql0C4hA9Iz276m5J5MQZGBYQiA/b8/F7vI0d9MEQRAEQRAEQWijssqthPtq0aiaL1wUgegZrLDagZ9Ohb6etX+u6R9FqcXJH3sLmrllgiAIgiAIgiC0VZllzVu6BUQgekYrrLKfclruP/WPMdEpxIfvtuTg8sjN2DJBEARBEARBENoiWZa9gWgzl3kUgegZrLDaftxERSciSRLTB8VyuNTCQ3N3Y3a4mrF1giAIgiAIgiC0NXsLqjE73MQFGpr1PCIQPYMVVjsaFIgCjE0O4YlxHdmQXsYtP+yoWWcqCIIgCIIgCMKZzep089zv+wk1ahjfJbRZzyUC0TOUy+2h1Owg1Lf+U3OPmtQzgrcu605uhY07Zu+k2CxKugiCIAiCIAjCme7t5Wlklll5/oJkTHp1s55LBKJnqGKzAxkIaeCI6FFDEwJ5d1J3iqrt3P3zTsqtzqZtoCAIgiAIgiAIbcbyg8XM2ZnPtAHRDIgNaPbziUC0nXLm51E2+4da2+zphymfPxfwTsuF+pVuOZFeUf68eWk3ssqs3PfrblxuUdZFEARBEARBEM40RdV2Xvr7AJ1Djdw+LL5FzikC0Xaq8L23yLzrVoq//B/gDULTJl1EzmMP4q6qrFnbGXaaI6JHDYgN4LnxyezNr+LP/YWNbrcgCIIgCIIgCG2HR5Z5/o8UbC4PL07ojFrZMiFik57lqaeeYtq0aU15SOEEIp57Cb/zLyDnsQfJfeEZ0iZdhMdcTcKPc1D6+lFY7Q1EG1K+5UTGJYfQMcSHLzdk4RZlXQRBEARBEAThtGzIKGNfQVVrN6OW77bksDGznAdHJxLfzJly/6nJAtF169bx888/N9XhhFNQaLXE/e8bdF26UjTjXZzZWXT4eR6GHr0AKKxyoFUp8NOpGn0uSZK4aXAsmWVWFqcUNfp4giAIgiAIgnC2qba7eGDObm6YtY33VhzC5nS3dpNIKaxmxqrDjEoK4rIe4S167iYJRC0WC8888wx9+/ZtisMJ9eTMy8VVXIxbUrA5eRh3Lc3jpu+343R7jtQQ1SBJUpOca3THYBKCDHyxIROPLEZF25tyq5OFewqwu8Q6X0EQBEEQhNawIrUEp1tmaEIgMzdnc+3MrezMrWy19ticbp5ZuB+TXs1T4zo1WdxQX00SiL799tsMHDiQgQMHNsXhhHo4uibU6fHw+gvz+fjSJ8gus7Izt5KftuVSVG1vVKKif1NIEjcOiuVQiYUZqw5TIkq6tArHaQSSf+8t4MqvNvP8nylM/24b6SWWE+4ry7K40SAIgiAIgnCaDpdYuOzzjaw5XFrnsUUpRUT4aXnr0m58MLkHdpeHm7/fztvL01pldPSdFYc4XGrxlmoxNG+pluNp9LzNbdu28eeff7JgwQK++OKLej9PqZQwmVpuDnJ7pVQqjttPqbO+QLaY8f1+AWlLS3lgVALDPviEF01D+WydEo1KwYik4Cbt4ymD4lh+qJRvNmXz/dYcxnYO48oB0QxJCEKhaLk7KCfqkzPdT5uzeGHhPh47P5nrBsedcv9Ss4P/LNzLwl35dI3w4/6xHXl78UGum7WNFy/pxiW9Ius859sNGbzx9wHuGZPE9YPjULXQYvW24Gx9X50u0V/1I/qpYUR/NYzor/o52/rpj935vPLnfsZ2DuXWkR0I99M16PlnW3+drhP10+HDZWSX23jktz28c0VvzusaBkC5xcGGjDJuGBpPQIAP5wf4MKxLGK//lcJ3m7JYm17GK5f1oH9c85dNAVhxoIhfduRx07B4zu8V1SLn/DdJlk9/+MPhcHDppZdyzz33cMEFF/D222+zdetWZs6cecrnOp1uystPPDIjeJlMhuP2k+xy4cjMYInNyHN/pPDjDf2IN6o4lJrFtL/zcHlkrhsQwz0jE5q8TeklFubsymPhngIqbC6iTTou7RHBRd3CCPJpfHKkUzlRn5ypnG4Pby5L45cdeWhVCjRKBXNuGoD/SYoMLz1YzGuLD1Jpc3HXqESu6hmOSqmgqNrOUwv3sy27gjuGxTN9UEzNNAxZlpn8xSaKqh3YXB46hvjw5LiOdI/wa6mX2qrOtvdVY4n+qh/RTw0j+qthRH/Vz9nSTx5Z5vN1mXy6LoMYk47cSjsKCS7rEcH1A2PqPVPubOmvxjpRP/1vXQafrM2gW7gv+wuqeP6CzozvEspvu/J46e+DfHNtH7qE+dZ6zqbMMl766wB5lXauGxjD3SOa/vr9n2RZ5tqZW7E63fxwfX80quYbeAgJ8T3hY40664wZM4iLi+OCCy5ozGGE0yCpVGg7JJJaZEatlIg16VFotSR1S+Kqvt67GmG+zRMUxgcZeGBUIgtvG8x/LkwmxKjlg1WHuejTDTwxfy8bMsrE9M4m9MZSbxA6rX80n1/dm2q7i682Zh133zKLgycX7OOxeXsJNWr55to+3D06qWZkM8SoZcblPRjfJZSP1qTzyuKDHL0XtTW7gqxyG4+P7chrF3eh3Orkxu+289rig1TbXS32egVBEARBaBi3R+aFP1P4dF0GE7qF8cP1/fn1xgFM6BrGLzvzuPTzjfx3SWpNeT+h+eRX2Qk0qJkxpQe9o/159vf9/LYrj0UpRUSbdHQONdZ5zoDYAL6/vj8Xdgvj641ZbM4sb9Y2rjpUyoEiMzcOjm3WIPRUGjU1d/78+RQVFdGnTx8AnE4nbrebPn36sG3btiZpoHByqcVmEgINtaZQ3jQ4lsMz8zB9ksWO8uxa+yuMCnocGtAk59aqFFzQJYwLuoTVGiVdfKCYGJOO1yd2IynEp0nOdbbakVPBrzvzmNovinvP6QDAhG5h/Lgthym9I4n0PzbdZsmBIl5bnEqV3cUdw+K5bkD0cafWqpUK/nNBsjdQ3ZRF5zBfJvWMYN7ufHw0Ss7tFIxOrWRgXAAfr0ln9vZclqWW8OCoDoxLDsHq9DBvdz5KhcSU3nWn9wqCIAiC0HI8ssxLfx/g972F3Do0jpsHxyJJEpH+Op46rxPTB8Xy5YZMftmZx5xdeVzWI4LOYUYyyqw43R5uHBR70llWQsMUVNoJ99Pho1HxzmXdeWTeXl76+yAScP3AmBMmBDJolDwxtiObMsqYsfowX1zd+7SSBxVW2Vl6sJhJPSOOG2TKsszn6zOJ8tcxvnNog4/flBoViM6cOROX69hIyVdffcXu3bt54403Gt0woX5Si80MiDXV2mbUqpg+7/g/Wk9182RNPTpKeufwBJYdLObt5Wk898d+vr6mz1m1zrApudweXll8kDBfLbcNja/ZftvQOBalFPHy3we4cXAsMSY9by9PY/GBYrqEGflwfE+Sgk9+A0CSJO4eEc++gireW3GInhF+LDlQzEXdwtCplYD3ffTwmCQu7BrGK4sO8tTC/fy4LZf0UguVNu/vfVyAnoEttJZBEARBEIS6XlucyoI9Bdw6JI5bhtTNIXG8gNTtkVEqJJBl1qWX8f7kHoQ1YZLLs1l+lY0OQd7rMJ1ayZuXdOOphftYmVbC+acI/LQqBbcMiePlRQdZmVbCOUnBDTr3lqxynlywj1KLE5dH5tr+0XX2WZdext78Kp4a17HVr9EbdfaoqCji4uJqvvz8/NDpdMTFnTqRitB4FVYnRdWOUwYdLUmrUjC+SyiPnZvEgSIz327OPvWThOOatSWHtGILj4xJwqBR1mwP99Nxx7B4tmZXcPtPO5nw6QZWpJVw5/B4vpjap97vB0mSeOq8jrg9Mrf/tAO7y8Mlx6kf1TXcly+v6cODoxPJq7TRN9qfj6/oSWyAnpcXHcTaBmpgCYIgCMLZKKvMyq8787iyTyQ3D4k96b5HA9IFtwzk5+n9WX3vMD64vCeFVXZu/n77SbPqC/UjyzL5lXbC/Y4F9RqVglcv7srcmwfWa6bgRd3DiQ3Q8+HqdNye+i11k2WZmZuyuGv2Tvx0KnpG+vHlhkyqbLWXVmWVWflg1WHCfbVM6BbWsBfXDMRQVTuWWmwGaJPTX8d0CmF0x2A+W5dBRqn4YGuonAorn63LYFRSEOckBdV5/Jr+0Sy+awj/ndiVGwfF8M21fZk+KBZVA7MXR/nruXtEAhU2Fx1DfI67bgFApZC4um8Uv982mP9e0o1+MSaeOq8juRU2Pl6TfjovURAEQRCERsqtsAHeeu/1ncYZbNQSd2RZV/9YE59c0QuH28PNP2xnT17r1bQ8E1TYXNhcnjqjy0qFREQ9sxerFBK3D4vnUImFhXsLTrl/td3F4/P38d7Kw4zqGMxX1/ThsXOTqLK5+GaTN6dIpc3J28vTuOKrzeSU23hodCLqNjBjsdHlW/7pgQceaMrDCaeQWuQNRDu2oRHRf3p0TCJXZJbzyuKDfDSlZ4sXyW2vZFnmv0vSUEoSD41OPOF+PhoVozoGM6pjw6Zt/NuUPpGkl1oYmhDYoJ9R32gTk3tF8P2WHEYmBtEvxtSodgiCIAiC0DB5ld5AtL5BzvEkhxn531W9ufuXXdwxeyczpqroESzKt5yOgkpvMqiGlsz5t3M7BdMz0o/3VhxiZIegE9b4PFRi5tHf9pJdbuW+czpwTb8oJEmiU6iR87uE8v3WHHw0Sr7dnE2lzcXEHuHcPiye4BaoclEfrR8KC6cttdiMv07VIiVTTkewUcudw+PZklXBitSS1m5Ou7H0YDFrDpdy27C4Rn+Q1YdCknhsbEdGJNYdeT2Ve0YmEBug54n5+8g/8sdQEARBEISWkVflLdESamzctWBMgJ7Pr+pFtEnPbd9uYU9+VRO18OySX+W9Fgpv5HpbhSTxxLiOVDvcvLMi7bj7/L2/kBtmbaPK7mLGlJ5c2z+61oDC7cPicHtkZqxOp2OokW+n9eXp8zq1mSAURCDarqUWm0kK8TnuKJbCePwf7Ym2N5dLe0aQEGTgvZWHcLqbJ1HSmaTa7uLNZWl0CvHhij6tU1y4IXw0Kt64pBsOt4eHf9uLTawXFQRBEIQWk19pI8SobZKkM8FGLR9O6UmIUcuTC/bVWV8onFp+zYho4xM/JQX7cP2AaBbuLWRjRlmtx2ZuyuKphfvpGGJk5rV9jzsrLcpfz+sTu/LOpO58eHkPOp1g+VVrEoFoO+WRZdKKzSdMTNPj0AB6FQ6q89VUpVvqS6WQuO+cDmSV2/h5Rx7r0ku5+ustjJ2xltt/2sEbS1OZuzOP3XmVIukN8PGadIqrHTw5rmOD13u2lvggAy9N6MyBwmpeXXywtZsjCIIgCGeNvEo7EU0Q9Bxl0qt554peFFTZefHvAzW1xptLcx+/peVX2dEoJQKaqBzO9EGxxJh0vLr4IA6Xd0Cn1OLg07UZjEwM4uMrehJ6ktHXEYlBDGvg0qsTctnwWf08iqqcxh/riCZdIyq0nNwKG1anp01lzD2RofEBDIoz8e6KQ7g9MtEmHaOSgkkrMfPbrnxsR36xJCDKpCM+0IBerUSrUtAr0o/zu4SiP1JS5Ez7wPqnvflV/LQtl8t7R9Itwq+1m9MgwzsEMX1wLF+sz2R8l1AGxwe2dpMEQRAE4YxXUGmjZ5R/kx6zT2wAd49I4N0Vh3j2jxRuGRJHbID+lM9bdrCYUouDyb1OXWPc5ZF5f+Uh/tpfxP+OTAk+E+QfqSHaVHlRdGolj5ybxL2/7Ob7rTlcPzCGWZtzsLs83DMyoUUTDmmy12DY8T9UxXupuORHaILXKALRdqomUVEbzJj7b5Ik8cCoRJ75fT8XdAnlyj5RNQV2PbJMboWNg0VmUovNpBWbySqzYnN5qLa7WLCngHdWHKJPtD+5FTayy608OK4Tk9tAyumm5PbIvLLoIIE+Gu4cHt/azTktNw6KZXFKEa8tSeX76/rV1CMVBEEQBOGYKpsLpUKqVZrtdLg9MgXVjkavRzyea/pFUWZx8OO2XP7eX8i45BCmD4ol8QQDIG6PzBtLUymqdpAcaqT7SW6ol1ucPLFwH5szy1EqJN5Ymsbbl3Vrc0ktD5WYifbX11yz1kdBla3J67EOiQ9kRIdAvlifybAOgczensN5nUOID2zZhFLqvE0AaHLWots7C1u3axt9TBGItlMHi81IQId2MCIKkBjsw3fX9auzXSFJRJv0RJv0jP5X9ldZltmZW8kvO/LYX1BNTIAeH42KNxcdoFeosU2WrTlds7fnsr+wmpcndMaobZ+/llqVgsfHJnHn7F18uTGLO4bFt3aTBEEQBKHNuWP2TtRKif9d1RtlI5bhFFXbcXvkJp2ae5QkSdwzsgNX94vmu83Z/Lwjl7/2FzG6YzA3Doqhc5hvrf03ZpZRWO1ApZD4v0UH+ebavsddYnSgsJpHfttDsdnBs+d3otLm4p0Vh1iZVnrccnUNkVNhxemSiQ9qfIBWanEw9ZutXNAllOfGJ9f7eflVdgbHBTT6/P/2wKhErvx6M7f9uAOb08ONg09eM7Y5qPI34QzthazxxWfNSzhix+DxPfXo98mINaLtVFqxmWiTrmbK6plIkiR6Rfnznws789P0/rx5aTfeurQbfjo1z/+ZgusMSX7kcnv4bF0Gg+MCGJcc0trNaZQBsQFc0CWUbzZm8eayNPbkV53R06kFQRCEs4t19y5kV+0kPpZdO5A99bsmSS02k1JYze68Kn7bnd+otuQ3UamQkwn20XDvOR2Yd8sgbhocy6bMMqZ9u437ft3F3n9k1l2wuwB/nYrnxydzsMjMD1vrriNclFLETd9vx+WR+fTKXlzcPZwr+0TSIcjAW8tSG5Xw0On2cOdPO7nph+2UmB2nfZyjNmSU4fbILNhTwJas8nq3obja0SSJiv4tJkDP1X2jqbS5OLdTCB2CWngwxu1AXbAdZ8Qgqka/jiR7MK54Ahp5jScC0XYqtch8wukRZzKTQc2LE7uRUljN28sPkVpkrlm83V5tza6g0uZicq+INjct5XQ8OCqRUUlB/LIjlxtmbePyLzfz2doMMsusrd00QRAEQThtjpxsDk4YS+adN9cEoxV//k7q+DEUffBuvY6xKKUIhQRdwox8uOow5RYnFVYnby1LY+nB4ga1J6+q8TVE68ukV3P7sHjm3zKIO4fHsze/mlt/3MGBwmqqbC6WpxZzfudQzuscwvAOgXyyJp0dORWAd9rujFWHeXLBPjqFGvn62r41uTBUSgWPnptEbqWdt5an4TnNwGbe7nxyK+1U2Vy8vfz45U4aYkN6Gf46FVH+Ov5v0UHs9bjWLKy2IwPhvs3z87hxcAxT+0Vxz8iEZjn+yaiKdiG57Tgj+uPxi8U86BG0GUvQpC9q3HGbqH1CC7I53WSVWzmvc/sePTtd47qGcXG3MH7anstP23NRShBt0pMQZKBDsA8dAg10CjWS0ARTM1rCyrQStCoFg+KbfipHazAZ1LxycVeqbC6WHiziz32FfLYug0/XZdA13JfxXUK5oEsopibKKCcIgiAILUETFU34w0+Q9+KzAJgumUzGrTeg696DoOunn/L5siyzaH8h/WNMPDg6kWu+2cITC/dxqNhMqcXJvN35dA/3PWkW1H9qylIh9WXUqpg+KJaLu4czbeZWHp+/l0t7ROBwy1zUPQxJknjs3CRu/XEHt/ywg6v7RZFRamXN4VIu7RHOI2OS6qy57Bdj4roBMXyzKQuPDE+M7digKct2l4cv1mfSM9KPQXEmPluXyYVdwxiacHqJE2VZZn1GOQPjApjYPYx7ftnNVxsyue0US47WTdgBl4H5xsPsyMio2a4wKpqkaoWPRsUDoxIbfZzToc7bDIAzwvs6rD1uQLfvB4yrnqM0ZgSoTi/ZlBgRbYcOl1rwyLSLjLnN5enzOzFrWl9eurAz1w+MISHIwOESC19vyOTp3/dzxVebmbMzr7WbeUqyLLMitYSBsaYzbpq1r07FJT0i+OiKXsy/dRD3jkzA5fbw1rI0Lv3fRj5fnyFK9giCIAjtSug99xPxzH8on/sr6dOvQde5K1Hf/crSfCcPzd3DHT/t4LddeZgddWtw7i+sJqvcxnmdQ0gM9uGqvtFsziwnxKjl9YldcXlk3mrAaF5+pR2TXt0q1w/BPhpeuagLuRU23l91mMRgA52P1KkM99Px/fX9mNQrgu+25LA+o4zHxybx1HmdTpj45+4R8dw4OJbfduXz9ML9lFnqP7321515FFY7uH1YHDcMjCU+UM9riw9SUGU/rdeWVmyhxOxgcHwAg+MDGd8llM/XZ7JwT8FJn1es9o6aBlbW3u6pbt8z9wDUeRtx+ccjG44MginVVI98EWVVFoatH572ccWIaDt08EjG3LNxau5RCkmiU6ixTnFeh8tDZpmVN5el8s7yQwyOD2iRKSun60CRmfwqOzcPaflF5y0pzFfLtAExTBsQw4HCaj5dm8HHazKYszOfr6/pQ5CPprWbKAiCIAj1ou3Yqeb/9vBorvj5AIXVDkKMGgxqJS/9fZA3lqYxNjmEid3D6R3lhyRJ/L2/CJVCYlSSNznjncPjGRBnYlCsCZVSwU2DY/lwdTqrD5UwvMOpE/fkVdqaJWNuffWO9ueekR14Z8UhLu4WXmt5kY9GxeNjO3Jh1zA0SqlOcqN/kySJO4bFY1Ar+XD1YdYeLuXqflFcPzDmpIF2eomFrzZk0j/GnwGx3pllT43rxJ0/72TS5xu5vHckNwyMIcBQ9zqjxOxgQ0YZ69PL2JJVzpTekdwwKJZ16aUADIo7eryOlJodvPBnCm5ZZmL38OO2pcTPO604sKr9L7OqRZZR52/GETem1mZn1FBsHS/BsPVDPPogbJ2vAHXDZiOKQLQdSis2o1UpzpiaS01Jo1KQFOLDM+cnc9XXm3n57wO8P7lHm117uTK1BAnq9QfnTNEp1Mgbl3ZjS1Y59/yyizeWpvHKxV1au1mCIAiCcEoVf/5Oxk3T0Pfpi+85Y1jw10YKqx08f14S47tFoJBgd14V83bnsyiliAV7CogN0HNRtzAWpRQxOD4A/yNLUzQqBcP+MX302v7R/LGvkFcWHeT+UR5GJwWhOkmdyPxKO3GBrXstOLVfFN0jfE9YrqVnZMPqol8/MIZzEoP4ZG06n6/PJLPMyv9dVPcaocTs4LN1GczdmYdOreSekR1qHusd7c/P0wfw2boMftiaw9yd+VzTP4orekeRWmxmXXoZ69NLOXBkYMekVxNoUPPxmnQGxgWwIaOMhCBDTRkWnVrJm5d245Hf9vLiXwf4dnM2Uf46OoT6Eqz3riON8tdRGCDjawaNq21ec54uZcVhFNYSnOH96zxmHvYsysosfFc+jc+GN7B2n4atxw14fOpXZlEEou3QwSIzHYIMjUr5faaL9Ndx94gO/HdpKvN253NJj4jWbtJxrUgroUek31k5ItgvxsRNg2P5eE0G41NDG522XRAEQRCakyMnm4ybr0PXvQeJP81F6W/ioH4OxuoK+iz+DmWPhwHoEelHj0g/HhydyNIDxczbnc+Hq9MBTlorXK1U8Pz4ZJ5csI8nF+wj1Kjh8t6RXNojvM6InizL5BRYSPzLzo5bNtR6rKnWJNbH0QoHTSk+yMArF3clab139tTY5BDGHCnxZ3W6mbU5m5mbsrG7PUzuFcnNQ2Lr9E+kv47nxidz3YAYPl6TzmfrMvlsXSYASoVEr0g/7hwez+D4AJJDjVTbXVz19Rae/yOFnAorl/euXZZEp1byxqXd+GZjFgeKqsmpsLEtuwKz4x9LjHpAXN6Zd22uOlI/1BkxsM5jHp8wyif/hipvE4Ydn2LY8gGGbZ9g73Qplt634A46+UCDCETbobRiM8M7nN4C7LPJ5b0jWHKgiLeXH2JwfGCTFxhurPxKGymF1dwzouWzn7UV1w2IYXFKMa8vOUi/GP92W0NVEARBOPNpoqKJ/eh/+I4chdLfhNsjs00bzkB9JWHT7qizv16tZEK3MCZ0CyO73Mr2nArO6xx60nN0DffllxsHsPZwKT9uy+HD1en8b10G53UO5co+kTVTXCusLhxqCKqse4wzYU0iwPUDYlh2sITXFh+kd5QfK1NL+GRtBsVmB6M7BnPX8HjiAk8+FTQhyMBrE7uyN7+KFWkldA3zpX+sPz6a2tcbfjo1T5/Xift+3Q0cm5b7T1qVgluGxtV87++vJz2vgpwKGznlNjY/fpCk7DMv/Y46bxMerQl3wAkSJUkSrsiBVEYORFF+GMPO/6Hb9xO6/T/hiDkHbpp3wmOLq752psTsoNTiPKvXh9aXQpJ45vxOXP31Fl5ZdJC3L+tWZ4pupc2Jn651srduzCgHYHji2XtTQa1U8PR5Hbnx++288GcKr1zc9bgFsAVBEAShLTBdfGnN//fmV1FudTLmwj4ofU5+XRZt0td7SZVSITEiMYgRiUEcLrHw07YcFu4tYMGeAnpF+vHouUk1ZU6CKs7cv5kqpYJnz+/EdbO2cen/NmJ1eugR4cerF3dp8Chs13BfuoaffJ3q0IRAJvWM4O+UQvpGn/r4kiQRYNAQYNDQPcKPyF0Zx70JoDC27+BUnbcJZ0R/kE79OjymBKpHvox54MPo9sxCv2fWSfcXgWg7k1rsnc/eMUQEovURbdJz54gE3lqWxu97C5nQ7dic9Xm783nxrwMMTQjghoGx9KnHh05TSi+1oFZKxAW0jzIzzaVbhB8PjErkzWVpvPBnCi9ckIyija7pFQRBEISj1hwuRSHB4GYsv5YQZOCxsR25c3gC8/fkM3NTNnf9vIup/aIACKo8s/9edgo1cs+IBBbuLeDmwbGM7hjcrHk/HhubxB3D49GdRibilpoO3ZIkaymq8jRsXa5o0PNkXQDWfndj7Xc3Jys2KQLRdibtSCB6Npduaagr+0SyJKWIN5el0T/WRJivFqvTzYer04k26WqKMveO8uOGgbEMTQhokeRGWeVWov31Yq0vcFXfqJqfiUoh8eCoRHx14uNJEARBaLvWHCqlR4RfTfKh5uSrUzG1XzTDOwRxyw/ba9acnskjokdd0z+aa/pHt8i5FJIk6pz/gzr/SP3Q8OYJstv3WPFZ6GCRmSAfzXHTUB+PYf3rBHx/LprUBXBkGsfZRiFJPDs+GbdH5tF5e7G7PMzanE2J2cHz45OZf8tAHh6dSF6lnfvn7OaamVv5e38hbk/z9ldGmZXYAJH5+Kjpg2K5aXAsC/YUMPF/G/hsbQaVNmdrN0sQBEEQ6iiutrO/sJphLZyzIzZAzweX98BXq0LrAB9bi55eOMuo8zYiKzS4Qns2y/FFINrOpBWbSQqu/1RObfoiVKUp+P91O6ZfLkG/9UPUOWuRHNXN2Mq2JzZAzwsXJLM3v4rn/9jPzE3ZjO4YTK8of3RqJVf2jWLOTQN49vxOON0enlq4n8u/3MTaw6XN0h63Rya7XASi/3b7sHi+ndaX/jEmPl2XwcWfbuTt5WknLUpdYRXBqiAIgtCy1h4uA6hVfqWldAwx8umVvbhlsQaJuiOi7X1NotB2qPM2e4NQla5Zji/mvrUjbo/MoRILk3vVsxSJ24myLBVL79twBySi3/YxxnX/B4CMhDuwE87Q3rjCjnwFdQVFw+fEtxejOgZz0+BYPl+fiVKCu/6VQl2tVHBx93AmdAtjeWoJn6xJ54E5u3lwVCJX9o1q0rYUVNlxumURiB5HcqiR/17SjQOF1XyzKYsft+bw07ZcxncJZdqAaDoEeaely7LMFxsy+XhNBo+dm1Qn1bogCIIgNJeUwmp8NMpWy9mRFOJD0pLBrXJu4SzhsqEq3Im1143NdgoRiLYjWeVW7C5PvT/0lBWHkTxOXMFdsSdPxtZ1KpKtDFXBdtSF21EVbEObvgj9/h8BsMePpfLCL+qVFau9unVoHOVWJ5F+uhOm/FZIEmM6BjMkPoCnF+7njWVpZJZZeWB0YpNldM0sswAQIwLRE+oUauSlCV24c3gC323JZu6ufBbsKWBEh0CuHxjD0oPFfLclB3+direXp9Ej0o/kUGNrN1sQBEE4C1TZXfjpVC2SU0IQWposy7gy1iN5HM22PhTE1Nx2JbWoYYmKVCUpALgCO9dsk3UBOONGYxnwAJUXfUPJjTsombYWc//70aYvRr/j86ZveBuikCQeH9uR6wbGnHJfvVrJ6xO7MrVfFD9tz+XhuXswO1xN0o7MMisAcSIQPaVIfx0Pj0liwS2DuGVILDtzK7n5hx18tyWHK/tE8tP0/vjr1Ty5YB+WfxaWFgRBEIRmUm13idrXwhlp4cL59OnTlfcf9WbKfeXbRbhcTXP9+2/iN6gdSS02o5AgIaieI6Kl+5ElxYkL0AJIEh6/WCwDH0JVsg+fdf+HM2oIrpDuTdTq9k2pkHhgVCJxAXpeX5LKLT/s4J3LuhPqq23UcTPLrBjUSoJ86pd0SgCTQc2tQ+OZNiCG+bvzkSSJy3tFIEkSL17YmTtn7+TamVvoEORDQpCB6YNiMWjO3KnmgiAIQuupdrgxir8xwhlm7drV3HnnzVitVoaO1LO/2M17X8yk0OzhtdfeavLziRHRdiSt2ExsgB6tqn4/NlVpCm7/hPotMJYkqkb/F48+EN+/7kBZvLeRrT2zTOoVyTuTupNTbuOhuXuwORs38pZZZiUmQC+m9JwGvVrJFX2imNI7sqb/+sWYeG58MjEBerIrrHy9MYuX/z6AfJZmihYEQRCaV7XdhY8YERXOMG+88SpWqxWFBENjlKzJcmO1Wvn++2+prq5q8vOJQLQdOVhkJim4/mvglCX7cQcl13t/WR9I1XkzUNhKCfjxfHwX34eiKvd0mnpGGhwfyIsTOpNSWM3Liw7WK8hJKzaTdWQa7j9litItTe7CrmG8O6kHP1zfnzuGx/N3ShE/bRPvX0EQBKHpmR1uMTVXOOMcPnwIvQp+uUJPkEHBwgPeKblKpZLCwsImP58IRNsJi8NNToWNpJB6lm5xWlFWZNRaH1qvp0UOpvTaNVj73I42dQEB349Bt/tbkD2n0eozz8jEIG4bFsef+wr5bkvOCffLLrfy9MJ9XPX1FqZ/t42cimPBqNPtIa/SJgLRZnT9wBhGdAjk7RWH+GVHLhszykgtMlNqcTR7fVhBEAThzGe2u8TUXOGMM6JvF5bd4MPEZBV3/25lzn5vICrLMpGRTVtBAsQa0XYjrfhooqL6jYiqyg4iIeNqwIjoUbLOhHnoU1i7T8N32aP4rngcbdpCKiZ8ASoRPE0fFEtKoZn3Vh4iKcSHQXEBNY+VWRx8vj6TX3bkoVRITO0XxfzdBTzy214+v7o3erWSZQM24bkGeCqXHXsLap6rMCrocaj5MpOdTRSSxAsXdGb6d9t4dXHqvx4Dk15NgEFNUrAPT47rJNaSCoIgCPUmy7JIViSccZRlaXwxNAO5SsFlP1qZl+INQg0GA/fd9zA6XdPXEhW/Qe1E6tFAtJ4jospSb8ZcdwNHRP/J4xdLxcTv0e35FuOKJ/Fd8SRVY96Cs3xdo0KSeH58Mjd+b+GpBfv46po+BPlo+G5LNjM3ZWNzupnYI5xbhsQRYtQyKC6AB+bs5j9/pvB/F3UhT+ddXxpWVrsfPdVi1Lkp+epUfHddP3IqbJRaHJRanJQd+bfU4qDE7GRxShFmh5v/XtKttZsrCIIgtBM2lwe3jAhEhdOiLD2ArNLj8Tt1BYeWosrdiP/vN4JCyaZ+b1C8fCY+PtsJCQnhvvseYurUac1z3mY5qtDkUovMGNRKIvxOcDfC7UBdsA2P2og7pBuqkv3ISi1u/7jGnViSsHWfhsJShM+mt3CG9cXWvXnejO2JQaPkjUu6cf2sbdz7yy6sTg/FZgejkoK4a3gC8UHHbhgMTQjk7hEJvLfyMNGr0zEHeqeGhpad3QF9S9CoFCQEGUgIOv4NnF925PLq4lTeWpbGy5N6tHDrBEEQhPao2u4dKTJqxWwaoWEU5gICZk8Alw1H3Bhs3a/DETsKFK33XtKkLsBv8X24jZFUXDyTDv7xzB1xdYucWwSi7URqsZnEYB8U/x6N9LgxLn8Mbep8FE4zsqSg8rwPUZXuxxWQBIqm+RFbBtyPqnA7xlXPIjktOGJG4A7qDNLZu8w42qTn/yZ04b45u+ke7surF3ehV5T/cfe9tn802eU2vtqYRWA/MFrAaGt4IKqoSMfjG1P/DyyPC2n/fPw3fIY6dwP2xAlY+tyBO+QEI4AeFwpzIR7fyAa3rT2a3CuS7HIb327OptjqZFzHYEZ0CESnFhcXgiAIwvFV270zm4yatnsZLdkrkTW+Z/0sttYgWYoxrnkBW5ercEYPq/WYYeNb4HFh7X0r2gNz8F94PW7fGKzdr8XW5SpkfVDLNVSW0W//FOPaF3GG96diwpfIuoBTP68Jtd3fIKGGLMukFps5t1NwncdUhdvR7/sBe+IEbB0nYtj+GX6L7kFWanB0GN90jZAUVI19F//512Jc+yIAHq0JZ+QgnFFDcEQNPSsD00HxASy+cwg+GuVJS7FIksSj5yZRanGwPLWExJyG/WFQ5W3CZ9PbaLJWYut4KVXj3jtpXyvMBej2foduzyyU5nzcxijsnS5Fk/Y7uoNzscedi3nIk3WyKhtXP4d+19c4w/pg63o19qSJyJr6Z2puj+4ZmYBGpWD+7gKWpRQR7KPhoyk9iQ8y4PLI/OfPFDZnlTMqKZhxySH0ivKre0NIEARBOGscGxFte5fRyuK9+Gx8E+3hv3D7xmDvcD6OhPNxRgxossEJ4eS06X+jOzAH3YE52JIuxjzsGTzGSKSSFLR7v+evsjjm/mlh6pVf0kuXhX731xjXvYLPhjexJ12EtdfNuEJ7Nns79VtnYFz/KvbECVSOfadV8sCId2Q7UFTtoNLmOm6iIk3GMmRJQdWoV5F1ATijh2OaMwVVyV5cgQ1PVHQysi6A8ikLUVTloM5dhzpnHZqc9WgP/wWAR+uPM3LwscA0uGuTnr+tqu8fIqVC4sULO3PP3etIqm8gKssYVz2NftfXePTB2BIvQndwLm6/GCyDH6uzrzp3PbpdX6M9/CeSx4Uj9hzkC9+kLHg4KJRIw59Ht3smhq0fEvDjOGxdrqJ6xAug0qPK34pu1zc4YkaiMBfgu+xRjKuex5Z0MbauV+EK73/szurR0jVnQECmkCTuGBbPoxd0YdHOHJ77I4U7f97Jh1N68tnaDP5OKaJvtD/zducze3suIUYN53YKYWynYHpEiqBUEAThbFPt8AaiPm0o0Z1kK8O45kW0+2cja3yx9L4NZXka+t0zMez4Hx5dAI74cdgTzsMRcw6o9d6KCB4XKDWt3fwziqp4Dx61EWuf2zBs+QBt+mKq+93L7t8/oZvRzbT/7aTMvptvvvmCF198leuum42yJAX9nplo9/+MNnUeFRO/wxk1tNnaqM5Yhs/617B1vISqce+32kCSJLdSxXen0015uaU1Tt2umEwGft+WzX2/7uaTK3vSN9pU+/HZE0ChonzybzXbJEsRxjUvYhlwP25Th2Zvo6Iq9x+B6TqUlRkAWHrdgnnYs00erJhMhnb93tnVYdNxExPVyZoryxhXPoV+9zdYet6EefBjoNJjXP44+r2zMA94EHvC+XiM4WhT56Pf9Q2qsgN4tP7YOl+Jtfs0PKYETCYD2dkFFBTkExUVg06nQ7KVYdj8Lvodn+OMGkLlBZ9hmjMFyVZK2dTlyGofVAVb0e37Ae3BeSicZlwBSdg7XoqyMgNNxjI8ukAqLvoKj19sC/Ze8zn6vkotMnP7TzuwON043TL3jEjguoExWBxuVqWVsPhAEWsPl+Jwy4T5ahmXHMLNQ2LxacNTtJpDe/89bCminxpG9FfDiP6qn6bup0UpRTy5YB/fX9ePpBCfJjvu6dIenI9x1dNI9gqsvW/F0udOZJ3J+6DDjCZrOdpDf6HJWILCXoGs0uHRmlBYiwEF1p7TsfS/F1nrXV4k3lf1c6J+Mv16GSBRPulXFJWZGFe/UDNo89RSG/+3ylGzr06nY9euA/j7mwDvDQXTr5NQWAopv3x+g6/jlWVpGLZ+gCZjGZXnf4QzakidfRSVmQT8dAEeYxRlk3/z3pRoRiEhvid8TASibZzJZOD9RSm8t/Iwi+8cgr9eXfOYZC0h6IveWAY+iGXAA63YytoUVbkYts5Av/trrN2mUX3Oy016p+Ws+ICUZYwrn0a/+2ssfe7APOTJYwG9x4XfH7egTV9U6ynOkJ5Ye1yPPWlizYeKy+XihRee4Ouvv0apVCHLHu677yHuv/9hJElCm/IrvkseQNYFoLAWUzDqfQoCBxEWFo5CceRn5jCjS52Pbt8PqPM349H644g5B03WCmS1gYpLfmyRGx7N7Z/vq5SCah6dv5fJPSO4bmDdrHbVdhcr00pYnOINSntH+/POZd3PqrWlZ8XvYRMQ/dQwor8aRvRX/TR1P83Zmcf/LTrI/FsGEn6iJJItRFGRQeC3w3GF9qRqzBu4g7qceGe3E3XuBjTpfyM5zMiGEBTmPLQpvyLrAjAPegRb16sxBfqJ91U9HPd9JXsI+qwL9s5TqB75Us3mGQ9cRFDpRh5bZMPqOra70ejL++9/zIQJF9dsU1RkEPDzxXi0/pRfPq9e6zaVJfswbH4fbep8UGnxaP2RXDbKJ8/DHZDoPW5VDtqDv6Hf8y2SvYKyKQvx+Mc3qg/q42SB6Nl1C7+dyi634a9T1QpCATSZy5GQccSObqWWHZ/HN5LqkS8ha4wYts4Aj4Pq0W+cEdM4W4QsY1z1jDcI7X1b7SAUQKGi8sLPUZaloirZj7IiA0fMcFxhfeoc6sUXn2PmzJnYbLaabe+++yahoWFcc8112JMnIav1+P11JxsrQhhx/q0oFBJ+fv7897/vcMEFE0Djg63rVdi6XoVkKfJ+ICpUKIv3Ypp3NaZfJ1Mx4UtcYb1boHNaRnKYkd9uHnjCx41aFRd2DePCrmH8ua+QZ3/fz5ML9vHShC4cKjGTUlhNQZWdwmoHIxODGNOx7vpuQRAEoX0yO44kK2oDa0TVhTuQkKke9drJg1AApRpnzHCcMcNrbbb2uhmf1c/ju+IJ9Lu+gvGvQMCJ/wYKJ6aoyPDOIguunRQyxR3Lj3+u4njDf3p97RFJj38cFRd+jmnulfjPm0rFxO9OGIyqCndi2Pwu2sN/4VH7YO17B5ZetyI5zQT8MhH/Bddh6X0L2oPz0eRtAMAZ1gfzuW+1SBB6KmdXZpl2KqvcSkxA3WFzTeZyPPqgFlnQ3GCShHnw45j734d+34/4rH+ttVvUPsgyPqueRb/rK28QOvTp4wfwkgJ3YCfsHSdi6X/PcYNQl8vFV199jsVS+26dxWLh3Xffqvne0eECrt3Yg7GfZuNw2LHZbBQWFnD77Teydevm2s0zhNQkO3AHd6X8sl+QVVpMv05Ct/sbjvsJe4Yb3yWUR89NYtWhUka9v4bp323n1cWpfLMpm5WpJTw+by8L9xS0djMFQRCEJlJtdyHhLeXW2lRFu5AValyBnU77GK6QHlRc+jMV4z9FctlQfTcJw8Y3vWtIhQZRFe8BqBOIXnPNdeh0da/lVSolw4ePrLPdFTGAygs+Q1V6AP/frkKyldV6XFmyD7/50wiYfSHqnHWY+99P6XXrMQ95EtkQfCSY/QKFOR/flU+jsJVgHvQIJdeupvzy+TgjBzfhqz59rX8rRziho2sJD91uJylbwY77vXcyFEYFPdL6oclc4a091FYz1UoSloEPo7CWYtj6AR6NEZRatAfnorCV4TZG4PGJwGOMwG2MxGOMwGOMxGMIQVZqQalGVhvP6JFUs9nM119/wfz5c/H39+PDi/0IKV+CpdetJw5C68lqteB0Oo77WFFRYc3/c3Nz+GXxOux2e619bDYb77//Dl9++e0Jz+EOSKLsij/wXXQvviueRFWwg+rRr7dqPazWcHnvSHy1Kg4Wm+kW7kuXMCMhRi1Ot4cH5+7hP3+lIElwYdew1m6qIAiC0EjVdhcGjbJNJKtTFe/xJqdsbMIhScKReCGlcWMIWvcMPpveRll6kOrRr9WsHT2lo4FrPa9LJUc1ytIDuML7nmaj2x5V8V5kharOjYFBgwZz//0P8dZbr6NSqVEoJCRJYtas2Wg0x//ZOeLPpeLCz/H//SZMc6+k/LLZyFp/FNW5mH67CmQP5kGPYe1xPbLWr+Z52dlZZGSk07FjMvLl80GWvQlE28D79d9EINqGeao9OJUypX4QVibV2q4q3IHCVoojrm1Ny61Dkqge+RIKSyHG9a8C3rWMzrA+KMz5qAu2okjLQ/I4j/t0tyEMZ9Rg3IGdkazFKGxlSANvAP/2/6FltVq54IIxpKenY7NZeft8LcnlWlY4etJ12DON/sAwGn0JDQ0jNzenzmN9+hzrv9zcHDQabZ1AVJZlDh8+dMrzyLoAKi/6GsPGN/HZ/C6Sx0nVuW+fdcHo+V1COf9f25QKJW9d2o375+zmuT9S+GpDFmM6BXNup2CSgn1OWvJHEARBaJuqHe7GTct1WkChBqX61PuejCyjKtqFPeG8xh3nn1Q63Bd9gMWYiM/al9Ee/hNn5BAc8WOxJ4w7cYJCWcbvj1tQlqdRMfF7PMaIE55CclSj2/UVhm0fo7CXUz3kSax972y619BEJHOht65nA65nVMV7cAckgaru2uEHHniEqVOnsXLlcnx8jIwZMxad7uRrjJ2xo7zB6MLp+C+4nooJX+H3x63gslF++QLcgR1r9rVardx223SWLVuCVuu9rrviiqt5/fW3Uf7remPJkr95//13KSzMZ/Toc7n33gcJCwuv9+tsKk0SiGZmZvJ///d/bNmyBb1ez4UXXsgDDzyAVqttisOf1Yr9ZWQJQstqv4E0GcuQkbwpuNs6hZLK8z5Ad2AOzvABtX5pAJA9SNZSlNW5KKrzUFiLwO30Tg8p3o06Zx26g7/hUfuAQoWUtgDNuPdxJE5ondfTRGbP/oHMzAxsNiuvjtVy/2Atb6+388TyTWy5pojQ0NBGHV+SJP7v//7LnXfeXDM9V5IkdDo9zz77n5r9OnVKPu7IqUqlZsiQeqYOlxRYBj0CSg0+G/4LyJgHPOhdf3CWB1s6tZJ3LuvOvN0FLDtYxJcbMvl8fSYxJh1jOoUwpmMwXcKMIigVBEFoJ8x2F0atsv5Z8P/J7STg54tBlim/9Cdkw+nnEFBU56GwleEK6XHaxzguScLa53acUUPRps5Dk74Y4+rnMK5+Dldg8pEyMONwhfauCdI0h/5Ae/gvZEmB/9wrqLj0p7rBqMOMfveRANRWhj1uDCjUGNf9H7JKh63njY1qtjblFyR7BbYe0xt97aGoyiFw1kicUYOpuODz4waWx6Mq3oMzetgJHw8LC2fKlKsa1BZn7Cgqx72P3993EjhrBApbGRUXfFbnevrppx9j2bKl2O32msGFn3/+kfj4Dtxzz/01+33yyYe88sp/aq4NMzLS+fXX2Sxfvp6wsJadudXoQNThcHD77beTlJTEDz/8QElJCU8++SQAjz/+eKMbeLYrDPCuuasTiGYuwxXaC1kf2BrNajiVHlvXqcd/TFIgG4JxGYLheOtdZRlcVlAbkGzlBP51I35/3YFlwAM4Q3vjMYbj8QlH1praVdCzaNGfWCwWuoUoeGyYlk+3OHjwLzu+vr5s2rShVga103XhhRcxf/4Cnn/+BQ4fPkTPnr145JEn6date80+fn7+3H773Xz66Yc1H0oKhQKDwcDdd9/foPNZ+t8HgM+G/6I7MAePPghnWF9cYX1xhvfFGdobNK2f6r6l6dRKrugTyRV9Iim1OFieWsLSA0V8uymLrzdmEeGnJSHIgK9WRVKwD1P7RaNRtc0p96UWB8/9kUJmmZVu4b70jPSjZ6QfnUJ8UCnbZpsFQRCaUrXdhVGjwlNtP+7jxwtOj9Lt/Q5VaQqyQoVp3tWUX/oTkr0S3f6fcIX1xRF/br3boSreDYAruPsp9jw9rtCeuEJ7Yh76NIryw2jTF6NJX4R+20cYtn6Ay5RI5QWf4faNxrj6eVxBXage+RJ+C67Df+4VVI98EWfMSHDZ0O/6GsO2j7yz+WJHkZ00nS/+3kF2RhrPdOlG4qpnUdjKsfS7C5QNH8jSHF6E7+L7kZBRmgswD368UdeE+p1fgMeJOnMl/r/fRMWF/wPVycucSNYSlOZ8rP9aH9oUHEkXUe2oxHfZo1j63o2jwwW1Hne5XPz00/d1ZrdZrVY+++yjmkDUYrHUCkIBnE4nlZWVzJjxLv/5z/81edtPptGB6M6dO8nMzGT27Nn4+PiQmJjIfffdx6uvvioC0SZwvEBUra9EVbAdy4D7W6lVLUySQG0AQNaZcE/9Fc+P1+Oz8c1au8lKLR6fMDw+Ybh9o7EMeKBNlxUJCwtHqVTyzEgNlXaZxxd7M9vKskxQUFCTnWfEiJH88sv8k+7zxBPPkJiYxAcfvEtJSTHDh4/kiSeeISoqusHns/S/D3uHC1DnbkRdsBVV/paaUjOypMAd2BlHzAjsSRfjCu3Vrm4eNIVAg4ZJPSOY1DOCcquTlWklrEwtobDaTnqplb/2F7HkQDEvTuhMfKChtZtbS2qRmQfn7qbU4mRkxxB2ZJWxKKUIAJ1KQbcIb2DaP8bEwLhTp5sXBEFoj6rtboJ8NMDRi34ZnX8R/jEpKNV2yrM6gzywzt83yVGFz6a3cEQOwtL/PvwXTifwu9FH6nmCrFBRed6Mes/4UhXtQkbCFdy1CV/d8XlMCVh734K19y1ItnI0GUswrnkJ088X44wcjLI6l7JxH+CKHEjFxbPw/+MWTPOvxeUfj8JRjcJajCP2HMwDHmRjrszkCyficjmx2+385uvDN5MMXMxbaA/MwTzsWRxxY+o9JVZZkoLvortxhfTAFdL9SMUGl7f++umsnXWY0e39HnviBByxo/Bd+jD+86dRde5bJ62drireC9RNVNRUbF2nYo8bi2wIweFwIMtyzexTh8OBy+U67vMqKytq/n/gwH6Uyrr96nQ6WbFiabO0+2QaHYh26NCBTz/9FB+fY6MckiThcBw/SYrQMIUmGYMNfI5V3yAwcUebLNvSYtQGKid8haIqG4W5AIU5H6U5H4U5/8j3BWjSF6PJWk35Jd+dOp15K7nhhpvZtfQHpnRT8cpqB2U27+9OQEAgAwe2bDYzSZK48sqpXHnlCUatG8gd2Al3YCds3a/1Ht9WhrpgG6r8LajzNqPf+TmG7Z/gCkii8sIv2vQNg+Zk0quZ2D2cid2PrctYfrCYl/4+wLSZW3l2fDLjkkNasYXw5YZMftyWi9sjU2V3EWhQ8+mVvRjaOYzycgv5lTZ25lbWfH2zMYsvN2QxtV8U953ToU0k8xAEQWhK1Q4XcYHe0TGVror+05/GGJpdax/PFy/gjBiAM3IQzshBuIK7od/6EQprCeYJX+MK603l+E8xbPjvkRrgF+O79GH8/r6LynGyNxg9xeenqmiPt0akumVvWso6E/bkyTijhnjrmmcswdZ5Cq5Ib8kXV0R/Sq5fjzbtD3R7v8Ot0mHpdy+uiP7Issydl/bFbK6uOV55lZnLv3fy1p0Tuc3/IP6/T8ejD8KeMB5Lv7tOGPxJ9ko0h//GZ+ObyGofKi/8HI9POCjUGLZ/gm7fDzgSzvMGlDEjjo20uh3eEnilKahKUlCWpqAsP4S94yVYBjyAbv9PKByVWHvdjCu8HyhUGFc8SeB3o7H0vw9H7Cg8+mAw1r5ZfyxjbvPdGCgww0O3X8WSJYuQZZkBAwbx9tvvk5jYkcTEJA4cSKndR5LE4MHHpgqHhITidB4/L0tERGSztftEJFlu2noLHo+HqVOnYjKZ+Pjjj0+6n9t99pV6aIjVQSt5Y7wVsx6e/ubYHZ3uU94nrN9OXPennHUJYQCUSgVu9ylSihenoPpuEjituK+ajRzVr2Ua10DZ74wlpGwL3T+XKDa7iYqKYu7ceSQlJTXZOerVXy3NWoaUshDlshdAocZ1zRwITm7VJrWlfsqrsHH/T9vZmlnOg2M7cvvIDq2yhvRAQRUXz1hD39gAksN8MWiUXDckjnA/3Qn7y+Jw8caiA8xcn8mEHuFc2juKDYdLsThc3DemI4E+jczs2M60pfdVeyD6q2FEf9VPU/fTwFeWML5bOOdeXkjCiJ9JHPM9B/66nrL07ridWkyx++j8dDlS5jqk8nQAZI0R3A7kzhfjvvSz4x/YXonyhytQZG9E9o9Bjj8HT4dRyHEjwafuWlLVe92RY4fivvTTJntt0MD+ctmQds9G7nwJ6PxOuXt2djZdu3auVd/8qJiYGNJS9iMd/APF/vlIB/8CjS+uqb9A6JHgzmFGSv0LxZ45SGmLkdx2ZP9Y3Jd9fuxaT5aRUheh2P8b0oHfkWwVyBojcvQgpMpsKE1D8nhHD2WFCoKSvNloszfg6X0dUsZqZH0A7umLjjWuMgfloidR7K89w0zWGMEQhGwIQqr2lmpz3bOrfn3XQG63mx49upGenl4z+nl0AOPAgYPs3LmDiy6agN1ux+12o1ar0ev1rFq1hi5djg3KnHfeONauXVNr0NBgMDBnzlxGjx7T5O1Wq08cqzR5IPryyy/z008/8fPPP9OxY8cT7ud0uikvt5zwccFr8peb6Rziw8sXHXkDyR6CvuyDI3oEVed90LqNayUmk6Fe7x1FZSam365GshZTOeFLnFH1TLzTQpRlaQR8N4qqnrewUnMevr6+dOnStckDjvr2V2tQHq2PJbupOHKHuLW0tX6yuzy8+FcKf+0vYnyXUB47N6lFi6fLssw9v+xib341v940AJO+dnbHk/WXLMvM3JTN+6sOA6BWSsgyhPlqeeuybnQIOnvWCbe191VbJ/qrYUR/1U9T99PQd1Zxdd8oRl2VzfD7b6cipyM7vn+y1j69CgcB3oRC6ryNqHM3oiw/RNWYN/D4Rp344E4Luv2z0WSvQp29FoWj0rs5uDvOmOE4YkbijBqKZK8g+IteVA99Gmuf25vstUHzvq+Kioro06crDkfd9bUdOiSyfv22mu+VpQfwn3c1ksuGefATqHPXoT38N5LLitsQhj3pIuwdJ+II6c38Bb8xa9ZM3G4XV1xxNZMnX4FKpQK3A3X2GrRpC1EXbMPtF4crKBl3YLL3X1Oid/quLGPY8Do+W94HoPK8D7F3nFinjcrivSgrM1FYSzBQhb0sH4W1BIW1FMlajKPDBc22dG7p0kXcfPP1VFdX19puMBh4/vmXuOGGmzlwIIUZM95l//599O8/gDvuuIfo6Jha+5eXl3HLLTewfv1a1GrvzeHnn3+J666b3iztDgnxPeFjTXZVI8syL7/8Mt9//z3vvvvuSYNQoX5cbg855VbGdjp2F0xVtAuFtaTtl21pAzx+sZRP+gX/36biP38aleM/rZUEQJO+GJ+1L+MM74cjfiyO6BEtmkhHm7bAO8W63x0MMrTu9MvW4g7sRMVlP+P/21WYfrkEa5/bMQ94oN7Z6c5kWpWCFy/0rhP9bF0GW7PKeXxsRzqHGXF7ZDwyeGQZt0fGLXu/j/TTNVmB9dWHStmQUc6DoxPrBKGnIkkS1w2MoU+0P1anm56RfqQWm3lo7h5u/G47z41PZnTH088UKQiC0FrsLg9Ot4xRqyJq8DI0PpWkr76s1j4K47HEbR5jBPaOl2DveEn9TqA2YOtxPbYe14PHhapwJ5rs1aizVqLf8TmGbR/jDOmBvdMkoPkSFTWXkJAQevbsxbZtW3C73TXb9Xo9111XO2uuO7AT5Zf9imne1fiueByPLgBb8mTsHSfijBhUMyvwnrtuY+HCeVgsZgC2bNnEvHlz+Pbbn5CUGpxxo3Ge6rpZkrAMfgyPIRRNzhrs/0oGVNOm4K7empyAzmTA3II3gg4fPnTcabUWi4WUFO+U3E6dknn33Q9PehyTKYDZs38jPz+P4uJiOnbs1GqVTpokEPV4PDz11FPMnz+ft99+m7FjxzbFYc96eZV23B6ZGNOxi3JNxjKA9lG2pQ3w+IRTftnP+M+/Fr8/bqJ6+AvYul+HqnA7fn/djkcXhDZtIfp9PyArNDijhuCIG4M9/lxv6ZFmpM7biCswGbkeQajb7WbRor/YuHE9UVFRTJo0hYCAdpIx+RTcpg6UXfk3PmtfxLB1BtqDv3nvciacjyus71k5/fwoSZK4eUgcQ+IDeOGvAzw4d89J9zfp1dwyJI5JPcMblcE2t8LGOysOERegZ0qvE9eCO5UekcemaXWP8OPra/rw6Ly9PDpvLxO6hfHw6MQWHeU9kxVV2/HVqtCdZAqUIAiNV233Ton0VUHSpX/g9OlP/JYbmudkChWu8L64wvtC/3vBaUF76A+Ma/6Dcc0LALhCaifGKSoq4uuvP2fr1s106dKNG2+85bQSDzanTz/9kosvPp+KioojwajMiBGjuOWWuiO7Hv84yi6fj6pkP86IgXVqr+7atZMFC+ZitVprtlksFtasWc2aNasYPnxkg9pm6zkdW8/mGRlsrK5du6NSqepkxvXx8aF37z4NPl54eATh4af/N74pNMkVwKuvvsr8+fN5//33GT1ajNSdjh05FagUEt0ijl24ZZZ7f6liTMfSRWsyl+MM7dWoulNnG1kfSMWlP+L79934rnwKde56NDlr8RhCKZv8G7LWhDpvI5qMpbVrZQUkYet2LdZu1zb9CJ3HjSpvC/ZOl51yV4vFwqWXXsDBgwcwm83odHpefvk//PLLPPr0aZtrXxtK1pmoHvMm9o6XYNj2Sc1dX48uAEfsKBxxY3DEjkLW/SsTq8OMNmMx9vhxLZ6soSV1i/Dj22v7svhAETanG4UkoVBIqBSS9/+St8rRnF15/HdpKrO35/DYuR3pH2tq0HmKzQ6+XJ/JrzvzUEjw1mXdm7QkS7ifjs+v7s3/1mXw1cYsVqeVMCA2gP6x/vSPMREboG8X9VTTS71JmjoE+RBi1DRrm4vNDg4WVZNaZKbY7GByr0hiA479TThcYuGLDZn8vb+QGJOe1yZ2JTH47Jn6LAgt7Wgg2q1yKcqqbKpHvNhyJ1cbsCdPxhE7GuPq55Gc5lp/Fw8fPsT554/CarVit9tZuXI5X3zxGb/99js9e/ZuuXaeQnR0DJs372LFiqXk5OTQu3dfevQ4Tvm+I2R90Alrc65evRKXy11nu8ViZsWKZQ0ORNuywYOH0qlTZ/bu3V0TjKpUKkymACZOPPX1ZFvU6EB0+/btfP311zz00EN0796doqKimsdCQs7O6YYNVWpxcO8vu7G53Nw+LJ7rB8agkCSyy7yBaPSRQFSylaEq2Iql3z2t2dx2Sdb4UjnhSwyb38Ow8U1krT8VF82sGY10Rg/DGT0M87BnvLWyMpagSfsD4+rn0W//DPPAh7AnT26y0TlVyT4UzmqcRzLMnczHH3/A/v37ahb222ze98Wtt05n48Yd7eLCvb6cMSOpiBnpzYSXucx7cyBjGboDc5AlhbfGWtwYHHFjUJYewGfdyyjNBTiDu1M54cu6xbPPIBqVggu7nrzQ9HmdQ1iZVsrby9O4Y/ZOJnQN5b5zOhBgOHmCoEqbk5mbsvlhaw5Ot4eJPcK5aXAcYb5NP1VHrVRwx/AEhncIYvb2XLZklbP4gPfvRqhRQ/9YE/1iTAyINRHh17pTtN0emef+2E9RtYOnzutEbICe3/cW8NLfB3AeSbYX5KPh8XOTGNUEU42Lqu1szCjnYJHZG3wWmym1HJuGpVRIzN6ey7QBMUT6aflzfxFbMsvRqhRc1jOCZQeLuWHWNh4ek8i5nULEaLMgNINqhzfoia7c5r1Z2oC6n01F1gdSNe69OtufffYJKisr8Xi8iYYcDgcOh4OHH76Pv/9e0dLNPCmlUsmYMeMadYxly5bwySczcDrrVurQanVNWgqvLZAkiV9+mc+rr77ITz/9gNvtYvz4i3juuRfR609e47StanSyotdee40vvvjiuI/t2bPHu1D4OESyomPeWJrKz9tzGZIQyOpDpQxLCOTZ8Z34Yn0mC/YWsOyuoUiShPbgPPz+vpOyyb9500mfpRq7iF6VvxVZ64c74NSZadVZq/FZ/wrqwh24AjphHvwojoTzG137Ur/jc4yrn6Pkug0nT1oADB3aj9TUg3WPodezYsV64uMTTvr8dp/MwuNGVbQTTfoSNJnLUBfuqHnIGdoLW/JkfNa/hqw2Yh72NB6fcNzGiAZPrW73/fQPNqebLzZkMnNTNj4aJfeMTODi7uF1SqlYnW5+2JrDzE3ZVNtdnNc5hNuGxhMTcOo/aE3VX7Isk1lmZUtWOZsyK9iSVU6Z1Rt8XdojnAdGJTbZuteGen/lIb7ZlI1O5R0VPicpiL/2F9Evxp/pg2LJKLWyYE8++wqquapvFHePSECrqj2CXJ9+Kq6289XGLObszMPhltEoJRKDfUgK9qFjqJFOIT4kBvvg8si8t+IQf+wrBCDGpOOCLmFc3juCAIOGomo7T8zfx47cSiQgMdiHXlF+9Iz0o1eUH5F+ujZ/4+pM+j1sCaK/6qcp+2lDRhl3/7yLjfGfEejMp+yqRad+UguJiwvHaq37OhUKBRkZBfVeB9ge3lcrVy7n2muvrLk5/296vYGNG3cQFnbyG7iN0R76qS04WbKiJs+aW18iEPXKLrcy5cvNXNw9jCfGdmT29jzeXZGGUavyfunUfD21NwC+Sx5Ac3gRJTfuOKvXzbX4L74sozn0Oz7rX0dVnoa9w3iqxryFrD11mvIT8f3zdtQF2yi9fsMp9x0xYiApKfvrbNfpdKxdu6VONrR/O9M+KCVLEZrM5aDUYU+aAJICZck+/BdOR1l1rI6bpdetmIc+CYr6jQqdaf0EcKjEzKuLDrItp5JekX7cd04HekT64XB5mLMzjy82ZFJqcTKiQyB3DI+nY4ix3sdurv6SZZm0EgsLdhfw3ZZsok06HhqTxMBYE+omnCZ8Kgv3FPD8nylM7hXBjYNi+c9fKWzIKOeibmE8Oa5jTVscLg/vrzrMD1tzUCslkoJ96BRqJDnUSOdQI/2TQrBb6maHLK62szKthOWpJWzKLEeWZS7qFs6VfSNJCPJBpThxwJhSUI1blukSZqwTWLo8MluyytmZU8mO3Ap251VhPjKCE+yjoVeUHxd0CWVEYlCbrPF6Jv4eNifRX/XTlP209EARj83fx67IV9H6BFAxcVaTHLcpdOnSgZKS4jrbNRoNGRkFKJX1u3ZsD++r884bxfbtW4/7mL+/P5999jWjRjV9KZJ/ag/99E8VFeV89NEH/P77Avz8/Lj11ju4+OJLm/0GZYtkzRVOzydrM1AqJG4ZEockSVzRJ5K+Mf48+/t+DhaZGd/tSLAje9BkLMcRe85ZHYS2CknCkTgBR8L56Hd8js/6VzDNvpDK8Z/WZE6rw2lFk7EE7aE/8PiEYx70yLF1prKMOm9jvcvJXHPN9bzyyn9qLcSXJIm4uPhTBqFnItkQgr3zlFrb3EFdKL16GcryQyjs5WjTFmLY8Smqkr1UD3sWWWdC1vgiq31AarlgprV1CPLh4yt7sWB3ATNWH+bG77czvEMgacVm8irt9Ivx57+XJNAz8vRvqjQ1SfIGc/eP6sDIpECe/yOF+3/djV6tYGBsANMHxdRaS98cduZW8vKiA/SP8efh0YmolArem9yDQyUWEoMMtf5oa1QKHhqdyMjEQNanl7G/oJrlB4v5bVc+AAoJ4gINJAYZ8NerMaiVbM+pYFdeFQDRJh1X9oni8t4RNcswTiU57MQ3DFQKiUFxAQyK864bc3tk0orN7MitZEdOBVuyKlhyoJi4AD03DIrhwq5hbTIgFYS2qtruvbGjtRfjCenUyq2p7brrpvPRRx/UGiXUarVccsmkegeh7UVq6oHjblcqlaxbt5XgYLE88KhFi/7kjTdeZefOHciyXDN1e/funezYsY1nnvlPq7VNjIi2EJdHJq/CRnqphYwyKxlH/t2WXcENA2O4a0Tt6ZVOt4dfduQxLDmUGB81qqLdBPw0nspz365zEX62ae07UKrcjfj9dQcKRwVVo17Fnny59wGXFU3mcrQH56NNX4TksuLRBaKwleIM6UHl+E/w+MWiqEgn6NvhVJ3zCrbu0055PqfTybXXXsGGDetwuVxoNBq0Wh2//fYHnToln/L5rd1frUW770d8lz+B5Km9dsSjNnoLW2t8cfvFYh7+HG5ThzO+nywON99tyWbWlmxiTHruGp7AwDjTad8Jban+sjndbMgoZ316KUsPFlNqcXJJ93DuHBFP4CnWvp6O/Eob18/ahkGj5MupfRpcuga8o7oFVXZSCqtJr7CzI7OMjDIrFVYn1XYXnUKNnJMUxKikYDr8K7Btbi6PzJKUImZuzialsJqekX48PjapQaPhzelM/z1saqK/6qcp++m7Ldm8vTyNQz7TsfW8CfPQp5rkuE3B4XBwyy3Xs2zZEtRqNS6Xi969+zJr1k8YjScelfq39vC+GjVqKHv37q6z3c/Pj5SUjBYJvNtDP82c+RVPP/34cadsg/dGxZYtewgNDW22Noipua1oUUoRn63NIKvcistzrKv9dSriAg10CTNyx/B4fDTHH5w++iY3bH4fnw2vUTx9W73KfZzJ2sIvvmQpwu/vO9HkrMPWeQq4nWjSF6FwmvHoArEnTsCedBHOyMFoMpbiu/g+kCSqz3kFXDb8lj5I6VWLcQd1rtf5ZFlmy5ZNbN68kYiISM4//0J0uvolcmkL/dValOWHUBXtRnJUITmqvf86vf8qHNWos1cjuZ1UjXwJ/eDrKa84/loToa7WeF9V2118vj6T77fmoFcruH1oPJN7R550GmtDWJ1ubvlhB9nlVr6c2oeEoMZnYv53P8my3CbWacqyzII9Bby38jCVNifJoUa6hvsyokMQQxMCGt1Gt0dmzeFScipsVFqdVNpcVNpdVNqO/N/mQqtS0CPCj97RfoxOCkanVrapz6sKq5NZW7JRKSQu7x3ZLDc+Gqst9Vdb1pT99OnadH5ct4+duluoHvYc1t63NPqYZWWlrFmzGoNBz/Dh56DRNO69duhQKvv27aNDh0S6dDnBzK2TaA/vq7/++oNbb72h1mwxg8HAgw8+yr33PtgibWjr/eRyuejSJYGKiooT7uPr68eMGZ8yfvyFzdYOEYi2Eqfbw0WfbsBXq+KcpGDiAvXEBeiJCzTU+y770Te56ddJ4LJSfsUfzdzqtq/N/OJ7XPhseB3D1g/xaE3YEy/AnjQRZ9QQqi023nnnDWbP/gGA26+8kMcS96Mt3olHFwCyh5KbdrXINNE2019tkKI6F99F96LJXY/sH4s9tA+OmJHeUe4jU+CVxXtRFe3GFdIdd2Cneq85PdO15vvqcImFN5elsiGjnLgAPb2j/Ik26YgN0BNt0hMToEffwHqaHlnmifn7WJ5azFuXdWdYQtPU6W3rv3/lVic/bM1hR24l+/K960kHxJp4cFQiSSH1KwNTUGXnztk70SgVjE0OJsCgYdbmbDLLjl0g+mpV+Om8X/46NX46FZU2F7vyKjE73IT7arl7RAJXDI6jopVvCLk9Mr/syOPTtelU2V3IMqiVEud3DqVnpB+JwT50CDac8AZyS2rr76+2oin76e3laezYuYWFygepHPd+vcqwncwXX3zG888/hUqlRpK8U0u///4X+vUb0CTtPR3t5X01Z84vvPDC0+Tl5WIymbjvvoe54467W+xmX1vvp8zMDEaOHITFcuI2+vj48Msv8+nbt3+ztUMEovVwoLAah9tD91OsPXJ5ZPYXVLE5s5xKm4vLe0cS6X/8kam/9xfy1ML9vDPp9C9qTCYDFQV5BH3eE0vfu7AMfvS0jnMmaWu/+JK50FvH60iRZbfbzfnnjyYlZV9NnSetVku3Ll1Y8dJl+Gx5F0f8OCov+KxF2tfW+qvN8bjR7fsen/y1yFkbUZrzcYb2pnrEC2hT56Pf+QWS7F1PIav0OEN64grrjSu0N86w3nh8oxudRbk9au33lSzLLE8t4fst2WSUWWuVOQFvYp4Yk46YI8Fp5zAjg+OOjfQVVtkpsTjoHOpN+PPp2nQ+W5fJ/ed04Jr+TVf8vbX7qSFcbg+/7szjk7UZVNpchPtqSQz2ITHY4P03yIe4QD26fwT55RYnt/y4naJqB4nBPuzMrQQgOdTIjYNi6BdjwqhVoTzBqLX7SHKl91YeJqWwmkh/HYlBBpJCvFmDOwT7EGvSo1G1zNpul9vDc3+k8HdKUU1ArlJKfLclm7/2FWFxHqtXGOmnpUOwT00fDY0PxP80pnI3Rnt6f7WmpuynF/9KwX5oFZ94nqf8kh9PWN+yPnbt2slFF42rNaoH4O9vYvfug/XOctvU2tv7yuFwoFarW3y2SVvvp+rqarp0Sai5Fv03pVJJQkIia9Zsata+E8mKTiG73MqtP+7A7HDTO8qPa/pF0y/GhK9Ohdsjk1JYzZascjZnlbM9u7LmD5FSIfH91hwu7RHO9EGxhP6r5t7P23OJ8tcxJD7geKetN3XWKiTZjSNudKOO055VV1fz3XffsGjR3yQkxDFt2o306NGrtZsFgOxTe1798uVLSEtLrfWLb7fbSTmYysLqHoy9ZhWypm2sx2rPdu/exRtvvMKuXTvp2LETDz/8OP37n7ouax0KJbZu16IbdivlZWa0B3/DuOpZAn65BBkJW/dpWLtPQ1WyH1XBdtSF29Hv+grJ7f35ug2hWAY9jK3L1WdlQNpaJElidMdgRh+p31ltd5FTbiOr3Or9KvP+u+ZwGSXmAgC6hBm5ZUgcW7Mr+GlbDg63TN9ofwbHB/DZukwu7hbG1H7eckq7OmzCU+2pc16FUUGPQ603UtGcVEoFV/SJ4vzOoczbnc+BIjNpxWY2ZpbV1E1VSN7a1h2CvMHp2sOl5FXaeXdSd/rFmCioslNYZad7hG+9LmyUComBcQF8fY2JP/cVsim7gn15laxNL8P9j+UsQT4awn21RPhpCfPVEe7n/X9cgIH4JphCDd71yE8s2MfqQ6XcPSKB6wZE17yGJ8d14vGxHcmtsJFWbOFQibdv0ootrE8vw+WR8dEomdoviqn9ohtcw9XlkUkrMmMyqJulfq/QdKrtbuJVVeAATyOXSn333Tc4HHVrYHo8blasWMp5513QqOOfLRo7lflMZTQamTRpCnPm/FKnzI1araZr1+58/fV3rbpc5KwPRB0uD08u2IdCkrhreDy/7MjjkXl7AYjy11Fhc9ZkSIsP1HNB11D6x5joG+OP0y3z5YZM5uzKZ97ufC7vHcn1A2MINGhILTKzLaeSe0cmNDojoTp3A7LKgCusT6Nfb3vh8XjIzs7CaDSiUqkYO/YcCgrysFqtrFql4Mcff+Dtt2cwadLlrd3UOrZv34bFYq6z3Wq1sH37VsaMGdsKrTqzbN68kcmTL8ZmsyHLMllZmaxbt4Yvv5zVuP6VJOydLsURMxL9zs9xxI7GFeGdruIO6nJsCpbbgao0BVXBdrQH5+K77FG0aQuxdb4CRVU2yqocJFsZCnsFzoiBWPreAUpxcdmcjFoVyWHG42aUtTjcLD1YxCdrMnhw7h4UElzQNYxOIT58vTGLrdkV9Ir04/GxHWv+IB8vCD3Z9jOJv17NtAHHMnK7PDLZZVbS/hF8pRWbWZlWgkKSeG1iV/rFmAAI89WeViClVEhM6BbGNcMSKC+34HB5yCizkFpsJrvcRkGlnfwqGweKzKw6VIrddezn0C3clym9IxmXHHLckdNSi4PNmeVIkoRS8p5LIUkoFN7v7S6ZzVnlrEwrIa/CxmPnJnF578g6x1FIEtEm7wj7OUlBNdudbg8HCqv5elM2n63L5MdtuUzrH80VfaJOWAPX4nCzK6+SnTmVbM/xltmxON2oFJK3ZNDgWDwemdxKO2UWJ2aHC6UkMapjcJ1atULLqra7iFB619w1NhCtqKjA7XbX2S7LMtXV1Y06tiAAvP762wD8+utslEolSqWSm266jWuuuY64uPjWbRxtcGru7rxKFuwpIMSoIT7QQFyggRiTvkEfvB5ZZlt2BRaHG41SgUopoVYqUCsl1Iqj30tolAq+3pjFj9ty+e/ErozqGIzL7WFzVjn7Cqo5UFiNr051JPA0Eexz/DsuORVW/rcuk9/3FqBRKriybxSFVXaWHChi4W2DTyvr4lEmkwE+HYWs8aXi0h9P+zjtydKli7jvvruorPR+QEdGRpGXl1tnaoGfnx979x5qc3fCfvzxOx5//CHM5trBqI+PD6+//jZTplzVou1p61NHTseFF45l8+aNdbZ36JDI+vXbTuuYp91Psgfd7m8wrn0ZyeW94+jR+uPRB4FSh6pkLy5TItUjX8IZNeSMWWPaHt9XdpeHFanFR6ZSetc/Wp1ulh4oZniH2tMqd4SeuMZvr8JB9T5ne+yn+rK7PDjdngaP/p1MffpLlmXKrU7yq+zsyKnklx25pJdaCTVquGFQLJd0D68JSDNKLdz18y4Kqo4/Ne0orUpBvxh/JvWMrBVkNtT+gio+WZvB6kOlBOjVTOoVwcXdwwg1atmaVcGaw6Vsz6ngQGE1bhkkICnEh95R/vSI9GVrVgXzdufjOcGVWahRw02DYxndMRh/vZrAAJ8z9v3VlJry9/D6Wdu4zfkNE61zKb79UKNmwixcOJ+77761zvWCVqtl69a9hIS0TnLKM/lzqym1p36qrq6ipKSEyMgo1OqWXULQZteI/r4tmxWpJQT7aEgIMrAyrYQ/9hWiVSlq3e1USBDpr/MGpgEGhnUIoH9M3bIDHllm2cFiPl+fycGiuiNSJ3Jln0geHpPU6NeUXmrhf+sy+Ht/ETIwoVsYz48/dXmNkzEZZFRvxGPpdzeWQY80uo1tXUrKfs4775w66yWOx2j05ddf59O7d98WaFn9WSwW+vbtRllZKUd/vSRJIjAwiC1bdmMwNM00svpqTx+U9RUTE3LcNQ8KhYLMzMLTujnR2H6SLEUoLEV4/GJrTb1WZy7Hd/kTKKuykFU6XMHdcYb2xBXaE1dIL9wBie2ytumZ+L76JxGIto7T6S9ZltmQUcb/1mWyI7eSIB8NF3cLo3e0P//5MwVZhpcmdCbIR4NHlvF4wCXLeDwyniNZjDuF+NRa+9pYO3Mr+Xx9BusOlyEDerUCq9ODVqWge4QvvaL86R3lR48IvzqBfHqJhb9TCgkwaIj00xHoo8ZXqyKnwsYnazLYleddh6uUIMioJUCvJtCgJshHc+zryPcJQYY2me3XIx+d6t12ksqUW5zYXG5CfbUnbdfkLzbxiuJDBkt7KL2+7g3RhnC73Vx99WQ2btyAxWJGkiR0Oh2PPPIkd999X6OO3Rjic6t+RD/VT5sMRPfnVTL+3VUY1EqsTjcyoFFKXNM/musHxqCQJDJLraSXWo58Wckos5BRasHhlukcamRq/yjGdAxBq1KQUWrhP38dYGduJfGBeqYPiiUu0IDL7cHplnF6vP/+83uHW0avVjC2UwhqZdNdCKYWm/ltVz5X9Y0kyr9+BcpPJKBiK6pvJ1Jx0Tc44sY0UQvbrocfvo9Zs7457lSVf9Pr9SxbtoYOHRp/E6GppaYe5I47bmbfvj0AdO3anY8++ozExI4t3pYz8YOyV6/O5OXl1tluNBpJS8s5rfUOzdpPTgvaw3+hKtyJunAHqqJdx0ZP1T64QrrjMYQiqwx4/GKwdb0aj09Y87SlkZRlqRg2v4vGmofDJxa3fzxu/wTcpnjc/vHImvrXqmvLRCDaOhrTX7IsszGznB+25rD2cCkeGUKMGmZc3rNJSvGcjvxKGwv3FlBU7WBoQiADY02NCnhlWWZLVgVpxWZKLQ6qXDJ5ZRZKzA7vl8VZa20teBN3RfjpUClArVTQJdyXAbEmukf4tkrm3wqrk3t/3U1RtZ0pvSO5rGdEo2aO1cfJ3lf5lTa+2pjFb7vycXlktCoFMSY9sQHer5gAb8WDhCADfjo153+0jlm610kyOiifsrDRbXO73SxcOI958+bi6+vLNddcd3r5DpqQ+NyqH9FP9dMmA9G0wmrWpxQwumMwbo/M4VILwT4aQownX1tid3n4fW8BszZ7MyUatUqGxAeyMq0ErUrB/ed04MKuYSfM0NfeBO75GOXylyi+aTeyztTazWl2kydfzKpVK065n1KppEuXbixduroFWnX6SktLAAgMPP2pXo11Jn5QfvbZx7z00vO1CjTr9Xpuu+0unnzy2dM6Zov2k8eNsiwVVdHRwHQ3kq0UyWVFUZ0PCjX2Tpdi6Xc3blOHlmnTEf9O0uMfnUJI541IGpmwq2W0aQuQVXoI74FcehjlkURANS9NH4wrMBlLv7txxoxo0bY3JRGIto6m6q/CKjvLU4sZ3iHohJntzwT/7i+PLFNpc1FidlBc7SCtxExKYTVF1Q48sozF4eZAkbkmWDXp1UT664j00xHpryPKX+v93l9PhJ+2UTfpyy1O9uRXsTuvEpVSYkLXMPRqJXf9vIvDJWa6R/ixNdu71lKpkDh61SZJ3inLkuTd1jHEyLjOIQxPCMSgUaJRKjBqlQ264Xii99WWrHLu+WUXsgwTu4fTKdSHzDJrzVdOha2mrxQS9I0xsT27gpV+zxIUHkflhK9Ou3/aMvG5VT+in+qnTQaijS3f4pFlNmWWs3BPASvTSugXY+LxsUmnDGTbGlmWWbz4L+bM+QWNRs1VV13L4MFDax4P+utGPCWHKZu6rBVb2XLeeecN3nrrdWw223Ef1+v1KJVKwsMjmD37N6Kimq7MwpnqTPyglGWZV155kU8+mYFSqcTlcjF16jRefvl1lMrTG21oK/2kKD+MYef/0O37EdxObF2vxtL/PjzGiBY5/z8DMP+Y/fSd9gKS0oXsViEZ9di6XIWl7534R8R4+8tpQVmR/o+vw2iyVqGsysYedy6u4K6oylLB7cDa6xacMcNb5HU0VlNlzW0r76v2QvRXw5xOf1kcbrblVHCwsJrcShu5Fd6vvEo7rn+MpkpAqK+WfjH+XNYjgp5RfmSUWtmVW0lsgJ5eUX41waDD5eFAUTW787yB5578KrLLvX/HFRLIsjfADDBoqLI5+e8l3RiaEEhqsZllB4pxuD0cPbP3qlRGlr2JsrZklXPgX8utQo0aBsUFMKxDIMM7BJ0yj8jx+sntkZn27VbMdhefXNmLcL+6Nyxcbg95lXYyy6zszK1g8YFiMsus7PG9B0XH86ge/d8G9X17IX4P60f0U/2ckYHoP8lH1ni0N7Isc/vtN/HXX3/8Y22AnltvvYOnnnoOZA/BX/bCFn8+1WPeaO3mtoiyslJGjBhEaWkpLpezzuMajYYZMz5k4sQp7fJn3hrO5A9Ks9lMTk42ERER+PqevAbwqbS1fpIsRfhsfhfdnlmAjL3Dhdi6TvVO2ZU9gAdk2Vvj9MiX2z/OW9O2EY4Goj4hmfSf/jQOsz+bv3wJp8W/1kjgSfvLZUO/80sMW95Hcppx+8cjOatRmgtwRA3F0v8+nFFDz4pyN23tfdXWif5qmKbsL7dHpqjaXis4zSyzsvpQKWaHG51Kge0f+TviAvT0ifbnYJGZA0XVNSV+Qo0aukX40T3cl24RvnQJ86XM6mDuznxWHyrl3nMSGBLfsNrq6SUWduRW4HDL2Jxu9uRXsTGjnCq7Cz+divOSQ4gNNKBVSmhUCjRK75dapUCrVNA5xoTxXx83c3fm8fKig7xyURfGJtcvKZAsy+SVW+j5fRcs/e45Y3N3iN/D+hH9VD9nfCDaXq1du5qpUy/HYrGgV4FHBrsbdDodK1duINHfTeB3o6ga/Qa2ri2babU1FRQU8J//PMPs2T/UeUySJC655FI+/fTrVmhZ+yQ+KOunrfaTojIT/c4v0e3/CYW94qT7ygo1jvhzsSVP8a4pVzZw3ZUsk9bzJ0K7riOq7yJkj5LNX7yMrcJbK7fegehRbjsggVLjDU73zMKw5QMU1iJcgcnYulyFK7gr7oAkPIbQ9hOYetwgu72v6xSa5H3ltqOozkdyVOMO6gyKf4z6e9yoCraiyVyB2xSPPemShv/c25C2+nvYVrVEf1mdbhalFLEnr4qu4UZ6RPqxO6+K33blk1ZsJjnUSPcI35rg89811ZuL2yOzObOc+XvyWXawGIf75JezXcKMnNsphHM7BRNgUDPp801Em/T876peDbqxLVmKCP6yD1UjX8LW44ZGvoq2Sfwe1o/op/o5WSB6ZtQRaKf+/vsPrFYroT4Sa2/0wV8HH25y8vlOWLp0MZ0He39wziN1DM8WYWFhTJ9+M3/++TtVVZW1HpNlmUOHDrVSywSh5Xn8YjEPfw7zoEfRZK1ActsBBbJCcSTbrqIm6646dz26lF/RHvoTjz4IW8dLsXe+HFdw9xMHebKMqmgn2rSFaNJ+Z/Dt6cgeBWXp3Uj546aaIPS0/LN2qkqHtddNWLtdg/bgbxh2fI5xzQvHXqfGD3dAEu6AJFwBSbgDk3FEDwNVK67vc1rx2fAaCnMBtm7X4owYiG7/jxg2vY3SXIBHH4TbLxZb12uwJU+qV2B6Qh4X6rxNqAp3oqjOQVmdi6I6D2VVLgprUc1ubmMEtuTLkTV+qAu2oM7diMJWeuzxDW9i7XmjN8A3JeDxCW+XWZmFtkOvVjKxezgTu4fXbOsQ5FPr+9agVEgMig9gUHwATrcHi8ONw+3xfrnkI/96vz9cYWfBjlw+WHWYD1YdJthHQ6nFyVuXdmvw7CqFxfv72NgaooIgiEC0Vfn4GPHVqZh3lYYIX4mlh108PVLDo8MgzfMHmsN+yPqAFk9W0hZ06pSM0+mos12tVjNyZPtNgCIIp02tx9Fh/El3cSSMwzzkCTSZK9Dun41+90wMOz/HFZCEvdMkbJ0uxeMXC7IHVcE2tKkL0R76HWVVNrJChTN6GHu/HU/R/oE4Lf7N8zpUOuxdrsTe+QoU5nyUZakoyw6iOvKvJmMZuv0/AeDRB2Htdi225Mvx+Me1aEClLD2I31+3oyw9gKz1Q5c6H4/aiMJZjTO8P7auU1GYC1EXbMV32cMYNr6BI+5cPL7RuH2jcPtG4/GNBr+E45/AaUFVsg9V0S7UBdvQZCxFYSvzvm61EY8xEo9vBPbgrniMkbiNkSAp0abOw7B1BpLswe0XhyNuNI64c3HEnoM6dyOGze/WCvBllQ63XxxuU8KR7MYJOCMGef+utJcRaEE4BbVSgb/+xJ8P5/YwcHn3MPIqbSw5UMyyg8Wc1zmEbhENX9KhsBQCeGdxCCdVWlrCkiWLUCgUjB17Hv7+ptZuktDGiKm5rcHjQmEtJu/gNtK+uImLO0lM+tHKbykuOgYqeHSEgRv76VG47XiSzqPk/C9au8Wt4tVXX+Ljjz/AYvG+TxQKBb6+vmzbtgOjsWHrS85mYupI/ZyJ/STZytGmLUR74Fc0ud61n86wPt6RNnM+skKDI2YE9sQJOBLGIesC6p2kpzn7S7KVoyrYhn73N2jSFyMh41H74A7qjL3DBdg7Xdas5W20Kb/gu/wJZLWeyrHv4YwciO7Ab6izVmLvdCmO+HHHgjhZRp21AsP2z1AV7ao1OgkgS0o8xgg8PuHIGh9kpQ5l+WGU5ane9b2ARxeII3YU9g7jcUYNPWWGdMlaArKMbAiu+6Aso6jKQVlx2PtVnn7s/xWZSB7vDT63Xyz2+HHYulyJO7hro/usqZyJv4fNSfRX/TRlP2n3z8ZvyQOUXLsaj398kxyzrWmK/vrpp+95+OH7jiQQlHC73XzwwcdMnHhZ0zSyDRC/f/Uj1oi2IlXuRvT7vkdhLkRh8X5J1lIkjnX7o0tcfLRdiSSBxyPz1VezGDWgB7qUn9Elj6bMkNyKr6D1yLLMzz//yAcfvEtJSTEjRpzD448/Ta9eXc+K905TaW8flCUlJXzyyQyWLVtCVFQ0d9xxD4MGDW7287a3fmooRWU22oNz0R76A49PuDf4jB+LrD29JE8t1V+KinQ02WtQlqagzt+CunAHsqTAGTMSW+cp2BPOA1Xj6jXXcFkxrnoO/d7vcEQMour8Gd6prUeUlpbwf//3H+bPn4tSqeKKK67mkUeewMfH59gxnBaUVTkoqrJRVudgcBTiLD6MwlyI5LIgOS24/WJxhXTHFdIDV3B3b0bklhid9LhRVmagzl6NJmMZmswVSB4HzuBuuEJ7edsV2tObSErRwAlTR6aM16xPdVrQ5K5HshQhOS01r11yWo/834z8/+yddVgUXRuHf7O9swuiEraI3Y3dLXZ3d3d3t752YHcXit2K3aIIoogKCIrEdsz3x8p+rjsLCyws6rnfy+vVszNnnj17ZuY85ymu4KeV1gOaXNXBCB0z1H3IkUVA8O4MwOFBnacO9I657S2SGRlpvDIythwn8eN1kPotRGT/AEAgSfqEP5DUjtenT6GoWrW8WQUEkUiEhw9fwtX177Amk/vPOogiag2MHtBrrYvx0WvBjf0IaBTQORe16C7GkUUg8/66AABdpnzQ067QS1yhp10Mf6ddocuUB9H8HLh+/Rr4fD5q1aoDsfj/iyoyyc0hY5I8/qTx+vr1K+rWrYaYmB9QqVTGTNKLFy9Hp05dbXINhmFw9OghrF69ElFRkahSpRqmTJmO8uVL/zHjlBGw17zi/giG8M0RiAKOghv/GQxXCL0kG/S0M/RiZ8PzVZz15/+dwdDO0ItdoJNmB/j0/ztimJ/PfL6xX8dzA8H79hrycsMgqzTORBlTqVSoUaMSPn8OhUZjyOgtFApRokQpnD17yWKcWUa+/yhlNISBJyEKPAXuj3fgKAx1j/ViF6gKeEGbpbBhbKXZoZNkAyPOYv6+YxiIXh+E5PZsUDo1tC4lwAgcwP/s9zOe+ZdDQQE8MRg+DYYvAbQKcH+6OTJcIdR564BXsB7iqUy//HbOYAQO6epGzAt/BMmDFeCH3jRarQEYXJwz5YVOkh16aTboJdmNY6OXZjdkrE5nd+eMPL8yErYcJ8mtWRC/2oeogW9t0l9GJLXjtXbtf1i0aC7UatMQK5FIjFmz5qFPn/6pFTFDQO4/6yDJipKA+/0tMp3pDY78K9S5qkOdpxZ0Th7QO+QCpY7/GcMUBN6PIHC/B4Eb8x6U3rAQ0UncoPZoDJVHU2hyVPr/woVhIL02GZRWieiOF6DLnN/i9R0BNG/eMh2+KYGQsVm7dhWio6ON8cEMw0ChkGPq1Alo06Y9BIJUJIP5ycqVS7F69Qqjy/eZM6dw7dplPHjwCFmy2Df5BiFpdE4ekFeeAHmlceB/9jPEVsrCwVF8AzfmA/jhD828ToCfGYVzVYc6XyNwf7yD8J0POIrvUOepDa1rKYgfrwe4AsQ022XIOPwbPj4nERkZYVRCAYNy+uaNP/z8bqNq1T+jPuqvMKLMUJbsZcz8SanjwQ+9AdHb4xC92md04zUez+H/VEyzGZQxSTbwogMg+Hgd6hyVoXUtDV7EU3DiPkFRvCvU7vWhy+QOhi8Bw6MNiad+V9TUMvC+vYYw6DREgafACT6H36OTGa7QZHPhVyVVJ80OTY5KYFKbOIbRg/vjPcQPV0P89ijiKQeEZGuNLLWGguJwIQi5An7YfXDiwyD4HgCOPNJESQUArVN+qD2aQJW/CbQupaxWSinlj5+u0x+gF2UxWKT/4MzHfwKpqRPMkUeSREVJoFIpodPpzNr1ep3FOvGA4Z3/4MF9REZ+RYUKFeHmRt7JfzsZThHlxIeB/+UuGIHjT+uhK/RiZ9N09UnBMODEfgSlVQAUF+BwwXB4AMX75e9cgMMDP+w+HC4MBbgiKAu3NST5+HDRvEuKC10md+gyF4A6X31oMxcEQEH4/jxErw9C/GIn9KLMULk3hDp/E3DkURB+uID4qtMTVUIJBML/uXz5AmuSKoZhEBj4FsWLl0hV/zKZDP/9txwKhcLYptfroVAoMH/+PFSvXhs/fvxAjRq1UKBAwVRdi5DGUBxoclWDJlc188/0OlDK7+DII8FRfANHHgneN39DcqaPVw2xsXlqQi/NAcH78xC+Pw9NtgqIbbgeeoccrJd7+vQJZDKZWbtGo8GrVy/soojGxcUiMjISuXLltskmDSOQQp2/KdT5mxpyGcgjDUp+fJghsdQvf+dFvgD3wwUAHMTVmGNQZlOSTEoggTZ7BWizV4Cs2gw48eMRF/4JHEUkOIoocORRpv+XRYAX9QocxTfjhjAAaLMUhjpvHajyNYY2WzmjLJQqFrorcyCJuAuhUASK4oChfmaapijD//Va8KLfgdLKodYBSx8wWOL3HQrdIRQq9BxHj56GtEx/KMr8YsXRaw3hNvHh4MjCwI0NheDjdYifbAD9eC100hxQeTSBJndNaLJXACNwADfmA7hR/uD9CP4ZvxsM7o/3ZnHFeqET1HlqGeRXx4GjjgOligUo6mcscRNosxa1nUv6PwibEppY+69w5JHQS/4O19K0omHDJli9eoXJuxYw5Ppo2JA96V5o6Ee0bdsckZGR4HAoqNVq9Os3EDNmzCV14/9i7KaIUup4SPyWgPf1GRihA3SSbOB9fwv+p9vmO9kU56fLlcG1VetSEuo8daB1K2uioPIiX0AYcBzC9+fBjQ2xWhZt1mKI8doOvUNOgxIb/wXcuFBw4j6B4dHQZS4IXaa8rG67qiLtAI0CgtBrEL7zhTDYF+I3BwEAGrdyUJTuZ/G6ERHheP8+GB4eBf4af3kCITU4O7sgMNDc3Umj0SBz5syp7j84OAg8nvljT6vVYu/ePTh27NjPXVwGnTp1xeLFK9LtBRgREY7jx48gNjYWtWrVhadnpQzz8o2J+YGDB/fhyZNHKFq0OLp06QEnpzz2FssyHC4Y2gU62gUJe/IqtIGsylRwowMNiYMSYmNrzgM35gN0jnkSjYvMn78AxGIaCoWpG5ZAIECePO5p8z0soFarMWHCaBw9eghcLg8cDgeTJ09H//6DbHcRDs+QZEmaHXAry34MwxjCWpKzUZzoNbmAQ3bodJlgbksxvzaligE35j34n+9AEHoL4mdbQT/ZaLAq5vBENC87eM93wUWoxaUPgEpHoUyp0ob3LaMHGD0oMGBAQZG9IraevoPVPk8RFPX/zTB//5eYN28mFi1a/pusPENmY+n/Ny4UZQeBUkZD8OEShO/OQvxqD+jnW3+6JIsMm+M/0UmyQeeUDyqPJv/PaJzJHdzYEAiDToP/2Q8MTwRG4AhGIIU+kzsojdz4HQEYNtWFDsjCd4Be4ABGIP15vAMYoQP0gkzQS9ygd8gJvSizQenm8KBzyJ1kQqy0hhf5Ahx5JNTZK2WIOEuBJBrOhR6BK1BCGBhhcPX/aQhhhJlMrNsceSR0WchGZWKULFkK3bv3wu7dO6FUKkBRFIRCIfr3H2xxk7d79074+DEEev3/NwO2b/dGuXIVidfgX4zdYkTx5QkY7/rQZi0GSisHJz4cetrFkJEwXyNDAW9ZxM8EP5HGv3Pjw8D9HgCK0UMvcIQmW3loXUtBEHoT/IjHhp3uXNWgdq8PvTgrKEZniAPS60AxWkCvBxgtqJ9tDE8IZeH2tnsQ6tTgf7oNwadbUJTsaSiV8BtqtRojRgzCmTOnIRQKoVKp0KZNeyxfvtpskUz8z81JzzHR6/W4f/8eoqO/o0IFT7i4/HnuOH/SHDp/3hcDB/Y2us0CAI/Hh6dnJZw4cTbV/UdGRqJcuWJQqVRJHkvTEmzY4I0mTbxSfd2kuHjxHPr16wm9Xg+1Wg2xWIz69Rth8+bt4HDsWwPy06dQNGxYCzKZHAqFHCKRCAKBEDdu3ESOHO52lS2lMAyDZ8+eIDY2FuXKlYdUajl+JYHY2BhUqFAKMTE/kPDa5HK5yJ49B+7ff8a6wQGkzf03duwIHD58EErl/xUbsZjG2rUb0bx5K5teKyV8+fIZK1YswfXrV+Hs7IKhQ0eiWbMWVp2bmvGiVLEQfLwKwcdr4H+5B27sR9z7pMPQswo8CjMsbsViMS5cuI7ChYuYnKvT6ZA7tyu0Wo1Zv46OjggK+pR8gTQK8CMeg//lHihVjKG2q3MxaJ3ym8YrJ+s7xkDw8Ro4cZ/AUcVBSCmhifsOSh0HSh0LjirO+HdKHWfmPpyAjnaF3jGPweVZlBmMOCv0oizQi7OCEWcx/l0vypKy9ZFWCW7cJ3BjQsCJDQE4PGhyVIaedoHEbwHE/vsBGNy9ta6lDDHDhm9o8j+Ags4hNzRuZaDLWhQMV2BQpiXZkyXX7/Pqmashi7jEORRFm29AptxvQVHsy2GGIzAoptIc0LiWgtj/AJRF2yO+5vzkjEiGQCaTYePGtTh69BAEAgG6d++Nnj37sK49IyKiERDwGpkzZ0Hu3MnfeGQYBvfu3cWJE0dAURy0a9cB5cuzuz0HBwehdu1qJs+0BKpUqYaTJ32Tff1fuX//HlauXIL374NRtmx5jB070SZeT3/S+sqeZMhkRVrZD0T/UKboAUcpoyEIvQX+p5vghz0EL/ottE4eUJboAWWR9obdqwzMrFlTsW2b92+LCDGGDRuF8eMnmxz7p05yrVYLLpebJhad9BqT4OB3aN++JaKjv4OiqJ8bCGPMfqOMzp82h1avXoFlyxaDz+dDo9GgZMlS2LXrALJmzWqT/vv06Y6LF89DpbIcp5JA/fqNsG/fYZtc1xJKpRLFinkgPj7epJ2mJVizZqPdd4J79+4KX98zJrvUFEWhcuUqOHnynB0lSxnBwUHo2LENoqIiweFwodVqMGfOAvTs2TfJcwMD32LkyMF4+vQJAArVqlXH6tUbkD07uzsvYPv7Ty6Xo3Bhd9b5W6JESVy5cttm10oJERHhqFWrCmJjY6DVagEANE1j9OjxGDlybJLn22q8Hjy4hwHdW+PL93gTHysul4uePfuYWTi1Wi1y5XI2mecJiMVihIREpFqmtCDR8WL0ho38+C+GGrUMA+jV4P74AF50oKFd8Q2U4js4yu8mrs6/opPmhCZnFWizFAI3NtQQyyrN9rMebT5DuaDYj+DGhoATY/g/VxbOLhLFAUBBUbof1HlqQRB6E7yIxwbjgHE5yvz/74we3Jj34KjjzOVyyA1t1qLQZK8ITQ5P6MVZDSFYXB4YDh+guIb/c7hwypoZP2L+v+Z65noPQscoVOwzBRRXi0/3m+JrQEWo47Kg5OPcP93BvxpcwuVfwVFEgRsTAl7kC1BaBeKrz0rU442N2NgYfPr0Cblz54aDQ8qylacGjUaDhg1rIygo0Pj8EItp1KlTDzt27DU59vTpIxg5ciQAQKvVoGTJ0ti+fW+aee89f/4UrVo1NXsPAkDx4iVw9eqdFPft63sGgwb1MboJc7lciEQinDlzCcWKFU9xv8Cft76yFxlSEbVp1lyN3BArkUHc2BKDYRh4eORgjTXKnDkLAgI+mLT9aZPcx+ckZs6cik+fQuHk5IRevfqic+fuyJvX3WZKaXqMCcMwqFKlPN6/f4dfbxGKouDpWRkzZ85FhQqeaSqDrUjL8fr+/RuCgoKQJ08eZMuWPVV93b59E5s2rcPXr19Ru3ZdVKzoiXz58sPDw7Yx1gqFAuPGjcKpU8d+uguJoFKpWHdia9asjSNHTtn0+r9z7doV9O3bA3FxsWafNWrUBLt3H0zT6ydF3rxuZnE+gCHWJzQ0Enz+n5NUhWEYVKhQCp8+fTS5r8ViGkePnrL6no6PjwOHwwVNJ23VsvX9Fx4eBk/PMqzz1dnZGf7+wTa7VkqYMWMKtm7dbBbrLRKJ4e8fBJqWYN++3di+3bAZ27JlGwwZMtxolbbVeJ05cxojRgxCXJy5AtOwYWPs2XPIrL11ay/4+d02UUa5XC68vFrA23tnqmVKC2w2vxjGYEFVfANH+R0cxfefSmoU+JEvwP/sB47yO/TCTD9diEPNYlt1kmzQZcoLvWNeROkc8DZKDXH2Yijg2RgcrRyCz3fA/R4AZZEOyatdy+jBjX4H7o93gF4LSq8xKL7f34L39Tl4Me+T7kLoCI1LKWhdS0PjWhr+LWQo2X45RA7f8XDHXMRHuBuPLRXhaXm9oteCExsKvUMuqxNK6XQ6TJ8+CXv27ASPx4dWq0GPHn0wZ86CVHm8hIR8QFxcHIoUKWrRI+NXTp06jpEjh5itP8ViMXx8LqJkyVIADNbDDh1a/uaZxEPJkqVw/vy1FMubGBqNBkWLeiA2NsakXSgUYfTocRgzZkKK+mUYBmXLFsWXL19M2imKQp069XHgwNEUywz8eWt0e/H3Z81NoYuLPWAYxuTm/hW2F+afxJUrFzF06ADjojU6OhorVy7DmjWrkDNnLmzevB1ly5a3s5TW4e//CuHhX/D7Po3B1cQPbds2x7Jl/6F9+052ktC+6PV6TJ06AXv27IRAIIRarUKDBo2xfv0WiESiZPfn7b0Jc+fONMbfvXr1Em5ubrh8+aatRYdYLMa6dZuwZMkKxMbGwMkpM4oXL2B2HE3TaNeuo82v/zuGhQj7fiDHVrF3qUAoFLIqolwu1+5uw8nl0aMHiI7+ZnZfK5UKbN/ubbUiao0rb1rh4uIKmhabKaIURVl0e0tPbt68zppwjM/nISDgDbZt24IzZ04Z34Nr1qzEqVMncPnyTQiFQpvJUa5cebPSEYDh/q9Tpz7rOcuX/4cmTepDqVRCoZCDpmk4ODhi7tyFNpMrw0JRYISOYISO0COfyUcKwBBPq4r9f7wkw4AbHQRO3CfoHXND55AL4Img0+kwbNhAnDmzBzweHwyjR54863DkyGm4FOucQtk40GUpaDEuk5J9BT/iCShNHCidFmC0PxVWLaDTgNJrIVJHgPr0COKnm0DrtagyBNBreXiyZ7qJEiqHHBUrlsKePYdQpEhR84txeNA75TNvT4SVK5dh795dP7PFGiyRu3fvgIuLi1VeAr/z+fMn9OzZBW/fvgGPxwOPx8eqVevQtGmzRM+7ffsmqxGEYYCHD+8bFdFNm9aZPfO1Wi3evHmNwMC3KFiwULJlTgo+n49Vq9Zi6NABUKvV0Ol0EIvFyJ49R6pi33/8iEZkZJRZO8MwePjwXmpEJtiIP2sV8RfA4XCMN/vvlC9fIdX9379/D15eDeDung2VKpXF4cMHUt2ntSxYMJd1warVahES8gFt27bA9+/f0k2e1BAXFwcu17ISoFAoMHnyONaFzr/A5s3rsW/fHqhUKsTFxUKlUuHSpfOYPn1SsvuKj4/H3LkzTJLAqFRKREREYNu2LbYU2wSJRILs2XNALBZjwwZviMViY+ZRiUSCihUrpYsiWrlyVVAs2UZpWoLOnbul+fWTomPHrmYKgkAgQJs2bRK9RzIiP35Es441wzCIjIxM02uHh4dh8uRxqFq1PNq2bY4rVy6lqB8ul4vZsxeY1JvmcDigaRpTpsy0lbgpJleuXKztGo0GCoUCp0+fMNmMValU+PQpFCdOpM4y8TvZs+dAz559TazWQqEQbm7Z0LFjF9ZzPDwK4MGDZ5g+fTZ69OiNOXMW4u7dJ4m6Xv8zUBxDgqMESyFFQZelIDR560CXuYChNA8Mm4pnz56GUqlEfHwcZDIZAgPfYsiQ5LmxJgdG4gq1RyOoCreDslgnKIt3g7JkLyhK94Oi3GDIKwyHvukK/Ojgi6j+bxDd9hTias5DTMdj0B+uixbiFmjw87+WaImPH0PQpo2XSamm1LB5s7lip1DIsXHj2uR/V4ZB27bN8erVi59jHI8fP6IxeHBfvHnzOtFzc+XKzbrZw+PxTDyavnz5bLZZZziOj8jIr8mW2VqaNWuJc+euonv3XmjYsDFmzZqHy5dvpcqNWSKRgstlV3WyZnVOcb8E20EUUTuwaNFy0DRtXMTxeDxIJBIsWLAkVf0+evQA7du3wIMH9yCXy/H+/TuMGzcKmzdvsIXYSfLhQ+LuMVqtFkePpm28na0oXboMa6zQr+j1etYMr/8CmzatN8seqlQqcfDgPmNcmLW8fPkcPJ65i5NKpcT586lLUGAtjRo1wYsXrzBmzAT06zcI3t47cfDgcavcnVKLQCDA9u17QNM0aJoGny+AWCxGq1Zt0KhRkzS/flJMmTIDFSp4QiymIZFIIJFIUKRIMaxZs87eoiWbChU8Wa11YjGNpk3TLilVeHgYateugp07tyMoKBA3b15H797dsHXr5hT117FjF+zYsQ+VKlVBjhw54eXVHOfOXUXRoslwd0wjhg4dZaIkA4Y57ulZGZ8+hbJuXsjlMty4cd3mssyduxArV65FxYqeKFSoCIYNG4WLF69DKpVaPMfRMRP69RuIZcv+Q48evSGR2D+ja3x8HPbt242lSxfi8uULSb6b7Mm2bZtZrWl37txGTMwP+wj1KzwRtNnKQVmyF7TZymHfvt2s7yylUoWrV1O2WfQrDMMgJiaG9bPo6Ohk93f37h2EhX0xq9GpVquxbVviz5MOHbqYvdMoivqZHK+hsa1u3fqsnk0ajRolSpRMtszJoWjRYliyZCX27DmE3r37p/r+EwgE6NSpK0Qi02eSWEynyBpNsD1/h2vuH0aFCp64ePEG1q1bhZcvX6J06TIYOnRkqmPhFiyYw7rrtmTJAvTu3S/NY7kKFiyER48eWPxcqVQgNPRjmspgK8RiMZYsWYlx40ayWnkB25UU+RP58YP9BarVaqFUKhNd6P1O5sxZWDNVAoCrq1uK5EsJefLkSXEcSmqpUaMWnj59DR+fU4iJiUGtWnXS/IVvLWKxGMePn8Hz50/h7/8KBQoURPnyFeHkJEm32BiNRoNz587i7ds3KFCgIJo0aWZ13czY2BicOnUCkZFfUaVKdUyYMBVLly40bqSIxWK4u+dDx45d00z+1atXIDY2zmSeKxRyzJs3E126dDdT3KyhTp16qFOnns1kVKvV8PO7Da1WgypVqlsV/8pG5cpVsHz5akyePA5arRZarRY1atTChg3eePz4EatFWiAQIHfu3Kn9CmZQFIXWrduhdet2rJ/r9XrcvHkdr1+/Qv78BVC3boMMZ+V/8+Y1WrRoBLVaA7lcBolEgkKFCuP48bMp/o3SEkuhRxwOBYVCgUyZnNJXoCSIiAhntXzq9Xp8+5Z6Dy6KolC0aDH4+78y+8ySd5wl9uzZiSlTxv908TVFp9Ph8+fEMzu7ubnhwIHjGDSoD6Kjv0Ov1yNfvvzYvn23yfqwX7+B2Lt3F6KiIo1eXzRNY8yYCXB0zNjJQNmYO3cRZDIZTp06DoFAAK1WiyFDhmcIjyPC35KsKIVERETg/ft3yJfPA25u2ewqiyWSEwhdtGg+1genWCxOF/ei27dvokuXdhYVN4lEgnXrtiQZx5AU6Rkc/uLFc8ydOwM3b1432YHk8fioWNEz1SnF04O0GK9Ondrg6tXLZu47Hh75cffuk2T3V6dONQQEvDbZmRaLaezbdxjVqtVItbzWQJIOJI/0Gq/IyEg0bVofUVGRxoV4pkxOOHfuSpLPbYOXSCvo9ToolUqIRCJUrVodgwYNw44dW/H9+zc0a9YSXbp0T7NFvZMTjeLFi7F6T0ilDjh50jfZC1Jb4+d3Gz16dIZeb3jG6XR6rF27Ec2apTxjs0ajwYcP75E5cxY4Ozv/7FeH8uVLIDw8zMSqR9M0bt16gFy5cqfbvIqLi0XLlk3x/n0wNBo1BAIBnJ1d4eNzIUPV9a5VqzJev/Y3aROJRBg+fDTGj59s9XgpFAqcPHkMT548QsGChdG+fcc0UQonTBiNPXt2mW0uurvnw717T5OVtFCtVsPf/yWkUodUl9qwNE4nTx7DqFFDzWInhUIRbty4i3z5PFJ1XcBwf3Xq1AZKpRIMw4CiKIhEIhw8eAKVK1exqo8bN66he/eOFtdXYrEYM2bMRd++A5Lsi2EYvH//Dny+wGJZFq1WjoULF+PixXNwdnbBoEFD0bCh/T10UsP3798QHh6OvHndbebpQNYN1vH3Z81N9rU1GDVqGE6dOmas49msWUusXr0hw2WATM4kb9y4Lh4/fmjWTtM0AgJCbJoIwhLXrl3BzJlTjLEKCdNLJBKhUKHCOHfuaqrdHe1x469cuRQrVy6FQCCARqNBsWLFsXv3IeMCKyOTFuP15s1rNG1aD0qlElqtFhwOB0KhEHv2HEKNGrWS3V9Y2Bd06dIewcFBxqyC06fPQb9+A20qd2L8LS+U4OAgfPz4EUWLFoebW9pZlNNrvAYO7IPTp0+YbFLweDw0bNjErOTAr+j1epQpUxTh4WEm7TRNY86chejRo3eayfwrTk406tevjxs3rpl9JhQK8eDB81RnnE4NcXGxKFWqsNlCXCQS4/btB8muH8gwDPR6vUXL4ocP79GnTzcEBQWCw+HAwcER69dvMT430mtejR8/Cvv37zGJ8+fxeKhfvyF27Uq/3AqJER4ehooVS7HWPc6TJy8ePnxh1XhFRkaiUaPaiI7+DplMBrGYhlAogI/PRRQqVNimMn/9+hUNGtTEjx8/oFDIwecLwOfzcODAMVSuXNXqfnx8TmLUqKHQ6xnodFrkyeMOb++dePDgHs6fPwtXVzf06tXP6k0cS+Ok0WjQvHlDvH7tb1TyaJpGhw6dsWTJSqvlTYrnz59ixYol8Pd/heLFS2DMmInJ2oDq0KEVrl27wvqZQCBAjhy5cOXKrWR5IyXG3/I+TGvIOFkHUUR/Y+HCudiwYa1J1kGRSIx+/QZixow5dpHJEsmZ5FeuXETv3t1MdszEYhr9+g3A9Onp+700Gg22bt2MvXt3QqPRoH37Thg0aJhNdqHsdePHxsbg5csXcHV1s0kh5JTAMAwuXDiH7du3ID4+Hi1btkG3bj0Tde1Lq/EKCfmAdev+w6NHD1GoUGEMGzYKzs7O2L7dGy9fPkfp0mXRs2ffZFkX3r4NwPfv31CiRCmbvVCt5U94oWi1WosbOfHxcejRozMePrwPgUAAlUqFjh27YMmSlWmS3Ta9xit3blfWmpk8Hg+fP3+zaGF5+fIFmjdvyJolsnz5ivD1vWxzWdlwcqJx/Php9OrV1SSuWiAQoGrV6jh06ES6yGGJQ4f2Y+LEsZDJTOv38fkCjB07wWp3dY1GgwUL5mDHjq2Qy2UoWrQYFi1ablH5CA39CKVSifz5C5jMz/SaVwUK5EJsrHnJJB6Ph48fv6ZLfHhSRESEo0KFkqyKaN687njw4HmS4xUd/R3du3fCw4f3zWoBlytXIU3ug7i4WOzfvxe3b99E/vwF0KtXX+TJk9fq89+8eY1GjWqbrGUoigKPxwOfz4dcLgeXy4VAILA6e31i46RUKrF79w4cO3YYYrEYvXr1RfPmrdKkDnpKqV69It6+DTBrpygOunXrgZkz59rUbfZPeB9mBMg4WQdRRH+jYMHcrMHjUqkDgoM/20EiyyR3kh8/fhQzZ05BVFQkhEIRBgwYjAkTpmS4uJfU8C/f+HPmTMe2bVuMcThisRiFChXGmTOXLMbMpdd4vXnzGl5e9aFWq6FSqSAUCiESieDre8Vuinty+HWcDAkmfoCmJVbHIqYlJ08ew+zZ0/HpUyiyZs1qTKr060JpwIDe8PX1MVm0isU0pk2bif79BwMwWEbu3fND5syZUbly1VQ9F9JrXuXJ48oaE5UaRbRcuQo4d47dumBrEsZp+3ZvzJkzAxRFQaNRo2rV6ti8ebvdY+a2bt2M2bOnsY7x4MHDMXv2fKv6GT58EE6ePG6ywSsWi5OdRCm95lW+fNlZ5waXy8XHj18zjHdUnTpV4e//yiQMQiQSYeTIsRg7dmKi43Xhgi/69+9l0Z2Ty+UhKCg0QyRk+pVJk8Zi585tZgl52JBIJPD3D04yzvpPXzdMnjweO3duM3N5pmka/v7BNg8tSM14hYZ+xKZN6/D06RMUL14CgwYNs4mLc0bkT59X6UViiug/mTU3Pj6etV0mi2dNWf0n0bp1Wzx79gZv34YgKCgUkydP/6uU0L8BpVKZorIvX758xpYtG02SQSgUCgQGBuLUqeO2FDFFjB8/CnFxcUZFSKVSITY2FlOmjLezZMnjzJlTKFOmKIoXL4ACBXJh8uTxdi3Tc/68L0aMGIxPn0IBAN++fcP8+bOxYcP/U/8rFAqcPXvazHKSUCJgz56daN26GcqUKYKRIweje/eOKFuWPW4xo9G0aXOzrMo8Hg+NGjWxqITGxcXi/PmzrJYksZi2S5KK3r374fXrYJw6dQ4PH77AwYPH7a6EAkCtWnVY22laYpJJMzEiIyNx4sRRs9qmKpUKq1evSLWMaUHjxl5mVk8Oh4OqVatnGCUUADZt2g4np8yQSCTgcrmQSCQoUaIUhgwZkeh58fFx6N+/t0UlFAB0Oi2GDRuIiIgIW4udKr58+WyVEgoYfrNnz5Kfl+BPY8SI0XBwcDCZs2IxjcmTZ2SopFX+/q9Qq1ZlbN/ujfv372L37h2oW7canj59bG/RUoRWq8WaNStRtmwxFC6cF4MH90syKRQhefyTimjZsuVZ20uVKo0TJ45i5cqluHDB1+oHYUaDoihIpQ5EAc1gvH0bAC+vBsiXLzvc3bOhR49OiIoyL7Rsibt377AukORyGS5cOGdLUZONXq/H/ft3zdoZhsGtWzftIFHK8PO7jSFD+iMs7As0Gg2USiX27t2FCRPG2E2m+fNnmy0m5XI5Vq5canS1UyjksLSHFhoaismTx+H27RvQaDSIi4tDfHw8IiLC0bFj6wy/+TZv3mLkypULEokUHA4HUqkU2bJlx6JF7ApOfHw86teviVWrlpuVZZBIJKhSpSq6dOmeHqKbIRKJUKJEyQyVHK9AgYLo0aM3aPr/VjGalqB27TqoXr2mVX18/PgBAoF5DgK9Xg9//5c2kzW5yGQyi+/x2bMXIFu27EZroEQiQebMWbB8+WqbyuDndxuDBvVF167tcfDgvmTXpixUqDCePPHHkiUrMWnSNOzcuR9nzlxM1AKoUqkwf/5s1lJFv3P+/Fk0blyHddPGXtSv3whisXXKlU6nh1Rq2dryt5A9ew5cvXobXbv2QL58HqhcuSq8vXdg4MAh9hbNhKlTJyA+Pt44z7VaLWQyGSZMGJ3oeXq9Hh8+vE/zes7JZejQAVi2bBE+f/6E6OhonDhxFPXr18T378nPqBwS8gF37txK0bl/M3+8a65cLoe//0tkyZLV6vInz549QatWTaFSqaDVasHlco21+9RqNRQKOcRiGrlz54GPz3m7pqsmZn9z0npM1Go1Tp48hvPnz8LFxRU9evRJdX2+Hz+iUbFiacTGxhgX/jweD/nyeeDmzftWxfBdvXoZffv2QHx8nEk7j8dDnz4DMG/eItbz0mMOMQyDvHndWN37HBwc8O5dxnJ5Z8PJiUaDBg1w/fpVs8+EQiFevgy0iwXLkgshj8dHQMB7ODg4gmEYVKpUJslavr8jkUhw7JiPxc25xEjPZ5NGo8GFC+cQEPAaBQsWRuPGTS1arTZv3oD582eZKe9cLhfbt+9N1JKaFmS0Z3jCO+/XMWAYBlevXsa+fbuhVqvQvn0nNG3a3OrNzO/fv6F06SJmygyXy0W7dp2wZo11tazVajUyZRJDoUjdJvDVq5cxceIYhIZ+hEAgQPfuvTBjxlwzN3ulUgkfn5N48eI5ChUqjJYt29g0Nn3VqmVYtWoZFAoFGIYBTUtQqlQpHD3qYzOr6+/zKzY2Bk2a1ENISAjUauuUS4lEimXLVqFt2w42kSm1KBQKNGhQEx8/hhjfKRwOBwzDmGycURSFfPk84Of3OMl72hb34ePHDzFlyng8ffoEDg4O6NNnAMaPn5wh4oltTUrHK2fOrKybLRRFISwsmnWtc+XKRYwcOQSxsXHQ6XSoWNETmzfvgIuLS4pktxUhIR9QvbqnWY4CkUiEMWMmYNSocVaNU3x8HPr06Y67d+8Y8zf06tUPc+YsyFBxyCkhPj4eJ08ew6tXL1G8eAm0atWW1dX/r3XN3blzK4oV80DHjq1Ru3ZVNGpUxyoXk9Kly+Ly5Zvo1KkrSpcui06duqJ48eKIifkBmSweer0eMlk8goODsHDh3HT4JuzExcWyLuwJaYdSqUTz5g0xfvxonDp1Ajt2bEXjxnVw5MhB4zEymQwnTx7D3r27jO6SSXHw4D6o1SqTl6hWq0VY2BfcunXDqj5q1KgFmqbNHlx8Pj/dMoBagqIodOzYxSwzs0gkQufO9rE+pYT374NZ2/l8vl3c1169emnRMuDo6AiJxLBopigKy5evhlhMG5UHaxa6HA7XYqhCWiCXy/HgwT28exeYrPP4fD68vJpjzJgJaN68JXg8Hnbt2o5SpQrDzS0TKlcuC1/fMwCAixfPs7oj0rQEHA71x7/4U8r161dRrVpF5MyZFfnz58KiRfOM1kKKolC3bn14e+/EuHGTcOjQAXh6lkaXLu1ZM7H/TpYsWdGpU1czK51QKMLIkUl7E4SFfUGnTm3g7p4NTk6OaN3aCyEhH1L0PZ8+fYxevbrgw4f30Ol0UCgU2LVrB8aONXdlFYlEaNeuI2bPno+uXXvYVAn9+vUrli9fDLlcbnzuy+UyPH/+HGfOnLLZdX7nv/9WICTkg9VKKGAIS/q9RIw9McQWX8HYsZOQO3ceMyWUoijQtAQ5c+bCvn2H0+WeDgx8izZtmuHx40fQ6/WIiYnBxo1rWefVv0zCO+l3RCIx6+/09m0AevfuhoiICCgUcqjVKty754cOHVrZ3Vvn5csXrDkilEol7t+/Z3U/Y8YMh5/fbSiVSsTGxkKlUmH37u3YvXuHDaVNfz59CkWlSmUwdepEeHtvxNSpE1GpUplkuy7bRBFVq9WYPn06KlasiGrVqmHLli226DZR7ty5hRkzpkIulyMuLg5KpQIvXjxD9+4drTrfw6MAVqxYg4sXr2PBgqV4+vSpSUY5wPC9jh07kmRfer0eO3duQ/XqFVG6dBGMGzcyVYvWZ8+eoHbtqihc2B1Zs2ZBr15dEB39PcX9/euEhX3B/Pmz0blzOyxePD/R3+bAgT148+Y15HKDBSphITNu3EjI5XLcveuHUqUKYfToYZg6dQKqVCln1WbF27cBrItjnU5nUfn5HR6Ph2PHfJA7dx5IJBI4ODhAKpVi9eoNNk/BnxJmzZqPihUrQSwWQyp1gEgkRpUq1TB16kx7i2Y1ZcuWZ92x1ev1SZaxiIyMtEkB9AS2bNmIpk3rsbrxiMU0Jk2aaiJrjRq1cPHidXTu3A2VK1dBly49IBSKEr2GVqtF+fIVbSZzYmzf7o2iRT3QqVMb1K1bHQ0a1ERERHiK+vL23oTp0ycjPDwMDMMgOPgdBg7sg0uXziN79hysv6FOp4OLS8apD5mePH78ED16dEJgYAAYhkF8fBw2bFiDadMmmhx3964fmjdvjAsXfBEa+hGXLp1H69ZerKVnfmfRouUYNWocsmZ1Bp/Ph6dnZZw8eTbJRGUajQZNm9bH9etXodVqodPp4Od3G02a1DOJh7eWpUsXmW3gKpUKnDhx1Kb3Z2IwDIM9e3awusvL5TKcPeuTZtc+efKYxZh2Pl/Aar2TSCR2eYcwDIOHD+/jwIG9eP78qclnUqkDhgwZjpiYGOj1ehOlhMvlolGjJnj48AU8PApYdS29Xo+rVy9j48a1uHz5QrJCrxLWAL+/wxUKBY4dO5wqd1KNRoOoqCijPHq9Hn5+t3Hs2OFke7hkBHr37geRyPS9IxKJ0K1bD1ZF1Nt7o9l81Wq1eP8+GC9ePEtTWZMiT5680Om0Zu18Pt/qBIzx8fE4e9bHzFtELjfkb0iM0NCPOHnyGO7e9bO7Us7GpElj8e1blHG9LJfL8O1bFCZPHpesfmziT7BkyRI8efIE27dvR3h4OCZMmIAcOXLAy8vLFt2zsnHjWpNU+IBh8gYEvMa7d4HIn99WWTqT/vEnThyDw4cPGF+a+/btxrlzZ3Hr1n04OWVO1tXCwr6gdWsvEyvFxYvn0a5dS1y6dOOf3c1PKf7+r9C8eUOoVCqo1WrcunUdW7ZsgK/vFRQsWMjs+BMnjrEqjFwuD/fu+WHAgF6IizN1jd20aR1q166LKlWqWZSjbNnyOHr0sPGGTYCiOChWrLjV36dQocJ48OA5Xr58DplMjjJlypo99O1Fgpvn69f+CAoKROHCRdJkcaPRaODndxtqtQqVK1ezqRVj3LhJuHjxvMnvRNM0Ro4cazEm682b1xg8uK8x8U+xYiWwceNWq0MF2IiMjMScOdNZ47ZcXFwxffpsdOrU1eyzQoUKY8WKNQAMi7zbt28gKMjc+phQUH3RouXpkujCz+82Zs2aZvLMfvnyBbp27YBLl6zzCEhAr9dj6dKFZs9/pVKB+fNn47//1uPkyaMm9zGXy0X27DlQpky51H2RP5Tly5ewLqL37t2FKVNmwMHBEQAwbdpEs3FVKBSYOnUibt5M3ALA5XIxevR4jB6dvORkFy6cQ0zMDxPFQK/XQ6GQ49Sp46zznI34+DiMHj0cFy+yx8sLBAJ8+fIJWbNmTZZ8yUWj0aBHj864des6q1WSy+Uic+bkrQt+hWEYhIeHgaZp1lABS1m+ORwOvL13YtasqQgN/WiMn+ZyuZBKHdC8easUy5QSYmJ+oF27lggMfAuKAvR6BuXKlcPevUeMz6TAwLfQ680VRq1Wixcvnhstpffu+eH+/XvIli0bvLxamLkG/vgRjTp1muLDhxBoNGrw+QJky5YNPj4Xk5wPL1++QJs2XoiJiWVVBoRCIT58CE62G6lOp8OiRfOwZcsGaLU6SKUSDB06Crt370BU1FcAgEajRatWbfDff+vTpARXWjBu3CR8+PAeZ8+ehlAohFqtRt269S2WEAwJCWHdFOByuQgLC0OpUmXSWGLLlCxZCkWKFMOLF89N4q35fAH69h1gVR8ymcziuj06Opq1nWEYjB8/CgcP7gefzwfDMHB1dcWkSdOwdu1/CAh4DTe3bBgzZgK6dOmeqF6g0Wjw/PlTCIUiFC9ewmY6BMMwuHz5kpkBT6fT4fLli8nqK9UzWy6X49ChQ5gyZQpKlCiB+vXro1+/ftizZ09qu06U8HD2nXQej4/ISOsTwAAGN5BKlSqb3egCgQCtW7dL9NwvXz7jwIG9Jju3Wq0WsbEx2LVre7LkAIC1a/8zW3xqNBoEBwfhyZNHye7PVjAMgxs3rmHBgjnYvHl9hgsot8TEiWMQFxdn3HFTqVSIi4vDlCnstfEcHNj92PV6HYKC3prddMD/F3SJ0bp1O2TKlMlkN1ooFKFkyVKoUMHT2q8DwKBElCxZGpUrV8kwSuivFC1aDM2bt0wTJfT+/XsoXrwAevfuioED+6B48QJWeS1YS+HCReDjcwG1atWFo6MjPDzyY+HCZRg1in2HLy4uFi1aNIK//yuo1Wqo1Wo8f/4UzZs3SlXyj2vXLptlik2gUaMmVi3OKYrCli074eTkBIlEAg6HA4FAgCxZsqBLl+7w8bmIzp2tW+SnFraNQ51Oh8DAt8nO3CuTxVt0J37/PhilSpXB8uWrIZU6wMHBAWKxGIULF8Xhwyf+2Y28gIA3rO08Hg9fvnwx/vvVK/bEQgEBr1mffbbg/ftgKJXm94pMJsO7d0FW99O3bw+cO3fG4ucajQbu7vlSJGNy2L7dG3fu3LR4/wsEAnTr1svi+U+fPkbPnl1QrVoFDB060GQj6caNayhXrjgqVSqD4sULoEuXdmZW3h49+phtmnE4HJQuXRZNmnjBx+cimjRpBj6fDy6Xi3r1GuDcuSuJJj/6+vUr1q5dhYkTx+DkyWPQaDS4cuUi2rdvidq1q2DevFnJtjZPmjQO/v6vIJfLIJPJoFDI8fDhAyxYMNt4jJOTk8XkTlmyZIFarUb79i3RuXNbLFo0FxMnjkHZskXh7//K5NiZM6ciMDAQMlk81Go1ZLJ4hISEYNKksYnKyDAMevXqgh8/foBh2Oe/SqWCu3vyS5MsWjQPmzdvgFxucEf9/v075s2bhY8fPyA+3vCMU6mUOHXqRJLri4wEn8/Hpk3bcPfuE2zduhu3bz/Ejh37LK5VqlevAZHIfO6p1SqUKlU6rcVNkgMHjqJhw0bg8/ng8/koVKgwDh06gbx53a0639XVFVmzOpu1czgc1KxZm/Wcgwf34ciRg1CplIiPj/s5Xz9g0KC+ePHiGdRqNUJDP2Lq1AnYtGmdxWtfuOCLokU90KFDKzRv3hAVK5ayqQs+l8uuQnI4yUuUmmqL6Js3b6BWq1G+/P+TXZQvXx7r169PtPh6aqlfvyFev35l9rDXajUoUaJksvv777/1aNq0PuRyOeRyGWhagly5cmHy5OmJnvfixXMIBEIzOZRKJZYuXQhHx0zo2bNPkgsgmUyGgQN749KlC6wvfIqiEBLyAeXKVUj2d0stGo0GXbu2x4MH9yCTySASibBgwRzs2XPI6qyKDMNAo9GAz+en22IwsUyud+6wZ3Lt3bsfbt68buYSljlzFuTMmZv1HIZhEk2RDxishRcuXMecOdNx7twZ8PkCdOrUBRMmTP1nF8cJhIZ+xO3bN5E5cxbUqVPP4o6+TCZD585tzCzSo0YNQZkyZVNlgfyVEiVK4vDhE1Yde+KEwQ3u153yBGuOr68PWrVqm2QfcXGxePnyBZydXYxWepFIxDovOBxOkvXyfqV48RJ4/NgfPj4nER4ehgoVPFGtWo00n3NKpRIHDuzByZMn4OTkZFHZ5PF4+PYtitU7wRISiRRSqRQ/fpjvJifUqmvXriOaN2+FV69ewNExU5rWsTVkhr6B8+fPQiKRokOHTjb0yLENJUuWRGhoiJlFR6vVImfOXMZ/Z8mSmXWTMVMmpzSzyBQrVhxCodCsPqJEIrX6Xf7xYwju3LltUfmjaRq9e/c3Wn7Tkj17dlh8HwiFIsybtxglS5Zi/fzKlUvo3bsrlEql0eX87NlTOH36AkQiEbp372jS9/XrV9G8eTOcPXvZ2Nav30DcuXML168b6uRyuVw4OmbCli07AAAuLi7YunWXSbxlYjx4cA8dOrSCVquDSqXEoUP7MXXqRMTFxRplCQoKxOHD+3H16h1kyZK0xVmv1+P06RNmWX1VKhUOHNiHefMWAwBy5MiJ0qXL4vHjhyYZsGmaxqBBw7B162bcv3/PWDYo4Zi+fbvjzp1Hxu/G5q6s1Wpw6tRxtG7dDk2aeLGOQ0DAG0RFWd50F4vFaNmyTbKtoRqNBlu2bDTbnGMYvZk7t0Ihx9atm9G9e69kXcPe5MyZy+TZYokePXrD23sToqK0xk0HmqbRsWMXZM+eI63FTBInp8zYvt1gbFKplMicOUuyzjfkb/gPffr0gEqlhF6vh0Ag+Fl6h12/8PbeZLYGZdML5HI5li1bjH79BpnpWiEhH8xqCMtkMrRp0wzPnr1JdX10iqLQokVrnDx53OQ+FggEaNGidbL6SrWWGBkZiUyZMpkkKXF2doZGo8H379/h6po2MTnNmrXAjh1bwTCxRvcXmqYxZcqMFLnq5c3rjocPX+DMmVMICfmA4sVLon79hkkq0jly5GT1IQcMD9WZM6fi+/dvGDOG3QKXwIQJo3H9+lWLu85arQ7FiydfwbYFhw7tx/37d403RkL8Tb9+PfHqVVCSmRX37t2FhQvnIDIyElmzOmPixKno2bNPmstNURSEQiFrwidLi/m6dRtg4MChWL9+Nfh8gfHY/fuPIkeOHKy7szQtSdJyDgBubm5Yt25zMr/F3wvDMJgzZzq2bt38M4unwWJ35Mgp1gXoxYvnWF2jtFodDh7cZ/JQ12q1OHXqOI4ePQSRSIyuXXugTp16NlfAQkM/ssaxKZVKhIYmnchq/frVWLRoPvh8PrRaDQoVKoI9ew6hbt0GrM8CoVBotatiAlKpNNnnpAaVSoVmzRoiMPCtcaHF4/HA5XLNXLAMG4emi/Lg4CCsXLkMDx8+gIdHfowaNRYVK1Yyfs7hcDB+/GTMmzfLZCEnEolN4pGFQmGab9wxDIMBA3ob3bl5PB42bFiDxYtX2KVW6e3bN7Fu3X/48uULateugyFDRsLV1RVjxkzElSuXTBYlYjGNfv0GmLwvhwwZgSVLTN2exWIaAwcOTTOZa9eui7x53REU9NaoLPD5fLi4uKJJk2ZW9fH58ycIhQKz7JYJfU2bNgt9+w60qdyWsBR7KBAIcPbsRZQsyW7lYRgGEyeONfmNdDodZDIZZs+eBg+P/GbvH41GA3//V3j58oXxmWlI5LUfr169xOPHD5EjRw7UqlXXbC1jzbOQYRgMHNjHJGu3TCYzy+KtVqvx/ft3bNmyCRMnTrHYX1BQIF6/9kfevHnNSiv9vy/TzYRt2/agS5d2CAx8Cx6PB7VajcGDh8PLqzlq1apsVrsWAD5//oyQkA9GC7hOx76uYhgGQ4b0Q8uWBvfX39FqtaAoy1afQYOGYfz4yRa/ryXi4mLNNl4S4/eQnr+JTJmccOnSTaxatRTnzp2Fo6MjBgwYkq7vLGugaTrFYSz16jXEmTMXsX79arx7F4gqVaph0KBhyJYtO+vxMpn1CQRVKhWrrrVv325otebPIpVKhatXL6NRoybJ+xIszJ+/GC9ePMenT6HQajXg8fjInTsP5s9nr+BgESaVHD9+nKlevbpJ28ePH5lChQoxoaGhFs/T6XSMWq1N1p+vX6OYPXv2MmXLlmVEIhEjlUoZPp/P5MqVi2ncuDHj63su2X3a4k/ZsuUYPp/PwBBQavZHIpEwsbHxFs+PiYljhEKhxfPFYjHTrFlzu3w3tVrLVKtWjVUuBwcH5vbtO4meu3XrNoamaZPzaJpmNm7clGJ5dDq91cf27z+AEYlEJtcXiUTM6NFjEj0vJCSU2bNnH3P2rC8jlytNvo9YLDb+3jRNM9WrV2fevw+x2NfDh4+ZTp06MSVLlmJ69erNvHr1Ol1/v+SMV3r+OXHiJCORSMzmVY4cORilUm12/IYNG83mUsKfwYOHGI9TKtVMw4aNTPqmaQkzZsxYm4/TkSPHGKlUaiaPVCplLl68ZDzu48dPzP37D5no6Bhj2+nTPmbfh8fjMZ6enoxarWXOnTvPSKVSxsHBgZFKpYxQKGQWL15i998tqfHassWboWnz3xWAyXOOpmlm+fIVJuc+ffqckUodGC6Xa/L827p1u8lxKpWGWb9+A5M9ew6Gw+EwBQoUYI4cOZakzPHxcubRoydMSEhoms5hkUjEfP0aleJ5lZI/v98fAoGAcXFxYT5+/MSo1Vrmxo2bjKenJ8Pn85ls2bIxS5YsZVQqjUkfSqWaGTduPCMWixkHBwdGJBIxI0aMZBQKVZrKHhX1nRk0aDCTOXNmJlOmTEyvXr2ZL1/CrT4/PPyr2XM+YQxGjhyVrvfFnDlzGbFYbCaLu7u72Xj/+uf79x8Mj8ezsIaQMvXr12f9zNHRkTl+/ESafJfXrwNYv4ulP+XLV2DtJy5Oxnh5Nfs5rxwZmqYZR0dHhqIok/M5HA7TsmUr1j6ePHnG+PqeY8LDvxrbihYtanHN9Pp1gPG4Nm3aWhzbhGfRgwePzK6pVKoZV1c3lv5pZuXKVSkeV6VSzTg7O1s1pkKhkBk/fkK6zmG1OuOuGzLan7QYp3HjxieqE/y+1pDJFGZ99OrV26I+smWLt81kVSrVjK/vOWblylWMr+851rWbWq1NVI9MdR1RX19fzJo1C/fu/T+Rwbt379C0aVPcuXPHYiB4cuuIbt26GbNnT4NGozHbcRSLaezYsRd16tRL2ZdIJeHhYRgxYjCuXbvC+jlN07h+/a5Fn/KoqCiUKVOENdMdRVEYN24SRo4cm2pTekpp1aop7ty5ZdYukUhx4sQZlC5d1uK55coVZy1xki1bdjx/HpAieZJT30omk6Fnz864f/8e+Hw+NBo1qleviW3b9qQ4vjIoKBAHDuzF9etX8fr1K2NAfr16DbB+vbfJrtmdO7fQuXM7o0sGl8uFSCTC6dMXUuRCnhIyWh3DBLp164ALF8wTi0ilUhw+fNIsm+uHD+9Ro0YlM6uHWEyjYcPGRjfsihUr4dKl82aWSpFIhJs371u8D1MyTlqtFvXr18S7d4FGt0CRSISSJUvDx+cCZDIZhgzph6tXL4PPF0Cn02LcuEkYPnw0OnRoxfrMEIlEuHHjHtzd80Emk+HSJUM5kjp16sPNzS1Z8qUllsarR4/OrLF6UqkU9eo1xIcP7+Hi4oKBA4eiVq06Jsd069YBFy+eZ7V8FytWAuvXb0lWcq9f2b9/D6ZNm2gME6hcuSqmTZsFb+9NuHv3DnLlyo2RI8eidu26Vvc5dOgAHD58wKxdKnXAqlVr0aJF63S5/1QqFYoW9TCrMczn89G7dz+jqyMbDMMgNjYGEonUaDWLj4/Hly+fkSNHDkil/4+b//btG5YuXYAzZwyJSHr06I3Bg4fbrCYmkPLn1axZ07Bjh7fxvudwOHBwcMSNG3fTzcWPYRj4+d3GmDEjEB4eBrlcBrFYDC6XhyNHTiZqoddqtfDwyMlq4cuVKzd69OiNFSuWmHn4CIVC3Lv3FDly5LTJd3j92h/r169BUNBbFCtWDAcPHrC6FEyTJl7YuXO/Wfv8+bOxadM6E9kT5gyXy4VSqYRYTEMioXH+/LUkM5QnsHr1CixbtthszPLmdcf9+8+MVt+IiHA0blwHX79+ZfVq4vP5mDJlJoYONS/FcufOLXTp0g46nR4qlRISiQQlSpTEkSOnzcqVJYf9+/dg0iRTC7hAIABFUdDr9dBoNKBpGq6ubrhw4VqyE1+mloy6bsho2HKcZDIZJk8eh6NHD0OjUYOiKDAMAx6P/zM0gjHRE2iaxtChI1mt8qdOHcfIkUPMvBdEIhGuXfOzWSiTtaRpHVE3NzfExsaaDE5kZCQEAgEyZcqU2u4BGFLPz5kzHUqlktXtRaGQY926/2xyreRw+/ZNVK9eEWXKFMW9e34WzewMwyRaNiBr1qysn3M4HLRu3Rrjx0+2mxIKAF279mB1SZBKpRbdjBL48uUza3tC2YW0RiKR4MiRU7h48TpWr96AK1duY9++I6lK8lOgQEEUKlQYgYEBUKvViIuLg0qlwuXLFzFu3EiTYydMGAOFQm50s0xwtZoxI/nuPH8bMhn7w5uiKFZ3anf3fOjff6DJXBSLafB4PJw7dwZhYV8QFvYFPj4nWd1lKYrDWoaCYRhERkaaPbCtgcfjwcfnPAYMGIIcOXIiV67cGDp0FI4cOQWKojBy5GBcvXoZKpUK8fFxUCgUWL58MU6fPmkx9ojP5xvLNUkkErRs2QadOnXNUEpoYri4uFiMJ+zdux8uXryOffuOmCmhAHD//l2LzwV//5do0aIRvn4Nx82b13H79k2LiUx+586dW5g0aRzi4uJ+JgFR4fbtW2jcuC6OHDmIkJAPuH37Jnr27Iz9+80T7Z06dRyVK5dFzpzOqFq1grH8hlAoZHVxpChAIEj5IjW5WIrBNSSVucz6GWBIilG8eAEUK5YfBQvmxsKFc6HX6yGVSlGoUGETJVQul6NRo9rYvXsHIiLC8fFjCJYtW4TevdPfBZmNmTPnYv78JShYsDCcnV3QsmUbXLp0I12U0ODgIPz333KUKFEQXbu2x9evEdBo1KhatRqmT5+Nhw+fJ+kmzuPx0K1bD7PELWIxjaFDR6Bnzz5wcHA0cbEVi2l069bNZkrojRvXft4TB/Do0QMcOLAfWq3GzD2Voiize1wspjF48HDWfnfv3mH2TE+4d8eOnYi2bdtjypQZ8PN7bLUSCgADBgxBqVKljVlyaZqGg4MjvL13mtyXbm7Z4O//Bs2bt2INJeLz+XB0ZI8frlq1Ou7ff44pU6Zj8ODh2Lx5O06c8E2VEgoAnTt3w4YNW1GsWAlkyuSEqlWr48SJs7h16wEGDRqGFi1aYfbsBbh2zS/dldCMhE6nw5o1q1CmTFEUKJALffv2SHGN4YxOv349cPz4EWPMJcMw4HA4aNu2A27cuItNm7Yhd+48oCgKmTI5YfToCRg3bhJrX02aNEPhwkVMwtBomkanTl3TXQlNilRbRBUKBSpVqoQtW7agUiVDHM+6detw8+ZNHDhgvlOcQHIsoiNGDMHBg3sTVVwKFSqMW7ceJE/4VPDy5Qt4eTUwiaURCATQ6XQmyrJYTKN3736YNWteov1dvnwBffp0h0qlgl6vB4/HB02L4ed3Dy4utnnJJAbDMNi3bzdWrlyKr1+/olix4pg9ewEqVaoMvV6PgQP74MKFc9BqtRAI+OBwuDh8+ESSL9dKlcqw1snMnTsPHj1iz9CYFNbsQMnlcqxatQyHDu0HwzBo06Y9xo6dYLKwSg01alRCQMBrs3ahUIg3bz5AIpFApVIhb143i7F+oaHpk3k4o+5s7t69A9OnTzJTGiUSCV6/fm9xs+D69avYt283lEoFChYsjK1bN1mlREqlUqxcuRYtW7Yxtl25chFjx44wZtr28mr+M+Nq6kvCxMT8QIkSBVkTqJQpUxa1a9fDhg1rzD6XSCTw9w9OVlIie2BpXiVkDf51p5+iKGTLlg2PH/snGlNepUq5RDOlCgQGxc+wMceAy+Vix459qFq1eqKydu3aHhcvnk/6S8EQs/T6dbBxwX/8+BGMGjX0t/hKMdav94aLiyvat29plnREKpXi1at3EIvF6XL/hYeHoWLFUqxzrWrVGjhxwtxCfe7cWQwc2NssbnTgwCGYMmWG2fGW7lexWAxf3ysptlT/TkrGKzg4CLdu3YSTkxMaNGicbvcOwzCYOHEM9u/fY5a0DDCM59691if1U6vVGD16GE6dOg6BQAiNRo1+/QZi+vQ5oCgK4eFhWLJkAS5ePA8HBwf07z8YI0cOQ2ys+cZdSr6Lp2cZhIS8N2lPuN/4fMHPmEmgSpXqiI39gZcvX4DP50OvZzBnzgKLCXXy5nVjTeDE4XAQEhKRIqUuNPQjDh7ch6ioKLi4OEOj0SJ79hxo3botHB3NjSBOTjQCAt7D07OUmSw0TePp09f/tML3Oxll3TBs2ECcPn3C+JtxOBw4OmbCrVsP0iwHTXKw1TiFhHxAjRqeZhs2PB4PnTp1NZZlAwzPCWsSfyqVSuzevQPHjh2GWCxGr1590bx5K7skyEzMIprqZEVisRitWrXC7NmzsWjRIkRGRmLbtm2YO3duars2Eh39PVEllM/nJ8udyhasXr3CzEVQrVaDx+MhV648+PLlEyQSKQYNGoqxYyda6OX/1KvXED4+F7B27X949y4Inp6VMHToSBQsWDBdHgbr16/G0qULjYuMx48fokOHljh2zAfly1fEli078OzZE9y5cxtZsmSBl1cLqxbrM2bMwZAh/c0WcdOnz07krNTBMAzatm2Oly9fGH8jb++NuHLlEi5fvmmTTM7fvrGXCKIoCnFxsZBIJODz+RAKhawv4D/xhafRaPDly2dkzZrVJgp9x45dcOjQPrx8+QIymQw8Hh88Hg+rVq1L1GJdq1YdozVt+fLFVhe9pygOGjRobPz3ixfP0bt3N5Pf5+zZ04iJ+YEDB46l8Fv9n+joaItKV0REBAYNGopDh/bj27coqFQqY23PuXMX2WQh/e3bN9y6dR00TaNmzTqp3sG3llKlymDhwmWYPHk8eDwe9HodMmfOgoMHjxvH49mzJ9ixYysiI7+iSZNmaNu2A0QiEYYPH43Jk8ebKXYJJLgI/vrs7dKlPZ49e81aUzEBS54ZbGg0anz6FGpMdDJ37kzWGpxz587A3btPMHToCKxZsxIcDgccDhcMw2DHjn3G3/D48eNYtmwZvn2LQr16DTBixFibW7ezZcsOT8/KuHvXzySDoVhMY9gwc3dDAFiyZAHL95Jjy5YNGDdukpkXzr17fhY9DZ4+fWwzRTQ5MAyDKVMmYO9egwWMy+WCy+Xi0KETKFu2fNIdJIPo6O/4+vUr8uTJa/xtfXxO4dChAxaz9Roynm6yWhEVCARYt24z5sxZiM+fQ5Evn4dJpt9s2bKbLEgB2Cyb8Y8f0fjy5ZNZO8MwEApFWLt2E8LCvqBCBU9j1t+QkA+Ijv6OIkWKJfnMvnDhnNmmbOnSZVL0XDp/3hcDBvSCVquDRqOGRCJBuXIVcODAsUTdxN3c3LB16y4MGNAHCWtxiuJg+/Y9f+Q7+W/n8+dPOHnymMn9lZCVfuvWTRazzvr53caiRfMQGBiAQoWKYNKkaahcuapV19Tr9Xjw4D6+fYtChQqe6abshoR8gEAgMFNEtVot3r41Lb9lrYekSCRC//6D0L//IJvJmRbYpLbK5MmTMWvWLPTs2RMSiQRDhw5F06ZNbdE1AIOV4ubNa6wvQYNLRSYMHz7aZtezBkt11cRiGt7eO1CiRKlklyopWbI0Nm3aZksxrUKtVmP58iVm46tQKLBo0TwcPnwSAFC6dNlE40HZ8PJqgY0buZg3byZCQj4gd+68mDp1Jpo1a2Ez+X/n5s3rePPG32SxqlKp8PFjCC5ePI8mTbxSfY2qVavDx+ek2RxwdMwEV1fDIpPD4aB7917YtWuHSQxLWmehTAt27dqOOXNmQKs1xGi3atUOy5atSpVyIxAIcPz4Wfj6nsGFC+fg4uKCrl27J6v0Ra5cuUHTtJlFNOFBLRQKwTCG58TevYdM3HrXr19ttoBMcNn8+DEEefLkTfTaT548wpIlC/H69SsULlwE48dPNqkJmytXbgiFQrP7isvlonr1WsiSJSuuXbuDrVs34+LF88iePQcGDhyKypWrWP39LbFp03rMmzcLfD7P6Ea3f//RZNesTSldunRHy5Zt8OjRAzg4OKBMmXLGZ+HevbswZcp4o/fHrVs34O29CWfPXkLnzt0QGvoRa9asZI2ZZ4eBj88pdO3aw+IRtWrVwdu3b83KRbCh0+mQJYshRT/DMKwx7oAhZhkAJkyYgk6duuLatSugaRqNGzc1btSsWLEEa9asNM7P0NCPOHHiKK5fvwdnZ/Packkhl8uxefN6HD162Bij2bVrD3C5XHh770SfPt3x6NGDn1mYdZg8eTrq12/E2ldo6EfWdkMd7Fgz+Tw88kMoNC9VxuFQyXKntBXv3wdj1qxpuHDB1yxkp1u3jnj+PCDJjO7WoFQqMXr0cPj4nPhp/dNj3LhJGDZsFHbt2pZkRtPk1tcEDOE6lvJrpBViMW1xveLg4IDGjc3XdHnzultVT3HOnIW4e9cPCoUCKpUSAoEAAoEAy5atTracKpXKbHNbJpPh0aMHOHLkoFm26vj4eFy9egl8PgeentVQv34j+Pu/w927d8DhcFC5clW7hj4RLJOQg4PtPf3w4X3WcxJKICXMj6ioW+jQoTV27dqfpMHqw4f3aNu2BaKjv4GiKKjVagwZMiLJMo62oHDhIqwbWny+wCxfxt9Gql1zU8qvrrnfvn3DxYuGpCUNGzY2q0OlVqvRokVjvHnjb5KIIGtWZ7Rt2wFDh45M9/ip4cMH48iRA2YvQKFQiOfPA5Jda8gS6eEe8fnzJ1StWp7Vcufi4oJXr96l6fWTS1Jjsnr1SixaNJc1Pfzo0eMwebK521lyCQ4OQsOGtSGXy3+6KxmsWevXb4GX1/+VbLVajREjBuPMmVPGB2qXLt2xcOGyNKvL9zupnUMXLviif//ev5XKEKFly7ZYs2aDLURMMXK5HOXKFTfxmqAoClmyZMXNm/fw/PlTCIUiVKpUxcwS3rBhbTx9+tisT0dHR+zZcyjRHVRDEqq2Zpb+XbsOmMQ+HjlyEGPHjjQpY0LTEly+fDPRBVxo6Ec8e/YUOXLkQNmy5ZO1ofX06WO0bNnE7H7OlCkTXr4MspllNCXzKj4+HsWLFzCzeIrFYsycORd9+gwAAMTFxaFNm2Z48+a1cUOJw+Gwbv7x+QJMmTKDNdFIAl+/fkXt2pURExNjjE0TCATGxEUJCIUieHk1w8aN/98QLFmyECIiws36zJ07Nx49emXxmrGxMShRoqDZDrdAIMCQISNY3V8TQ6PRoHHjuggMDDD2mZCoK6FGJGCYO5GRX1G4cFFj7NzvvH8fjHbtWrAqo5kzZ4G//zszJS4iIgKVK5c1KS3A5XKRJ09e+Pk9ttnzzJp5df36VfTo0RlKpYLVW0oqleLAgePw9KzEcnbyGDVqKI4dO2zyO9I0jVWr1mHr1k24d8+8VnUCYrEYU6fOwoABg1MthyVsuUYYMqQ/Tp8+YbYg5nK5qFSpCjZt2gY3t2wp6jsqKgq7dm3D48cPUbRocfTp0z9F8bt+frfRrVsHs3rSAFC9ek0cO+Zj/PflyxfQt29P49zUajVYtGg5unTpnqLv8C+REVxz374NQP36Nc2SUfF4PPTs2QcLFy4zO6datQqsMfNFihTFjRv3zNoTYBgG1atXxLt3QSbvGZqmsXnzdjRsyF7uxJbjNHr0MBw7dtj47qYoCg4ODrhx457N4sDtRZomK0otBw/uQ9myRTF58jhMnjwOZcoUxZEjB02OEQgEOHnSFwsXLkP9+g3Rrl1HnDx5Dq9eBWHOnAVpooQ+efIIixfPx+rVK/HxY4jZ56NGjTFzRTEkDuhpMyU0vcia1RkA+0LX3d0jfYWxAblz54ZQaO4mRNMSuLllx4ULvrhy5ZJFdypr8PAogCtXbqNLl+4oXLgIGjZsjCNHTpkooYBh7m7cuBWPHr3CgQPH8OzZGyxevCLdlFBbsHLlUjPFQalU4sSJI2ZZOtMbmqZx5swFlC5d1rjLXqZMOfj4nIezswvq1m2AatVqsLpje3pWNtaK/RWVSoVChQonet1p0yayumtOm2bqht+uXUfs338EdevWR8GChdClS3dcvXrbohKq1+sxatRQVK1aHiNGDEabNoZaeV+/fk1iJP7Pvn27Wee2Xq/HjRtXre4npbx9G4ArVy6yKm9Pnjxi/S0UCgVOnTph/LeDgwNOnz6P4cNHIWfOXHBzy4ZmzVqyJk3j8bioWbN2ojK5urri6tU76NWrL/LnL4AqVaph27bdmDVrPiQSKSQSKYRCIRo3booVK9aanDtx4lSIxabXFYvFmDhxWqLX9Pd/xWppUavVuH6dPcN6Yvj6+iA4OMhEIVIo5LhwwRevX/sb23LnzoNy5SpYVEKPHDmIWrUqs7ori8ViTJkyg9WS6ObmhmPHTqNAgYLGmMEqVarh5EnfdH2e6fV6DBs2EAqF3GLIjsGakfLnewJyuRxHjx4y20xIyEHQtm0Hs7mRgFgsRt687ola6jMaS5asRLVqNcw2q3Q6He7fv4tWrbxSnGTQ2dkZY8ZMwJ49hzB16swUJ5Hi8/kWZfj1vR8T8wN9+vSAXC5DfHwc4uPjoFQqMWnSWAQHW45DJ2QcChUqjHLlyps9RwUCAfr3N9/cYRjGYuK2t28Tr9IQEPAGnz9/MtvslMvl2Lo1feq/L1v2HyZMmIIcOXJCKpWiUaMmOH/+6h+vhCaFXS2iL18GoGrV8mYPeZFIhHv3nqZbyvVfYRgGEyaMxqFD+6FSqcDl8sDlcrBixRq0a9fR5NgXL55h+vTJePToAZycMmPgwKEYMmS4TV/K6bUrNWfOdGzduuW3QubmFp6MQFJjolQqUb58CXz7FmV8qFAUBbFY/DMVNs/YtnPnflSrViNd5LYXqZ1DpUoVRnh4mFl7UmWJ0pvv3w0ucL97VFgiLOwLatashLi4OOM8oWka3bv3xty5CxM9N3v2zBYL10dExKQ4GcDOndswY8YUk/uQx+OhcuWqJjv9iTF4cD8cPXrIrJ0tWVNq+H1excT8QNeuHfDixTPweHyo1Sp07twNixYtNz4Tnz59jNatvViTSzVt2hw7duy1eD2GYdC3b3dcuXLZ6A5J0xK0aNEaq1ebF6O3FqVSiZCQD3BxcbE4d3bu3IYlSxYgKioSLi6umDJlRpJWleDgd6hdu6rZbj5FUfDyaoFt23YnS84JE0Zjx46tZu0ikRhz5ixAr159k+zDkpUWAHLmzIlZs+ZbNT8iIyOhUMhw6dJFhIZ+RIUKnmjUqEmK4u/Dw8Pw5s1r5M3rjnz5PODkRCM0NBxv3rzB0aMHcfasD7hcLjp16oaRI8fgy5dPqFu3eqKx4VKpFP7+wanKjp4gm6dnadbxyprVGU+fvkbbts3w6tVLyGQycLk8AAyKFi2Obt16oFOnbqybJ7YkLdYIEyeOxe7d2828iiQSCfbvP2p1vF1aoNPpULp0EXz9GmHSTtMSrF+/BU2bNgMAHDiwF5MnjzN71vB4fIwaNRYTJkxJN5n/RDKCRRQA4uPjMG7cKGMolIdHfixbttpiCEvRoh6sOTxcXV3x8qXlDYgHD+6hU6c2rJb2smXL4/x59k3cjDJOGZ00TVaUGk6fNo+xAwCGAU6fPoEBA4aku0y3b9/E4cMHjNYOvV4NjQYYM2Y46tdvaBLQXrJkaZw4cTbdZUwLpk6dBZFIjI0b10EulyN79uyYN29RhlNCDdl992HVqlWIjY1B48ZeGDFitMkCUiQSwcfnAoYM6Y/nz5+Coih4eOTH+/fBZpairl074OXLtzbLpvs3UrFiJZw5c8rsXuXx+Blqp85aBTSB7Nlz4OLFG5g/fzZu3bqOLFmyYMCAoejRo7cV18qCyEjzrMdOTplTlZHO23ujmfVZq9Xi/v27+Pbtm1VxY15eLeDre8Ysdk2j0aB69Vopli0pRowYjKdPH/+M7TQ8Pw8d2o+iRYujd+9+AAxx5lmzOkMuN7Vm0TSNPn36J9o/RVHw9t4FH5+TOHBgL7hcLjp37p7qmG+RSITChYskekzPnn3Qs2cfaDQaq2tmenjkR6lSpfHkySMT91+RSIQhQ9jLXCRGjhy5WOOleDye1e6S169fs6gsVqlS3epNivDwMLRu3RQajQYKhQISiQTu7h44ffq81RmndTodxowZjmPHDhtrMVeqVAVFixbB9u3bzLLQrlu3Cnfu3MSGDd4WN4EAg2K+du3mVCuhAODq6gapVGqmiFIUhUqVqkAoFOLECV+cP++LK1cuwtXVDZ06dc0wm3Mp5fv3b6yhLQCFoKC3UCqVcHJyQunSZa163ikUCuh0OptkI+dyudiz5yDatWsJvd5QpYBhGHTo0MnkWWC4pvn6UqfTJhnXS8g4SKUO2LhxK1Sq9VCplKwZkX9l+PDRWLJkvslGlVhMY8SIMYmeV7JkaVZLu0gkQosWrVIkO8E67OofqFarWBVRvV4HlcraRBW25fjxI6yxkjweD1euXLKDROkDl8vFhAlTEBQUiuDgz3j8+JWZm2lGYMaMKRg61LDgDQ5+B2/vjahXrwZiY2NMjsuXzwO+vpfx8mUgnj8PQMuWbaDXmz9kKIoy1gQksDNp0jSIxWITS79YTGP69Nk2LWRvD9zd82HLlh14/fo9Xr16jZ49+1i1sBo6dJSZpcNQR29YquSJj49nbedyuSaxeYnRpIkXKleuApo2uGZyOByIxWJMmzY7zRKgxMXF4vLli2YJhuRyQxbWBCiKwoEDx5A9ew5IpVI4ODhAKBRi1KhxSbrXAobv0qJFa+zbdwS7dx9E06bN0jUVfXLn+65d+1G9usHVUSKRIlOmTFixYk2KkkZ16tTFzGU2wdOjfv2GVvVhkJ+t7imVrIQtgwb1RWxsrPFdKZPJEBT0FmvWrLS6j/Xr1+DEiaNQqVSIjY2FUqnErVs3sGXLZqhUKrNFoVKpxLNnTxEaGorixUuajQWXy0XDhk1w69Z9o1UstXA4HMybt9gkizWXy4VEIjHG+PJ4PGPZp4kTp9pUCdVoNHj06AFevnyRLnW3E6hWrTqrJVelUmLy5PHo27cHWrXyQuXKZY1Ju9j4+vUrunRphwIFcqFQoTxo2LC2iRt5SilTphxevHiLVavWYfbsBbhy5TaWLFlp8iyoW7c+65iJxWI0bmyb+fEvExERAW/vjfjvv+V48eJ5ml9PKBQmqYQCwODBwzB06EhIJBKIxWJIJBIMHz6a1ZX3V0QiEZYuXQWxWGx8tojFNPLmdUfPnkl7m6QVKpUKc+fORJEi7nB3z45evbqyhgv+ydjVNdfP7yEaNapj5rokEolw6dLNJOO00oIJE0Zj585tZg8wqdQBK1eusZlbm7UQs///iYgIR4UKJc0sAiKRGBMnTk00WcnMmVOxYcMas3aRSISZM+ehb98BNpc3o2CLOfT2bQAWL56PBw/uIWfOnBg1ajwaNWIP3v9TSc44MQyDBQvmYPPm9eBwONDp9OjTpz9mzJiTKtf8KVPGY+fObSYWNMDgNvn4sb/VSpdOp8O5c2dx+vRxODpmQteuPZKd8Topfh0vQx3L0mYlrQDAxcUVr16ZukTp9Xrcv38X0dHR8PSsnO4ZQm2FoZSAAjRtOeOooX5hMH78+AEPj/yp2ry5c+cWBg7sg7i4ODCMHrlz58WOHXtRoIB1mablcjmKFy9gtqlB0zQOHDhmlctlWNgXeHqWYf2t8+TJi4cPX1glS+nSRRAW9sWqYxMQCASYPn02mjdvhdatvfD161dQlEFha9u2A1asWJMm8aq3bt3AypVL8fFjCCpU8MS4cROTld37VxiGseo+Pn/eF8OGDYBOpwfD6JE1qzP27DmEIkWKmhyXFmuE+Ph41KxZCREREcZM02x10jkcDvLmdcfdu0/MvpNer0e1ahUQEvLBaF1NSL5y//6zZHuwpISlSxdi7dr/oFIpodfrQdMSNG/eEqtXb7BLLcU/icTm1dmzPhg8uK8x2ZtAIEDHjl2wePGKDDOuKpUKUVGRcHZ2SVaCvpcvX2D79i2IiAhHgwaN0L5950Td69N6jd65c1vcvn3T6JXB4XDg5JQZfn6P/qh8NIm55tpNEVWrtdiz5wA2bVqLp0+fQKPRgKIoCIVCDBgwBFOnzrSHWLh//x5rgXKxmMarV4Hp7sJJFNH/c+GCLwYP7o+4uFizz2rXrotDh05YPPfWrRvo1q2jmUuOSCTCtWt34OFRwNbiZhjIHLKOlIyTXC5HePgXuLllt5gcJjlERUWhXr3q+PEjGgqFAjweH3w+H7t3H7DKYpie/DpeDMOgXLni+PzZtA4hl8tF+/adsHq1fbMr2xq9Xo9Vq5Zh3brVkMtlcHZ2wezZ89GmTXuzY219/+n1egQGvoVAIEC+fMlPJnft2hX06tUFFEUZlYoBA4Zg2rRZVp0fERGB8uVLsCYD8vDIj7t3n1jVj4dHDoseAJaQSqVYu3YzmjZtBoZhcO+eH8LCvqBcuQqptkQGBLyBj88pAAyaNWuZpLt2crl48RxmzJiCd++C4OzsgtGjx6Ffv0GsC/cPH96jZs3KZpv0zs7OePYswGQzI62e79++fcOKFYtx5sxpiMU0xGIRXr16abZJL5FIcOLEWbONruvXr6J3765mv7FYLMbkydMxaFDqvEes5dGjBzh0aD8oSo8mTVqiZs3aGUZZyshYmlfx8fEoUaIAa4x24cJFMHv2AtStWz89RMwQpOX66s2b12jUqLaZl6ZIJMb48ZPSvWxlasiQMaJv3rzGyJGDER8fD5FIDJrmo3Xr9ujRoxfKlCmX4n7lcjkOHz6AW7euI08ed/To0TtZLyhPz0ro128gtmzZAL1eDy6XB4ZhsGGDN4kjtDNubtlYY4MSSggkRrVqNVC3br3fEp3Q6Nmzz1+thFpDZGQk7t69DUfHTBYzzBLYoWnapvPH2dkZt27dx969u3Hr1g24u+dDnz794eGR32bXSAsoisLKlWvRs2cXqNUq6HQ6CIVCSKVSTJw41d7i2Zzlyxdj3br/jIuxiIhwjB49DFKp1GKaf1vB4XBSpSTVrl0Xz58H4OxZH8hkMtStWz9ZCq2bmxuKFCmCly9fmITWiERisxqOiVGjRi2cP+/LGp7DBofDgYODIxo0MNRFpSjKZklz/vtvOZYvX2y03P3333KMHj0eo0ePt0n/169fRb9+PX+pbRiJ+fNnQy6XY+TIsWbH79u3GzqdeYymUqnC1auX0nyOAYZapvPnL8H8+UsAAK1aNWV1deVwuIiOjjZrDwn5wPq+VigUFjObpgXly1dE+fIVyYasjbhx45rF+rwBAW/Qu3dXLF++2iy5JyH5vH796mcCNFOUSgUePXpoB4nSBrtZRB89eoQKFSoY/83lclG8eCmo1Sp8+vQRBQsWwvTpc1C9es0k+9LpdFi/fg22bNmAiIjwn65yOvD5CdaEg6hRI3lJOoKCAnHp0nmIxTS8vFqkqPi4LSAPz//DMAxq166CwMC3JokUxGIa585dQdGixRI9X6/Xw9f3DI4ePQSBQIDOnbv9E7ujic2hVauWY/nyRcYyJmKxCIcPn0KxYsXTU8QMwZ9+ryV4laTXRgLbeAUEvMHmzevx7l0QqlSpjr59B9jt2ZlWaDQaFCqUlzVmt2TJ0rh8+aZJ258+r9gIDg5C8+aNoFAooFarwePxUa5ceezff9RqNzhDLWZDaI6hDx44HC4oCmaJing8HsqVq4ANG7yRO3cem38XQ3Zj8+z9V6/eTrEL7q80blwXjx+bLxwdHBwQEBBids+OHDkE+/fvMTuepiVYtGgZOnXqamxLr/m1efMGzJ8/i9U68+pVIBwcHE3aHz9+iDZtmplZzmhaggULlqR7Lc+/8T5MSyyN19mzPhg+fCBrdtkEnJ1d8PJl4B9Vpi6lpOW8evbsCVq2bGJ2DwmFQgwbNhoTJ/45mZ8zpGvuw4cPUbFixUSPsbZ8yIgRg3Hy5DHWJEMAkC1bdjx9+vqPvCnIw9OUiIgIDBzYE48ePQKXy4NYLMLKlevQuHFTe4uWYbE0h+7cuYXOnduZuaFny5YNT5+++SPvl9Twp95r798HY+zYEfDzuw2KolC/fkMsW7Yarq6uaXrdP3W8Usu3b99QunQRVtfUTJkyITAw1KTtbx0nlUqFCxd88fnzJ5QrVxEVK3ome1MvPDwMmzatx6NHD1CoUGEMGjQMFKXBrFlz8OaNP4oVK47+/QehRIlSJhnrbcnatf9h4cK5xljIBPh8PiZOnIYRI1Lv/laoUB78+PHDrF0oFOLxY3+4uLhAJpPh9u0b4HA4iI6OxoQJo81KjwiFIty4cdfEgp1e80smk6Fx4zr4+DEECoUCFEVBJBJh+vQ56NdvoNnxDMOgdWsvPH780Kjk83g8uLq64c6dR2le1uZ3/tb7MK1IzDW3ePECZuuGX/l1Xv/tpOW8YhgGTZvWx4sXz0wSAUqlDvDze2R1tvSMQIZ0zbUGhUKBOXNmmO0w/0pY2BccP36EtYB7AjExMfjw4b3N3Nv0ej38/G7j27coVKxYyS71Tv9V3NzccO3aDbx58w5xcXHw8Mhv0U2EkDg7dnizvkzi4+Nx//49i3W6UoNarcaLF89A0xIUKVL0r7dGpzXx8XFo2rQ+oqO/G10cL126iGbNGsLP7xG5N9IAJycn0DTNqogWKZK4V8bfhFAoRPPmrVLVR7Zs2TFz5lyTNicnGrt3H0hVv8mByzVYYdl4/vwp2rZtDqFQhO7de6Fx46Ypemblz18Qjx49MGsXCoXInDkzzp71wZAh/cHlGjb/9Ho9smXLji9fPhs32GmaRocOnVMUF2wLJBIJzp27in37duPsWR+4uDijT5+BFt8TFEVh//6jWL58Efbt2wONRoOmTZth5MixWLBgDo4dOwyGYdCmTTtMmjTNzKJKyJhIpVJs2OCNQYP6sGa3Bgy/vaPj3/l76vV6REdHw8HBIVmZxlMCRVE4ePAYJk4ci1OnTkCn06JMmbJYtmz1H6WEJkWGN3kEBgYk+vnr1/5JugLp9Tqb7b69fx+MChVKonv3jhg1ahg8PUtj9uxp6ZpanWBYwBQsWIgstFNBTEwMaztFcSCXW59ERC6XY/782ShdughKliyEmTOnsCaU8vE5iaJFPdC+fSs0aVIXVaqUx7t3gSmWnwAcP34UCoXcJM5Oq9UgKuorrl27bEfJTFEqlYiMjPwrnpNcLheTJ0+HWPx7+R4xpk6dleJ+NRoNbt26gStXLppZwghpR7NmLUBR5kshrVaLCxfO4ebN67h06TwGD+6LyZPHpegaU6bMMCkBAxgUy1GjxiEqKhKDBvWFXC5DXFwc4uLiIJPJ8PnzZ4wZMwHly1dEjRq1sGbNRixevCJF17cVEokE/fsPwvHjPti8eUeSm5UJZaP8/d8hMPAjVqxYgz59umPHDm9ERUXi27co7Ny5DV5eDROtDUvIWDRt2gwPHrxAx45dzLKAi8VidO3aM1mZav8Ujh49hJIlC6F06cIoWDAPZs6caqHeru1wdMyEDRu88fFjBEJCInDu3FWUKFEyTa9pLT9+ROP792+JHiOXy3Hz5vVEj8kQiihNSyzuMiZlbXR3dzerXfcrXC4XJUqUQrZs2VMlI2Awk3fv3hFfvnxGfHw84uPjoFKpsH27N6lFSfjjaNWqLesGjVargadnZav60Ov1aNOmGTZuXIuwsC+IiAjH1q2b4eXV0OQBHRj4FkOHDkBcXCzi4+Mgl8vx/v07tGnTnCxAUkFAwBvW7IUajQbv3gWxnJG+KJVKjB49DAUL5kbZssVQunQRnDlz2t5ipZrevfth+fLVyJcvP8RiGmXLlsP+/UeT7UVw+/ZN1KtXA9mzZ0Hu3C7o3Lkt+vfvhWLF8uPEiaPG47RaLc6f98X69Wtw5cql3zYetDh+/Aj69euBsWNH4OnTxzb7nv8CuXPnwdy5CyEUiiASGf7weDzw+XyTrLVyuRz79u1GcHDy76saNWph27bdKFSoCDgcDtzcsmHmzHkYOnQkjh8/CoYxT9jE4VDInDkLfH0v4+jR02jevNUf70Fy9eolhIR8MFmzqdVqhIZ+xKVLF+woGSG5uLm5YfXqDZg5cx4cHBxA0zREIhE6duyCOXMW2Fs8m3PlykWMGTMckZFfoVaroVDIsWOHNyZMsE1Cs6TgcrkZRrkPCfkAL68GKF68AEqVKox69WogIOCN2XFHjx5CsWIe6NWrK0sv/8furrl8Ph99+vRHpkyZsHLlUpNFlVgsxvjxiQfjengUgKdnZdy752fmnkvTNFxd3eDtvdMmsgYGvkVoaKhZlj+5XI6tWzfBy6u5Ta5DIKQHbdt2wO7dO+Dv/wpyuQxcLhcCgQDz5y+xOkP0zZvXERDw2uTeMywsQnDhwjljYXm22pgMwyAuLg63b9/McKVJbEV09Hfw+QJIpdI06b9kyVKQSCRmFjQej4dixUqkyTWTw4gRg3Du3Fnj/AgPD8OQIf1w+PApeHpWsrN0qaNduw5o165Dis+/f/8eunRpZ5LbQKVSGcdqxIghKFWqDCQSKZo1a4CoqCio1SoIBELkyZMXp075QiKRol27Fnj69Ankchk4HA6OHDloMW4vLYiNjYG39yacOXMKTk6Z0b//4D8uZr9Xr75o2LAxfH0NG8pPnz7BwYP7zI6jKAo3b95IUabsevUaol69hmbt8fFxrJvpGo0m0YQwfyIvXjxnDQeRy2V4+fL5X1eb+l9gwIDB6NWrL8LCviBrVuc0e9fZm6VLF5rloVEoFNi61Rvjxk1N95hne6FWq9GsWUNERn416kIvXz5H8+YN8ejRS6OLfUDAG4wePdysBBUbdreIajQayOVyjBgxBqNHj4ejoyP4fD6yZMmKOXMWWvWi37FjH1q0aA2BQAgej4e8ed0xduxE7N9/FHfvPkHOnLksnhsREY6+fXsgd24X5MnjhqFDByA6+jvrsfHxcRZdQWNjzV0RCYSMjEAgwIkTZ7Fs2So0b94S3br1hI/PBXTr1tPqPp4/f8Yany2TyfDs2f/rCYaHh1lwYWHw7VtUSsTPsLx+7Y8NG9aiatXyKFGiIAoXzotOndrg69evNr9Wy5Zt4OSU2STrpkAgQP78BVCtWg2bXy85REVFwdf3jFk2UoVCgf/+W24nqTIOixbNtZhgDzBYOg8e3Ifx40fi8+dPkMniodFoIJPF4927QMyZMwOnTh03KqGAwUPBkFthOn78MC+pYWvi4+PRoEEtrFy5DC9ePMfNm9cxaFAfLFo0L82vbWty5MiJvn0Hom/fgciTJ68xk/ivcLlcZM5s26RJderUM3PbBQybSX9bPcY8efKaubQDBq+4pEqwETIuAoEAefO6/7VKKAB8/PiRtZ2iKIs6w9/I+fNnIZPFmxjkGIaBWq3G8eP/9+LZs2cntFoNWxdm2F0RFQqFcHNzA0VRGDlyLAICQuDv/w7+/u/Qs2cfq/qQSqVYt24zgoM/IyDgAx48eI6JE6eiSpVqiWb+VCqVaNSoDnx9faBSqaBUKnDixFE0a8Yer1CiRClwOOauMSKRGK1atbH+SxMIGQSBQIB27Tpi69bdWLp0FUqWLJ2s8/PkyQOhUGTW/vvCon79hqw7hhqNFpUq2T4pkj2Ii4tFixaN0ahRbcycOQVBQYHQaDTQaDS4ceMaWrVqYvMYSZFIhPPnr6FVq7aQSCRwdMyELl264/jxM3Z34wsL+2IxmcP79+/SWZqMx5s3rxP9XKvV4Pv3b7h06YLZJo5arcaJE0dx+vQJoxL6K3w+H3fu3LapvGzs27cb4eFhUKn+v9kgl8uxbt1/iIr6czeYOnbswrrpzOFw0aBBY5teq3z5ivDyagGalhjbaJpGx45dkixJ9qdh+J60ybqMw+GApsVo1qylHSUjEBKnVKkyrO08Hh+urm7pK4wdCQ0NZTU+yOVyhIR8MP47KirS6vhZuyuiHA4HHTt2Mf6by+UiUyanFJWOEAgEycq85uNzEjExP0wGS6PRICzsC65evcTa/8qVayEWi40vKZqm4e7ujl69+iVbXgLhT6dxYy+zhQVFURAKBWjZ8v+bM61bt0O+fPkhEv1/55+mafTp0w85cuS0mTw6nQ6XL1/A5s3rcfPm9XRNjjN+/GiTUgW/otVqERYWBj8/2ysHrq6uWL9+C96/D0NQUCiWLFmZITJQ5svnAY3G/EXE5XJRseKf7ZZrC5LK4k7TEjRo0MjiHNbp9HBwyMS64cAwhsQyac2lS+dZrboCgRCPH5tnif1TyJMnLzZt2gap1AEODg6QSqVwdXXDkSMnWa2XqYGiKKxduwmbN29Dixat0bp1O2zbtsfuiYnSApFIhLNnL8HTszJ4PB54PB4qVqwEH5+LNh9XQsqJi4tFSEhIhsjfoFQqM4QcU6aYJ6ijaRqzZs0yS9j0N1O6dBnWDWaJRIqyZcsb/92oUROr30F2U0Q5HA4yZ86MHTv22XQhmhzevHnNmp1QqVQhIIA9W2/z5q1w4cJ19O7dD02bNsf8+Utw/vy1v9olgUCwhFAoxJkzF1G+fEXw+QLw+XyUKlUGPj4XTe6JhOMmT56OsmXLo0iRYsiSJSsOHz6I3r274u3bxLNjW0NkZCSqVi2P/v17Ye7cmejRozPq16/JmsHX1qjVapw+fTLRxGkMw+Djx5A0lyWt+f79Gw4d2o89e/YkmjFPKpVi2LCRJpZwiqIgFosxcuTY9BA1QzNx4lSLi2+alqBy5SqoX78RqlevZbYxy+Px0LRpM/To0ctkcycBoVCYLq7Z2bPnYN001ut1cHFJ2zq2aU2TJl54/ToYu3YdwMGDx/Hs2RuUKVMuTa5FURQaNmwCb++d2LRpG+rWrW93j4a0Im9ed5w6dQ5v337E27chOH36vN1K0hD+z+fPn7Bz5zY0b94QRYt6oHTpkihWLD+OHj1kF3nu3LmF6tUrwt09G/Lly4HJk8cnWqYxrSlZsjROnjyLmjVrwdExEwoXLoJVq9Zh2LDhxmMYhsGHD+/x/n3wX5Ehno2qVaujWLESEIn+7wknFAqRK1cukxhvL68WKFq0OKsr/u9QjJ1GKyYmDnK51iS2Kb05dGg/Jk4cY6aMSiRSbNq0FQ0b2j9wnhRhNiexMbl27Qpmz56OoKC3yJEjJyZMmIq2bduns4QZi/SaQz9+RINhGGTOnCXR49au/e9n4L9BJoqiIJFIcPHideTPXzDF1+/ZszMuXjxv4uEgEAjQuXM3LF26KsnzUzNOMpkMBQrkSnTnViwW48yZSxkm9XpKOHLkIMaMGf6z9iIFrVaLFSvWoF27jqzHMwyDAwf2Yu3aVYiKikSlSlUwdeosFC5cJJ0ltx+Jzavz530xbdpEhIR8+OnOnge5c+dB27Yd0LJlG3C5XHz8GIImTepBLpdBJpNBIpEiS5YsOHfuKlxcXLBhwxosWDDXuCsvEAhw6NBxi65ktuTFi2do1qyhiVWUy+XC3d0Dd+48TJEyRd55yYOMl3WQcbLM0qULsXr1Smg0arNknGKxGPv3H0XVqtXTTR5//1do0qSuyXNFJBKhcWMvbN68Pd3ksIaEefXy5Qv069cDYWFfAABubtmwdeuuZIc7/QkoFAqsWrUMBw/ug06nQ+vW7TBu3EQ4OmYyOU6lUuHQof04ceIobty4ZrE/uymiGo3O7g8FhUKBSpXKIDLyq3EByePxkTt3bty5kzGKwf9rD8+4uFh8//4dOXLktOjuYGlMrl+/ih49Opk8vMRiMebNW4zu3XullcgZHrbx+vQpFHv27MSHD+9RtWp1tGvXMV2yvimVShQtms9s84fL5aJVq7bYsME7Rf3qdDrkzu3CGpPg6OiIoKBPSfaR2nutZs3KePPGn/UzkUiMatWqY//+o6yf/wl8+fIZlSuXNXM9FolEuHv3id08WzI61swrjUYDHo9nUXGTy+U4deo4AgPfonjxEvDyamGSyv/bt2+4c+cmpFIHVK9eM11dxY4ePYQJE0aDYQCdTgsPjwLYvfsAcuXKnaL+/rV3Xmoh42UdZJzYuXv3Djp2bMOazTiB2rXr4tChE+km0+DB/XD8+BEzpVgoFOLhwxdwc8uWbrIkhZMTjU+fvqJs2aJmtdkdHR3x5Il/hgiVsTcuLpYrMdg9RtSeiMVinDt3BfXrNwSXa6gb5uXVHGfOXEq1EmrIXDgDRYt6IH/+nBg8uB/Cw8NsJHn6odPpcOPGNRw9esgkENnWKJVKDBs2EEWL5ketWpVRtKgHdu1K3s7X3LkzWNNrL1w45691k0gJd+/eQfXqFbF27SocO3YYM2ZMRq1aVdIl81tIyAfWxbZOp8ODB/dT3C/DMGYvrQS02vSJL1m5cg0kEolRCeBwOKAoCq6ubhg5cgx27tyfLnKkFadPn2C9jxiGwenTJ9JfoL8IPp+fqPWQpml06tQV06fPRps27c3qyWXNmhXNm7dCnTr10j1eqW3bDnj9+j2OHj2FK1du4erV2ylWQgkEQvqyd++uJEtshIayZ4xNKwICXrO+zwUCYYYMb/HxOcm6Ca7VanHy5HE7SPRnYfc6ovYmR46c2L37oHGBZau4jC5d2uHhwwfGbIInTx7DrVs34Of3yOoajfbmw4f3aN3aCzExP8AwhiyOHTt2wdKlq2wevzJ27EicPn0carUKCWF206dPRvbs2a3OUhgUFMja/uPHD8hk8X/MuKclDMNgyJD+JvV65XI5wsI+Y9WqZZg9O20LUbu6uprVE00gb96Up+/n8XioVq0Gbt++afIC4/F4aNLEK8X9Jofy5Svi+vW78PbehICAN6hY0RO9evWDs7Nzulw/rVGp1Kyuxzqdzq6xOwT7IxAITBJVEAiEPwOFQpHoRj2Px0PlylXTUSKgTJlyePPmNUu2cBXy509+Dd+0JiIinDVJoUKh+CMNUOnNP20R/RWKomymXD19+hhPnjwySWmv1WoRFxeLw4cP2OQa6UGvXl0QFvYF8fHxkMnioVKpcOTIQZsHr8fGxuDUqWMs9QblWLVqmdX95M6dh7WdpiUmqfH/VYKD36FFi8b49CnU7DO1Wg0fn1NpLkPmzFnQrFlLk0B3wOCdMHr0+FT1vXz5amTOnMXoYiyRSODmlg2zZs1PcZ9fvnzG+vVrsGzZIjx9+jjJ4/PkyYs5cxbg4MFjGDdu0h+thOp0OuzcuQ316tVAjRqeCA//whrTz+PxMkQ8PYFAIBCSR8uWbSyujzgcDsRiMUaNGpeuMg0bNsrM60MsFqNLl+7IkiVruspiDRUqeJqtaQCDJwvJEJ80RBFNA/z9X7G2y+VyPH78KJ2lSRnv3wcjODjYzD1CLpdj69bNNr1WVFQUuFx24/znz5+t7mfSpOlmWSjFYhqjRo1LUTmgv4no6O9o0qQu7t+/a/GY9Eqfv3LlWqN7oUgkRtaszli5ci2qV6+Zqn7d3fPhwYPnmDt3EQYOHIrFi1fAz+8x3NxSVuPr1KnjqFy5LBYsmI1lyxahZcumGDt2xD/j5j1oUF/MmDEFL148Q0DAG+zevRNSqQPEYjE4HI5xkdK//yAUKVLU3uISCAQCIZk0bdoM1avXMCqjCSElmTJlQosWrXHx4g3kzeuerjJ5eOSHj89F1KxZCyKRGG5u2TB+/BQsXGi9YSI9qVq1OsqUKWeSwVwkEqNEiVKoUaOWHSX7M/jnXXPTAnf3fKzWVZFI9Ecs2F68eIYhQ/pbjBtgK3mTGnLnzgMej614OAeenpWt7sfLqzlWrFiD2bOn4+vXCDg6OmLUqHEYPHh40if/oWg0GkREhCNLlqyJJhvav38vlEqlRSVKLKbTrRauSCTCqlXrMH/+EsTGxsDV1c1micGkUqlNElPFxcVi+PBBJlZ6hUKOo0cPoXnzVqhdu26qr5GR8fd/hQsXfE1irlUqJXg8LkaNGoeoqCgIhTx4ebVC+fIV7SgpgUAgEFIKl8vFrl0HcPXqJfj6nkGmTE7o2LELPD3L2jW5U/HiJXDkyGm7XT85UBSFAweOYdu2Ldi3bzcABp06dUW/foP+eSOINRBFNA2oUqUacuXKg+DgIGM8HEVREAgE6NSpm52lM0WpVGLnzq04evQQhEIRWrRohYUL5yI+Pp71eKFQhFat2thUBj6fj2nTZmPmzKnGzG0cDgc0TWPChCnJ6qtt2w5o06Y9VCoVhELhX1uLDQC8vTdi0aJ50Gi0YBgG3bv3xOzZC1jdJ1++fM5aeB4wjH/jxk3Ru3f6KKIJSCQSqwsepzfXrl1ltdLL5XIcPXr4r1dEHzy4x9ouk8nw5ctnLF26imShJBAIhN/Q6XTQ6/XpnjQsNXA4HNSr1xD16jW0tyh/LEKhEIMHD8PgwcPsLcofB1HV0wCKonDixFk0bNgYfD4fXC4X5cqVh4/PRWTNmnH827VaLVq1aoL58+fg6dMnuHfPDzNnTjVJZPMrIpEIefO6Y8CAwTaXpVevvti8eTvKl6+A7NlzoHnzVrhw4ToKFEh+XUmKoiASif5qJfTEiaOYO3cWYmNjoVDIoVQqsGfPTsydO4P1+FKlyrC63goEAqxfvwWbNm3LEOWKMgqWdjEpigKH8/fOqwTc3LKxKuJCoRA5c5KMqAQCgfArP35EY+DAPsiTxxW5c7ugWbOGCAh4Y2+xCIQMzz9dRzQ9UKsNmSZTGn9nK6tDdPR3fPjwHrlz5zUmUDl9+iRGjBhklastj8dD9+69MXv2fNag7PSEWGKAGjU8WV9yYrEYgYGhEAgExjYnJxofPnxG5crl8ONHtDHuVygUomTJ0jhz5uJfrbRby6/zKj4+HiVKFDDblBGLaezdeyjV8awZHY1GgzJliiIqKtLEnZumady79xRubtnIfWglZJySBxmv5EHGyzrScpwYhkH9+jUREPAa6p9p/ymKgoODA+7de5ahDBDWQuaVdZBxsg5SR9SOCASCdEsCw4Zer8fkyeNQqlRhtGvXEmXLFsXQoQOh0Whw/foVq+M9eTweBgwYZHcllGDAUkpwnU6H+Pg4s3Ynp8w4d+4K6tatDx6PcJu6xQAASklJREFUD5qm0bFjFxw6dIIooSxIpVJs2rQdIpEYYjENPl8AkUiM7t17oVq1GvYWL83h8/k4dcoXhQoVhkgkBk3TyJYtO/bvP5qhiokTCASCvXnw4D6Cg4OMSihgUE7Vag327dtlR8kIhIwPiRH9y1m37j/s27cHKpXKWOvv9OkTcHFxRrZs2SEQCEwenglQFGW0hIhEItSuXQ/58yffTZaQNpQuXQY3blw3a3d0zAQnp8ys57i758O+fUfSWrS/hkaNmuDx41c4ffoE5HI56tVr8EckG7MV+fMXxM2b9/Hhw3uo1WoUKFCQJF4gEAiE3wgODgKbb6FSqYC/v3/6C2Qler0et27dQGjoR5QuXRYlSpS0t0h2IT4+Djt3bsPZsz7ImjUr+vcfTLLdpiNEEf3L2bRpvTEBUAJKpQI7dmzDrVv3sWbNSrNznJwyw9OzEm7evA6xmEaPHr0xbtwkm8gTHByEs2fPgMvlwsurOfLkyWuTfv81pk2bjZYtm0Kp/H8xarFYjDlzFhBlwYY4OzunexKnjIa7ez57i0AgEAgZlqJFiwEw10TFYhply5ZLf4GsIDw8DC1bNkFk5Ffo9XowDINq1Wpix469JqE9fzvx8fFo0KAWPn/+ZMySf+PGNUyYMAVDhoyws3T/BmTF+pcTE/ODtV2hkMPNLRt27NiHLFmyQCqVgqZpuLvnw6lT57BnzyGEhETgzZv3mDJlhk0eTP/9txy1a1fFwoVzMX/+LFSrVhHbt3unut9/kTJlysHH5zzq1m0AFxdXlC9fEdu370G7dh3tLRqBQCAQCP8MpUuXRZky5SAU/j90icPhQCKRoFOnLnaUzDJDhvTHx48hiI+Ph1wuh0KhwK1b17F+/Wp7i5au7N27E1++fDYp1SaXy7Fo0TyL62eCbSGK6F9O2bIVWNsLFSoCPp+POnXq4eXLIBw/fga+vldw797TNHE/fPs2AMuXL4ZSqYRGo4ZarYZKpcSMGVPw5ctnm1/vX6BkydLYv/8IXr0Kgq/vZdSt2yDNr8kwDE6dOo42bZqhceO62Lx5vcWyMAQCgUAg/Avs23cEvXr1RaZMThCLxWjatDkuXLgGR8dM9hbNjNjYGNy75wedTmfSrlQqsXv3DvsIZSfOnTvLuobh8wV4/PiRHST69yCuuX858+YtRIsWjaFSqaDT6cDhcCAUirBo0TLjMTweD6VLl01TOU6fPmmsqforFAX4+vqgb9+BaXp9NuRyOb5//wY3t2x/VM0vezJx4hgcOrTfmE329etXOHLkEM6cuUjGkEAgEAj/JDRNY+7chZg7d6G9RUkStVpjMUlhQi6RfwVXVzeTnCgJ6PW6PzLb8Z8IsYj+BSiVBstigQK5kSuXMzp1aovg4CAAhvqRly7dRNu2HVCkSFG0aNEaZ85ctEvmz4ySnVWr1WLy5PEoUsQd1apVRNGi+eDtvdHeYmV43r8Pxv79e01KmigUCgQGBuDMmVN2lIxAIBAIBII1ODs7s8b+8/l8NGvWwg4S2Y++fQdCJDKtbMHhcJAtWw6ULFnaTlL9WxBF9C+gV68u2L7dG7GxMVCr1bh69RIaNaqLqKgoAECBAgWxdu0m3LhxD5s3b7dLZrRmzVqAxzM3wDMMg8aNvdJVltmzp2Pfvl1QKpVQKOSIjY3F3LmzcOrU8XSV40/j3j0/cLlcs3aZTIYrVy7bQSICgUAgEAjJZe3aTZBKpca4VpqWIHv2HBg/foqdJUtfPD0rYc6c+RCLaTg4OIKmaRQoUBAHDx7LMMaTvx2iiP7hBAS8wZ07t6FS/T/QmmEYKJVK7Nq1zY6SmVK4cBGMHj0BIpEIfD4fAoEAQqEIs2cvQM6cudJNDpVKhV27tpnFBCgUcixbtjjd5PgTyZo1K2tGXj6fj2zZSG1JAoFAIBDsCcMw0Ov1SR5Xpkw53L37FGPHTkTHjl2wYMES3Lhx7590R+3Zsy/8/d9h9+4DOHv2Mm7evE8qOqQjJEb0D+ft2zeslkaVSoknTzJWoPXo0ePQokVLnD17BhwOB82bt0z3mz02NtbiQzo8/Eu6yvKnUbt2PYhEIshk8SbxFDweD126dLejZAQCgUAg/Lv8+BGNSZPG4fTpE9DpdKhevSaWLl2FfPk8LJ7j6uqKUaPGpqOUGReJRIKqVavbW4x/EqKI/uF4eBSATqc1axcKhShRopQdJEqc/PkLYvjwUXa7ftasWSGVOrAG5JcunTHrfWUU+Hw+jh8/g27dOiAyMhIcDgUul4t16zaTWpMEAoFAINgBhmHQqpUXAgPfGpNC3rp1A40b18WDB88yZOZeAiGBVLvmfvv2DWPHjkXlypVRpUoVTJ48GbGxsbaQjWAFxYuXQNmy5SAUCk3aBQIBevXqayepMi4cDgezZ8+HWPz/4HSKoiAW05g+fZb9BPtDKFy4CO7ff4azZy/hyJFT8PcPRsOGTewtFoFAIBAI/yR+frcREvIeGo3a2KbX66FUKnHw4H47SkYgJE2qFdGxY8ciIiIC27dvx+bNm/H27VtMnTrVFrIRrGTPnsNo164jhEIhKIqCp2dlnD59AW5uJG6PjQ4dOmPbtt0oX74CXFxcUa9eQ/j4XECpUmXsLdofAUVRKFasOMqVq0BKthAIBAKBYEeCggJZQ44UCjlev35lB4kIBOtJlWtueHg4/Pz84OvrCw8Pgx/61KlT0bVrVygUChOrEyHtkEqlWLlyLVasWAO9Xs+a2ZRgSr16DVGvXkN7i0EgEAgEAoGQYgoXLsqaSJCmaZQqRUqQEDI2qbKISqVSbN68Ge7u7sY2iqKg1+uhVqstn0hIEyiKIkoogUAgEAgEwj+Cp2clFCxY2CREi8PhQCKRol27jnaUjEBImlQrorVq1TLZidm1axcKFSqETJlIcDSBQCAQCAQCgZBWUBSFY8dOo1Onrj9rgwrRuLEXLly4BqnUId3kePnyBWbPno5p0ybi3r27Jtn1CQRLUEwSM0WlUiE8PJz1M0MGUqnx3zt27MDixYuxdetWVK1aNdEL6/V66HRkkiYFl8uBTpd0Tah/CTImyYOMl3WQcUoeZLysg4xT8iDjlTzIeFlHRhmn2NhYLF68CAcPHgCXy0WvXr0xZsxYs4ST9ia547V8+TLMmTMbarUaer0eYrEY3bp1x9q169JQSvuTUeZVRofPt+ytmaQi+vDhQ3Tt2pX1s4ULF6JNmzYAgG3btmHJkiWYPn26xeN/RaPR4ccPeZLH/es4OdFknH6DjEnyIONlHWSckgcZL+sg45Q8yHglDzJe1pERxkmj0aBevRp4//6dsYScSCRG+fIVcezYaVAUZVf5fiU54/X58ydUrlzWrCweTdM4fPgkKlaslBYiZggywrz6E3BxsWyZTzJZUYUKFRAQEJDoMatXr8a6deswbdo0q5RQAoFAIBAIBALhX+HcubMIDQ0xUdiUSgWePHmEBw/uw9Pzz1TYLl26wJosSaFQ4OzZ03+1IkpIPaku37Jz506sX78ec+bMQffu3W0hE4FAIBAIBAKB8Nfw8OF9yGQys3atVoMnTx7aQSLbIBAIWBVRLpcLoVBkB4lsg1qtxtu3AYiKirK3KH81qVJEv3z5gmXLlqFz586oW7cuIiMjjX90Op2tZCQQCAQCgUAgEP5Y8uTJC7GYNmsXCATImTO3HSSyDY0bN2WNk+TxeGjbtoMdJEo9+/fvQbFiHmjUqDbKli2Krl3bIy4u1t5i/ZWkShG9cuUK1Go19u3bh+rVq5v8+fTpk61kJBAIBAKBQCAQ/ljatm0PHs80Io7D4YCmJWjYsLGdpEo9mTNnwcaNWyEWiyGRSEDTNIRCIWbMmIuCBQvZW7xkc+vWDUyaNA6xsbGQyWRQqVS4fv0q+vfvZW/R/kqSjBFNjG7duqFbt262koVAIBAIBAKBQPjrcHLKjJMnfTF4cF98+PAeDMOgePGS2LRpGwQCgb3FSxVNmzbD8+cBOH/eF1qtFvXqNYSbm5u9xUoRa9eugkJhmoBIrVbj9u2bCA8PQ7Zs2e0k2d9JqhRRAoFAIBAIBAKBkDQlSpTEzZv3ERERDi6XB2dnZ3uLZDMyZXJChw6d7S1Gqvn0KZS1XSAQ4OvXCKKI2phUJysiEAgEAoFAIBAI1uHmlu2vUkL/JmrUqA0ej2/WrtPpUKDAn+dqnNEhiiiBQCAQCAQCgUD45xkxYjSkUqlJPK9YTGPSpOmgafNkU4TUQRRRAoFAIBAIBAKB8M+TPXsOXL16G507d0PevO7w9KyMzZu3Y9CgofYW7a+ExIgSCAQCgUAgEAgEAoCcOXNh+fLV9hbjn4BYRAkEAoFAIBAIBAKBkK4QRZRAIBAIBAKBQCAQCOkKUUQJhAzMtWtX0LVrezRqVAerVi1HXFysvUUiEAgEAoFAIBBSDYkRJRAyKGvX/odlyxZCLjcUVn79+hX27duNK1duQip1sLN0BAKBQCAQCARCyiEWUQIhAxIT8wNLlsw3KqEAoFQqER4ehp07t9tRMgKBQCAQCAQCIfUQRZRAyIA8ffoEfL7ArF2pVODixXN2kIhAIBAIBAKBQLAdRBElEDIgWbJkhU6nM2unKApubtnsIBGBQCAQCAQCgWA7iCJKIGRASpQoiVy5coHL5Zq0i0Ri9Os30E5SEQgEAoFAIBAItoEoogRCBoSiKBw8eByFCxeBWEzDwcEBNE1j3rxFqFixkr3FIxAIBAKBQCAQUgXJmksgZFBy5syFa9f88PZtAKKjo1GyZCnQNG1vsQgEAoFAIBAIhFRDFFECIYNTqFBhe4tAIBAIBAKBQCDYFOKaSyAQCAQCgUAgEAiEdIUoogQCgUAgEAgEAoFASFeIIkogEAgEAoFAIBAIhHSFKKIEAoFAIBAIBAKBQEhXiCJKIBAIBAKBQCAQCIR0hSiiBAKBQCAQCAQCgUBIV4giSiAQCAQCgUAgEAiEdIUoogQCgUAgEAgEAoFASFeIIkogEAgEAoFAIBAIhHSFKKIEAoFAIBAIBAKBQEhXiCJKIBAIBAKBQCAQCIR0hSiiBAKBQCAQCAQCgUBIV4giSiAQCAQCgUAgEAiEdIUoogQCgUAgEAgEAoFASFeIIkogEAgEAoFAIBAIhHSFKKIEAoFAIBAIBAKBQEhXiCJKIPyvvfuOiuJ6/zj+RopURQUsFFvsJXajYkVRFCzf2HuLPRo1scWGXSOWoEHsJUajxtiw9xjLz2jsBRTpgqJipMOyvz8IkyAYdpUmPK9zOEfvbLnzYWaZZ+fOHSGEEEIIIUS2kkI0C8THx9OvX3euXr2itHl6rqZduxYMHtyXgAB/pT0uLo5+/boTGRmZE139aHTt6sLw4YNQq9Wp2q9f/wN7+3okJiZq/ZpPn4Zgb1/vnT9v3rzJrO5/sMTEROzt63H9+h853RWRjuPHj6bZfqZOnags/9D9f8yYYe/cTnft+inL1ut9zJkzg/nzZ+d0N4QQQgiRy+nldAfymri4OFxdp/Pkia/S5uPjzd69u3B39+Tgwf2sWbOKBQu+A2D//r04ODhiamqaU13+aNy9e5uDB/fRv3+fTH3dNWs2UbJkyTTt8jsRmvLz86VZs5ZMnDhZaTMwKAhk3v7fvXsv+vQZkKbdxES2UyGEEEJ8fOSMaCZ68sSX4cMHERwclKo9IMCPsmXLU7FiZeztmxEQ4AdAXFwsBw78SrduPXOgtx+fEiVKsmbNKl69epWpr2tubk6xYhZpfnR0dDL1fUTe5ef3hPLlP0m1/ZiZmQGZt/8bGhqlu50aGhpm1WoJIYQQQmQZKUQz0c2bf9KgQUM8PTelai9evAQhIcFERkby8OF9rKxKALB37x7atGn70Z3RUKvVnDhxlD59utGpkxObN68nLi4uy9+3R48+GBsb4+a29J2P+euvv1i8eD4uLo44OjbH1XU6f/31+r3f89ChfbRo8Rn+/n4AhIQE07q1PUeOHFL+P2nSV7Rp04wuXdqzdetG5bnPnoUxdepEWre253//64CHhzsJCQnK8ps3b/DFF/1p1aoJfft25+hRr1TvvWnTOpyd2+Ds3Fp5vxTx8fGsXOmGs3Nr2rd3YMaMKbx8+QL4Z8jx2bOn6NGjM5991oDJk8cTERGhPP/q1SsMGdIPB4fk975w4bzG/RJp+fn5YmdXOt1l2bH/v3r1ig4dHFi3zkNp+/rrsYwdOwK1Wo1KpWLDBk86d3bC0bE5U6ZMIDw8HEjenzdvXk/nzk60bducCRO+JDAwQHmdyMhI5s2bRdu2zenYsS2LF88nOjpKWX7z5p8MHNibVq2aMGvWVOLiYlP17fz5s/Tt2x0HhyYMHtyHy5cvKsvGjBnG5s3rmTDhSxwcmtC/fw8uXbqgLH/9OgJX1+m0bdscZ+c2rFzppgzDz6hfQgghhMjdpBDNRJ07f86oUePSnKGoXr0mtWvXoX37Vuza9RNDhw4nNjYWL6/9H+XZ0HnzZvHFFwM5ceIYly79zqxZ0+nc2SlVkZUVDA0NGTduIvv37+PWrRvpPmbatK959OghixcvY+XKHwgI8Gfu3Jnv/Z7Ozp2pWbMWK1YkD6VcsmQ+tWvXxcnJmfj4eCZMGIOurh6enhuZMmUGP/20lePHj6BWq5k27RvMzAqxYcOPzJo1j4sXf2PNmlUAvHgRzjffjKNNGye2bt3JoEFDWbHiO6Ug3L9/L7t27WDq1JksX74aL68Dqfrl6bmaO3dusXjxClat8kStTmLSpPGprqHdtm0zM2fOY9WqH7h37y4//bQVAH9/P775ZhxNmjRl8+YddOzYhZkzpxAcHJRhvz4Gt8td5abVlTQ/t8tdzZL3S0hIIDg4iIsXL9CzZxe6d++Eh4c78fHxQPbs/0WKFGHMmPHs2LGN4OAgjh07zM2bfzJlygx0dHTYuHEtBw78yuTJ37Ju3Rbi4uKYNy95v/jll585evQwM2bMYe3aLdjY2DBu3EhiY5MLyoULXXn9OoLVq9ezZMkKAgP9lWtAX716xaRJX9GgQUM2b96OnV0Zzp07o/TLx8ebuXNn0rfvALZu/ZmOHbswbdo3+Pg8VB6zbdsmWrd2ZMOGH7GzK8OiRfNQqVQATJv2DaGhIaxcuYaFC5dy/vwZtm/fkmG/hBBCCJH7Zeo1oj/88AN79uzh9OnTmfmyAHjdDePAndBMf93/0rF6CTpUK54pr+XqupDx4yMwNTVFT0+P7du30LZte2JiYpg8eQKhoaH07z8IF5fOmfJ+WSUkJJi1az1SnQGNiYnmwYP7HDq0ny5dumbp+zdt2oLmzVvg5raYDRu2pVr26JEPN25c58cfd1OmTFkAZs2aS+/eXfH1fUy5cuXTfc2BA3ulGYbbuHFTXF0XADBp0rf0798TV9fpPHhwn23bfgbgjz/+j/Dw56xfvw1TU1PKlfuECRMmY2hoxLVrVwkJCcbTcxO6urqULl2GCRMmM2HCGEaO/JK9e3dTp05dunfvBYCNjS3+/n7s3r0De/tmHDy4j65de9CkSdO/+zCN/v2Ti5bY2Fj27t2Fp+cmKlasDMCMGXNo396BW7duYGWVvM0OGvQF1apVx9zcGEfHdjx4cA+AQ4f2U6VKNQYPHgaAra0d0dFRxMTEcPjwwf/s18cgKTJJq/YPFRgYgEqlwtDQiLlzFxMSEsTKlW5ER0cr14xmxv7/009b2b17R5r2X389gqmpKU5Ozhw7dhg3t0V4ez9gxIgvKVXKGrVazf79exk+fDSNGtkD8PXXUzl0aD9JSUn89NM2xo37mrp16wMwfvwkLl26yNmzp6hR41POnz+Ll9dJChUqDMD06a507erC06dPOX36BIUKmTNy5Fh0dHQYMmQ4Fy/+c0Zz585tdOjQkXbtOgBgbd2Ve/fusmfPz0ydmlwIf/ZZY9q3dwFg4MChDBzYi+fPnxMdHcXNm3+yc+ev2NjYKv1+8SKc4OCgd/YrLCyU4sVLvPfvUwghhBDZI9MK0cePH+Ph4YGlpWVmvWSeY25uDkB0dDSHDx9k3bqtrFq1nMqVqzJr1nz69u1Gw4aNlEIiN7p48QJ6evpphuJGRUVx/PjRLC9EAaZOnUbHjs7s2bNTKcQA/P2fYGxsohShAHZ2ZTAzK4S//5N3FqKLFy9Pc+BqZGSk/NvGxpb+/Qexfv0aJkyYjKWlFZA8HNPa2jbVRDOOjk5A8lmmyMg3tGvXQlmmVqtJSEggLCwUf/8nXL58kTZtmirLVSoV5uZFlNfu12+QsqxcuU+UyW9CQoJISEhg9OgvUvU5Pj6ewEB/ZfuxtrZRlhkbmyhDGv38fKlUqUqq5w4cOBSAzZvX/We/RFrlypXHy+skhQubA1ChQkXUajWzZ3/LuHET0dNL/pj90P3fxaULPXr0TtNubGys/Pubb6bRt283KlSoxP/+1w2AiIgIIiJeUanSP/uKtbUNw4ePJjo6mmfPwpgzZzoFCvwzQCZ5WwrAzKwQarWazz93TvO+/v5+f18bWz7VFzmVK1dRzgb7+fnh6/sIL6/9yvLExESqVKmWqi8pTExMAFCpEvHze4KJiYlShAI0atQEgN9//+2d/QoMDJBCVAghhPgIZEohmpSUxLfffkuNGjUIDc2as5YdqhXPtLOTOW3Pnp04OTljbGzM3bu3GTlyLBYWFtjZlf77GrLcu55FihRJdxIfPT09pUDLatbW1vTvP5iNG9fx9ddTlHYDA4N0H5+UpCIp6d1nw4oXL5HqYDc9jx75oKury/XrfygH+Hp6+u98vEqlwsbGliVLVqRZZmVVHJVKRevWbZUCMMW/iwFIfasaXV1d5bUB3N3Xpplt1dy8CG/e/AWAvn7q/qUM282o3xn3S7wtpQhNUbp0WRITE4mIiMDCwiLVsvfd/83MzDLcTv39/UhKSiIgwI8XL8KxsLBMsx38W8q25Oq6gDJlyqVaZmpqxq1bNzAyMmLTprS3iClXzhYvr6Npbqmkp6enFKIqlYqePfvSoUPHVI/5d5/S2x7VajX6+u/+86RSqd7Zr2LFLNJ5hhBCCCFym0w5uty6dSuGhoZ06dIlM14uT4uKiuToUS8+/7zH3y06qNXJRZJKpeKtY7pcp1mzlunO0qmvr0+/fgOzrR+9e/fHwsIi1eQspUuXITo6Cj+/J0rbkye+REVFvXMiGU1cuHCeixcvsGTJCn7//Ty//XYWAFtbW0JCgoiK+ucekBs2eDJ//mxsbUvz7FkYhQoVxsbGFhsbW168eMGaNatISkrC1rY0gYEByjIbG1uuXLnEwYP7AChbtjz3799TXjcoKJCYmGgg+QySrq4ur19HKM81Ny+Cu/syQkOfZrg+tra2qa7RA/jqq1EcOPBrhv0SaZ07dxoXF8dU10j7+DzE1NSMYsWKpXpsVu7/0dHRuLktYsiQ4VSqVIVly5YAybchMjcvgrf3P7/zwMAAXFwcUauTKFKkKOHh4crvu2TJUnh6rubRI2/s7EoTExNDUpJKWQ7g7r6MqKgoypUrj4+Pd6r7+P77fezsShMSEpxqezp27DDnz5/NcH1sbOyIiopKNQv5oUP7GDt2RIb9EkIIIUTu98GFaGBgIGvWrGHOnDmZ0Z88b9euHTg7d1KGflauXIWTJ49x584t/P2fpBpqmhvp6+vzyy8Hsba2wcTEBDMzM0xMTFi58gcqVKiYrf2YMGEyT5+GKG12dmVo3Lgp8+fP4v79u9y/f5f582dTs2YtKlSo9M7XioiI4MWL8DQ/8fHxREdHsWzZYvr2HUDDho3o1asfy5YtISoqkgYNkodRLl48Hz+/J1y69Du7d+/ks88a06DBZ5QqZc2cOdPx8fH+e2KhuRQoUICCBQvyv/91w8fHmzVrVhEYGMCZMyfx8PheORv2+efd+eWXXZw+fRJf30csXjxPOStpbGyCi0tnli9fwrVrV/H392P+/Fk8fvwIW9v/PmMG0LlzV+7du8O2bZsJCgpk164d3Llzi7p162fYL5FWrVp1UKvVLFkyn4AAfy5evMDq1Svp3btfmtEDH7L/x8bGpLudvnnzBoB16zwwMDCgV69+jB8/iYsXf+PcueTr9bt168nGjWu5evUyfn5PWLZsMRUrVqZQocL06NGb9evXcP78WYKCAnFzW8TVq1coU6YsZcqUpWHDxsydO5O7d+/g4+PNvHmzePnyJZaWljg4OBIfH8eKFd8REODHjz9u5u7d20qfu3fvzdmzp/j55+0EBQWyb98etm7dmGo47ruULVuOevUasGjRXHx8vLl58wabN2+gQYPP/rNfb5+BFkIIIUTupKN+e1zVW+Li4t453LZYsWKMHj2apk2bMnToUHbv3o2Hh4dGkxUlJSWhUuXy038foHr1qqxbt55GjRorbW/evKFXrx7s3v2LciD69GkI48ePJzAwgNGjx9C7d59Ur6OrWwCVKmsmWfkQarWa69evERUVRYMGDbP8XoaOjq0ZNmwEXbt2TZXJpEnfcPiwFzdu3EJPT4/XryNYsGA+Z8+eRVdXl5YtWzFp0mQKFy6c5jWDg4Np27bNO99z1aofuHjxAufP/8b+/QcoWLAgsbGxdOrkgr19U2bMmImfnx/z5s3lzz+vU7RoUfr160///gMACAoKYuHCBfzf/12hYMGCODi0ZtKkycp1cJcvX2L58uV4ez/EwsKC3r37MGjQYOX9t27dwqZNG4mLi+OLL4axdq0nK1e606BBA2JjY3FzW8rRo0eIi4ujVq3aTJv2LWXKlFHW6/DhI9jZlUZXtwDff/89ly9fZtu2HwE4f/4cy5cvw9/fn7JlyzJx4jc0btxYo37ldheKnUf1RpWmXddMF/sX755w6UP2tfv377FkyRLu3LmNmZkZ3bp1Z8SIkakK0ffZ/1MMHDiAP/5If9bfNm0cGTx4CH379sbDw1P5PS5b5sbBgwc4cOAQRkZGrFixnP3795GQkECTJk2YNm06xYoVQ6VS4eHxA3v3/sJff/1F5cpVmDx5MjVq1ASSZ8ZdtGgB586dQ0dHh0aNGjFt2rcUL14clSqJe/fuMXeuK97e3tSrV18pBOfPT57s68iRI3h4rCYwMBBra2uGDRtBx44dlfWqU6cOY8eOA0iz7YaHP2fevHn8/vsFjI2N6dy5C2PHjkNXV/ed/bKwyF3zFOTWz/DcSvLSjuSlGclJO5KXZiQnzejr675zWYaF6B9//EGfPukfHM2dO5edO3eye/dudHV1tSpEExJUREREZ/i4/M7c3Fhyeotkoh3JSzOSk3YkL81ITtqRvLQjeWlGctKO5KUZyUkzlpZm71yW4WRF9erV4+HDh+ku69ev39/fgtcDkmdDTEhIoHbt2nh5eVGqVKn37LIQQgghhBBCiLzqg2bNXbp0qXLTc4CjR4+yfft2tm3bhpVV9sygKoQQQgghhBDi4/JBhWjx4qknMClatCh6enqULv3+M5QKIYQQQgghhMjb5OaAQgghhBBCCCGyVaYWot26ddNooiIhhBBCCCGEEPmXnBEVQgghhBBCCJGtpBAVQgghhBBCCJGtpBAVQgghhBBCCJGtpBAVQgghhBBCCJGtpBDNRMePH8Xevl6qn6lTJwLg6bmadu1aMHhwXwIC/JXnxMXF0a9fdyIjI3Oq2x+V+Ph4OnfuyNWrV1K1f2i+Y8YMS/O7S/nZteunLFmX9zVnzgzmz5+d090QQgghhBDivX3QfURFan5+vjRr1pKJEycrbQYGBfHx8Wbv3l24u3ty8OB+1qxZxYIF3wGwf/9eHBwcMTU1zalufzTi4uJwdZ3Oo0ePUrVnVr7du/eiT58BadpNTOR3I4QQQgghRGaSM6KZyM/vCeXLf0KxYhbKj5mZGQEBfpQtW56KFStjb9+MgAA/AOLiYjlw4Fe6deuZsx3/CDx54svw4YMIDg5Ksyyz8jU0NEr1u0v5MTQ0zMxVEUIIIYQQIt+TQjQT+fn5YmdXOk178eIlCAkJJjIykocP72NlVQKAvXv30KZN24/qjJs6KYmwZUtIeP78n7bEREK/W4jqdUSWve/Nm3/SoEFDPD03pVmWHfm+evWKDh0cWLfOQ2n7+uuxjB07ArVajUqlYsMGTzp3dsLRsTlTpkwgPDwcALVazebN6+nc2Ym2bZszYcKXBAYGKK8TGRnJvHmzaNu2OR07tmXx4vlER0elWveBA3vTqlUTZs2aSlxcbKq+nT9/lr59u+Pg0ITBg/tw+fJFZdmYMcNYs8aDCRO+xMGhCf379+DSpQvK8tevI3B1nU7bts1xdm7DypVuJCYmatQvIYQQQggh3pcUopkkISGB4OAgLl68QM+eXejevRMeHu7Ex8dTvXpNateuQ/v2rdi16yeGDh1ObGwsXl77P7qzoXGPfAhb6Ybv584kPH+OOjGRgDHDCPtuIa+PeGXZ+3bu/DmjRo1L9+xkduRbpEgRxowZz44d2wgODuLYscPcvPknU6bMQEdHh40b13LgwK9Mnvwt69ZtIS4ujnnzZgLwyy8/c/ToYWbMmMPatVuwsbFh3LiRxMYmF5QLF7ry+nUEq1evZ8mSFQQG+ivXgL569YpJk76iQYOGbN68HTu7Mpw7d0bpl4+PN3PnzqRv3wFs3fozHTt2Ydq0b/Dxeag8Zv36dbRu7ciGDT9iZ1eGRYvmoVKpAJg27RtCQ0NYuXINCxcu5fz5M2zfviXDfgkhhBBCCPEhPpprRAs+2IPh/Z3Z+p6xVXoSV7mrRo8NDAxApVJhaGjE3LmLCQkJYuVKN6Kjo5k4cTKurgsZPz4CU1NT9PT02L59C23bticmJobJkycQGhpK//6DcHHpnLUr9YEMK1ai3Pbd+PbpxuPOTuiXKEXkb2cpOd2Voj375Fi/MiPfn37ayu7dO9K0//rrEUxNTXFycubYscO4uS3C2/sBI0Z8SalS1qjVavbv38vw4aNp1MgegK+/nsqhQ/tJSkrip5+2MW7c19StWx+A8eMncenSRc6ePUWNGp9y/vxZvLxOUqhQYQCmT3ela1cXwsJCuXDhPIUKmTNy5Fh0dHQYMmQ4Fy/+c0Zz585tdOjQkXbtOgBgbd2Ve/fusmfPz0ydmlwI29s3pX17FwAGDhzKwIG9eP78OdHRUdy8+Sc7d/6KjY2t0u8XL8IJDg76z34VL17iQ35dQgghhBAin/toCtHcrly58nh5naRwYXMAKlSoiFqtZvbsbxk3biJ6enqYmycvi46O5vDhg6xbt5VVq5ZTuXJVZs2aT9++3WjYsBFWVsVzbkU0YGrfjLJbd+LbrRNxPt5YjvwSq7Hjc7pbH5yvi0sXevTonabd2NhY+fc330yjb99uVKhQif/9rxsAERERRES8olKlysrjrK1tGD58NNHR0Tx7FsacOdMpUOCfAQjx8fEEBgZgZlYItVrN5587p3nfwMCAv687Lo+Ojo7SXrlyFeLj4wHw8/PD1/cRXl77leWJiYlUqVJN+b+tra3ybxMTEwBUqkT8/J5gYmKiFKEAjRo1AeD333/7z35JISqEEEIIIT7ER1OIxlXuqvHZyZySUoSmKF26LImJiURERGBhYaG079mzEycnZ4yNjbl79zYjR47FwsICO7vSf1/jmLsLUXViIi93bFP+/+bMSRLGfIW+pWUO9uof75uvmZlZqqIsPf7+fiQlJREQ4MeLF+FYWFiir6//zsenDIF1dV1AmTLlUi0zNTXj1q0bGBkZsWlT2lvEFCtmwblzZ1Cr1ana9fT0lEJUpVLRs2dfOnTomOox/+5Tev1Tq9Xo679791epVP/ZLyGEEEIIIT6EXCOaSc6dO42LiyMJCQlKm4/PQ0xNzShWrJjSFhUVydGjXnz+eY+/W3RQq5OA5IP/t2qOXCflmtCIvXsoOd2V8nsPEefvp1wzmtOyMt/o6Gjc3BYxZMhwKlWqwrJlSwAwNTXF3LwI3t7/XJcZGBiAi4sjanUSRYoUJTw8HBsbW2xsbClZshSenqt59MgbO7vSxMTEkJSkUpYDuLsvIyoqinLlyuPj461MIASkeh87u9KEhAQrz7WxseXYscOcP382w/WxsbEjKioq1UzEhw7tY+zYERn2SwghhBBCiA8hhWgmqVWrDmq1miVL5hMQ4M/FixdYvXolvXv3SzWscteuHTg7d8LIyAhIHmZ58uQx7ty5hb//EypWrPyut8gV4vye8NeJ45Sc7orV2PGY2jej3PbdxAcEEPnb2Zzu3gflGxsbw4sX4Wl+3rx5A8C6dR4YGBjQq1c/xo+fxMWLv3Hu3GkAunXrycaNa7l69TJ+fk9YtmwxFStWplChwvTo0Zv169dw/vxZgoICcXNbxNWrVyhTpixlypSlYcPGzJ07k7t37+Dj4828ebN4+fIlFhYWODg4Eh8fx4oV3xEQ4MePP27m7t3bSp+7d+/N2bOn+Pnn7QQFBbJv3x62bt2ItbVNhlmVLVuOevUasGjRXHx8vLl58wabN2+gQYPPMuyXEEIIIYTIHx498mHUqC9o1KgOffv24I8//i9TXldH/fa4v2ySkKAiIiI6J946y3h7P8DdfTkPHtzDxMSUTp3+x8CBQ5VCNDIyki++6M+mTT8ps7+GhoYyY8YkgoODGTJk2L/O5CUzNzfOdTklhIWhX7x4hm1Zxd6+HsuXr6Z+/Yap2t8n3xRjxgzjxo3r6S5r0aIVffoMYMSIwSxdupL69T8D4IcfvufYscNs374HQ0NDPD1Xc+TIQRISEmjQoBETJkyiSJGiqFQqNm1ax6FD+3nz5i8qVqzEl19OoGrV6kDyNaYrVy7l4sXf0NHRoV69howf/40yBPbhwwcsXbqQx48fUatWHeUM+7ffzgbg1KnjbNq0juDgIEqUKMmAAUOUyYvGjBlGgwb16d//CwCePg2hW7eOygRFL16Es2zZYq5cuYSxsQnt27vwxRcj0dXVzbBfeU1u3NdyM8lLM5KTdiQv7UhempGctCN5aSa/5HT37h2cndsQGxurXHJmZGTE+vVbaNOmXYbPt7Q0e+cyKURzufyykWtDMtGO5KUZyUk7kpdmJCftSF7akbw0IzlpR/LSTH7JqXv3Lpw9eypNu42NLdeu3Uk18jM9/1WIytBcIYQQQgghhBBpXLuW/jDcsLBQ/vrr9Qe9thSiQgghhBBCCCHSeNclWbq6uhgbm3zQa0shKoQQQgghhBAijTFjvsLY2DhVm6GhIX369P/PWxhqQgpRIYQQQgghhBBp9Os3kGHDRmFoaISpqRkFCxbExaULrq4LPvi1331HeyGEEEIIIYQQ+ZaOjg7Tps1k7Njx+Pv7U7JkSYoWLZYpry2FqBBCCCGEEEKIdzI1NaNateqZ+poyNFcIIYQQQgghRLaSQlQIIYQQQgghRLaSQlQIIYQQQgghRLaSQlQIIYQQQgghRLaSQlQIIYQQQgghRLaSQlQIIYQQQgghRLaSQlQIIYQQQgghRLaSQlQIIYQQQgghRLbSUavV6pzuhBBCCCGEEEKI/EPOiAohhBBCCCGEyFZSiAohhBBCCCGEyFZSiAohhBBCCCGEyFZSiAohhBBCCCGEyFZSiAohhBBCCCGEyFZSiAohhBBCCCGEyFZSiAohhBBCCCGEyFZSiAohhBDvISIiAgC5HbcQQgihPSlEc4C/vz/R0dE53Y1cJSwsLKe78FG5cuUKb968yelu5HobN25kxIgRREZG5nRXPgq+vr6SlQb27t2Li4sLp06dAkBHRyeHe5T7BQYGEhcXl9Pd+GjI30TNhIaGKv+WL4Q0J1n9NzlOzz56Od2B/OTGjRu4urqSmJhIQkIC/fv3x9nZmUKFCqFWq/Plwcy1a9eYP38+AGZmZgwfPpzGjRvn2zw0ERgYyIABA1i0aBEdO3akQAH5PultV69eZfr06URFRTFx4kRMTU1zuku52p07d5g6dSrx8fHo6ekxcOBAOnXqhIGBQU53LVf5v//7P2bMmEFERASvX7+mUKFCAPJ59R9u3rzJ7NmzSUhIwMDAgKFDh9K+ffuc7laudevWLWbPnq3se/369aNFixaYmJjIdvYvt27dYtasWSQmJmJhYcHgwYNp2rQpIPvju7x8+ZK1a9cyZcoUyecd5Dg9+8kRbDZ59OgRc+bMoVmzZixZsgRnZ2e2bNmCl5cXkD+/Ub9//z4zZ86kZcuWfPXVV1haWjJ58mQgf+ahqZCQECD5rMzTp09zuDe5z9q1a+nXrx8dOnTg9OnTdOnSJae7lKvFx8ezYsUK6tevz+rVq2nVqhXHjx/n9evXOd21XOPly5cMGTKEwYMH065dOw4cOEC9evUwNjYG5PPqXcLDw1m0aBFNmzZlwYIFNGzYkBkzZvDy5cuc7lquFBYWxoIFC6hfvz7Tpk3js88+44cffmDDhg2oVCrZzv4WFRWFm5sb9evXZ/LkyZQuXZqpU6dy8OBBKRb+w+3bt9m1axeHDx8GICkpKYd7lLvIcXrOkDOi2eT27dv89ddfjBo1ioIFC1KlShW8vb25dOkSPXr0QEdHJ99t5NevXwdg9OjRFChQAEtLS65evUpgYCC2trY53LvcJ+UPrJGREbVq1eL69evs3buXUaNGoaurm9PdyzUKFy5M0aJFGTt2bLrL5UAltXv37nHt2jWmTJnCJ598wsSJE1Mtl7ySv/QxMDDgxIkTlCxZkvj4eO7evauMRpCM0nfv3j38/PxYv349JiYmVKlShT179hASEkLRokUlt7fcvHkTb29vvv/+e6ysrKhZsyZ6enqcPn2asmXL4uLiQlJSUr4fBfPgwQPu37/PkiVLKF68OPb29gDs378fU1NTWrZsKdvWv6RkERgYSGxsLNu3b6dRo0YUKVJEcgJln5Lj9JyRvz/NspCPj0+qb33DwsKwsbEhNjZWaWvcuDE3btwgMTExX2zcb2cC0LRpUxITEwH45ZdfKFSoEDdu3JDrY0ibV8o2cuvWLWrVqoWbmxsbNmzA29s7p7qYK7ydU48ePUhKSlK+9T18+DBLlizhxx9/JCwsLF/sa//l7byMjY2xtLSkYMGCAHh5ebFo0SI2b95MeHh4vs3Lx8eH8PBwAIYOHYqHhwclS5YkKSmJ58+fU7p0aV69egXIN+Up3t62ihQpQuHChbl06RIA27Ztw9LSkkePHqX6jM+v16u9nVd8fDx2dnbK30SAzz//nODgYHbt2sWLFy/yZRF6+/ZtAFQqFQCFChWiWLFiyv4HyftowYIFOXz4MK9evcr3++S/M9PR0eH58+ds27aNxo0bo6ury+bNm4H8/dmVklHK509oaCi2trb5+jg9J+S/T7Qs5u/vT/fu3Rk+fDjdu3fn+++/JzIyksaNG+Po6JhqQ3706BElS5bEwMBA+YDNi9LL5NWrVzg5OdG3b1/lTMPJkyexsbHhu+++Y+TIkUohkd+8ndeqVasIDg5WlltZWREWFkbbtm2xtrZmy5YtAPnuwvr0cgoKCgKge/fubN26lQULFuDu7k5oaChr1qxhzJgxsl39K6/w8HD09fUxMTHh4cOHrF69mu+//57Xr1+zefNmRo8ezfHjx3O669nq3zn17Nkz1XaVmJhIgQIFsLa25vnz5yQkJAAyxO3tbcvd3Z3nz59jZ2dH1apVmTFjBm3atMHDw4PKlSvj4eHBqFGj2L59O5D/DobTy+v169eUKlWKmJgYbt26pTzW0NAQa2trEhMT2bt3bw72OmdERETQrVs37t27p4z8SUhIoFixYvz555/K42xsbGjdujVhYWGcPXs2h3qbO6SXWVhYGIUKFWL+/Pk0adKE3377jQcPHgD58/MrvYxq1qxJu3bt8uVxek6SQjSTbd68GSsrK7Zs2cLQoUO5fPkys2fP5pNPPqF3796YmZkpO/3Dhw+xs7MDyNNDK9/O5MqVK8ybNw8DAwNsbGwAqFGjBrt27cLDw4OjR49SqlQpTp48mS+vgUxvG1q2bJkym+nt27eV7WXlypXs27ePAQMG4Obmlq9mPH07p0uXLrF8+XJiYmKwt7cnLi6OmzdvsnbtWpYtW8aJEyewtrbm6NGjqQr7/CK9vBYtWkTp0qWxsLDg4MGDPHnyhPnz57Nw4UKOHDmClZUVXl5eSiGWH6S3/y1fvpzIyEj09PSIj48HoG7duly9ehUgX56l+rf0Mlu8eDH6+vosXryYL7/8EkNDQzw9PVm2bBkHDhygdu3aHDlyhIcPH+Z097Ndevvi3LlzqV27NrVq1WL79u2sXbuW4OBgXF1dsbW1pUaNGjx58kS5ZVB+8fjxYwCWL1+utFWtWhUrKysuX75MYGCg0t62bVvMzc25c+dOvvpb+Lb0MqtevTpjxoyhePHiNGrUCCsrK+WsaH78/EovoyZNmtClSxcKFSqU747Tc1L+2/qy0MuXL7l06RIdO3bE1taWnj17MnjwYEJDQ1m5cqXyuAIFCvD69WsePHhAq1atcrDHWS+9TAYNGsSzZ89wd3cHkodFlChRAisrK1QqFcbGxnTu3Jn79++nGqKUH6SX18CBA3n27BkrVqwAwMLCgkqVKgFw6dIlChYsyM2bN+nQoUO+mR32XdvV06dPWbNmDZ9++ikFCxakVq1alCpVCpVKhZGREZ07d+bx48fKmaz84l15BQUFsX79eqZPn86xY8e4fv06FSpUAMDIyIj//e9/PH78ON8c1P3X/pfyeZUym6m5uTmJiYnExMTkZJdz3H/tiytWrEBfX5+GDRtiZWVF+fLlAShYsCAdO3ZEpVIpw5/zi3cdJwQFBeHh4cHcuXOpVasWO3fuxMXFhZcvX7J8+XI+++wzrl+/rgyhzy9Onz5NuXLluHDhAvv27VPaBw4cyO3bt7l06ZLy5ZCxsTFNmzblwoUL+XrG7/QyS0pKonnz5kDymb+mTZvi7e2t3IIqv50VTS8jlUqlbDf56Tg9p0khmomKFi1KQkICL168UNrs7e1xcnLi6NGjPHjwQDnlf+HCBWJiYqhbt67y2HPnzinf0uQV78qkXbt2qTKJjo4mJiZG+capYsWKhISE5Lvhpv+V14kTJwgICODZs2ccOnSIDh06sGvXLmbOnIlKpcLX1zffXGv1rpw6dOjA3r17CQ0NZceOHUydOhVdXV1lu/rkk0/w9/cnKioqp7qeI96Vl4uLC9u2bcPY2Jg+ffoQFxeX6prj6tWr8+TJk3xzz1pNPq9S1K1blzNnzuS7wuBt78qsffv2HDt2DG9vb16+fElkZGSqv292dnbcvXs3333Z+F/74s6dO/Hz82PixIns3LmTo0ePsnXrVmUyv1evXqW6LjKvCwsL49q1a4wcOZKvvvqKxYsXK8tq1qxJ8+bN8fLyUkYmANSqVYsXL17ky9FU8O7MUs56phSczZo1o0KFCuzYsUO55CC/eFdGurq6qY6h8stxek7LP1teJvmvA/24uDgcHBw4c+aMcuNuQ0NDmjVrRo0aNfD09FQee+7cOapXr46VlRUXLlygbdu2zJkz56P8ViozMjl+/Djbtm3j0aNHAJw4cYIGDRpQrly5rF+BbPa+eVWtWhV3d3c++eQT3rx5Q5cuXdixYweff/45/fr1Y/bs2Xlqkqf3zalmzZosW7YMHR0dQkJCOHz4sDIU9/jx4zRs2JBPPvkkW9YhO71vXtWqVeP777/n66+/pnDhwvz888/cvHkTgDNnzlC7dm0qVqyYLeuQHTLrM9zBwQEDAwPlur28fP3Qh2xbmzZt4tNPP+XNmzccOHBAKebPnz9P7dq1qVmzZrasQ3b6kG1s9erVStuDBw+Ug94zZ87QpEkTSpUqlfUrkE0y+uJUV1eXevXqKV9sGBgY4Obmpiz/8ssvMTIyYtu2bVy5cgW1Ws2ZM2eoU6dOnp15/0MyU6lUSsFpa2tLixYtiIqK4scff9TotT8WH5LRv4/Bz58/n2eO03MzKUS1oFKpUs2m9fbGWLBgQapVq0ZMTAxHjx5V2lN2+JCQEOXi+ujoaAwNDZk8eTLDhg3D0dGRU6dOKcPiPhYfmklwcDD+/v4YGBhw8OBBBgwYwODBg1m1ahVt27ZFX18/29YlO3xIXg4ODjx9+hQjIyMOHz7M0KFDMTExAWDSpElMnTqVEiVKZM+KZLEPyally5aEhoby+PFjrly5woIFCxg6dCjDhg3D3d2ddu3a5bmzWB+SV+vWrbl37x4vXrxg9uzZxMTEMHDgQHr37s2CBQtwcnKicOHC2bYuWSkzPsNTinRdXV2cnZ3ZsGEDiYmJefb6oQ/JrFWrVnh7exMeHs6ECRN4+PCh8hnv6uqKo6MjRYoUybZ1yQ6Z8dn14MEDnj9/zu7du/niiy8YNmwY27Zto3Xr1tm2Hlkto5zUajUWFhZ8/fXXFClSBBsbG0aPHs3GjRt5/vw5kHx2ecyYMVhaWjJixAi6du2Kh4cHjo6OefIM34dmpqurS1JSklKoNWrUiHLlynHy5EmioqLyxKRhmZFRyqU7eeU4PbfLe3tqFtmyZQudO3fmyy+/ZOHChUDyUIeUHTplw23WrBnFixfn1KlTqc5OffrppxQoUEC5nsjb25vLly8TGxvLb7/9lubefR+DzMrk6dOntG/fnk2bNjFr1iwcHBw4deoUXbt2zf6VykIfmlfNmjVJSkrCzMwMQ0NDIHm2yZQP2j59+mTn6mSZzNiudHR0CA8Pp0uXLqxbt47+/ftTt25dTp06Rbdu3bJ/pbJQZuSlr69PYGAgDRo0YPny5WzYsIGePXty5swZ2a7+lvJ5lXK9rJGREXXr1qVTp06pDu7ykszatgICAmjdujUrV65k0aJFODk5cfLkSfr27Zv9K5WFMuuz69WrV5QvX54ZM2YwduxY6tWrx7Fjx2jfvn32r1QWyCint0cXJCUloaOjg6OjIzVq1GDmzJnKsurVqzN37lw2b95M//79OX36dJ47doDMy+zf98I0Nzdn8ODBeHp6Kl9qf8wyK6OUEyB54Tj9YyCFqAY2b97Mzp07GT58OLVr1+bEiRMsXboUSN6QVSqVsuHq6enRvHlzoqKilNtqAJQuXZpHjx4pEzNMmTKFXbt2sXLlSooVK5b9K/WBMiuTx48fK/dRs7CwwNHRkT59+lC0aNHsX6kslBl5lSlTBh8fH16/fp3qtfPSN7+ZuV2l7GtVqlShV69eDB8+XLar/8gr5dozAwMD6tSpQ8eOHfPM2arM/Az/9/7XqlUrRowYgYGBQZ44m/BvWfEZb2VlRcuWLenWrVue2bZSZMW+aGVlRefOnRk2bFie+ezSJCddXV10dHQIDg4mPj5e+Rtnbm7OmDFjOH/+PNeuXQP+Ke4//fRTOnXqlOe2K8jczHR0dFIVZOXLl88TRWhmb1fw8R+nfyz0croDuZFarU51UHH58mUcHBxwdnYmKSmJV69eERwcTFJSkjIcKyQkhKlTp/LmzRvc3d158+YNa9asoWTJkjg4OODt7Y2dnR2ffvopwEc3C1dWZlK9evWcWq0sI3lpJitzqlGjRk6tVpaR7Uoz2ZVTXvoSSLYt7UhemnnfnL799lvCw8NZv349xYsXV55fv3592rZty+jRo7l8+XKeu3wHsj6zvHAZQVZlNGrUKK5cuQJAy5Yts3el8qm881c0Ez179gxI/hbl9evXqaYBL1CgALdv36Z8+fLKxDonT57ExcWFYsWKsXTpUqytrenatStjxoxh/fr1DBkyhHHjxtGiRQtKly6dI+v0obIyk5R7NOUlkpdmJCftSF6akZy0J5lpR/LSzPvmVKRIEdzd3VMVC5B8je2wYcMYPHgwkHcm2Pk3ySxjWZXRkCFDgLyR0cdCRy1pp7J69Wpu377NmjVrlLbvv/+eX3/9lVKlSuHt7U3RokUpVKgQjx49YsSIEVSsWBE7OzvlHmn/9vLlSx4/fkyZMmWwtLTMzlXJNJKJdiQvzUhO2pG8NCM5aU8y047kpZnMzik/kMwyJhnlMWqRSt++fdWNGjVSnz17Vml78+aN+tKlS+qePXuqJ0yYoE5ISFAnJiaq9+/fr3ZyclLv3r07B3uc9SQT7UhempGctCN5aUZy0p5kph3JSzOSk/Yks4xJRnmLDM39l7CwMAICAihVqhRbt24lPj4eAGNjYz777DNq1apFgwYN0NPTQ1dXl44dO1K2bFllPHlevI+cZKIdyUszkpN2JC/NSE7ak8y0I3lpRnLSnmSWMcko78m3hejbs4+q1WoOHDiAnZ0d/fr1Izo6mu3btwPJ012r1Wp+//135QbUKbfMqFChgjIG/WO/AFwy0Y7kpRnJSTuSl2YkJ+1JZtqRvDQjOWlPMsuYZJQ/5LtC9Ny5czRr1owJEybQqVMnjhw5wqtXr9DR0cHIyAgnJyccHByoVasWR48eJSQkRLnvUqtWrfD09OTevXskJSURHx/PvXv36NKlS06v1geRTLQjeWlGctKO5KUZyUl7kpl2JC/NSE7ak8wyJhnlL/lisiL139M8x8XFMWjQIBo3bkynTp346aefuH37NjVq1GDy5MmpnnPlyhXWrFlDhQoVmDZtmtLeo0cPwsLCqFq1KiEhIQC4ubl9dBdASybakbw0IzlpR/LSjOSkPclMO5KXZiQn7UlmGZOM8rEsvgY1xyUkJCj//vPPP9X29vbqoKAgpe3HH39UOzs7q728vNRqtVodFxenPG/t2rXqLl26qK9evao8PiwsTH3y5Em1u7u7eufOndm0FplLMtGO5KUZyUk7kpdmJCftSWbakbw0IzlpTzLLmGSUv+nldCGcldatW8eFCxewsrKiadOmNGzYkMjISGJjY5XHtGnTBl9fX9asWYOTkxMGBgYkJiaip6dH8+bNuX37Njt27KBevXoAWFhY4ODggIODQ06t1geRTLQjeWlGctKO5KUZyUl7kpl2JC/NSE7ak8wyJhmJPHmNaExMDF999RUHDhzA2dmZxMREVq1axdq1a3FxcWHHjh3KY62srHByckJPT49NmzYByRc9A1SsWJEWLVpw9+5d9uzZAyTfKPdjJJloR/LSjOSkHclLM5KT9iQz7UhempGctCeZZUwyEiny5G8rJCQEPz8/XF1d6datG4sXL2bAgAEcO3YMY2NjAgMDuX37tvL4SpUqYW9vz++//85ff/2Frq6uMttWw4YNcXR0pGzZsjm1OplCMtGO5KUZyUk7kpdmJCftSWbakbw0IzlpTzLLmGQkUuTJQtTPz48HDx5Qp04dAAwMDChSpAiWlpaUL18elUrF/v37lcebmZlRuXJl4uLiiIyMBP75RsXa2poJEyZQt27d7F+RTCSZaEfy0ozkpB3JSzOSk/YkM+1IXpqRnLQnmWVMMhIp8mQhWrVqVVq1akVAQIDyjYmenh6+vr60bduWJk2acP/+ffbt26c8x9ramhs3bqDOo5MISybakbw0IzlpR/LSjOSkPclMO5KXZiQn7UlmGZOMRIo8OVlRiRIlWLhwIaampso48ps3b1K+fHkKFSqEk5MTL1++5LvvvsPS0pKKFSty8eJF7O3tKVasWA73PmtIJtqRvDQjOWlH8tKM5KQ9yUw7kpdmJCftSWYZk4xEijxZiOro6FC4cOFUbdeuXaN27dpA8g4wcuRIXr9+zYwZM9DR0SEyMpJ58+ZhaGiYE13OcpKJdiQvzUhO2pG8NCM5aU8y047kpRnJSXuSWcYkI6HIodvGZKtHjx6pq1WrptyDSK1WqyMjI9VqtVr96tUr9fnz53OqazlGMtGO5KUZyUk7kpdmJCftSWbakbw0IzlpTzLLmGSUf+XJa0RTqP8eR3779m309fVp1aoVAGvXrqVu3brs3bsXc3NzmjZtmpPdzFaSiXYkL81ITtqRvDQjOWlPMtOO5KUZyUl7klnGJCORJ4fmpkgZd37//n1q1qzJyZMnWb58OWq1Gg8PD1q2bJnDPcx+kol2JC/NSE7akbw0IzlpTzLTjuSlGclJe5JZxiQjkacL0RS6urpcuXKFW7duMXLkSIYNG5bTXcpxkol2JC/NSE7akbw0IzlpTzLTjuSlGclJe5JZxiSj/EtHrc778yDv27ePoKAghg0bhoGBQU53J1eQTLQjeWlGctKO5KUZyUl7kpl2JC/NSE7ak8wyJhnlX/miEFWr1crpf5FMMtGO5KUZyUk7kpdmJCftSWbakbw0IzlpTzLLmGSUf+WLQlQIIYQQQgghRO6Rp2fNFUIIIYQQQgiR+0ghKoQQQgghhBAiW0khKoQQQgghhBAiW0khKoQQQgghhBAiW0khKoQQQgghhBAiW+nldAeEEEKIj02rVq0IDg4GQEdHByMjIypVqsTo0aNp2rSpRq9x+fJlihYtSsWKFbOyq0IIIUSuJGdEhRBCiPcwZcoULly4wLlz5/j555+pU6cOw4cP5+LFixo9f8CAAYSHh2dxL4UQQojcSc6ICiGEEO/B1NQUS0tLAIoXL86kSZN4/vw5Cxcu5ODBgzncOyGEECJ3kzOiQgghRCbp0aMH3t7e+Pv78/jxY4YOHUrt2rWpUaMGvXr1wsfHB0ge2gswaNAg3N3dAfjjjz/o2rUrNWvWpEOHDuzbty+nVkMIIYTIclKICiGEEJmkfPnyAPj4+DBq1ChKlSrF/v372blzJ0lJSSxZsgSAPXv2ALBixQoGDx7M8+fPGTZsGC4uLhw8eJDRo0czb948Tp8+nWPrIoQQQmQlGZorhBBCZBIzMzMAoqOj6dq1K71798bExASALl264OnpCUDRokUBKFy4MCYmJqxbt46GDRsyYMAAAEqXLo2vry9btmxRzp4KIYQQeYkUokIIIUQmiYyMBJKvH3VwcGD//v3cuXMHX19f7t27h7m5ebrP8/X15bfffqN27dpKW2JiolKwCiGEEHmNFKJCCCFEJnn48CEA1tbWdO3alcKFC9O6dWucnZ3x9fVl7dq16T4vMTGRDh06MGrUqFTtBQrIFTRCCCHyJilEhRBCiEzyyy+/UK1aNUJCQggNDeXAgQPo6+sDcOHCBdRqdbrPK1u2LH/88QelS5dW2rZv386zZ88YP358tvRdCCGEyE7yVasQQgjxHiIjI3n+/DnPnj3j4cOHzJ8/n8OHDzNlyhTMzc2JiYnhxIkTBAUFsXv3brZv3058fLzyfGNjY3x8fHjz5g29e/fm/v37uLm54efnx9GjR/nuu+8oXrx4Dq6hEEIIkXV01O/6elYIIYQQ6WrVqhXBwcHK/4sVK0bVqlUZMWIE9erVA2DVqlVs376duLg4KlasSPfu3Zk6dSpnzpyhVKlSrFy5kg0bNtCrVy+mTp3KpUuXWLp0KQ8fPsTS0pK+ffsyZMiQnFpFIYQQIktJISqEEEIIIYQQIlvJ0FwhhBBCCCGEENlKClEhhBBCCCGEENlKClEhhBBCCCGEENlKClEhhBBCCCGEENlKClEhhBBCCCGEENlKClEhhBBCCCGEENlKClEhhBBCCCGEENlKClEhhBBCCCGEENnq/wF+ELQMQ8XCgAAAAABJRU5ErkJggg==\n",
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",
       "text/plain": [
-       "<Figure size 1152x432 with 1 Axes>"
+       "<Figure size 1600x600 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -738,7 +661,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -760,14 +683,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 1152x432 with 1 Axes>"
+       "<Figure size 1600x600 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -807,7 +730,7 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -821,7 +744,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.8"
+   "version": "3.10.11"
   }
  },
  "nbformat": 4,
diff --git a/_sources/univariate/univariate_volatility_modeling.ipynb.txt b/_sources/univariate/univariate_volatility_modeling.ipynb.txt
index 9c8fc4c53a..f1165dca2e 100644
--- a/_sources/univariate/univariate_volatility_modeling.ipynb.txt
+++ b/_sources/univariate/univariate_volatility_modeling.ipynb.txt
@@ -134,10 +134,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Iteration:      5,   Func. Count:     35,   Neg. LLF: 6970.2765831170655\n",
-      "Iteration:     10,   Func. Count:     63,   Neg. LLF: 6936.718477482658\n",
+      "Iteration:      5,   Func. Count:     35,   Neg. LLF: 6970.2779565773335\n",
+      "Iteration:     10,   Func. Count:     63,   Neg. LLF: 6936.718477482076\n",
       "Optimization terminated successfully    (Exit mode 0)\n",
-      "            Current function value: 6936.718476988985\n",
+      "            Current function value: 6936.718476988951\n",
       "            Iterations: 11\n",
       "            Function evaluations: 68\n",
       "            Gradient evaluations: 11\n",
@@ -149,8 +149,8 @@
       "Distribution:                  Normal   AIC:                           13881.4\n",
       "Method:            Maximum Likelihood   BIC:                           13907.5\n",
       "                                        No. Observations:                 5030\n",
-      "Date:                Wed, Apr 12 2023   Df Residuals:                     5029\n",
-      "Time:                        15:58:55   Df Model:                            1\n",
+      "Date:                Tue, May 30 2023   Df Residuals:                     5029\n",
+      "Time:                        10:55:28   Df Model:                            1\n",
       "                                 Mean Model                                 \n",
       "============================================================================\n",
       "                 coef    std err          t      P>|t|      95.0% Conf. Int.\n",
@@ -251,8 +251,8 @@
       "Distribution:                  Normal   AIC:                           13655.8\n",
       "Method:            Maximum Likelihood   BIC:                           13688.4\n",
       "                                        No. Observations:                 5030\n",
-      "Date:                Wed, Apr 12 2023   Df Residuals:                     5029\n",
-      "Time:                        15:58:56   Df Model:                            1\n",
+      "Date:                Tue, May 30 2023   Df Residuals:                     5029\n",
+      "Time:                        10:55:29   Df Model:                            1\n",
       "                                  Mean Model                                 \n",
       "=============================================================================\n",
       "                 coef    std err          t      P>|t|       95.0% Conf. Int.\n",
@@ -263,7 +263,7 @@
       "                 coef    std err          t      P>|t|       95.0% Conf. Int.\n",
       "-----------------------------------------------------------------------------\n",
       "omega          0.0196  4.051e-03      4.830  1.362e-06  [1.163e-02,2.751e-02]\n",
-      "alpha[1]   2.2928e-10  1.026e-02  2.234e-08      1.000 [-2.011e-02,2.011e-02]\n",
+      "alpha[1]   2.4027e-10  1.026e-02  2.341e-08      1.000 [-2.011e-02,2.011e-02]\n",
       "gamma[1]       0.1831  2.266e-02      8.079  6.543e-16      [  0.139,  0.227]\n",
       "beta[1]        0.8922  1.458e-02     61.200      0.000      [  0.864,  0.921]\n",
       "=============================================================================\n",
@@ -321,12 +321,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Iteration:      5,   Func. Count:     45,   Neg. LLF: 6829.203002130782\n",
-      "Iteration:     10,   Func. Count:     79,   Neg. LLF: 6799.178617397159\n",
+      "Iteration:      5,   Func. Count:     45,   Neg. LLF: 6829.197763035331\n",
+      "Iteration:     10,   Func. Count:     79,   Neg. LLF: 6799.178613053096\n",
       "Optimization terminated successfully    (Exit mode 0)\n",
-      "            Current function value: 6799.178521515934\n",
-      "            Iterations: 13\n",
-      "            Function evaluations: 96\n",
+      "            Current function value: 6799.178523172141\n",
+      "            Iterations: 14\n",
+      "            Function evaluations: 103\n",
       "            Gradient evaluations: 13\n",
       "                  Constant Mean - TARCH/ZARCH Model Results                   \n",
       "==============================================================================\n",
@@ -336,21 +336,21 @@
       "Distribution:                  Normal   AIC:                           13608.4\n",
       "Method:            Maximum Likelihood   BIC:                           13641.0\n",
       "                                        No. Observations:                 5030\n",
-      "Date:                Wed, Apr 12 2023   Df Residuals:                     5029\n",
-      "Time:                        15:58:56   Df Model:                            1\n",
+      "Date:                Tue, May 30 2023   Df Residuals:                     5029\n",
+      "Time:                        10:55:29   Df Model:                            1\n",
       "                                  Mean Model                                 \n",
       "=============================================================================\n",
       "                 coef    std err          t      P>|t|       95.0% Conf. Int.\n",
       "-----------------------------------------------------------------------------\n",
-      "mu             0.0143  1.091e-02      1.311      0.190 [-7.087e-03,3.570e-02]\n",
+      "mu             0.0143  1.091e-02      1.311      0.190 [-7.086e-03,3.570e-02]\n",
       "                               Volatility Model                              \n",
       "=============================================================================\n",
       "                 coef    std err          t      P>|t|       95.0% Conf. Int.\n",
       "-----------------------------------------------------------------------------\n",
-      "omega          0.0258  4.100e-03      6.299  2.990e-10  [1.779e-02,3.386e-02]\n",
-      "alpha[1]       0.0000  9.156e-03      0.000      1.000 [-1.794e-02,1.794e-02]\n",
-      "gamma[1]       0.1707  1.601e-02     10.664  1.505e-26      [  0.139,  0.202]\n",
-      "beta[1]        0.9098  9.672e-03     94.066      0.000      [  0.891,  0.929]\n",
+      "omega          0.0258  4.100e-03      6.299  2.987e-10  [1.779e-02,3.386e-02]\n",
+      "alpha[1]   5.9528e-09  9.156e-03  6.502e-07      1.000 [-1.794e-02,1.794e-02]\n",
+      "gamma[1]       0.1707  1.601e-02     10.664  1.501e-26      [  0.139,  0.202]\n",
+      "beta[1]        0.9098  9.671e-03     94.067      0.000      [  0.891,  0.929]\n",
       "=============================================================================\n",
       "\n",
       "Covariance estimator: robust\n"
@@ -393,12 +393,12 @@
      "output_type": "stream",
      "text": [
       "Iteration:      5,   Func. Count:     50,   Neg. LLF: 6728.995165345449\n",
-      "Iteration:     10,   Func. Count:     90,   Neg. LLF: 6722.151188120595\n",
+      "Iteration:     10,   Func. Count:     90,   Neg. LLF: 6722.15117736426\n",
       "Optimization terminated successfully    (Exit mode 0)\n",
-      "            Current function value: 6722.151187386988\n",
-      "            Iterations: 11\n",
-      "            Function evaluations: 97\n",
-      "            Gradient evaluations: 11\n",
+      "            Current function value: 6722.1511833987915\n",
+      "            Iterations: 13\n",
+      "            Function evaluations: 110\n",
+      "            Gradient evaluations: 12\n",
       "                     Constant Mean - TARCH/ZARCH Model Results                      \n",
       "====================================================================================\n",
       "Dep. Variable:                    Adj Close   R-squared:                       0.000\n",
@@ -407,26 +407,26 @@
       "Distribution:      Standardized Student's t   AIC:                           13456.3\n",
       "Method:                  Maximum Likelihood   BIC:                           13495.4\n",
       "                                              No. Observations:                 5030\n",
-      "Date:                      Wed, Apr 12 2023   Df Residuals:                     5029\n",
-      "Time:                              15:58:56   Df Model:                            1\n",
+      "Date:                      Tue, May 30 2023   Df Residuals:                     5029\n",
+      "Time:                              10:55:29   Df Model:                            1\n",
       "                                 Mean Model                                 \n",
       "============================================================================\n",
       "                 coef    std err          t      P>|t|      95.0% Conf. Int.\n",
       "----------------------------------------------------------------------------\n",
-      "mu             0.0323  2.364e-03     13.650  2.020e-42 [2.763e-02,3.690e-02]\n",
+      "mu             0.0323  2.292e-03     14.077  5.292e-45 [2.777e-02,3.676e-02]\n",
       "                               Volatility Model                              \n",
       "=============================================================================\n",
       "                 coef    std err          t      P>|t|       95.0% Conf. Int.\n",
       "-----------------------------------------------------------------------------\n",
-      "omega          0.0201  3.493e-03      5.744  9.249e-09  [1.322e-02,2.691e-02]\n",
-      "alpha[1]   1.4150e-08  8.223e-03  1.721e-06      1.000 [-1.612e-02,1.612e-02]\n",
-      "gamma[1]       0.1721  1.512e-02     11.385  4.978e-30      [  0.143,  0.202]\n",
-      "beta[1]        0.9139  9.578e-03     95.415      0.000      [  0.895,  0.933]\n",
+      "omega          0.0201  3.492e-03      5.745  9.179e-09  [1.322e-02,2.691e-02]\n",
+      "alpha[1]       0.0000  8.223e-03      0.000      1.000 [-1.612e-02,1.612e-02]\n",
+      "gamma[1]       0.1721  1.512e-02     11.386  4.925e-30      [  0.143,  0.202]\n",
+      "beta[1]        0.9139  9.578e-03     95.414      0.000      [  0.895,  0.933]\n",
       "                              Distribution                              \n",
       "========================================================================\n",
       "                 coef    std err          t      P>|t|  95.0% Conf. Int.\n",
       "------------------------------------------------------------------------\n",
-      "nu             7.9553      0.881      9.034  1.648e-19 [  6.229,  9.681]\n",
+      "nu             7.9549      0.880      9.036  1.620e-19 [  6.229,  9.680]\n",
       "========================================================================\n",
       "\n",
       "Covariance estimator: robust\n"
@@ -470,8 +470,8 @@
       "Distribution:       Standardized Student's t   AIC:                           13829.9\n",
       "Method:            User-specified Parameters   BIC:                           13869.0\n",
       "                                               No. Observations:                 5030\n",
-      "Date:                       Wed, Apr 12 2023                                         \n",
-      "Time:                               15:58:56                                         \n",
+      "Date:                       Tue, May 30 2023                                         \n",
+      "Time:                               10:55:29                                         \n",
       "      Mean Model     \n",
       "=====================\n",
       "                 coef\n",
@@ -523,7 +523,7 @@
     },
     {
      "data": {
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",
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",
       "text/plain": [
        "<Figure size 1600x600 with 1 Axes>"
       ]
@@ -646,8 +646,8 @@
       "Distribution:                  Normal   AIC:                           6609.68\n",
       "Method:            Maximum Likelihood   BIC:                           6632.57\n",
       "                                        No. Observations:                  719\n",
-      "Date:                Wed, Apr 12 2023   Df Residuals:                      715\n",
-      "Time:                        15:58:57   Df Model:                            4\n",
+      "Date:                Tue, May 30 2023   Df Residuals:                      715\n",
+      "Time:                        10:55:30   Df Model:                            4\n",
       "                                   Mean Model                                  \n",
       "===============================================================================\n",
       "                   coef    std err          t      P>|t|       95.0% Conf. Int.\n",
@@ -709,24 +709,24 @@
       "Distribution:                  Normal   AIC:                           6369.19\n",
       "Method:            Maximum Likelihood   BIC:                           6414.97\n",
       "                                        No. Observations:                  719\n",
-      "Date:                Wed, Apr 12 2023   Df Residuals:                      715\n",
-      "Time:                        15:58:57   Df Model:                            4\n",
+      "Date:                Tue, May 30 2023   Df Residuals:                      715\n",
+      "Time:                        10:55:30   Df Model:                            4\n",
       "                                   Mean Model                                  \n",
       "===============================================================================\n",
       "                   coef    std err          t      P>|t|       95.0% Conf. Int.\n",
       "-------------------------------------------------------------------------------\n",
       "Const            2.8500      1.883      1.513      0.130      [ -0.841,  6.541]\n",
-      "CPILFESL[1]      1.0859  3.534e-02     30.726 2.594e-207      [  1.017,  1.155]\n",
+      "CPILFESL[1]      1.0859  3.534e-02     30.726 2.596e-207      [  1.017,  1.155]\n",
       "CPILFESL[3]     -0.0788  3.855e-02     -2.045  4.085e-02   [ -0.154,-3.282e-03]\n",
       "CPILFESL[12]    -0.0189  1.157e-02     -1.630      0.103 [-4.154e-02,3.820e-03]\n",
       "                             Volatility Model                             \n",
       "==========================================================================\n",
       "                 coef    std err          t      P>|t|    95.0% Conf. Int.\n",
       "--------------------------------------------------------------------------\n",
-      "omega         76.8684     16.016      4.799  1.592e-06 [ 45.477,1.083e+02]\n",
+      "omega         76.8696     16.017      4.799  1.592e-06 [ 45.478,1.083e+02]\n",
       "alpha[1]       0.1345  4.003e-02      3.359  7.827e-04 [5.600e-02,  0.213]\n",
-      "alpha[2]       0.2280  6.284e-02      3.628  2.860e-04   [  0.105,  0.351]\n",
-      "alpha[3]       0.1838  6.802e-02      2.702  6.894e-03 [5.047e-02,  0.317]\n",
+      "alpha[2]       0.2280  6.284e-02      3.628  2.861e-04   [  0.105,  0.351]\n",
+      "alpha[3]       0.1838  6.802e-02      2.702  6.894e-03 [5.046e-02,  0.317]\n",
       "alpha[4]       0.2538  7.826e-02      3.242  1.185e-03   [  0.100,  0.407]\n",
       "alpha[5]       0.1954  7.091e-02      2.756  5.856e-03 [5.643e-02,  0.334]\n",
       "==========================================================================\n",
@@ -761,7 +761,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 1600x600 with 2 Axes>"
       ]
@@ -811,31 +811,31 @@
       "Distribution:      Standardized Student's t   AIC:                           6358.51\n",
       "Method:                  Maximum Likelihood   BIC:                           6408.86\n",
       "                                              No. Observations:                  719\n",
-      "Date:                      Wed, Apr 12 2023   Df Residuals:                      715\n",
-      "Time:                              15:58:58   Df Model:                            4\n",
+      "Date:                      Tue, May 30 2023   Df Residuals:                      715\n",
+      "Time:                              10:55:31   Df Model:                            4\n",
       "                                   Mean Model                                  \n",
       "===============================================================================\n",
       "                   coef    std err          t      P>|t|       95.0% Conf. Int.\n",
       "-------------------------------------------------------------------------------\n",
-      "Const            3.1221      1.861      1.678  9.342e-02      [ -0.525,  6.770]\n",
-      "CPILFESL[1]      1.0843  3.525e-02     30.762 8.338e-208      [  1.015,  1.153]\n",
-      "CPILFESL[3]     -0.0730  3.873e-02     -1.885  5.947e-02    [ -0.149,2.916e-03]\n",
-      "CPILFESL[12]    -0.0236  1.316e-02     -1.791  7.331e-02 [-4.935e-02,2.224e-03]\n",
+      "Const            3.1220      1.861      1.678  9.343e-02      [ -0.526,  6.770]\n",
+      "CPILFESL[1]      1.0843  3.525e-02     30.762 8.350e-208      [  1.015,  1.153]\n",
+      "CPILFESL[3]     -0.0730  3.873e-02     -1.885  5.948e-02    [ -0.149,2.919e-03]\n",
+      "CPILFESL[12]    -0.0236  1.316e-02     -1.791  7.329e-02 [-4.935e-02,2.223e-03]\n",
       "                             Volatility Model                             \n",
       "==========================================================================\n",
       "                 coef    std err          t      P>|t|    95.0% Conf. Int.\n",
       "--------------------------------------------------------------------------\n",
-      "omega         87.3616     20.628      4.235  2.285e-05 [ 46.931,1.278e+02]\n",
-      "alpha[1]       0.1715  5.064e-02      3.386  7.090e-04 [7.221e-02,  0.271]\n",
-      "alpha[2]       0.2202  6.394e-02      3.443  5.743e-04 [9.485e-02,  0.345]\n",
-      "alpha[3]       0.1547  6.327e-02      2.445  1.447e-02 [3.071e-02,  0.279]\n",
+      "omega         87.3503     20.625      4.235  2.283e-05 [ 46.927,1.278e+02]\n",
+      "alpha[1]       0.1715  5.064e-02      3.386  7.087e-04 [7.222e-02,  0.271]\n",
+      "alpha[2]       0.2202  6.394e-02      3.444  5.741e-04 [9.486e-02,  0.345]\n",
+      "alpha[3]       0.1547  6.327e-02      2.446  1.447e-02 [3.072e-02,  0.279]\n",
       "alpha[4]       0.2117  7.287e-02      2.905  3.677e-03 [6.884e-02,  0.354]\n",
-      "alpha[5]       0.1959  7.854e-02      2.494  1.261e-02 [4.198e-02,  0.350]\n",
+      "alpha[5]       0.1959  7.853e-02      2.495  1.261e-02 [4.199e-02,  0.350]\n",
       "                              Distribution                              \n",
       "========================================================================\n",
       "                 coef    std err          t      P>|t|  95.0% Conf. Int.\n",
       "------------------------------------------------------------------------\n",
-      "nu             9.0448      3.366      2.687  7.208e-03 [  2.447, 15.642]\n",
+      "nu             9.0451      3.366      2.687  7.205e-03 [  2.448, 15.642]\n",
       "========================================================================\n",
       "\n",
       "Covariance estimator: robust\n"
@@ -1191,33 +1191,33 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>2.729532</td>\n",
-       "      <td>2.293291</td>\n",
-       "      <td>2.700167</td>\n",
+       "      <td>2.242949</td>\n",
+       "      <td>2.325649</td>\n",
+       "      <td>2.213584</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>1.310209</td>\n",
-       "      <td>2.294658</td>\n",
-       "      <td>1.280844</td>\n",
+       "      <td>0.404109</td>\n",
+       "      <td>2.301873</td>\n",
+       "      <td>0.374743</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>-3.711848</td>\n",
-       "      <td>2.240700</td>\n",
-       "      <td>-3.741213</td>\n",
+       "      <td>-0.106860</td>\n",
+       "      <td>2.232734</td>\n",
+       "      <td>-0.136226</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>2.212867</td>\n",
-       "      <td>2.417869</td>\n",
-       "      <td>2.183502</td>\n",
+       "      <td>-1.678962</td>\n",
+       "      <td>2.165269</td>\n",
+       "      <td>-1.708327</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>2.987078</td>\n",
-       "      <td>2.387496</td>\n",
-       "      <td>2.957713</td>\n",
+       "      <td>-1.388209</td>\n",
+       "      <td>2.155309</td>\n",
+       "      <td>-1.417574</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1225,11 +1225,11 @@
       ],
       "text/plain": [
        "       data  volatility    errors\n",
-       "0  2.729532    2.293291  2.700167\n",
-       "1  1.310209    2.294658  1.280844\n",
-       "2 -3.711848    2.240700 -3.741213\n",
-       "3  2.212867    2.417869  2.183502\n",
-       "4  2.987078    2.387496  2.957713"
+       "0  2.242949    2.325649  2.213584\n",
+       "1  0.404109    2.301873  0.374743\n",
+       "2 -0.106860    2.232734 -0.136226\n",
+       "3 -1.678962    2.165269 -1.708327\n",
+       "4 -1.388209    2.155309 -1.417574"
       ]
      },
      "execution_count": 18,
@@ -1483,7 +1483,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.10.11"
   },
   "pycharm": {
    "stem_cell": {
diff --git a/_sources/univariate/univariate_volatility_scenarios.ipynb.txt b/_sources/univariate/univariate_volatility_scenarios.ipynb.txt
index 8fe8387b47..8204c7e6e5 100644
--- a/_sources/univariate/univariate_volatility_scenarios.ipynb.txt
+++ b/_sources/univariate/univariate_volatility_scenarios.ipynb.txt
@@ -91,22 +91,13 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\git\\arch\\arch\\univariate\\distribution.py:63: FutureWarning: random_state is deprecated and will be removed in a future update. Use seed instead.\n",
-      "  warnings.warn(\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "rets = 100 * nasdaq.pct_change().dropna()\n",
     "\n",
     "# Build components to set the state for the distribution\n",
     "random_state = np.random.RandomState(1)\n",
-    "dist = Normal(random_state=random_state)\n",
+    "dist = Normal(seed=random_state)\n",
     "volatility = GARCH(1, 1, 1)\n",
     "\n",
     "mod = ConstantMean(rets, volatility=volatility, distribution=dist)"
@@ -135,8 +126,8 @@
        "Distribution:                  Normal   AIC:                           16403.5\n",
        "Method:            Maximum Likelihood   BIC:                           16436.1\n",
        "                                        No. Observations:                 5030\n",
-       "Date:                Wed, Apr 12 2023   Df Residuals:                     5029\n",
-       "Time:                        15:59:51   Df Model:                            1\n",
+       "Date:                Tue, May 30 2023   Df Residuals:                     5029\n",
+       "Time:                        10:55:35   Df Model:                            1\n",
        "                                 Mean Model                                 \n",
        "============================================================================\n",
        "                 coef    std err          t      P>|t|      95.0% Conf. Int.\n",
@@ -153,7 +144,7 @@
        "=============================================================================\n",
        "\n",
        "Covariance estimator: robust\n",
-       "ARCHModelResult, id: 0x1548c0a8850"
+       "ARCHModelResult, id: 0x202821906a0"
       ]
      },
      "execution_count": 4,
@@ -173,50 +164,18 @@
     "GJR-GARCH models support analytical forecasts, which is the default.  The forecasts are produced for all of 2017 using the estimated model parameters."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                h.01      h.02      h.03      h.04      h.05      h.06   \n",
-      "Date                                                                     \n",
-      "2017-01-03  0.623295  0.637504  0.651549  0.665431  0.679154  0.692717  \\\n",
-      "2017-01-04  0.599455  0.613940  0.628257  0.642408  0.656397  0.670223   \n",
-      "2017-01-05  0.567297  0.582153  0.596837  0.611352  0.625699  0.639880   \n",
-      "2017-01-06  0.542506  0.557649  0.572616  0.587410  0.602034  0.616488   \n",
-      "2017-01-09  0.515452  0.530906  0.546183  0.561282  0.576208  0.590961   \n",
-      "\n",
-      "                h.07      h.08      h.09      h.10  \n",
-      "Date                                                \n",
-      "2017-01-03  0.706124  0.719376  0.732475  0.745423  \n",
-      "2017-01-04  0.683890  0.697399  0.710752  0.723950  \n",
-      "2017-01-05  0.653897  0.667753  0.681448  0.694985  \n",
-      "2017-01-06  0.630776  0.644899  0.658858  0.672656  \n",
-      "2017-01-09  0.605543  0.619957  0.634205  0.648288  \n"
-     ]
-    }
-   ],
-   "source": [
-    "forecasts = res.forecast(start=\"1-1-2017\", horizon=10, reindex=False)\n",
-    "print(forecasts.residual_variance.dropna().head())"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "All GARCH specification are complete models in the sense that they specify a distribution. This allows simulations to be produced using the assumptions in the model.  The `forecast` function can be made to produce simulations using the assumed distribution by setting `method='simulation'`.  \n",
     "\n",
-    "These forecasts are similar to the analytical forecasts above.  As the number of simulation increases towards $\\infty$, the simulation-based forecasts will converge to the analytical values above. "
+    "These forecasts are similar to the analytical forecasts above.  As the number of simulation increases towards $\\infty$, the simulation-based forecasts will converge to the analytical values above."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -242,9 +201,7 @@
     }
    ],
    "source": [
-    "sim_forecasts = res.forecast(\n",
-    "    start=\"1-1-2017\", method=\"simulation\", horizon=10, reindex=False\n",
-    ")\n",
+    "sim_forecasts = res.forecast(start=\"1-1-2017\", method=\"simulation\", horizon=10)\n",
     "print(sim_forecasts.residual_variance.dropna().head())"
    ]
   },
@@ -258,6 +215,43 @@
     "The forecast variances are much larger and grow faster than those from either method previously illustrated. This reflects the increase in volatility in the first 5 days."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                h.01      h.02      h.03      h.04      h.05      h.06   \n",
+      "Date                                                                     \n",
+      "2017-01-03  0.623295  0.637504  0.651549  0.665431  0.679154  0.692717  \\\n",
+      "2017-01-04  0.599455  0.613940  0.628257  0.642408  0.656397  0.670223   \n",
+      "2017-01-05  0.567297  0.582153  0.596837  0.611352  0.625699  0.639880   \n",
+      "2017-01-06  0.542506  0.557649  0.572616  0.587410  0.602034  0.616488   \n",
+      "2017-01-09  0.515452  0.530906  0.546183  0.561282  0.576208  0.590961   \n",
+      "\n",
+      "                h.07      h.08      h.09      h.10  \n",
+      "Date                                                \n",
+      "2017-01-03  0.706124  0.719376  0.732475  0.745423  \n",
+      "2017-01-04  0.683890  0.697399  0.710752  0.723950  \n",
+      "2017-01-05  0.653897  0.667753  0.681448  0.694985  \n",
+      "2017-01-06  0.630776  0.644899  0.658858  0.672656  \n",
+      "2017-01-09  0.605543  0.619957  0.634205  0.648288  \n"
+     ]
+    }
+   ],
+   "source": [
+    "forecasts = res.forecast(start=\"1-1-2017\", horizon=10)\n",
+    "print(forecasts.residual_variance.dropna().head())"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 7,
@@ -280,7 +274,7 @@
       "2017-01-03  0.970796  0.977504  0.982202  0.992547  \n",
       "2017-01-04  0.955295  0.965540  0.966432  0.974248  \n",
       "2017-01-05  0.905952  0.915208  0.930777  0.938636  \n",
-      "2017-01-06  0.856175  0.873866  0.886221  0.890002  \n",
+      "2017-01-06  0.856175  0.873865  0.886221  0.890002  \n",
       "2017-01-09  0.851104  0.864591  0.874696  0.894397  \n"
      ]
     }
@@ -298,7 +292,7 @@
     "\n",
     "\n",
     "scenario_forecasts = res.forecast(\n",
-    "    start=\"1-1-2017\", method=\"simulation\", horizon=10, rng=scenario_rng, reindex=False\n",
+    "    start=\"1-1-2017\", method=\"simulation\", horizon=10, rng=scenario_rng\n",
     ")\n",
     "print(scenario_forecasts.residual_variance.dropna().head())"
    ]
@@ -335,7 +329,7 @@
       "Date                                                \n",
       "2017-01-03  0.958215  1.043704  1.124684  1.203893  \n",
       "2017-01-04  0.945120  1.013400  1.084042  1.158148  \n",
-      "2017-01-05  0.889032  0.961424  1.022413  1.097192  \n",
+      "2017-01-05  0.889032  0.961424  1.022412  1.097192  \n",
       "2017-01-06  0.840667  0.895559  0.957266  1.019497  \n",
       "2017-01-09  0.820788  0.887791  0.938708  1.028614  \n"
      ]
@@ -358,11 +352,7 @@
     "shocks = std_shocks[\"2008-08-01\":\"2008-11-10\"]\n",
     "scenario_bootstrap = ScenarioBootstrapRNG(shocks, random_state)\n",
     "bs_forecasts = res.forecast(\n",
-    "    start=\"1-1-2017\",\n",
-    "    method=\"simulation\",\n",
-    "    horizon=10,\n",
-    "    rng=scenario_bootstrap.rng,\n",
-    "    reindex=False,\n",
+    "    start=\"1-1-2017\", method=\"simulation\", horizon=10, rng=scenario_bootstrap.rng\n",
     ")\n",
     "print(bs_forecasts.residual_variance.dropna().head())"
    ]
@@ -382,7 +372,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 1600x600 with 1 Axes>"
       ]
@@ -434,7 +424,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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ORK5CYII=",
       "text/plain": [
        "<Figure size 1600x600 with 2 Axes>"
       ]
@@ -540,7 +530,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.10.11"
   }
  },
  "nbformat": 4,
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diff --git a/_static/fonts/5ce47d5195e59af38114d0b70217baf2.woff2 b/_static/fonts/5ce47d5195e59af38114d0b70217baf2.woff2
new file mode 100644
index 0000000000000000000000000000000000000000..f496f9e2c631a490b21fa8937a0ebfc374539c33
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diff --git a/_static/mathjax/LICENSE b/_static/mathjax/LICENSE
new file mode 100644
index 0000000000..d645695673
--- /dev/null
+++ b/_static/mathjax/LICENSE
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diff --git a/_static/mathjax/output/chtml/fonts/woff-v2/MathJax_AMS-Regular.woff b/_static/mathjax/output/chtml/fonts/woff-v2/MathJax_AMS-Regular.woff
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this.adaptor.next(t)},t.prototype.handleContainer=function(t,e){this.pushString();var r=this.adaptor.getAttribute(t,"class")||"",n=this.adaptor.kind(t)||"",o=this.processHtmlClass.exec(r),i=t;return!this.adaptor.firstChild(t)||this.adaptor.getAttribute(t,"data-MJX")||!o&&this.skipHtmlTags.exec(n)?i=this.adaptor.next(t):(this.adaptor.next(t)&&this.stack.push([this.adaptor.next(t),e]),i=this.adaptor.firstChild(t),e=(e||this.ignoreHtmlClass.exec(r))&&!o),[i,e]},t.prototype.handleOther=function(t,e){return this.pushString(),this.adaptor.next(t)},t.prototype.find=function(t){var e,r;this.init();for(var o=this.adaptor.next(t),i=!1,s=this.options.includeHtmlTags;t&&t!==o;){var a=this.adaptor.kind(t);"#text"===a?t=this.handleText(t,i):s.hasOwnProperty(a)?t=this.handleTag(t,i):a?(t=(e=n(this.handleContainer(t,i),2))[0],i=e[1]):t=this.handleOther(t,i),!t&&this.stack.length&&(this.pushString(),t=(r=n(this.stack.pop(),2))[0],i=r[1])}this.pushString();var l=[this.strings,this.nodes];return this.init(),l},t.OPTIONS={skipHtmlTags:["script","noscript","style","textarea","pre","code","annotation","annotation-xml"],includeHtmlTags:{br:"\n",wbr:"","#comment":""},ignoreHtmlClass:"mathjax_ignore",processHtmlClass:"mathjax_process"},t}();e.HTMLDomStrings=i},3726:function(t,e,r){var n,o=this&&this.__extends||(n=function(t,e){return n=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,e){t.__proto__=e}||function(t,e){for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&(t[r]=e[r])},n(t,e)},function(t,e){if("function"!=typeof e&&null!==e)throw new TypeError("Class extends value "+String(e)+" is not a constructor or null");function r(){this.constructor=t}n(t,e),t.prototype=null===e?Object.create(e):(r.prototype=e.prototype,new r)});Object.defineProperty(e,"__esModule",{value:!0}),e.HTMLHandler=void 0;var i=r(3670),s=r(3683),a=function(t){function e(){var e=null!==t&&t.apply(this,arguments)||this;return e.documentClass=s.HTMLDocument,e}return o(e,t),e.prototype.handlesDocument=function(t){var e=this.adaptor;if("string"==typeof t)try{t=e.parse(t,"text/html")}catch(t){}return t instanceof e.window.Document||t instanceof e.window.HTMLElement||t instanceof e.window.DocumentFragment},e.prototype.create=function(e,r){var n=this.adaptor;if("string"==typeof e)e=n.parse(e,"text/html");else if(e instanceof n.window.HTMLElement||e instanceof n.window.DocumentFragment){var o=e;e=n.parse("","text/html"),n.append(n.body(e),o)}return t.prototype.create.call(this,e,r)},e}(i.AbstractHandler);e.HTMLHandler=a},3363:function(t,e,r){var n,o=this&&this.__extends||(n=function(t,e){return n=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,e){t.__proto__=e}||function(t,e){for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&(t[r]=e[r])},n(t,e)},function(t,e){if("function"!=typeof e&&null!==e)throw new TypeError("Class extends value "+String(e)+" is not a constructor or null");function r(){this.constructor=t}n(t,e),t.prototype=null===e?Object.create(e):(r.prototype=e.prototype,new r)});Object.defineProperty(e,"__esModule",{value:!0}),e.HTMLMathItem=void 0;var i=r(4474),s=function(t){function e(e,r,n,o,i){return void 0===n&&(n=!0),void 0===o&&(o={node:null,n:0,delim:""}),void 0===i&&(i={node:null,n:0,delim:""}),t.call(this,e,r,n,o,i)||this}return o(e,t),Object.defineProperty(e.prototype,"adaptor",{get:function(){return this.inputJax.adaptor},enumerable:!1,configurable:!0}),e.prototype.updateDocument=function(t){if(this.state()<i.STATE.INSERTED){if(this.inputJax.processStrings){var e=this.start.node;if(e===this.end.node)this.end.n&&this.end.n<this.adaptor.value(this.end.node).length&&this.adaptor.split(this.end.node,this.end.n),this.start.n&&(e=this.adaptor.split(this.start.node,this.start.n)),this.adaptor.replace(this.typesetRoot,e);else{for(this.start.n&&(e=this.adaptor.split(e,this.start.n));e!==this.end.node;){var r=this.adaptor.next(e);this.adaptor.remove(e),e=r}this.adaptor.insert(this.typesetRoot,e),this.end.n<this.adaptor.value(e).length&&this.adaptor.split(e,this.end.n),this.adaptor.remove(e)}}else this.adaptor.replace(this.typesetRoot,this.start.node);this.start.node=this.end.node=this.typesetRoot,this.start.n=this.end.n=0,this.state(i.STATE.INSERTED)}},e.prototype.updateStyleSheet=function(t){t.addStyleSheet()},e.prototype.removeFromDocument=function(t){if(void 0===t&&(t=!1),this.state()>=i.STATE.TYPESET){var e=this.adaptor,r=this.start.node,n=e.text("");if(t){var o=this.start.delim+this.math+this.end.delim;if(this.inputJax.processStrings)n=e.text(o);else{var s=e.parse(o,"text/html");n=e.firstChild(e.body(s))}}e.parent(r)&&e.replace(n,r),this.start.node=this.end.node=n,this.start.n=this.end.n=0}},e}(i.AbstractMathItem);e.HTMLMathItem=s},3335:function(t,e,r){var n,o=this&&this.__extends||(n=function(t,e){return n=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,e){t.__proto__=e}||function(t,e){for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&(t[r]=e[r])},n(t,e)},function(t,e){if("function"!=typeof e&&null!==e)throw new TypeError("Class extends value "+String(e)+" is not a constructor or null");function r(){this.constructor=t}n(t,e),t.prototype=null===e?Object.create(e):(r.prototype=e.prototype,new r)});Object.defineProperty(e,"__esModule",{value:!0}),e.HTMLMathList=void 0;var i=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return o(e,t),e}(r(9e3).AbstractMathList);e.HTMLMathList=i},2892:function(t,e,r){var n,o=this&&this.__extends||(n=function(t,e){return n=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,e){t.__proto__=e}||function(t,e){for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&(t[r]=e[r])},n(t,e)},function(t,e){if("function"!=typeof e&&null!==e)throw new TypeError("Class extends value "+String(e)+" is not a constructor or null");function r(){this.constructor=t}n(t,e),t.prototype=null===e?Object.create(e):(r.prototype=e.prototype,new r)}),i=this&&this.__read||function(t,e){var r="function"==typeof Symbol&&t[Symbol.iterator];if(!r)return t;var n,o,i=r.call(t),s=[];try{for(;(void 0===e||e-- >0)&&!(n=i.next()).done;)s.push(n.value)}catch(t){o={error:t}}finally{try{n&&!n.done&&(r=i.return)&&r.call(i)}finally{if(o)throw o.error}}return s};Object.defineProperty(e,"__esModule",{value:!0}),e.MathML=void 0;var s=r(9206),a=r(7233),l=r(7525),c=r(625),u=r(2769),p=function(t){function e(e){void 0===e&&(e={});var r=this,n=i((0,a.separateOptions)(e,c.FindMathML.OPTIONS,u.MathMLCompile.OPTIONS),3),o=n[0],s=n[1],p=n[2];return(r=t.call(this,o)||this).findMathML=r.options.FindMathML||new c.FindMathML(s),r.mathml=r.options.MathMLCompile||new u.MathMLCompile(p),r.mmlFilters=new l.FunctionList,r}return o(e,t),e.prototype.setAdaptor=function(e){t.prototype.setAdaptor.call(this,e),this.findMathML.adaptor=e,this.mathml.adaptor=e},e.prototype.setMmlFactory=function(e){t.prototype.setMmlFactory.call(this,e),this.mathml.setMmlFactory(e)},Object.defineProperty(e.prototype,"processStrings",{get:function(){return!1},enumerable:!1,configurable:!0}),e.prototype.compile=function(t,e){var r=t.start.node;if(!r||!t.end.node||this.options.forceReparse||"#text"===this.adaptor.kind(r)){var n=this.executeFilters(this.preFilters,t,e,(t.math||"<math></math>").trim()),o=this.checkForErrors(this.adaptor.parse(n,"text/"+this.options.parseAs)),i=this.adaptor.body(o);1!==this.adaptor.childNodes(i).length&&this.error("MathML must consist of a single element"),r=this.adaptor.remove(this.adaptor.firstChild(i)),"math"!==this.adaptor.kind(r).replace(/^[a-z]+:/,"")&&this.error("MathML must be formed by a <math> element, not <"+this.adaptor.kind(r)+">")}return r=this.executeFilters(this.mmlFilters,t,e,r),this.executeFilters(this.postFilters,t,e,this.mathml.compile(r))},e.prototype.checkForErrors=function(t){var e=this.adaptor.tags(this.adaptor.body(t),"parsererror")[0];return e&&(""===this.adaptor.textContent(e)&&this.error("Error processing MathML"),this.options.parseError.call(this,e)),t},e.prototype.error=function(t){throw new Error(t)},e.prototype.findMath=function(t){return this.findMathML.findMath(t)},e.NAME="MathML",e.OPTIONS=(0,a.defaultOptions)({parseAs:"html",forceReparse:!1,FindMathML:null,MathMLCompile:null,parseError:function(t){this.error(this.adaptor.textContent(t).replace(/\n.*/g,""))}},s.AbstractInputJax.OPTIONS),e}(s.AbstractInputJax);e.MathML=p},625:function(t,e,r){var n,o=this&&this.__extends||(n=function(t,e){return n=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,e){t.__proto__=e}||function(t,e){for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&(t[r]=e[r])},n(t,e)},function(t,e){if("function"!=typeof e&&null!==e)throw new TypeError("Class extends value "+String(e)+" is not a constructor or null");function r(){this.constructor=t}n(t,e),t.prototype=null===e?Object.create(e):(r.prototype=e.prototype,new r)}),i=this&&this.__values||function(t){var e="function"==typeof Symbol&&Symbol.iterator,r=e&&t[e],n=0;if(r)return r.call(t);if(t&&"number"==typeof t.length)return{next:function(){return t&&n>=t.length&&(t=void 0),{value:t&&t[n++],done:!t}}};throw new TypeError(e?"Object is not iterable.":"Symbol.iterator is not defined.")};Object.defineProperty(e,"__esModule",{value:!0}),e.FindMathML=void 0;var s=r(3494),a="http://www.w3.org/1998/Math/MathML",l=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return o(e,t),e.prototype.findMath=function(t){var e=new Set;this.findMathNodes(t,e),this.findMathPrefixed(t,e);var r=this.adaptor.root(this.adaptor.document);return"html"===this.adaptor.kind(r)&&0===e.size&&this.findMathNS(t,e),this.processMath(e)},e.prototype.findMathNodes=function(t,e){var r,n;try{for(var o=i(this.adaptor.tags(t,"math")),s=o.next();!s.done;s=o.next()){var a=s.value;e.add(a)}}catch(t){r={error:t}}finally{try{s&&!s.done&&(n=o.return)&&n.call(o)}finally{if(r)throw r.error}}},e.prototype.findMathPrefixed=function(t,e){var r,n,o,s,l=this.adaptor.root(this.adaptor.document);try{for(var c=i(this.adaptor.allAttributes(l)),u=c.next();!u.done;u=c.next()){var p=u.value;if("xmlns:"===p.name.substr(0,6)&&p.value===a){var h=p.name.substr(6);try{for(var f=(o=void 0,i(this.adaptor.tags(t,h+":math"))),d=f.next();!d.done;d=f.next()){var m=d.value;e.add(m)}}catch(t){o={error:t}}finally{try{d&&!d.done&&(s=f.return)&&s.call(f)}finally{if(o)throw o.error}}}}}catch(t){r={error:t}}finally{try{u&&!u.done&&(n=c.return)&&n.call(c)}finally{if(r)throw r.error}}},e.prototype.findMathNS=function(t,e){var r,n;try{for(var o=i(this.adaptor.tags(t,"math",a)),s=o.next();!s.done;s=o.next()){var l=s.value;e.add(l)}}catch(t){r={error:t}}finally{try{s&&!s.done&&(n=o.return)&&n.call(o)}finally{if(r)throw r.error}}},e.prototype.processMath=function(t){var e,r,n=[];try{for(var o=i(Array.from(t)),s=o.next();!s.done;s=o.next()){var a=s.value,l="block"===this.adaptor.getAttribute(a,"display")||"display"===this.adaptor.getAttribute(a,"mode"),c={node:a,n:0,delim:""},u={node:a,n:0,delim:""};n.push({math:this.adaptor.outerHTML(a),start:c,end:u,display:l})}}catch(t){e={error:t}}finally{try{s&&!s.done&&(r=o.return)&&r.call(o)}finally{if(e)throw e.error}}return n},e.OPTIONS={},e}(s.AbstractFindMath);e.FindMathML=l},2769:function(t,e,r){var n=this&&this.__assign||function(){return n=Object.assign||function(t){for(var e,r=1,n=arguments.length;r<n;r++)for(var o in e=arguments[r])Object.prototype.hasOwnProperty.call(e,o)&&(t[o]=e[o]);return t},n.apply(this,arguments)},o=this&&this.__createBinding||(Object.create?function(t,e,r,n){void 0===n&&(n=r);var o=Object.getOwnPropertyDescriptor(e,r);o&&!("get"in o?!e.__esModule:o.writable||o.configurable)||(o={enumerable:!0,get:function(){return e[r]}}),Object.defineProperty(t,n,o)}:function(t,e,r,n){void 0===n&&(n=r),t[n]=e[r]}),i=this&&this.__setModuleDefault||(Object.create?function(t,e){Object.defineProperty(t,"default",{enumerable:!0,value:e})}:function(t,e){t.default=e}),s=this&&this.__importStar||function(t){if(t&&t.__esModule)return t;var e={};if(null!=t)for(var r in t)"default"!==r&&Object.prototype.hasOwnProperty.call(t,r)&&o(e,t,r);return i(e,t),e},a=this&&this.__values||function(t){var e="function"==typeof Symbol&&Symbol.iterator,r=e&&t[e],n=0;if(r)return r.call(t);if(t&&"number"==typeof t.length)return{next:function(){return t&&n>=t.length&&(t=void 0),{value:t&&t[n++],done:!t}}};throw new TypeError(e?"Object is not iterable.":"Symbol.iterator is not defined.")};Object.defineProperty(e,"__esModule",{value:!0}),e.MathMLCompile=void 0;var l=r(9007),c=r(7233),u=s(r(5368)),p=function(){function t(t){void 0===t&&(t={});var e=this.constructor;this.options=(0,c.userOptions)((0,c.defaultOptions)({},e.OPTIONS),t)}return t.prototype.setMmlFactory=function(t){this.factory=t},t.prototype.compile=function(t){var e=this.makeNode(t);return e.verifyTree(this.options.verify),e.setInheritedAttributes({},!1,0,!1),e.walkTree(this.markMrows),e},t.prototype.makeNode=function(t){var e,r,n=this.adaptor,o=!1,i=n.kind(t).replace(/^.*:/,""),s=n.getAttribute(t,"data-mjx-texclass")||"";s&&(s=this.filterAttribute("data-mjx-texclass",s)||"");var c=s&&"mrow"===i?"TeXAtom":i;try{for(var u=a(this.filterClassList(n.allClasses(t))),p=u.next();!p.done;p=u.next()){var h=p.value;h.match(/^MJX-TeXAtom-/)&&"mrow"===i?(s=h.substr(12),c="TeXAtom"):"MJX-fixedlimits"===h&&(o=!0)}}catch(t){e={error:t}}finally{try{p&&!p.done&&(r=u.return)&&r.call(u)}finally{if(e)throw e.error}}this.factory.getNodeClass(c)||this.error('Unknown node type "'+c+'"');var f=this.factory.create(c);return"TeXAtom"!==c||"OP"!==s||o||(f.setProperty("movesupsub",!0),f.attributes.setInherited("movablelimits",!0)),s&&(f.texClass=l.TEXCLASS[s],f.setProperty("texClass",f.texClass)),this.addAttributes(f,t),this.checkClass(f,t),this.addChildren(f,t),f},t.prototype.addAttributes=function(t,e){var r,n,o=!1;try{for(var i=a(this.adaptor.allAttributes(e)),s=i.next();!s.done;s=i.next()){var l=s.value,c=l.name,u=this.filterAttribute(c,l.value);if(null!==u&&"xmlns"!==c)if("data-mjx-"===c.substr(0,9))switch(c.substr(9)){case"alternate":t.setProperty("variantForm",!0);break;case"variant":t.attributes.set("mathvariant",u),o=!0;break;case"smallmatrix":t.setProperty("scriptlevel",1),t.setProperty("useHeight",!1);break;case"accent":t.setProperty("mathaccent","true"===u);break;case"auto-op":t.setProperty("autoOP","true"===u);break;case"script-align":t.setProperty("scriptalign",u)}else if("class"!==c){var p=u.toLowerCase();"true"===p||"false"===p?t.attributes.set(c,"true"===p):o&&"mathvariant"===c||t.attributes.set(c,u)}}}catch(t){r={error:t}}finally{try{s&&!s.done&&(n=i.return)&&n.call(i)}finally{if(r)throw r.error}}},t.prototype.filterAttribute=function(t,e){return e},t.prototype.filterClassList=function(t){return t},t.prototype.addChildren=function(t,e){var r,n;if(0!==t.arity){var o=this.adaptor;try{for(var i=a(o.childNodes(e)),s=i.next();!s.done;s=i.next()){var l=s.value,c=o.kind(l);if("#comment"!==c)if("#text"===c)this.addText(t,l);else if(t.isKind("annotation-xml"))t.appendChild(this.factory.create("XML").setXML(l,o));else{var u=t.appendChild(this.makeNode(l));0===u.arity&&o.childNodes(l).length&&(this.options.fixMisplacedChildren?this.addChildren(t,l):u.mError("There should not be children for "+u.kind+" nodes",this.options.verify,!0))}}}catch(t){r={error:t}}finally{try{s&&!s.done&&(n=i.return)&&n.call(i)}finally{if(r)throw 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null;var t=this.stack.Top().First;return this.configuration.popParser(),t},t.prototype.convertDelimiter=function(t){var e=this.lookup("delimiter",t);return e?e.char:null},t.prototype.getCodePoint=function(){var t=this.string.codePointAt(this.i);return void 0===t?"":String.fromCodePoint(t)},t.prototype.nextIsSpace=function(){return!!this.string.charAt(this.i).match(/\s/)},t.prototype.GetNext=function(){for(;this.nextIsSpace();)this.i++;return this.getCodePoint()},t.prototype.GetCS=function(){var t=this.string.slice(this.i).match(/^(([a-z]+) ?|[\uD800-\uDBFF].|.)/i);return t?(this.i+=t[0].length,t[2]||t[1]):(this.i++," ")},t.prototype.GetArgument=function(t,e){switch(this.GetNext()){case"":if(!e)throw new c.default("MissingArgFor","Missing argument for %1",this.currentCS);return null;case"}":if(!e)throw new c.default("ExtraCloseMissingOpen","Extra close brace or missing open brace");return null;case"\\":return this.i++,"\\"+this.GetCS();case"{":for(var r=++this.i,n=1;this.i<this.string.length;)switch(this.string.charAt(this.i++)){case"\\":this.i++;break;case"{":n++;break;case"}":if(0==--n)return this.string.slice(r,this.i-1)}throw new c.default("MissingCloseBrace","Missing close brace")}var o=this.getCodePoint();return this.i+=o.length,o},t.prototype.GetBrackets=function(t,e){if("["!==this.GetNext())return e;for(var r=++this.i,n=0;this.i<this.string.length;)switch(this.string.charAt(this.i++)){case"{":n++;break;case"\\":this.i++;break;case"}":if(n--<=0)throw new c.default("ExtraCloseLooking","Extra close brace while looking for %1","']'");break;case"]":if(0===n)return this.string.slice(r,this.i-1)}throw new c.default("MissingCloseBracket","Could not find closing ']' for argument to %1",this.currentCS)},t.prototype.GetDelimiter=function(t,e){var r=this.GetNext();if(this.i+=r.length,this.i<=this.string.length&&("\\"===r?r+=this.GetCS():"{"===r&&e&&(this.i--,r=this.GetArgument(t).trim()),this.contains("delimiter",r)))return this.convertDelimiter(r);throw new c.default("MissingOrUnrecognizedDelim","Missing or unrecognized delimiter for %1",this.currentCS)},t.prototype.GetDimen=function(t){if("{"===this.GetNext()){var e=this.GetArgument(t),r=o(a.default.matchDimen(e),2),n=r[0],i=r[1];if(n)return n+i}else{e=this.string.slice(this.i);var s=o(a.default.matchDimen(e,!0),3),l=(n=s[0],i=s[1],s[2]);if(n)return this.i+=l,n+i}throw new c.default("MissingDimOrUnits","Missing dimension or its units for %1",this.currentCS)},t.prototype.GetUpTo=function(t,e){for(;this.nextIsSpace();)this.i++;for(var r=this.i,n=0;this.i<this.string.length;){var o=this.i,i=this.GetNext();switch(this.i+=i.length,i){case"\\":i+=this.GetCS();break;case"{":n++;break;case"}":if(0===n)throw new c.default("ExtraCloseLooking","Extra close brace while looking for %1",e);n--}if(0===n&&i===e)return this.string.slice(r,o)}throw new c.default("TokenNotFoundForCommand","Could not find %1 for %2",e,this.currentCS)},t.prototype.ParseArg=function(e){return new t(this.GetArgument(e),this.stack.env,this.configuration).mml()},t.prototype.ParseUpTo=function(e,r){return new t(this.GetUpTo(e,r),this.stack.env,this.configuration).mml()},t.prototype.GetDelimiterArg=function(t){var e=a.default.trimSpaces(this.GetArgument(t));if(""===e)return null;if(this.contains("delimiter",e))return e;throw new c.default("MissingOrUnrecognizedDelim","Missing or unrecognized delimiter for %1",this.currentCS)},t.prototype.GetStar=function(){var t="*"===this.GetNext();return t&&this.i++,t},t.prototype.create=function(t){for(var e,r=[],n=1;n<arguments.length;n++)r[n-1]=arguments[n];return(e=this.configuration.nodeFactory).create.apply(e,i([t],o(r),!1))},t}();e.default=p},8021:function(t,e,r){var n,o,i=this&&this.__extends||(n=function(t,e){return n=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,e){t.__proto__=e}||function(t,e){for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&(t[r]=e[r])},n(t,e)},function(t,e){if("function"!=typeof e&&null!==e)throw new TypeError("Class extends value "+String(e)+" is not a constructor or null");function r(){this.constructor=t}n(t,e),t.prototype=null===e?Object.create(e):(r.prototype=e.prototype,new r)});Object.defineProperty(e,"__esModule",{value:!0}),e.AmsConfiguration=e.AmsTags=void 0;var s=r(9899),a=r(2790),l=r(6521),c=r(4387);r(7379);var u=r(9140),p=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return i(e,t),e}(l.AbstractTags);e.AmsTags=p;e.AmsConfiguration=s.Configuration.create("ams",{handler:{character:["AMSmath-operatorLetter"],delimiter:["AMSsymbols-delimiter","AMSmath-delimiter"],macro:["AMSsymbols-mathchar0mi","AMSsymbols-mathchar0mo","AMSsymbols-delimiter","AMSsymbols-macros","AMSmath-mathchar0mo","AMSmath-macros","AMSmath-delimiter"],environment:["AMSmath-environment"]},items:(o={},o[a.MultlineItem.prototype.kind]=a.MultlineItem,o[a.FlalignItem.prototype.kind]=a.FlalignItem,o),tags:{ams:p},init:function(t){new u.CommandMap(c.NEW_OPS,{},{}),t.append(s.Configuration.local({handler:{macro:[c.NEW_OPS]},priority:-1}))},config:function(t,e){e.parseOptions.options.multlineWidth&&(e.parseOptions.options.ams.multlineWidth=e.parseOptions.options.multlineWidth),delete e.parseOptions.options.multlineWidth},options:{multlineWidth:"",ams:{multlineWidth:"100%",multlineIndent:"1em"}}})},2790:function(t,e,r){var n,o=this&&this.__extends||(n=function(t,e){return n=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,e){t.__proto__=e}||function(t,e){for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&(t[r]=e[r])},n(t,e)},function(t,e){if("function"!=typeof e&&null!==e)throw new TypeError("Class extends value "+String(e)+" is not a constructor or null");function r(){this.constructor=t}n(t,e),t.prototype=null===e?Object.create(e):(r.prototype=e.prototype,new r)}),i=this&&this.__assign||function(){return i=Object.assign||function(t){for(var e,r=1,n=arguments.length;r<n;r++)for(var o in e=arguments[r])Object.prototype.hasOwnProperty.call(e,o)&&(t[o]=e[o]);return t},i.apply(this,arguments)},s=this&&this.__importDefault||function(t){return t&&t.__esModule?t:{default:t}};Object.defineProperty(e,"__esModule",{value:!0}),e.FlalignItem=e.MultlineItem=void 0;var a=r(1181),l=s(r(1130)),c=s(r(1256)),u=s(r(3971)),p=r(8317),h=function(t){function e(e){for(var r=[],n=1;n<arguments.length;n++)r[n-1]=arguments[n];var o=t.call(this,e)||this;return o.factory.configuration.tags.start("multline",!0,r[0]),o}return o(e,t),Object.defineProperty(e.prototype,"kind",{get:function(){return"multline"},enumerable:!1,configurable:!0}),e.prototype.EndEntry=function(){this.table.length&&l.default.fixInitialMO(this.factory.configuration,this.nodes);var t=this.getProperty("shove"),e=this.create("node","mtd",this.nodes,t?{columnalign:t}:{});this.setProperty("shove",null),this.row.push(e),this.Clear()},e.prototype.EndRow=function(){if(1!==this.row.length)throw new u.default("MultlineRowsOneCol","The rows within the %1 environment must have exactly one column","multline");var t=this.create("node","mtr",this.row);this.table.push(t),this.row=[]},e.prototype.EndTable=function(){if(t.prototype.EndTable.call(this),this.table.length){var e=this.table.length-1,r=-1;c.default.getAttribute(c.default.getChildren(this.table[0])[0],"columnalign")||c.default.setAttribute(c.default.getChildren(this.table[0])[0],"columnalign",p.TexConstant.Align.LEFT),c.default.getAttribute(c.default.getChildren(this.table[e])[0],"columnalign")||c.default.setAttribute(c.default.getChildren(this.table[e])[0],"columnalign",p.TexConstant.Align.RIGHT);var n=this.factory.configuration.tags.getTag();if(n){r=this.arraydef.side===p.TexConstant.Align.LEFT?0:this.table.length-1;var o=this.table[r],i=this.create("node","mlabeledtr",[n].concat(c.default.getChildren(o)));c.default.copyAttributes(o,i),this.table[r]=i}}this.factory.configuration.tags.end()},e}(a.ArrayItem);e.MultlineItem=h;var f=function(t){function e(e,r,n,o,i){var s=t.call(this,e)||this;return s.name=r,s.numbered=n,s.padded=o,s.center=i,s.factory.configuration.tags.start(r,n,n),s}return o(e,t),Object.defineProperty(e.prototype,"kind",{get:function(){return"flalign"},enumerable:!1,configurable:!0}),e.prototype.EndEntry=function(){t.prototype.EndEntry.call(this);var e=this.getProperty("xalignat");if(e&&this.row.length>e)throw new u.default("XalignOverflow","Extra %1 in row of %2","&",this.name)},e.prototype.EndRow=function(){for(var e,r=this.row,n=this.getProperty("xalignat");r.length<n;)r.push(this.create("node","mtd"));for(this.row=[],this.padded&&this.row.push(this.create("node","mtd"));e=r.shift();)this.row.push(e),(e=r.shift())&&this.row.push(e),(r.length||this.padded)&&this.row.push(this.create("node","mtd"));this.row.length>this.maxrow&&(this.maxrow=this.row.length),t.prototype.EndRow.call(this);var o=this.table[this.table.length-1];if(this.getProperty("zeroWidthLabel")&&o.isKind("mlabeledtr")){var s=c.default.getChildren(o)[0],a=this.factory.configuration.options.tagSide,l=i({width:0},"right"===a?{lspace:"-1width"}:{}),u=this.create("node","mpadded",c.default.getChildren(s),l);s.setChildren([u])}},e.prototype.EndTable=function(){(t.prototype.EndTable.call(this),this.center)&&(this.maxrow<=2&&(delete this.arraydef.width,delete this.global.indentalign))},e}(a.EqnArrayItem);e.FlalignItem=f},7379:function(t,e,r){var n=this&&this.__createBinding||(Object.create?function(t,e,r,n){void 0===n&&(n=r);var o=Object.getOwnPropertyDescriptor(e,r);o&&!("get"in o?!e.__esModule:o.writable||o.configurable)||(o={enumerable:!0,get:function(){return e[r]}}),Object.defineProperty(t,n,o)}:function(t,e,r,n){void 0===n&&(n=r),t[n]=e[r]}),o=this&&this.__setModuleDefault||(Object.create?function(t,e){Object.defineProperty(t,"default",{enumerable:!0,value:e})}:function(t,e){t.default=e}),i=this&&this.__importStar||function(t){if(t&&t.__esModule)return t;var e={};if(null!=t)for(var r in t)"default"!==r&&Object.prototype.hasOwnProperty.call(t,r)&&n(e,t,r);return o(e,t),e},s=this&&this.__importDefault||function(t){return t&&t.__esModule?t:{default:t}};Object.defineProperty(e,"__esModule",{value:!0});var a=r(4387),l=i(r(9140)),c=r(8317),u=s(r(5450)),p=s(r(1130)),h=r(9007),f=r(6010);new l.CharacterMap("AMSmath-mathchar0mo",u.default.mathchar0mo,{iiiint:["\u2a0c",{texClass:h.TEXCLASS.OP}]}),new l.RegExpMap("AMSmath-operatorLetter",a.AmsMethods.operatorLetter,/[-*]/i),new l.CommandMap("AMSmath-macros",{mathring:["Accent","02DA"],nobreakspace:"Tilde",negmedspace:["Spacer",f.MATHSPACE.negativemediummathspace],negthickspace:["Spacer",f.MATHSPACE.negativethickmathspace],idotsint:["MultiIntegral","\\int\\cdots\\int"],dddot:["Accent","20DB"],ddddot:["Accent","20DC"],sideset:"SideSet",boxed:["Macro","\\fbox{$\\displaystyle{#1}$}",1],tag:"HandleTag",notag:"HandleNoTag",eqref:["HandleRef",!0],substack:["Macro","\\begin{subarray}{c}#1\\end{subarray}",1],injlim:["NamedOp","inj&thinsp;lim"],projlim:["NamedOp","proj&thinsp;lim"],varliminf:["Macro","\\mathop{\\underline{\\mmlToken{mi}{lim}}}"],varlimsup:["Macro","\\mathop{\\overline{\\mmlToken{mi}{lim}}}"],varinjlim:["Macro","\\mathop{\\underrightarrow{\\mmlToken{mi}{lim}}}"],varprojlim:["Macro","\\mathop{\\underleftarrow{\\mmlToken{mi}{lim}}}"],DeclareMathOperator:"HandleDeclareOp",operatorname:"HandleOperatorName",genfrac:"Genfrac",frac:["Genfrac","","","",""],tfrac:["Genfrac","","","","1"],dfrac:["Genfrac","","","","0"],binom:["Genfrac","(",")","0",""],tbinom:["Genfrac","(",")","0","1"],dbinom:["Genfrac","(",")","0","0"],cfrac:"CFrac",shoveleft:["HandleShove",c.TexConstant.Align.LEFT],shoveright:["HandleShove",c.TexConstant.Align.RIGHT],xrightarrow:["xArrow",8594,5,10],xleftarrow:["xArrow",8592,10,5]},a.AmsMethods),new l.EnvironmentMap("AMSmath-environment",u.default.environment,{"equation*":["Equation",null,!1],"eqnarray*":["EqnArray",null,!1,!0,"rcl",p.default.cols(0,f.MATHSPACE.thickmathspace),".5em"],align:["EqnArray",null,!0,!0,"rl",p.default.cols(0,2)],"align*":["EqnArray",null,!1,!0,"rl",p.default.cols(0,2)],multline:["Multline",null,!0],"multline*":["Multline",null,!1],split:["EqnArray",null,!1,!1,"rl",p.default.cols(0)],gather:["EqnArray",null,!0,!0,"c"],"gather*":["EqnArray",null,!1,!0,"c"],alignat:["AlignAt",null,!0,!0],"alignat*":["AlignAt",null,!1,!0],alignedat:["AlignAt",null,!1,!1],aligned:["AmsEqnArray",null,null,null,"rl",p.default.cols(0,2),".5em","D"],gathered:["AmsEqnArray",null,null,null,"c",null,".5em","D"],xalignat:["XalignAt",null,!0,!0],"xalignat*":["XalignAt",null,!1,!0],xxalignat:["XalignAt",null,!1,!1],flalign:["FlalignArray",null,!0,!1,!0,"rlc","auto auto fit"],"flalign*":["FlalignArray",null,!1,!1,!0,"rlc","auto auto fit"],subarray:["Array",null,null,null,null,p.default.cols(0),"0.1em","S",1],smallmatrix:["Array",null,null,null,"c",p.default.cols(1/3),".2em","S",1],matrix:["Array",null,null,null,"c"],pmatrix:["Array",null,"(",")","c"],bmatrix:["Array",null,"[","]","c"],Bmatrix:["Array",null,"\\{","\\}","c"],vmatrix:["Array",null,"\\vert","\\vert","c"],Vmatrix:["Array",null,"\\Vert","\\Vert","c"],cases:["Array",null,"\\{",".","ll",null,".2em","T"]},a.AmsMethods),new l.DelimiterMap("AMSmath-delimiter",u.default.delimiter,{"\\lvert":["|",{texClass:h.TEXCLASS.OPEN}],"\\rvert":["|",{texClass:h.TEXCLASS.CLOSE}],"\\lVert":["\u2016",{texClass:h.TEXCLASS.OPEN}],"\\rVert":["\u2016",{texClass:h.TEXCLASS.CLOSE}]}),new l.CharacterMap("AMSsymbols-mathchar0mi",u.default.mathchar0mi,{digamma:"\u03dd",varkappa:"\u03f0",varGamma:["\u0393",{mathvariant:c.TexConstant.Variant.ITALIC}],varDelta:["\u0394",{mathvariant:c.TexConstant.Variant.ITALIC}],varTheta:["\u0398",{mathvariant:c.TexConstant.Variant.ITALIC}],varLambda:["\u039b",{mathvariant:c.TexConstant.Variant.ITALIC}],varXi:["\u039e",{mathvariant:c.TexConstant.Variant.ITALIC}],varPi:["\u03a0",{mathvariant:c.TexConstant.Variant.ITALIC}],varSigma:["\u03a3",{mathvariant:c.TexConstant.Variant.ITALIC}],varUpsilon:["\u03a5",{mathvariant:c.TexConstant.Variant.ITALIC}],varPhi:["\u03a6",{mathvariant:c.TexConstant.Variant.ITALIC}],varPsi:["\u03a8",{mathvariant:c.TexConstant.Variant.ITALIC}],varOmega:["\u03a9",{mathvariant:c.TexConstant.Variant.ITALIC}],beth:"\u2136",gimel:"\u2137",daleth:"\u2138",backprime:["\u2035",{variantForm:!0}],hslash:"\u210f",varnothing:["\u2205",{variantForm:!0}],blacktriangle:"\u25b4",triangledown:["\u25bd",{variantForm:!0}],blacktriangledown:"\u25be",square:"\u25fb",Box:"\u25fb",blacksquare:"\u25fc",lozenge:"\u25ca",Diamond:"\u25ca",blacklozenge:"\u29eb",circledS:["\u24c8",{mathvariant:c.TexConstant.Variant.NORMAL}],bigstar:"\u2605",sphericalangle:"\u2222",measuredangle:"\u2221",nexists:"\u2204",complement:"\u2201",mho:"\u2127",eth:["\xf0",{mathvariant:c.TexConstant.Variant.NORMAL}],Finv:"\u2132",diagup:"\u2571",Game:"\u2141",diagdown:"\u2572",Bbbk:["k",{mathvariant:c.TexConstant.Variant.DOUBLESTRUCK}],yen:"\xa5",circledR:"\xae",checkmark:"\u2713",maltese:"\u2720"}),new l.CharacterMap("AMSsymbols-mathchar0mo",u.default.mathchar0mo,{dotplus:"\u2214",ltimes:"\u22c9",smallsetminus:["\u2216",{variantForm:!0}],rtimes:"\u22ca",Cap:"\u22d2",doublecap:"\u22d2",leftthreetimes:"\u22cb",Cup:"\u22d3",doublecup:"\u22d3",rightthreetimes:"\u22cc",barwedge:"\u22bc",curlywedge:"\u22cf",veebar:"\u22bb",curlyvee:"\u22ce",doublebarwedge:"\u2a5e",boxminus:"\u229f",circleddash:"\u229d",boxtimes:"\u22a0",circledast:"\u229b",boxdot:"\u22a1",circledcirc:"\u229a",boxplus:"\u229e",centerdot:["\u22c5",{variantForm:!0}],divideontimes:"\u22c7",intercal:"\u22ba",leqq:"\u2266",geqq:"\u2267",leqslant:"\u2a7d",geqslant:"\u2a7e",eqslantless:"\u2a95",eqslantgtr:"\u2a96",lesssim:"\u2272",gtrsim:"\u2273",lessapprox:"\u2a85",gtrapprox:"\u2a86",approxeq:"\u224a",lessdot:"\u22d6",gtrdot:"\u22d7",lll:"\u22d8",llless:"\u22d8",ggg:"\u22d9",gggtr:"\u22d9",lessgtr:"\u2276",gtrless:"\u2277",lesseqgtr:"\u22da",gtreqless:"\u22db",lesseqqgtr:"\u2a8b",gtreqqless:"\u2a8c",doteqdot:"\u2251",Doteq:"\u2251",eqcirc:"\u2256",risingdotseq:"\u2253",circeq:"\u2257",fallingdotseq:"\u2252",triangleq:"\u225c",backsim:"\u223d",thicksim:["\u223c",{variantForm:!0}],backsimeq:"\u22cd",thickapprox:["\u2248",{variantForm:!0}],subseteqq:"\u2ac5",supseteqq:"\u2ac6",Subset:"\u22d0",Supset:"\u22d1",sqsubset:"\u228f",sqsupset:"\u2290",preccurlyeq:"\u227c",succcurlyeq:"\u227d",curlyeqprec:"\u22de",curlyeqsucc:"\u22df",precsim:"\u227e",succsim:"\u227f",precapprox:"\u2ab7",succapprox:"\u2ab8",vartriangleleft:"\u22b2",lhd:"\u22b2",vartriangleright:"\u22b3",rhd:"\u22b3",trianglelefteq:"\u22b4",unlhd:"\u22b4",trianglerighteq:"\u22b5",unrhd:"\u22b5",vDash:["\u22a8",{variantForm:!0}],Vdash:"\u22a9",Vvdash:"\u22aa",smallsmile:["\u2323",{variantForm:!0}],shortmid:["\u2223",{variantForm:!0}],smallfrown:["\u2322",{variantForm:!0}],shortparallel:["\u2225",{variantForm:!0}],bumpeq:"\u224f",between:"\u226c",Bumpeq:"\u224e",pitchfork:"\u22d4",varpropto:["\u221d",{variantForm:!0}],backepsilon:"\u220d",blacktriangleleft:"\u25c2",blacktriangleright:"\u25b8",therefore:"\u2234",because:"\u2235",eqsim:"\u2242",vartriangle:["\u25b3",{variantForm:!0}],Join:"\u22c8",nless:"\u226e",ngtr:"\u226f",nleq:"\u2270",ngeq:"\u2271",nleqslant:["\u2a87",{variantForm:!0}],ngeqslant:["\u2a88",{variantForm:!0}],nleqq:["\u2270",{variantForm:!0}],ngeqq:["\u2271",{variantForm:!0}],lneq:"\u2a87",gneq:"\u2a88",lneqq:"\u2268",gneqq:"\u2269",lvertneqq:["\u2268",{variantForm:!0}],gvertneqq:["\u2269",{variantForm:!0}],lnsim:"\u22e6",gnsim:"\u22e7",lnapprox:"\u2a89",gnapprox:"\u2a8a",nprec:"\u2280",nsucc:"\u2281",npreceq:["\u22e0",{variantForm:!0}],nsucceq:["\u22e1",{variantForm:!0}],precneqq:"\u2ab5",succneqq:"\u2ab6",precnsim:"\u22e8",succnsim:"\u22e9",precnapprox:"\u2ab9",succnapprox:"\u2aba",nsim:"\u2241",ncong:"\u2247",nshortmid:["\u2224",{variantForm:!0}],nshortparallel:["\u2226",{variantForm:!0}],nmid:"\u2224",nparallel:"\u2226",nvdash:"\u22ac",nvDash:"\u22ad",nVdash:"\u22ae",nVDash:"\u22af",ntriangleleft:"\u22ea",ntriangleright:"\u22eb",ntrianglelefteq:"\u22ec",ntrianglerighteq:"\u22ed",nsubseteq:"\u2288",nsupseteq:"\u2289",nsubseteqq:["\u2288",{variantForm:!0}],nsupseteqq:["\u2289",{variantForm:!0}],subsetneq:"\u228a",supsetneq:"\u228b",varsubsetneq:["\u228a",{variantForm:!0}],varsupsetneq:["\u228b",{variantForm:!0}],subsetneqq:"\u2acb",supsetneqq:"\u2acc",varsubsetneqq:["\u2acb",{variantForm:!0}],varsupsetneqq:["\u2acc",{variantForm:!0}],leftleftarrows:"\u21c7",rightrightarrows:"\u21c9",leftrightarrows:"\u21c6",rightleftarrows:"\u21c4",Lleftarrow:"\u21da",Rrightarrow:"\u21db",twoheadleftarrow:"\u219e",twoheadrightarrow:"\u21a0",leftarrowtail:"\u21a2",rightarrowtail:"\u21a3",looparrowleft:"\u21ab",looparrowright:"\u21ac",leftrightharpoons:"\u21cb",rightleftharpoons:["\u21cc",{variantForm:!0}],curvearrowleft:"\u21b6",curvearrowright:"\u21b7",circlearrowleft:"\u21ba",circlearrowright:"\u21bb",Lsh:"\u21b0",Rsh:"\u21b1",upuparrows:"\u21c8",downdownarrows:"\u21ca",upharpoonleft:"\u21bf",upharpoonright:"\u21be",downharpoonleft:"\u21c3",restriction:"\u21be",multimap:"\u22b8",downharpoonright:"\u21c2",leftrightsquigarrow:"\u21ad",rightsquigarrow:"\u21dd",leadsto:"\u21dd",dashrightarrow:"\u21e2",dashleftarrow:"\u21e0",nleftarrow:"\u219a",nrightarrow:"\u219b",nLeftarrow:"\u21cd",nRightarrow:"\u21cf",nleftrightarrow:"\u21ae",nLeftrightarrow:"\u21ce"}),new l.DelimiterMap("AMSsymbols-delimiter",u.default.delimiter,{"\\ulcorner":"\u231c","\\urcorner":"\u231d","\\llcorner":"\u231e","\\lrcorner":"\u231f"}),new l.CommandMap("AMSsymbols-macros",{implies:["Macro","\\;\\Longrightarrow\\;"],impliedby:["Macro","\\;\\Longleftarrow\\;"]},a.AmsMethods)},4387:function(t,e,r){var n=this&&this.__assign||function(){return n=Object.assign||function(t){for(var e,r=1,n=arguments.length;r<n;r++)for(var o in e=arguments[r])Object.prototype.hasOwnProperty.call(e,o)&&(t[o]=e[o]);return t},n.apply(this,arguments)},o=this&&this.__read||function(t,e){var r="function"==typeof Symbol&&t[Symbol.iterator];if(!r)return t;var n,o,i=r.call(t),s=[];try{for(;(void 0===e||e-- >0)&&!(n=i.next()).done;)s.push(n.value)}catch(t){o={error:t}}finally{try{n&&!n.done&&(r=i.return)&&r.call(i)}finally{if(o)throw o.error}}return s},i=this&&this.__importDefault||function(t){return t&&t.__esModule?t:{default:t}};Object.defineProperty(e,"__esModule",{value:!0}),e.NEW_OPS=e.AmsMethods=void 0;var s=i(r(1130)),a=i(r(5450)),l=i(r(1256)),c=r(8317),u=i(r(8417)),p=i(r(3971)),h=r(8803),f=i(r(7693)),d=r(9007);function m(t){if(!t||t.isInferred&&0===t.childNodes.length)return[null,null];if(t.isKind("msubsup")&&y(t))return[t,null];var e=l.default.getChildAt(t,0);return t.isInferred&&e&&y(e)?(t.childNodes.splice(0,1),[e,t]):[null,t]}function y(t){var e=t.childNodes[0];return e&&e.isKind("mi")&&""===e.getText()}e.AmsMethods={},e.AmsMethods.AmsEqnArray=function(t,e,r,n,o,i,a){var l=t.GetBrackets("\\begin{"+e.getName()+"}"),c=f.default.EqnArray(t,e,r,n,o,i,a);return s.default.setArrayAlign(c,l)},e.AmsMethods.AlignAt=function(t,r,n,o){var i,a,l=r.getName(),c="",u=[];if(o||(a=t.GetBrackets("\\begin{"+l+"}")),(i=t.GetArgument("\\begin{"+l+"}")).match(/[^0-9]/))throw new p.default("PositiveIntegerArg","Argument to %1 must me a positive integer","\\begin{"+l+"}");for(var h=parseInt(i,10);h>0;)c+="rl",u.push("0em 0em"),h--;var f=u.join(" ");if(o)return e.AmsMethods.EqnArray(t,r,n,o,c,f);var d=e.AmsMethods.EqnArray(t,r,n,o,c,f);return s.default.setArrayAlign(d,a)},e.AmsMethods.Multline=function(t,e,r){t.Push(e),s.default.checkEqnEnv(t);var n=t.itemFactory.create("multline",r,t.stack);return n.arraydef={displaystyle:!0,rowspacing:".5em",columnspacing:"100%",width:t.options.ams.multlineWidth,side:t.options.tagSide,minlabelspacing:t.options.tagIndent,framespacing:t.options.ams.multlineIndent+" 0",frame:"","data-width-includes-label":!0},n},e.AmsMethods.XalignAt=function(t,r,n,o){var i=t.GetArgument("\\begin{"+r.getName()+"}");if(i.match(/[^0-9]/))throw new p.default("PositiveIntegerArg","Argument to %1 must me a positive integer","\\begin{"+r.getName()+"}");var s=o?"crl":"rlc",a=o?"fit auto auto":"auto auto fit",l=e.AmsMethods.FlalignArray(t,r,n,o,!1,s,a,!0);return l.setProperty("xalignat",2*parseInt(i)),l},e.AmsMethods.FlalignArray=function(t,e,r,n,o,i,a,l){void 0===l&&(l=!1),t.Push(e),s.default.checkEqnEnv(t),i=i.split("").join(" ").replace(/r/g,"right").replace(/l/g,"left").replace(/c/g,"center");var c=t.itemFactory.create("flalign",e.getName(),r,n,o,t.stack);return c.arraydef={width:"100%",displaystyle:!0,columnalign:i,columnspacing:"0em",columnwidth:a,rowspacing:"3pt",side:t.options.tagSide,minlabelspacing:l?"0":t.options.tagIndent,"data-width-includes-label":!0},c.setProperty("zeroWidthLabel",l),c},e.NEW_OPS="ams-declare-ops",e.AmsMethods.HandleDeclareOp=function(t,r){var n=t.GetStar()?"*":"",o=s.default.trimSpaces(t.GetArgument(r));"\\"===o.charAt(0)&&(o=o.substr(1));var i=t.GetArgument(r);t.configuration.handlers.retrieve(e.NEW_OPS).add(o,new h.Macro(o,e.AmsMethods.Macro,["\\operatorname".concat(n,"{").concat(i,"}")]))},e.AmsMethods.HandleOperatorName=function(t,e){var r=t.GetStar(),o=s.default.trimSpaces(t.GetArgument(e)),i=new u.default(o,n(n({},t.stack.env),{font:c.TexConstant.Variant.NORMAL,multiLetterIdentifiers:/^[-*a-z]+/i,operatorLetters:!0}),t.configuration).mml();if(i.isKind("mi")||(i=t.create("node","TeXAtom",[i])),l.default.setProperties(i,{movesupsub:r,movablelimits:!0,texClass:d.TEXCLASS.OP}),!r){var a=t.GetNext(),p=t.i;"\\"===a&&++t.i&&"limits"!==t.GetCS()&&(t.i=p)}t.Push(i)},e.AmsMethods.SideSet=function(t,e){var r=o(m(t.ParseArg(e)),2),n=r[0],i=r[1],a=o(m(t.ParseArg(e)),2),c=a[0],u=a[1],p=t.ParseArg(e),h=p;n&&(i?n.replaceChild(t.create("node","mphantom",[t.create("node","mpadded",[s.default.copyNode(p,t)],{width:0})]),l.default.getChildAt(n,0)):(h=t.create("node","mmultiscripts",[p]),c&&l.default.appendChildren(h,[l.default.getChildAt(c,1)||t.create("node","none"),l.default.getChildAt(c,2)||t.create("node","none")]),l.default.setProperty(h,"scriptalign","left"),l.default.appendChildren(h,[t.create("node","mprescripts"),l.default.getChildAt(n,1)||t.create("node","none"),l.default.getChildAt(n,2)||t.create("node","none")]))),c&&h===p&&(c.replaceChild(p,l.default.getChildAt(c,0)),h=c);var 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f=t.create("node","munderover",[h]),m=t.create("node","mpadded",[c,p],i);if(l.default.setAttribute(m,"voffset","-.2em"),l.default.setAttribute(m,"height","-.2em"),l.default.setChild(f,f.over,m),a){var y=new u.default(a,t.stack.env,t.configuration).mml(),g=t.create("node","mspace",[],{height:".75em"});m=t.create("node","mpadded",[y,g],i),l.default.setAttribute(m,"voffset",".15em"),l.default.setAttribute(m,"depth","-.15em"),l.default.setChild(f,f.under,m)}l.default.setProperty(f,"subsupOK",!0),t.Push(f)},e.AmsMethods.HandleShove=function(t,e,r){var n=t.stack.Top();if("multline"!==n.kind)throw new p.default("CommandOnlyAllowedInEnv","%1 only allowed in %2 environment",t.currentCS,"multline");if(n.Size())throw new p.default("CommandAtTheBeginingOfLine","%1 must come at the beginning of the line",t.currentCS);n.setProperty("shove",r)},e.AmsMethods.CFrac=function(t,e){var r=s.default.trimSpaces(t.GetBrackets(e,"")),n=t.GetArgument(e),o=t.GetArgument(e),i={l:c.TexConstant.Align.LEFT,r:c.TexConstant.Align.RIGHT,"":""},a=new u.default("\\strut\\textstyle{"+n+"}",t.stack.env,t.configuration).mml(),h=new u.default("\\strut\\textstyle{"+o+"}",t.stack.env,t.configuration).mml(),f=t.create("node","mfrac",[a,h]);if(null==(r=i[r]))throw new p.default("IllegalAlign","Illegal alignment specified in %1",t.currentCS);r&&l.default.setProperties(f,{numalign:r,denomalign:r}),t.Push(f)},e.AmsMethods.Genfrac=function(t,e,r,n,o,i){null==r&&(r=t.GetDelimiterArg(e)),null==n&&(n=t.GetDelimiterArg(e)),null==o&&(o=t.GetArgument(e)),null==i&&(i=s.default.trimSpaces(t.GetArgument(e)));var a=t.ParseArg(e),c=t.ParseArg(e),u=t.create("node","mfrac",[a,c]);if(""!==o&&l.default.setAttribute(u,"linethickness",o),(r||n)&&(l.default.setProperty(u,"withDelims",!0),u=s.default.fixedFence(t.configuration,r,u,n)),""!==i){var h=parseInt(i,10),f=["D","T","S","SS"][h];if(null==f)throw new p.default("BadMathStyleFor","Bad math style for %1",t.currentCS);u=t.create("node","mstyle",[u]),"D"===f?l.default.setProperties(u,{displaystyle:!0,scriptlevel:0}):l.default.setProperties(u,{displaystyle:!1,scriptlevel:h-1})}t.Push(u)},e.AmsMethods.HandleTag=function(t,e){if(!t.tags.currentTag.taggable&&t.tags.env)throw new p.default("CommandNotAllowedInEnv","%1 not allowed in %2 environment",t.currentCS,t.tags.env);if(t.tags.currentTag.tag)throw new p.default("MultipleCommand","Multiple %1",t.currentCS);var r=t.GetStar(),n=s.default.trimSpaces(t.GetArgument(e));t.tags.tag(n,r)},e.AmsMethods.HandleNoTag=f.default.HandleNoTag,e.AmsMethods.HandleRef=f.default.HandleRef,e.AmsMethods.Macro=f.default.Macro,e.AmsMethods.Accent=f.default.Accent,e.AmsMethods.Tilde=f.default.Tilde,e.AmsMethods.Array=f.default.Array,e.AmsMethods.Spacer=f.default.Spacer,e.AmsMethods.NamedOp=f.default.NamedOp,e.AmsMethods.EqnArray=f.default.EqnArray,e.AmsMethods.Equation=f.default.Equation},1275:function(t,e,r){var n=this&&this.__read||function(t,e){var r="function"==typeof Symbol&&t[Symbol.iterator];if(!r)return t;var n,o,i=r.call(t),s=[];try{for(;(void 0===e||e-- >0)&&!(n=i.next()).done;)s.push(n.value)}catch(t){o={error:t}}finally{try{n&&!n.done&&(r=i.return)&&r.call(i)}finally{if(o)throw o.error}}return s},o=this&&this.__values||function(t){var e="function"==typeof Symbol&&Symbol.iterator,r=e&&t[e],n=0;if(r)return r.call(t);if(t&&"number"==typeof t.length)return{next:function(){return t&&n>=t.length&&(t=void 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o(e,t),Object.defineProperty(e.prototype,"kind",{get:function(){return"array"},enumerable:!1,configurable:!0}),Object.defineProperty(e.prototype,"isOpen",{get:function(){return!0},enumerable:!1,configurable:!0}),Object.defineProperty(e.prototype,"copyEnv",{get:function(){return!1},enumerable:!1,configurable:!0}),e.prototype.checkItem=function(e){if(e.isClose&&!e.isKind("over")){if(e.getProperty("isEntry"))return this.EndEntry(),this.clearEnv(),d.BaseItem.fail;if(e.getProperty("isCR"))return this.EndEntry(),this.EndRow(),this.clearEnv(),d.BaseItem.fail;this.EndTable(),this.clearEnv();var r=this.factory.create("mml",this.createMml());if(this.getProperty("requireClose")){if(e.isKind("close"))return[[r],!0];throw new p.default("MissingCloseBrace","Missing close brace")}return[[r,e],!0]}return t.prototype.checkItem.call(this,e)},e.prototype.createMml=function(){var t=this.arraydef.scriptlevel;delete this.arraydef.scriptlevel;var e=this.create("node","mtable",this.table,this.arraydef);return t&&e.setProperty("scriptlevel",t),4===this.frame.length?f.default.setAttribute(e,"frame",this.dashed?"dashed":"solid"):this.frame.length&&(this.arraydef.rowlines&&(this.arraydef.rowlines=this.arraydef.rowlines.replace(/none( none)+$/,"none")),f.default.setAttribute(e,"frame",""),e=this.create("node","menclose",[e],{notation:this.frame.join(" ")}),"none"===(this.arraydef.columnlines||"none")&&"none"===(this.arraydef.rowlines||"none")||f.default.setAttribute(e,"data-padding",0)),(this.getProperty("open")||this.getProperty("close"))&&(e=h.default.fenced(this.factory.configuration,this.getProperty("open"),e,this.getProperty("close"))),e},e.prototype.EndEntry=function(){var t=this.create("node","mtd",this.nodes);this.hfill.length&&(0===this.hfill[0]&&f.default.setAttribute(t,"columnalign","right"),this.hfill[this.hfill.length-1]===this.Size()&&f.default.setAttribute(t,"columnalign",f.default.getAttribute(t,"columnalign")?"center":"left")),this.row.push(t),this.Clear(),this.hfill=[]},e.prototype.EndRow=function(){var t;this.getProperty("isNumbered")&&3===this.row.length?(this.row.unshift(this.row.pop()),t=this.create("node","mlabeledtr",this.row)):t=this.create("node","mtr",this.row),this.table.push(t),this.row=[]},e.prototype.EndTable=function(){(this.Size()||this.row.length)&&(this.EndEntry(),this.EndRow()),this.checkLines()},e.prototype.checkLines=function(){if(this.arraydef.rowlines){var t=this.arraydef.rowlines.split(/ /);t.length===this.table.length?(this.frame.push("bottom"),t.pop(),this.arraydef.rowlines=t.join(" ")):t.length<this.table.length-1&&(this.arraydef.rowlines+=" none")}if(this.getProperty("rowspacing")){for(var e=this.arraydef.rowspacing.split(/ /);e.length<this.table.length;)e.push(this.getProperty("rowspacing")+"em");this.arraydef.rowspacing=e.join(" ")}},e.prototype.addRowSpacing=function(t){if(this.arraydef.rowspacing){var e=this.arraydef.rowspacing.split(/ /);if(!this.getProperty("rowspacing")){var r=h.default.dimen2em(e[0]);this.setProperty("rowspacing",r)}for(var n=this.getProperty("rowspacing");e.length<this.table.length;)e.push(h.default.Em(n));e[this.table.length-1]=h.default.Em(Math.max(0,n+h.default.dimen2em(t))),this.arraydef.rowspacing=e.join(" ")}},e}(d.BaseItem);e.ArrayItem=k;var j=function(t){function e(e){for(var r=[],n=1;n<arguments.length;n++)r[n-1]=arguments[n];var o=t.call(this,e)||this;return o.maxrow=0,o.factory.configuration.tags.start(r[0],r[2],r[1]),o}return o(e,t),Object.defineProperty(e.prototype,"kind",{get:function(){return"eqnarray"},enumerable:!1,configurable:!0}),e.prototype.EndEntry=function(){this.row.length&&h.default.fixInitialMO(this.factory.configuration,this.nodes);var t=this.create("node","mtd",this.nodes);this.row.push(t),this.Clear()},e.prototype.EndRow=function(){this.row.length>this.maxrow&&(this.maxrow=this.row.length);var t="mtr",e=this.factory.configuration.tags.getTag();e&&(this.row=[e].concat(this.row),t="mlabeledtr"),this.factory.configuration.tags.clearTag();var r=this.create("node",t,this.row);this.table.push(r),this.row=[]},e.prototype.EndTable=function(){t.prototype.EndTable.call(this),this.factory.configuration.tags.end(),this.extendArray("columnalign",this.maxrow),this.extendArray("columnwidth",this.maxrow),this.extendArray("columnspacing",this.maxrow-1)},e.prototype.extendArray=function(t,e){if(this.arraydef[t]){var r=this.arraydef[t].split(/ /),n=s([],i(r),!1);if(n.length>1){for(;n.length<e;)n.push.apply(n,s([],i(r),!1));this.arraydef[t]=n.slice(0,e).join(" ")}}},e}(k);e.EqnArrayItem=j;var B=function(t){function e(e){for(var r=[],n=1;n<arguments.length;n++)r[n-1]=arguments[n];var o=t.call(this,e)||this;return o.factory.configuration.tags.start("equation",!0,r[0]),o}return o(e,t),Object.defineProperty(e.prototype,"kind",{get:function(){return"equation"},enumerable:!1,configurable:!0}),Object.defineProperty(e.prototype,"isOpen",{get:function(){return!0},enumerable:!1,configurable:!0}),e.prototype.checkItem=function(e){if(e.isKind("end")){var r=this.toMml(),n=this.factory.configuration.tags.getTag();return this.factory.configuration.tags.end(),[[n?this.factory.configuration.tags.enTag(r,n):r,e],!0]}if(e.isKind("stop"))throw new p.default("EnvMissingEnd","Missing \\end{%1}",this.getName());return t.prototype.checkItem.call(this,e)},e}(d.BaseItem);e.EquationItem=B},1267:function(t,e,r){var n=this&&this.__createBinding||(Object.create?function(t,e,r,n){void 0===n&&(n=r);var o=Object.getOwnPropertyDescriptor(e,r);o&&!("get"in o?!e.__esModule:o.writable||o.configurable)||(o={enumerable:!0,get:function(){return e[r]}}),Object.defineProperty(t,n,o)}:function(t,e,r,n){void 0===n&&(n=r),t[n]=e[r]}),o=this&&this.__setModuleDefault||(Object.create?function(t,e){Object.defineProperty(t,"default",{enumerable:!0,value:e})}:function(t,e){t.default=e}),i=this&&this.__importStar||function(t){if(t&&t.__esModule)return t;var e={};if(null!=t)for(var r in t)"default"!==r&&Object.prototype.hasOwnProperty.call(t,r)&&n(e,t,r);return o(e,t),e},s=this&&this.__importDefault||function(t){return t&&t.__esModule?t:{default:t}};Object.defineProperty(e,"__esModule",{value:!0});var a=i(r(9140)),l=r(8317),c=s(r(7693)),u=s(r(5450)),p=s(r(1130)),h=r(9007),f=r(6010);new a.RegExpMap("letter",u.default.variable,/[a-z]/i),new a.RegExpMap("digit",u.default.digit,/[0-9.,]/),new a.RegExpMap("command",u.default.controlSequence,/^\\/),new a.MacroMap("special",{"{":"Open","}":"Close","~":"Tilde","^":"Superscript",_:"Subscript"," ":"Space","\t":"Space","\r":"Space","\n":"Space","'":"Prime","%":"Comment","&":"Entry","#":"Hash","\xa0":"Space","\u2019":"Prime"},c.default),new a.CharacterMap("mathchar0mi",u.default.mathchar0mi,{alpha:"\u03b1",beta:"\u03b2",gamma:"\u03b3",delta:"\u03b4",epsilon:"\u03f5",zeta:"\u03b6",eta:"\u03b7",theta:"\u03b8",iota:"\u03b9",kappa:"\u03ba",lambda:"\u03bb",mu:"\u03bc",nu:"\u03bd",xi:"\u03be",omicron:"\u03bf",pi:"\u03c0",rho:"\u03c1",sigma:"\u03c3",tau:"\u03c4",upsilon:"\u03c5",phi:"\u03d5",chi:"\u03c7",psi:"\u03c8",omega:"\u03c9",varepsilon:"\u03b5",vartheta:"\u03d1",varpi:"\u03d6",varrho:"\u03f1",varsigma:"\u03c2",varphi:"\u03c6",S:["\xa7",{mathvariant:l.TexConstant.Variant.NORMAL}],aleph:["\u2135",{mathvariant:l.TexConstant.Variant.NORMAL}],hbar:["\u210f",{variantForm:!0}],imath:"\u0131",jmath:"\u0237",ell:"\u2113",wp:["\u2118",{mathvariant:l.TexConstant.Variant.NORMAL}],Re:["\u211c",{mathvariant:l.TexConstant.Variant.NORMAL}],Im:["\u2111",{mathvariant:l.TexConstant.Variant.NORMAL}],partial:["\u2202",{mathvariant:l.TexConstant.Variant.ITALIC}],infty:["\u221e",{mathvariant:l.TexConstant.Variant.NORMAL}],prime:["\u2032",{variantForm:!0}],emptyset:["\u2205",{mathvariant:l.TexConstant.Variant.NORMAL}],nabla:["\u2207",{mathvariant:l.TexConstant.Variant.NORMAL}],top:["\u22a4",{mathvariant:l.TexConstant.Variant.NORMAL}],bot:["\u22a5",{mathvariant:l.TexConstant.Variant.NORMAL}],angle:["\u2220",{mathvariant:l.TexConstant.Variant.NORMAL}],triangle:["\u25b3",{mathvariant:l.TexConstant.Variant.NORMAL}],backslash:["\u2216",{mathvariant:l.TexConstant.Variant.NORMAL}],forall:["\u2200",{mathvariant:l.TexConstant.Variant.NORMAL}],exists:["\u2203",{mathvariant:l.TexConstant.Variant.NORMAL}],neg:["\xac",{mathvariant:l.TexConstant.Variant.NORMAL}],lnot:["\xac",{mathvariant:l.TexConstant.Variant.NORMAL}],flat:["\u266d",{mathvariant:l.TexConstant.Variant.NORMAL}],natural:["\u266e",{mathvariant:l.TexConstant.Variant.NORMAL}],sharp:["\u266f",{mathvariant:l.TexConstant.Variant.NORMAL}],clubsuit:["\u2663",{mathvariant:l.TexConstant.Variant.NORMAL}],diamondsuit:["\u2662",{mathvariant:l.TexConstant.Variant.NORMAL}],heartsuit:["\u2661",{mathvariant:l.TexConstant.Variant.NORMAL}],spadesuit:["\u2660",{mathvariant:l.TexConstant.Variant.NORMAL}]}),new a.CharacterMap("mathchar0mo",u.default.mathchar0mo,{surd:"\u221a",coprod:["\u2210",{texClass:h.TEXCLASS.OP,movesupsub:!0}],bigvee:["\u22c1",{texClass:h.TEXCLASS.OP,movesupsub:!0}],bigwedge:["\u22c0",{texClass:h.TEXCLASS.OP,movesupsub:!0}],biguplus:["\u2a04",{texClass:h.TEXCLASS.OP,movesupsub:!0}],bigcap:["\u22c2",{texClass:h.TEXCLASS.OP,movesupsub:!0}],bigcup:["\u22c3",{texClass:h.TEXCLASS.OP,movesupsub:!0}],int:["\u222b",{texClass:h.TEXCLASS.OP}],intop:["\u222b",{texClass:h.TEXCLASS.OP,movesupsub:!0,movablelimits:!0}],iint:["\u222c",{texClass:h.TEXCLASS.OP}],iiint:["\u222d",{texClass:h.TEXCLASS.OP}],prod:["\u220f",{texClass:h.TEXCLASS.OP,movesupsub:!0}],sum:["\u2211",{texClass:h.TEXCLASS.OP,movesupsub:!0}],bigotimes:["\u2a02",{texClass:h.TEXCLASS.OP,movesupsub:!0}],bigoplus:["\u2a01",{texClass:h.TEXCLASS.OP,movesupsub:!0}],bigodot:["\u2a00",{texClass:h.TEXCLASS.OP,movesupsub:!0}],oint:["\u222e",{texClass:h.TEXCLASS.OP}],bigsqcup:["\u2a06",{texClass:h.TEXCLASS.OP,movesupsub:!0}],smallint:["\u222b",{largeop:!1}],triangleleft:"\u25c3",triangleright:"\u25b9",bigtriangleup:"\u25b3",bigtriangledown:"\u25bd",wedge:"\u2227",land:"\u2227",vee:"\u2228",lor:"\u2228",cap:"\u2229",cup:"\u222a",ddagger:"\u2021",dagger:"\u2020",sqcap:"\u2293",sqcup:"\u2294",uplus:"\u228e",amalg:"\u2a3f",diamond:"\u22c4",bullet:"\u2219",wr:"\u2240",div:"\xf7",divsymbol:"\xf7",odot:["\u2299",{largeop:!1}],oslash:["\u2298",{largeop:!1}],otimes:["\u2297",{largeop:!1}],ominus:["\u2296",{largeop:!1}],oplus:["\u2295",{largeop:!1}],mp:"\u2213",pm:"\xb1",circ:"\u2218",bigcirc:"\u25ef",setminus:"\u2216",cdot:"\u22c5",ast:"\u2217",times:"\xd7",star:"\u22c6",propto:"\u221d",sqsubseteq:"\u2291",sqsupseteq:"\u2292",parallel:"\u2225",mid:"\u2223",dashv:"\u22a3",vdash:"\u22a2",leq:"\u2264",le:"\u2264",geq:"\u2265",ge:"\u2265",lt:"<",gt:">",succ:"\u227b",prec:"\u227a",approx:"\u2248",succeq:"\u2ab0",preceq:"\u2aaf",supset:"\u2283",subset:"\u2282",supseteq:"\u2287",subseteq:"\u2286",in:"\u2208",ni:"\u220b",notin:"\u2209",owns:"\u220b",gg:"\u226b",ll:"\u226a",sim:"\u223c",simeq:"\u2243",perp:"\u22a5",equiv:"\u2261",asymp:"\u224d",smile:"\u2323",frown:"\u2322",ne:"\u2260",neq:"\u2260",cong:"\u2245",doteq:"\u2250",bowtie:"\u22c8",models:"\u22a8",notChar:"\u29f8",Leftrightarrow:"\u21d4",Leftarrow:"\u21d0",Rightarrow:"\u21d2",leftrightarrow:"\u2194",leftarrow:"\u2190",gets:"\u2190",rightarrow:"\u2192",to:["\u2192",{accent:!1}],mapsto:"\u21a6",leftharpoonup:"\u21bc",leftharpoondown:"\u21bd",rightharpoonup:"\u21c0",rightharpoondown:"\u21c1",nearrow:"\u2197",searrow:"\u2198",nwarrow:"\u2196",swarrow:"\u2199",rightleftharpoons:"\u21cc",hookrightarrow:"\u21aa",hookleftarrow:"\u21a9",longleftarrow:"\u27f5",Longleftarrow:"\u27f8",longrightarrow:"\u27f6",Longrightarrow:"\u27f9",Longleftrightarrow:"\u27fa",longleftrightarrow:"\u27f7",longmapsto:"\u27fc",ldots:"\u2026",cdots:"\u22ef",vdots:"\u22ee",ddots:"\u22f1",dotsc:"\u2026",dotsb:"\u22ef",dotsm:"\u22ef",dotsi:"\u22ef",dotso:"\u2026",ldotp:[".",{texClass:h.TEXCLASS.PUNCT}],cdotp:["\u22c5",{texClass:h.TEXCLASS.PUNCT}],colon:[":",{texClass:h.TEXCLASS.PUNCT}]}),new a.CharacterMap("mathchar7",u.default.mathchar7,{Gamma:"\u0393",Delta:"\u0394",Theta:"\u0398",Lambda:"\u039b",Xi:"\u039e",Pi:"\u03a0",Sigma:"\u03a3",Upsilon:"\u03a5",Phi:"\u03a6",Psi:"\u03a8",Omega:"\u03a9",_:"_","#":"#",$:"$","%":"%","&":"&",And:"&"}),new a.DelimiterMap("delimiter",u.default.delimiter,{"(":"(",")":")","[":"[","]":"]","<":"\u27e8",">":"\u27e9","\\lt":"\u27e8","\\gt":"\u27e9","/":"/","|":["|",{texClass:h.TEXCLASS.ORD}],".":"","\\\\":"\\","\\lmoustache":"\u23b0","\\rmoustache":"\u23b1","\\lgroup":"\u27ee","\\rgroup":"\u27ef","\\arrowvert":"\u23d0","\\Arrowvert":"\u2016","\\bracevert":"\u23aa","\\Vert":["\u2016",{texClass:h.TEXCLASS.ORD}],"\\|":["\u2016",{texClass:h.TEXCLASS.ORD}],"\\vert":["|",{texClass:h.TEXCLASS.ORD}],"\\uparrow":"\u2191","\\downarrow":"\u2193","\\updownarrow":"\u2195","\\Uparrow":"\u21d1","\\Downarrow":"\u21d3","\\Updownarrow":"\u21d5","\\backslash":"\\","\\rangle":"\u27e9","\\langle":"\u27e8","\\rbrace":"}","\\lbrace":"{","\\}":"}","\\{":"{","\\rceil":"\u2309","\\lceil":"\u2308","\\rfloor":"\u230b","\\lfloor":"\u230a","\\lbrack":"[","\\rbrack":"]"}),new 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e=t.padding/2,r=t.thickness;return[2*e,e,e+r,3*e+r]},border:function(t){return[0,0,t.thickness,0]},remove:"bottom"}],h.Arrow("up"),h.Arrow("down"),h.Arrow("left"),h.Arrow("right"),h.Arrow("updown"),h.Arrow("leftright"),h.DiagonalArrow("updiagonal"),h.DiagonalArrow("northeast"),h.DiagonalArrow("southeast"),h.DiagonalArrow("northwest"),h.DiagonalArrow("southwest"),h.DiagonalArrow("northeastsouthwest"),h.DiagonalArrow("northwestsoutheast"),["longdiv",{renderer:function(t,e){var r=t.adaptor;r.setStyle(e,"border-top",t.em(t.thickness)+" solid");var n=r.append(t.chtml,t.html("dbox")),o=t.thickness,i=t.padding;o!==h.THICKNESS&&r.setStyle(n,"border-width",t.em(o)),i!==h.PADDING&&(r.setStyle(n,"left",t.em(-1.5*i)),r.setStyle(n,"width",t.em(3*i)),r.setStyle(n,"clip-path","inset(0 0 0 "+t.em(1.5*i)+")"))},bbox:function(t){var e=t.padding,r=t.thickness;return[e+r,e,e,2*e+r/2]}}],["radical",{renderer:function(t,e){t.msqrt.toCHTML(e);var 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mjx-cell":{"text-align":"right"},'[script-align="left"] > mjx-row > mjx-cell':{"text-align":"left"},'[script-align="center"] > mjx-row > mjx-cell':{"text-align":"center"},'[script-align="right"] > mjx-row > mjx-cell':{"text-align":"right"}},e}((0,a.CommonMmultiscriptsMixin)(s.CHTMLmsubsup));e.CHTMLmmultiscripts=u},6460:function(t,e,r){var n,o=this&&this.__extends||(n=function(t,e){return n=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,e){t.__proto__=e}||function(t,e){for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&(t[r]=e[r])},n(t,e)},function(t,e){if("function"!=typeof e&&null!==e)throw new TypeError("Class extends value "+String(e)+" is not a constructor or null");function r(){this.constructor=t}n(t,e),t.prototype=null===e?Object.create(e):(r.prototype=e.prototype,new r)});Object.defineProperty(e,"__esModule",{value:!0}),e.CHTMLmn=void 0;var i=r(5355),s=r(5023),a=r(3050),l=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return 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l=i(this.tableRows),c=l.next();!c.done;c=l.next()){var u=c.value,p=0;try{for(var h=(r=void 0,i(u.tableCells)),f=h.next();!f.done;f=h.next()){var d=f.value,m=s[p++],y=s[p],g=d?d.chtml:this.adaptor.childNodes(u.chtml)[p];(p>1&&"0.4em"!==m||a&&1===p)&&this.adaptor.setStyle(g,"paddingLeft",m),(p<this.numCols&&"0.4em"!==y||a&&p===this.numCols)&&this.adaptor.setStyle(g,"paddingRight",y)}}catch(t){r={error:t}}finally{try{f&&!f.done&&(n=h.return)&&n.call(h)}finally{if(r)throw r.error}}}}catch(e){t={error:e}}finally{try{c&&!c.done&&(e=l.return)&&e.call(l)}finally{if(t)throw t.error}}},e.prototype.handleColumnLines=function(){var t,e,r,n;if("none"!==this.node.attributes.get("columnlines")){var o=this.getColumnAttributes("columnlines");try{for(var s=i(this.childNodes),a=s.next();!a.done;a=s.next()){var l=a.value,c=0;try{for(var u=(r=void 0,i(this.adaptor.childNodes(l.chtml).slice(1))),p=u.next();!p.done;p=u.next()){var h=p.value,f=o[c++];"none"!==f&&this.adaptor.setStyle(h,"borderLeft",".07em "+f)}}catch(t){r={error:t}}finally{try{p&&!p.done&&(n=u.return)&&n.call(u)}finally{if(r)throw r.error}}}}catch(e){t={error:e}}finally{try{a&&!a.done&&(e=s.return)&&e.call(s)}finally{if(t)throw t.error}}}},e.prototype.handleColumnWidths=function(){var t,e,r,n;try{for(var o=i(this.childNodes),s=o.next();!s.done;s=o.next()){var a=s.value,l=0;try{for(var c=(r=void 0,i(this.adaptor.childNodes(a.chtml))),u=c.next();!u.done;u=c.next()){var p=u.value,h=this.cWidths[l++];if(null!==h){var f="number"==typeof h?this.em(h):h;this.adaptor.setStyle(p,"width",f),this.adaptor.setStyle(p,"maxWidth",f),this.adaptor.setStyle(p,"minWidth",f)}}}catch(t){r={error:t}}finally{try{u&&!u.done&&(n=c.return)&&n.call(c)}finally{if(r)throw r.error}}}}catch(e){t={error:e}}finally{try{s&&!s.done&&(e=o.return)&&e.call(o)}finally{if(t)throw t.error}}},e.prototype.handleRowSpacing=function(){var t,e,r,n,o=this.childNodes[0]?1/this.childNodes[0].getBBox().rscale:1,s=this.getEmHalfSpacing(this.fSpace[1],this.rSpace,o),a=this.frame,l=0;try{for(var c=i(this.childNodes),u=c.next();!u.done;u=c.next()){var p=u.value,h=s[l++],f=s[l];try{for(var d=(r=void 0,i(p.childNodes)),m=d.next();!m.done;m=d.next()){var y=m.value;(l>1&&"0.215em"!==h||a&&1===l)&&this.adaptor.setStyle(y.chtml,"paddingTop",h),(l<this.numRows&&"0.215em"!==f||a&&l===this.numRows)&&this.adaptor.setStyle(y.chtml,"paddingBottom",f)}}catch(t){r={error:t}}finally{try{m&&!m.done&&(n=d.return)&&n.call(d)}finally{if(r)throw r.error}}}}catch(e){t={error:e}}finally{try{u&&!u.done&&(e=c.return)&&e.call(c)}finally{if(t)throw t.error}}},e.prototype.handleRowLines=function(){var t,e,r,n;if("none"!==this.node.attributes.get("rowlines")){var o=this.getRowAttributes("rowlines"),s=0;try{for(var a=i(this.childNodes.slice(1)),l=a.next();!l.done;l=a.next()){var c=l.value,u=o[s++];if("none"!==u)try{for(var p=(r=void 0,i(this.adaptor.childNodes(c.chtml))),h=p.next();!h.done;h=p.next()){var f=h.value;this.adaptor.setStyle(f,"borderTop",".07em "+u)}}catch(t){r={error:t}}finally{try{h&&!h.done&&(n=p.return)&&n.call(p)}finally{if(r)throw r.error}}}}catch(e){t={error:e}}finally{try{l&&!l.done&&(e=a.return)&&e.call(a)}finally{if(t)throw t.error}}}},e.prototype.handleRowHeights=function(){this.node.attributes.get("equalrows")&&this.handleEqualRows()},e.prototype.handleEqualRows=function(){for(var t=this.getRowHalfSpacing(),e=this.getTableData(),r=e.H,n=e.D,o=e.NH,i=e.ND,s=this.getEqualRowHeight(),a=0;a<this.numRows;a++){var l=this.childNodes[a];this.setRowHeight(l,s+t[a]+t[a+1]+this.rLines[a]),s!==o[a]+i[a]&&this.setRowBaseline(l,s,(s-r[a]+n[a])/2)}},e.prototype.setRowHeight=function(t,e){this.adaptor.setStyle(t.chtml,"height",this.em(e))},e.prototype.setRowBaseline=function(t,e,r){var n,o,s=t.node.attributes.get("rowalign");try{for(var a=i(t.childNodes),l=a.next();!l.done;l=a.next()){var c=l.value;if(this.setCellBaseline(c,s,e,r))break}}catch(t){n={error:t}}finally{try{l&&!l.done&&(o=a.return)&&o.call(a)}finally{if(n)throw n.error}}},e.prototype.setCellBaseline=function(t,e,r,n){var o=t.node.attributes.get("rowalign");if("baseline"===o||"axis"===o){var i=this.adaptor,s=i.lastChild(t.chtml);i.setStyle(s,"height",this.em(r)),i.setStyle(s,"verticalAlign",this.em(-n));var a=t.parent;if(!(a.node.isKind("mlabeledtr")&&t===a.childNodes[0]||"baseline"!==e&&"axis"!==e))return!0}return!1},e.prototype.handleFrame=function(){this.frame&&this.fLine&&this.adaptor.setStyle(this.itable,"border",".07em "+this.node.attributes.get("frame"))},e.prototype.handleWidth=function(){var t=this.adaptor,e=this.getBBox(),r=e.w,n=e.L,o=e.R;t.setStyle(this.chtml,"minWidth",this.em(n+r+o));var i=this.node.attributes.get("width");if((0,u.isPercent)(i))t.setStyle(this.chtml,"width",""),t.setAttribute(this.chtml,"width","full");else if(!this.hasLabels){if("auto"===i)return;i=this.em(this.length2em(i)+2*this.fLine)}var s=t.firstChild(this.chtml);if(t.setStyle(s,"width",i),t.setStyle(s,"minWidth",this.em(r)),n||o){t.setStyle(this.chtml,"margin","");var a=this.node.attributes.get("data-width-includes-label")?"padding":"margin";n===o?t.setStyle(s,a,"0 "+this.em(o)):t.setStyle(s,a,"0 "+this.em(o)+" 0 "+this.em(n))}t.setAttribute(this.itable,"width","full")},e.prototype.handleAlign=function(){var t=s(this.getAlignmentRow(),2),e=t[0],r=t[1];if(null===r)"axis"!==e&&this.adaptor.setAttribute(this.chtml,"align",e);else{var n=this.getVerticalPosition(r,e);this.adaptor.setAttribute(this.chtml,"align","top"),this.adaptor.setStyle(this.chtml,"verticalAlign",this.em(n))}},e.prototype.handleJustify=function(){var t=this.getAlignShift()[0];"center"!==t&&this.adaptor.setAttribute(this.chtml,"justify",t)},e.prototype.handleLabels=function(){if(this.hasLabels){var t=this.labels,e=this.node.attributes,r=this.adaptor,n=e.get("side");r.setAttribute(this.chtml,"side",n),r.setAttribute(t,"align",n),r.setStyle(t,n,"0");var o=s(this.addLabelPadding(n),2),i=o[0],a=o[1];if(a){var l=r.firstChild(this.chtml);this.setIndent(l,i,a)}this.updateRowHeights(),this.addLabelSpacing()}},e.prototype.addLabelPadding=function(t){var e=s(this.getPadAlignShift(t),3),r=e[1],n=e[2],o={};if("right"===t&&!this.node.attributes.get("data-width-includes-label")){var i=this.node.attributes.get("width"),a=this.getBBox(),l=a.w,c=a.L,p=a.R;o.style={width:(0,u.isPercent)(i)?"calc("+i+" + "+this.em(c+p)+")":this.em(c+l+p)}}return this.adaptor.append(this.chtml,this.html("mjx-labels",o,[this.labels])),[r,n]},e.prototype.updateRowHeights=function(){for(var t=this.getTableData(),e=t.H,r=t.D,n=t.NH,o=t.ND,i=this.getRowHalfSpacing(),s=0;s<this.numRows;s++){var a=this.childNodes[s];this.setRowHeight(a,e[s]+r[s]+i[s]+i[s+1]+this.rLines[s]),e[s]!==n[s]||r[s]!==o[s]?this.setRowBaseline(a,e[s]+r[s],r[s]):a.node.isKind("mlabeledtr")&&this.setCellBaseline(a.childNodes[0],"",e[s]+r[s],r[s])}},e.prototype.addLabelSpacing=function(){for(var t=this.adaptor,e=this.node.attributes.get("equalrows"),r=this.getTableData(),n=r.H,o=r.D,i=e?this.getEqualRowHeight():0,s=this.getRowHalfSpacing(),a=this.fLine,l=t.firstChild(this.labels),c=0;c<this.numRows;c++){this.childNodes[c].node.isKind("mlabeledtr")?(a&&t.insert(this.html("mjx-mtr",{style:{height:this.em(a)}}),l),t.setStyle(l,"height",this.em((e?i:n[c]+o[c])+s[c]+s[c+1])),l=t.next(l),a=this.rLines[c]):a+=s[c]+(e?i:n[c]+o[c])+s[c+1]+this.rLines[c]}},e.kind=c.MmlMtable.prototype.kind,e.styles={"mjx-mtable":{"vertical-align":".25em","text-align":"center",position:"relative","box-sizing":"border-box","border-spacing":0,"border-collapse":"collapse"},'mjx-mstyle[size="s"] mjx-mtable':{"vertical-align":".354em"},"mjx-labels":{position:"absolute",left:0,top:0},"mjx-table":{display:"inline-block","vertical-align":"-.5ex","box-sizing":"border-box"},"mjx-table > mjx-itable":{"vertical-align":"middle","text-align":"left","box-sizing":"border-box"},"mjx-labels > mjx-itable":{position:"absolute",top:0},'mjx-mtable[justify="left"]':{"text-align":"left"},'mjx-mtable[justify="right"]':{"text-align":"right"},'mjx-mtable[justify="left"][side="left"]':{"padding-right":"0 ! important"},'mjx-mtable[justify="left"][side="right"]':{"padding-left":"0 ! important"},'mjx-mtable[justify="right"][side="left"]':{"padding-right":"0 ! important"},'mjx-mtable[justify="right"][side="right"]':{"padding-left":"0 ! important"},"mjx-mtable[align]":{"vertical-align":"baseline"},'mjx-mtable[align="top"] > mjx-table':{"vertical-align":"top"},'mjx-mtable[align="bottom"] > mjx-table':{"vertical-align":"bottom"},'mjx-mtable[side="right"] mjx-labels':{"min-width":"100%"}},e}((0,l.CommonMtableMixin)(a.CHTMLWrapper));e.CHTMLmtable=p},7056:function(t,e,r){var n,o=this&&this.__extends||(n=function(t,e){return n=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,e){t.__proto__=e}||function(t,e){for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&(t[r]=e[r])},n(t,e)},function(t,e){if("function"!=typeof e&&null!==e)throw new TypeError("Class extends value "+String(e)+" is not a constructor or null");function r(){this.constructor=t}n(t,e),t.prototype=null===e?Object.create(e):(r.prototype=e.prototype,new r)});Object.defineProperty(e,"__esModule",{value:!0}),e.CHTMLmtd=void 0;var i=r(5355),s=r(5164),a=r(4359),l=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return o(e,t),e.prototype.toCHTML=function(e){t.prototype.toCHTML.call(this,e);var r=this.node.attributes.get("rowalign"),n=this.node.attributes.get("columnalign");r!==this.parent.node.attributes.get("rowalign")&&this.adaptor.setAttribute(this.chtml,"rowalign",r),"center"===n||"mlabeledtr"===this.parent.kind&&this===this.parent.childNodes[0]&&n===this.parent.parent.node.attributes.get("side")||this.adaptor.setStyle(this.chtml,"textAlign",n),this.parent.parent.node.getProperty("useHeight")&&this.adaptor.append(this.chtml,this.html("mjx-tstrut"))},e.kind=a.MmlMtd.prototype.kind,e.styles={"mjx-mtd":{display:"table-cell","text-align":"center",padding:".215em .4em"},"mjx-mtd:first-child":{"padding-left":0},"mjx-mtd:last-child":{"padding-right":0},"mjx-mtable > * > mjx-itable > *:first-child > mjx-mtd":{"padding-top":0},"mjx-mtable > * > mjx-itable > *:last-child > mjx-mtd":{"padding-bottom":0},"mjx-tstrut":{display:"inline-block",height:"1em","vertical-align":"-.25em"},'mjx-labels[align="left"] > mjx-mtr > mjx-mtd':{"text-align":"left"},'mjx-labels[align="right"] > mjx-mtr > mjx-mtd':{"text-align":"right"},"mjx-mtd[extra]":{padding:0},'mjx-mtd[rowalign="top"]':{"vertical-align":"top"},'mjx-mtd[rowalign="center"]':{"vertical-align":"middle"},'mjx-mtd[rowalign="bottom"]':{"vertical-align":"bottom"},'mjx-mtd[rowalign="baseline"]':{"vertical-align":"baseline"},'mjx-mtd[rowalign="axis"]':{"vertical-align":".25em"}},e}((0,s.CommonMtdMixin)(i.CHTMLWrapper));e.CHTMLmtd=l},1259:function(t,e,r){var n,o=this&&this.__extends||(n=function(t,e){return n=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,e){t.__proto__=e}||function(t,e){for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&(t[r]=e[r])},n(t,e)},function(t,e){if("function"!=typeof e&&null!==e)throw new TypeError("Class extends value "+String(e)+" is not a constructor or null");function r(){this.constructor=t}n(t,e),t.prototype=null===e?Object.create(e):(r.prototype=e.prototype,new r)});Object.defineProperty(e,"__esModule",{value:!0}),e.CHTMLmtext=void 0;var i=r(5355),s=r(6319),a=r(4770),l=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return o(e,t),e.kind=a.MmlMtext.prototype.kind,e}((0,s.CommonMtextMixin)(i.CHTMLWrapper));e.CHTMLmtext=l},3571:function(t,e,r){var n,o=this&&this.__extends||(n=function(t,e){return n=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,e){t.__proto__=e}||function(t,e){for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&(t[r]=e[r])},n(t,e)},function(t,e){if("function"!=typeof e&&null!==e)throw new TypeError("Class extends value "+String(e)+" is not a constructor or null");function r(){this.constructor=t}n(t,e),t.prototype=null===e?Object.create(e):(r.prototype=e.prototype,new r)});Object.defineProperty(e,"__esModule",{value:!0}),e.CHTMLmlabeledtr=e.CHTMLmtr=void 0;var i=r(5355),s=r(5766),a=r(5766),l=r(5022),c=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return o(e,t),e.prototype.toCHTML=function(e){t.prototype.toCHTML.call(this,e);var r=this.node.attributes.get("rowalign");"baseline"!==r&&this.adaptor.setAttribute(this.chtml,"rowalign",r)},e.kind=l.MmlMtr.prototype.kind,e.styles={"mjx-mtr":{display:"table-row"},'mjx-mtr[rowalign="top"] > mjx-mtd':{"vertical-align":"top"},'mjx-mtr[rowalign="center"] > mjx-mtd':{"vertical-align":"middle"},'mjx-mtr[rowalign="bottom"] > mjx-mtd':{"vertical-align":"bottom"},'mjx-mtr[rowalign="baseline"] > mjx-mtd':{"vertical-align":"baseline"},'mjx-mtr[rowalign="axis"] > mjx-mtd':{"vertical-align":".25em"}},e}((0,s.CommonMtrMixin)(i.CHTMLWrapper));e.CHTMLmtr=c;var u=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return o(e,t),e.prototype.toCHTML=function(e){t.prototype.toCHTML.call(this,e);var r=this.adaptor.firstChild(this.chtml);if(r){this.adaptor.remove(r);var n=this.node.attributes.get("rowalign"),o="baseline"!==n&&"axis"!==n?{rowalign:n}:{},i=this.html("mjx-mtr",o,[r]);this.adaptor.append(this.parent.labels,i)}},e.prototype.markUsed=function(){t.prototype.markUsed.call(this),this.jax.wrapperUsage.add(c.kind)},e.kind=l.MmlMlabeledtr.prototype.kind,e.styles={"mjx-mlabeledtr":{display:"table-row"},'mjx-mlabeledtr[rowalign="top"] > mjx-mtd':{"vertical-align":"top"},'mjx-mlabeledtr[rowalign="center"] > mjx-mtd':{"vertical-align":"middle"},'mjx-mlabeledtr[rowalign="bottom"] > mjx-mtd':{"vertical-align":"bottom"},'mjx-mlabeledtr[rowalign="baseline"] > mjx-mtd':{"vertical-align":"baseline"},'mjx-mlabeledtr[rowalign="axis"] > mjx-mtd':{"vertical-align":".25em"}},e}((0,a.CommonMlabeledtrMixin)(c));e.CHTMLmlabeledtr=u},6590:function(t,e,r){var n,o=this&&this.__extends||(n=function(t,e){return n=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,e){t.__proto__=e}||function(t,e){for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&(t[r]=e[r])},n(t,e)},function(t,e){if("function"!=typeof e&&null!==e)throw new TypeError("Class extends value "+String(e)+" is not a constructor or null");function r(){this.constructor=t}n(t,e),t.prototype=null===e?Object.create(e):(r.prototype=e.prototype,new r)});Object.defineProperty(e,"__esModule",{value:!0}),e.CHTMLmunderover=e.CHTMLmover=e.CHTMLmunder=void 0;var i=r(4300),s=r(1971),a=r(1971),l=r(1971),c=r(5184),u=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return o(e,t),e.prototype.toCHTML=function(e){if(this.hasMovableLimits())return t.prototype.toCHTML.call(this,e),void this.adaptor.setAttribute(this.chtml,"limits","false");this.chtml=this.standardCHTMLnode(e);var r=this.adaptor.append(this.adaptor.append(this.chtml,this.html("mjx-row")),this.html("mjx-base")),n=this.adaptor.append(this.adaptor.append(this.chtml,this.html("mjx-row")),this.html("mjx-under"));this.baseChild.toCHTML(r),this.scriptChild.toCHTML(n);var o=this.baseChild.getOuterBBox(),i=this.scriptChild.getOuterBBox(),s=this.getUnderKV(o,i)[0],a=this.isLineBelow?0:this.getDelta(!0);this.adaptor.setStyle(n,"paddingTop",this.em(s)),this.setDeltaW([r,n],this.getDeltaW([o,i],[0,-a])),this.adjustUnderDepth(n,i)},e.kind=c.MmlMunder.prototype.kind,e.styles={"mjx-over":{"text-align":"left"},'mjx-munder:not([limits="false"])':{display:"inline-table"},"mjx-munder > mjx-row":{"text-align":"left"},"mjx-under":{"padding-bottom":".1em"}},e}((0,s.CommonMunderMixin)(i.CHTMLmsub));e.CHTMLmunder=u;var p=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return o(e,t),e.prototype.toCHTML=function(e){if(this.hasMovableLimits())return t.prototype.toCHTML.call(this,e),void 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e=[],r=0;r<arguments.length;r++)e[r]=arguments[r];var n=t.apply(this,i([],o(e),!1))||this;if(n.bevel=null,n.pad=n.node.getProperty("withDelims")?0:n.font.params.nulldelimiterspace,n.node.attributes.get("bevelled")){var s=n.getBevelData(n.isDisplay()).H,a=n.bevel=n.createMo("/");a.node.attributes.set("symmetric",!0),a.canStretch(1),a.getStretchedVariant([s],!0)}return n}return n(e,t),e.prototype.computeBBox=function(t,e){void 0===e&&(e=!1),t.empty();var r=this.node.attributes.getList("linethickness","bevelled"),n=r.linethickness,o=r.bevelled,i=this.isDisplay(),s=null;if(o)this.getBevelledBBox(t,i);else{var a=this.length2em(String(n),.06);s=-2*this.pad,0===a?this.getAtopBBox(t,i):(this.getFractionBBox(t,i,a),s-=.2),s+=t.w}t.clean(),this.setChildPWidths(e,s)},e.prototype.getFractionBBox=function(t,e,r){var n=this.childNodes[0].getOuterBBox(),o=this.childNodes[1].getOuterBBox(),i=this.font.params.axis_height,s=this.getTUV(e,r),a=s.T,l=s.u,c=s.v;t.combine(n,0,i+a+Math.max(n.d*n.rscale,l)),t.combine(o,0,i-a-Math.max(o.h*o.rscale,c)),t.w+=2*this.pad+.2},e.prototype.getTUV=function(t,e){var r=this.font.params,n=r.axis_height,o=(t?3.5:1.5)*e;return{T:(t?3.5:1.5)*e,u:(t?r.num1:r.num2)-n-o,v:(t?r.denom1:r.denom2)+n-o}},e.prototype.getAtopBBox=function(t,e){var r=this.getUVQ(e),n=r.u,o=r.v,i=r.nbox,s=r.dbox;t.combine(i,0,n),t.combine(s,0,-o),t.w+=2*this.pad},e.prototype.getUVQ=function(t){var e=this.childNodes[0].getOuterBBox(),r=this.childNodes[1].getOuterBBox(),n=this.font.params,i=o(t?[n.num1,n.denom1]:[n.num3,n.denom2],2),s=i[0],a=i[1],l=(t?7:3)*n.rule_thickness,c=s-e.d*e.scale-(r.h*r.scale-a);return c<l&&(s+=(l-c)/2,a+=(l-c)/2,c=l),{u:s,v:a,q:c,nbox:e,dbox:r}},e.prototype.getBevelledBBox=function(t,e){var r=this.getBevelData(e),n=r.u,o=r.v,i=r.delta,s=r.nbox,a=r.dbox,l=this.bevel.getOuterBBox();t.combine(s,0,n),t.combine(l,t.w-i/2,0),t.combine(a,t.w-i/2,o)},e.prototype.getBevelData=function(t){var e=this.childNodes[0].getOuterBBox(),r=this.childNodes[1].getOuterBBox(),n=t?.4:.15,o=Math.max(e.scale*(e.h+e.d),r.scale*(r.h+r.d))+2*n,i=this.font.params.axis_height;return{H:o,delta:n,u:e.scale*(e.d-e.h)/2+i+n,v:r.scale*(r.d-r.h)/2+i-n,nbox:e,dbox:r}},e.prototype.canStretch=function(t){return!1},e.prototype.isDisplay=function(){var t=this.node.attributes.getList("displaystyle","scriptlevel"),e=t.displaystyle,r=t.scriptlevel;return e&&0===r},e.prototype.getWrapWidth=function(t){var e=this.node.attributes;return e.get("bevelled")?this.childNodes[t].getOuterBBox().w:this.getBBox().w-(this.length2em(e.get("linethickness"))?.2:0)-2*this.pad},e.prototype.getChildAlign=function(t){var e=this.node.attributes;return e.get("bevelled")?"left":e.get(["numalign","denomalign"][t])},e}(t)}},5636:function(t,e){var r,n=this&&this.__extends||(r=function(t,e){return r=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,e){t.__proto__=e}||function(t,e){for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&(t[r]=e[r])},r(t,e)},function(t,e){if("function"!=typeof e&&null!==e)throw new TypeError("Class extends value "+String(e)+" is not a constructor or null");function n(){this.constructor=t}r(t,e),t.prototype=null===e?Object.create(e):(n.prototype=e.prototype,new n)}),o=this&&this.__read||function(t,e){var r="function"==typeof Symbol&&t[Symbol.iterator];if(!r)return t;var n,o,i=r.call(t),s=[];try{for(;(void 0===e||e-- >0)&&!(n=i.next()).done;)s.push(n.value)}catch(t){o={error:t}}finally{try{n&&!n.done&&(r=i.return)&&r.call(i)}finally{if(o)throw o.error}}return s},i=this&&this.__spreadArray||function(t,e,r){if(r||2===arguments.length)for(var n,o=0,i=e.length;o<i;o++)!n&&o in e||(n||(n=Array.prototype.slice.call(e,0,o)),n[o]=e[o]);return t.concat(n||Array.prototype.slice.call(e))};Object.defineProperty(e,"__esModule",{value:!0}),e.CommonMglyphMixin=void 0,e.CommonMglyphMixin=function(t){return function(t){function e(){for(var e=[],r=0;r<arguments.length;r++)e[r]=arguments[r];var n=t.apply(this,i([],o(e),!1))||this;return n.getParameters(),n}return n(e,t),e.prototype.getParameters=function(){var t=this.node.attributes.getList("width","height","valign","src","index"),e=t.width,r=t.height,n=t.valign,o=t.src,i=t.index;if(o)this.width="auto"===e?1:this.length2em(e),this.height="auto"===r?1:this.length2em(r),this.valign=this.length2em(n||"0");else{var s=String.fromCodePoint(parseInt(i)),a=this.node.factory;this.charWrapper=this.wrap(a.create("text").setText(s)),this.charWrapper.parent=this}},e.prototype.computeBBox=function(t,e){void 0===e&&(e=!1),this.charWrapper?t.updateFrom(this.charWrapper.getBBox()):(t.w=this.width,t.h=this.height+this.valign,t.d=-this.valign)},e}(t)}},5723:function(t,e){var r,n=this&&this.__extends||(r=function(t,e){return r=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,e){t.__proto__=e}||function(t,e){for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&(t[r]=e[r])},r(t,e)},function(t,e){if("function"!=typeof e&&null!==e)throw new TypeError("Class extends value "+String(e)+" is not a constructor or null");function n(){this.constructor=t}r(t,e),t.prototype=null===e?Object.create(e):(n.prototype=e.prototype,new n)});Object.defineProperty(e,"__esModule",{value:!0}),e.CommonMiMixin=void 0,e.CommonMiMixin=function(t){return function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return n(e,t),e.prototype.computeBBox=function(e,r){void 0===r&&(r=!1),t.prototype.computeBBox.call(this,e),this.copySkewIC(e)},e}(t)}},8009:function(t,e,r){var n,o=this&&this.__extends||(n=function(t,e){return n=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,e){t.__proto__=e}||function(t,e){for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&(t[r]=e[r])},n(t,e)},function(t,e){if("function"!=typeof e&&null!==e)throw new TypeError("Class extends value "+String(e)+" is not a constructor or null");function r(){this.constructor=t}n(t,e),t.prototype=null===e?Object.create(e):(r.prototype=e.prototype,new r)}),i=this&&this.__read||function(t,e){var r="function"==typeof Symbol&&t[Symbol.iterator];if(!r)return t;var n,o,i=r.call(t),s=[];try{for(;(void 0===e||e-- >0)&&!(n=i.next()).done;)s.push(n.value)}catch(t){o={error:t}}finally{try{n&&!n.done&&(r=i.return)&&r.call(i)}finally{if(o)throw o.error}}return s},s=this&&this.__spreadArray||function(t,e,r){if(r||2===arguments.length)for(var n,o=0,i=e.length;o<i;o++)!n&&o in e||(n||(n=Array.prototype.slice.call(e,0,o)),n[o]=e[o]);return t.concat(n||Array.prototype.slice.call(e))},a=this&&this.__values||function(t){var e="function"==typeof Symbol&&Symbol.iterator,r=e&&t[e],n=0;if(r)return r.call(t);if(t&&"number"==typeof t.length)return{next:function(){return t&&n>=t.length&&(t=void 0),{value:t&&t[n++],done:!t}}};throw new TypeError(e?"Object is not iterable.":"Symbol.iterator is not defined.")};Object.defineProperty(e,"__esModule",{value:!0}),e.CommonMmultiscriptsMixin=e.ScriptNames=e.NextScript=void 0;var l=r(6469);e.NextScript={base:"subList",subList:"supList",supList:"subList",psubList:"psupList",psupList:"psubList"},e.ScriptNames=["sup","sup","psup","psub"],e.CommonMmultiscriptsMixin=function(t){return function(t){function r(){for(var e=[],r=0;r<arguments.length;r++)e[r]=arguments[r];var n=t.apply(this,s([],i(e),!1))||this;return n.scriptData=null,n.firstPrescript=0,n.getScriptData(),n}return o(r,t),r.prototype.combinePrePost=function(t,e){var r=new l.BBox(t);return r.combine(e,0,0),r},r.prototype.computeBBox=function(t,e){void 0===e&&(e=!1);var r=this.font.params.scriptspace,n=this.scriptData,o=this.combinePrePost(n.sub,n.psub),s=this.combinePrePost(n.sup,n.psup),a=i(this.getUVQ(o,s),2),l=a[0],c=a[1];if(t.empty(),n.numPrescripts&&(t.combine(n.psup,r,l),t.combine(n.psub,r,c)),t.append(n.base),n.numScripts){var u=t.w;t.combine(n.sup,u,l),t.combine(n.sub,u,c),t.w+=r}t.clean(),this.setChildPWidths(e)},r.prototype.getScriptData=function(){var t=this.scriptData={base:null,sub:l.BBox.empty(),sup:l.BBox.empty(),psub:l.BBox.empty(),psup:l.BBox.empty(),numPrescripts:0,numScripts:0},e=this.getScriptBBoxLists();this.combineBBoxLists(t.sub,t.sup,e.subList,e.supList),this.combineBBoxLists(t.psub,t.psup,e.psubList,e.psupList),t.base=e.base[0],t.numPrescripts=e.psubList.length,t.numScripts=e.subList.length},r.prototype.getScriptBBoxLists=function(){var t,r,n={base:[],subList:[],supList:[],psubList:[],psupList:[]},o="base";try{for(var i=a(this.childNodes),s=i.next();!s.done;s=i.next()){var l=s.value;l.node.isKind("mprescripts")?o="psubList":(n[o].push(l.getOuterBBox()),o=e.NextScript[o])}}catch(e){t={error:e}}finally{try{s&&!s.done&&(r=i.return)&&r.call(i)}finally{if(t)throw t.error}}return this.firstPrescript=n.subList.length+n.supList.length+2,this.padLists(n.subList,n.supList),this.padLists(n.psubList,n.psupList),n},r.prototype.padLists=function(t,e){t.length>e.length&&e.push(l.BBox.empty())},r.prototype.combineBBoxLists=function(t,e,r,n){for(var o=0;o<r.length;o++){var s=i(this.getScaledWHD(r[o]),3),a=s[0],l=s[1],c=s[2],u=i(this.getScaledWHD(n[o]),3),p=u[0],h=u[1],f=u[2],d=Math.max(a,p);t.w+=d,e.w+=d,l>t.h&&(t.h=l),c>t.d&&(t.d=c),h>e.h&&(e.h=h),f>e.d&&(e.d=f)}},r.prototype.getScaledWHD=function(t){var e=t.w,r=t.h,n=t.d,o=t.rscale;return[e*o,r*o,n*o]},r.prototype.getUVQ=function(e,r){var n;if(!this.UVQ){var o=i([0,0,0],3),s=o[0],a=o[1],l=o[2];0===e.h&&0===e.d?s=this.getU():0===r.h&&0===r.d?s=-this.getV():(s=(n=i(t.prototype.getUVQ.call(this,e,r),3))[0],a=n[1],l=n[2]),this.UVQ=[s,a,l]}return this.UVQ},r}(t)}},5023:function(t,e){var r,n=this&&this.__extends||(r=function(t,e){return r=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,e){t.__proto__=e}||function(t,e){for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&(t[r]=e[r])},r(t,e)},function(t,e){if("function"!=typeof e&&null!==e)throw new TypeError("Class extends value "+String(e)+" is not a constructor or null");function n(){this.constructor=t}r(t,e),t.prototype=null===e?Object.create(e):(n.prototype=e.prototype,new n)});Object.defineProperty(e,"__esModule",{value:!0}),e.CommonMnMixin=void 0,e.CommonMnMixin=function(t){return function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return n(e,t),e.prototype.remapChars=function(t){if(t.length){var e=this.font.getRemappedChar("mn",t[0]);if(e){var 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n=t.apply(this,l([],a(e),!1))||this;return n.size=null,n.isAccent=n.node.isAccent,n}return i(e,t),e.prototype.computeBBox=function(t,e){if(void 0===e&&(e=!1),this.protoBBox(t),this.node.attributes.get("symmetric")&&2!==this.stretch.dir){var r=this.getCenterOffset(t);t.h+=r,t.d-=r}this.node.getProperty("mathaccent")&&(0===this.stretch.dir||this.size>=0)&&(t.w=0)},e.prototype.protoBBox=function(e){var r=0!==this.stretch.dir;r&&null===this.size&&this.getStretchedVariant([0]),r&&this.size<0||(t.prototype.computeBBox.call(this,e),this.copySkewIC(e))},e.prototype.getAccentOffset=function(){var t=u.BBox.empty();return this.protoBBox(t),-t.w/2},e.prototype.getCenterOffset=function(e){return void 0===e&&(e=null),e||(e=u.BBox.empty(),t.prototype.computeBBox.call(this,e)),(e.h+e.d)/2+this.font.params.axis_height-e.h},e.prototype.getVariant=function(){this.node.attributes.get("largeop")?this.variant=this.node.attributes.get("displaystyle")?"-largeop":"-smallop":this.node.attributes.getExplicit("mathvariant")||!1!==this.node.getProperty("pseudoscript")?t.prototype.getVariant.call(this):this.variant="-tex-variant"},e.prototype.canStretch=function(t){if(0!==this.stretch.dir)return this.stretch.dir===t;if(!this.node.attributes.get("stretchy"))return!1;var e=this.getText();if(1!==Array.from(e).length)return!1;var r=this.font.getDelimiter(e.codePointAt(0));return this.stretch=r&&r.dir===t?r:h.NOSTRETCH,0!==this.stretch.dir},e.prototype.getStretchedVariant=function(t,e){var r,n;if(void 0===e&&(e=!1),0!==this.stretch.dir){var o=this.getWH(t),i=this.getSize("minsize",0),a=this.getSize("maxsize",1/0),l=this.node.getProperty("mathaccent");o=Math.max(i,Math.min(a,o));var 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t.concat(n||Array.prototype.slice.call(e))},a=this&&this.__values||function(t){var e="function"==typeof Symbol&&Symbol.iterator,r=e&&t[e],n=0;if(r)return r.call(t);if(t&&"number"==typeof t.length)return{next:function(){return t&&n>=t.length&&(t=void 0),{value:t&&t[n++],done:!t}}};throw new TypeError(e?"Object is not iterable.":"Symbol.iterator is not defined.")};Object.defineProperty(e,"__esModule",{value:!0}),e.CommonMtableMixin=void 0;var l=r(6469),c=r(505),u=r(7875);e.CommonMtableMixin=function(t){return function(t){function e(){for(var e=[],r=0;r<arguments.length;r++)e[r]=arguments[r];var n=t.apply(this,s([],i(e),!1))||this;n.numCols=0,n.numRows=0,n.data=null,n.pwidthCells=[],n.pWidth=0,n.numCols=(0,u.max)(n.tableRows.map((function(t){return t.numCells}))),n.numRows=n.childNodes.length,n.hasLabels=n.childNodes.reduce((function(t,e){return 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o=(n-(e[t]+r[t]))/2;o<1e-5||(e[t]+=o,r[t]+=o)},e.prototype.recordPWidthCell=function(t,e){t.childNodes[0]&&t.childNodes[0].getBBox().pwidth&&this.pwidthCells.push([t,e])},e.prototype.computeBBox=function(t,e){void 0===e&&(e=!1);var r,n,o=this.getTableData(),s=o.H,a=o.D;if(this.node.attributes.get("equalrows")){var l=this.getEqualRowHeight();r=(0,u.sum)([].concat(this.rLines,this.rSpace))+l*this.numRows}else r=(0,u.sum)(s.concat(a,this.rLines,this.rSpace));r+=2*(this.fLine+this.fSpace[1]);var p=this.getComputedWidths();n=(0,u.sum)(p.concat(this.cLines,this.cSpace))+2*(this.fLine+this.fSpace[0]);var h=this.node.attributes.get("width");"auto"!==h&&(n=Math.max(this.length2em(h,0)+2*this.fLine,n));var f=i(this.getBBoxHD(r),2),d=f[0],m=f[1];t.h=d,t.d=m,t.w=n;var y=i(this.getBBoxLR(),2),g=y[0],b=y[1];t.L=g,t.R=b,(0,c.isPercent)(h)||this.setColumnPWidths()},e.prototype.setChildPWidths=function(t,e,r){var n=this.node.attributes.get("width");if(!(0,c.isPercent)(n))return!1;this.hasLabels||(this.bbox.pwidth="",this.container.bbox.pwidth="");var o=this.bbox,i=o.w,s=o.L,a=o.R,l=this.node.attributes.get("data-width-includes-label"),p=Math.max(i,this.length2em(n,Math.max(e,s+i+a)))-(l?s+a:0),h=this.node.attributes.get("equalcolumns")?Array(this.numCols).fill(this.percent(1/Math.max(1,this.numCols))):this.getColumnAttributes("columnwidth",0);this.cWidths=this.getColumnWidthsFixed(h,p);var f=this.getComputedWidths();return this.pWidth=(0,u.sum)(f.concat(this.cLines,this.cSpace))+2*(this.fLine+this.fSpace[0]),this.isTop&&(this.bbox.w=this.pWidth),this.setColumnPWidths(),this.pWidth!==i&&this.parent.invalidateBBox(),this.pWidth!==i},e.prototype.setColumnPWidths=function(){var t,e,r=this.cWidths;try{for(var n=a(this.pwidthCells),o=n.next();!o.done;o=n.next()){var s=i(o.value,2),l=s[0],c=s[1];l.setChildPWidths(!1,r[c])&&(l.invalidateBBox(),l.getBBox())}}catch(e){t={error:e}}finally{try{o&&!o.done&&(e=n.return)&&e.call(n)}finally{if(t)throw t.error}}},e.prototype.getBBoxHD=function(t){var e=i(this.getAlignmentRow(),2),r=e[0],n=e[1];if(null===n){var o=this.font.params.axis_height,s=t/2;return{top:[0,t],center:[s,s],bottom:[t,0],baseline:[s,s],axis:[s+o,s-o]}[r]||[s,s]}var a=this.getVerticalPosition(n,r);return[a,t-a]},e.prototype.getBBoxLR=function(){if(this.hasLabels){var t=this.node.attributes,e=t.get("side"),r=i(this.getPadAlignShift(e),2),n=r[0],o=r[1],s=this.hasLabels&&!!t.get("data-width-includes-label");return s&&this.frame&&this.fSpace[0]&&(n-=this.fSpace[0]),"center"!==o||s?"left"===e?[n,0]:[0,n]:[n,n]}return[0,0]},e.prototype.getPadAlignShift=function(t){var 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this.node.attributes.get("equalcolumns")&&(r=Array(r.length).fill((0,u.max)(r))),r},e.prototype.getColumnWidths=function(){var t=this.node.attributes.get("width");if(this.node.attributes.get("equalcolumns"))return this.getEqualColumns(t);var e=this.getColumnAttributes("columnwidth",0);return"auto"===t?this.getColumnWidthsAuto(e):(0,c.isPercent)(t)?this.getColumnWidthsPercent(e):this.getColumnWidthsFixed(e,this.length2em(t))},e.prototype.getEqualColumns=function(t){var e,r=Math.max(1,this.numCols);if("auto"===t){var n=this.getTableData().W;e=(0,u.max)(n)}else if((0,c.isPercent)(t))e=this.percent(1/r);else{var o=(0,u.sum)([].concat(this.cLines,this.cSpace))+2*this.fSpace[0];e=Math.max(0,this.length2em(t)-o)/r}return Array(this.numCols).fill(e)},e.prototype.getColumnWidthsAuto=function(t){var e=this;return t.map((function(t){return"auto"===t||"fit"===t?null:(0,c.isPercent)(t)?t:e.length2em(t)}))},e.prototype.getColumnWidthsPercent=function(t){var 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e=this.store;this.moving||e.insertTaborder(),e.active.focus()}},e.prototype.left=function(t){this.move_(this.store.previous())},e.prototype.right=function(t){this.move_(this.store.next())},Object.defineProperty(e.prototype,"frame",{get:function(){return this._frame},enumerable:!1,configurable:!0}),Object.defineProperty(e.prototype,"store",{get:function(){return this._store},enumerable:!1,configurable:!0}),e.prototype.post=function(e,r){if(void 0!==r)return this.moving||this.store.removeTaborder(),void t.prototype.post.call(this,e,r);var n,o,i,s=e;if(s instanceof Event?(n=s.target,this.stop(s)):n=s,s instanceof MouseEvent&&(o=s.pageX,i=s.pageY,o||i||!s.clientX||(o=s.clientX+document.body.scrollLeft+document.documentElement.scrollLeft,i=s.clientY+document.body.scrollTop+document.documentElement.scrollTop)),!o&&!i&&n){var a=window.pageXOffset||document.documentElement.scrollLeft,l=window.pageYOffset||document.documentElement.scrollTop,c=n.getBoundingClientRect();o=(c.right+c.left)/2+a,i=(c.bottom+c.top)/2+l}this.store.active=n,this.anchor=this.store.active;var u=this.html;o+u.offsetWidth>document.body.offsetWidth-5&&(o=document.body.offsetWidth-u.offsetWidth-5),this.post(o,i)},e.prototype.registerWidget=function(t){this.widgets.push(t)},e.prototype.unregisterWidget=function(t){var e=this.widgets.indexOf(t);e>-1&&this.widgets.splice(e,1),0===this.widgets.length&&this.unpost()},e.prototype.unpostWidgets=function(){this.widgets.forEach((function(t){return t.unpost()}))},e.prototype.toJson=function(){return{type:""}},e.prototype.move_=function(t){this.anchor&&t!==this.anchor&&(this.moving=!0,this.unpost(),this.post(t),this.moving=!1)},e}(i.AbstractMenu);e.ContextMenu=c},7309:function(t,e,r){Object.defineProperty(e,"__esModule",{value:!0}),e.CssStyles=void 0;var n=r(2165);!function(t){function e(t){return"."+(n.HtmlClasses[t]||t)}var r={};r[e("INFOCLOSE")]="{  top:.2em; right:.2em;}",r[e("INFOCONTENT")]="{  overflow:auto; text-align:left; font-size:80%;  padding:.4em .6em; border:1px inset; margin:1em 0px;  max-height:20em; max-width:30em; background-color:#EEEEEE;  white-space:normal;}",r[e("INFO")+e("MOUSEPOST")]="{outline:none;}",r[e("INFO")]='{  position:fixed; left:50%; width:auto; text-align:center;  border:3px outset; padding:1em 2em; background-color:#DDDDDD;  color:black;  cursor:default; font-family:message-box; font-size:120%;  font-style:normal; text-indent:0; text-transform:none;  line-height:normal; letter-spacing:normal; word-spacing:normal;  word-wrap:normal; white-space:nowrap; float:none; z-index:201;  border-radius: 15px;                     /* Opera 10.5 and IE9 */  -webkit-border-radius:15px;               /* Safari and Chrome */  -moz-border-radius:15px;                  /* Firefox */  -khtml-border-radius:15px;                /* Konqueror */  box-shadow:0px 10px 20px #808080;         /* Opera 10.5 and IE9 */  -webkit-box-shadow:0px 10px 20px #808080; /* Safari 3 & Chrome */  -moz-box-shadow:0px 10px 20px #808080;    /* Forefox 3.5 */  -khtml-box-shadow:0px 10px 20px #808080;  /* Konqueror */  filter:progid:DXImageTransform.Microsoft.dropshadow(OffX=2, OffY=2, Color="gray", Positive="true"); /* IE */}';var o={};o[e("MENU")]="{  position:absolute;  background-color:white;  color:black;  width:auto; padding:5px 0px;  border:1px solid #CCCCCC; margin:0; cursor:default;  font: menu; text-align:left; text-indent:0; text-transform:none;  line-height:normal; letter-spacing:normal; word-spacing:normal;  word-wrap:normal; white-space:nowrap; float:none; z-index:201;  border-radius: 5px;                     /* Opera 10.5 and IE9 */  -webkit-border-radius: 5px;             /* Safari and Chrome */  -moz-border-radius: 5px;                /* Firefox */  -khtml-border-radius: 5px;              /* Konqueror */  box-shadow:0px 10px 20px #808080;         /* Opera 10.5 and IE9 */  -webkit-box-shadow:0px 10px 20px #808080; /* Safari 3 & Chrome */  -moz-box-shadow:0px 10px 20px #808080;    /* Forefox 3.5 */  -khtml-box-shadow:0px 10px 20px #808080;  /* Konqueror */}",o[e("MENUITEM")]="{  padding: 1px 2em;  background:transparent;}",o[e("MENUARROW")]="{  position:absolute; right:.5em; padding-top:.25em; color:#666666;  font-family: null; font-size: .75em}",o[e("MENUACTIVE")+" "+e("MENUARROW")]="{color:white}",o[e("MENUARROW")+e("RTL")]="{left:.5em; right:auto}",o[e("MENUCHECK")]="{  position:absolute; left:.7em;  font-family: null}",o[e("MENUCHECK")+e("RTL")]="{ right:.7em; left:auto }",o[e("MENURADIOCHECK")]="{  position:absolute; left: .7em;}",o[e("MENURADIOCHECK")+e("RTL")]="{  right: .7em; left:auto}",o[e("MENUINPUTBOX")]="{  padding-left: 1em; right:.5em; color:#666666;  font-family: null;}",o[e("MENUINPUTBOX")+e("RTL")]="{  left: .1em;}",o[e("MENUCOMBOBOX")]="{  left:.1em; padding-bottom:.5em;}",o[e("MENUSLIDER")]="{ 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background-color:#FFFFFF;}",o[e("SELECTIONDIVIDER")]="{  clear: both; border-top: 2px solid #000000;}",o[e("MENU")+" "+e("MENUCLOSE")]="{  top:-10px; left:-10px}";var i={};i[e("MENUCLOSE")]='{  position:absolute;  cursor:pointer;  display:inline-block;  border:2px solid #AAA;  border-radius:18px;  -webkit-border-radius: 18px;             /* Safari and Chrome */  -moz-border-radius: 18px;                /* Firefox */  -khtml-border-radius: 18px;              /* Konqueror */  font-family: "Courier New", Courier;  font-size:24px;  color:#F0F0F0}',i[e("MENUCLOSE")+" span"]="{  display:block; background-color:#AAA; border:1.5px solid;  border-radius:18px;  -webkit-border-radius: 18px;             /* Safari and Chrome */  -moz-border-radius: 18px;                /* Firefox */  -khtml-border-radius: 18px;              /* Konqueror */  line-height:0;  padding:8px 0 6px     /* may need to be browser-specific */}",i[e("MENUCLOSE")+":hover"]="{  color:white!important;  border:2px solid #CCC!important}",i[e("MENUCLOSE")+":hover span"]="{  background-color:#CCC!important}",i[e("MENUCLOSE")+":hover:focus"]="{  outline:none}";var s=!1,a=!1,l=!1;function c(t){l||(u(i,t),l=!0)}function u(t,e){var r=e||document,n=r.createElement("style");n.type="text/css";var o="";for(var i in t)o+=i,o+=" ",o+=t[i],o+="\n";n.innerHTML=o,r.head.appendChild(n)}t.addMenuStyles=function(t){a||(u(o,t),a=!0,c(t))},t.addInfoStyles=function(t){s||(u(r,t),s=!0,c(t))}}(e.CssStyles||(e.CssStyles={}))},2165:function(t,e){Object.defineProperty(e,"__esModule",{value:!0}),e.HtmlAttrs=e.HtmlClasses=void 0;function r(t){return"CtxtMenu_"+t}function n(t){return r(t)}function o(t){return r(t)}e.HtmlClasses={ATTACHED:n("Attached"),CONTEXTMENU:n("ContextMenu"),MENU:n("Menu"),MENUARROW:n("MenuArrow"),MENUACTIVE:n("MenuActive"),MENUCHECK:n("MenuCheck"),MENUCLOSE:n("MenuClose"),MENUCOMBOBOX:n("MenuComboBox"),MENUDISABLED:n("MenuDisabled"),MENUFRAME:n("MenuFrame"),MENUITEM:n("MenuItem"),MENULABEL:n("MenuLabel"),MENURADIOCHECK:n("MenuRadioCheck"),MENUINPUTBOX:n("MenuInputBox"),MENURULE:n("MenuRule"),MENUSLIDER:n("MenuSlider"),MOUSEPOST:n("MousePost"),RTL:n("RTL"),INFO:n("Info"),INFOCLOSE:n("InfoClose"),INFOCONTENT:n("InfoContent"),INFOSIGNATURE:n("InfoSignature"),INFOTITLE:n("InfoTitle"),SLIDERVALUE:n("SliderValue"),SLIDERBAR:n("SliderBar"),SELECTION:n("Selection"),SELECTIONBOX:n("SelectionBox"),SELECTIONMENU:n("SelectionMenu"),SELECTIONDIVIDER:n("SelectionDivider"),SELECTIONITEM:n("SelectionItem")},e.HtmlAttrs={COUNTER:o("Counter"),KEYDOWNFUNC:o("keydownFunc"),CONTEXTMENUFUNC:o("contextmenuFunc"),OLDTAB:o("Oldtabindex"),TOUCHFUNC:o("TouchFunc")}},4922:function(t,e,r){var n,o=this&&this.__extends||(n=function(t,e){return n=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,e){t.__proto__=e}||function(t,e){for(var r in e)e.hasOwnProperty(r)&&(t[r]=e[r])},n(t,e)},function(t,e){function r(){this.constructor=t}n(t,e),t.prototype=null===e?Object.create(e):(r.prototype=e.prototype,new r)});Object.defineProperty(e,"__esModule",{value:!0}),e.Info=void 0;var i=r(5179),s=r(2165),a=function(t){function e(e,r,n){var o=t.call(this)||this;return o.title=e,o.signature=n,o.className=s.HtmlClasses.INFO,o.role="dialog",o.contentDiv=o.generateContent(),o.close=o.generateClose(),o.content=r||function(){return""},o}return o(e,t),e.prototype.attachMenu=function(t){this.menu=t},e.prototype.generateHtml=function(){t.prototype.generateHtml.call(this);var e=this.html;e.appendChild(this.generateTitle()),e.appendChild(this.contentDiv),e.appendChild(this.generateSignature()),e.appendChild(this.close.html),e.setAttribute("tabindex","0")},e.prototype.post=function(){t.prototype.post.call(this);var e=document.documentElement,r=this.html,n=window.innerHeight||e.clientHeight||e.scrollHeight||0,o=Math.floor(-r.offsetWidth/2),i=Math.floor((n-r.offsetHeight)/3);r.setAttribute("style","margin-left: "+o+"px; top: "+i+"px;"),window.event instanceof MouseEvent&&r.classList.add(s.HtmlClasses.MOUSEPOST),r.focus()},e.prototype.display=function(){this.menu.registerWidget(this),this.contentDiv.innerHTML=this.content();var t=this.menu.html;t.parentNode&&t.parentNode.removeChild(t),this.menu.frame.appendChild(this.html)},e.prototype.click=function(t){},e.prototype.keydown=function(e){this.bubbleKey(),t.prototype.keydown.call(this,e)},e.prototype.escape=function(t){this.unpost()},e.prototype.unpost=function(){t.prototype.unpost.call(this),this.html.classList.remove(s.HtmlClasses.MOUSEPOST),this.menu.unregisterWidget(this)},e.prototype.generateClose=function(){var t=new i.CloseButton(this),e=t.html;return e.classList.add(s.HtmlClasses.INFOCLOSE),e.setAttribute("aria-label","Close Dialog Box"),t},e.prototype.generateTitle=function(){var t=document.createElement("span");return t.innerHTML=this.title,t.classList.add(s.HtmlClasses.INFOTITLE),t},e.prototype.generateContent=function(){var t=document.createElement("div");return t.classList.add(s.HtmlClasses.INFOCONTENT),t.setAttribute("tabindex","0"),t},e.prototype.generateSignature=function(){var t=document.createElement("span");return t.innerHTML=this.signature,t.classList.add(s.HtmlClasses.INFOSIGNATURE),t},e.prototype.toJson=function(){return{type:""}},e}(r(8372).AbstractPostable);e.Info=a},1409:function(t,e,r){var n,o=this&&this.__extends||(n=function(t,e){return n=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,e){t.__proto__=e}||function(t,e){for(var r in e)e.hasOwnProperty(r)&&(t[r]=e[r])},n(t,e)},function(t,e){function r(){this.constructor=t}n(t,e),t.prototype=null===e?Object.create(e):(r.prototype=e.prototype,new r)});Object.defineProperty(e,"__esModule",{value:!0}),e.Checkbox=void 0;var i=r(6765),s=r(2556),a=r(2165),l=function(t){function e(e,r,n,o){var i=t.call(this,e,"checkbox",r,o)||this;return i.role="menuitemcheckbox",i.variable=e.pool.lookup(n),i.register(),i}return o(e,t),e.fromJson=function(t,e,r){return new this(r,e.content,e.variable,e.id)},e.prototype.executeAction=function(){this.variable.setValue(!this.variable.getValue()),s.MenuUtil.close(this)},e.prototype.generateSpan=function(){this.span=document.createElement("span"),this.span.textContent="\u2713",this.span.classList.add(a.HtmlClasses.MENUCHECK)},e.prototype.updateAria=function(){this.html.setAttribute("aria-checked",this.variable.getValue()?"true":"false")},e.prototype.updateSpan=function(){this.span.style.display=this.variable.getValue()?"":"none"},e.prototype.toJson=function(){return{type:""}},e}(i.AbstractVariableItem);e.Checkbox=l},9886:function(t,e,r){var n,o=this&&this.__extends||(n=function(t,e){return n=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,e){t.__proto__=e}||function(t,e){for(var r in e)e.hasOwnProperty(r)&&(t[r]=e[r])},n(t,e)},function(t,e){function r(){this.constructor=t}n(t,e),t.prototype=null===e?Object.create(e):(r.prototype=e.prototype,new r)});Object.defineProperty(e,"__esModule",{value:!0}),e.Combo=void 0;var i=r(6765),s=r(2556),a=r(2165),l=r(3205),c=function(t){function e(e,r,n,o){var i=t.call(this,e,"combobox",r,o)||this;return i.role="combobox",i.inputEvent=!1,i.variable=e.pool.lookup(n),i.register(),i}return o(e,t),e.fromJson=function(t,e,r){return new this(r,e.content,e.variable,e.id)},e.prototype.executeAction=function(){this.variable.setValue(this.input.value,s.MenuUtil.getActiveElement(this))},e.prototype.space=function(e){t.prototype.space.call(this,e),s.MenuUtil.close(this)},e.prototype.focus=function(){t.prototype.focus.call(this),this.input.focus()},e.prototype.unfocus=function(){t.prototype.unfocus.call(this),this.updateSpan()},e.prototype.generateHtml=function(){t.prototype.generateHtml.call(this),this.html.classList.add(a.HtmlClasses.MENUCOMBOBOX)},e.prototype.generateSpan=function(){this.span=document.createElement("span"),this.span.classList.add(a.HtmlClasses.MENUINPUTBOX),this.input=document.createElement("input"),this.input.addEventListener("keydown",this.inputKey.bind(this)),this.input.setAttribute("size","10em"),this.input.setAttribute("type","text"),this.input.setAttribute("tabindex","-1"),this.span.appendChild(this.input)},e.prototype.inputKey=function(t){this.bubbleKey(),this.inputEvent=!0},e.prototype.keydown=function(e){if(this.inputEvent&&e.keyCode!==l.KEY.ESCAPE&&e.keyCode!==l.KEY.RETURN)return this.inputEvent=!1,void e.stopPropagation();t.prototype.keydown.call(this,e),e.stopPropagation()},e.prototype.updateAria=function(){},e.prototype.updateSpan=function(){var t;try{t=this.variable.getValue(s.MenuUtil.getActiveElement(this))}catch(e){t=""}this.input.value=t},e.prototype.toJson=function(){return{type:""}},e}(i.AbstractVariableItem);e.Combo=c},3467:function(t,e,r){var n,o=this&&this.__extends||(n=function(t,e){return n=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,e){t.__proto__=e}||function(t,e){for(var r in e)e.hasOwnProperty(r)&&(t[r]=e[r])},n(t,e)},function(t,e){function r(){this.constructor=t}n(t,e),t.prototype=null===e?Object.create(e):(r.prototype=e.prototype,new r)});Object.defineProperty(e,"__esModule",{value:!0}),e.Command=void 0;var i=r(1340),s=r(2556),a=function(t){function e(e,r,n,o){var i=t.call(this,e,"command",r,o)||this;return i.command=n,i}return o(e,t),e.fromJson=function(t,e,r){return new this(r,e.content,e.action,e.id)},e.prototype.executeAction=function(){try{this.command(s.MenuUtil.getActiveElement(this))}catch(t){s.MenuUtil.error(t,"Illegal command callback.")}s.MenuUtil.close(this)},e.prototype.toJson=function(){return{type:""}},e}(i.AbstractItem);e.Command=a},2965:function(t,e,r){var n,o=this&&this.__extends||(n=function(t,e){return n=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,e){t.__proto__=e}||function(t,e){for(var r in e)e.hasOwnProperty(r)&&(t[r]=e[r])},n(t,e)},function(t,e){function r(){this.constructor=t}n(t,e),t.prototype=null===e?Object.create(e):(r.prototype=e.prototype,new r)});Object.defineProperty(e,"__esModule",{value:!0}),e.Label=void 0;var i=r(1340),s=r(2165),a=function(t){function e(e,r,n){return t.call(this,e,"label",r,n)||this}return o(e,t),e.fromJson=function(t,e,r){return new this(r,e.content,e.id)},e.prototype.generateHtml=function(){t.prototype.generateHtml.call(this),this.html.classList.add(s.HtmlClasses.MENULABEL)},e.prototype.toJson=function(){return{type:""}},e}(i.AbstractItem);e.Label=a},385:function(t,e,r){var n,o=this&&this.__extends||(n=function(t,e){return n=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,e){t.__proto__=e}||function(t,e){for(var r in e)e.hasOwnProperty(r)&&(t[r]=e[r])},n(t,e)},function(t,e){function r(){this.constructor=t}n(t,e),t.prototype=null===e?Object.create(e):(r.prototype=e.prototype,new r)});Object.defineProperty(e,"__esModule",{value:!0}),e.Radio=void 0;var i=r(6765),s=r(2556),a=r(2165),l=function(t){function e(e,r,n,o){var i=t.call(this,e,"radio",r,o)||this;return i.role="menuitemradio",i.variable=e.pool.lookup(n),i.register(),i}return o(e,t),e.fromJson=function(t,e,r){return new this(r,e.content,e.variable,e.id)},e.prototype.executeAction=function(){this.variable.setValue(this.id),s.MenuUtil.close(this)},e.prototype.generateSpan=function(){this.span=document.createElement("span"),this.span.textContent="\u2713",this.span.classList.add(a.HtmlClasses.MENURADIOCHECK)},e.prototype.updateAria=function(){this.html.setAttribute("aria-checked",this.variable.getValue()===this.id?"true":"false")},e.prototype.updateSpan=function(){this.span.style.display=this.variable.getValue()===this.id?"":"none"},e.prototype.toJson=function(){return{type:""}},e}(i.AbstractVariableItem);e.Radio=l},3463:function(t,e,r){var n,o=this&&this.__extends||(n=function(t,e){return n=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,e){t.__proto__=e}||function(t,e){for(var r in e)e.hasOwnProperty(r)&&(t[r]=e[r])},n(t,e)},function(t,e){function r(){this.constructor=t}n(t,e),t.prototype=null===e?Object.create(e):(r.prototype=e.prototype,new r)});Object.defineProperty(e,"__esModule",{value:!0}),e.Rule=void 0;var i=r(9329),s=r(2165),a=function(t){function e(e){var r=t.call(this,e,"rule")||this;return r.className=s.HtmlClasses.MENUITEM,r.role="separator",r}return o(e,t),e.fromJson=function(t,e,r){return new this(r)},e.prototype.generateHtml=function(){t.prototype.generateHtml.call(this);var e=this.html;e.classList.add(s.HtmlClasses.MENURULE),e.setAttribute("aria-orientation","vertical")},e.prototype.addEvents=function(t){},e.prototype.toJson=function(){return{type:"rule"}},e}(i.AbstractEntry);e.Rule=a},7625:function(t,e,r){var n,o=this&&this.__extends||(n=function(t,e){return n=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,e){t.__proto__=e}||function(t,e){for(var r in e)e.hasOwnProperty(r)&&(t[r]=e[r])},n(t,e)},function(t,e){function r(){this.constructor=t}n(t,e),t.prototype=null===e?Object.create(e):(r.prototype=e.prototype,new r)});Object.defineProperty(e,"__esModule",{value:!0}),e.Slider=void 0;var i=r(6765),s=r(2556),a=r(2165),l=r(3205),c=function(t){function e(e,r,n,o){var i=t.call(this,e,"slider",r,o)||this;return i.role="slider",i.labelId="ctx_slideLabel"+s.MenuUtil.counter(),i.valueId="ctx_slideValue"+s.MenuUtil.counter(),i.inputEvent=!1,i.variable=e.pool.lookup(n),i.register(),i}return o(e,t),e.fromJson=function(t,e,r){return new 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r=e.keyCode;return r===l.KEY.UP||r===l.KEY.DOWN?(e.preventDefault(),void t.prototype.keydown.call(this,e)):this.inputEvent&&r!==l.KEY.ESCAPE&&r!==l.KEY.RETURN?(this.inputEvent=!1,void e.stopPropagation()):(t.prototype.keydown.call(this,e),void e.stopPropagation())},e.prototype.updateAria=function(){var t=this.variable.getValue();t&&this.input&&(this.input.setAttribute("aria-valuenow",t),this.input.setAttribute("aria-valuetext",t+"%"))},e.prototype.updateSpan=function(){var t;try{t=this.variable.getValue(s.MenuUtil.getActiveElement(this)),this.valueSpan.innerHTML=t+"%"}catch(e){t=""}this.input.value=t},e.prototype.toJson=function(){return{type:""}},e}(i.AbstractVariableItem);e.Slider=c},6186:function(t,e,r){var n,o=this&&this.__extends||(n=function(t,e){return n=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,e){t.__proto__=e}||function(t,e){for(var r in e)e.hasOwnProperty(r)&&(t[r]=e[r])},n(t,e)},function(t,e){function 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s=a.selections.slice(t,t+o),l=i(a.rowDiv(s),4),c=l[0],u=l[1],p=l[2],h=l[3];e.push(c),r=Math.max(r,u),s.forEach((function(t){return t.html.style.height=p+"px"})),n=a.combineColumn(n,h)},a=this,l=0;l<this.selections.length;l+=o)s(l);this._balanced&&(this.balanceColumn(e,n),r=n.reduce((function(t,e){return t+e}),20)),e.forEach((function(t){return t.style.width=r+"px"}))}},e.prototype.getChunkSize=function(t){switch(this.grid){case"square":return Math.floor(Math.sqrt(t));case"horizontal":return Math.floor(t/e.chunkSize);default:return e.chunkSize}},e.prototype.balanceColumn=function(t,e){t.forEach((function(t){for(var r=Array.from(t.children),n=0,o=void 0;o=r[n];n++)o.style.width=e[n]+"px"}))},e.prototype.combineColumn=function(t,e){for(var r=[],n=0;t[n]||e[n];){if(!t[n]){r=r.concat(e.slice(n));break}if(!e[n]){r=r.concat(t.slice(n));break}r.push(Math.max(t[n],e[n])),n++}return r},e.prototype.left=function(t){var e=this;this.move(t,(function(t){return(0===t?e.selections.length:t)-1}))},e.prototype.right=function(t){var e=this;this.move(t,(function(t){return t===e.selections.length-1?0:t+1}))},e.prototype.generateHtml=function(){t.prototype.generateHtml.call(this),this.html.classList.add(a.HtmlClasses.SELECTION)},e.prototype.generateContent=function(){var e=t.prototype.generateContent.call(this);return e.classList.add(a.HtmlClasses.SELECTIONBOX),e.removeAttribute("tabindex"),e},e.prototype.findSelection=function(t){var e=t.target,r=null;if(e.id&&(r=this.selections.find((function(t){return t.html.id===e.id}))),!r){var n=e.parentElement.id;r=this.selections.find((function(t){return t.html.id===n}))}return r},e.prototype.move=function(t,e){var r=this.findSelection(t);r.focused&&r.focused.unfocus();var 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this.semantic.contentNodes.length&&o.walkTree(this.semantic.contentNodes[0]),this.semantic.childNodes.length&&o.walkTree(this.semantic.childNodes[0]),(0,i.setAttributes)(this.mml,this.semantic),this.mml}}e.CaseLine=s},6839:function(t,e,r){Object.defineProperty(e,"__esModule",{value:!0}),e.CaseMultiindex=void 0;const n=r(5740),o=r(9268),i=r(5452),s=r(2298);class a extends o.AbstractEnrichCase{constructor(t){super(t),this.mml=t.mathmlTree}static multiscriptIndex(t){return"punctuated"===t.type&&"dummy"===t.contentNodes[0].role?i.collapsePunctuated(t):(i.walkTree(t),t.id)}static createNone_(t){const e=n.createElement("none");return t&&(0,s.setAttributes)(e,t),e.setAttribute(s.Attribute.ADDED,"true"),e}completeMultiscript(t,e){const r=n.toArray(this.mml.childNodes).slice(1);let o=0;const l=t=>{for(let e,n=0;e=t[n];n++){const t=r[o];if(t&&e===parseInt(i.getInnerNode(t).getAttribute(s.Attribute.ID)))i.getInnerNode(t).setAttribute(s.Attribute.PARENT,this.semantic.id.toString()),o++;else{const r=this.semantic.querySelectorAll((t=>t.id===e));this.mml.insertBefore(a.createNone_(r[0]),t||null)}}};l(t),r[o]&&"MPRESCRIPTS"!==n.tagName(r[o])?this.mml.insertBefore(r[o],n.createElement("mprescripts")):o++,l(e)}}e.CaseMultiindex=a},7014:function(t,e,r){Object.defineProperty(e,"__esModule",{value:!0}),e.CaseMultiscripts=void 0;const n=r(5740),o=r(5656),i=r(6839),s=r(5452),a=r(2298);class l extends i.CaseMultiindex{static test(t){if(!t.mathmlTree)return!1;return"MMULTISCRIPTS"===n.tagName(t.mathmlTree)&&("superscript"===t.type||"subscript"===t.type)}constructor(t){super(t)}getMathml(){let t,e,r;if((0,a.setAttributes)(this.mml,this.semantic),this.semantic.childNodes[0]&&"subsup"===this.semantic.childNodes[0].role){const n=this.semantic.childNodes[0];t=n.childNodes[0],e=i.CaseMultiindex.multiscriptIndex(this.semantic.childNodes[1]),r=i.CaseMultiindex.multiscriptIndex(n.childNodes[1]);const l=[this.semantic.id,[n.id,t.id,r],e];s.addCollapsedAttribute(this.mml,l),this.mml.setAttribute(a.Attribute.TYPE,n.role),this.completeMultiscript(o.SemanticSkeleton.interleaveIds(r,e),[])}else{t=this.semantic.childNodes[0],e=i.CaseMultiindex.multiscriptIndex(this.semantic.childNodes[1]);const r=[this.semantic.id,t.id,e];s.addCollapsedAttribute(this.mml,r)}const n=o.SemanticSkeleton.collapsedLeafs(r||[],e),l=s.walkTree(t);return s.getInnerNode(l).setAttribute(a.Attribute.PARENT,this.semantic.id.toString()),n.unshift(t.id),this.mml.setAttribute(a.Attribute.CHILDREN,n.join(",")),this.mml}}e.CaseMultiscripts=l},3416:function(t,e,r){Object.defineProperty(e,"__esModule",{value:!0}),e.CaseProof=void 0;const n=r(9268),o=r(5452),i=r(2298);class s extends n.AbstractEnrichCase{constructor(t){super(t),this.mml=t.mathmlTree}static test(t){return!!t.mathmlTree&&("inference"===t.type||"premises"===t.type)}getMathml(){return this.semantic.childNodes.length?(this.semantic.contentNodes.forEach((function(t){o.walkTree(t),(0,i.setAttributes)(t.mathmlTree,t)})),this.semantic.childNodes.forEach((function(t){o.walkTree(t)})),(0,i.setAttributes)(this.mml,this.semantic),this.mml.getAttribute("data-semantic-id")===this.mml.getAttribute("data-semantic-parent")&&this.mml.removeAttribute("data-semantic-parent"),this.mml):this.mml}}e.CaseProof=s},5699:function(t,e,r){Object.defineProperty(e,"__esModule",{value:!0}),e.CaseTable=void 0;const n=r(5740),o=r(9268),i=r(5452),s=r(2298);class a extends o.AbstractEnrichCase{constructor(t){super(t),this.inner=[],this.mml=t.mathmlTree}static test(t){return"matrix"===t.type||"vector"===t.type||"cases"===t.type}getMathml(){const t=i.cloneContentNode(this.semantic.contentNodes[0]),e=this.semantic.contentNodes[1]?i.cloneContentNode(this.semantic.contentNodes[1]):null;if(this.inner=this.semantic.childNodes.map(i.walkTree),this.mml)if("MFENCED"===n.tagName(this.mml)){const 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l=[this.semantic.id,this.semantic.childNodes[0].id,t,e,r,a];i.addCollapsedAttribute(this.mml,l);const c=n.SemanticSkeleton.collapsedLeafs(t,e,r,a);return c.unshift(this.semantic.childNodes[0].id),this.mml.setAttribute(s.Attribute.CHILDREN,c.join(",")),this.completeMultiscript(n.SemanticSkeleton.interleaveIds(r,a),n.SemanticSkeleton.interleaveIds(t,e)),this.mml}}e.CaseTensor=a},9236:function(t,e,r){Object.defineProperty(e,"__esModule",{value:!0}),e.CaseText=void 0;const n=r(9268),o=r(5452),i=r(2298);class s extends n.AbstractEnrichCase{constructor(t){super(t),this.mml=t.mathmlTree}static test(t){return"punctuated"===t.type&&("text"===t.role||t.contentNodes.every((t=>"dummy"===t.role)))}getMathml(){const t=[],e=o.collapsePunctuated(this.semantic,t);return this.mml=o.introduceNewLayer(t,this.semantic),(0,i.setAttributes)(this.mml,this.semantic),this.mml.removeAttribute(i.Attribute.CONTENT),o.addCollapsedAttribute(this.mml,e),this.mml}}e.CaseText=s},5714:function(t,e,r){Object.defineProperty(e,"__esModule",{value:!0}),e.prepareMmlString=e.testTranslation__=e.semanticMathml=e.semanticMathmlSync=e.semanticMathmlNode=void 0;const n=r(2057),o=r(5740),i=r(5897),s=r(1414),a=r(5452),l=r(2298);function c(t){const e=o.cloneNode(t),r=s.getTree(e);return a.enrich(e,r)}function u(t){return c(o.parseInput(t))}function p(t){return t.match(/^<math/)||(t="<math>"+t),t.match(/\/math>$/)||(t+="</math>"),t}r(1513),e.semanticMathmlNode=c,e.semanticMathmlSync=u,e.semanticMathml=function(t,e){i.EnginePromise.getall().then((()=>{const r=o.parseInput(t);e(c(r))}))},e.testTranslation__=function(t){n.Debugger.getInstance().init();const 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h.CaseText(t)})},5452:function(t,e,r){Object.defineProperty(e,"__esModule",{value:!0}),e.printNodeList__=e.collapsePunctuated=e.formattedOutput_=e.formattedOutput=e.getInnerNode=e.setOperatorAttribute_=e.createInvisibleOperator_=e.rewriteMfenced=e.cloneContentNode=e.addCollapsedAttribute=e.parentNode_=e.isIgnorable_=e.unitChild_=e.descendNode_=e.ascendNewNode=e.validLca_=e.pathToRoot_=e.attachedElement_=e.prunePath_=e.mathmlLca_=e.lcaType=e.functionApplication_=e.isDescendant_=e.insertNewChild_=e.mergeChildren_=e.collectChildNodes_=e.collateChildNodes_=e.childrenSubset_=e.moveSemanticAttributes_=e.introduceLayerAboveLca=e.introduceNewLayer=e.walkTree=e.enrich=e.SETTINGS=void 0;const n=r(2057),o=r(5740),i=r(5897),s=r(3588),a=r(7516),l=r(5656),c=r(4795),u=r(2298),p=r(3532);function h(t){const e=(0,p.getCase)(t);let r;if(e)return r=e.getMathml(),N(r);if(1===t.mathml.length)return n.Debugger.getInstance().output("Walktree Case 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r=t.context,o=t.dynamicCstr.getValues();for(let t=0,i=o.length;t<i;t++)e=this.addNode_(e,o[t],n.TrieNodeKind.DYNAMIC,r);e=this.addNode_(e,t.precondition.query,n.TrieNodeKind.QUERY,r);const i=t.precondition.constraints;for(let t=0,o=i.length;t<o;t++)e=this.addNode_(e,i[t],n.TrieNodeKind.BOOLEAN,r);e.setRule(t)}lookupRules(t,e){let r=[this.root];const o=[];for(;e.length;){const t=e.shift(),o=[];for(;r.length;){r.shift().getChildren().forEach((e=>{e.getKind()===n.TrieNodeKind.DYNAMIC&&-1===t.indexOf(e.getConstraint())||o.push(e)}))}r=o.slice()}for(;r.length;){const e=r.shift();if(e.getRule){const t=e.getRule();t&&o.push(t)}const n=e.findChildren(t);r=r.concat(n)}return o}hasSubtrie(t){let e=this.root;for(let r=0,n=t.length;r<n;r++){const n=t[r];if(e=e.getChild(n),!e)return!1}return!0}toString(){return i.printWithDepth_(this.root,0,"")}collectRules(){return i.collectRules_(this.root)}order(){return i.order_(this.root)}enumerate(t){return this.enumerate_(this.root,t)}byConstraint(t){let 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e,r=this.speechRules_.length-1;e=this.speechRules_[r];r--)e!==t&&t.dynamicCstr.equal(e.dynamicCstr)&&a.comparePreconditions_(e,t)&&this.speechRules_.splice(r,1)}getSpeechRules(){return this.speechRules_}setSpeechRules(t){this.speechRules_=t}getPreconditions(){return this.preconditions}parseCstr(t){try{return this.parser.parse(this.locale+"."+this.modality+(this.domain?"."+this.domain:"")+"."+t)}catch(e){if("RuleError"===e.name)return console.error("Rule Error ",`Illegal Dynamic Constraint: ${t}.`,e.message),null;throw e}}parsePrecondition(t,e){try{const r=this.parsePrecondition_(t);t=r[0];let n=r.slice(1);for(const t of e)n=n.concat(this.parsePrecondition_(t));return new i.Precondition(t,...n)}catch(r){if("RuleError"===r.name)return console.error("Rule Error ",`Illegal preconditions: ${t}, ${e}.`,r.message),null;throw r}}parseAction(t){try{return i.Action.fromString(t)}catch(e){if("RuleError"===e.name)return console.error("Rule Error ",`Illegal action: ${t}.`,e.message),null;throw e}}parse(t){this.modality=t.modality||this.modality,this.locale=t.locale||this.locale,this.domain=t.domain||this.domain,this.context.parse(t.functions||[]),"actions"!==t.kind&&(this.kind=t.kind||this.kind,this.inheritRules()),this.parseRules(t.rules||[])}parseRules(t){for(let e,r=0;e=t[r];r++){const t=e[0],r=this.parseMethods[t];t&&r&&r.apply(this,e.slice(1))}}generateRules(t){const e=this.context.customGenerators.lookup(t);e&&e(this)}defineAction(t,e){let r;try{r=i.Action.fromString(e)}catch(t){if("RuleError"===t.name)return void console.error("Action Error ",e,t.message);throw t}const n=this.getFullPreconditions(t);if(!n)return void console.error(`Action Error: No precondition for action ${t}`);this.ignoreRules(t);const o=new RegExp("^\\w+\\.\\w+\\."+(this.domain?"\\w+\\.":""));n.conditions.forEach((([e,n])=>{const s=this.parseCstr(e.toString().replace(o,""));this.addRule(new i.SpeechRule(t,s,n,r))}))}getFullPreconditions(t){const e=this.preconditions.get(t);return e||!this.inherits?e:this.inherits.getFullPreconditions(t)}definePrecondition(t,e,r,...n){const o=this.parsePrecondition(r,n),i=this.parseCstr(e);o&&i?(o.rank=this.rank++,this.preconditions.set(t,new l(i,o))):console.error(`Precondition Error: ${r}, (${e})`)}inheritRules(){if(!this.inherits||!this.inherits.getSpeechRules().length)return;const t=new RegExp("^\\w+\\.\\w+\\."+(this.domain?"\\w+\\.":""));this.inherits.getSpeechRules().forEach((e=>{const r=this.parseCstr(e.dynamicCstr.toString().replace(t,""));this.addRule(new i.SpeechRule(e.name,r,e.precondition,e.action))}))}ignoreRules(t,...e){let r=this.findAllRules((e=>e.name===t));if(!e.length)return void r.forEach(this.deleteRule.bind(this));let n=[];for(const t of e){const e=this.parseCstr(t);for(const t of r)e.equal(t.dynamicCstr)?this.deleteRule(t):n.push(t);r=n,n=[]}}parsePrecondition_(t){const e=this.context.customGenerators.lookup(t);return e?e():[t]}}e.BaseRuleStore=a;class l{constructor(t,e){this.base=t,this._conditions=[],this.constraints=[],this.allCstr={},this.constraints.push(t),this.addCondition(t,e)}get conditions(){return this._conditions}addConstraint(t){if(this.constraints.filter((e=>e.equal(t))).length)return;this.constraints.push(t);const e=[];for(const[r,n]of this.conditions)this.base.equal(r)&&e.push([t,n]);this._conditions=this._conditions.concat(e)}addBaseCondition(t){this.addCondition(this.base,t)}addFullCondition(t){this.constraints.forEach((e=>this.addCondition(e,t)))}addCondition(t,e){const r=t.toString()+" "+e.toString();this.allCstr.condStr||(this.allCstr[r]=!0,this._conditions.push([t,e]))}}e.Condition=l},2469:function(t,e,r){Object.defineProperty(e,"__esModule",{value:!0}),e.BrailleStore=void 0;const n=r(7630),o=r(9935);class i extends o.MathStore{constructor(){super(...arguments),this.modality="braille",this.customTranscriptions={"\u22ca":"\u2808\u2821\u2833"}}evaluateString(t){const e=[],r=Array.from(t);for(let t=0;t<r.length;t++)e.push(this.evaluateCharacter(r[t]));return e}annotations(){for(let t,e=0;t=this.annotators[e];e++)(0,n.activate)(this.locale,t)}}e.BrailleStore=i},1676:function(t,e){var r;Object.defineProperty(e,"__esModule",{value:!0}),e.DefaultComparator=e.DynamicCstrParser=e.DynamicCstr=e.DynamicProperties=e.Axis=void 0,function(t){t.DOMAIN="domain",t.STYLE="style",t.LOCALE="locale",t.TOPIC="topic",t.MODALITY="modality"}(r=e.Axis||(e.Axis={}));class n{constructor(t,e=Object.keys(t)){this.properties=t,this.order=e}static createProp(...t){const e=o.DEFAULT_ORDER,r={};for(let n=0,o=t.length,i=e.length;n<o&&n<i;n++)r[e[n]]=t[n];return new n(r)}getProperties(){return this.properties}getOrder(){return this.order}getAxes(){return this.order}getProperty(t){return this.properties[t]}updateProperties(t){this.properties=t}allProperties(){const t=[];return this.order.forEach((e=>t.push(this.getProperty(e).slice()))),t}toString(){const t=[];return this.order.forEach((e=>t.push(e+": "+this.getProperty(e).toString()))),t.join("\n")}}e.DynamicProperties=n;class o extends n{constructor(t,e){const r={};for(const[e,n]of Object.entries(t))r[e]=[n];super(r,e),this.components=t}static createCstr(...t){const e=o.DEFAULT_ORDER,r={};for(let n=0,o=t.length,i=e.length;n<o&&n<i;n++)r[e[n]]=t[n];return new o(r)}static defaultCstr(){return o.createCstr.apply(null,o.DEFAULT_ORDER.map((function(t){return o.DEFAULT_VALUES[t]})))}static validOrder(t){const e=o.DEFAULT_ORDER.slice();return t.every((t=>{const r=e.indexOf(t);return-1!==r&&e.splice(r,1)}))}getComponents(){return this.components}getValue(t){return this.components[t]}getValues(){return this.order.map((t=>this.getValue(t)))}allProperties(){const t=super.allProperties();for(let e,r,n=0;e=t[n],r=this.order[n];n++){const t=this.getValue(r);-1===e.indexOf(t)&&e.unshift(t)}return t}toString(){return this.getValues().join(".")}equal(t){const e=t.getAxes();if(this.order.length!==e.length)return!1;for(let r,n=0;r=e[n];n++){const e=this.getValue(r);if(!e||t.getValue(r)!==e)return!1}return!0}}e.DynamicCstr=o,o.DEFAULT_ORDER=[r.LOCALE,r.MODALITY,r.DOMAIN,r.STYLE,r.TOPIC],o.BASE_LOCALE="base",o.DEFAULT_VALUE="default",o.DEFAULT_VALUES={[r.LOCALE]:"en",[r.DOMAIN]:o.DEFAULT_VALUE,[r.STYLE]:o.DEFAULT_VALUE,[r.TOPIC]:o.DEFAULT_VALUE,[r.MODALITY]:"speech"};e.DynamicCstrParser=class{constructor(t){this.order=t}parse(t){const e=t.split("."),r={};if(e.length>this.order.length)throw new Error("Invalid dynamic constraint: "+r);let n=0;for(let t,o=0;t=this.order[o],e.length;o++,n++){const n=e.shift();r[t]=n}return new o(r,this.order.slice(0,n))}};e.DefaultComparator=class{constructor(t,e=new n(t.getProperties(),t.getOrder())){this.reference=t,this.fallback=e,this.order=this.reference.getOrder()}getReference(){return this.reference}setReference(t,e){this.reference=t,this.fallback=e||new n(t.getProperties(),t.getOrder()),this.order=this.reference.getOrder()}match(t){const e=t.getAxes();return e.length===this.reference.getAxes().length&&e.every((e=>{const r=t.getValue(e);return r===this.reference.getValue(e)||-1!==this.fallback.getProperty(e).indexOf(r)}))}compare(t,e){let r=!1;for(let n,o=0;n=this.order[o];o++){const o=t.getValue(n),i=e.getValue(n);if(!r){const t=this.reference.getValue(n);if(t===o&&t!==i)return-1;if(t===i&&t!==o)return 1;if(t===o&&t===i)continue;t!==o&&t!==i&&(r=!0)}const s=this.fallback.getProperty(n),a=s.indexOf(o),l=s.indexOf(i);if(a<l)return-1;if(l<a)return 1}return 0}toString(){return this.reference.toString()+"\n"+this.fallback.toString()}}},2105:function(t,e,r){Object.defineProperty(e,"__esModule",{value:!0}),e.numbersToAlpha=e.Grammar=e.ATTRIBUTE=void 0;const n=r(5740),o=r(5897),i=r(2536),s=r(4356);e.ATTRIBUTE="grammar";class a{constructor(){this.currentFlags={},this.parameters_={},this.corrections_={},this.preprocessors_={},this.stateStack_=[]}static getInstance(){return a.instance=a.instance||new a,a.instance}static parseInput(t){const e={},r=t.split(":");for(let t=0,n=r.length;t<n;t++){const n=r[t].split("="),o=n[0].trim();n[1]?e[o]=n[1].trim():o.match(/^!/)?e[o.slice(1)]=!1:e[o]=!0}return e}static parseState(t){const e={},r=t.split(" ");for(let t=0,n=r.length;t<n;t++){const n=r[t].split(":"),o=n[0],i=n[1];e[o]=i||!0}return e}static translateString_(t){if(t.match(/:unit$/))return a.translateUnit_(t);const e=o.default.getInstance();let r=e.evaluator(t,e.dynamicCstr);return r=null===r?t:r,a.getInstance().getParameter("plural")&&(r=s.LOCALE.FUNCTIONS.plural(r)),r}static translateUnit_(t){t=a.prepareUnit_(t);const e=o.default.getInstance(),r=a.getInstance().getParameter("plural"),n=e.strict,i=`${e.locale}.${e.modality}.default`;let l,c;return e.strict=!0,r&&(l=e.defaultParser.parse(i+".plural"),c=e.evaluator(t,l)),c?(e.strict=n,c):(l=e.defaultParser.parse(i+".default"),c=e.evaluator(t,l),e.strict=n,c?(r&&(c=s.LOCALE.FUNCTIONS.plural(c)),c):a.cleanUnit_(t))}static prepareUnit_(t){const e=t.match(/:unit$/);return e?t.slice(0,e.index).replace(/\s+/g," ")+t.slice(e.index):t}static cleanUnit_(t){return t.match(/:unit$/)?t.replace(/:unit$/,""):t}clear(){this.parameters_={},this.stateStack_=[]}setParameter(t,e){const r=this.parameters_[t];return e?this.parameters_[t]=e:delete this.parameters_[t],r}getParameter(t){return this.parameters_[t]}setCorrection(t,e){this.corrections_[t]=e}setPreprocessor(t,e){this.preprocessors_[t]=e}getCorrection(t){return this.corrections_[t]}getState(){const t=[];for(const e in this.parameters_){const r=this.parameters_[e];t.push("string"==typeof r?e+":"+r:e)}return t.join(" ")}pushState(t){for(const e in t)t[e]=this.setParameter(e,t[e]);this.stateStack_.push(t)}popState(){const t=this.stateStack_.pop();for(const e in t)this.setParameter(e,t[e])}setAttribute(t){if(t&&t.nodeType===n.NodeType.ELEMENT_NODE){const r=this.getState();r&&t.setAttribute(e.ATTRIBUTE,r)}}preprocess(t){return this.runProcessors_(t,this.preprocessors_)}correct(t){return this.runProcessors_(t,this.corrections_)}apply(t,e){return this.currentFlags=e||{},t=this.currentFlags.adjust||this.currentFlags.preprocess?a.getInstance().preprocess(t):t,(this.parameters_.translate||this.currentFlags.translate)&&(t=a.translateString_(t)),t=this.currentFlags.adjust||this.currentFlags.correct?a.getInstance().correct(t):t,this.currentFlags={},t}runProcessors_(t,e){for(const r in this.parameters_){const n=e[r];if(!n)continue;const o=this.parameters_[r];t=!0===o?n(t):n(t,o)}return t}}function l(t,e){if(!e||!t)return t;const r=s.LOCALE.FUNCTIONS.fontRegexp(i.localFont(e));return t.replace(r,"")}function c(t){return t.match(/\d+/)?s.LOCALE.NUMBERS.numberToWords(parseInt(t,10)):t}e.Grammar=a,e.numbersToAlpha=c,a.getInstance().setCorrection("localFont",i.localFont),a.getInstance().setCorrection("localRole",i.localRole),a.getInstance().setCorrection("localEnclose",i.localEnclose),a.getInstance().setCorrection("ignoreFont",l),a.getInstance().setPreprocessor("annotation",(function(t,e){return t+":"+e})),a.getInstance().setPreprocessor("noTranslateText",(function(t){return t.match(new RegExp("^["+s.LOCALE.MESSAGES.regexp.TEXT+"]+$"))&&(a.getInstance().currentFlags.translate=!1),t})),a.getInstance().setCorrection("ignoreCaps",(function(t){let e=s.LOCALE.ALPHABETS.capPrefix[o.default.getInstance().domain];return void 0===e&&(e=s.LOCALE.ALPHABETS.capPrefix.default),l(t,e)})),a.getInstance().setPreprocessor("numbers2alpha",c)},2780:function(t,e,r){Object.defineProperty(e,"__esModule",{value:!0}),e.enumerate=e.lookupString=e.lookupCategory=e.lookupRule=e.addSiUnitRules=e.addUnitRules=e.addFunctionRules=e.addSymbolRules=e.defineRule=e.defineRules=e.setSiPrefixes=void 0;const n=r(2057),o=r(5897),i=r(7491),s=r(4658),a=r(1676);let l=a.DynamicCstr.DEFAULT_VALUES[a.Axis.LOCALE],c=a.DynamicCstr.DEFAULT_VALUES[a.Axis.MODALITY],u={};e.setSiPrefixes=function(t){u=t};const p={};function h(t,e,r,n){const o=_(e);S(o,r),o.defineRulesFromMappings(t,l,c,e,n)}function f(t){if(v(t))return;const e=t.names,r=t.mappings,n=t.category;for(let t,o=0;t=e[o];o++)h(t,t,n,r)}function d(t){for(const e of Object.keys(u)){const r=Object.assign({},t);r.mappings={};const n=u[e];r.key=e+r.key,r.names=r.names.map((function(t){return e+t}));for(const e of Object.keys(t.mappings)){r.mappings[e]={};for(const o of Object.keys(t.mappings[e]))r.mappings[e][o]=i.locales[l]().FUNCTIONS.si(n,t.mappings[e][o])}b(r)}b(t)}function m(t,e){const r=p[t];return r?r.lookupRule(null,e):null}function y(t,e){const r=m(t,e);return r?r.action:null}function g(t,e){return e=e||{},t.length?(e[t[0]]=g(t.slice(1),e[t[0]]),e):e}function b(t){const e=t.names;e&&(t.names=e.map((function(t){return t+":unit"}))),f(t)}function v(t){return!(!t.locale&&!t.modality)&&(l=t.locale||l,c=t.modality||c,!0)}function _(t){let e=p[t];return e?(n.Debugger.getInstance().output("Store exists! "+t),e):(e=new s.MathSimpleStore,p[t]=e,e)}function S(t,e){e&&(t.category=e)}e.defineRules=h,e.defineRule=function(t,e,r,n,o,i){const s=_(o);S(s,n),s.defineRuleFromStrings(t,l,c,e,r,o,i)},e.addSymbolRules=function(t){if(v(t))return;const e=s.MathSimpleStore.parseUnicode(t.key);h(t.key,e,t.category,t.mappings)},e.addFunctionRules=f,e.addUnitRules=function(t){v(t)||(t.si?d(t):b(t))},e.addSiUnitRules=d,e.lookupRule=m,e.lookupCategory=function(t){const e=p[t];return e?e.category:""},e.lookupString=y,o.default.getInstance().evaluator=y,e.enumerate=function(t={}){for(const e of Object.values(p))for(const[,r]of e.rules.entries())for(const{cstr:e}of r)t=g(e.getValues(),t);return t}},4658:function(t,e,r){Object.defineProperty(e,"__esModule",{value:!0}),e.MathSimpleStore=void 0;const n=r(5897),o=r(1676);class i{constructor(){this.category="",this.rules=new Map}static parseUnicode(t){const e=parseInt(t,16);return String.fromCodePoint(e)}static testDynamicConstraints_(t,e){return n.default.getInstance().strict?e.cstr.equal(t):n.default.getInstance().comparator.match(e.cstr)}defineRulesFromMappings(t,e,r,n,o){for(const i in o)for(const s in o[i]){const a=o[i][s];this.defineRuleFromStrings(t,e,r,i,s,n,a)}}getRules(t){let e=this.rules.get(t);return e||(e=[],this.rules.set(t,e)),e}defineRuleFromStrings(t,e,r,o,i,s,a){let l=this.getRules(e);const c=n.default.getInstance().parsers[o]||n.default.getInstance().defaultParser,u=n.default.getInstance().comparators[o],p=`${e}.${r}.${o}.${i}`,h=c.parse(p),f=u?u():n.default.getInstance().comparator,d=f.getReference();f.setReference(h);const m={cstr:h,action:a};l=l.filter((t=>!h.equal(t.cstr))),l.push(m),this.rules.set(e,l),f.setReference(d)}lookupRule(t,e){let r=this.getRules(e.getValue(o.Axis.LOCALE));return r=r.filter((function(t){return i.testDynamicConstraints_(e,t)})),1===r.length?r[0]:r.length?r.sort(((t,e)=>n.default.getInstance().comparator.compare(t.cstr,e.cstr)))[0]:null}}e.MathSimpleStore=i},9935:function(t,e,r){Object.defineProperty(e,"__esModule",{value:!0}),e.MathStore=void 0;const n=r(707),o=r(4356),i=r(7630),s=r(4504),a=r(4650);class l extends s.BaseRuleStore{constructor(){super(),this.annotators=[],this.parseMethods.Alias=this.defineAlias,this.parseMethods.SpecializedRule=this.defineSpecializedRule,this.parseMethods.Specialized=this.defineSpecialized}initialize(){this.initialized||(this.annotations(),this.initialized=!0)}annotations(){for(let t,e=0;t=this.annotators[e];e++)(0,i.activate)(this.domain,t)}defineAlias(t,e,...r){const n=this.parsePrecondition(e,r);if(!n)return void console.error(`Precondition Error: ${e} ${r}`);const o=this.preconditions.get(t);o?o.addFullCondition(n):console.error(`Alias Error: No precondition by the name of ${t}`)}defineRulesAlias(t,e,...r){const n=this.findAllRules((function(e){return e.name===t}));if(0===n.length)throw new a.OutputError("Rule with name "+t+" does not exist.");const o=[];n.forEach((t=>{(t=>{const e=t.dynamicCstr.toString(),r=t.action.toString();for(let t,n=0;t=o[n];n++)if(t.action===r&&t.cstr===e)return!1;return o.push({cstr:e,action:r}),!0})(t)&&this.addAlias_(t,e,r)}))}defineSpecializedRule(t,e,r,n){const o=this.parseCstr(e),i=this.findRule((e=>e.name===t&&o.equal(e.dynamicCstr))),s=this.parseCstr(r);if(!i&&n)throw new a.OutputError("Rule named "+t+" with style "+e+" does not exist.");const l=n?a.Action.fromString(n):i.action,c=new a.SpeechRule(i.name,s,i.precondition,l);this.addRule(c)}defineSpecialized(t,e,r){const n=this.parseCstr(r);if(!n)return void console.error(`Dynamic Constraint Error: ${r}`);const o=this.preconditions.get(t);o?o.addConstraint(n):console.error(`Alias Error: No precondition by the name of ${t}`)}evaluateString(t){const e=[];if(t.match(/^\s+$/))return e;let r=this.matchNumber_(t);if(r&&r.length===t.length)return e.push(this.evaluateCharacter(r.number)),e;const i=n.removeEmpty(t.replace(/\s/g," ").split(" "));for(let t,n=0;t=i[n];n++)if(1===t.length)e.push(this.evaluateCharacter(t));else if(t.match(new RegExp("^["+o.LOCALE.MESSAGES.regexp.TEXT+"]+$")))e.push(this.evaluateCharacter(t));else{let n=t;for(;n;){r=this.matchNumber_(n);const t=n.match(new RegExp("^["+o.LOCALE.MESSAGES.regexp.TEXT+"]+"));if(r)e.push(this.evaluateCharacter(r.number)),n=n.substring(r.length);else if(t)e.push(this.evaluateCharacter(t[0])),n=n.substring(t[0].length);else{const t=Array.from(n),r=t[0];e.push(this.evaluateCharacter(r)),n=t.slice(1).join("")}}}return e}parse(t){super.parse(t),this.annotators=t.annotators||[]}addAlias_(t,e,r){const n=this.parsePrecondition(e,r),o=new a.SpeechRule(t.name,t.dynamicCstr,n,t.action);o.name=t.name,this.addRule(o)}matchNumber_(t){const e=t.match(new RegExp("^"+o.LOCALE.MESSAGES.regexp.NUMBER)),r=t.match(new RegExp("^"+l.regexp.NUMBER));if(!e&&!r)return null;const n=r&&r[0]===t;if(e&&e[0]===t||!n)return e?{number:e[0],length:e[0].length}:null;return{number:r[0].replace(new RegExp(l.regexp.DIGIT_GROUP,"g"),"X").replace(new RegExp(l.regexp.DECIMAL_MARK,"g"),o.LOCALE.MESSAGES.regexp.DECIMAL_MARK).replace(/X/g,o.LOCALE.MESSAGES.regexp.DIGIT_GROUP.replace(/\\/g,"")),length:r[0].length}}}e.MathStore=l,l.regexp={NUMBER:"((\\d{1,3})(?=(,| ))((,| )\\d{3})*(\\.\\d+)?)|^\\d*\\.\\d+|^\\d+",DECIMAL_MARK:"\\.",DIGIT_GROUP:","}},4650:function(t,e,r){Object.defineProperty(e,"__esModule",{value:!0}),e.OutputError=e.Precondition=e.Action=e.Component=e.ActionType=e.SpeechRule=void 0;const n=r(5897),o=r(2105);var i;function s(t){switch(t){case"[n]":return i.NODE;case"[m]":return i.MULTI;case"[t]":return i.TEXT;case"[p]":return i.PERSONALITY;default:throw"Parse error: "+t}}e.SpeechRule=class{constructor(t,e,r,n){this.name=t,this.dynamicCstr=e,this.precondition=r,this.action=n,this.context=null}toString(){return this.name+" | "+this.dynamicCstr.toString()+" | "+this.precondition.toString()+" ==> "+this.action.toString()}},function(t){t.NODE="NODE",t.MULTI="MULTI",t.TEXT="TEXT",t.PERSONALITY="PERSONALITY"}(i=e.ActionType||(e.ActionType={}));class a{constructor({type:t,content:e,attributes:r,grammar:n}){this.type=t,this.content=e,this.attributes=r,this.grammar=n}static grammarFromString(t){return o.Grammar.parseInput(t)}static fromString(t){const e={type:s(t.substring(0,3))};let r=t.slice(3).trim();if(!r)throw new u("Missing content.");switch(e.type){case i.TEXT:if('"'===r[0]){const t=p(r,"\\(")[0].trim();if('"'!==t.slice(-1))throw new u("Invalid string syntax.");e.content=t,r=r.slice(t.length).trim(),-1===r.indexOf("(")&&(r="");break}case i.NODE:case i.MULTI:{const t=r.indexOf(" (");if(-1===t){e.content=r.trim(),r="";break}e.content=r.substring(0,t).trim(),r=r.slice(t).trim()}}if(r){const t=a.attributesFromString(r);t.grammar&&(e.grammar=t.grammar,delete t.grammar),Object.keys(t).length&&(e.attributes=t)}return new a(e)}static attributesFromString(t){if("("!==t[0]||")"!==t.slice(-1))throw new u("Invalid attribute expression: "+t);const e={},r=p(t.slice(1,-1),",");for(let t=0,n=r.length;t<n;t++){const n=r[t],i=n.indexOf(":");if(-1===i)e[n.trim()]="true";else{const t=n.substring(0,i).trim(),r=n.slice(i+1).trim();e[t]=t===o.ATTRIBUTE?a.grammarFromString(r):r}}return e}toString(){let t="";t+=function(t){switch(t){case i.NODE:return"[n]";case i.MULTI:return"[m]";case i.TEXT:return"[t]";case i.PERSONALITY:return"[p]";default:throw"Unknown type error: "+t}}(this.type),t+=this.content?" "+this.content:"";const e=this.attributesToString();return t+=e?" "+e:"",t}grammarToString(){return this.getGrammar().join(":")}getGrammar(){const t=[];for(const e in this.grammar)!0===this.grammar[e]?t.push(e):!1===this.grammar[e]?t.push("!"+e):t.push(e+"="+this.grammar[e]);return t}attributesToString(){const t=this.getAttributes(),e=this.grammarToString();return e&&t.push("grammar:"+e),t.length>0?"("+t.join(", ")+")":""}getAttributes(){const t=[];for(const e in this.attributes){const r=this.attributes[e];"true"===r?t.push(e):t.push(e+":"+r)}return t}}e.Component=a;class l{constructor(t){this.components=t}static fromString(t){const e=p(t,";").filter((function(t){return t.match(/\S/)})).map((function(t){return t.trim()})),r=[];for(let t=0,n=e.length;t<n;t++){const n=a.fromString(e[t]);n&&r.push(n)}return new l(r)}toString(){return this.components.map((function(t){return t.toString()})).join("; ")}}e.Action=l;class c{constructor(t,...e){this.query=t,this.constraints=e;const[r,n]=this.presetPriority();this.priority=r?n:this.calculatePriority()}static constraintValue(t,e){for(let r,n=0;r=e[n];n++)if(t.match(r))return++n;return 0}toString(){const t=this.constraints.join(", ");return`${this.query}, ${t} (${this.priority}, ${this.rank})`}calculatePriority(){const t=c.constraintValue(this.query,c.queryPriorities);if(!t)return 0;const e=this.query.match(/^self::.+\[(.+)\]/)[1];return 100*t+10*c.constraintValue(e,c.attributePriorities)}presetPriority(){if(!this.constraints.length)return[!1,0];const t=this.constraints[this.constraints.length-1].match(/^priority=(.*$)/);if(!t)return[!1,0];this.constraints.pop();const e=parseFloat(t[1]);return[!0,isNaN(e)?0:e]}}e.Precondition=c,c.queryPriorities=[/^self::\*\[.+\]$/,/^self::[\w-]+\[.+\]$/],c.attributePriorities=[/^@[\w-]+$/,/^@[\w-]+!=".+"$/,/^not\(contains\(@[\w-]+,\s*".+"\)\)$/,/^contains\(@[\w-]+,".+"\)$/,/^@[\w-]+=".+"$/];class u extends n.SREError{constructor(t){super(t),this.name="RuleError"}}function p(t,e){const r=[];let n="";for(;""!==t;){const o=t.search(e);if(-1===o){if((t.match(/"/g)||[]).length%2!=0)throw new u("Invalid string in expression: "+t);r.push(n+t),n="",t=""}else if((t.substring(0,o).match(/"/g)||[]).length%2==0)r.push(n+t.substring(0,o)),n="",t=t.substring(o+1);else{const e=t.substring(o).search('"');if(-1===e)throw new u("Invalid string in expression: "+t);n+=t.substring(0,o+e+1),t=t.substring(o+e+1)}}return n&&r.push(n),r}e.OutputError=u},4106:function(t,e,r){Object.defineProperty(e,"__esModule",{value:!0}),e.SpeechRuleContext=void 0;const n=r(5274),o=r(5662);e.SpeechRuleContext=class{constructor(){this.customQueries=new o.CustomQueries,this.customStrings=new o.CustomStrings,this.contextFunctions=new o.ContextFunctions,this.customGenerators=new o.CustomGenerators}applyCustomQuery(t,e){const r=this.customQueries.lookup(e);return r?r(t):null}applySelector(t,e){return this.applyCustomQuery(t,e)||n.evalXPath(e,t)}applyQuery(t,e){const r=this.applySelector(t,e);return r.length>0?r[0]:null}applyConstraint(t,e){return!!this.applyQuery(t,e)||n.evaluateBoolean(e,t)}constructString(t,e){if(!e)return"";if('"'===e.charAt(0))return e.slice(1,-1);const r=this.customStrings.lookup(e);return r?r(t):n.evaluateString(e,t)}parse(t){const e=Array.isArray(t)?t:Object.entries(t);for(let t,r=0;t=e[r];r++){switch(t[0].slice(0,3)){case"CQF":this.customQueries.add(t[0],t[1]);break;case"CSF":this.customStrings.add(t[0],t[1]);break;case"CTF":this.contextFunctions.add(t[0],t[1]);break;case"CGF":this.customGenerators.add(t[0],t[1]);break;default:console.error("FunctionError: Invalid function name "+t[0])}}}}},2362:function(t,e,r){Object.defineProperty(e,"__esModule",{value:!0}),e.storeFactory=e.SpeechRuleEngine=void 0;const n=r(7052),o=r(2057),i=r(5740),s=r(5897),a=r(4440),l=r(5274),c=r(7283),u=r(7599),p=r(2469),h=r(1676),f=r(2105),d=r(9935),m=r(4650),y=r(4508);class g{constructor(){this.trie=null,this.evaluators_={},this.trie=new y.Trie}static getInstance(){return g.instance=g.instance||new g,g.instance}static debugSpeechRule(t,e){const r=t.precondition,n=t.context.applyQuery(e,r.query);o.Debugger.getInstance().output(r.query,n?n.toString():n),r.constraints.forEach((r=>o.Debugger.getInstance().output(r,t.context.applyConstraint(e,r))))}static debugNamedSpeechRule(t,e){const r=g.getInstance().trie.collectRules().filter((e=>e.name==t));for(let n,i=0;n=r[i];i++)o.Debugger.getInstance().output("Rule",t,"DynamicCstr:",n.dynamicCstr.toString(),"number",i),g.debugSpeechRule(n,e)}evaluateNode(t){(0,l.updateEvaluator)(t);const e=(new Date).getTime();let r=[];try{r=this.evaluateNode_(t)}catch(t){console.error("Something went wrong computing speech."),o.Debugger.getInstance().output(t)}const n=(new Date).getTime();return o.Debugger.getInstance().output("Time:",n-e),r}toString(){return this.trie.collectRules().map((t=>t.toString())).join("\n")}runInSetting(t,e){const r=s.default.getInstance(),n={};for(const e in t)n[e]=r[e],r[e]=t[e];r.setDynamicCstr();const o=e();for(const t in n)r[t]=n[t];return r.setDynamicCstr(),o}addStore(t){const e=v(t);"abstract"!==e.kind&&e.getSpeechRules().forEach((t=>this.trie.addRule(t))),this.addEvaluator(e)}processGrammar(t,e,r){const n={};for(const o in r){const i=r[o];n[o]="string"==typeof i?t.constructString(e,i):i}f.Grammar.getInstance().pushState(n)}addEvaluator(t){const e=t.evaluateDefault.bind(t),r=this.evaluators_[t.locale];if(r)return void(r[t.modality]=e);const n={};n[t.modality]=e,this.evaluators_[t.locale]=n}getEvaluator(t,e){const r=this.evaluators_[t]||this.evaluators_[h.DynamicCstr.DEFAULT_VALUES[h.Axis.LOCALE]];return r[e]||r[h.DynamicCstr.DEFAULT_VALUES[h.Axis.MODALITY]]}enumerate(t){return this.trie.enumerate(t)}evaluateNode_(t){return t?(this.updateConstraint_(),this.evaluateTree_(t)):[]}evaluateTree_(t){const e=s.default.getInstance();let r;o.Debugger.getInstance().output(e.mode!==a.Mode.HTTP?t.toString():t),f.Grammar.getInstance().setAttribute(t);const i=this.lookupRule(t,e.dynamicCstr);if(!i)return e.strict?[]:(r=this.getEvaluator(e.locale,e.modality)(t),t.attributes&&this.addPersonality_(r,{},!1,t),r);o.Debugger.getInstance().generateOutput((()=>["Apply Rule:",i.name,i.dynamicCstr.toString(),(e.mode,a.Mode.HTTP,t).toString()]));const c=i.context,u=i.action.components;r=[];for(let e,o=0;e=u[o];o++){let o=[];const i=e.content||"",a=e.attributes||{};let u=!1;e.grammar&&this.processGrammar(c,t,e.grammar);let p=null;if(a.engine){p=s.default.getInstance().dynamicCstr.getComponents();const t=f.Grammar.parseInput(a.engine);s.default.getInstance().setDynamicCstr(t)}switch(e.type){case m.ActionType.NODE:{const e=c.applyQuery(t,i);e&&(o=this.evaluateTree_(e))}break;case m.ActionType.MULTI:{u=!0;const e=c.applySelector(t,i);e.length>0&&(o=this.evaluateNodeList_(c,e,a.sepFunc,c.constructString(t,a.separator),a.ctxtFunc,c.constructString(t,a.context)))}break;case m.ActionType.TEXT:{const e=a.span,r={};if(e){const n=(0,l.evalXPath)(e,t);n.length&&(r.extid=n[0].getAttribute("extid"))}const s=c.constructString(t,i);(s||""===s)&&(o=Array.isArray(s)?s.map((function(t){return n.AuditoryDescription.create({text:t.speech,attributes:t.attributes},{adjust:!0})})):[n.AuditoryDescription.create({text:s,attributes:r},{adjust:!0})])}break;case m.ActionType.PERSONALITY:default:o=[n.AuditoryDescription.create({text:i})]}o[0]&&!u&&(a.context&&(o[0].context=c.constructString(t,a.context)+(o[0].context||"")),a.annotation&&(o[0].annotation=a.annotation)),this.addLayout(o,a,u),e.grammar&&f.Grammar.getInstance().popState(),r=r.concat(this.addPersonality_(o,a,u,t)),p&&s.default.getInstance().setDynamicCstr(p)}return r}evaluateNodeList_(t,e,r,o,i,s){if(!e.length)return[];const a=o||"",l=s||"",c=t.contextFunctions.lookup(i),u=c?c(e,l):function(){return l},p=t.contextFunctions.lookup(r),h=p?p(e,a):function(){return[n.AuditoryDescription.create({text:a},{translate:!0})]};let f=[];for(let t,r=0;t=e[r];r++){const n=this.evaluateTree_(t);if(n.length>0&&(n[0].context=u()+(n[0].context||""),f=f.concat(n),r<e.length-1)){const t=h();f=f.concat(t)}}return f}addLayout(t,e,r){const o=e.layout;o&&(o.match(/^begin/)?t.unshift(new n.AuditoryDescription({text:"",layout:o})):o.match(/^end/)?t.push(new n.AuditoryDescription({text:"",layout:o})):(t.unshift(new n.AuditoryDescription({text:"",layout:`begin${o}`})),t.push(new n.AuditoryDescription({text:"",layout:`end${o}`}))))}addPersonality_(t,e,r,o){const i={};let s=null;for(const t of a.personalityPropList){const r=e[t];if(void 0===r)continue;const n=parseFloat(r),o=isNaN(n)?'"'===r.charAt(0)?r.slice(1,-1):r:n;t===a.personalityProps.PAUSE?s=o:i[t]=o}for(let e,r=0;e=t[r];r++)this.addRelativePersonality_(e,i),this.addExternalAttributes_(e,o);if(r&&t.length&&delete t[t.length-1].personality[a.personalityProps.JOIN],s&&t.length){const e=t[t.length-1];e.text||Object.keys(e.personality).length?t.push(n.AuditoryDescription.create({text:"",personality:{pause:s}})):e.personality[a.personalityProps.PAUSE]=s}return t}addExternalAttributes_(t,e){if(e.hasAttributes()){const r=e.attributes;for(let e=r.length-1;e>=0;e--){const n=r[e].name;!t.attributes[n]&&n.match(/^ext/)&&(t.attributes[n]=r[e].value)}}}addRelativePersonality_(t,e){if(!t.personality)return t.personality=e,t;const r=t.personality;for(const t in e)r[t]&&"number"==typeof r[t]&&"number"==typeof e[t]?r[t]=r[t]+e[t]:r[t]||(r[t]=e[t]);return t}updateConstraint_(){const t=s.default.getInstance().dynamicCstr,e=s.default.getInstance().strict,r=this.trie,n={};let o=t.getValue(h.Axis.LOCALE),i=t.getValue(h.Axis.MODALITY),a=t.getValue(h.Axis.DOMAIN);r.hasSubtrie([o,i,a])||(a=h.DynamicCstr.DEFAULT_VALUES[h.Axis.DOMAIN],r.hasSubtrie([o,i,a])||(i=h.DynamicCstr.DEFAULT_VALUES[h.Axis.MODALITY],r.hasSubtrie([o,i,a])||(o=h.DynamicCstr.DEFAULT_VALUES[h.Axis.LOCALE]))),n[h.Axis.LOCALE]=[o],n[h.Axis.MODALITY]=["summary"!==i?i:h.DynamicCstr.DEFAULT_VALUES[h.Axis.MODALITY]],n[h.Axis.DOMAIN]=["speech"!==i?h.DynamicCstr.DEFAULT_VALUES[h.Axis.DOMAIN]:a];const l=t.getOrder();for(let r,o=0;r=l[o];o++)if(!n[r]){const o=t.getValue(r),i=this.makeSet_(o,t.preference),s=h.DynamicCstr.DEFAULT_VALUES[r];e||o===s||i.push(s),n[r]=i}t.updateProperties(n)}makeSet_(t,e){return e&&Object.keys(e).length?t.split(":"):[t]}lookupRule(t,e){if(!t||t.nodeType!==i.NodeType.ELEMENT_NODE&&t.nodeType!==i.NodeType.TEXT_NODE)return null;const r=this.lookupRules(t,e);return r.length>0?this.pickMostConstraint_(e,r):null}lookupRules(t,e){return this.trie.lookupRules(t,e.allProperties())}pickMostConstraint_(t,e){const r=s.default.getInstance().comparator;return e.sort((function(t,e){return r.compare(t.dynamicCstr,e.dynamicCstr)||e.precondition.priority-t.precondition.priority||e.precondition.constraints.length-t.precondition.constraints.length||e.precondition.rank-t.precondition.rank})),o.Debugger.getInstance().generateOutput((()=>e.map((t=>t.name+"("+t.dynamicCstr.toString()+")"))).bind(this)),e[0]}}e.SpeechRuleEngine=g;const b=new Map;function v(t){const e=`${t.locale}.${t.modality}.${t.domain}`;if("actions"===t.kind){const r=b.get(e);return r.parse(t),r}u.init(),t&&!t.functions&&(t.functions=c.getStore(t.locale,t.modality,t.domain));const r="braille"===t.modality?new p.BrailleStore:new d.MathStore;return 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n=t.comp.shift(),o=null,i=[];for(;t.comp.length;)if(i=[],n.length)o&&e.push(o),r.push(n),o=t.rel.shift(),n=t.comp.shift();else{for(o&&i.push(o);!n.length&&t.comp.length;)n=t.comp.shift(),i.push(t.rel.shift());o=p(i,n,r)}i.length||n.length?(e.push(o),r.push(n)):(i.push(o),p(i,n,r));return{rel:e,comp:r}}(e):e,t=e.comp[0];for(let r,n,o=1;r=e.comp[o],n=e.rel[o-1];o++)t.push(n),t=t.concat(r);return e=u.partitionNodes(t,(function(t){return t.textContent===i.invisibleTimes()&&("operator"===t.type||"infixop"===t.type)})),e.rel.length?h(e.comp.shift(),e.rel,e.comp):t}))),s.add(new a.SemanticTreeHeuristic("simple2prefix",(t=>(t.textContent.length>1&&!t.textContent[0].match(/[A-Z]/)&&(t.role="prefix function"),t)),(t=>"braille"===o.default.getInstance().modality&&"identifier"===t.type))),s.add(new a.SemanticTreeHeuristic("detect_cycle",(t=>{t.type="matrix",t.role="cycle";const e=t.childNodes[0];return e.type="row",e.role="cycle",e.textContent="",e.contentNodes=[],t}),(t=>"fenced"===t.type&&"infixop"===t.childNodes[0].type&&"implicit"===t.childNodes[0].role&&t.childNodes[0].childNodes.every((function(t){return"number"===t.type}))&&t.childNodes[0].contentNodes.every((function(t){return"space"===t.role})))))},7228:function(t,e,r){Object.defineProperty(e,"__esModule",{value:!0}),e.SemanticMathml=void 0;const n=r(5740),o=r(5250),i=r(5609),s=r(3308),a=r(4795);class l extends o.SemanticAbstractParser{constructor(){super("MathML"),this.parseMap_={SEMANTICS:this.semantics_.bind(this),MATH:this.rows_.bind(this),MROW:this.rows_.bind(this),MPADDED:this.rows_.bind(this),MSTYLE:this.rows_.bind(this),MFRAC:this.fraction_.bind(this),MSUB:this.limits_.bind(this),MSUP:this.limits_.bind(this),MSUBSUP:this.limits_.bind(this),MOVER:this.limits_.bind(this),MUNDER:this.limits_.bind(this),MUNDEROVER:this.limits_.bind(this),MROOT:this.root_.bind(this),MSQRT:this.sqrt_.bind(this),MTABLE:this.table_.bind(this),MLABELEDTR:this.tableLabeledRow_.bind(this),MTR:this.tableRow_.bind(this),MTD:this.tableCell_.bind(this),MS:this.text_.bind(this),MTEXT:this.text_.bind(this),MSPACE:this.space_.bind(this),"ANNOTATION-XML":this.text_.bind(this),MI:this.identifier_.bind(this),MN:this.number_.bind(this),MO:this.operator_.bind(this),MFENCED:this.fenced_.bind(this),MENCLOSE:this.enclosed_.bind(this),MMULTISCRIPTS:this.multiscripts_.bind(this),ANNOTATION:this.empty_.bind(this),NONE:this.empty_.bind(this),MACTION:this.action_.bind(this)};const t={type:"identifier",role:"numbersetletter",font:"double-struck"};["C","H","N","P","Q","R","Z","\u2102","\u210d","\u2115","\u2119","\u211a","\u211d","\u2124"].forEach((e=>this.getFactory().defaultMap.add(e,t)).bind(this))}static getAttribute_(t,e,r){if(!t.hasAttribute(e))return r;const n=t.getAttribute(e);return n.match(/^\s*$/)?null:n}parse(t){s.default.getInstance().setNodeFactory(this.getFactory());const e=n.toArray(t.childNodes),r=n.tagName(t),o=this.parseMap_[r],i=(o||this.dummy_.bind(this))(t,e);return a.addAttributes(i,t),-1!==["MATH","MROW","MPADDED","MSTYLE","SEMANTICS"].indexOf(r)||(i.mathml.unshift(t),i.mathmlTree=t),i}semantics_(t,e){return e.length?this.parse(e[0]):this.getFactory().makeEmptyNode()}rows_(t,e){const r=t.getAttribute("semantics");if(r&&r.match("bspr_"))return s.default.proof(t,r,this.parseList.bind(this));let n;return 1===(e=a.purgeNodes(e)).length?(n=this.parse(e[0]),"empty"!==n.type||n.mathmlTree||(n.mathmlTree=t)):n=s.default.getInstance().row(this.parseList(e)),n.mathml.unshift(t),n}fraction_(t,e){if(!e.length)return this.getFactory().makeEmptyNode();const r=this.parse(e[0]),n=e[1]?this.parse(e[1]):this.getFactory().makeEmptyNode();return s.default.getInstance().fractionLikeNode(r,n,t.getAttribute("linethickness"),"true"===t.getAttribute("bevelled"))}limits_(t,e){return s.default.getInstance().limitNode(n.tagName(t),this.parseList(e))}root_(t,e){return e[1]?this.getFactory().makeBranchNode("root",[this.parse(e[1]),this.parse(e[0])],[]):this.sqrt_(t,e)}sqrt_(t,e){const r=this.parseList(a.purgeNodes(e));return this.getFactory().makeBranchNode("sqrt",[s.default.getInstance().row(r)],[])}table_(t,e){const r=t.getAttribute("semantics");if(r&&r.match("bspr_"))return s.default.proof(t,r,this.parseList.bind(this));const n=this.getFactory().makeBranchNode("table",this.parseList(e),[]);return n.mathmlTree=t,s.default.tableToMultiline(n),n}tableRow_(t,e){const r=this.getFactory().makeBranchNode("row",this.parseList(e),[]);return r.role="table",r}tableLabeledRow_(t,e){if(!e.length)return this.tableRow_(t,e);const r=this.parse(e[0]);r.role="label";const n=this.getFactory().makeBranchNode("row",this.parseList(e.slice(1)),[r]);return n.role="table",n}tableCell_(t,e){const r=this.parseList(a.purgeNodes(e));let n;n=r.length?1===r.length&&i.isType(r[0],"empty")?r:[s.default.getInstance().row(r)]:[];const o=this.getFactory().makeBranchNode("cell",n,[]);return o.role="table",o}space_(t,e){const r=t.getAttribute("width"),o=r&&r.match(/[a-z]*$/);if(!o)return this.empty_(t,e);const i=o[0],a=parseFloat(r.slice(0,o.index)),l={cm:.4,pc:.5,em:.5,ex:1,in:.15,pt:5,mm:5}[i];if(!l||isNaN(a)||a<l)return this.empty_(t,e);const c=this.getFactory().makeUnprocessed(t);return s.default.getInstance().text(c,n.tagName(t))}text_(t,e){const r=this.leaf_(t,e);return t.textContent?(r.updateContent(t.textContent,!0),s.default.getInstance().text(r,n.tagName(t))):r}identifier_(t,e){const r=this.leaf_(t,e);return s.default.getInstance().identifierNode(r,s.default.getInstance().font(t.getAttribute("mathvariant")),t.getAttribute("class"))}number_(t,e){const r=this.leaf_(t,e);return s.default.number(r),r}operator_(t,e){const r=this.leaf_(t,e);return s.default.getInstance().operatorNode(r),r}fenced_(t,e){const r=this.parseList(a.purgeNodes(e)),n=l.getAttribute_(t,"separators",","),o=l.getAttribute_(t,"open","("),i=l.getAttribute_(t,"close",")"),c=s.default.getInstance().mfenced(o,i,n,r);return s.default.getInstance().tablesInRow([c])[0]}enclosed_(t,e){const r=this.parseList(a.purgeNodes(e)),n=this.getFactory().makeBranchNode("enclose",[s.default.getInstance().row(r)],[]);return n.role=t.getAttribute("notation")||"unknown",n}multiscripts_(t,e){if(!e.length)return this.getFactory().makeEmptyNode();const r=this.parse(e.shift());if(!e.length)return r;const o=[],i=[],l=[],c=[];let u=!1,p=0;for(let t,r=0;t=e[r];r++)"MPRESCRIPTS"!==n.tagName(t)?(u?1&p?o.push(t):i.push(t):1&p?l.push(t):c.push(t),p++):(u=!0,p=0);return a.purgeNodes(o).length||a.purgeNodes(i).length?s.default.getInstance().tensor(r,this.parseList(i),this.parseList(o),this.parseList(c),this.parseList(l)):s.default.getInstance().pseudoTensor(r,this.parseList(c),this.parseList(l))}empty_(t,e){return this.getFactory().makeEmptyNode()}action_(t,e){return e.length>1?this.parse(e[1]):this.getFactory().makeUnprocessed(t)}dummy_(t,e){const r=this.getFactory().makeUnprocessed(t);return r.role=t.tagName,r.textContent=t.textContent,r}leaf_(t,e){if(1===e.length&&e[0].nodeType!==n.NodeType.TEXT_NODE){const r=this.getFactory().makeUnprocessed(t);return r.role=e[0].tagName,a.addAttributes(r,e[0]),r}return this.getFactory().makeLeafNode(t.textContent,s.default.getInstance().font(t.getAttribute("mathvariant")))}}e.SemanticMathml=l},5952:function(t,e,r){Object.defineProperty(e,"__esModule",{value:!0}),e.SemanticNode=void 0;const n=r(5740),o=r(3588),i=r(4795);class s{constructor(t){this.id=t,this.mathml=[],this.parent=null,this.type="unknown",this.role="unknown",this.font="unknown",this.embellished=null,this.fencePointer="",this.childNodes=[],this.textContent="",this.mathmlTree=null,this.contentNodes=[],this.annotation={},this.attributes={},this.nobreaking=!1}static fromXml(t){const e=parseInt(t.getAttribute("id"),10),r=new s(e);return r.type=t.tagName,s.setAttribute(r,t,"role"),s.setAttribute(r,t,"font"),s.setAttribute(r,t,"embellished"),s.setAttribute(r,t,"fencepointer","fencePointer"),t.getAttribute("annotation")&&r.parseAnnotation(t.getAttribute("annotation")),i.addAttributes(r,t),s.processChildren(r,t),r}static setAttribute(t,e,r,n){n=n||r;const o=e.getAttribute(r);o&&(t[n]=o)}static processChildren(t,e){for(const r of n.toArray(e.childNodes)){if(r.nodeType===n.NodeType.TEXT_NODE){t.textContent=r.textContent;continue}const e=n.toArray(r.childNodes).map(s.fromXml);e.forEach((e=>e.parent=t)),"CONTENT"===n.tagName(r)?t.contentNodes=e:t.childNodes=e}}querySelectorAll(t){let e=[];for(let r,n=0;r=this.childNodes[n];n++)e=e.concat(r.querySelectorAll(t));for(let r,n=0;r=this.contentNodes[n];n++)e=e.concat(r.querySelectorAll(t));return t(this)&&e.unshift(this),e}xml(t,e){const r=function(r,n){const o=n.map((function(r){return r.xml(t,e)})),i=t.createElementNS("",r);for(let t,e=0;t=o[e];e++)i.appendChild(t);return i},n=t.createElementNS("",this.type);return e||this.xmlAttributes(n),n.textContent=this.textContent,this.contentNodes.length>0&&n.appendChild(r("content",this.contentNodes)),this.childNodes.length>0&&n.appendChild(r("children",this.childNodes)),n}toString(t=!1){const e=n.parseInput("<snode/>");return n.serializeXml(this.xml(e,t))}allAttributes(){const t=[];return t.push(["role",this.role]),"unknown"!==this.font&&t.push(["font",this.font]),Object.keys(this.annotation).length&&t.push(["annotation",this.xmlAnnotation()]),this.embellished&&t.push(["embellished",this.embellished]),this.fencePointer&&t.push(["fencepointer",this.fencePointer]),t.push(["id",this.id.toString()]),t}xmlAnnotation(){const t=[];for(const e in this.annotation)this.annotation[e].forEach((function(r){t.push(e+":"+r)}));return t.join(";")}toJson(){const t={};t.type=this.type;const e=this.allAttributes();for(let r,n=0;r=e[n];n++)t[r[0]]=r[1].toString();return this.textContent&&(t.$t=this.textContent),this.childNodes.length&&(t.children=this.childNodes.map((function(t){return t.toJson()}))),this.contentNodes.length&&(t.content=this.contentNodes.map((function(t){return t.toJson()}))),t}updateContent(t,e){const r=e?t.replace(/^[ \f\n\r\t\v\u200b]*/,"").replace(/[ \f\n\r\t\v\u200b]*$/,""):t.trim();if(t=t&&!r?t:r,this.textContent===t)return;const n=(0,o.lookupMeaning)(t);this.textContent=t,this.role=n.role,this.type=n.type,this.font=n.font}addMathmlNodes(t){for(let e,r=0;e=t[r];r++)-1===this.mathml.indexOf(e)&&this.mathml.push(e)}appendChild(t){this.childNodes.push(t),this.addMathmlNodes(t.mathml),t.parent=this}replaceChild(t,e){const r=this.childNodes.indexOf(t);if(-1===r)return;t.parent=null,e.parent=this,this.childNodes[r]=e;const n=t.mathml.filter((function(t){return-1===e.mathml.indexOf(t)})),o=e.mathml.filter((function(e){return-1===t.mathml.indexOf(e)}));this.removeMathmlNodes(n),this.addMathmlNodes(o)}appendContentNode(t){t&&(this.contentNodes.push(t),this.addMathmlNodes(t.mathml),t.parent=this)}removeContentNode(t){if(t){const e=this.contentNodes.indexOf(t);-1!==e&&this.contentNodes.slice(e,1)}}equals(t){if(!t)return!1;if(this.type!==t.type||this.role!==t.role||this.textContent!==t.textContent||this.childNodes.length!==t.childNodes.length||this.contentNodes.length!==t.contentNodes.length)return!1;for(let e,r,n=0;e=this.childNodes[n],r=t.childNodes[n];n++)if(!e.equals(r))return!1;for(let e,r,n=0;e=this.contentNodes[n],r=t.contentNodes[n];n++)if(!e.equals(r))return!1;return!0}displayTree(){console.info(this.displayTree_(0))}addAnnotation(t,e){e&&this.addAnnotation_(t,e)}getAnnotation(t){const e=this.annotation[t];return e||[]}hasAnnotation(t,e){const r=this.annotation[t];return!!r&&-1!==r.indexOf(e)}parseAnnotation(t){const e=t.split(";");for(let t=0,r=e.length;t<r;t++){const r=e[t].split(":");this.addAnnotation(r[0],r[1])}}meaning(){return{type:this.type,role:this.role,font:this.font}}xmlAttributes(t){const e=this.allAttributes();for(let r,n=0;r=e[n];n++)t.setAttribute(r[0],r[1]);this.addExternalAttributes(t)}addExternalAttributes(t){for(const e in this.attributes)t.setAttribute(e,this.attributes[e])}removeMathmlNodes(t){const e=this.mathml;for(let r,n=0;r=t[n];n++){const t=e.indexOf(r);-1!==t&&e.splice(t,1)}this.mathml=e}displayTree_(t){t++;const e=Array(t).join("  ");let r="";r+="\n"+e+this.toString(),r+="\n"+e+"MathmlTree:",r+="\n"+e+this.mathmlTreeString(),r+="\n"+e+"MathML:";for(let t,n=0;t=this.mathml[n];n++)r+="\n"+e+t.toString();return r+="\n"+e+"Begin Content",this.contentNodes.forEach((function(e){r+=e.displayTree_(t)})),r+="\n"+e+"End Content",r+="\n"+e+"Begin Children",this.childNodes.forEach((function(e){r+=e.displayTree_(t)})),r+="\n"+e+"End Children",r}mathmlTreeString(){return this.mathmlTree?this.mathmlTree.toString():"EMPTY"}addAnnotation_(t,e){const r=this.annotation[t];r?r.push(e):this.annotation[t]=[e]}}e.SemanticNode=s},6537:function(t,e,r){Object.defineProperty(e,"__esModule",{value:!0}),e.SemanticNodeFactory=void 0;const n=r(8158),o=r(8158),i=r(5952);e.SemanticNodeFactory=class{constructor(){this.leafMap=new o.SemanticNodeCollator,this.defaultMap=new n.SemanticDefault,this.idCounter_=-1}makeNode(t){return this.createNode_(t)}makeUnprocessed(t){const e=this.createNode_();return e.mathml=[t],e.mathmlTree=t,e}makeEmptyNode(){const t=this.createNode_();return t.type="empty",t}makeContentNode(t){const e=this.createNode_();return e.updateContent(t),e}makeMultipleContentNodes(t,e){const r=[];for(let n=0;n<t;n++)r.push(this.makeContentNode(e));return r}makeLeafNode(t,e){if(!t)return this.makeEmptyNode();const r=this.makeContentNode(t);r.font=e||r.font;const n=this.defaultMap.retrieveNode(r);return n&&(r.type=n.type,r.role=n.role,r.font=n.font),this.leafMap.addNode(r),r}makeBranchNode(t,e,r,n){const o=this.createNode_();return n&&o.updateContent(n),o.type=t,o.childNodes=e,o.contentNodes=r,e.concat(r).forEach((function(t){t.parent=o,o.addMathmlNodes(t.mathml)})),o}createNode_(t){return void 0!==t?this.idCounter_=Math.max(this.idCounter_,t):t=++this.idCounter_,new i.SemanticNode(t)}}},3882:function(t,e){Object.defineProperty(e,"__esModule",{value:!0}),e.SemanticComparator=e.reduce=e.sort=e.apply=e.add=void 0;const r=[];function n(t){r.push(t)}function o(t,e){for(let n,o=0;n=r[o];o++){const r=n.compare(t,e);if(0!==r)return r}return 0}function i(t){t.sort(o)}e.add=n,e.apply=o,e.sort=i,e.reduce=function(t){if(t.length<=1)return t;const e=t.slice();i(e);const r=[];let n;do{n=e.pop(),r.push(n)}while(n&&e.length&&0===o(e[e.length-1],n));return r};class s{constructor(t,e=null){this.comparator=t,this.type=e,n(this)}compare(t,e){return this.type&&this.type===t.type&&this.type===e.type?this.comparator(t,e):0}}e.SemanticComparator=s,new s((function(t,e){return"simple function"===t.role?1:"simple function"===e.role?-1:0}),"identifier")},5250:function(t,e,r){Object.defineProperty(e,"__esModule",{value:!0}),e.SemanticAbstractParser=void 0;const n=r(6537);e.SemanticAbstractParser=class{constructor(t){this.type=t,this.factory_=new n.SemanticNodeFactory}getFactory(){return this.factory_}setFactory(t){this.factory_=t}getType(){return this.type}parseList(t){const e=[];for(let r,n=0;r=t[n];n++)e.push(this.parse(r));return e}}},5609:function(t,e,r){Object.defineProperty(e,"__esModule",{value:!0}),e.isMembership=e.elligibleRightNeutral=e.elligibleLeftNeutral=e.compareNeutralFences=e.isNeutralFence=e.isImplicitOp=e.isImplicit=e.isPureUnit=e.isUnitCounter=e.isNumber=e.isSingletonSetContent=e.scriptedElement_=e.illegalSingleton_=e.isSetNode=e.isRightBrace=e.isLeftBrace=e.isSimpleFunction=e.singlePunctAtPosition=e.isSimpleFunctionHead=e.isLimitBase=e.isBinomial=e.lineIsLabelled=e.tableIsMultiline=e.tableIsCases=e.isFencedElement=e.tableIsMatrixOrVector=e.isTableOrMultiline=e.isElligibleEmbellishedFence=e.isFence=e.isPunctuation=e.isRelation=e.isOperator=e.isEmbellished=e.isGeneralFunctionBoundary=e.isIntegralDxBoundarySingle=e.isIntegralDxBoundary=e.isBigOpBoundary=e.isPrefixFunctionBoundary=e.isSimpleFunctionScope=e.isAccent=e.isRole=e.embellishedType=e.isType=void 0;const n=r(3588),o=r(4795);function i(t,e){return t.type===e}function s(t,e){return t.embellished===e}function a(t,e){return t.role===e}function l(t){return u(t)||p(t)}function c(t){return i(t,"operator")||s(t,"operator")}function u(t){return i(t,"relation")||s(t,"relation")}function p(t){return i(t,"punctuation")||s(t,"punctuation")}function h(t){return i(t,"fence")||s(t,"fence")}function f(t){return!t.embellished||!function(t){return i(t,"tensor")&&(!i(t.childNodes[1],"empty")||!i(t.childNodes[2],"empty"))&&(!i(t.childNodes[3],"empty")||!i(t.childNodes[4],"empty"))}(t)&&((!a(t,"close")||!i(t,"tensor"))&&((!a(t,"open")||!i(t,"subscript")&&!i(t,"superscript"))&&f(t.childNodes[0])))}function d(t){return!!t&&(i(t,"table")||i(t,"multiline"))}function m(t){return!!t&&i(t,"fenced")&&(a(t,"leftright")||v(t))&&1===t.childNodes.length}function y(t){return!!t&&-1!==["{","\ufe5b","\uff5b"].indexOf(t.textContent)}function g(t){return!!t&&-1!==["}","\ufe5c","\uff5d"].indexOf(t.textContent)}function b(t){return"number"===t.type&&("integer"===t.role||"float"===t.role)}function v(t){return"neutral"===t.role||"metric"===t.role}e.isType=i,e.embellishedType=s,e.isRole=a,e.isAccent=function(t){const e=new RegExp("\u221e|\u1ab2");return i(t,"fence")||i(t,"punctuation")||i(t,"operator")&&!t.textContent.match(e)||i(t,"relation")||i(t,"identifier")&&a(t,"unknown")&&!t.textContent.match(n.allLettersRegExp)&&!t.textContent.match(e)},e.isSimpleFunctionScope=function(t){const e=t.childNodes;if(0===e.length)return!0;if(e.length>1)return!1;const r=e[0];if("infixop"===r.type){if("implicit"!==r.role)return!1;if(r.childNodes.some((t=>i(t,"infixop"))))return!1}return!0},e.isPrefixFunctionBoundary=function(t){return c(t)&&!a(t,"division")||i(t,"appl")||l(t)},e.isBigOpBoundary=function(t){return c(t)||l(t)},e.isIntegralDxBoundary=function(t,e){return!!e&&i(e,"identifier")&&n.lookupSecondary("d",t.textContent)},e.isIntegralDxBoundarySingle=function(t){if(i(t,"identifier")){const e=t.textContent[0];return e&&t.textContent[1]&&n.lookupSecondary("d",e)}return!1},e.isGeneralFunctionBoundary=l,e.isEmbellished=function(t){return t.embellished?t.embellished:n.isEmbellishedType(t.type)?t.type:null},e.isOperator=c,e.isRelation=u,e.isPunctuation=p,e.isFence=h,e.isElligibleEmbellishedFence=function(t){return!(!t||!h(t))&&(!t.embellished||f(t))},e.isTableOrMultiline=d,e.tableIsMatrixOrVector=function(t){return!!t&&m(t)&&d(t.childNodes[0])},e.isFencedElement=m,e.tableIsCases=function(t,e){return e.length>0&&a(e[e.length-1],"openfence")},e.tableIsMultiline=function(t){return t.childNodes.every((function(t){return t.childNodes.length<=1}))},e.lineIsLabelled=function(t){return i(t,"line")&&t.contentNodes.length&&a(t.contentNodes[0],"label")},e.isBinomial=function(t){return 2===t.childNodes.length},e.isLimitBase=function t(e){return i(e,"largeop")||i(e,"limboth")||i(e,"limlower")||i(e,"limupper")||i(e,"function")&&a(e,"limit function")||(i(e,"overscore")||i(e,"underscore"))&&t(e.childNodes[0])},e.isSimpleFunctionHead=function(t){return"identifier"===t.type||"latinletter"===t.role||"greekletter"===t.role||"otherletter"===t.role},e.singlePunctAtPosition=function(t,e,r){return 1===e.length&&("punctuation"===t[r].type||"punctuation"===t[r].embellished)&&t[r]===e[0]},e.isSimpleFunction=function(t){return i(t,"identifier")&&a(t,"simple function")},e.isLeftBrace=y,e.isRightBrace=g,e.isSetNode=function(t){return y(t.contentNodes[0])&&g(t.contentNodes[1])},e.illegalSingleton_=["punctuation","punctuated","relseq","multirel","table","multiline","cases","inference"],e.scriptedElement_=["limupper","limlower","limboth","subscript","superscript","underscore","overscore","tensor"],e.isSingletonSetContent=function t(r){const n=r.type;return-1===e.illegalSingleton_.indexOf(n)&&("infixop"!==n||"implicit"===r.role)&&("fenced"===n?"leftright"!==r.role||t(r.childNodes[0]):-1===e.scriptedElement_.indexOf(n)||t(r.childNodes[0]))},e.isNumber=b,e.isUnitCounter=function(t){return b(t)||"vulgar"===t.role||"mixed"===t.role},e.isPureUnit=function(t){const e=t.childNodes;return"unit"===t.role&&(!e.length||"unit"===e[0].role)},e.isImplicit=function(t){return"implicit"===t.role||"unit"===t.role&&!!t.contentNodes.length&&t.contentNodes[0].textContent===n.invisibleTimes()},e.isImplicitOp=function(t){return"infixop"===t.type&&"implicit"===t.role},e.isNeutralFence=v,e.compareNeutralFences=function(t,e){return v(t)&&v(e)&&(0,o.getEmbellishedInner)(t).textContent===(0,o.getEmbellishedInner)(e).textContent},e.elligibleLeftNeutral=function(t){return!!v(t)&&(!t.embellished||"superscript"!==t.type&&"subscript"!==t.type&&("tensor"!==t.type||"empty"===t.childNodes[3].type&&"empty"===t.childNodes[4].type))},e.elligibleRightNeutral=function(t){return!!v(t)&&(!t.embellished||("tensor"!==t.type||"empty"===t.childNodes[1].type&&"empty"===t.childNodes[2].type))},e.isMembership=function(t){return["element","nonelement","reelement","renonelement"].includes(t.role)}},3308:function(t,e,r){Object.defineProperty(e,"__esModule",{value:!0});const n=r(5740),o=r(3588),i=r(7516),s=r(6537),a=r(5609),l=r(4795);class c{constructor(){this.funcAppls={},this.factory_=new s.SemanticNodeFactory,i.updateFactory(this.factory_)}static getInstance(){return c.instance=c.instance||new c,c.instance}static tableToMultiline(t){if(a.tableIsMultiline(t)){t.type="multiline";for(let e,r=0;e=t.childNodes[r];r++)c.rowToLine_(e,"multiline");1===t.childNodes.length&&!a.lineIsLabelled(t.childNodes[0])&&a.isFencedElement(t.childNodes[0].childNodes[0])&&c.tableToMatrixOrVector_(c.rewriteFencedLine_(t)),c.binomialForm_(t),c.classifyMultiline(t)}else c.classifyTable(t)}static number(t){"unknown"!==t.type&&"identifier"!==t.type||(t.type="number"),c.numberRole_(t),c.exprFont_(t)}static classifyMultiline(t){let e=0;const r=t.childNodes.length;let n;for(;e<r&&(!(n=t.childNodes[e])||!n.childNodes.length);)e++;if(e>=r)return;const o=n.childNodes[0].role;"unknown"!==o&&t.childNodes.every((function(t){const e=t.childNodes[0];return!e||e.role===o&&(a.isType(e,"relation")||a.isType(e,"relseq"))}))&&(t.role=o)}static classifyTable(t){const e=c.computeColumns_(t);c.classifyByColumns_(t,e,"equality")||c.classifyByColumns_(t,e,"inequality",["equality"])||c.classifyByColumns_(t,e,"arrow")||c.detectCaleyTable(t)}static detectCaleyTable(t){if(!t.mathmlTree)return!1;const e=t.mathmlTree,r=e.getAttribute("columnlines"),n=e.getAttribute("rowlines");return!(!r||!n)&&(!(!c.cayleySpacing(r)||!c.cayleySpacing(n))&&(t.role="cayley",!0))}static cayleySpacing(t){const e=t.split(" ");return("solid"===e[0]||"dashed"===e[0])&&e.slice(1).every((t=>"none"===t))}static proof(t,e,r){const n=c.separateSemantics(e);return c.getInstance().proof(t,n,r)}static findSemantics(t,e,r){const n=null==r?null:r,o=c.getSemantics(t);return!!o&&(!!o[e]&&(null==n||o[e]===n))}static getSemantics(t){const e=t.getAttribute("semantics");return e?c.separateSemantics(e):null}static removePrefix(t){const[,...e]=t.split("_");return e.join("_")}static separateSemantics(t){const e={};return t.split(";").forEach((function(t){const[r,n]=t.split(":");e[c.removePrefix(r)]=n})),e}static matchSpaces_(t,e){for(let r,n=0;r=e[n];n++){const e=t[n].mathmlTree,o=t[n+1].mathmlTree;if(!e||!o)continue;const i=e.nextSibling;if(!i||i===o)continue;const s=c.getSpacer_(i);s&&(r.mathml.push(s),r.mathmlTree=s,r.role="space")}}static getSpacer_(t){if("MSPACE"===n.tagName(t))return t;for(;l.hasEmptyTag(t)&&1===t.childNodes.length;)if(t=t.childNodes[0],"MSPACE"===n.tagName(t))return t;return null}static fenceToPunct_(t){const e=c.FENCE_TO_PUNCT_[t.role];if(e){for(;t.embellished;)t.embellished="punctuation",a.isRole(t,"subsup")||a.isRole(t,"underover")||(t.role=e),t=t.childNodes[0];t.type="punctuation",t.role=e}}static classifyFunction_(t,e){if("appl"===t.type||"bigop"===t.type||"integral"===t.type)return"";if(e[0]&&e[0].textContent===o.functionApplication()){c.getInstance().funcAppls[t.id]=e.shift();let r="simple function";return i.run("simple2prefix",t),"prefix function"!==t.role&&"limit function"!==t.role||(r=t.role),c.propagateFunctionRole_(t,r),"prefix"}const r=c.CLASSIFY_FUNCTION_[t.role];return r||(a.isSimpleFunctionHead(t)?"simple":"")}static propagateFunctionRole_(t,e){if(t){if("infixop"===t.type)return;a.isRole(t,"subsup")||a.isRole(t,"underover")||(t.role=e),c.propagateFunctionRole_(t.childNodes[0],e)}}static getFunctionOp_(t,e){if(e(t))return t;for(let r,n=0;r=t.childNodes[n];n++){const t=c.getFunctionOp_(r,e);if(t)return t}return null}static tableToMatrixOrVector_(t){const e=t.childNodes[0];a.isType(e,"multiline")?c.tableToVector_(t):c.tableToMatrix_(t),t.contentNodes.forEach(e.appendContentNode.bind(e));for(let t,r=0;t=e.childNodes[r];r++)c.assignRoleToRow_(t,c.getComponentRoles_(e));return e.parent=null,e}static tableToVector_(t){const e=t.childNodes[0];e.type="vector",1!==e.childNodes.length?c.binomialForm_(e):c.tableToSquare_(t)}static binomialForm_(t){a.isBinomial(t)&&(t.role="binomial",t.childNodes[0].role="binomial",t.childNodes[1].role="binomial")}static tableToMatrix_(t){const e=t.childNodes[0];e.type="matrix",e.childNodes&&e.childNodes.length>0&&e.childNodes[0].childNodes&&e.childNodes.length===e.childNodes[0].childNodes.length?c.tableToSquare_(t):e.childNodes&&1===e.childNodes.length&&(e.role="rowvector")}static tableToSquare_(t){const e=t.childNodes[0];a.isNeutralFence(t)?e.role="determinant":e.role="squarematrix"}static getComponentRoles_(t){const e=t.role;return e&&"unknown"!==e?e:t.type.toLowerCase()||"unknown"}static tableToCases_(t,e){for(let e,r=0;e=t.childNodes[r];r++)c.assignRoleToRow_(e,"cases");return t.type="cases",t.appendContentNode(e),a.tableIsMultiline(t)&&c.binomialForm_(t),t}static rewriteFencedLine_(t){const e=t.childNodes[0],r=t.childNodes[0].childNodes[0],n=t.childNodes[0].childNodes[0].childNodes[0];return r.parent=t.parent,t.parent=r,n.parent=e,r.childNodes=[t],e.childNodes=[n],r}static rowToLine_(t,e){const r=e||"unknown";a.isType(t,"row")&&(t.type="line",t.role=r,1===t.childNodes.length&&a.isType(t.childNodes[0],"cell")&&(t.childNodes=t.childNodes[0].childNodes,t.childNodes.forEach((function(e){e.parent=t}))))}static assignRoleToRow_(t,e){a.isType(t,"line")?t.role=e:a.isType(t,"row")&&(t.role=e,t.childNodes.forEach((function(t){a.isType(t,"cell")&&(t.role=e)})))}static nextSeparatorFunction_(t){let e;if(t){if(t.match(/^\s+$/))return null;e=t.replace(/\s/g,"").split("").filter((function(t){return t}))}else e=[","];return function(){return e.length>1?e.shift():e[0]}}static numberRole_(t){if("unknown"!==t.role)return;const e=[...t.textContent].filter((t=>t.match(/[^\s]/))),r=e.map(o.lookupMeaning);if(r.every((function(t){return"number"===t.type&&"integer"===t.role||"punctuation"===t.type&&"comma"===t.role})))return t.role="integer",void("0"===e[0]&&t.addAnnotation("general","basenumber"));r.every((function(t){return"number"===t.type&&"integer"===t.role||"punctuation"===t.type}))?t.role="float":t.role="othernumber"}static exprFont_(t){if("unknown"!==t.font)return;const e=[...t.textContent].map(o.lookupMeaning).reduce((function(t,e){return t&&e.font&&"unknown"!==e.font&&e.font!==t?"unknown"===t?e.font:null:t}),"unknown");e&&(t.font=e)}static purgeFences_(t){const e=t.rel,r=t.comp,n=[],o=[];for(;e.length>0;){const t=e.shift();let i=r.shift();a.isElligibleEmbellishedFence(t)?(n.push(t),o.push(i)):(c.fenceToPunct_(t),i.push(t),i=i.concat(r.shift()),r.unshift(i))}return o.push(r.shift()),{rel:n,comp:o}}static rewriteFencedNode_(t){const e=t.contentNodes[0],r=t.contentNodes[1];let n=c.rewriteFence_(t,e);return t.contentNodes[0]=n.fence,n=c.rewriteFence_(n.node,r),t.contentNodes[1]=n.fence,t.contentNodes[0].parent=t,t.contentNodes[1].parent=t,n.node.parent=null,n.node}static rewriteFence_(t,e){if(!e.embellished)return{node:t,fence:e};const r=e.childNodes[0],n=c.rewriteFence_(t,r);return a.isType(e,"superscript")||a.isType(e,"subscript")||a.isType(e,"tensor")?(a.isRole(e,"subsup")||(e.role=t.role),r!==n.node&&(e.replaceChild(r,n.node),r.parent=t),c.propagateFencePointer_(e,r),{node:e,fence:n.fence}):(e.replaceChild(r,n.fence),e.mathmlTree&&-1===e.mathml.indexOf(e.mathmlTree)&&e.mathml.push(e.mathmlTree),{node:n.node,fence:e})}static propagateFencePointer_(t,e){t.fencePointer=e.fencePointer||e.id.toString(),t.embellished=null}static classifyByColumns_(t,e,r,n){return!!(3===e.length&&c.testColumns_(e,1,(t=>c.isPureRelation_(t,r)))||2===e.length&&(c.testColumns_(e,1,(t=>c.isEndRelation_(t,r)||c.isPureRelation_(t,r)))||c.testColumns_(e,0,(t=>c.isEndRelation_(t,r,!0)||c.isPureRelation_(t,r)))))&&(t.role=r,!0)}static isEndRelation_(t,e,r){const n=r?t.childNodes.length-1:0;return a.isType(t,"relseq")&&a.isRole(t,e)&&a.isType(t.childNodes[n],"empty")}static isPureRelation_(t,e){return a.isType(t,"relation")&&a.isRole(t,e)}static computeColumns_(t){const e=[];for(let r,n=0;r=t.childNodes[n];n++)for(let t,n=0;t=r.childNodes[n];n++){e[n]?e[n].push(t):e[n]=[t]}return e}static testColumns_(t,e,r){const n=t[e];return!!n&&(n.some((function(t){return t.childNodes.length&&r(t.childNodes[0])}))&&n.every((function(t){return!t.childNodes.length||r(t.childNodes[0])})))}setNodeFactory(t){c.getInstance().factory_=t,i.updateFactory(c.getInstance().factory_)}getNodeFactory(){return c.getInstance().factory_}identifierNode(t,e,r){if("MathML-Unit"===r)t.type="identifier",t.role="unit";else if(!e&&1===t.textContent.length&&("integer"===t.role||"latinletter"===t.role||"greekletter"===t.role)&&"normal"===t.font)return t.font="italic",i.run("simpleNamedFunction",t);return"unknown"===t.type&&(t.type="identifier"),c.exprFont_(t),i.run("simpleNamedFunction",t)}implicitNode(t){if(t=c.getInstance().getMixedNumbers_(t),1===(t=c.getInstance().combineUnits_(t)).length)return t[0];const e=c.getInstance().implicitNode_(t);return i.run("combine_juxtaposition",e)}text(t,e){return c.exprFont_(t),t.type="text","MS"===e?(t.role="string",t):"MSPACE"===e||t.textContent.match(/^\s*$/)?(t.role="space",t):t}row(t){return 0===(t=t.filter((function(t){return!a.isType(t,"empty")}))).length?c.getInstance().factory_.makeEmptyNode():(t=c.getInstance().getFencesInRow_(t),t=c.getInstance().tablesInRow(t),t=c.getInstance().getPunctuationInRow_(t),t=c.getInstance().getTextInRow_(t),t=c.getInstance().getFunctionsInRow_(t),c.getInstance().relationsInRow_(t))}limitNode(t,e){if(!e.length)return c.getInstance().factory_.makeEmptyNode();let r,n=e[0],o="unknown";if(!e[1])return n;if(a.isLimitBase(n)){r=c.MML_TO_LIMIT_[t];const i=r.length;if(o=r.type,e=e.slice(0,r.length+1),1===i&&a.isAccent(e[1])||2===i&&a.isAccent(e[1])&&a.isAccent(e[2]))return r=c.MML_TO_BOUNDS_[t],c.getInstance().accentNode_(n,e,r.type,r.length,r.accent);if(2===i){if(a.isAccent(e[1]))return n=c.getInstance().accentNode_(n,[n,e[1]],{MSUBSUP:"subscript",MUNDEROVER:"underscore"}[t],1,!0),e[2]?c.getInstance().makeLimitNode_(n,[n,e[2]],null,"limupper"):n;if(e[2]&&a.isAccent(e[2]))return n=c.getInstance().accentNode_(n,[n,e[2]],{MSUBSUP:"superscript",MUNDEROVER:"overscore"}[t],1,!0),c.getInstance().makeLimitNode_(n,[n,e[1]],null,"limlower");e[i]||(o="limlower")}return c.getInstance().makeLimitNode_(n,e,null,o)}return r=c.MML_TO_BOUNDS_[t],c.getInstance().accentNode_(n,e,r.type,r.length,r.accent)}tablesInRow(t){let e=l.partitionNodes(t,a.tableIsMatrixOrVector),r=[];for(let t,n=0;t=e.rel[n];n++)r=r.concat(e.comp.shift()),r.push(c.tableToMatrixOrVector_(t));r=r.concat(e.comp.shift()),e=l.partitionNodes(r,a.isTableOrMultiline),r=[];for(let t,n=0;t=e.rel[n];n++){const n=e.comp.shift();a.tableIsCases(t,n)&&c.tableToCases_(t,n.pop()),r=r.concat(n),r.push(t)}return r.concat(e.comp.shift())}mfenced(t,e,r,n){if(r&&n.length>0){const t=c.nextSeparatorFunction_(r),e=[n.shift()];n.forEach((r=>{e.push(c.getInstance().factory_.makeContentNode(t())),e.push(r)})),n=e}return t&&e?c.getInstance().horizontalFencedNode_(c.getInstance().factory_.makeContentNode(t),c.getInstance().factory_.makeContentNode(e),n):(t&&n.unshift(c.getInstance().factory_.makeContentNode(t)),e&&n.push(c.getInstance().factory_.makeContentNode(e)),c.getInstance().row(n))}fractionLikeNode(t,e,r,n){let o;if(!n&&l.isZeroLength(r)){const r=c.getInstance().factory_.makeBranchNode("line",[t],[]),n=c.getInstance().factory_.makeBranchNode("line",[e],[]);return o=c.getInstance().factory_.makeBranchNode("multiline",[r,n],[]),c.binomialForm_(o),c.classifyMultiline(o),o}return o=c.getInstance().fractionNode_(t,e),n&&o.addAnnotation("general","bevelled"),o}tensor(t,e,r,n,o){const i=c.getInstance().factory_.makeBranchNode("tensor",[t,c.getInstance().scriptNode_(e,"leftsub"),c.getInstance().scriptNode_(r,"leftsuper"),c.getInstance().scriptNode_(n,"rightsub"),c.getInstance().scriptNode_(o,"rightsuper")],[]);return i.role=t.role,i.embellished=a.isEmbellished(t),i}pseudoTensor(t,e,r){const n=t=>!a.isType(t,"empty"),o=e.filter(n).length,i=r.filter(n).length;if(!o&&!i)return t;const s=o?i?"MSUBSUP":"MSUB":"MSUP",l=[t];return o&&l.push(c.getInstance().scriptNode_(e,"rightsub",!0)),i&&l.push(c.getInstance().scriptNode_(r,"rightsuper",!0)),c.getInstance().limitNode(s,l)}font(t){const e=c.MATHJAX_FONTS[t];return e||t}proof(t,e,r){if(e.inference||e.axiom||console.log("Noise"),e.axiom){const e=c.getInstance().cleanInference(t.childNodes),n=e.length?c.getInstance().factory_.makeBranchNode("inference",r(e),[]):c.getInstance().factory_.makeEmptyNode();return n.role="axiom",n.mathmlTree=t,n}const n=c.getInstance().inference(t,e,r);return e.proof&&(n.role="proof",n.childNodes[0].role="final"),n}inference(t,e,r){if(e.inferenceRule){const e=c.getInstance().getFormulas(t,[],r);return c.getInstance().factory_.makeBranchNode("inference",[e.conclusion,e.premises],[])}const o=e.labelledRule,i=n.toArray(t.childNodes),s=[];"left"!==o&&"both"!==o||s.push(c.getInstance().getLabel(t,i,r,"left")),"right"!==o&&"both"!==o||s.push(c.getInstance().getLabel(t,i,r,"right"));const a=c.getInstance().getFormulas(t,i,r),l=c.getInstance().factory_.makeBranchNode("inference",[a.conclusion,a.premises],s);return l.mathmlTree=t,l}getLabel(t,e,r,o){const i=c.getInstance().findNestedRow(e,"prooflabel",o),s=c.getInstance().factory_.makeBranchNode("rulelabel",r(n.toArray(i.childNodes)),[]);return s.role=o,s.mathmlTree=i,s}getFormulas(t,e,r){const o=e.length?c.getInstance().findNestedRow(e,"inferenceRule"):t,i="up"===c.getSemantics(o).inferenceRule,s=i?o.childNodes[1]:o.childNodes[0],a=i?o.childNodes[0]:o.childNodes[1],l=s.childNodes[0].childNodes[0],u=n.toArray(l.childNodes[0].childNodes),p=[];let h=1;for(const t of u)h%2&&p.push(t.childNodes[0]),h++;const f=r(p),d=r(n.toArray(a.childNodes[0].childNodes))[0],m=c.getInstance().factory_.makeBranchNode("premises",f,[]);m.mathmlTree=l;const y=c.getInstance().factory_.makeBranchNode("conclusion",[d],[]);return y.mathmlTree=a.childNodes[0].childNodes[0],{conclusion:y,premises:m}}findNestedRow(t,e,r){return c.getInstance().findNestedRow_(t,e,0,r)}cleanInference(t){return n.toArray(t).filter((function(t){return"MSPACE"!==n.tagName(t)}))}operatorNode(t){return"unknown"===t.type&&(t.type="operator"),i.run("multioperator",t)}implicitNode_(t){const e=c.getInstance().factory_.makeMultipleContentNodes(t.length-1,o.invisibleTimes());c.matchSpaces_(t,e);const r=c.getInstance().infixNode_(t,e[0]);return r.role="implicit",e.forEach((function(t){t.parent=r})),r.contentNodes=e,r}infixNode_(t,e){const r=c.getInstance().factory_.makeBranchNode("infixop",t,[e],l.getEmbellishedInner(e).textContent);return r.role=e.role,i.run("propagateSimpleFunction",r)}explicitMixed_(t){const e=l.partitionNodes(t,(function(t){return t.textContent===o.invisiblePlus()}));if(!e.rel.length)return t;let r=[];for(let t,n=0;t=e.rel[n];n++){const o=e.comp[n],i=e.comp[n+1],s=o.length-1;if(o[s]&&i[0]&&a.isType(o[s],"number")&&!a.isRole(o[s],"mixed")&&a.isType(i[0],"fraction")){const t=c.getInstance().factory_.makeBranchNode("number",[o[s],i[0]],[]);t.role="mixed",r=r.concat(o.slice(0,s)),r.push(t),i.shift()}else r=r.concat(o),r.push(t)}return r.concat(e.comp[e.comp.length-1])}concatNode_(t,e,r){if(0===e.length)return t;const n=e.map((function(t){return l.getEmbellishedInner(t).textContent})).join(" "),o=c.getInstance().factory_.makeBranchNode(r,[t],e,n);return e.length>1&&(o.role="multiop"),o}prefixNode_(t,e){const r=l.partitionNodes(e,(t=>a.isRole(t,"subtraction")));let n=c.getInstance().concatNode_(t,r.comp.pop(),"prefixop");for(1===n.contentNodes.length&&"addition"===n.contentNodes[0].role&&"+"===n.contentNodes[0].textContent&&(n.role="positive");r.rel.length>0;)n=c.getInstance().concatNode_(n,[r.rel.pop()],"prefixop"),n.role="negative",n=c.getInstance().concatNode_(n,r.comp.pop(),"prefixop");return n}postfixNode_(t,e){return e.length?c.getInstance().concatNode_(t,e,"postfixop"):t}combineUnits_(t){const e=l.partitionNodes(t,(function(t){return!a.isRole(t,"unit")}));if(t.length===e.rel.length)return e.rel;const r=[];let n,o;do{const t=e.comp.shift();n=e.rel.shift();let i=null;o=r.pop(),o&&(t.length&&a.isUnitCounter(o)?t.unshift(o):r.push(o)),1===t.length&&(i=t.pop()),t.length>1&&(i=c.getInstance().implicitNode_(t),i.role="unit"),i&&r.push(i),n&&r.push(n)}while(n);return r}getMixedNumbers_(t){const e=l.partitionNodes(t,(function(t){return a.isType(t,"fraction")&&a.isRole(t,"vulgar")}));if(!e.rel.length)return t;let r=[];for(let t,n=0;t=e.rel[n];n++){const o=e.comp[n],i=o.length-1;if(o[i]&&a.isType(o[i],"number")&&(a.isRole(o[i],"integer")||a.isRole(o[i],"float"))){const e=c.getInstance().factory_.makeBranchNode("number",[o[i],t],[]);e.role="mixed",r=r.concat(o.slice(0,i)),r.push(e)}else r=r.concat(o),r.push(t)}return r.concat(e.comp[e.comp.length-1])}getTextInRow_(t){if(t.length<=1)return t;const e=l.partitionNodes(t,(t=>a.isType(t,"text")));if(0===e.rel.length)return t;const r=[];let n=e.comp[0];n.length>0&&r.push(c.getInstance().row(n));for(let t,o=0;t=e.rel[o];o++)r.push(t),n=e.comp[o+1],n.length>0&&r.push(c.getInstance().row(n));return[c.getInstance().dummyNode_(r)]}relationsInRow_(t){const e=l.partitionNodes(t,a.isRelation),r=e.rel[0];if(!r)return c.getInstance().operationsInRow_(t);if(1===t.length)return t[0];const n=e.comp.map(c.getInstance().operationsInRow_);let o;return e.rel.some((function(t){return!t.equals(r)}))?(o=c.getInstance().factory_.makeBranchNode("multirel",n,e.rel),e.rel.every((function(t){return t.role===r.role}))&&(o.role=r.role),o):(o=c.getInstance().factory_.makeBranchNode("relseq",n,e.rel,l.getEmbellishedInner(r).textContent),o.role=r.role,o)}operationsInRow_(t){if(0===t.length)return c.getInstance().factory_.makeEmptyNode();if(1===(t=c.getInstance().explicitMixed_(t)).length)return t[0];const e=[];for(;t.length>0&&a.isOperator(t[0]);)e.push(t.shift());if(0===t.length)return c.getInstance().prefixNode_(e.pop(),e);if(1===t.length)return c.getInstance().prefixNode_(t[0],e);t=i.run("convert_juxtaposition",t);const r=l.sliceNodes(t,a.isOperator),n=c.getInstance().prefixNode_(c.getInstance().implicitNode(r.head),e);return r.div?c.getInstance().operationsTree_(r.tail,n,r.div):n}operationsTree_(t,e,r,n){const o=n||[];if(0===t.length){if(o.unshift(r),"infixop"===e.type){const t=c.getInstance().postfixNode_(e.childNodes.pop(),o);return e.appendChild(t),e}return c.getInstance().postfixNode_(e,o)}const i=l.sliceNodes(t,a.isOperator);if(0===i.head.length)return o.push(i.div),c.getInstance().operationsTree_(i.tail,e,r,o);const s=c.getInstance().prefixNode_(c.getInstance().implicitNode(i.head),o),u=c.getInstance().appendOperand_(e,r,s);return i.div?c.getInstance().operationsTree_(i.tail,u,i.div,[]):u}appendOperand_(t,e,r){if("infixop"!==t.type)return c.getInstance().infixNode_([t,r],e);const n=c.getInstance().appendDivisionOp_(t,e,r);return n||(c.getInstance().appendExistingOperator_(t,e,r)?t:"multiplication"===e.role?c.getInstance().appendMultiplicativeOp_(t,e,r):c.getInstance().appendAdditiveOp_(t,e,r))}appendDivisionOp_(t,e,r){return"division"===e.role?a.isImplicit(t)?c.getInstance().infixNode_([t,r],e):c.getInstance().appendLastOperand_(t,e,r):"division"===t.role?c.getInstance().infixNode_([t,r],e):null}appendLastOperand_(t,e,r){let n=t,o=t.childNodes[t.childNodes.length-1];for(;o&&"infixop"===o.type&&!a.isImplicit(o);)n=o,o=n.childNodes[t.childNodes.length-1];const i=c.getInstance().infixNode_([n.childNodes.pop(),r],e);return n.appendChild(i),t}appendMultiplicativeOp_(t,e,r){if(a.isImplicit(t))return c.getInstance().infixNode_([t,r],e);let n=t,o=t.childNodes[t.childNodes.length-1];for(;o&&"infixop"===o.type&&!a.isImplicit(o);)n=o,o=n.childNodes[t.childNodes.length-1];const i=c.getInstance().infixNode_([n.childNodes.pop(),r],e);return n.appendChild(i),t}appendAdditiveOp_(t,e,r){return c.getInstance().infixNode_([t,r],e)}appendExistingOperator_(t,e,r){return!(!t||"infixop"!==t.type||a.isImplicit(t))&&(t.contentNodes[0].equals(e)?(t.appendContentNode(e),t.appendChild(r),!0):c.getInstance().appendExistingOperator_(t.childNodes[t.childNodes.length-1],e,r))}getFencesInRow_(t){let e=l.partitionNodes(t,a.isFence);e=c.purgeFences_(e);const r=e.comp.shift();return c.getInstance().fences_(e.rel,e.comp,[],[r])}fences_(t,e,r,n){if(0===t.length&&0===r.length)return n[0];const o=t=>a.isRole(t,"open");if(0===t.length){const t=n.shift();for(;r.length>0;){if(o(r[0])){const e=r.shift();c.fenceToPunct_(e),t.push(e)}else{const e=l.sliceNodes(r,o),i=e.head.length-1,s=c.getInstance().neutralFences_(e.head,n.slice(0,i));n=n.slice(i),t.push(...s),e.div&&e.tail.unshift(e.div),r=e.tail}t.push(...n.shift())}return t}const i=r[r.length-1],s=t[0].role;if("open"===s||a.isNeutralFence(t[0])&&(!i||!a.compareNeutralFences(t[0],i))){r.push(t.shift());const o=e.shift();return o&&n.push(o),c.getInstance().fences_(t,e,r,n)}if(i&&"close"===s&&"open"===i.role){const o=c.getInstance().horizontalFencedNode_(r.pop(),t.shift(),n.pop());return n.push(n.pop().concat([o],e.shift())),c.getInstance().fences_(t,e,r,n)}if(i&&a.compareNeutralFences(t[0],i)){if(!a.elligibleLeftNeutral(i)||!a.elligibleRightNeutral(t[0])){r.push(t.shift());const o=e.shift();return o&&n.push(o),c.getInstance().fences_(t,e,r,n)}const o=c.getInstance().horizontalFencedNode_(r.pop(),t.shift(),n.pop());return n.push(n.pop().concat([o],e.shift())),c.getInstance().fences_(t,e,r,n)}if(i&&"close"===s&&a.isNeutralFence(i)&&r.some(o)){const i=l.sliceNodes(r,o,!0),s=n.pop(),a=n.length-i.tail.length+1,u=c.getInstance().neutralFences_(i.tail,n.slice(a));n=n.slice(0,a);const p=c.getInstance().horizontalFencedNode_(i.div,t.shift(),n.pop().concat(u,s));return n.push(n.pop().concat([p],e.shift())),c.getInstance().fences_(t,e,i.head,n)}const u=t.shift();return c.fenceToPunct_(u),n.push(n.pop().concat([u],e.shift())),c.getInstance().fences_(t,e,r,n)}neutralFences_(t,e){if(0===t.length)return t;if(1===t.length)return c.fenceToPunct_(t[0]),t;const r=t.shift();if(!a.elligibleLeftNeutral(r)){c.fenceToPunct_(r);const n=e.shift();return n.unshift(r),n.concat(c.getInstance().neutralFences_(t,e))}const n=l.sliceNodes(t,(function(t){return a.compareNeutralFences(t,r)}));if(!n.div){c.fenceToPunct_(r);const n=e.shift();return n.unshift(r),n.concat(c.getInstance().neutralFences_(t,e))}if(!a.elligibleRightNeutral(n.div))return c.fenceToPunct_(n.div),t.unshift(r),c.getInstance().neutralFences_(t,e);const o=c.getInstance().combineFencedContent_(r,n.div,n.head,e);if(n.tail.length>0){const t=o.shift(),e=c.getInstance().neutralFences_(n.tail,o);return t.concat(e)}return o[0]}combineFencedContent_(t,e,r,n){if(0===r.length){const r=c.getInstance().horizontalFencedNode_(t,e,n.shift());return n.length>0?n[0].unshift(r):n=[[r]],n}const o=n.shift(),i=r.length-1,s=n.slice(0,i),a=(n=n.slice(i)).shift(),l=c.getInstance().neutralFences_(r,s);o.push(...l),o.push(...a);const u=c.getInstance().horizontalFencedNode_(t,e,o);return n.length>0?n[0].unshift(u):n=[[u]],n}horizontalFencedNode_(t,e,r){const n=c.getInstance().row(r);let o=c.getInstance().factory_.makeBranchNode("fenced",[n],[t,e]);return"open"===t.role?(c.getInstance().classifyHorizontalFence_(o),o=i.run("propagateComposedFunction",o)):o.role=t.role,o=i.run("detect_cycle",o),c.rewriteFencedNode_(o)}classifyHorizontalFence_(t){t.role="leftright";const e=t.childNodes;if(!a.isSetNode(t)||e.length>1)return;if(0===e.length||"empty"===e[0].type)return void(t.role="set empty");const r=e[0].type;if(1===e.length&&a.isSingletonSetContent(e[0]))return void(t.role="set singleton");const n=e[0].role;if("punctuated"===r&&"sequence"===n){if("comma"!==e[0].contentNodes[0].role)return 1!==e[0].contentNodes.length||"vbar"!==e[0].contentNodes[0].role&&"colon"!==e[0].contentNodes[0].role?void 0:(t.role="set extended",void c.getInstance().setExtension_(t));t.role="set collection"}}setExtension_(t){const e=t.childNodes[0].childNodes[0];e&&"infixop"===e.type&&1===e.contentNodes.length&&a.isMembership(e.contentNodes[0])&&(e.addAnnotation("set","intensional"),e.contentNodes[0].addAnnotation("set","intensional"))}getPunctuationInRow_(t){if(t.length<=1)return t;const e=t=>{const e=t.type;return"punctuation"===e||"text"===e||"operator"===e||"relation"===e},r=l.partitionNodes(t,(function(r){if(!a.isPunctuation(r))return!1;if(a.isPunctuation(r)&&!a.isRole(r,"ellipsis"))return!0;const n=t.indexOf(r);if(0===n)return!t[1]||!e(t[1]);const o=t[n-1];if(n===t.length-1)return!e(o);const i=t[n+1];return!e(o)||!e(i)}));if(0===r.rel.length)return t;const n=[];let o=r.comp.shift();o.length>0&&n.push(c.getInstance().row(o));let i=0;for(;r.comp.length>0;)n.push(r.rel[i++]),o=r.comp.shift(),o.length>0&&n.push(c.getInstance().row(o));return[c.getInstance().punctuatedNode_(n,r.rel)]}punctuatedNode_(t,e){const r=c.getInstance().factory_.makeBranchNode("punctuated",t,e);if(e.length===t.length){const t=e[0].role;if("unknown"!==t&&e.every((function(e){return e.role===t})))return r.role=t,r}return a.singlePunctAtPosition(t,e,0)?r.role="startpunct":a.singlePunctAtPosition(t,e,t.length-1)?r.role="endpunct":e.every((t=>a.isRole(t,"dummy")))?r.role="text":e.every((t=>a.isRole(t,"space")))?r.role="space":r.role="sequence",r}dummyNode_(t){const e=c.getInstance().factory_.makeMultipleContentNodes(t.length-1,o.invisibleComma());return e.forEach((function(t){t.role="dummy"})),c.getInstance().punctuatedNode_(t,e)}accentRole_(t,e){if(!a.isAccent(t))return!1;const r=t.textContent,n=o.lookupSecondary("bar",r)||o.lookupSecondary("tilde",r)||t.role;return t.role="underscore"===e?"underaccent":"overaccent",t.addAnnotation("accent",n),!0}accentNode_(t,e,r,n,o){const i=(e=e.slice(0,n+1))[1],s=e[2];let a;if(!o&&s&&(a=c.getInstance().factory_.makeBranchNode("subscript",[t,i],[]),a.role="subsup",e=[a,s],r="superscript"),o){const n=c.getInstance().accentRole_(i,r);if(s){c.getInstance().accentRole_(s,"overscore")&&!n?(a=c.getInstance().factory_.makeBranchNode("overscore",[t,s],[]),e=[a,i],r="underscore"):(a=c.getInstance().factory_.makeBranchNode("underscore",[t,i],[]),e=[a,s],r="overscore"),a.role="underover"}}return c.getInstance().makeLimitNode_(t,e,a,r)}makeLimitNode_(t,e,r,n){if("limupper"===n&&"limlower"===t.type)return t.childNodes.push(e[1]),e[1].parent=t,t.type="limboth",t;if("limlower"===n&&"limupper"===t.type)return t.childNodes.splice(1,-1,e[1]),e[1].parent=t,t.type="limboth",t;const o=c.getInstance().factory_.makeBranchNode(n,e,[]),i=a.isEmbellished(t);return r&&(r.embellished=i),o.embellished=i,o.role=t.role,o}getFunctionsInRow_(t,e){const r=e||[];if(0===t.length)return r;const n=t.shift(),o=c.classifyFunction_(n,t);if(!o)return r.push(n),c.getInstance().getFunctionsInRow_(t,r);const i=c.getInstance().getFunctionsInRow_(t,[]),s=c.getInstance().getFunctionArgs_(n,i,o);return r.concat(s)}getFunctionArgs_(t,e,r){let n,o,i;switch(r){case"integral":{const r=c.getInstance().getIntegralArgs_(e);if(!r.intvar&&!r.integrand.length)return r.rest.unshift(t),r.rest;const n=c.getInstance().row(r.integrand);return i=c.getInstance().integralNode_(t,n,r.intvar),r.rest.unshift(i),r.rest}case"prefix":if(e[0]&&"fenced"===e[0].type){const r=e.shift();return a.isNeutralFence(r)||(r.role="leftright"),i=c.getInstance().functionNode_(t,r),e.unshift(i),e}if(n=l.sliceNodes(e,a.isPrefixFunctionBoundary),n.head.length)o=c.getInstance().row(n.head),n.div&&n.tail.unshift(n.div);else{if(!n.div||!a.isType(n.div,"appl"))return e.unshift(t),e;o=n.div}return i=c.getInstance().functionNode_(t,o),n.tail.unshift(i),n.tail;case"bigop":return n=l.sliceNodes(e,a.isBigOpBoundary),n.head.length?(o=c.getInstance().row(n.head),i=c.getInstance().bigOpNode_(t,o),n.div&&n.tail.unshift(n.div),n.tail.unshift(i),n.tail):(e.unshift(t),e);default:{if(0===e.length)return[t];const r=e[0];return"fenced"===r.type&&!a.isNeutralFence(r)&&a.isSimpleFunctionScope(r)?(r.role="leftright",c.propagateFunctionRole_(t,"simple function"),i=c.getInstance().functionNode_(t,e.shift()),e.unshift(i),e):(e.unshift(t),e)}}}getIntegralArgs_(t,e=[]){if(0===t.length)return{integrand:e,intvar:null,rest:t};const r=t[0];if(a.isGeneralFunctionBoundary(r))return{integrand:e,intvar:null,rest:t};if(a.isIntegralDxBoundarySingle(r))return r.role="integral",{integrand:e,intvar:r,rest:t.slice(1)};if(t[1]&&a.isIntegralDxBoundary(r,t[1])){const n=c.getInstance().prefixNode_(t[1],[r]);return n.role="integral",{integrand:e,intvar:n,rest:t.slice(2)}}return e.push(t.shift()),c.getInstance().getIntegralArgs_(t,e)}functionNode_(t,e){const r=c.getInstance().factory_.makeContentNode(o.functionApplication()),n=c.getInstance().funcAppls[t.id];n&&(r.mathmlTree=n.mathmlTree,r.mathml=n.mathml,r.annotation=n.annotation,r.attributes=n.attributes,delete c.getInstance().funcAppls[t.id]),r.type="punctuation",r.role="application";const i=c.getFunctionOp_(t,(function(t){return a.isType(t,"function")||a.isType(t,"identifier")&&a.isRole(t,"simple function")}));return c.getInstance().functionalNode_("appl",[t,e],i,[r])}bigOpNode_(t,e){const r=c.getFunctionOp_(t,(t=>a.isType(t,"largeop")));return c.getInstance().functionalNode_("bigop",[t,e],r,[])}integralNode_(t,e,r){e=e||c.getInstance().factory_.makeEmptyNode(),r=r||c.getInstance().factory_.makeEmptyNode();const n=c.getFunctionOp_(t,(t=>a.isType(t,"largeop")));return c.getInstance().functionalNode_("integral",[t,e,r],n,[])}functionalNode_(t,e,r,n){const o=e[0];let i;r&&(i=r.parent,n.push(r));const s=c.getInstance().factory_.makeBranchNode(t,e,n);return s.role=o.role,i&&(r.parent=i),s}fractionNode_(t,e){const r=c.getInstance().factory_.makeBranchNode("fraction",[t,e],[]);return r.role=r.childNodes.every((function(t){return a.isType(t,"number")&&a.isRole(t,"integer")}))?"vulgar":r.childNodes.every(a.isPureUnit)?"unit":"division",i.run("propagateSimpleFunction",r)}scriptNode_(t,e,r){let n;switch(t.length){case 0:n=c.getInstance().factory_.makeEmptyNode();break;case 1:if(n=t[0],r)return n;break;default:n=c.getInstance().dummyNode_(t)}return n.role=e,n}findNestedRow_(t,e,r,o){if(r>3)return null;for(let i,s=0;i=t[s];s++){const t=n.tagName(i);if("MSPACE"!==t){if("MROW"===t)return c.getInstance().findNestedRow_(n.toArray(i.childNodes),e,r+1,o);if(c.findSemantics(i,e,o))return i}}return null}}e.default=c,c.FENCE_TO_PUNCT_={metric:"metric",neutral:"vbar",open:"openfence",close:"closefence"},c.MML_TO_LIMIT_={MSUB:{type:"limlower",length:1},MUNDER:{type:"limlower",length:1},MSUP:{type:"limupper",length:1},MOVER:{type:"limupper",length:1},MSUBSUP:{type:"limboth",length:2},MUNDEROVER:{type:"limboth",length:2}},c.MML_TO_BOUNDS_={MSUB:{type:"subscript",length:1,accent:!1},MSUP:{type:"superscript",length:1,accent:!1},MSUBSUP:{type:"subscript",length:2,accent:!1},MUNDER:{type:"underscore",length:1,accent:!0},MOVER:{type:"overscore",length:1,accent:!0},MUNDEROVER:{type:"underscore",length:2,accent:!0}},c.CLASSIFY_FUNCTION_={integral:"integral",sum:"bigop","prefix function":"prefix","limit function":"prefix","simple function":"prefix","composed function":"prefix"},c.MATHJAX_FONTS={"-tex-caligraphic":"caligraphic","-tex-caligraphic-bold":"caligraphic-bold","-tex-calligraphic":"caligraphic","-tex-calligraphic-bold":"caligraphic-bold","-tex-oldstyle":"oldstyle","-tex-oldstyle-bold":"oldstyle-bold","-tex-mathit":"italic"}},5656:function(t,e,r){Object.defineProperty(e,"__esModule",{value:!0}),e.SemanticSkeleton=void 0;const n=r(707),o=r(5274),i=r(2298);class s{constructor(t){this.parents=null,this.levelsMap=null,t=0===t?t:t||[],this.array=t}static fromTree(t){return s.fromNode(t.root)}static fromNode(t){return new s(s.fromNode_(t))}static fromString(t){return new s(s.fromString_(t))}static simpleCollapseStructure(t){return"number"==typeof t}static contentCollapseStructure(t){return!!t&&!s.simpleCollapseStructure(t)&&"c"===t[0]}static interleaveIds(t,e){return n.interleaveLists(s.collapsedLeafs(t),s.collapsedLeafs(e))}static collapsedLeafs(...t){return t.reduce(((t,e)=>{return t.concat((r=e,s.simpleCollapseStructure(r)?[r]:(r=r,s.contentCollapseStructure(r[1])?r.slice(2):r.slice(1))));var r}),[])}static fromStructure(t,e){return new s(s.tree_(t,e.root))}static combineContentChildren(t,e,r){switch(t.type){case"relseq":case"infixop":case"multirel":return n.interleaveLists(r,e);case"prefixop":return e.concat(r);case"postfixop":return r.concat(e);case"fenced":return r.unshift(e[0]),r.push(e[1]),r;case"appl":return[r[0],e[0],r[1]];case"root":return[r[1],r[0]];case"row":case"line":return e.length&&r.unshift(e[0]),r;default:return r}}static makeSexp_(t){return s.simpleCollapseStructure(t)?t.toString():s.contentCollapseStructure(t)?"(c "+t.slice(1).map(s.makeSexp_).join(" ")+")":"("+t.map(s.makeSexp_).join(" ")+")"}static fromString_(t){let e=t.replace(/\(/g,"[");return e=e.replace(/\)/g,"]"),e=e.replace(/ /g,","),e=e.replace(/c/g,'"c"'),JSON.parse(e)}static fromNode_(t){if(!t)return[];const e=t.contentNodes;let r;e.length&&(r=e.map(s.fromNode_),r.unshift("c"));const n=t.childNodes;if(!n.length)return e.length?[t.id,r]:t.id;const o=n.map(s.fromNode_);return e.length&&o.unshift(r),o.unshift(t.id),o}static tree_(t,e){if(!e)return[];if(!e.childNodes.length)return e.id;const r=e.id,n=[r],a=o.evalXPath(`.//self::*[@${i.Attribute.ID}=${r}]`,t)[0],l=s.combineContentChildren(e,e.contentNodes.map((function(t){return t})),e.childNodes.map((function(t){return t})));a&&s.addOwns_(a,l);for(let e,r=0;e=l[r];r++)n.push(s.tree_(t,e));return n}static addOwns_(t,e){const r=t.getAttribute(i.Attribute.COLLAPSED),n=r?s.realLeafs_(s.fromString(r).array):e.map((t=>t.id));t.setAttribute(i.Attribute.OWNS,n.join(" "))}static realLeafs_(t){if(s.simpleCollapseStructure(t))return[t];if(s.contentCollapseStructure(t))return[];t=t;let e=[];for(let r=1;r<t.length;r++)e=e.concat(s.realLeafs_(t[r]));return e}populate(){this.parents&&this.levelsMap||(this.parents={},this.levelsMap={},this.populate_(this.array,this.array,[]))}toString(){return s.makeSexp_(this.array)}populate_(t,e,r){if(s.simpleCollapseStructure(t))return t=t,this.levelsMap[t]=e,void(this.parents[t]=t===r[0]?r.slice(1):r);const n=s.contentCollapseStructure(t)?t.slice(1):t,o=[n[0]].concat(r);for(let e=0,r=n.length;e<r;e++){const r=n[e];this.populate_(r,t,o)}}isRoot(t){return t===this.levelsMap[t][0]}directChildren(t){if(!this.isRoot(t))return[];return this.levelsMap[t].slice(1).map((t=>s.simpleCollapseStructure(t)?t:s.contentCollapseStructure(t)?t[1]:t[0]))}subtreeNodes(t){if(!this.isRoot(t))return[];const e=(t,r)=>{s.simpleCollapseStructure(t)?r.push(t):(t=t,s.contentCollapseStructure(t)&&(t=t.slice(1)),t.forEach((t=>e(t,r))))},r=this.levelsMap[t],n=[];return e(r.slice(1),n),n}}e.SemanticSkeleton=s},7075:function(t,e,r){Object.defineProperty(e,"__esModule",{value:!0}),e.SemanticTree=void 0;const n=r(5740),o=r(7630),i=r(9265),s=r(7228),a=r(5952),l=r(5609);r(94);class c{constructor(t){this.mathml=t,this.parser=new s.SemanticMathml,this.root=this.parser.parse(t),this.collator=this.parser.getFactory().leafMap.collateMeaning();const e=this.collator.newDefault();e&&(this.parser=new s.SemanticMathml,this.parser.getFactory().defaultMap=e,this.root=this.parser.parse(t)),u.visit(this.root,{}),(0,o.annotate)(this.root)}static empty(){const t=n.parseInput("<math/>"),e=new c(t);return e.mathml=t,e}static fromNode(t,e){const r=c.empty();return r.root=t,e&&(r.mathml=e),r}static fromRoot(t,e){let r=t;for(;r.parent;)r=r.parent;const n=c.fromNode(r);return e&&(n.mathml=e),n}static fromXml(t){const e=c.empty();return t.childNodes[0]&&(e.root=a.SemanticNode.fromXml(t.childNodes[0])),e}xml(t){const e=n.parseInput("<stree></stree>"),r=this.root.xml(e.ownerDocument,t);return e.appendChild(r),e}toString(t){return n.serializeXml(this.xml(t))}formatXml(t){const e=this.toString(t);return n.formatXml(e)}displayTree(){this.root.displayTree()}replaceNode(t,e){const r=t.parent;r?r.replaceChild(t,e):this.root=e}toJson(){const t={};return t.stree=this.root.toJson(),t}}e.SemanticTree=c;const u=new i.SemanticVisitor("general","unit",((t,e)=>{if("infixop"===t.type&&("multiplication"===t.role||"implicit"===t.role)){const e=t.childNodes;e.length&&(l.isPureUnit(e[0])||l.isUnitCounter(e[0]))&&t.childNodes.slice(1).every(l.isPureUnit)&&(t.role="unit")}return!1}))},4795:function(t,e,r){Object.defineProperty(e,"__esModule",{value:!0}),e.partitionNodes=e.sliceNodes=e.getEmbellishedInner=e.addAttributes=e.isZeroLength=e.purgeNodes=e.isOrphanedGlyph=e.hasDisplayTag=e.hasEmptyTag=e.hasIgnoreTag=e.hasLeafTag=e.hasMathTag=e.directSpeechKeys=e.DISPLAYTAGS=e.EMPTYTAGS=e.IGNORETAGS=e.LEAFTAGS=void 0;const n=r(5740);function o(t){return!!t&&-1!==e.LEAFTAGS.indexOf(n.tagName(t))}function i(t,e,r){r&&t.reverse();const n=[];for(let o,i=0;o=t[i];i++){if(e(o))return r?{head:t.slice(i+1).reverse(),div:o,tail:n.reverse()}:{head:n,div:o,tail:t.slice(i+1)};n.push(o)}return r?{head:[],div:null,tail:n.reverse()}:{head:n,div:null,tail:[]}}e.LEAFTAGS=["MO","MI","MN","MTEXT","MS","MSPACE"],e.IGNORETAGS=["MERROR","MPHANTOM","MALIGNGROUP","MALIGNMARK","MPRESCRIPTS","ANNOTATION","ANNOTATION-XML"],e.EMPTYTAGS=["MATH","MROW","MPADDED","MACTION","NONE","MSTYLE","SEMANTICS"],e.DISPLAYTAGS=["MROOT","MSQRT"],e.directSpeechKeys=["aria-label","exact-speech","alt"],e.hasMathTag=function(t){return!!t&&"MATH"===n.tagName(t)},e.hasLeafTag=o,e.hasIgnoreTag=function(t){return!!t&&-1!==e.IGNORETAGS.indexOf(n.tagName(t))},e.hasEmptyTag=function(t){return!!t&&-1!==e.EMPTYTAGS.indexOf(n.tagName(t))},e.hasDisplayTag=function(t){return!!t&&-1!==e.DISPLAYTAGS.indexOf(n.tagName(t))},e.isOrphanedGlyph=function(t){return!!t&&"MGLYPH"===n.tagName(t)&&!o(t.parentNode)},e.purgeNodes=function(t){const r=[];for(let o,i=0;o=t[i];i++){if(o.nodeType!==n.NodeType.ELEMENT_NODE)continue;const t=n.tagName(o);-1===e.IGNORETAGS.indexOf(t)&&(-1!==e.EMPTYTAGS.indexOf(t)&&0===o.childNodes.length||r.push(o))}return r},e.isZeroLength=function(t){if(!t)return!1;if(-1!==["negativeveryverythinmathspace","negativeverythinmathspace","negativethinmathspace","negativemediummathspace","negativethickmathspace","negativeverythickmathspace","negativeveryverythickmathspace"].indexOf(t))return!0;const e=t.match(/[0-9.]+/);return!!e&&0===parseFloat(e[0])},e.addAttributes=function(t,r){if(r.hasAttributes()){const n=r.attributes;for(let r=n.length-1;r>=0;r--){const o=n[r].name;o.match(/^ext/)&&(t.attributes[o]=n[r].value,t.nobreaking=!0),-1!==e.directSpeechKeys.indexOf(o)&&(t.attributes["ext-speech"]=n[r].value,t.nobreaking=!0),o.match(/texclass$/)&&(t.attributes.texclass=n[r].value),"href"===o&&(t.attributes.href=n[r].value,t.nobreaking=!0)}}},e.getEmbellishedInner=function t(e){return e&&e.embellished&&e.childNodes.length>0?t(e.childNodes[0]):e},e.sliceNodes=i,e.partitionNodes=function(t,e){let r=t;const n=[],o=[];let s=null;do{s=i(r,e),o.push(s.head),n.push(s.div),r=s.tail}while(s.div);return n.pop(),{rel:n,comp:o}}},6278:function(t,e,r){Object.defineProperty(e,"__esModule",{value:!0}),e.AbstractSpeechGenerator=void 0;const n=r(6828),o=r(2298),i=r(1214),s=r(9543);e.AbstractSpeechGenerator=class{constructor(){this.modality=o.addPrefix("speech"),this.rebuilt_=null,this.options_={}}getRebuilt(){return this.rebuilt_}setRebuilt(t){this.rebuilt_=t}setOptions(t){this.options_=t||{},this.modality=o.addPrefix(this.options_.modality||"speech")}getOptions(){return this.options_}start(){}end(){}generateSpeech(t,e){return this.rebuilt_||(this.rebuilt_=new i.RebuildStree(e)),(0,n.setup)(this.options_),s.computeMarkup(this.getRebuilt().xml)}}},1452:function(t,e,r){Object.defineProperty(e,"__esModule",{value:!0}),e.AdhocSpeechGenerator=void 0;const n=r(6278);class o extends n.AbstractSpeechGenerator{getSpeech(t,e){const r=this.generateSpeech(t,e);return t.setAttribute(this.modality,r),r}}e.AdhocSpeechGenerator=o},5152:function(t,e,r){Object.defineProperty(e,"__esModule",{value:!0}),e.ColorGenerator=void 0;const n=r(2298),o=r(8396),i=r(1214),s=r(1204),a=r(6278);class l extends a.AbstractSpeechGenerator{constructor(){super(...arguments),this.modality=(0,n.addPrefix)("foreground"),this.contrast=new o.ContrastPicker}static visitStree_(t,e,r){if(t.childNodes.length){if(t.contentNodes.length&&("punctuated"===t.type&&t.contentNodes.forEach((t=>r[t.id]=!0)),"implicit"!==t.role&&e.push(t.contentNodes.map((t=>t.id)))),t.childNodes.length){if("implicit"===t.role){const n=[];let o=[];for(const e of t.childNodes){const t=[];l.visitStree_(e,t,r),t.length<=2&&n.push(t.shift()),o=o.concat(t)}return e.push(n),void o.forEach((t=>e.push(t)))}t.childNodes.forEach((t=>l.visitStree_(t,e,r)))}}else r[t.id]||e.push(t.id)}getSpeech(t,e){return s.getAttribute(t,this.modality)}generateSpeech(t,e){return this.getRebuilt()||this.setRebuilt(new i.RebuildStree(t)),this.colorLeaves_(t),s.getAttribute(t,this.modality)}colorLeaves_(t){const e=[];l.visitStree_(this.getRebuilt().streeRoot,e,{});for(const r of e){const e=this.contrast.generate();let n=!1;n=Array.isArray(r)?r.map((r=>this.colorLeave_(t,r,e))).reduce(((t,e)=>t||e),!1):this.colorLeave_(t,r.toString(),e),n&&this.contrast.increment()}}colorLeave_(t,e,r){const n=s.getBySemanticId(t,e);return!!n&&(n.setAttribute(this.modality,r),!0)}}e.ColorGenerator=l},6604:function(t,e,r){Object.defineProperty(e,"__esModule",{value:!0}),e.DirectSpeechGenerator=void 0;const n=r(1204),o=r(6278);class i extends o.AbstractSpeechGenerator{getSpeech(t,e){return n.getAttribute(t,this.modality)}}e.DirectSpeechGenerator=i},3123:function(t,e,r){Object.defineProperty(e,"__esModule",{value:!0}),e.DummySpeechGenerator=void 0;const n=r(6278);class o extends n.AbstractSpeechGenerator{getSpeech(t,e){return""}}e.DummySpeechGenerator=o},5858:function(t,e,r){Object.defineProperty(e,"__esModule",{value:!0}),e.NodeSpeechGenerator=void 0;const n=r(1204),o=r(4598);class i extends o.TreeSpeechGenerator{getSpeech(t,e){return super.getSpeech(t,e),n.getAttribute(t,this.modality)}}e.NodeSpeechGenerator=i},9552:function(t,e,r){Object.defineProperty(e,"__esModule",{value:!0}),e.generatorMapping_=e.generator=void 0;const n=r(1452),o=r(5152),i=r(6604),s=r(3123),a=r(5858),l=r(597),c=r(4598);e.generator=function(t){return(e.generatorMapping_[t]||e.generatorMapping_.Direct)()},e.generatorMapping_={Adhoc:()=>new n.AdhocSpeechGenerator,Color:()=>new o.ColorGenerator,Direct:()=>new i.DirectSpeechGenerator,Dummy:()=>new s.DummySpeechGenerator,Node:()=>new a.NodeSpeechGenerator,Summary:()=>new l.SummarySpeechGenerator,Tree:()=>new c.TreeSpeechGenerator}},9543:function(t,e,r){Object.defineProperty(e,"__esModule",{value:!0}),e.computeSummary_=e.retrieveSummary=e.connectAllMactions=e.connectMactions=e.nodeAtPosition_=e.computePrefix_=e.retrievePrefix=e.addPrefix=e.addModality=e.addSpeech=e.recomputeMarkup=e.computeMarkup=e.recomputeSpeech=e.computeSpeech=void 0;const n=r(8290),o=r(5740),i=r(5274),s=r(2298),a=r(2362),l=r(7075),c=r(1204);function u(t){return a.SpeechRuleEngine.getInstance().evaluateNode(t)}function p(t){return u(l.SemanticTree.fromNode(t).xml())}function h(t){const e=p(t);return n.markup(e)}function f(t){const e=d(t);return n.markup(e)}function d(t){const e=l.SemanticTree.fromRoot(t),r=i.evalXPath('.//*[@id="'+t.id+'"]',e.xml());let n=r[0];return r.length>1&&(n=m(t,r)||n),n?a.SpeechRuleEngine.getInstance().runInSetting({modality:"prefix",domain:"default",style:"default",strict:!0,speech:!0},(function(){return a.SpeechRuleEngine.getInstance().evaluateNode(n)})):[]}function m(t,e){const r=e[0];if(!t.parent)return r;const n=[];for(;t;)n.push(t.id),t=t.parent;const o=function(t,e){for(;e.length&&e.shift().toString()===t.getAttribute("id")&&t.parentNode&&t.parentNode.parentNode;)t=t.parentNode.parentNode;return!e.length};for(let t,r=0;t=e[r];r++)if(o(t,n.slice()))return t;return r}function y(t){return t?a.SpeechRuleEngine.getInstance().runInSetting({modality:"summary",strict:!1,speech:!0},(function(){return a.SpeechRuleEngine.getInstance().evaluateNode(t)})):[]}e.computeSpeech=u,e.recomputeSpeech=p,e.computeMarkup=function(t){const e=u(t);return n.markup(e)},e.recomputeMarkup=h,e.addSpeech=function(t,e,r){const i=o.querySelectorAllByAttrValue(r,"id",e.id.toString())[0],a=i?n.markup(u(i)):h(e);t.setAttribute(s.Attribute.SPEECH,a)},e.addModality=function(t,e,r){const n=h(e);t.setAttribute(r,n)},e.addPrefix=function(t,e){const r=f(e);r&&t.setAttribute(s.Attribute.PREFIX,r)},e.retrievePrefix=f,e.computePrefix_=d,e.nodeAtPosition_=m,e.connectMactions=function(t,e,r){const n=o.querySelectorAll(e,"maction");for(let e,i=0;e=n[i];i++){const n=e.getAttribute("id"),i=o.querySelectorAllByAttrValue(t,"id",n)[0];if(!i)continue;const a=e.childNodes[1],l=a.getAttribute(s.Attribute.ID);let u=c.getBySemanticId(t,l);if(u&&"dummy"!==u.getAttribute(s.Attribute.TYPE))continue;if(u=i.childNodes[0],u.getAttribute("sre-highlighter-added"))continue;const p=a.getAttribute(s.Attribute.PARENT);p&&u.setAttribute(s.Attribute.PARENT,p),u.setAttribute(s.Attribute.TYPE,"dummy"),u.setAttribute(s.Attribute.ID,l);o.querySelectorAllByAttrValue(r,"id",l)[0].setAttribute("alternative",l)}},e.connectAllMactions=function(t,e){const r=o.querySelectorAll(t,"maction");for(let t,n=0;t=r[n];n++){const r=t.childNodes[1].getAttribute(s.Attribute.ID);o.querySelectorAllByAttrValue(e,"id",r)[0].setAttribute("alternative",r)}},e.retrieveSummary=function(t){const e=y(t);return n.markup(e)},e.computeSummary_=y},597:function(t,e,r){Object.defineProperty(e,"__esModule",{value:!0}),e.SummarySpeechGenerator=void 0;const n=r(6278),o=r(9543);class i extends n.AbstractSpeechGenerator{getSpeech(t,e){return o.connectAllMactions(e,this.getRebuilt().xml),this.generateSpeech(t,e)}}e.SummarySpeechGenerator=i},4598:function(t,e,r){Object.defineProperty(e,"__esModule",{value:!0}),e.TreeSpeechGenerator=void 0;const n=r(2298),o=r(1204),i=r(6278),s=r(9543);class a extends i.AbstractSpeechGenerator{getSpeech(t,e){const r=this.generateSpeech(t,e),i=this.getRebuilt().nodeDict;for(const r in i){const a=i[r],l=o.getBySemanticId(e,r),c=o.getBySemanticId(t,r);l&&c&&(this.modality&&this.modality!==n.Attribute.SPEECH?s.addModality(c,a,this.modality):s.addSpeech(c,a,this.getRebuilt().xml),this.modality===n.Attribute.SPEECH&&s.addPrefix(c,a))}return r}}e.TreeSpeechGenerator=a},313:function(t,e,r){Object.defineProperty(e,"__esModule",{value:!0}),e.INTERVALS=e.makeLetter=e.numberRules=e.alphabetRules=e.getFont=e.makeInterval=e.generate=e.makeDomains_=e.Domains_=e.Base=e.Embellish=e.Font=void 0;const n=r(5897),o=r(7491),i=r(4356),s=r(2536),a=r(2780);var l,c,u;function p(){const t=i.LOCALE.ALPHABETS,r=(t,e)=>{const r={};return Object.keys(t).forEach((t=>r[t]=!0)),Object.keys(e).forEach((t=>r[t]=!0)),Object.keys(r)};e.Domains_.small=r(t.smallPrefix,t.letterTrans),e.Domains_.capital=r(t.capPrefix,t.letterTrans),e.Domains_.digit=r(t.digitPrefix,t.digitTrans)}function h(t){const e=t.toString(16).toUpperCase();return e.length>3?e:("000"+e).slice(-4)}function f([t,e],r){const n=parseInt(t,16),o=parseInt(e,16),i=[];for(let t=n;t<=o;t++){let e=h(t);!1!==r[e]&&(e=r[e]||e,i.push(e))}return i}function d(t){const e="normal"===t||"fullwidth"===t?"":i.LOCALE.MESSAGES.font[t]||i.LOCALE.MESSAGES.embellish[t]||"";return(0,s.localeFontCombiner)(e)}function m(t,r,n,o,s,a){const l=d(o);for(let o,c,u,p=0;o=t[p],c=r[p],u=n[p];p++){const t=a?i.LOCALE.ALPHABETS.capPrefix:i.LOCALE.ALPHABETS.smallPrefix,r=a?e.Domains_.capital:e.Domains_.small;g(l.combiner,o,c,u,l.font,t,s,i.LOCALE.ALPHABETS.letterTrans,r)}}function y(t,r,n,o,s){const a=d(n);for(let n,l,c=0;n=t[c],l=r[c];c++){const t=i.LOCALE.ALPHABETS.digitPrefix,r=c+s;g(a.combiner,n,l,r,a.font,t,o,i.LOCALE.ALPHABETS.digitTrans,e.Domains_.digit)}}function g(t,e,r,n,o,i,s,l,c){for(let u,p=0;u=c[p];p++){const c=u in l?l[u]:l.default,p=u in 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smartPreferences(t,e){const r=c.getLocalePreferences()[e];if(!r)return[];const n=t.explorers.speech,i=c.relevantPreferences(n.walker.getFocus().getSemanticPrimary()),s=o.DOMAIN_TO_STYLES.clearspeak;return[{type:"radio",content:"No Preferences",id:"clearspeak-default",variable:"speechRules"},{type:"radio",content:"Current Preferences",id:"clearspeak-"+s,variable:"speechRules"},{type:"rule"},{type:"label",content:"Preferences for "+i},{type:"rule"}].concat(r[i].map((function(t){const e=t.split("_");return{type:"radio",content:e[1],id:"clearspeak-"+c.addPreference(s,e[0],e[1]),variable:"speechRules"}})))}static relevantPreferences(t){const e=d[t.type];return e&&(e[t.role]||e[""])||"ImpliedTimes"}static findPreference(t,e){if("default"===t)return c.AUTO;return c.fromPreference(t)[e]||c.AUTO}static addPreference(t,e,r){if("default"===t)return e+"_"+r;const n=c.fromPreference(t);return n[e]=r,c.toPreference(n)}static getLocalePreferences_(t){const e={};for(const r in 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p extends s.DefaultComparator{constructor(t,e){super(t,e),this.preference=t instanceof c?t.preference:{}}match(t){if(!(t instanceof c))return super.match(t);if("default"===t.getComponents()[s.Axis.STYLE])return!0;const e=Object.keys(t.preference);for(let r,n=0;r=e[n];n++)if(this.preference[r]!==t.preference[r])return!1;return!0}compare(t,e){const r=super.compare(t,e);if(0!==r)return r;const n=t instanceof c,o=e instanceof c;if(!n&&o)return 1;if(n&&!o)return-1;if(!n&&!o)return 0;const i=Object.keys(t.preference).length,s=Object.keys(e.preference).length;return i>s?-1:i<s?1:0}}e.Comparator=p;class h extends s.DynamicCstrParser{constructor(){super([s.Axis.LOCALE,s.Axis.MODALITY,s.Axis.DOMAIN,s.Axis.STYLE])}parse(t){const e=super.parse(t);let r=e.getValue(s.Axis.STYLE);const n=e.getValue(s.Axis.LOCALE),o=e.getValue(s.Axis.MODALITY);let a={};return r!==i.DynamicCstr.DEFAULT_VALUE&&(a=this.fromPreference(r),r=this.toPreference(a)),new c({locale:n,modality:o,domain:"clearspeak",style:r},a)}fromPreference(t){return c.fromPreference(t)}toPreference(t){return c.toPreference(t)}}e.Parser=h;const f=[["AbsoluteValue","fenced","neutral","metric"],["Bar","overscore","overaccent"],["Caps","identifier","latinletter"],["CombinationPermutation","appl","unknown"],["Ellipses","punctuation","ellipsis"],["Exponent","superscript",""],["Fraction","fraction",""],["Functions","appl","simple function"],["ImpliedTimes","operator","implicit"],["Log","appl","prefix 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n=r(1676),o=r(365),i=r(9912),s=r(1378),a=r(8437),l=r(7283);e.ClearspeakRules=function(){l.addStore(n.DynamicCstr.BASE_LOCALE+".speech.clearspeak","",{CTFpauseSeparator:o.pauseSeparator,CTFnodeCounter:i.nodeCounter,CTFcontentIterator:o.contentIterator,CSFvulgarFraction:a.vulgarFraction,CQFvulgarFractionSmall:i.isSmallVulgarFraction,CQFcellsSimple:i.allCellsSimple,CSFordinalExponent:i.ordinalExponent,CSFwordOrdinal:i.wordOrdinal,CQFmatchingFences:i.matchingFences,CSFnestingDepth:i.nestingDepth,CQFfencedArguments:i.fencedArguments,CQFsimpleArguments:i.simpleArguments,CQFspaceoutNumber:s.spaceoutNumber})}},9912:function(t,e,r){Object.defineProperty(e,"__esModule",{value:!0}),e.wordOrdinal=e.layoutFactor_=e.fencedFactor_=e.simpleFactor_=e.simpleArguments=e.fencedArguments=e.insertNesting=e.matchingFences=e.nestingDepth=e.NESTING_DEPTH=e.ordinalExponent=e.allTextLastContent_=e.isUnitExpression=e.isSmallVulgarFraction=e.allCellsSimple=e.allIndices_=e.isInteger_=e.simpleCell_=e.simpleNode=e.hasPreference=e.isSimpleFraction_=e.isSimpleNumber_=e.isNumber_=e.isLetter_=e.isSimple_=e.isSimpleLetters_=e.isSimpleDegree_=e.isSimpleNegative_=e.isSimpleFunction_=e.isSimpleExpression=e.nodeCounter=void 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l.concat(c&&u&&"space"===a.getAttribute("role")?[n.AuditoryDescription.create({text:"\u2800"},{})]:[])}}},8437:function(t,e,r){Object.defineProperty(e,"__esModule",{value:!0}),e.ordinalPosition=e.vulgarFraction=e.wordCounter=e.ordinalCounter=void 0;const n=r(9536),o=r(5740),i=r(4356),s=r(4977);e.ordinalCounter=function(t,e){let r=0;return function(){return i.LOCALE.NUMBERS.numericOrdinal(++r)+" "+e}},e.wordCounter=function(t,e){let r=0;return function(){return i.LOCALE.NUMBERS.numberToOrdinal(++r,!1)+" "+e}},e.vulgarFraction=function(t){const e=(0,s.convertVulgarFraction)(t,i.LOCALE.MESSAGES.MS.FRAC_OVER);return e.convertible&&e.enumerator&&e.denominator?[new n.Span(i.LOCALE.NUMBERS.numberToWords(e.enumerator),{extid:t.childNodes[0].childNodes[0].getAttribute("extid"),separator:""}),new n.Span(i.LOCALE.NUMBERS.vulgarSep,{separator:""}),new n.Span(i.LOCALE.NUMBERS.numberToOrdinal(e.denominator,1!==e.enumerator),{extid:t.childNodes[0].childNodes[1].getAttribute("extid")})]:[new 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g.RebuildStree(this.getXml()),this.rootId=this.rebuilt_.stree.root.id.toString(),this.generator.setRebuilt(this.rebuilt_),this.skeleton=h.SemanticSkeleton.fromTree(this.rebuilt_.stree),this.skeleton.populate(),this.focus_=this.singletonFocus(this.rootId),this.levels=this.initLevels(),d.connectMactions(this.node,this.getXml(),this.rebuilt_.xml)}previousLevel(){const t=this.getFocus().getDomPrimary();return t?v.getAttribute(t,c.Attribute.PARENT):this.getFocus().getSemanticPrimary().parent.id.toString()}nextLevel(){const t=this.getFocus().getDomPrimary();let e,r;if(t){e=v.splitAttribute(v.getAttribute(t,c.Attribute.CHILDREN)),r=v.splitAttribute(v.getAttribute(t,c.Attribute.CONTENT));const n=v.getAttribute(t,c.Attribute.TYPE),o=v.getAttribute(t,c.Attribute.ROLE);return this.combineContentChildren(n,o,r,e)}const n=t=>t.id.toString(),o=this.getRebuilt().nodeDict[this.primaryId()];return e=o.childNodes.map(n),r=o.contentNodes.map(n),0===e.length?[]:this.combineContentChildren(o.type,o.role,r,e)}singletonFocus(t){this.getRebuilt();const e=this.retrieveVisuals(t);return this.focusFromId(t,e)}retrieveVisuals(t){if(!this.skeleton)return[t];const e=parseInt(t,10),r=this.skeleton.subtreeNodes(e);if(!r.length)return[t];r.unshift(e);const n={},o=[];_.updateEvaluator(this.getXml());for(const t of r)n[t]||(o.push(t.toString()),n[t]=!0,this.subtreeIds(t,n));return o}subtreeIds(t,e){const r=_.evalXPath(`//*[@data-semantic-id="${t}"]`,this.getXml());_.evalXPath("*//@data-semantic-id",r[0]).forEach((t=>e[parseInt(t.textContent,10)]=!0))}focusFromId(t,e){return y.Focus.factory(t,e,this.getRebuilt(),this.node)}summary(){return this.moved=this.isSpeech()?b.WalkerMoves.SUMMARY:b.WalkerMoves.REPEAT,this.getFocus().clone()}detail(){return this.moved=this.isSpeech()?b.WalkerMoves.DETAIL:b.WalkerMoves.REPEAT,this.getFocus().clone()}specialMove(){return null}virtualize(t){return this.cursors.push({focus:this.getFocus(),levels:this.levels,undo:t||!this.cursors.length}),this.levels=this.levels.clone(),this.getFocus().clone()}previous(){const t=this.cursors.pop();return t?(this.levels=t.levels,t.focus):this.getFocus()}undo(){let t;do{t=this.cursors.pop()}while(t&&!t.undo);return t?(this.levels=t.levels,t.focus):this.getFocus()}update(t){this.generator.setOptions(t),(0,a.setup)(t).then((()=>f.generator("Tree").getSpeech(this.node,this.getXml())))}nextRules(){const t=this.generator.getOptions();return"speech"!==t.modality?this.getFocus():(s.DOMAIN_TO_STYLES[t.domain]=t.style,t.domain="mathspeak"===t.domain?"clearspeak":"mathspeak",t.style=s.DOMAIN_TO_STYLES[t.domain],this.update(t),this.moved=b.WalkerMoves.REPEAT,this.getFocus().clone())}nextStyle(t,e){if("mathspeak"===t){const t=["default","brief","sbrief"],r=t.indexOf(e);return-1===r?e:r>=t.length-1?t[0]:t[r+1]}if("clearspeak"===t){const t=m.ClearspeakPreferences.getLocalePreferences().en;if(!t)return"default";const r=m.ClearspeakPreferences.relevantPreferences(this.getFocus().getSemanticPrimary()),n=m.ClearspeakPreferences.findPreference(e,r),o=t[r].map((function(t){return t.split("_")[1]})),i=o.indexOf(n);if(-1===i)return e;const s=i>=o.length-1?o[0]:o[i+1];return m.ClearspeakPreferences.addPreference(e,r,s)}return e}previousRules(){const t=this.generator.getOptions();return"speech"!==t.modality?this.getFocus():(t.style=this.nextStyle(t.domain,t.style),this.update(t),this.moved=b.WalkerMoves.REPEAT,this.getFocus().clone())}refocus(){let t,e=this.getFocus();for(;!e.getNodes().length;){t=this.levels.peek();const r=this.up();if(!r)break;this.setFocus(r),e=this.getFocus(!0)}this.levels.push(t),this.setFocus(e)}toggleActive_(){this.active_=!this.active_}mergePrefix_(t,e=[]){const r=this.isSpeech()?this.prefix_():"";r&&t.unshift(r);const n=this.isSpeech()?this.postfix_():"";return n&&t.push(n),o.finalize(o.merge(e.concat(t)))}prefix_(){const t=this.getFocus().getDomNodes(),e=this.getFocus().getSemanticNodes();return t[0]?v.getAttribute(t[0],c.Attribute.PREFIX):d.retrievePrefix(e[0])}postfix_(){const t=this.getFocus().getDomNodes();return t[0]?v.getAttribute(t[0],c.Attribute.POSTFIX):""}depth_(){const t=p.Grammar.getInstance().getParameter("depth");p.Grammar.getInstance().setParameter("depth",!0);const e=this.getFocus().getDomPrimary(),r=this.expandable(e)?u.LOCALE.MESSAGES.navigate.EXPANDABLE:this.collapsible(e)?u.LOCALE.MESSAGES.navigate.COLLAPSIBLE:"",i=u.LOCALE.MESSAGES.navigate.LEVEL+" "+this.getDepth(),s=this.getFocus().getSemanticNodes(),a=d.retrievePrefix(s[0]),l=[new n.AuditoryDescription({text:i,personality:{}}),new n.AuditoryDescription({text:a,personality:{}}),new n.AuditoryDescription({text:r,personality:{}})];return p.Grammar.getInstance().setParameter("depth",t),o.finalize(o.markup(l))}actionable_(t){const e=null==t?void 0:t.parentNode;return e&&this.highlighter.isMactionNode(e)?e:null}summary_(){const t=this.getFocus().getSemanticPrimary().id.toString(),e=this.getRebuilt().xml.getAttribute("id")===t?this.getRebuilt().xml:i.querySelectorAllByAttrValue(this.getRebuilt().xml,"id",t)[0],r=d.retrieveSummary(e);return this.mergePrefix_([r])}detail_(){const t=this.getFocus().getSemanticPrimary().id.toString(),e=this.getRebuilt().xml.getAttribute("id")===t?this.getRebuilt().xml:i.querySelectorAllByAttrValue(this.getRebuilt().xml,"id",t)[0],r=e.getAttribute("alternative");e.removeAttribute("alternative");const n=d.computeMarkup(e),o=this.mergePrefix_([n]);return e.setAttribute("alternative",r),o}}e.AbstractWalker=S,S.ID_COUNTER=0,S.SRE_ID_ATTR="sre-explorer-id"},162:function(t,e,r){Object.defineProperty(e,"__esModule",{value:!0}),e.DummyWalker=void 0;const n=r(3284);class o extends n.AbstractWalker{up(){return null}down(){return null}left(){return null}right(){return null}repeat(){return null}depth(){return null}home(){return null}getDepth(){return 0}initLevels(){return null}combineContentChildren(t,e,r,n){return[]}findFocusOnLevel(t){return null}}e.DummyWalker=o},7730:function(t,e,r){Object.defineProperty(e,"__esModule",{value:!0}),e.Focus=void 0;const n=r(1204);class o{constructor(t,e){this.nodes=t,this.primary=e,this.domNodes=[],this.domPrimary_=null,this.allNodes=[]}static factory(t,e,r,i){const s=t=>n.getBySemanticId(i,t),a=r.nodeDict,l=s(t),c=e.map(s),u=e.map((function(t){return a[t]})),p=new o(u,a[t]);return p.domNodes=c,p.domPrimary_=l,p.allNodes=o.generateAllVisibleNodes_(e,c,a,i),p}static generateAllVisibleNodes_(t,e,r,i){const s=t=>n.getBySemanticId(i,t);let a=[];for(let n=0,l=t.length;n<l;n++){if(e[n]){a.push(e[n]);continue}const l=r[t[n]];if(!l)continue;const c=l.childNodes.map((function(t){return t.id.toString()})),u=c.map(s);a=a.concat(o.generateAllVisibleNodes_(c,u,r,i))}return a}getSemanticPrimary(){return this.primary}getSemanticNodes(){return this.nodes}getNodes(){return this.allNodes}getDomNodes(){return this.domNodes}getDomPrimary(){return this.domPrimary_}toString(){return"Primary:"+this.domPrimary_+" Nodes:"+this.domNodes}clone(){const t=new o(this.nodes,this.primary);return t.domNodes=this.domNodes,t.domPrimary_=this.domPrimary_,t.allNodes=this.allNodes,t}}e.Focus=o},9797:function(t,e){Object.defineProperty(e,"__esModule",{value:!0}),e.Levels=void 0;class r{constructor(){this.level_=[]}push(t){this.level_.push(t)}pop(){return this.level_.pop()}peek(){return this.level_[this.level_.length-1]||null}indexOf(t){const e=this.peek();return e?e.indexOf(t):null}find(t){const e=this.peek();if(!e)return null;for(let r=0,n=e.length;r<n;r++)if(t(e[r]))return e[r];return null}get(t){const e=this.peek();return!e||t<0||t>=e.length?null:e[t]}depth(){return this.level_.length}clone(){const t=new r;return t.level_=this.level_.slice(0),t}toString(){let t="";for(let e,r=0;e=this.level_[r];r++)t+="\n"+e.map((function(t){return t.toString()}));return t}}e.Levels=r},1214:function(t,e,r){Object.defineProperty(e,"__esModule",{value:!0}),e.RebuildStree=void 0;const n=r(5740),o=r(2298),i=r(3588),s=r(6537),a=r(3308),l=r(5656),c=r(7075),u=r(4795),p=r(1204);class h{constructor(t){this.mathml=t,this.factory=new s.SemanticNodeFactory,this.nodeDict={},this.mmlRoot=p.getSemanticRoot(t),this.streeRoot=this.assembleTree(this.mmlRoot),this.stree=c.SemanticTree.fromNode(this.streeRoot,this.mathml),this.xml=this.stree.xml(),a.default.getInstance().setNodeFactory(this.factory)}static addAttributes(t,e,r){r&&1===e.childNodes.length&&e.childNodes[0].nodeType!==n.NodeType.TEXT_NODE&&u.addAttributes(t,e.childNodes[0]),u.addAttributes(t,e)}static textContent(t,e,r){if(!r&&e.textContent)return void(t.textContent=e.textContent);const n=p.splitAttribute(p.getAttribute(e,o.Attribute.OPERATOR));n.length>1&&(t.textContent=n[1])}static isPunctuated(t){return!l.SemanticSkeleton.simpleCollapseStructure(t)&&t[1]&&l.SemanticSkeleton.contentCollapseStructure(t[1])}getTree(){return this.stree}assembleTree(t){const e=this.makeNode(t),r=p.splitAttribute(p.getAttribute(t,o.Attribute.CHILDREN)),n=p.splitAttribute(p.getAttribute(t,o.Attribute.CONTENT));if(h.addAttributes(e,t,!(r.length||n.length)),0===n.length&&0===r.length)return h.textContent(e,t),e;if(n.length>0){const t=p.getBySemanticId(this.mathml,n[0]);t&&h.textContent(e,t,!0)}e.contentNodes=n.map((t=>this.setParent(t,e))),e.childNodes=r.map((t=>this.setParent(t,e)));const i=p.getAttribute(t,o.Attribute.COLLAPSED);return i?this.postProcess(e,i):e}makeNode(t){const e=p.getAttribute(t,o.Attribute.TYPE),r=p.getAttribute(t,o.Attribute.ROLE),n=p.getAttribute(t,o.Attribute.FONT),i=p.getAttribute(t,o.Attribute.ANNOTATION)||"",s=p.getAttribute(t,o.Attribute.ID),a=p.getAttribute(t,o.Attribute.EMBELLISHED),l=p.getAttribute(t,o.Attribute.FENCEPOINTER),c=this.createNode(parseInt(s,10));return c.type=e,c.role=r,c.font=n||"unknown",c.parseAnnotation(i),l&&(c.fencePointer=l),a&&(c.embellished=a),c}makePunctuation(t){const e=this.createNode(t);return e.updateContent((0,i.invisibleComma)()),e.role="dummy",e}makePunctuated(t,e,r){const n=this.createNode(e[0]);n.type="punctuated",n.embellished=t.embellished,n.fencePointer=t.fencePointer,n.role=r;const o=e.splice(1,1)[0].slice(1);n.contentNodes=o.map(this.makePunctuation.bind(this)),this.collapsedChildren_(e)}makeEmpty(t,e,r){const n=this.createNode(e);n.type="empty",n.embellished=t.embellished,n.fencePointer=t.fencePointer,n.role=r}makeIndex(t,e,r){if(h.isPunctuated(e))return this.makePunctuated(t,e,r),void(e=e[0]);l.SemanticSkeleton.simpleCollapseStructure(e)&&!this.nodeDict[e.toString()]&&this.makeEmpty(t,e,r)}postProcess(t,e){const r=l.SemanticSkeleton.fromString(e).array;if("subsup"===t.type){const e=this.createNode(r[1][0]);return e.type="subscript",e.role="subsup",t.type="superscript",e.embellished=t.embellished,e.fencePointer=t.fencePointer,this.makeIndex(t,r[1][2],"rightsub"),this.makeIndex(t,r[2],"rightsuper"),this.collapsedChildren_(r),t}if("subscript"===t.type)return this.makeIndex(t,r[2],"rightsub"),this.collapsedChildren_(r),t;if("superscript"===t.type)return this.makeIndex(t,r[2],"rightsuper"),this.collapsedChildren_(r),t;if("tensor"===t.type)return this.makeIndex(t,r[2],"leftsub"),this.makeIndex(t,r[3],"leftsuper"),this.makeIndex(t,r[4],"rightsub"),this.makeIndex(t,r[5],"rightsuper"),this.collapsedChildren_(r),t;if("punctuated"===t.type){if(h.isPunctuated(r)){const e=r.splice(1,1)[0].slice(1);t.contentNodes=e.map(this.makePunctuation.bind(this))}return t}if("underover"===t.type){const e=this.createNode(r[1][0]);return"overaccent"===t.childNodes[1].role?(e.type="overscore",t.type="underscore"):(e.type="underscore",t.type="overscore"),e.role="underover",e.embellished=t.embellished,e.fencePointer=t.fencePointer,this.collapsedChildren_(r),t}return t}createNode(t){const e=this.factory.makeNode(t);return this.nodeDict[t.toString()]=e,e}collapsedChildren_(t){const e=t=>{const r=this.nodeDict[t[0]];r.childNodes=[];for(let n=1,o=t.length;n<o;n++){const o=t[n];r.childNodes.push(l.SemanticSkeleton.simpleCollapseStructure(o)?this.nodeDict[o]:e(o))}return r};e(t)}setParent(t,e){const r=p.getBySemanticId(this.mathml,t),n=this.assembleTree(r);return n.parent=e,n}}e.RebuildStree=h},6295:function(t,e,r){Object.defineProperty(e,"__esModule",{value:!0}),e.SemanticWalker=void 0;const n=r(3284),o=r(9797);class i extends n.AbstractWalker{constructor(t,e,r,n){super(t,e,r,n),this.node=t,this.generator=e,this.highlighter=r,this.levels=null,this.restoreState()}initLevels(){const t=new o.Levels;return t.push([this.getFocus()]),t}up(){super.up();const t=this.previousLevel();if(!t)return null;this.levels.pop();return this.levels.find((function(e){return e.getSemanticNodes().some((function(e){return e.id.toString()===t}))}))}down(){super.down();const t=this.nextLevel();return 0===t.length?null:(this.levels.push(t),t[0])}combineContentChildren(t,e,r,n){switch(t){case"relseq":case"infixop":case"multirel":return this.makePairList(n,r);case"prefixop":return[this.focusFromId(n[0],r.concat(n))];case"postfixop":return[this.focusFromId(n[0],n.concat(r))];case"matrix":case"vector":case"fenced":return[this.focusFromId(n[0],[r[0],n[0],r[1]])];case"cases":return[this.focusFromId(n[0],[r[0],n[0]])];case"punctuated":return"text"===e?n.map(this.singletonFocus.bind(this)):n.length===r.length?r.map(this.singletonFocus.bind(this)):this.combinePunctuations(n,r,[],[]);case"appl":return[this.focusFromId(n[0],[n[0],r[0]]),this.singletonFocus(n[1])];case"root":return[this.singletonFocus(n[1]),this.singletonFocus(n[0])];default:return n.map(this.singletonFocus.bind(this))}}combinePunctuations(t,e,r,n){if(0===t.length)return n;const o=t.shift(),i=e.shift();return o===i?(r.push(i),this.combinePunctuations(t,e,r,n)):(e.unshift(i),r.push(o),t.length===e.length?(n.push(this.focusFromId(o,r.concat(e))),n):(n.push(this.focusFromId(o,r)),this.combinePunctuations(t,e,[],n)))}makePairList(t,e){if(0===t.length)return[];if(1===t.length)return[this.singletonFocus(t[0])];const r=[this.singletonFocus(t.shift())];for(let n=0,o=t.length;n<o;n++)r.push(this.focusFromId(t[n],[e[n],t[n]]));return r}left(){super.left();const t=this.levels.indexOf(this.getFocus());if(null===t)return null;const e=this.levels.get(t-1);return e||null}right(){super.right();const t=this.levels.indexOf(this.getFocus());if(null===t)return null;const e=this.levels.get(t+1);return e||null}findFocusOnLevel(t){return this.levels.find((e=>e.getSemanticPrimary().id===t))}}e.SemanticWalker=i},9806:function(t,e,r){Object.defineProperty(e,"__esModule",{value:!0}),e.SyntaxWalker=void 0;const n=r(707),o=r(3284),i=r(9797);class s extends o.AbstractWalker{constructor(t,e,r,n){super(t,e,r,n),this.node=t,this.generator=e,this.highlighter=r,this.levels=null,this.restoreState()}initLevels(){const t=new i.Levels;return t.push([this.primaryId()]),t}up(){super.up();const t=this.previousLevel();return t?(this.levels.pop(),this.singletonFocus(t)):null}down(){super.down();const t=this.nextLevel();if(0===t.length)return null;const e=this.singletonFocus(t[0]);return e&&this.levels.push(t),e}combineContentChildren(t,e,r,o){switch(t){case"relseq":case"infixop":case"multirel":return(0,n.interleaveLists)(o,r);case"prefixop":return r.concat(o);case"postfixop":return o.concat(r);case"matrix":case"vector":case"fenced":return o.unshift(r[0]),o.push(r[1]),o;case"cases":return o.unshift(r[0]),o;case"punctuated":return"text"===e?(0,n.interleaveLists)(o,r):o;case"appl":return[o[0],r[0],o[1]];case"root":return[o[1],o[0]];default:return o}}left(){super.left();const t=this.levels.indexOf(this.primaryId());if(null===t)return null;const e=this.levels.get(t-1);return e?this.singletonFocus(e):null}right(){super.right();const t=this.levels.indexOf(this.primaryId());if(null===t)return null;const e=this.levels.get(t+1);return e?this.singletonFocus(e):null}findFocusOnLevel(t){return this.singletonFocus(t.toString())}focusDomNodes(){return[this.getFocus().getDomPrimary()]}focusSemanticNodes(){return[this.getFocus().getSemanticPrimary()]}}e.SyntaxWalker=s},1799:function(t,e,r){Object.defineProperty(e,"__esModule",{value:!0}),e.TableWalker=void 0;const n=r(5740),o=r(8496),i=r(9806),s=r(179);class a extends i.SyntaxWalker{constructor(t,e,r,n){super(t,e,r,n),this.node=t,this.generator=e,this.highlighter=r,this.firstJump=null,this.key_=null,this.row_=0,this.currentTable_=null,this.keyMapping.set(o.KeyCode.ZERO,this.jumpCell.bind(this)),this.keyMapping.set(o.KeyCode.ONE,this.jumpCell.bind(this)),this.keyMapping.set(o.KeyCode.TWO,this.jumpCell.bind(this)),this.keyMapping.set(o.KeyCode.THREE,this.jumpCell.bind(this)),this.keyMapping.set(o.KeyCode.FOUR,this.jumpCell.bind(this)),this.keyMapping.set(o.KeyCode.FIVE,this.jumpCell.bind(this)),this.keyMapping.set(o.KeyCode.SIX,this.jumpCell.bind(this)),this.keyMapping.set(o.KeyCode.SEVEN,this.jumpCell.bind(this)),this.keyMapping.set(o.KeyCode.EIGHT,this.jumpCell.bind(this)),this.keyMapping.set(o.KeyCode.NINE,this.jumpCell.bind(this))}move(t){this.key_=t;const e=super.move(t);return this.modifier=!1,e}up(){return this.moved=s.WalkerMoves.UP,this.eligibleCell_()?this.verticalMove_(!1):super.up()}down(){return this.moved=s.WalkerMoves.DOWN,this.eligibleCell_()?this.verticalMove_(!0):super.down()}jumpCell(){if(!this.isInTable_()||null===this.key_)return this.getFocus();if(this.moved===s.WalkerMoves.ROW){this.moved=s.WalkerMoves.CELL;const t=this.key_-o.KeyCode.ZERO;return this.isLegalJump_(this.row_,t)?this.jumpCell_(this.row_,t):this.getFocus()}const t=this.key_-o.KeyCode.ZERO;return t>this.currentTable_.childNodes.length?this.getFocus():(this.row_=t,this.moved=s.WalkerMoves.ROW,this.getFocus().clone())}undo(){const t=super.undo();return t===this.firstJump&&(this.firstJump=null),t}eligibleCell_(){const t=this.getFocus().getSemanticPrimary();return this.modifier&&"cell"===t.type&&-1!==a.ELIGIBLE_CELL_ROLES.indexOf(t.role)}verticalMove_(t){const e=this.previousLevel();if(!e)return null;const r=this.getFocus(),n=this.levels.indexOf(this.primaryId()),o=this.levels.pop(),i=this.levels.indexOf(e),s=this.levels.get(t?i+1:i-1);if(!s)return this.levels.push(o),null;this.setFocus(this.singletonFocus(s));const a=this.nextLevel();return a[n]?(this.levels.push(a),this.singletonFocus(a[n])):(this.setFocus(r),this.levels.push(o),null)}jumpCell_(t,e){this.firstJump?this.virtualize(!1):(this.firstJump=this.getFocus(),this.virtualize(!0));const r=this.currentTable_.id.toString();let n;do{n=this.levels.pop()}while(-1===n.indexOf(r));this.levels.push(n),this.setFocus(this.singletonFocus(r)),this.levels.push(this.nextLevel());const o=this.currentTable_.childNodes[t-1];return this.setFocus(this.singletonFocus(o.id.toString())),this.levels.push(this.nextLevel()),this.singletonFocus(o.childNodes[e-1].id.toString())}isLegalJump_(t,e){const r=n.querySelectorAllByAttrValue(this.getRebuilt().xml,"id",this.currentTable_.id.toString())[0];if(!r||r.hasAttribute("alternative"))return!1;const o=this.currentTable_.childNodes[t-1];if(!o)return!1;const i=n.querySelectorAllByAttrValue(r,"id",o.id.toString())[0];return!(!i||i.hasAttribute("alternative"))&&!(!o||!o.childNodes[e-1])}isInTable_(){let t=this.getFocus().getSemanticPrimary();for(;t;){if(-1!==a.ELIGIBLE_TABLE_TYPES.indexOf(t.type))return this.currentTable_=t,!0;t=t.parent}return!1}}e.TableWalker=a,a.ELIGIBLE_CELL_ROLES=["determinant","rowvector","binomial","squarematrix","multiline","matrix","vector","cases","table"],a.ELIGIBLE_TABLE_TYPES=["multiline","matrix","vector","cases","table"]},179:function(t,e){Object.defineProperty(e,"__esModule",{value:!0}),e.WalkerState=e.WalkerMoves=void 0,function(t){t.UP="up",t.DOWN="down",t.LEFT="left",t.RIGHT="right",t.REPEAT="repeat",t.DEPTH="depth",t.ENTER="enter",t.EXPAND="expand",t.HOME="home",t.SUMMARY="summary",t.DETAIL="detail",t.ROW="row",t.CELL="cell"}(e.WalkerMoves||(e.WalkerMoves={}));class r{static resetState(t){delete r.STATE[t]}static setState(t,e){r.STATE[t]=e}static getState(t){return r.STATE[t]}}e.WalkerState=r,r.STATE={}},3362:function(t,e,r){Object.defineProperty(e,"__esModule",{value:!0}),e.walkerMapping_=e.walker=void 0;const n=r(162),o=r(6295),i=r(9806),s=r(1799);e.walker=function(t,r,n,o,i){return(e.walkerMapping_[t.toLowerCase()]||e.walkerMapping_.dummy)(r,n,o,i)},e.walkerMapping_={dummy:(t,e,r,o)=>new n.DummyWalker(t,e,r,o),semantic:(t,e,r,n)=>new o.SemanticWalker(t,e,r,n),syntax:(t,e,r,n)=>new i.SyntaxWalker(t,e,r,n),table:(t,e,r,n)=>new s.TableWalker(t,e,r,n)}},1204:function(t,e,r){Object.defineProperty(e,"__esModule",{value:!0}),e.getBySemanticId=e.getSemanticRoot=e.getAttribute=e.splitAttribute=void 0;const n=r(5740),o=r(2298);e.splitAttribute=function(t){return t?t.split(/,/):[]},e.getAttribute=function(t,e){return t.getAttribute(e)},e.getSemanticRoot=function(t){if(t.hasAttribute(o.Attribute.TYPE)&&!t.hasAttribute(o.Attribute.PARENT))return t;const e=n.querySelectorAllByAttr(t,o.Attribute.TYPE);for(let 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index 0000000000..ec77f5d06f
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ki({alert$:e}){cn.default.isSupported()&&new U(t=>{new cn.default("[data-clipboard-target], [data-clipboard-text]",{text:r=>r.getAttribute("data-clipboard-text")||Vs(B(r.getAttribute("data-clipboard-target")))}).on("success",r=>t.next(r))}).pipe(M(t=>{t.trigger.focus()}),d(()=>ae("clipboard.copied"))).subscribe(e)}function Ns(e){if(e.length<2)return[""];let[t,r]=[...e].sort((o,i)=>o.length-i.length).map(o=>o.replace(/[^/]+$/,"")),n=0;if(t===r)n=t.length;else for(;t.charCodeAt(n)===r.charCodeAt(n);)n++;return e.map(o=>o.replace(t.slice(0,n),""))}function hr(e){let t=__md_get("__sitemap",sessionStorage,e);if(t)return C(t);{let r=ce();return zo(new URL("sitemap.xml",e||r.base)).pipe(d(n=>Ns(D("loc",n).map(o=>o.textContent))),le(()=>k),De([]),M(n=>__md_set("__sitemap",n,sessionStorage,e)))}}function Pi({document$:e,location$:t,viewport$:r}){let n=ce();if(location.protocol==="file:")return;"scrollRestoration"in 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Set,u=new Set;for(let l of D("script",document))l.src?f.add(new URL(l.src,document.baseURI).toString()):u.add(l.outerHTML);e.pipe(Pe(1),mt(l=>ze(this,null,function*(){var p;for(let m of["title","link[rel=canonical]","meta[name=author]","meta[name=description]","[data-md-component=announce]","[data-md-component=container]","[data-md-component=header-topic]","[data-md-component=outdated]","[data-md-component=logo]","[data-md-component=skip]",...Z("navigation.tabs.sticky")?["[data-md-component=tabs]"]:[]]){let h=ie(m),b=ie(m,l);typeof h!="undefined"&&typeof b!="undefined"&&h.replaceWith(b)}((p=window.MathJax)==null?void 0:p.typesetPromise)!==void 0&&(yield window.MathJax.typesetPromise());for(let m of D("script",l))if(m.src){let h=new URL(m.src,document.baseURI).toString();if(!f.has(h)){let b=document.createElement("script");for(let q of m.getAttributeNames())b.setAttribute(q,m.getAttribute(q));let w;b.src=h,b.async||(w=new 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$i=Je(rn());function Ii(e,t){let r=new RegExp(e.separator,"img"),n=(o,i,a)=>`${i}<mark data-md-highlight>${a}</mark>`;return o=>{o=o.replace(/[\s*+\-:~^]+/g," ").trim();let i=new RegExp(`(^|${e.separator})(${o.replace(/[|\\{}()[\]^$+*?.-]/g,"\\$&").replace(r,"|")})`,"img");return a=>(t?(0,$i.default)(a):a).replace(i,n).replace(/<\/mark>(\s+)<mark[^>]*>/img,"$1")}}function ji({document$:e}){var a;let t=ce(),r=t.version.staticVersions?C(t.version.staticVersions):Ge(new URL((a=t.version.versionPath)!=null?a:"../versions.json",t.base)),n=new URL("..",t.base),o=c=>new URL(c,n).toString().replace(/\/*$/,""),i=r.pipe(d(c=>{let s=t.base.toString().replace(/\/*$/,"");return c.find(({version:f,aliases:u})=>o(f)===s||u.find(l=>o(l)===s))||c[0]}));r.pipe(d(c=>new Map(c.map(s=>[`${new URL(`../${s.version}/`,t.base)}`,s]))),_(c=>x(document.body,"click").pipe(A(s=>!s.metaKey&&!s.ctrlKey),me(i),_(([s,f])=>{if(s.target instanceof Element){let u=s.target.closest("a");if(u&&!u.target&&c.has(u.href)){let l=u.href;return!s.target.closest(".md-version")&&c.get(l)===f?k:(s.preventDefault(),C(l))}}return k}),_(s=>{let{version:f}=c.get(s);return hr(new URL(s)).pipe(d(u=>{let p=Oe().href.replace(t.base,"");return u.includes(p.split("#")[0])?new URL(`../${f}/${p}`,t.base):new URL(s)}))})))).subscribe(c=>lr(c)),G([r,i]).subscribe(([c,s])=>{B(".md-header__topic").appendChild(oi(c,s))}),e.pipe(_(()=>i)).subscribe(c=>{var f;let s=__md_get("__outdated",sessionStorage);if(s===null){let u=((f=t.version)==null?void 0:f.default)||"latest";s=!c.aliases.includes(u),__md_set("__outdated",s,sessionStorage)}if(s)for(let u of ee("outdated"))u.hidden=!1})}function Fi(e,{location$:t}){let r={lang:[],separator:"\\s+"};return G([t.pipe(z(Oe()),A(n=>!!n.searchParams.get("h")))]).pipe(d(([n])=>Ii(r,!0)(n.searchParams.get("h"))),d(n=>{var a;let o=new Map,i=document.createNodeIterator(e,NodeFilter.SHOW_TEXT);for(let c=i.nextNode();c;c=i.nextNode())if((a=c.parentElement)!=null&&a.offsetHeight){let s=c.textContent,f=n(s);f.length>s.length&&o.set(c,f)}for(let[c,s]of o){let{childNodes:f}=O("span",null,s);c.replaceWith(...Array.from(f))}return{ref:e,nodes:o}}))}function Ds(e,{viewport$:t,main$:r}){let n=e.parentElement,o=n.offsetTop-n.parentElement.offsetTop;return G([r,t]).pipe(d(([{offset:i,height:a},{offset:{y:c}}])=>(a=a+Math.min(o,Math.max(0,c-i))-o,{height:a,locked:c>=i+o})),Q((i,a)=>i.height===a.height&&i.locked===a.locked))}function fn(e,n){var o=n,{header$:t}=o,r=hn(o,["header$"]);let i=B(".md-sidebar__scrollwrap",e),{y:a}=Be(i);return I(()=>{let c=new L;return c.pipe(Re(0,fe),me(t)).subscribe({next([{height:s},{height:f}]){i.style.height=`${s-2*a}px`,e.style.top=`${f}px`},complete(){i.style.height="",e.style.top=""}}),c.pipe(Ce(fe),ve(1)).subscribe(()=>{for(let s of D(".md-nav__link--active[href]",e)){let f=ko(s);if(typeof f!="undefined"){let u=s.offsetTop-f.offsetTop,{height:l}=Se(f);f.scrollTo({top:u-l/2})}}}),Ds(e,r).pipe(M(s=>c.next(s)),R(()=>c.complete()),d(s=>V({ref:e},s)))})}function Ui(e,t){if(typeof t!="undefined"){let r=`https://api.github.com/repos/${e}/${t}`;return Ir(Ge(`${r}/releases/latest`).pipe(le(()=>k),d(n=>({version:n.tag_name})),De({})),Ge(r).pipe(le(()=>k),d(n=>({stars:n.stargazers_count,forks:n.forks_count})),De({}))).pipe(d(([n,o])=>V(V({},n),o)))}else{let r=`https://api.github.com/users/${e}`;return Ge(r).pipe(d(n=>({repositories:n.public_repos})),De({}))}}function Vi(e,t){let r=`https://${e}/api/v4/projects/${encodeURIComponent(t)}`;return Ge(r).pipe(le(()=>k),d(({star_count:n,forks_count:o})=>({stars:n,forks:o})),De({}))}function Ni(e){let[t]=e.match(/(git(?:hub|lab))/i)||[];switch(t.toLowerCase()){case"github":let[,r,n]=e.match(/^.+github\.com\/([^/]+)\/?([^/]+)?/i);return Ui(r,n);case"gitlab":let[,o,i]=e.match(/^.+?([^/]*gitlab[^/]+)\/(.+?)\/?$/i);return Vi(o,i);default:return k}}var Ws;function zs(e){return Ws||(Ws=I(()=>{let t=__md_get("__source",sessionStorage);if(t)return C(t);if(ee("consent").length){let n=__md_get("__consent");if(!(n&&n.github))return k}return Ni(e.href).pipe(M(n=>__md_set("__source",n,sessionStorage)))}).pipe(le(()=>k),A(t=>Object.keys(t).length>0),d(t=>({facts:t})),X(1)))}function Di(e){let t=B(":scope > :last-child",e);return I(()=>{let r=new L;return r.subscribe(({facts:n})=>{t.appendChild(ri(n)),t.classList.add("md-source__repository--active")}),zs(e).pipe(M(n=>r.next(n)),R(()=>r.complete()),d(n=>V({ref:e},n)))})}function qs(e,{viewport$:t,header$:r}){return de(document.body).pipe(_(()=>mr(e,{header$:r,viewport$:t})),d(({offset:{y:n}})=>({hidden:n>=10})),J("hidden"))}function Wi(e,t){return I(()=>{let r=new L;return r.subscribe({next({hidden:n}){e.hidden=n},complete(){e.hidden=!1}}),(Z("navigation.tabs.sticky")?C({hidden:!1}):qs(e,t)).pipe(M(n=>r.next(n)),R(()=>r.complete()),d(n=>V({ref:e},n)))})}function Ks(e,{viewport$:t,header$:r,excludedLinks:n}){let o=new Map,i=D("a[href]",e);for(let s of i){if(n!=null&&n.has(s))continue;let f=s.getAttribute("href"),u;if(f.startsWith("#")){let l=decodeURIComponent(s.hash.substring(1));u=ie(`[id="${l}"]`)}else u=ie(`a.pseudo-toc-entry[href=${CSS.escape(f)}]`);if(typeof u!="undefined"){let l=s.closest(".md-nav__link");l!==null&&o.set(l,u)}}let a=r.pipe(J("height"),d(({height:s})=>{let f=_e("main"),u=B(":scope > :first-child",f);return s+.8*(u.offsetTop-f.offsetTop)}),pe());return de(document.body).pipe(J("height"),_(s=>I(()=>{let f=[];return C([...o].reduce((u,[l,p])=>{for(;f.length&&o.get(f[f.length-1]).tagName>=p.tagName;)f.pop();let m=p.offsetTop;for(;!m&&p.parentElement;)p=p.parentElement,m=p.offsetTop;return u.set([...f=[...f,l]].reverse(),m)},new Map))}).pipe(d(f=>new Map([...f].sort(([,u],[,l])=>u-l))),St(a),_(([f,u])=>t.pipe(Nr(([l,p],{offset:{y:m},size:h})=>{let b=m+h.height>=Math.floor(s.height);for(;p.length;){let[,w]=p[0];if(w-u<m||b)l=[...l,p.shift()];else break}for(;l.length;){let[,w]=l[l.length-1];if(w-u>=m&&!b)p=[l.pop(),...p];else break}return[l,p]},[[],[...f]]),Q((l,p)=>l[0]===p[0]&&l[1]===p[1])))))).pipe(d(([s,f])=>({prev:s.map(([u])=>u),next:f.map(([u])=>u)})),z({prev:[],next:[]}),Ne(2,1),d(([s,f])=>s.prev.length<f.prev.length?{prev:f.prev.slice(Math.max(0,s.prev.length-1),f.prev.length),next:[]}:{prev:f.prev.slice(-1),next:f.next.slice(0,f.next.length-s.next.length)}))}function ln(e,{viewport$:t,header$:r,target$:n,localToc:o}){return I(()=>{let i=new L,a=o?"md-nav__link--active":"md-nav__link--in-viewport";if(i.subscribe(({prev:s,next:f})=>{for(let[u]of f)u.classList.remove("md-nav__link--passed"),u.classList.remove(a);for(let[u,[l]]of s.entries())l.classList.add("md-nav__link--passed"),l.classList.toggle(a,u===s.length-1)}),Z("toc.follow")&&(o||!Z("toc.integrate"))){let s=!o||Z("toc.integrate");i.pipe(He(1)).subscribe(({prev:f})=>{var m;let u;if(f.length===0&&s&&(u=(m=e.querySelector("a[href='#']"))!=null?m:e),s=!1,f.length!==0&&(u=f[f.length-1][0]),u===void 0||!u.offsetHeight)return;let l=u.parentElement,p=5;for(;l!==null&&l.scrollHeight-p<=l.clientHeight;)l=l.parentElement;if(l!==null&&l!==document.body&&l!==document.documentElement){let h=u.getBoundingClientRect(),b=l.getBoundingClientRect();l.scrollTo({top:l.scrollTop+(h.y-b.height/2-b.y)})}})}o&&Z("navigation.tracking")&&t.pipe(te(i.pipe(ue(1))),J("offset"),He(250),Pe(1),te(n.pipe(Pe(1))),Ot({delay:250}),me(i)).subscribe(([,{prev:s}])=>{let f=Oe(),u=s[s.length-1];if(u&&u.length){let[l]=u,{hash:p}=new URL(l.href);f.hash!==p&&(f.hash=p,history.replaceState({},"",`${f}`))}else f.hash="",history.replaceState({},"",`${f}`)}),Z("toc.sticky")&&de(document.body).pipe(J("width"),He(0)).subscribe(()=>{let s=new Map,f="--md-nav__header-height";for(let u of D(".md-nav__link",e)){let l=u.nextElementSibling;if(!(l instanceof HTMLElement)||l.tagName!=="NAV")continue;let p="",m=NaN,h=l.parentElement.closest("nav");if(h!==null){let b=s.get(h);b!==void 0&&(p=`${b.height} + `,m=b.zindex-1)}isNaN(m)&&(m=100),p+=`${u.offsetHeight}px + 0.625em`,u.classList.add("md-nav__sticky"),u.style.setProperty("--md-nav__sticky-zindex",m.toString()),l.style.setProperty(f,`calc(${p})`),s.set(l,{height:p,zindex:m})}});let c=o?void 0:new Set(D("[data-md-component='toc'] a[href]",e));return Ks(e,{viewport$:t,header$:r,excludedLinks:c}).pipe(M(s=>i.next(s)),R(()=>i.complete()),d(s=>V({ref:e},s)))})}function Bs(e,{viewport$:t,main$:r,target$:n}){let o=t.pipe(d(({offset:{y:a}})=>a),Ne(2,1),d(([a,c])=>a>c&&c>0),Q()),i=r.pipe(d(({active:a})=>a));return G([i,o]).pipe(d(([a,c])=>!(a&&c)),Q(),te(n.pipe(Pe(1))),Vr(!0),Ot({delay:250}),d(a=>({hidden:a})))}function zi(e,{viewport$:t,header$:r,main$:n,target$:o}){let i=new L,a=i.pipe(ue(1));return i.subscribe({next({hidden:c}){e.hidden=c,c?(e.setAttribute("tabindex","-1"),e.blur()):e.removeAttribute("tabindex")},complete(){e.style.top="",e.hidden=!0,e.removeAttribute("tabindex")}}),r.pipe(te(a),J("height")).subscribe(({height:c})=>{e.style.top=`${c+16}px`}),Bs(e,{viewport$:t,main$:n,target$:o}).pipe(M(c=>i.next(c)),R(()=>i.complete()),d(c=>V({ref:e},c)))}function qi({document$:e,tablet$:t}){e.pipe(_(()=>D(".md-toggle--indeterminate, [data-md-state=indeterminate]")),M(r=>{r.indeterminate=!0,r.checked=!1}),oe(r=>x(r,"change").pipe(zr(()=>r.classList.contains("md-toggle--indeterminate")),d(()=>r))),me(t)).subscribe(([r,n])=>{r.classList.remove("md-toggle--indeterminate"),n&&(r.checked=!1)})}function Ys(){return/(iPad|iPhone|iPod)/.test(navigator.userAgent)}function Ki({document$:e}){e.pipe(_(()=>D("[data-md-scrollfix]")),M(t=>t.removeAttribute("data-md-scrollfix")),A(Ys),oe(t=>x(t,"touchstart").pipe(d(()=>t)))).subscribe(t=>{let r=t.scrollTop;r===0?t.scrollTop=1:r+t.offsetHeight===t.scrollHeight&&(t.scrollTop=r-1)})}function Bi({viewport$:e,tablet$:t}){G([ht("search"),t]).pipe(d(([r,n])=>r&&!n),_(r=>C(r).pipe(ke(r?400:100))),me(e)).subscribe(([r,{offset:{y:n}}])=>{if(r)document.body.setAttribute("data-md-scrolllock",""),document.body.style.top=`-${n}px`;else{let o=-1*parseInt(document.body.style.top,10);document.body.removeAttribute("data-md-scrolllock"),document.body.style.top="",o&&window.scrollTo(0,o)}})}Object.entries||(Object.entries=function(e){let t=[];for(let r of Object.keys(e))t.push([r,e[r]]);return t});Object.values||(Object.values=function(e){let t=[];for(let r of Object.keys(e))t.push(e[r]);return t});typeof Element!="undefined"&&(Element.prototype.scrollTo||(Element.prototype.scrollTo=function(e,t){typeof e=="object"?(this.scrollLeft=e.left,this.scrollTop=e.top):(this.scrollLeft=e,this.scrollTop=t)}),Element.prototype.replaceWith||(Element.prototype.replaceWith=function(...e){let t=this.parentNode;if(t){e.length===0&&t.removeChild(this);for(let r=e.length-1;r>=0;r--){let n=e[r];typeof n!="object"?n=document.createTextNode(n):n.parentNode&&n.parentNode.removeChild(n),r?t.insertBefore(this.previousSibling,n):t.replaceChild(n,this)}}}));document.documentElement.classList.remove("no-js");document.documentElement.classList.add("js");var nt=So(),vr=jo(),rt=Do(),un=Io(),ge=Jo(),gr=Yr("(min-width: 960px)"),Gi=Yr("(min-width: 1220px)"),Ji=Wo(),Gs=ce(),pn=new L;ki({alert$:pn});Z("navigation.instant")&&Pi({document$:nt,location$:vr,viewport$:ge});var Yi;((Yi=Gs.version)==null?void 0:Yi.provider)==="mike"&&ji({document$:nt});P(vr,rt).pipe(ke(125)).subscribe(()=>{Ye("drawer",!1),Ye("search",!1)});un.pipe(A(({mode:e})=>e==="global")).subscribe(e=>{switch(e.type){case"p":case",":let t=ie("[href][rel=prev]");typeof t!="undefined"&&t.click();break;case"n":case".":let r=ie("[href][rel=next]");typeof r!="undefined"&&r.click();break}});qi({document$:nt,tablet$:gr});Ki({document$:nt});Bi({viewport$:ge,tablet$:gr});var We=xi(_e("header"),{viewport$:ge}),br=nt.pipe(d(()=>_e("main")),_(e=>Si(e,{viewport$:ge,header$:We})),X(1)),Js=P(...ee("consent").map(e=>Xo(e,{target$:rt})),...ee("dialog").map(e=>yi(e,{alert$:pn})),...ee("header").map(e=>wi(e,{viewport$:ge,header$:We,main$:br})),...ee("palette").map(e=>Oi(e)),...ee("search").map(e=>Hi(e,{keyboard$:un})),...ee("source").map(e=>Di(e))),Qs=I(()=>P(...ee("announce").map(e=>Qo(e)),...ee("content").map(e=>gi(e,{viewport$:ge,target$:rt,print$:Ji})),...ee("content").map(e=>Z("search.highlight")?Fi(e,{location$:vr}):k),...ee("header-title").map(e=>Ei(e,{viewport$:ge,header$:We})),...ee("sidebar").map(e=>e.getAttribute("data-md-type")==="navigation"?Gr(Gi,()=>fn(e,{viewport$:ge,header$:We,main$:br})):Gr(gr,()=>fn(e,{viewport$:ge,header$:We,main$:br}))),...ee("tabs").map(e=>Wi(e,{viewport$:ge,header$:We})),...ee("toc").map(e=>ln(e,{viewport$:ge,header$:We,target$:rt,localToc:!0})),...ee("sidebar").filter(e=>e.getAttribute("data-md-type")==="navigation").map(e=>ln(e,{viewport$:ge,header$:We,target$:rt,localToc:!1})),...ee("top").map(e=>zi(e,{viewport$:ge,header$:We,main$:br,target$:rt})))),Qi=nt.pipe(_(()=>Qs),tt(Js),X(1));Qi.subscribe();window.document$=nt;window.location$=vr;window.target$=rt;window.keyboard$=un;window.viewport$=ge;window.tablet$=gr;window.screen$=Gi;window.print$=Ji;window.alert$=pn;window.component$=Qi;})();
diff --git a/_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css b/_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css
new file mode 100644
index 0000000000..1d0a46e53c
--- /dev/null
+++ b/_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css
@@ -0,0 +1,4 @@
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diff --git a/api.html b/api.html
index 0a22078900..bdf4431805 100644
--- a/api.html
+++ b/api.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>API Reference - arch 6.1.0</title>
+        <title>API Reference - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL(".",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -601,11 +601,11 @@
                   
 
 
-<h1 id="api-reference">API Reference<a class="headerlink" href="#api-reference" title="Permalink to this heading">¶</a></h1>
+<h1 id="api-reference">API Reference<a class="headerlink" href="#api-reference" title="Link to this heading">¶</a></h1>
 <p>This page lists contains a list of the essential end-user API functions
 and classes.</p>
-<h2 id="volatility-modeling">Volatility Modeling<a class="headerlink" href="#volatility-modeling" title="Permalink to this heading">¶</a></h2>
-<h3 id="high-level">High-level<a class="headerlink" href="#high-level" title="Permalink to this heading">¶</a></h3>
+<h2 id="volatility-modeling">Volatility Modeling<a class="headerlink" href="#volatility-modeling" title="Link to this heading">¶</a></h2>
+<h3 id="high-level">High-level<a class="headerlink" href="#high-level" title="Link to this heading">¶</a></h3>
 <table class="autosummary longtable docutils data align-default">
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="univariate/introduction.html#arch.univariate.arch_model" title="arch.univariate.arch_model (Python function) — Initialization of common ARCH model specifications"><code class="xref py py-obj docutils literal notranslate"><span class="pre">arch_model</span></code></a>(y[, x, mean, lags, vol, p, o, q, ...])</p></td>
@@ -613,7 +613,7 @@ <h3 id="high-level">High-level<a class="headerlink" href="#high-level" title="Pe
 </tr>
 </tbody>
 </table>
-<h3 id="mean-specification">Mean Specification<a class="headerlink" href="#mean-specification" title="Permalink to this heading">¶</a></h3>
+<h3 id="mean-specification">Mean Specification<a class="headerlink" href="#mean-specification" title="Link to this heading">¶</a></h3>
 <table class="autosummary longtable docutils data align-default">
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="univariate/generated/arch.univariate.ConstantMean.html#arch.univariate.ConstantMean" title="arch.univariate.ConstantMean (Python class) — Constant mean model estimation and simulation."><code class="xref py py-obj docutils literal notranslate"><span class="pre">ConstantMean</span></code></a>([y, hold_back, volatility, ...])</p></td>
@@ -633,7 +633,7 @@ <h3 id="mean-specification">Mean Specification<a class="headerlink" href="#mean-
 </tr>
 </tbody>
 </table>
-<h3 id="volatility-process-specification">Volatility Process Specification<a class="headerlink" href="#volatility-process-specification" title="Permalink to this heading">¶</a></h3>
+<h3 id="volatility-process-specification">Volatility Process Specification<a class="headerlink" href="#volatility-process-specification" title="Link to this heading">¶</a></h3>
 <table class="autosummary longtable docutils data align-default">
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="univariate/generated/arch.univariate.GARCH.html#arch.univariate.GARCH" title="arch.univariate.GARCH (Python class) — GARCH and related model estimation"><code class="xref py py-obj docutils literal notranslate"><span class="pre">GARCH</span></code></a>([p, o, q, power])</p></td>
@@ -665,7 +665,7 @@ <h3 id="volatility-process-specification">Volatility Process Specification<a cla
 </tr>
 </tbody>
 </table>
-<h3 id="shock-distributions">Shock Distributions<a class="headerlink" href="#shock-distributions" title="Permalink to this heading">¶</a></h3>
+<h3 id="shock-distributions">Shock Distributions<a class="headerlink" href="#shock-distributions" title="Link to this heading">¶</a></h3>
 <table class="autosummary longtable docutils data align-default">
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="univariate/generated/arch.univariate.Normal.html#arch.univariate.Normal" title="arch.univariate.Normal (Python class) — Standard normal distribution for use with ARCH models"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Normal</span></code></a>([random_state, seed])</p></td>
@@ -682,7 +682,7 @@ <h3 id="shock-distributions">Shock Distributions<a class="headerlink" href="#sho
 </tr>
 </tbody>
 </table>
-<h2 id="unit-root-testing">Unit Root Testing<a class="headerlink" href="#unit-root-testing" title="Permalink to this heading">¶</a></h2>
+<h2 id="unit-root-testing">Unit Root Testing<a class="headerlink" href="#unit-root-testing" title="Link to this heading">¶</a></h2>
 <table class="autosummary longtable docutils data align-default">
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="unitroot/generated/arch.unitroot.ADF.html#arch.unitroot.ADF" title="arch.unitroot.ADF (Python class) — Augmented Dickey-Fuller unit root test"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ADF</span></code></a>(y[, lags, trend, max_lags, method, ...])</p></td>
@@ -705,7 +705,7 @@ <h2 id="unit-root-testing">Unit Root Testing<a class="headerlink" href="#unit-ro
 </tr>
 </tbody>
 </table>
-<h2 id="cointegration-testing">Cointegration Testing<a class="headerlink" href="#cointegration-testing" title="Permalink to this heading">¶</a></h2>
+<h2 id="cointegration-testing">Cointegration Testing<a class="headerlink" href="#cointegration-testing" title="Link to this heading">¶</a></h2>
 <table class="autosummary longtable docutils data align-default">
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="unitroot/generated/arch.unitroot.cointegration.engle_granger.html#arch.unitroot.cointegration.engle_granger" title="arch.unitroot.cointegration.engle_granger (Python function) — Test for cointegration within a set of time series."><code class="xref py py-obj docutils literal notranslate"><span class="pre">engle_granger</span></code></a>(y, x[, trend, lags, max_lags, ...])</p></td>
@@ -716,7 +716,7 @@ <h2 id="cointegration-testing">Cointegration Testing<a class="headerlink" href="
 </tr>
 </tbody>
 </table>
-<h2 id="cointegrating-relationship-estimation">Cointegrating Relationship Estimation<a class="headerlink" href="#cointegrating-relationship-estimation" title="Permalink to this heading">¶</a></h2>
+<h2 id="cointegrating-relationship-estimation">Cointegrating Relationship Estimation<a class="headerlink" href="#cointegrating-relationship-estimation" title="Link to this heading">¶</a></h2>
 <table class="autosummary longtable docutils data align-default">
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="unitroot/generated/arch.unitroot.cointegration.CanonicalCointegratingReg.html#arch.unitroot.cointegration.CanonicalCointegratingReg" title="arch.unitroot.cointegration.CanonicalCointegratingReg (Python class) — Canonical Cointegrating Regression cointegrating vector estimation."><code class="xref py py-obj docutils literal notranslate"><span class="pre">CanonicalCointegratingReg</span></code></a>(y, x[, trend, x_trend])</p></td>
@@ -730,7 +730,7 @@ <h2 id="cointegrating-relationship-estimation">Cointegrating Relationship Estima
 </tr>
 </tbody>
 </table>
-<h2 id="bootstraps">Bootstraps<a class="headerlink" href="#bootstraps" title="Permalink to this heading">¶</a></h2>
+<h2 id="bootstraps">Bootstraps<a class="headerlink" href="#bootstraps" title="Link to this heading">¶</a></h2>
 <table class="autosummary longtable docutils data align-default">
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="bootstrap/generated/arch.bootstrap.IIDBootstrap.html#arch.bootstrap.IIDBootstrap" title="arch.bootstrap.IIDBootstrap (Python class) — Bootstrap using uniform resampling"><code class="xref py py-obj docutils literal notranslate"><span class="pre">IIDBootstrap</span></code></a>(*args[, random_state, seed])</p></td>
@@ -750,7 +750,7 @@ <h2 id="bootstraps">Bootstraps<a class="headerlink" href="#bootstraps" title="Pe
 </tr>
 </tbody>
 </table>
-<h3 id="block-length-selection">Block-length Selection<a class="headerlink" href="#block-length-selection" title="Permalink to this heading">¶</a></h3>
+<h3 id="block-length-selection">Block-length Selection<a class="headerlink" href="#block-length-selection" title="Link to this heading">¶</a></h3>
 <table class="autosummary longtable docutils data align-default">
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="bootstrap/generated/arch.bootstrap.optimal_block_length.html#arch.bootstrap.optimal_block_length" title="arch.bootstrap.optimal_block_length (Python function) — Estimate optimal window length for time-series bootstraps"><code class="xref py py-obj docutils literal notranslate"><span class="pre">optimal_block_length</span></code></a>(x)</p></td>
@@ -758,7 +758,7 @@ <h3 id="block-length-selection">Block-length Selection<a class="headerlink" href
 </tr>
 </tbody>
 </table>
-<h2 id="testing-with-multiple-comparison">Testing with Multiple-Comparison<a class="headerlink" href="#testing-with-multiple-comparison" title="Permalink to this heading">¶</a></h2>
+<h2 id="testing-with-multiple-comparison">Testing with Multiple-Comparison<a class="headerlink" href="#testing-with-multiple-comparison" title="Link to this heading">¶</a></h2>
 <table class="autosummary longtable docutils data align-default">
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="multiple-comparison/generated/arch.bootstrap.SPA.html#arch.bootstrap.SPA" title="arch.bootstrap.SPA (Python class) — Test of Superior Predictive Ability (SPA) of White and Hansen."><code class="xref py py-obj docutils literal notranslate"><span class="pre">SPA</span></code></a>(benchmark, models[, block_size, reps, ...])</p></td>
@@ -772,7 +772,7 @@ <h2 id="testing-with-multiple-comparison">Testing with Multiple-Comparison<a cla
 </tr>
 </tbody>
 </table>
-<h2 id="long-run-covariance-hac-estimation">Long-run Covariance (HAC) Estimation<a class="headerlink" href="#long-run-covariance-hac-estimation" title="Permalink to this heading">¶</a></h2>
+<h2 id="long-run-covariance-hac-estimation">Long-run Covariance (HAC) Estimation<a class="headerlink" href="#long-run-covariance-hac-estimation" title="Link to this heading">¶</a></h2>
 <table class="autosummary longtable docutils data align-default">
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="covariance/generated/arch.covariance.kernel.Bartlett.html#arch.covariance.kernel.Bartlett" title="arch.covariance.kernel.Bartlett (Python class) — Bartlett's (Newey-West) kernel covariance estimation."><code class="xref py py-obj docutils literal notranslate"><span class="pre">Bartlett</span></code></a>(x[, bandwidth, df_adjust, center, ...])</p></td>
@@ -871,7 +871,7 @@ <h2 id="long-run-covariance-hac-estimation">Long-run Covariance (HAC) Estimation
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -918,7 +918,7 @@ <h2 id="long-run-covariance-hac-estimation">Long-run Covariance (HAC) Estimation
     <script id="__config" type="application/json">{"base": ".", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/background.html b/bootstrap/background.html
index d8572171fd..e0b626b61a 100644
--- a/bootstrap/background.html
+++ b/bootstrap/background.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>References - arch 6.1.0</title>
+        <title>References - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -546,7 +546,7 @@
                   
 
 
-<h1 id="references">References<a class="headerlink" href="#references" title="Permalink to this heading">¶</a></h1>
+<h1 id="references">References<a class="headerlink" href="#references" title="Link to this heading">¶</a></h1>
 <p>The bootstrap is a large area with a number of high-quality books. Leading
 references include</p>
 <p class="rubric">References</p>
@@ -663,7 +663,7 @@ <h1 id="references">References<a class="headerlink" href="#references" title="Pe
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -710,7 +710,7 @@ <h1 id="references">References<a class="headerlink" href="#references" title="Pe
     <script id="__config" type="application/json">{"base": "..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/bootstrap.html b/bootstrap/bootstrap.html
index 6eb45bc0c3..646d1b0135 100644
--- a/bootstrap/bootstrap.html
+++ b/bootstrap/bootstrap.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Bootstrapping - arch 6.1.0</title>
+        <title>Bootstrapping - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -646,7 +646,7 @@
                   
 
 
-<h1 id="module-arch.bootstrap"><span id="bootstrapping"></span>Bootstrapping<a class="headerlink" href="#module-arch.bootstrap" title="Permalink to this heading">¶</a></h1>
+<h1 id="module-arch.bootstrap"><span id="bootstrapping"></span>Bootstrapping<a class="headerlink" href="#module-arch.bootstrap" title="Link to this heading">¶</a></h1>
 <p>The bootstrap module provides both high- and low-level interfaces for
 bootstrapping data contained in NumPy arrays or pandas Series or DataFrames.</p>
 <p>All bootstraps have the same interfaces and only differ in their name, setup
@@ -732,7 +732,7 @@ <h1 id="module-arch.bootstrap"><span id="bootstrapping"></span>Bootstrapping<a c
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -779,7 +779,7 @@ <h1 id="module-arch.bootstrap"><span id="bootstrapping"></span>Bootstrapping<a c
     <script id="__config" type="application/json">{"base": "..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/bootstrap_examples.html b/bootstrap/bootstrap_examples.html
index b6550420a4..0d25b4355c 100644
--- a/bootstrap/bootstrap_examples.html
+++ b/bootstrap/bootstrap_examples.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Bootstrap Examples - arch 6.1.0</title>
+        <title>Bootstrap Examples - arch 6.2.0</title>
       
     
 
@@ -47,9 +47,9 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css" />
-        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css?v=2aa19091" />
+        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -93,7 +93,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -103,7 +103,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -190,10 +190,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -735,7 +735,7 @@
                   
 
 
-<h1 id="Bootstrap-Examples">Bootstrap Examples<a class="headerlink" href="#Bootstrap-Examples" title="Permalink to this heading">¶</a></h1>
+<h1 id="Bootstrap-Examples">Bootstrap Examples<a class="headerlink" href="#Bootstrap-Examples" title="Link to this heading">¶</a></h1>
 <p><em>This setup code is required to run in an IPython notebook</em></p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[1]:
@@ -753,7 +753,7 @@ <h1 id="Bootstrap-Examples">Bootstrap Examples<a class="headerlink" href="#Boots
 </code></pre></div>
 </div>
 </div>
-<h2 id="Sharpe-Ratio">Sharpe Ratio<a class="headerlink" href="#Sharpe-Ratio" title="Permalink to this heading">¶</a></h2>
+<h2 id="Sharpe-Ratio">Sharpe Ratio<a class="headerlink" href="#Sharpe-Ratio" title="Link to this heading">¶</a></h2>
 <p>The Sharpe Ratio is an important measure of return per unit of risk. The example shows how to estimate the variance of the Sharpe Ratio and how to construct confidence intervals for the Sharpe Ratio using a long series of U.S. equity data.</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[2]:
@@ -828,9 +828,9 @@ <h2 id="Sharpe-Ratio">Sharpe Ratio<a class="headerlink" href="#Sharpe-Ratio" tit
 dtype: float64
 </pre></div></div>
 </div>
-<h2 id="Reproducibility">Reproducibility<a class="headerlink" href="#Reproducibility" title="Permalink to this heading">¶</a></h2>
+<h2 id="Reproducibility">Reproducibility<a class="headerlink" href="#Reproducibility" title="Link to this heading">¶</a></h2>
 <p>All bootstraps accept the keyword argument <code class="docutils literal notranslate"><span class="pre">seed</span></code> which can contain a NumPy <code class="docutils literal notranslate"><span class="pre">Generator</span></code> or <code class="docutils literal notranslate"><span class="pre">RandomState</span></code> or an <code class="docutils literal notranslate"><span class="pre">int</span></code>. When using an <code class="docutils literal notranslate"><span class="pre">int</span></code>, the argument is passed <code class="docutils literal notranslate"><span class="pre">np.random.default_rng</span></code> to create the core generator. This allows the same pseudo random values to be used across multiple runs.</p>
-<h3 id="Warning"><em>Warning</em><a class="headerlink" href="#Warning" title="Permalink to this heading">¶</a></h3>
+<h3 id="Warning"><em>Warning</em><a class="headerlink" href="#Warning" title="Link to this heading">¶</a></h3>
 <p><em>The bootstrap chosen must be appropriate for the data. Squared returns are serially correlated, and so a time-series bootstrap is required.</em></p>
 <p>Bootstraps are initialized with any bootstrap specific parameters and the data to be used in the bootstrap. Here the <code class="docutils literal notranslate"><span class="pre">12</span></code> is the average window length in the Stationary Bootstrap, and the next input is the data to be bootstrapped.</p>
 <div class="nbinput docutils container">
@@ -929,7 +929,7 @@ <h3 id="Warning"><em>Warning</em><a class="headerlink" href="#Warning" title="Pe
 Upper  11.471040  22.129620  0.691471
 </pre></div></div>
 </div>
-<h3 id="Optimal-Block-Length-Estimation">Optimal Block Length Estimation<a class="headerlink" href="#Optimal-Block-Length-Estimation" title="Permalink to this heading">¶</a></h3>
+<h3 id="Optimal-Block-Length-Estimation">Optimal Block Length Estimation<a class="headerlink" href="#Optimal-Block-Length-Estimation" title="Link to this heading">¶</a></h3>
 <p>The function <code class="docutils literal notranslate"><span class="pre">optimal_block_length</span></code> can be used to estimate the optimal block lengths for the Stationary and Circular bootstraps. Here we use the squared market return since the Sharpe ratio depends on the mean and the variance, and the autocorrelation in the squares is stronger than in the returns.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[10]:
@@ -973,7 +973,7 @@ <h3 id="Optimal-Block-Length-Estimation">Optimal Block Length Estimation<a class
 <img alt="../_images/bootstrap_bootstrap_examples_23_0.png" src="../_images/bootstrap_bootstrap_examples_23_0.png" />
 </div>
 </div>
-<h2 id="Probit-(statsmodels)">Probit (statsmodels)<a class="headerlink" href="#Probit-(statsmodels)" title="Permalink to this heading">¶</a></h2>
+<h2 id="Probit-(statsmodels)">Probit (statsmodels)<a class="headerlink" href="#Probit-(statsmodels)" title="Link to this heading">¶</a></h2>
 <p>The second example makes use of a Probit model from statsmodels. The demo data is university admissions data which contains a binary variable for being admitted, GRE score, GPA score and quartile rank. This data is downloaded from the internet and imported using pandas.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[12]:
@@ -1003,7 +1003,7 @@ <h2 id="Probit-(statsmodels)">Probit (statsmodels)<a class="headerlink" href="#P
 max      1.000000  800.000000    4.000000    4.00000
 </pre></div></div>
 </div>
-<h3 id="Fitting-the-model-directly">Fitting the model directly<a class="headerlink" href="#Fitting-the-model-directly" title="Permalink to this heading">¶</a></h3>
+<h3 id="Fitting-the-model-directly">Fitting the model directly<a class="headerlink" href="#Fitting-the-model-directly" title="Link to this heading">¶</a></h3>
 <p>The first steps are to build the regressor and the dependent variable arrays. Then, using these arrays, the model can be estimated by calling <code class="docutils literal notranslate"><span class="pre">fit</span></code></p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[13]:
@@ -1036,7 +1036,7 @@ <h3 id="Fitting-the-model-directly">Fitting the model directly<a class="headerli
 dtype: float64
 </pre></div></div>
 </div>
-<h3 id="The-wrapper-function">The wrapper function<a class="headerlink" href="#The-wrapper-function" title="Permalink to this heading">¶</a></h3>
+<h3 id="The-wrapper-function">The wrapper function<a class="headerlink" href="#The-wrapper-function" title="Link to this heading">¶</a></h3>
 <p>Most models in statsmodels are implemented as classes, require an explicit call to <code class="docutils literal notranslate"><span class="pre">fit</span></code> and return a class containing parameter estimates and other quantities. These classes cannot be directly used with the bootstrap methods. However, a simple wrapper can be written that takes the data as the only inputs and returns parameters estimated using a Statsmodel model.</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[14]:
@@ -1140,7 +1140,7 @@ <h3 id="The-wrapper-function">The wrapper function<a class="headerlink" href="#T
 Upper -1.622607  0.002906  0.871725
 </pre></div></div>
 </div>
-<h3 id="Speeding-things-up">Speeding things up<a class="headerlink" href="#Speeding-things-up" title="Permalink to this heading">¶</a></h3>
+<h3 id="Speeding-things-up">Speeding things up<a class="headerlink" href="#Speeding-things-up" title="Link to this heading">¶</a></h3>
 <p>Starting values can be provided to <code class="docutils literal notranslate"><span class="pre">fit</span></code> which can save time finding starting values. Since the bootstrap parameter estimates should be close to the original sample estimates, the full sample estimated parameters are reasonable starting values. These can be passed using the <code class="docutils literal notranslate"><span class="pre">extra_kwargs</span></code> dictionary to a modified wrapper that will accept a keyword argument containing starting values.</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[19]:
@@ -1175,7 +1175,7 @@ <h3 id="Speeding-things-up">Speeding things up<a class="headerlink" href="#Speed
 gpa   -0.102525 -5.815162e-05  0.039859
 </pre></div></div>
 </div>
-<h2 id="Bootstrapping-Uneven-Length-Samples">Bootstrapping Uneven Length Samples<a class="headerlink" href="#Bootstrapping-Uneven-Length-Samples" title="Permalink to this heading">¶</a></h2>
+<h2 id="Bootstrapping-Uneven-Length-Samples">Bootstrapping Uneven Length Samples<a class="headerlink" href="#Bootstrapping-Uneven-Length-Samples" title="Link to this heading">¶</a></h2>
 <p>Independent samples of uneven length are common in experiment settings, e.g., A/B testing of a website. The <code class="docutils literal notranslate"><span class="pre">IIDBootstrap</span></code> allows for arbitrary dependence within an observation index and so cannot be naturally applied to these data sets. The <code class="docutils literal notranslate"><span class="pre">IndependentSamplesBootstrap</span></code> allows datasets with variables of different lengths to be sampled by exploiting the independence of the values to separately bootstrap each component. Below is an example showing how a confidence interval can be constructed
 for the difference in means of two groups.</p>
 <div class="nbinput docutils container">
@@ -1274,7 +1274,7 @@ <h2 id="Bootstrapping-Uneven-Length-Samples">Bootstrapping Uneven Length Samples
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1321,10 +1321,10 @@ <h2 id="Bootstrapping-Uneven-Length-Samples">Bootstrapping Uneven Length Samples
     <script id="__config" type="application/json">{"base": "..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+        <script id="MathJax-script" src="../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
     
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 </html>
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diff --git a/bootstrap/confidence-intervals.html b/bootstrap/confidence-intervals.html
index 21b613e60e..712663ae7c 100644
--- a/bootstrap/confidence-intervals.html
+++ b/bootstrap/confidence-intervals.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Confidence Intervals - arch 6.1.0</title>
+        <title>Confidence Intervals - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -776,7 +776,7 @@
                   
 
 
-<h1 id="confidence-intervals">Confidence Intervals<a class="headerlink" href="#confidence-intervals" title="Permalink to this heading">¶</a></h1>
+<h1 id="confidence-intervals">Confidence Intervals<a class="headerlink" href="#confidence-intervals" title="Link to this heading">¶</a></h1>
 <p>The confidence interval function allows three types of confidence intervals to
 be constructed:</p>
 <ul class="simple">
@@ -815,7 +815,7 @@ <h1 id="confidence-intervals">Confidence Intervals<a class="headerlink" href="#c
 </li>
 </ul>
 </nav>
-<h2 id="setup"><a class="toc-backref" href="#id1" role="doc-backlink">Setup</a><a class="headerlink" href="#setup" title="Permalink to this heading">¶</a></h2>
+<h2 id="setup"><a class="toc-backref" href="#id1" role="doc-backlink">Setup</a><a class="headerlink" href="#setup" title="Link to this heading">¶</a></h2>
 <p>All examples will construct confidence intervals for the Sharpe ratio of the
 S&amp;P 500, which is the ratio of the annualized mean to the annualized standard
 deviation.  The parameters will be the annualized mean, the annualized standard
@@ -846,14 +846,14 @@ <h2 id="setup"><a class="toc-backref" href="#id1" role="doc-backlink">Setup</a><
 <p class="admonition-title">Note</p>
 <p>Functions must return 1-d NumPy arrays or Pandas Series.</p>
 </div>
-<h2 id="confidence-interval-types"><a class="toc-backref" href="#id2" role="doc-backlink">Confidence Interval Types</a><a class="headerlink" href="#confidence-interval-types" title="Permalink to this heading">¶</a></h2>
+<h2 id="confidence-interval-types"><a class="toc-backref" href="#id2" role="doc-backlink">Confidence Interval Types</a><a class="headerlink" href="#confidence-interval-types" title="Link to this heading">¶</a></h2>
 <p>Three types of confidence intervals can be computed.  The simplest are
 non-parametric; these only make use of parameter estimates from both the
 original data as well as the resampled data.  Semi-parametric mix the original
 data with a limited form of resampling, usually for residuals.  Finally,
 parametric bootstrap confidence intervals make use of a parametric distribution
 to construct “as-if” exact confidence intervals.</p>
-<h3 id="nonparametric-confidence-intervals"><a class="toc-backref" href="#id3" role="doc-backlink">Nonparametric Confidence Intervals</a><a class="headerlink" href="#nonparametric-confidence-intervals" title="Permalink to this heading">¶</a></h3>
+<h3 id="nonparametric-confidence-intervals"><a class="toc-backref" href="#id3" role="doc-backlink">Nonparametric Confidence Intervals</a><a class="headerlink" href="#nonparametric-confidence-intervals" title="Link to this heading">¶</a></h3>
 <p>Non-parametric sampling is the simplest method to construct confidence
 intervals.</p>
 <p>This example makes use of the percentile bootstrap which is conceptually the
@@ -872,11 +872,11 @@ <h3 id="nonparametric-confidence-intervals"><a class="toc-backref" href="#id3" r
 time-series bootstrap with an appropriately chosen block size should be
 used.</p>
 </div>
-<h3 id="semi-parametric-confidence-intervals"><a class="toc-backref" href="#id4" role="doc-backlink">Semi-parametric Confidence Intervals</a><a class="headerlink" href="#semi-parametric-confidence-intervals" title="Permalink to this heading">¶</a></h3>
+<h3 id="semi-parametric-confidence-intervals"><a class="toc-backref" href="#id4" role="doc-backlink">Semi-parametric Confidence Intervals</a><a class="headerlink" href="#semi-parametric-confidence-intervals" title="Link to this heading">¶</a></h3>
 <p>See <a class="reference internal" href="semiparametric-parametric-bootstrap.html"><span class="doc">Semiparametric Bootstraps</span></a></p>
-<h3 id="parametric-confidence-intervals"><a class="toc-backref" href="#id5" role="doc-backlink">Parametric Confidence Intervals</a><a class="headerlink" href="#parametric-confidence-intervals" title="Permalink to this heading">¶</a></h3>
+<h3 id="parametric-confidence-intervals"><a class="toc-backref" href="#id5" role="doc-backlink">Parametric Confidence Intervals</a><a class="headerlink" href="#parametric-confidence-intervals" title="Link to this heading">¶</a></h3>
 <p>See <a class="reference internal" href="semiparametric-parametric-bootstrap.html"><span class="doc">Parametric Bootstraps</span></a></p>
-<h2 id="confidence-interval-methods"><a class="toc-backref" href="#id6" role="doc-backlink">Confidence Interval Methods</a><a class="headerlink" href="#confidence-interval-methods" title="Permalink to this heading">¶</a></h2>
+<h2 id="confidence-interval-methods"><a class="toc-backref" href="#id6" role="doc-backlink">Confidence Interval Methods</a><a class="headerlink" href="#confidence-interval-methods" title="Link to this heading">¶</a></h2>
 <div class="note admonition">
 <p class="admonition-title">Note</p>
 <p><code class="docutils literal notranslate"><span class="pre">conf_int</span></code> can construct two-sided, upper or lower (one-sided) confidence
@@ -884,7 +884,7 @@ <h2 id="confidence-interval-methods"><a class="toc-backref" href="#id6" role="do
 default).  This can be modified using the keyword inputs <code class="docutils literal notranslate"><span class="pre">type</span></code>
 (<code class="docutils literal notranslate"><span class="pre">'upper'</span></code>, <code class="docutils literal notranslate"><span class="pre">'lower'</span></code> or <code class="docutils literal notranslate"><span class="pre">'two-sided'</span></code>) and <code class="docutils literal notranslate"><span class="pre">size</span></code>.</p>
 </div>
-<h3 id="basic-basic"><a class="toc-backref" href="#id7" role="doc-backlink">Basic (<code class="docutils literal notranslate"><span class="pre">basic</span></code>)</a><a class="headerlink" href="#basic-basic" title="Permalink to this heading">¶</a></h3>
+<h3 id="basic-basic"><a class="toc-backref" href="#id7" role="doc-backlink">Basic (<code class="docutils literal notranslate"><span class="pre">basic</span></code>)</a><a class="headerlink" href="#basic-basic" title="Link to this heading">¶</a></h3>
 <p>Basic confidence intervals construct many bootstrap replications
 <span class="math notranslate nohighlight">\(\hat{\theta}_b^\star\)</span> and then constructs the confidence interval as</p>
 <div class="math notranslate nohighlight">
@@ -900,7 +900,7 @@ <h3 id="basic-basic"><a class="toc-backref" href="#id7" role="doc-backlink">Basi
 <span class="n">ci</span> <span class="o">=</span> <span class="n">bs</span><span class="o">.</span><span class="n">conf_int</span><span class="p">(</span><span class="n">sharpe_ratio</span><span class="p">,</span> <span class="mi">1000</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;basic&#39;</span><span class="p">)</span>
 </code></pre></div>
 </div>
-<h3 id="percentile-percentile"><a class="toc-backref" href="#id8" role="doc-backlink">Percentile (<code class="docutils literal notranslate"><span class="pre">percentile</span></code>)</a><a class="headerlink" href="#percentile-percentile" title="Permalink to this heading">¶</a></h3>
+<h3 id="percentile-percentile"><a class="toc-backref" href="#id8" role="doc-backlink">Percentile (<code class="docutils literal notranslate"><span class="pre">percentile</span></code>)</a><a class="headerlink" href="#percentile-percentile" title="Link to this heading">¶</a></h3>
 <p>The percentile method directly constructs confidence intervals from the empirical
 CDF of the bootstrap parameter estimates, <span class="math notranslate nohighlight">\(\hat{\theta}_b^\star\)</span>.
 The confidence interval is then defined.</p>
@@ -915,7 +915,7 @@ <h3 id="percentile-percentile"><a class="toc-backref" href="#id8" role="doc-back
 <span class="n">ci</span> <span class="o">=</span> <span class="n">bs</span><span class="o">.</span><span class="n">conf_int</span><span class="p">(</span><span class="n">sharpe_ratio</span><span class="p">,</span> <span class="mi">1000</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;percentile&#39;</span><span class="p">)</span>
 </code></pre></div>
 </div>
-<h3 id="asymptotic-normal-approximation-norm-cov-or-var"><a class="toc-backref" href="#id9" role="doc-backlink">Asymptotic Normal Approximation (<code class="docutils literal notranslate"><span class="pre">norm</span></code>, <code class="docutils literal notranslate"><span class="pre">cov</span></code> or <code class="docutils literal notranslate"><span class="pre">var</span></code>)</a><a class="headerlink" href="#asymptotic-normal-approximation-norm-cov-or-var" title="Permalink to this heading">¶</a></h3>
+<h3 id="asymptotic-normal-approximation-norm-cov-or-var"><a class="toc-backref" href="#id9" role="doc-backlink">Asymptotic Normal Approximation (<code class="docutils literal notranslate"><span class="pre">norm</span></code>, <code class="docutils literal notranslate"><span class="pre">cov</span></code> or <code class="docutils literal notranslate"><span class="pre">var</span></code>)</a><a class="headerlink" href="#asymptotic-normal-approximation-norm-cov-or-var" title="Link to this heading">¶</a></h3>
 <p>The asymptotic normal approximation method estimates the covariance of
 the parameters and then combines this with the usual quantiles from a normal
 distribution.  The confidence interval is then</p>
@@ -930,7 +930,7 @@ <h3 id="asymptotic-normal-approximation-norm-cov-or-var"><a class="toc-backref"
 <span class="n">ci</span> <span class="o">=</span> <span class="n">bs</span><span class="o">.</span><span class="n">conf_int</span><span class="p">(</span><span class="n">sharpe_ratio</span><span class="p">,</span> <span class="mi">1000</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;norm&#39;</span><span class="p">)</span>
 </code></pre></div>
 </div>
-<h3 id="studentized-studentized"><a class="toc-backref" href="#id10" role="doc-backlink">Studentized (<code class="docutils literal notranslate"><span class="pre">studentized</span></code>)</a><a class="headerlink" href="#studentized-studentized" title="Permalink to this heading">¶</a></h3>
+<h3 id="studentized-studentized"><a class="toc-backref" href="#id10" role="doc-backlink">Studentized (<code class="docutils literal notranslate"><span class="pre">studentized</span></code>)</a><a class="headerlink" href="#studentized-studentized" title="Link to this heading">¶</a></h3>
 <p>The studentized bootstrap may be more accurate than some of the other methods.
 The studentized bootstrap makes use of either a standard error function, when
 parameter standard errors can be analytically computed, or a nested bootstrap,
@@ -991,7 +991,7 @@ <h3 id="studentized-studentized"><a class="toc-backref" href="#id10" role="doc-b
 <code class="docutils literal notranslate"><span class="pre">std_err_func(params,</span> <span class="pre">*args,</span> <span class="pre">**kwargs)</span></code> where <code class="docutils literal notranslate"><span class="pre">params</span></code> is an array
 of estimated parameters constructed using <code class="docutils literal notranslate"><span class="pre">*args</span></code> and <code class="docutils literal notranslate"><span class="pre">**kwargs</span></code>.</p>
 </div>
-<h3 id="bias-corrected-bc-bias-corrected-or-debiased"><a class="toc-backref" href="#id11" role="doc-backlink">Bias-corrected (<code class="docutils literal notranslate"><span class="pre">bc</span></code>, <code class="docutils literal notranslate"><span class="pre">bias-corrected</span></code> or <code class="docutils literal notranslate"><span class="pre">debiased</span></code>)</a><a class="headerlink" href="#bias-corrected-bc-bias-corrected-or-debiased" title="Permalink to this heading">¶</a></h3>
+<h3 id="bias-corrected-bc-bias-corrected-or-debiased"><a class="toc-backref" href="#id11" role="doc-backlink">Bias-corrected (<code class="docutils literal notranslate"><span class="pre">bc</span></code>, <code class="docutils literal notranslate"><span class="pre">bias-corrected</span></code> or <code class="docutils literal notranslate"><span class="pre">debiased</span></code>)</a><a class="headerlink" href="#bias-corrected-bc-bias-corrected-or-debiased" title="Link to this heading">¶</a></h3>
 <p>The bias corrected bootstrap makes use of a bootstrap estimate of the bias to
 improve confidence intervals.</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><code><span class="kn">from</span> <span class="nn">arch.bootstrap</span> <span class="kn">import</span> <span class="n">IIDBootstrap</span>
@@ -1001,7 +1001,7 @@ <h3 id="bias-corrected-bc-bias-corrected-or-debiased"><a class="toc-backref" hre
 </div>
 <p>The bias-corrected confidence interval is identical to the bias-corrected and
 accelerated where <span class="math notranslate nohighlight">\(a=0\)</span>.</p>
-<h3 id="bias-corrected-and-accelerated-bca"><a class="toc-backref" href="#id12" role="doc-backlink">Bias-corrected and accelerated (<code class="docutils literal notranslate"><span class="pre">bca</span></code>)</a><a class="headerlink" href="#bias-corrected-and-accelerated-bca" title="Permalink to this heading">¶</a></h3>
+<h3 id="bias-corrected-and-accelerated-bca"><a class="toc-backref" href="#id12" role="doc-backlink">Bias-corrected and accelerated (<code class="docutils literal notranslate"><span class="pre">bca</span></code>)</a><a class="headerlink" href="#bias-corrected-and-accelerated-bca" title="Link to this heading">¶</a></h3>
 <p>Bias-corrected and accelerated confidence intervals make use of both
 a bootstrap bias estimate and a jackknife acceleration term.  BCa intervals may
 offer higher-order accuracy if some conditions are satisfied. Bias-corrected
@@ -1093,7 +1093,7 @@ <h3 id="bias-corrected-and-accelerated-bca"><a class="toc-backref" href="#id12"
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1140,10 +1140,10 @@ <h3 id="bias-corrected-and-accelerated-bca"><a class="toc-backref" href="#id12"
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+        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
 </html>
\ No newline at end of file
diff --git a/bootstrap/generated/arch.bootstrap.CircularBlockBootstrap.html b/bootstrap/generated/arch.bootstrap.CircularBlockBootstrap.html
index 283df3f9f8..4fcee42ff0 100644
--- a/bootstrap/generated/arch.bootstrap.CircularBlockBootstrap.html
+++ b/bootstrap/generated/arch.bootstrap.CircularBlockBootstrap.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap - arch 6.1.0</title>
+        <title>arch.bootstrap.CircularBlockBootstrap - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1309,64 +1309,64 @@
                   
 
 
-<h1 id="arch-bootstrap-circularblockbootstrap">arch.bootstrap.CircularBlockBootstrap<a class="headerlink" href="#arch-bootstrap-circularblockbootstrap" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-circularblockbootstrap">arch.bootstrap.CircularBlockBootstrap<a class="headerlink" href="#arch-bootstrap-circularblockbootstrap" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.CircularBlockBootstrap">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.bootstrap.</span></span><span class="sig-name descname"><span class="pre">CircularBlockBootstrap</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.__init__.block_size" title="arch.bootstrap.CircularBlockBootstrap.__init__.block_size (Python parameter) — Size of block to use"><span class="n"><span class="pre">block_size</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.__init__.args" title="arch.bootstrap.CircularBlockBootstrap.__init__.args (Python parameter) — Positional arguments to bootstrap"><span class="n"><span class="pre">args</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.__init__.random_state" title="arch.bootstrap.CircularBlockBootstrap.__init__.random_state (Python parameter) — RandomState to use to ensure reproducable results. Cannot be used with seed"><span class="n"><span class="pre">random_state</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.__init__.seed" title="arch.bootstrap.CircularBlockBootstrap.__init__.seed (Python parameter) — Seed to use to ensure reproducable results. If an int, passes the value to value to np.random.default_rng. If None, a fresh Generator is constructed with system-provided entropy."><span class="n"><span class="pre">seed</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">**</span></span><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.__init__.kwargs" title="arch.bootstrap.CircularBlockBootstrap.__init__.kwargs (Python parameter) — Keyword arguments to bootstrap"><span class="n"><span class="pre">kwargs</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/bootstrap/base.html#CircularBlockBootstrap"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.bootstrap.</span></span><span class="sig-name descname"><span class="pre">CircularBlockBootstrap</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.__init__.block_size" title="arch.bootstrap.CircularBlockBootstrap.__init__.block_size (Python parameter) — Size of block to use"><span class="n"><span class="pre">block_size</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.__init__.args" title="arch.bootstrap.CircularBlockBootstrap.__init__.args (Python parameter) — Positional arguments to bootstrap"><span class="n"><span class="pre">args</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">ArrayLike</span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.__init__.random_state" title="arch.bootstrap.CircularBlockBootstrap.__init__.random_state (Python parameter) — RandomState to use to ensure reproducable results. Cannot be used with seed"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">RandomState</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.__init__.seed" title="arch.bootstrap.CircularBlockBootstrap.__init__.seed (Python parameter) — Seed to use to ensure reproducable results. If an int, passes the value to value to np.random.default_rng. If None, a fresh Generator is constructed with system-provided entropy."><span class="n"><span class="pre">seed</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Generator</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">RandomState</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">**</span></span><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.__init__.kwargs" title="arch.bootstrap.CircularBlockBootstrap.__init__.kwargs (Python parameter) — Keyword arguments to bootstrap"><span class="n"><span class="pre">kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">ArrayLike</span></span></span></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/bootstrap/base.html#CircularBlockBootstrap"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap" title="Link to this definition">¶</a></dt>
 <dd><p>Bootstrap using blocks of the same length with end-to-start wrap around</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.CircularBlockBootstrap-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.CircularBlockBootstrap.__init__.block_size"><span class="sig-name descname">block_size</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.__init__.block_size" title="Permalink to this definition">¶</a></dt><dd><p>Size of block to use</p>
+<dt id="arch.bootstrap.CircularBlockBootstrap.__init__.block_size"><span class="n sig-name">block_size</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.__init__.block_size" title="Permalink to this definition">¶</a></dt><dd><p>Size of block to use</p>
 </dd>
-<dt id="arch.bootstrap.CircularBlockBootstrap.__init__.args"><span class="o">*</span><span class="n sig-name">args</span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.__init__.args" title="Permalink to this definition">¶</a></dt><dd><p>Positional arguments to bootstrap</p>
+<dt id="arch.bootstrap.CircularBlockBootstrap.__init__.args"><span class="o">*</span><span class="n sig-name">args</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype">ArrayLike</span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.__init__.args" title="Permalink to this definition">¶</a></dt><dd><p>Positional arguments to bootstrap</p>
 </dd>
-<dt id="arch.bootstrap.CircularBlockBootstrap.__init__.seed"><span class="sig-name descname">seed</span><span class="p"> : </span><span class="desctype"><span class="desctype">{Generator, RandomState, int}, optional</span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.__init__.seed" title="Permalink to this definition">¶</a></dt><dd><p>Seed to use to ensure reproducable results. If an int, passes the
+<dt id="arch.bootstrap.CircularBlockBootstrap.__init__.seed"><span class="n sig-name">seed</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span>Generator<span class="w"> </span><span class="p">|</span><span class="w"> </span>RandomState</span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.__init__.seed" title="Permalink to this definition">¶</a></dt><dd><p>Seed to use to ensure reproducable results. If an int, passes the
 value to value to <code class="docutils literal notranslate"><span class="pre">np.random.default_rng</span></code>. If None, a fresh
 Generator is constructed with system-provided entropy.</p>
 </dd>
-<dt id="arch.bootstrap.CircularBlockBootstrap.__init__.random_state"><span class="sig-name descname">random_state</span><span class="p"> : </span><span class="desctype"><span class="desctype">RandomState, optional</span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.__init__.random_state" title="Permalink to this definition">¶</a></dt><dd><p><p><code class="docutils literal notranslate"><span class="pre">RandomState</span></code> to use to ensure reproducable results. Cannot
+<dt id="arch.bootstrap.CircularBlockBootstrap.__init__.random_state"><span class="n sig-name">random_state</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype">RandomState<span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.__init__.random_state" title="Permalink to this definition">¶</a></dt><dd><p><p><code class="docutils literal notranslate"><span class="pre">RandomState</span></code> to use to ensure reproducable results. Cannot
 be used with <code class="docutils literal notranslate"><span class="pre">seed</span></code></p>
-<div class="deprecated">
-<p><span class="versionmodified deprecated">Deprecated since version 5.0: </span>The random_state keyword argument has been deprecated. Use seed instead.</p>
+<div class="admonition deprecated">
+<p class="admonition-title"><span class="versionmodified deprecated">Deprecated since version 5.0: </span>The random_state keyword argument has been deprecated. Use seed instead.</p>
 </div>
 </p>
 </dd>
-<dt id="arch.bootstrap.CircularBlockBootstrap.__init__.kwargs"><span class="o">**</span><span class="n sig-name">kwargs</span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.__init__.kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Keyword arguments to bootstrap</p>
+<dt id="arch.bootstrap.CircularBlockBootstrap.__init__.kwargs"><span class="o">**</span><span class="n sig-name">kwargs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype">ArrayLike</span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.__init__.kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Keyword arguments to bootstrap</p>
 </dd>
 </dl>
 </dd>
 </dl>
 <dl class="py attribute objdesc">
 <dt class="sig sig-object highlight py" id="arch.bootstrap.CircularBlockBootstrap.data">
-<span class="sig-name descname"><span class="pre">data</span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.data" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">data</span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.data" title="Link to this definition">¶</a></dt>
 <dd><p>Two-element tuple with the pos_data in the first position and kw_data
 in the second (pos_data, kw_data)</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.CircularBlockBootstrap.data-type">Type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.data-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py attribute objdesc">
 <dt class="sig sig-object highlight py" id="arch.bootstrap.CircularBlockBootstrap.pos_data">
-<span class="sig-name descname"><span class="pre">pos_data</span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.pos_data" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">pos_data</span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.pos_data" title="Link to this definition">¶</a></dt>
 <dd><p>Tuple containing the positional arguments (in the order entered)</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.CircularBlockBootstrap.pos_data-type">Type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.pos_data-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py attribute objdesc">
 <dt class="sig sig-object highlight py" id="arch.bootstrap.CircularBlockBootstrap.kw_data">
-<span class="sig-name descname"><span class="pre">kw_data</span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.kw_data" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">kw_data</span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.kw_data" title="Link to this definition">¶</a></dt>
 <dd><p>Dictionary containing the keyword arguments</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.CircularBlockBootstrap.kw_data-type">Type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.kw_data-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1426,8 +1426,8 @@ <h1 id="arch-bootstrap-circularblockbootstrap">arch.bootstrap.CircularBlockBoots
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="generated/arch.bootstrap.CircularBlockBootstrap.conf_int.html#arch.bootstrap.CircularBlockBootstrap.conf_int" title="arch.bootstrap.CircularBlockBootstrap.conf_int (Python method) — Function the computes parameter values.  See Notes for requirements"><code class="xref py py-obj docutils literal notranslate"><span class="pre">conf_int</span></code></a>(func[, reps, method, size, tail, ...])</p></td>
 <td><p><dl class="field-list simple">
-<dt class="field-odd">type func<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.11)"><code class="xref py py-data docutils literal notranslate"><span class="pre">Callable</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/constants.html#Ellipsis" title="(in Python v3.11)"><code class="xref py py-data docutils literal notranslate"><span class="pre">...</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>]</p>
+<dt class="field-odd">param func<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Function the computes parameter values.  See Notes for requirements</p>
 </dd>
 </dl>
 </p></td>
@@ -1541,7 +1541,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap">arch.bootstrap.CircularBlockBoots
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1588,7 +1588,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap">arch.bootstrap.CircularBlockBoots
     <script id="__config" type="application/json">{"base": "../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/arch.bootstrap.IIDBootstrap.html b/bootstrap/generated/arch.bootstrap.IIDBootstrap.html
index 10cec3f2b0..bda0ece3db 100644
--- a/bootstrap/generated/arch.bootstrap.IIDBootstrap.html
+++ b/bootstrap/generated/arch.bootstrap.IIDBootstrap.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap - arch 6.1.0</title>
+        <title>arch.bootstrap.IIDBootstrap - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1250,62 +1250,62 @@
                   
 
 
-<h1 id="arch-bootstrap-iidbootstrap">arch.bootstrap.IIDBootstrap<a class="headerlink" href="#arch-bootstrap-iidbootstrap" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-iidbootstrap">arch.bootstrap.IIDBootstrap<a class="headerlink" href="#arch-bootstrap-iidbootstrap" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.IIDBootstrap">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.bootstrap.</span></span><span class="sig-name descname"><span class="pre">IIDBootstrap</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.__init__.args" title="arch.bootstrap.IIDBootstrap.__init__.args (Python parameter) — Positional arguments to bootstrap"><span class="n"><span class="pre">args</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.__init__.random_state" title="arch.bootstrap.IIDBootstrap.__init__.random_state (Python parameter) — RandomState to use to ensure reproducable results. Cannot be used with seed"><span class="n"><span class="pre">random_state</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.__init__.seed" title="arch.bootstrap.IIDBootstrap.__init__.seed (Python parameter) — Seed to use to ensure reproducable results. If an int, passes the value to value to np.random.default_rng. If None, a fresh Generator is constructed with system-provided entropy."><span class="n"><span class="pre">seed</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">**</span></span><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.__init__.kwargs" title="arch.bootstrap.IIDBootstrap.__init__.kwargs (Python parameter) — Keyword arguments to bootstrap"><span class="n"><span class="pre">kwargs</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/bootstrap/base.html#IIDBootstrap"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.IIDBootstrap" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.bootstrap.</span></span><span class="sig-name descname"><span class="pre">IIDBootstrap</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.__init__.args" title="arch.bootstrap.IIDBootstrap.__init__.args (Python parameter) — Positional arguments to bootstrap"><span class="n"><span class="pre">args</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">ArrayLike</span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.__init__.random_state" title="arch.bootstrap.IIDBootstrap.__init__.random_state (Python parameter) — RandomState to use to ensure reproducable results. Cannot be used with seed"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">RandomState</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.__init__.seed" title="arch.bootstrap.IIDBootstrap.__init__.seed (Python parameter) — Seed to use to ensure reproducable results. If an int, passes the value to value to np.random.default_rng. If None, a fresh Generator is constructed with system-provided entropy."><span class="n"><span class="pre">seed</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Generator</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">RandomState</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">**</span></span><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.__init__.kwargs" title="arch.bootstrap.IIDBootstrap.__init__.kwargs (Python parameter) — Keyword arguments to bootstrap"><span class="n"><span class="pre">kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">ArrayLike</span></span></span></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/bootstrap/base.html#IIDBootstrap"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.IIDBootstrap" title="Link to this definition">¶</a></dt>
 <dd><p>Bootstrap using uniform resampling</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.IIDBootstrap-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.IIDBootstrap.__init__.args"><span class="o">*</span><span class="n sig-name">args</span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.__init__.args" title="Permalink to this definition">¶</a></dt><dd><p>Positional arguments to bootstrap</p>
+<dt id="arch.bootstrap.IIDBootstrap.__init__.args"><span class="o">*</span><span class="n sig-name">args</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype">ArrayLike</span></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.__init__.args" title="Permalink to this definition">¶</a></dt><dd><p>Positional arguments to bootstrap</p>
 </dd>
-<dt id="arch.bootstrap.IIDBootstrap.__init__.seed"><span class="sig-name descname">seed</span><span class="p"> : </span><span class="desctype"><span class="desctype">{Generator, RandomState, int}, optional</span></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.__init__.seed" title="Permalink to this definition">¶</a></dt><dd><p>Seed to use to ensure reproducable results. If an int, passes the
+<dt id="arch.bootstrap.IIDBootstrap.__init__.seed"><span class="n sig-name">seed</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span>Generator<span class="w"> </span><span class="p">|</span><span class="w"> </span>RandomState</span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.__init__.seed" title="Permalink to this definition">¶</a></dt><dd><p>Seed to use to ensure reproducable results. If an int, passes the
 value to value to <code class="docutils literal notranslate"><span class="pre">np.random.default_rng</span></code>. If None, a fresh
 Generator is constructed with system-provided entropy.</p>
 </dd>
-<dt id="arch.bootstrap.IIDBootstrap.__init__.random_state"><span class="sig-name descname">random_state</span><span class="p"> : </span><span class="desctype"><span class="desctype">RandomState, optional</span></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.__init__.random_state" title="Permalink to this definition">¶</a></dt><dd><p><p><code class="docutils literal notranslate"><span class="pre">RandomState</span></code> to use to ensure reproducable results. Cannot
+<dt id="arch.bootstrap.IIDBootstrap.__init__.random_state"><span class="n sig-name">random_state</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype">RandomState<span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.__init__.random_state" title="Permalink to this definition">¶</a></dt><dd><p><p><code class="docutils literal notranslate"><span class="pre">RandomState</span></code> to use to ensure reproducable results. Cannot
 be used with <code class="docutils literal notranslate"><span class="pre">seed</span></code></p>
-<div class="deprecated">
-<p><span class="versionmodified deprecated">Deprecated since version 5.0: </span>The random_state keyword argument has been deprecated. Use seed instead.</p>
+<div class="admonition deprecated">
+<p class="admonition-title"><span class="versionmodified deprecated">Deprecated since version 5.0: </span>The random_state keyword argument has been deprecated. Use seed instead.</p>
 </div>
 </p>
 </dd>
-<dt id="arch.bootstrap.IIDBootstrap.__init__.kwargs"><span class="o">**</span><span class="n sig-name">kwargs</span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.__init__.kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Keyword arguments to bootstrap</p>
+<dt id="arch.bootstrap.IIDBootstrap.__init__.kwargs"><span class="o">**</span><span class="n sig-name">kwargs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype">ArrayLike</span></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.__init__.kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Keyword arguments to bootstrap</p>
 </dd>
 </dl>
 </dd>
 </dl>
 <dl class="py attribute objdesc">
 <dt class="sig sig-object highlight py" id="arch.bootstrap.IIDBootstrap.data">
-<span class="sig-name descname"><span class="pre">data</span></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.data" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">data</span></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.data" title="Link to this definition">¶</a></dt>
 <dd><p>Two-element tuple with the pos_data in the first position and kw_data
 in the second (pos_data, kw_data)</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.IIDBootstrap.data-type">Type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.data-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py attribute objdesc">
 <dt class="sig sig-object highlight py" id="arch.bootstrap.IIDBootstrap.pos_data">
-<span class="sig-name descname"><span class="pre">pos_data</span></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.pos_data" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">pos_data</span></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.pos_data" title="Link to this definition">¶</a></dt>
 <dd><p>Tuple containing the positional arguments (in the order entered)</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.IIDBootstrap.pos_data-type">Type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.pos_data-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py attribute objdesc">
 <dt class="sig sig-object highlight py" id="arch.bootstrap.IIDBootstrap.kw_data">
-<span class="sig-name descname"><span class="pre">kw_data</span></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.kw_data" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">kw_data</span></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.kw_data" title="Link to this definition">¶</a></dt>
 <dd><p>Dictionary containing the keyword arguments</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.IIDBootstrap.kw_data-type">Type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.kw_data-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1365,8 +1365,8 @@ <h1 id="arch-bootstrap-iidbootstrap">arch.bootstrap.IIDBootstrap<a class="header
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="generated/arch.bootstrap.IIDBootstrap.conf_int.html#arch.bootstrap.IIDBootstrap.conf_int" title="arch.bootstrap.IIDBootstrap.conf_int (Python method) — Function the computes parameter values.  See Notes for requirements"><code class="xref py py-obj docutils literal notranslate"><span class="pre">conf_int</span></code></a>(func[, reps, method, size, tail, ...])</p></td>
 <td><p><dl class="field-list simple">
-<dt class="field-odd">type func<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.11)"><code class="xref py py-data docutils literal notranslate"><span class="pre">Callable</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/constants.html#Ellipsis" title="(in Python v3.11)"><code class="xref py py-data docutils literal notranslate"><span class="pre">...</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>]</p>
+<dt class="field-odd">param func<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Function the computes parameter values.  See Notes for requirements</p>
 </dd>
 </dl>
 </p></td>
@@ -1480,7 +1480,7 @@ <h1 id="arch-bootstrap-iidbootstrap">arch.bootstrap.IIDBootstrap<a class="header
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1527,7 +1527,7 @@ <h1 id="arch-bootstrap-iidbootstrap">arch.bootstrap.IIDBootstrap<a class="header
     <script id="__config" type="application/json">{"base": "../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/arch.bootstrap.IndependentSamplesBootstrap.html b/bootstrap/generated/arch.bootstrap.IndependentSamplesBootstrap.html
index 3a54aba769..032b44d366 100644
--- a/bootstrap/generated/arch.bootstrap.IndependentSamplesBootstrap.html
+++ b/bootstrap/generated/arch.bootstrap.IndependentSamplesBootstrap.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap - arch 6.1.0</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1222,51 +1222,51 @@
                   
 
 
-<h1 id="arch-bootstrap-independentsamplesbootstrap">arch.bootstrap.IndependentSamplesBootstrap<a class="headerlink" href="#arch-bootstrap-independentsamplesbootstrap" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-independentsamplesbootstrap">arch.bootstrap.IndependentSamplesBootstrap<a class="headerlink" href="#arch-bootstrap-independentsamplesbootstrap" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.IndependentSamplesBootstrap">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.bootstrap.</span></span><span class="sig-name descname"><span class="pre">IndependentSamplesBootstrap</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.__init__.args" title="arch.bootstrap.IndependentSamplesBootstrap.__init__.args (Python parameter) — Positional arguments to bootstrap"><span class="n"><span class="pre">args</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">**</span></span><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.__init__.kwargs" title="arch.bootstrap.IndependentSamplesBootstrap.__init__.kwargs (Python parameter) — Keyword arguments to bootstrap"><span class="n"><span class="pre">kwargs</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/bootstrap/base.html#IndependentSamplesBootstrap"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.bootstrap.</span></span><span class="sig-name descname"><span class="pre">IndependentSamplesBootstrap</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.__init__.args" title="arch.bootstrap.IndependentSamplesBootstrap.__init__.args (Python parameter) — Positional arguments to bootstrap"><span class="n"><span class="pre">args</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">ArrayLike</span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">RandomState</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Generator</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">RandomState</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">**</span></span><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.__init__.kwargs" title="arch.bootstrap.IndependentSamplesBootstrap.__init__.kwargs (Python parameter) — Keyword arguments to bootstrap"><span class="n"><span class="pre">kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">ArrayLike</span></span></span></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/bootstrap/base.html#IndependentSamplesBootstrap"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap" title="Link to this definition">¶</a></dt>
 <dd><p>Bootstrap where each input is independently resampled</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.IndependentSamplesBootstrap-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.IndependentSamplesBootstrap.__init__.args"><span class="o">*</span><span class="n sig-name">args</span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.__init__.args" title="Permalink to this definition">¶</a></dt><dd><p>Positional arguments to bootstrap</p>
+<dt id="arch.bootstrap.IndependentSamplesBootstrap.__init__.args"><span class="o">*</span><span class="n sig-name">args</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype">ArrayLike</span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.__init__.args" title="Permalink to this definition">¶</a></dt><dd><p>Positional arguments to bootstrap</p>
 </dd>
-<dt id="arch.bootstrap.IndependentSamplesBootstrap.__init__.kwargs"><span class="o">**</span><span class="n sig-name">kwargs</span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.__init__.kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Keyword arguments to bootstrap</p>
+<dt id="arch.bootstrap.IndependentSamplesBootstrap.__init__.kwargs"><span class="o">**</span><span class="n sig-name">kwargs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype">ArrayLike</span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.__init__.kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Keyword arguments to bootstrap</p>
 </dd>
 </dl>
 </dd>
 </dl>
 <dl class="py attribute objdesc">
 <dt class="sig sig-object highlight py" id="arch.bootstrap.IndependentSamplesBootstrap.data">
-<span class="sig-name descname"><span class="pre">data</span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.data" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">data</span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.data" title="Link to this definition">¶</a></dt>
 <dd><p>Two-element tuple with the pos_data in the first position and kw_data
 in the second (pos_data, kw_data)</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.IndependentSamplesBootstrap.data-type">Type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.data-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py attribute objdesc">
 <dt class="sig sig-object highlight py" id="arch.bootstrap.IndependentSamplesBootstrap.pos_data">
-<span class="sig-name descname"><span class="pre">pos_data</span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.pos_data" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">pos_data</span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.pos_data" title="Link to this definition">¶</a></dt>
 <dd><p>Tuple containing the positional arguments (in the order entered)</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.IndependentSamplesBootstrap.pos_data-type">Type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.pos_data-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py attribute objdesc">
 <dt class="sig sig-object highlight py" id="arch.bootstrap.IndependentSamplesBootstrap.kw_data">
-<span class="sig-name descname"><span class="pre">kw_data</span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.kw_data" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">kw_data</span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.kw_data" title="Link to this definition">¶</a></dt>
 <dd><p>Dictionary containing the keyword arguments</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.IndependentSamplesBootstrap.kw_data-type">Type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.kw_data-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1327,8 +1327,8 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap">arch.bootstrap.IndependentSa
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="generated/arch.bootstrap.IndependentSamplesBootstrap.conf_int.html#arch.bootstrap.IndependentSamplesBootstrap.conf_int" title="arch.bootstrap.IndependentSamplesBootstrap.conf_int (Python method) — Function the computes parameter values.  See Notes for requirements"><code class="xref py py-obj docutils literal notranslate"><span class="pre">conf_int</span></code></a>(func[, reps, method, size, tail, ...])</p></td>
 <td><p><dl class="field-list simple">
-<dt class="field-odd">type func<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.11)"><code class="xref py py-data docutils literal notranslate"><span class="pre">Callable</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/constants.html#Ellipsis" title="(in Python v3.11)"><code class="xref py py-data docutils literal notranslate"><span class="pre">...</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>]</p>
+<dt class="field-odd">param func<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Function the computes parameter values.  See Notes for requirements</p>
 </dd>
 </dl>
 </p></td>
@@ -1442,7 +1442,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap">arch.bootstrap.IndependentSa
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1489,7 +1489,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap">arch.bootstrap.IndependentSa
     <script id="__config" type="application/json">{"base": "../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/arch.bootstrap.MovingBlockBootstrap.html b/bootstrap/generated/arch.bootstrap.MovingBlockBootstrap.html
index 5ccfabdad6..1cdb074b0b 100644
--- a/bootstrap/generated/arch.bootstrap.MovingBlockBootstrap.html
+++ b/bootstrap/generated/arch.bootstrap.MovingBlockBootstrap.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap - arch 6.1.0</title>
+        <title>arch.bootstrap.MovingBlockBootstrap - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1309,64 +1309,64 @@
                   
 
 
-<h1 id="arch-bootstrap-movingblockbootstrap">arch.bootstrap.MovingBlockBootstrap<a class="headerlink" href="#arch-bootstrap-movingblockbootstrap" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-movingblockbootstrap">arch.bootstrap.MovingBlockBootstrap<a class="headerlink" href="#arch-bootstrap-movingblockbootstrap" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.MovingBlockBootstrap">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.bootstrap.</span></span><span class="sig-name descname"><span class="pre">MovingBlockBootstrap</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.__init__.block_size" title="arch.bootstrap.MovingBlockBootstrap.__init__.block_size (Python parameter) — Size of block to use"><span class="n"><span class="pre">block_size</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.__init__.args" title="arch.bootstrap.MovingBlockBootstrap.__init__.args (Python parameter) — Positional arguments to bootstrap"><span class="n"><span class="pre">args</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.__init__.random_state" title="arch.bootstrap.MovingBlockBootstrap.__init__.random_state (Python parameter) — RandomState to use to ensure reproducable results. Cannot be used with seed"><span class="n"><span class="pre">random_state</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.__init__.seed" title="arch.bootstrap.MovingBlockBootstrap.__init__.seed (Python parameter) — Seed to use to ensure reproducable results. If an int, passes the value to value to np.random.default_rng. If None, a fresh Generator is constructed with system-provided entropy."><span class="n"><span class="pre">seed</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">**</span></span><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.__init__.kwargs" title="arch.bootstrap.MovingBlockBootstrap.__init__.kwargs (Python parameter) — Keyword arguments to bootstrap"><span class="n"><span class="pre">kwargs</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/bootstrap/base.html#MovingBlockBootstrap"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.bootstrap.</span></span><span class="sig-name descname"><span class="pre">MovingBlockBootstrap</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.__init__.block_size" title="arch.bootstrap.MovingBlockBootstrap.__init__.block_size (Python parameter) — Size of block to use"><span class="n"><span class="pre">block_size</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.__init__.args" title="arch.bootstrap.MovingBlockBootstrap.__init__.args (Python parameter) — Positional arguments to bootstrap"><span class="n"><span class="pre">args</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">ArrayLike</span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.__init__.random_state" title="arch.bootstrap.MovingBlockBootstrap.__init__.random_state (Python parameter) — RandomState to use to ensure reproducable results. Cannot be used with seed"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">RandomState</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.__init__.seed" title="arch.bootstrap.MovingBlockBootstrap.__init__.seed (Python parameter) — Seed to use to ensure reproducable results. If an int, passes the value to value to np.random.default_rng. If None, a fresh Generator is constructed with system-provided entropy."><span class="n"><span class="pre">seed</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Generator</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">RandomState</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">**</span></span><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.__init__.kwargs" title="arch.bootstrap.MovingBlockBootstrap.__init__.kwargs (Python parameter) — Keyword arguments to bootstrap"><span class="n"><span class="pre">kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">ArrayLike</span></span></span></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/bootstrap/base.html#MovingBlockBootstrap"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap" title="Link to this definition">¶</a></dt>
 <dd><p>Bootstrap using blocks of the same length without wrap around</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.MovingBlockBootstrap-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.MovingBlockBootstrap.__init__.block_size"><span class="sig-name descname">block_size</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.__init__.block_size" title="Permalink to this definition">¶</a></dt><dd><p>Size of block to use</p>
+<dt id="arch.bootstrap.MovingBlockBootstrap.__init__.block_size"><span class="n sig-name">block_size</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.__init__.block_size" title="Permalink to this definition">¶</a></dt><dd><p>Size of block to use</p>
 </dd>
-<dt id="arch.bootstrap.MovingBlockBootstrap.__init__.args"><span class="o">*</span><span class="n sig-name">args</span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.__init__.args" title="Permalink to this definition">¶</a></dt><dd><p>Positional arguments to bootstrap</p>
+<dt id="arch.bootstrap.MovingBlockBootstrap.__init__.args"><span class="o">*</span><span class="n sig-name">args</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype">ArrayLike</span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.__init__.args" title="Permalink to this definition">¶</a></dt><dd><p>Positional arguments to bootstrap</p>
 </dd>
-<dt id="arch.bootstrap.MovingBlockBootstrap.__init__.seed"><span class="sig-name descname">seed</span><span class="p"> : </span><span class="desctype"><span class="desctype">{Generator, RandomState, int}, optional</span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.__init__.seed" title="Permalink to this definition">¶</a></dt><dd><p>Seed to use to ensure reproducable results. If an int, passes the
+<dt id="arch.bootstrap.MovingBlockBootstrap.__init__.seed"><span class="n sig-name">seed</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span>Generator<span class="w"> </span><span class="p">|</span><span class="w"> </span>RandomState</span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.__init__.seed" title="Permalink to this definition">¶</a></dt><dd><p>Seed to use to ensure reproducable results. If an int, passes the
 value to value to <code class="docutils literal notranslate"><span class="pre">np.random.default_rng</span></code>. If None, a fresh
 Generator is constructed with system-provided entropy.</p>
 </dd>
-<dt id="arch.bootstrap.MovingBlockBootstrap.__init__.random_state"><span class="sig-name descname">random_state</span><span class="p"> : </span><span class="desctype"><span class="desctype">RandomState, optional</span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.__init__.random_state" title="Permalink to this definition">¶</a></dt><dd><p><p><code class="docutils literal notranslate"><span class="pre">RandomState</span></code> to use to ensure reproducable results. Cannot
+<dt id="arch.bootstrap.MovingBlockBootstrap.__init__.random_state"><span class="n sig-name">random_state</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype">RandomState<span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.__init__.random_state" title="Permalink to this definition">¶</a></dt><dd><p><p><code class="docutils literal notranslate"><span class="pre">RandomState</span></code> to use to ensure reproducable results. Cannot
 be used with <code class="docutils literal notranslate"><span class="pre">seed</span></code></p>
-<div class="deprecated">
-<p><span class="versionmodified deprecated">Deprecated since version 5.0: </span>The random_state keyword argument has been deprecated. Use seed instead.</p>
+<div class="admonition deprecated">
+<p class="admonition-title"><span class="versionmodified deprecated">Deprecated since version 5.0: </span>The random_state keyword argument has been deprecated. Use seed instead.</p>
 </div>
 </p>
 </dd>
-<dt id="arch.bootstrap.MovingBlockBootstrap.__init__.kwargs"><span class="o">**</span><span class="n sig-name">kwargs</span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.__init__.kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Keyword arguments to bootstrap</p>
+<dt id="arch.bootstrap.MovingBlockBootstrap.__init__.kwargs"><span class="o">**</span><span class="n sig-name">kwargs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype">ArrayLike</span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.__init__.kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Keyword arguments to bootstrap</p>
 </dd>
 </dl>
 </dd>
 </dl>
 <dl class="py attribute objdesc">
 <dt class="sig sig-object highlight py" id="arch.bootstrap.MovingBlockBootstrap.data">
-<span class="sig-name descname"><span class="pre">data</span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.data" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">data</span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.data" title="Link to this definition">¶</a></dt>
 <dd><p>Two-element tuple with the pos_data in the first position and kw_data
 in the second (pos_data, kw_data)</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.MovingBlockBootstrap.data-type">Type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.data-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py attribute objdesc">
 <dt class="sig sig-object highlight py" id="arch.bootstrap.MovingBlockBootstrap.pos_data">
-<span class="sig-name descname"><span class="pre">pos_data</span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.pos_data" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">pos_data</span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.pos_data" title="Link to this definition">¶</a></dt>
 <dd><p>Tuple containing the positional arguments (in the order entered)</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.MovingBlockBootstrap.pos_data-type">Type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.pos_data-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py attribute objdesc">
 <dt class="sig sig-object highlight py" id="arch.bootstrap.MovingBlockBootstrap.kw_data">
-<span class="sig-name descname"><span class="pre">kw_data</span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.kw_data" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">kw_data</span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.kw_data" title="Link to this definition">¶</a></dt>
 <dd><p>Dictionary containing the keyword arguments</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.MovingBlockBootstrap.kw_data-type">Type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.kw_data-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1428,8 +1428,8 @@ <h1 id="arch-bootstrap-movingblockbootstrap">arch.bootstrap.MovingBlockBootstrap
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="generated/arch.bootstrap.MovingBlockBootstrap.conf_int.html#arch.bootstrap.MovingBlockBootstrap.conf_int" title="arch.bootstrap.MovingBlockBootstrap.conf_int (Python method) — Function the computes parameter values.  See Notes for requirements"><code class="xref py py-obj docutils literal notranslate"><span class="pre">conf_int</span></code></a>(func[, reps, method, size, tail, ...])</p></td>
 <td><p><dl class="field-list simple">
-<dt class="field-odd">type func<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.11)"><code class="xref py py-data docutils literal notranslate"><span class="pre">Callable</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/constants.html#Ellipsis" title="(in Python v3.11)"><code class="xref py py-data docutils literal notranslate"><span class="pre">...</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>]</p>
+<dt class="field-odd">param func<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Function the computes parameter values.  See Notes for requirements</p>
 </dd>
 </dl>
 </p></td>
@@ -1543,7 +1543,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap">arch.bootstrap.MovingBlockBootstrap
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1590,7 +1590,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap">arch.bootstrap.MovingBlockBootstrap
     <script id="__config" type="application/json">{"base": "../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/arch.bootstrap.StationaryBootstrap.html b/bootstrap/generated/arch.bootstrap.StationaryBootstrap.html
index bc92892094..89fb5992bc 100644
--- a/bootstrap/generated/arch.bootstrap.StationaryBootstrap.html
+++ b/bootstrap/generated/arch.bootstrap.StationaryBootstrap.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap - arch 6.1.0</title>
+        <title>arch.bootstrap.StationaryBootstrap - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1309,64 +1309,64 @@
                   
 
 
-<h1 id="arch-bootstrap-stationarybootstrap">arch.bootstrap.StationaryBootstrap<a class="headerlink" href="#arch-bootstrap-stationarybootstrap" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-stationarybootstrap">arch.bootstrap.StationaryBootstrap<a class="headerlink" href="#arch-bootstrap-stationarybootstrap" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.StationaryBootstrap">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.bootstrap.</span></span><span class="sig-name descname"><span class="pre">StationaryBootstrap</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.__init__.block_size" title="arch.bootstrap.StationaryBootstrap.__init__.block_size (Python parameter) — Average size of block to use"><span class="n"><span class="pre">block_size</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.__init__.args" title="arch.bootstrap.StationaryBootstrap.__init__.args (Python parameter) — Positional arguments to bootstrap"><span class="n"><span class="pre">args</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.__init__.random_state" title="arch.bootstrap.StationaryBootstrap.__init__.random_state (Python parameter) — RandomState to use to ensure reproducable results. Cannot be used with seed"><span class="n"><span class="pre">random_state</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.__init__.seed" title="arch.bootstrap.StationaryBootstrap.__init__.seed (Python parameter) — Seed to use to ensure reproducable results. If an int, passes the value to value to np.random.default_rng. If None, a fresh Generator is constructed with system-provided entropy."><span class="n"><span class="pre">seed</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">**</span></span><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.__init__.kwargs" title="arch.bootstrap.StationaryBootstrap.__init__.kwargs (Python parameter) — Keyword arguments to bootstrap"><span class="n"><span class="pre">kwargs</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/bootstrap/base.html#StationaryBootstrap"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.bootstrap.</span></span><span class="sig-name descname"><span class="pre">StationaryBootstrap</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.__init__.block_size" title="arch.bootstrap.StationaryBootstrap.__init__.block_size (Python parameter) — Average size of block to use"><span class="n"><span class="pre">block_size</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.__init__.args" title="arch.bootstrap.StationaryBootstrap.__init__.args (Python parameter) — Positional arguments to bootstrap"><span class="n"><span class="pre">args</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">ArrayLike</span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.__init__.random_state" title="arch.bootstrap.StationaryBootstrap.__init__.random_state (Python parameter) — RandomState to use to ensure reproducable results. Cannot be used with seed"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">RandomState</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.__init__.seed" title="arch.bootstrap.StationaryBootstrap.__init__.seed (Python parameter) — Seed to use to ensure reproducable results. If an int, passes the value to value to np.random.default_rng. If None, a fresh Generator is constructed with system-provided entropy."><span class="n"><span class="pre">seed</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Generator</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">RandomState</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">**</span></span><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.__init__.kwargs" title="arch.bootstrap.StationaryBootstrap.__init__.kwargs (Python parameter) — Keyword arguments to bootstrap"><span class="n"><span class="pre">kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">ArrayLike</span></span></span></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/bootstrap/base.html#StationaryBootstrap"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap" title="Link to this definition">¶</a></dt>
 <dd><p>Politis and Romano (1994) bootstrap with expon distributed block sizes</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.StationaryBootstrap-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.StationaryBootstrap.__init__.block_size"><span class="sig-name descname">block_size</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.__init__.block_size" title="Permalink to this definition">¶</a></dt><dd><p>Average size of block to use</p>
+<dt id="arch.bootstrap.StationaryBootstrap.__init__.block_size"><span class="n sig-name">block_size</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.__init__.block_size" title="Permalink to this definition">¶</a></dt><dd><p>Average size of block to use</p>
 </dd>
-<dt id="arch.bootstrap.StationaryBootstrap.__init__.args"><span class="o">*</span><span class="n sig-name">args</span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.__init__.args" title="Permalink to this definition">¶</a></dt><dd><p>Positional arguments to bootstrap</p>
+<dt id="arch.bootstrap.StationaryBootstrap.__init__.args"><span class="o">*</span><span class="n sig-name">args</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype">ArrayLike</span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.__init__.args" title="Permalink to this definition">¶</a></dt><dd><p>Positional arguments to bootstrap</p>
 </dd>
-<dt id="arch.bootstrap.StationaryBootstrap.__init__.seed"><span class="sig-name descname">seed</span><span class="p"> : </span><span class="desctype"><span class="desctype">{Generator, RandomState, int}, optional</span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.__init__.seed" title="Permalink to this definition">¶</a></dt><dd><p>Seed to use to ensure reproducable results. If an int, passes the
+<dt id="arch.bootstrap.StationaryBootstrap.__init__.seed"><span class="n sig-name">seed</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span>Generator<span class="w"> </span><span class="p">|</span><span class="w"> </span>RandomState</span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.__init__.seed" title="Permalink to this definition">¶</a></dt><dd><p>Seed to use to ensure reproducable results. If an int, passes the
 value to value to <code class="docutils literal notranslate"><span class="pre">np.random.default_rng</span></code>. If None, a fresh
 Generator is constructed with system-provided entropy.</p>
 </dd>
-<dt id="arch.bootstrap.StationaryBootstrap.__init__.random_state"><span class="sig-name descname">random_state</span><span class="p"> : </span><span class="desctype"><span class="desctype">RandomState, optional</span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.__init__.random_state" title="Permalink to this definition">¶</a></dt><dd><p><p><code class="docutils literal notranslate"><span class="pre">RandomState</span></code> to use to ensure reproducable results. Cannot
+<dt id="arch.bootstrap.StationaryBootstrap.__init__.random_state"><span class="n sig-name">random_state</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype">RandomState<span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.__init__.random_state" title="Permalink to this definition">¶</a></dt><dd><p><p><code class="docutils literal notranslate"><span class="pre">RandomState</span></code> to use to ensure reproducable results. Cannot
 be used with <code class="docutils literal notranslate"><span class="pre">seed</span></code></p>
-<div class="deprecated">
-<p><span class="versionmodified deprecated">Deprecated since version 5.0: </span>The random_state keyword argument has been deprecated. Use seed instead.</p>
+<div class="admonition deprecated">
+<p class="admonition-title"><span class="versionmodified deprecated">Deprecated since version 5.0: </span>The random_state keyword argument has been deprecated. Use seed instead.</p>
 </div>
 </p>
 </dd>
-<dt id="arch.bootstrap.StationaryBootstrap.__init__.kwargs"><span class="o">**</span><span class="n sig-name">kwargs</span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.__init__.kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Keyword arguments to bootstrap</p>
+<dt id="arch.bootstrap.StationaryBootstrap.__init__.kwargs"><span class="o">**</span><span class="n sig-name">kwargs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype">ArrayLike</span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.__init__.kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Keyword arguments to bootstrap</p>
 </dd>
 </dl>
 </dd>
 </dl>
 <dl class="py attribute objdesc">
 <dt class="sig sig-object highlight py" id="arch.bootstrap.StationaryBootstrap.data">
-<span class="sig-name descname"><span class="pre">data</span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.data" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">data</span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.data" title="Link to this definition">¶</a></dt>
 <dd><p>Two-element tuple with the pos_data in the first position and kw_data
 in the second (pos_data, kw_data)</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.StationaryBootstrap.data-type">Type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.data-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py attribute objdesc">
 <dt class="sig sig-object highlight py" id="arch.bootstrap.StationaryBootstrap.pos_data">
-<span class="sig-name descname"><span class="pre">pos_data</span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.pos_data" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">pos_data</span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.pos_data" title="Link to this definition">¶</a></dt>
 <dd><p>Tuple containing the positional arguments (in the order entered)</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.StationaryBootstrap.pos_data-type">Type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.pos_data-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py attribute objdesc">
 <dt class="sig sig-object highlight py" id="arch.bootstrap.StationaryBootstrap.kw_data">
-<span class="sig-name descname"><span class="pre">kw_data</span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.kw_data" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">kw_data</span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.kw_data" title="Link to this definition">¶</a></dt>
 <dd><p>Dictionary containing the keyword arguments</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.StationaryBootstrap.kw_data-type">Type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.kw_data-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1426,8 +1426,8 @@ <h1 id="arch-bootstrap-stationarybootstrap">arch.bootstrap.StationaryBootstrap<a
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="generated/arch.bootstrap.StationaryBootstrap.conf_int.html#arch.bootstrap.StationaryBootstrap.conf_int" title="arch.bootstrap.StationaryBootstrap.conf_int (Python method) — Function the computes parameter values.  See Notes for requirements"><code class="xref py py-obj docutils literal notranslate"><span class="pre">conf_int</span></code></a>(func[, reps, method, size, tail, ...])</p></td>
 <td><p><dl class="field-list simple">
-<dt class="field-odd">type func<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.11)"><code class="xref py py-data docutils literal notranslate"><span class="pre">Callable</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/constants.html#Ellipsis" title="(in Python v3.11)"><code class="xref py py-data docutils literal notranslate"><span class="pre">...</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>]</p>
+<dt class="field-odd">param func<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Function the computes parameter values.  See Notes for requirements</p>
 </dd>
 </dl>
 </p></td>
@@ -1541,7 +1541,7 @@ <h1 id="arch-bootstrap-stationarybootstrap">arch.bootstrap.StationaryBootstrap<a
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1588,7 +1588,7 @@ <h1 id="arch-bootstrap-stationarybootstrap">arch.bootstrap.StationaryBootstrap<a
     <script id="__config" type="application/json">{"base": "../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/arch.bootstrap.optimal_block_length.html b/bootstrap/generated/arch.bootstrap.optimal_block_length.html
index b8a25a0fcc..4bd30914be 100644
--- a/bootstrap/generated/arch.bootstrap.optimal_block_length.html
+++ b/bootstrap/generated/arch.bootstrap.optimal_block_length.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.optimal_block_length - arch 6.1.0</title>
+        <title>arch.bootstrap.optimal_block_length - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -763,15 +763,15 @@
                   
 
 
-<h1 id="arch-bootstrap-optimal-block-length">arch.bootstrap.optimal_block_length<a class="headerlink" href="#arch-bootstrap-optimal-block-length" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-optimal-block-length">arch.bootstrap.optimal_block_length<a class="headerlink" href="#arch-bootstrap-optimal-block-length" title="Link to this heading">¶</a></h1>
 <dl class="py function objdesc">
-<dt class="sig sig-object highlight py" id="arch.bootstrap.optimal_block_length">
-<span class="sig-prename descclassname"><span class="pre">arch.bootstrap.</span></span><span class="sig-name descname"><span class="pre">optimal_block_length</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.optimal_block_length.x" title="arch.bootstrap.optimal_block_length.x (Python parameter) — A one-dimensional or two-dimensional array-like.  Operates columns by column if 2-dimensional."><span class="n"><span class="pre">x</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/bootstrap/base.html#optimal_block_length"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.optimal_block_length" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.optimal_block_length">
+<span class="sig-prename descclassname"><span class="pre">arch.bootstrap.</span></span><span class="sig-name descname"><span class="pre">optimal_block_length</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.optimal_block_length.x" title="arch.bootstrap.optimal_block_length.x (Python parameter) — A one-dimensional or two-dimensional array-like.  Operates columns by column if 2-dimensional."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">ArrayLike1D</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">ArrayLike2D</span></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><span class="pre">pd.DataFrame</span></span></span></span><a class="reference internal" href="../../_modules/arch/bootstrap/base.html#optimal_block_length"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.optimal_block_length" title="Link to this definition">¶</a></dt>
 <dd><p>Estimate optimal window length for time-series bootstraps</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.optimal_block_length-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.optimal_block_length-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.optimal_block_length.x"><span class="sig-name descname">x</span><span class="p"> : </span><span class="desctype"><span class="desctype">array_like</span></span><a class="headerlink" href="#arch.bootstrap.optimal_block_length.x" title="Permalink to this definition">¶</a></dt><dd><p>A one-dimensional or two-dimensional array-like.  Operates columns by
+<dt id="arch.bootstrap.optimal_block_length.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype">ArrayLike1D<span class="w"> </span><span class="p">|</span><span class="w"> </span>ArrayLike2D</span></span><a class="headerlink" href="#arch.bootstrap.optimal_block_length.x" title="Permalink to this definition">¶</a></dt><dd><p>A one-dimensional or two-dimensional array-like.  Operates columns by
 column if 2-dimensional.</p>
 </dd>
 </dl>
@@ -782,7 +782,7 @@ <h1 id="arch-bootstrap-optimal-block-length">arch.bootstrap.optimal_block_length
 size for the circular bootstrap.</p>
 </dd>
 <dt class="field-odd" id="arch.bootstrap.optimal_block_length-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.optimal_block_length-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>DataFrame</p>
+<dd class="field-odd"><p><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code></a></p>
 </dd>
 </dl>
 <div class="seealso admonition">
@@ -903,7 +903,7 @@ <h1 id="arch-bootstrap-optimal-block-length">arch.bootstrap.optimal_block_length
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -950,10 +950,10 @@ <h1 id="arch-bootstrap-optimal-block-length">arch.bootstrap.optimal_block_length
     <script id="__config" type="application/json">{"base": "../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
         <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+        <script id="MathJax-script" src="../../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
     
   </body>
 </html>
\ No newline at end of file
diff --git a/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.apply.html b/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.apply.html
index bce190f922..cbb2a0136e 100644
--- a/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.apply.html
+++ b/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.apply.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.apply - arch 6.1.0</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.apply - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1081,19 +1081,19 @@
                   
 
 
-<h1 id="arch-bootstrap-circularblockbootstrap-apply">arch.bootstrap.CircularBlockBootstrap.apply<a class="headerlink" href="#arch-bootstrap-circularblockbootstrap-apply" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-circularblockbootstrap-apply">arch.bootstrap.CircularBlockBootstrap.apply<a class="headerlink" href="#arch-bootstrap-circularblockbootstrap-apply" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.bootstrap.CircularBlockBootstrap.apply">
-<span class="sig-prename descclassname"><span class="pre">CircularBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.apply.func" title="arch.bootstrap.CircularBlockBootstrap.apply.func (Python parameter) — Function the computes parameter values.  See Notes for requirements"><span class="n"><span class="pre">func</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.apply.reps" title="arch.bootstrap.CircularBlockBootstrap.apply.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.apply.extra_kwargs" title="arch.bootstrap.CircularBlockBootstrap.apply.extra_kwargs (Python parameter) — Extra keyword arguments to use when calling func.  Must not conflict with keyword arguments used to initialize bootstrap"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.apply" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.CircularBlockBootstrap.apply">
+<span class="sig-prename descclassname"><span class="pre">CircularBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.apply.func" title="arch.bootstrap.CircularBlockBootstrap.apply.func (Python parameter) — Function the computes parameter values.  See Notes for requirements"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.apply.reps" title="arch.bootstrap.CircularBlockBootstrap.apply.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.apply.extra_kwargs" title="arch.bootstrap.CircularBlockBootstrap.apply.extra_kwargs (Python parameter) — Extra keyword arguments to use when calling func.  Must not conflict with keyword arguments used to initialize bootstrap"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.apply" title="Link to this definition">¶</a></dt>
 <dd><p>Applies a function to bootstrap replicated data</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.CircularBlockBootstrap.apply-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.apply-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.CircularBlockBootstrap.apply.func"><span class="sig-name descname">func</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable</span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.apply.func" title="Permalink to this definition">¶</a></dt><dd><p>Function the computes parameter values.  See Notes for requirements</p>
+<dt id="arch.bootstrap.CircularBlockBootstrap.apply.func"><span class="n sig-name">func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.apply.func" title="Permalink to this definition">¶</a></dt><dd><p>Function the computes parameter values.  See Notes for requirements</p>
 </dd>
-<dt id="arch.bootstrap.CircularBlockBootstrap.apply.reps"><span class="sig-name descname">reps</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, default 1000</span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.apply.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
+<dt id="arch.bootstrap.CircularBlockBootstrap.apply.reps"><span class="n sig-name">reps</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.apply.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
 </dd>
-<dt id="arch.bootstrap.CircularBlockBootstrap.apply.extra_kwargs"><span class="sig-name descname">extra_kwargs</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a>, default None</span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.apply.extra_kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Extra keyword arguments to use when calling func.  Must not
+<dt id="arch.bootstrap.CircularBlockBootstrap.apply.extra_kwargs"><span class="n sig-name">extra_kwargs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)">Any</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.apply.extra_kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Extra keyword arguments to use when calling func.  Must not
 conflict with keyword arguments used to initialize bootstrap</p>
 </dd>
 </dl>
@@ -1103,7 +1103,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap-apply">arch.bootstrap.CircularBloc
 corresponds to a bootstrap iteration</p>
 </dd>
 <dt class="field-odd" id="arch.bootstrap.CircularBlockBootstrap.apply-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.apply-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -1193,7 +1193,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap-apply">arch.bootstrap.CircularBloc
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1240,7 +1240,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap-apply">arch.bootstrap.CircularBloc
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.bootstrap.html b/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.bootstrap.html
index aa45a66b3f..7d4e511f52 100644
--- a/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.bootstrap.html
+++ b/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.bootstrap.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.bootstrap - arch 6.1.0</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.bootstrap - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1053,15 +1053,15 @@
                   
 
 
-<h1 id="arch-bootstrap-circularblockbootstrap-bootstrap">arch.bootstrap.CircularBlockBootstrap.bootstrap<a class="headerlink" href="#arch-bootstrap-circularblockbootstrap-bootstrap" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-circularblockbootstrap-bootstrap">arch.bootstrap.CircularBlockBootstrap.bootstrap<a class="headerlink" href="#arch-bootstrap-circularblockbootstrap-bootstrap" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.bootstrap.CircularBlockBootstrap.bootstrap">
-<span class="sig-prename descclassname"><span class="pre">CircularBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">bootstrap</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.bootstrap.reps" title="arch.bootstrap.CircularBlockBootstrap.bootstrap.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.bootstrap" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.CircularBlockBootstrap.bootstrap">
+<span class="sig-prename descclassname"><span class="pre">CircularBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">bootstrap</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.bootstrap.reps" title="arch.bootstrap.CircularBlockBootstrap.bootstrap.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Generator" title="(in Python v3.11)"><span class="pre">Generator</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.bootstrap" title="Link to this definition">¶</a></dt>
 <dd><p>Iterator for use when bootstrapping</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.CircularBlockBootstrap.bootstrap-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.bootstrap-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.CircularBlockBootstrap.bootstrap.reps"><span class="sig-name descname">reps</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.bootstrap.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
+<dt id="arch.bootstrap.CircularBlockBootstrap.bootstrap.reps"><span class="n sig-name">reps</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.bootstrap.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
 </dd>
 </dl>
 </dd>
@@ -1069,7 +1069,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap-bootstrap">arch.bootstrap.Circular
 <dd class="field-even"><p>Generator to iterate over in bootstrap calculations</p>
 </dd>
 <dt class="field-odd" id="arch.bootstrap.CircularBlockBootstrap.bootstrap-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.bootstrap-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>generator</p>
+<dd class="field-odd"><p><a class="reference internal" href="arch.bootstrap.CircularBlockBootstrap.generator.html#arch.bootstrap.CircularBlockBootstrap.generator" title="arch.bootstrap.CircularBlockBootstrap.generator (Python property) — Set or get the instance PRNG"><code class="xref py py-class docutils literal notranslate"><span class="pre">generator</span></code></a></p>
 </dd>
 </dl>
 <p class="example rubric">Examples</p>
@@ -1161,7 +1161,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap-bootstrap">arch.bootstrap.Circular
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1208,7 +1208,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap-bootstrap">arch.bootstrap.Circular
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.clone.html b/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.clone.html
index 2138892a8d..89e7b82dab 100644
--- a/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.clone.html
+++ b/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.clone.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.clone - arch 6.1.0</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.clone - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1081,19 +1081,19 @@
                   
 
 
-<h1 id="arch-bootstrap-circularblockbootstrap-clone">arch.bootstrap.CircularBlockBootstrap.clone<a class="headerlink" href="#arch-bootstrap-circularblockbootstrap-clone" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-circularblockbootstrap-clone">arch.bootstrap.CircularBlockBootstrap.clone<a class="headerlink" href="#arch-bootstrap-circularblockbootstrap-clone" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.bootstrap.CircularBlockBootstrap.clone">
-<span class="sig-prename descclassname"><span class="pre">CircularBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">clone</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.clone.args" title="arch.bootstrap.CircularBlockBootstrap.clone.args (Python parameter) — Positional arguments to bootstrap"><span class="n"><span class="pre">args</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.clone.seed" title="arch.bootstrap.CircularBlockBootstrap.clone.seed (Python parameter) — The seed value to pass to the closed generator"><span class="n"><span class="pre">seed</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">**</span></span><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.clone.kwargs" title="arch.bootstrap.CircularBlockBootstrap.clone.kwargs (Python parameter) — Keyword arguments to bootstrap"><span class="n"><span class="pre">kwargs</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/bootstrap/base.html#CircularBlockBootstrap.clone"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.clone" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.CircularBlockBootstrap.clone">
+<span class="sig-prename descclassname"><span class="pre">CircularBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">clone</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.clone.args" title="arch.bootstrap.CircularBlockBootstrap.clone.args (Python parameter) — Positional arguments to bootstrap"><span class="n"><span class="pre">args</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">ArrayLike</span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.clone.seed" title="arch.bootstrap.CircularBlockBootstrap.clone.seed (Python parameter) — The seed value to pass to the closed generator"><span class="n"><span class="pre">seed</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Generator</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">RandomState</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">**</span></span><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.clone.kwargs" title="arch.bootstrap.CircularBlockBootstrap.clone.kwargs (Python parameter) — Keyword arguments to bootstrap"><span class="n"><span class="pre">kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">ArrayLike</span></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.bootstrap.CircularBlockBootstrap.html#arch.bootstrap.CircularBlockBootstrap" title="arch.bootstrap.CircularBlockBootstrap (Python class) — Bootstrap using blocks of the same length with end-to-start wrap around"><span class="pre">CircularBlockBootstrap</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/bootstrap/base.html#CircularBlockBootstrap.clone"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.clone" title="Link to this definition">¶</a></dt>
 <dd><p>Clones the bootstrap using different data with a fresh prng.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.CircularBlockBootstrap.clone-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.clone-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.CircularBlockBootstrap.clone.args"><span class="o">*</span><span class="n sig-name">args</span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.clone.args" title="Permalink to this definition">¶</a></dt><dd><p>Positional arguments to bootstrap</p>
+<dt id="arch.bootstrap.CircularBlockBootstrap.clone.args"><span class="o">*</span><span class="n sig-name">args</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype">ArrayLike</span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.clone.args" title="Permalink to this definition">¶</a></dt><dd><p>Positional arguments to bootstrap</p>
 </dd>
-<dt id="arch.bootstrap.CircularBlockBootstrap.clone.seed"><span class="n sig-name">seed</span><span class="o">=</span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.clone.seed" title="Permalink to this definition">¶</a></dt><dd><p>The seed value to pass to the closed generator</p>
+<dt id="arch.bootstrap.CircularBlockBootstrap.clone.seed"><span class="n sig-name">seed</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span>Generator<span class="w"> </span><span class="p">|</span><span class="w"> </span>RandomState</span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.clone.seed" title="Permalink to this definition">¶</a></dt><dd><p>The seed value to pass to the closed generator</p>
 </dd>
-<dt id="arch.bootstrap.CircularBlockBootstrap.clone.kwargs"><span class="o">**</span><span class="n sig-name">kwargs</span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.clone.kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Keyword arguments to bootstrap</p>
+<dt id="arch.bootstrap.CircularBlockBootstrap.clone.kwargs"><span class="o">**</span><span class="n sig-name">kwargs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype">ArrayLike</span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.clone.kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Keyword arguments to bootstrap</p>
 </dd>
 </dl>
 </dd>
@@ -1101,7 +1101,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap-clone">arch.bootstrap.CircularBloc
 <dd class="field-even"><p>Bootstrap instance</p>
 </dd>
 <dt class="field-odd" id="arch.bootstrap.CircularBlockBootstrap.clone-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.clone-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>bs</p>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">bs</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1173,7 +1173,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap-clone">arch.bootstrap.CircularBloc
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1220,7 +1220,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap-clone">arch.bootstrap.CircularBloc
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.conf_int.html b/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.conf_int.html
index dbc1c16866..4791632671 100644
--- a/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.conf_int.html
+++ b/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.conf_int.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.conf_int - arch 6.1.0</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.conf_int - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
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       <img src="../../../_static/bw-logo.svg" alt="logo">
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-<h1 id="arch-bootstrap-circularblockbootstrap-conf-int">arch.bootstrap.CircularBlockBootstrap.conf_int<a class="headerlink" href="#arch-bootstrap-circularblockbootstrap-conf-int" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-circularblockbootstrap-conf-int">arch.bootstrap.CircularBlockBootstrap.conf_int<a class="headerlink" href="#arch-bootstrap-circularblockbootstrap-conf-int" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.CircularBlockBootstrap.conf_int">
-<span class="sig-prename descclassname"><span class="pre">CircularBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">conf_int</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.func" title="arch.bootstrap.CircularBlockBootstrap.conf_int.func (Python parameter) — Function the computes parameter values.  See Notes for requirements"><span class="n"><span class="pre">func</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.reps" title="arch.bootstrap.CircularBlockBootstrap.conf_int.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.method" title="arch.bootstrap.CircularBlockBootstrap.conf_int.method (Python parameter) — One of 'basic', 'percentile', 'studentized', 'norm' (identical to 'var', 'cov'), 'bc' (identical to 'debiased', 'bias-corrected'), or 'bca'"><span class="n"><span class="pre">method</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;basic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.size" title="arch.bootstrap.CircularBlockBootstrap.conf_int.size (Python parameter) — Coverage of confidence interval"><span class="n"><span class="pre">size</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.95</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.tail" title="arch.bootstrap.CircularBlockBootstrap.conf_int.tail (Python parameter) — One of 'two', 'upper' or 'lower'."><span class="n"><span class="pre">tail</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;two&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.extra_kwargs" title="arch.bootstrap.CircularBlockBootstrap.conf_int.extra_kwargs (Python parameter) — Extra keyword arguments to use when calling func and std_err_func, when appropriate"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.reuse" title="arch.bootstrap.CircularBlockBootstrap.conf_int.reuse (Python parameter) — Flag indicating whether to reuse previously computed bootstrap results.  This allows alternative methods to be compared without rerunning the bootstrap simulation.  Reuse is ignored if reps is not the same across multiple runs, func changes across calls, or method is 'studentized'."><span class="n"><span class="pre">reuse</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.sampling" title="arch.bootstrap.CircularBlockBootstrap.conf_int.sampling (Python parameter) — Type of sampling to use: 'nonparametric', 'semi-parametric' (or 'semi') or 'parametric'.  The default is 'nonparametric'.  See notes about the changes to func required when using 'semi' or 'parametric'."><span class="n"><span class="pre">sampling</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;nonparametric&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.std_err_func" title="arch.bootstrap.CircularBlockBootstrap.conf_int.std_err_func (Python parameter) — Function to use when standardizing estimated parameters when using the studentized bootstrap.  Providing an analytical function eliminates the need for a nested bootstrap"><span class="n"><span class="pre">std_err_func</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.studentize_reps" title="arch.bootstrap.CircularBlockBootstrap.conf_int.studentize_reps (Python parameter) — Number of bootstraps to use in the inner bootstrap when using the studentized bootstrap.  Ignored when std_err_func is provided"><span class="n"><span class="pre">studentize_reps</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.conf_int" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">CircularBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">conf_int</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.func" title="arch.bootstrap.CircularBlockBootstrap.conf_int.func (Python parameter) — Function the computes parameter values.  See Notes for requirements"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.reps" title="arch.bootstrap.CircularBlockBootstrap.conf_int.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.method" title="arch.bootstrap.CircularBlockBootstrap.conf_int.method (Python parameter) — One of 'basic', 'percentile', 'studentized', 'norm' (identical to 'var', 'cov'), 'bc' (identical to 'debiased', 'bias-corrected'), or 'bca'"><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'basic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'percentile'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'studentized'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'norm'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bc'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bca'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;basic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.size" title="arch.bootstrap.CircularBlockBootstrap.conf_int.size (Python parameter) — Coverage of confidence interval"><span class="n"><span class="pre">size</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.95</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.tail" title="arch.bootstrap.CircularBlockBootstrap.conf_int.tail (Python parameter) — One of 'two', 'upper' or 'lower'."><span class="n"><span class="pre">tail</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'two'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'upper'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'lower'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;two&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.extra_kwargs" title="arch.bootstrap.CircularBlockBootstrap.conf_int.extra_kwargs (Python parameter) — Extra keyword arguments to use when calling func and std_err_func, when appropriate"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.reuse" title="arch.bootstrap.CircularBlockBootstrap.conf_int.reuse (Python parameter) — Flag indicating whether to reuse previously computed bootstrap results.  This allows alternative methods to be compared without rerunning the bootstrap simulation.  Reuse is ignored if reps is not the same across multiple runs, func changes across calls, or method is 'studentized'."><span class="n"><span class="pre">reuse</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.sampling" title="arch.bootstrap.CircularBlockBootstrap.conf_int.sampling (Python parameter) — Type of sampling to use: 'nonparametric', 'semi-parametric' (or 'semi') or 'parametric'.  The default is 'nonparametric'.  See notes about the changes to func required when using 'semi' or 'parametric'."><span class="n"><span class="pre">sampling</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'nonparametric'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'semi-parametric'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'semi'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'parametric'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'semiparametric'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;nonparametric&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.std_err_func" title="arch.bootstrap.CircularBlockBootstrap.conf_int.std_err_func (Python parameter) — Function to use when standardizing estimated parameters when using the studentized bootstrap.  Providing an analytical function eliminates the need for a nested bootstrap"><span class="n"><span class="pre">std_err_func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.studentize_reps" title="arch.bootstrap.CircularBlockBootstrap.conf_int.studentize_reps (Python parameter) — Number of bootstraps to use in the inner bootstrap when using the studentized bootstrap.  Ignored when std_err_func is provided"><span class="n"><span class="pre">studentize_reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.conf_int" title="Link to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.CircularBlockBootstrap.conf_int-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.conf_int-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.CircularBlockBootstrap.conf_int.func"><span class="sig-name descname">func</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable</span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.func" title="Permalink to this definition">¶</a></dt><dd><p>Function the computes parameter values.  See Notes for requirements</p>
+<dt id="arch.bootstrap.CircularBlockBootstrap.conf_int.func"><span class="n sig-name">func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.func" title="Permalink to this definition">¶</a></dt><dd><p>Function the computes parameter values.  See Notes for requirements</p>
 </dd>
-<dt id="arch.bootstrap.CircularBlockBootstrap.conf_int.reps"><span class="sig-name descname">reps</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, default 1000</span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
+<dt id="arch.bootstrap.CircularBlockBootstrap.conf_int.reps"><span class="n sig-name">reps</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
 </dd>
-<dt id="arch.bootstrap.CircularBlockBootstrap.conf_int.method"><span class="sig-name descname">method</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, default &quot;basic&quot;</span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.method" title="Permalink to this definition">¶</a></dt><dd><p>One of ‘basic’, ‘percentile’, ‘studentized’, ‘norm’ (identical to
+<dt id="arch.bootstrap.CircularBlockBootstrap.conf_int.method"><span class="n sig-name">method</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'basic'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'percentile'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'studentized'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'norm'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'bc'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'bca'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;basic&#39;</span></code><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.method" title="Permalink to this definition">¶</a></dt><dd><p>One of ‘basic’, ‘percentile’, ‘studentized’, ‘norm’ (identical to
 ‘var’, ‘cov’), ‘bc’ (identical to ‘debiased’, ‘bias-corrected’), or
 ‘bca’</p>
 </dd>
-<dt id="arch.bootstrap.CircularBlockBootstrap.conf_int.size"><span class="sig-name descname">size</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>, default 0.95</span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.size" title="Permalink to this definition">¶</a></dt><dd><p>Coverage of confidence interval</p>
+<dt id="arch.bootstrap.CircularBlockBootstrap.conf_int.size"><span class="n sig-name">size</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.95</span></code><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.size" title="Permalink to this definition">¶</a></dt><dd><p>Coverage of confidence interval</p>
 </dd>
-<dt id="arch.bootstrap.CircularBlockBootstrap.conf_int.tail"><span class="sig-name descname">tail</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, default &quot;two&quot;</span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.tail" title="Permalink to this definition">¶</a></dt><dd><p>One of ‘two’, ‘upper’ or ‘lower’.</p>
+<dt id="arch.bootstrap.CircularBlockBootstrap.conf_int.tail"><span class="n sig-name">tail</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'two'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'upper'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'lower'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;two&#39;</span></code><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.tail" title="Permalink to this definition">¶</a></dt><dd><p>One of ‘two’, ‘upper’ or ‘lower’.</p>
 </dd>
-<dt id="arch.bootstrap.CircularBlockBootstrap.conf_int.reuse"><span class="sig-name descname">reuse</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, default False</span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.reuse" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to reuse previously computed bootstrap
+<dt id="arch.bootstrap.CircularBlockBootstrap.conf_int.reuse"><span class="n sig-name">reuse</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.reuse" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to reuse previously computed bootstrap
 results.  This allows alternative methods to be compared without
 rerunning the bootstrap simulation.  Reuse is ignored if reps is
 not the same across multiple runs, func changes across calls, or
 method is ‘studentized’.</p>
 </dd>
-<dt id="arch.bootstrap.CircularBlockBootstrap.conf_int.sampling"><span class="sig-name descname">sampling</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, default &quot;nonparametric&quot;</span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.sampling" title="Permalink to this definition">¶</a></dt><dd><p>Type of sampling to use: ‘nonparametric’, ‘semi-parametric’ (or
+<dt id="arch.bootstrap.CircularBlockBootstrap.conf_int.sampling"><span class="n sig-name">sampling</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'nonparametric'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'semi-parametric'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'semi'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'parametric'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'semiparametric'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;nonparametric&#39;</span></code><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.sampling" title="Permalink to this definition">¶</a></dt><dd><p>Type of sampling to use: ‘nonparametric’, ‘semi-parametric’ (or
 ‘semi’) or ‘parametric’.  The default is ‘nonparametric’.  See
 notes about the changes to func required when using ‘semi’ or
 ‘parametric’.</p>
 </dd>
-<dt id="arch.bootstrap.CircularBlockBootstrap.conf_int.extra_kwargs"><span class="sig-name descname">extra_kwargs</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a>, default None</span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.extra_kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Extra keyword arguments to use when calling func and std_err_func,
+<dt id="arch.bootstrap.CircularBlockBootstrap.conf_int.extra_kwargs"><span class="n sig-name">extra_kwargs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)">Any</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.extra_kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Extra keyword arguments to use when calling func and std_err_func,
 when appropriate</p>
 </dd>
-<dt id="arch.bootstrap.CircularBlockBootstrap.conf_int.std_err_func"><span class="sig-name descname">std_err_func</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable, default None</span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.std_err_func" title="Permalink to this definition">¶</a></dt><dd><p>Function to use when standardizing estimated parameters when using
+<dt id="arch.bootstrap.CircularBlockBootstrap.conf_int.std_err_func"><span class="n sig-name">std_err_func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.std_err_func" title="Permalink to this definition">¶</a></dt><dd><p>Function to use when standardizing estimated parameters when using
 the studentized bootstrap.  Providing an analytical function
 eliminates the need for a nested bootstrap</p>
 </dd>
-<dt id="arch.bootstrap.CircularBlockBootstrap.conf_int.studentize_reps"><span class="sig-name descname">studentize_reps</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, default 1000</span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.studentize_reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstraps to use in the inner bootstrap when using the
+<dt id="arch.bootstrap.CircularBlockBootstrap.conf_int.studentize_reps"><span class="n sig-name">studentize_reps</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.studentize_reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstraps to use in the inner bootstrap when using the
 studentized bootstrap.  Ignored when <code class="docutils literal notranslate"><span class="pre">std_err_func</span></code> is provided</p>
 </dd>
 </dl>
@@ -1228,7 +1228,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap-conf-int">arch.bootstrap.CircularB
 Similarly, ‘upper’ sets all lower bounds to -inf.</p>
 </dd>
 <dt class="field-odd" id="arch.bootstrap.CircularBlockBootstrap.conf_int-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.conf_int-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 <p class="example rubric">Examples</p>
@@ -1340,7 +1340,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap-conf-int">arch.bootstrap.CircularB
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1387,7 +1387,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap-conf-int">arch.bootstrap.CircularB
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.cov.html b/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.cov.html
index a3c6283848..72ab125167 100644
--- a/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.cov.html
+++ b/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.cov.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.cov - arch 6.1.0</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.cov - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1095,24 +1095,24 @@
                   
 
 
-<h1 id="arch-bootstrap-circularblockbootstrap-cov">arch.bootstrap.CircularBlockBootstrap.cov<a class="headerlink" href="#arch-bootstrap-circularblockbootstrap-cov" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-circularblockbootstrap-cov">arch.bootstrap.CircularBlockBootstrap.cov<a class="headerlink" href="#arch-bootstrap-circularblockbootstrap-cov" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.CircularBlockBootstrap.cov">
-<span class="sig-prename descclassname"><span class="pre">CircularBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">cov</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.cov.func" title="arch.bootstrap.CircularBlockBootstrap.cov.func (Python parameter) — Callable function that returns the statistic of interest as a 1-d array"><span class="n"><span class="pre">func</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.cov.reps" title="arch.bootstrap.CircularBlockBootstrap.cov.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.cov.recenter" title="arch.bootstrap.CircularBlockBootstrap.cov.recenter (Python parameter) — Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."><span class="n"><span class="pre">recenter</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.cov.extra_kwargs" title="arch.bootstrap.CircularBlockBootstrap.cov.extra_kwargs (Python parameter) — Dictionary of extra keyword arguments to pass to func"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.cov" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">CircularBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">cov</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.cov.func" title="arch.bootstrap.CircularBlockBootstrap.cov.func (Python parameter) — Callable function that returns the statistic of interest as a 1-d array"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.cov.reps" title="arch.bootstrap.CircularBlockBootstrap.cov.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.cov.recenter" title="arch.bootstrap.CircularBlockBootstrap.cov.recenter (Python parameter) — Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."><span class="n"><span class="pre">recenter</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.cov.extra_kwargs" title="arch.bootstrap.CircularBlockBootstrap.cov.extra_kwargs (Python parameter) — Dictionary of extra keyword arguments to pass to func"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.cov" title="Link to this definition">¶</a></dt>
 <dd><p>Compute parameter covariance using bootstrap</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.CircularBlockBootstrap.cov-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.cov-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.CircularBlockBootstrap.cov.func"><span class="sig-name descname">func</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable</span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.cov.func" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that returns the statistic of interest as a
+<dt id="arch.bootstrap.CircularBlockBootstrap.cov.func"><span class="n sig-name">func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.cov.func" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that returns the statistic of interest as a
 1-d array</p>
 </dd>
-<dt id="arch.bootstrap.CircularBlockBootstrap.cov.reps"><span class="sig-name descname">reps</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, default 1000</span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.cov.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
+<dt id="arch.bootstrap.CircularBlockBootstrap.cov.reps"><span class="n sig-name">reps</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.cov.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
 </dd>
-<dt id="arch.bootstrap.CircularBlockBootstrap.cov.recenter"><span class="sig-name descname">recenter</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, default True</span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.cov.recenter" title="Permalink to this definition">¶</a></dt><dd><p>Whether to center the bootstrap variance estimator on the average
+<dt id="arch.bootstrap.CircularBlockBootstrap.cov.recenter"><span class="n sig-name">recenter</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.cov.recenter" title="Permalink to this definition">¶</a></dt><dd><p>Whether to center the bootstrap variance estimator on the average
 of the bootstrap samples (True) or to center on the original sample
 estimate (False).  Default is True.</p>
 </dd>
-<dt id="arch.bootstrap.CircularBlockBootstrap.cov.extra_kwargs"><span class="sig-name descname">extra_kwargs</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a>, default None</span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.cov.extra_kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Dictionary of extra keyword arguments to pass to func</p>
+<dt id="arch.bootstrap.CircularBlockBootstrap.cov.extra_kwargs"><span class="n sig-name">extra_kwargs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)">Any</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.cov.extra_kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Dictionary of extra keyword arguments to pass to func</p>
 </dd>
 </dl>
 </dd>
@@ -1120,7 +1120,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap-cov">arch.bootstrap.CircularBlockB
 <dd class="field-even"><p>Bootstrap covariance estimator</p>
 </dd>
 <dt class="field-odd" id="arch.bootstrap.CircularBlockBootstrap.cov-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.cov-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -1227,7 +1227,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap-cov">arch.bootstrap.CircularBlockB
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1274,7 +1274,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap-cov">arch.bootstrap.CircularBlockB
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.generator.html b/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.generator.html
index 377c3996e1..506785d8c7 100644
--- a/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.generator.html
+++ b/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.generator.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.generator - arch 6.1.0</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.generator - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1027,15 +1027,15 @@
                   
 
 
-<h1 id="arch-bootstrap-circularblockbootstrap-generator">arch.bootstrap.CircularBlockBootstrap.generator<a class="headerlink" href="#arch-bootstrap-circularblockbootstrap-generator" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-circularblockbootstrap-generator">arch.bootstrap.CircularBlockBootstrap.generator<a class="headerlink" href="#arch-bootstrap-circularblockbootstrap-generator" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.bootstrap.CircularBlockBootstrap.generator">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CircularBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">generator</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.24)"><span class="pre">Generator</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.24)"><span class="pre">RandomState</span></a></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.generator" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CircularBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">generator</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><span class="pre">Generator</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">RandomState</span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.generator" title="Link to this definition">¶</a></dt>
 <dd><p>Set or get the instance PRNG</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.CircularBlockBootstrap.generator-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.generator-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt><span class="sig-name descname">seed</span><span class="p"> : </span><span class="desctype"><span class="desctype">{Generator, RandomState}, optional</span></span></dt><dd><p>Generator or RandomState used to produce the pseudo-random
+<dt><span class="sig-name descname">seed</span><span class="p"> : </span><span class="desctype"><code class="docutils literal notranslate"><span class="pre">{Generator,</span> <span class="pre">RandomState}</span></code>, <em>optional</em></span></dt><dd><p>Generator or RandomState used to produce the pseudo-random
 values used in the bootstrap</p>
 </dd>
 </dl>
@@ -1045,7 +1045,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap-generator">arch.bootstrap.Circular
 by bootstrap</p>
 </dd>
 <dt class="field-odd" id="arch.bootstrap.CircularBlockBootstrap.generator-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.generator-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>{Generator, RandomState}</p>
+<dd class="field-odd"><p><code class="docutils literal notranslate"><span class="pre">{Generator,</span> <span class="pre">RandomState}</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1117,7 +1117,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap-generator">arch.bootstrap.Circular
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1164,7 +1164,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap-generator">arch.bootstrap.Circular
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.get_state.html b/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.get_state.html
index f626f233a6..546150b953 100644
--- a/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.get_state.html
+++ b/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.get_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.get_state - arch 6.1.0</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.get_state - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1013,17 +1013,17 @@
                   
 
 
-<h1 id="arch-bootstrap-circularblockbootstrap-get-state">arch.bootstrap.CircularBlockBootstrap.get_state<a class="headerlink" href="#arch-bootstrap-circularblockbootstrap-get-state" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-circularblockbootstrap-get-state">arch.bootstrap.CircularBlockBootstrap.get_state<a class="headerlink" href="#arch-bootstrap-circularblockbootstrap-get-state" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.bootstrap.CircularBlockBootstrap.get_state">
-<span class="sig-prename descclassname"><span class="pre">CircularBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">get_state</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.get_state" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.CircularBlockBootstrap.get_state">
+<span class="sig-prename descclassname"><span class="pre">CircularBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">get_state</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Mapping" title="(in Python v3.11)"><span class="pre">Mapping</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.get_state" title="Link to this definition">¶</a></dt>
 <dd><p>Gets the state of the bootstrap’s random number generator</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.CircularBlockBootstrap.get_state-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.get_state-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>Dictionary containing the state.</p>
 </dd>
 <dt class="field-even" id="arch.bootstrap.CircularBlockBootstrap.get_state-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.get_state-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1095,7 +1095,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap-get-state">arch.bootstrap.Circular
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1142,7 +1142,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap-get-state">arch.bootstrap.Circular
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.index.html b/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.index.html
index 9236a6d68f..bb564cbedc 100644
--- a/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.index.html
+++ b/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.index.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.index - arch 6.1.0</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.index - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -973,10 +973,10 @@
                   
 
 
-<h1 id="arch-bootstrap-circularblockbootstrap-index">arch.bootstrap.CircularBlockBootstrap.index<a class="headerlink" href="#arch-bootstrap-circularblockbootstrap-index" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-circularblockbootstrap-index">arch.bootstrap.CircularBlockBootstrap.index<a class="headerlink" href="#arch-bootstrap-circularblockbootstrap-index" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.CircularBlockBootstrap.index">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CircularBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">index</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">numpy.ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">numpy.ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">numpy.ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.index" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CircularBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">index</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.index" title="Link to this definition">¶</a></dt>
 <dd><p>The current index of the bootstrap</p>
 </dd></dl>
 
@@ -1047,7 +1047,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap-index">arch.bootstrap.CircularBloc
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1094,7 +1094,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap-index">arch.bootstrap.CircularBloc
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.random_state.html b/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.random_state.html
index 3c95113979..49c6cc8d97 100644
--- a/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.random_state.html
+++ b/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.random_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.random_state - arch 6.1.0</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.random_state - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1027,15 +1027,15 @@
                   
 
 
-<h1 id="arch-bootstrap-circularblockbootstrap-random-state">arch.bootstrap.CircularBlockBootstrap.random_state<a class="headerlink" href="#arch-bootstrap-circularblockbootstrap-random-state" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-circularblockbootstrap-random-state">arch.bootstrap.CircularBlockBootstrap.random_state<a class="headerlink" href="#arch-bootstrap-circularblockbootstrap-random-state" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.CircularBlockBootstrap.random_state">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CircularBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">random_state</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.24)"><span class="pre">Generator</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.24)"><span class="pre">RandomState</span></a></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.random_state" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CircularBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">random_state</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><span class="pre">Generator</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">RandomState</span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.random_state" title="Link to this definition">¶</a></dt>
 <dd><p>Set or get the instance random state</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.CircularBlockBootstrap.random_state-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.random_state-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt><span class="sig-name descname">random_state</span><span class="p"> : </span><span class="desctype"><span class="desctype">RandomState</span></span></dt><dd><p>RandomState instance used by bootstrap</p>
+<dt><span class="sig-name descname">random_state</span><span class="p"> : </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.random.RandomState</span></code></a></span></dt><dd><p>RandomState instance used by bootstrap</p>
 </dd>
 </dl>
 </dd>
@@ -1043,7 +1043,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap-random-state">arch.bootstrap.Circu
 <dd class="field-even"><p>RandomState instance used by bootstrap</p>
 </dd>
 <dt class="field-odd" id="arch.bootstrap.CircularBlockBootstrap.random_state-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.random_state-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>RandomState</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.random.RandomState</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1115,7 +1115,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap-random-state">arch.bootstrap.Circu
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1162,7 +1162,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap-random-state">arch.bootstrap.Circu
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.reset.html b/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.reset.html
index d09ed93bad..79afc54a5b 100644
--- a/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.reset.html
+++ b/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.reset.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.reset - arch 6.1.0</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.reset - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -645,13 +645,6 @@
       </ul>
     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.CircularBlockBootstrap.reset-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
       </ul>
@@ -1011,13 +1004,6 @@
       </ul>
     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.CircularBlockBootstrap.reset-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
       </ul>
@@ -1039,23 +1025,20 @@
                   
 
 
-<h1 id="arch-bootstrap-circularblockbootstrap-reset">arch.bootstrap.CircularBlockBootstrap.reset<a class="headerlink" href="#arch-bootstrap-circularblockbootstrap-reset" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-circularblockbootstrap-reset">arch.bootstrap.CircularBlockBootstrap.reset<a class="headerlink" href="#arch-bootstrap-circularblockbootstrap-reset" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.bootstrap.CircularBlockBootstrap.reset">
-<span class="sig-prename descclassname"><span class="pre">CircularBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">reset</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.reset.use_seed" title="arch.bootstrap.CircularBlockBootstrap.reset.use_seed (Python parameter) — Flag indicating whether to use the last seed if provided.  If False or if no seed has been set, the bootstrap will be reset to the initial state.  Default is True"><span class="n"><span class="pre">use_seed</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.reset" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">CircularBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">reset</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.reset.use_seed" title="arch.bootstrap.CircularBlockBootstrap.reset.use_seed (Python parameter) — Flag indicating whether to use the last seed if provided.  If False or if no seed has been set, the bootstrap will be reset to the initial state.  Default is True"><span class="n"><span class="pre">use_seed</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.reset" title="Link to this definition">¶</a></dt>
 <dd><p>Resets the bootstrap to either its initial state or the last seed.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.CircularBlockBootstrap.reset-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.reset-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.CircularBlockBootstrap.reset.use_seed"><span class="sig-name descname">use_seed</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, default True</span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.reset.use_seed" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to use the last seed if provided.  If
+<dt id="arch.bootstrap.CircularBlockBootstrap.reset.use_seed"><span class="n sig-name">use_seed</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.reset.use_seed" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to use the last seed if provided.  If
 False or if no seed has been set, the bootstrap will be reset
 to the initial state.  Default is True</p>
 </dd>
 </dl>
 </dd>
-<dt class="field-even" id="arch.bootstrap.CircularBlockBootstrap.reset-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.reset-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code></a></p>
-</dd>
 </dl>
 </dd></dl>
 
@@ -1126,7 +1109,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap-reset">arch.bootstrap.CircularBloc
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1173,7 +1156,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap-reset">arch.bootstrap.CircularBloc
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.seed.html b/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.seed.html
index 641ae92f37..e2a381b8bf 100644
--- a/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.seed.html
+++ b/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.seed.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.seed - arch 6.1.0</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.seed - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -660,13 +660,6 @@
       </ul>
     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.CircularBlockBootstrap.seed-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
       </ul>
@@ -1011,13 +1004,6 @@
       </ul>
     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.CircularBlockBootstrap.seed-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
       </ul>
@@ -1039,21 +1025,18 @@
                   
 
 
-<h1 id="arch-bootstrap-circularblockbootstrap-seed">arch.bootstrap.CircularBlockBootstrap.seed<a class="headerlink" href="#arch-bootstrap-circularblockbootstrap-seed" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-circularblockbootstrap-seed">arch.bootstrap.CircularBlockBootstrap.seed<a class="headerlink" href="#arch-bootstrap-circularblockbootstrap-seed" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.bootstrap.CircularBlockBootstrap.seed">
-<span class="sig-prename descclassname"><span class="pre">CircularBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">seed</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.seed.value" title="arch.bootstrap.CircularBlockBootstrap.seed.value (Python parameter) — Value to use as the seed."><span class="n"><span class="pre">value</span></span></a></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.seed" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.CircularBlockBootstrap.seed">
+<span class="sig-prename descclassname"><span class="pre">CircularBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">seed</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.seed.value" title="arch.bootstrap.CircularBlockBootstrap.seed.value (Python parameter) — Value to use as the seed."><span class="n"><span class="pre">value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.seed" title="Link to this definition">¶</a></dt>
 <dd><p>Reseeds the bootstrap’s random number generator</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.CircularBlockBootstrap.seed-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.seed-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.CircularBlockBootstrap.seed.value"><span class="sig-name descname">value</span><span class="p"> : </span><span class="desctype"><span class="desctype">{int, List[<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>], ndarray}</span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.seed.value" title="Permalink to this definition">¶</a></dt><dd><p>Value to use as the seed.</p>
+<dt id="arch.bootstrap.CircularBlockBootstrap.seed.value"><span class="n sig-name">value</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.seed.value" title="Permalink to this definition">¶</a></dt><dd><p>Value to use as the seed.</p>
 </dd>
 </dl>
 </dd>
-<dt class="field-even" id="arch.bootstrap.CircularBlockBootstrap.seed-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.seed-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code></a></p>
-</dd>
 </dl>
 </dd></dl>
 
@@ -1124,7 +1107,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap-seed">arch.bootstrap.CircularBlock
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1171,7 +1154,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap-seed">arch.bootstrap.CircularBlock
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.set_state.html b/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.set_state.html
index e59e3f2119..f90652f0f1 100644
--- a/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.set_state.html
+++ b/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.set_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.set_state - arch 6.1.0</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.set_state - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -675,13 +675,6 @@
       </ul>
     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.CircularBlockBootstrap.set_state-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
       </ul>
@@ -1011,13 +1004,6 @@
       </ul>
     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.CircularBlockBootstrap.set_state-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
       </ul>
@@ -1039,21 +1025,18 @@
                   
 
 
-<h1 id="arch-bootstrap-circularblockbootstrap-set-state">arch.bootstrap.CircularBlockBootstrap.set_state<a class="headerlink" href="#arch-bootstrap-circularblockbootstrap-set-state" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-circularblockbootstrap-set-state">arch.bootstrap.CircularBlockBootstrap.set_state<a class="headerlink" href="#arch-bootstrap-circularblockbootstrap-set-state" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.bootstrap.CircularBlockBootstrap.set_state">
-<span class="sig-prename descclassname"><span class="pre">CircularBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">set_state</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.set_state.state" title="arch.bootstrap.CircularBlockBootstrap.set_state.state (Python parameter) — Dictionary or tuple containing the state."><span class="n"><span class="pre">state</span></span></a></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.set_state" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.CircularBlockBootstrap.set_state">
+<span class="sig-prename descclassname"><span class="pre">CircularBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">set_state</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.set_state.state" title="arch.bootstrap.CircularBlockBootstrap.set_state.state (Python parameter) — Dictionary or tuple containing the state."><span class="n"><span class="pre">state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.set_state" title="Link to this definition">¶</a></dt>
 <dd><p>Sets the state of the bootstrap’s random number generator</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.CircularBlockBootstrap.set_state-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.set_state-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.CircularBlockBootstrap.set_state.state"><span class="sig-name descname">state</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a></span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.set_state.state" title="Permalink to this definition">¶</a></dt><dd><p>Dictionary or tuple containing the state.</p>
+<dt id="arch.bootstrap.CircularBlockBootstrap.set_state.state"><span class="n sig-name">state</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)">Any</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.set_state.state" title="Permalink to this definition">¶</a></dt><dd><p>Dictionary or tuple containing the state.</p>
 </dd>
 </dl>
 </dd>
-<dt class="field-even" id="arch.bootstrap.CircularBlockBootstrap.set_state-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.set_state-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code></a></p>
-</dd>
 </dl>
 </dd></dl>
 
@@ -1124,7 +1107,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap-set-state">arch.bootstrap.Circular
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1171,7 +1154,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap-set-state">arch.bootstrap.Circular
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.state.html b/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.state.html
index 6eb358a951..0ed8cdcd30 100644
--- a/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.state.html
+++ b/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.state - arch 6.1.0</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.state - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1013,10 +1013,10 @@
                   
 
 
-<h1 id="arch-bootstrap-circularblockbootstrap-state">arch.bootstrap.CircularBlockBootstrap.state<a class="headerlink" href="#arch-bootstrap-circularblockbootstrap-state" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-circularblockbootstrap-state">arch.bootstrap.CircularBlockBootstrap.state<a class="headerlink" href="#arch-bootstrap-circularblockbootstrap-state" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.CircularBlockBootstrap.state">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CircularBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">state</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">numpy.ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Mapping" title="(in Python v3.11)"><span class="pre">Mapping</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.state" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CircularBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">state</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Mapping" title="(in Python v3.11)"><span class="pre">Mapping</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.state" title="Link to this definition">¶</a></dt>
 <dd><p>Set or get the generator’s state</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.CircularBlockBootstrap.state-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.state-returns" title="Permalink to this headline">¶</a></dt>
@@ -1025,7 +1025,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap-state">arch.bootstrap.CircularBloc
 Otherwise it is a dictionary.</p>
 </dd>
 <dt class="field-even" id="arch.bootstrap.CircularBlockBootstrap.state-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.state-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p>{tuple, dict}</p>
+<dd class="field-even"><p><code class="docutils literal notranslate"><span class="pre">{tuple,</span> <span class="pre">dict}</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1097,7 +1097,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap-state">arch.bootstrap.CircularBloc
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1144,7 +1144,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap-state">arch.bootstrap.CircularBloc
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.update_indices.html b/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.update_indices.html
index 4b31dd12c0..1c69c9d3f0 100644
--- a/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.update_indices.html
+++ b/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.update_indices.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.update_indices - arch 6.1.0</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.update_indices - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -669,19 +669,6 @@
     <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="arch.bootstrap.CircularBlockBootstrap.update_indices (Python method) — Update indices for the next iteration of the bootstrap.  This must be overridden when creating new bootstraps." class="md-ellipsis">Circular<wbr>Block<wbr>Bootstrap.<wbr>update_<wbr>indices</span>
   </a>
   
-    <nav class="md-nav" aria-label="CircularBlockBootstrap.update_indices">
-      <ul class="md-nav__list">
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.CircularBlockBootstrap.update_indices-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
-</li>
-        
-      </ul>
-    </nav>
-  
 </li>
       
     </ul>
@@ -970,19 +957,6 @@
     <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="arch.bootstrap.CircularBlockBootstrap.update_indices (Python method) — Update indices for the next iteration of the bootstrap.  This must be overridden when creating new bootstraps." class="md-ellipsis">Circular<wbr>Block<wbr>Bootstrap.<wbr>update_<wbr>indices</span>
   </a>
   
-    <nav class="md-nav" aria-label="CircularBlockBootstrap.update_indices">
-      <ul class="md-nav__list">
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.CircularBlockBootstrap.update_indices-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
-</li>
-        
-      </ul>
-    </nav>
-  
 </li>
       
     </ul>
@@ -999,17 +973,12 @@
                   
 
 
-<h1 id="arch-bootstrap-circularblockbootstrap-update-indices">arch.bootstrap.CircularBlockBootstrap.update_indices<a class="headerlink" href="#arch-bootstrap-circularblockbootstrap-update-indices" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-circularblockbootstrap-update-indices">arch.bootstrap.CircularBlockBootstrap.update_indices<a class="headerlink" href="#arch-bootstrap-circularblockbootstrap-update-indices" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.bootstrap.CircularBlockBootstrap.update_indices">
-<span class="sig-prename descclassname"><span class="pre">CircularBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">update_indices</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/bootstrap/base.html#CircularBlockBootstrap.update_indices"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.update_indices" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">CircularBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">update_indices</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/bootstrap/base.html#CircularBlockBootstrap.update_indices"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.update_indices" title="Link to this definition">¶</a></dt>
 <dd><p>Update indices for the next iteration of the bootstrap.  This must
 be overridden when creating new bootstraps.</p>
-<dl class="field-list simple">
-<dt class="field-odd" id="arch.bootstrap.CircularBlockBootstrap.update_indices-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.update_indices-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a></p>
-</dd>
-</dl>
 </dd></dl>
 
 
@@ -1079,7 +1048,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap-update-indices">arch.bootstrap.Cir
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1126,7 +1095,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap-update-indices">arch.bootstrap.Cir
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.var.html b/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.var.html
index 15b36ef592..0a49c1d555 100644
--- a/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.var.html
+++ b/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.var.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.var - arch 6.1.0</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.var - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1095,24 +1095,24 @@
                   
 
 
-<h1 id="arch-bootstrap-circularblockbootstrap-var">arch.bootstrap.CircularBlockBootstrap.var<a class="headerlink" href="#arch-bootstrap-circularblockbootstrap-var" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-circularblockbootstrap-var">arch.bootstrap.CircularBlockBootstrap.var<a class="headerlink" href="#arch-bootstrap-circularblockbootstrap-var" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.CircularBlockBootstrap.var">
-<span class="sig-prename descclassname"><span class="pre">CircularBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">var</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.var.func" title="arch.bootstrap.CircularBlockBootstrap.var.func (Python parameter) — Callable function that returns the statistic of interest as a 1-d array"><span class="n"><span class="pre">func</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.var.reps" title="arch.bootstrap.CircularBlockBootstrap.var.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.var.recenter" title="arch.bootstrap.CircularBlockBootstrap.var.recenter (Python parameter) — Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."><span class="n"><span class="pre">recenter</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.var.extra_kwargs" title="arch.bootstrap.CircularBlockBootstrap.var.extra_kwargs (Python parameter) — Dictionary of extra keyword arguments to pass to func"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.var" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">CircularBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">var</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.var.func" title="arch.bootstrap.CircularBlockBootstrap.var.func (Python parameter) — Callable function that returns the statistic of interest as a 1-d array"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.var.reps" title="arch.bootstrap.CircularBlockBootstrap.var.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.var.recenter" title="arch.bootstrap.CircularBlockBootstrap.var.recenter (Python parameter) — Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."><span class="n"><span class="pre">recenter</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.var.extra_kwargs" title="arch.bootstrap.CircularBlockBootstrap.var.extra_kwargs (Python parameter) — Dictionary of extra keyword arguments to pass to func"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.var" title="Link to this definition">¶</a></dt>
 <dd><p>Compute parameter variance using bootstrap</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.CircularBlockBootstrap.var-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.var-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.CircularBlockBootstrap.var.func"><span class="sig-name descname">func</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable</span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.var.func" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that returns the statistic of interest as a
+<dt id="arch.bootstrap.CircularBlockBootstrap.var.func"><span class="n sig-name">func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.var.func" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that returns the statistic of interest as a
 1-d array</p>
 </dd>
-<dt id="arch.bootstrap.CircularBlockBootstrap.var.reps"><span class="sig-name descname">reps</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, default 1000</span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.var.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
+<dt id="arch.bootstrap.CircularBlockBootstrap.var.reps"><span class="n sig-name">reps</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.var.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
 </dd>
-<dt id="arch.bootstrap.CircularBlockBootstrap.var.recenter"><span class="sig-name descname">recenter</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, default True</span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.var.recenter" title="Permalink to this definition">¶</a></dt><dd><p>Whether to center the bootstrap variance estimator on the average
+<dt id="arch.bootstrap.CircularBlockBootstrap.var.recenter"><span class="n sig-name">recenter</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.var.recenter" title="Permalink to this definition">¶</a></dt><dd><p>Whether to center the bootstrap variance estimator on the average
 of the bootstrap samples (True) or to center on the original sample
 estimate (False).  Default is True.</p>
 </dd>
-<dt id="arch.bootstrap.CircularBlockBootstrap.var.extra_kwargs"><span class="sig-name descname">extra_kwargs</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a>, default None</span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.var.extra_kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Dictionary of extra keyword arguments to pass to func</p>
+<dt id="arch.bootstrap.CircularBlockBootstrap.var.extra_kwargs"><span class="n sig-name">extra_kwargs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)">Any</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.var.extra_kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Dictionary of extra keyword arguments to pass to func</p>
 </dd>
 </dl>
 </dd>
@@ -1120,7 +1120,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap-var">arch.bootstrap.CircularBlockB
 <dd class="field-even"><p>Bootstrap variance estimator</p>
 </dd>
 <dt class="field-odd" id="arch.bootstrap.CircularBlockBootstrap.var-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.var-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -1227,7 +1227,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap-var">arch.bootstrap.CircularBlockB
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1274,7 +1274,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap-var">arch.bootstrap.CircularBlockB
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.apply.html b/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.apply.html
index fbe67f6223..1ddc3e3287 100644
--- a/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.apply.html
+++ b/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.apply.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.apply - arch 6.1.0</title>
+        <title>arch.bootstrap.IIDBootstrap.apply - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1036,19 +1036,19 @@
                   
 
 
-<h1 id="arch-bootstrap-iidbootstrap-apply">arch.bootstrap.IIDBootstrap.apply<a class="headerlink" href="#arch-bootstrap-iidbootstrap-apply" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-iidbootstrap-apply">arch.bootstrap.IIDBootstrap.apply<a class="headerlink" href="#arch-bootstrap-iidbootstrap-apply" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.bootstrap.IIDBootstrap.apply">
-<span class="sig-prename descclassname"><span class="pre">IIDBootstrap.</span></span><span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.apply.func" title="arch.bootstrap.IIDBootstrap.apply.func (Python parameter) — Function the computes parameter values.  See Notes for requirements"><span class="n"><span class="pre">func</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.apply.reps" title="arch.bootstrap.IIDBootstrap.apply.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.apply.extra_kwargs" title="arch.bootstrap.IIDBootstrap.apply.extra_kwargs (Python parameter) — Extra keyword arguments to use when calling func.  Must not conflict with keyword arguments used to initialize bootstrap"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/bootstrap/base.html#IIDBootstrap.apply"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.apply" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.IIDBootstrap.apply">
+<span class="sig-prename descclassname"><span class="pre">IIDBootstrap.</span></span><span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.apply.func" title="arch.bootstrap.IIDBootstrap.apply.func (Python parameter) — Function the computes parameter values.  See Notes for requirements"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.apply.reps" title="arch.bootstrap.IIDBootstrap.apply.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.apply.extra_kwargs" title="arch.bootstrap.IIDBootstrap.apply.extra_kwargs (Python parameter) — Extra keyword arguments to use when calling func.  Must not conflict with keyword arguments used to initialize bootstrap"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/bootstrap/base.html#IIDBootstrap.apply"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.apply" title="Link to this definition">¶</a></dt>
 <dd><p>Applies a function to bootstrap replicated data</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.IIDBootstrap.apply-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.apply-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.IIDBootstrap.apply.func"><span class="sig-name descname">func</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable</span></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.apply.func" title="Permalink to this definition">¶</a></dt><dd><p>Function the computes parameter values.  See Notes for requirements</p>
+<dt id="arch.bootstrap.IIDBootstrap.apply.func"><span class="n sig-name">func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.apply.func" title="Permalink to this definition">¶</a></dt><dd><p>Function the computes parameter values.  See Notes for requirements</p>
 </dd>
-<dt id="arch.bootstrap.IIDBootstrap.apply.reps"><span class="sig-name descname">reps</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, default 1000</span></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.apply.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
+<dt id="arch.bootstrap.IIDBootstrap.apply.reps"><span class="n sig-name">reps</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.apply.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
 </dd>
-<dt id="arch.bootstrap.IIDBootstrap.apply.extra_kwargs"><span class="sig-name descname">extra_kwargs</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a>, default None</span></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.apply.extra_kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Extra keyword arguments to use when calling func.  Must not
+<dt id="arch.bootstrap.IIDBootstrap.apply.extra_kwargs"><span class="n sig-name">extra_kwargs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)">Any</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.apply.extra_kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Extra keyword arguments to use when calling func.  Must not
 conflict with keyword arguments used to initialize bootstrap</p>
 </dd>
 </dl>
@@ -1058,7 +1058,7 @@ <h1 id="arch-bootstrap-iidbootstrap-apply">arch.bootstrap.IIDBootstrap.apply<a c
 corresponds to a bootstrap iteration</p>
 </dd>
 <dt class="field-odd" id="arch.bootstrap.IIDBootstrap.apply-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.apply-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -1148,7 +1148,7 @@ <h1 id="arch-bootstrap-iidbootstrap-apply">arch.bootstrap.IIDBootstrap.apply<a c
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1195,7 +1195,7 @@ <h1 id="arch-bootstrap-iidbootstrap-apply">arch.bootstrap.IIDBootstrap.apply<a c
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.bootstrap.html b/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.bootstrap.html
index f812a7b934..a5e0dca143 100644
--- a/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.bootstrap.html
+++ b/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.bootstrap.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.bootstrap - arch 6.1.0</title>
+        <title>arch.bootstrap.IIDBootstrap.bootstrap - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1008,15 +1008,15 @@
                   
 
 
-<h1 id="arch-bootstrap-iidbootstrap-bootstrap">arch.bootstrap.IIDBootstrap.bootstrap<a class="headerlink" href="#arch-bootstrap-iidbootstrap-bootstrap" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-iidbootstrap-bootstrap">arch.bootstrap.IIDBootstrap.bootstrap<a class="headerlink" href="#arch-bootstrap-iidbootstrap-bootstrap" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.bootstrap.IIDBootstrap.bootstrap">
-<span class="sig-prename descclassname"><span class="pre">IIDBootstrap.</span></span><span class="sig-name descname"><span class="pre">bootstrap</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.bootstrap.reps" title="arch.bootstrap.IIDBootstrap.bootstrap.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/bootstrap/base.html#IIDBootstrap.bootstrap"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.bootstrap" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.IIDBootstrap.bootstrap">
+<span class="sig-prename descclassname"><span class="pre">IIDBootstrap.</span></span><span class="sig-name descname"><span class="pre">bootstrap</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.bootstrap.reps" title="arch.bootstrap.IIDBootstrap.bootstrap.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Generator" title="(in Python v3.11)"><span class="pre">Generator</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/bootstrap/base.html#IIDBootstrap.bootstrap"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.bootstrap" title="Link to this definition">¶</a></dt>
 <dd><p>Iterator for use when bootstrapping</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.IIDBootstrap.bootstrap-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.bootstrap-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.IIDBootstrap.bootstrap.reps"><span class="sig-name descname">reps</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.bootstrap.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
+<dt id="arch.bootstrap.IIDBootstrap.bootstrap.reps"><span class="n sig-name">reps</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.bootstrap.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
 </dd>
 </dl>
 </dd>
@@ -1024,7 +1024,7 @@ <h1 id="arch-bootstrap-iidbootstrap-bootstrap">arch.bootstrap.IIDBootstrap.boots
 <dd class="field-even"><p>Generator to iterate over in bootstrap calculations</p>
 </dd>
 <dt class="field-odd" id="arch.bootstrap.IIDBootstrap.bootstrap-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.bootstrap-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>generator</p>
+<dd class="field-odd"><p><a class="reference internal" href="arch.bootstrap.IIDBootstrap.generator.html#arch.bootstrap.IIDBootstrap.generator" title="arch.bootstrap.IIDBootstrap.generator (Python property) — Set or get the instance PRNG"><code class="xref py py-class docutils literal notranslate"><span class="pre">generator</span></code></a></p>
 </dd>
 </dl>
 <p class="example rubric">Examples</p>
@@ -1116,7 +1116,7 @@ <h1 id="arch-bootstrap-iidbootstrap-bootstrap">arch.bootstrap.IIDBootstrap.boots
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1163,7 +1163,7 @@ <h1 id="arch-bootstrap-iidbootstrap-bootstrap">arch.bootstrap.IIDBootstrap.boots
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.clone.html b/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.clone.html
index a105d93b70..f0115bedda 100644
--- a/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.clone.html
+++ b/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.clone.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.clone - arch 6.1.0</title>
+        <title>arch.bootstrap.IIDBootstrap.clone - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1036,19 +1036,19 @@
                   
 
 
-<h1 id="arch-bootstrap-iidbootstrap-clone">arch.bootstrap.IIDBootstrap.clone<a class="headerlink" href="#arch-bootstrap-iidbootstrap-clone" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-iidbootstrap-clone">arch.bootstrap.IIDBootstrap.clone<a class="headerlink" href="#arch-bootstrap-iidbootstrap-clone" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.bootstrap.IIDBootstrap.clone">
-<span class="sig-prename descclassname"><span class="pre">IIDBootstrap.</span></span><span class="sig-name descname"><span class="pre">clone</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.clone.args" title="arch.bootstrap.IIDBootstrap.clone.args (Python parameter) — Positional arguments to bootstrap"><span class="n"><span class="pre">args</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.clone.seed" title="arch.bootstrap.IIDBootstrap.clone.seed (Python parameter) — The seed value to pass to the closed generator"><span class="n"><span class="pre">seed</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">**</span></span><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.clone.kwargs" title="arch.bootstrap.IIDBootstrap.clone.kwargs (Python parameter) — Keyword arguments to bootstrap"><span class="n"><span class="pre">kwargs</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/bootstrap/base.html#IIDBootstrap.clone"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.clone" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.IIDBootstrap.clone">
+<span class="sig-prename descclassname"><span class="pre">IIDBootstrap.</span></span><span class="sig-name descname"><span class="pre">clone</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.clone.args" title="arch.bootstrap.IIDBootstrap.clone.args (Python parameter) — Positional arguments to bootstrap"><span class="n"><span class="pre">args</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">ArrayLike</span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.clone.seed" title="arch.bootstrap.IIDBootstrap.clone.seed (Python parameter) — The seed value to pass to the closed generator"><span class="n"><span class="pre">seed</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Generator</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">RandomState</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">**</span></span><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.clone.kwargs" title="arch.bootstrap.IIDBootstrap.clone.kwargs (Python parameter) — Keyword arguments to bootstrap"><span class="n"><span class="pre">kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">ArrayLike</span></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.bootstrap.IIDBootstrap.html#arch.bootstrap.IIDBootstrap" title="arch.bootstrap.IIDBootstrap (Python class) — Bootstrap using uniform resampling"><span class="pre">IIDBootstrap</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/bootstrap/base.html#IIDBootstrap.clone"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.clone" title="Link to this definition">¶</a></dt>
 <dd><p>Clones the bootstrap using different data with a fresh prng.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.IIDBootstrap.clone-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.clone-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.IIDBootstrap.clone.args"><span class="o">*</span><span class="n sig-name">args</span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.clone.args" title="Permalink to this definition">¶</a></dt><dd><p>Positional arguments to bootstrap</p>
+<dt id="arch.bootstrap.IIDBootstrap.clone.args"><span class="o">*</span><span class="n sig-name">args</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype">ArrayLike</span></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.clone.args" title="Permalink to this definition">¶</a></dt><dd><p>Positional arguments to bootstrap</p>
 </dd>
-<dt id="arch.bootstrap.IIDBootstrap.clone.seed"><span class="n sig-name">seed</span><span class="o">=</span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.clone.seed" title="Permalink to this definition">¶</a></dt><dd><p>The seed value to pass to the closed generator</p>
+<dt id="arch.bootstrap.IIDBootstrap.clone.seed"><span class="n sig-name">seed</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span>Generator<span class="w"> </span><span class="p">|</span><span class="w"> </span>RandomState</span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.clone.seed" title="Permalink to this definition">¶</a></dt><dd><p>The seed value to pass to the closed generator</p>
 </dd>
-<dt id="arch.bootstrap.IIDBootstrap.clone.kwargs"><span class="o">**</span><span class="n sig-name">kwargs</span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.clone.kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Keyword arguments to bootstrap</p>
+<dt id="arch.bootstrap.IIDBootstrap.clone.kwargs"><span class="o">**</span><span class="n sig-name">kwargs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype">ArrayLike</span></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.clone.kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Keyword arguments to bootstrap</p>
 </dd>
 </dl>
 </dd>
@@ -1056,7 +1056,7 @@ <h1 id="arch-bootstrap-iidbootstrap-clone">arch.bootstrap.IIDBootstrap.clone<a c
 <dd class="field-even"><p>Bootstrap instance</p>
 </dd>
 <dt class="field-odd" id="arch.bootstrap.IIDBootstrap.clone-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.clone-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>bs</p>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">bs</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1128,7 +1128,7 @@ <h1 id="arch-bootstrap-iidbootstrap-clone">arch.bootstrap.IIDBootstrap.clone<a c
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1175,7 +1175,7 @@ <h1 id="arch-bootstrap-iidbootstrap-clone">arch.bootstrap.IIDBootstrap.clone<a c
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.conf_int.html b/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.conf_int.html
index 18c39fddc4..8e237b5969 100644
--- a/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.conf_int.html
+++ b/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.conf_int.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.conf_int - arch 6.1.0</title>
+        <title>arch.bootstrap.IIDBootstrap.conf_int - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1134,44 +1134,44 @@
                   
 
 
-<h1 id="arch-bootstrap-iidbootstrap-conf-int">arch.bootstrap.IIDBootstrap.conf_int<a class="headerlink" href="#arch-bootstrap-iidbootstrap-conf-int" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-iidbootstrap-conf-int">arch.bootstrap.IIDBootstrap.conf_int<a class="headerlink" href="#arch-bootstrap-iidbootstrap-conf-int" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.IIDBootstrap.conf_int">
-<span class="sig-prename descclassname"><span class="pre">IIDBootstrap.</span></span><span class="sig-name descname"><span class="pre">conf_int</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.conf_int.func" title="arch.bootstrap.IIDBootstrap.conf_int.func (Python parameter) — Function the computes parameter values.  See Notes for requirements"><span class="n"><span class="pre">func</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.conf_int.reps" title="arch.bootstrap.IIDBootstrap.conf_int.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.conf_int.method" title="arch.bootstrap.IIDBootstrap.conf_int.method (Python parameter) — One of 'basic', 'percentile', 'studentized', 'norm' (identical to 'var', 'cov'), 'bc' (identical to 'debiased', 'bias-corrected'), or 'bca'"><span class="n"><span class="pre">method</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;basic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.conf_int.size" title="arch.bootstrap.IIDBootstrap.conf_int.size (Python parameter) — Coverage of confidence interval"><span class="n"><span class="pre">size</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.95</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.conf_int.tail" title="arch.bootstrap.IIDBootstrap.conf_int.tail (Python parameter) — One of 'two', 'upper' or 'lower'."><span class="n"><span class="pre">tail</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;two&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.conf_int.extra_kwargs" title="arch.bootstrap.IIDBootstrap.conf_int.extra_kwargs (Python parameter) — Extra keyword arguments to use when calling func and std_err_func, when appropriate"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.conf_int.reuse" title="arch.bootstrap.IIDBootstrap.conf_int.reuse (Python parameter) — Flag indicating whether to reuse previously computed bootstrap results.  This allows alternative methods to be compared without rerunning the bootstrap simulation.  Reuse is ignored if reps is not the same across multiple runs, func changes across calls, or method is 'studentized'."><span class="n"><span class="pre">reuse</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.conf_int.sampling" title="arch.bootstrap.IIDBootstrap.conf_int.sampling (Python parameter) — Type of sampling to use: 'nonparametric', 'semi-parametric' (or 'semi') or 'parametric'.  The default is 'nonparametric'.  See notes about the changes to func required when using 'semi' or 'parametric'."><span class="n"><span class="pre">sampling</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;nonparametric&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.conf_int.std_err_func" title="arch.bootstrap.IIDBootstrap.conf_int.std_err_func (Python parameter) — Function to use when standardizing estimated parameters when using the studentized bootstrap.  Providing an analytical function eliminates the need for a nested bootstrap"><span class="n"><span class="pre">std_err_func</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.conf_int.studentize_reps" title="arch.bootstrap.IIDBootstrap.conf_int.studentize_reps (Python parameter) — Number of bootstraps to use in the inner bootstrap when using the studentized bootstrap.  Ignored when std_err_func is provided"><span class="n"><span class="pre">studentize_reps</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/bootstrap/base.html#IIDBootstrap.conf_int"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.conf_int" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">IIDBootstrap.</span></span><span class="sig-name descname"><span class="pre">conf_int</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.conf_int.func" title="arch.bootstrap.IIDBootstrap.conf_int.func (Python parameter) — Function the computes parameter values.  See Notes for requirements"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.conf_int.reps" title="arch.bootstrap.IIDBootstrap.conf_int.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.conf_int.method" title="arch.bootstrap.IIDBootstrap.conf_int.method (Python parameter) — One of 'basic', 'percentile', 'studentized', 'norm' (identical to 'var', 'cov'), 'bc' (identical to 'debiased', 'bias-corrected'), or 'bca'"><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'basic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'percentile'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'studentized'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'norm'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bc'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bca'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;basic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.conf_int.size" title="arch.bootstrap.IIDBootstrap.conf_int.size (Python parameter) — Coverage of confidence interval"><span class="n"><span class="pre">size</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.95</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.conf_int.tail" title="arch.bootstrap.IIDBootstrap.conf_int.tail (Python parameter) — One of 'two', 'upper' or 'lower'."><span class="n"><span class="pre">tail</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'two'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'upper'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'lower'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;two&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.conf_int.extra_kwargs" title="arch.bootstrap.IIDBootstrap.conf_int.extra_kwargs (Python parameter) — Extra keyword arguments to use when calling func and std_err_func, when appropriate"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.conf_int.reuse" title="arch.bootstrap.IIDBootstrap.conf_int.reuse (Python parameter) — Flag indicating whether to reuse previously computed bootstrap results.  This allows alternative methods to be compared without rerunning the bootstrap simulation.  Reuse is ignored if reps is not the same across multiple runs, func changes across calls, or method is 'studentized'."><span class="n"><span class="pre">reuse</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.conf_int.sampling" title="arch.bootstrap.IIDBootstrap.conf_int.sampling (Python parameter) — Type of sampling to use: 'nonparametric', 'semi-parametric' (or 'semi') or 'parametric'.  The default is 'nonparametric'.  See notes about the changes to func required when using 'semi' or 'parametric'."><span class="n"><span class="pre">sampling</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'nonparametric'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'semi-parametric'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'semi'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'parametric'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'semiparametric'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;nonparametric&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.conf_int.std_err_func" title="arch.bootstrap.IIDBootstrap.conf_int.std_err_func (Python parameter) — Function to use when standardizing estimated parameters when using the studentized bootstrap.  Providing an analytical function eliminates the need for a nested bootstrap"><span class="n"><span class="pre">std_err_func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.conf_int.studentize_reps" title="arch.bootstrap.IIDBootstrap.conf_int.studentize_reps (Python parameter) — Number of bootstraps to use in the inner bootstrap when using the studentized bootstrap.  Ignored when std_err_func is provided"><span class="n"><span class="pre">studentize_reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/bootstrap/base.html#IIDBootstrap.conf_int"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.conf_int" title="Link to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.IIDBootstrap.conf_int-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.conf_int-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.IIDBootstrap.conf_int.func"><span class="sig-name descname">func</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable</span></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.conf_int.func" title="Permalink to this definition">¶</a></dt><dd><p>Function the computes parameter values.  See Notes for requirements</p>
+<dt id="arch.bootstrap.IIDBootstrap.conf_int.func"><span class="n sig-name">func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.conf_int.func" title="Permalink to this definition">¶</a></dt><dd><p>Function the computes parameter values.  See Notes for requirements</p>
 </dd>
-<dt id="arch.bootstrap.IIDBootstrap.conf_int.reps"><span class="sig-name descname">reps</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, default 1000</span></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.conf_int.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
+<dt id="arch.bootstrap.IIDBootstrap.conf_int.reps"><span class="n sig-name">reps</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.conf_int.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
 </dd>
-<dt id="arch.bootstrap.IIDBootstrap.conf_int.method"><span class="sig-name descname">method</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, default &quot;basic&quot;</span></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.conf_int.method" title="Permalink to this definition">¶</a></dt><dd><p>One of ‘basic’, ‘percentile’, ‘studentized’, ‘norm’ (identical to
+<dt id="arch.bootstrap.IIDBootstrap.conf_int.method"><span class="n sig-name">method</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'basic'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'percentile'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'studentized'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'norm'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'bc'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'bca'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;basic&#39;</span></code><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.conf_int.method" title="Permalink to this definition">¶</a></dt><dd><p>One of ‘basic’, ‘percentile’, ‘studentized’, ‘norm’ (identical to
 ‘var’, ‘cov’), ‘bc’ (identical to ‘debiased’, ‘bias-corrected’), or
 ‘bca’</p>
 </dd>
-<dt id="arch.bootstrap.IIDBootstrap.conf_int.size"><span class="sig-name descname">size</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>, default 0.95</span></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.conf_int.size" title="Permalink to this definition">¶</a></dt><dd><p>Coverage of confidence interval</p>
+<dt id="arch.bootstrap.IIDBootstrap.conf_int.size"><span class="n sig-name">size</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.95</span></code><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.conf_int.size" title="Permalink to this definition">¶</a></dt><dd><p>Coverage of confidence interval</p>
 </dd>
-<dt id="arch.bootstrap.IIDBootstrap.conf_int.tail"><span class="sig-name descname">tail</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, default &quot;two&quot;</span></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.conf_int.tail" title="Permalink to this definition">¶</a></dt><dd><p>One of ‘two’, ‘upper’ or ‘lower’.</p>
+<dt id="arch.bootstrap.IIDBootstrap.conf_int.tail"><span class="n sig-name">tail</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'two'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'upper'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'lower'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;two&#39;</span></code><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.conf_int.tail" title="Permalink to this definition">¶</a></dt><dd><p>One of ‘two’, ‘upper’ or ‘lower’.</p>
 </dd>
-<dt id="arch.bootstrap.IIDBootstrap.conf_int.reuse"><span class="sig-name descname">reuse</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, default False</span></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.conf_int.reuse" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to reuse previously computed bootstrap
+<dt id="arch.bootstrap.IIDBootstrap.conf_int.reuse"><span class="n sig-name">reuse</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.conf_int.reuse" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to reuse previously computed bootstrap
 results.  This allows alternative methods to be compared without
 rerunning the bootstrap simulation.  Reuse is ignored if reps is
 not the same across multiple runs, func changes across calls, or
 method is ‘studentized’.</p>
 </dd>
-<dt id="arch.bootstrap.IIDBootstrap.conf_int.sampling"><span class="sig-name descname">sampling</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, default &quot;nonparametric&quot;</span></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.conf_int.sampling" title="Permalink to this definition">¶</a></dt><dd><p>Type of sampling to use: ‘nonparametric’, ‘semi-parametric’ (or
+<dt id="arch.bootstrap.IIDBootstrap.conf_int.sampling"><span class="n sig-name">sampling</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'nonparametric'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'semi-parametric'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'semi'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'parametric'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'semiparametric'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;nonparametric&#39;</span></code><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.conf_int.sampling" title="Permalink to this definition">¶</a></dt><dd><p>Type of sampling to use: ‘nonparametric’, ‘semi-parametric’ (or
 ‘semi’) or ‘parametric’.  The default is ‘nonparametric’.  See
 notes about the changes to func required when using ‘semi’ or
 ‘parametric’.</p>
 </dd>
-<dt id="arch.bootstrap.IIDBootstrap.conf_int.extra_kwargs"><span class="sig-name descname">extra_kwargs</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a>, default None</span></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.conf_int.extra_kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Extra keyword arguments to use when calling func and std_err_func,
+<dt id="arch.bootstrap.IIDBootstrap.conf_int.extra_kwargs"><span class="n sig-name">extra_kwargs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)">Any</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.conf_int.extra_kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Extra keyword arguments to use when calling func and std_err_func,
 when appropriate</p>
 </dd>
-<dt id="arch.bootstrap.IIDBootstrap.conf_int.std_err_func"><span class="sig-name descname">std_err_func</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable, default None</span></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.conf_int.std_err_func" title="Permalink to this definition">¶</a></dt><dd><p>Function to use when standardizing estimated parameters when using
+<dt id="arch.bootstrap.IIDBootstrap.conf_int.std_err_func"><span class="n sig-name">std_err_func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.conf_int.std_err_func" title="Permalink to this definition">¶</a></dt><dd><p>Function to use when standardizing estimated parameters when using
 the studentized bootstrap.  Providing an analytical function
 eliminates the need for a nested bootstrap</p>
 </dd>
-<dt id="arch.bootstrap.IIDBootstrap.conf_int.studentize_reps"><span class="sig-name descname">studentize_reps</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, default 1000</span></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.conf_int.studentize_reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstraps to use in the inner bootstrap when using the
+<dt id="arch.bootstrap.IIDBootstrap.conf_int.studentize_reps"><span class="n sig-name">studentize_reps</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.conf_int.studentize_reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstraps to use in the inner bootstrap when using the
 studentized bootstrap.  Ignored when <code class="docutils literal notranslate"><span class="pre">std_err_func</span></code> is provided</p>
 </dd>
 </dl>
@@ -1183,7 +1183,7 @@ <h1 id="arch-bootstrap-iidbootstrap-conf-int">arch.bootstrap.IIDBootstrap.conf_i
 Similarly, ‘upper’ sets all lower bounds to -inf.</p>
 </dd>
 <dt class="field-odd" id="arch.bootstrap.IIDBootstrap.conf_int-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.conf_int-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 <p class="example rubric">Examples</p>
@@ -1295,7 +1295,7 @@ <h1 id="arch-bootstrap-iidbootstrap-conf-int">arch.bootstrap.IIDBootstrap.conf_i
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1342,7 +1342,7 @@ <h1 id="arch-bootstrap-iidbootstrap-conf-int">arch.bootstrap.IIDBootstrap.conf_i
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.cov.html b/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.cov.html
index 1a2f8bc446..cac91fd5b0 100644
--- a/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.cov.html
+++ b/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.cov.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.cov - arch 6.1.0</title>
+        <title>arch.bootstrap.IIDBootstrap.cov - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1050,24 +1050,24 @@
                   
 
 
-<h1 id="arch-bootstrap-iidbootstrap-cov">arch.bootstrap.IIDBootstrap.cov<a class="headerlink" href="#arch-bootstrap-iidbootstrap-cov" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-iidbootstrap-cov">arch.bootstrap.IIDBootstrap.cov<a class="headerlink" href="#arch-bootstrap-iidbootstrap-cov" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.bootstrap.IIDBootstrap.cov">
-<span class="sig-prename descclassname"><span class="pre">IIDBootstrap.</span></span><span class="sig-name descname"><span class="pre">cov</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.cov.func" title="arch.bootstrap.IIDBootstrap.cov.func (Python parameter) — Callable function that returns the statistic of interest as a 1-d array"><span class="n"><span class="pre">func</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.cov.reps" title="arch.bootstrap.IIDBootstrap.cov.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.cov.recenter" title="arch.bootstrap.IIDBootstrap.cov.recenter (Python parameter) — Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."><span class="n"><span class="pre">recenter</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.cov.extra_kwargs" title="arch.bootstrap.IIDBootstrap.cov.extra_kwargs (Python parameter) — Dictionary of extra keyword arguments to pass to func"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/bootstrap/base.html#IIDBootstrap.cov"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.cov" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.IIDBootstrap.cov">
+<span class="sig-prename descclassname"><span class="pre">IIDBootstrap.</span></span><span class="sig-name descname"><span class="pre">cov</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.cov.func" title="arch.bootstrap.IIDBootstrap.cov.func (Python parameter) — Callable function that returns the statistic of interest as a 1-d array"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.cov.reps" title="arch.bootstrap.IIDBootstrap.cov.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.cov.recenter" title="arch.bootstrap.IIDBootstrap.cov.recenter (Python parameter) — Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."><span class="n"><span class="pre">recenter</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.cov.extra_kwargs" title="arch.bootstrap.IIDBootstrap.cov.extra_kwargs (Python parameter) — Dictionary of extra keyword arguments to pass to func"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/bootstrap/base.html#IIDBootstrap.cov"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.cov" title="Link to this definition">¶</a></dt>
 <dd><p>Compute parameter covariance using bootstrap</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.IIDBootstrap.cov-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.cov-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.IIDBootstrap.cov.func"><span class="sig-name descname">func</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable</span></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.cov.func" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that returns the statistic of interest as a
+<dt id="arch.bootstrap.IIDBootstrap.cov.func"><span class="n sig-name">func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.cov.func" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that returns the statistic of interest as a
 1-d array</p>
 </dd>
-<dt id="arch.bootstrap.IIDBootstrap.cov.reps"><span class="sig-name descname">reps</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, default 1000</span></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.cov.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
+<dt id="arch.bootstrap.IIDBootstrap.cov.reps"><span class="n sig-name">reps</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.cov.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
 </dd>
-<dt id="arch.bootstrap.IIDBootstrap.cov.recenter"><span class="sig-name descname">recenter</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, default True</span></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.cov.recenter" title="Permalink to this definition">¶</a></dt><dd><p>Whether to center the bootstrap variance estimator on the average
+<dt id="arch.bootstrap.IIDBootstrap.cov.recenter"><span class="n sig-name">recenter</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.cov.recenter" title="Permalink to this definition">¶</a></dt><dd><p>Whether to center the bootstrap variance estimator on the average
 of the bootstrap samples (True) or to center on the original sample
 estimate (False).  Default is True.</p>
 </dd>
-<dt id="arch.bootstrap.IIDBootstrap.cov.extra_kwargs"><span class="sig-name descname">extra_kwargs</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a>, default None</span></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.cov.extra_kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Dictionary of extra keyword arguments to pass to func</p>
+<dt id="arch.bootstrap.IIDBootstrap.cov.extra_kwargs"><span class="n sig-name">extra_kwargs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)">Any</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.cov.extra_kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Dictionary of extra keyword arguments to pass to func</p>
 </dd>
 </dl>
 </dd>
@@ -1075,7 +1075,7 @@ <h1 id="arch-bootstrap-iidbootstrap-cov">arch.bootstrap.IIDBootstrap.cov<a class
 <dd class="field-even"><p>Bootstrap covariance estimator</p>
 </dd>
 <dt class="field-odd" id="arch.bootstrap.IIDBootstrap.cov-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.cov-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -1182,7 +1182,7 @@ <h1 id="arch-bootstrap-iidbootstrap-cov">arch.bootstrap.IIDBootstrap.cov<a class
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1229,7 +1229,7 @@ <h1 id="arch-bootstrap-iidbootstrap-cov">arch.bootstrap.IIDBootstrap.cov<a class
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.generator.html b/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.generator.html
index d944483d64..9f84430946 100644
--- a/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.generator.html
+++ b/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.generator.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.generator - arch 6.1.0</title>
+        <title>arch.bootstrap.IIDBootstrap.generator - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -982,15 +982,15 @@
                   
 
 
-<h1 id="arch-bootstrap-iidbootstrap-generator">arch.bootstrap.IIDBootstrap.generator<a class="headerlink" href="#arch-bootstrap-iidbootstrap-generator" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-iidbootstrap-generator">arch.bootstrap.IIDBootstrap.generator<a class="headerlink" href="#arch-bootstrap-iidbootstrap-generator" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.bootstrap.IIDBootstrap.generator">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">IIDBootstrap.</span></span><span class="sig-name descname"><span class="pre">generator</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.24)"><span class="pre">Generator</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.24)"><span class="pre">RandomState</span></a></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.generator" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">IIDBootstrap.</span></span><span class="sig-name descname"><span class="pre">generator</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><span class="pre">Generator</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">RandomState</span></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.generator" title="Link to this definition">¶</a></dt>
 <dd><p>Set or get the instance PRNG</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.IIDBootstrap.generator-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.generator-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt><span class="sig-name descname">seed</span><span class="p"> : </span><span class="desctype"><span class="desctype">{Generator, RandomState}, optional</span></span></dt><dd><p>Generator or RandomState used to produce the pseudo-random
+<dt><span class="sig-name descname">seed</span><span class="p"> : </span><span class="desctype"><code class="docutils literal notranslate"><span class="pre">{Generator,</span> <span class="pre">RandomState}</span></code>, <em>optional</em></span></dt><dd><p>Generator or RandomState used to produce the pseudo-random
 values used in the bootstrap</p>
 </dd>
 </dl>
@@ -1000,7 +1000,7 @@ <h1 id="arch-bootstrap-iidbootstrap-generator">arch.bootstrap.IIDBootstrap.gener
 by bootstrap</p>
 </dd>
 <dt class="field-odd" id="arch.bootstrap.IIDBootstrap.generator-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.generator-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>{Generator, RandomState}</p>
+<dd class="field-odd"><p><code class="docutils literal notranslate"><span class="pre">{Generator,</span> <span class="pre">RandomState}</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1072,7 +1072,7 @@ <h1 id="arch-bootstrap-iidbootstrap-generator">arch.bootstrap.IIDBootstrap.gener
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1119,7 +1119,7 @@ <h1 id="arch-bootstrap-iidbootstrap-generator">arch.bootstrap.IIDBootstrap.gener
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.get_state.html b/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.get_state.html
index f20d404961..88fc0f5e67 100644
--- a/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.get_state.html
+++ b/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.get_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.get_state - arch 6.1.0</title>
+        <title>arch.bootstrap.IIDBootstrap.get_state - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -968,17 +968,17 @@
                   
 
 
-<h1 id="arch-bootstrap-iidbootstrap-get-state">arch.bootstrap.IIDBootstrap.get_state<a class="headerlink" href="#arch-bootstrap-iidbootstrap-get-state" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-iidbootstrap-get-state">arch.bootstrap.IIDBootstrap.get_state<a class="headerlink" href="#arch-bootstrap-iidbootstrap-get-state" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.bootstrap.IIDBootstrap.get_state">
-<span class="sig-prename descclassname"><span class="pre">IIDBootstrap.</span></span><span class="sig-name descname"><span class="pre">get_state</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/bootstrap/base.html#IIDBootstrap.get_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.get_state" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.IIDBootstrap.get_state">
+<span class="sig-prename descclassname"><span class="pre">IIDBootstrap.</span></span><span class="sig-name descname"><span class="pre">get_state</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Mapping" title="(in Python v3.11)"><span class="pre">Mapping</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/bootstrap/base.html#IIDBootstrap.get_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.get_state" title="Link to this definition">¶</a></dt>
 <dd><p>Gets the state of the bootstrap’s random number generator</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.IIDBootstrap.get_state-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.get_state-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>Dictionary containing the state.</p>
 </dd>
 <dt class="field-even" id="arch.bootstrap.IIDBootstrap.get_state-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.get_state-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1050,7 +1050,7 @@ <h1 id="arch-bootstrap-iidbootstrap-get-state">arch.bootstrap.IIDBootstrap.get_s
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1097,7 +1097,7 @@ <h1 id="arch-bootstrap-iidbootstrap-get-state">arch.bootstrap.IIDBootstrap.get_s
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.index.html b/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.index.html
index ea4775076e..d1be52be2e 100644
--- a/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.index.html
+++ b/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.index.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.index - arch 6.1.0</title>
+        <title>arch.bootstrap.IIDBootstrap.index - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -928,10 +928,10 @@
                   
 
 
-<h1 id="arch-bootstrap-iidbootstrap-index">arch.bootstrap.IIDBootstrap.index<a class="headerlink" href="#arch-bootstrap-iidbootstrap-index" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-iidbootstrap-index">arch.bootstrap.IIDBootstrap.index<a class="headerlink" href="#arch-bootstrap-iidbootstrap-index" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.IIDBootstrap.index">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">IIDBootstrap.</span></span><span class="sig-name descname"><span class="pre">index</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">numpy.ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">numpy.ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">numpy.ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.index" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">IIDBootstrap.</span></span><span class="sig-name descname"><span class="pre">index</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.index" title="Link to this definition">¶</a></dt>
 <dd><p>The current index of the bootstrap</p>
 </dd></dl>
 
@@ -1002,7 +1002,7 @@ <h1 id="arch-bootstrap-iidbootstrap-index">arch.bootstrap.IIDBootstrap.index<a c
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1049,7 +1049,7 @@ <h1 id="arch-bootstrap-iidbootstrap-index">arch.bootstrap.IIDBootstrap.index<a c
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.random_state.html b/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.random_state.html
index aa0053778c..909b410620 100644
--- a/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.random_state.html
+++ b/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.random_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.random_state - arch 6.1.0</title>
+        <title>arch.bootstrap.IIDBootstrap.random_state - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -982,15 +982,15 @@
                   
 
 
-<h1 id="arch-bootstrap-iidbootstrap-random-state">arch.bootstrap.IIDBootstrap.random_state<a class="headerlink" href="#arch-bootstrap-iidbootstrap-random-state" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-iidbootstrap-random-state">arch.bootstrap.IIDBootstrap.random_state<a class="headerlink" href="#arch-bootstrap-iidbootstrap-random-state" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.bootstrap.IIDBootstrap.random_state">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">IIDBootstrap.</span></span><span class="sig-name descname"><span class="pre">random_state</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.24)"><span class="pre">Generator</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.24)"><span class="pre">RandomState</span></a></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.random_state" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">IIDBootstrap.</span></span><span class="sig-name descname"><span class="pre">random_state</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><span class="pre">Generator</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">RandomState</span></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.random_state" title="Link to this definition">¶</a></dt>
 <dd><p>Set or get the instance random state</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.IIDBootstrap.random_state-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.random_state-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt><span class="sig-name descname">random_state</span><span class="p"> : </span><span class="desctype"><span class="desctype">RandomState</span></span></dt><dd><p>RandomState instance used by bootstrap</p>
+<dt><span class="sig-name descname">random_state</span><span class="p"> : </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.random.RandomState</span></code></a></span></dt><dd><p>RandomState instance used by bootstrap</p>
 </dd>
 </dl>
 </dd>
@@ -998,7 +998,7 @@ <h1 id="arch-bootstrap-iidbootstrap-random-state">arch.bootstrap.IIDBootstrap.ra
 <dd class="field-even"><p>RandomState instance used by bootstrap</p>
 </dd>
 <dt class="field-odd" id="arch.bootstrap.IIDBootstrap.random_state-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.random_state-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>RandomState</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.random.RandomState</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1070,7 +1070,7 @@ <h1 id="arch-bootstrap-iidbootstrap-random-state">arch.bootstrap.IIDBootstrap.ra
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1117,7 +1117,7 @@ <h1 id="arch-bootstrap-iidbootstrap-random-state">arch.bootstrap.IIDBootstrap.ra
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.reset.html b/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.reset.html
index 9d82a3e2cf..3b1fb7d528 100644
--- a/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.reset.html
+++ b/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.reset.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.reset - arch 6.1.0</title>
+        <title>arch.bootstrap.IIDBootstrap.reset - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -600,13 +600,6 @@
       </ul>
     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.IIDBootstrap.reset-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
       </ul>
@@ -966,13 +959,6 @@
       </ul>
     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.IIDBootstrap.reset-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
       </ul>
@@ -994,23 +980,20 @@
                   
 
 
-<h1 id="arch-bootstrap-iidbootstrap-reset">arch.bootstrap.IIDBootstrap.reset<a class="headerlink" href="#arch-bootstrap-iidbootstrap-reset" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-iidbootstrap-reset">arch.bootstrap.IIDBootstrap.reset<a class="headerlink" href="#arch-bootstrap-iidbootstrap-reset" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.bootstrap.IIDBootstrap.reset">
-<span class="sig-prename descclassname"><span class="pre">IIDBootstrap.</span></span><span class="sig-name descname"><span class="pre">reset</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.reset.use_seed" title="arch.bootstrap.IIDBootstrap.reset.use_seed (Python parameter) — Flag indicating whether to use the last seed if provided.  If False or if no seed has been set, the bootstrap will be reset to the initial state.  Default is True"><span class="n"><span class="pre">use_seed</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/bootstrap/base.html#IIDBootstrap.reset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.reset" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">IIDBootstrap.</span></span><span class="sig-name descname"><span class="pre">reset</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.reset.use_seed" title="arch.bootstrap.IIDBootstrap.reset.use_seed (Python parameter) — Flag indicating whether to use the last seed if provided.  If False or if no seed has been set, the bootstrap will be reset to the initial state.  Default is True"><span class="n"><span class="pre">use_seed</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/bootstrap/base.html#IIDBootstrap.reset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.reset" title="Link to this definition">¶</a></dt>
 <dd><p>Resets the bootstrap to either its initial state or the last seed.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.IIDBootstrap.reset-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.reset-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.IIDBootstrap.reset.use_seed"><span class="sig-name descname">use_seed</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, default True</span></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.reset.use_seed" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to use the last seed if provided.  If
+<dt id="arch.bootstrap.IIDBootstrap.reset.use_seed"><span class="n sig-name">use_seed</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.reset.use_seed" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to use the last seed if provided.  If
 False or if no seed has been set, the bootstrap will be reset
 to the initial state.  Default is True</p>
 </dd>
 </dl>
 </dd>
-<dt class="field-even" id="arch.bootstrap.IIDBootstrap.reset-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.reset-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code></a></p>
-</dd>
 </dl>
 </dd></dl>
 
@@ -1081,7 +1064,7 @@ <h1 id="arch-bootstrap-iidbootstrap-reset">arch.bootstrap.IIDBootstrap.reset<a c
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1128,7 +1111,7 @@ <h1 id="arch-bootstrap-iidbootstrap-reset">arch.bootstrap.IIDBootstrap.reset<a c
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.seed.html b/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.seed.html
index 44d3087f48..0d4c948154 100644
--- a/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.seed.html
+++ b/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.seed.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.seed - arch 6.1.0</title>
+        <title>arch.bootstrap.IIDBootstrap.seed - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -615,13 +615,6 @@
       </ul>
     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.IIDBootstrap.seed-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
       </ul>
@@ -966,13 +959,6 @@
       </ul>
     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.IIDBootstrap.seed-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
       </ul>
@@ -994,21 +980,18 @@
                   
 
 
-<h1 id="arch-bootstrap-iidbootstrap-seed">arch.bootstrap.IIDBootstrap.seed<a class="headerlink" href="#arch-bootstrap-iidbootstrap-seed" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-iidbootstrap-seed">arch.bootstrap.IIDBootstrap.seed<a class="headerlink" href="#arch-bootstrap-iidbootstrap-seed" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.bootstrap.IIDBootstrap.seed">
-<span class="sig-prename descclassname"><span class="pre">IIDBootstrap.</span></span><span class="sig-name descname"><span class="pre">seed</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.seed.value" title="arch.bootstrap.IIDBootstrap.seed.value (Python parameter) — Value to use as the seed."><span class="n"><span class="pre">value</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/bootstrap/base.html#IIDBootstrap.seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.seed" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">IIDBootstrap.</span></span><span class="sig-name descname"><span class="pre">seed</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.seed.value" title="arch.bootstrap.IIDBootstrap.seed.value (Python parameter) — Value to use as the seed."><span class="n"><span class="pre">value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/bootstrap/base.html#IIDBootstrap.seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.seed" title="Link to this definition">¶</a></dt>
 <dd><p>Reseeds the bootstrap’s random number generator</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.IIDBootstrap.seed-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.seed-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.IIDBootstrap.seed.value"><span class="sig-name descname">value</span><span class="p"> : </span><span class="desctype"><span class="desctype">{int, List[<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>], ndarray}</span></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.seed.value" title="Permalink to this definition">¶</a></dt><dd><p>Value to use as the seed.</p>
+<dt id="arch.bootstrap.IIDBootstrap.seed.value"><span class="n sig-name">value</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.seed.value" title="Permalink to this definition">¶</a></dt><dd><p>Value to use as the seed.</p>
 </dd>
 </dl>
 </dd>
-<dt class="field-even" id="arch.bootstrap.IIDBootstrap.seed-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.seed-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code></a></p>
-</dd>
 </dl>
 </dd></dl>
 
@@ -1079,7 +1062,7 @@ <h1 id="arch-bootstrap-iidbootstrap-seed">arch.bootstrap.IIDBootstrap.seed<a cla
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1126,7 +1109,7 @@ <h1 id="arch-bootstrap-iidbootstrap-seed">arch.bootstrap.IIDBootstrap.seed<a cla
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.set_state.html b/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.set_state.html
index 19bfddf7fe..a2df61928b 100644
--- a/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.set_state.html
+++ b/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.set_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.set_state - arch 6.1.0</title>
+        <title>arch.bootstrap.IIDBootstrap.set_state - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -630,13 +630,6 @@
       </ul>
     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.IIDBootstrap.set_state-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
       </ul>
@@ -966,13 +959,6 @@
       </ul>
     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.IIDBootstrap.set_state-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
       </ul>
@@ -994,21 +980,18 @@
                   
 
 
-<h1 id="arch-bootstrap-iidbootstrap-set-state">arch.bootstrap.IIDBootstrap.set_state<a class="headerlink" href="#arch-bootstrap-iidbootstrap-set-state" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-iidbootstrap-set-state">arch.bootstrap.IIDBootstrap.set_state<a class="headerlink" href="#arch-bootstrap-iidbootstrap-set-state" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.bootstrap.IIDBootstrap.set_state">
-<span class="sig-prename descclassname"><span class="pre">IIDBootstrap.</span></span><span class="sig-name descname"><span class="pre">set_state</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.set_state.state" title="arch.bootstrap.IIDBootstrap.set_state.state (Python parameter) — Dictionary or tuple containing the state."><span class="n"><span class="pre">state</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/bootstrap/base.html#IIDBootstrap.set_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.set_state" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.IIDBootstrap.set_state">
+<span class="sig-prename descclassname"><span class="pre">IIDBootstrap.</span></span><span class="sig-name descname"><span class="pre">set_state</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.set_state.state" title="arch.bootstrap.IIDBootstrap.set_state.state (Python parameter) — Dictionary or tuple containing the state."><span class="n"><span class="pre">state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/bootstrap/base.html#IIDBootstrap.set_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.set_state" title="Link to this definition">¶</a></dt>
 <dd><p>Sets the state of the bootstrap’s random number generator</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.IIDBootstrap.set_state-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.set_state-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.IIDBootstrap.set_state.state"><span class="sig-name descname">state</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a></span></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.set_state.state" title="Permalink to this definition">¶</a></dt><dd><p>Dictionary or tuple containing the state.</p>
+<dt id="arch.bootstrap.IIDBootstrap.set_state.state"><span class="n sig-name">state</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)">Any</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.set_state.state" title="Permalink to this definition">¶</a></dt><dd><p>Dictionary or tuple containing the state.</p>
 </dd>
 </dl>
 </dd>
-<dt class="field-even" id="arch.bootstrap.IIDBootstrap.set_state-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.set_state-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code></a></p>
-</dd>
 </dl>
 </dd></dl>
 
@@ -1079,7 +1062,7 @@ <h1 id="arch-bootstrap-iidbootstrap-set-state">arch.bootstrap.IIDBootstrap.set_s
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1126,7 +1109,7 @@ <h1 id="arch-bootstrap-iidbootstrap-set-state">arch.bootstrap.IIDBootstrap.set_s
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.state.html b/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.state.html
index 4a0b0e144a..a58db433ad 100644
--- a/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.state.html
+++ b/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.state - arch 6.1.0</title>
+        <title>arch.bootstrap.IIDBootstrap.state - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -968,10 +968,10 @@
                   
 
 
-<h1 id="arch-bootstrap-iidbootstrap-state">arch.bootstrap.IIDBootstrap.state<a class="headerlink" href="#arch-bootstrap-iidbootstrap-state" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-iidbootstrap-state">arch.bootstrap.IIDBootstrap.state<a class="headerlink" href="#arch-bootstrap-iidbootstrap-state" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.IIDBootstrap.state">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">IIDBootstrap.</span></span><span class="sig-name descname"><span class="pre">state</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">numpy.ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Mapping" title="(in Python v3.11)"><span class="pre">Mapping</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.state" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">IIDBootstrap.</span></span><span class="sig-name descname"><span class="pre">state</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Mapping" title="(in Python v3.11)"><span class="pre">Mapping</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.state" title="Link to this definition">¶</a></dt>
 <dd><p>Set or get the generator’s state</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.IIDBootstrap.state-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.state-returns" title="Permalink to this headline">¶</a></dt>
@@ -980,7 +980,7 @@ <h1 id="arch-bootstrap-iidbootstrap-state">arch.bootstrap.IIDBootstrap.state<a c
 Otherwise it is a dictionary.</p>
 </dd>
 <dt class="field-even" id="arch.bootstrap.IIDBootstrap.state-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.state-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p>{tuple, dict}</p>
+<dd class="field-even"><p><code class="docutils literal notranslate"><span class="pre">{tuple,</span> <span class="pre">dict}</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1052,7 +1052,7 @@ <h1 id="arch-bootstrap-iidbootstrap-state">arch.bootstrap.IIDBootstrap.state<a c
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1099,7 +1099,7 @@ <h1 id="arch-bootstrap-iidbootstrap-state">arch.bootstrap.IIDBootstrap.state<a c
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.update_indices.html b/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.update_indices.html
index 775f5da3eb..6f7f4336f3 100644
--- a/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.update_indices.html
+++ b/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.update_indices.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.update_indices - arch 6.1.0</title>
+        <title>arch.bootstrap.IIDBootstrap.update_indices - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -624,19 +624,6 @@
     <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="arch.bootstrap.IIDBootstrap.update_indices (Python method) — Update indices for the next iteration of the bootstrap.  This must be overridden when creating new bootstraps." class="md-ellipsis">IIDBootstrap.<wbr>update_<wbr>indices</span>
   </a>
   
-    <nav class="md-nav" aria-label="IIDBootstrap.update_indices">
-      <ul class="md-nav__list">
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.IIDBootstrap.update_indices-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
-</li>
-        
-      </ul>
-    </nav>
-  
 </li>
       
     </ul>
@@ -925,19 +912,6 @@
     <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="arch.bootstrap.IIDBootstrap.update_indices (Python method) — Update indices for the next iteration of the bootstrap.  This must be overridden when creating new bootstraps." class="md-ellipsis">IIDBootstrap.<wbr>update_<wbr>indices</span>
   </a>
   
-    <nav class="md-nav" aria-label="IIDBootstrap.update_indices">
-      <ul class="md-nav__list">
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.IIDBootstrap.update_indices-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
-</li>
-        
-      </ul>
-    </nav>
-  
 </li>
       
     </ul>
@@ -954,17 +928,12 @@
                   
 
 
-<h1 id="arch-bootstrap-iidbootstrap-update-indices">arch.bootstrap.IIDBootstrap.update_indices<a class="headerlink" href="#arch-bootstrap-iidbootstrap-update-indices" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-iidbootstrap-update-indices">arch.bootstrap.IIDBootstrap.update_indices<a class="headerlink" href="#arch-bootstrap-iidbootstrap-update-indices" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.bootstrap.IIDBootstrap.update_indices">
-<span class="sig-prename descclassname"><span class="pre">IIDBootstrap.</span></span><span class="sig-name descname"><span class="pre">update_indices</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/bootstrap/base.html#IIDBootstrap.update_indices"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.update_indices" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.IIDBootstrap.update_indices">
+<span class="sig-prename descclassname"><span class="pre">IIDBootstrap.</span></span><span class="sig-name descname"><span class="pre">update_indices</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/bootstrap/base.html#IIDBootstrap.update_indices"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.update_indices" title="Link to this definition">¶</a></dt>
 <dd><p>Update indices for the next iteration of the bootstrap.  This must
 be overridden when creating new bootstraps.</p>
-<dl class="field-list simple">
-<dt class="field-odd" id="arch.bootstrap.IIDBootstrap.update_indices-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.update_indices-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.11)"><code class="xref py py-data docutils literal notranslate"><span class="pre">Union</span></code></a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>, <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>, <a class="reference external" href="https://docs.python.org/3/library/constants.html#Ellipsis" title="(in Python v3.11)"><code class="xref py py-data docutils literal notranslate"><span class="pre">...</span></code></a>], <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span></code></a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>], <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>]]]</p>
-</dd>
-</dl>
 </dd></dl>
 
 
@@ -1034,7 +1003,7 @@ <h1 id="arch-bootstrap-iidbootstrap-update-indices">arch.bootstrap.IIDBootstrap.
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1081,7 +1050,7 @@ <h1 id="arch-bootstrap-iidbootstrap-update-indices">arch.bootstrap.IIDBootstrap.
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.var.html b/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.var.html
index bac6a93d81..5b041354d9 100644
--- a/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.var.html
+++ b/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.var.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.var - arch 6.1.0</title>
+        <title>arch.bootstrap.IIDBootstrap.var - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1050,24 +1050,24 @@
                   
 
 
-<h1 id="arch-bootstrap-iidbootstrap-var">arch.bootstrap.IIDBootstrap.var<a class="headerlink" href="#arch-bootstrap-iidbootstrap-var" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-iidbootstrap-var">arch.bootstrap.IIDBootstrap.var<a class="headerlink" href="#arch-bootstrap-iidbootstrap-var" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.bootstrap.IIDBootstrap.var">
-<span class="sig-prename descclassname"><span class="pre">IIDBootstrap.</span></span><span class="sig-name descname"><span class="pre">var</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.var.func" title="arch.bootstrap.IIDBootstrap.var.func (Python parameter) — Callable function that returns the statistic of interest as a 1-d array"><span class="n"><span class="pre">func</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.var.reps" title="arch.bootstrap.IIDBootstrap.var.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.var.recenter" title="arch.bootstrap.IIDBootstrap.var.recenter (Python parameter) — Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."><span class="n"><span class="pre">recenter</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.var.extra_kwargs" title="arch.bootstrap.IIDBootstrap.var.extra_kwargs (Python parameter) — Dictionary of extra keyword arguments to pass to func"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/bootstrap/base.html#IIDBootstrap.var"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.var" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.IIDBootstrap.var">
+<span class="sig-prename descclassname"><span class="pre">IIDBootstrap.</span></span><span class="sig-name descname"><span class="pre">var</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.var.func" title="arch.bootstrap.IIDBootstrap.var.func (Python parameter) — Callable function that returns the statistic of interest as a 1-d array"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.var.reps" title="arch.bootstrap.IIDBootstrap.var.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.var.recenter" title="arch.bootstrap.IIDBootstrap.var.recenter (Python parameter) — Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."><span class="n"><span class="pre">recenter</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.var.extra_kwargs" title="arch.bootstrap.IIDBootstrap.var.extra_kwargs (Python parameter) — Dictionary of extra keyword arguments to pass to func"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/bootstrap/base.html#IIDBootstrap.var"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.var" title="Link to this definition">¶</a></dt>
 <dd><p>Compute parameter variance using bootstrap</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.IIDBootstrap.var-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.var-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.IIDBootstrap.var.func"><span class="sig-name descname">func</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable</span></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.var.func" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that returns the statistic of interest as a
+<dt id="arch.bootstrap.IIDBootstrap.var.func"><span class="n sig-name">func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.var.func" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that returns the statistic of interest as a
 1-d array</p>
 </dd>
-<dt id="arch.bootstrap.IIDBootstrap.var.reps"><span class="sig-name descname">reps</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, default 1000</span></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.var.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
+<dt id="arch.bootstrap.IIDBootstrap.var.reps"><span class="n sig-name">reps</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.var.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
 </dd>
-<dt id="arch.bootstrap.IIDBootstrap.var.recenter"><span class="sig-name descname">recenter</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, default True</span></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.var.recenter" title="Permalink to this definition">¶</a></dt><dd><p>Whether to center the bootstrap variance estimator on the average
+<dt id="arch.bootstrap.IIDBootstrap.var.recenter"><span class="n sig-name">recenter</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.var.recenter" title="Permalink to this definition">¶</a></dt><dd><p>Whether to center the bootstrap variance estimator on the average
 of the bootstrap samples (True) or to center on the original sample
 estimate (False).  Default is True.</p>
 </dd>
-<dt id="arch.bootstrap.IIDBootstrap.var.extra_kwargs"><span class="sig-name descname">extra_kwargs</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a>, default None</span></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.var.extra_kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Dictionary of extra keyword arguments to pass to func</p>
+<dt id="arch.bootstrap.IIDBootstrap.var.extra_kwargs"><span class="n sig-name">extra_kwargs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)">Any</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.var.extra_kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Dictionary of extra keyword arguments to pass to func</p>
 </dd>
 </dl>
 </dd>
@@ -1075,7 +1075,7 @@ <h1 id="arch-bootstrap-iidbootstrap-var">arch.bootstrap.IIDBootstrap.var<a class
 <dd class="field-even"><p>Bootstrap variance estimator</p>
 </dd>
 <dt class="field-odd" id="arch.bootstrap.IIDBootstrap.var-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.var-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -1182,7 +1182,7 @@ <h1 id="arch-bootstrap-iidbootstrap-var">arch.bootstrap.IIDBootstrap.var<a class
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1229,7 +1229,7 @@ <h1 id="arch-bootstrap-iidbootstrap-var">arch.bootstrap.IIDBootstrap.var<a class
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.apply.html b/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.apply.html
index 331b680498..c9f3b92a56 100644
--- a/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.apply.html
+++ b/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.apply.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.apply - arch 6.1.0</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.apply - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1036,19 +1036,19 @@
                   
 
 
-<h1 id="arch-bootstrap-independentsamplesbootstrap-apply">arch.bootstrap.IndependentSamplesBootstrap.apply<a class="headerlink" href="#arch-bootstrap-independentsamplesbootstrap-apply" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-independentsamplesbootstrap-apply">arch.bootstrap.IndependentSamplesBootstrap.apply<a class="headerlink" href="#arch-bootstrap-independentsamplesbootstrap-apply" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.IndependentSamplesBootstrap.apply">
-<span class="sig-prename descclassname"><span class="pre">IndependentSamplesBootstrap.</span></span><span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.apply.func" title="arch.bootstrap.IndependentSamplesBootstrap.apply.func (Python parameter) — Function the computes parameter values.  See Notes for requirements"><span class="n"><span class="pre">func</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.apply.reps" title="arch.bootstrap.IndependentSamplesBootstrap.apply.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.apply.extra_kwargs" title="arch.bootstrap.IndependentSamplesBootstrap.apply.extra_kwargs (Python parameter) — Extra keyword arguments to use when calling func.  Must not conflict with keyword arguments used to initialize bootstrap"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.apply" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">IndependentSamplesBootstrap.</span></span><span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.apply.func" title="arch.bootstrap.IndependentSamplesBootstrap.apply.func (Python parameter) — Function the computes parameter values.  See Notes for requirements"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.apply.reps" title="arch.bootstrap.IndependentSamplesBootstrap.apply.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.apply.extra_kwargs" title="arch.bootstrap.IndependentSamplesBootstrap.apply.extra_kwargs (Python parameter) — Extra keyword arguments to use when calling func.  Must not conflict with keyword arguments used to initialize bootstrap"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.apply" title="Link to this definition">¶</a></dt>
 <dd><p>Applies a function to bootstrap replicated data</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.IndependentSamplesBootstrap.apply-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.apply-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.IndependentSamplesBootstrap.apply.func"><span class="sig-name descname">func</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable</span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.apply.func" title="Permalink to this definition">¶</a></dt><dd><p>Function the computes parameter values.  See Notes for requirements</p>
+<dt id="arch.bootstrap.IndependentSamplesBootstrap.apply.func"><span class="n sig-name">func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.apply.func" title="Permalink to this definition">¶</a></dt><dd><p>Function the computes parameter values.  See Notes for requirements</p>
 </dd>
-<dt id="arch.bootstrap.IndependentSamplesBootstrap.apply.reps"><span class="sig-name descname">reps</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, default 1000</span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.apply.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
+<dt id="arch.bootstrap.IndependentSamplesBootstrap.apply.reps"><span class="n sig-name">reps</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.apply.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
 </dd>
-<dt id="arch.bootstrap.IndependentSamplesBootstrap.apply.extra_kwargs"><span class="sig-name descname">extra_kwargs</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a>, default None</span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.apply.extra_kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Extra keyword arguments to use when calling func.  Must not
+<dt id="arch.bootstrap.IndependentSamplesBootstrap.apply.extra_kwargs"><span class="n sig-name">extra_kwargs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)">Any</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.apply.extra_kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Extra keyword arguments to use when calling func.  Must not
 conflict with keyword arguments used to initialize bootstrap</p>
 </dd>
 </dl>
@@ -1058,7 +1058,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap-apply">arch.bootstrap.Indepen
 corresponds to a bootstrap iteration</p>
 </dd>
 <dt class="field-odd" id="arch.bootstrap.IndependentSamplesBootstrap.apply-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.apply-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -1148,7 +1148,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap-apply">arch.bootstrap.Indepen
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1195,7 +1195,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap-apply">arch.bootstrap.Indepen
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.bootstrap.html b/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.bootstrap.html
index 85cdd0690b..1d818b6549 100644
--- a/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.bootstrap.html
+++ b/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.bootstrap.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.bootstrap - arch 6.1.0</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.bootstrap - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1008,15 +1008,15 @@
                   
 
 
-<h1 id="arch-bootstrap-independentsamplesbootstrap-bootstrap">arch.bootstrap.IndependentSamplesBootstrap.bootstrap<a class="headerlink" href="#arch-bootstrap-independentsamplesbootstrap-bootstrap" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-independentsamplesbootstrap-bootstrap">arch.bootstrap.IndependentSamplesBootstrap.bootstrap<a class="headerlink" href="#arch-bootstrap-independentsamplesbootstrap-bootstrap" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.bootstrap.IndependentSamplesBootstrap.bootstrap">
-<span class="sig-prename descclassname"><span class="pre">IndependentSamplesBootstrap.</span></span><span class="sig-name descname"><span class="pre">bootstrap</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.bootstrap.reps" title="arch.bootstrap.IndependentSamplesBootstrap.bootstrap.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.bootstrap" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.IndependentSamplesBootstrap.bootstrap">
+<span class="sig-prename descclassname"><span class="pre">IndependentSamplesBootstrap.</span></span><span class="sig-name descname"><span class="pre">bootstrap</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.bootstrap.reps" title="arch.bootstrap.IndependentSamplesBootstrap.bootstrap.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Generator" title="(in Python v3.11)"><span class="pre">Generator</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.bootstrap" title="Link to this definition">¶</a></dt>
 <dd><p>Iterator for use when bootstrapping</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.IndependentSamplesBootstrap.bootstrap-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.bootstrap-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.IndependentSamplesBootstrap.bootstrap.reps"><span class="sig-name descname">reps</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.bootstrap.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
+<dt id="arch.bootstrap.IndependentSamplesBootstrap.bootstrap.reps"><span class="n sig-name">reps</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.bootstrap.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
 </dd>
 </dl>
 </dd>
@@ -1024,7 +1024,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap-bootstrap">arch.bootstrap.Ind
 <dd class="field-even"><p>Generator to iterate over in bootstrap calculations</p>
 </dd>
 <dt class="field-odd" id="arch.bootstrap.IndependentSamplesBootstrap.bootstrap-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.bootstrap-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>generator</p>
+<dd class="field-odd"><p><a class="reference internal" href="arch.bootstrap.IndependentSamplesBootstrap.generator.html#arch.bootstrap.IndependentSamplesBootstrap.generator" title="arch.bootstrap.IndependentSamplesBootstrap.generator (Python property) — Set or get the instance PRNG"><code class="xref py py-class docutils literal notranslate"><span class="pre">generator</span></code></a></p>
 </dd>
 </dl>
 <p class="example rubric">Examples</p>
@@ -1116,7 +1116,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap-bootstrap">arch.bootstrap.Ind
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1163,7 +1163,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap-bootstrap">arch.bootstrap.Ind
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.clone.html b/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.clone.html
index 2fe8c42ab5..953acb2ca2 100644
--- a/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.clone.html
+++ b/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.clone.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.clone - arch 6.1.0</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.clone - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1036,19 +1036,19 @@
                   
 
 
-<h1 id="arch-bootstrap-independentsamplesbootstrap-clone">arch.bootstrap.IndependentSamplesBootstrap.clone<a class="headerlink" href="#arch-bootstrap-independentsamplesbootstrap-clone" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-independentsamplesbootstrap-clone">arch.bootstrap.IndependentSamplesBootstrap.clone<a class="headerlink" href="#arch-bootstrap-independentsamplesbootstrap-clone" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.bootstrap.IndependentSamplesBootstrap.clone">
-<span class="sig-prename descclassname"><span class="pre">IndependentSamplesBootstrap.</span></span><span class="sig-name descname"><span class="pre">clone</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.clone.args" title="arch.bootstrap.IndependentSamplesBootstrap.clone.args (Python parameter) — Positional arguments to bootstrap"><span class="n"><span class="pre">args</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.clone.seed" title="arch.bootstrap.IndependentSamplesBootstrap.clone.seed (Python parameter) — The seed value to pass to the closed generator"><span class="n"><span class="pre">seed</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">**</span></span><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.clone.kwargs" title="arch.bootstrap.IndependentSamplesBootstrap.clone.kwargs (Python parameter) — Keyword arguments to bootstrap"><span class="n"><span class="pre">kwargs</span></span></a></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.clone" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.IndependentSamplesBootstrap.clone">
+<span class="sig-prename descclassname"><span class="pre">IndependentSamplesBootstrap.</span></span><span class="sig-name descname"><span class="pre">clone</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.clone.args" title="arch.bootstrap.IndependentSamplesBootstrap.clone.args (Python parameter) — Positional arguments to bootstrap"><span class="n"><span class="pre">args</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">ArrayLike</span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.clone.seed" title="arch.bootstrap.IndependentSamplesBootstrap.clone.seed (Python parameter) — The seed value to pass to the closed generator"><span class="n"><span class="pre">seed</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Generator</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">RandomState</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">**</span></span><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.clone.kwargs" title="arch.bootstrap.IndependentSamplesBootstrap.clone.kwargs (Python parameter) — Keyword arguments to bootstrap"><span class="n"><span class="pre">kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">ArrayLike</span></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.bootstrap.IIDBootstrap.html#arch.bootstrap.IIDBootstrap" title="arch.bootstrap.IIDBootstrap (Python class) — Bootstrap using uniform resampling"><span class="pre">IIDBootstrap</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.clone" title="Link to this definition">¶</a></dt>
 <dd><p>Clones the bootstrap using different data with a fresh prng.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.IndependentSamplesBootstrap.clone-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.clone-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.IndependentSamplesBootstrap.clone.args"><span class="o">*</span><span class="n sig-name">args</span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.clone.args" title="Permalink to this definition">¶</a></dt><dd><p>Positional arguments to bootstrap</p>
+<dt id="arch.bootstrap.IndependentSamplesBootstrap.clone.args"><span class="o">*</span><span class="n sig-name">args</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype">ArrayLike</span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.clone.args" title="Permalink to this definition">¶</a></dt><dd><p>Positional arguments to bootstrap</p>
 </dd>
-<dt id="arch.bootstrap.IndependentSamplesBootstrap.clone.seed"><span class="n sig-name">seed</span><span class="o">=</span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.clone.seed" title="Permalink to this definition">¶</a></dt><dd><p>The seed value to pass to the closed generator</p>
+<dt id="arch.bootstrap.IndependentSamplesBootstrap.clone.seed"><span class="n sig-name">seed</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span>Generator<span class="w"> </span><span class="p">|</span><span class="w"> </span>RandomState</span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.clone.seed" title="Permalink to this definition">¶</a></dt><dd><p>The seed value to pass to the closed generator</p>
 </dd>
-<dt id="arch.bootstrap.IndependentSamplesBootstrap.clone.kwargs"><span class="o">**</span><span class="n sig-name">kwargs</span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.clone.kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Keyword arguments to bootstrap</p>
+<dt id="arch.bootstrap.IndependentSamplesBootstrap.clone.kwargs"><span class="o">**</span><span class="n sig-name">kwargs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype">ArrayLike</span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.clone.kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Keyword arguments to bootstrap</p>
 </dd>
 </dl>
 </dd>
@@ -1056,7 +1056,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap-clone">arch.bootstrap.Indepen
 <dd class="field-even"><p>Bootstrap instance</p>
 </dd>
 <dt class="field-odd" id="arch.bootstrap.IndependentSamplesBootstrap.clone-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.clone-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>bs</p>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">bs</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1128,7 +1128,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap-clone">arch.bootstrap.Indepen
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1175,7 +1175,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap-clone">arch.bootstrap.Indepen
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.conf_int.html b/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.conf_int.html
index 358399bac0..8137474d71 100644
--- a/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.conf_int.html
+++ b/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.conf_int.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.conf_int - arch 6.1.0</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.conf_int - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1134,44 +1134,44 @@
                   
 
 
-<h1 id="arch-bootstrap-independentsamplesbootstrap-conf-int">arch.bootstrap.IndependentSamplesBootstrap.conf_int<a class="headerlink" href="#arch-bootstrap-independentsamplesbootstrap-conf-int" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-independentsamplesbootstrap-conf-int">arch.bootstrap.IndependentSamplesBootstrap.conf_int<a class="headerlink" href="#arch-bootstrap-independentsamplesbootstrap-conf-int" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.IndependentSamplesBootstrap.conf_int">
-<span class="sig-prename descclassname"><span class="pre">IndependentSamplesBootstrap.</span></span><span class="sig-name descname"><span class="pre">conf_int</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.func" title="arch.bootstrap.IndependentSamplesBootstrap.conf_int.func (Python parameter) — Function the computes parameter values.  See Notes for requirements"><span class="n"><span class="pre">func</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.reps" title="arch.bootstrap.IndependentSamplesBootstrap.conf_int.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.method" title="arch.bootstrap.IndependentSamplesBootstrap.conf_int.method (Python parameter) — One of 'basic', 'percentile', 'studentized', 'norm' (identical to 'var', 'cov'), 'bc' (identical to 'debiased', 'bias-corrected'), or 'bca'"><span class="n"><span class="pre">method</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;basic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.size" title="arch.bootstrap.IndependentSamplesBootstrap.conf_int.size (Python parameter) — Coverage of confidence interval"><span class="n"><span class="pre">size</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.95</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.tail" title="arch.bootstrap.IndependentSamplesBootstrap.conf_int.tail (Python parameter) — One of 'two', 'upper' or 'lower'."><span class="n"><span class="pre">tail</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;two&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.extra_kwargs" title="arch.bootstrap.IndependentSamplesBootstrap.conf_int.extra_kwargs (Python parameter) — Extra keyword arguments to use when calling func and std_err_func, when appropriate"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.reuse" title="arch.bootstrap.IndependentSamplesBootstrap.conf_int.reuse (Python parameter) — Flag indicating whether to reuse previously computed bootstrap results.  This allows alternative methods to be compared without rerunning the bootstrap simulation.  Reuse is ignored if reps is not the same across multiple runs, func changes across calls, or method is 'studentized'."><span class="n"><span class="pre">reuse</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.sampling" title="arch.bootstrap.IndependentSamplesBootstrap.conf_int.sampling (Python parameter) — Type of sampling to use: 'nonparametric', 'semi-parametric' (or 'semi') or 'parametric'.  The default is 'nonparametric'.  See notes about the changes to func required when using 'semi' or 'parametric'."><span class="n"><span class="pre">sampling</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;nonparametric&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.std_err_func" title="arch.bootstrap.IndependentSamplesBootstrap.conf_int.std_err_func (Python parameter) — Function to use when standardizing estimated parameters when using the studentized bootstrap.  Providing an analytical function eliminates the need for a nested bootstrap"><span class="n"><span class="pre">std_err_func</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.studentize_reps" title="arch.bootstrap.IndependentSamplesBootstrap.conf_int.studentize_reps (Python parameter) — Number of bootstraps to use in the inner bootstrap when using the studentized bootstrap.  Ignored when std_err_func is provided"><span class="n"><span class="pre">studentize_reps</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">IndependentSamplesBootstrap.</span></span><span class="sig-name descname"><span class="pre">conf_int</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.func" title="arch.bootstrap.IndependentSamplesBootstrap.conf_int.func (Python parameter) — Function the computes parameter values.  See Notes for requirements"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.reps" title="arch.bootstrap.IndependentSamplesBootstrap.conf_int.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.method" title="arch.bootstrap.IndependentSamplesBootstrap.conf_int.method (Python parameter) — One of 'basic', 'percentile', 'studentized', 'norm' (identical to 'var', 'cov'), 'bc' (identical to 'debiased', 'bias-corrected'), or 'bca'"><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'basic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'percentile'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'studentized'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'norm'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bc'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bca'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;basic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.size" title="arch.bootstrap.IndependentSamplesBootstrap.conf_int.size (Python parameter) — Coverage of confidence interval"><span class="n"><span class="pre">size</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.95</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.tail" title="arch.bootstrap.IndependentSamplesBootstrap.conf_int.tail (Python parameter) — One of 'two', 'upper' or 'lower'."><span class="n"><span class="pre">tail</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'two'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'upper'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'lower'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;two&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.extra_kwargs" title="arch.bootstrap.IndependentSamplesBootstrap.conf_int.extra_kwargs (Python parameter) — Extra keyword arguments to use when calling func and std_err_func, when appropriate"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.reuse" title="arch.bootstrap.IndependentSamplesBootstrap.conf_int.reuse (Python parameter) — Flag indicating whether to reuse previously computed bootstrap results.  This allows alternative methods to be compared without rerunning the bootstrap simulation.  Reuse is ignored if reps is not the same across multiple runs, func changes across calls, or method is 'studentized'."><span class="n"><span class="pre">reuse</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.sampling" title="arch.bootstrap.IndependentSamplesBootstrap.conf_int.sampling (Python parameter) — Type of sampling to use: 'nonparametric', 'semi-parametric' (or 'semi') or 'parametric'.  The default is 'nonparametric'.  See notes about the changes to func required when using 'semi' or 'parametric'."><span class="n"><span class="pre">sampling</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'nonparametric'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'semi-parametric'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'semi'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'parametric'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'semiparametric'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;nonparametric&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.std_err_func" title="arch.bootstrap.IndependentSamplesBootstrap.conf_int.std_err_func (Python parameter) — Function to use when standardizing estimated parameters when using the studentized bootstrap.  Providing an analytical function eliminates the need for a nested bootstrap"><span class="n"><span class="pre">std_err_func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.studentize_reps" title="arch.bootstrap.IndependentSamplesBootstrap.conf_int.studentize_reps (Python parameter) — Number of bootstraps to use in the inner bootstrap when using the studentized bootstrap.  Ignored when std_err_func is provided"><span class="n"><span class="pre">studentize_reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int" title="Link to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.IndependentSamplesBootstrap.conf_int-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.IndependentSamplesBootstrap.conf_int.func"><span class="sig-name descname">func</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable</span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.func" title="Permalink to this definition">¶</a></dt><dd><p>Function the computes parameter values.  See Notes for requirements</p>
+<dt id="arch.bootstrap.IndependentSamplesBootstrap.conf_int.func"><span class="n sig-name">func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.func" title="Permalink to this definition">¶</a></dt><dd><p>Function the computes parameter values.  See Notes for requirements</p>
 </dd>
-<dt id="arch.bootstrap.IndependentSamplesBootstrap.conf_int.reps"><span class="sig-name descname">reps</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, default 1000</span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
+<dt id="arch.bootstrap.IndependentSamplesBootstrap.conf_int.reps"><span class="n sig-name">reps</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
 </dd>
-<dt id="arch.bootstrap.IndependentSamplesBootstrap.conf_int.method"><span class="sig-name descname">method</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, default &quot;basic&quot;</span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.method" title="Permalink to this definition">¶</a></dt><dd><p>One of ‘basic’, ‘percentile’, ‘studentized’, ‘norm’ (identical to
+<dt id="arch.bootstrap.IndependentSamplesBootstrap.conf_int.method"><span class="n sig-name">method</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'basic'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'percentile'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'studentized'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'norm'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'bc'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'bca'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;basic&#39;</span></code><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.method" title="Permalink to this definition">¶</a></dt><dd><p>One of ‘basic’, ‘percentile’, ‘studentized’, ‘norm’ (identical to
 ‘var’, ‘cov’), ‘bc’ (identical to ‘debiased’, ‘bias-corrected’), or
 ‘bca’</p>
 </dd>
-<dt id="arch.bootstrap.IndependentSamplesBootstrap.conf_int.size"><span class="sig-name descname">size</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>, default 0.95</span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.size" title="Permalink to this definition">¶</a></dt><dd><p>Coverage of confidence interval</p>
+<dt id="arch.bootstrap.IndependentSamplesBootstrap.conf_int.size"><span class="n sig-name">size</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.95</span></code><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.size" title="Permalink to this definition">¶</a></dt><dd><p>Coverage of confidence interval</p>
 </dd>
-<dt id="arch.bootstrap.IndependentSamplesBootstrap.conf_int.tail"><span class="sig-name descname">tail</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, default &quot;two&quot;</span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.tail" title="Permalink to this definition">¶</a></dt><dd><p>One of ‘two’, ‘upper’ or ‘lower’.</p>
+<dt id="arch.bootstrap.IndependentSamplesBootstrap.conf_int.tail"><span class="n sig-name">tail</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'two'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'upper'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'lower'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;two&#39;</span></code><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.tail" title="Permalink to this definition">¶</a></dt><dd><p>One of ‘two’, ‘upper’ or ‘lower’.</p>
 </dd>
-<dt id="arch.bootstrap.IndependentSamplesBootstrap.conf_int.reuse"><span class="sig-name descname">reuse</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, default False</span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.reuse" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to reuse previously computed bootstrap
+<dt id="arch.bootstrap.IndependentSamplesBootstrap.conf_int.reuse"><span class="n sig-name">reuse</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.reuse" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to reuse previously computed bootstrap
 results.  This allows alternative methods to be compared without
 rerunning the bootstrap simulation.  Reuse is ignored if reps is
 not the same across multiple runs, func changes across calls, or
 method is ‘studentized’.</p>
 </dd>
-<dt id="arch.bootstrap.IndependentSamplesBootstrap.conf_int.sampling"><span class="sig-name descname">sampling</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, default &quot;nonparametric&quot;</span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.sampling" title="Permalink to this definition">¶</a></dt><dd><p>Type of sampling to use: ‘nonparametric’, ‘semi-parametric’ (or
+<dt id="arch.bootstrap.IndependentSamplesBootstrap.conf_int.sampling"><span class="n sig-name">sampling</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'nonparametric'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'semi-parametric'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'semi'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'parametric'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'semiparametric'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;nonparametric&#39;</span></code><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.sampling" title="Permalink to this definition">¶</a></dt><dd><p>Type of sampling to use: ‘nonparametric’, ‘semi-parametric’ (or
 ‘semi’) or ‘parametric’.  The default is ‘nonparametric’.  See
 notes about the changes to func required when using ‘semi’ or
 ‘parametric’.</p>
 </dd>
-<dt id="arch.bootstrap.IndependentSamplesBootstrap.conf_int.extra_kwargs"><span class="sig-name descname">extra_kwargs</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a>, default None</span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.extra_kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Extra keyword arguments to use when calling func and std_err_func,
+<dt id="arch.bootstrap.IndependentSamplesBootstrap.conf_int.extra_kwargs"><span class="n sig-name">extra_kwargs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)">Any</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.extra_kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Extra keyword arguments to use when calling func and std_err_func,
 when appropriate</p>
 </dd>
-<dt id="arch.bootstrap.IndependentSamplesBootstrap.conf_int.std_err_func"><span class="sig-name descname">std_err_func</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable, default None</span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.std_err_func" title="Permalink to this definition">¶</a></dt><dd><p>Function to use when standardizing estimated parameters when using
+<dt id="arch.bootstrap.IndependentSamplesBootstrap.conf_int.std_err_func"><span class="n sig-name">std_err_func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.std_err_func" title="Permalink to this definition">¶</a></dt><dd><p>Function to use when standardizing estimated parameters when using
 the studentized bootstrap.  Providing an analytical function
 eliminates the need for a nested bootstrap</p>
 </dd>
-<dt id="arch.bootstrap.IndependentSamplesBootstrap.conf_int.studentize_reps"><span class="sig-name descname">studentize_reps</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, default 1000</span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.studentize_reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstraps to use in the inner bootstrap when using the
+<dt id="arch.bootstrap.IndependentSamplesBootstrap.conf_int.studentize_reps"><span class="n sig-name">studentize_reps</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.studentize_reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstraps to use in the inner bootstrap when using the
 studentized bootstrap.  Ignored when <code class="docutils literal notranslate"><span class="pre">std_err_func</span></code> is provided</p>
 </dd>
 </dl>
@@ -1183,7 +1183,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap-conf-int">arch.bootstrap.Inde
 Similarly, ‘upper’ sets all lower bounds to -inf.</p>
 </dd>
 <dt class="field-odd" id="arch.bootstrap.IndependentSamplesBootstrap.conf_int-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 <p class="example rubric">Examples</p>
@@ -1295,7 +1295,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap-conf-int">arch.bootstrap.Inde
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1342,7 +1342,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap-conf-int">arch.bootstrap.Inde
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.cov.html b/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.cov.html
index 15e6ccef84..7124656910 100644
--- a/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.cov.html
+++ b/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.cov.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.cov - arch 6.1.0</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.cov - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1050,24 +1050,24 @@
                   
 
 
-<h1 id="arch-bootstrap-independentsamplesbootstrap-cov">arch.bootstrap.IndependentSamplesBootstrap.cov<a class="headerlink" href="#arch-bootstrap-independentsamplesbootstrap-cov" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-independentsamplesbootstrap-cov">arch.bootstrap.IndependentSamplesBootstrap.cov<a class="headerlink" href="#arch-bootstrap-independentsamplesbootstrap-cov" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.IndependentSamplesBootstrap.cov">
-<span class="sig-prename descclassname"><span class="pre">IndependentSamplesBootstrap.</span></span><span class="sig-name descname"><span class="pre">cov</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.cov.func" title="arch.bootstrap.IndependentSamplesBootstrap.cov.func (Python parameter) — Callable function that returns the statistic of interest as a 1-d array"><span class="n"><span class="pre">func</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.cov.reps" title="arch.bootstrap.IndependentSamplesBootstrap.cov.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.cov.recenter" title="arch.bootstrap.IndependentSamplesBootstrap.cov.recenter (Python parameter) — Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."><span class="n"><span class="pre">recenter</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.cov.extra_kwargs" title="arch.bootstrap.IndependentSamplesBootstrap.cov.extra_kwargs (Python parameter) — Dictionary of extra keyword arguments to pass to func"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.cov" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">IndependentSamplesBootstrap.</span></span><span class="sig-name descname"><span class="pre">cov</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.cov.func" title="arch.bootstrap.IndependentSamplesBootstrap.cov.func (Python parameter) — Callable function that returns the statistic of interest as a 1-d array"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.cov.reps" title="arch.bootstrap.IndependentSamplesBootstrap.cov.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.cov.recenter" title="arch.bootstrap.IndependentSamplesBootstrap.cov.recenter (Python parameter) — Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."><span class="n"><span class="pre">recenter</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.cov.extra_kwargs" title="arch.bootstrap.IndependentSamplesBootstrap.cov.extra_kwargs (Python parameter) — Dictionary of extra keyword arguments to pass to func"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.cov" title="Link to this definition">¶</a></dt>
 <dd><p>Compute parameter covariance using bootstrap</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.IndependentSamplesBootstrap.cov-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.cov-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.IndependentSamplesBootstrap.cov.func"><span class="sig-name descname">func</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable</span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.cov.func" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that returns the statistic of interest as a
+<dt id="arch.bootstrap.IndependentSamplesBootstrap.cov.func"><span class="n sig-name">func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.cov.func" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that returns the statistic of interest as a
 1-d array</p>
 </dd>
-<dt id="arch.bootstrap.IndependentSamplesBootstrap.cov.reps"><span class="sig-name descname">reps</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, default 1000</span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.cov.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
+<dt id="arch.bootstrap.IndependentSamplesBootstrap.cov.reps"><span class="n sig-name">reps</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.cov.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
 </dd>
-<dt id="arch.bootstrap.IndependentSamplesBootstrap.cov.recenter"><span class="sig-name descname">recenter</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, default True</span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.cov.recenter" title="Permalink to this definition">¶</a></dt><dd><p>Whether to center the bootstrap variance estimator on the average
+<dt id="arch.bootstrap.IndependentSamplesBootstrap.cov.recenter"><span class="n sig-name">recenter</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.cov.recenter" title="Permalink to this definition">¶</a></dt><dd><p>Whether to center the bootstrap variance estimator on the average
 of the bootstrap samples (True) or to center on the original sample
 estimate (False).  Default is True.</p>
 </dd>
-<dt id="arch.bootstrap.IndependentSamplesBootstrap.cov.extra_kwargs"><span class="sig-name descname">extra_kwargs</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a>, default None</span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.cov.extra_kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Dictionary of extra keyword arguments to pass to func</p>
+<dt id="arch.bootstrap.IndependentSamplesBootstrap.cov.extra_kwargs"><span class="n sig-name">extra_kwargs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)">Any</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.cov.extra_kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Dictionary of extra keyword arguments to pass to func</p>
 </dd>
 </dl>
 </dd>
@@ -1075,7 +1075,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap-cov">arch.bootstrap.Independe
 <dd class="field-even"><p>Bootstrap covariance estimator</p>
 </dd>
 <dt class="field-odd" id="arch.bootstrap.IndependentSamplesBootstrap.cov-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.cov-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -1182,7 +1182,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap-cov">arch.bootstrap.Independe
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1229,7 +1229,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap-cov">arch.bootstrap.Independe
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.generator.html b/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.generator.html
index 180f68f6c4..eb927fa763 100644
--- a/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.generator.html
+++ b/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.generator.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.generator - arch 6.1.0</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.generator - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -982,15 +982,15 @@
                   
 
 
-<h1 id="arch-bootstrap-independentsamplesbootstrap-generator">arch.bootstrap.IndependentSamplesBootstrap.generator<a class="headerlink" href="#arch-bootstrap-independentsamplesbootstrap-generator" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-independentsamplesbootstrap-generator">arch.bootstrap.IndependentSamplesBootstrap.generator<a class="headerlink" href="#arch-bootstrap-independentsamplesbootstrap-generator" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.IndependentSamplesBootstrap.generator">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">IndependentSamplesBootstrap.</span></span><span class="sig-name descname"><span class="pre">generator</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.24)"><span class="pre">Generator</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.24)"><span class="pre">RandomState</span></a></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.generator" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">IndependentSamplesBootstrap.</span></span><span class="sig-name descname"><span class="pre">generator</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><span class="pre">Generator</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">RandomState</span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.generator" title="Link to this definition">¶</a></dt>
 <dd><p>Set or get the instance PRNG</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.IndependentSamplesBootstrap.generator-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.generator-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt><span class="sig-name descname">seed</span><span class="p"> : </span><span class="desctype"><span class="desctype">{Generator, RandomState}, optional</span></span></dt><dd><p>Generator or RandomState used to produce the pseudo-random
+<dt><span class="sig-name descname">seed</span><span class="p"> : </span><span class="desctype"><code class="docutils literal notranslate"><span class="pre">{Generator,</span> <span class="pre">RandomState}</span></code>, <em>optional</em></span></dt><dd><p>Generator or RandomState used to produce the pseudo-random
 values used in the bootstrap</p>
 </dd>
 </dl>
@@ -1000,7 +1000,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap-generator">arch.bootstrap.Ind
 by bootstrap</p>
 </dd>
 <dt class="field-odd" id="arch.bootstrap.IndependentSamplesBootstrap.generator-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.generator-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>{Generator, RandomState}</p>
+<dd class="field-odd"><p><code class="docutils literal notranslate"><span class="pre">{Generator,</span> <span class="pre">RandomState}</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1072,7 +1072,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap-generator">arch.bootstrap.Ind
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1119,7 +1119,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap-generator">arch.bootstrap.Ind
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.get_state.html b/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.get_state.html
index e9d007074a..d1398208ea 100644
--- a/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.get_state.html
+++ b/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.get_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.get_state - arch 6.1.0</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.get_state - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -968,17 +968,17 @@
                   
 
 
-<h1 id="arch-bootstrap-independentsamplesbootstrap-get-state">arch.bootstrap.IndependentSamplesBootstrap.get_state<a class="headerlink" href="#arch-bootstrap-independentsamplesbootstrap-get-state" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-independentsamplesbootstrap-get-state">arch.bootstrap.IndependentSamplesBootstrap.get_state<a class="headerlink" href="#arch-bootstrap-independentsamplesbootstrap-get-state" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.bootstrap.IndependentSamplesBootstrap.get_state">
-<span class="sig-prename descclassname"><span class="pre">IndependentSamplesBootstrap.</span></span><span class="sig-name descname"><span class="pre">get_state</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.get_state" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.IndependentSamplesBootstrap.get_state">
+<span class="sig-prename descclassname"><span class="pre">IndependentSamplesBootstrap.</span></span><span class="sig-name descname"><span class="pre">get_state</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Mapping" title="(in Python v3.11)"><span class="pre">Mapping</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.get_state" title="Link to this definition">¶</a></dt>
 <dd><p>Gets the state of the bootstrap’s random number generator</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.IndependentSamplesBootstrap.get_state-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.get_state-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>Dictionary containing the state.</p>
 </dd>
 <dt class="field-even" id="arch.bootstrap.IndependentSamplesBootstrap.get_state-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.get_state-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1050,7 +1050,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap-get-state">arch.bootstrap.Ind
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1097,7 +1097,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap-get-state">arch.bootstrap.Ind
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.index.html b/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.index.html
index b9edf60f97..0f6ddc6d10 100644
--- a/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.index.html
+++ b/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.index.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.index - arch 6.1.0</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.index - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -968,10 +968,10 @@
                   
 
 
-<h1 id="arch-bootstrap-independentsamplesbootstrap-index">arch.bootstrap.IndependentSamplesBootstrap.index<a class="headerlink" href="#arch-bootstrap-independentsamplesbootstrap-index" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-independentsamplesbootstrap-index">arch.bootstrap.IndependentSamplesBootstrap.index<a class="headerlink" href="#arch-bootstrap-independentsamplesbootstrap-index" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.IndependentSamplesBootstrap.index">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">IndependentSamplesBootstrap.</span></span><span class="sig-name descname"><span class="pre">index</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">numpy.ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">numpy.ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">numpy.ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.index" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">IndependentSamplesBootstrap.</span></span><span class="sig-name descname"><span class="pre">index</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.index" title="Link to this definition">¶</a></dt>
 <dd><p>Returns the current index of the bootstrap</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.IndependentSamplesBootstrap.index-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.index-returns" title="Permalink to this headline">¶</a></dt>
@@ -980,7 +980,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap-index">arch.bootstrap.Indepen
 dictionary contains the indices of keyword arguments.</p>
 </dd>
 <dt class="field-even" id="arch.bootstrap.IndependentSamplesBootstrap.index-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.index-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a>[<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a>[ndarray], <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a>[<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, ndarray]]</p>
+<dd class="field-even"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple[list[ndarray]</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">dict[str</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray]]</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1052,7 +1052,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap-index">arch.bootstrap.Indepen
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1099,7 +1099,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap-index">arch.bootstrap.Indepen
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.random_state.html b/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.random_state.html
index 04f52d7732..e61df2fcde 100644
--- a/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.random_state.html
+++ b/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.random_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.random_state - arch 6.1.0</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.random_state - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -982,15 +982,15 @@
                   
 
 
-<h1 id="arch-bootstrap-independentsamplesbootstrap-random-state">arch.bootstrap.IndependentSamplesBootstrap.random_state<a class="headerlink" href="#arch-bootstrap-independentsamplesbootstrap-random-state" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-independentsamplesbootstrap-random-state">arch.bootstrap.IndependentSamplesBootstrap.random_state<a class="headerlink" href="#arch-bootstrap-independentsamplesbootstrap-random-state" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.IndependentSamplesBootstrap.random_state">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">IndependentSamplesBootstrap.</span></span><span class="sig-name descname"><span class="pre">random_state</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.24)"><span class="pre">Generator</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.24)"><span class="pre">RandomState</span></a></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.random_state" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">IndependentSamplesBootstrap.</span></span><span class="sig-name descname"><span class="pre">random_state</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><span class="pre">Generator</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">RandomState</span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.random_state" title="Link to this definition">¶</a></dt>
 <dd><p>Set or get the instance random state</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.IndependentSamplesBootstrap.random_state-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.random_state-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt><span class="sig-name descname">random_state</span><span class="p"> : </span><span class="desctype"><span class="desctype">RandomState</span></span></dt><dd><p>RandomState instance used by bootstrap</p>
+<dt><span class="sig-name descname">random_state</span><span class="p"> : </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.random.RandomState</span></code></a></span></dt><dd><p>RandomState instance used by bootstrap</p>
 </dd>
 </dl>
 </dd>
@@ -998,7 +998,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap-random-state">arch.bootstrap.
 <dd class="field-even"><p>RandomState instance used by bootstrap</p>
 </dd>
 <dt class="field-odd" id="arch.bootstrap.IndependentSamplesBootstrap.random_state-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.random_state-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>RandomState</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.random.RandomState</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1070,7 +1070,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap-random-state">arch.bootstrap.
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1117,7 +1117,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap-random-state">arch.bootstrap.
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.reset.html b/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.reset.html
index a0d24c206f..a5a2e3bd6f 100644
--- a/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.reset.html
+++ b/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.reset.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.reset - arch 6.1.0</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.reset - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -615,13 +615,6 @@
       </ul>
     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.IndependentSamplesBootstrap.reset-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
       </ul>
@@ -966,13 +959,6 @@
       </ul>
     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.IndependentSamplesBootstrap.reset-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
       </ul>
@@ -994,23 +980,20 @@
                   
 
 
-<h1 id="arch-bootstrap-independentsamplesbootstrap-reset">arch.bootstrap.IndependentSamplesBootstrap.reset<a class="headerlink" href="#arch-bootstrap-independentsamplesbootstrap-reset" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-independentsamplesbootstrap-reset">arch.bootstrap.IndependentSamplesBootstrap.reset<a class="headerlink" href="#arch-bootstrap-independentsamplesbootstrap-reset" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.bootstrap.IndependentSamplesBootstrap.reset">
-<span class="sig-prename descclassname"><span class="pre">IndependentSamplesBootstrap.</span></span><span class="sig-name descname"><span class="pre">reset</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.reset.use_seed" title="arch.bootstrap.IndependentSamplesBootstrap.reset.use_seed (Python parameter) — Flag indicating whether to use the last seed if provided.  If False or if no seed has been set, the bootstrap will be reset to the initial state.  Default is True"><span class="n"><span class="pre">use_seed</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/bootstrap/base.html#IndependentSamplesBootstrap.reset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.reset" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">IndependentSamplesBootstrap.</span></span><span class="sig-name descname"><span class="pre">reset</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.reset.use_seed" title="arch.bootstrap.IndependentSamplesBootstrap.reset.use_seed (Python parameter) — Flag indicating whether to use the last seed if provided.  If False or if no seed has been set, the bootstrap will be reset to the initial state.  Default is True"><span class="n"><span class="pre">use_seed</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/bootstrap/base.html#IndependentSamplesBootstrap.reset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.reset" title="Link to this definition">¶</a></dt>
 <dd><p>Resets the bootstrap to either its initial state or the last seed.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.IndependentSamplesBootstrap.reset-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.reset-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.IndependentSamplesBootstrap.reset.use_seed"><span class="sig-name descname">use_seed</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, default True</span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.reset.use_seed" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to use the last seed if provided.  If
+<dt id="arch.bootstrap.IndependentSamplesBootstrap.reset.use_seed"><span class="n sig-name">use_seed</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.reset.use_seed" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to use the last seed if provided.  If
 False or if no seed has been set, the bootstrap will be reset
 to the initial state.  Default is True</p>
 </dd>
 </dl>
 </dd>
-<dt class="field-even" id="arch.bootstrap.IndependentSamplesBootstrap.reset-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.reset-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code></a></p>
-</dd>
 </dl>
 </dd></dl>
 
@@ -1081,7 +1064,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap-reset">arch.bootstrap.Indepen
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1128,7 +1111,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap-reset">arch.bootstrap.Indepen
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.seed.html b/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.seed.html
index 2ffb1d1fe5..f4a60bda2b 100644
--- a/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.seed.html
+++ b/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.seed.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.seed - arch 6.1.0</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.seed - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -630,13 +630,6 @@
       </ul>
     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.IndependentSamplesBootstrap.seed-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
       </ul>
@@ -966,13 +959,6 @@
       </ul>
     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.IndependentSamplesBootstrap.seed-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
       </ul>
@@ -994,21 +980,18 @@
                   
 
 
-<h1 id="arch-bootstrap-independentsamplesbootstrap-seed">arch.bootstrap.IndependentSamplesBootstrap.seed<a class="headerlink" href="#arch-bootstrap-independentsamplesbootstrap-seed" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-independentsamplesbootstrap-seed">arch.bootstrap.IndependentSamplesBootstrap.seed<a class="headerlink" href="#arch-bootstrap-independentsamplesbootstrap-seed" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.bootstrap.IndependentSamplesBootstrap.seed">
-<span class="sig-prename descclassname"><span class="pre">IndependentSamplesBootstrap.</span></span><span class="sig-name descname"><span class="pre">seed</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.seed.value" title="arch.bootstrap.IndependentSamplesBootstrap.seed.value (Python parameter) — Value to use as the seed."><span class="n"><span class="pre">value</span></span></a></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.seed" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.IndependentSamplesBootstrap.seed">
+<span class="sig-prename descclassname"><span class="pre">IndependentSamplesBootstrap.</span></span><span class="sig-name descname"><span class="pre">seed</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.seed.value" title="arch.bootstrap.IndependentSamplesBootstrap.seed.value (Python parameter) — Value to use as the seed."><span class="n"><span class="pre">value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.seed" title="Link to this definition">¶</a></dt>
 <dd><p>Reseeds the bootstrap’s random number generator</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.IndependentSamplesBootstrap.seed-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.seed-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.IndependentSamplesBootstrap.seed.value"><span class="sig-name descname">value</span><span class="p"> : </span><span class="desctype"><span class="desctype">{int, List[<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>], ndarray}</span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.seed.value" title="Permalink to this definition">¶</a></dt><dd><p>Value to use as the seed.</p>
+<dt id="arch.bootstrap.IndependentSamplesBootstrap.seed.value"><span class="n sig-name">value</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.seed.value" title="Permalink to this definition">¶</a></dt><dd><p>Value to use as the seed.</p>
 </dd>
 </dl>
 </dd>
-<dt class="field-even" id="arch.bootstrap.IndependentSamplesBootstrap.seed-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.seed-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code></a></p>
-</dd>
 </dl>
 </dd></dl>
 
@@ -1079,7 +1062,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap-seed">arch.bootstrap.Independ
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1126,7 +1109,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap-seed">arch.bootstrap.Independ
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.set_state.html b/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.set_state.html
index 95bd57017b..26fdda808c 100644
--- a/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.set_state.html
+++ b/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.set_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.set_state - arch 6.1.0</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.set_state - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -645,13 +645,6 @@
       </ul>
     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.IndependentSamplesBootstrap.set_state-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
       </ul>
@@ -966,13 +959,6 @@
       </ul>
     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.IndependentSamplesBootstrap.set_state-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
       </ul>
@@ -994,21 +980,18 @@
                   
 
 
-<h1 id="arch-bootstrap-independentsamplesbootstrap-set-state">arch.bootstrap.IndependentSamplesBootstrap.set_state<a class="headerlink" href="#arch-bootstrap-independentsamplesbootstrap-set-state" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-independentsamplesbootstrap-set-state">arch.bootstrap.IndependentSamplesBootstrap.set_state<a class="headerlink" href="#arch-bootstrap-independentsamplesbootstrap-set-state" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.bootstrap.IndependentSamplesBootstrap.set_state">
-<span class="sig-prename descclassname"><span class="pre">IndependentSamplesBootstrap.</span></span><span class="sig-name descname"><span class="pre">set_state</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.set_state.state" title="arch.bootstrap.IndependentSamplesBootstrap.set_state.state (Python parameter) — Dictionary or tuple containing the state."><span class="n"><span class="pre">state</span></span></a></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.set_state" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.IndependentSamplesBootstrap.set_state">
+<span class="sig-prename descclassname"><span class="pre">IndependentSamplesBootstrap.</span></span><span class="sig-name descname"><span class="pre">set_state</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.set_state.state" title="arch.bootstrap.IndependentSamplesBootstrap.set_state.state (Python parameter) — Dictionary or tuple containing the state."><span class="n"><span class="pre">state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.set_state" title="Link to this definition">¶</a></dt>
 <dd><p>Sets the state of the bootstrap’s random number generator</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.IndependentSamplesBootstrap.set_state-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.set_state-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.IndependentSamplesBootstrap.set_state.state"><span class="sig-name descname">state</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a></span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.set_state.state" title="Permalink to this definition">¶</a></dt><dd><p>Dictionary or tuple containing the state.</p>
+<dt id="arch.bootstrap.IndependentSamplesBootstrap.set_state.state"><span class="n sig-name">state</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)">Any</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.set_state.state" title="Permalink to this definition">¶</a></dt><dd><p>Dictionary or tuple containing the state.</p>
 </dd>
 </dl>
 </dd>
-<dt class="field-even" id="arch.bootstrap.IndependentSamplesBootstrap.set_state-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.set_state-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code></a></p>
-</dd>
 </dl>
 </dd></dl>
 
@@ -1079,7 +1062,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap-set-state">arch.bootstrap.Ind
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1126,7 +1109,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap-set-state">arch.bootstrap.Ind
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.state.html b/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.state.html
index 2609dba888..d99ca91967 100644
--- a/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.state.html
+++ b/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.state - arch 6.1.0</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.state - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -968,10 +968,10 @@
                   
 
 
-<h1 id="arch-bootstrap-independentsamplesbootstrap-state">arch.bootstrap.IndependentSamplesBootstrap.state<a class="headerlink" href="#arch-bootstrap-independentsamplesbootstrap-state" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-independentsamplesbootstrap-state">arch.bootstrap.IndependentSamplesBootstrap.state<a class="headerlink" href="#arch-bootstrap-independentsamplesbootstrap-state" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.IndependentSamplesBootstrap.state">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">IndependentSamplesBootstrap.</span></span><span class="sig-name descname"><span class="pre">state</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">numpy.ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Mapping" title="(in Python v3.11)"><span class="pre">Mapping</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.state" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">IndependentSamplesBootstrap.</span></span><span class="sig-name descname"><span class="pre">state</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Mapping" title="(in Python v3.11)"><span class="pre">Mapping</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.state" title="Link to this definition">¶</a></dt>
 <dd><p>Set or get the generator’s state</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.IndependentSamplesBootstrap.state-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.state-returns" title="Permalink to this headline">¶</a></dt>
@@ -980,7 +980,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap-state">arch.bootstrap.Indepen
 Otherwise it is a dictionary.</p>
 </dd>
 <dt class="field-even" id="arch.bootstrap.IndependentSamplesBootstrap.state-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.state-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p>{tuple, dict}</p>
+<dd class="field-even"><p><code class="docutils literal notranslate"><span class="pre">{tuple,</span> <span class="pre">dict}</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1052,7 +1052,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap-state">arch.bootstrap.Indepen
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1099,7 +1099,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap-state">arch.bootstrap.Indepen
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.update_indices.html b/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.update_indices.html
index 4b4ef5d88e..932b1d433d 100644
--- a/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.update_indices.html
+++ b/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.update_indices.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.update_indices - arch 6.1.0</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.update_indices - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -639,19 +639,6 @@
     <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="arch.bootstrap.IndependentSamplesBootstrap.update_indices (Python method) — Update indices for the next iteration of the bootstrap.  This must be overridden when creating new bootstraps." class="md-ellipsis">Independent<wbr>Samples<wbr>Bootstrap.<wbr>update_<wbr>indices</span>
   </a>
   
-    <nav class="md-nav" aria-label="IndependentSamplesBootstrap.update_indices">
-      <ul class="md-nav__list">
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.IndependentSamplesBootstrap.update_indices-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
-</li>
-        
-      </ul>
-    </nav>
-  
 </li>
       
     </ul>
@@ -925,19 +912,6 @@
     <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="arch.bootstrap.IndependentSamplesBootstrap.update_indices (Python method) — Update indices for the next iteration of the bootstrap.  This must be overridden when creating new bootstraps." class="md-ellipsis">Independent<wbr>Samples<wbr>Bootstrap.<wbr>update_<wbr>indices</span>
   </a>
   
-    <nav class="md-nav" aria-label="IndependentSamplesBootstrap.update_indices">
-      <ul class="md-nav__list">
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.IndependentSamplesBootstrap.update_indices-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
-</li>
-        
-      </ul>
-    </nav>
-  
 </li>
       
     </ul>
@@ -954,17 +928,12 @@
                   
 
 
-<h1 id="arch-bootstrap-independentsamplesbootstrap-update-indices">arch.bootstrap.IndependentSamplesBootstrap.update_indices<a class="headerlink" href="#arch-bootstrap-independentsamplesbootstrap-update-indices" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-independentsamplesbootstrap-update-indices">arch.bootstrap.IndependentSamplesBootstrap.update_indices<a class="headerlink" href="#arch-bootstrap-independentsamplesbootstrap-update-indices" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.bootstrap.IndependentSamplesBootstrap.update_indices">
-<span class="sig-prename descclassname"><span class="pre">IndependentSamplesBootstrap.</span></span><span class="sig-name descname"><span class="pre">update_indices</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/bootstrap/base.html#IndependentSamplesBootstrap.update_indices"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.update_indices" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.IndependentSamplesBootstrap.update_indices">
+<span class="sig-prename descclassname"><span class="pre">IndependentSamplesBootstrap.</span></span><span class="sig-name descname"><span class="pre">update_indices</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/bootstrap/base.html#IndependentSamplesBootstrap.update_indices"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.update_indices" title="Link to this definition">¶</a></dt>
 <dd><p>Update indices for the next iteration of the bootstrap.  This must
 be overridden when creating new bootstraps.</p>
-<dl class="field-list simple">
-<dt class="field-odd" id="arch.bootstrap.IndependentSamplesBootstrap.update_indices-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.update_indices-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span></code></a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>], <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>[<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>]]</p>
-</dd>
-</dl>
 </dd></dl>
 
 
@@ -1034,7 +1003,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap-update-indices">arch.bootstra
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1081,7 +1050,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap-update-indices">arch.bootstra
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.var.html b/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.var.html
index 7bbe6624ff..eec0fcc64d 100644
--- a/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.var.html
+++ b/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.var.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.var - arch 6.1.0</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.var - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1050,24 +1050,24 @@
                   
 
 
-<h1 id="arch-bootstrap-independentsamplesbootstrap-var">arch.bootstrap.IndependentSamplesBootstrap.var<a class="headerlink" href="#arch-bootstrap-independentsamplesbootstrap-var" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-independentsamplesbootstrap-var">arch.bootstrap.IndependentSamplesBootstrap.var<a class="headerlink" href="#arch-bootstrap-independentsamplesbootstrap-var" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.IndependentSamplesBootstrap.var">
-<span class="sig-prename descclassname"><span class="pre">IndependentSamplesBootstrap.</span></span><span class="sig-name descname"><span class="pre">var</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.var.func" title="arch.bootstrap.IndependentSamplesBootstrap.var.func (Python parameter) — Callable function that returns the statistic of interest as a 1-d array"><span class="n"><span class="pre">func</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.var.reps" title="arch.bootstrap.IndependentSamplesBootstrap.var.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.var.recenter" title="arch.bootstrap.IndependentSamplesBootstrap.var.recenter (Python parameter) — Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."><span class="n"><span class="pre">recenter</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.var.extra_kwargs" title="arch.bootstrap.IndependentSamplesBootstrap.var.extra_kwargs (Python parameter) — Dictionary of extra keyword arguments to pass to func"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.var" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">IndependentSamplesBootstrap.</span></span><span class="sig-name descname"><span class="pre">var</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.var.func" title="arch.bootstrap.IndependentSamplesBootstrap.var.func (Python parameter) — Callable function that returns the statistic of interest as a 1-d array"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.var.reps" title="arch.bootstrap.IndependentSamplesBootstrap.var.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.var.recenter" title="arch.bootstrap.IndependentSamplesBootstrap.var.recenter (Python parameter) — Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."><span class="n"><span class="pre">recenter</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.var.extra_kwargs" title="arch.bootstrap.IndependentSamplesBootstrap.var.extra_kwargs (Python parameter) — Dictionary of extra keyword arguments to pass to func"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.var" title="Link to this definition">¶</a></dt>
 <dd><p>Compute parameter variance using bootstrap</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.IndependentSamplesBootstrap.var-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.var-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.IndependentSamplesBootstrap.var.func"><span class="sig-name descname">func</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable</span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.var.func" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that returns the statistic of interest as a
+<dt id="arch.bootstrap.IndependentSamplesBootstrap.var.func"><span class="n sig-name">func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.var.func" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that returns the statistic of interest as a
 1-d array</p>
 </dd>
-<dt id="arch.bootstrap.IndependentSamplesBootstrap.var.reps"><span class="sig-name descname">reps</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, default 1000</span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.var.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
+<dt id="arch.bootstrap.IndependentSamplesBootstrap.var.reps"><span class="n sig-name">reps</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.var.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
 </dd>
-<dt id="arch.bootstrap.IndependentSamplesBootstrap.var.recenter"><span class="sig-name descname">recenter</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, default True</span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.var.recenter" title="Permalink to this definition">¶</a></dt><dd><p>Whether to center the bootstrap variance estimator on the average
+<dt id="arch.bootstrap.IndependentSamplesBootstrap.var.recenter"><span class="n sig-name">recenter</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.var.recenter" title="Permalink to this definition">¶</a></dt><dd><p>Whether to center the bootstrap variance estimator on the average
 of the bootstrap samples (True) or to center on the original sample
 estimate (False).  Default is True.</p>
 </dd>
-<dt id="arch.bootstrap.IndependentSamplesBootstrap.var.extra_kwargs"><span class="sig-name descname">extra_kwargs</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a>, default None</span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.var.extra_kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Dictionary of extra keyword arguments to pass to func</p>
+<dt id="arch.bootstrap.IndependentSamplesBootstrap.var.extra_kwargs"><span class="n sig-name">extra_kwargs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)">Any</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.var.extra_kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Dictionary of extra keyword arguments to pass to func</p>
 </dd>
 </dl>
 </dd>
@@ -1075,7 +1075,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap-var">arch.bootstrap.Independe
 <dd class="field-even"><p>Bootstrap variance estimator</p>
 </dd>
 <dt class="field-odd" id="arch.bootstrap.IndependentSamplesBootstrap.var-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.var-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -1182,7 +1182,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap-var">arch.bootstrap.Independe
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1229,7 +1229,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap-var">arch.bootstrap.Independe
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.apply.html b/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.apply.html
index f24f19c815..d2ba40ffd9 100644
--- a/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.apply.html
+++ b/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.apply.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.apply - arch 6.1.0</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.apply - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1081,19 +1081,19 @@
                   
 
 
-<h1 id="arch-bootstrap-movingblockbootstrap-apply">arch.bootstrap.MovingBlockBootstrap.apply<a class="headerlink" href="#arch-bootstrap-movingblockbootstrap-apply" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-movingblockbootstrap-apply">arch.bootstrap.MovingBlockBootstrap.apply<a class="headerlink" href="#arch-bootstrap-movingblockbootstrap-apply" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.bootstrap.MovingBlockBootstrap.apply">
-<span class="sig-prename descclassname"><span class="pre">MovingBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.apply.func" title="arch.bootstrap.MovingBlockBootstrap.apply.func (Python parameter) — Function the computes parameter values.  See Notes for requirements"><span class="n"><span class="pre">func</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.apply.reps" title="arch.bootstrap.MovingBlockBootstrap.apply.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.apply.extra_kwargs" title="arch.bootstrap.MovingBlockBootstrap.apply.extra_kwargs (Python parameter) — Extra keyword arguments to use when calling func.  Must not conflict with keyword arguments used to initialize bootstrap"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.apply" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.MovingBlockBootstrap.apply">
+<span class="sig-prename descclassname"><span class="pre">MovingBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.apply.func" title="arch.bootstrap.MovingBlockBootstrap.apply.func (Python parameter) — Function the computes parameter values.  See Notes for requirements"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.apply.reps" title="arch.bootstrap.MovingBlockBootstrap.apply.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.apply.extra_kwargs" title="arch.bootstrap.MovingBlockBootstrap.apply.extra_kwargs (Python parameter) — Extra keyword arguments to use when calling func.  Must not conflict with keyword arguments used to initialize bootstrap"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.apply" title="Link to this definition">¶</a></dt>
 <dd><p>Applies a function to bootstrap replicated data</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.MovingBlockBootstrap.apply-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.apply-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.MovingBlockBootstrap.apply.func"><span class="sig-name descname">func</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable</span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.apply.func" title="Permalink to this definition">¶</a></dt><dd><p>Function the computes parameter values.  See Notes for requirements</p>
+<dt id="arch.bootstrap.MovingBlockBootstrap.apply.func"><span class="n sig-name">func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.apply.func" title="Permalink to this definition">¶</a></dt><dd><p>Function the computes parameter values.  See Notes for requirements</p>
 </dd>
-<dt id="arch.bootstrap.MovingBlockBootstrap.apply.reps"><span class="sig-name descname">reps</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, default 1000</span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.apply.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
+<dt id="arch.bootstrap.MovingBlockBootstrap.apply.reps"><span class="n sig-name">reps</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.apply.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
 </dd>
-<dt id="arch.bootstrap.MovingBlockBootstrap.apply.extra_kwargs"><span class="sig-name descname">extra_kwargs</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a>, default None</span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.apply.extra_kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Extra keyword arguments to use when calling func.  Must not
+<dt id="arch.bootstrap.MovingBlockBootstrap.apply.extra_kwargs"><span class="n sig-name">extra_kwargs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)">Any</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.apply.extra_kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Extra keyword arguments to use when calling func.  Must not
 conflict with keyword arguments used to initialize bootstrap</p>
 </dd>
 </dl>
@@ -1103,7 +1103,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap-apply">arch.bootstrap.MovingBlockBoo
 corresponds to a bootstrap iteration</p>
 </dd>
 <dt class="field-odd" id="arch.bootstrap.MovingBlockBootstrap.apply-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.apply-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -1193,7 +1193,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap-apply">arch.bootstrap.MovingBlockBoo
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1240,7 +1240,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap-apply">arch.bootstrap.MovingBlockBoo
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.bootstrap.html b/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.bootstrap.html
index 81bcff2c36..66fef79562 100644
--- a/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.bootstrap.html
+++ b/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.bootstrap.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.bootstrap - arch 6.1.0</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.bootstrap - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1053,15 +1053,15 @@
                   
 
 
-<h1 id="arch-bootstrap-movingblockbootstrap-bootstrap">arch.bootstrap.MovingBlockBootstrap.bootstrap<a class="headerlink" href="#arch-bootstrap-movingblockbootstrap-bootstrap" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-movingblockbootstrap-bootstrap">arch.bootstrap.MovingBlockBootstrap.bootstrap<a class="headerlink" href="#arch-bootstrap-movingblockbootstrap-bootstrap" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.bootstrap.MovingBlockBootstrap.bootstrap">
-<span class="sig-prename descclassname"><span class="pre">MovingBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">bootstrap</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.bootstrap.reps" title="arch.bootstrap.MovingBlockBootstrap.bootstrap.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.bootstrap" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.MovingBlockBootstrap.bootstrap">
+<span class="sig-prename descclassname"><span class="pre">MovingBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">bootstrap</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.bootstrap.reps" title="arch.bootstrap.MovingBlockBootstrap.bootstrap.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Generator" title="(in Python v3.11)"><span class="pre">Generator</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.bootstrap" title="Link to this definition">¶</a></dt>
 <dd><p>Iterator for use when bootstrapping</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.MovingBlockBootstrap.bootstrap-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.bootstrap-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.MovingBlockBootstrap.bootstrap.reps"><span class="sig-name descname">reps</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.bootstrap.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
+<dt id="arch.bootstrap.MovingBlockBootstrap.bootstrap.reps"><span class="n sig-name">reps</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.bootstrap.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
 </dd>
 </dl>
 </dd>
@@ -1069,7 +1069,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap-bootstrap">arch.bootstrap.MovingBloc
 <dd class="field-even"><p>Generator to iterate over in bootstrap calculations</p>
 </dd>
 <dt class="field-odd" id="arch.bootstrap.MovingBlockBootstrap.bootstrap-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.bootstrap-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>generator</p>
+<dd class="field-odd"><p><a class="reference internal" href="arch.bootstrap.MovingBlockBootstrap.generator.html#arch.bootstrap.MovingBlockBootstrap.generator" title="arch.bootstrap.MovingBlockBootstrap.generator (Python property) — Set or get the instance PRNG"><code class="xref py py-class docutils literal notranslate"><span class="pre">generator</span></code></a></p>
 </dd>
 </dl>
 <p class="example rubric">Examples</p>
@@ -1161,7 +1161,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap-bootstrap">arch.bootstrap.MovingBloc
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1208,7 +1208,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap-bootstrap">arch.bootstrap.MovingBloc
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.clone.html b/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.clone.html
index 5c4eb40ff3..fd318a8c47 100644
--- a/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.clone.html
+++ b/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.clone.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.clone - arch 6.1.0</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.clone - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1081,19 +1081,19 @@
                   
 
 
-<h1 id="arch-bootstrap-movingblockbootstrap-clone">arch.bootstrap.MovingBlockBootstrap.clone<a class="headerlink" href="#arch-bootstrap-movingblockbootstrap-clone" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-movingblockbootstrap-clone">arch.bootstrap.MovingBlockBootstrap.clone<a class="headerlink" href="#arch-bootstrap-movingblockbootstrap-clone" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.bootstrap.MovingBlockBootstrap.clone">
-<span class="sig-prename descclassname"><span class="pre">MovingBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">clone</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.clone.args" title="arch.bootstrap.MovingBlockBootstrap.clone.args (Python parameter) — Positional arguments to bootstrap"><span class="n"><span class="pre">args</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.clone.seed" title="arch.bootstrap.MovingBlockBootstrap.clone.seed (Python parameter) — The seed value to pass to the closed generator"><span class="n"><span class="pre">seed</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">**</span></span><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.clone.kwargs" title="arch.bootstrap.MovingBlockBootstrap.clone.kwargs (Python parameter) — Keyword arguments to bootstrap"><span class="n"><span class="pre">kwargs</span></span></a></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.clone" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.MovingBlockBootstrap.clone">
+<span class="sig-prename descclassname"><span class="pre">MovingBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">clone</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.clone.args" title="arch.bootstrap.MovingBlockBootstrap.clone.args (Python parameter) — Positional arguments to bootstrap"><span class="n"><span class="pre">args</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">ArrayLike</span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.clone.seed" title="arch.bootstrap.MovingBlockBootstrap.clone.seed (Python parameter) — The seed value to pass to the closed generator"><span class="n"><span class="pre">seed</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Generator</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">RandomState</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">**</span></span><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.clone.kwargs" title="arch.bootstrap.MovingBlockBootstrap.clone.kwargs (Python parameter) — Keyword arguments to bootstrap"><span class="n"><span class="pre">kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">ArrayLike</span></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.bootstrap.CircularBlockBootstrap.html#arch.bootstrap.CircularBlockBootstrap" title="arch.bootstrap.CircularBlockBootstrap (Python class) — Bootstrap using blocks of the same length with end-to-start wrap around"><span class="pre">CircularBlockBootstrap</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.clone" title="Link to this definition">¶</a></dt>
 <dd><p>Clones the bootstrap using different data with a fresh prng.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.MovingBlockBootstrap.clone-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.clone-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.MovingBlockBootstrap.clone.args"><span class="o">*</span><span class="n sig-name">args</span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.clone.args" title="Permalink to this definition">¶</a></dt><dd><p>Positional arguments to bootstrap</p>
+<dt id="arch.bootstrap.MovingBlockBootstrap.clone.args"><span class="o">*</span><span class="n sig-name">args</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype">ArrayLike</span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.clone.args" title="Permalink to this definition">¶</a></dt><dd><p>Positional arguments to bootstrap</p>
 </dd>
-<dt id="arch.bootstrap.MovingBlockBootstrap.clone.seed"><span class="n sig-name">seed</span><span class="o">=</span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.clone.seed" title="Permalink to this definition">¶</a></dt><dd><p>The seed value to pass to the closed generator</p>
+<dt id="arch.bootstrap.MovingBlockBootstrap.clone.seed"><span class="n sig-name">seed</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span>Generator<span class="w"> </span><span class="p">|</span><span class="w"> </span>RandomState</span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.clone.seed" title="Permalink to this definition">¶</a></dt><dd><p>The seed value to pass to the closed generator</p>
 </dd>
-<dt id="arch.bootstrap.MovingBlockBootstrap.clone.kwargs"><span class="o">**</span><span class="n sig-name">kwargs</span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.clone.kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Keyword arguments to bootstrap</p>
+<dt id="arch.bootstrap.MovingBlockBootstrap.clone.kwargs"><span class="o">**</span><span class="n sig-name">kwargs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype">ArrayLike</span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.clone.kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Keyword arguments to bootstrap</p>
 </dd>
 </dl>
 </dd>
@@ -1101,7 +1101,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap-clone">arch.bootstrap.MovingBlockBoo
 <dd class="field-even"><p>Bootstrap instance</p>
 </dd>
 <dt class="field-odd" id="arch.bootstrap.MovingBlockBootstrap.clone-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.clone-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>bs</p>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">bs</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1173,7 +1173,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap-clone">arch.bootstrap.MovingBlockBoo
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1220,7 +1220,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap-clone">arch.bootstrap.MovingBlockBoo
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.conf_int.html b/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.conf_int.html
index baf8f1bb9a..4fc722fdab 100644
--- a/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.conf_int.html
+++ b/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.conf_int.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.conf_int - arch 6.1.0</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.conf_int - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1179,44 +1179,44 @@
                   
 
 
-<h1 id="arch-bootstrap-movingblockbootstrap-conf-int">arch.bootstrap.MovingBlockBootstrap.conf_int<a class="headerlink" href="#arch-bootstrap-movingblockbootstrap-conf-int" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-movingblockbootstrap-conf-int">arch.bootstrap.MovingBlockBootstrap.conf_int<a class="headerlink" href="#arch-bootstrap-movingblockbootstrap-conf-int" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.MovingBlockBootstrap.conf_int">
-<span class="sig-prename descclassname"><span class="pre">MovingBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">conf_int</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.func" title="arch.bootstrap.MovingBlockBootstrap.conf_int.func (Python parameter) — Function the computes parameter values.  See Notes for requirements"><span class="n"><span class="pre">func</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.reps" title="arch.bootstrap.MovingBlockBootstrap.conf_int.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.method" title="arch.bootstrap.MovingBlockBootstrap.conf_int.method (Python parameter) — One of 'basic', 'percentile', 'studentized', 'norm' (identical to 'var', 'cov'), 'bc' (identical to 'debiased', 'bias-corrected'), or 'bca'"><span class="n"><span class="pre">method</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;basic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.size" title="arch.bootstrap.MovingBlockBootstrap.conf_int.size (Python parameter) — Coverage of confidence interval"><span class="n"><span class="pre">size</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.95</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.tail" title="arch.bootstrap.MovingBlockBootstrap.conf_int.tail (Python parameter) — One of 'two', 'upper' or 'lower'."><span class="n"><span class="pre">tail</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;two&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.extra_kwargs" title="arch.bootstrap.MovingBlockBootstrap.conf_int.extra_kwargs (Python parameter) — Extra keyword arguments to use when calling func and std_err_func, when appropriate"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.reuse" title="arch.bootstrap.MovingBlockBootstrap.conf_int.reuse (Python parameter) — Flag indicating whether to reuse previously computed bootstrap results.  This allows alternative methods to be compared without rerunning the bootstrap simulation.  Reuse is ignored if reps is not the same across multiple runs, func changes across calls, or method is 'studentized'."><span class="n"><span class="pre">reuse</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.sampling" title="arch.bootstrap.MovingBlockBootstrap.conf_int.sampling (Python parameter) — Type of sampling to use: 'nonparametric', 'semi-parametric' (or 'semi') or 'parametric'.  The default is 'nonparametric'.  See notes about the changes to func required when using 'semi' or 'parametric'."><span class="n"><span class="pre">sampling</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;nonparametric&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.std_err_func" title="arch.bootstrap.MovingBlockBootstrap.conf_int.std_err_func (Python parameter) — Function to use when standardizing estimated parameters when using the studentized bootstrap.  Providing an analytical function eliminates the need for a nested bootstrap"><span class="n"><span class="pre">std_err_func</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.studentize_reps" title="arch.bootstrap.MovingBlockBootstrap.conf_int.studentize_reps (Python parameter) — Number of bootstraps to use in the inner bootstrap when using the studentized bootstrap.  Ignored when std_err_func is provided"><span class="n"><span class="pre">studentize_reps</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.conf_int" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">MovingBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">conf_int</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.func" title="arch.bootstrap.MovingBlockBootstrap.conf_int.func (Python parameter) — Function the computes parameter values.  See Notes for requirements"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.reps" title="arch.bootstrap.MovingBlockBootstrap.conf_int.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.method" title="arch.bootstrap.MovingBlockBootstrap.conf_int.method (Python parameter) — One of 'basic', 'percentile', 'studentized', 'norm' (identical to 'var', 'cov'), 'bc' (identical to 'debiased', 'bias-corrected'), or 'bca'"><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'basic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'percentile'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'studentized'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'norm'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bc'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bca'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;basic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.size" title="arch.bootstrap.MovingBlockBootstrap.conf_int.size (Python parameter) — Coverage of confidence interval"><span class="n"><span class="pre">size</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.95</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.tail" title="arch.bootstrap.MovingBlockBootstrap.conf_int.tail (Python parameter) — One of 'two', 'upper' or 'lower'."><span class="n"><span class="pre">tail</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'two'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'upper'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'lower'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;two&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.extra_kwargs" title="arch.bootstrap.MovingBlockBootstrap.conf_int.extra_kwargs (Python parameter) — Extra keyword arguments to use when calling func and std_err_func, when appropriate"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.reuse" title="arch.bootstrap.MovingBlockBootstrap.conf_int.reuse (Python parameter) — Flag indicating whether to reuse previously computed bootstrap results.  This allows alternative methods to be compared without rerunning the bootstrap simulation.  Reuse is ignored if reps is not the same across multiple runs, func changes across calls, or method is 'studentized'."><span class="n"><span class="pre">reuse</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.sampling" title="arch.bootstrap.MovingBlockBootstrap.conf_int.sampling (Python parameter) — Type of sampling to use: 'nonparametric', 'semi-parametric' (or 'semi') or 'parametric'.  The default is 'nonparametric'.  See notes about the changes to func required when using 'semi' or 'parametric'."><span class="n"><span class="pre">sampling</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'nonparametric'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'semi-parametric'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'semi'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'parametric'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'semiparametric'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;nonparametric&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.std_err_func" title="arch.bootstrap.MovingBlockBootstrap.conf_int.std_err_func (Python parameter) — Function to use when standardizing estimated parameters when using the studentized bootstrap.  Providing an analytical function eliminates the need for a nested bootstrap"><span class="n"><span class="pre">std_err_func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.studentize_reps" title="arch.bootstrap.MovingBlockBootstrap.conf_int.studentize_reps (Python parameter) — Number of bootstraps to use in the inner bootstrap when using the studentized bootstrap.  Ignored when std_err_func is provided"><span class="n"><span class="pre">studentize_reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.conf_int" title="Link to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.MovingBlockBootstrap.conf_int-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.conf_int-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.MovingBlockBootstrap.conf_int.func"><span class="sig-name descname">func</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable</span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.func" title="Permalink to this definition">¶</a></dt><dd><p>Function the computes parameter values.  See Notes for requirements</p>
+<dt id="arch.bootstrap.MovingBlockBootstrap.conf_int.func"><span class="n sig-name">func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.func" title="Permalink to this definition">¶</a></dt><dd><p>Function the computes parameter values.  See Notes for requirements</p>
 </dd>
-<dt id="arch.bootstrap.MovingBlockBootstrap.conf_int.reps"><span class="sig-name descname">reps</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, default 1000</span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
+<dt id="arch.bootstrap.MovingBlockBootstrap.conf_int.reps"><span class="n sig-name">reps</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
 </dd>
-<dt id="arch.bootstrap.MovingBlockBootstrap.conf_int.method"><span class="sig-name descname">method</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, default &quot;basic&quot;</span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.method" title="Permalink to this definition">¶</a></dt><dd><p>One of ‘basic’, ‘percentile’, ‘studentized’, ‘norm’ (identical to
+<dt id="arch.bootstrap.MovingBlockBootstrap.conf_int.method"><span class="n sig-name">method</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'basic'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'percentile'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'studentized'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'norm'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'bc'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'bca'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;basic&#39;</span></code><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.method" title="Permalink to this definition">¶</a></dt><dd><p>One of ‘basic’, ‘percentile’, ‘studentized’, ‘norm’ (identical to
 ‘var’, ‘cov’), ‘bc’ (identical to ‘debiased’, ‘bias-corrected’), or
 ‘bca’</p>
 </dd>
-<dt id="arch.bootstrap.MovingBlockBootstrap.conf_int.size"><span class="sig-name descname">size</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>, default 0.95</span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.size" title="Permalink to this definition">¶</a></dt><dd><p>Coverage of confidence interval</p>
+<dt id="arch.bootstrap.MovingBlockBootstrap.conf_int.size"><span class="n sig-name">size</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.95</span></code><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.size" title="Permalink to this definition">¶</a></dt><dd><p>Coverage of confidence interval</p>
 </dd>
-<dt id="arch.bootstrap.MovingBlockBootstrap.conf_int.tail"><span class="sig-name descname">tail</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, default &quot;two&quot;</span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.tail" title="Permalink to this definition">¶</a></dt><dd><p>One of ‘two’, ‘upper’ or ‘lower’.</p>
+<dt id="arch.bootstrap.MovingBlockBootstrap.conf_int.tail"><span class="n sig-name">tail</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'two'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'upper'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'lower'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;two&#39;</span></code><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.tail" title="Permalink to this definition">¶</a></dt><dd><p>One of ‘two’, ‘upper’ or ‘lower’.</p>
 </dd>
-<dt id="arch.bootstrap.MovingBlockBootstrap.conf_int.reuse"><span class="sig-name descname">reuse</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, default False</span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.reuse" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to reuse previously computed bootstrap
+<dt id="arch.bootstrap.MovingBlockBootstrap.conf_int.reuse"><span class="n sig-name">reuse</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.reuse" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to reuse previously computed bootstrap
 results.  This allows alternative methods to be compared without
 rerunning the bootstrap simulation.  Reuse is ignored if reps is
 not the same across multiple runs, func changes across calls, or
 method is ‘studentized’.</p>
 </dd>
-<dt id="arch.bootstrap.MovingBlockBootstrap.conf_int.sampling"><span class="sig-name descname">sampling</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, default &quot;nonparametric&quot;</span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.sampling" title="Permalink to this definition">¶</a></dt><dd><p>Type of sampling to use: ‘nonparametric’, ‘semi-parametric’ (or
+<dt id="arch.bootstrap.MovingBlockBootstrap.conf_int.sampling"><span class="n sig-name">sampling</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'nonparametric'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'semi-parametric'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'semi'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'parametric'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'semiparametric'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;nonparametric&#39;</span></code><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.sampling" title="Permalink to this definition">¶</a></dt><dd><p>Type of sampling to use: ‘nonparametric’, ‘semi-parametric’ (or
 ‘semi’) or ‘parametric’.  The default is ‘nonparametric’.  See
 notes about the changes to func required when using ‘semi’ or
 ‘parametric’.</p>
 </dd>
-<dt id="arch.bootstrap.MovingBlockBootstrap.conf_int.extra_kwargs"><span class="sig-name descname">extra_kwargs</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a>, default None</span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.extra_kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Extra keyword arguments to use when calling func and std_err_func,
+<dt id="arch.bootstrap.MovingBlockBootstrap.conf_int.extra_kwargs"><span class="n sig-name">extra_kwargs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)">Any</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.extra_kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Extra keyword arguments to use when calling func and std_err_func,
 when appropriate</p>
 </dd>
-<dt id="arch.bootstrap.MovingBlockBootstrap.conf_int.std_err_func"><span class="sig-name descname">std_err_func</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable, default None</span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.std_err_func" title="Permalink to this definition">¶</a></dt><dd><p>Function to use when standardizing estimated parameters when using
+<dt id="arch.bootstrap.MovingBlockBootstrap.conf_int.std_err_func"><span class="n sig-name">std_err_func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.std_err_func" title="Permalink to this definition">¶</a></dt><dd><p>Function to use when standardizing estimated parameters when using
 the studentized bootstrap.  Providing an analytical function
 eliminates the need for a nested bootstrap</p>
 </dd>
-<dt id="arch.bootstrap.MovingBlockBootstrap.conf_int.studentize_reps"><span class="sig-name descname">studentize_reps</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, default 1000</span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.studentize_reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstraps to use in the inner bootstrap when using the
+<dt id="arch.bootstrap.MovingBlockBootstrap.conf_int.studentize_reps"><span class="n sig-name">studentize_reps</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.studentize_reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstraps to use in the inner bootstrap when using the
 studentized bootstrap.  Ignored when <code class="docutils literal notranslate"><span class="pre">std_err_func</span></code> is provided</p>
 </dd>
 </dl>
@@ -1228,7 +1228,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap-conf-int">arch.bootstrap.MovingBlock
 Similarly, ‘upper’ sets all lower bounds to -inf.</p>
 </dd>
 <dt class="field-odd" id="arch.bootstrap.MovingBlockBootstrap.conf_int-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.conf_int-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 <p class="example rubric">Examples</p>
@@ -1340,7 +1340,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap-conf-int">arch.bootstrap.MovingBlock
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1387,7 +1387,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap-conf-int">arch.bootstrap.MovingBlock
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.cov.html b/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.cov.html
index 5b745af91d..ef63a363cc 100644
--- a/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.cov.html
+++ b/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.cov.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.cov - arch 6.1.0</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.cov - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1095,24 +1095,24 @@
                   
 
 
-<h1 id="arch-bootstrap-movingblockbootstrap-cov">arch.bootstrap.MovingBlockBootstrap.cov<a class="headerlink" href="#arch-bootstrap-movingblockbootstrap-cov" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-movingblockbootstrap-cov">arch.bootstrap.MovingBlockBootstrap.cov<a class="headerlink" href="#arch-bootstrap-movingblockbootstrap-cov" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.MovingBlockBootstrap.cov">
-<span class="sig-prename descclassname"><span class="pre">MovingBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">cov</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.cov.func" title="arch.bootstrap.MovingBlockBootstrap.cov.func (Python parameter) — Callable function that returns the statistic of interest as a 1-d array"><span class="n"><span class="pre">func</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.cov.reps" title="arch.bootstrap.MovingBlockBootstrap.cov.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.cov.recenter" title="arch.bootstrap.MovingBlockBootstrap.cov.recenter (Python parameter) — Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."><span class="n"><span class="pre">recenter</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.cov.extra_kwargs" title="arch.bootstrap.MovingBlockBootstrap.cov.extra_kwargs (Python parameter) — Dictionary of extra keyword arguments to pass to func"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.cov" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">MovingBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">cov</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.cov.func" title="arch.bootstrap.MovingBlockBootstrap.cov.func (Python parameter) — Callable function that returns the statistic of interest as a 1-d array"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.cov.reps" title="arch.bootstrap.MovingBlockBootstrap.cov.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.cov.recenter" title="arch.bootstrap.MovingBlockBootstrap.cov.recenter (Python parameter) — Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."><span class="n"><span class="pre">recenter</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.cov.extra_kwargs" title="arch.bootstrap.MovingBlockBootstrap.cov.extra_kwargs (Python parameter) — Dictionary of extra keyword arguments to pass to func"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.cov" title="Link to this definition">¶</a></dt>
 <dd><p>Compute parameter covariance using bootstrap</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.MovingBlockBootstrap.cov-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.cov-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.MovingBlockBootstrap.cov.func"><span class="sig-name descname">func</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable</span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.cov.func" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that returns the statistic of interest as a
+<dt id="arch.bootstrap.MovingBlockBootstrap.cov.func"><span class="n sig-name">func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.cov.func" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that returns the statistic of interest as a
 1-d array</p>
 </dd>
-<dt id="arch.bootstrap.MovingBlockBootstrap.cov.reps"><span class="sig-name descname">reps</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, default 1000</span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.cov.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
+<dt id="arch.bootstrap.MovingBlockBootstrap.cov.reps"><span class="n sig-name">reps</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.cov.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
 </dd>
-<dt id="arch.bootstrap.MovingBlockBootstrap.cov.recenter"><span class="sig-name descname">recenter</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, default True</span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.cov.recenter" title="Permalink to this definition">¶</a></dt><dd><p>Whether to center the bootstrap variance estimator on the average
+<dt id="arch.bootstrap.MovingBlockBootstrap.cov.recenter"><span class="n sig-name">recenter</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.cov.recenter" title="Permalink to this definition">¶</a></dt><dd><p>Whether to center the bootstrap variance estimator on the average
 of the bootstrap samples (True) or to center on the original sample
 estimate (False).  Default is True.</p>
 </dd>
-<dt id="arch.bootstrap.MovingBlockBootstrap.cov.extra_kwargs"><span class="sig-name descname">extra_kwargs</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a>, default None</span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.cov.extra_kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Dictionary of extra keyword arguments to pass to func</p>
+<dt id="arch.bootstrap.MovingBlockBootstrap.cov.extra_kwargs"><span class="n sig-name">extra_kwargs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)">Any</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.cov.extra_kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Dictionary of extra keyword arguments to pass to func</p>
 </dd>
 </dl>
 </dd>
@@ -1120,7 +1120,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap-cov">arch.bootstrap.MovingBlockBoots
 <dd class="field-even"><p>Bootstrap covariance estimator</p>
 </dd>
 <dt class="field-odd" id="arch.bootstrap.MovingBlockBootstrap.cov-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.cov-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -1227,7 +1227,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap-cov">arch.bootstrap.MovingBlockBoots
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1274,7 +1274,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap-cov">arch.bootstrap.MovingBlockBoots
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.generator.html b/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.generator.html
index aae83c6f97..99f5df6ad7 100644
--- a/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.generator.html
+++ b/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.generator.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.generator - arch 6.1.0</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.generator - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1027,15 +1027,15 @@
                   
 
 
-<h1 id="arch-bootstrap-movingblockbootstrap-generator">arch.bootstrap.MovingBlockBootstrap.generator<a class="headerlink" href="#arch-bootstrap-movingblockbootstrap-generator" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-movingblockbootstrap-generator">arch.bootstrap.MovingBlockBootstrap.generator<a class="headerlink" href="#arch-bootstrap-movingblockbootstrap-generator" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.bootstrap.MovingBlockBootstrap.generator">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">MovingBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">generator</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.24)"><span class="pre">Generator</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.24)"><span class="pre">RandomState</span></a></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.generator" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">MovingBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">generator</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><span class="pre">Generator</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">RandomState</span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.generator" title="Link to this definition">¶</a></dt>
 <dd><p>Set or get the instance PRNG</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.MovingBlockBootstrap.generator-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.generator-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt><span class="sig-name descname">seed</span><span class="p"> : </span><span class="desctype"><span class="desctype">{Generator, RandomState}, optional</span></span></dt><dd><p>Generator or RandomState used to produce the pseudo-random
+<dt><span class="sig-name descname">seed</span><span class="p"> : </span><span class="desctype"><code class="docutils literal notranslate"><span class="pre">{Generator,</span> <span class="pre">RandomState}</span></code>, <em>optional</em></span></dt><dd><p>Generator or RandomState used to produce the pseudo-random
 values used in the bootstrap</p>
 </dd>
 </dl>
@@ -1045,7 +1045,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap-generator">arch.bootstrap.MovingBloc
 by bootstrap</p>
 </dd>
 <dt class="field-odd" id="arch.bootstrap.MovingBlockBootstrap.generator-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.generator-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>{Generator, RandomState}</p>
+<dd class="field-odd"><p><code class="docutils literal notranslate"><span class="pre">{Generator,</span> <span class="pre">RandomState}</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1117,7 +1117,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap-generator">arch.bootstrap.MovingBloc
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1164,7 +1164,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap-generator">arch.bootstrap.MovingBloc
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.get_state.html b/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.get_state.html
index a3ccefbba3..39ae10022c 100644
--- a/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.get_state.html
+++ b/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.get_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.get_state - arch 6.1.0</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.get_state - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1013,17 +1013,17 @@
                   
 
 
-<h1 id="arch-bootstrap-movingblockbootstrap-get-state">arch.bootstrap.MovingBlockBootstrap.get_state<a class="headerlink" href="#arch-bootstrap-movingblockbootstrap-get-state" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-movingblockbootstrap-get-state">arch.bootstrap.MovingBlockBootstrap.get_state<a class="headerlink" href="#arch-bootstrap-movingblockbootstrap-get-state" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.bootstrap.MovingBlockBootstrap.get_state">
-<span class="sig-prename descclassname"><span class="pre">MovingBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">get_state</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.get_state" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.MovingBlockBootstrap.get_state">
+<span class="sig-prename descclassname"><span class="pre">MovingBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">get_state</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Mapping" title="(in Python v3.11)"><span class="pre">Mapping</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.get_state" title="Link to this definition">¶</a></dt>
 <dd><p>Gets the state of the bootstrap’s random number generator</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.MovingBlockBootstrap.get_state-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.get_state-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>Dictionary containing the state.</p>
 </dd>
 <dt class="field-even" id="arch.bootstrap.MovingBlockBootstrap.get_state-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.get_state-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1095,7 +1095,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap-get-state">arch.bootstrap.MovingBloc
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1142,7 +1142,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap-get-state">arch.bootstrap.MovingBloc
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.index.html b/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.index.html
index 2795193369..881ee2e9ca 100644
--- a/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.index.html
+++ b/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.index.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.index - arch 6.1.0</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.index - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -973,10 +973,10 @@
                   
 
 
-<h1 id="arch-bootstrap-movingblockbootstrap-index">arch.bootstrap.MovingBlockBootstrap.index<a class="headerlink" href="#arch-bootstrap-movingblockbootstrap-index" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-movingblockbootstrap-index">arch.bootstrap.MovingBlockBootstrap.index<a class="headerlink" href="#arch-bootstrap-movingblockbootstrap-index" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.MovingBlockBootstrap.index">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">MovingBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">index</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">numpy.ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">numpy.ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">numpy.ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.index" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">MovingBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">index</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.index" title="Link to this definition">¶</a></dt>
 <dd><p>The current index of the bootstrap</p>
 </dd></dl>
 
@@ -1047,7 +1047,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap-index">arch.bootstrap.MovingBlockBoo
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1094,7 +1094,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap-index">arch.bootstrap.MovingBlockBoo
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.random_state.html b/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.random_state.html
index 2d2e2ac90c..687a76b99d 100644
--- a/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.random_state.html
+++ b/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.random_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.random_state - arch 6.1.0</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.random_state - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1027,15 +1027,15 @@
                   
 
 
-<h1 id="arch-bootstrap-movingblockbootstrap-random-state">arch.bootstrap.MovingBlockBootstrap.random_state<a class="headerlink" href="#arch-bootstrap-movingblockbootstrap-random-state" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-movingblockbootstrap-random-state">arch.bootstrap.MovingBlockBootstrap.random_state<a class="headerlink" href="#arch-bootstrap-movingblockbootstrap-random-state" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.bootstrap.MovingBlockBootstrap.random_state">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">MovingBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">random_state</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.24)"><span class="pre">Generator</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.24)"><span class="pre">RandomState</span></a></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.random_state" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">MovingBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">random_state</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><span class="pre">Generator</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">RandomState</span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.random_state" title="Link to this definition">¶</a></dt>
 <dd><p>Set or get the instance random state</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.MovingBlockBootstrap.random_state-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.random_state-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt><span class="sig-name descname">random_state</span><span class="p"> : </span><span class="desctype"><span class="desctype">RandomState</span></span></dt><dd><p>RandomState instance used by bootstrap</p>
+<dt><span class="sig-name descname">random_state</span><span class="p"> : </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.random.RandomState</span></code></a></span></dt><dd><p>RandomState instance used by bootstrap</p>
 </dd>
 </dl>
 </dd>
@@ -1043,7 +1043,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap-random-state">arch.bootstrap.MovingB
 <dd class="field-even"><p>RandomState instance used by bootstrap</p>
 </dd>
 <dt class="field-odd" id="arch.bootstrap.MovingBlockBootstrap.random_state-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.random_state-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>RandomState</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.random.RandomState</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1115,7 +1115,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap-random-state">arch.bootstrap.MovingB
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1162,7 +1162,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap-random-state">arch.bootstrap.MovingB
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.reset.html b/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.reset.html
index fdbf999689..507d5da8cd 100644
--- a/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.reset.html
+++ b/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.reset.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.reset - arch 6.1.0</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.reset - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -660,13 +660,6 @@
       </ul>
     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.MovingBlockBootstrap.reset-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
       </ul>
@@ -1011,13 +1004,6 @@
       </ul>
     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.MovingBlockBootstrap.reset-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
       </ul>
@@ -1039,23 +1025,20 @@
                   
 
 
-<h1 id="arch-bootstrap-movingblockbootstrap-reset">arch.bootstrap.MovingBlockBootstrap.reset<a class="headerlink" href="#arch-bootstrap-movingblockbootstrap-reset" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-movingblockbootstrap-reset">arch.bootstrap.MovingBlockBootstrap.reset<a class="headerlink" href="#arch-bootstrap-movingblockbootstrap-reset" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.bootstrap.MovingBlockBootstrap.reset">
-<span class="sig-prename descclassname"><span class="pre">MovingBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">reset</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.reset.use_seed" title="arch.bootstrap.MovingBlockBootstrap.reset.use_seed (Python parameter) — Flag indicating whether to use the last seed if provided.  If False or if no seed has been set, the bootstrap will be reset to the initial state.  Default is True"><span class="n"><span class="pre">use_seed</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.reset" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">MovingBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">reset</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.reset.use_seed" title="arch.bootstrap.MovingBlockBootstrap.reset.use_seed (Python parameter) — Flag indicating whether to use the last seed if provided.  If False or if no seed has been set, the bootstrap will be reset to the initial state.  Default is True"><span class="n"><span class="pre">use_seed</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.reset" title="Link to this definition">¶</a></dt>
 <dd><p>Resets the bootstrap to either its initial state or the last seed.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.MovingBlockBootstrap.reset-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.reset-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.MovingBlockBootstrap.reset.use_seed"><span class="sig-name descname">use_seed</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, default True</span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.reset.use_seed" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to use the last seed if provided.  If
+<dt id="arch.bootstrap.MovingBlockBootstrap.reset.use_seed"><span class="n sig-name">use_seed</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.reset.use_seed" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to use the last seed if provided.  If
 False or if no seed has been set, the bootstrap will be reset
 to the initial state.  Default is True</p>
 </dd>
 </dl>
 </dd>
-<dt class="field-even" id="arch.bootstrap.MovingBlockBootstrap.reset-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.reset-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code></a></p>
-</dd>
 </dl>
 </dd></dl>
 
@@ -1126,7 +1109,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap-reset">arch.bootstrap.MovingBlockBoo
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1173,7 +1156,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap-reset">arch.bootstrap.MovingBlockBoo
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.seed.html b/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.seed.html
index 2842e7b1dd..7b4ff07681 100644
--- a/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.seed.html
+++ b/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.seed.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.seed - arch 6.1.0</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.seed - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -675,13 +675,6 @@
       </ul>
     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.MovingBlockBootstrap.seed-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
       </ul>
@@ -1011,13 +1004,6 @@
       </ul>
     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.MovingBlockBootstrap.seed-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
       </ul>
@@ -1039,21 +1025,18 @@
                   
 
 
-<h1 id="arch-bootstrap-movingblockbootstrap-seed">arch.bootstrap.MovingBlockBootstrap.seed<a class="headerlink" href="#arch-bootstrap-movingblockbootstrap-seed" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-movingblockbootstrap-seed">arch.bootstrap.MovingBlockBootstrap.seed<a class="headerlink" href="#arch-bootstrap-movingblockbootstrap-seed" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.bootstrap.MovingBlockBootstrap.seed">
-<span class="sig-prename descclassname"><span class="pre">MovingBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">seed</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.seed.value" title="arch.bootstrap.MovingBlockBootstrap.seed.value (Python parameter) — Value to use as the seed."><span class="n"><span class="pre">value</span></span></a></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.seed" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">MovingBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">seed</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.seed.value" title="arch.bootstrap.MovingBlockBootstrap.seed.value (Python parameter) — Value to use as the seed."><span class="n"><span class="pre">value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.seed" title="Link to this definition">¶</a></dt>
 <dd><p>Reseeds the bootstrap’s random number generator</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.MovingBlockBootstrap.seed-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.seed-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.MovingBlockBootstrap.seed.value"><span class="sig-name descname">value</span><span class="p"> : </span><span class="desctype"><span class="desctype">{int, List[<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>], ndarray}</span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.seed.value" title="Permalink to this definition">¶</a></dt><dd><p>Value to use as the seed.</p>
+<dt id="arch.bootstrap.MovingBlockBootstrap.seed.value"><span class="n sig-name">value</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.seed.value" title="Permalink to this definition">¶</a></dt><dd><p>Value to use as the seed.</p>
 </dd>
 </dl>
 </dd>
-<dt class="field-even" id="arch.bootstrap.MovingBlockBootstrap.seed-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.seed-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code></a></p>
-</dd>
 </dl>
 </dd></dl>
 
@@ -1124,7 +1107,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap-seed">arch.bootstrap.MovingBlockBoot
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1171,7 +1154,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap-seed">arch.bootstrap.MovingBlockBoot
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.set_state.html b/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.set_state.html
index ee6e2a23eb..75018f8df7 100644
--- a/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.set_state.html
+++ b/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.set_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.set_state - arch 6.1.0</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.set_state - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -690,13 +690,6 @@
       </ul>
     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.MovingBlockBootstrap.set_state-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
       </ul>
@@ -1011,13 +1004,6 @@
       </ul>
     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.MovingBlockBootstrap.set_state-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
       </ul>
@@ -1039,21 +1025,18 @@
                   
 
 
-<h1 id="arch-bootstrap-movingblockbootstrap-set-state">arch.bootstrap.MovingBlockBootstrap.set_state<a class="headerlink" href="#arch-bootstrap-movingblockbootstrap-set-state" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-movingblockbootstrap-set-state">arch.bootstrap.MovingBlockBootstrap.set_state<a class="headerlink" href="#arch-bootstrap-movingblockbootstrap-set-state" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.bootstrap.MovingBlockBootstrap.set_state">
-<span class="sig-prename descclassname"><span class="pre">MovingBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">set_state</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.set_state.state" title="arch.bootstrap.MovingBlockBootstrap.set_state.state (Python parameter) — Dictionary or tuple containing the state."><span class="n"><span class="pre">state</span></span></a></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.set_state" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.MovingBlockBootstrap.set_state">
+<span class="sig-prename descclassname"><span class="pre">MovingBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">set_state</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.set_state.state" title="arch.bootstrap.MovingBlockBootstrap.set_state.state (Python parameter) — Dictionary or tuple containing the state."><span class="n"><span class="pre">state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.set_state" title="Link to this definition">¶</a></dt>
 <dd><p>Sets the state of the bootstrap’s random number generator</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.MovingBlockBootstrap.set_state-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.set_state-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.MovingBlockBootstrap.set_state.state"><span class="sig-name descname">state</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a></span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.set_state.state" title="Permalink to this definition">¶</a></dt><dd><p>Dictionary or tuple containing the state.</p>
+<dt id="arch.bootstrap.MovingBlockBootstrap.set_state.state"><span class="n sig-name">state</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)">Any</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.set_state.state" title="Permalink to this definition">¶</a></dt><dd><p>Dictionary or tuple containing the state.</p>
 </dd>
 </dl>
 </dd>
-<dt class="field-even" id="arch.bootstrap.MovingBlockBootstrap.set_state-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.set_state-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code></a></p>
-</dd>
 </dl>
 </dd></dl>
 
@@ -1124,7 +1107,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap-set-state">arch.bootstrap.MovingBloc
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1171,7 +1154,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap-set-state">arch.bootstrap.MovingBloc
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.state.html b/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.state.html
index fe12dec8da..fb144bd6d2 100644
--- a/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.state.html
+++ b/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.state - arch 6.1.0</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.state - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1013,10 +1013,10 @@
                   
 
 
-<h1 id="arch-bootstrap-movingblockbootstrap-state">arch.bootstrap.MovingBlockBootstrap.state<a class="headerlink" href="#arch-bootstrap-movingblockbootstrap-state" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-movingblockbootstrap-state">arch.bootstrap.MovingBlockBootstrap.state<a class="headerlink" href="#arch-bootstrap-movingblockbootstrap-state" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.MovingBlockBootstrap.state">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">MovingBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">state</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">numpy.ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Mapping" title="(in Python v3.11)"><span class="pre">Mapping</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.state" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">MovingBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">state</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Mapping" title="(in Python v3.11)"><span class="pre">Mapping</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.state" title="Link to this definition">¶</a></dt>
 <dd><p>Set or get the generator’s state</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.MovingBlockBootstrap.state-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.state-returns" title="Permalink to this headline">¶</a></dt>
@@ -1025,7 +1025,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap-state">arch.bootstrap.MovingBlockBoo
 Otherwise it is a dictionary.</p>
 </dd>
 <dt class="field-even" id="arch.bootstrap.MovingBlockBootstrap.state-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.state-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p>{tuple, dict}</p>
+<dd class="field-even"><p><code class="docutils literal notranslate"><span class="pre">{tuple,</span> <span class="pre">dict}</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1097,7 +1097,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap-state">arch.bootstrap.MovingBlockBoo
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1144,7 +1144,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap-state">arch.bootstrap.MovingBlockBoo
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.update_indices.html b/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.update_indices.html
index db540d296b..45d21cf340 100644
--- a/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.update_indices.html
+++ b/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.update_indices.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.update_indices - arch 6.1.0</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.update_indices - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -684,19 +684,6 @@
     <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="arch.bootstrap.MovingBlockBootstrap.update_indices (Python method) — Update indices for the next iteration of the bootstrap.  This must be overridden when creating new bootstraps." class="md-ellipsis">Moving<wbr>Block<wbr>Bootstrap.<wbr>update_<wbr>indices</span>
   </a>
   
-    <nav class="md-nav" aria-label="MovingBlockBootstrap.update_indices">
-      <ul class="md-nav__list">
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.MovingBlockBootstrap.update_indices-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
-</li>
-        
-      </ul>
-    </nav>
-  
 </li>
       
     </ul>
@@ -970,19 +957,6 @@
     <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="arch.bootstrap.MovingBlockBootstrap.update_indices (Python method) — Update indices for the next iteration of the bootstrap.  This must be overridden when creating new bootstraps." class="md-ellipsis">Moving<wbr>Block<wbr>Bootstrap.<wbr>update_<wbr>indices</span>
   </a>
   
-    <nav class="md-nav" aria-label="MovingBlockBootstrap.update_indices">
-      <ul class="md-nav__list">
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.MovingBlockBootstrap.update_indices-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
-</li>
-        
-      </ul>
-    </nav>
-  
 </li>
       
     </ul>
@@ -999,17 +973,12 @@
                   
 
 
-<h1 id="arch-bootstrap-movingblockbootstrap-update-indices">arch.bootstrap.MovingBlockBootstrap.update_indices<a class="headerlink" href="#arch-bootstrap-movingblockbootstrap-update-indices" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-movingblockbootstrap-update-indices">arch.bootstrap.MovingBlockBootstrap.update_indices<a class="headerlink" href="#arch-bootstrap-movingblockbootstrap-update-indices" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.bootstrap.MovingBlockBootstrap.update_indices">
-<span class="sig-prename descclassname"><span class="pre">MovingBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">update_indices</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/bootstrap/base.html#MovingBlockBootstrap.update_indices"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.update_indices" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">MovingBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">update_indices</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/bootstrap/base.html#MovingBlockBootstrap.update_indices"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.update_indices" title="Link to this definition">¶</a></dt>
 <dd><p>Update indices for the next iteration of the bootstrap.  This must
 be overridden when creating new bootstraps.</p>
-<dl class="field-list simple">
-<dt class="field-odd" id="arch.bootstrap.MovingBlockBootstrap.update_indices-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.update_indices-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a></p>
-</dd>
-</dl>
 </dd></dl>
 
 
@@ -1079,7 +1048,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap-update-indices">arch.bootstrap.Movin
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1126,7 +1095,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap-update-indices">arch.bootstrap.Movin
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.var.html b/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.var.html
index 9e5756f5f9..4de1162999 100644
--- a/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.var.html
+++ b/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.var.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.var - arch 6.1.0</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.var - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1095,24 +1095,24 @@
                   
 
 
-<h1 id="arch-bootstrap-movingblockbootstrap-var">arch.bootstrap.MovingBlockBootstrap.var<a class="headerlink" href="#arch-bootstrap-movingblockbootstrap-var" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-movingblockbootstrap-var">arch.bootstrap.MovingBlockBootstrap.var<a class="headerlink" href="#arch-bootstrap-movingblockbootstrap-var" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.MovingBlockBootstrap.var">
-<span class="sig-prename descclassname"><span class="pre">MovingBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">var</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.var.func" title="arch.bootstrap.MovingBlockBootstrap.var.func (Python parameter) — Callable function that returns the statistic of interest as a 1-d array"><span class="n"><span class="pre">func</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.var.reps" title="arch.bootstrap.MovingBlockBootstrap.var.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.var.recenter" title="arch.bootstrap.MovingBlockBootstrap.var.recenter (Python parameter) — Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."><span class="n"><span class="pre">recenter</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.var.extra_kwargs" title="arch.bootstrap.MovingBlockBootstrap.var.extra_kwargs (Python parameter) — Dictionary of extra keyword arguments to pass to func"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.var" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">MovingBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">var</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.var.func" title="arch.bootstrap.MovingBlockBootstrap.var.func (Python parameter) — Callable function that returns the statistic of interest as a 1-d array"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.var.reps" title="arch.bootstrap.MovingBlockBootstrap.var.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.var.recenter" title="arch.bootstrap.MovingBlockBootstrap.var.recenter (Python parameter) — Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."><span class="n"><span class="pre">recenter</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.var.extra_kwargs" title="arch.bootstrap.MovingBlockBootstrap.var.extra_kwargs (Python parameter) — Dictionary of extra keyword arguments to pass to func"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.var" title="Link to this definition">¶</a></dt>
 <dd><p>Compute parameter variance using bootstrap</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.MovingBlockBootstrap.var-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.var-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.MovingBlockBootstrap.var.func"><span class="sig-name descname">func</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable</span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.var.func" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that returns the statistic of interest as a
+<dt id="arch.bootstrap.MovingBlockBootstrap.var.func"><span class="n sig-name">func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.var.func" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that returns the statistic of interest as a
 1-d array</p>
 </dd>
-<dt id="arch.bootstrap.MovingBlockBootstrap.var.reps"><span class="sig-name descname">reps</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, default 1000</span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.var.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
+<dt id="arch.bootstrap.MovingBlockBootstrap.var.reps"><span class="n sig-name">reps</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.var.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
 </dd>
-<dt id="arch.bootstrap.MovingBlockBootstrap.var.recenter"><span class="sig-name descname">recenter</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, default True</span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.var.recenter" title="Permalink to this definition">¶</a></dt><dd><p>Whether to center the bootstrap variance estimator on the average
+<dt id="arch.bootstrap.MovingBlockBootstrap.var.recenter"><span class="n sig-name">recenter</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.var.recenter" title="Permalink to this definition">¶</a></dt><dd><p>Whether to center the bootstrap variance estimator on the average
 of the bootstrap samples (True) or to center on the original sample
 estimate (False).  Default is True.</p>
 </dd>
-<dt id="arch.bootstrap.MovingBlockBootstrap.var.extra_kwargs"><span class="sig-name descname">extra_kwargs</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a>, default None</span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.var.extra_kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Dictionary of extra keyword arguments to pass to func</p>
+<dt id="arch.bootstrap.MovingBlockBootstrap.var.extra_kwargs"><span class="n sig-name">extra_kwargs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)">Any</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.var.extra_kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Dictionary of extra keyword arguments to pass to func</p>
 </dd>
 </dl>
 </dd>
@@ -1120,7 +1120,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap-var">arch.bootstrap.MovingBlockBoots
 <dd class="field-even"><p>Bootstrap variance estimator</p>
 </dd>
 <dt class="field-odd" id="arch.bootstrap.MovingBlockBootstrap.var-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.var-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -1227,7 +1227,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap-var">arch.bootstrap.MovingBlockBoots
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1274,7 +1274,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap-var">arch.bootstrap.MovingBlockBoots
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.apply.html b/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.apply.html
index 135b236c98..92ccf29fb5 100644
--- a/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.apply.html
+++ b/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.apply.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.apply - arch 6.1.0</title>
+        <title>arch.bootstrap.StationaryBootstrap.apply - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1081,19 +1081,19 @@
                   
 
 
-<h1 id="arch-bootstrap-stationarybootstrap-apply">arch.bootstrap.StationaryBootstrap.apply<a class="headerlink" href="#arch-bootstrap-stationarybootstrap-apply" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-stationarybootstrap-apply">arch.bootstrap.StationaryBootstrap.apply<a class="headerlink" href="#arch-bootstrap-stationarybootstrap-apply" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.bootstrap.StationaryBootstrap.apply">
-<span class="sig-prename descclassname"><span class="pre">StationaryBootstrap.</span></span><span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.apply.func" title="arch.bootstrap.StationaryBootstrap.apply.func (Python parameter) — Function the computes parameter values.  See Notes for requirements"><span class="n"><span class="pre">func</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.apply.reps" title="arch.bootstrap.StationaryBootstrap.apply.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.apply.extra_kwargs" title="arch.bootstrap.StationaryBootstrap.apply.extra_kwargs (Python parameter) — Extra keyword arguments to use when calling func.  Must not conflict with keyword arguments used to initialize bootstrap"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.apply" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.StationaryBootstrap.apply">
+<span class="sig-prename descclassname"><span class="pre">StationaryBootstrap.</span></span><span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.apply.func" title="arch.bootstrap.StationaryBootstrap.apply.func (Python parameter) — Function the computes parameter values.  See Notes for requirements"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.apply.reps" title="arch.bootstrap.StationaryBootstrap.apply.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.apply.extra_kwargs" title="arch.bootstrap.StationaryBootstrap.apply.extra_kwargs (Python parameter) — Extra keyword arguments to use when calling func.  Must not conflict with keyword arguments used to initialize bootstrap"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.apply" title="Link to this definition">¶</a></dt>
 <dd><p>Applies a function to bootstrap replicated data</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.StationaryBootstrap.apply-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.apply-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.StationaryBootstrap.apply.func"><span class="sig-name descname">func</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable</span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.apply.func" title="Permalink to this definition">¶</a></dt><dd><p>Function the computes parameter values.  See Notes for requirements</p>
+<dt id="arch.bootstrap.StationaryBootstrap.apply.func"><span class="n sig-name">func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.apply.func" title="Permalink to this definition">¶</a></dt><dd><p>Function the computes parameter values.  See Notes for requirements</p>
 </dd>
-<dt id="arch.bootstrap.StationaryBootstrap.apply.reps"><span class="sig-name descname">reps</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, default 1000</span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.apply.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
+<dt id="arch.bootstrap.StationaryBootstrap.apply.reps"><span class="n sig-name">reps</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.apply.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
 </dd>
-<dt id="arch.bootstrap.StationaryBootstrap.apply.extra_kwargs"><span class="sig-name descname">extra_kwargs</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a>, default None</span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.apply.extra_kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Extra keyword arguments to use when calling func.  Must not
+<dt id="arch.bootstrap.StationaryBootstrap.apply.extra_kwargs"><span class="n sig-name">extra_kwargs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)">Any</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.apply.extra_kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Extra keyword arguments to use when calling func.  Must not
 conflict with keyword arguments used to initialize bootstrap</p>
 </dd>
 </dl>
@@ -1103,7 +1103,7 @@ <h1 id="arch-bootstrap-stationarybootstrap-apply">arch.bootstrap.StationaryBoots
 corresponds to a bootstrap iteration</p>
 </dd>
 <dt class="field-odd" id="arch.bootstrap.StationaryBootstrap.apply-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.apply-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -1193,7 +1193,7 @@ <h1 id="arch-bootstrap-stationarybootstrap-apply">arch.bootstrap.StationaryBoots
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1240,7 +1240,7 @@ <h1 id="arch-bootstrap-stationarybootstrap-apply">arch.bootstrap.StationaryBoots
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.bootstrap.html b/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.bootstrap.html
index 2b83f7224e..c10c8f2629 100644
--- a/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.bootstrap.html
+++ b/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.bootstrap.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.bootstrap - arch 6.1.0</title>
+        <title>arch.bootstrap.StationaryBootstrap.bootstrap - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1053,15 +1053,15 @@
                   
 
 
-<h1 id="arch-bootstrap-stationarybootstrap-bootstrap">arch.bootstrap.StationaryBootstrap.bootstrap<a class="headerlink" href="#arch-bootstrap-stationarybootstrap-bootstrap" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-stationarybootstrap-bootstrap">arch.bootstrap.StationaryBootstrap.bootstrap<a class="headerlink" href="#arch-bootstrap-stationarybootstrap-bootstrap" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.bootstrap.StationaryBootstrap.bootstrap">
-<span class="sig-prename descclassname"><span class="pre">StationaryBootstrap.</span></span><span class="sig-name descname"><span class="pre">bootstrap</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.bootstrap.reps" title="arch.bootstrap.StationaryBootstrap.bootstrap.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.bootstrap" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.StationaryBootstrap.bootstrap">
+<span class="sig-prename descclassname"><span class="pre">StationaryBootstrap.</span></span><span class="sig-name descname"><span class="pre">bootstrap</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.bootstrap.reps" title="arch.bootstrap.StationaryBootstrap.bootstrap.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Generator" title="(in Python v3.11)"><span class="pre">Generator</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.bootstrap" title="Link to this definition">¶</a></dt>
 <dd><p>Iterator for use when bootstrapping</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.StationaryBootstrap.bootstrap-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.bootstrap-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.StationaryBootstrap.bootstrap.reps"><span class="sig-name descname">reps</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.bootstrap.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
+<dt id="arch.bootstrap.StationaryBootstrap.bootstrap.reps"><span class="n sig-name">reps</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.bootstrap.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
 </dd>
 </dl>
 </dd>
@@ -1069,7 +1069,7 @@ <h1 id="arch-bootstrap-stationarybootstrap-bootstrap">arch.bootstrap.StationaryB
 <dd class="field-even"><p>Generator to iterate over in bootstrap calculations</p>
 </dd>
 <dt class="field-odd" id="arch.bootstrap.StationaryBootstrap.bootstrap-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.bootstrap-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>generator</p>
+<dd class="field-odd"><p><a class="reference internal" href="arch.bootstrap.StationaryBootstrap.generator.html#arch.bootstrap.StationaryBootstrap.generator" title="arch.bootstrap.StationaryBootstrap.generator (Python property) — Set or get the instance PRNG"><code class="xref py py-class docutils literal notranslate"><span class="pre">generator</span></code></a></p>
 </dd>
 </dl>
 <p class="example rubric">Examples</p>
@@ -1161,7 +1161,7 @@ <h1 id="arch-bootstrap-stationarybootstrap-bootstrap">arch.bootstrap.StationaryB
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1208,7 +1208,7 @@ <h1 id="arch-bootstrap-stationarybootstrap-bootstrap">arch.bootstrap.StationaryB
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.clone.html b/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.clone.html
index 3e3316bd54..241db1fe8f 100644
--- a/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.clone.html
+++ b/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.clone.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.clone - arch 6.1.0</title>
+        <title>arch.bootstrap.StationaryBootstrap.clone - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1081,19 +1081,19 @@
                   
 
 
-<h1 id="arch-bootstrap-stationarybootstrap-clone">arch.bootstrap.StationaryBootstrap.clone<a class="headerlink" href="#arch-bootstrap-stationarybootstrap-clone" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-stationarybootstrap-clone">arch.bootstrap.StationaryBootstrap.clone<a class="headerlink" href="#arch-bootstrap-stationarybootstrap-clone" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.bootstrap.StationaryBootstrap.clone">
-<span class="sig-prename descclassname"><span class="pre">StationaryBootstrap.</span></span><span class="sig-name descname"><span class="pre">clone</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.clone.args" title="arch.bootstrap.StationaryBootstrap.clone.args (Python parameter) — Positional arguments to bootstrap"><span class="n"><span class="pre">args</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.clone.seed" title="arch.bootstrap.StationaryBootstrap.clone.seed (Python parameter) — The seed value to pass to the closed generator"><span class="n"><span class="pre">seed</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">**</span></span><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.clone.kwargs" title="arch.bootstrap.StationaryBootstrap.clone.kwargs (Python parameter) — Keyword arguments to bootstrap"><span class="n"><span class="pre">kwargs</span></span></a></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.clone" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.StationaryBootstrap.clone">
+<span class="sig-prename descclassname"><span class="pre">StationaryBootstrap.</span></span><span class="sig-name descname"><span class="pre">clone</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.clone.args" title="arch.bootstrap.StationaryBootstrap.clone.args (Python parameter) — Positional arguments to bootstrap"><span class="n"><span class="pre">args</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.clone.seed" title="arch.bootstrap.StationaryBootstrap.clone.seed (Python parameter) — The seed value to pass to the closed generator"><span class="n"><span class="pre">seed</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><span class="pre">Generator</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">**</span></span><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.clone.kwargs" title="arch.bootstrap.StationaryBootstrap.clone.kwargs (Python parameter) — Keyword arguments to bootstrap"><span class="n"><span class="pre">kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.bootstrap.CircularBlockBootstrap.html#arch.bootstrap.CircularBlockBootstrap" title="arch.bootstrap.base.CircularBlockBootstrap (Python class)"><span class="pre">CircularBlockBootstrap</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.clone" title="Link to this definition">¶</a></dt>
 <dd><p>Clones the bootstrap using different data with a fresh prng.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.StationaryBootstrap.clone-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.clone-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.StationaryBootstrap.clone.args"><span class="o">*</span><span class="n sig-name">args</span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.clone.args" title="Permalink to this definition">¶</a></dt><dd><p>Positional arguments to bootstrap</p>
+<dt id="arch.bootstrap.StationaryBootstrap.clone.args"><span class="o">*</span><span class="n sig-name">args</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.clone.args" title="Permalink to this definition">¶</a></dt><dd><p>Positional arguments to bootstrap</p>
 </dd>
-<dt id="arch.bootstrap.StationaryBootstrap.clone.seed"><span class="n sig-name">seed</span><span class="o">=</span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.clone.seed" title="Permalink to this definition">¶</a></dt><dd><p>The seed value to pass to the closed generator</p>
+<dt id="arch.bootstrap.StationaryBootstrap.clone.seed"><span class="n sig-name">seed</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)">Generator</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)">RandomState</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.clone.seed" title="Permalink to this definition">¶</a></dt><dd><p>The seed value to pass to the closed generator</p>
 </dd>
-<dt id="arch.bootstrap.StationaryBootstrap.clone.kwargs"><span class="o">**</span><span class="n sig-name">kwargs</span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.clone.kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Keyword arguments to bootstrap</p>
+<dt id="arch.bootstrap.StationaryBootstrap.clone.kwargs"><span class="o">**</span><span class="n sig-name">kwargs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.clone.kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Keyword arguments to bootstrap</p>
 </dd>
 </dl>
 </dd>
@@ -1101,7 +1101,7 @@ <h1 id="arch-bootstrap-stationarybootstrap-clone">arch.bootstrap.StationaryBoots
 <dd class="field-even"><p>Bootstrap instance</p>
 </dd>
 <dt class="field-odd" id="arch.bootstrap.StationaryBootstrap.clone-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.clone-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>bs</p>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">bs</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1173,7 +1173,7 @@ <h1 id="arch-bootstrap-stationarybootstrap-clone">arch.bootstrap.StationaryBoots
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1220,7 +1220,7 @@ <h1 id="arch-bootstrap-stationarybootstrap-clone">arch.bootstrap.StationaryBoots
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diff --git a/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.conf_int.html b/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.conf_int.html
index ad08cee915..89008c9db2 100644
--- a/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.conf_int.html
+++ b/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.conf_int.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.conf_int - arch 6.1.0</title>
+        <title>arch.bootstrap.StationaryBootstrap.conf_int - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
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@@ -92,7 +92,7 @@
 
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@@ -102,7 +102,7 @@
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           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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@@ -1179,44 +1179,44 @@
                   
 
 
-<h1 id="arch-bootstrap-stationarybootstrap-conf-int">arch.bootstrap.StationaryBootstrap.conf_int<a class="headerlink" href="#arch-bootstrap-stationarybootstrap-conf-int" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-stationarybootstrap-conf-int">arch.bootstrap.StationaryBootstrap.conf_int<a class="headerlink" href="#arch-bootstrap-stationarybootstrap-conf-int" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.StationaryBootstrap.conf_int">
-<span class="sig-prename descclassname"><span class="pre">StationaryBootstrap.</span></span><span class="sig-name descname"><span class="pre">conf_int</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.conf_int.func" title="arch.bootstrap.StationaryBootstrap.conf_int.func (Python parameter) — Function the computes parameter values.  See Notes for requirements"><span class="n"><span class="pre">func</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.conf_int.reps" title="arch.bootstrap.StationaryBootstrap.conf_int.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.conf_int.method" title="arch.bootstrap.StationaryBootstrap.conf_int.method (Python parameter) — One of 'basic', 'percentile', 'studentized', 'norm' (identical to 'var', 'cov'), 'bc' (identical to 'debiased', 'bias-corrected'), or 'bca'"><span class="n"><span class="pre">method</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;basic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.conf_int.size" title="arch.bootstrap.StationaryBootstrap.conf_int.size (Python parameter) — Coverage of confidence interval"><span class="n"><span class="pre">size</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.95</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.conf_int.tail" title="arch.bootstrap.StationaryBootstrap.conf_int.tail (Python parameter) — One of 'two', 'upper' or 'lower'."><span class="n"><span class="pre">tail</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;two&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.conf_int.extra_kwargs" title="arch.bootstrap.StationaryBootstrap.conf_int.extra_kwargs (Python parameter) — Extra keyword arguments to use when calling func and std_err_func, when appropriate"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.conf_int.reuse" title="arch.bootstrap.StationaryBootstrap.conf_int.reuse (Python parameter) — Flag indicating whether to reuse previously computed bootstrap results.  This allows alternative methods to be compared without rerunning the bootstrap simulation.  Reuse is ignored if reps is not the same across multiple runs, func changes across calls, or method is 'studentized'."><span class="n"><span class="pre">reuse</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.conf_int.sampling" title="arch.bootstrap.StationaryBootstrap.conf_int.sampling (Python parameter) — Type of sampling to use: 'nonparametric', 'semi-parametric' (or 'semi') or 'parametric'.  The default is 'nonparametric'.  See notes about the changes to func required when using 'semi' or 'parametric'."><span class="n"><span class="pre">sampling</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;nonparametric&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.conf_int.std_err_func" title="arch.bootstrap.StationaryBootstrap.conf_int.std_err_func (Python parameter) — Function to use when standardizing estimated parameters when using the studentized bootstrap.  Providing an analytical function eliminates the need for a nested bootstrap"><span class="n"><span class="pre">std_err_func</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.conf_int.studentize_reps" title="arch.bootstrap.StationaryBootstrap.conf_int.studentize_reps (Python parameter) — Number of bootstraps to use in the inner bootstrap when using the studentized bootstrap.  Ignored when std_err_func is provided"><span class="n"><span class="pre">studentize_reps</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.conf_int" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">StationaryBootstrap.</span></span><span class="sig-name descname"><span class="pre">conf_int</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.conf_int.func" title="arch.bootstrap.StationaryBootstrap.conf_int.func (Python parameter) — Function the computes parameter values.  See Notes for requirements"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.conf_int.reps" title="arch.bootstrap.StationaryBootstrap.conf_int.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.conf_int.method" title="arch.bootstrap.StationaryBootstrap.conf_int.method (Python parameter) — One of 'basic', 'percentile', 'studentized', 'norm' (identical to 'var', 'cov'), 'bc' (identical to 'debiased', 'bias-corrected'), or 'bca'"><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'basic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'percentile'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'studentized'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'norm'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bc'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bca'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;basic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.conf_int.size" title="arch.bootstrap.StationaryBootstrap.conf_int.size (Python parameter) — Coverage of confidence interval"><span class="n"><span class="pre">size</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.95</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.conf_int.tail" title="arch.bootstrap.StationaryBootstrap.conf_int.tail (Python parameter) — One of 'two', 'upper' or 'lower'."><span class="n"><span class="pre">tail</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'two'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'upper'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'lower'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;two&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.conf_int.extra_kwargs" title="arch.bootstrap.StationaryBootstrap.conf_int.extra_kwargs (Python parameter) — Extra keyword arguments to use when calling func and std_err_func, when appropriate"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.conf_int.reuse" title="arch.bootstrap.StationaryBootstrap.conf_int.reuse (Python parameter) — Flag indicating whether to reuse previously computed bootstrap results.  This allows alternative methods to be compared without rerunning the bootstrap simulation.  Reuse is ignored if reps is not the same across multiple runs, func changes across calls, or method is 'studentized'."><span class="n"><span class="pre">reuse</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.conf_int.sampling" title="arch.bootstrap.StationaryBootstrap.conf_int.sampling (Python parameter) — Type of sampling to use: 'nonparametric', 'semi-parametric' (or 'semi') or 'parametric'.  The default is 'nonparametric'.  See notes about the changes to func required when using 'semi' or 'parametric'."><span class="n"><span class="pre">sampling</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'nonparametric'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'semi-parametric'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'semi'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'parametric'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'semiparametric'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;nonparametric&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.conf_int.std_err_func" title="arch.bootstrap.StationaryBootstrap.conf_int.std_err_func (Python parameter) — Function to use when standardizing estimated parameters when using the studentized bootstrap.  Providing an analytical function eliminates the need for a nested bootstrap"><span class="n"><span class="pre">std_err_func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.conf_int.studentize_reps" title="arch.bootstrap.StationaryBootstrap.conf_int.studentize_reps (Python parameter) — Number of bootstraps to use in the inner bootstrap when using the studentized bootstrap.  Ignored when std_err_func is provided"><span class="n"><span class="pre">studentize_reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.conf_int" title="Link to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.StationaryBootstrap.conf_int-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.conf_int-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.StationaryBootstrap.conf_int.func"><span class="sig-name descname">func</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable</span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.conf_int.func" title="Permalink to this definition">¶</a></dt><dd><p>Function the computes parameter values.  See Notes for requirements</p>
+<dt id="arch.bootstrap.StationaryBootstrap.conf_int.func"><span class="n sig-name">func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.conf_int.func" title="Permalink to this definition">¶</a></dt><dd><p>Function the computes parameter values.  See Notes for requirements</p>
 </dd>
-<dt id="arch.bootstrap.StationaryBootstrap.conf_int.reps"><span class="sig-name descname">reps</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, default 1000</span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.conf_int.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
+<dt id="arch.bootstrap.StationaryBootstrap.conf_int.reps"><span class="n sig-name">reps</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.conf_int.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
 </dd>
-<dt id="arch.bootstrap.StationaryBootstrap.conf_int.method"><span class="sig-name descname">method</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, default &quot;basic&quot;</span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.conf_int.method" title="Permalink to this definition">¶</a></dt><dd><p>One of ‘basic’, ‘percentile’, ‘studentized’, ‘norm’ (identical to
+<dt id="arch.bootstrap.StationaryBootstrap.conf_int.method"><span class="n sig-name">method</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'basic'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'percentile'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'studentized'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'norm'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'bc'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'bca'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;basic&#39;</span></code><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.conf_int.method" title="Permalink to this definition">¶</a></dt><dd><p>One of ‘basic’, ‘percentile’, ‘studentized’, ‘norm’ (identical to
 ‘var’, ‘cov’), ‘bc’ (identical to ‘debiased’, ‘bias-corrected’), or
 ‘bca’</p>
 </dd>
-<dt id="arch.bootstrap.StationaryBootstrap.conf_int.size"><span class="sig-name descname">size</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>, default 0.95</span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.conf_int.size" title="Permalink to this definition">¶</a></dt><dd><p>Coverage of confidence interval</p>
+<dt id="arch.bootstrap.StationaryBootstrap.conf_int.size"><span class="n sig-name">size</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.95</span></code><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.conf_int.size" title="Permalink to this definition">¶</a></dt><dd><p>Coverage of confidence interval</p>
 </dd>
-<dt id="arch.bootstrap.StationaryBootstrap.conf_int.tail"><span class="sig-name descname">tail</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, default &quot;two&quot;</span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.conf_int.tail" title="Permalink to this definition">¶</a></dt><dd><p>One of ‘two’, ‘upper’ or ‘lower’.</p>
+<dt id="arch.bootstrap.StationaryBootstrap.conf_int.tail"><span class="n sig-name">tail</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'two'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'upper'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'lower'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;two&#39;</span></code><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.conf_int.tail" title="Permalink to this definition">¶</a></dt><dd><p>One of ‘two’, ‘upper’ or ‘lower’.</p>
 </dd>
-<dt id="arch.bootstrap.StationaryBootstrap.conf_int.reuse"><span class="sig-name descname">reuse</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, default False</span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.conf_int.reuse" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to reuse previously computed bootstrap
+<dt id="arch.bootstrap.StationaryBootstrap.conf_int.reuse"><span class="n sig-name">reuse</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.conf_int.reuse" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to reuse previously computed bootstrap
 results.  This allows alternative methods to be compared without
 rerunning the bootstrap simulation.  Reuse is ignored if reps is
 not the same across multiple runs, func changes across calls, or
 method is ‘studentized’.</p>
 </dd>
-<dt id="arch.bootstrap.StationaryBootstrap.conf_int.sampling"><span class="sig-name descname">sampling</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, default &quot;nonparametric&quot;</span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.conf_int.sampling" title="Permalink to this definition">¶</a></dt><dd><p>Type of sampling to use: ‘nonparametric’, ‘semi-parametric’ (or
+<dt id="arch.bootstrap.StationaryBootstrap.conf_int.sampling"><span class="n sig-name">sampling</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'nonparametric'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'semi-parametric'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'semi'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'parametric'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'semiparametric'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;nonparametric&#39;</span></code><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.conf_int.sampling" title="Permalink to this definition">¶</a></dt><dd><p>Type of sampling to use: ‘nonparametric’, ‘semi-parametric’ (or
 ‘semi’) or ‘parametric’.  The default is ‘nonparametric’.  See
 notes about the changes to func required when using ‘semi’ or
 ‘parametric’.</p>
 </dd>
-<dt id="arch.bootstrap.StationaryBootstrap.conf_int.extra_kwargs"><span class="sig-name descname">extra_kwargs</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a>, default None</span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.conf_int.extra_kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Extra keyword arguments to use when calling func and std_err_func,
+<dt id="arch.bootstrap.StationaryBootstrap.conf_int.extra_kwargs"><span class="n sig-name">extra_kwargs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)">Any</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.conf_int.extra_kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Extra keyword arguments to use when calling func and std_err_func,
 when appropriate</p>
 </dd>
-<dt id="arch.bootstrap.StationaryBootstrap.conf_int.std_err_func"><span class="sig-name descname">std_err_func</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable, default None</span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.conf_int.std_err_func" title="Permalink to this definition">¶</a></dt><dd><p>Function to use when standardizing estimated parameters when using
+<dt id="arch.bootstrap.StationaryBootstrap.conf_int.std_err_func"><span class="n sig-name">std_err_func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.conf_int.std_err_func" title="Permalink to this definition">¶</a></dt><dd><p>Function to use when standardizing estimated parameters when using
 the studentized bootstrap.  Providing an analytical function
 eliminates the need for a nested bootstrap</p>
 </dd>
-<dt id="arch.bootstrap.StationaryBootstrap.conf_int.studentize_reps"><span class="sig-name descname">studentize_reps</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, default 1000</span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.conf_int.studentize_reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstraps to use in the inner bootstrap when using the
+<dt id="arch.bootstrap.StationaryBootstrap.conf_int.studentize_reps"><span class="n sig-name">studentize_reps</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.conf_int.studentize_reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstraps to use in the inner bootstrap when using the
 studentized bootstrap.  Ignored when <code class="docutils literal notranslate"><span class="pre">std_err_func</span></code> is provided</p>
 </dd>
 </dl>
@@ -1228,7 +1228,7 @@ <h1 id="arch-bootstrap-stationarybootstrap-conf-int">arch.bootstrap.StationaryBo
 Similarly, ‘upper’ sets all lower bounds to -inf.</p>
 </dd>
 <dt class="field-odd" id="arch.bootstrap.StationaryBootstrap.conf_int-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.conf_int-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 <p class="example rubric">Examples</p>
@@ -1340,7 +1340,7 @@ <h1 id="arch-bootstrap-stationarybootstrap-conf-int">arch.bootstrap.StationaryBo
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1387,7 +1387,7 @@ <h1 id="arch-bootstrap-stationarybootstrap-conf-int">arch.bootstrap.StationaryBo
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.cov.html b/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.cov.html
index e6750896a5..cf53b9ecee 100644
--- a/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.cov.html
+++ b/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.cov.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.cov - arch 6.1.0</title>
+        <title>arch.bootstrap.StationaryBootstrap.cov - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1095,24 +1095,24 @@
                   
 
 
-<h1 id="arch-bootstrap-stationarybootstrap-cov">arch.bootstrap.StationaryBootstrap.cov<a class="headerlink" href="#arch-bootstrap-stationarybootstrap-cov" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-stationarybootstrap-cov">arch.bootstrap.StationaryBootstrap.cov<a class="headerlink" href="#arch-bootstrap-stationarybootstrap-cov" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.StationaryBootstrap.cov">
-<span class="sig-prename descclassname"><span class="pre">StationaryBootstrap.</span></span><span class="sig-name descname"><span class="pre">cov</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.cov.func" title="arch.bootstrap.StationaryBootstrap.cov.func (Python parameter) — Callable function that returns the statistic of interest as a 1-d array"><span class="n"><span class="pre">func</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.cov.reps" title="arch.bootstrap.StationaryBootstrap.cov.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.cov.recenter" title="arch.bootstrap.StationaryBootstrap.cov.recenter (Python parameter) — Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."><span class="n"><span class="pre">recenter</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.cov.extra_kwargs" title="arch.bootstrap.StationaryBootstrap.cov.extra_kwargs (Python parameter) — Dictionary of extra keyword arguments to pass to func"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.cov" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">StationaryBootstrap.</span></span><span class="sig-name descname"><span class="pre">cov</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.cov.func" title="arch.bootstrap.StationaryBootstrap.cov.func (Python parameter) — Callable function that returns the statistic of interest as a 1-d array"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.cov.reps" title="arch.bootstrap.StationaryBootstrap.cov.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.cov.recenter" title="arch.bootstrap.StationaryBootstrap.cov.recenter (Python parameter) — Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."><span class="n"><span class="pre">recenter</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.cov.extra_kwargs" title="arch.bootstrap.StationaryBootstrap.cov.extra_kwargs (Python parameter) — Dictionary of extra keyword arguments to pass to func"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.cov" title="Link to this definition">¶</a></dt>
 <dd><p>Compute parameter covariance using bootstrap</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.StationaryBootstrap.cov-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.cov-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.StationaryBootstrap.cov.func"><span class="sig-name descname">func</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable</span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.cov.func" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that returns the statistic of interest as a
+<dt id="arch.bootstrap.StationaryBootstrap.cov.func"><span class="n sig-name">func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.cov.func" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that returns the statistic of interest as a
 1-d array</p>
 </dd>
-<dt id="arch.bootstrap.StationaryBootstrap.cov.reps"><span class="sig-name descname">reps</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, default 1000</span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.cov.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
+<dt id="arch.bootstrap.StationaryBootstrap.cov.reps"><span class="n sig-name">reps</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.cov.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
 </dd>
-<dt id="arch.bootstrap.StationaryBootstrap.cov.recenter"><span class="sig-name descname">recenter</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, default True</span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.cov.recenter" title="Permalink to this definition">¶</a></dt><dd><p>Whether to center the bootstrap variance estimator on the average
+<dt id="arch.bootstrap.StationaryBootstrap.cov.recenter"><span class="n sig-name">recenter</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.cov.recenter" title="Permalink to this definition">¶</a></dt><dd><p>Whether to center the bootstrap variance estimator on the average
 of the bootstrap samples (True) or to center on the original sample
 estimate (False).  Default is True.</p>
 </dd>
-<dt id="arch.bootstrap.StationaryBootstrap.cov.extra_kwargs"><span class="sig-name descname">extra_kwargs</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a>, default None</span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.cov.extra_kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Dictionary of extra keyword arguments to pass to func</p>
+<dt id="arch.bootstrap.StationaryBootstrap.cov.extra_kwargs"><span class="n sig-name">extra_kwargs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)">Any</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.cov.extra_kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Dictionary of extra keyword arguments to pass to func</p>
 </dd>
 </dl>
 </dd>
@@ -1120,7 +1120,7 @@ <h1 id="arch-bootstrap-stationarybootstrap-cov">arch.bootstrap.StationaryBootstr
 <dd class="field-even"><p>Bootstrap covariance estimator</p>
 </dd>
 <dt class="field-odd" id="arch.bootstrap.StationaryBootstrap.cov-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.cov-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -1227,7 +1227,7 @@ <h1 id="arch-bootstrap-stationarybootstrap-cov">arch.bootstrap.StationaryBootstr
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1274,7 +1274,7 @@ <h1 id="arch-bootstrap-stationarybootstrap-cov">arch.bootstrap.StationaryBootstr
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.generator.html b/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.generator.html
index 4d24b7cb98..110634a8e0 100644
--- a/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.generator.html
+++ b/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.generator.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.generator - arch 6.1.0</title>
+        <title>arch.bootstrap.StationaryBootstrap.generator - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1027,15 +1027,15 @@
                   
 
 
-<h1 id="arch-bootstrap-stationarybootstrap-generator">arch.bootstrap.StationaryBootstrap.generator<a class="headerlink" href="#arch-bootstrap-stationarybootstrap-generator" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-stationarybootstrap-generator">arch.bootstrap.StationaryBootstrap.generator<a class="headerlink" href="#arch-bootstrap-stationarybootstrap-generator" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.bootstrap.StationaryBootstrap.generator">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">StationaryBootstrap.</span></span><span class="sig-name descname"><span class="pre">generator</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.24)"><span class="pre">Generator</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.24)"><span class="pre">RandomState</span></a></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.generator" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">StationaryBootstrap.</span></span><span class="sig-name descname"><span class="pre">generator</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><span class="pre">Generator</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.generator" title="Link to this definition">¶</a></dt>
 <dd><p>Set or get the instance PRNG</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.StationaryBootstrap.generator-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.generator-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt><span class="sig-name descname">seed</span><span class="p"> : </span><span class="desctype"><span class="desctype">{Generator, RandomState}, optional</span></span></dt><dd><p>Generator or RandomState used to produce the pseudo-random
+<dt><span class="sig-name descname">seed</span><span class="p"> : </span><span class="desctype"><code class="docutils literal notranslate"><span class="pre">{Generator,</span> <span class="pre">RandomState}</span></code>, <em>optional</em></span></dt><dd><p>Generator or RandomState used to produce the pseudo-random
 values used in the bootstrap</p>
 </dd>
 </dl>
@@ -1045,7 +1045,7 @@ <h1 id="arch-bootstrap-stationarybootstrap-generator">arch.bootstrap.StationaryB
 by bootstrap</p>
 </dd>
 <dt class="field-odd" id="arch.bootstrap.StationaryBootstrap.generator-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.generator-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>{Generator, RandomState}</p>
+<dd class="field-odd"><p><code class="docutils literal notranslate"><span class="pre">{Generator,</span> <span class="pre">RandomState}</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1117,7 +1117,7 @@ <h1 id="arch-bootstrap-stationarybootstrap-generator">arch.bootstrap.StationaryB
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1164,7 +1164,7 @@ <h1 id="arch-bootstrap-stationarybootstrap-generator">arch.bootstrap.StationaryB
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.get_state.html b/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.get_state.html
index 11cb4dc73f..9283e857ef 100644
--- a/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.get_state.html
+++ b/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.get_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.get_state - arch 6.1.0</title>
+        <title>arch.bootstrap.StationaryBootstrap.get_state - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1013,17 +1013,17 @@
                   
 
 
-<h1 id="arch-bootstrap-stationarybootstrap-get-state">arch.bootstrap.StationaryBootstrap.get_state<a class="headerlink" href="#arch-bootstrap-stationarybootstrap-get-state" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-stationarybootstrap-get-state">arch.bootstrap.StationaryBootstrap.get_state<a class="headerlink" href="#arch-bootstrap-stationarybootstrap-get-state" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.bootstrap.StationaryBootstrap.get_state">
-<span class="sig-prename descclassname"><span class="pre">StationaryBootstrap.</span></span><span class="sig-name descname"><span class="pre">get_state</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.get_state" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.StationaryBootstrap.get_state">
+<span class="sig-prename descclassname"><span class="pre">StationaryBootstrap.</span></span><span class="sig-name descname"><span class="pre">get_state</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Mapping" title="(in Python v3.11)"><span class="pre">Mapping</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.get_state" title="Link to this definition">¶</a></dt>
 <dd><p>Gets the state of the bootstrap’s random number generator</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.StationaryBootstrap.get_state-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.get_state-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>Dictionary containing the state.</p>
 </dd>
 <dt class="field-even" id="arch.bootstrap.StationaryBootstrap.get_state-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.get_state-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1095,7 +1095,7 @@ <h1 id="arch-bootstrap-stationarybootstrap-get-state">arch.bootstrap.StationaryB
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1142,7 +1142,7 @@ <h1 id="arch-bootstrap-stationarybootstrap-get-state">arch.bootstrap.StationaryB
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.index.html b/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.index.html
index 907704d47c..3491d9a169 100644
--- a/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.index.html
+++ b/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.index.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.index - arch 6.1.0</title>
+        <title>arch.bootstrap.StationaryBootstrap.index - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -973,10 +973,10 @@
                   
 
 
-<h1 id="arch-bootstrap-stationarybootstrap-index">arch.bootstrap.StationaryBootstrap.index<a class="headerlink" href="#arch-bootstrap-stationarybootstrap-index" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-stationarybootstrap-index">arch.bootstrap.StationaryBootstrap.index<a class="headerlink" href="#arch-bootstrap-stationarybootstrap-index" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.StationaryBootstrap.index">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">StationaryBootstrap.</span></span><span class="sig-name descname"><span class="pre">index</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">numpy.ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">numpy.ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">numpy.ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.index" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">StationaryBootstrap.</span></span><span class="sig-name descname"><span class="pre">index</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.index" title="Link to this definition">¶</a></dt>
 <dd><p>The current index of the bootstrap</p>
 </dd></dl>
 
@@ -1047,7 +1047,7 @@ <h1 id="arch-bootstrap-stationarybootstrap-index">arch.bootstrap.StationaryBoots
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1094,7 +1094,7 @@ <h1 id="arch-bootstrap-stationarybootstrap-index">arch.bootstrap.StationaryBoots
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.random_state.html b/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.random_state.html
index 0c0ab9b728..c32b1f4796 100644
--- a/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.random_state.html
+++ b/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.random_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.random_state - arch 6.1.0</title>
+        <title>arch.bootstrap.StationaryBootstrap.random_state - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1027,15 +1027,15 @@
                   
 
 
-<h1 id="arch-bootstrap-stationarybootstrap-random-state">arch.bootstrap.StationaryBootstrap.random_state<a class="headerlink" href="#arch-bootstrap-stationarybootstrap-random-state" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-stationarybootstrap-random-state">arch.bootstrap.StationaryBootstrap.random_state<a class="headerlink" href="#arch-bootstrap-stationarybootstrap-random-state" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.bootstrap.StationaryBootstrap.random_state">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">StationaryBootstrap.</span></span><span class="sig-name descname"><span class="pre">random_state</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.24)"><span class="pre">Generator</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.24)"><span class="pre">RandomState</span></a></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.random_state" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">StationaryBootstrap.</span></span><span class="sig-name descname"><span class="pre">random_state</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><span class="pre">Generator</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.random_state" title="Link to this definition">¶</a></dt>
 <dd><p>Set or get the instance random state</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.StationaryBootstrap.random_state-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.random_state-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt><span class="sig-name descname">random_state</span><span class="p"> : </span><span class="desctype"><span class="desctype">RandomState</span></span></dt><dd><p>RandomState instance used by bootstrap</p>
+<dt><span class="sig-name descname">random_state</span><span class="p"> : </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.random.RandomState</span></code></a></span></dt><dd><p>RandomState instance used by bootstrap</p>
 </dd>
 </dl>
 </dd>
@@ -1043,7 +1043,7 @@ <h1 id="arch-bootstrap-stationarybootstrap-random-state">arch.bootstrap.Stationa
 <dd class="field-even"><p>RandomState instance used by bootstrap</p>
 </dd>
 <dt class="field-odd" id="arch.bootstrap.StationaryBootstrap.random_state-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.random_state-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>RandomState</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.random.RandomState</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1115,7 +1115,7 @@ <h1 id="arch-bootstrap-stationarybootstrap-random-state">arch.bootstrap.Stationa
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1162,7 +1162,7 @@ <h1 id="arch-bootstrap-stationarybootstrap-random-state">arch.bootstrap.Stationa
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.reset.html b/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.reset.html
index 8f62aeb8aa..7176ecb8ae 100644
--- a/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.reset.html
+++ b/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.reset.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.reset - arch 6.1.0</title>
+        <title>arch.bootstrap.StationaryBootstrap.reset - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -630,13 +630,6 @@
       </ul>
     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.StationaryBootstrap.reset-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
       </ul>
@@ -1011,13 +1004,6 @@
       </ul>
     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.StationaryBootstrap.reset-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
       </ul>
@@ -1039,23 +1025,20 @@
                   
 
 
-<h1 id="arch-bootstrap-stationarybootstrap-reset">arch.bootstrap.StationaryBootstrap.reset<a class="headerlink" href="#arch-bootstrap-stationarybootstrap-reset" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-stationarybootstrap-reset">arch.bootstrap.StationaryBootstrap.reset<a class="headerlink" href="#arch-bootstrap-stationarybootstrap-reset" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.bootstrap.StationaryBootstrap.reset">
-<span class="sig-prename descclassname"><span class="pre">StationaryBootstrap.</span></span><span class="sig-name descname"><span class="pre">reset</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.reset.use_seed" title="arch.bootstrap.StationaryBootstrap.reset.use_seed (Python parameter) — Flag indicating whether to use the last seed if provided.  If False or if no seed has been set, the bootstrap will be reset to the initial state.  Default is True"><span class="n"><span class="pre">use_seed</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.reset" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">StationaryBootstrap.</span></span><span class="sig-name descname"><span class="pre">reset</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.reset.use_seed" title="arch.bootstrap.StationaryBootstrap.reset.use_seed (Python parameter) — Flag indicating whether to use the last seed if provided.  If False or if no seed has been set, the bootstrap will be reset to the initial state.  Default is True"><span class="n"><span class="pre">use_seed</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.reset" title="Link to this definition">¶</a></dt>
 <dd><p>Resets the bootstrap to either its initial state or the last seed.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.StationaryBootstrap.reset-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.reset-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.StationaryBootstrap.reset.use_seed"><span class="sig-name descname">use_seed</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, default True</span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.reset.use_seed" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to use the last seed if provided.  If
+<dt id="arch.bootstrap.StationaryBootstrap.reset.use_seed"><span class="n sig-name">use_seed</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.reset.use_seed" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to use the last seed if provided.  If
 False or if no seed has been set, the bootstrap will be reset
 to the initial state.  Default is True</p>
 </dd>
 </dl>
 </dd>
-<dt class="field-even" id="arch.bootstrap.StationaryBootstrap.reset-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.reset-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code></a></p>
-</dd>
 </dl>
 </dd></dl>
 
@@ -1126,7 +1109,7 @@ <h1 id="arch-bootstrap-stationarybootstrap-reset">arch.bootstrap.StationaryBoots
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1173,7 +1156,7 @@ <h1 id="arch-bootstrap-stationarybootstrap-reset">arch.bootstrap.StationaryBoots
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.seed.html b/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.seed.html
index 99cfde3e92..19955c8077 100644
--- a/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.seed.html
+++ b/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.seed.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.seed - arch 6.1.0</title>
+        <title>arch.bootstrap.StationaryBootstrap.seed - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -645,13 +645,6 @@
       </ul>
     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.StationaryBootstrap.seed-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
       </ul>
@@ -1011,13 +1004,6 @@
       </ul>
     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.StationaryBootstrap.seed-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
       </ul>
@@ -1039,21 +1025,18 @@
                   
 
 
-<h1 id="arch-bootstrap-stationarybootstrap-seed">arch.bootstrap.StationaryBootstrap.seed<a class="headerlink" href="#arch-bootstrap-stationarybootstrap-seed" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-stationarybootstrap-seed">arch.bootstrap.StationaryBootstrap.seed<a class="headerlink" href="#arch-bootstrap-stationarybootstrap-seed" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.bootstrap.StationaryBootstrap.seed">
-<span class="sig-prename descclassname"><span class="pre">StationaryBootstrap.</span></span><span class="sig-name descname"><span class="pre">seed</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.seed.value" title="arch.bootstrap.StationaryBootstrap.seed.value (Python parameter) — Value to use as the seed."><span class="n"><span class="pre">value</span></span></a></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.seed" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">StationaryBootstrap.</span></span><span class="sig-name descname"><span class="pre">seed</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.seed.value" title="arch.bootstrap.StationaryBootstrap.seed.value (Python parameter) — Value to use as the seed."><span class="n"><span class="pre">value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.seed" title="Link to this definition">¶</a></dt>
 <dd><p>Reseeds the bootstrap’s random number generator</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.StationaryBootstrap.seed-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.seed-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.StationaryBootstrap.seed.value"><span class="sig-name descname">value</span><span class="p"> : </span><span class="desctype"><span class="desctype">{int, List[<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>], ndarray}</span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.seed.value" title="Permalink to this definition">¶</a></dt><dd><p>Value to use as the seed.</p>
+<dt id="arch.bootstrap.StationaryBootstrap.seed.value"><span class="n sig-name">value</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.seed.value" title="Permalink to this definition">¶</a></dt><dd><p>Value to use as the seed.</p>
 </dd>
 </dl>
 </dd>
-<dt class="field-even" id="arch.bootstrap.StationaryBootstrap.seed-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.seed-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code></a></p>
-</dd>
 </dl>
 </dd></dl>
 
@@ -1124,7 +1107,7 @@ <h1 id="arch-bootstrap-stationarybootstrap-seed">arch.bootstrap.StationaryBootst
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1171,7 +1154,7 @@ <h1 id="arch-bootstrap-stationarybootstrap-seed">arch.bootstrap.StationaryBootst
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.set_state.html b/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.set_state.html
index 8c7c7b1841..4000eb7de7 100644
--- a/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.set_state.html
+++ b/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.set_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.set_state - arch 6.1.0</title>
+        <title>arch.bootstrap.StationaryBootstrap.set_state - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -660,13 +660,6 @@
       </ul>
     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.StationaryBootstrap.set_state-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
       </ul>
@@ -1011,13 +1004,6 @@
       </ul>
     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.StationaryBootstrap.set_state-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
       </ul>
@@ -1039,21 +1025,18 @@
                   
 
 
-<h1 id="arch-bootstrap-stationarybootstrap-set-state">arch.bootstrap.StationaryBootstrap.set_state<a class="headerlink" href="#arch-bootstrap-stationarybootstrap-set-state" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-stationarybootstrap-set-state">arch.bootstrap.StationaryBootstrap.set_state<a class="headerlink" href="#arch-bootstrap-stationarybootstrap-set-state" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.bootstrap.StationaryBootstrap.set_state">
-<span class="sig-prename descclassname"><span class="pre">StationaryBootstrap.</span></span><span class="sig-name descname"><span class="pre">set_state</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.set_state.state" title="arch.bootstrap.StationaryBootstrap.set_state.state (Python parameter) — Dictionary or tuple containing the state."><span class="n"><span class="pre">state</span></span></a></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.set_state" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.StationaryBootstrap.set_state">
+<span class="sig-prename descclassname"><span class="pre">StationaryBootstrap.</span></span><span class="sig-name descname"><span class="pre">set_state</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.set_state.state" title="arch.bootstrap.StationaryBootstrap.set_state.state (Python parameter) — Dictionary or tuple containing the state."><span class="n"><span class="pre">state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.set_state" title="Link to this definition">¶</a></dt>
 <dd><p>Sets the state of the bootstrap’s random number generator</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.StationaryBootstrap.set_state-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.set_state-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.StationaryBootstrap.set_state.state"><span class="sig-name descname">state</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a></span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.set_state.state" title="Permalink to this definition">¶</a></dt><dd><p>Dictionary or tuple containing the state.</p>
+<dt id="arch.bootstrap.StationaryBootstrap.set_state.state"><span class="n sig-name">state</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)">Any</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.set_state.state" title="Permalink to this definition">¶</a></dt><dd><p>Dictionary or tuple containing the state.</p>
 </dd>
 </dl>
 </dd>
-<dt class="field-even" id="arch.bootstrap.StationaryBootstrap.set_state-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.set_state-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code></a></p>
-</dd>
 </dl>
 </dd></dl>
 
@@ -1124,7 +1107,7 @@ <h1 id="arch-bootstrap-stationarybootstrap-set-state">arch.bootstrap.StationaryB
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1171,7 +1154,7 @@ <h1 id="arch-bootstrap-stationarybootstrap-set-state">arch.bootstrap.StationaryB
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.state.html b/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.state.html
index 589c225d18..65e25a085c 100644
--- a/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.state.html
+++ b/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.state - arch 6.1.0</title>
+        <title>arch.bootstrap.StationaryBootstrap.state - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1013,10 +1013,10 @@
                   
 
 
-<h1 id="arch-bootstrap-stationarybootstrap-state">arch.bootstrap.StationaryBootstrap.state<a class="headerlink" href="#arch-bootstrap-stationarybootstrap-state" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-stationarybootstrap-state">arch.bootstrap.StationaryBootstrap.state<a class="headerlink" href="#arch-bootstrap-stationarybootstrap-state" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.StationaryBootstrap.state">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">StationaryBootstrap.</span></span><span class="sig-name descname"><span class="pre">state</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">numpy.ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Mapping" title="(in Python v3.11)"><span class="pre">Mapping</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.state" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">StationaryBootstrap.</span></span><span class="sig-name descname"><span class="pre">state</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Mapping" title="(in Python v3.11)"><span class="pre">Mapping</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.state" title="Link to this definition">¶</a></dt>
 <dd><p>Set or get the generator’s state</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.StationaryBootstrap.state-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.state-returns" title="Permalink to this headline">¶</a></dt>
@@ -1025,7 +1025,7 @@ <h1 id="arch-bootstrap-stationarybootstrap-state">arch.bootstrap.StationaryBoots
 Otherwise it is a dictionary.</p>
 </dd>
 <dt class="field-even" id="arch.bootstrap.StationaryBootstrap.state-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.state-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p>{tuple, dict}</p>
+<dd class="field-even"><p><code class="docutils literal notranslate"><span class="pre">{tuple,</span> <span class="pre">dict}</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1097,7 +1097,7 @@ <h1 id="arch-bootstrap-stationarybootstrap-state">arch.bootstrap.StationaryBoots
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1144,7 +1144,7 @@ <h1 id="arch-bootstrap-stationarybootstrap-state">arch.bootstrap.StationaryBoots
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.update_indices.html b/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.update_indices.html
index 58b78ce291..0d53439441 100644
--- a/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.update_indices.html
+++ b/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.update_indices.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.update_indices - arch 6.1.0</title>
+        <title>arch.bootstrap.StationaryBootstrap.update_indices - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -654,19 +654,6 @@
     <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="arch.bootstrap.StationaryBootstrap.update_indices (Python method) — Update indices for the next iteration of the bootstrap.  This must be overridden when creating new bootstraps." class="md-ellipsis">Stationary<wbr>Bootstrap.<wbr>update_<wbr>indices</span>
   </a>
   
-    <nav class="md-nav" aria-label="StationaryBootstrap.update_indices">
-      <ul class="md-nav__list">
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.StationaryBootstrap.update_indices-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
-</li>
-        
-      </ul>
-    </nav>
-  
 </li>
       
     </ul>
@@ -970,19 +957,6 @@
     <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="arch.bootstrap.StationaryBootstrap.update_indices (Python method) — Update indices for the next iteration of the bootstrap.  This must be overridden when creating new bootstraps." class="md-ellipsis">Stationary<wbr>Bootstrap.<wbr>update_<wbr>indices</span>
   </a>
   
-    <nav class="md-nav" aria-label="StationaryBootstrap.update_indices">
-      <ul class="md-nav__list">
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.StationaryBootstrap.update_indices-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
-</li>
-        
-      </ul>
-    </nav>
-  
 </li>
       
     </ul>
@@ -999,17 +973,12 @@
                   
 
 
-<h1 id="arch-bootstrap-stationarybootstrap-update-indices">arch.bootstrap.StationaryBootstrap.update_indices<a class="headerlink" href="#arch-bootstrap-stationarybootstrap-update-indices" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-stationarybootstrap-update-indices">arch.bootstrap.StationaryBootstrap.update_indices<a class="headerlink" href="#arch-bootstrap-stationarybootstrap-update-indices" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.bootstrap.StationaryBootstrap.update_indices">
-<span class="sig-prename descclassname"><span class="pre">StationaryBootstrap.</span></span><span class="sig-name descname"><span class="pre">update_indices</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/bootstrap/base.html#StationaryBootstrap.update_indices"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.update_indices" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">StationaryBootstrap.</span></span><span class="sig-name descname"><span class="pre">update_indices</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/bootstrap/base.html#StationaryBootstrap.update_indices"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.update_indices" title="Link to this definition">¶</a></dt>
 <dd><p>Update indices for the next iteration of the bootstrap.  This must
 be overridden when creating new bootstraps.</p>
-<dl class="field-list simple">
-<dt class="field-odd" id="arch.bootstrap.StationaryBootstrap.update_indices-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.update_indices-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a></p>
-</dd>
-</dl>
 </dd></dl>
 
 
@@ -1079,7 +1048,7 @@ <h1 id="arch-bootstrap-stationarybootstrap-update-indices">arch.bootstrap.Statio
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1126,7 +1095,7 @@ <h1 id="arch-bootstrap-stationarybootstrap-update-indices">arch.bootstrap.Statio
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.var.html b/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.var.html
index 4d13b576e9..78db2063c9 100644
--- a/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.var.html
+++ b/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.var.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.var - arch 6.1.0</title>
+        <title>arch.bootstrap.StationaryBootstrap.var - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1095,24 +1095,24 @@
                   
 
 
-<h1 id="arch-bootstrap-stationarybootstrap-var">arch.bootstrap.StationaryBootstrap.var<a class="headerlink" href="#arch-bootstrap-stationarybootstrap-var" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-stationarybootstrap-var">arch.bootstrap.StationaryBootstrap.var<a class="headerlink" href="#arch-bootstrap-stationarybootstrap-var" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.StationaryBootstrap.var">
-<span class="sig-prename descclassname"><span class="pre">StationaryBootstrap.</span></span><span class="sig-name descname"><span class="pre">var</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.var.func" title="arch.bootstrap.StationaryBootstrap.var.func (Python parameter) — Callable function that returns the statistic of interest as a 1-d array"><span class="n"><span class="pre">func</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.var.reps" title="arch.bootstrap.StationaryBootstrap.var.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.var.recenter" title="arch.bootstrap.StationaryBootstrap.var.recenter (Python parameter) — Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."><span class="n"><span class="pre">recenter</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.var.extra_kwargs" title="arch.bootstrap.StationaryBootstrap.var.extra_kwargs (Python parameter) — Dictionary of extra keyword arguments to pass to func"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.var" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">StationaryBootstrap.</span></span><span class="sig-name descname"><span class="pre">var</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.var.func" title="arch.bootstrap.StationaryBootstrap.var.func (Python parameter) — Callable function that returns the statistic of interest as a 1-d array"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.var.reps" title="arch.bootstrap.StationaryBootstrap.var.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.var.recenter" title="arch.bootstrap.StationaryBootstrap.var.recenter (Python parameter) — Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."><span class="n"><span class="pre">recenter</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.var.extra_kwargs" title="arch.bootstrap.StationaryBootstrap.var.extra_kwargs (Python parameter) — Dictionary of extra keyword arguments to pass to func"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.var" title="Link to this definition">¶</a></dt>
 <dd><p>Compute parameter variance using bootstrap</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.StationaryBootstrap.var-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.var-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.StationaryBootstrap.var.func"><span class="sig-name descname">func</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable</span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.var.func" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that returns the statistic of interest as a
+<dt id="arch.bootstrap.StationaryBootstrap.var.func"><span class="n sig-name">func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.var.func" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that returns the statistic of interest as a
 1-d array</p>
 </dd>
-<dt id="arch.bootstrap.StationaryBootstrap.var.reps"><span class="sig-name descname">reps</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, default 1000</span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.var.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
+<dt id="arch.bootstrap.StationaryBootstrap.var.reps"><span class="n sig-name">reps</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.var.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
 </dd>
-<dt id="arch.bootstrap.StationaryBootstrap.var.recenter"><span class="sig-name descname">recenter</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, default True</span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.var.recenter" title="Permalink to this definition">¶</a></dt><dd><p>Whether to center the bootstrap variance estimator on the average
+<dt id="arch.bootstrap.StationaryBootstrap.var.recenter"><span class="n sig-name">recenter</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.var.recenter" title="Permalink to this definition">¶</a></dt><dd><p>Whether to center the bootstrap variance estimator on the average
 of the bootstrap samples (True) or to center on the original sample
 estimate (False).  Default is True.</p>
 </dd>
-<dt id="arch.bootstrap.StationaryBootstrap.var.extra_kwargs"><span class="sig-name descname">extra_kwargs</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a>, default None</span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.var.extra_kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Dictionary of extra keyword arguments to pass to func</p>
+<dt id="arch.bootstrap.StationaryBootstrap.var.extra_kwargs"><span class="n sig-name">extra_kwargs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)">Any</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.var.extra_kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Dictionary of extra keyword arguments to pass to func</p>
 </dd>
 </dl>
 </dd>
@@ -1120,7 +1120,7 @@ <h1 id="arch-bootstrap-stationarybootstrap-var">arch.bootstrap.StationaryBootstr
 <dd class="field-even"><p>Bootstrap variance estimator</p>
 </dd>
 <dt class="field-odd" id="arch.bootstrap.StationaryBootstrap.var-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.var-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -1227,7 +1227,7 @@ <h1 id="arch-bootstrap-stationarybootstrap-var">arch.bootstrap.StationaryBootstr
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1274,7 +1274,7 @@ <h1 id="arch-bootstrap-stationarybootstrap-var">arch.bootstrap.StationaryBootstr
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/iid-bootstraps.html b/bootstrap/iid-bootstraps.html
index 8d7d4b2e3e..f4b1095f44 100644
--- a/bootstrap/iid-bootstraps.html
+++ b/bootstrap/iid-bootstraps.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Independent, Identical Distributed Data (i.i.d.) - arch 6.1.0</title>
+        <title>Independent, Identical Distributed Data (i.i.d.) - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -708,7 +708,7 @@
                   
 
 
-<h1 id="independent-identical-distributed-data-i-i-d">Independent, Identical Distributed Data (i.i.d.)<a class="headerlink" href="#independent-identical-distributed-data-i-i-d" title="Permalink to this heading">¶</a></h1>
+<h1 id="independent-identical-distributed-data-i-i-d">Independent, Identical Distributed Data (i.i.d.)<a class="headerlink" href="#independent-identical-distributed-data-i-i-d" title="Link to this heading">¶</a></h1>
 <p><a class="reference internal" href="generated/arch.bootstrap.IIDBootstrap.html#arch.bootstrap.IIDBootstrap" title="arch.bootstrap.IIDBootstrap (Python class) — Bootstrap using uniform resampling"><code class="xref py py-class docutils literal notranslate"><span class="pre">IIDBootstrap</span></code></a> is the standard bootstrap that is appropriate for data that is
 either i.i.d. or at least not serially dependant.</p>
 <table class="autosummary longtable docutils data align-default">
@@ -718,7 +718,7 @@ <h1 id="independent-identical-distributed-data-i-i-d">Independent, Identical Dis
 </tr>
 </tbody>
 </table>
-<h1 id="independent-samples">Independent Samples<a class="headerlink" href="#independent-samples" title="Permalink to this heading">¶</a></h1>
+<h1 id="independent-samples">Independent Samples<a class="headerlink" href="#independent-samples" title="Link to this heading">¶</a></h1>
 <p><a class="reference internal" href="generated/arch.bootstrap.IndependentSamplesBootstrap.html#arch.bootstrap.IndependentSamplesBootstrap" title="arch.bootstrap.IndependentSamplesBootstrap (Python class) — Bootstrap where each input is independently resampled"><code class="xref py py-class docutils literal notranslate"><span class="pre">IndependentSamplesBootstrap</span></code></a> is a bootstrap that is appropriate for
 data is totally independent, and where each variable may have a different sample size. This
 type of data arises naturally in experimental settings, e.g., website A/B testing.</p>
@@ -796,7 +796,7 @@ <h1 id="independent-samples">Independent Samples<a class="headerlink" href="#ind
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -843,7 +843,7 @@ <h1 id="independent-samples">Independent Samples<a class="headerlink" href="#ind
     <script id="__config" type="application/json">{"base": "..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/low-level-interface.html b/bootstrap/low-level-interface.html
index c7d29e6bfd..72e0da5416 100644
--- a/bootstrap/low-level-interface.html
+++ b/bootstrap/low-level-interface.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Low-level Interfaces - arch 6.1.0</title>
+        <title>Low-level Interfaces - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -612,8 +612,8 @@
                   
 
 
-<h1 id="low-level-interfaces">Low-level Interfaces<a class="headerlink" href="#low-level-interfaces" title="Permalink to this heading">¶</a></h1>
-<h2 id="constructing-parameter-estimates">Constructing Parameter Estimates<a class="headerlink" href="#constructing-parameter-estimates" title="Permalink to this heading">¶</a></h2>
+<h1 id="low-level-interfaces">Low-level Interfaces<a class="headerlink" href="#low-level-interfaces" title="Link to this heading">¶</a></h1>
+<h2 id="constructing-parameter-estimates">Constructing Parameter Estimates<a class="headerlink" href="#constructing-parameter-estimates" title="Link to this heading">¶</a></h2>
 <p>The bootstrap method apply can be use to directly compute parameter estimates
 from a function and the bootstrapped data.</p>
 <p>This example makes use of monthly S&amp;P 500 data.</p>
@@ -649,7 +649,7 @@ <h2 id="constructing-parameter-estimates">Constructing Parameter Estimates<a cla
 </code></pre></div>
 </div>
 <img alt="../_images/bootstrap_histogram.png" src="../_images/bootstrap_histogram.png" />
-<h2 id="the-bootstrap-iterator">The Bootstrap Iterator<a class="headerlink" href="#the-bootstrap-iterator" title="Permalink to this heading">¶</a></h2>
+<h2 id="the-bootstrap-iterator">The Bootstrap Iterator<a class="headerlink" href="#the-bootstrap-iterator" title="Link to this heading">¶</a></h2>
 <p>The lowest-level method to use a bootstrap is the iterator.  This is used
 internally in all higher-level methods that estimate a function using multiple
 bootstrap replications.  The iterator returns a two-element tuple where the
@@ -741,7 +741,7 @@ <h2 id="the-bootstrap-iterator">The Bootstrap Iterator<a class="headerlink" href
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -788,7 +788,7 @@ <h2 id="the-bootstrap-iterator">The Bootstrap Iterator<a class="headerlink" href
     <script id="__config" type="application/json">{"base": "..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/parameter-covariance-estimation.html b/bootstrap/parameter-covariance-estimation.html
index 54e7783136..59aa7eae42 100644
--- a/bootstrap/parameter-covariance-estimation.html
+++ b/bootstrap/parameter-covariance-estimation.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Covariance Estimation - arch 6.1.0</title>
+        <title>Covariance Estimation - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -546,7 +546,7 @@
                   
 
 
-<h1 id="covariance-estimation">Covariance Estimation<a class="headerlink" href="#covariance-estimation" title="Permalink to this heading">¶</a></h1>
+<h1 id="covariance-estimation">Covariance Estimation<a class="headerlink" href="#covariance-estimation" title="Link to this heading">¶</a></h1>
 <p>The bootstrap can be used to estimate parameter covariances in applications where
 analytical computation is challenging, or simply as an alternative to
 traditional estimators.</p>
@@ -675,7 +675,7 @@ <h1 id="covariance-estimation">Covariance Estimation<a class="headerlink" href="
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -722,7 +722,7 @@ <h1 id="covariance-estimation">Covariance Estimation<a class="headerlink" href="
     <script id="__config" type="application/json">{"base": "..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/semiparametric-parametric-bootstrap.html b/bootstrap/semiparametric-parametric-bootstrap.html
index 8a70d1167c..aba3d6e3c7 100644
--- a/bootstrap/semiparametric-parametric-bootstrap.html
+++ b/bootstrap/semiparametric-parametric-bootstrap.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Semiparametric Bootstraps - arch 6.1.0</title>
+        <title>Semiparametric Bootstraps - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -659,7 +659,7 @@
                   
 
 
-<h1 id="semiparametric-bootstraps"><span id="id1"></span>Semiparametric Bootstraps<a class="headerlink" href="#semiparametric-bootstraps" title="Permalink to this heading">¶</a></h1>
+<h1 id="semiparametric-bootstraps"><span id="id1"></span>Semiparametric Bootstraps<a class="headerlink" href="#semiparametric-bootstraps" title="Link to this heading">¶</a></h1>
 <p>Functions for semi-parametric bootstraps differ from those used in
 nonparametric bootstraps.  At a minimum they must accept the keyword
 argument <code class="docutils literal notranslate"><span class="pre">params</span></code> which will contain the parameters estimated on
@@ -710,7 +710,7 @@ <h1 id="semiparametric-bootstraps"><span id="id1"></span>Semiparametric Bootstra
                  <span class="n">sampling</span><span class="o">=</span><span class="s1">&#39;semi&#39;</span><span class="p">,</span> <span class="n">extra_kwargs</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;x_orig&#39;</span><span class="p">:</span> <span class="n">x</span><span class="p">})</span>
 </code></pre></div>
 </div>
-<h2 id="using-partial-instead-of-extra-kwargs">Using <code class="docutils literal notranslate"><span class="pre">partial</span></code> instead of <code class="docutils literal notranslate"><span class="pre">extra_kwargs</span></code><a class="headerlink" href="#using-partial-instead-of-extra-kwargs" title="Permalink to this heading">¶</a></h2>
+<h2 id="using-partial-instead-of-extra-kwargs">Using <code class="docutils literal notranslate"><span class="pre">partial</span></code> instead of <code class="docutils literal notranslate"><span class="pre">extra_kwargs</span></code><a class="headerlink" href="#using-partial-instead-of-extra-kwargs" title="Link to this heading">¶</a></h2>
 <p><code class="docutils literal notranslate"><span class="pre">functools.partial</span></code> can be used instead to provide a wrapper function which
 can then be used in the bootstrap.  This example fixed the value of <code class="docutils literal notranslate"><span class="pre">x_orig</span></code>
 so that it is not necessary to use <code class="docutils literal notranslate"><span class="pre">extra_kwargs</span></code>.</p>
@@ -719,7 +719,7 @@ <h2 id="using-partial-instead-of-extra-kwargs">Using <code class="docutils liter
 <span class="n">ci</span> <span class="o">=</span> <span class="n">bs</span><span class="o">.</span><span class="n">conf_int</span><span class="p">(</span><span class="n">ols_partial</span><span class="p">,</span> <span class="mi">1000</span><span class="p">,</span> <span class="n">sampling</span><span class="o">=</span><span class="s1">&#39;semi&#39;</span><span class="p">)</span>
 </code></pre></div>
 </div>
-<h2 id="semiparametric-bootstrap-alternative-method">Semiparametric Bootstrap (Alternative Method)<a class="headerlink" href="#semiparametric-bootstrap-alternative-method" title="Permalink to this heading">¶</a></h2>
+<h2 id="semiparametric-bootstrap-alternative-method">Semiparametric Bootstrap (Alternative Method)<a class="headerlink" href="#semiparametric-bootstrap-alternative-method" title="Link to this heading">¶</a></h2>
 <p>Since semiparametric bootstraps are effectively bootstrapping residuals, an
 alternative method can be used to conduct a semiparametric bootstrap. This
 requires passing both the data and the estimated residuals when initializing
@@ -749,7 +749,7 @@ <h2 id="semiparametric-bootstrap-alternative-method">Semiparametric Bootstrap (A
 relatively expensive when compared to simulating data or estimating
 parameters.  These circumstances are rarely encountered in actual problems.</p>
 </div>
-<h1 id="parametric-bootstraps"><span id="id2"></span>Parametric Bootstraps<a class="headerlink" href="#parametric-bootstraps" title="Permalink to this heading">¶</a></h1>
+<h1 id="parametric-bootstraps"><span id="id2"></span>Parametric Bootstraps<a class="headerlink" href="#parametric-bootstraps" title="Link to this heading">¶</a></h1>
 <p>Parametric bootstraps are meaningfully different from their nonparametric or
 semiparametric cousins.  Instead of sampling the data to simulate the data
 (or residuals, in the case of a semiparametric bootstrap), a parametric
@@ -766,7 +766,7 @@ <h1 id="parametric-bootstraps"><span id="id2"></span>Parametric Bootstraps<a cla
 bootstrap, requires specific keyword arguments. The first is <code class="docutils literal notranslate"><span class="pre">params</span></code>,
 which, when present, will contain the parameters estimated on the original
 data.  The second is <code class="docutils literal notranslate"><span class="pre">rng</span></code> which will contain the
-<a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.random.RandomState</span></code></a> instance that is used by the bootstrap.
+<a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.random.RandomState</span></code></a> instance that is used by the bootstrap.
 This is provided to facilitate simulation in a reproducible manner.</p>
 <p>A parametric bootstrap function must:</p>
 <ul class="simple">
@@ -871,7 +871,7 @@ <h1 id="parametric-bootstraps"><span id="id2"></span>Parametric Bootstraps<a cla
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -918,7 +918,7 @@ <h1 id="parametric-bootstraps"><span id="id2"></span>Parametric Bootstraps<a cla
     <script id="__config" type="application/json">{"base": "..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/bootstrap/timeseries-bootstraps.html b/bootstrap/timeseries-bootstraps.html
index 4afe93b6d2..861f6cd54e 100644
--- a/bootstrap/timeseries-bootstraps.html
+++ b/bootstrap/timeseries-bootstraps.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Time-series Bootstraps - arch 6.1.0</title>
+        <title>Time-series Bootstraps - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -689,7 +689,7 @@
                   
 
 
-<h1 id="time-series-bootstraps">Time-series Bootstraps<a class="headerlink" href="#time-series-bootstraps" title="Permalink to this heading">¶</a></h1>
+<h1 id="time-series-bootstraps">Time-series Bootstraps<a class="headerlink" href="#time-series-bootstraps" title="Link to this heading">¶</a></h1>
 <p>Bootstraps for time-series data come in a variety of forms.  The three contained
 in this package are the stationary bootstrap
 (<a class="reference internal" href="generated/arch.bootstrap.StationaryBootstrap.html#arch.bootstrap.StationaryBootstrap" title="arch.bootstrap.StationaryBootstrap (Python class) — Politis and Romano (1994) bootstrap with expon distributed block sizes"><code class="xref py py-class docutils literal notranslate"><span class="pre">StationaryBootstrap</span></code></a>), which uses blocks with an
@@ -783,7 +783,7 @@ <h1 id="time-series-bootstraps">Time-series Bootstraps<a class="headerlink" href
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -830,7 +830,7 @@ <h1 id="time-series-bootstraps">Time-series Bootstraps<a class="headerlink" href
     <script id="__config" type="application/json">{"base": "..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/changes.html b/changes.html
index d430841f4a..8f322ebebf 100644
--- a/changes.html
+++ b/changes.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Change Logs - arch 6.1.0</title>
+        <title>Change Logs - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL(".",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -513,6 +513,13 @@
     <nav class="md-nav" aria-label="Version 6">
       <ul class="md-nav__list">
         
+          <li class="md-nav__item">
+  <a href="#release-6-2" class="md-nav__link">
+    <span title="/changes.rst#release-6-2 (reference label)" class="md-ellipsis">Release 6.<wbr>2</span>
+  </a>
+  
+</li>
+        
           <li class="md-nav__item">
   <a href="#release-6-1" class="md-nav__link">
     <span title="/changes.rst#release-6-1 (reference label)" class="md-ellipsis">Release 6.<wbr>1</span>
@@ -621,6 +628,13 @@
     <nav class="md-nav" aria-label="Version 6">
       <ul class="md-nav__list">
         
+          <li class="md-nav__item">
+  <a href="#release-6-2" class="md-nav__link">
+    <span title="/changes.rst#release-6-2 (reference label)" class="md-ellipsis">Release 6.<wbr>2</span>
+  </a>
+  
+</li>
+        
           <li class="md-nav__item">
   <a href="#release-6-1" class="md-nav__link">
     <span title="/changes.rst#release-6-1 (reference label)" class="md-ellipsis">Release 6.<wbr>1</span>
@@ -661,9 +675,22 @@
                   
 
 
-<h1 id="change-logs">Change Logs<a class="headerlink" href="#change-logs" title="Permalink to this heading">¶</a></h1>
-<h2 id="version-6">Version 6<a class="headerlink" href="#version-6" title="Permalink to this heading">¶</a></h2>
-<h3 id="release-6-1">Release 6.1<a class="headerlink" href="#release-6-1" title="Permalink to this heading">¶</a></h3>
+<h1 id="change-logs">Change Logs<a class="headerlink" href="#change-logs" title="Link to this heading">¶</a></h1>
+<h2 id="version-6">Version 6<a class="headerlink" href="#version-6" title="Link to this heading">¶</a></h2>
+<h3 id="release-6-2">Release 6.2<a class="headerlink" href="#release-6-2" title="Link to this heading">¶</a></h3>
+<ul class="simple">
+<li><p>Fixed a bug that affected forecasting from  <a class="reference internal" href="univariate/generated/arch.univariate.FIGARCH.html#arch.univariate.FIGARCH" title="arch.univariate.volatility.FIGARCH (Python class)"><code class="xref py py-class docutils literal notranslate"><span class="pre">FIGARCH</span></code></a> models (<a class="reference external" href="https://github.com/bashtage/arch/issues/606">GH606</a>).</p></li>
+<li><p>Added a performance warning when testing for unit roots in large series using a lag-length
+search with no-max-lag specified.</p></li>
+<li><p>Fixed a bug that affected forecasting from <code class="xref py py-class docutils literal notranslate"><span class="pre">arch.univariate.volatility.FIGARCH'</span>
+<span class="pre">models</span> <span class="pre">(:issue:`606</span></code>).</p></li>
+<li><p>Changed the default value of <code class="docutils literal notranslate"><span class="pre">reindex</span></code> to <code class="docutils literal notranslate"><span class="pre">False</span></code> so that forecasts
+will not match the input by default.  Set <code class="docutils literal notranslate"><span class="pre">reindex</span></code> to <code class="docutils literal notranslate"><span class="pre">True</span></code> if this is
+required.</p></li>
+<li><p>Made <code class="docutils literal notranslate"><span class="pre">from</span> <span class="pre">__future__</span> <span class="pre">import</span> <span class="pre">reindex</span></code> a no-op.</p></li>
+<li><p>Updated notebooks to reflect best practices</p></li>
+</ul>
+<h3 id="release-6-1">Release 6.1<a class="headerlink" href="#release-6-1" title="Link to this heading">¶</a></h3>
 <ul class="simple">
 <li><p>Pushed back the adoption of Cython 3 until a later date</p></li>
 <li><p>Fixed a bug that occurred when:</p>
@@ -673,14 +700,14 @@ <h3 id="release-6-1">Release 6.1<a class="headerlink" href="#release-6-1" title=
 </ul>
 </li>
 </ul>
-<h3 id="release-6-0">Release 6.0<a class="headerlink" href="#release-6-0" title="Permalink to this heading">¶</a></h3>
+<h3 id="release-6-0">Release 6.0<a class="headerlink" href="#release-6-0" title="Link to this heading">¶</a></h3>
 <ul class="simple">
 <li><p>Minimum supported Python is 3.9</p></li>
 <li><p>Bumped minimum NumPy, SciPy, pandas, statsmodels and Cython</p></li>
 <li><p>Removed dependence on property-cached</p></li>
 <li><p>Added compatability with Cython 3</p></li>
 </ul>
-<h2 id="past-releases">Past Releases<a class="headerlink" href="#past-releases" title="Permalink to this heading">¶</a></h2>
+<h2 id="past-releases">Past Releases<a class="headerlink" href="#past-releases" title="Link to this heading">¶</a></h2>
 <div class="toctree-wrapper compound">
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="changes/5.0.html">Version 5</a></li>
@@ -757,7 +784,7 @@ <h2 id="past-releases">Past Releases<a class="headerlink" href="#past-releases"
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -804,7 +831,7 @@ <h2 id="past-releases">Past Releases<a class="headerlink" href="#past-releases"
     <script id="__config" type="application/json">{"base": ".", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/changes/1.0.html b/changes/1.0.html
index d646dd860a..9d3201b147 100644
--- a/changes/1.0.html
+++ b/changes/1.0.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Version 1 - arch 6.1.0</title>
+        <title>Version 1 - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -563,8 +563,8 @@
                   
 
 
-<h1 id="version-1">Version 1<a class="headerlink" href="#version-1" title="Permalink to this heading">¶</a></h1>
-<h2 id="version-1-1">Version 1.1<a class="headerlink" href="#version-1-1" title="Permalink to this heading">¶</a></h2>
+<h1 id="version-1">Version 1<a class="headerlink" href="#version-1" title="Link to this heading">¶</a></h1>
+<h2 id="version-1-1">Version 1.1<a class="headerlink" href="#version-1-1" title="Link to this heading">¶</a></h2>
 <ul class="simple">
 <li><p>Refactored to move the univariate routines to <cite>arch.univariate</cite> and added
 deprecation warnings in the old locations</p></li>
@@ -626,7 +626,7 @@ <h2 id="version-1-1">Version 1.1<a class="headerlink" href="#version-1-1" title=
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -673,7 +673,7 @@ <h2 id="version-1-1">Version 1.1<a class="headerlink" href="#version-1-1" title=
     <script id="__config" type="application/json">{"base": "..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/changes/2.0.html b/changes/2.0.html
index 455b63f630..a59edd20f9 100644
--- a/changes/2.0.html
+++ b/changes/2.0.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Version 2 - arch 6.1.0</title>
+        <title>Version 2 - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -577,13 +577,13 @@
                   
 
 
-<h1 id="version-2">Version 2<a class="headerlink" href="#version-2" title="Permalink to this heading">¶</a></h1>
-<h2 id="version-2-2">Version 2.2<a class="headerlink" href="#version-2-2" title="Permalink to this heading">¶</a></h2>
+<h1 id="version-2">Version 2<a class="headerlink" href="#version-2" title="Link to this heading">¶</a></h1>
+<h2 id="version-2-2">Version 2.2<a class="headerlink" href="#version-2-2" title="Link to this heading">¶</a></h2>
 <ul class="simple">
 <li><p>Added multiple comparison procedures</p></li>
 <li><p>Typographical and other small changes</p></li>
 </ul>
-<h2 id="version-2-1">Version 2.1<a class="headerlink" href="#version-2-1" title="Permalink to this heading">¶</a></h2>
+<h2 id="version-2-1">Version 2.1<a class="headerlink" href="#version-2-1" title="Link to this heading">¶</a></h2>
 <ul class="simple">
 <li><p>Add unit root tests:
 * Augmented Dickey-Fuller
@@ -660,7 +660,7 @@ <h2 id="version-2-1">Version 2.1<a class="headerlink" href="#version-2-1" title=
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -707,7 +707,7 @@ <h2 id="version-2-1">Version 2.1<a class="headerlink" href="#version-2-1" title=
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-        <script src="../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/changes/3.0.html b/changes/3.0.html
index cdd092f033..d6e3a7f4c2 100644
--- a/changes/3.0.html
+++ b/changes/3.0.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Version 3 - arch 6.1.0</title>
+        <title>Version 3 - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -511,7 +511,7 @@
                   
 
 
-<h1 id="version-3">Version 3<a class="headerlink" href="#version-3" title="Permalink to this heading">¶</a></h1>
+<h1 id="version-3">Version 3<a class="headerlink" href="#version-3" title="Link to this heading">¶</a></h1>
 <ul class="simple">
 <li><p>Added forecast code for mean forecasting</p></li>
 <li><p>Added volatility hedgehog plot</p></li>
@@ -595,7 +595,7 @@ <h1 id="version-3">Version 3<a class="headerlink" href="#version-3" title="Perma
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -642,7 +642,7 @@ <h1 id="version-3">Version 3<a class="headerlink" href="#version-3" title="Perma
     <script id="__config" type="application/json">{"base": "..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/changes/4.0.html b/changes/4.0.html
index 05ba61fcd4..25cc6e1dbb 100644
--- a/changes/4.0.html
+++ b/changes/4.0.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Version 4 - arch 6.1.0</title>
+        <title>Version 4 - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -815,8 +815,8 @@
                   
 
 
-<h1 id="version-4">Version 4<a class="headerlink" href="#version-4" title="Permalink to this heading">¶</a></h1>
-<h2 id="release-4-19">Release 4.19<a class="headerlink" href="#release-4-19" title="Permalink to this heading">¶</a></h2>
+<h1 id="version-4">Version 4<a class="headerlink" href="#version-4" title="Link to this heading">¶</a></h1>
+<h2 id="release-4-19">Release 4.19<a class="headerlink" href="#release-4-19" title="Link to this heading">¶</a></h2>
 <ul class="simple">
 <li><p>Added the keyword argument <code class="docutils literal notranslate"><span class="pre">reindex</span></code> to <a class="reference internal" href="../univariate/generated/generated/arch.univariate.HARX.forecast.html#arch.univariate.HARX.forecast" title="arch.univariate.HARX.forecast (Python method) — Construct forecasts from estimated model"><code class="xref py py-func docutils literal notranslate"><span class="pre">forecast()</span></code></a>
 that allows the returned forecasts to have minimal size when <code class="docutils literal notranslate"><span class="pre">reindex=False</span></code>.
@@ -833,29 +833,29 @@ <h2 id="release-4-19">Release 4.19<a class="headerlink" href="#release-4-19" tit
 <li><p>Fixed handling of exogenous regressors in <a class="reference internal" href="../univariate/generated/generated/arch.univariate.HARX.forecast.html#arch.univariate.HARX.forecast" title="arch.univariate.HARX.forecast (Python method) — Construct forecasts from estimated model"><code class="xref py py-func docutils literal notranslate"><span class="pre">forecast()</span></code></a>.
 It is now possible to pass values for <span class="math notranslate nohighlight">\(E_t[X_{t+h}]\)</span> using the <code class="docutils literal notranslate"><span class="pre">x</span></code> argument.</p></li>
 </ul>
-<h2 id="release-4-18">Release 4.18<a class="headerlink" href="#release-4-18" title="Permalink to this heading">¶</a></h2>
+<h2 id="release-4-18">Release 4.18<a class="headerlink" href="#release-4-18" title="Link to this heading">¶</a></h2>
 <ul class="simple">
 <li><p>Improved <a class="reference internal" href="../univariate/generated/generated/arch.univariate.HARX.fit.html#arch.univariate.HARX.fit" title="arch.univariate.HARX.fit (Python method) — Estimate model parameters"><code class="xref py py-func docutils literal notranslate"><span class="pre">fit()</span></code></a> performance of ARCH models.</p></li>
 <li><p>Fixed a bug where <code class="docutils literal notranslate"><span class="pre">`typing_extensions</span></code> was subtly introduced as a run-time dependency.</p></li>
 </ul>
-<h2 id="release-4-17">Release 4.17<a class="headerlink" href="#release-4-17" title="Permalink to this heading">¶</a></h2>
+<h2 id="release-4-17">Release 4.17<a class="headerlink" href="#release-4-17" title="Link to this heading">¶</a></h2>
 <ul class="simple">
 <li><p>Fixed a bug that produced incorrect conditional volatility from EWMA models (<a class="reference external" href="https://github.com/bashtage/arch/issues/458">GH458</a>).</p></li>
 </ul>
-<h2 id="release-4-16">Release 4.16<a class="headerlink" href="#release-4-16" title="Permalink to this heading">¶</a></h2>
+<h2 id="release-4-16">Release 4.16<a class="headerlink" href="#release-4-16" title="Link to this heading">¶</a></h2>
 <ul class="simple">
 <li><p>Added <a class="reference internal" href="../univariate/generated/arch.univariate.APARCH.html#arch.univariate.APARCH" title="arch.univariate.APARCH (Python class) — Asymmetric Power ARCH (APARCH) volatility process"><code class="xref py py-class docutils literal notranslate"><span class="pre">APARCH</span></code></a> volatilty process (<a class="reference external" href="https://github.com/bashtage/arch/issues/443">GH443</a>).</p></li>
 <li><p>Added support for Python 3.9 in pyproject.toml (<a class="reference external" href="https://github.com/bashtage/arch/issues/438">GH438</a>).</p></li>
 <li><p>Fixed a bug in model degree-of-freedom calculation (<a class="reference external" href="https://github.com/bashtage/arch/issues/437">GH437</a>).</p></li>
 <li><p>Improved HARX initialization (<a class="reference external" href="https://github.com/bashtage/arch/issues/417">GH417</a>).</p></li>
 </ul>
-<h2 id="release-4-15">Release 4.15<a class="headerlink" href="#release-4-15" title="Permalink to this heading">¶</a></h2>
+<h2 id="release-4-15">Release 4.15<a class="headerlink" href="#release-4-15" title="Link to this heading">¶</a></h2>
 <ul class="simple">
 <li><p>This is a minor release with doc fixes and other small updates. The only notable
 feature is <code class="xref py py-func docutils literal notranslate"><span class="pre">regression()</span></code> which returns
 regression results from the model estimated as part of the test (<a class="reference external" href="https://github.com/bashtage/arch/issues/395">GH395</a>).</p></li>
 </ul>
-<h2 id="release-4-14">Release 4.14<a class="headerlink" href="#release-4-14" title="Permalink to this heading">¶</a></h2>
+<h2 id="release-4-14">Release 4.14<a class="headerlink" href="#release-4-14" title="Link to this heading">¶</a></h2>
 <ul class="simple">
 <li><p>Added Kernel-based long-run variance estimation in <code class="docutils literal notranslate"><span class="pre">arch.covariance.kernel</span></code>.
 Examples include the <a class="reference internal" href="../covariance/generated/arch.covariance.kernel.Bartlett.html#arch.covariance.kernel.Bartlett" title="arch.covariance.kernel.Bartlett (Python class) — Bartlett's (Newey-West) kernel covariance estimation."><code class="xref py py-class docutils literal notranslate"><span class="pre">Bartlett</span></code></a> and the
@@ -882,11 +882,11 @@ <h2 id="release-4-14">Release 4.14<a class="headerlink" href="#release-4-14" tit
 user-provided <cite>RandomState</cite> when one was provided. This prevented
 reproducing simulated values (<a class="reference external" href="https://github.com/bashtage/arch/issues/353">GH353</a>).</p></li>
 </ul>
-<h2 id="release-4-13">Release 4.13<a class="headerlink" href="#release-4-13" title="Permalink to this heading">¶</a></h2>
+<h2 id="release-4-13">Release 4.13<a class="headerlink" href="#release-4-13" title="Link to this heading">¶</a></h2>
 <ul class="simple">
 <li><p>Restored the vendored copy of <code class="docutils literal notranslate"><span class="pre">property_cached</span></code> for conda package building.</p></li>
 </ul>
-<h2 id="release-4-12">Release 4.12<a class="headerlink" href="#release-4-12" title="Permalink to this heading">¶</a></h2>
+<h2 id="release-4-12">Release 4.12<a class="headerlink" href="#release-4-12" title="Link to this heading">¶</a></h2>
 <ul class="simple">
 <li><p>Added typing support to all classes, functions and methods (<a class="reference external" href="https://github.com/bashtage/arch/issues/338">GH338</a>,
 <a class="reference external" href="https://github.com/bashtage/arch/issues/341">GH341</a>, <a class="reference external" href="https://github.com/bashtage/arch/issues/342">GH342</a>, <a class="reference external" href="https://github.com/bashtage/arch/issues/343">GH343</a>, <a class="reference external" href="https://github.com/bashtage/arch/issues/345">GH345</a>, <a class="reference external" href="https://github.com/bashtage/arch/issues/346">GH346</a>).</p></li>
@@ -898,7 +898,7 @@ <h2 id="release-4-12">Release 4.12<a class="headerlink" href="#release-4-12" tit
 <li><p>Fixed a bug that produced an OverflowError when a time series has no
 variance (<a class="reference external" href="https://github.com/bashtage/arch/issues/331">GH331</a>).</p></li>
 </ul>
-<h2 id="release-4-11">Release 4.11<a class="headerlink" href="#release-4-11" title="Permalink to this heading">¶</a></h2>
+<h2 id="release-4-11">Release 4.11<a class="headerlink" href="#release-4-11" title="Link to this heading">¶</a></h2>
 <ul class="simple">
 <li><p>Added <a class="reference internal" href="../univariate/generated/generated/arch.univariate.base.ARCHModelResult.std_resid.html#arch.univariate.base.ARCHModelResult.std_resid" title="arch.univariate.base.ARCHModelResult.std_resid (Python property) — Residuals standardized by conditional volatility"><code class="xref py py-func docutils literal notranslate"><span class="pre">std_resid()</span></code></a> (<a class="reference external" href="https://github.com/bashtage/arch/issues/326">GH326</a>).</p></li>
 <li><p>Error if inputs are not ndarrays, DataFrames or Series (<a class="reference external" href="https://github.com/bashtage/arch/issues/315">GH315</a>).</p></li>
@@ -906,14 +906,14 @@ <h2 id="release-4-11">Release 4.11<a class="headerlink" href="#release-4-11" tit
 If the function bootstrapped produces statistics with 0 variance, it is not possible to
 studentized (<a class="reference external" href="https://github.com/bashtage/arch/issues/322">GH322</a>).</p></li>
 </ul>
-<h2 id="release-4-10">Release 4.10<a class="headerlink" href="#release-4-10" title="Permalink to this heading">¶</a></h2>
+<h2 id="release-4-10">Release 4.10<a class="headerlink" href="#release-4-10" title="Link to this heading">¶</a></h2>
 <ul class="simple">
 <li><p>Fixed a bug in arch_lm_test that assumed that the model data is contained in
 a pandas Series. (<a class="reference external" href="https://github.com/bashtage/arch/issues/313">GH313</a>).</p></li>
 <li><p>Fixed a bug that can affect use in certain environments that reload modules
 (<a class="reference external" href="https://github.com/bashtage/arch/issues/317">GH317</a>).</p></li>
 </ul>
-<h2 id="release-4-9">Release 4.9<a class="headerlink" href="#release-4-9" title="Permalink to this heading">¶</a></h2>
+<h2 id="release-4-9">Release 4.9<a class="headerlink" href="#release-4-9" title="Link to this heading">¶</a></h2>
 <ul class="simple">
 <li><p>Removed support for Python 2.7.</p></li>
 <li><p>Added <a class="reference internal" href="../unitroot/generated/arch.unitroot.auto_bandwidth.html#arch.unitroot.auto_bandwidth" title="arch.unitroot.auto_bandwidth (Python function) — Automatic bandwidth selection of Andrews (1991) and Newey &amp; West (1994)."><code class="xref py py-func docutils literal notranslate"><span class="pre">auto_bandwidth()</span></code></a> to compute optimized bandwidth
@@ -933,17 +933,17 @@ <h2 id="release-4-9">Release 4.9<a class="headerlink" href="#release-4-9" title=
 <li><p>Added <a class="reference internal" href="../univariate/generated/generated/arch.univariate.base.ARCHModelResult.optimization_result.html#arch.univariate.base.ARCHModelResult.optimization_result" title="arch.univariate.base.ARCHModelResult.optimization_result (Python property) — Information about the convergence of the loglikelihood optimization"><code class="xref py py-func docutils literal notranslate"><span class="pre">optimization_result()</span></code></a> to
 simplify checking for convergence of the numerical optimizer (<a class="reference external" href="https://github.com/bashtage/arch/issues/292">GH292</a>).</p></li>
 <li><p>Added <cite>random_state</cite> argument to <a class="reference internal" href="../univariate/generated/generated/arch.univariate.HARX.forecast.html#arch.univariate.HARX.forecast" title="arch.univariate.HARX.forecast (Python method) — Construct forecasts from estimated model"><code class="xref py py-func docutils literal notranslate"><span class="pre">forecast()</span></code></a>
-to allow a <a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">RandomState</span></code></a> object to be passed in when
+to allow a <a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">RandomState</span></code></a> object to be passed in when
 forecasting when <cite>method=’bootstrap’</cite>. This allows the repeatable forecast to
 be produced (<a class="reference external" href="https://github.com/bashtage/arch/issues/290">GH290</a>).</p></li>
 <li><p>Fixed a bug in <a class="reference internal" href="../unitroot/generated/arch.unitroot.VarianceRatio.html#arch.unitroot.VarianceRatio" title="arch.unitroot.VarianceRatio (Python class) — Variance Ratio test of a random walk."><code class="xref py py-class docutils literal notranslate"><span class="pre">VarianceRatio</span></code></a> that used the wrong
 variance in nonrobust inference with overlapping samples (<a class="reference external" href="https://github.com/bashtage/arch/issues/286">GH286</a>).</p></li>
 </ul>
-<h2 id="release-4-8-1">Release 4.8.1<a class="headerlink" href="#release-4-8-1" title="Permalink to this heading">¶</a></h2>
+<h2 id="release-4-8-1">Release 4.8.1<a class="headerlink" href="#release-4-8-1" title="Link to this heading">¶</a></h2>
 <ul class="simple">
 <li><p>Fixed a bug which prevented extension modules from being correctly imported.</p></li>
 </ul>
-<h2 id="release-4-8">Release 4.8<a class="headerlink" href="#release-4-8" title="Permalink to this heading">¶</a></h2>
+<h2 id="release-4-8">Release 4.8<a class="headerlink" href="#release-4-8" title="Link to this heading">¶</a></h2>
 <ul class="simple">
 <li><p>Added Zivot-Andrews unit root test <a class="reference internal" href="../unitroot/generated/arch.unitroot.ZivotAndrews.html#arch.unitroot.ZivotAndrews" title="arch.unitroot.ZivotAndrews (Python class) — Zivot-Andrews structural-break unit-root test"><code class="xref py py-class docutils literal notranslate"><span class="pre">ZivotAndrews</span></code></a>.
 This code was originally written by Jim Varanelli.</p></li>
@@ -961,7 +961,7 @@ <h2 id="release-4-8">Release 4.8<a class="headerlink" href="#release-4-8" title=
 <li><p>Added ability to set the <code class="docutils literal notranslate"><span class="pre">random_state</span></code> when initializing a bootstrap
 (<a class="reference external" href="https://github.com/bashtage/arch/issues/259">GH259</a>).</p></li>
 </ul>
-<h2 id="release-4-7">Release 4.7<a class="headerlink" href="#release-4-7" title="Permalink to this heading">¶</a></h2>
+<h2 id="release-4-7">Release 4.7<a class="headerlink" href="#release-4-7" title="Link to this heading">¶</a></h2>
 <ul class="simple">
 <li><p>Added support for Fractionally Integrated GARCH (FIGARCH) in
 <a class="reference internal" href="../univariate/generated/arch.univariate.FIGARCH.html#arch.univariate.FIGARCH" title="arch.univariate.FIGARCH (Python class) — FIGARCH model"><code class="xref py py-class docutils literal notranslate"><span class="pre">FIGARCH</span></code></a>.</p></li>
@@ -970,13 +970,13 @@ <h2 id="release-4-7">Release 4.7<a class="headerlink" href="#release-4-7" title=
 <li><p>Fixed a big where parameter-less models where incorrectly reported as
 having constant variance (<a class="reference external" href="https://github.com/bashtage/arch/issues/248">GH248</a>).</p></li>
 </ul>
-<h2 id="release-4-6">Release 4.6<a class="headerlink" href="#release-4-6" title="Permalink to this heading">¶</a></h2>
+<h2 id="release-4-6">Release 4.6<a class="headerlink" href="#release-4-6" title="Link to this heading">¶</a></h2>
 <ul class="simple">
 <li><p>Added support for MIDAS volatility processes using Hyperbolic
 weighting in <code class="xref py py-class docutils literal notranslate"><span class="pre">MidasHyperbolic</span></code>
 (<a class="reference external" href="https://github.com/bashtage/arch/issues/233">GH233</a>).</p></li>
 </ul>
-<h2 id="release-4-5">Release 4.5<a class="headerlink" href="#release-4-5" title="Permalink to this heading">¶</a></h2>
+<h2 id="release-4-5">Release 4.5<a class="headerlink" href="#release-4-5" title="Link to this heading">¶</a></h2>
 <ul class="simple">
 <li><p>Added a parameter to forecast that allows a user-provided callable random
 generator to be used in place of the model random generator (<a class="reference external" href="https://github.com/bashtage/arch/issues/225">GH225</a>).</p></li>
@@ -984,7 +984,7 @@ <h2 id="release-4-5">Release 4.5<a class="headerlink" href="#release-4-5" title=
 large time-series.</p></li>
 <li><p>Improved performance of automatic lag selection in ADF and related tests.</p></li>
 </ul>
-<h2 id="release-4-4">Release 4.4<a class="headerlink" href="#release-4-4" title="Permalink to this heading">¶</a></h2>
+<h2 id="release-4-4">Release 4.4<a class="headerlink" href="#release-4-4" title="Link to this heading">¶</a></h2>
 <ul class="simple">
 <li><p>Added named parameters to Dickey-Fuller regressions.</p></li>
 <li><p>Removed use of the module-level NumPy RandomState. All random number
@@ -994,7 +994,7 @@ <h2 id="release-4-4">Release 4.4<a class="headerlink" href="#release-4-4" title=
 <li><p>Fixed a bug in MCS when using the max method that prevented all included
 models from being listed.</p></li>
 </ul>
-<h2 id="release-4-3">Release 4.3<a class="headerlink" href="#release-4-3" title="Permalink to this heading">¶</a></h2>
+<h2 id="release-4-3">Release 4.3<a class="headerlink" href="#release-4-3" title="Link to this heading">¶</a></h2>
 <ul class="simple">
 <li><p>Added <a class="reference internal" href="../univariate/generated/arch.univariate.FixedVariance.html#arch.univariate.FixedVariance" title="arch.univariate.FixedVariance (Python class) — Fixed volatility process"><code class="xref py py-class docutils literal notranslate"><span class="pre">FixedVariance</span></code></a> volatility process which
 allows pre-specified variances to be used with a mean model.  This has been
@@ -1002,7 +1002,7 @@ <h2 id="release-4-3">Release 4.3<a class="headerlink" href="#release-4-3" title=
 with a fixed variance, and then a variance model is estimated on the residuals
 using a <code class="docutils literal notranslate"><span class="pre">ZeroMean</span></code> variance process.</p></li>
 </ul>
-<h2 id="release-4-2">Release 4.2<a class="headerlink" href="#release-4-2" title="Permalink to this heading">¶</a></h2>
+<h2 id="release-4-2">Release 4.2<a class="headerlink" href="#release-4-2" title="Link to this heading">¶</a></h2>
 <ul class="simple">
 <li><p>Fixed a bug that prevented <code class="docutils literal notranslate"><span class="pre">fix</span></code> from being used with a new model
 (<a class="reference external" href="https://github.com/bashtage/arch/issues/156">GH156</a>).</p></li>
@@ -1079,7 +1079,7 @@ <h2 id="release-4-2">Release 4.2<a class="headerlink" href="#release-4-2" title=
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1126,10 +1126,10 @@ <h2 id="release-4-2">Release 4.2<a class="headerlink" href="#release-4-2" title=
     <script id="__config" type="application/json">{"base": "..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
         <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+        <script id="MathJax-script" src="../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
     
   </body>
 </html>
\ No newline at end of file
diff --git a/changes/5.0.html b/changes/5.0.html
index eb9149b417..967e8dcf30 100644
--- a/changes/5.0.html
+++ b/changes/5.0.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Version 5 - arch 6.1.0</title>
+        <title>Version 5 - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -741,33 +741,33 @@
                   
 
 
-<h1 id="version-5">Version 5<a class="headerlink" href="#version-5" title="Permalink to this heading">¶</a></h1>
-<h2 id="changes-since-5-5">Changes Since 5.5<a class="headerlink" href="#changes-since-5-5" title="Permalink to this heading">¶</a></h2>
+<h1 id="version-5">Version 5<a class="headerlink" href="#version-5" title="Link to this heading">¶</a></h1>
+<h2 id="changes-since-5-5">Changes Since 5.5<a class="headerlink" href="#changes-since-5-5" title="Link to this heading">¶</a></h2>
 <ul class="simple">
 <li><p>Removed dependence on property-cached</p></li>
 <li><p>Bumped minimum NumPy, SciPy, pandas, statsmodels and Cython</p></li>
 <li><p>Added compatability with Cython 3</p></li>
 </ul>
-<h2 id="release-5-5">Release 5.5<a class="headerlink" href="#release-5-5" title="Permalink to this heading">¶</a></h2>
+<h2 id="release-5-5">Release 5.5<a class="headerlink" href="#release-5-5" title="Link to this heading">¶</a></h2>
 <ul class="simple">
 <li><p>NumPy 1.25 fixes</p></li>
 <li><p>Initial pandas copy-on-write support</p></li>
 <li><p>Switched doc theme to sphinx-immaterial</p></li>
 <li><p>Small fixes for typing issues</p></li>
 </ul>
-<h2 id="release-5-4">Release 5.4<a class="headerlink" href="#release-5-4" title="Permalink to this heading">¶</a></h2>
+<h2 id="release-5-4">Release 5.4<a class="headerlink" href="#release-5-4" title="Link to this heading">¶</a></h2>
 <ul class="simple">
 <li><p>Compatability release with pandas 2.0</p></li>
 <li><p>Add testing and wheel support for Python 3.11</p></li>
 </ul>
-<h2 id="release-5-3">Release 5.3<a class="headerlink" href="#release-5-3" title="Permalink to this heading">¶</a></h2>
+<h2 id="release-5-3">Release 5.3<a class="headerlink" href="#release-5-3" title="Link to this heading">¶</a></h2>
 <ul class="simple">
 <li><p>Fixed a bug in <code class="xref py py-func docutils literal notranslate"><span class="pre">arch_model()</span></code> where <code class="docutils literal notranslate"><span class="pre">power</span></code> was
 not passed to the <a class="reference internal" href="../univariate/generated/arch.univariate.FIGARCH.html#arch.univariate.FIGARCH" title="arch.univariate.FIGARCH (Python class) — FIGARCH model"><code class="xref py py-class docutils literal notranslate"><span class="pre">FIGARCH</span></code></a> constructor (<a class="reference external" href="https://github.com/bashtage/arch/issues/572">GH572</a>).</p></li>
 <li><p>Fixed a bug that affected downstream projects due to an overly specific assert
 (<a class="reference external" href="https://github.com/bashtage/arch/issues/569">GH569</a>).</p></li>
 </ul>
-<h2 id="release-5-2">Release 5.2<a class="headerlink" href="#release-5-2" title="Permalink to this heading">¶</a></h2>
+<h2 id="release-5-2">Release 5.2<a class="headerlink" href="#release-5-2" title="Link to this heading">¶</a></h2>
 <ul class="simple">
 <li><p>Fixed a bug in in <a class="reference internal" href="../univariate/generated/generated/arch.univariate.base.ARCHModelResult.std_resid.html#arch.univariate.base.ARCHModelResult.std_resid" title="arch.univariate.base.ARCHModelResult.std_resid (Python property) — Residuals standardized by conditional volatility"><code class="xref py py-func docutils literal notranslate"><span class="pre">std_resid()</span></code></a> that
 would raise an exception when the data used to construct the model with a NumPy
@@ -776,39 +776,39 @@ <h2 id="release-5-2">Release 5.2<a class="headerlink" href="#release-5-2" title=
 <code class="docutils literal notranslate"><span class="pre">forecast</span></code> methods when producing multi-step forecasts usign simulation with
 exogenous variables (<a class="reference external" href="https://github.com/bashtage/arch/issues/551">GH551</a>).</p></li>
 </ul>
-<h2 id="release-5-1">Release 5.1<a class="headerlink" href="#release-5-1" title="Permalink to this heading">¶</a></h2>
-<h3 id="unit-root">Unit Root<a class="headerlink" href="#unit-root" title="Permalink to this heading">¶</a></h3>
+<h2 id="release-5-1">Release 5.1<a class="headerlink" href="#release-5-1" title="Link to this heading">¶</a></h2>
+<h3 id="unit-root">Unit Root<a class="headerlink" href="#unit-root" title="Link to this heading">¶</a></h3>
 <ul class="simple">
 <li><p>Improved automatic lag length selection in <a class="reference internal" href="../unitroot/generated/arch.unitroot.DFGLS.html#arch.unitroot.DFGLS" title="arch.unitroot.DFGLS (Python class) — Elliott, Rothenberg and Stock's (1) GLS detrended Dickey-Fuller"><code class="xref py py-class docutils literal notranslate"><span class="pre">DFGLS</span></code></a>
 by using OLS rather than GLS detrended data when selecting the lag length.
 This problem was studied by Perron, P., &amp; Qu, Z. (2007).</p></li>
 </ul>
-<h2 id="release-5-0">Release 5.0<a class="headerlink" href="#release-5-0" title="Permalink to this heading">¶</a></h2>
-<h3 id="id1">Unit Root<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h3>
+<h2 id="release-5-0">Release 5.0<a class="headerlink" href="#release-5-0" title="Link to this heading">¶</a></h2>
+<h3 id="id1">Unit Root<a class="headerlink" href="#id1" title="Link to this heading">¶</a></h3>
 <ul class="simple">
 <li><p>All unit root tests are now immutable, and so properties such as <code class="docutils literal notranslate"><span class="pre">trend</span></code> cannot
 be set after the test is created.</p></li>
 </ul>
-<h3 id="bootstrap">Bootstrap<a class="headerlink" href="#bootstrap" title="Permalink to this heading">¶</a></h3>
+<h3 id="bootstrap">Bootstrap<a class="headerlink" href="#bootstrap" title="Link to this heading">¶</a></h3>
 <ul class="simple">
 <li><p>Added <code class="docutils literal notranslate"><span class="pre">seed</span></code> keyword argument to all bootstraps (e.g., <a class="reference internal" href="../bootstrap/generated/arch.bootstrap.IIDBootstrap.html#arch.bootstrap.IIDBootstrap" title="arch.bootstrap.IIDBootstrap (Python class) — Bootstrap using uniform resampling"><code class="xref py py-class docutils literal notranslate"><span class="pre">IIDBootstrap</span></code></a>
-and <a class="reference internal" href="../bootstrap/generated/arch.bootstrap.StationaryBootstrap.html#arch.bootstrap.StationaryBootstrap" title="arch.bootstrap.StationaryBootstrap (Python class) — Politis and Romano (1994) bootstrap with expon distributed block sizes"><code class="xref py py-class docutils literal notranslate"><span class="pre">StationaryBootstrap</span></code></a>) that allows a NumPy <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.random.Generator</span></code></a>
-to be used. The <code class="docutils literal notranslate"><span class="pre">seed</span></code> keyword argument also accepts legacy <a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.random.RandomState</span></code></a>
+and <a class="reference internal" href="../bootstrap/generated/arch.bootstrap.StationaryBootstrap.html#arch.bootstrap.StationaryBootstrap" title="arch.bootstrap.StationaryBootstrap (Python class) — Politis and Romano (1994) bootstrap with expon distributed block sizes"><code class="xref py py-class docutils literal notranslate"><span class="pre">StationaryBootstrap</span></code></a>) that allows a NumPy <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.random.Generator</span></code></a>
+to be used. The <code class="docutils literal notranslate"><span class="pre">seed</span></code> keyword argument also accepts legacy <a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.random.RandomState</span></code></a>
 instances and integers.  If an integer is passed, the random number generator is constructed by
-calling <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.default_rng" title="(in NumPy v1.24)"><code class="xref py py-func docutils literal notranslate"><span class="pre">numpy.random.default_rng()</span></code></a> The <code class="docutils literal notranslate"><span class="pre">seed</span></code> keyword argument replaces the
+calling <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.default_rng" title="(in NumPy v1.26)"><code class="xref py py-func docutils literal notranslate"><span class="pre">numpy.random.default_rng()</span></code></a> The <code class="docutils literal notranslate"><span class="pre">seed</span></code> keyword argument replaces the
 <code class="docutils literal notranslate"><span class="pre">random_state</span></code> keyword argument.</p></li>
 <li><p>The <a class="reference internal" href="../bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.random_state.html#arch.bootstrap.IIDBootstrap.random_state" title="arch.bootstrap.IIDBootstrap.random_state (Python property) — Set or get the instance random state"><code class="xref py py-func docutils literal notranslate"><span class="pre">random_state()</span></code></a> property has also been deprecated in favor
 of <a class="reference internal" href="../bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.generator.html#arch.bootstrap.IIDBootstrap.generator" title="arch.bootstrap.IIDBootstrap.generator (Python property) — Set or get the instance PRNG"><code class="xref py py-func docutils literal notranslate"><span class="pre">generator()</span></code></a>.</p></li>
 <li><p>The <a class="reference internal" href="../bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.get_state.html#arch.bootstrap.IIDBootstrap.get_state" title="arch.bootstrap.IIDBootstrap.get_state (Python method) — Gets the state of the bootstrap's random number generator"><code class="xref py py-func docutils literal notranslate"><span class="pre">get_state()</span></code></a> and <a class="reference internal" href="../bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.set_state.html#arch.bootstrap.IIDBootstrap.set_state" title="arch.bootstrap.IIDBootstrap.set_state (Python method) — Sets the state of the bootstrap's random number generator"><code class="xref py py-func docutils literal notranslate"><span class="pre">set_state()</span></code></a>
 methods have been replaced by the <a class="reference internal" href="../bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.state.html#arch.bootstrap.IIDBootstrap.state" title="arch.bootstrap.IIDBootstrap.state (Python property) — Set or get the generator's state"><code class="xref py py-func docutils literal notranslate"><span class="pre">state()</span></code></a> property.</p></li>
 </ul>
-<h3 id="volatility-modeling">Volatility Modeling<a class="headerlink" href="#volatility-modeling" title="Permalink to this heading">¶</a></h3>
+<h3 id="volatility-modeling">Volatility Modeling<a class="headerlink" href="#volatility-modeling" title="Link to this heading">¶</a></h3>
 <ul class="simple">
 <li><p>Added <code class="docutils literal notranslate"><span class="pre">seed</span></code> keyword argument to all distributions (e.g., <a class="reference internal" href="../univariate/generated/arch.univariate.Normal.html#arch.univariate.Normal" title="arch.univariate.distribution.Normal (Python class)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Normal</span></code></a>
-and <a class="reference internal" href="../univariate/generated/arch.univariate.StudentsT.html#arch.univariate.StudentsT" title="arch.univariate.distribution.StudentsT (Python class)"><code class="xref py py-class docutils literal notranslate"><span class="pre">StudentsT</span></code></a>) that allows a NumPy <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.random.Generator</span></code></a>
-to be used. The <code class="docutils literal notranslate"><span class="pre">seed</span></code> keyword argument also accepts legacy <a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.random.RandomState</span></code></a>
+and <a class="reference internal" href="../univariate/generated/arch.univariate.StudentsT.html#arch.univariate.StudentsT" title="arch.univariate.distribution.StudentsT (Python class)"><code class="xref py py-class docutils literal notranslate"><span class="pre">StudentsT</span></code></a>) that allows a NumPy <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.random.Generator</span></code></a>
+to be used. The <code class="docutils literal notranslate"><span class="pre">seed</span></code> keyword argument also accepts legacy <a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.random.RandomState</span></code></a>
 instances and integers.  If an integer is passed, the random number generator is constructed by
-calling <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.default_rng" title="(in NumPy v1.24)"><code class="xref py py-func docutils literal notranslate"><span class="pre">numpy.random.default_rng()</span></code></a> The <code class="docutils literal notranslate"><span class="pre">seed</span></code> keyword argument replaces the
+calling <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.default_rng" title="(in NumPy v1.26)"><code class="xref py py-func docutils literal notranslate"><span class="pre">numpy.random.default_rng()</span></code></a> The <code class="docutils literal notranslate"><span class="pre">seed</span></code> keyword argument replaces the
 <code class="docutils literal notranslate"><span class="pre">random_state</span></code> keyword argument.</p></li>
 <li><p>The <code class="xref py py-func docutils literal notranslate"><span class="pre">random_state()</span></code> property has also been deprecated in favor
 of <code class="xref py py-func docutils literal notranslate"><span class="pre">generator()</span></code>.</p></li>
@@ -818,7 +818,7 @@ <h3 id="volatility-modeling">Volatility Modeling<a class="headerlink" href="#vol
 <a class="reference internal" href="../univariate/generated/arch.univariate.recursions_python.VolatilityUpdater.html#arch.univariate.recursions_python.VolatilityUpdater" title="arch.univariate.recursions_python.VolatilityUpdater (Python class) — Base class that all volatility updaters must inherit from."><code class="xref py py-class docutils literal notranslate"><span class="pre">VolatilityUpdater</span></code></a> to allow
 <a class="reference internal" href="../univariate/generated/arch.univariate.ARCHInMean.html#arch.univariate.ARCHInMean" title="arch.univariate.ARCHInMean (Python class) — (G)ARCH-in-mean model and simulation"><code class="xref py py-class docutils literal notranslate"><span class="pre">ARCHInMean</span></code></a> to be created from different volatility processes.</p></li>
 </ul>
-<h3 id="setup">Setup<a class="headerlink" href="#setup" title="Permalink to this heading">¶</a></h3>
+<h3 id="setup">Setup<a class="headerlink" href="#setup" title="Link to this heading">¶</a></h3>
 <ul class="simple">
 <li><p>Added support for using an environmental variable to disable C-extension compilation.</p>
 <ul>
@@ -895,7 +895,7 @@ <h3 id="setup">Setup<a class="headerlink" href="#setup" title="Permalink to this
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -942,7 +942,7 @@ <h3 id="setup">Setup<a class="headerlink" href="#setup" title="Permalink to this
     <script id="__config" type="application/json">{"base": "..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/changes/6.0.html b/changes/6.0.html
index ed04e47d32..9497c6eb2f 100644
--- a/changes/6.0.html
+++ b/changes/6.0.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Version 6 - arch 6.1.0</title>
+        <title>Version 6 - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -363,8 +363,21 @@
                   
 
 
-<h1 id="version-6">Version 6<a class="headerlink" href="#version-6" title="Permalink to this heading">¶</a></h1>
-<h2 id="release-6-1">Release 6.1<a class="headerlink" href="#release-6-1" title="Permalink to this heading">¶</a></h2>
+<h1 id="version-6">Version 6<a class="headerlink" href="#version-6" title="Link to this heading">¶</a></h1>
+<h2 id="release-6-2">Release 6.2<a class="headerlink" href="#release-6-2" title="Link to this heading">¶</a></h2>
+<ul class="simple">
+<li><p>Fixed a bug that affected forecasting from  <a class="reference internal" href="../univariate/generated/arch.univariate.FIGARCH.html#arch.univariate.FIGARCH" title="arch.univariate.volatility.FIGARCH (Python class)"><code class="xref py py-class docutils literal notranslate"><span class="pre">FIGARCH</span></code></a> models (<a class="reference external" href="https://github.com/bashtage/arch/issues/606">GH606</a>).</p></li>
+<li><p>Added a performance warning when testing for unit roots in large series using a lag-length
+search with no-max-lag specified.</p></li>
+<li><p>Fixed a bug that affected forecasting from <code class="xref py py-class docutils literal notranslate"><span class="pre">arch.univariate.volatility.FIGARCH'</span>
+<span class="pre">models</span> <span class="pre">(:issue:`606</span></code>).</p></li>
+<li><p>Changed the default value of <code class="docutils literal notranslate"><span class="pre">reindex</span></code> to <code class="docutils literal notranslate"><span class="pre">False</span></code> so that forecasts
+will not match the input by default.  Set <code class="docutils literal notranslate"><span class="pre">reindex</span></code> to <code class="docutils literal notranslate"><span class="pre">True</span></code> if this is
+required.</p></li>
+<li><p>Made <code class="docutils literal notranslate"><span class="pre">from</span> <span class="pre">__future__</span> <span class="pre">import</span> <span class="pre">reindex</span></code> a no-op.</p></li>
+<li><p>Updated notebooks to reflect best practices</p></li>
+</ul>
+<h2 id="release-6-1">Release 6.1<a class="headerlink" href="#release-6-1" title="Link to this heading">¶</a></h2>
 <ul class="simple">
 <li><p>Pushed back the adoption of Cython 3 until a later date</p></li>
 <li><p>Fixed a bug that occurred when:</p>
@@ -374,7 +387,7 @@ <h2 id="release-6-1">Release 6.1<a class="headerlink" href="#release-6-1" title=
 </ul>
 </li>
 </ul>
-<h2 id="release-6-0">Release 6.0<a class="headerlink" href="#release-6-0" title="Permalink to this heading">¶</a></h2>
+<h2 id="release-6-0">Release 6.0<a class="headerlink" href="#release-6-0" title="Link to this heading">¶</a></h2>
 <ul class="simple">
 <li><p>Minimum supported Python is 3.9</p></li>
 <li><p>Bumped minimum NumPy, SciPy, pandas, statsmodels and Cython</p></li>
@@ -412,7 +425,7 @@ <h2 id="release-6-0">Release 6.0<a class="headerlink" href="#release-6-0" title=
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -459,7 +472,7 @@ <h2 id="release-6-0">Release 6.0<a class="headerlink" href="#release-6-0" title=
     <script id="__config" type="application/json">{"base": "..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/covariance/covariance.html b/covariance/covariance.html
index 73950186d1..49082cd706 100644
--- a/covariance/covariance.html
+++ b/covariance/covariance.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Long-run Covariance Estimation - arch 6.1.0</title>
+        <title>Long-run Covariance Estimation - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -841,8 +841,8 @@
                   
 
 
-<h1 id="long-run-covariance-estimation">Long-run Covariance Estimation<a class="headerlink" href="#long-run-covariance-estimation" title="Permalink to this heading">¶</a></h1>
-<h2 id="module-arch.covariance.kernel"><span id="long-run-covariance-estimators"></span>Long-run Covariance Estimators<a class="headerlink" href="#module-arch.covariance.kernel" title="Permalink to this heading">¶</a></h2>
+<h1 id="long-run-covariance-estimation">Long-run Covariance Estimation<a class="headerlink" href="#long-run-covariance-estimation" title="Link to this heading">¶</a></h1>
+<h2 id="module-arch.covariance.kernel"><span id="long-run-covariance-estimators"></span>Long-run Covariance Estimators<a class="headerlink" href="#module-arch.covariance.kernel" title="Link to this heading">¶</a></h2>
 <table class="autosummary longtable docutils data align-default">
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="generated/arch.covariance.kernel.Andrews.html#arch.covariance.kernel.Andrews" title="arch.covariance.kernel.Andrews (Python class) — Alternative name of the QuadraticSpectral covariance estimator."><code class="xref py py-obj docutils literal notranslate"><span class="pre">Andrews</span></code></a>(x[, bandwidth, df_adjust, center, ...])</p></td>
@@ -883,7 +883,7 @@ <h2 id="module-arch.covariance.kernel"><span id="long-run-covariance-estimators"
 </tr>
 </tbody>
 </table>
-<h2 id="results">Results<a class="headerlink" href="#results" title="Permalink to this heading">¶</a></h2>
+<h2 id="results">Results<a class="headerlink" href="#results" title="Link to this heading">¶</a></h2>
 <table class="autosummary longtable docutils data align-default">
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="generated/arch.covariance.kernel.CovarianceEstimate.html#arch.covariance.kernel.CovarianceEstimate" title="arch.covariance.kernel.CovarianceEstimate (Python class) — Covariance estimate using a long-run covariance estimator"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CovarianceEstimate</span></code></a>(short_run, one_sided_strict)</p></td>
@@ -958,7 +958,7 @@ <h2 id="results">Results<a class="headerlink" href="#results" title="Permalink t
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1005,7 +1005,7 @@ <h2 id="results">Results<a class="headerlink" href="#results" title="Permalink t
     <script id="__config" type="application/json">{"base": "..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/covariance/generated/arch.covariance.kernel.Andrews.html b/covariance/generated/arch.covariance.kernel.Andrews.html
index 683da42307..c95760ce8a 100644
--- a/covariance/generated/arch.covariance.kernel.Andrews.html
+++ b/covariance/generated/arch.covariance.kernel.Andrews.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.Andrews - arch 6.1.0</title>
+        <title>arch.covariance.kernel.Andrews - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -977,10 +977,10 @@
                   
 
 
-<h1 id="arch-covariance-kernel-andrews">arch.covariance.kernel.Andrews<a class="headerlink" href="#arch-covariance-kernel-andrews" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-andrews">arch.covariance.kernel.Andrews<a class="headerlink" href="#arch-covariance-kernel-andrews" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.covariance.kernel.Andrews">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.covariance.kernel.</span></span><span class="sig-name descname"><span class="pre">Andrews</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">bandwidth</span></span><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">df_adjust</span></span><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">center</span></span><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">weights</span></span><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">force_int</span></span><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/covariance/kernel.html#Andrews"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.covariance.kernel.Andrews" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.covariance.kernel.</span></span><span class="sig-name descname"><span class="pre">Andrews</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">bandwidth</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">df_adjust</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">center</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">weights</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">force_int</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/covariance/kernel.html#Andrews"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.covariance.kernel.Andrews" title="Link to this definition">¶</a></dt>
 <dd><p>Alternative name of the QuadraticSpectral covariance estimator.</p>
 <div class="seealso admonition">
 <p class="admonition-title">See also</p>
@@ -1095,7 +1095,7 @@ <h1 id="arch-covariance-kernel-andrews">arch.covariance.kernel.Andrews<a class="
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
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@@ -1142,7 +1142,7 @@ <h1 id="arch-covariance-kernel-andrews">arch.covariance.kernel.Andrews<a class="
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diff --git a/covariance/generated/arch.covariance.kernel.Bartlett.html b/covariance/generated/arch.covariance.kernel.Bartlett.html
index 1cd3f53ef3..cf286ce0c4 100644
--- a/covariance/generated/arch.covariance.kernel.Bartlett.html
+++ b/covariance/generated/arch.covariance.kernel.Bartlett.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.Bartlett - arch 6.1.0</title>
+        <title>arch.covariance.kernel.Bartlett - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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@@ -92,7 +92,7 @@
 
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@@ -102,7 +102,7 @@
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@@ -1093,29 +1093,29 @@
                   
 
 
-<h1 id="arch-covariance-kernel-bartlett">arch.covariance.kernel.Bartlett<a class="headerlink" href="#arch-covariance-kernel-bartlett" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-bartlett">arch.covariance.kernel.Bartlett<a class="headerlink" href="#arch-covariance-kernel-bartlett" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.covariance.kernel.Bartlett">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.covariance.kernel.</span></span><span class="sig-name descname"><span class="pre">Bartlett</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.Bartlett.__init__.x" title="arch.covariance.kernel.Bartlett.__init__.x (Python parameter) — The data to use in covariance estimation."><span class="n"><span class="pre">x</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.Bartlett.__init__.bandwidth" title="arch.covariance.kernel.Bartlett.__init__.bandwidth (Python parameter) — The kernel's bandwidth.  If None, optimal bandwidth is estimated."><span class="n"><span class="pre">bandwidth</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.Bartlett.__init__.df_adjust" title="arch.covariance.kernel.Bartlett.__init__.df_adjust (Python parameter) — Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."><span class="n"><span class="pre">df_adjust</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.Bartlett.__init__.center" title="arch.covariance.kernel.Bartlett.__init__.center (Python parameter) — A flag indicating whether x should be demeaned before estimating the covariance."><span class="n"><span class="pre">center</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.Bartlett.__init__.weights" title="arch.covariance.kernel.Bartlett.__init__.weights (Python parameter) — An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."><span class="n"><span class="pre">weights</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.Bartlett.__init__.force_int" title="arch.covariance.kernel.Bartlett.__init__.force_int (Python parameter) — Force bandwidth to be an integer."><span class="n"><span class="pre">force_int</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/covariance/kernel.html#Bartlett"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.covariance.kernel.Bartlett" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.covariance.kernel.</span></span><span class="sig-name descname"><span class="pre">Bartlett</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.Bartlett.__init__.x" title="arch.covariance.kernel.Bartlett.__init__.x (Python parameter) — The data to use in covariance estimation."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.Bartlett.__init__.bandwidth" title="arch.covariance.kernel.Bartlett.__init__.bandwidth (Python parameter) — The kernel's bandwidth.  If None, optimal bandwidth is estimated."><span class="n"><span class="pre">bandwidth</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.Bartlett.__init__.df_adjust" title="arch.covariance.kernel.Bartlett.__init__.df_adjust (Python parameter) — Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."><span class="n"><span class="pre">df_adjust</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.Bartlett.__init__.center" title="arch.covariance.kernel.Bartlett.__init__.center (Python parameter) — A flag indicating whether x should be demeaned before estimating the covariance."><span class="n"><span class="pre">center</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.Bartlett.__init__.weights" title="arch.covariance.kernel.Bartlett.__init__.weights (Python parameter) — An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."><span class="n"><span class="pre">weights</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.Bartlett.__init__.force_int" title="arch.covariance.kernel.Bartlett.__init__.force_int (Python parameter) — Force bandwidth to be an integer."><span class="n"><span class="pre">force_int</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/covariance/kernel.html#Bartlett"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.covariance.kernel.Bartlett" title="Link to this definition">¶</a></dt>
 <dd><p>Bartlett’s (Newey-West) kernel covariance estimation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.Bartlett-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Bartlett-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.covariance.kernel.Bartlett.__init__.x"><span class="sig-name descname">x</span><span class="p"> : </span><span class="desctype"><span class="desctype">array_like</span></span><a class="headerlink" href="#arch.covariance.kernel.Bartlett.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>The data to use in covariance estimation.</p>
+<dt id="arch.covariance.kernel.Bartlett.__init__.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.covariance.kernel.Bartlett.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>The data to use in covariance estimation.</p>
 </dd>
-<dt id="arch.covariance.kernel.Bartlett.__init__.bandwidth"><span class="sig-name descname">bandwidth</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>, default None</span></span><a class="headerlink" href="#arch.covariance.kernel.Bartlett.__init__.bandwidth" title="Permalink to this definition">¶</a></dt><dd><p>The kernel’s bandwidth.  If None, optimal bandwidth is estimated.</p>
+<dt id="arch.covariance.kernel.Bartlett.__init__.bandwidth"><span class="n sig-name">bandwidth</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.covariance.kernel.Bartlett.__init__.bandwidth" title="Permalink to this definition">¶</a></dt><dd><p>The kernel’s bandwidth.  If None, optimal bandwidth is estimated.</p>
 </dd>
-<dt id="arch.covariance.kernel.Bartlett.__init__.df_adjust"><span class="sig-name descname">df_adjust</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, default 0</span></span><a class="headerlink" href="#arch.covariance.kernel.Bartlett.__init__.df_adjust" title="Permalink to this definition">¶</a></dt><dd><p>Degrees of freedom to remove when adjusting the covariance. Uses the
+<dt id="arch.covariance.kernel.Bartlett.__init__.df_adjust"><span class="n sig-name">df_adjust</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code><a class="headerlink" href="#arch.covariance.kernel.Bartlett.__init__.df_adjust" title="Permalink to this definition">¶</a></dt><dd><p>Degrees of freedom to remove when adjusting the covariance. Uses the
 number of observations in x minus df_adjust when dividing
 inner-products.</p>
 </dd>
-<dt id="arch.covariance.kernel.Bartlett.__init__.center"><span class="sig-name descname">center</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, default True</span></span><a class="headerlink" href="#arch.covariance.kernel.Bartlett.__init__.center" title="Permalink to this definition">¶</a></dt><dd><p>A flag indicating whether x should be demeaned before estimating the
+<dt id="arch.covariance.kernel.Bartlett.__init__.center"><span class="n sig-name">center</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.covariance.kernel.Bartlett.__init__.center" title="Permalink to this definition">¶</a></dt><dd><p>A flag indicating whether x should be demeaned before estimating the
 covariance.</p>
 </dd>
-<dt id="arch.covariance.kernel.Bartlett.__init__.weights"><span class="sig-name descname">weights</span><span class="p"> : </span><span class="desctype"><span class="desctype">array_like, default None</span></span><a class="headerlink" href="#arch.covariance.kernel.Bartlett.__init__.weights" title="Permalink to this definition">¶</a></dt><dd><p>An array of weights used to combine when estimating optimal bandwidth.
+<dt id="arch.covariance.kernel.Bartlett.__init__.weights"><span class="n sig-name">weights</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.covariance.kernel.Bartlett.__init__.weights" title="Permalink to this definition">¶</a></dt><dd><p>An array of weights used to combine when estimating optimal bandwidth.
 If not provided, a vector of 1s is used. Must have nvar elements.</p>
 </dd>
-<dt id="arch.covariance.kernel.Bartlett.__init__.force_int"><span class="sig-name descname">force_int</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, default False</span></span><a class="headerlink" href="#arch.covariance.kernel.Bartlett.__init__.force_int" title="Permalink to this definition">¶</a></dt><dd><p>Force bandwidth to be an integer.</p>
+<dt id="arch.covariance.kernel.Bartlett.__init__.force_int"><span class="n sig-name">force_int</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.covariance.kernel.Bartlett.__init__.force_int" title="Permalink to this definition">¶</a></dt><dd><p>Force bandwidth to be an integer.</p>
 </dd>
 </dl>
 </dd>
@@ -1234,7 +1234,7 @@ <h1 id="arch-covariance-kernel-bartlett">arch.covariance.kernel.Bartlett<a class
   
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diff --git a/covariance/generated/arch.covariance.kernel.CovarianceEstimate.html b/covariance/generated/arch.covariance.kernel.CovarianceEstimate.html
index 23369be226..dfc2522524 100644
--- a/covariance/generated/arch.covariance.kernel.CovarianceEstimate.html
+++ b/covariance/generated/arch.covariance.kernel.CovarianceEstimate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.CovarianceEstimate - arch 6.1.0</title>
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@@ -47,8 +47,8 @@
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+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
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+            arch 6.2.0
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+    arch 6.2.0
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@@ -782,25 +782,25 @@
                   
 
 
-<h1 id="arch-covariance-kernel-covarianceestimate">arch.covariance.kernel.CovarianceEstimate<a class="headerlink" href="#arch-covariance-kernel-covarianceestimate" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-covarianceestimate">arch.covariance.kernel.CovarianceEstimate<a class="headerlink" href="#arch-covariance-kernel-covarianceestimate" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.covariance.kernel.CovarianceEstimate">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.covariance.kernel.</span></span><span class="sig-name descname"><span class="pre">CovarianceEstimate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.CovarianceEstimate.__init__.short_run" title="arch.covariance.kernel.CovarianceEstimate.__init__.short_run (Python parameter) — The short-run covariance estimate."><span class="n"><span class="pre">short_run</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.CovarianceEstimate.__init__.one_sided_strict" title="arch.covariance.kernel.CovarianceEstimate.__init__.one_sided_strict (Python parameter) — The one-sided-strict covariance estimate. If not provided, computed from short_run and one_sided_strict."><span class="n"><span class="pre">one_sided_strict</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.CovarianceEstimate.__init__.columns" title="arch.covariance.kernel.CovarianceEstimate.__init__.columns (Python parameter) — Column labels to use if covariance estimates are returned as DataFrames."><span class="n"><span class="pre">columns</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.CovarianceEstimate.__init__.long_run" title="arch.covariance.kernel.CovarianceEstimate.__init__.long_run (Python parameter) — The long-run covariance estimate. If not provided, computed from short_run and one_sided_strict."><span class="n"><span class="pre">long_run</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">one_sided</span></span><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/covariance/kernel.html#CovarianceEstimate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.covariance.kernel.CovarianceEstimate" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.covariance.kernel.</span></span><span class="sig-name descname"><span class="pre">CovarianceEstimate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.CovarianceEstimate.__init__.short_run" title="arch.covariance.kernel.CovarianceEstimate.__init__.short_run (Python parameter) — The short-run covariance estimate."><span class="n"><span class="pre">short_run</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.CovarianceEstimate.__init__.one_sided_strict" title="arch.covariance.kernel.CovarianceEstimate.__init__.one_sided_strict (Python parameter) — The one-sided-strict covariance estimate. If not provided, computed from short_run and one_sided_strict."><span class="n"><span class="pre">one_sided_strict</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.CovarianceEstimate.__init__.columns" title="arch.covariance.kernel.CovarianceEstimate.__init__.columns (Python parameter) — Column labels to use if covariance estimates are returned as DataFrames."><span class="n"><span class="pre">columns</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Index.html#pandas.Index" title="(in pandas v2.1.1)"><span class="pre">Index</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.CovarianceEstimate.__init__.long_run" title="arch.covariance.kernel.CovarianceEstimate.__init__.long_run (Python parameter) — The long-run covariance estimate. If not provided, computed from short_run and one_sided_strict."><span class="n"><span class="pre">long_run</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">one_sided</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/covariance/kernel.html#CovarianceEstimate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.covariance.kernel.CovarianceEstimate" title="Link to this definition">¶</a></dt>
 <dd><p>Covariance estimate using a long-run covariance estimator</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.CovarianceEstimate-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.CovarianceEstimate-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.covariance.kernel.CovarianceEstimate.__init__.short_run"><span class="sig-name descname">short_run</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.covariance.kernel.CovarianceEstimate.__init__.short_run" title="Permalink to this definition">¶</a></dt><dd><p>The short-run covariance estimate.</p>
+<dt id="arch.covariance.kernel.CovarianceEstimate.__init__.short_run"><span class="n sig-name">short_run</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.covariance.kernel.CovarianceEstimate.__init__.short_run" title="Permalink to this definition">¶</a></dt><dd><p>The short-run covariance estimate.</p>
 </dd>
-<dt id="arch.covariance.kernel.CovarianceEstimate.__init__.one_sided_strict"><span class="sig-name descname">one_sided_strict</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray, default None</span></span><a class="headerlink" href="#arch.covariance.kernel.CovarianceEstimate.__init__.one_sided_strict" title="Permalink to this definition">¶</a></dt><dd><p>The one-sided strict covariance estimate.</p>
+<dt id="arch.covariance.kernel.CovarianceEstimate.__init__.one_sided_strict"><span class="n sig-name">one_sided_strict</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.covariance.kernel.CovarianceEstimate.__init__.one_sided_strict" title="Permalink to this definition">¶</a></dt><dd><p>The one-sided strict covariance estimate.</p>
 </dd>
-<dt id="arch.covariance.kernel.CovarianceEstimate.__init__.columns"><span class="sig-name descname">columns</span><span class="p"> : </span><span class="desctype"><span class="desctype">{None, <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a>[<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>]}</span></span><a class="headerlink" href="#arch.covariance.kernel.CovarianceEstimate.__init__.columns" title="Permalink to this definition">¶</a></dt><dd><p>Column labels to use if covariance estimates are returned as
+<dt id="arch.covariance.kernel.CovarianceEstimate.__init__.columns"><span class="n sig-name">columns</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Index.html#pandas.Index" title="(in pandas v2.1.1)">Index</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.covariance.kernel.CovarianceEstimate.__init__.columns" title="Permalink to this definition">¶</a></dt><dd><p>Column labels to use if covariance estimates are returned as
 DataFrames.</p>
 </dd>
-<dt id="arch.covariance.kernel.CovarianceEstimate.__init__.long_run"><span class="sig-name descname">long_run</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray, default None</span></span><a class="headerlink" href="#arch.covariance.kernel.CovarianceEstimate.__init__.long_run" title="Permalink to this definition">¶</a></dt><dd><p>The long-run covariance estimate. If not provided, computed from
+<dt id="arch.covariance.kernel.CovarianceEstimate.__init__.long_run"><span class="n sig-name">long_run</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.covariance.kernel.CovarianceEstimate.__init__.long_run" title="Permalink to this definition">¶</a></dt><dd><p>The long-run covariance estimate. If not provided, computed from
 short_run and one_sided_strict.</p>
 </dd>
-<dt id="arch.covariance.kernel.CovarianceEstimate.__init__.one_sided_strict"><span class="n sig-name">one_sided_strict</span><a class="headerlink" href="#arch.covariance.kernel.CovarianceEstimate.__init__.one_sided_strict" title="Permalink to this definition">¶</a></dt><dd><p>The one-sided-strict covariance estimate. If not provided, computed
+<dt id="arch.covariance.kernel.CovarianceEstimate.__init__.one_sided_strict"><span class="n sig-name">one_sided_strict</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.covariance.kernel.CovarianceEstimate.__init__.one_sided_strict" title="Permalink to this definition">¶</a></dt><dd><p>The one-sided-strict covariance estimate. If not provided, computed
 from short_run and one_sided_strict.</p>
 </dd>
 </dl>
@@ -905,7 +905,7 @@ <h1 id="arch-covariance-kernel-covarianceestimate">arch.covariance.kernel.Covari
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
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@@ -952,10 +952,10 @@ <h1 id="arch-covariance-kernel-covarianceestimate">arch.covariance.kernel.Covari
     <script id="__config" type="application/json">{"base": "../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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diff --git a/covariance/generated/arch.covariance.kernel.Gallant.html b/covariance/generated/arch.covariance.kernel.Gallant.html
index 42ec1eedd2..36d354274e 100644
--- a/covariance/generated/arch.covariance.kernel.Gallant.html
+++ b/covariance/generated/arch.covariance.kernel.Gallant.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.Gallant - arch 6.1.0</title>
+        <title>arch.covariance.kernel.Gallant - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch 6.1.0
+    arch 6.2.0
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@@ -977,10 +977,10 @@
                   
 
 
-<h1 id="arch-covariance-kernel-gallant">arch.covariance.kernel.Gallant<a class="headerlink" href="#arch-covariance-kernel-gallant" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-gallant">arch.covariance.kernel.Gallant<a class="headerlink" href="#arch-covariance-kernel-gallant" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.covariance.kernel.Gallant">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.covariance.kernel.</span></span><span class="sig-name descname"><span class="pre">Gallant</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">bandwidth</span></span><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">df_adjust</span></span><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">center</span></span><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">weights</span></span><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">force_int</span></span><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/covariance/kernel.html#Gallant"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.covariance.kernel.Gallant" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.covariance.kernel.</span></span><span class="sig-name descname"><span class="pre">Gallant</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">bandwidth</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">df_adjust</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">center</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">weights</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">force_int</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/covariance/kernel.html#Gallant"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.covariance.kernel.Gallant" title="Link to this definition">¶</a></dt>
 <dd><p>Alternative name for Parzen covariance estimator.</p>
 <div class="seealso admonition">
 <p class="admonition-title">See also</p>
@@ -1095,7 +1095,7 @@ <h1 id="arch-covariance-kernel-gallant">arch.covariance.kernel.Gallant<a class="
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1142,7 +1142,7 @@ <h1 id="arch-covariance-kernel-gallant">arch.covariance.kernel.Gallant<a class="
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diff --git a/covariance/generated/arch.covariance.kernel.NeweyWest.html b/covariance/generated/arch.covariance.kernel.NeweyWest.html
index d2eeba1be5..eaf2a69489 100644
--- a/covariance/generated/arch.covariance.kernel.NeweyWest.html
+++ b/covariance/generated/arch.covariance.kernel.NeweyWest.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.NeweyWest - arch 6.1.0</title>
+        <title>arch.covariance.kernel.NeweyWest - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -977,10 +977,10 @@
                   
 
 
-<h1 id="arch-covariance-kernel-neweywest">arch.covariance.kernel.NeweyWest<a class="headerlink" href="#arch-covariance-kernel-neweywest" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-neweywest">arch.covariance.kernel.NeweyWest<a class="headerlink" href="#arch-covariance-kernel-neweywest" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.covariance.kernel.NeweyWest">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.covariance.kernel.</span></span><span class="sig-name descname"><span class="pre">NeweyWest</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">bandwidth</span></span><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">df_adjust</span></span><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">center</span></span><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">weights</span></span><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">force_int</span></span><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/covariance/kernel.html#NeweyWest"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.covariance.kernel.NeweyWest" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.covariance.kernel.</span></span><span class="sig-name descname"><span class="pre">NeweyWest</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">bandwidth</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">df_adjust</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">center</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">weights</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">force_int</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/covariance/kernel.html#NeweyWest"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.covariance.kernel.NeweyWest" title="Link to this definition">¶</a></dt>
 <dd><p>Alternative name for Bartlett covariance estimator.</p>
 <div class="seealso admonition">
 <p class="admonition-title">See also</p>
@@ -1095,7 +1095,7 @@ <h1 id="arch-covariance-kernel-neweywest">arch.covariance.kernel.NeweyWest<a cla
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1142,7 +1142,7 @@ <h1 id="arch-covariance-kernel-neweywest">arch.covariance.kernel.NeweyWest<a cla
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diff --git a/covariance/generated/arch.covariance.kernel.Parzen.html b/covariance/generated/arch.covariance.kernel.Parzen.html
index 37e0efa39b..3136007264 100644
--- a/covariance/generated/arch.covariance.kernel.Parzen.html
+++ b/covariance/generated/arch.covariance.kernel.Parzen.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.Parzen - arch 6.1.0</title>
+        <title>arch.covariance.kernel.Parzen - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1093,29 +1093,29 @@
                   
 
 
-<h1 id="arch-covariance-kernel-parzen">arch.covariance.kernel.Parzen<a class="headerlink" href="#arch-covariance-kernel-parzen" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-parzen">arch.covariance.kernel.Parzen<a class="headerlink" href="#arch-covariance-kernel-parzen" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.covariance.kernel.Parzen">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.covariance.kernel.</span></span><span class="sig-name descname"><span class="pre">Parzen</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.Parzen.__init__.x" title="arch.covariance.kernel.Parzen.__init__.x (Python parameter) — The data to use in covariance estimation."><span class="n"><span class="pre">x</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.Parzen.__init__.bandwidth" title="arch.covariance.kernel.Parzen.__init__.bandwidth (Python parameter) — The kernel's bandwidth.  If None, optimal bandwidth is estimated."><span class="n"><span class="pre">bandwidth</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.Parzen.__init__.df_adjust" title="arch.covariance.kernel.Parzen.__init__.df_adjust (Python parameter) — Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."><span class="n"><span class="pre">df_adjust</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.Parzen.__init__.center" title="arch.covariance.kernel.Parzen.__init__.center (Python parameter) — A flag indicating whether x should be demeaned before estimating the covariance."><span class="n"><span class="pre">center</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.Parzen.__init__.weights" title="arch.covariance.kernel.Parzen.__init__.weights (Python parameter) — An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."><span class="n"><span class="pre">weights</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.Parzen.__init__.force_int" title="arch.covariance.kernel.Parzen.__init__.force_int (Python parameter) — Force bandwidth to be an integer."><span class="n"><span class="pre">force_int</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/covariance/kernel.html#Parzen"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.covariance.kernel.Parzen" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.covariance.kernel.</span></span><span class="sig-name descname"><span class="pre">Parzen</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.Parzen.__init__.x" title="arch.covariance.kernel.Parzen.__init__.x (Python parameter) — The data to use in covariance estimation."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.Parzen.__init__.bandwidth" title="arch.covariance.kernel.Parzen.__init__.bandwidth (Python parameter) — The kernel's bandwidth.  If None, optimal bandwidth is estimated."><span class="n"><span class="pre">bandwidth</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.Parzen.__init__.df_adjust" title="arch.covariance.kernel.Parzen.__init__.df_adjust (Python parameter) — Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."><span class="n"><span class="pre">df_adjust</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.Parzen.__init__.center" title="arch.covariance.kernel.Parzen.__init__.center (Python parameter) — A flag indicating whether x should be demeaned before estimating the covariance."><span class="n"><span class="pre">center</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.Parzen.__init__.weights" title="arch.covariance.kernel.Parzen.__init__.weights (Python parameter) — An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."><span class="n"><span class="pre">weights</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.Parzen.__init__.force_int" title="arch.covariance.kernel.Parzen.__init__.force_int (Python parameter) — Force bandwidth to be an integer."><span class="n"><span class="pre">force_int</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/covariance/kernel.html#Parzen"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.covariance.kernel.Parzen" title="Link to this definition">¶</a></dt>
 <dd><p>Parzen’s kernel covariance estimation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.Parzen-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Parzen-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.covariance.kernel.Parzen.__init__.x"><span class="sig-name descname">x</span><span class="p"> : </span><span class="desctype"><span class="desctype">array_like</span></span><a class="headerlink" href="#arch.covariance.kernel.Parzen.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>The data to use in covariance estimation.</p>
+<dt id="arch.covariance.kernel.Parzen.__init__.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.covariance.kernel.Parzen.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>The data to use in covariance estimation.</p>
 </dd>
-<dt id="arch.covariance.kernel.Parzen.__init__.bandwidth"><span class="sig-name descname">bandwidth</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>, default None</span></span><a class="headerlink" href="#arch.covariance.kernel.Parzen.__init__.bandwidth" title="Permalink to this definition">¶</a></dt><dd><p>The kernel’s bandwidth.  If None, optimal bandwidth is estimated.</p>
+<dt id="arch.covariance.kernel.Parzen.__init__.bandwidth"><span class="n sig-name">bandwidth</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.covariance.kernel.Parzen.__init__.bandwidth" title="Permalink to this definition">¶</a></dt><dd><p>The kernel’s bandwidth.  If None, optimal bandwidth is estimated.</p>
 </dd>
-<dt id="arch.covariance.kernel.Parzen.__init__.df_adjust"><span class="sig-name descname">df_adjust</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, default 0</span></span><a class="headerlink" href="#arch.covariance.kernel.Parzen.__init__.df_adjust" title="Permalink to this definition">¶</a></dt><dd><p>Degrees of freedom to remove when adjusting the covariance. Uses the
+<dt id="arch.covariance.kernel.Parzen.__init__.df_adjust"><span class="n sig-name">df_adjust</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code><a class="headerlink" href="#arch.covariance.kernel.Parzen.__init__.df_adjust" title="Permalink to this definition">¶</a></dt><dd><p>Degrees of freedom to remove when adjusting the covariance. Uses the
 number of observations in x minus df_adjust when dividing
 inner-products.</p>
 </dd>
-<dt id="arch.covariance.kernel.Parzen.__init__.center"><span class="sig-name descname">center</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, default True</span></span><a class="headerlink" href="#arch.covariance.kernel.Parzen.__init__.center" title="Permalink to this definition">¶</a></dt><dd><p>A flag indicating whether x should be demeaned before estimating the
+<dt id="arch.covariance.kernel.Parzen.__init__.center"><span class="n sig-name">center</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.covariance.kernel.Parzen.__init__.center" title="Permalink to this definition">¶</a></dt><dd><p>A flag indicating whether x should be demeaned before estimating the
 covariance.</p>
 </dd>
-<dt id="arch.covariance.kernel.Parzen.__init__.weights"><span class="sig-name descname">weights</span><span class="p"> : </span><span class="desctype"><span class="desctype">array_like, default None</span></span><a class="headerlink" href="#arch.covariance.kernel.Parzen.__init__.weights" title="Permalink to this definition">¶</a></dt><dd><p>An array of weights used to combine when estimating optimal bandwidth.
+<dt id="arch.covariance.kernel.Parzen.__init__.weights"><span class="n sig-name">weights</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.covariance.kernel.Parzen.__init__.weights" title="Permalink to this definition">¶</a></dt><dd><p>An array of weights used to combine when estimating optimal bandwidth.
 If not provided, a vector of 1s is used. Must have nvar elements.</p>
 </dd>
-<dt id="arch.covariance.kernel.Parzen.__init__.force_int"><span class="sig-name descname">force_int</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, default False</span></span><a class="headerlink" href="#arch.covariance.kernel.Parzen.__init__.force_int" title="Permalink to this definition">¶</a></dt><dd><p>Force bandwidth to be an integer.</p>
+<dt id="arch.covariance.kernel.Parzen.__init__.force_int"><span class="n sig-name">force_int</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.covariance.kernel.Parzen.__init__.force_int" title="Permalink to this definition">¶</a></dt><dd><p>Force bandwidth to be an integer.</p>
 </dd>
 </dl>
 </dd>
@@ -1234,7 +1234,7 @@ <h1 id="arch-covariance-kernel-parzen">arch.covariance.kernel.Parzen<a class="he
   
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diff --git a/covariance/generated/arch.covariance.kernel.ParzenCauchy.html b/covariance/generated/arch.covariance.kernel.ParzenCauchy.html
index 111bb1b923..c938c9f0b9 100644
--- a/covariance/generated/arch.covariance.kernel.ParzenCauchy.html
+++ b/covariance/generated/arch.covariance.kernel.ParzenCauchy.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.ParzenCauchy - arch 6.1.0</title>
+        <title>arch.covariance.kernel.ParzenCauchy - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
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+            arch 6.2.0
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@@ -1093,29 +1093,29 @@
                   
 
 
-<h1 id="arch-covariance-kernel-parzencauchy">arch.covariance.kernel.ParzenCauchy<a class="headerlink" href="#arch-covariance-kernel-parzencauchy" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-parzencauchy">arch.covariance.kernel.ParzenCauchy<a class="headerlink" href="#arch-covariance-kernel-parzencauchy" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.covariance.kernel.ParzenCauchy">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.covariance.kernel.</span></span><span class="sig-name descname"><span class="pre">ParzenCauchy</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenCauchy.__init__.x" title="arch.covariance.kernel.ParzenCauchy.__init__.x (Python parameter) — The data to use in covariance estimation."><span class="n"><span class="pre">x</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenCauchy.__init__.bandwidth" title="arch.covariance.kernel.ParzenCauchy.__init__.bandwidth (Python parameter) — The kernel's bandwidth.  If None, optimal bandwidth is estimated."><span class="n"><span class="pre">bandwidth</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenCauchy.__init__.df_adjust" title="arch.covariance.kernel.ParzenCauchy.__init__.df_adjust (Python parameter) — Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."><span class="n"><span class="pre">df_adjust</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenCauchy.__init__.center" title="arch.covariance.kernel.ParzenCauchy.__init__.center (Python parameter) — A flag indicating whether x should be demeaned before estimating the covariance."><span class="n"><span class="pre">center</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenCauchy.__init__.weights" title="arch.covariance.kernel.ParzenCauchy.__init__.weights (Python parameter) — An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."><span class="n"><span class="pre">weights</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenCauchy.__init__.force_int" title="arch.covariance.kernel.ParzenCauchy.__init__.force_int (Python parameter) — Force bandwidth to be an integer."><span class="n"><span class="pre">force_int</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/covariance/kernel.html#ParzenCauchy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.covariance.kernel.</span></span><span class="sig-name descname"><span class="pre">ParzenCauchy</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenCauchy.__init__.x" title="arch.covariance.kernel.ParzenCauchy.__init__.x (Python parameter) — The data to use in covariance estimation."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenCauchy.__init__.bandwidth" title="arch.covariance.kernel.ParzenCauchy.__init__.bandwidth (Python parameter) — The kernel's bandwidth.  If None, optimal bandwidth is estimated."><span class="n"><span class="pre">bandwidth</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenCauchy.__init__.df_adjust" title="arch.covariance.kernel.ParzenCauchy.__init__.df_adjust (Python parameter) — Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."><span class="n"><span class="pre">df_adjust</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenCauchy.__init__.center" title="arch.covariance.kernel.ParzenCauchy.__init__.center (Python parameter) — A flag indicating whether x should be demeaned before estimating the covariance."><span class="n"><span class="pre">center</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenCauchy.__init__.weights" title="arch.covariance.kernel.ParzenCauchy.__init__.weights (Python parameter) — An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."><span class="n"><span class="pre">weights</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenCauchy.__init__.force_int" title="arch.covariance.kernel.ParzenCauchy.__init__.force_int (Python parameter) — Force bandwidth to be an integer."><span class="n"><span class="pre">force_int</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/covariance/kernel.html#ParzenCauchy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy" title="Link to this definition">¶</a></dt>
 <dd><p>Parzen’s Cauchy kernel covariance estimation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.ParzenCauchy-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.covariance.kernel.ParzenCauchy.__init__.x"><span class="sig-name descname">x</span><span class="p"> : </span><span class="desctype"><span class="desctype">array_like</span></span><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>The data to use in covariance estimation.</p>
+<dt id="arch.covariance.kernel.ParzenCauchy.__init__.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>The data to use in covariance estimation.</p>
 </dd>
-<dt id="arch.covariance.kernel.ParzenCauchy.__init__.bandwidth"><span class="sig-name descname">bandwidth</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>, default None</span></span><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy.__init__.bandwidth" title="Permalink to this definition">¶</a></dt><dd><p>The kernel’s bandwidth.  If None, optimal bandwidth is estimated.</p>
+<dt id="arch.covariance.kernel.ParzenCauchy.__init__.bandwidth"><span class="n sig-name">bandwidth</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy.__init__.bandwidth" title="Permalink to this definition">¶</a></dt><dd><p>The kernel’s bandwidth.  If None, optimal bandwidth is estimated.</p>
 </dd>
-<dt id="arch.covariance.kernel.ParzenCauchy.__init__.df_adjust"><span class="sig-name descname">df_adjust</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, default 0</span></span><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy.__init__.df_adjust" title="Permalink to this definition">¶</a></dt><dd><p>Degrees of freedom to remove when adjusting the covariance. Uses the
+<dt id="arch.covariance.kernel.ParzenCauchy.__init__.df_adjust"><span class="n sig-name">df_adjust</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy.__init__.df_adjust" title="Permalink to this definition">¶</a></dt><dd><p>Degrees of freedom to remove when adjusting the covariance. Uses the
 number of observations in x minus df_adjust when dividing
 inner-products.</p>
 </dd>
-<dt id="arch.covariance.kernel.ParzenCauchy.__init__.center"><span class="sig-name descname">center</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, default True</span></span><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy.__init__.center" title="Permalink to this definition">¶</a></dt><dd><p>A flag indicating whether x should be demeaned before estimating the
+<dt id="arch.covariance.kernel.ParzenCauchy.__init__.center"><span class="n sig-name">center</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy.__init__.center" title="Permalink to this definition">¶</a></dt><dd><p>A flag indicating whether x should be demeaned before estimating the
 covariance.</p>
 </dd>
-<dt id="arch.covariance.kernel.ParzenCauchy.__init__.weights"><span class="sig-name descname">weights</span><span class="p"> : </span><span class="desctype"><span class="desctype">array_like, default None</span></span><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy.__init__.weights" title="Permalink to this definition">¶</a></dt><dd><p>An array of weights used to combine when estimating optimal bandwidth.
+<dt id="arch.covariance.kernel.ParzenCauchy.__init__.weights"><span class="n sig-name">weights</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy.__init__.weights" title="Permalink to this definition">¶</a></dt><dd><p>An array of weights used to combine when estimating optimal bandwidth.
 If not provided, a vector of 1s is used. Must have nvar elements.</p>
 </dd>
-<dt id="arch.covariance.kernel.ParzenCauchy.__init__.force_int"><span class="sig-name descname">force_int</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, default False</span></span><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy.__init__.force_int" title="Permalink to this definition">¶</a></dt><dd><p>Force bandwidth to be an integer.</p>
+<dt id="arch.covariance.kernel.ParzenCauchy.__init__.force_int"><span class="n sig-name">force_int</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy.__init__.force_int" title="Permalink to this definition">¶</a></dt><dd><p>Force bandwidth to be an integer.</p>
 </dd>
 </dl>
 </dd>
@@ -1234,7 +1234,7 @@ <h1 id="arch-covariance-kernel-parzencauchy">arch.covariance.kernel.ParzenCauchy
   
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     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
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diff --git a/covariance/generated/arch.covariance.kernel.ParzenGeometric.html b/covariance/generated/arch.covariance.kernel.ParzenGeometric.html
index 36154a161f..0e14ba8db7 100644
--- a/covariance/generated/arch.covariance.kernel.ParzenGeometric.html
+++ b/covariance/generated/arch.covariance.kernel.ParzenGeometric.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.ParzenGeometric - arch 6.1.0</title>
+        <title>arch.covariance.kernel.ParzenGeometric - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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+    arch 6.2.0
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@@ -1093,29 +1093,29 @@
                   
 
 
-<h1 id="arch-covariance-kernel-parzengeometric">arch.covariance.kernel.ParzenGeometric<a class="headerlink" href="#arch-covariance-kernel-parzengeometric" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-parzengeometric">arch.covariance.kernel.ParzenGeometric<a class="headerlink" href="#arch-covariance-kernel-parzengeometric" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.covariance.kernel.ParzenGeometric">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.covariance.kernel.</span></span><span class="sig-name descname"><span class="pre">ParzenGeometric</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenGeometric.__init__.x" title="arch.covariance.kernel.ParzenGeometric.__init__.x (Python parameter) — The data to use in covariance estimation."><span class="n"><span class="pre">x</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenGeometric.__init__.bandwidth" title="arch.covariance.kernel.ParzenGeometric.__init__.bandwidth (Python parameter) — The kernel's bandwidth.  If None, optimal bandwidth is estimated."><span class="n"><span class="pre">bandwidth</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenGeometric.__init__.df_adjust" title="arch.covariance.kernel.ParzenGeometric.__init__.df_adjust (Python parameter) — Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."><span class="n"><span class="pre">df_adjust</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenGeometric.__init__.center" title="arch.covariance.kernel.ParzenGeometric.__init__.center (Python parameter) — A flag indicating whether x should be demeaned before estimating the covariance."><span class="n"><span class="pre">center</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenGeometric.__init__.weights" title="arch.covariance.kernel.ParzenGeometric.__init__.weights (Python parameter) — An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."><span class="n"><span class="pre">weights</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenGeometric.__init__.force_int" title="arch.covariance.kernel.ParzenGeometric.__init__.force_int (Python parameter) — Force bandwidth to be an integer."><span class="n"><span class="pre">force_int</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/covariance/kernel.html#ParzenGeometric"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.covariance.kernel.</span></span><span class="sig-name descname"><span class="pre">ParzenGeometric</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenGeometric.__init__.x" title="arch.covariance.kernel.ParzenGeometric.__init__.x (Python parameter) — The data to use in covariance estimation."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenGeometric.__init__.bandwidth" title="arch.covariance.kernel.ParzenGeometric.__init__.bandwidth (Python parameter) — The kernel's bandwidth.  If None, optimal bandwidth is estimated."><span class="n"><span class="pre">bandwidth</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenGeometric.__init__.df_adjust" title="arch.covariance.kernel.ParzenGeometric.__init__.df_adjust (Python parameter) — Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."><span class="n"><span class="pre">df_adjust</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenGeometric.__init__.center" title="arch.covariance.kernel.ParzenGeometric.__init__.center (Python parameter) — A flag indicating whether x should be demeaned before estimating the covariance."><span class="n"><span class="pre">center</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenGeometric.__init__.weights" title="arch.covariance.kernel.ParzenGeometric.__init__.weights (Python parameter) — An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."><span class="n"><span class="pre">weights</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenGeometric.__init__.force_int" title="arch.covariance.kernel.ParzenGeometric.__init__.force_int (Python parameter) — Force bandwidth to be an integer."><span class="n"><span class="pre">force_int</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/covariance/kernel.html#ParzenGeometric"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric" title="Link to this definition">¶</a></dt>
 <dd><p>Parzen’s Geometric kernel covariance estimation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.ParzenGeometric-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.covariance.kernel.ParzenGeometric.__init__.x"><span class="sig-name descname">x</span><span class="p"> : </span><span class="desctype"><span class="desctype">array_like</span></span><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>The data to use in covariance estimation.</p>
+<dt id="arch.covariance.kernel.ParzenGeometric.__init__.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>The data to use in covariance estimation.</p>
 </dd>
-<dt id="arch.covariance.kernel.ParzenGeometric.__init__.bandwidth"><span class="sig-name descname">bandwidth</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>, default None</span></span><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric.__init__.bandwidth" title="Permalink to this definition">¶</a></dt><dd><p>The kernel’s bandwidth.  If None, optimal bandwidth is estimated.</p>
+<dt id="arch.covariance.kernel.ParzenGeometric.__init__.bandwidth"><span class="n sig-name">bandwidth</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric.__init__.bandwidth" title="Permalink to this definition">¶</a></dt><dd><p>The kernel’s bandwidth.  If None, optimal bandwidth is estimated.</p>
 </dd>
-<dt id="arch.covariance.kernel.ParzenGeometric.__init__.df_adjust"><span class="sig-name descname">df_adjust</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, default 0</span></span><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric.__init__.df_adjust" title="Permalink to this definition">¶</a></dt><dd><p>Degrees of freedom to remove when adjusting the covariance. Uses the
+<dt id="arch.covariance.kernel.ParzenGeometric.__init__.df_adjust"><span class="n sig-name">df_adjust</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric.__init__.df_adjust" title="Permalink to this definition">¶</a></dt><dd><p>Degrees of freedom to remove when adjusting the covariance. Uses the
 number of observations in x minus df_adjust when dividing
 inner-products.</p>
 </dd>
-<dt id="arch.covariance.kernel.ParzenGeometric.__init__.center"><span class="sig-name descname">center</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, default True</span></span><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric.__init__.center" title="Permalink to this definition">¶</a></dt><dd><p>A flag indicating whether x should be demeaned before estimating the
+<dt id="arch.covariance.kernel.ParzenGeometric.__init__.center"><span class="n sig-name">center</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric.__init__.center" title="Permalink to this definition">¶</a></dt><dd><p>A flag indicating whether x should be demeaned before estimating the
 covariance.</p>
 </dd>
-<dt id="arch.covariance.kernel.ParzenGeometric.__init__.weights"><span class="sig-name descname">weights</span><span class="p"> : </span><span class="desctype"><span class="desctype">array_like, default None</span></span><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric.__init__.weights" title="Permalink to this definition">¶</a></dt><dd><p>An array of weights used to combine when estimating optimal bandwidth.
+<dt id="arch.covariance.kernel.ParzenGeometric.__init__.weights"><span class="n sig-name">weights</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric.__init__.weights" title="Permalink to this definition">¶</a></dt><dd><p>An array of weights used to combine when estimating optimal bandwidth.
 If not provided, a vector of 1s is used. Must have nvar elements.</p>
 </dd>
-<dt id="arch.covariance.kernel.ParzenGeometric.__init__.force_int"><span class="sig-name descname">force_int</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, default False</span></span><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric.__init__.force_int" title="Permalink to this definition">¶</a></dt><dd><p>Force bandwidth to be an integer.</p>
+<dt id="arch.covariance.kernel.ParzenGeometric.__init__.force_int"><span class="n sig-name">force_int</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric.__init__.force_int" title="Permalink to this definition">¶</a></dt><dd><p>Force bandwidth to be an integer.</p>
 </dd>
 </dl>
 </dd>
@@ -1234,7 +1234,7 @@ <h1 id="arch-covariance-kernel-parzengeometric">arch.covariance.kernel.ParzenGeo
   
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     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
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diff --git a/covariance/generated/arch.covariance.kernel.ParzenRiesz.html b/covariance/generated/arch.covariance.kernel.ParzenRiesz.html
index 576dc9e42b..4e46a4451e 100644
--- a/covariance/generated/arch.covariance.kernel.ParzenRiesz.html
+++ b/covariance/generated/arch.covariance.kernel.ParzenRiesz.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.ParzenRiesz - arch 6.1.0</title>
+        <title>arch.covariance.kernel.ParzenRiesz - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
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+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1093,29 +1093,29 @@
                   
 
 
-<h1 id="arch-covariance-kernel-parzenriesz">arch.covariance.kernel.ParzenRiesz<a class="headerlink" href="#arch-covariance-kernel-parzenriesz" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-parzenriesz">arch.covariance.kernel.ParzenRiesz<a class="headerlink" href="#arch-covariance-kernel-parzenriesz" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.covariance.kernel.ParzenRiesz">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.covariance.kernel.</span></span><span class="sig-name descname"><span class="pre">ParzenRiesz</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenRiesz.__init__.x" title="arch.covariance.kernel.ParzenRiesz.__init__.x (Python parameter) — The data to use in covariance estimation."><span class="n"><span class="pre">x</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenRiesz.__init__.bandwidth" title="arch.covariance.kernel.ParzenRiesz.__init__.bandwidth (Python parameter) — The kernel's bandwidth.  If None, optimal bandwidth is estimated."><span class="n"><span class="pre">bandwidth</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenRiesz.__init__.df_adjust" title="arch.covariance.kernel.ParzenRiesz.__init__.df_adjust (Python parameter) — Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."><span class="n"><span class="pre">df_adjust</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenRiesz.__init__.center" title="arch.covariance.kernel.ParzenRiesz.__init__.center (Python parameter) — A flag indicating whether x should be demeaned before estimating the covariance."><span class="n"><span class="pre">center</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenRiesz.__init__.weights" title="arch.covariance.kernel.ParzenRiesz.__init__.weights (Python parameter) — An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."><span class="n"><span class="pre">weights</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenRiesz.__init__.force_int" title="arch.covariance.kernel.ParzenRiesz.__init__.force_int (Python parameter) — Force bandwidth to be an integer."><span class="n"><span class="pre">force_int</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/covariance/kernel.html#ParzenRiesz"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.covariance.kernel.</span></span><span class="sig-name descname"><span class="pre">ParzenRiesz</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenRiesz.__init__.x" title="arch.covariance.kernel.ParzenRiesz.__init__.x (Python parameter) — The data to use in covariance estimation."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenRiesz.__init__.bandwidth" title="arch.covariance.kernel.ParzenRiesz.__init__.bandwidth (Python parameter) — The kernel's bandwidth.  If None, optimal bandwidth is estimated."><span class="n"><span class="pre">bandwidth</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenRiesz.__init__.df_adjust" title="arch.covariance.kernel.ParzenRiesz.__init__.df_adjust (Python parameter) — Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."><span class="n"><span class="pre">df_adjust</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenRiesz.__init__.center" title="arch.covariance.kernel.ParzenRiesz.__init__.center (Python parameter) — A flag indicating whether x should be demeaned before estimating the covariance."><span class="n"><span class="pre">center</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenRiesz.__init__.weights" title="arch.covariance.kernel.ParzenRiesz.__init__.weights (Python parameter) — An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."><span class="n"><span class="pre">weights</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenRiesz.__init__.force_int" title="arch.covariance.kernel.ParzenRiesz.__init__.force_int (Python parameter) — Force bandwidth to be an integer."><span class="n"><span class="pre">force_int</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/covariance/kernel.html#ParzenRiesz"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz" title="Link to this definition">¶</a></dt>
 <dd><p>Parzen-Reisz kernel covariance estimation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.ParzenRiesz-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.covariance.kernel.ParzenRiesz.__init__.x"><span class="sig-name descname">x</span><span class="p"> : </span><span class="desctype"><span class="desctype">array_like</span></span><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>The data to use in covariance estimation.</p>
+<dt id="arch.covariance.kernel.ParzenRiesz.__init__.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>The data to use in covariance estimation.</p>
 </dd>
-<dt id="arch.covariance.kernel.ParzenRiesz.__init__.bandwidth"><span class="sig-name descname">bandwidth</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>, default None</span></span><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz.__init__.bandwidth" title="Permalink to this definition">¶</a></dt><dd><p>The kernel’s bandwidth.  If None, optimal bandwidth is estimated.</p>
+<dt id="arch.covariance.kernel.ParzenRiesz.__init__.bandwidth"><span class="n sig-name">bandwidth</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz.__init__.bandwidth" title="Permalink to this definition">¶</a></dt><dd><p>The kernel’s bandwidth.  If None, optimal bandwidth is estimated.</p>
 </dd>
-<dt id="arch.covariance.kernel.ParzenRiesz.__init__.df_adjust"><span class="sig-name descname">df_adjust</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, default 0</span></span><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz.__init__.df_adjust" title="Permalink to this definition">¶</a></dt><dd><p>Degrees of freedom to remove when adjusting the covariance. Uses the
+<dt id="arch.covariance.kernel.ParzenRiesz.__init__.df_adjust"><span class="n sig-name">df_adjust</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz.__init__.df_adjust" title="Permalink to this definition">¶</a></dt><dd><p>Degrees of freedom to remove when adjusting the covariance. Uses the
 number of observations in x minus df_adjust when dividing
 inner-products.</p>
 </dd>
-<dt id="arch.covariance.kernel.ParzenRiesz.__init__.center"><span class="sig-name descname">center</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, default True</span></span><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz.__init__.center" title="Permalink to this definition">¶</a></dt><dd><p>A flag indicating whether x should be demeaned before estimating the
+<dt id="arch.covariance.kernel.ParzenRiesz.__init__.center"><span class="n sig-name">center</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz.__init__.center" title="Permalink to this definition">¶</a></dt><dd><p>A flag indicating whether x should be demeaned before estimating the
 covariance.</p>
 </dd>
-<dt id="arch.covariance.kernel.ParzenRiesz.__init__.weights"><span class="sig-name descname">weights</span><span class="p"> : </span><span class="desctype"><span class="desctype">array_like, default None</span></span><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz.__init__.weights" title="Permalink to this definition">¶</a></dt><dd><p>An array of weights used to combine when estimating optimal bandwidth.
+<dt id="arch.covariance.kernel.ParzenRiesz.__init__.weights"><span class="n sig-name">weights</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz.__init__.weights" title="Permalink to this definition">¶</a></dt><dd><p>An array of weights used to combine when estimating optimal bandwidth.
 If not provided, a vector of 1s is used. Must have nvar elements.</p>
 </dd>
-<dt id="arch.covariance.kernel.ParzenRiesz.__init__.force_int"><span class="sig-name descname">force_int</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, default False</span></span><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz.__init__.force_int" title="Permalink to this definition">¶</a></dt><dd><p>Force bandwidth to be an integer.</p>
+<dt id="arch.covariance.kernel.ParzenRiesz.__init__.force_int"><span class="n sig-name">force_int</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz.__init__.force_int" title="Permalink to this definition">¶</a></dt><dd><p>Force bandwidth to be an integer.</p>
 </dd>
 </dl>
 </dd>
@@ -1234,7 +1234,7 @@ <h1 id="arch-covariance-kernel-parzenriesz">arch.covariance.kernel.ParzenRiesz<a
   
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diff --git a/covariance/generated/arch.covariance.kernel.QuadraticSpectral.html b/covariance/generated/arch.covariance.kernel.QuadraticSpectral.html
index 833a8e0306..17c5e75380 100644
--- a/covariance/generated/arch.covariance.kernel.QuadraticSpectral.html
+++ b/covariance/generated/arch.covariance.kernel.QuadraticSpectral.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.QuadraticSpectral - arch 6.1.0</title>
+        <title>arch.covariance.kernel.QuadraticSpectral - arch 6.2.0</title>
       
     
 
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@@ -92,7 +92,7 @@
 
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+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
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       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1093,29 +1093,29 @@
                   
 
 
-<h1 id="arch-covariance-kernel-quadraticspectral">arch.covariance.kernel.QuadraticSpectral<a class="headerlink" href="#arch-covariance-kernel-quadraticspectral" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-quadraticspectral">arch.covariance.kernel.QuadraticSpectral<a class="headerlink" href="#arch-covariance-kernel-quadraticspectral" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.covariance.kernel.QuadraticSpectral">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.covariance.kernel.</span></span><span class="sig-name descname"><span class="pre">QuadraticSpectral</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.QuadraticSpectral.__init__.x" title="arch.covariance.kernel.QuadraticSpectral.__init__.x (Python parameter) — The data to use in covariance estimation."><span class="n"><span class="pre">x</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.QuadraticSpectral.__init__.bandwidth" title="arch.covariance.kernel.QuadraticSpectral.__init__.bandwidth (Python parameter) — The kernel's bandwidth.  If None, optimal bandwidth is estimated."><span class="n"><span class="pre">bandwidth</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.QuadraticSpectral.__init__.df_adjust" title="arch.covariance.kernel.QuadraticSpectral.__init__.df_adjust (Python parameter) — Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."><span class="n"><span class="pre">df_adjust</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.QuadraticSpectral.__init__.center" title="arch.covariance.kernel.QuadraticSpectral.__init__.center (Python parameter) — A flag indicating whether x should be demeaned before estimating the covariance."><span class="n"><span class="pre">center</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.QuadraticSpectral.__init__.weights" title="arch.covariance.kernel.QuadraticSpectral.__init__.weights (Python parameter) — An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."><span class="n"><span class="pre">weights</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.QuadraticSpectral.__init__.force_int" title="arch.covariance.kernel.QuadraticSpectral.__init__.force_int (Python parameter) — Force bandwidth to be an integer."><span class="n"><span class="pre">force_int</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/covariance/kernel.html#QuadraticSpectral"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.covariance.kernel.</span></span><span class="sig-name descname"><span class="pre">QuadraticSpectral</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.QuadraticSpectral.__init__.x" title="arch.covariance.kernel.QuadraticSpectral.__init__.x (Python parameter) — The data to use in covariance estimation."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.QuadraticSpectral.__init__.bandwidth" title="arch.covariance.kernel.QuadraticSpectral.__init__.bandwidth (Python parameter) — The kernel's bandwidth.  If None, optimal bandwidth is estimated."><span class="n"><span class="pre">bandwidth</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.QuadraticSpectral.__init__.df_adjust" title="arch.covariance.kernel.QuadraticSpectral.__init__.df_adjust (Python parameter) — Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."><span class="n"><span class="pre">df_adjust</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.QuadraticSpectral.__init__.center" title="arch.covariance.kernel.QuadraticSpectral.__init__.center (Python parameter) — A flag indicating whether x should be demeaned before estimating the covariance."><span class="n"><span class="pre">center</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.QuadraticSpectral.__init__.weights" title="arch.covariance.kernel.QuadraticSpectral.__init__.weights (Python parameter) — An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."><span class="n"><span class="pre">weights</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.QuadraticSpectral.__init__.force_int" title="arch.covariance.kernel.QuadraticSpectral.__init__.force_int (Python parameter) — Force bandwidth to be an integer."><span class="n"><span class="pre">force_int</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/covariance/kernel.html#QuadraticSpectral"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral" title="Link to this definition">¶</a></dt>
 <dd><p>Quadratic-Spectral (Andrews’) kernel covariance estimation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.QuadraticSpectral-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.covariance.kernel.QuadraticSpectral.__init__.x"><span class="sig-name descname">x</span><span class="p"> : </span><span class="desctype"><span class="desctype">array_like</span></span><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>The data to use in covariance estimation.</p>
+<dt id="arch.covariance.kernel.QuadraticSpectral.__init__.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>The data to use in covariance estimation.</p>
 </dd>
-<dt id="arch.covariance.kernel.QuadraticSpectral.__init__.bandwidth"><span class="sig-name descname">bandwidth</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>, default None</span></span><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral.__init__.bandwidth" title="Permalink to this definition">¶</a></dt><dd><p>The kernel’s bandwidth.  If None, optimal bandwidth is estimated.</p>
+<dt id="arch.covariance.kernel.QuadraticSpectral.__init__.bandwidth"><span class="n sig-name">bandwidth</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral.__init__.bandwidth" title="Permalink to this definition">¶</a></dt><dd><p>The kernel’s bandwidth.  If None, optimal bandwidth is estimated.</p>
 </dd>
-<dt id="arch.covariance.kernel.QuadraticSpectral.__init__.df_adjust"><span class="sig-name descname">df_adjust</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, default 0</span></span><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral.__init__.df_adjust" title="Permalink to this definition">¶</a></dt><dd><p>Degrees of freedom to remove when adjusting the covariance. Uses the
+<dt id="arch.covariance.kernel.QuadraticSpectral.__init__.df_adjust"><span class="n sig-name">df_adjust</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral.__init__.df_adjust" title="Permalink to this definition">¶</a></dt><dd><p>Degrees of freedom to remove when adjusting the covariance. Uses the
 number of observations in x minus df_adjust when dividing
 inner-products.</p>
 </dd>
-<dt id="arch.covariance.kernel.QuadraticSpectral.__init__.center"><span class="sig-name descname">center</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, default True</span></span><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral.__init__.center" title="Permalink to this definition">¶</a></dt><dd><p>A flag indicating whether x should be demeaned before estimating the
+<dt id="arch.covariance.kernel.QuadraticSpectral.__init__.center"><span class="n sig-name">center</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral.__init__.center" title="Permalink to this definition">¶</a></dt><dd><p>A flag indicating whether x should be demeaned before estimating the
 covariance.</p>
 </dd>
-<dt id="arch.covariance.kernel.QuadraticSpectral.__init__.weights"><span class="sig-name descname">weights</span><span class="p"> : </span><span class="desctype"><span class="desctype">array_like, default None</span></span><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral.__init__.weights" title="Permalink to this definition">¶</a></dt><dd><p>An array of weights used to combine when estimating optimal bandwidth.
+<dt id="arch.covariance.kernel.QuadraticSpectral.__init__.weights"><span class="n sig-name">weights</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral.__init__.weights" title="Permalink to this definition">¶</a></dt><dd><p>An array of weights used to combine when estimating optimal bandwidth.
 If not provided, a vector of 1s is used. Must have nvar elements.</p>
 </dd>
-<dt id="arch.covariance.kernel.QuadraticSpectral.__init__.force_int"><span class="sig-name descname">force_int</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, default False</span></span><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral.__init__.force_int" title="Permalink to this definition">¶</a></dt><dd><p>Force bandwidth to be an integer.</p>
+<dt id="arch.covariance.kernel.QuadraticSpectral.__init__.force_int"><span class="n sig-name">force_int</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral.__init__.force_int" title="Permalink to this definition">¶</a></dt><dd><p>Force bandwidth to be an integer.</p>
 </dd>
 </dl>
 </dd>
@@ -1234,7 +1234,7 @@ <h1 id="arch-covariance-kernel-quadraticspectral">arch.covariance.kernel.Quadrat
   
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-    7.0.1.
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@@ -1281,10 +1281,10 @@ <h1 id="arch-covariance-kernel-quadraticspectral">arch.covariance.kernel.Quadrat
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diff --git a/covariance/generated/arch.covariance.kernel.TukeyHamming.html b/covariance/generated/arch.covariance.kernel.TukeyHamming.html
index 5f1569c935..6098cbbf14 100644
--- a/covariance/generated/arch.covariance.kernel.TukeyHamming.html
+++ b/covariance/generated/arch.covariance.kernel.TukeyHamming.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.TukeyHamming - arch 6.1.0</title>
+        <title>arch.covariance.kernel.TukeyHamming - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -1093,29 +1093,29 @@
                   
 
 
-<h1 id="arch-covariance-kernel-tukeyhamming">arch.covariance.kernel.TukeyHamming<a class="headerlink" href="#arch-covariance-kernel-tukeyhamming" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-tukeyhamming">arch.covariance.kernel.TukeyHamming<a class="headerlink" href="#arch-covariance-kernel-tukeyhamming" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.covariance.kernel.TukeyHamming">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.covariance.kernel.</span></span><span class="sig-name descname"><span class="pre">TukeyHamming</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyHamming.__init__.x" title="arch.covariance.kernel.TukeyHamming.__init__.x (Python parameter) — The data to use in covariance estimation."><span class="n"><span class="pre">x</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyHamming.__init__.bandwidth" title="arch.covariance.kernel.TukeyHamming.__init__.bandwidth (Python parameter) — The kernel's bandwidth.  If None, optimal bandwidth is estimated."><span class="n"><span class="pre">bandwidth</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyHamming.__init__.df_adjust" title="arch.covariance.kernel.TukeyHamming.__init__.df_adjust (Python parameter) — Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."><span class="n"><span class="pre">df_adjust</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyHamming.__init__.center" title="arch.covariance.kernel.TukeyHamming.__init__.center (Python parameter) — A flag indicating whether x should be demeaned before estimating the covariance."><span class="n"><span class="pre">center</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyHamming.__init__.weights" title="arch.covariance.kernel.TukeyHamming.__init__.weights (Python parameter) — An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."><span class="n"><span class="pre">weights</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyHamming.__init__.force_int" title="arch.covariance.kernel.TukeyHamming.__init__.force_int (Python parameter) — Force bandwidth to be an integer."><span class="n"><span class="pre">force_int</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/covariance/kernel.html#TukeyHamming"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.covariance.kernel.</span></span><span class="sig-name descname"><span class="pre">TukeyHamming</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyHamming.__init__.x" title="arch.covariance.kernel.TukeyHamming.__init__.x (Python parameter) — The data to use in covariance estimation."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyHamming.__init__.bandwidth" title="arch.covariance.kernel.TukeyHamming.__init__.bandwidth (Python parameter) — The kernel's bandwidth.  If None, optimal bandwidth is estimated."><span class="n"><span class="pre">bandwidth</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyHamming.__init__.df_adjust" title="arch.covariance.kernel.TukeyHamming.__init__.df_adjust (Python parameter) — Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."><span class="n"><span class="pre">df_adjust</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyHamming.__init__.center" title="arch.covariance.kernel.TukeyHamming.__init__.center (Python parameter) — A flag indicating whether x should be demeaned before estimating the covariance."><span class="n"><span class="pre">center</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyHamming.__init__.weights" title="arch.covariance.kernel.TukeyHamming.__init__.weights (Python parameter) — An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."><span class="n"><span class="pre">weights</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyHamming.__init__.force_int" title="arch.covariance.kernel.TukeyHamming.__init__.force_int (Python parameter) — Force bandwidth to be an integer."><span class="n"><span class="pre">force_int</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/covariance/kernel.html#TukeyHamming"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming" title="Link to this definition">¶</a></dt>
 <dd><p>Tukey-Hamming kernel covariance estimation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.TukeyHamming-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.covariance.kernel.TukeyHamming.__init__.x"><span class="sig-name descname">x</span><span class="p"> : </span><span class="desctype"><span class="desctype">array_like</span></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>The data to use in covariance estimation.</p>
+<dt id="arch.covariance.kernel.TukeyHamming.__init__.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>The data to use in covariance estimation.</p>
 </dd>
-<dt id="arch.covariance.kernel.TukeyHamming.__init__.bandwidth"><span class="sig-name descname">bandwidth</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>, default None</span></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming.__init__.bandwidth" title="Permalink to this definition">¶</a></dt><dd><p>The kernel’s bandwidth.  If None, optimal bandwidth is estimated.</p>
+<dt id="arch.covariance.kernel.TukeyHamming.__init__.bandwidth"><span class="n sig-name">bandwidth</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming.__init__.bandwidth" title="Permalink to this definition">¶</a></dt><dd><p>The kernel’s bandwidth.  If None, optimal bandwidth is estimated.</p>
 </dd>
-<dt id="arch.covariance.kernel.TukeyHamming.__init__.df_adjust"><span class="sig-name descname">df_adjust</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, default 0</span></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming.__init__.df_adjust" title="Permalink to this definition">¶</a></dt><dd><p>Degrees of freedom to remove when adjusting the covariance. Uses the
+<dt id="arch.covariance.kernel.TukeyHamming.__init__.df_adjust"><span class="n sig-name">df_adjust</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming.__init__.df_adjust" title="Permalink to this definition">¶</a></dt><dd><p>Degrees of freedom to remove when adjusting the covariance. Uses the
 number of observations in x minus df_adjust when dividing
 inner-products.</p>
 </dd>
-<dt id="arch.covariance.kernel.TukeyHamming.__init__.center"><span class="sig-name descname">center</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, default True</span></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming.__init__.center" title="Permalink to this definition">¶</a></dt><dd><p>A flag indicating whether x should be demeaned before estimating the
+<dt id="arch.covariance.kernel.TukeyHamming.__init__.center"><span class="n sig-name">center</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming.__init__.center" title="Permalink to this definition">¶</a></dt><dd><p>A flag indicating whether x should be demeaned before estimating the
 covariance.</p>
 </dd>
-<dt id="arch.covariance.kernel.TukeyHamming.__init__.weights"><span class="sig-name descname">weights</span><span class="p"> : </span><span class="desctype"><span class="desctype">array_like, default None</span></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming.__init__.weights" title="Permalink to this definition">¶</a></dt><dd><p>An array of weights used to combine when estimating optimal bandwidth.
+<dt id="arch.covariance.kernel.TukeyHamming.__init__.weights"><span class="n sig-name">weights</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming.__init__.weights" title="Permalink to this definition">¶</a></dt><dd><p>An array of weights used to combine when estimating optimal bandwidth.
 If not provided, a vector of 1s is used. Must have nvar elements.</p>
 </dd>
-<dt id="arch.covariance.kernel.TukeyHamming.__init__.force_int"><span class="sig-name descname">force_int</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, default False</span></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming.__init__.force_int" title="Permalink to this definition">¶</a></dt><dd><p>Force bandwidth to be an integer.</p>
+<dt id="arch.covariance.kernel.TukeyHamming.__init__.force_int"><span class="n sig-name">force_int</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming.__init__.force_int" title="Permalink to this definition">¶</a></dt><dd><p>Force bandwidth to be an integer.</p>
 </dd>
 </dl>
 </dd>
@@ -1234,7 +1234,7 @@ <h1 id="arch-covariance-kernel-tukeyhamming">arch.covariance.kernel.TukeyHamming
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1281,10 +1281,10 @@ <h1 id="arch-covariance-kernel-tukeyhamming">arch.covariance.kernel.TukeyHamming
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diff --git a/covariance/generated/arch.covariance.kernel.TukeyHanning.html b/covariance/generated/arch.covariance.kernel.TukeyHanning.html
index 58ba742490..8e7094d4be 100644
--- a/covariance/generated/arch.covariance.kernel.TukeyHanning.html
+++ b/covariance/generated/arch.covariance.kernel.TukeyHanning.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.TukeyHanning - arch 6.1.0</title>
+        <title>arch.covariance.kernel.TukeyHanning - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1093,29 +1093,29 @@
                   
 
 
-<h1 id="arch-covariance-kernel-tukeyhanning">arch.covariance.kernel.TukeyHanning<a class="headerlink" href="#arch-covariance-kernel-tukeyhanning" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-tukeyhanning">arch.covariance.kernel.TukeyHanning<a class="headerlink" href="#arch-covariance-kernel-tukeyhanning" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.covariance.kernel.TukeyHanning">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.covariance.kernel.</span></span><span class="sig-name descname"><span class="pre">TukeyHanning</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyHanning.__init__.x" title="arch.covariance.kernel.TukeyHanning.__init__.x (Python parameter) — The data to use in covariance estimation."><span class="n"><span class="pre">x</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyHanning.__init__.bandwidth" title="arch.covariance.kernel.TukeyHanning.__init__.bandwidth (Python parameter) — The kernel's bandwidth.  If None, optimal bandwidth is estimated."><span class="n"><span class="pre">bandwidth</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyHanning.__init__.df_adjust" title="arch.covariance.kernel.TukeyHanning.__init__.df_adjust (Python parameter) — Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."><span class="n"><span class="pre">df_adjust</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyHanning.__init__.center" title="arch.covariance.kernel.TukeyHanning.__init__.center (Python parameter) — A flag indicating whether x should be demeaned before estimating the covariance."><span class="n"><span class="pre">center</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyHanning.__init__.weights" title="arch.covariance.kernel.TukeyHanning.__init__.weights (Python parameter) — An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."><span class="n"><span class="pre">weights</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyHanning.__init__.force_int" title="arch.covariance.kernel.TukeyHanning.__init__.force_int (Python parameter) — Force bandwidth to be an integer."><span class="n"><span class="pre">force_int</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/covariance/kernel.html#TukeyHanning"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.covariance.kernel.</span></span><span class="sig-name descname"><span class="pre">TukeyHanning</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyHanning.__init__.x" title="arch.covariance.kernel.TukeyHanning.__init__.x (Python parameter) — The data to use in covariance estimation."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyHanning.__init__.bandwidth" title="arch.covariance.kernel.TukeyHanning.__init__.bandwidth (Python parameter) — The kernel's bandwidth.  If None, optimal bandwidth is estimated."><span class="n"><span class="pre">bandwidth</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyHanning.__init__.df_adjust" title="arch.covariance.kernel.TukeyHanning.__init__.df_adjust (Python parameter) — Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."><span class="n"><span class="pre">df_adjust</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyHanning.__init__.center" title="arch.covariance.kernel.TukeyHanning.__init__.center (Python parameter) — A flag indicating whether x should be demeaned before estimating the covariance."><span class="n"><span class="pre">center</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyHanning.__init__.weights" title="arch.covariance.kernel.TukeyHanning.__init__.weights (Python parameter) — An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."><span class="n"><span class="pre">weights</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyHanning.__init__.force_int" title="arch.covariance.kernel.TukeyHanning.__init__.force_int (Python parameter) — Force bandwidth to be an integer."><span class="n"><span class="pre">force_int</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/covariance/kernel.html#TukeyHanning"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning" title="Link to this definition">¶</a></dt>
 <dd><p>Tukey-Hanning kernel covariance estimation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.TukeyHanning-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.covariance.kernel.TukeyHanning.__init__.x"><span class="sig-name descname">x</span><span class="p"> : </span><span class="desctype"><span class="desctype">array_like</span></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>The data to use in covariance estimation.</p>
+<dt id="arch.covariance.kernel.TukeyHanning.__init__.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>The data to use in covariance estimation.</p>
 </dd>
-<dt id="arch.covariance.kernel.TukeyHanning.__init__.bandwidth"><span class="sig-name descname">bandwidth</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>, default None</span></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning.__init__.bandwidth" title="Permalink to this definition">¶</a></dt><dd><p>The kernel’s bandwidth.  If None, optimal bandwidth is estimated.</p>
+<dt id="arch.covariance.kernel.TukeyHanning.__init__.bandwidth"><span class="n sig-name">bandwidth</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning.__init__.bandwidth" title="Permalink to this definition">¶</a></dt><dd><p>The kernel’s bandwidth.  If None, optimal bandwidth is estimated.</p>
 </dd>
-<dt id="arch.covariance.kernel.TukeyHanning.__init__.df_adjust"><span class="sig-name descname">df_adjust</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, default 0</span></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning.__init__.df_adjust" title="Permalink to this definition">¶</a></dt><dd><p>Degrees of freedom to remove when adjusting the covariance. Uses the
+<dt id="arch.covariance.kernel.TukeyHanning.__init__.df_adjust"><span class="n sig-name">df_adjust</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning.__init__.df_adjust" title="Permalink to this definition">¶</a></dt><dd><p>Degrees of freedom to remove when adjusting the covariance. Uses the
 number of observations in x minus df_adjust when dividing
 inner-products.</p>
 </dd>
-<dt id="arch.covariance.kernel.TukeyHanning.__init__.center"><span class="sig-name descname">center</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, default True</span></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning.__init__.center" title="Permalink to this definition">¶</a></dt><dd><p>A flag indicating whether x should be demeaned before estimating the
+<dt id="arch.covariance.kernel.TukeyHanning.__init__.center"><span class="n sig-name">center</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning.__init__.center" title="Permalink to this definition">¶</a></dt><dd><p>A flag indicating whether x should be demeaned before estimating the
 covariance.</p>
 </dd>
-<dt id="arch.covariance.kernel.TukeyHanning.__init__.weights"><span class="sig-name descname">weights</span><span class="p"> : </span><span class="desctype"><span class="desctype">array_like, default None</span></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning.__init__.weights" title="Permalink to this definition">¶</a></dt><dd><p>An array of weights used to combine when estimating optimal bandwidth.
+<dt id="arch.covariance.kernel.TukeyHanning.__init__.weights"><span class="n sig-name">weights</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning.__init__.weights" title="Permalink to this definition">¶</a></dt><dd><p>An array of weights used to combine when estimating optimal bandwidth.
 If not provided, a vector of 1s is used. Must have nvar elements.</p>
 </dd>
-<dt id="arch.covariance.kernel.TukeyHanning.__init__.force_int"><span class="sig-name descname">force_int</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, default False</span></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning.__init__.force_int" title="Permalink to this definition">¶</a></dt><dd><p>Force bandwidth to be an integer.</p>
+<dt id="arch.covariance.kernel.TukeyHanning.__init__.force_int"><span class="n sig-name">force_int</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning.__init__.force_int" title="Permalink to this definition">¶</a></dt><dd><p>Force bandwidth to be an integer.</p>
 </dd>
 </dl>
 </dd>
@@ -1234,7 +1234,7 @@ <h1 id="arch-covariance-kernel-tukeyhanning">arch.covariance.kernel.TukeyHanning
   
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     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1281,10 +1281,10 @@ <h1 id="arch-covariance-kernel-tukeyhanning">arch.covariance.kernel.TukeyHanning
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diff --git a/covariance/generated/arch.covariance.kernel.TukeyParzen.html b/covariance/generated/arch.covariance.kernel.TukeyParzen.html
index 4a27a37b0a..5fcf99c394 100644
--- a/covariance/generated/arch.covariance.kernel.TukeyParzen.html
+++ b/covariance/generated/arch.covariance.kernel.TukeyParzen.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.TukeyParzen - arch 6.1.0</title>
+        <title>arch.covariance.kernel.TukeyParzen - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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@@ -102,7 +102,7 @@
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-            arch 6.1.0
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@@ -1093,29 +1093,29 @@
                   
 
 
-<h1 id="arch-covariance-kernel-tukeyparzen">arch.covariance.kernel.TukeyParzen<a class="headerlink" href="#arch-covariance-kernel-tukeyparzen" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-tukeyparzen">arch.covariance.kernel.TukeyParzen<a class="headerlink" href="#arch-covariance-kernel-tukeyparzen" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.covariance.kernel.TukeyParzen">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.covariance.kernel.</span></span><span class="sig-name descname"><span class="pre">TukeyParzen</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyParzen.__init__.x" title="arch.covariance.kernel.TukeyParzen.__init__.x (Python parameter) — The data to use in covariance estimation."><span class="n"><span class="pre">x</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyParzen.__init__.bandwidth" title="arch.covariance.kernel.TukeyParzen.__init__.bandwidth (Python parameter) — The kernel's bandwidth.  If None, optimal bandwidth is estimated."><span class="n"><span class="pre">bandwidth</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyParzen.__init__.df_adjust" title="arch.covariance.kernel.TukeyParzen.__init__.df_adjust (Python parameter) — Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."><span class="n"><span class="pre">df_adjust</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyParzen.__init__.center" title="arch.covariance.kernel.TukeyParzen.__init__.center (Python parameter) — A flag indicating whether x should be demeaned before estimating the covariance."><span class="n"><span class="pre">center</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyParzen.__init__.weights" title="arch.covariance.kernel.TukeyParzen.__init__.weights (Python parameter) — An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."><span class="n"><span class="pre">weights</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyParzen.__init__.force_int" title="arch.covariance.kernel.TukeyParzen.__init__.force_int (Python parameter) — Force bandwidth to be an integer."><span class="n"><span class="pre">force_int</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/covariance/kernel.html#TukeyParzen"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.covariance.kernel.</span></span><span class="sig-name descname"><span class="pre">TukeyParzen</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyParzen.__init__.x" title="arch.covariance.kernel.TukeyParzen.__init__.x (Python parameter) — The data to use in covariance estimation."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyParzen.__init__.bandwidth" title="arch.covariance.kernel.TukeyParzen.__init__.bandwidth (Python parameter) — The kernel's bandwidth.  If None, optimal bandwidth is estimated."><span class="n"><span class="pre">bandwidth</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyParzen.__init__.df_adjust" title="arch.covariance.kernel.TukeyParzen.__init__.df_adjust (Python parameter) — Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."><span class="n"><span class="pre">df_adjust</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyParzen.__init__.center" title="arch.covariance.kernel.TukeyParzen.__init__.center (Python parameter) — A flag indicating whether x should be demeaned before estimating the covariance."><span class="n"><span class="pre">center</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyParzen.__init__.weights" title="arch.covariance.kernel.TukeyParzen.__init__.weights (Python parameter) — An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."><span class="n"><span class="pre">weights</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyParzen.__init__.force_int" title="arch.covariance.kernel.TukeyParzen.__init__.force_int (Python parameter) — Force bandwidth to be an integer."><span class="n"><span class="pre">force_int</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/covariance/kernel.html#TukeyParzen"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen" title="Link to this definition">¶</a></dt>
 <dd><p>Tukey-Parzen kernel covariance estimation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.TukeyParzen-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.covariance.kernel.TukeyParzen.__init__.x"><span class="sig-name descname">x</span><span class="p"> : </span><span class="desctype"><span class="desctype">array_like</span></span><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>The data to use in covariance estimation.</p>
+<dt id="arch.covariance.kernel.TukeyParzen.__init__.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>The data to use in covariance estimation.</p>
 </dd>
-<dt id="arch.covariance.kernel.TukeyParzen.__init__.bandwidth"><span class="sig-name descname">bandwidth</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>, default None</span></span><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen.__init__.bandwidth" title="Permalink to this definition">¶</a></dt><dd><p>The kernel’s bandwidth.  If None, optimal bandwidth is estimated.</p>
+<dt id="arch.covariance.kernel.TukeyParzen.__init__.bandwidth"><span class="n sig-name">bandwidth</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen.__init__.bandwidth" title="Permalink to this definition">¶</a></dt><dd><p>The kernel’s bandwidth.  If None, optimal bandwidth is estimated.</p>
 </dd>
-<dt id="arch.covariance.kernel.TukeyParzen.__init__.df_adjust"><span class="sig-name descname">df_adjust</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, default 0</span></span><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen.__init__.df_adjust" title="Permalink to this definition">¶</a></dt><dd><p>Degrees of freedom to remove when adjusting the covariance. Uses the
+<dt id="arch.covariance.kernel.TukeyParzen.__init__.df_adjust"><span class="n sig-name">df_adjust</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen.__init__.df_adjust" title="Permalink to this definition">¶</a></dt><dd><p>Degrees of freedom to remove when adjusting the covariance. Uses the
 number of observations in x minus df_adjust when dividing
 inner-products.</p>
 </dd>
-<dt id="arch.covariance.kernel.TukeyParzen.__init__.center"><span class="sig-name descname">center</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, default True</span></span><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen.__init__.center" title="Permalink to this definition">¶</a></dt><dd><p>A flag indicating whether x should be demeaned before estimating the
+<dt id="arch.covariance.kernel.TukeyParzen.__init__.center"><span class="n sig-name">center</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen.__init__.center" title="Permalink to this definition">¶</a></dt><dd><p>A flag indicating whether x should be demeaned before estimating the
 covariance.</p>
 </dd>
-<dt id="arch.covariance.kernel.TukeyParzen.__init__.weights"><span class="sig-name descname">weights</span><span class="p"> : </span><span class="desctype"><span class="desctype">array_like, default None</span></span><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen.__init__.weights" title="Permalink to this definition">¶</a></dt><dd><p>An array of weights used to combine when estimating optimal bandwidth.
+<dt id="arch.covariance.kernel.TukeyParzen.__init__.weights"><span class="n sig-name">weights</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen.__init__.weights" title="Permalink to this definition">¶</a></dt><dd><p>An array of weights used to combine when estimating optimal bandwidth.
 If not provided, a vector of 1s is used. Must have nvar elements.</p>
 </dd>
-<dt id="arch.covariance.kernel.TukeyParzen.__init__.force_int"><span class="sig-name descname">force_int</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, default False</span></span><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen.__init__.force_int" title="Permalink to this definition">¶</a></dt><dd><p>Force bandwidth to be an integer.</p>
+<dt id="arch.covariance.kernel.TukeyParzen.__init__.force_int"><span class="n sig-name">force_int</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen.__init__.force_int" title="Permalink to this definition">¶</a></dt><dd><p>Force bandwidth to be an integer.</p>
 </dd>
 </dl>
 </dd>
@@ -1234,7 +1234,7 @@ <h1 id="arch-covariance-kernel-tukeyparzen">arch.covariance.kernel.TukeyParzen<a
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1281,10 +1281,10 @@ <h1 id="arch-covariance-kernel-tukeyparzen">arch.covariance.kernel.TukeyParzen<a
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diff --git a/covariance/generated/generated/arch.covariance.kernel.Andrews.bandwidth.html b/covariance/generated/generated/arch.covariance.kernel.Andrews.bandwidth.html
index 96f3a22d92..7df62ed16b 100644
--- a/covariance/generated/generated/arch.covariance.kernel.Andrews.bandwidth.html
+++ b/covariance/generated/generated/arch.covariance.kernel.Andrews.bandwidth.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.Andrews.bandwidth - arch 6.1.0</title>
+        <title>arch.covariance.kernel.Andrews.bandwidth - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
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+    arch 6.2.0
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@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-andrews-bandwidth">arch.covariance.kernel.Andrews.bandwidth<a class="headerlink" href="#arch-covariance-kernel-andrews-bandwidth" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-andrews-bandwidth">arch.covariance.kernel.Andrews.bandwidth<a class="headerlink" href="#arch-covariance-kernel-andrews-bandwidth" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.Andrews.bandwidth">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Andrews.</span></span><span class="sig-name descname"><span class="pre">bandwidth</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.covariance.kernel.Andrews.bandwidth" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Andrews.</span></span><span class="sig-name descname"><span class="pre">bandwidth</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.covariance.kernel.Andrews.bandwidth" title="Link to this definition">¶</a></dt>
 <dd><p>The bandwidth used by the covariance estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.Andrews.bandwidth-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Andrews.bandwidth-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The user-provided or estimated optimal bandwidth.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.Andrews.bandwidth-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Andrews.bandwidth-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-andrews-bandwidth">arch.covariance.kernel.Andrews
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-andrews-bandwidth">arch.covariance.kernel.Andrews
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diff --git a/covariance/generated/generated/arch.covariance.kernel.Andrews.bandwidth_scale.html b/covariance/generated/generated/arch.covariance.kernel.Andrews.bandwidth_scale.html
index f9a22fe2f8..825ac295e7 100644
--- a/covariance/generated/generated/arch.covariance.kernel.Andrews.bandwidth_scale.html
+++ b/covariance/generated/generated/arch.covariance.kernel.Andrews.bandwidth_scale.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.Andrews.bandwidth_scale - arch 6.1.0</title>
+        <title>arch.covariance.kernel.Andrews.bandwidth_scale - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-andrews-bandwidth-scale">arch.covariance.kernel.Andrews.bandwidth_scale<a class="headerlink" href="#arch-covariance-kernel-andrews-bandwidth-scale" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-andrews-bandwidth-scale">arch.covariance.kernel.Andrews.bandwidth_scale<a class="headerlink" href="#arch-covariance-kernel-andrews-bandwidth-scale" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.Andrews.bandwidth_scale">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Andrews.</span></span><span class="sig-name descname"><span class="pre">bandwidth_scale</span></span><a class="headerlink" href="#arch.covariance.kernel.Andrews.bandwidth_scale" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Andrews.</span></span><span class="sig-name descname"><span class="pre">bandwidth_scale</span></span><a class="headerlink" href="#arch.covariance.kernel.Andrews.bandwidth_scale" title="Link to this definition">¶</a></dt>
 <dd><p>The power used in optimal bandwidth calculation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.Andrews.bandwidth_scale-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Andrews.bandwidth_scale-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The power value used in the optimal bandwidth calculation.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.Andrews.bandwidth_scale-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Andrews.bandwidth_scale-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-andrews-bandwidth-scale">arch.covariance.kernel.A
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
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@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-andrews-bandwidth-scale">arch.covariance.kernel.A
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.Andrews.centered.html b/covariance/generated/generated/arch.covariance.kernel.Andrews.centered.html
index a18ea06b3a..60d75288fe 100644
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+++ b/covariance/generated/generated/arch.covariance.kernel.Andrews.centered.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.Andrews.centered - arch 6.1.0</title>
+        <title>arch.covariance.kernel.Andrews.centered - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-andrews-centered">arch.covariance.kernel.Andrews.centered<a class="headerlink" href="#arch-covariance-kernel-andrews-centered" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-andrews-centered">arch.covariance.kernel.Andrews.centered<a class="headerlink" href="#arch-covariance-kernel-andrews-centered" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.Andrews.centered">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Andrews.</span></span><span class="sig-name descname"><span class="pre">centered</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.covariance.kernel.Andrews.centered" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Andrews.</span></span><span class="sig-name descname"><span class="pre">centered</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.covariance.kernel.Andrews.centered" title="Link to this definition">¶</a></dt>
 <dd><p>Flag indicating whether the data are centered (demeaned).</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.Andrews.centered-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Andrews.centered-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>A flag indicating whether the estimator is centered.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.Andrews.centered-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Andrews.centered-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-andrews-centered">arch.covariance.kernel.Andrews.
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-andrews-centered">arch.covariance.kernel.Andrews.
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diff --git a/covariance/generated/generated/arch.covariance.kernel.Andrews.cov.html b/covariance/generated/generated/arch.covariance.kernel.Andrews.cov.html
index 8e3d6c06c5..4f4db3489d 100644
--- a/covariance/generated/generated/arch.covariance.kernel.Andrews.cov.html
+++ b/covariance/generated/generated/arch.covariance.kernel.Andrews.cov.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.Andrews.cov - arch 6.1.0</title>
+        <title>arch.covariance.kernel.Andrews.cov - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,10 +938,10 @@
                   
 
 
-<h1 id="arch-covariance-kernel-andrews-cov">arch.covariance.kernel.Andrews.cov<a class="headerlink" href="#arch-covariance-kernel-andrews-cov" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-andrews-cov">arch.covariance.kernel.Andrews.cov<a class="headerlink" href="#arch-covariance-kernel-andrews-cov" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.Andrews.cov">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Andrews.</span></span><span class="sig-name descname"><span class="pre">cov</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.covariance.kernel.CovarianceEstimate.html#arch.covariance.kernel.CovarianceEstimate" title="arch.covariance.kernel.CovarianceEstimate (Python class) — Covariance estimate using a long-run covariance estimator"><span class="pre">CovarianceEstimate</span></a></span><a class="headerlink" href="#arch.covariance.kernel.Andrews.cov" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Andrews.</span></span><span class="sig-name descname"><span class="pre">cov</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.covariance.kernel.CovarianceEstimate.html#arch.covariance.kernel.CovarianceEstimate" title="arch.covariance.kernel.CovarianceEstimate (Python class) — Covariance estimate using a long-run covariance estimator"><span class="pre">CovarianceEstimate</span></a></span><a class="headerlink" href="#arch.covariance.kernel.Andrews.cov" title="Link to this definition">¶</a></dt>
 <dd><p>The estimated covariances.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.Andrews.cov-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Andrews.cov-returns" title="Permalink to this headline">¶</a></dt>
@@ -955,7 +955,7 @@ <h1 id="arch-covariance-kernel-andrews-cov">arch.covariance.kernel.Andrews.cov<a
 </p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.Andrews.cov-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Andrews.cov-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference internal" href="../arch.covariance.kernel.CovarianceEstimate.html#arch.covariance.kernel.CovarianceEstimate" title="arch.covariance.kernel.CovarianceEstimate (Python class) — Covariance estimate using a long-run covariance estimator">CovarianceEstimate</a></p>
+<dd class="field-even"><p><a class="reference internal" href="../arch.covariance.kernel.CovarianceEstimate.html#arch.covariance.kernel.CovarianceEstimate" title="arch.covariance.kernel.CovarianceEstimate (Python class) — Covariance estimate using a long-run covariance estimator"><code class="xref py py-class docutils literal notranslate"><span class="pre">CovarianceEstimate</span></code></a></p>
 </dd>
 </dl>
 <div class="seealso admonition">
@@ -1031,7 +1031,7 @@ <h1 id="arch-covariance-kernel-andrews-cov">arch.covariance.kernel.Andrews.cov<a
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1078,7 +1078,7 @@ <h1 id="arch-covariance-kernel-andrews-cov">arch.covariance.kernel.Andrews.cov<a
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.Andrews.force_int.html b/covariance/generated/generated/arch.covariance.kernel.Andrews.force_int.html
index 765e4fecf9..6772baabad 100644
--- a/covariance/generated/generated/arch.covariance.kernel.Andrews.force_int.html
+++ b/covariance/generated/generated/arch.covariance.kernel.Andrews.force_int.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.Andrews.force_int - arch 6.1.0</title>
+        <title>arch.covariance.kernel.Andrews.force_int - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -898,10 +898,10 @@
                   
 
 
-<h1 id="arch-covariance-kernel-andrews-force-int">arch.covariance.kernel.Andrews.force_int<a class="headerlink" href="#arch-covariance-kernel-andrews-force-int" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-andrews-force-int">arch.covariance.kernel.Andrews.force_int<a class="headerlink" href="#arch-covariance-kernel-andrews-force-int" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.Andrews.force_int">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Andrews.</span></span><span class="sig-name descname"><span class="pre">force_int</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.covariance.kernel.Andrews.force_int" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Andrews.</span></span><span class="sig-name descname"><span class="pre">force_int</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.covariance.kernel.Andrews.force_int" title="Link to this definition">¶</a></dt>
 <dd><p>Flag indicating whether the bandwidth is restricted to be an integer.</p>
 </dd></dl>
 
@@ -972,7 +972,7 @@ <h1 id="arch-covariance-kernel-andrews-force-int">arch.covariance.kernel.Andrews
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1019,7 +1019,7 @@ <h1 id="arch-covariance-kernel-andrews-force-int">arch.covariance.kernel.Andrews
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.Andrews.kernel_const.html b/covariance/generated/generated/arch.covariance.kernel.Andrews.kernel_const.html
index 1acdd33cda..323642f90b 100644
--- a/covariance/generated/generated/arch.covariance.kernel.Andrews.kernel_const.html
+++ b/covariance/generated/generated/arch.covariance.kernel.Andrews.kernel_const.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.Andrews.kernel_const - arch 6.1.0</title>
+        <title>arch.covariance.kernel.Andrews.kernel_const - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-andrews-kernel-const">arch.covariance.kernel.Andrews.kernel_const<a class="headerlink" href="#arch-covariance-kernel-andrews-kernel-const" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-andrews-kernel-const">arch.covariance.kernel.Andrews.kernel_const<a class="headerlink" href="#arch-covariance-kernel-andrews-kernel-const" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.Andrews.kernel_const">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Andrews.</span></span><span class="sig-name descname"><span class="pre">kernel_const</span></span><a class="headerlink" href="#arch.covariance.kernel.Andrews.kernel_const" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Andrews.</span></span><span class="sig-name descname"><span class="pre">kernel_const</span></span><a class="headerlink" href="#arch.covariance.kernel.Andrews.kernel_const" title="Link to this definition">¶</a></dt>
 <dd><p>The constant used in optimal bandwidth calculation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.Andrews.kernel_const-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Andrews.kernel_const-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The constant value used in the optimal bandwidth calculation.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.Andrews.kernel_const-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Andrews.kernel_const-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-andrews-kernel-const">arch.covariance.kernel.Andr
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-andrews-kernel-const">arch.covariance.kernel.Andr
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.Andrews.kernel_weights.html b/covariance/generated/generated/arch.covariance.kernel.Andrews.kernel_weights.html
index edf7ee256d..c5923532f6 100644
--- a/covariance/generated/generated/arch.covariance.kernel.Andrews.kernel_weights.html
+++ b/covariance/generated/generated/arch.covariance.kernel.Andrews.kernel_weights.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.Andrews.kernel_weights - arch 6.1.0</title>
+        <title>arch.covariance.kernel.Andrews.kernel_weights - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-andrews-kernel-weights">arch.covariance.kernel.Andrews.kernel_weights<a class="headerlink" href="#arch-covariance-kernel-andrews-kernel-weights" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-andrews-kernel-weights">arch.covariance.kernel.Andrews.kernel_weights<a class="headerlink" href="#arch-covariance-kernel-andrews-kernel-weights" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.Andrews.kernel_weights">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Andrews.</span></span><span class="sig-name descname"><span class="pre">kernel_weights</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">ndarray</span></a></span><a class="headerlink" href="#arch.covariance.kernel.Andrews.kernel_weights" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Andrews.</span></span><span class="sig-name descname"><span class="pre">kernel_weights</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span><a class="headerlink" href="#arch.covariance.kernel.Andrews.kernel_weights" title="Link to this definition">¶</a></dt>
 <dd><p>Weights used in covariance calculation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.Andrews.kernel_weights-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Andrews.kernel_weights-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The weight vector including 1 in position 0.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.Andrews.kernel_weights-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Andrews.kernel_weights-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p>ndarray</p>
+<dd class="field-even"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-andrews-kernel-weights">arch.covariance.kernel.An
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-andrews-kernel-weights">arch.covariance.kernel.An
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.Andrews.name.html b/covariance/generated/generated/arch.covariance.kernel.Andrews.name.html
index 79b945b4d0..1e8d988429 100644
--- a/covariance/generated/generated/arch.covariance.kernel.Andrews.name.html
+++ b/covariance/generated/generated/arch.covariance.kernel.Andrews.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.Andrews.name - arch 6.1.0</title>
+        <title>arch.covariance.kernel.Andrews.name - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-andrews-name">arch.covariance.kernel.Andrews.name<a class="headerlink" href="#arch-covariance-kernel-andrews-name" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-andrews-name">arch.covariance.kernel.Andrews.name<a class="headerlink" href="#arch-covariance-kernel-andrews-name" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.Andrews.name">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Andrews.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.covariance.kernel.Andrews.name" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Andrews.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.covariance.kernel.Andrews.name" title="Link to this definition">¶</a></dt>
 <dd><p>The covariance estimator’s name.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.Andrews.name-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Andrews.name-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The covariance estimator’s name.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.Andrews.name-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Andrews.name-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-andrews-name">arch.covariance.kernel.Andrews.name
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-andrews-name">arch.covariance.kernel.Andrews.name
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.Andrews.opt_bandwidth.html b/covariance/generated/generated/arch.covariance.kernel.Andrews.opt_bandwidth.html
index 317cde7d2b..c34b70612c 100644
--- a/covariance/generated/generated/arch.covariance.kernel.Andrews.opt_bandwidth.html
+++ b/covariance/generated/generated/arch.covariance.kernel.Andrews.opt_bandwidth.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.Andrews.opt_bandwidth - arch 6.1.0</title>
+        <title>arch.covariance.kernel.Andrews.opt_bandwidth - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-andrews-opt-bandwidth">arch.covariance.kernel.Andrews.opt_bandwidth<a class="headerlink" href="#arch-covariance-kernel-andrews-opt-bandwidth" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-andrews-opt-bandwidth">arch.covariance.kernel.Andrews.opt_bandwidth<a class="headerlink" href="#arch-covariance-kernel-andrews-opt-bandwidth" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.Andrews.opt_bandwidth">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Andrews.</span></span><span class="sig-name descname"><span class="pre">opt_bandwidth</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.covariance.kernel.Andrews.opt_bandwidth" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Andrews.</span></span><span class="sig-name descname"><span class="pre">opt_bandwidth</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.covariance.kernel.Andrews.opt_bandwidth" title="Link to this definition">¶</a></dt>
 <dd><p>Estimate optimal bandwidth.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.Andrews.opt_bandwidth-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Andrews.opt_bandwidth-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The estimated optimal bandwidth.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.Andrews.opt_bandwidth-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Andrews.opt_bandwidth-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -1026,7 +1026,7 @@ <h1 id="arch-covariance-kernel-andrews-opt-bandwidth">arch.covariance.kernel.And
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1073,10 +1073,10 @@ <h1 id="arch-covariance-kernel-andrews-opt-bandwidth">arch.covariance.kernel.And
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+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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\ No newline at end of file
diff --git a/covariance/generated/generated/arch.covariance.kernel.Andrews.rate.html b/covariance/generated/generated/arch.covariance.kernel.Andrews.rate.html
index 1d7736af83..50b1670eae 100644
--- a/covariance/generated/generated/arch.covariance.kernel.Andrews.rate.html
+++ b/covariance/generated/generated/arch.covariance.kernel.Andrews.rate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.Andrews.rate - arch 6.1.0</title>
+        <title>arch.covariance.kernel.Andrews.rate - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -938,10 +938,10 @@
                   
 
 
-<h1 id="arch-covariance-kernel-andrews-rate">arch.covariance.kernel.Andrews.rate<a class="headerlink" href="#arch-covariance-kernel-andrews-rate" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-andrews-rate">arch.covariance.kernel.Andrews.rate<a class="headerlink" href="#arch-covariance-kernel-andrews-rate" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.Andrews.rate">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Andrews.</span></span><span class="sig-name descname"><span class="pre">rate</span></span><a class="headerlink" href="#arch.covariance.kernel.Andrews.rate" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Andrews.</span></span><span class="sig-name descname"><span class="pre">rate</span></span><a class="headerlink" href="#arch.covariance.kernel.Andrews.rate" title="Link to this definition">¶</a></dt>
 <dd><p>The optimal rate used in bandwidth selection.</p>
 <p>Controls the number of lags used in the variance estimate that
 determines the estimate of the optimal bandwidth.</p>
@@ -950,7 +950,7 @@ <h1 id="arch-covariance-kernel-andrews-rate">arch.covariance.kernel.Andrews.rate
 <dd class="field-odd"><p>The rate used in bandwidth selection.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.Andrews.rate-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Andrews.rate-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1022,7 +1022,7 @@ <h1 id="arch-covariance-kernel-andrews-rate">arch.covariance.kernel.Andrews.rate
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1069,7 +1069,7 @@ <h1 id="arch-covariance-kernel-andrews-rate">arch.covariance.kernel.Andrews.rate
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.Bartlett.bandwidth.html b/covariance/generated/generated/arch.covariance.kernel.Bartlett.bandwidth.html
index 565f72635a..2297e78472 100644
--- a/covariance/generated/generated/arch.covariance.kernel.Bartlett.bandwidth.html
+++ b/covariance/generated/generated/arch.covariance.kernel.Bartlett.bandwidth.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.Bartlett.bandwidth - arch 6.1.0</title>
+        <title>arch.covariance.kernel.Bartlett.bandwidth - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-bartlett-bandwidth">arch.covariance.kernel.Bartlett.bandwidth<a class="headerlink" href="#arch-covariance-kernel-bartlett-bandwidth" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-bartlett-bandwidth">arch.covariance.kernel.Bartlett.bandwidth<a class="headerlink" href="#arch-covariance-kernel-bartlett-bandwidth" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.Bartlett.bandwidth">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Bartlett.</span></span><span class="sig-name descname"><span class="pre">bandwidth</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.covariance.kernel.Bartlett.bandwidth" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Bartlett.</span></span><span class="sig-name descname"><span class="pre">bandwidth</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.covariance.kernel.Bartlett.bandwidth" title="Link to this definition">¶</a></dt>
 <dd><p>The bandwidth used by the covariance estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.Bartlett.bandwidth-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Bartlett.bandwidth-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The user-provided or estimated optimal bandwidth.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.Bartlett.bandwidth-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Bartlett.bandwidth-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-bartlett-bandwidth">arch.covariance.kernel.Bartle
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-bartlett-bandwidth">arch.covariance.kernel.Bartle
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.Bartlett.bandwidth_scale.html b/covariance/generated/generated/arch.covariance.kernel.Bartlett.bandwidth_scale.html
index 4d3e8526a7..ffb1221e4b 100644
--- a/covariance/generated/generated/arch.covariance.kernel.Bartlett.bandwidth_scale.html
+++ b/covariance/generated/generated/arch.covariance.kernel.Bartlett.bandwidth_scale.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.Bartlett.bandwidth_scale - arch 6.1.0</title>
+        <title>arch.covariance.kernel.Bartlett.bandwidth_scale - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-bartlett-bandwidth-scale">arch.covariance.kernel.Bartlett.bandwidth_scale<a class="headerlink" href="#arch-covariance-kernel-bartlett-bandwidth-scale" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-bartlett-bandwidth-scale">arch.covariance.kernel.Bartlett.bandwidth_scale<a class="headerlink" href="#arch-covariance-kernel-bartlett-bandwidth-scale" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.Bartlett.bandwidth_scale">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Bartlett.</span></span><span class="sig-name descname"><span class="pre">bandwidth_scale</span></span><a class="headerlink" href="#arch.covariance.kernel.Bartlett.bandwidth_scale" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Bartlett.</span></span><span class="sig-name descname"><span class="pre">bandwidth_scale</span></span><a class="headerlink" href="#arch.covariance.kernel.Bartlett.bandwidth_scale" title="Link to this definition">¶</a></dt>
 <dd><p>The power used in optimal bandwidth calculation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.Bartlett.bandwidth_scale-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Bartlett.bandwidth_scale-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The power value used in the optimal bandwidth calculation.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.Bartlett.bandwidth_scale-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Bartlett.bandwidth_scale-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-bartlett-bandwidth-scale">arch.covariance.kernel.
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-bartlett-bandwidth-scale">arch.covariance.kernel.
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.Bartlett.centered.html b/covariance/generated/generated/arch.covariance.kernel.Bartlett.centered.html
index 0bd722135d..fc8e58f1e9 100644
--- a/covariance/generated/generated/arch.covariance.kernel.Bartlett.centered.html
+++ b/covariance/generated/generated/arch.covariance.kernel.Bartlett.centered.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.Bartlett.centered - arch 6.1.0</title>
+        <title>arch.covariance.kernel.Bartlett.centered - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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@@ -92,7 +92,7 @@
 
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@@ -102,7 +102,7 @@
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+            arch 6.2.0
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@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-bartlett-centered">arch.covariance.kernel.Bartlett.centered<a class="headerlink" href="#arch-covariance-kernel-bartlett-centered" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-bartlett-centered">arch.covariance.kernel.Bartlett.centered<a class="headerlink" href="#arch-covariance-kernel-bartlett-centered" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.Bartlett.centered">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Bartlett.</span></span><span class="sig-name descname"><span class="pre">centered</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.covariance.kernel.Bartlett.centered" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Bartlett.</span></span><span class="sig-name descname"><span class="pre">centered</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.covariance.kernel.Bartlett.centered" title="Link to this definition">¶</a></dt>
 <dd><p>Flag indicating whether the data are centered (demeaned).</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.Bartlett.centered-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Bartlett.centered-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>A flag indicating whether the estimator is centered.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.Bartlett.centered-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Bartlett.centered-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-bartlett-centered">arch.covariance.kernel.Bartlet
   
     Created using
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-    7.0.1.
+    7.2.6.
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@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-bartlett-centered">arch.covariance.kernel.Bartlet
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diff --git a/covariance/generated/generated/arch.covariance.kernel.Bartlett.cov.html b/covariance/generated/generated/arch.covariance.kernel.Bartlett.cov.html
index 11bfe4cdef..98d41904bc 100644
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+++ b/covariance/generated/generated/arch.covariance.kernel.Bartlett.cov.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.Bartlett.cov - arch 6.1.0</title>
+        <title>arch.covariance.kernel.Bartlett.cov - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+    arch 6.2.0
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     <div class="md-nav__source">
@@ -938,10 +938,10 @@
                   
 
 
-<h1 id="arch-covariance-kernel-bartlett-cov">arch.covariance.kernel.Bartlett.cov<a class="headerlink" href="#arch-covariance-kernel-bartlett-cov" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-bartlett-cov">arch.covariance.kernel.Bartlett.cov<a class="headerlink" href="#arch-covariance-kernel-bartlett-cov" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.Bartlett.cov">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Bartlett.</span></span><span class="sig-name descname"><span class="pre">cov</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.covariance.kernel.CovarianceEstimate.html#arch.covariance.kernel.CovarianceEstimate" title="arch.covariance.kernel.CovarianceEstimate (Python class) — Covariance estimate using a long-run covariance estimator"><span class="pre">CovarianceEstimate</span></a></span><a class="headerlink" href="#arch.covariance.kernel.Bartlett.cov" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Bartlett.</span></span><span class="sig-name descname"><span class="pre">cov</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.covariance.kernel.CovarianceEstimate.html#arch.covariance.kernel.CovarianceEstimate" title="arch.covariance.kernel.CovarianceEstimate (Python class) — Covariance estimate using a long-run covariance estimator"><span class="pre">CovarianceEstimate</span></a></span><a class="headerlink" href="#arch.covariance.kernel.Bartlett.cov" title="Link to this definition">¶</a></dt>
 <dd><p>The estimated covariances.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.Bartlett.cov-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Bartlett.cov-returns" title="Permalink to this headline">¶</a></dt>
@@ -955,7 +955,7 @@ <h1 id="arch-covariance-kernel-bartlett-cov">arch.covariance.kernel.Bartlett.cov
 </p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.Bartlett.cov-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Bartlett.cov-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference internal" href="../arch.covariance.kernel.CovarianceEstimate.html#arch.covariance.kernel.CovarianceEstimate" title="arch.covariance.kernel.CovarianceEstimate (Python class) — Covariance estimate using a long-run covariance estimator">CovarianceEstimate</a></p>
+<dd class="field-even"><p><a class="reference internal" href="../arch.covariance.kernel.CovarianceEstimate.html#arch.covariance.kernel.CovarianceEstimate" title="arch.covariance.kernel.CovarianceEstimate (Python class) — Covariance estimate using a long-run covariance estimator"><code class="xref py py-class docutils literal notranslate"><span class="pre">CovarianceEstimate</span></code></a></p>
 </dd>
 </dl>
 <div class="seealso admonition">
@@ -1031,7 +1031,7 @@ <h1 id="arch-covariance-kernel-bartlett-cov">arch.covariance.kernel.Bartlett.cov
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1078,7 +1078,7 @@ <h1 id="arch-covariance-kernel-bartlett-cov">arch.covariance.kernel.Bartlett.cov
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diff --git a/covariance/generated/generated/arch.covariance.kernel.Bartlett.force_int.html b/covariance/generated/generated/arch.covariance.kernel.Bartlett.force_int.html
index daf6fb57f0..5a81610f66 100644
--- a/covariance/generated/generated/arch.covariance.kernel.Bartlett.force_int.html
+++ b/covariance/generated/generated/arch.covariance.kernel.Bartlett.force_int.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.Bartlett.force_int - arch 6.1.0</title>
+        <title>arch.covariance.kernel.Bartlett.force_int - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -898,10 +898,10 @@
                   
 
 
-<h1 id="arch-covariance-kernel-bartlett-force-int">arch.covariance.kernel.Bartlett.force_int<a class="headerlink" href="#arch-covariance-kernel-bartlett-force-int" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-bartlett-force-int">arch.covariance.kernel.Bartlett.force_int<a class="headerlink" href="#arch-covariance-kernel-bartlett-force-int" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.Bartlett.force_int">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Bartlett.</span></span><span class="sig-name descname"><span class="pre">force_int</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.covariance.kernel.Bartlett.force_int" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Bartlett.</span></span><span class="sig-name descname"><span class="pre">force_int</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.covariance.kernel.Bartlett.force_int" title="Link to this definition">¶</a></dt>
 <dd><p>Flag indicating whether the bandwidth is restricted to be an integer.</p>
 </dd></dl>
 
@@ -972,7 +972,7 @@ <h1 id="arch-covariance-kernel-bartlett-force-int">arch.covariance.kernel.Bartle
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1019,7 +1019,7 @@ <h1 id="arch-covariance-kernel-bartlett-force-int">arch.covariance.kernel.Bartle
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
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diff --git a/covariance/generated/generated/arch.covariance.kernel.Bartlett.kernel_const.html b/covariance/generated/generated/arch.covariance.kernel.Bartlett.kernel_const.html
index 7bf5bf2106..2949e92d82 100644
--- a/covariance/generated/generated/arch.covariance.kernel.Bartlett.kernel_const.html
+++ b/covariance/generated/generated/arch.covariance.kernel.Bartlett.kernel_const.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.Bartlett.kernel_const - arch 6.1.0</title>
+        <title>arch.covariance.kernel.Bartlett.kernel_const - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
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-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-bartlett-kernel-const">arch.covariance.kernel.Bartlett.kernel_const<a class="headerlink" href="#arch-covariance-kernel-bartlett-kernel-const" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-bartlett-kernel-const">arch.covariance.kernel.Bartlett.kernel_const<a class="headerlink" href="#arch-covariance-kernel-bartlett-kernel-const" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.Bartlett.kernel_const">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Bartlett.</span></span><span class="sig-name descname"><span class="pre">kernel_const</span></span><a class="headerlink" href="#arch.covariance.kernel.Bartlett.kernel_const" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Bartlett.</span></span><span class="sig-name descname"><span class="pre">kernel_const</span></span><a class="headerlink" href="#arch.covariance.kernel.Bartlett.kernel_const" title="Link to this definition">¶</a></dt>
 <dd><p>The constant used in optimal bandwidth calculation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.Bartlett.kernel_const-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Bartlett.kernel_const-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The constant value used in the optimal bandwidth calculation.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.Bartlett.kernel_const-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Bartlett.kernel_const-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-bartlett-kernel-const">arch.covariance.kernel.Bar
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-bartlett-kernel-const">arch.covariance.kernel.Bar
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.Bartlett.kernel_weights.html b/covariance/generated/generated/arch.covariance.kernel.Bartlett.kernel_weights.html
index 7fad3dc33f..7840eafdab 100644
--- a/covariance/generated/generated/arch.covariance.kernel.Bartlett.kernel_weights.html
+++ b/covariance/generated/generated/arch.covariance.kernel.Bartlett.kernel_weights.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.Bartlett.kernel_weights - arch 6.1.0</title>
+        <title>arch.covariance.kernel.Bartlett.kernel_weights - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-bartlett-kernel-weights">arch.covariance.kernel.Bartlett.kernel_weights<a class="headerlink" href="#arch-covariance-kernel-bartlett-kernel-weights" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-bartlett-kernel-weights">arch.covariance.kernel.Bartlett.kernel_weights<a class="headerlink" href="#arch-covariance-kernel-bartlett-kernel-weights" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.Bartlett.kernel_weights">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Bartlett.</span></span><span class="sig-name descname"><span class="pre">kernel_weights</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">ndarray</span></a></span><a class="headerlink" href="#arch.covariance.kernel.Bartlett.kernel_weights" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Bartlett.</span></span><span class="sig-name descname"><span class="pre">kernel_weights</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span><a class="headerlink" href="#arch.covariance.kernel.Bartlett.kernel_weights" title="Link to this definition">¶</a></dt>
 <dd><p>Weights used in covariance calculation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.Bartlett.kernel_weights-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Bartlett.kernel_weights-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The weight vector including 1 in position 0.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.Bartlett.kernel_weights-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Bartlett.kernel_weights-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p>ndarray</p>
+<dd class="field-even"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-bartlett-kernel-weights">arch.covariance.kernel.B
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-bartlett-kernel-weights">arch.covariance.kernel.B
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diff --git a/covariance/generated/generated/arch.covariance.kernel.Bartlett.name.html b/covariance/generated/generated/arch.covariance.kernel.Bartlett.name.html
index f8a40ecf3a..ce87be3362 100644
--- a/covariance/generated/generated/arch.covariance.kernel.Bartlett.name.html
+++ b/covariance/generated/generated/arch.covariance.kernel.Bartlett.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.Bartlett.name - arch 6.1.0</title>
+        <title>arch.covariance.kernel.Bartlett.name - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-bartlett-name">arch.covariance.kernel.Bartlett.name<a class="headerlink" href="#arch-covariance-kernel-bartlett-name" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-bartlett-name">arch.covariance.kernel.Bartlett.name<a class="headerlink" href="#arch-covariance-kernel-bartlett-name" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.Bartlett.name">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Bartlett.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.covariance.kernel.Bartlett.name" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Bartlett.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.covariance.kernel.Bartlett.name" title="Link to this definition">¶</a></dt>
 <dd><p>The covariance estimator’s name.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.Bartlett.name-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Bartlett.name-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The covariance estimator’s name.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.Bartlett.name-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Bartlett.name-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-bartlett-name">arch.covariance.kernel.Bartlett.na
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-bartlett-name">arch.covariance.kernel.Bartlett.na
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diff --git a/covariance/generated/generated/arch.covariance.kernel.Bartlett.opt_bandwidth.html b/covariance/generated/generated/arch.covariance.kernel.Bartlett.opt_bandwidth.html
index 6044978493..3870a8fb20 100644
--- a/covariance/generated/generated/arch.covariance.kernel.Bartlett.opt_bandwidth.html
+++ b/covariance/generated/generated/arch.covariance.kernel.Bartlett.opt_bandwidth.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.Bartlett.opt_bandwidth - arch 6.1.0</title>
+        <title>arch.covariance.kernel.Bartlett.opt_bandwidth - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-bartlett-opt-bandwidth">arch.covariance.kernel.Bartlett.opt_bandwidth<a class="headerlink" href="#arch-covariance-kernel-bartlett-opt-bandwidth" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-bartlett-opt-bandwidth">arch.covariance.kernel.Bartlett.opt_bandwidth<a class="headerlink" href="#arch-covariance-kernel-bartlett-opt-bandwidth" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.Bartlett.opt_bandwidth">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Bartlett.</span></span><span class="sig-name descname"><span class="pre">opt_bandwidth</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.covariance.kernel.Bartlett.opt_bandwidth" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Bartlett.</span></span><span class="sig-name descname"><span class="pre">opt_bandwidth</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.covariance.kernel.Bartlett.opt_bandwidth" title="Link to this definition">¶</a></dt>
 <dd><p>Estimate optimal bandwidth.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.Bartlett.opt_bandwidth-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Bartlett.opt_bandwidth-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The estimated optimal bandwidth.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.Bartlett.opt_bandwidth-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Bartlett.opt_bandwidth-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -1026,7 +1026,7 @@ <h1 id="arch-covariance-kernel-bartlett-opt-bandwidth">arch.covariance.kernel.Ba
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1073,10 +1073,10 @@ <h1 id="arch-covariance-kernel-bartlett-opt-bandwidth">arch.covariance.kernel.Ba
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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+        <script id="MathJax-script" src="../../../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
     
   </body>
 </html>
\ No newline at end of file
diff --git a/covariance/generated/generated/arch.covariance.kernel.Bartlett.rate.html b/covariance/generated/generated/arch.covariance.kernel.Bartlett.rate.html
index 4d214f3ecb..3697fb994c 100644
--- a/covariance/generated/generated/arch.covariance.kernel.Bartlett.rate.html
+++ b/covariance/generated/generated/arch.covariance.kernel.Bartlett.rate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.Bartlett.rate - arch 6.1.0</title>
+        <title>arch.covariance.kernel.Bartlett.rate - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,10 +938,10 @@
                   
 
 
-<h1 id="arch-covariance-kernel-bartlett-rate">arch.covariance.kernel.Bartlett.rate<a class="headerlink" href="#arch-covariance-kernel-bartlett-rate" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-bartlett-rate">arch.covariance.kernel.Bartlett.rate<a class="headerlink" href="#arch-covariance-kernel-bartlett-rate" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.Bartlett.rate">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Bartlett.</span></span><span class="sig-name descname"><span class="pre">rate</span></span><a class="headerlink" href="#arch.covariance.kernel.Bartlett.rate" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Bartlett.</span></span><span class="sig-name descname"><span class="pre">rate</span></span><a class="headerlink" href="#arch.covariance.kernel.Bartlett.rate" title="Link to this definition">¶</a></dt>
 <dd><p>The optimal rate used in bandwidth selection.</p>
 <p>Controls the number of lags used in the variance estimate that
 determines the estimate of the optimal bandwidth.</p>
@@ -950,7 +950,7 @@ <h1 id="arch-covariance-kernel-bartlett-rate">arch.covariance.kernel.Bartlett.ra
 <dd class="field-odd"><p>The rate used in bandwidth selection.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.Bartlett.rate-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Bartlett.rate-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1022,7 +1022,7 @@ <h1 id="arch-covariance-kernel-bartlett-rate">arch.covariance.kernel.Bartlett.ra
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1069,7 +1069,7 @@ <h1 id="arch-covariance-kernel-bartlett-rate">arch.covariance.kernel.Bartlett.ra
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+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.CovarianceEstimate.long_run.html b/covariance/generated/generated/arch.covariance.kernel.CovarianceEstimate.long_run.html
index 6a6a99293f..de240be2cb 100644
--- a/covariance/generated/generated/arch.covariance.kernel.CovarianceEstimate.long_run.html
+++ b/covariance/generated/generated/arch.covariance.kernel.CovarianceEstimate.long_run.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.CovarianceEstimate.long_run - arch 6.1.0</title>
+        <title>arch.covariance.kernel.CovarianceEstimate.long_run - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -643,10 +643,10 @@
                   
 
 
-<h1 id="arch-covariance-kernel-covarianceestimate-long-run">arch.covariance.kernel.CovarianceEstimate.long_run<a class="headerlink" href="#arch-covariance-kernel-covarianceestimate-long-run" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-covarianceestimate-long-run">arch.covariance.kernel.CovarianceEstimate.long_run<a class="headerlink" href="#arch-covariance-kernel-covarianceestimate-long-run" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.CovarianceEstimate.long_run">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CovarianceEstimate.</span></span><span class="sig-name descname"><span class="pre">long_run</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.0.1)"><span class="pre">DataFrame</span></a></span><a class="headerlink" href="#arch.covariance.kernel.CovarianceEstimate.long_run" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CovarianceEstimate.</span></span><span class="sig-name descname"><span class="pre">long_run</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><span class="pre">Float64Array</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">DataFrame</span></span><a class="headerlink" href="#arch.covariance.kernel.CovarianceEstimate.long_run" title="Link to this definition">¶</a></dt>
 <dd><p>The long-run covariance estimate.</p>
 </dd></dl>
 
@@ -717,7 +717,7 @@ <h1 id="arch-covariance-kernel-covarianceestimate-long-run">arch.covariance.kern
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -764,7 +764,7 @@ <h1 id="arch-covariance-kernel-covarianceestimate-long-run">arch.covariance.kern
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.CovarianceEstimate.one_sided.html b/covariance/generated/generated/arch.covariance.kernel.CovarianceEstimate.one_sided.html
index ed27380f82..3e65ef3426 100644
--- a/covariance/generated/generated/arch.covariance.kernel.CovarianceEstimate.one_sided.html
+++ b/covariance/generated/generated/arch.covariance.kernel.CovarianceEstimate.one_sided.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.CovarianceEstimate.one_sided - arch 6.1.0</title>
+        <title>arch.covariance.kernel.CovarianceEstimate.one_sided - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -643,10 +643,10 @@
                   
 
 
-<h1 id="arch-covariance-kernel-covarianceestimate-one-sided">arch.covariance.kernel.CovarianceEstimate.one_sided<a class="headerlink" href="#arch-covariance-kernel-covarianceestimate-one-sided" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-covarianceestimate-one-sided">arch.covariance.kernel.CovarianceEstimate.one_sided<a class="headerlink" href="#arch-covariance-kernel-covarianceestimate-one-sided" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.CovarianceEstimate.one_sided">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CovarianceEstimate.</span></span><span class="sig-name descname"><span class="pre">one_sided</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.0.1)"><span class="pre">DataFrame</span></a></span><a class="headerlink" href="#arch.covariance.kernel.CovarianceEstimate.one_sided" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CovarianceEstimate.</span></span><span class="sig-name descname"><span class="pre">one_sided</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><span class="pre">Float64Array</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">DataFrame</span></span><a class="headerlink" href="#arch.covariance.kernel.CovarianceEstimate.one_sided" title="Link to this definition">¶</a></dt>
 <dd><p>The one-sided covariance estimate.</p>
 </dd></dl>
 
@@ -717,7 +717,7 @@ <h1 id="arch-covariance-kernel-covarianceestimate-one-sided">arch.covariance.ker
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -764,7 +764,7 @@ <h1 id="arch-covariance-kernel-covarianceestimate-one-sided">arch.covariance.ker
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+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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diff --git a/covariance/generated/generated/arch.covariance.kernel.CovarianceEstimate.one_sided_strict.html b/covariance/generated/generated/arch.covariance.kernel.CovarianceEstimate.one_sided_strict.html
index 3100786908..d1fa31d875 100644
--- a/covariance/generated/generated/arch.covariance.kernel.CovarianceEstimate.one_sided_strict.html
+++ b/covariance/generated/generated/arch.covariance.kernel.CovarianceEstimate.one_sided_strict.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.CovarianceEstimate.one_sided_strict - arch 6.1.0</title>
+        <title>arch.covariance.kernel.CovarianceEstimate.one_sided_strict - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -643,10 +643,10 @@
                   
 
 
-<h1 id="arch-covariance-kernel-covarianceestimate-one-sided-strict">arch.covariance.kernel.CovarianceEstimate.one_sided_strict<a class="headerlink" href="#arch-covariance-kernel-covarianceestimate-one-sided-strict" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-covarianceestimate-one-sided-strict">arch.covariance.kernel.CovarianceEstimate.one_sided_strict<a class="headerlink" href="#arch-covariance-kernel-covarianceestimate-one-sided-strict" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.CovarianceEstimate.one_sided_strict">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CovarianceEstimate.</span></span><span class="sig-name descname"><span class="pre">one_sided_strict</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.0.1)"><span class="pre">DataFrame</span></a></span><a class="headerlink" href="#arch.covariance.kernel.CovarianceEstimate.one_sided_strict" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CovarianceEstimate.</span></span><span class="sig-name descname"><span class="pre">one_sided_strict</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a></span><a class="headerlink" href="#arch.covariance.kernel.CovarianceEstimate.one_sided_strict" title="Link to this definition">¶</a></dt>
 <dd><p>The one-sided strict covariance estimate.</p>
 </dd></dl>
 
@@ -717,7 +717,7 @@ <h1 id="arch-covariance-kernel-covarianceestimate-one-sided-strict">arch.covaria
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
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@@ -764,7 +764,7 @@ <h1 id="arch-covariance-kernel-covarianceestimate-one-sided-strict">arch.covaria
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
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diff --git a/covariance/generated/generated/arch.covariance.kernel.CovarianceEstimate.short_run.html b/covariance/generated/generated/arch.covariance.kernel.CovarianceEstimate.short_run.html
index 8be8ebedeb..137c225c4d 100644
--- a/covariance/generated/generated/arch.covariance.kernel.CovarianceEstimate.short_run.html
+++ b/covariance/generated/generated/arch.covariance.kernel.CovarianceEstimate.short_run.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.CovarianceEstimate.short_run - arch 6.1.0</title>
+        <title>arch.covariance.kernel.CovarianceEstimate.short_run - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -643,10 +643,10 @@
                   
 
 
-<h1 id="arch-covariance-kernel-covarianceestimate-short-run">arch.covariance.kernel.CovarianceEstimate.short_run<a class="headerlink" href="#arch-covariance-kernel-covarianceestimate-short-run" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-covarianceestimate-short-run">arch.covariance.kernel.CovarianceEstimate.short_run<a class="headerlink" href="#arch-covariance-kernel-covarianceestimate-short-run" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.CovarianceEstimate.short_run">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CovarianceEstimate.</span></span><span class="sig-name descname"><span class="pre">short_run</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.0.1)"><span class="pre">DataFrame</span></a></span><a class="headerlink" href="#arch.covariance.kernel.CovarianceEstimate.short_run" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CovarianceEstimate.</span></span><span class="sig-name descname"><span class="pre">short_run</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a></span><a class="headerlink" href="#arch.covariance.kernel.CovarianceEstimate.short_run" title="Link to this definition">¶</a></dt>
 <dd><p>The short-run covariance estimate.</p>
 </dd></dl>
 
@@ -717,7 +717,7 @@ <h1 id="arch-covariance-kernel-covarianceestimate-short-run">arch.covariance.ker
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -764,7 +764,7 @@ <h1 id="arch-covariance-kernel-covarianceestimate-short-run">arch.covariance.ker
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.Gallant.bandwidth.html b/covariance/generated/generated/arch.covariance.kernel.Gallant.bandwidth.html
index f51075901f..a19de205c9 100644
--- a/covariance/generated/generated/arch.covariance.kernel.Gallant.bandwidth.html
+++ b/covariance/generated/generated/arch.covariance.kernel.Gallant.bandwidth.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.Gallant.bandwidth - arch 6.1.0</title>
+        <title>arch.covariance.kernel.Gallant.bandwidth - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-gallant-bandwidth">arch.covariance.kernel.Gallant.bandwidth<a class="headerlink" href="#arch-covariance-kernel-gallant-bandwidth" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-gallant-bandwidth">arch.covariance.kernel.Gallant.bandwidth<a class="headerlink" href="#arch-covariance-kernel-gallant-bandwidth" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.Gallant.bandwidth">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Gallant.</span></span><span class="sig-name descname"><span class="pre">bandwidth</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.covariance.kernel.Gallant.bandwidth" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Gallant.</span></span><span class="sig-name descname"><span class="pre">bandwidth</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.covariance.kernel.Gallant.bandwidth" title="Link to this definition">¶</a></dt>
 <dd><p>The bandwidth used by the covariance estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.Gallant.bandwidth-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Gallant.bandwidth-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The user-provided or estimated optimal bandwidth.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.Gallant.bandwidth-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Gallant.bandwidth-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-gallant-bandwidth">arch.covariance.kernel.Gallant
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-gallant-bandwidth">arch.covariance.kernel.Gallant
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.Gallant.bandwidth_scale.html b/covariance/generated/generated/arch.covariance.kernel.Gallant.bandwidth_scale.html
index 5647e0e027..db8765d225 100644
--- a/covariance/generated/generated/arch.covariance.kernel.Gallant.bandwidth_scale.html
+++ b/covariance/generated/generated/arch.covariance.kernel.Gallant.bandwidth_scale.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.Gallant.bandwidth_scale - arch 6.1.0</title>
+        <title>arch.covariance.kernel.Gallant.bandwidth_scale - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-gallant-bandwidth-scale">arch.covariance.kernel.Gallant.bandwidth_scale<a class="headerlink" href="#arch-covariance-kernel-gallant-bandwidth-scale" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-gallant-bandwidth-scale">arch.covariance.kernel.Gallant.bandwidth_scale<a class="headerlink" href="#arch-covariance-kernel-gallant-bandwidth-scale" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.Gallant.bandwidth_scale">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Gallant.</span></span><span class="sig-name descname"><span class="pre">bandwidth_scale</span></span><a class="headerlink" href="#arch.covariance.kernel.Gallant.bandwidth_scale" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Gallant.</span></span><span class="sig-name descname"><span class="pre">bandwidth_scale</span></span><a class="headerlink" href="#arch.covariance.kernel.Gallant.bandwidth_scale" title="Link to this definition">¶</a></dt>
 <dd><p>The power used in optimal bandwidth calculation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.Gallant.bandwidth_scale-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Gallant.bandwidth_scale-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The power value used in the optimal bandwidth calculation.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.Gallant.bandwidth_scale-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Gallant.bandwidth_scale-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-gallant-bandwidth-scale">arch.covariance.kernel.G
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-gallant-bandwidth-scale">arch.covariance.kernel.G
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.Gallant.centered.html b/covariance/generated/generated/arch.covariance.kernel.Gallant.centered.html
index a7d162bc4f..245d38daf5 100644
--- a/covariance/generated/generated/arch.covariance.kernel.Gallant.centered.html
+++ b/covariance/generated/generated/arch.covariance.kernel.Gallant.centered.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.Gallant.centered - arch 6.1.0</title>
+        <title>arch.covariance.kernel.Gallant.centered - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-gallant-centered">arch.covariance.kernel.Gallant.centered<a class="headerlink" href="#arch-covariance-kernel-gallant-centered" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-gallant-centered">arch.covariance.kernel.Gallant.centered<a class="headerlink" href="#arch-covariance-kernel-gallant-centered" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.Gallant.centered">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Gallant.</span></span><span class="sig-name descname"><span class="pre">centered</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.covariance.kernel.Gallant.centered" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Gallant.</span></span><span class="sig-name descname"><span class="pre">centered</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.covariance.kernel.Gallant.centered" title="Link to this definition">¶</a></dt>
 <dd><p>Flag indicating whether the data are centered (demeaned).</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.Gallant.centered-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Gallant.centered-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>A flag indicating whether the estimator is centered.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.Gallant.centered-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Gallant.centered-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-gallant-centered">arch.covariance.kernel.Gallant.
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-gallant-centered">arch.covariance.kernel.Gallant.
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.Gallant.cov.html b/covariance/generated/generated/arch.covariance.kernel.Gallant.cov.html
index b5a2c3f248..88a79c36de 100644
--- a/covariance/generated/generated/arch.covariance.kernel.Gallant.cov.html
+++ b/covariance/generated/generated/arch.covariance.kernel.Gallant.cov.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.Gallant.cov - arch 6.1.0</title>
+        <title>arch.covariance.kernel.Gallant.cov - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,10 +938,10 @@
                   
 
 
-<h1 id="arch-covariance-kernel-gallant-cov">arch.covariance.kernel.Gallant.cov<a class="headerlink" href="#arch-covariance-kernel-gallant-cov" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-gallant-cov">arch.covariance.kernel.Gallant.cov<a class="headerlink" href="#arch-covariance-kernel-gallant-cov" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.Gallant.cov">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Gallant.</span></span><span class="sig-name descname"><span class="pre">cov</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.covariance.kernel.CovarianceEstimate.html#arch.covariance.kernel.CovarianceEstimate" title="arch.covariance.kernel.CovarianceEstimate (Python class) — Covariance estimate using a long-run covariance estimator"><span class="pre">CovarianceEstimate</span></a></span><a class="headerlink" href="#arch.covariance.kernel.Gallant.cov" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Gallant.</span></span><span class="sig-name descname"><span class="pre">cov</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.covariance.kernel.CovarianceEstimate.html#arch.covariance.kernel.CovarianceEstimate" title="arch.covariance.kernel.CovarianceEstimate (Python class) — Covariance estimate using a long-run covariance estimator"><span class="pre">CovarianceEstimate</span></a></span><a class="headerlink" href="#arch.covariance.kernel.Gallant.cov" title="Link to this definition">¶</a></dt>
 <dd><p>The estimated covariances.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.Gallant.cov-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Gallant.cov-returns" title="Permalink to this headline">¶</a></dt>
@@ -955,7 +955,7 @@ <h1 id="arch-covariance-kernel-gallant-cov">arch.covariance.kernel.Gallant.cov<a
 </p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.Gallant.cov-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Gallant.cov-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference internal" href="../arch.covariance.kernel.CovarianceEstimate.html#arch.covariance.kernel.CovarianceEstimate" title="arch.covariance.kernel.CovarianceEstimate (Python class) — Covariance estimate using a long-run covariance estimator">CovarianceEstimate</a></p>
+<dd class="field-even"><p><a class="reference internal" href="../arch.covariance.kernel.CovarianceEstimate.html#arch.covariance.kernel.CovarianceEstimate" title="arch.covariance.kernel.CovarianceEstimate (Python class) — Covariance estimate using a long-run covariance estimator"><code class="xref py py-class docutils literal notranslate"><span class="pre">CovarianceEstimate</span></code></a></p>
 </dd>
 </dl>
 <div class="seealso admonition">
@@ -1031,7 +1031,7 @@ <h1 id="arch-covariance-kernel-gallant-cov">arch.covariance.kernel.Gallant.cov<a
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1078,7 +1078,7 @@ <h1 id="arch-covariance-kernel-gallant-cov">arch.covariance.kernel.Gallant.cov<a
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.Gallant.force_int.html b/covariance/generated/generated/arch.covariance.kernel.Gallant.force_int.html
index 498339428b..401c08ccc9 100644
--- a/covariance/generated/generated/arch.covariance.kernel.Gallant.force_int.html
+++ b/covariance/generated/generated/arch.covariance.kernel.Gallant.force_int.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.Gallant.force_int - arch 6.1.0</title>
+        <title>arch.covariance.kernel.Gallant.force_int - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -898,10 +898,10 @@
                   
 
 
-<h1 id="arch-covariance-kernel-gallant-force-int">arch.covariance.kernel.Gallant.force_int<a class="headerlink" href="#arch-covariance-kernel-gallant-force-int" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-gallant-force-int">arch.covariance.kernel.Gallant.force_int<a class="headerlink" href="#arch-covariance-kernel-gallant-force-int" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.Gallant.force_int">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Gallant.</span></span><span class="sig-name descname"><span class="pre">force_int</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.covariance.kernel.Gallant.force_int" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Gallant.</span></span><span class="sig-name descname"><span class="pre">force_int</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.covariance.kernel.Gallant.force_int" title="Link to this definition">¶</a></dt>
 <dd><p>Flag indicating whether the bandwidth is restricted to be an integer.</p>
 </dd></dl>
 
@@ -972,7 +972,7 @@ <h1 id="arch-covariance-kernel-gallant-force-int">arch.covariance.kernel.Gallant
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1019,7 +1019,7 @@ <h1 id="arch-covariance-kernel-gallant-force-int">arch.covariance.kernel.Gallant
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.Gallant.kernel_const.html b/covariance/generated/generated/arch.covariance.kernel.Gallant.kernel_const.html
index bda086b35e..71ad70faf6 100644
--- a/covariance/generated/generated/arch.covariance.kernel.Gallant.kernel_const.html
+++ b/covariance/generated/generated/arch.covariance.kernel.Gallant.kernel_const.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.Gallant.kernel_const - arch 6.1.0</title>
+        <title>arch.covariance.kernel.Gallant.kernel_const - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-gallant-kernel-const">arch.covariance.kernel.Gallant.kernel_const<a class="headerlink" href="#arch-covariance-kernel-gallant-kernel-const" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-gallant-kernel-const">arch.covariance.kernel.Gallant.kernel_const<a class="headerlink" href="#arch-covariance-kernel-gallant-kernel-const" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.Gallant.kernel_const">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Gallant.</span></span><span class="sig-name descname"><span class="pre">kernel_const</span></span><a class="headerlink" href="#arch.covariance.kernel.Gallant.kernel_const" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Gallant.</span></span><span class="sig-name descname"><span class="pre">kernel_const</span></span><a class="headerlink" href="#arch.covariance.kernel.Gallant.kernel_const" title="Link to this definition">¶</a></dt>
 <dd><p>The constant used in optimal bandwidth calculation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.Gallant.kernel_const-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Gallant.kernel_const-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The constant value used in the optimal bandwidth calculation.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.Gallant.kernel_const-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Gallant.kernel_const-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-gallant-kernel-const">arch.covariance.kernel.Gall
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-gallant-kernel-const">arch.covariance.kernel.Gall
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.Gallant.kernel_weights.html b/covariance/generated/generated/arch.covariance.kernel.Gallant.kernel_weights.html
index 944e34bc54..6e926818bf 100644
--- a/covariance/generated/generated/arch.covariance.kernel.Gallant.kernel_weights.html
+++ b/covariance/generated/generated/arch.covariance.kernel.Gallant.kernel_weights.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.Gallant.kernel_weights - arch 6.1.0</title>
+        <title>arch.covariance.kernel.Gallant.kernel_weights - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-gallant-kernel-weights">arch.covariance.kernel.Gallant.kernel_weights<a class="headerlink" href="#arch-covariance-kernel-gallant-kernel-weights" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-gallant-kernel-weights">arch.covariance.kernel.Gallant.kernel_weights<a class="headerlink" href="#arch-covariance-kernel-gallant-kernel-weights" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.Gallant.kernel_weights">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Gallant.</span></span><span class="sig-name descname"><span class="pre">kernel_weights</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">ndarray</span></a></span><a class="headerlink" href="#arch.covariance.kernel.Gallant.kernel_weights" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Gallant.</span></span><span class="sig-name descname"><span class="pre">kernel_weights</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span><a class="headerlink" href="#arch.covariance.kernel.Gallant.kernel_weights" title="Link to this definition">¶</a></dt>
 <dd><p>Weights used in covariance calculation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.Gallant.kernel_weights-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Gallant.kernel_weights-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The weight vector including 1 in position 0.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.Gallant.kernel_weights-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Gallant.kernel_weights-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p>ndarray</p>
+<dd class="field-even"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-gallant-kernel-weights">arch.covariance.kernel.Ga
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-gallant-kernel-weights">arch.covariance.kernel.Ga
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.Gallant.name.html b/covariance/generated/generated/arch.covariance.kernel.Gallant.name.html
index 6ff8584c2c..e120429fe4 100644
--- a/covariance/generated/generated/arch.covariance.kernel.Gallant.name.html
+++ b/covariance/generated/generated/arch.covariance.kernel.Gallant.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.Gallant.name - arch 6.1.0</title>
+        <title>arch.covariance.kernel.Gallant.name - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-gallant-name">arch.covariance.kernel.Gallant.name<a class="headerlink" href="#arch-covariance-kernel-gallant-name" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-gallant-name">arch.covariance.kernel.Gallant.name<a class="headerlink" href="#arch-covariance-kernel-gallant-name" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.Gallant.name">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Gallant.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.covariance.kernel.Gallant.name" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Gallant.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.covariance.kernel.Gallant.name" title="Link to this definition">¶</a></dt>
 <dd><p>The covariance estimator’s name.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.Gallant.name-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Gallant.name-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The covariance estimator’s name.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.Gallant.name-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Gallant.name-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-gallant-name">arch.covariance.kernel.Gallant.name
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-gallant-name">arch.covariance.kernel.Gallant.name
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.Gallant.opt_bandwidth.html b/covariance/generated/generated/arch.covariance.kernel.Gallant.opt_bandwidth.html
index c0456bb7dc..23a94f1d4c 100644
--- a/covariance/generated/generated/arch.covariance.kernel.Gallant.opt_bandwidth.html
+++ b/covariance/generated/generated/arch.covariance.kernel.Gallant.opt_bandwidth.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.Gallant.opt_bandwidth - arch 6.1.0</title>
+        <title>arch.covariance.kernel.Gallant.opt_bandwidth - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-gallant-opt-bandwidth">arch.covariance.kernel.Gallant.opt_bandwidth<a class="headerlink" href="#arch-covariance-kernel-gallant-opt-bandwidth" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-gallant-opt-bandwidth">arch.covariance.kernel.Gallant.opt_bandwidth<a class="headerlink" href="#arch-covariance-kernel-gallant-opt-bandwidth" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.Gallant.opt_bandwidth">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Gallant.</span></span><span class="sig-name descname"><span class="pre">opt_bandwidth</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.covariance.kernel.Gallant.opt_bandwidth" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Gallant.</span></span><span class="sig-name descname"><span class="pre">opt_bandwidth</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.covariance.kernel.Gallant.opt_bandwidth" title="Link to this definition">¶</a></dt>
 <dd><p>Estimate optimal bandwidth.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.Gallant.opt_bandwidth-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Gallant.opt_bandwidth-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The estimated optimal bandwidth.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.Gallant.opt_bandwidth-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Gallant.opt_bandwidth-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -1026,7 +1026,7 @@ <h1 id="arch-covariance-kernel-gallant-opt-bandwidth">arch.covariance.kernel.Gal
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1073,10 +1073,10 @@ <h1 id="arch-covariance-kernel-gallant-opt-bandwidth">arch.covariance.kernel.Gal
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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+        <script id="MathJax-script" src="../../../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
     
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 </html>
\ No newline at end of file
diff --git a/covariance/generated/generated/arch.covariance.kernel.Gallant.rate.html b/covariance/generated/generated/arch.covariance.kernel.Gallant.rate.html
index aea77dd784..00e60707b1 100644
--- a/covariance/generated/generated/arch.covariance.kernel.Gallant.rate.html
+++ b/covariance/generated/generated/arch.covariance.kernel.Gallant.rate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.Gallant.rate - arch 6.1.0</title>
+        <title>arch.covariance.kernel.Gallant.rate - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,10 +938,10 @@
                   
 
 
-<h1 id="arch-covariance-kernel-gallant-rate">arch.covariance.kernel.Gallant.rate<a class="headerlink" href="#arch-covariance-kernel-gallant-rate" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-gallant-rate">arch.covariance.kernel.Gallant.rate<a class="headerlink" href="#arch-covariance-kernel-gallant-rate" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.Gallant.rate">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Gallant.</span></span><span class="sig-name descname"><span class="pre">rate</span></span><a class="headerlink" href="#arch.covariance.kernel.Gallant.rate" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Gallant.</span></span><span class="sig-name descname"><span class="pre">rate</span></span><a class="headerlink" href="#arch.covariance.kernel.Gallant.rate" title="Link to this definition">¶</a></dt>
 <dd><p>The optimal rate used in bandwidth selection.</p>
 <p>Controls the number of lags used in the variance estimate that
 determines the estimate of the optimal bandwidth.</p>
@@ -950,7 +950,7 @@ <h1 id="arch-covariance-kernel-gallant-rate">arch.covariance.kernel.Gallant.rate
 <dd class="field-odd"><p>The rate used in bandwidth selection.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.Gallant.rate-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Gallant.rate-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1022,7 +1022,7 @@ <h1 id="arch-covariance-kernel-gallant-rate">arch.covariance.kernel.Gallant.rate
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1069,7 +1069,7 @@ <h1 id="arch-covariance-kernel-gallant-rate">arch.covariance.kernel.Gallant.rate
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.NeweyWest.bandwidth.html b/covariance/generated/generated/arch.covariance.kernel.NeweyWest.bandwidth.html
index 7770677bc3..c7efc3484f 100644
--- a/covariance/generated/generated/arch.covariance.kernel.NeweyWest.bandwidth.html
+++ b/covariance/generated/generated/arch.covariance.kernel.NeweyWest.bandwidth.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.NeweyWest.bandwidth - arch 6.1.0</title>
+        <title>arch.covariance.kernel.NeweyWest.bandwidth - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-neweywest-bandwidth">arch.covariance.kernel.NeweyWest.bandwidth<a class="headerlink" href="#arch-covariance-kernel-neweywest-bandwidth" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-neweywest-bandwidth">arch.covariance.kernel.NeweyWest.bandwidth<a class="headerlink" href="#arch-covariance-kernel-neweywest-bandwidth" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.NeweyWest.bandwidth">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">NeweyWest.</span></span><span class="sig-name descname"><span class="pre">bandwidth</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.covariance.kernel.NeweyWest.bandwidth" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">NeweyWest.</span></span><span class="sig-name descname"><span class="pre">bandwidth</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.covariance.kernel.NeweyWest.bandwidth" title="Link to this definition">¶</a></dt>
 <dd><p>The bandwidth used by the covariance estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.NeweyWest.bandwidth-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.NeweyWest.bandwidth-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The user-provided or estimated optimal bandwidth.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.NeweyWest.bandwidth-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.NeweyWest.bandwidth-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-neweywest-bandwidth">arch.covariance.kernel.Newey
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-neweywest-bandwidth">arch.covariance.kernel.Newey
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.NeweyWest.bandwidth_scale.html b/covariance/generated/generated/arch.covariance.kernel.NeweyWest.bandwidth_scale.html
index 56a004c4ae..ccc2dddfce 100644
--- a/covariance/generated/generated/arch.covariance.kernel.NeweyWest.bandwidth_scale.html
+++ b/covariance/generated/generated/arch.covariance.kernel.NeweyWest.bandwidth_scale.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.NeweyWest.bandwidth_scale - arch 6.1.0</title>
+        <title>arch.covariance.kernel.NeweyWest.bandwidth_scale - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-neweywest-bandwidth-scale">arch.covariance.kernel.NeweyWest.bandwidth_scale<a class="headerlink" href="#arch-covariance-kernel-neweywest-bandwidth-scale" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-neweywest-bandwidth-scale">arch.covariance.kernel.NeweyWest.bandwidth_scale<a class="headerlink" href="#arch-covariance-kernel-neweywest-bandwidth-scale" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.NeweyWest.bandwidth_scale">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">NeweyWest.</span></span><span class="sig-name descname"><span class="pre">bandwidth_scale</span></span><a class="headerlink" href="#arch.covariance.kernel.NeweyWest.bandwidth_scale" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">NeweyWest.</span></span><span class="sig-name descname"><span class="pre">bandwidth_scale</span></span><a class="headerlink" href="#arch.covariance.kernel.NeweyWest.bandwidth_scale" title="Link to this definition">¶</a></dt>
 <dd><p>The power used in optimal bandwidth calculation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.NeweyWest.bandwidth_scale-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.NeweyWest.bandwidth_scale-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The power value used in the optimal bandwidth calculation.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.NeweyWest.bandwidth_scale-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.NeweyWest.bandwidth_scale-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-neweywest-bandwidth-scale">arch.covariance.kernel
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-neweywest-bandwidth-scale">arch.covariance.kernel
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.NeweyWest.centered.html b/covariance/generated/generated/arch.covariance.kernel.NeweyWest.centered.html
index 870e069a23..26b4dcc487 100644
--- a/covariance/generated/generated/arch.covariance.kernel.NeweyWest.centered.html
+++ b/covariance/generated/generated/arch.covariance.kernel.NeweyWest.centered.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.NeweyWest.centered - arch 6.1.0</title>
+        <title>arch.covariance.kernel.NeweyWest.centered - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
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+    arch 6.2.0
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@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-neweywest-centered">arch.covariance.kernel.NeweyWest.centered<a class="headerlink" href="#arch-covariance-kernel-neweywest-centered" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-neweywest-centered">arch.covariance.kernel.NeweyWest.centered<a class="headerlink" href="#arch-covariance-kernel-neweywest-centered" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.NeweyWest.centered">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">NeweyWest.</span></span><span class="sig-name descname"><span class="pre">centered</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.covariance.kernel.NeweyWest.centered" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">NeweyWest.</span></span><span class="sig-name descname"><span class="pre">centered</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.covariance.kernel.NeweyWest.centered" title="Link to this definition">¶</a></dt>
 <dd><p>Flag indicating whether the data are centered (demeaned).</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.NeweyWest.centered-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.NeweyWest.centered-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>A flag indicating whether the estimator is centered.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.NeweyWest.centered-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.NeweyWest.centered-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-neweywest-centered">arch.covariance.kernel.NeweyW
   
     Created using
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-    7.0.1.
+    7.2.6.
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@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-neweywest-centered">arch.covariance.kernel.NeweyW
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.NeweyWest.cov.html b/covariance/generated/generated/arch.covariance.kernel.NeweyWest.cov.html
index 759eb38f4b..e7e99273f5 100644
--- a/covariance/generated/generated/arch.covariance.kernel.NeweyWest.cov.html
+++ b/covariance/generated/generated/arch.covariance.kernel.NeweyWest.cov.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.NeweyWest.cov - arch 6.1.0</title>
+        <title>arch.covariance.kernel.NeweyWest.cov - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+    arch 6.2.0
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     <div class="md-nav__source">
@@ -938,10 +938,10 @@
                   
 
 
-<h1 id="arch-covariance-kernel-neweywest-cov">arch.covariance.kernel.NeweyWest.cov<a class="headerlink" href="#arch-covariance-kernel-neweywest-cov" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-neweywest-cov">arch.covariance.kernel.NeweyWest.cov<a class="headerlink" href="#arch-covariance-kernel-neweywest-cov" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.NeweyWest.cov">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">NeweyWest.</span></span><span class="sig-name descname"><span class="pre">cov</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.covariance.kernel.CovarianceEstimate.html#arch.covariance.kernel.CovarianceEstimate" title="arch.covariance.kernel.CovarianceEstimate (Python class) — Covariance estimate using a long-run covariance estimator"><span class="pre">CovarianceEstimate</span></a></span><a class="headerlink" href="#arch.covariance.kernel.NeweyWest.cov" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">NeweyWest.</span></span><span class="sig-name descname"><span class="pre">cov</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.covariance.kernel.CovarianceEstimate.html#arch.covariance.kernel.CovarianceEstimate" title="arch.covariance.kernel.CovarianceEstimate (Python class) — Covariance estimate using a long-run covariance estimator"><span class="pre">CovarianceEstimate</span></a></span><a class="headerlink" href="#arch.covariance.kernel.NeweyWest.cov" title="Link to this definition">¶</a></dt>
 <dd><p>The estimated covariances.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.NeweyWest.cov-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.NeweyWest.cov-returns" title="Permalink to this headline">¶</a></dt>
@@ -955,7 +955,7 @@ <h1 id="arch-covariance-kernel-neweywest-cov">arch.covariance.kernel.NeweyWest.c
 </p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.NeweyWest.cov-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.NeweyWest.cov-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference internal" href="../arch.covariance.kernel.CovarianceEstimate.html#arch.covariance.kernel.CovarianceEstimate" title="arch.covariance.kernel.CovarianceEstimate (Python class) — Covariance estimate using a long-run covariance estimator">CovarianceEstimate</a></p>
+<dd class="field-even"><p><a class="reference internal" href="../arch.covariance.kernel.CovarianceEstimate.html#arch.covariance.kernel.CovarianceEstimate" title="arch.covariance.kernel.CovarianceEstimate (Python class) — Covariance estimate using a long-run covariance estimator"><code class="xref py py-class docutils literal notranslate"><span class="pre">CovarianceEstimate</span></code></a></p>
 </dd>
 </dl>
 <div class="seealso admonition">
@@ -1031,7 +1031,7 @@ <h1 id="arch-covariance-kernel-neweywest-cov">arch.covariance.kernel.NeweyWest.c
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1078,7 +1078,7 @@ <h1 id="arch-covariance-kernel-neweywest-cov">arch.covariance.kernel.NeweyWest.c
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diff --git a/covariance/generated/generated/arch.covariance.kernel.NeweyWest.force_int.html b/covariance/generated/generated/arch.covariance.kernel.NeweyWest.force_int.html
index 795bbd860b..4c55958ae0 100644
--- a/covariance/generated/generated/arch.covariance.kernel.NeweyWest.force_int.html
+++ b/covariance/generated/generated/arch.covariance.kernel.NeweyWest.force_int.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.NeweyWest.force_int - arch 6.1.0</title>
+        <title>arch.covariance.kernel.NeweyWest.force_int - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -898,10 +898,10 @@
                   
 
 
-<h1 id="arch-covariance-kernel-neweywest-force-int">arch.covariance.kernel.NeweyWest.force_int<a class="headerlink" href="#arch-covariance-kernel-neweywest-force-int" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-neweywest-force-int">arch.covariance.kernel.NeweyWest.force_int<a class="headerlink" href="#arch-covariance-kernel-neweywest-force-int" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.NeweyWest.force_int">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">NeweyWest.</span></span><span class="sig-name descname"><span class="pre">force_int</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.covariance.kernel.NeweyWest.force_int" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">NeweyWest.</span></span><span class="sig-name descname"><span class="pre">force_int</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.covariance.kernel.NeweyWest.force_int" title="Link to this definition">¶</a></dt>
 <dd><p>Flag indicating whether the bandwidth is restricted to be an integer.</p>
 </dd></dl>
 
@@ -972,7 +972,7 @@ <h1 id="arch-covariance-kernel-neweywest-force-int">arch.covariance.kernel.Newey
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1019,7 +1019,7 @@ <h1 id="arch-covariance-kernel-neweywest-force-int">arch.covariance.kernel.Newey
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diff --git a/covariance/generated/generated/arch.covariance.kernel.NeweyWest.kernel_const.html b/covariance/generated/generated/arch.covariance.kernel.NeweyWest.kernel_const.html
index 68b2b46571..3e7d3c0618 100644
--- a/covariance/generated/generated/arch.covariance.kernel.NeweyWest.kernel_const.html
+++ b/covariance/generated/generated/arch.covariance.kernel.NeweyWest.kernel_const.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.NeweyWest.kernel_const - arch 6.1.0</title>
+        <title>arch.covariance.kernel.NeweyWest.kernel_const - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-neweywest-kernel-const">arch.covariance.kernel.NeweyWest.kernel_const<a class="headerlink" href="#arch-covariance-kernel-neweywest-kernel-const" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-neweywest-kernel-const">arch.covariance.kernel.NeweyWest.kernel_const<a class="headerlink" href="#arch-covariance-kernel-neweywest-kernel-const" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.NeweyWest.kernel_const">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">NeweyWest.</span></span><span class="sig-name descname"><span class="pre">kernel_const</span></span><a class="headerlink" href="#arch.covariance.kernel.NeweyWest.kernel_const" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">NeweyWest.</span></span><span class="sig-name descname"><span class="pre">kernel_const</span></span><a class="headerlink" href="#arch.covariance.kernel.NeweyWest.kernel_const" title="Link to this definition">¶</a></dt>
 <dd><p>The constant used in optimal bandwidth calculation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.NeweyWest.kernel_const-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.NeweyWest.kernel_const-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The constant value used in the optimal bandwidth calculation.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.NeweyWest.kernel_const-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.NeweyWest.kernel_const-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-neweywest-kernel-const">arch.covariance.kernel.Ne
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
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@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-neweywest-kernel-const">arch.covariance.kernel.Ne
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diff --git a/covariance/generated/generated/arch.covariance.kernel.NeweyWest.kernel_weights.html b/covariance/generated/generated/arch.covariance.kernel.NeweyWest.kernel_weights.html
index 92ecbfc923..ce173be278 100644
--- a/covariance/generated/generated/arch.covariance.kernel.NeweyWest.kernel_weights.html
+++ b/covariance/generated/generated/arch.covariance.kernel.NeweyWest.kernel_weights.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.NeweyWest.kernel_weights - arch 6.1.0</title>
+        <title>arch.covariance.kernel.NeweyWest.kernel_weights - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-neweywest-kernel-weights">arch.covariance.kernel.NeweyWest.kernel_weights<a class="headerlink" href="#arch-covariance-kernel-neweywest-kernel-weights" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-neweywest-kernel-weights">arch.covariance.kernel.NeweyWest.kernel_weights<a class="headerlink" href="#arch-covariance-kernel-neweywest-kernel-weights" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.NeweyWest.kernel_weights">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">NeweyWest.</span></span><span class="sig-name descname"><span class="pre">kernel_weights</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">ndarray</span></a></span><a class="headerlink" href="#arch.covariance.kernel.NeweyWest.kernel_weights" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">NeweyWest.</span></span><span class="sig-name descname"><span class="pre">kernel_weights</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span><a class="headerlink" href="#arch.covariance.kernel.NeweyWest.kernel_weights" title="Link to this definition">¶</a></dt>
 <dd><p>Weights used in covariance calculation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.NeweyWest.kernel_weights-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.NeweyWest.kernel_weights-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The weight vector including 1 in position 0.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.NeweyWest.kernel_weights-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.NeweyWest.kernel_weights-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p>ndarray</p>
+<dd class="field-even"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-neweywest-kernel-weights">arch.covariance.kernel.
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-neweywest-kernel-weights">arch.covariance.kernel.
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diff --git a/covariance/generated/generated/arch.covariance.kernel.NeweyWest.name.html b/covariance/generated/generated/arch.covariance.kernel.NeweyWest.name.html
index 019d262a14..c9f2c3ef85 100644
--- a/covariance/generated/generated/arch.covariance.kernel.NeweyWest.name.html
+++ b/covariance/generated/generated/arch.covariance.kernel.NeweyWest.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.NeweyWest.name - arch 6.1.0</title>
+        <title>arch.covariance.kernel.NeweyWest.name - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-neweywest-name">arch.covariance.kernel.NeweyWest.name<a class="headerlink" href="#arch-covariance-kernel-neweywest-name" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-neweywest-name">arch.covariance.kernel.NeweyWest.name<a class="headerlink" href="#arch-covariance-kernel-neweywest-name" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.NeweyWest.name">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">NeweyWest.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.covariance.kernel.NeweyWest.name" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">NeweyWest.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.covariance.kernel.NeweyWest.name" title="Link to this definition">¶</a></dt>
 <dd><p>The covariance estimator’s name.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.NeweyWest.name-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.NeweyWest.name-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The covariance estimator’s name.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.NeweyWest.name-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.NeweyWest.name-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-neweywest-name">arch.covariance.kernel.NeweyWest.
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-neweywest-name">arch.covariance.kernel.NeweyWest.
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diff --git a/covariance/generated/generated/arch.covariance.kernel.NeweyWest.opt_bandwidth.html b/covariance/generated/generated/arch.covariance.kernel.NeweyWest.opt_bandwidth.html
index 478ea8f530..b31bb3a166 100644
--- a/covariance/generated/generated/arch.covariance.kernel.NeweyWest.opt_bandwidth.html
+++ b/covariance/generated/generated/arch.covariance.kernel.NeweyWest.opt_bandwidth.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.NeweyWest.opt_bandwidth - arch 6.1.0</title>
+        <title>arch.covariance.kernel.NeweyWest.opt_bandwidth - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-neweywest-opt-bandwidth">arch.covariance.kernel.NeweyWest.opt_bandwidth<a class="headerlink" href="#arch-covariance-kernel-neweywest-opt-bandwidth" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-neweywest-opt-bandwidth">arch.covariance.kernel.NeweyWest.opt_bandwidth<a class="headerlink" href="#arch-covariance-kernel-neweywest-opt-bandwidth" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.NeweyWest.opt_bandwidth">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">NeweyWest.</span></span><span class="sig-name descname"><span class="pre">opt_bandwidth</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.covariance.kernel.NeweyWest.opt_bandwidth" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">NeweyWest.</span></span><span class="sig-name descname"><span class="pre">opt_bandwidth</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.covariance.kernel.NeweyWest.opt_bandwidth" title="Link to this definition">¶</a></dt>
 <dd><p>Estimate optimal bandwidth.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.NeweyWest.opt_bandwidth-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.NeweyWest.opt_bandwidth-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The estimated optimal bandwidth.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.NeweyWest.opt_bandwidth-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.NeweyWest.opt_bandwidth-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -1026,7 +1026,7 @@ <h1 id="arch-covariance-kernel-neweywest-opt-bandwidth">arch.covariance.kernel.N
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1073,10 +1073,10 @@ <h1 id="arch-covariance-kernel-neweywest-opt-bandwidth">arch.covariance.kernel.N
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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+        <script id="MathJax-script" src="../../../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
     
   </body>
 </html>
\ No newline at end of file
diff --git a/covariance/generated/generated/arch.covariance.kernel.NeweyWest.rate.html b/covariance/generated/generated/arch.covariance.kernel.NeweyWest.rate.html
index 3a9d32640a..1a65f7ecb2 100644
--- a/covariance/generated/generated/arch.covariance.kernel.NeweyWest.rate.html
+++ b/covariance/generated/generated/arch.covariance.kernel.NeweyWest.rate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.NeweyWest.rate - arch 6.1.0</title>
+        <title>arch.covariance.kernel.NeweyWest.rate - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,10 +938,10 @@
                   
 
 
-<h1 id="arch-covariance-kernel-neweywest-rate">arch.covariance.kernel.NeweyWest.rate<a class="headerlink" href="#arch-covariance-kernel-neweywest-rate" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-neweywest-rate">arch.covariance.kernel.NeweyWest.rate<a class="headerlink" href="#arch-covariance-kernel-neweywest-rate" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.NeweyWest.rate">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">NeweyWest.</span></span><span class="sig-name descname"><span class="pre">rate</span></span><a class="headerlink" href="#arch.covariance.kernel.NeweyWest.rate" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">NeweyWest.</span></span><span class="sig-name descname"><span class="pre">rate</span></span><a class="headerlink" href="#arch.covariance.kernel.NeweyWest.rate" title="Link to this definition">¶</a></dt>
 <dd><p>The optimal rate used in bandwidth selection.</p>
 <p>Controls the number of lags used in the variance estimate that
 determines the estimate of the optimal bandwidth.</p>
@@ -950,7 +950,7 @@ <h1 id="arch-covariance-kernel-neweywest-rate">arch.covariance.kernel.NeweyWest.
 <dd class="field-odd"><p>The rate used in bandwidth selection.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.NeweyWest.rate-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.NeweyWest.rate-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1022,7 +1022,7 @@ <h1 id="arch-covariance-kernel-neweywest-rate">arch.covariance.kernel.NeweyWest.
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1069,7 +1069,7 @@ <h1 id="arch-covariance-kernel-neweywest-rate">arch.covariance.kernel.NeweyWest.
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.Parzen.bandwidth.html b/covariance/generated/generated/arch.covariance.kernel.Parzen.bandwidth.html
index dee40be089..fa9a5788a2 100644
--- a/covariance/generated/generated/arch.covariance.kernel.Parzen.bandwidth.html
+++ b/covariance/generated/generated/arch.covariance.kernel.Parzen.bandwidth.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.Parzen.bandwidth - arch 6.1.0</title>
+        <title>arch.covariance.kernel.Parzen.bandwidth - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-parzen-bandwidth">arch.covariance.kernel.Parzen.bandwidth<a class="headerlink" href="#arch-covariance-kernel-parzen-bandwidth" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-parzen-bandwidth">arch.covariance.kernel.Parzen.bandwidth<a class="headerlink" href="#arch-covariance-kernel-parzen-bandwidth" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.Parzen.bandwidth">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Parzen.</span></span><span class="sig-name descname"><span class="pre">bandwidth</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.covariance.kernel.Parzen.bandwidth" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Parzen.</span></span><span class="sig-name descname"><span class="pre">bandwidth</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.covariance.kernel.Parzen.bandwidth" title="Link to this definition">¶</a></dt>
 <dd><p>The bandwidth used by the covariance estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.Parzen.bandwidth-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Parzen.bandwidth-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The user-provided or estimated optimal bandwidth.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.Parzen.bandwidth-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Parzen.bandwidth-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-parzen-bandwidth">arch.covariance.kernel.Parzen.b
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-parzen-bandwidth">arch.covariance.kernel.Parzen.b
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.Parzen.bandwidth_scale.html b/covariance/generated/generated/arch.covariance.kernel.Parzen.bandwidth_scale.html
index 852df2738b..e7113475bf 100644
--- a/covariance/generated/generated/arch.covariance.kernel.Parzen.bandwidth_scale.html
+++ b/covariance/generated/generated/arch.covariance.kernel.Parzen.bandwidth_scale.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.Parzen.bandwidth_scale - arch 6.1.0</title>
+        <title>arch.covariance.kernel.Parzen.bandwidth_scale - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-parzen-bandwidth-scale">arch.covariance.kernel.Parzen.bandwidth_scale<a class="headerlink" href="#arch-covariance-kernel-parzen-bandwidth-scale" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-parzen-bandwidth-scale">arch.covariance.kernel.Parzen.bandwidth_scale<a class="headerlink" href="#arch-covariance-kernel-parzen-bandwidth-scale" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.Parzen.bandwidth_scale">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Parzen.</span></span><span class="sig-name descname"><span class="pre">bandwidth_scale</span></span><a class="headerlink" href="#arch.covariance.kernel.Parzen.bandwidth_scale" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Parzen.</span></span><span class="sig-name descname"><span class="pre">bandwidth_scale</span></span><a class="headerlink" href="#arch.covariance.kernel.Parzen.bandwidth_scale" title="Link to this definition">¶</a></dt>
 <dd><p>The power used in optimal bandwidth calculation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.Parzen.bandwidth_scale-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Parzen.bandwidth_scale-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The power value used in the optimal bandwidth calculation.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.Parzen.bandwidth_scale-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Parzen.bandwidth_scale-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-parzen-bandwidth-scale">arch.covariance.kernel.Pa
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
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@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-parzen-bandwidth-scale">arch.covariance.kernel.Pa
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.Parzen.centered.html b/covariance/generated/generated/arch.covariance.kernel.Parzen.centered.html
index c7d480f686..70f271929c 100644
--- a/covariance/generated/generated/arch.covariance.kernel.Parzen.centered.html
+++ b/covariance/generated/generated/arch.covariance.kernel.Parzen.centered.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.Parzen.centered - arch 6.1.0</title>
+        <title>arch.covariance.kernel.Parzen.centered - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-parzen-centered">arch.covariance.kernel.Parzen.centered<a class="headerlink" href="#arch-covariance-kernel-parzen-centered" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-parzen-centered">arch.covariance.kernel.Parzen.centered<a class="headerlink" href="#arch-covariance-kernel-parzen-centered" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.Parzen.centered">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Parzen.</span></span><span class="sig-name descname"><span class="pre">centered</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.covariance.kernel.Parzen.centered" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Parzen.</span></span><span class="sig-name descname"><span class="pre">centered</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.covariance.kernel.Parzen.centered" title="Link to this definition">¶</a></dt>
 <dd><p>Flag indicating whether the data are centered (demeaned).</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.Parzen.centered-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Parzen.centered-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>A flag indicating whether the estimator is centered.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.Parzen.centered-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Parzen.centered-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-parzen-centered">arch.covariance.kernel.Parzen.ce
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-parzen-centered">arch.covariance.kernel.Parzen.ce
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.Parzen.cov.html b/covariance/generated/generated/arch.covariance.kernel.Parzen.cov.html
index bd6be3131a..ce6c692bf5 100644
--- a/covariance/generated/generated/arch.covariance.kernel.Parzen.cov.html
+++ b/covariance/generated/generated/arch.covariance.kernel.Parzen.cov.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.Parzen.cov - arch 6.1.0</title>
+        <title>arch.covariance.kernel.Parzen.cov - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,10 +938,10 @@
                   
 
 
-<h1 id="arch-covariance-kernel-parzen-cov">arch.covariance.kernel.Parzen.cov<a class="headerlink" href="#arch-covariance-kernel-parzen-cov" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-parzen-cov">arch.covariance.kernel.Parzen.cov<a class="headerlink" href="#arch-covariance-kernel-parzen-cov" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.Parzen.cov">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Parzen.</span></span><span class="sig-name descname"><span class="pre">cov</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.covariance.kernel.CovarianceEstimate.html#arch.covariance.kernel.CovarianceEstimate" title="arch.covariance.kernel.CovarianceEstimate (Python class) — Covariance estimate using a long-run covariance estimator"><span class="pre">CovarianceEstimate</span></a></span><a class="headerlink" href="#arch.covariance.kernel.Parzen.cov" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Parzen.</span></span><span class="sig-name descname"><span class="pre">cov</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.covariance.kernel.CovarianceEstimate.html#arch.covariance.kernel.CovarianceEstimate" title="arch.covariance.kernel.CovarianceEstimate (Python class) — Covariance estimate using a long-run covariance estimator"><span class="pre">CovarianceEstimate</span></a></span><a class="headerlink" href="#arch.covariance.kernel.Parzen.cov" title="Link to this definition">¶</a></dt>
 <dd><p>The estimated covariances.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.Parzen.cov-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Parzen.cov-returns" title="Permalink to this headline">¶</a></dt>
@@ -955,7 +955,7 @@ <h1 id="arch-covariance-kernel-parzen-cov">arch.covariance.kernel.Parzen.cov<a c
 </p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.Parzen.cov-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Parzen.cov-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference internal" href="../arch.covariance.kernel.CovarianceEstimate.html#arch.covariance.kernel.CovarianceEstimate" title="arch.covariance.kernel.CovarianceEstimate (Python class) — Covariance estimate using a long-run covariance estimator">CovarianceEstimate</a></p>
+<dd class="field-even"><p><a class="reference internal" href="../arch.covariance.kernel.CovarianceEstimate.html#arch.covariance.kernel.CovarianceEstimate" title="arch.covariance.kernel.CovarianceEstimate (Python class) — Covariance estimate using a long-run covariance estimator"><code class="xref py py-class docutils literal notranslate"><span class="pre">CovarianceEstimate</span></code></a></p>
 </dd>
 </dl>
 <div class="seealso admonition">
@@ -1031,7 +1031,7 @@ <h1 id="arch-covariance-kernel-parzen-cov">arch.covariance.kernel.Parzen.cov<a c
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1078,7 +1078,7 @@ <h1 id="arch-covariance-kernel-parzen-cov">arch.covariance.kernel.Parzen.cov<a c
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.Parzen.force_int.html b/covariance/generated/generated/arch.covariance.kernel.Parzen.force_int.html
index cb154b94cf..c0c2ccca00 100644
--- a/covariance/generated/generated/arch.covariance.kernel.Parzen.force_int.html
+++ b/covariance/generated/generated/arch.covariance.kernel.Parzen.force_int.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.Parzen.force_int - arch 6.1.0</title>
+        <title>arch.covariance.kernel.Parzen.force_int - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -898,10 +898,10 @@
                   
 
 
-<h1 id="arch-covariance-kernel-parzen-force-int">arch.covariance.kernel.Parzen.force_int<a class="headerlink" href="#arch-covariance-kernel-parzen-force-int" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-parzen-force-int">arch.covariance.kernel.Parzen.force_int<a class="headerlink" href="#arch-covariance-kernel-parzen-force-int" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.Parzen.force_int">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Parzen.</span></span><span class="sig-name descname"><span class="pre">force_int</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.covariance.kernel.Parzen.force_int" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Parzen.</span></span><span class="sig-name descname"><span class="pre">force_int</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.covariance.kernel.Parzen.force_int" title="Link to this definition">¶</a></dt>
 <dd><p>Flag indicating whether the bandwidth is restricted to be an integer.</p>
 </dd></dl>
 
@@ -972,7 +972,7 @@ <h1 id="arch-covariance-kernel-parzen-force-int">arch.covariance.kernel.Parzen.f
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1019,7 +1019,7 @@ <h1 id="arch-covariance-kernel-parzen-force-int">arch.covariance.kernel.Parzen.f
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.Parzen.kernel_const.html b/covariance/generated/generated/arch.covariance.kernel.Parzen.kernel_const.html
index 4128c43536..f907fe357d 100644
--- a/covariance/generated/generated/arch.covariance.kernel.Parzen.kernel_const.html
+++ b/covariance/generated/generated/arch.covariance.kernel.Parzen.kernel_const.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.Parzen.kernel_const - arch 6.1.0</title>
+        <title>arch.covariance.kernel.Parzen.kernel_const - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-parzen-kernel-const">arch.covariance.kernel.Parzen.kernel_const<a class="headerlink" href="#arch-covariance-kernel-parzen-kernel-const" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-parzen-kernel-const">arch.covariance.kernel.Parzen.kernel_const<a class="headerlink" href="#arch-covariance-kernel-parzen-kernel-const" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.Parzen.kernel_const">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Parzen.</span></span><span class="sig-name descname"><span class="pre">kernel_const</span></span><a class="headerlink" href="#arch.covariance.kernel.Parzen.kernel_const" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Parzen.</span></span><span class="sig-name descname"><span class="pre">kernel_const</span></span><a class="headerlink" href="#arch.covariance.kernel.Parzen.kernel_const" title="Link to this definition">¶</a></dt>
 <dd><p>The constant used in optimal bandwidth calculation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.Parzen.kernel_const-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Parzen.kernel_const-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The constant value used in the optimal bandwidth calculation.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.Parzen.kernel_const-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Parzen.kernel_const-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-parzen-kernel-const">arch.covariance.kernel.Parze
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-parzen-kernel-const">arch.covariance.kernel.Parze
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.Parzen.kernel_weights.html b/covariance/generated/generated/arch.covariance.kernel.Parzen.kernel_weights.html
index d7dc380152..81bae6dbc5 100644
--- a/covariance/generated/generated/arch.covariance.kernel.Parzen.kernel_weights.html
+++ b/covariance/generated/generated/arch.covariance.kernel.Parzen.kernel_weights.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.Parzen.kernel_weights - arch 6.1.0</title>
+        <title>arch.covariance.kernel.Parzen.kernel_weights - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-parzen-kernel-weights">arch.covariance.kernel.Parzen.kernel_weights<a class="headerlink" href="#arch-covariance-kernel-parzen-kernel-weights" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-parzen-kernel-weights">arch.covariance.kernel.Parzen.kernel_weights<a class="headerlink" href="#arch-covariance-kernel-parzen-kernel-weights" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.Parzen.kernel_weights">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Parzen.</span></span><span class="sig-name descname"><span class="pre">kernel_weights</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">ndarray</span></a></span><a class="headerlink" href="#arch.covariance.kernel.Parzen.kernel_weights" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Parzen.</span></span><span class="sig-name descname"><span class="pre">kernel_weights</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span><a class="headerlink" href="#arch.covariance.kernel.Parzen.kernel_weights" title="Link to this definition">¶</a></dt>
 <dd><p>Weights used in covariance calculation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.Parzen.kernel_weights-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Parzen.kernel_weights-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The weight vector including 1 in position 0.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.Parzen.kernel_weights-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Parzen.kernel_weights-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p>ndarray</p>
+<dd class="field-even"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-parzen-kernel-weights">arch.covariance.kernel.Par
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-parzen-kernel-weights">arch.covariance.kernel.Par
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.Parzen.name.html b/covariance/generated/generated/arch.covariance.kernel.Parzen.name.html
index 12408f8143..3e9c007e06 100644
--- a/covariance/generated/generated/arch.covariance.kernel.Parzen.name.html
+++ b/covariance/generated/generated/arch.covariance.kernel.Parzen.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.Parzen.name - arch 6.1.0</title>
+        <title>arch.covariance.kernel.Parzen.name - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-parzen-name">arch.covariance.kernel.Parzen.name<a class="headerlink" href="#arch-covariance-kernel-parzen-name" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-parzen-name">arch.covariance.kernel.Parzen.name<a class="headerlink" href="#arch-covariance-kernel-parzen-name" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.Parzen.name">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Parzen.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.covariance.kernel.Parzen.name" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Parzen.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.covariance.kernel.Parzen.name" title="Link to this definition">¶</a></dt>
 <dd><p>The covariance estimator’s name.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.Parzen.name-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Parzen.name-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The covariance estimator’s name.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.Parzen.name-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Parzen.name-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-parzen-name">arch.covariance.kernel.Parzen.name<a
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-parzen-name">arch.covariance.kernel.Parzen.name<a
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.Parzen.opt_bandwidth.html b/covariance/generated/generated/arch.covariance.kernel.Parzen.opt_bandwidth.html
index 27588df4da..8e42950445 100644
--- a/covariance/generated/generated/arch.covariance.kernel.Parzen.opt_bandwidth.html
+++ b/covariance/generated/generated/arch.covariance.kernel.Parzen.opt_bandwidth.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.Parzen.opt_bandwidth - arch 6.1.0</title>
+        <title>arch.covariance.kernel.Parzen.opt_bandwidth - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-parzen-opt-bandwidth">arch.covariance.kernel.Parzen.opt_bandwidth<a class="headerlink" href="#arch-covariance-kernel-parzen-opt-bandwidth" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-parzen-opt-bandwidth">arch.covariance.kernel.Parzen.opt_bandwidth<a class="headerlink" href="#arch-covariance-kernel-parzen-opt-bandwidth" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.Parzen.opt_bandwidth">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Parzen.</span></span><span class="sig-name descname"><span class="pre">opt_bandwidth</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.covariance.kernel.Parzen.opt_bandwidth" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Parzen.</span></span><span class="sig-name descname"><span class="pre">opt_bandwidth</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.covariance.kernel.Parzen.opt_bandwidth" title="Link to this definition">¶</a></dt>
 <dd><p>Estimate optimal bandwidth.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.Parzen.opt_bandwidth-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Parzen.opt_bandwidth-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The estimated optimal bandwidth.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.Parzen.opt_bandwidth-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Parzen.opt_bandwidth-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -1026,7 +1026,7 @@ <h1 id="arch-covariance-kernel-parzen-opt-bandwidth">arch.covariance.kernel.Parz
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1073,10 +1073,10 @@ <h1 id="arch-covariance-kernel-parzen-opt-bandwidth">arch.covariance.kernel.Parz
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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+        <script id="MathJax-script" src="../../../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
     
   </body>
 </html>
\ No newline at end of file
diff --git a/covariance/generated/generated/arch.covariance.kernel.Parzen.rate.html b/covariance/generated/generated/arch.covariance.kernel.Parzen.rate.html
index 43f3ae5841..4cfbf59434 100644
--- a/covariance/generated/generated/arch.covariance.kernel.Parzen.rate.html
+++ b/covariance/generated/generated/arch.covariance.kernel.Parzen.rate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.Parzen.rate - arch 6.1.0</title>
+        <title>arch.covariance.kernel.Parzen.rate - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,10 +938,10 @@
                   
 
 
-<h1 id="arch-covariance-kernel-parzen-rate">arch.covariance.kernel.Parzen.rate<a class="headerlink" href="#arch-covariance-kernel-parzen-rate" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-parzen-rate">arch.covariance.kernel.Parzen.rate<a class="headerlink" href="#arch-covariance-kernel-parzen-rate" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.Parzen.rate">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Parzen.</span></span><span class="sig-name descname"><span class="pre">rate</span></span><a class="headerlink" href="#arch.covariance.kernel.Parzen.rate" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Parzen.</span></span><span class="sig-name descname"><span class="pre">rate</span></span><a class="headerlink" href="#arch.covariance.kernel.Parzen.rate" title="Link to this definition">¶</a></dt>
 <dd><p>The optimal rate used in bandwidth selection.</p>
 <p>Controls the number of lags used in the variance estimate that
 determines the estimate of the optimal bandwidth.</p>
@@ -950,7 +950,7 @@ <h1 id="arch-covariance-kernel-parzen-rate">arch.covariance.kernel.Parzen.rate<a
 <dd class="field-odd"><p>The rate used in bandwidth selection.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.Parzen.rate-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Parzen.rate-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1022,7 +1022,7 @@ <h1 id="arch-covariance-kernel-parzen-rate">arch.covariance.kernel.Parzen.rate<a
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1069,7 +1069,7 @@ <h1 id="arch-covariance-kernel-parzen-rate">arch.covariance.kernel.Parzen.rate<a
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.ParzenCauchy.bandwidth.html b/covariance/generated/generated/arch.covariance.kernel.ParzenCauchy.bandwidth.html
index 63f29e8ef5..00ae0b42af 100644
--- a/covariance/generated/generated/arch.covariance.kernel.ParzenCauchy.bandwidth.html
+++ b/covariance/generated/generated/arch.covariance.kernel.ParzenCauchy.bandwidth.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.ParzenCauchy.bandwidth - arch 6.1.0</title>
+        <title>arch.covariance.kernel.ParzenCauchy.bandwidth - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch 6.1.0
+    arch 6.2.0
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@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-parzencauchy-bandwidth">arch.covariance.kernel.ParzenCauchy.bandwidth<a class="headerlink" href="#arch-covariance-kernel-parzencauchy-bandwidth" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-parzencauchy-bandwidth">arch.covariance.kernel.ParzenCauchy.bandwidth<a class="headerlink" href="#arch-covariance-kernel-parzencauchy-bandwidth" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.ParzenCauchy.bandwidth">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenCauchy.</span></span><span class="sig-name descname"><span class="pre">bandwidth</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy.bandwidth" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenCauchy.</span></span><span class="sig-name descname"><span class="pre">bandwidth</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy.bandwidth" title="Link to this definition">¶</a></dt>
 <dd><p>The bandwidth used by the covariance estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.ParzenCauchy.bandwidth-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy.bandwidth-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The user-provided or estimated optimal bandwidth.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.ParzenCauchy.bandwidth-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy.bandwidth-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-parzencauchy-bandwidth">arch.covariance.kernel.Pa
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
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@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-parzencauchy-bandwidth">arch.covariance.kernel.Pa
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.ParzenCauchy.bandwidth_scale.html b/covariance/generated/generated/arch.covariance.kernel.ParzenCauchy.bandwidth_scale.html
index e2e631ae11..4dc541e6c3 100644
--- a/covariance/generated/generated/arch.covariance.kernel.ParzenCauchy.bandwidth_scale.html
+++ b/covariance/generated/generated/arch.covariance.kernel.ParzenCauchy.bandwidth_scale.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.ParzenCauchy.bandwidth_scale - arch 6.1.0</title>
+        <title>arch.covariance.kernel.ParzenCauchy.bandwidth_scale - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-parzencauchy-bandwidth-scale">arch.covariance.kernel.ParzenCauchy.bandwidth_scale<a class="headerlink" href="#arch-covariance-kernel-parzencauchy-bandwidth-scale" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-parzencauchy-bandwidth-scale">arch.covariance.kernel.ParzenCauchy.bandwidth_scale<a class="headerlink" href="#arch-covariance-kernel-parzencauchy-bandwidth-scale" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.ParzenCauchy.bandwidth_scale">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenCauchy.</span></span><span class="sig-name descname"><span class="pre">bandwidth_scale</span></span><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy.bandwidth_scale" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenCauchy.</span></span><span class="sig-name descname"><span class="pre">bandwidth_scale</span></span><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy.bandwidth_scale" title="Link to this definition">¶</a></dt>
 <dd><p>The power used in optimal bandwidth calculation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.ParzenCauchy.bandwidth_scale-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy.bandwidth_scale-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The power value used in the optimal bandwidth calculation.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.ParzenCauchy.bandwidth_scale-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy.bandwidth_scale-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-parzencauchy-bandwidth-scale">arch.covariance.ker
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-parzencauchy-bandwidth-scale">arch.covariance.ker
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.ParzenCauchy.centered.html b/covariance/generated/generated/arch.covariance.kernel.ParzenCauchy.centered.html
index 6eeade3548..ab80b33e8b 100644
--- a/covariance/generated/generated/arch.covariance.kernel.ParzenCauchy.centered.html
+++ b/covariance/generated/generated/arch.covariance.kernel.ParzenCauchy.centered.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.ParzenCauchy.centered - arch 6.1.0</title>
+        <title>arch.covariance.kernel.ParzenCauchy.centered - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-parzencauchy-centered">arch.covariance.kernel.ParzenCauchy.centered<a class="headerlink" href="#arch-covariance-kernel-parzencauchy-centered" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-parzencauchy-centered">arch.covariance.kernel.ParzenCauchy.centered<a class="headerlink" href="#arch-covariance-kernel-parzencauchy-centered" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.ParzenCauchy.centered">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenCauchy.</span></span><span class="sig-name descname"><span class="pre">centered</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy.centered" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenCauchy.</span></span><span class="sig-name descname"><span class="pre">centered</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy.centered" title="Link to this definition">¶</a></dt>
 <dd><p>Flag indicating whether the data are centered (demeaned).</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.ParzenCauchy.centered-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy.centered-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>A flag indicating whether the estimator is centered.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.ParzenCauchy.centered-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy.centered-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-parzencauchy-centered">arch.covariance.kernel.Par
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-parzencauchy-centered">arch.covariance.kernel.Par
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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diff --git a/covariance/generated/generated/arch.covariance.kernel.ParzenCauchy.cov.html b/covariance/generated/generated/arch.covariance.kernel.ParzenCauchy.cov.html
index 6aa34ccf89..a7b109ba0d 100644
--- a/covariance/generated/generated/arch.covariance.kernel.ParzenCauchy.cov.html
+++ b/covariance/generated/generated/arch.covariance.kernel.ParzenCauchy.cov.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.ParzenCauchy.cov - arch 6.1.0</title>
+        <title>arch.covariance.kernel.ParzenCauchy.cov - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,10 +938,10 @@
                   
 
 
-<h1 id="arch-covariance-kernel-parzencauchy-cov">arch.covariance.kernel.ParzenCauchy.cov<a class="headerlink" href="#arch-covariance-kernel-parzencauchy-cov" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-parzencauchy-cov">arch.covariance.kernel.ParzenCauchy.cov<a class="headerlink" href="#arch-covariance-kernel-parzencauchy-cov" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.ParzenCauchy.cov">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenCauchy.</span></span><span class="sig-name descname"><span class="pre">cov</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.covariance.kernel.CovarianceEstimate.html#arch.covariance.kernel.CovarianceEstimate" title="arch.covariance.kernel.CovarianceEstimate (Python class) — Covariance estimate using a long-run covariance estimator"><span class="pre">CovarianceEstimate</span></a></span><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy.cov" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenCauchy.</span></span><span class="sig-name descname"><span class="pre">cov</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.covariance.kernel.CovarianceEstimate.html#arch.covariance.kernel.CovarianceEstimate" title="arch.covariance.kernel.CovarianceEstimate (Python class) — Covariance estimate using a long-run covariance estimator"><span class="pre">CovarianceEstimate</span></a></span><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy.cov" title="Link to this definition">¶</a></dt>
 <dd><p>The estimated covariances.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.ParzenCauchy.cov-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy.cov-returns" title="Permalink to this headline">¶</a></dt>
@@ -955,7 +955,7 @@ <h1 id="arch-covariance-kernel-parzencauchy-cov">arch.covariance.kernel.ParzenCa
 </p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.ParzenCauchy.cov-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy.cov-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference internal" href="../arch.covariance.kernel.CovarianceEstimate.html#arch.covariance.kernel.CovarianceEstimate" title="arch.covariance.kernel.CovarianceEstimate (Python class) — Covariance estimate using a long-run covariance estimator">CovarianceEstimate</a></p>
+<dd class="field-even"><p><a class="reference internal" href="../arch.covariance.kernel.CovarianceEstimate.html#arch.covariance.kernel.CovarianceEstimate" title="arch.covariance.kernel.CovarianceEstimate (Python class) — Covariance estimate using a long-run covariance estimator"><code class="xref py py-class docutils literal notranslate"><span class="pre">CovarianceEstimate</span></code></a></p>
 </dd>
 </dl>
 <div class="seealso admonition">
@@ -1031,7 +1031,7 @@ <h1 id="arch-covariance-kernel-parzencauchy-cov">arch.covariance.kernel.ParzenCa
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1078,7 +1078,7 @@ <h1 id="arch-covariance-kernel-parzencauchy-cov">arch.covariance.kernel.ParzenCa
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diff --git a/covariance/generated/generated/arch.covariance.kernel.ParzenCauchy.force_int.html b/covariance/generated/generated/arch.covariance.kernel.ParzenCauchy.force_int.html
index 56313e4dad..48501276b1 100644
--- a/covariance/generated/generated/arch.covariance.kernel.ParzenCauchy.force_int.html
+++ b/covariance/generated/generated/arch.covariance.kernel.ParzenCauchy.force_int.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.ParzenCauchy.force_int - arch 6.1.0</title>
+        <title>arch.covariance.kernel.ParzenCauchy.force_int - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -898,10 +898,10 @@
                   
 
 
-<h1 id="arch-covariance-kernel-parzencauchy-force-int">arch.covariance.kernel.ParzenCauchy.force_int<a class="headerlink" href="#arch-covariance-kernel-parzencauchy-force-int" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-parzencauchy-force-int">arch.covariance.kernel.ParzenCauchy.force_int<a class="headerlink" href="#arch-covariance-kernel-parzencauchy-force-int" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.ParzenCauchy.force_int">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenCauchy.</span></span><span class="sig-name descname"><span class="pre">force_int</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy.force_int" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenCauchy.</span></span><span class="sig-name descname"><span class="pre">force_int</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy.force_int" title="Link to this definition">¶</a></dt>
 <dd><p>Flag indicating whether the bandwidth is restricted to be an integer.</p>
 </dd></dl>
 
@@ -972,7 +972,7 @@ <h1 id="arch-covariance-kernel-parzencauchy-force-int">arch.covariance.kernel.Pa
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1019,7 +1019,7 @@ <h1 id="arch-covariance-kernel-parzencauchy-force-int">arch.covariance.kernel.Pa
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.ParzenCauchy.kernel_const.html b/covariance/generated/generated/arch.covariance.kernel.ParzenCauchy.kernel_const.html
index 3c8ebf9eff..2bba1827a1 100644
--- a/covariance/generated/generated/arch.covariance.kernel.ParzenCauchy.kernel_const.html
+++ b/covariance/generated/generated/arch.covariance.kernel.ParzenCauchy.kernel_const.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.ParzenCauchy.kernel_const - arch 6.1.0</title>
+        <title>arch.covariance.kernel.ParzenCauchy.kernel_const - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-parzencauchy-kernel-const">arch.covariance.kernel.ParzenCauchy.kernel_const<a class="headerlink" href="#arch-covariance-kernel-parzencauchy-kernel-const" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-parzencauchy-kernel-const">arch.covariance.kernel.ParzenCauchy.kernel_const<a class="headerlink" href="#arch-covariance-kernel-parzencauchy-kernel-const" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.ParzenCauchy.kernel_const">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenCauchy.</span></span><span class="sig-name descname"><span class="pre">kernel_const</span></span><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy.kernel_const" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenCauchy.</span></span><span class="sig-name descname"><span class="pre">kernel_const</span></span><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy.kernel_const" title="Link to this definition">¶</a></dt>
 <dd><p>The constant used in optimal bandwidth calculation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.ParzenCauchy.kernel_const-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy.kernel_const-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The constant value used in the optimal bandwidth calculation.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.ParzenCauchy.kernel_const-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy.kernel_const-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-parzencauchy-kernel-const">arch.covariance.kernel
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-parzencauchy-kernel-const">arch.covariance.kernel
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.ParzenCauchy.kernel_weights.html b/covariance/generated/generated/arch.covariance.kernel.ParzenCauchy.kernel_weights.html
index 530baa51f1..e7b5f06dc2 100644
--- a/covariance/generated/generated/arch.covariance.kernel.ParzenCauchy.kernel_weights.html
+++ b/covariance/generated/generated/arch.covariance.kernel.ParzenCauchy.kernel_weights.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.ParzenCauchy.kernel_weights - arch 6.1.0</title>
+        <title>arch.covariance.kernel.ParzenCauchy.kernel_weights - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch 6.1.0
+    arch 6.2.0
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@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-parzencauchy-kernel-weights">arch.covariance.kernel.ParzenCauchy.kernel_weights<a class="headerlink" href="#arch-covariance-kernel-parzencauchy-kernel-weights" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-parzencauchy-kernel-weights">arch.covariance.kernel.ParzenCauchy.kernel_weights<a class="headerlink" href="#arch-covariance-kernel-parzencauchy-kernel-weights" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.ParzenCauchy.kernel_weights">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenCauchy.</span></span><span class="sig-name descname"><span class="pre">kernel_weights</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">ndarray</span></a></span><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy.kernel_weights" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenCauchy.</span></span><span class="sig-name descname"><span class="pre">kernel_weights</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy.kernel_weights" title="Link to this definition">¶</a></dt>
 <dd><p>Weights used in covariance calculation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.ParzenCauchy.kernel_weights-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy.kernel_weights-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The weight vector including 1 in position 0.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.ParzenCauchy.kernel_weights-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy.kernel_weights-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p>ndarray</p>
+<dd class="field-even"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-parzencauchy-kernel-weights">arch.covariance.kern
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-parzencauchy-kernel-weights">arch.covariance.kern
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+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.ParzenCauchy.name.html b/covariance/generated/generated/arch.covariance.kernel.ParzenCauchy.name.html
index d3d0b95fec..2632b1ea9f 100644
--- a/covariance/generated/generated/arch.covariance.kernel.ParzenCauchy.name.html
+++ b/covariance/generated/generated/arch.covariance.kernel.ParzenCauchy.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.ParzenCauchy.name - arch 6.1.0</title>
+        <title>arch.covariance.kernel.ParzenCauchy.name - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-parzencauchy-name">arch.covariance.kernel.ParzenCauchy.name<a class="headerlink" href="#arch-covariance-kernel-parzencauchy-name" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-parzencauchy-name">arch.covariance.kernel.ParzenCauchy.name<a class="headerlink" href="#arch-covariance-kernel-parzencauchy-name" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.ParzenCauchy.name">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenCauchy.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy.name" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenCauchy.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy.name" title="Link to this definition">¶</a></dt>
 <dd><p>The covariance estimator’s name.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.ParzenCauchy.name-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy.name-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The covariance estimator’s name.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.ParzenCauchy.name-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy.name-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-parzencauchy-name">arch.covariance.kernel.ParzenC
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-parzencauchy-name">arch.covariance.kernel.ParzenC
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+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.ParzenCauchy.opt_bandwidth.html b/covariance/generated/generated/arch.covariance.kernel.ParzenCauchy.opt_bandwidth.html
index 138b1cae03..ca4e418831 100644
--- a/covariance/generated/generated/arch.covariance.kernel.ParzenCauchy.opt_bandwidth.html
+++ b/covariance/generated/generated/arch.covariance.kernel.ParzenCauchy.opt_bandwidth.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.ParzenCauchy.opt_bandwidth - arch 6.1.0</title>
+        <title>arch.covariance.kernel.ParzenCauchy.opt_bandwidth - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-parzencauchy-opt-bandwidth">arch.covariance.kernel.ParzenCauchy.opt_bandwidth<a class="headerlink" href="#arch-covariance-kernel-parzencauchy-opt-bandwidth" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-parzencauchy-opt-bandwidth">arch.covariance.kernel.ParzenCauchy.opt_bandwidth<a class="headerlink" href="#arch-covariance-kernel-parzencauchy-opt-bandwidth" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.ParzenCauchy.opt_bandwidth">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenCauchy.</span></span><span class="sig-name descname"><span class="pre">opt_bandwidth</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy.opt_bandwidth" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenCauchy.</span></span><span class="sig-name descname"><span class="pre">opt_bandwidth</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy.opt_bandwidth" title="Link to this definition">¶</a></dt>
 <dd><p>Estimate optimal bandwidth.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.ParzenCauchy.opt_bandwidth-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy.opt_bandwidth-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The estimated optimal bandwidth.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.ParzenCauchy.opt_bandwidth-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy.opt_bandwidth-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -1026,7 +1026,7 @@ <h1 id="arch-covariance-kernel-parzencauchy-opt-bandwidth">arch.covariance.kerne
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1073,10 +1073,10 @@ <h1 id="arch-covariance-kernel-parzencauchy-opt-bandwidth">arch.covariance.kerne
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
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+        <script id="MathJax-script" src="../../../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
     
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diff --git a/covariance/generated/generated/arch.covariance.kernel.ParzenCauchy.rate.html b/covariance/generated/generated/arch.covariance.kernel.ParzenCauchy.rate.html
index 108f28d422..af510d706a 100644
--- a/covariance/generated/generated/arch.covariance.kernel.ParzenCauchy.rate.html
+++ b/covariance/generated/generated/arch.covariance.kernel.ParzenCauchy.rate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.ParzenCauchy.rate - arch 6.1.0</title>
+        <title>arch.covariance.kernel.ParzenCauchy.rate - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,10 +938,10 @@
                   
 
 
-<h1 id="arch-covariance-kernel-parzencauchy-rate">arch.covariance.kernel.ParzenCauchy.rate<a class="headerlink" href="#arch-covariance-kernel-parzencauchy-rate" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-parzencauchy-rate">arch.covariance.kernel.ParzenCauchy.rate<a class="headerlink" href="#arch-covariance-kernel-parzencauchy-rate" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.ParzenCauchy.rate">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenCauchy.</span></span><span class="sig-name descname"><span class="pre">rate</span></span><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy.rate" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenCauchy.</span></span><span class="sig-name descname"><span class="pre">rate</span></span><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy.rate" title="Link to this definition">¶</a></dt>
 <dd><p>The optimal rate used in bandwidth selection.</p>
 <p>Controls the number of lags used in the variance estimate that
 determines the estimate of the optimal bandwidth.</p>
@@ -950,7 +950,7 @@ <h1 id="arch-covariance-kernel-parzencauchy-rate">arch.covariance.kernel.ParzenC
 <dd class="field-odd"><p>The rate used in bandwidth selection.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.ParzenCauchy.rate-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy.rate-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1022,7 +1022,7 @@ <h1 id="arch-covariance-kernel-parzencauchy-rate">arch.covariance.kernel.ParzenC
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1069,7 +1069,7 @@ <h1 id="arch-covariance-kernel-parzencauchy-rate">arch.covariance.kernel.ParzenC
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+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.ParzenGeometric.bandwidth.html b/covariance/generated/generated/arch.covariance.kernel.ParzenGeometric.bandwidth.html
index 5eda3fd078..b35f01d023 100644
--- a/covariance/generated/generated/arch.covariance.kernel.ParzenGeometric.bandwidth.html
+++ b/covariance/generated/generated/arch.covariance.kernel.ParzenGeometric.bandwidth.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.ParzenGeometric.bandwidth - arch 6.1.0</title>
+        <title>arch.covariance.kernel.ParzenGeometric.bandwidth - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-parzengeometric-bandwidth">arch.covariance.kernel.ParzenGeometric.bandwidth<a class="headerlink" href="#arch-covariance-kernel-parzengeometric-bandwidth" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-parzengeometric-bandwidth">arch.covariance.kernel.ParzenGeometric.bandwidth<a class="headerlink" href="#arch-covariance-kernel-parzengeometric-bandwidth" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.ParzenGeometric.bandwidth">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenGeometric.</span></span><span class="sig-name descname"><span class="pre">bandwidth</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric.bandwidth" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenGeometric.</span></span><span class="sig-name descname"><span class="pre">bandwidth</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric.bandwidth" title="Link to this definition">¶</a></dt>
 <dd><p>The bandwidth used by the covariance estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.ParzenGeometric.bandwidth-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric.bandwidth-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The user-provided or estimated optimal bandwidth.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.ParzenGeometric.bandwidth-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric.bandwidth-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-parzengeometric-bandwidth">arch.covariance.kernel
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-parzengeometric-bandwidth">arch.covariance.kernel
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.ParzenGeometric.bandwidth_scale.html b/covariance/generated/generated/arch.covariance.kernel.ParzenGeometric.bandwidth_scale.html
index 71be6c0524..51841368ad 100644
--- a/covariance/generated/generated/arch.covariance.kernel.ParzenGeometric.bandwidth_scale.html
+++ b/covariance/generated/generated/arch.covariance.kernel.ParzenGeometric.bandwidth_scale.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.ParzenGeometric.bandwidth_scale - arch 6.1.0</title>
+        <title>arch.covariance.kernel.ParzenGeometric.bandwidth_scale - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-parzengeometric-bandwidth-scale">arch.covariance.kernel.ParzenGeometric.bandwidth_scale<a class="headerlink" href="#arch-covariance-kernel-parzengeometric-bandwidth-scale" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-parzengeometric-bandwidth-scale">arch.covariance.kernel.ParzenGeometric.bandwidth_scale<a class="headerlink" href="#arch-covariance-kernel-parzengeometric-bandwidth-scale" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.ParzenGeometric.bandwidth_scale">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenGeometric.</span></span><span class="sig-name descname"><span class="pre">bandwidth_scale</span></span><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric.bandwidth_scale" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenGeometric.</span></span><span class="sig-name descname"><span class="pre">bandwidth_scale</span></span><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric.bandwidth_scale" title="Link to this definition">¶</a></dt>
 <dd><p>The power used in optimal bandwidth calculation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.ParzenGeometric.bandwidth_scale-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric.bandwidth_scale-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The power value used in the optimal bandwidth calculation.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.ParzenGeometric.bandwidth_scale-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric.bandwidth_scale-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-parzengeometric-bandwidth-scale">arch.covariance.
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-parzengeometric-bandwidth-scale">arch.covariance.
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diff --git a/covariance/generated/generated/arch.covariance.kernel.ParzenGeometric.centered.html b/covariance/generated/generated/arch.covariance.kernel.ParzenGeometric.centered.html
index c2f1e8fb3d..fb1589075e 100644
--- a/covariance/generated/generated/arch.covariance.kernel.ParzenGeometric.centered.html
+++ b/covariance/generated/generated/arch.covariance.kernel.ParzenGeometric.centered.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.ParzenGeometric.centered - arch 6.1.0</title>
+        <title>arch.covariance.kernel.ParzenGeometric.centered - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-parzengeometric-centered">arch.covariance.kernel.ParzenGeometric.centered<a class="headerlink" href="#arch-covariance-kernel-parzengeometric-centered" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-parzengeometric-centered">arch.covariance.kernel.ParzenGeometric.centered<a class="headerlink" href="#arch-covariance-kernel-parzengeometric-centered" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.ParzenGeometric.centered">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenGeometric.</span></span><span class="sig-name descname"><span class="pre">centered</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric.centered" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenGeometric.</span></span><span class="sig-name descname"><span class="pre">centered</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric.centered" title="Link to this definition">¶</a></dt>
 <dd><p>Flag indicating whether the data are centered (demeaned).</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.ParzenGeometric.centered-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric.centered-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>A flag indicating whether the estimator is centered.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.ParzenGeometric.centered-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric.centered-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-parzengeometric-centered">arch.covariance.kernel.
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-parzengeometric-centered">arch.covariance.kernel.
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diff --git a/covariance/generated/generated/arch.covariance.kernel.ParzenGeometric.cov.html b/covariance/generated/generated/arch.covariance.kernel.ParzenGeometric.cov.html
index 875a3a7df3..76b0ed131b 100644
--- a/covariance/generated/generated/arch.covariance.kernel.ParzenGeometric.cov.html
+++ b/covariance/generated/generated/arch.covariance.kernel.ParzenGeometric.cov.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.ParzenGeometric.cov - arch 6.1.0</title>
+        <title>arch.covariance.kernel.ParzenGeometric.cov - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,10 +938,10 @@
                   
 
 
-<h1 id="arch-covariance-kernel-parzengeometric-cov">arch.covariance.kernel.ParzenGeometric.cov<a class="headerlink" href="#arch-covariance-kernel-parzengeometric-cov" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-parzengeometric-cov">arch.covariance.kernel.ParzenGeometric.cov<a class="headerlink" href="#arch-covariance-kernel-parzengeometric-cov" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.ParzenGeometric.cov">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenGeometric.</span></span><span class="sig-name descname"><span class="pre">cov</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.covariance.kernel.CovarianceEstimate.html#arch.covariance.kernel.CovarianceEstimate" title="arch.covariance.kernel.CovarianceEstimate (Python class) — Covariance estimate using a long-run covariance estimator"><span class="pre">CovarianceEstimate</span></a></span><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric.cov" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenGeometric.</span></span><span class="sig-name descname"><span class="pre">cov</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.covariance.kernel.CovarianceEstimate.html#arch.covariance.kernel.CovarianceEstimate" title="arch.covariance.kernel.CovarianceEstimate (Python class) — Covariance estimate using a long-run covariance estimator"><span class="pre">CovarianceEstimate</span></a></span><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric.cov" title="Link to this definition">¶</a></dt>
 <dd><p>The estimated covariances.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.ParzenGeometric.cov-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric.cov-returns" title="Permalink to this headline">¶</a></dt>
@@ -955,7 +955,7 @@ <h1 id="arch-covariance-kernel-parzengeometric-cov">arch.covariance.kernel.Parze
 </p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.ParzenGeometric.cov-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric.cov-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference internal" href="../arch.covariance.kernel.CovarianceEstimate.html#arch.covariance.kernel.CovarianceEstimate" title="arch.covariance.kernel.CovarianceEstimate (Python class) — Covariance estimate using a long-run covariance estimator">CovarianceEstimate</a></p>
+<dd class="field-even"><p><a class="reference internal" href="../arch.covariance.kernel.CovarianceEstimate.html#arch.covariance.kernel.CovarianceEstimate" title="arch.covariance.kernel.CovarianceEstimate (Python class) — Covariance estimate using a long-run covariance estimator"><code class="xref py py-class docutils literal notranslate"><span class="pre">CovarianceEstimate</span></code></a></p>
 </dd>
 </dl>
 <div class="seealso admonition">
@@ -1031,7 +1031,7 @@ <h1 id="arch-covariance-kernel-parzengeometric-cov">arch.covariance.kernel.Parze
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1078,7 +1078,7 @@ <h1 id="arch-covariance-kernel-parzengeometric-cov">arch.covariance.kernel.Parze
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diff --git a/covariance/generated/generated/arch.covariance.kernel.ParzenGeometric.force_int.html b/covariance/generated/generated/arch.covariance.kernel.ParzenGeometric.force_int.html
index 7c42a84a9f..bfaa9766ef 100644
--- a/covariance/generated/generated/arch.covariance.kernel.ParzenGeometric.force_int.html
+++ b/covariance/generated/generated/arch.covariance.kernel.ParzenGeometric.force_int.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.ParzenGeometric.force_int - arch 6.1.0</title>
+        <title>arch.covariance.kernel.ParzenGeometric.force_int - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -898,10 +898,10 @@
                   
 
 
-<h1 id="arch-covariance-kernel-parzengeometric-force-int">arch.covariance.kernel.ParzenGeometric.force_int<a class="headerlink" href="#arch-covariance-kernel-parzengeometric-force-int" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-parzengeometric-force-int">arch.covariance.kernel.ParzenGeometric.force_int<a class="headerlink" href="#arch-covariance-kernel-parzengeometric-force-int" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.ParzenGeometric.force_int">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenGeometric.</span></span><span class="sig-name descname"><span class="pre">force_int</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric.force_int" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenGeometric.</span></span><span class="sig-name descname"><span class="pre">force_int</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric.force_int" title="Link to this definition">¶</a></dt>
 <dd><p>Flag indicating whether the bandwidth is restricted to be an integer.</p>
 </dd></dl>
 
@@ -972,7 +972,7 @@ <h1 id="arch-covariance-kernel-parzengeometric-force-int">arch.covariance.kernel
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1019,7 +1019,7 @@ <h1 id="arch-covariance-kernel-parzengeometric-force-int">arch.covariance.kernel
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.ParzenGeometric.kernel_const.html b/covariance/generated/generated/arch.covariance.kernel.ParzenGeometric.kernel_const.html
index a17da3e531..c2b89e3361 100644
--- a/covariance/generated/generated/arch.covariance.kernel.ParzenGeometric.kernel_const.html
+++ b/covariance/generated/generated/arch.covariance.kernel.ParzenGeometric.kernel_const.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.ParzenGeometric.kernel_const - arch 6.1.0</title>
+        <title>arch.covariance.kernel.ParzenGeometric.kernel_const - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-parzengeometric-kernel-const">arch.covariance.kernel.ParzenGeometric.kernel_const<a class="headerlink" href="#arch-covariance-kernel-parzengeometric-kernel-const" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-parzengeometric-kernel-const">arch.covariance.kernel.ParzenGeometric.kernel_const<a class="headerlink" href="#arch-covariance-kernel-parzengeometric-kernel-const" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.ParzenGeometric.kernel_const">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenGeometric.</span></span><span class="sig-name descname"><span class="pre">kernel_const</span></span><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric.kernel_const" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenGeometric.</span></span><span class="sig-name descname"><span class="pre">kernel_const</span></span><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric.kernel_const" title="Link to this definition">¶</a></dt>
 <dd><p>The constant used in optimal bandwidth calculation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.ParzenGeometric.kernel_const-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric.kernel_const-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The constant value used in the optimal bandwidth calculation.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.ParzenGeometric.kernel_const-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric.kernel_const-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-parzengeometric-kernel-const">arch.covariance.ker
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-parzengeometric-kernel-const">arch.covariance.ker
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.ParzenGeometric.kernel_weights.html b/covariance/generated/generated/arch.covariance.kernel.ParzenGeometric.kernel_weights.html
index 938df76b56..9e29bb4532 100644
--- a/covariance/generated/generated/arch.covariance.kernel.ParzenGeometric.kernel_weights.html
+++ b/covariance/generated/generated/arch.covariance.kernel.ParzenGeometric.kernel_weights.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.ParzenGeometric.kernel_weights - arch 6.1.0</title>
+        <title>arch.covariance.kernel.ParzenGeometric.kernel_weights - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-parzengeometric-kernel-weights">arch.covariance.kernel.ParzenGeometric.kernel_weights<a class="headerlink" href="#arch-covariance-kernel-parzengeometric-kernel-weights" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-parzengeometric-kernel-weights">arch.covariance.kernel.ParzenGeometric.kernel_weights<a class="headerlink" href="#arch-covariance-kernel-parzengeometric-kernel-weights" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.ParzenGeometric.kernel_weights">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenGeometric.</span></span><span class="sig-name descname"><span class="pre">kernel_weights</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">ndarray</span></a></span><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric.kernel_weights" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenGeometric.</span></span><span class="sig-name descname"><span class="pre">kernel_weights</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric.kernel_weights" title="Link to this definition">¶</a></dt>
 <dd><p>Weights used in covariance calculation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.ParzenGeometric.kernel_weights-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric.kernel_weights-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The weight vector including 1 in position 0.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.ParzenGeometric.kernel_weights-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric.kernel_weights-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p>ndarray</p>
+<dd class="field-even"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-parzengeometric-kernel-weights">arch.covariance.k
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-parzengeometric-kernel-weights">arch.covariance.k
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.ParzenGeometric.name.html b/covariance/generated/generated/arch.covariance.kernel.ParzenGeometric.name.html
index 7bae11a32a..3cd23e5920 100644
--- a/covariance/generated/generated/arch.covariance.kernel.ParzenGeometric.name.html
+++ b/covariance/generated/generated/arch.covariance.kernel.ParzenGeometric.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.ParzenGeometric.name - arch 6.1.0</title>
+        <title>arch.covariance.kernel.ParzenGeometric.name - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-parzengeometric-name">arch.covariance.kernel.ParzenGeometric.name<a class="headerlink" href="#arch-covariance-kernel-parzengeometric-name" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-parzengeometric-name">arch.covariance.kernel.ParzenGeometric.name<a class="headerlink" href="#arch-covariance-kernel-parzengeometric-name" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.ParzenGeometric.name">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenGeometric.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric.name" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenGeometric.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric.name" title="Link to this definition">¶</a></dt>
 <dd><p>The covariance estimator’s name.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.ParzenGeometric.name-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric.name-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The covariance estimator’s name.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.ParzenGeometric.name-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric.name-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-parzengeometric-name">arch.covariance.kernel.Parz
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-parzengeometric-name">arch.covariance.kernel.Parz
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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diff --git a/covariance/generated/generated/arch.covariance.kernel.ParzenGeometric.opt_bandwidth.html b/covariance/generated/generated/arch.covariance.kernel.ParzenGeometric.opt_bandwidth.html
index 434f5dcef4..7050c2050c 100644
--- a/covariance/generated/generated/arch.covariance.kernel.ParzenGeometric.opt_bandwidth.html
+++ b/covariance/generated/generated/arch.covariance.kernel.ParzenGeometric.opt_bandwidth.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.ParzenGeometric.opt_bandwidth - arch 6.1.0</title>
+        <title>arch.covariance.kernel.ParzenGeometric.opt_bandwidth - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-parzengeometric-opt-bandwidth">arch.covariance.kernel.ParzenGeometric.opt_bandwidth<a class="headerlink" href="#arch-covariance-kernel-parzengeometric-opt-bandwidth" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-parzengeometric-opt-bandwidth">arch.covariance.kernel.ParzenGeometric.opt_bandwidth<a class="headerlink" href="#arch-covariance-kernel-parzengeometric-opt-bandwidth" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.ParzenGeometric.opt_bandwidth">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenGeometric.</span></span><span class="sig-name descname"><span class="pre">opt_bandwidth</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric.opt_bandwidth" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenGeometric.</span></span><span class="sig-name descname"><span class="pre">opt_bandwidth</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric.opt_bandwidth" title="Link to this definition">¶</a></dt>
 <dd><p>Estimate optimal bandwidth.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.ParzenGeometric.opt_bandwidth-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric.opt_bandwidth-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The estimated optimal bandwidth.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.ParzenGeometric.opt_bandwidth-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric.opt_bandwidth-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -1026,7 +1026,7 @@ <h1 id="arch-covariance-kernel-parzengeometric-opt-bandwidth">arch.covariance.ke
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1073,10 +1073,10 @@ <h1 id="arch-covariance-kernel-parzengeometric-opt-bandwidth">arch.covariance.ke
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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+        <script id="MathJax-script" src="../../../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
     
   </body>
 </html>
\ No newline at end of file
diff --git a/covariance/generated/generated/arch.covariance.kernel.ParzenGeometric.rate.html b/covariance/generated/generated/arch.covariance.kernel.ParzenGeometric.rate.html
index ee7bdc95ae..2dc2d42ae7 100644
--- a/covariance/generated/generated/arch.covariance.kernel.ParzenGeometric.rate.html
+++ b/covariance/generated/generated/arch.covariance.kernel.ParzenGeometric.rate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.ParzenGeometric.rate - arch 6.1.0</title>
+        <title>arch.covariance.kernel.ParzenGeometric.rate - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,10 +938,10 @@
                   
 
 
-<h1 id="arch-covariance-kernel-parzengeometric-rate">arch.covariance.kernel.ParzenGeometric.rate<a class="headerlink" href="#arch-covariance-kernel-parzengeometric-rate" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-parzengeometric-rate">arch.covariance.kernel.ParzenGeometric.rate<a class="headerlink" href="#arch-covariance-kernel-parzengeometric-rate" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.ParzenGeometric.rate">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenGeometric.</span></span><span class="sig-name descname"><span class="pre">rate</span></span><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric.rate" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenGeometric.</span></span><span class="sig-name descname"><span class="pre">rate</span></span><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric.rate" title="Link to this definition">¶</a></dt>
 <dd><p>The optimal rate used in bandwidth selection.</p>
 <p>Controls the number of lags used in the variance estimate that
 determines the estimate of the optimal bandwidth.</p>
@@ -950,7 +950,7 @@ <h1 id="arch-covariance-kernel-parzengeometric-rate">arch.covariance.kernel.Parz
 <dd class="field-odd"><p>The rate used in bandwidth selection.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.ParzenGeometric.rate-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric.rate-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1022,7 +1022,7 @@ <h1 id="arch-covariance-kernel-parzengeometric-rate">arch.covariance.kernel.Parz
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1069,7 +1069,7 @@ <h1 id="arch-covariance-kernel-parzengeometric-rate">arch.covariance.kernel.Parz
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.ParzenRiesz.bandwidth.html b/covariance/generated/generated/arch.covariance.kernel.ParzenRiesz.bandwidth.html
index 1786ee5ddf..c108c47825 100644
--- a/covariance/generated/generated/arch.covariance.kernel.ParzenRiesz.bandwidth.html
+++ b/covariance/generated/generated/arch.covariance.kernel.ParzenRiesz.bandwidth.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.ParzenRiesz.bandwidth - arch 6.1.0</title>
+        <title>arch.covariance.kernel.ParzenRiesz.bandwidth - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-parzenriesz-bandwidth">arch.covariance.kernel.ParzenRiesz.bandwidth<a class="headerlink" href="#arch-covariance-kernel-parzenriesz-bandwidth" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-parzenriesz-bandwidth">arch.covariance.kernel.ParzenRiesz.bandwidth<a class="headerlink" href="#arch-covariance-kernel-parzenriesz-bandwidth" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.ParzenRiesz.bandwidth">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenRiesz.</span></span><span class="sig-name descname"><span class="pre">bandwidth</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz.bandwidth" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenRiesz.</span></span><span class="sig-name descname"><span class="pre">bandwidth</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz.bandwidth" title="Link to this definition">¶</a></dt>
 <dd><p>The bandwidth used by the covariance estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.ParzenRiesz.bandwidth-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz.bandwidth-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The user-provided or estimated optimal bandwidth.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.ParzenRiesz.bandwidth-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz.bandwidth-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-parzenriesz-bandwidth">arch.covariance.kernel.Par
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-parzenriesz-bandwidth">arch.covariance.kernel.Par
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.ParzenRiesz.bandwidth_scale.html b/covariance/generated/generated/arch.covariance.kernel.ParzenRiesz.bandwidth_scale.html
index e5e596fc55..94af483713 100644
--- a/covariance/generated/generated/arch.covariance.kernel.ParzenRiesz.bandwidth_scale.html
+++ b/covariance/generated/generated/arch.covariance.kernel.ParzenRiesz.bandwidth_scale.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.ParzenRiesz.bandwidth_scale - arch 6.1.0</title>
+        <title>arch.covariance.kernel.ParzenRiesz.bandwidth_scale - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-parzenriesz-bandwidth-scale">arch.covariance.kernel.ParzenRiesz.bandwidth_scale<a class="headerlink" href="#arch-covariance-kernel-parzenriesz-bandwidth-scale" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-parzenriesz-bandwidth-scale">arch.covariance.kernel.ParzenRiesz.bandwidth_scale<a class="headerlink" href="#arch-covariance-kernel-parzenriesz-bandwidth-scale" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.ParzenRiesz.bandwidth_scale">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenRiesz.</span></span><span class="sig-name descname"><span class="pre">bandwidth_scale</span></span><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz.bandwidth_scale" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenRiesz.</span></span><span class="sig-name descname"><span class="pre">bandwidth_scale</span></span><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz.bandwidth_scale" title="Link to this definition">¶</a></dt>
 <dd><p>The power used in optimal bandwidth calculation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.ParzenRiesz.bandwidth_scale-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz.bandwidth_scale-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The power value used in the optimal bandwidth calculation.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.ParzenRiesz.bandwidth_scale-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz.bandwidth_scale-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-parzenriesz-bandwidth-scale">arch.covariance.kern
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-parzenriesz-bandwidth-scale">arch.covariance.kern
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.ParzenRiesz.centered.html b/covariance/generated/generated/arch.covariance.kernel.ParzenRiesz.centered.html
index 71063f1dea..ea095bc66c 100644
--- a/covariance/generated/generated/arch.covariance.kernel.ParzenRiesz.centered.html
+++ b/covariance/generated/generated/arch.covariance.kernel.ParzenRiesz.centered.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.ParzenRiesz.centered - arch 6.1.0</title>
+        <title>arch.covariance.kernel.ParzenRiesz.centered - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-parzenriesz-centered">arch.covariance.kernel.ParzenRiesz.centered<a class="headerlink" href="#arch-covariance-kernel-parzenriesz-centered" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-parzenriesz-centered">arch.covariance.kernel.ParzenRiesz.centered<a class="headerlink" href="#arch-covariance-kernel-parzenriesz-centered" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.ParzenRiesz.centered">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenRiesz.</span></span><span class="sig-name descname"><span class="pre">centered</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz.centered" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenRiesz.</span></span><span class="sig-name descname"><span class="pre">centered</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz.centered" title="Link to this definition">¶</a></dt>
 <dd><p>Flag indicating whether the data are centered (demeaned).</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.ParzenRiesz.centered-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz.centered-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>A flag indicating whether the estimator is centered.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.ParzenRiesz.centered-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz.centered-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-parzenriesz-centered">arch.covariance.kernel.Parz
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-parzenriesz-centered">arch.covariance.kernel.Parz
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.ParzenRiesz.cov.html b/covariance/generated/generated/arch.covariance.kernel.ParzenRiesz.cov.html
index f6cd6d8dc5..6c5c38ccb8 100644
--- a/covariance/generated/generated/arch.covariance.kernel.ParzenRiesz.cov.html
+++ b/covariance/generated/generated/arch.covariance.kernel.ParzenRiesz.cov.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.ParzenRiesz.cov - arch 6.1.0</title>
+        <title>arch.covariance.kernel.ParzenRiesz.cov - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,10 +938,10 @@
                   
 
 
-<h1 id="arch-covariance-kernel-parzenriesz-cov">arch.covariance.kernel.ParzenRiesz.cov<a class="headerlink" href="#arch-covariance-kernel-parzenriesz-cov" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-parzenriesz-cov">arch.covariance.kernel.ParzenRiesz.cov<a class="headerlink" href="#arch-covariance-kernel-parzenriesz-cov" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.ParzenRiesz.cov">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenRiesz.</span></span><span class="sig-name descname"><span class="pre">cov</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.covariance.kernel.CovarianceEstimate.html#arch.covariance.kernel.CovarianceEstimate" title="arch.covariance.kernel.CovarianceEstimate (Python class) — Covariance estimate using a long-run covariance estimator"><span class="pre">CovarianceEstimate</span></a></span><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz.cov" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenRiesz.</span></span><span class="sig-name descname"><span class="pre">cov</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.covariance.kernel.CovarianceEstimate.html#arch.covariance.kernel.CovarianceEstimate" title="arch.covariance.kernel.CovarianceEstimate (Python class) — Covariance estimate using a long-run covariance estimator"><span class="pre">CovarianceEstimate</span></a></span><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz.cov" title="Link to this definition">¶</a></dt>
 <dd><p>The estimated covariances.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.ParzenRiesz.cov-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz.cov-returns" title="Permalink to this headline">¶</a></dt>
@@ -955,7 +955,7 @@ <h1 id="arch-covariance-kernel-parzenriesz-cov">arch.covariance.kernel.ParzenRie
 </p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.ParzenRiesz.cov-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz.cov-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference internal" href="../arch.covariance.kernel.CovarianceEstimate.html#arch.covariance.kernel.CovarianceEstimate" title="arch.covariance.kernel.CovarianceEstimate (Python class) — Covariance estimate using a long-run covariance estimator">CovarianceEstimate</a></p>
+<dd class="field-even"><p><a class="reference internal" href="../arch.covariance.kernel.CovarianceEstimate.html#arch.covariance.kernel.CovarianceEstimate" title="arch.covariance.kernel.CovarianceEstimate (Python class) — Covariance estimate using a long-run covariance estimator"><code class="xref py py-class docutils literal notranslate"><span class="pre">CovarianceEstimate</span></code></a></p>
 </dd>
 </dl>
 <div class="seealso admonition">
@@ -1031,7 +1031,7 @@ <h1 id="arch-covariance-kernel-parzenriesz-cov">arch.covariance.kernel.ParzenRie
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1078,7 +1078,7 @@ <h1 id="arch-covariance-kernel-parzenriesz-cov">arch.covariance.kernel.ParzenRie
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.ParzenRiesz.force_int.html b/covariance/generated/generated/arch.covariance.kernel.ParzenRiesz.force_int.html
index 6dcf8f874e..6018a10a2f 100644
--- a/covariance/generated/generated/arch.covariance.kernel.ParzenRiesz.force_int.html
+++ b/covariance/generated/generated/arch.covariance.kernel.ParzenRiesz.force_int.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.ParzenRiesz.force_int - arch 6.1.0</title>
+        <title>arch.covariance.kernel.ParzenRiesz.force_int - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -898,10 +898,10 @@
                   
 
 
-<h1 id="arch-covariance-kernel-parzenriesz-force-int">arch.covariance.kernel.ParzenRiesz.force_int<a class="headerlink" href="#arch-covariance-kernel-parzenriesz-force-int" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-parzenriesz-force-int">arch.covariance.kernel.ParzenRiesz.force_int<a class="headerlink" href="#arch-covariance-kernel-parzenriesz-force-int" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.ParzenRiesz.force_int">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenRiesz.</span></span><span class="sig-name descname"><span class="pre">force_int</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz.force_int" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenRiesz.</span></span><span class="sig-name descname"><span class="pre">force_int</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz.force_int" title="Link to this definition">¶</a></dt>
 <dd><p>Flag indicating whether the bandwidth is restricted to be an integer.</p>
 </dd></dl>
 
@@ -972,7 +972,7 @@ <h1 id="arch-covariance-kernel-parzenriesz-force-int">arch.covariance.kernel.Par
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1019,7 +1019,7 @@ <h1 id="arch-covariance-kernel-parzenriesz-force-int">arch.covariance.kernel.Par
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.ParzenRiesz.kernel_const.html b/covariance/generated/generated/arch.covariance.kernel.ParzenRiesz.kernel_const.html
index 4fa8da99af..c63f00803c 100644
--- a/covariance/generated/generated/arch.covariance.kernel.ParzenRiesz.kernel_const.html
+++ b/covariance/generated/generated/arch.covariance.kernel.ParzenRiesz.kernel_const.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.ParzenRiesz.kernel_const - arch 6.1.0</title>
+        <title>arch.covariance.kernel.ParzenRiesz.kernel_const - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-parzenriesz-kernel-const">arch.covariance.kernel.ParzenRiesz.kernel_const<a class="headerlink" href="#arch-covariance-kernel-parzenriesz-kernel-const" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-parzenriesz-kernel-const">arch.covariance.kernel.ParzenRiesz.kernel_const<a class="headerlink" href="#arch-covariance-kernel-parzenriesz-kernel-const" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.ParzenRiesz.kernel_const">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenRiesz.</span></span><span class="sig-name descname"><span class="pre">kernel_const</span></span><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz.kernel_const" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenRiesz.</span></span><span class="sig-name descname"><span class="pre">kernel_const</span></span><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz.kernel_const" title="Link to this definition">¶</a></dt>
 <dd><p>The constant used in optimal bandwidth calculation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.ParzenRiesz.kernel_const-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz.kernel_const-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The constant value used in the optimal bandwidth calculation.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.ParzenRiesz.kernel_const-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz.kernel_const-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-parzenriesz-kernel-const">arch.covariance.kernel.
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-parzenriesz-kernel-const">arch.covariance.kernel.
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.ParzenRiesz.kernel_weights.html b/covariance/generated/generated/arch.covariance.kernel.ParzenRiesz.kernel_weights.html
index 15ee607ea3..a2caf0d85f 100644
--- a/covariance/generated/generated/arch.covariance.kernel.ParzenRiesz.kernel_weights.html
+++ b/covariance/generated/generated/arch.covariance.kernel.ParzenRiesz.kernel_weights.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.ParzenRiesz.kernel_weights - arch 6.1.0</title>
+        <title>arch.covariance.kernel.ParzenRiesz.kernel_weights - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-parzenriesz-kernel-weights">arch.covariance.kernel.ParzenRiesz.kernel_weights<a class="headerlink" href="#arch-covariance-kernel-parzenriesz-kernel-weights" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-parzenriesz-kernel-weights">arch.covariance.kernel.ParzenRiesz.kernel_weights<a class="headerlink" href="#arch-covariance-kernel-parzenriesz-kernel-weights" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.ParzenRiesz.kernel_weights">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenRiesz.</span></span><span class="sig-name descname"><span class="pre">kernel_weights</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">ndarray</span></a></span><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz.kernel_weights" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenRiesz.</span></span><span class="sig-name descname"><span class="pre">kernel_weights</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz.kernel_weights" title="Link to this definition">¶</a></dt>
 <dd><p>Weights used in covariance calculation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.ParzenRiesz.kernel_weights-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz.kernel_weights-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The weight vector including 1 in position 0.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.ParzenRiesz.kernel_weights-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz.kernel_weights-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p>ndarray</p>
+<dd class="field-even"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-parzenriesz-kernel-weights">arch.covariance.kerne
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-parzenriesz-kernel-weights">arch.covariance.kerne
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.ParzenRiesz.name.html b/covariance/generated/generated/arch.covariance.kernel.ParzenRiesz.name.html
index 0c67db273f..09071dbde3 100644
--- a/covariance/generated/generated/arch.covariance.kernel.ParzenRiesz.name.html
+++ b/covariance/generated/generated/arch.covariance.kernel.ParzenRiesz.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.ParzenRiesz.name - arch 6.1.0</title>
+        <title>arch.covariance.kernel.ParzenRiesz.name - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-parzenriesz-name">arch.covariance.kernel.ParzenRiesz.name<a class="headerlink" href="#arch-covariance-kernel-parzenriesz-name" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-parzenriesz-name">arch.covariance.kernel.ParzenRiesz.name<a class="headerlink" href="#arch-covariance-kernel-parzenriesz-name" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.ParzenRiesz.name">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenRiesz.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz.name" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenRiesz.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz.name" title="Link to this definition">¶</a></dt>
 <dd><p>The covariance estimator’s name.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.ParzenRiesz.name-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz.name-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The covariance estimator’s name.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.ParzenRiesz.name-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz.name-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-parzenriesz-name">arch.covariance.kernel.ParzenRi
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-parzenriesz-name">arch.covariance.kernel.ParzenRi
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.ParzenRiesz.opt_bandwidth.html b/covariance/generated/generated/arch.covariance.kernel.ParzenRiesz.opt_bandwidth.html
index 60d29c80d3..72b0d39f1e 100644
--- a/covariance/generated/generated/arch.covariance.kernel.ParzenRiesz.opt_bandwidth.html
+++ b/covariance/generated/generated/arch.covariance.kernel.ParzenRiesz.opt_bandwidth.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.ParzenRiesz.opt_bandwidth - arch 6.1.0</title>
+        <title>arch.covariance.kernel.ParzenRiesz.opt_bandwidth - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-parzenriesz-opt-bandwidth">arch.covariance.kernel.ParzenRiesz.opt_bandwidth<a class="headerlink" href="#arch-covariance-kernel-parzenriesz-opt-bandwidth" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-parzenriesz-opt-bandwidth">arch.covariance.kernel.ParzenRiesz.opt_bandwidth<a class="headerlink" href="#arch-covariance-kernel-parzenriesz-opt-bandwidth" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.ParzenRiesz.opt_bandwidth">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenRiesz.</span></span><span class="sig-name descname"><span class="pre">opt_bandwidth</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz.opt_bandwidth" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenRiesz.</span></span><span class="sig-name descname"><span class="pre">opt_bandwidth</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz.opt_bandwidth" title="Link to this definition">¶</a></dt>
 <dd><p>Estimate optimal bandwidth.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.ParzenRiesz.opt_bandwidth-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz.opt_bandwidth-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The estimated optimal bandwidth.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.ParzenRiesz.opt_bandwidth-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz.opt_bandwidth-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -1026,7 +1026,7 @@ <h1 id="arch-covariance-kernel-parzenriesz-opt-bandwidth">arch.covariance.kernel
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1073,10 +1073,10 @@ <h1 id="arch-covariance-kernel-parzenriesz-opt-bandwidth">arch.covariance.kernel
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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         <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+        <script id="MathJax-script" src="../../../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
     
   </body>
 </html>
\ No newline at end of file
diff --git a/covariance/generated/generated/arch.covariance.kernel.ParzenRiesz.rate.html b/covariance/generated/generated/arch.covariance.kernel.ParzenRiesz.rate.html
index cdf129063d..ccf2430ddb 100644
--- a/covariance/generated/generated/arch.covariance.kernel.ParzenRiesz.rate.html
+++ b/covariance/generated/generated/arch.covariance.kernel.ParzenRiesz.rate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.ParzenRiesz.rate - arch 6.1.0</title>
+        <title>arch.covariance.kernel.ParzenRiesz.rate - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,10 +938,10 @@
                   
 
 
-<h1 id="arch-covariance-kernel-parzenriesz-rate">arch.covariance.kernel.ParzenRiesz.rate<a class="headerlink" href="#arch-covariance-kernel-parzenriesz-rate" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-parzenriesz-rate">arch.covariance.kernel.ParzenRiesz.rate<a class="headerlink" href="#arch-covariance-kernel-parzenriesz-rate" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.ParzenRiesz.rate">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenRiesz.</span></span><span class="sig-name descname"><span class="pre">rate</span></span><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz.rate" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ParzenRiesz.</span></span><span class="sig-name descname"><span class="pre">rate</span></span><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz.rate" title="Link to this definition">¶</a></dt>
 <dd><p>The optimal rate used in bandwidth selection.</p>
 <p>Controls the number of lags used in the variance estimate that
 determines the estimate of the optimal bandwidth.</p>
@@ -950,7 +950,7 @@ <h1 id="arch-covariance-kernel-parzenriesz-rate">arch.covariance.kernel.ParzenRi
 <dd class="field-odd"><p>The rate used in bandwidth selection.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.ParzenRiesz.rate-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz.rate-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1022,7 +1022,7 @@ <h1 id="arch-covariance-kernel-parzenriesz-rate">arch.covariance.kernel.ParzenRi
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1069,7 +1069,7 @@ <h1 id="arch-covariance-kernel-parzenriesz-rate">arch.covariance.kernel.ParzenRi
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.QuadraticSpectral.bandwidth.html b/covariance/generated/generated/arch.covariance.kernel.QuadraticSpectral.bandwidth.html
index 2fa7450e25..893c0955c3 100644
--- a/covariance/generated/generated/arch.covariance.kernel.QuadraticSpectral.bandwidth.html
+++ b/covariance/generated/generated/arch.covariance.kernel.QuadraticSpectral.bandwidth.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.QuadraticSpectral.bandwidth - arch 6.1.0</title>
+        <title>arch.covariance.kernel.QuadraticSpectral.bandwidth - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-quadraticspectral-bandwidth">arch.covariance.kernel.QuadraticSpectral.bandwidth<a class="headerlink" href="#arch-covariance-kernel-quadraticspectral-bandwidth" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-quadraticspectral-bandwidth">arch.covariance.kernel.QuadraticSpectral.bandwidth<a class="headerlink" href="#arch-covariance-kernel-quadraticspectral-bandwidth" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.QuadraticSpectral.bandwidth">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">QuadraticSpectral.</span></span><span class="sig-name descname"><span class="pre">bandwidth</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral.bandwidth" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">QuadraticSpectral.</span></span><span class="sig-name descname"><span class="pre">bandwidth</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral.bandwidth" title="Link to this definition">¶</a></dt>
 <dd><p>The bandwidth used by the covariance estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.QuadraticSpectral.bandwidth-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral.bandwidth-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The user-provided or estimated optimal bandwidth.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.QuadraticSpectral.bandwidth-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral.bandwidth-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-quadraticspectral-bandwidth">arch.covariance.kern
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-quadraticspectral-bandwidth">arch.covariance.kern
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.QuadraticSpectral.bandwidth_scale.html b/covariance/generated/generated/arch.covariance.kernel.QuadraticSpectral.bandwidth_scale.html
index 5bbcaf00f1..0b9c03d0a4 100644
--- a/covariance/generated/generated/arch.covariance.kernel.QuadraticSpectral.bandwidth_scale.html
+++ b/covariance/generated/generated/arch.covariance.kernel.QuadraticSpectral.bandwidth_scale.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.QuadraticSpectral.bandwidth_scale - arch 6.1.0</title>
+        <title>arch.covariance.kernel.QuadraticSpectral.bandwidth_scale - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-quadraticspectral-bandwidth-scale">arch.covariance.kernel.QuadraticSpectral.bandwidth_scale<a class="headerlink" href="#arch-covariance-kernel-quadraticspectral-bandwidth-scale" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-quadraticspectral-bandwidth-scale">arch.covariance.kernel.QuadraticSpectral.bandwidth_scale<a class="headerlink" href="#arch-covariance-kernel-quadraticspectral-bandwidth-scale" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.QuadraticSpectral.bandwidth_scale">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">QuadraticSpectral.</span></span><span class="sig-name descname"><span class="pre">bandwidth_scale</span></span><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral.bandwidth_scale" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">QuadraticSpectral.</span></span><span class="sig-name descname"><span class="pre">bandwidth_scale</span></span><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral.bandwidth_scale" title="Link to this definition">¶</a></dt>
 <dd><p>The power used in optimal bandwidth calculation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.QuadraticSpectral.bandwidth_scale-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral.bandwidth_scale-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The power value used in the optimal bandwidth calculation.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.QuadraticSpectral.bandwidth_scale-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral.bandwidth_scale-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-quadraticspectral-bandwidth-scale">arch.covarianc
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-quadraticspectral-bandwidth-scale">arch.covarianc
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.QuadraticSpectral.centered.html b/covariance/generated/generated/arch.covariance.kernel.QuadraticSpectral.centered.html
index fa9d3db248..2af191996a 100644
--- a/covariance/generated/generated/arch.covariance.kernel.QuadraticSpectral.centered.html
+++ b/covariance/generated/generated/arch.covariance.kernel.QuadraticSpectral.centered.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.QuadraticSpectral.centered - arch 6.1.0</title>
+        <title>arch.covariance.kernel.QuadraticSpectral.centered - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-quadraticspectral-centered">arch.covariance.kernel.QuadraticSpectral.centered<a class="headerlink" href="#arch-covariance-kernel-quadraticspectral-centered" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-quadraticspectral-centered">arch.covariance.kernel.QuadraticSpectral.centered<a class="headerlink" href="#arch-covariance-kernel-quadraticspectral-centered" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.QuadraticSpectral.centered">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">QuadraticSpectral.</span></span><span class="sig-name descname"><span class="pre">centered</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral.centered" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">QuadraticSpectral.</span></span><span class="sig-name descname"><span class="pre">centered</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral.centered" title="Link to this definition">¶</a></dt>
 <dd><p>Flag indicating whether the data are centered (demeaned).</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.QuadraticSpectral.centered-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral.centered-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>A flag indicating whether the estimator is centered.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.QuadraticSpectral.centered-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral.centered-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-quadraticspectral-centered">arch.covariance.kerne
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
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@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-quadraticspectral-centered">arch.covariance.kerne
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.QuadraticSpectral.cov.html b/covariance/generated/generated/arch.covariance.kernel.QuadraticSpectral.cov.html
index 9140fd71c8..d224678e2e 100644
--- a/covariance/generated/generated/arch.covariance.kernel.QuadraticSpectral.cov.html
+++ b/covariance/generated/generated/arch.covariance.kernel.QuadraticSpectral.cov.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.QuadraticSpectral.cov - arch 6.1.0</title>
+        <title>arch.covariance.kernel.QuadraticSpectral.cov - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,10 +938,10 @@
                   
 
 
-<h1 id="arch-covariance-kernel-quadraticspectral-cov">arch.covariance.kernel.QuadraticSpectral.cov<a class="headerlink" href="#arch-covariance-kernel-quadraticspectral-cov" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-quadraticspectral-cov">arch.covariance.kernel.QuadraticSpectral.cov<a class="headerlink" href="#arch-covariance-kernel-quadraticspectral-cov" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.QuadraticSpectral.cov">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">QuadraticSpectral.</span></span><span class="sig-name descname"><span class="pre">cov</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.covariance.kernel.CovarianceEstimate.html#arch.covariance.kernel.CovarianceEstimate" title="arch.covariance.kernel.CovarianceEstimate (Python class) — Covariance estimate using a long-run covariance estimator"><span class="pre">CovarianceEstimate</span></a></span><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral.cov" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">QuadraticSpectral.</span></span><span class="sig-name descname"><span class="pre">cov</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.covariance.kernel.CovarianceEstimate.html#arch.covariance.kernel.CovarianceEstimate" title="arch.covariance.kernel.CovarianceEstimate (Python class) — Covariance estimate using a long-run covariance estimator"><span class="pre">CovarianceEstimate</span></a></span><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral.cov" title="Link to this definition">¶</a></dt>
 <dd><p>The estimated covariances.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.QuadraticSpectral.cov-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral.cov-returns" title="Permalink to this headline">¶</a></dt>
@@ -955,7 +955,7 @@ <h1 id="arch-covariance-kernel-quadraticspectral-cov">arch.covariance.kernel.Qua
 </p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.QuadraticSpectral.cov-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral.cov-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference internal" href="../arch.covariance.kernel.CovarianceEstimate.html#arch.covariance.kernel.CovarianceEstimate" title="arch.covariance.kernel.CovarianceEstimate (Python class) — Covariance estimate using a long-run covariance estimator">CovarianceEstimate</a></p>
+<dd class="field-even"><p><a class="reference internal" href="../arch.covariance.kernel.CovarianceEstimate.html#arch.covariance.kernel.CovarianceEstimate" title="arch.covariance.kernel.CovarianceEstimate (Python class) — Covariance estimate using a long-run covariance estimator"><code class="xref py py-class docutils literal notranslate"><span class="pre">CovarianceEstimate</span></code></a></p>
 </dd>
 </dl>
 <div class="seealso admonition">
@@ -1031,7 +1031,7 @@ <h1 id="arch-covariance-kernel-quadraticspectral-cov">arch.covariance.kernel.Qua
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1078,7 +1078,7 @@ <h1 id="arch-covariance-kernel-quadraticspectral-cov">arch.covariance.kernel.Qua
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.QuadraticSpectral.force_int.html b/covariance/generated/generated/arch.covariance.kernel.QuadraticSpectral.force_int.html
index a425b96ba7..0d39434286 100644
--- a/covariance/generated/generated/arch.covariance.kernel.QuadraticSpectral.force_int.html
+++ b/covariance/generated/generated/arch.covariance.kernel.QuadraticSpectral.force_int.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.QuadraticSpectral.force_int - arch 6.1.0</title>
+        <title>arch.covariance.kernel.QuadraticSpectral.force_int - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -898,10 +898,10 @@
                   
 
 
-<h1 id="arch-covariance-kernel-quadraticspectral-force-int">arch.covariance.kernel.QuadraticSpectral.force_int<a class="headerlink" href="#arch-covariance-kernel-quadraticspectral-force-int" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-quadraticspectral-force-int">arch.covariance.kernel.QuadraticSpectral.force_int<a class="headerlink" href="#arch-covariance-kernel-quadraticspectral-force-int" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.QuadraticSpectral.force_int">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">QuadraticSpectral.</span></span><span class="sig-name descname"><span class="pre">force_int</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral.force_int" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">QuadraticSpectral.</span></span><span class="sig-name descname"><span class="pre">force_int</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral.force_int" title="Link to this definition">¶</a></dt>
 <dd><p>Flag indicating whether the bandwidth is restricted to be an integer.</p>
 </dd></dl>
 
@@ -972,7 +972,7 @@ <h1 id="arch-covariance-kernel-quadraticspectral-force-int">arch.covariance.kern
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1019,7 +1019,7 @@ <h1 id="arch-covariance-kernel-quadraticspectral-force-int">arch.covariance.kern
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.QuadraticSpectral.kernel_const.html b/covariance/generated/generated/arch.covariance.kernel.QuadraticSpectral.kernel_const.html
index 920aa24f8b..aeed843487 100644
--- a/covariance/generated/generated/arch.covariance.kernel.QuadraticSpectral.kernel_const.html
+++ b/covariance/generated/generated/arch.covariance.kernel.QuadraticSpectral.kernel_const.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.QuadraticSpectral.kernel_const - arch 6.1.0</title>
+        <title>arch.covariance.kernel.QuadraticSpectral.kernel_const - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-quadraticspectral-kernel-const">arch.covariance.kernel.QuadraticSpectral.kernel_const<a class="headerlink" href="#arch-covariance-kernel-quadraticspectral-kernel-const" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-quadraticspectral-kernel-const">arch.covariance.kernel.QuadraticSpectral.kernel_const<a class="headerlink" href="#arch-covariance-kernel-quadraticspectral-kernel-const" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.QuadraticSpectral.kernel_const">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">QuadraticSpectral.</span></span><span class="sig-name descname"><span class="pre">kernel_const</span></span><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral.kernel_const" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">QuadraticSpectral.</span></span><span class="sig-name descname"><span class="pre">kernel_const</span></span><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral.kernel_const" title="Link to this definition">¶</a></dt>
 <dd><p>The constant used in optimal bandwidth calculation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.QuadraticSpectral.kernel_const-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral.kernel_const-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The constant value used in the optimal bandwidth calculation.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.QuadraticSpectral.kernel_const-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral.kernel_const-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-quadraticspectral-kernel-const">arch.covariance.k
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-quadraticspectral-kernel-const">arch.covariance.k
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+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.QuadraticSpectral.kernel_weights.html b/covariance/generated/generated/arch.covariance.kernel.QuadraticSpectral.kernel_weights.html
index bd58c88a1f..ed5da4822e 100644
--- a/covariance/generated/generated/arch.covariance.kernel.QuadraticSpectral.kernel_weights.html
+++ b/covariance/generated/generated/arch.covariance.kernel.QuadraticSpectral.kernel_weights.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.QuadraticSpectral.kernel_weights - arch 6.1.0</title>
+        <title>arch.covariance.kernel.QuadraticSpectral.kernel_weights - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-quadraticspectral-kernel-weights">arch.covariance.kernel.QuadraticSpectral.kernel_weights<a class="headerlink" href="#arch-covariance-kernel-quadraticspectral-kernel-weights" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-quadraticspectral-kernel-weights">arch.covariance.kernel.QuadraticSpectral.kernel_weights<a class="headerlink" href="#arch-covariance-kernel-quadraticspectral-kernel-weights" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.QuadraticSpectral.kernel_weights">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">QuadraticSpectral.</span></span><span class="sig-name descname"><span class="pre">kernel_weights</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">ndarray</span></a></span><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral.kernel_weights" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">QuadraticSpectral.</span></span><span class="sig-name descname"><span class="pre">kernel_weights</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral.kernel_weights" title="Link to this definition">¶</a></dt>
 <dd><p>Weights used in covariance calculation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.QuadraticSpectral.kernel_weights-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral.kernel_weights-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The weight vector including 1 in position 0.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.QuadraticSpectral.kernel_weights-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral.kernel_weights-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p>ndarray</p>
+<dd class="field-even"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-quadraticspectral-kernel-weights">arch.covariance
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-quadraticspectral-kernel-weights">arch.covariance
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.QuadraticSpectral.name.html b/covariance/generated/generated/arch.covariance.kernel.QuadraticSpectral.name.html
index 2bdf73661e..86fbbb6875 100644
--- a/covariance/generated/generated/arch.covariance.kernel.QuadraticSpectral.name.html
+++ b/covariance/generated/generated/arch.covariance.kernel.QuadraticSpectral.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.QuadraticSpectral.name - arch 6.1.0</title>
+        <title>arch.covariance.kernel.QuadraticSpectral.name - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-quadraticspectral-name">arch.covariance.kernel.QuadraticSpectral.name<a class="headerlink" href="#arch-covariance-kernel-quadraticspectral-name" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-quadraticspectral-name">arch.covariance.kernel.QuadraticSpectral.name<a class="headerlink" href="#arch-covariance-kernel-quadraticspectral-name" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.QuadraticSpectral.name">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">QuadraticSpectral.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral.name" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">QuadraticSpectral.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral.name" title="Link to this definition">¶</a></dt>
 <dd><p>The covariance estimator’s name.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.QuadraticSpectral.name-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral.name-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The covariance estimator’s name.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.QuadraticSpectral.name-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral.name-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-quadraticspectral-name">arch.covariance.kernel.Qu
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-quadraticspectral-name">arch.covariance.kernel.Qu
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diff --git a/covariance/generated/generated/arch.covariance.kernel.QuadraticSpectral.opt_bandwidth.html b/covariance/generated/generated/arch.covariance.kernel.QuadraticSpectral.opt_bandwidth.html
index ef54cedaef..1913b5de4e 100644
--- a/covariance/generated/generated/arch.covariance.kernel.QuadraticSpectral.opt_bandwidth.html
+++ b/covariance/generated/generated/arch.covariance.kernel.QuadraticSpectral.opt_bandwidth.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.QuadraticSpectral.opt_bandwidth - arch 6.1.0</title>
+        <title>arch.covariance.kernel.QuadraticSpectral.opt_bandwidth - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-quadraticspectral-opt-bandwidth">arch.covariance.kernel.QuadraticSpectral.opt_bandwidth<a class="headerlink" href="#arch-covariance-kernel-quadraticspectral-opt-bandwidth" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-quadraticspectral-opt-bandwidth">arch.covariance.kernel.QuadraticSpectral.opt_bandwidth<a class="headerlink" href="#arch-covariance-kernel-quadraticspectral-opt-bandwidth" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.QuadraticSpectral.opt_bandwidth">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">QuadraticSpectral.</span></span><span class="sig-name descname"><span class="pre">opt_bandwidth</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral.opt_bandwidth" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">QuadraticSpectral.</span></span><span class="sig-name descname"><span class="pre">opt_bandwidth</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral.opt_bandwidth" title="Link to this definition">¶</a></dt>
 <dd><p>Estimate optimal bandwidth.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.QuadraticSpectral.opt_bandwidth-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral.opt_bandwidth-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The estimated optimal bandwidth.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.QuadraticSpectral.opt_bandwidth-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral.opt_bandwidth-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -1026,7 +1026,7 @@ <h1 id="arch-covariance-kernel-quadraticspectral-opt-bandwidth">arch.covariance.
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1073,10 +1073,10 @@ <h1 id="arch-covariance-kernel-quadraticspectral-opt-bandwidth">arch.covariance.
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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+        <script id="MathJax-script" src="../../../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
     
   </body>
 </html>
\ No newline at end of file
diff --git a/covariance/generated/generated/arch.covariance.kernel.QuadraticSpectral.rate.html b/covariance/generated/generated/arch.covariance.kernel.QuadraticSpectral.rate.html
index 6fb9dfb867..7c5b9d6508 100644
--- a/covariance/generated/generated/arch.covariance.kernel.QuadraticSpectral.rate.html
+++ b/covariance/generated/generated/arch.covariance.kernel.QuadraticSpectral.rate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.QuadraticSpectral.rate - arch 6.1.0</title>
+        <title>arch.covariance.kernel.QuadraticSpectral.rate - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,10 +938,10 @@
                   
 
 
-<h1 id="arch-covariance-kernel-quadraticspectral-rate">arch.covariance.kernel.QuadraticSpectral.rate<a class="headerlink" href="#arch-covariance-kernel-quadraticspectral-rate" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-quadraticspectral-rate">arch.covariance.kernel.QuadraticSpectral.rate<a class="headerlink" href="#arch-covariance-kernel-quadraticspectral-rate" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.QuadraticSpectral.rate">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">QuadraticSpectral.</span></span><span class="sig-name descname"><span class="pre">rate</span></span><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral.rate" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">QuadraticSpectral.</span></span><span class="sig-name descname"><span class="pre">rate</span></span><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral.rate" title="Link to this definition">¶</a></dt>
 <dd><p>The optimal rate used in bandwidth selection.</p>
 <p>Controls the number of lags used in the variance estimate that
 determines the estimate of the optimal bandwidth.</p>
@@ -950,7 +950,7 @@ <h1 id="arch-covariance-kernel-quadraticspectral-rate">arch.covariance.kernel.Qu
 <dd class="field-odd"><p>The rate used in bandwidth selection.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.QuadraticSpectral.rate-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral.rate-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1022,7 +1022,7 @@ <h1 id="arch-covariance-kernel-quadraticspectral-rate">arch.covariance.kernel.Qu
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1069,7 +1069,7 @@ <h1 id="arch-covariance-kernel-quadraticspectral-rate">arch.covariance.kernel.Qu
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.TukeyHamming.bandwidth.html b/covariance/generated/generated/arch.covariance.kernel.TukeyHamming.bandwidth.html
index e2aa834420..b7fb95142a 100644
--- a/covariance/generated/generated/arch.covariance.kernel.TukeyHamming.bandwidth.html
+++ b/covariance/generated/generated/arch.covariance.kernel.TukeyHamming.bandwidth.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.TukeyHamming.bandwidth - arch 6.1.0</title>
+        <title>arch.covariance.kernel.TukeyHamming.bandwidth - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-tukeyhamming-bandwidth">arch.covariance.kernel.TukeyHamming.bandwidth<a class="headerlink" href="#arch-covariance-kernel-tukeyhamming-bandwidth" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-tukeyhamming-bandwidth">arch.covariance.kernel.TukeyHamming.bandwidth<a class="headerlink" href="#arch-covariance-kernel-tukeyhamming-bandwidth" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.TukeyHamming.bandwidth">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyHamming.</span></span><span class="sig-name descname"><span class="pre">bandwidth</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming.bandwidth" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyHamming.</span></span><span class="sig-name descname"><span class="pre">bandwidth</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming.bandwidth" title="Link to this definition">¶</a></dt>
 <dd><p>The bandwidth used by the covariance estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.TukeyHamming.bandwidth-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming.bandwidth-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The user-provided or estimated optimal bandwidth.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.TukeyHamming.bandwidth-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming.bandwidth-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-tukeyhamming-bandwidth">arch.covariance.kernel.Tu
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-tukeyhamming-bandwidth">arch.covariance.kernel.Tu
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
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diff --git a/covariance/generated/generated/arch.covariance.kernel.TukeyHamming.bandwidth_scale.html b/covariance/generated/generated/arch.covariance.kernel.TukeyHamming.bandwidth_scale.html
index 0dad7c15b3..3b759ed9b9 100644
--- a/covariance/generated/generated/arch.covariance.kernel.TukeyHamming.bandwidth_scale.html
+++ b/covariance/generated/generated/arch.covariance.kernel.TukeyHamming.bandwidth_scale.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.TukeyHamming.bandwidth_scale - arch 6.1.0</title>
+        <title>arch.covariance.kernel.TukeyHamming.bandwidth_scale - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-tukeyhamming-bandwidth-scale">arch.covariance.kernel.TukeyHamming.bandwidth_scale<a class="headerlink" href="#arch-covariance-kernel-tukeyhamming-bandwidth-scale" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-tukeyhamming-bandwidth-scale">arch.covariance.kernel.TukeyHamming.bandwidth_scale<a class="headerlink" href="#arch-covariance-kernel-tukeyhamming-bandwidth-scale" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.TukeyHamming.bandwidth_scale">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyHamming.</span></span><span class="sig-name descname"><span class="pre">bandwidth_scale</span></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming.bandwidth_scale" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyHamming.</span></span><span class="sig-name descname"><span class="pre">bandwidth_scale</span></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming.bandwidth_scale" title="Link to this definition">¶</a></dt>
 <dd><p>The power used in optimal bandwidth calculation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.TukeyHamming.bandwidth_scale-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming.bandwidth_scale-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The power value used in the optimal bandwidth calculation.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.TukeyHamming.bandwidth_scale-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming.bandwidth_scale-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-tukeyhamming-bandwidth-scale">arch.covariance.ker
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-tukeyhamming-bandwidth-scale">arch.covariance.ker
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.TukeyHamming.centered.html b/covariance/generated/generated/arch.covariance.kernel.TukeyHamming.centered.html
index 559be05305..5d53638fa9 100644
--- a/covariance/generated/generated/arch.covariance.kernel.TukeyHamming.centered.html
+++ b/covariance/generated/generated/arch.covariance.kernel.TukeyHamming.centered.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.TukeyHamming.centered - arch 6.1.0</title>
+        <title>arch.covariance.kernel.TukeyHamming.centered - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-tukeyhamming-centered">arch.covariance.kernel.TukeyHamming.centered<a class="headerlink" href="#arch-covariance-kernel-tukeyhamming-centered" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-tukeyhamming-centered">arch.covariance.kernel.TukeyHamming.centered<a class="headerlink" href="#arch-covariance-kernel-tukeyhamming-centered" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.TukeyHamming.centered">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyHamming.</span></span><span class="sig-name descname"><span class="pre">centered</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming.centered" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyHamming.</span></span><span class="sig-name descname"><span class="pre">centered</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming.centered" title="Link to this definition">¶</a></dt>
 <dd><p>Flag indicating whether the data are centered (demeaned).</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.TukeyHamming.centered-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming.centered-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>A flag indicating whether the estimator is centered.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.TukeyHamming.centered-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming.centered-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-tukeyhamming-centered">arch.covariance.kernel.Tuk
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-tukeyhamming-centered">arch.covariance.kernel.Tuk
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.TukeyHamming.cov.html b/covariance/generated/generated/arch.covariance.kernel.TukeyHamming.cov.html
index 63c701ef8b..bb16bd344f 100644
--- a/covariance/generated/generated/arch.covariance.kernel.TukeyHamming.cov.html
+++ b/covariance/generated/generated/arch.covariance.kernel.TukeyHamming.cov.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.TukeyHamming.cov - arch 6.1.0</title>
+        <title>arch.covariance.kernel.TukeyHamming.cov - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,10 +938,10 @@
                   
 
 
-<h1 id="arch-covariance-kernel-tukeyhamming-cov">arch.covariance.kernel.TukeyHamming.cov<a class="headerlink" href="#arch-covariance-kernel-tukeyhamming-cov" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-tukeyhamming-cov">arch.covariance.kernel.TukeyHamming.cov<a class="headerlink" href="#arch-covariance-kernel-tukeyhamming-cov" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.TukeyHamming.cov">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyHamming.</span></span><span class="sig-name descname"><span class="pre">cov</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.covariance.kernel.CovarianceEstimate.html#arch.covariance.kernel.CovarianceEstimate" title="arch.covariance.kernel.CovarianceEstimate (Python class) — Covariance estimate using a long-run covariance estimator"><span class="pre">CovarianceEstimate</span></a></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming.cov" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyHamming.</span></span><span class="sig-name descname"><span class="pre">cov</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.covariance.kernel.CovarianceEstimate.html#arch.covariance.kernel.CovarianceEstimate" title="arch.covariance.kernel.CovarianceEstimate (Python class) — Covariance estimate using a long-run covariance estimator"><span class="pre">CovarianceEstimate</span></a></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming.cov" title="Link to this definition">¶</a></dt>
 <dd><p>The estimated covariances.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.TukeyHamming.cov-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming.cov-returns" title="Permalink to this headline">¶</a></dt>
@@ -955,7 +955,7 @@ <h1 id="arch-covariance-kernel-tukeyhamming-cov">arch.covariance.kernel.TukeyHam
 </p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.TukeyHamming.cov-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming.cov-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference internal" href="../arch.covariance.kernel.CovarianceEstimate.html#arch.covariance.kernel.CovarianceEstimate" title="arch.covariance.kernel.CovarianceEstimate (Python class) — Covariance estimate using a long-run covariance estimator">CovarianceEstimate</a></p>
+<dd class="field-even"><p><a class="reference internal" href="../arch.covariance.kernel.CovarianceEstimate.html#arch.covariance.kernel.CovarianceEstimate" title="arch.covariance.kernel.CovarianceEstimate (Python class) — Covariance estimate using a long-run covariance estimator"><code class="xref py py-class docutils literal notranslate"><span class="pre">CovarianceEstimate</span></code></a></p>
 </dd>
 </dl>
 <div class="seealso admonition">
@@ -1031,7 +1031,7 @@ <h1 id="arch-covariance-kernel-tukeyhamming-cov">arch.covariance.kernel.TukeyHam
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1078,7 +1078,7 @@ <h1 id="arch-covariance-kernel-tukeyhamming-cov">arch.covariance.kernel.TukeyHam
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.TukeyHamming.force_int.html b/covariance/generated/generated/arch.covariance.kernel.TukeyHamming.force_int.html
index acf97727d0..6af036f997 100644
--- a/covariance/generated/generated/arch.covariance.kernel.TukeyHamming.force_int.html
+++ b/covariance/generated/generated/arch.covariance.kernel.TukeyHamming.force_int.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.TukeyHamming.force_int - arch 6.1.0</title>
+        <title>arch.covariance.kernel.TukeyHamming.force_int - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -898,10 +898,10 @@
                   
 
 
-<h1 id="arch-covariance-kernel-tukeyhamming-force-int">arch.covariance.kernel.TukeyHamming.force_int<a class="headerlink" href="#arch-covariance-kernel-tukeyhamming-force-int" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-tukeyhamming-force-int">arch.covariance.kernel.TukeyHamming.force_int<a class="headerlink" href="#arch-covariance-kernel-tukeyhamming-force-int" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.TukeyHamming.force_int">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyHamming.</span></span><span class="sig-name descname"><span class="pre">force_int</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming.force_int" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyHamming.</span></span><span class="sig-name descname"><span class="pre">force_int</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming.force_int" title="Link to this definition">¶</a></dt>
 <dd><p>Flag indicating whether the bandwidth is restricted to be an integer.</p>
 </dd></dl>
 
@@ -972,7 +972,7 @@ <h1 id="arch-covariance-kernel-tukeyhamming-force-int">arch.covariance.kernel.Tu
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1019,7 +1019,7 @@ <h1 id="arch-covariance-kernel-tukeyhamming-force-int">arch.covariance.kernel.Tu
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.TukeyHamming.kernel_const.html b/covariance/generated/generated/arch.covariance.kernel.TukeyHamming.kernel_const.html
index 6ac7189ea5..6fb6bd5ffd 100644
--- a/covariance/generated/generated/arch.covariance.kernel.TukeyHamming.kernel_const.html
+++ b/covariance/generated/generated/arch.covariance.kernel.TukeyHamming.kernel_const.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.TukeyHamming.kernel_const - arch 6.1.0</title>
+        <title>arch.covariance.kernel.TukeyHamming.kernel_const - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-tukeyhamming-kernel-const">arch.covariance.kernel.TukeyHamming.kernel_const<a class="headerlink" href="#arch-covariance-kernel-tukeyhamming-kernel-const" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-tukeyhamming-kernel-const">arch.covariance.kernel.TukeyHamming.kernel_const<a class="headerlink" href="#arch-covariance-kernel-tukeyhamming-kernel-const" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.TukeyHamming.kernel_const">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyHamming.</span></span><span class="sig-name descname"><span class="pre">kernel_const</span></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming.kernel_const" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyHamming.</span></span><span class="sig-name descname"><span class="pre">kernel_const</span></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming.kernel_const" title="Link to this definition">¶</a></dt>
 <dd><p>The constant used in optimal bandwidth calculation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.TukeyHamming.kernel_const-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming.kernel_const-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The constant value used in the optimal bandwidth calculation.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.TukeyHamming.kernel_const-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming.kernel_const-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-tukeyhamming-kernel-const">arch.covariance.kernel
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-tukeyhamming-kernel-const">arch.covariance.kernel
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.TukeyHamming.kernel_weights.html b/covariance/generated/generated/arch.covariance.kernel.TukeyHamming.kernel_weights.html
index 79674b5182..513f9dc207 100644
--- a/covariance/generated/generated/arch.covariance.kernel.TukeyHamming.kernel_weights.html
+++ b/covariance/generated/generated/arch.covariance.kernel.TukeyHamming.kernel_weights.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.TukeyHamming.kernel_weights - arch 6.1.0</title>
+        <title>arch.covariance.kernel.TukeyHamming.kernel_weights - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-tukeyhamming-kernel-weights">arch.covariance.kernel.TukeyHamming.kernel_weights<a class="headerlink" href="#arch-covariance-kernel-tukeyhamming-kernel-weights" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-tukeyhamming-kernel-weights">arch.covariance.kernel.TukeyHamming.kernel_weights<a class="headerlink" href="#arch-covariance-kernel-tukeyhamming-kernel-weights" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.TukeyHamming.kernel_weights">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyHamming.</span></span><span class="sig-name descname"><span class="pre">kernel_weights</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">ndarray</span></a></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming.kernel_weights" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyHamming.</span></span><span class="sig-name descname"><span class="pre">kernel_weights</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming.kernel_weights" title="Link to this definition">¶</a></dt>
 <dd><p>Weights used in covariance calculation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.TukeyHamming.kernel_weights-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming.kernel_weights-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The weight vector including 1 in position 0.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.TukeyHamming.kernel_weights-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming.kernel_weights-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p>ndarray</p>
+<dd class="field-even"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-tukeyhamming-kernel-weights">arch.covariance.kern
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-tukeyhamming-kernel-weights">arch.covariance.kern
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.TukeyHamming.name.html b/covariance/generated/generated/arch.covariance.kernel.TukeyHamming.name.html
index aa57ba4b0f..fca265e5cf 100644
--- a/covariance/generated/generated/arch.covariance.kernel.TukeyHamming.name.html
+++ b/covariance/generated/generated/arch.covariance.kernel.TukeyHamming.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.TukeyHamming.name - arch 6.1.0</title>
+        <title>arch.covariance.kernel.TukeyHamming.name - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-tukeyhamming-name">arch.covariance.kernel.TukeyHamming.name<a class="headerlink" href="#arch-covariance-kernel-tukeyhamming-name" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-tukeyhamming-name">arch.covariance.kernel.TukeyHamming.name<a class="headerlink" href="#arch-covariance-kernel-tukeyhamming-name" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.TukeyHamming.name">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyHamming.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming.name" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyHamming.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming.name" title="Link to this definition">¶</a></dt>
 <dd><p>The covariance estimator’s name.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.TukeyHamming.name-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming.name-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The covariance estimator’s name.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.TukeyHamming.name-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming.name-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-tukeyhamming-name">arch.covariance.kernel.TukeyHa
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-tukeyhamming-name">arch.covariance.kernel.TukeyHa
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.TukeyHamming.opt_bandwidth.html b/covariance/generated/generated/arch.covariance.kernel.TukeyHamming.opt_bandwidth.html
index 2b0b3f48cd..431903a54d 100644
--- a/covariance/generated/generated/arch.covariance.kernel.TukeyHamming.opt_bandwidth.html
+++ b/covariance/generated/generated/arch.covariance.kernel.TukeyHamming.opt_bandwidth.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.TukeyHamming.opt_bandwidth - arch 6.1.0</title>
+        <title>arch.covariance.kernel.TukeyHamming.opt_bandwidth - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-tukeyhamming-opt-bandwidth">arch.covariance.kernel.TukeyHamming.opt_bandwidth<a class="headerlink" href="#arch-covariance-kernel-tukeyhamming-opt-bandwidth" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-tukeyhamming-opt-bandwidth">arch.covariance.kernel.TukeyHamming.opt_bandwidth<a class="headerlink" href="#arch-covariance-kernel-tukeyhamming-opt-bandwidth" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.TukeyHamming.opt_bandwidth">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyHamming.</span></span><span class="sig-name descname"><span class="pre">opt_bandwidth</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming.opt_bandwidth" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyHamming.</span></span><span class="sig-name descname"><span class="pre">opt_bandwidth</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming.opt_bandwidth" title="Link to this definition">¶</a></dt>
 <dd><p>Estimate optimal bandwidth.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.TukeyHamming.opt_bandwidth-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming.opt_bandwidth-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The estimated optimal bandwidth.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.TukeyHamming.opt_bandwidth-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming.opt_bandwidth-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -1026,7 +1026,7 @@ <h1 id="arch-covariance-kernel-tukeyhamming-opt-bandwidth">arch.covariance.kerne
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1073,10 +1073,10 @@ <h1 id="arch-covariance-kernel-tukeyhamming-opt-bandwidth">arch.covariance.kerne
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+        <script id="MathJax-script" src="../../../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
     
   </body>
 </html>
\ No newline at end of file
diff --git a/covariance/generated/generated/arch.covariance.kernel.TukeyHamming.rate.html b/covariance/generated/generated/arch.covariance.kernel.TukeyHamming.rate.html
index 106bdb4350..bab9059c22 100644
--- a/covariance/generated/generated/arch.covariance.kernel.TukeyHamming.rate.html
+++ b/covariance/generated/generated/arch.covariance.kernel.TukeyHamming.rate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.TukeyHamming.rate - arch 6.1.0</title>
+        <title>arch.covariance.kernel.TukeyHamming.rate - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,10 +938,10 @@
                   
 
 
-<h1 id="arch-covariance-kernel-tukeyhamming-rate">arch.covariance.kernel.TukeyHamming.rate<a class="headerlink" href="#arch-covariance-kernel-tukeyhamming-rate" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-tukeyhamming-rate">arch.covariance.kernel.TukeyHamming.rate<a class="headerlink" href="#arch-covariance-kernel-tukeyhamming-rate" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.TukeyHamming.rate">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyHamming.</span></span><span class="sig-name descname"><span class="pre">rate</span></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming.rate" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyHamming.</span></span><span class="sig-name descname"><span class="pre">rate</span></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming.rate" title="Link to this definition">¶</a></dt>
 <dd><p>The optimal rate used in bandwidth selection.</p>
 <p>Controls the number of lags used in the variance estimate that
 determines the estimate of the optimal bandwidth.</p>
@@ -950,7 +950,7 @@ <h1 id="arch-covariance-kernel-tukeyhamming-rate">arch.covariance.kernel.TukeyHa
 <dd class="field-odd"><p>The rate used in bandwidth selection.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.TukeyHamming.rate-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming.rate-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1022,7 +1022,7 @@ <h1 id="arch-covariance-kernel-tukeyhamming-rate">arch.covariance.kernel.TukeyHa
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1069,7 +1069,7 @@ <h1 id="arch-covariance-kernel-tukeyhamming-rate">arch.covariance.kernel.TukeyHa
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.TukeyHanning.bandwidth.html b/covariance/generated/generated/arch.covariance.kernel.TukeyHanning.bandwidth.html
index 328383aace..c2a6ccae26 100644
--- a/covariance/generated/generated/arch.covariance.kernel.TukeyHanning.bandwidth.html
+++ b/covariance/generated/generated/arch.covariance.kernel.TukeyHanning.bandwidth.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.TukeyHanning.bandwidth - arch 6.1.0</title>
+        <title>arch.covariance.kernel.TukeyHanning.bandwidth - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
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+    arch 6.2.0
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@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-tukeyhanning-bandwidth">arch.covariance.kernel.TukeyHanning.bandwidth<a class="headerlink" href="#arch-covariance-kernel-tukeyhanning-bandwidth" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-tukeyhanning-bandwidth">arch.covariance.kernel.TukeyHanning.bandwidth<a class="headerlink" href="#arch-covariance-kernel-tukeyhanning-bandwidth" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.TukeyHanning.bandwidth">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyHanning.</span></span><span class="sig-name descname"><span class="pre">bandwidth</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning.bandwidth" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyHanning.</span></span><span class="sig-name descname"><span class="pre">bandwidth</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning.bandwidth" title="Link to this definition">¶</a></dt>
 <dd><p>The bandwidth used by the covariance estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.TukeyHanning.bandwidth-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning.bandwidth-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The user-provided or estimated optimal bandwidth.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.TukeyHanning.bandwidth-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning.bandwidth-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-tukeyhanning-bandwidth">arch.covariance.kernel.Tu
   
     Created using
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-    7.0.1.
+    7.2.6.
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@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-tukeyhanning-bandwidth">arch.covariance.kernel.Tu
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diff --git a/covariance/generated/generated/arch.covariance.kernel.TukeyHanning.bandwidth_scale.html b/covariance/generated/generated/arch.covariance.kernel.TukeyHanning.bandwidth_scale.html
index cc4b397fb7..695e315b34 100644
--- a/covariance/generated/generated/arch.covariance.kernel.TukeyHanning.bandwidth_scale.html
+++ b/covariance/generated/generated/arch.covariance.kernel.TukeyHanning.bandwidth_scale.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.TukeyHanning.bandwidth_scale - arch 6.1.0</title>
+        <title>arch.covariance.kernel.TukeyHanning.bandwidth_scale - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-tukeyhanning-bandwidth-scale">arch.covariance.kernel.TukeyHanning.bandwidth_scale<a class="headerlink" href="#arch-covariance-kernel-tukeyhanning-bandwidth-scale" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-tukeyhanning-bandwidth-scale">arch.covariance.kernel.TukeyHanning.bandwidth_scale<a class="headerlink" href="#arch-covariance-kernel-tukeyhanning-bandwidth-scale" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.TukeyHanning.bandwidth_scale">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyHanning.</span></span><span class="sig-name descname"><span class="pre">bandwidth_scale</span></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning.bandwidth_scale" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyHanning.</span></span><span class="sig-name descname"><span class="pre">bandwidth_scale</span></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning.bandwidth_scale" title="Link to this definition">¶</a></dt>
 <dd><p>The power used in optimal bandwidth calculation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.TukeyHanning.bandwidth_scale-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning.bandwidth_scale-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The power value used in the optimal bandwidth calculation.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.TukeyHanning.bandwidth_scale-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning.bandwidth_scale-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-tukeyhanning-bandwidth-scale">arch.covariance.ker
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-tukeyhanning-bandwidth-scale">arch.covariance.ker
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
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diff --git a/covariance/generated/generated/arch.covariance.kernel.TukeyHanning.centered.html b/covariance/generated/generated/arch.covariance.kernel.TukeyHanning.centered.html
index 4c84f99913..bb366da2aa 100644
--- a/covariance/generated/generated/arch.covariance.kernel.TukeyHanning.centered.html
+++ b/covariance/generated/generated/arch.covariance.kernel.TukeyHanning.centered.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.TukeyHanning.centered - arch 6.1.0</title>
+        <title>arch.covariance.kernel.TukeyHanning.centered - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-tukeyhanning-centered">arch.covariance.kernel.TukeyHanning.centered<a class="headerlink" href="#arch-covariance-kernel-tukeyhanning-centered" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-tukeyhanning-centered">arch.covariance.kernel.TukeyHanning.centered<a class="headerlink" href="#arch-covariance-kernel-tukeyhanning-centered" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.TukeyHanning.centered">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyHanning.</span></span><span class="sig-name descname"><span class="pre">centered</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning.centered" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyHanning.</span></span><span class="sig-name descname"><span class="pre">centered</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning.centered" title="Link to this definition">¶</a></dt>
 <dd><p>Flag indicating whether the data are centered (demeaned).</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.TukeyHanning.centered-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning.centered-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>A flag indicating whether the estimator is centered.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.TukeyHanning.centered-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning.centered-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-tukeyhanning-centered">arch.covariance.kernel.Tuk
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-tukeyhanning-centered">arch.covariance.kernel.Tuk
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
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diff --git a/covariance/generated/generated/arch.covariance.kernel.TukeyHanning.cov.html b/covariance/generated/generated/arch.covariance.kernel.TukeyHanning.cov.html
index 18812a9922..9af7ec9c57 100644
--- a/covariance/generated/generated/arch.covariance.kernel.TukeyHanning.cov.html
+++ b/covariance/generated/generated/arch.covariance.kernel.TukeyHanning.cov.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.TukeyHanning.cov - arch 6.1.0</title>
+        <title>arch.covariance.kernel.TukeyHanning.cov - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,10 +938,10 @@
                   
 
 
-<h1 id="arch-covariance-kernel-tukeyhanning-cov">arch.covariance.kernel.TukeyHanning.cov<a class="headerlink" href="#arch-covariance-kernel-tukeyhanning-cov" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-tukeyhanning-cov">arch.covariance.kernel.TukeyHanning.cov<a class="headerlink" href="#arch-covariance-kernel-tukeyhanning-cov" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.TukeyHanning.cov">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyHanning.</span></span><span class="sig-name descname"><span class="pre">cov</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.covariance.kernel.CovarianceEstimate.html#arch.covariance.kernel.CovarianceEstimate" title="arch.covariance.kernel.CovarianceEstimate (Python class) — Covariance estimate using a long-run covariance estimator"><span class="pre">CovarianceEstimate</span></a></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning.cov" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyHanning.</span></span><span class="sig-name descname"><span class="pre">cov</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.covariance.kernel.CovarianceEstimate.html#arch.covariance.kernel.CovarianceEstimate" title="arch.covariance.kernel.CovarianceEstimate (Python class) — Covariance estimate using a long-run covariance estimator"><span class="pre">CovarianceEstimate</span></a></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning.cov" title="Link to this definition">¶</a></dt>
 <dd><p>The estimated covariances.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.TukeyHanning.cov-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning.cov-returns" title="Permalink to this headline">¶</a></dt>
@@ -955,7 +955,7 @@ <h1 id="arch-covariance-kernel-tukeyhanning-cov">arch.covariance.kernel.TukeyHan
 </p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.TukeyHanning.cov-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning.cov-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference internal" href="../arch.covariance.kernel.CovarianceEstimate.html#arch.covariance.kernel.CovarianceEstimate" title="arch.covariance.kernel.CovarianceEstimate (Python class) — Covariance estimate using a long-run covariance estimator">CovarianceEstimate</a></p>
+<dd class="field-even"><p><a class="reference internal" href="../arch.covariance.kernel.CovarianceEstimate.html#arch.covariance.kernel.CovarianceEstimate" title="arch.covariance.kernel.CovarianceEstimate (Python class) — Covariance estimate using a long-run covariance estimator"><code class="xref py py-class docutils literal notranslate"><span class="pre">CovarianceEstimate</span></code></a></p>
 </dd>
 </dl>
 <div class="seealso admonition">
@@ -1031,7 +1031,7 @@ <h1 id="arch-covariance-kernel-tukeyhanning-cov">arch.covariance.kernel.TukeyHan
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1078,7 +1078,7 @@ <h1 id="arch-covariance-kernel-tukeyhanning-cov">arch.covariance.kernel.TukeyHan
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.TukeyHanning.force_int.html b/covariance/generated/generated/arch.covariance.kernel.TukeyHanning.force_int.html
index f87e353608..a985f8de18 100644
--- a/covariance/generated/generated/arch.covariance.kernel.TukeyHanning.force_int.html
+++ b/covariance/generated/generated/arch.covariance.kernel.TukeyHanning.force_int.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.TukeyHanning.force_int - arch 6.1.0</title>
+        <title>arch.covariance.kernel.TukeyHanning.force_int - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -898,10 +898,10 @@
                   
 
 
-<h1 id="arch-covariance-kernel-tukeyhanning-force-int">arch.covariance.kernel.TukeyHanning.force_int<a class="headerlink" href="#arch-covariance-kernel-tukeyhanning-force-int" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-tukeyhanning-force-int">arch.covariance.kernel.TukeyHanning.force_int<a class="headerlink" href="#arch-covariance-kernel-tukeyhanning-force-int" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.TukeyHanning.force_int">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyHanning.</span></span><span class="sig-name descname"><span class="pre">force_int</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning.force_int" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyHanning.</span></span><span class="sig-name descname"><span class="pre">force_int</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning.force_int" title="Link to this definition">¶</a></dt>
 <dd><p>Flag indicating whether the bandwidth is restricted to be an integer.</p>
 </dd></dl>
 
@@ -972,7 +972,7 @@ <h1 id="arch-covariance-kernel-tukeyhanning-force-int">arch.covariance.kernel.Tu
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1019,7 +1019,7 @@ <h1 id="arch-covariance-kernel-tukeyhanning-force-int">arch.covariance.kernel.Tu
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.TukeyHanning.kernel_const.html b/covariance/generated/generated/arch.covariance.kernel.TukeyHanning.kernel_const.html
index 80a3c183e7..e839923bd0 100644
--- a/covariance/generated/generated/arch.covariance.kernel.TukeyHanning.kernel_const.html
+++ b/covariance/generated/generated/arch.covariance.kernel.TukeyHanning.kernel_const.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.TukeyHanning.kernel_const - arch 6.1.0</title>
+        <title>arch.covariance.kernel.TukeyHanning.kernel_const - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-tukeyhanning-kernel-const">arch.covariance.kernel.TukeyHanning.kernel_const<a class="headerlink" href="#arch-covariance-kernel-tukeyhanning-kernel-const" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-tukeyhanning-kernel-const">arch.covariance.kernel.TukeyHanning.kernel_const<a class="headerlink" href="#arch-covariance-kernel-tukeyhanning-kernel-const" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.TukeyHanning.kernel_const">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyHanning.</span></span><span class="sig-name descname"><span class="pre">kernel_const</span></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning.kernel_const" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyHanning.</span></span><span class="sig-name descname"><span class="pre">kernel_const</span></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning.kernel_const" title="Link to this definition">¶</a></dt>
 <dd><p>The constant used in optimal bandwidth calculation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.TukeyHanning.kernel_const-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning.kernel_const-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The constant value used in the optimal bandwidth calculation.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.TukeyHanning.kernel_const-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning.kernel_const-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-tukeyhanning-kernel-const">arch.covariance.kernel
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-tukeyhanning-kernel-const">arch.covariance.kernel
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.TukeyHanning.kernel_weights.html b/covariance/generated/generated/arch.covariance.kernel.TukeyHanning.kernel_weights.html
index 7811585618..972e7f3d2a 100644
--- a/covariance/generated/generated/arch.covariance.kernel.TukeyHanning.kernel_weights.html
+++ b/covariance/generated/generated/arch.covariance.kernel.TukeyHanning.kernel_weights.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.TukeyHanning.kernel_weights - arch 6.1.0</title>
+        <title>arch.covariance.kernel.TukeyHanning.kernel_weights - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
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+    arch 6.2.0
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@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-tukeyhanning-kernel-weights">arch.covariance.kernel.TukeyHanning.kernel_weights<a class="headerlink" href="#arch-covariance-kernel-tukeyhanning-kernel-weights" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-tukeyhanning-kernel-weights">arch.covariance.kernel.TukeyHanning.kernel_weights<a class="headerlink" href="#arch-covariance-kernel-tukeyhanning-kernel-weights" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.TukeyHanning.kernel_weights">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyHanning.</span></span><span class="sig-name descname"><span class="pre">kernel_weights</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">ndarray</span></a></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning.kernel_weights" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyHanning.</span></span><span class="sig-name descname"><span class="pre">kernel_weights</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning.kernel_weights" title="Link to this definition">¶</a></dt>
 <dd><p>Weights used in covariance calculation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.TukeyHanning.kernel_weights-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning.kernel_weights-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The weight vector including 1 in position 0.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.TukeyHanning.kernel_weights-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning.kernel_weights-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p>ndarray</p>
+<dd class="field-even"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-tukeyhanning-kernel-weights">arch.covariance.kern
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-tukeyhanning-kernel-weights">arch.covariance.kern
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diff --git a/covariance/generated/generated/arch.covariance.kernel.TukeyHanning.name.html b/covariance/generated/generated/arch.covariance.kernel.TukeyHanning.name.html
index eaea1d8408..25946aaf4d 100644
--- a/covariance/generated/generated/arch.covariance.kernel.TukeyHanning.name.html
+++ b/covariance/generated/generated/arch.covariance.kernel.TukeyHanning.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.TukeyHanning.name - arch 6.1.0</title>
+        <title>arch.covariance.kernel.TukeyHanning.name - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-tukeyhanning-name">arch.covariance.kernel.TukeyHanning.name<a class="headerlink" href="#arch-covariance-kernel-tukeyhanning-name" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-tukeyhanning-name">arch.covariance.kernel.TukeyHanning.name<a class="headerlink" href="#arch-covariance-kernel-tukeyhanning-name" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.TukeyHanning.name">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyHanning.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning.name" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyHanning.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning.name" title="Link to this definition">¶</a></dt>
 <dd><p>The covariance estimator’s name.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.TukeyHanning.name-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning.name-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The covariance estimator’s name.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.TukeyHanning.name-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning.name-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-tukeyhanning-name">arch.covariance.kernel.TukeyHa
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-tukeyhanning-name">arch.covariance.kernel.TukeyHa
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.TukeyHanning.opt_bandwidth.html b/covariance/generated/generated/arch.covariance.kernel.TukeyHanning.opt_bandwidth.html
index f52dfc7fe9..482c53a24e 100644
--- a/covariance/generated/generated/arch.covariance.kernel.TukeyHanning.opt_bandwidth.html
+++ b/covariance/generated/generated/arch.covariance.kernel.TukeyHanning.opt_bandwidth.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.TukeyHanning.opt_bandwidth - arch 6.1.0</title>
+        <title>arch.covariance.kernel.TukeyHanning.opt_bandwidth - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-tukeyhanning-opt-bandwidth">arch.covariance.kernel.TukeyHanning.opt_bandwidth<a class="headerlink" href="#arch-covariance-kernel-tukeyhanning-opt-bandwidth" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-tukeyhanning-opt-bandwidth">arch.covariance.kernel.TukeyHanning.opt_bandwidth<a class="headerlink" href="#arch-covariance-kernel-tukeyhanning-opt-bandwidth" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.TukeyHanning.opt_bandwidth">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyHanning.</span></span><span class="sig-name descname"><span class="pre">opt_bandwidth</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning.opt_bandwidth" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyHanning.</span></span><span class="sig-name descname"><span class="pre">opt_bandwidth</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning.opt_bandwidth" title="Link to this definition">¶</a></dt>
 <dd><p>Estimate optimal bandwidth.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.TukeyHanning.opt_bandwidth-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning.opt_bandwidth-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The estimated optimal bandwidth.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.TukeyHanning.opt_bandwidth-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning.opt_bandwidth-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -1026,7 +1026,7 @@ <h1 id="arch-covariance-kernel-tukeyhanning-opt-bandwidth">arch.covariance.kerne
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1073,10 +1073,10 @@ <h1 id="arch-covariance-kernel-tukeyhanning-opt-bandwidth">arch.covariance.kerne
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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+        <script id="MathJax-script" src="../../../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
     
   </body>
 </html>
\ No newline at end of file
diff --git a/covariance/generated/generated/arch.covariance.kernel.TukeyHanning.rate.html b/covariance/generated/generated/arch.covariance.kernel.TukeyHanning.rate.html
index f8ec9919e1..150aa1dac9 100644
--- a/covariance/generated/generated/arch.covariance.kernel.TukeyHanning.rate.html
+++ b/covariance/generated/generated/arch.covariance.kernel.TukeyHanning.rate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.TukeyHanning.rate - arch 6.1.0</title>
+        <title>arch.covariance.kernel.TukeyHanning.rate - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,10 +938,10 @@
                   
 
 
-<h1 id="arch-covariance-kernel-tukeyhanning-rate">arch.covariance.kernel.TukeyHanning.rate<a class="headerlink" href="#arch-covariance-kernel-tukeyhanning-rate" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-tukeyhanning-rate">arch.covariance.kernel.TukeyHanning.rate<a class="headerlink" href="#arch-covariance-kernel-tukeyhanning-rate" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.TukeyHanning.rate">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyHanning.</span></span><span class="sig-name descname"><span class="pre">rate</span></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning.rate" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyHanning.</span></span><span class="sig-name descname"><span class="pre">rate</span></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning.rate" title="Link to this definition">¶</a></dt>
 <dd><p>The optimal rate used in bandwidth selection.</p>
 <p>Controls the number of lags used in the variance estimate that
 determines the estimate of the optimal bandwidth.</p>
@@ -950,7 +950,7 @@ <h1 id="arch-covariance-kernel-tukeyhanning-rate">arch.covariance.kernel.TukeyHa
 <dd class="field-odd"><p>The rate used in bandwidth selection.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.TukeyHanning.rate-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning.rate-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1022,7 +1022,7 @@ <h1 id="arch-covariance-kernel-tukeyhanning-rate">arch.covariance.kernel.TukeyHa
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1069,7 +1069,7 @@ <h1 id="arch-covariance-kernel-tukeyhanning-rate">arch.covariance.kernel.TukeyHa
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.bandwidth.html b/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.bandwidth.html
index 4212d2b993..0fe73a2ac1 100644
--- a/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.bandwidth.html
+++ b/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.bandwidth.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.TukeyParzen.bandwidth - arch 6.1.0</title>
+        <title>arch.covariance.kernel.TukeyParzen.bandwidth - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-tukeyparzen-bandwidth">arch.covariance.kernel.TukeyParzen.bandwidth<a class="headerlink" href="#arch-covariance-kernel-tukeyparzen-bandwidth" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-tukeyparzen-bandwidth">arch.covariance.kernel.TukeyParzen.bandwidth<a class="headerlink" href="#arch-covariance-kernel-tukeyparzen-bandwidth" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.TukeyParzen.bandwidth">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyParzen.</span></span><span class="sig-name descname"><span class="pre">bandwidth</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen.bandwidth" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyParzen.</span></span><span class="sig-name descname"><span class="pre">bandwidth</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen.bandwidth" title="Link to this definition">¶</a></dt>
 <dd><p>The bandwidth used by the covariance estimator.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.TukeyParzen.bandwidth-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen.bandwidth-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The user-provided or estimated optimal bandwidth.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.TukeyParzen.bandwidth-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen.bandwidth-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-tukeyparzen-bandwidth">arch.covariance.kernel.Tuk
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-tukeyparzen-bandwidth">arch.covariance.kernel.Tuk
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.bandwidth_scale.html b/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.bandwidth_scale.html
index fa95ff93a3..cfa6c3c07c 100644
--- a/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.bandwidth_scale.html
+++ b/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.bandwidth_scale.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.TukeyParzen.bandwidth_scale - arch 6.1.0</title>
+        <title>arch.covariance.kernel.TukeyParzen.bandwidth_scale - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-tukeyparzen-bandwidth-scale">arch.covariance.kernel.TukeyParzen.bandwidth_scale<a class="headerlink" href="#arch-covariance-kernel-tukeyparzen-bandwidth-scale" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-tukeyparzen-bandwidth-scale">arch.covariance.kernel.TukeyParzen.bandwidth_scale<a class="headerlink" href="#arch-covariance-kernel-tukeyparzen-bandwidth-scale" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.TukeyParzen.bandwidth_scale">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyParzen.</span></span><span class="sig-name descname"><span class="pre">bandwidth_scale</span></span><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen.bandwidth_scale" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyParzen.</span></span><span class="sig-name descname"><span class="pre">bandwidth_scale</span></span><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen.bandwidth_scale" title="Link to this definition">¶</a></dt>
 <dd><p>The power used in optimal bandwidth calculation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.TukeyParzen.bandwidth_scale-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen.bandwidth_scale-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The power value used in the optimal bandwidth calculation.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.TukeyParzen.bandwidth_scale-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen.bandwidth_scale-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-tukeyparzen-bandwidth-scale">arch.covariance.kern
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-tukeyparzen-bandwidth-scale">arch.covariance.kern
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.centered.html b/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.centered.html
index 191c9090d1..950aef7d4b 100644
--- a/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.centered.html
+++ b/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.centered.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.TukeyParzen.centered - arch 6.1.0</title>
+        <title>arch.covariance.kernel.TukeyParzen.centered - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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@@ -102,7 +102,7 @@
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+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-tukeyparzen-centered">arch.covariance.kernel.TukeyParzen.centered<a class="headerlink" href="#arch-covariance-kernel-tukeyparzen-centered" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-tukeyparzen-centered">arch.covariance.kernel.TukeyParzen.centered<a class="headerlink" href="#arch-covariance-kernel-tukeyparzen-centered" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.TukeyParzen.centered">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyParzen.</span></span><span class="sig-name descname"><span class="pre">centered</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen.centered" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyParzen.</span></span><span class="sig-name descname"><span class="pre">centered</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen.centered" title="Link to this definition">¶</a></dt>
 <dd><p>Flag indicating whether the data are centered (demeaned).</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.TukeyParzen.centered-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen.centered-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>A flag indicating whether the estimator is centered.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.TukeyParzen.centered-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen.centered-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-tukeyparzen-centered">arch.covariance.kernel.Tuke
   
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-    7.0.1.
+    7.2.6.
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@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-tukeyparzen-centered">arch.covariance.kernel.Tuke
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diff --git a/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.cov.html b/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.cov.html
index 1810fc3424..f9948004fb 100644
--- a/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.cov.html
+++ b/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.cov.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.TukeyParzen.cov - arch 6.1.0</title>
+        <title>arch.covariance.kernel.TukeyParzen.cov - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
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+    arch 6.2.0
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     <div class="md-nav__source">
@@ -938,10 +938,10 @@
                   
 
 
-<h1 id="arch-covariance-kernel-tukeyparzen-cov">arch.covariance.kernel.TukeyParzen.cov<a class="headerlink" href="#arch-covariance-kernel-tukeyparzen-cov" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-tukeyparzen-cov">arch.covariance.kernel.TukeyParzen.cov<a class="headerlink" href="#arch-covariance-kernel-tukeyparzen-cov" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.TukeyParzen.cov">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyParzen.</span></span><span class="sig-name descname"><span class="pre">cov</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.covariance.kernel.CovarianceEstimate.html#arch.covariance.kernel.CovarianceEstimate" title="arch.covariance.kernel.CovarianceEstimate (Python class) — Covariance estimate using a long-run covariance estimator"><span class="pre">CovarianceEstimate</span></a></span><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen.cov" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyParzen.</span></span><span class="sig-name descname"><span class="pre">cov</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.covariance.kernel.CovarianceEstimate.html#arch.covariance.kernel.CovarianceEstimate" title="arch.covariance.kernel.CovarianceEstimate (Python class) — Covariance estimate using a long-run covariance estimator"><span class="pre">CovarianceEstimate</span></a></span><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen.cov" title="Link to this definition">¶</a></dt>
 <dd><p>The estimated covariances.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.TukeyParzen.cov-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen.cov-returns" title="Permalink to this headline">¶</a></dt>
@@ -955,7 +955,7 @@ <h1 id="arch-covariance-kernel-tukeyparzen-cov">arch.covariance.kernel.TukeyParz
 </p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.TukeyParzen.cov-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen.cov-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference internal" href="../arch.covariance.kernel.CovarianceEstimate.html#arch.covariance.kernel.CovarianceEstimate" title="arch.covariance.kernel.CovarianceEstimate (Python class) — Covariance estimate using a long-run covariance estimator">CovarianceEstimate</a></p>
+<dd class="field-even"><p><a class="reference internal" href="../arch.covariance.kernel.CovarianceEstimate.html#arch.covariance.kernel.CovarianceEstimate" title="arch.covariance.kernel.CovarianceEstimate (Python class) — Covariance estimate using a long-run covariance estimator"><code class="xref py py-class docutils literal notranslate"><span class="pre">CovarianceEstimate</span></code></a></p>
 </dd>
 </dl>
 <div class="seealso admonition">
@@ -1031,7 +1031,7 @@ <h1 id="arch-covariance-kernel-tukeyparzen-cov">arch.covariance.kernel.TukeyParz
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1078,7 +1078,7 @@ <h1 id="arch-covariance-kernel-tukeyparzen-cov">arch.covariance.kernel.TukeyParz
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diff --git a/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.force_int.html b/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.force_int.html
index fd5e1fa034..dfb091e443 100644
--- a/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.force_int.html
+++ b/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.force_int.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.TukeyParzen.force_int - arch 6.1.0</title>
+        <title>arch.covariance.kernel.TukeyParzen.force_int - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -898,10 +898,10 @@
                   
 
 
-<h1 id="arch-covariance-kernel-tukeyparzen-force-int">arch.covariance.kernel.TukeyParzen.force_int<a class="headerlink" href="#arch-covariance-kernel-tukeyparzen-force-int" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-tukeyparzen-force-int">arch.covariance.kernel.TukeyParzen.force_int<a class="headerlink" href="#arch-covariance-kernel-tukeyparzen-force-int" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.TukeyParzen.force_int">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyParzen.</span></span><span class="sig-name descname"><span class="pre">force_int</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen.force_int" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyParzen.</span></span><span class="sig-name descname"><span class="pre">force_int</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen.force_int" title="Link to this definition">¶</a></dt>
 <dd><p>Flag indicating whether the bandwidth is restricted to be an integer.</p>
 </dd></dl>
 
@@ -972,7 +972,7 @@ <h1 id="arch-covariance-kernel-tukeyparzen-force-int">arch.covariance.kernel.Tuk
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
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@@ -1019,7 +1019,7 @@ <h1 id="arch-covariance-kernel-tukeyparzen-force-int">arch.covariance.kernel.Tuk
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.kernel_const.html b/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.kernel_const.html
index 50d72875cb..68408cc254 100644
--- a/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.kernel_const.html
+++ b/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.kernel_const.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.TukeyParzen.kernel_const - arch 6.1.0</title>
+        <title>arch.covariance.kernel.TukeyParzen.kernel_const - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-tukeyparzen-kernel-const">arch.covariance.kernel.TukeyParzen.kernel_const<a class="headerlink" href="#arch-covariance-kernel-tukeyparzen-kernel-const" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-tukeyparzen-kernel-const">arch.covariance.kernel.TukeyParzen.kernel_const<a class="headerlink" href="#arch-covariance-kernel-tukeyparzen-kernel-const" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.TukeyParzen.kernel_const">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyParzen.</span></span><span class="sig-name descname"><span class="pre">kernel_const</span></span><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen.kernel_const" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyParzen.</span></span><span class="sig-name descname"><span class="pre">kernel_const</span></span><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen.kernel_const" title="Link to this definition">¶</a></dt>
 <dd><p>The constant used in optimal bandwidth calculation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.TukeyParzen.kernel_const-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen.kernel_const-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The constant value used in the optimal bandwidth calculation.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.TukeyParzen.kernel_const-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen.kernel_const-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-tukeyparzen-kernel-const">arch.covariance.kernel.
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
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@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-tukeyparzen-kernel-const">arch.covariance.kernel.
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+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.kernel_weights.html b/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.kernel_weights.html
index 701bfcf987..d647e05a10 100644
--- a/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.kernel_weights.html
+++ b/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.kernel_weights.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.TukeyParzen.kernel_weights - arch 6.1.0</title>
+        <title>arch.covariance.kernel.TukeyParzen.kernel_weights - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-tukeyparzen-kernel-weights">arch.covariance.kernel.TukeyParzen.kernel_weights<a class="headerlink" href="#arch-covariance-kernel-tukeyparzen-kernel-weights" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-tukeyparzen-kernel-weights">arch.covariance.kernel.TukeyParzen.kernel_weights<a class="headerlink" href="#arch-covariance-kernel-tukeyparzen-kernel-weights" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.TukeyParzen.kernel_weights">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyParzen.</span></span><span class="sig-name descname"><span class="pre">kernel_weights</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">ndarray</span></a></span><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen.kernel_weights" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyParzen.</span></span><span class="sig-name descname"><span class="pre">kernel_weights</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen.kernel_weights" title="Link to this definition">¶</a></dt>
 <dd><p>Weights used in covariance calculation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.TukeyParzen.kernel_weights-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen.kernel_weights-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The weight vector including 1 in position 0.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.TukeyParzen.kernel_weights-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen.kernel_weights-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p>ndarray</p>
+<dd class="field-even"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-tukeyparzen-kernel-weights">arch.covariance.kerne
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-tukeyparzen-kernel-weights">arch.covariance.kerne
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.name.html b/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.name.html
index 6b7bac9d65..4f35a3b3f3 100644
--- a/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.name.html
+++ b/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.TukeyParzen.name - arch 6.1.0</title>
+        <title>arch.covariance.kernel.TukeyParzen.name - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-tukeyparzen-name">arch.covariance.kernel.TukeyParzen.name<a class="headerlink" href="#arch-covariance-kernel-tukeyparzen-name" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-tukeyparzen-name">arch.covariance.kernel.TukeyParzen.name<a class="headerlink" href="#arch-covariance-kernel-tukeyparzen-name" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.TukeyParzen.name">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyParzen.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen.name" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyParzen.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen.name" title="Link to this definition">¶</a></dt>
 <dd><p>The covariance estimator’s name.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.TukeyParzen.name-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen.name-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The covariance estimator’s name.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.TukeyParzen.name-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen.name-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1020,7 +1020,7 @@ <h1 id="arch-covariance-kernel-tukeyparzen-name">arch.covariance.kernel.TukeyPar
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-tukeyparzen-name">arch.covariance.kernel.TukeyPar
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.opt_bandwidth.html b/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.opt_bandwidth.html
index 215814b54f..3e32d88dc6 100644
--- a/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.opt_bandwidth.html
+++ b/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.opt_bandwidth.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.TukeyParzen.opt_bandwidth - arch 6.1.0</title>
+        <title>arch.covariance.kernel.TukeyParzen.opt_bandwidth - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,17 +938,17 @@
                   
 
 
-<h1 id="arch-covariance-kernel-tukeyparzen-opt-bandwidth">arch.covariance.kernel.TukeyParzen.opt_bandwidth<a class="headerlink" href="#arch-covariance-kernel-tukeyparzen-opt-bandwidth" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-tukeyparzen-opt-bandwidth">arch.covariance.kernel.TukeyParzen.opt_bandwidth<a class="headerlink" href="#arch-covariance-kernel-tukeyparzen-opt-bandwidth" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.TukeyParzen.opt_bandwidth">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyParzen.</span></span><span class="sig-name descname"><span class="pre">opt_bandwidth</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen.opt_bandwidth" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyParzen.</span></span><span class="sig-name descname"><span class="pre">opt_bandwidth</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen.opt_bandwidth" title="Link to this definition">¶</a></dt>
 <dd><p>Estimate optimal bandwidth.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.TukeyParzen.opt_bandwidth-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen.opt_bandwidth-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The estimated optimal bandwidth.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.TukeyParzen.opt_bandwidth-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen.opt_bandwidth-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -1026,7 +1026,7 @@ <h1 id="arch-covariance-kernel-tukeyparzen-opt-bandwidth">arch.covariance.kernel
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1073,10 +1073,10 @@ <h1 id="arch-covariance-kernel-tukeyparzen-opt-bandwidth">arch.covariance.kernel
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+        <script id="MathJax-script" src="../../../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
     
   </body>
 </html>
\ No newline at end of file
diff --git a/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.rate.html b/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.rate.html
index 929e813ff5..2ba21a30b4 100644
--- a/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.rate.html
+++ b/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.rate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.TukeyParzen.rate - arch 6.1.0</title>
+        <title>arch.covariance.kernel.TukeyParzen.rate - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,10 +938,10 @@
                   
 
 
-<h1 id="arch-covariance-kernel-tukeyparzen-rate">arch.covariance.kernel.TukeyParzen.rate<a class="headerlink" href="#arch-covariance-kernel-tukeyparzen-rate" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-covariance-kernel-tukeyparzen-rate">arch.covariance.kernel.TukeyParzen.rate<a class="headerlink" href="#arch-covariance-kernel-tukeyparzen-rate" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.TukeyParzen.rate">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyParzen.</span></span><span class="sig-name descname"><span class="pre">rate</span></span><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen.rate" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TukeyParzen.</span></span><span class="sig-name descname"><span class="pre">rate</span></span><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen.rate" title="Link to this definition">¶</a></dt>
 <dd><p>The optimal rate used in bandwidth selection.</p>
 <p>Controls the number of lags used in the variance estimate that
 determines the estimate of the optimal bandwidth.</p>
@@ -950,7 +950,7 @@ <h1 id="arch-covariance-kernel-tukeyparzen-rate">arch.covariance.kernel.TukeyPar
 <dd class="field-odd"><p>The rate used in bandwidth selection.</p>
 </dd>
 <dt class="field-even" id="arch.covariance.kernel.TukeyParzen.rate-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen.rate-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1022,7 +1022,7 @@ <h1 id="arch-covariance-kernel-tukeyparzen-rate">arch.covariance.kernel.TukeyPar
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1069,7 +1069,7 @@ <h1 id="arch-covariance-kernel-tukeyparzen-rate">arch.covariance.kernel.TukeyPar
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/devel/_modules/arch/bootstrap/base.html b/devel/_modules/arch/bootstrap/base.html
index c287cc4212..4ded26817a 100644
--- a/devel/_modules/arch/bootstrap/base.html
+++ b/devel/_modules/arch/bootstrap/base.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.base - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.base - arch 6.2.0</title>
       
     
 
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/_modules/arch/bootstrap/multiple_comparison.html b/devel/_modules/arch/bootstrap/multiple_comparison.html
index 756c25637e..63ceecdfab 100644
--- a/devel/_modules/arch/bootstrap/multiple_comparison.html
+++ b/devel/_modules/arch/bootstrap/multiple_comparison.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.multiple_comparison - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.multiple_comparison - arch 6.2.0</title>
       
     
 
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/_modules/arch/covariance/kernel.html b/devel/_modules/arch/covariance/kernel.html
index 88715a8829..eee4c96bde 100644
--- a/devel/_modules/arch/covariance/kernel.html
+++ b/devel/_modules/arch/covariance/kernel.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel - arch v6.1.1 (+15)</title>
+        <title>arch.covariance.kernel - arch 6.2.0</title>
       
     
 
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/_modules/arch/unitroot/_engle_granger.html b/devel/_modules/arch/unitroot/_engle_granger.html
index a9d8c10887..4b24df7e27 100644
--- a/devel/_modules/arch/unitroot/_engle_granger.html
+++ b/devel/_modules/arch/unitroot/_engle_granger.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot._engle_granger - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot._engle_granger - arch 6.2.0</title>
       
     
 
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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--- a/devel/_modules/arch/unitroot/_phillips_ouliaris.html
+++ b/devel/_modules/arch/unitroot/_phillips_ouliaris.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.unitroot._phillips_ouliaris - arch 6.2.0</title>
       
     
 
@@ -87,7 +87,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+            arch 6.2.0
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@@ -184,10 +184,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+    arch 6.2.0
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--- a/devel/_modules/arch/unitroot/_shared.html
+++ b/devel/_modules/arch/unitroot/_shared.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.unitroot._shared - arch 6.2.0</title>
       
     
 
@@ -87,7 +87,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+            arch 6.2.0
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+    arch 6.2.0
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--- a/devel/_modules/arch/unitroot/cointegration.html
+++ b/devel/_modules/arch/unitroot/cointegration.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.unitroot.cointegration - arch 6.2.0</title>
       
     
 
@@ -87,7 +87,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -97,7 +97,7 @@
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+            arch 6.2.0
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@@ -184,10 +184,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+    arch 6.2.0
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--- a/devel/_modules/arch/unitroot/unitroot.html
+++ b/devel/_modules/arch/unitroot/unitroot.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.unitroot.unitroot - arch 6.2.0</title>
       
     
 
@@ -87,7 +87,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -97,7 +97,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -184,10 +184,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index f489ac67f5..8d024a07db 100644
--- a/devel/_modules/arch/univariate/base.html
+++ b/devel/_modules/arch/univariate/base.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.univariate.base - arch 6.2.0</title>
       
     
 
@@ -87,7 +87,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -97,7 +97,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -184,10 +184,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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--- a/devel/_modules/arch/univariate/distribution.html
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@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.univariate.distribution - arch 6.2.0</title>
       
     
 
@@ -87,7 +87,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -97,7 +97,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -184,10 +184,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index c72d0658b4..f2a79cd24e 100644
--- a/devel/_modules/arch/univariate/mean.html
+++ b/devel/_modules/arch/univariate/mean.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.univariate.mean - arch 6.2.0</title>
       
     
 
@@ -87,7 +87,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -97,7 +97,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -184,10 +184,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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--- a/devel/_modules/arch/univariate/recursions_python.html
+++ b/devel/_modules/arch/univariate/recursions_python.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.recursions_python - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.recursions_python - arch 6.2.0</title>
       
     
 
@@ -87,7 +87,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -97,7 +97,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -184,10 +184,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index 1d46fb11f7..83cf32a019 100644
--- a/devel/_modules/arch/univariate/volatility.html
+++ b/devel/_modules/arch/univariate/volatility.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.univariate.volatility - arch 6.2.0</title>
       
     
 
@@ -87,7 +87,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -97,7 +97,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -184,10 +184,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/_modules/arch/utility/testing.html b/devel/_modules/arch/utility/testing.html
index a206b5c06c..a48e6813c1 100644
--- a/devel/_modules/arch/utility/testing.html
+++ b/devel/_modules/arch/utility/testing.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.utility.testing - arch 6.2.0</title>
       
     
 
@@ -87,7 +87,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -97,7 +97,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -184,10 +184,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+    arch 6.2.0
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index db4a351d96..e42be02d1e 100644
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+++ b/devel/_modules/functools.html
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+        <title>functools - arch 6.2.0</title>
       
     
 
@@ -87,7 +87,7 @@
 
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-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+            arch 6.2.0
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@@ -184,10 +184,10 @@
 
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-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
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+    arch 6.2.0
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diff --git a/devel/_modules/index.html b/devel/_modules/index.html
index 9b9dcae347..e4782e24e7 100644
--- a/devel/_modules/index.html
+++ b/devel/_modules/index.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Overview: module code - arch v6.1.1 (+15)</title>
+        <title>Overview: module code - arch 6.2.0</title>
       
     
 
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/api.html b/devel/api.html
index 26586499f9..bdf4431805 100644
--- a/devel/api.html
+++ b/devel/api.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>API Reference - arch v6.1.1 (+15)</title>
+        <title>API Reference - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/background.html b/devel/bootstrap/background.html
index c0c79b560f..e0b626b61a 100644
--- a/devel/bootstrap/background.html
+++ b/devel/bootstrap/background.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>References - arch v6.1.1 (+15)</title>
+        <title>References - arch 6.2.0</title>
       
     
 
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/bootstrap.html b/devel/bootstrap/bootstrap.html
index abc88b5b0e..646d1b0135 100644
--- a/devel/bootstrap/bootstrap.html
+++ b/devel/bootstrap/bootstrap.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Bootstrapping - arch v6.1.1 (+15)</title>
+        <title>Bootstrapping - arch 6.2.0</title>
       
     
 
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/bootstrap_examples.html b/devel/bootstrap/bootstrap_examples.html
index d7f0ef2ec1..0d25b4355c 100644
--- a/devel/bootstrap/bootstrap_examples.html
+++ b/devel/bootstrap/bootstrap_examples.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Bootstrap Examples - arch v6.1.1 (+15)</title>
+        <title>Bootstrap Examples - arch 6.2.0</title>
       
     
 
@@ -93,7 +93,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -103,7 +103,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -190,10 +190,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/confidence-intervals.html b/devel/bootstrap/confidence-intervals.html
index ab21e3f692..712663ae7c 100644
--- a/devel/bootstrap/confidence-intervals.html
+++ b/devel/bootstrap/confidence-intervals.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Confidence Intervals - arch v6.1.1 (+15)</title>
+        <title>Confidence Intervals - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/arch.bootstrap.CircularBlockBootstrap.html b/devel/bootstrap/generated/arch.bootstrap.CircularBlockBootstrap.html
index 1c9b31239b..4fcee42ff0 100644
--- a/devel/bootstrap/generated/arch.bootstrap.CircularBlockBootstrap.html
+++ b/devel/bootstrap/generated/arch.bootstrap.CircularBlockBootstrap.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.CircularBlockBootstrap - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/arch.bootstrap.IIDBootstrap.html b/devel/bootstrap/generated/arch.bootstrap.IIDBootstrap.html
index 345203f287..bda0ece3db 100644
--- a/devel/bootstrap/generated/arch.bootstrap.IIDBootstrap.html
+++ b/devel/bootstrap/generated/arch.bootstrap.IIDBootstrap.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.IIDBootstrap - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/arch.bootstrap.IndependentSamplesBootstrap.html b/devel/bootstrap/generated/arch.bootstrap.IndependentSamplesBootstrap.html
index 4c546ed7f9..032b44d366 100644
--- a/devel/bootstrap/generated/arch.bootstrap.IndependentSamplesBootstrap.html
+++ b/devel/bootstrap/generated/arch.bootstrap.IndependentSamplesBootstrap.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/arch.bootstrap.MovingBlockBootstrap.html b/devel/bootstrap/generated/arch.bootstrap.MovingBlockBootstrap.html
index 024eb3ec8e..1cdb074b0b 100644
--- a/devel/bootstrap/generated/arch.bootstrap.MovingBlockBootstrap.html
+++ b/devel/bootstrap/generated/arch.bootstrap.MovingBlockBootstrap.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.MovingBlockBootstrap - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/arch.bootstrap.StationaryBootstrap.html b/devel/bootstrap/generated/arch.bootstrap.StationaryBootstrap.html
index 48d7f13cad..89fb5992bc 100644
--- a/devel/bootstrap/generated/arch.bootstrap.StationaryBootstrap.html
+++ b/devel/bootstrap/generated/arch.bootstrap.StationaryBootstrap.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.StationaryBootstrap - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/arch.bootstrap.optimal_block_length.html b/devel/bootstrap/generated/arch.bootstrap.optimal_block_length.html
index 86ffda8396..4bd30914be 100644
--- a/devel/bootstrap/generated/arch.bootstrap.optimal_block_length.html
+++ b/devel/bootstrap/generated/arch.bootstrap.optimal_block_length.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.optimal_block_length - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.optimal_block_length - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.apply.html b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.apply.html
index 9be4a403d0..cbb2a0136e 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.apply.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.apply.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.apply - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.apply - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.bootstrap.html b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.bootstrap.html
index 4583939ed5..7d4e511f52 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.bootstrap.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.bootstrap.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.bootstrap - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.bootstrap - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.clone.html b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.clone.html
index f699369826..89e7b82dab 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.clone.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.clone.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.clone - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.clone - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.conf_int.html b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.conf_int.html
index f0e1733775..4791632671 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.conf_int.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.conf_int.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.conf_int - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.conf_int - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.cov.html b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.cov.html
index 90ec6d7dac..72ab125167 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.cov.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.cov.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.cov - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.cov - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.generator.html b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.generator.html
index 56177d5b52..506785d8c7 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.generator.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.generator.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.generator - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.generator - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.get_state.html b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.get_state.html
index 96dec62ec7..546150b953 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.get_state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.get_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.get_state - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.get_state - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.index.html b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.index.html
index bd5f6ea6aa..bb564cbedc 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.index.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.index.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.index - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.index - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.random_state.html b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.random_state.html
index 4aacab779e..49c6cc8d97 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.random_state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.random_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.random_state - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.random_state - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.reset.html b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.reset.html
index d888efc782..79afc54a5b 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.reset.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.reset.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.reset - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.reset - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.seed.html b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.seed.html
index 49d6dbb335..e2a381b8bf 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.seed.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.seed.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.seed - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.seed - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.set_state.html b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.set_state.html
index 1c01ca3994..f90652f0f1 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.set_state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.set_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.set_state - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.set_state - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.state.html b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.state.html
index d736684ec0..0ed8cdcd30 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.state - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.state - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.update_indices.html b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.update_indices.html
index 134baaf286..1c69c9d3f0 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.update_indices.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.update_indices.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.update_indices - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.update_indices - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.var.html b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.var.html
index 48ba268566..0a49c1d555 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.var.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.var.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.var - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.var - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.apply.html b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.apply.html
index 1725b3f9c4..1ddc3e3287 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.apply.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.apply.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.apply - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.IIDBootstrap.apply - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.bootstrap.html b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.bootstrap.html
index 775791c23f..a5e0dca143 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.bootstrap.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.bootstrap.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.bootstrap - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.IIDBootstrap.bootstrap - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.clone.html b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.clone.html
index 74dd1fe4bd..f0115bedda 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.clone.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.clone.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.clone - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.IIDBootstrap.clone - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.conf_int.html b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.conf_int.html
index 158f3b0013..8e237b5969 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.conf_int.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.conf_int.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.conf_int - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.IIDBootstrap.conf_int - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.cov.html b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.cov.html
index 8da174c01e..cac91fd5b0 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.cov.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.cov.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.cov - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.IIDBootstrap.cov - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.generator.html b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.generator.html
index 5627f2f170..9f84430946 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.generator.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.generator.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.generator - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.IIDBootstrap.generator - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.get_state.html b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.get_state.html
index 2fcc0cf47d..88fc0f5e67 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.get_state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.get_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.get_state - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.IIDBootstrap.get_state - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.index.html b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.index.html
index c457de3d03..d1be52be2e 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.index.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.index.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.index - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.IIDBootstrap.index - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.random_state.html b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.random_state.html
index 263d1c2b89..909b410620 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.random_state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.random_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.random_state - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.IIDBootstrap.random_state - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.reset.html b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.reset.html
index cd7b3d186b..3b1fb7d528 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.reset.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.reset.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.reset - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.IIDBootstrap.reset - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.seed.html b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.seed.html
index 38008347e8..0d4c948154 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.seed.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.seed.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.seed - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.IIDBootstrap.seed - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.set_state.html b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.set_state.html
index 70e2e1c881..a2df61928b 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.set_state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.set_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.set_state - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.IIDBootstrap.set_state - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.state.html b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.state.html
index 5b75d63e96..a58db433ad 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.state - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.IIDBootstrap.state - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.update_indices.html b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.update_indices.html
index 789c442a3a..6f7f4336f3 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.update_indices.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.update_indices.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.update_indices - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.IIDBootstrap.update_indices - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.var.html b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.var.html
index 6aeeb4c209..5b041354d9 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.var.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.var.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.var - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.IIDBootstrap.var - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.apply.html b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.apply.html
index ae1d06f81a..c9f3b92a56 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.apply.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.apply.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.apply - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.apply - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.bootstrap.html b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.bootstrap.html
index 2f35601753..1d818b6549 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.bootstrap.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.bootstrap.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.bootstrap - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.bootstrap - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.clone.html b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.clone.html
index 4b666b75fd..953acb2ca2 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.clone.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.clone.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.clone - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.clone - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.conf_int.html b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.conf_int.html
index cacd91737f..8137474d71 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.conf_int.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.conf_int.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.conf_int - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.conf_int - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.cov.html b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.cov.html
index 5c55b8ee4d..7124656910 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.cov.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.cov.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.cov - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.cov - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.generator.html b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.generator.html
index 7370daf76b..eb927fa763 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.generator.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.generator.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.generator - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.generator - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.get_state.html b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.get_state.html
index ee303f18a7..d1398208ea 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.get_state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.get_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.get_state - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.get_state - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.index.html b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.index.html
index da630ab2de..0f6ddc6d10 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.index.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.index.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.index - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.index - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.random_state.html b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.random_state.html
index 45b313a6f0..e61df2fcde 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.random_state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.random_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.random_state - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.random_state - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.reset.html b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.reset.html
index 40c06f96d4..a5a2e3bd6f 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.reset.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.reset.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.reset - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.reset - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.seed.html b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.seed.html
index c31a7dcee2..f4a60bda2b 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.seed.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.seed.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.seed - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.seed - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.set_state.html b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.set_state.html
index a78bd24ecd..26fdda808c 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.set_state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.set_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.set_state - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.set_state - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.state.html b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.state.html
index 8757ab98ce..d99ca91967 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.state - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.state - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.update_indices.html b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.update_indices.html
index 919f670595..932b1d433d 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.update_indices.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.update_indices.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.update_indices - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.update_indices - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.var.html b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.var.html
index 6d1820ebd1..eec0fcc64d 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.var.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.var.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.var - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.var - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.apply.html b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.apply.html
index 4b26cdff01..d2ba40ffd9 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.apply.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.apply.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.apply - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.apply - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.bootstrap.html b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.bootstrap.html
index 0192dddb57..66fef79562 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.bootstrap.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.bootstrap.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.bootstrap - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.bootstrap - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.clone.html b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.clone.html
index b198d698aa..fd318a8c47 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.clone.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.clone.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.clone - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.clone - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.conf_int.html b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.conf_int.html
index eaac1349ef..4fc722fdab 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.conf_int.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.conf_int.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.conf_int - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.conf_int - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.cov.html b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.cov.html
index e618a2f33e..ef63a363cc 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.cov.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.cov.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.cov - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.cov - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.generator.html b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.generator.html
index d1b5665806..99f5df6ad7 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.generator.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.generator.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.generator - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.generator - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.get_state.html b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.get_state.html
index 449f75e2e0..39ae10022c 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.get_state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.get_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.get_state - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.get_state - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.index.html b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.index.html
index 11f907707a..881ee2e9ca 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.index.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.index.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.index - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.index - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.random_state.html b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.random_state.html
index cbe96ebcec..687a76b99d 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.random_state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.random_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.random_state - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.random_state - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.reset.html b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.reset.html
index 6e37205c61..507d5da8cd 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.reset.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.reset.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.reset - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.reset - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.seed.html b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.seed.html
index 9c18c320a8..7b4ff07681 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.seed.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.seed.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.seed - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.seed - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.set_state.html b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.set_state.html
index eefc780ad7..75018f8df7 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.set_state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.set_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.set_state - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.set_state - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.state.html b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.state.html
index b760582adf..fb144bd6d2 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.state - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.state - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.update_indices.html b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.update_indices.html
index 94bc18d28a..45d21cf340 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.update_indices.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.update_indices.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.update_indices - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.update_indices - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.var.html b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.var.html
index 1a22ae4972..4de1162999 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.var.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.var.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.var - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.var - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.apply.html b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.apply.html
index 8523f4c402..92ccf29fb5 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.apply.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.apply.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.apply - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.StationaryBootstrap.apply - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.bootstrap.html b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.bootstrap.html
index 03de18cb7b..c10c8f2629 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.bootstrap.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.bootstrap.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.bootstrap - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.StationaryBootstrap.bootstrap - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.clone.html b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.clone.html
index 79b9ad0cdd..241db1fe8f 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.clone.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.clone.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.clone - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.StationaryBootstrap.clone - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.conf_int.html b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.conf_int.html
index ead95b4242..89008c9db2 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.conf_int.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.conf_int.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.conf_int - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.StationaryBootstrap.conf_int - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.cov.html b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.cov.html
index a96f6014c9..cf53b9ecee 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.cov.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.cov.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.cov - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.StationaryBootstrap.cov - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.generator.html b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.generator.html
index bf5b531faa..110634a8e0 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.generator.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.generator.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.generator - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.StationaryBootstrap.generator - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.get_state.html b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.get_state.html
index c051d84009..9283e857ef 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.get_state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.get_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.get_state - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.StationaryBootstrap.get_state - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.index.html b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.index.html
index ec76f797ff..3491d9a169 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.index.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.index.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.index - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.StationaryBootstrap.index - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.random_state.html b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.random_state.html
index 257fa7aa73..c32b1f4796 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.random_state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.random_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.random_state - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.StationaryBootstrap.random_state - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.reset.html b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.reset.html
index f730944e94..7176ecb8ae 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.reset.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.reset.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.reset - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.StationaryBootstrap.reset - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.seed.html b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.seed.html
index 7cb9ec5ba7..19955c8077 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.seed.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.seed.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.seed - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.StationaryBootstrap.seed - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.set_state.html b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.set_state.html
index 604b384d7d..4000eb7de7 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.set_state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.set_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.set_state - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.StationaryBootstrap.set_state - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.state.html b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.state.html
index d72c17eb64..65e25a085c 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.state - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.StationaryBootstrap.state - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.update_indices.html b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.update_indices.html
index ab340920b9..0d53439441 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.update_indices.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.update_indices.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.update_indices - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.StationaryBootstrap.update_indices - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.var.html b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.var.html
index 6cc8c8d5b1..78db2063c9 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.var.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.var.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.var - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.StationaryBootstrap.var - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/iid-bootstraps.html b/devel/bootstrap/iid-bootstraps.html
index e8e97ac2b7..f4b1095f44 100644
--- a/devel/bootstrap/iid-bootstraps.html
+++ b/devel/bootstrap/iid-bootstraps.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Independent, Identical Distributed Data (i.i.d.) - arch v6.1.1 (+15)</title>
+        <title>Independent, Identical Distributed Data (i.i.d.) - arch 6.2.0</title>
       
     
 
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/low-level-interface.html b/devel/bootstrap/low-level-interface.html
index 70e4e6300d..72e0da5416 100644
--- a/devel/bootstrap/low-level-interface.html
+++ b/devel/bootstrap/low-level-interface.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Low-level Interfaces - arch v6.1.1 (+15)</title>
+        <title>Low-level Interfaces - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/parameter-covariance-estimation.html b/devel/bootstrap/parameter-covariance-estimation.html
index d5cce61650..59aa7eae42 100644
--- a/devel/bootstrap/parameter-covariance-estimation.html
+++ b/devel/bootstrap/parameter-covariance-estimation.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Covariance Estimation - arch v6.1.1 (+15)</title>
+        <title>Covariance Estimation - arch 6.2.0</title>
       
     
 
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/semiparametric-parametric-bootstrap.html b/devel/bootstrap/semiparametric-parametric-bootstrap.html
index 03e5cdd76b..aba3d6e3c7 100644
--- a/devel/bootstrap/semiparametric-parametric-bootstrap.html
+++ b/devel/bootstrap/semiparametric-parametric-bootstrap.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Semiparametric Bootstraps - arch v6.1.1 (+15)</title>
+        <title>Semiparametric Bootstraps - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/bootstrap/timeseries-bootstraps.html b/devel/bootstrap/timeseries-bootstraps.html
index c674dcadc6..861f6cd54e 100644
--- a/devel/bootstrap/timeseries-bootstraps.html
+++ b/devel/bootstrap/timeseries-bootstraps.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Time-series Bootstraps - arch v6.1.1 (+15)</title>
+        <title>Time-series Bootstraps - arch 6.2.0</title>
       
     
 
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/changes.html b/devel/changes.html
index 517c277f41..8f322ebebf 100644
--- a/devel/changes.html
+++ b/devel/changes.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Change Logs - arch v6.1.1 (+15)</title>
+        <title>Change Logs - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/changes/1.0.html b/devel/changes/1.0.html
index bf54330385..9d3201b147 100644
--- a/devel/changes/1.0.html
+++ b/devel/changes/1.0.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Version 1 - arch v6.1.1 (+15)</title>
+        <title>Version 1 - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/changes/2.0.html b/devel/changes/2.0.html
index 0754b6ed49..a59edd20f9 100644
--- a/devel/changes/2.0.html
+++ b/devel/changes/2.0.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Version 2 - arch v6.1.1 (+15)</title>
+        <title>Version 2 - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/changes/3.0.html b/devel/changes/3.0.html
index 0bc8f22989..d6e3a7f4c2 100644
--- a/devel/changes/3.0.html
+++ b/devel/changes/3.0.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Version 3 - arch v6.1.1 (+15)</title>
+        <title>Version 3 - arch 6.2.0</title>
       
     
 
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/changes/4.0.html b/devel/changes/4.0.html
index b949275e7f..25cc6e1dbb 100644
--- a/devel/changes/4.0.html
+++ b/devel/changes/4.0.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Version 4 - arch v6.1.1 (+15)</title>
+        <title>Version 4 - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/changes/5.0.html b/devel/changes/5.0.html
index a61598e268..967e8dcf30 100644
--- a/devel/changes/5.0.html
+++ b/devel/changes/5.0.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Version 5 - arch v6.1.1 (+15)</title>
+        <title>Version 5 - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
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@@ -92,7 +92,7 @@
 
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@@ -189,10 +189,10 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+    arch 6.2.0
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index 5c99f7b07b..88a79c36de 100644
--- a/devel/covariance/generated/generated/arch.covariance.kernel.Gallant.cov.html
+++ b/devel/covariance/generated/generated/arch.covariance.kernel.Gallant.cov.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.covariance.kernel.Gallant.cov - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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+++ b/devel/covariance/generated/generated/arch.covariance.kernel.Gallant.force_int.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.covariance.kernel.Gallant.force_int - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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+++ b/devel/covariance/generated/generated/arch.covariance.kernel.Gallant.kernel_const.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.covariance.kernel.Gallant.kernel_const - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index cc3c3c65b9..23a94f1d4c 100644
--- a/devel/covariance/generated/generated/arch.covariance.kernel.Gallant.opt_bandwidth.html
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index 9155aca395..00e60707b1 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index e32f7e1aac..c7efc3484f 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index 25ba8b3a6a..ccc2dddfce 100644
--- a/devel/covariance/generated/generated/arch.covariance.kernel.NeweyWest.bandwidth_scale.html
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index 54690ab222..26b4dcc487 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/covariance/generated/generated/arch.covariance.kernel.NeweyWest.cov.html b/devel/covariance/generated/generated/arch.covariance.kernel.NeweyWest.cov.html
index 4d13dd4813..e7e99273f5 100644
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@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.covariance.kernel.NeweyWest.cov - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+    arch 6.2.0
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index 8cde021abf..4c55958ae0 100644
--- a/devel/covariance/generated/generated/arch.covariance.kernel.NeweyWest.force_int.html
+++ b/devel/covariance/generated/generated/arch.covariance.kernel.NeweyWest.force_int.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.covariance.kernel.NeweyWest.force_int - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index 365b0a7a04..3e7d3c0618 100644
--- a/devel/covariance/generated/generated/arch.covariance.kernel.NeweyWest.kernel_const.html
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@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.covariance.kernel.NeweyWest.kernel_const - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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--- a/devel/covariance/generated/generated/arch.covariance.kernel.NeweyWest.kernel_weights.html
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.NeweyWest.name - arch v6.1.1 (+15)</title>
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.covariance.kernel.NeweyWest.opt_bandwidth - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index b6ee7bf1d6..1a65f7ecb2 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.NeweyWest.rate - arch v6.1.1 (+15)</title>
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/covariance/generated/generated/arch.covariance.kernel.Parzen.bandwidth.html b/devel/covariance/generated/generated/arch.covariance.kernel.Parzen.bandwidth.html
index 53d261d42f..fa9a5788a2 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index ff6d44338c..e7113475bf 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index e64b641d62..70f271929c 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
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index 3c3ccdae06..ce6c692bf5 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.Parzen.cov - arch v6.1.1 (+15)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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diff --git a/devel/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.kernel_weights.html b/devel/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.kernel_weights.html
index 6e85331812..d647e05a10 100644
--- a/devel/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.kernel_weights.html
+++ b/devel/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.kernel_weights.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.TukeyParzen.kernel_weights - arch v6.1.1 (+15)</title>
+        <title>arch.covariance.kernel.TukeyParzen.kernel_weights - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.name.html b/devel/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.name.html
index c42be43588..4f35a3b3f3 100644
--- a/devel/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.name.html
+++ b/devel/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.TukeyParzen.name - arch v6.1.1 (+15)</title>
+        <title>arch.covariance.kernel.TukeyParzen.name - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.opt_bandwidth.html b/devel/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.opt_bandwidth.html
index 02d422067f..3e32d88dc6 100644
--- a/devel/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.opt_bandwidth.html
+++ b/devel/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.opt_bandwidth.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.TukeyParzen.opt_bandwidth - arch v6.1.1 (+15)</title>
+        <title>arch.covariance.kernel.TukeyParzen.opt_bandwidth - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.rate.html b/devel/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.rate.html
index e1cd10a8d4..2ba21a30b4 100644
--- a/devel/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.rate.html
+++ b/devel/covariance/generated/generated/arch.covariance.kernel.TukeyParzen.rate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.TukeyParzen.rate - arch v6.1.1 (+15)</title>
+        <title>arch.covariance.kernel.TukeyParzen.rate - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/genindex.html b/devel/genindex.html
index 0dfc78f21f..e16a125d71 100644
--- a/devel/genindex.html
+++ b/devel/genindex.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>None - arch v6.1.1 (+15)</title>
+        <title>None - arch 6.2.0</title>
       
     
 
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/index.html b/devel/index.html
index 6e153955e7..88623d78cb 100644
--- a/devel/index.html
+++ b/devel/index.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch v6.1.1 (+15)</title>
+        <title>arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="#" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="#" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -197,10 +197,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="#" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="#" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/multiple-comparison/background.html b/devel/multiple-comparison/background.html
index 1452d4d9dc..a8978d15e5 100644
--- a/devel/multiple-comparison/background.html
+++ b/devel/multiple-comparison/background.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>References - arch v6.1.1 (+15)</title>
+        <title>References - arch 6.2.0</title>
       
     
 
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/multiple-comparison/generated/arch.bootstrap.MCS.html b/devel/multiple-comparison/generated/arch.bootstrap.MCS.html
index a56f451d76..73a192e6aa 100644
--- a/devel/multiple-comparison/generated/arch.bootstrap.MCS.html
+++ b/devel/multiple-comparison/generated/arch.bootstrap.MCS.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MCS - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.MCS - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/multiple-comparison/generated/arch.bootstrap.SPA.html b/devel/multiple-comparison/generated/arch.bootstrap.SPA.html
index 516f886172..69ae25b0cc 100644
--- a/devel/multiple-comparison/generated/arch.bootstrap.SPA.html
+++ b/devel/multiple-comparison/generated/arch.bootstrap.SPA.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.SPA - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.SPA - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/multiple-comparison/generated/arch.bootstrap.StepM.html b/devel/multiple-comparison/generated/arch.bootstrap.StepM.html
index 8e9c5cbe6a..bcb6a238fd 100644
--- a/devel/multiple-comparison/generated/arch.bootstrap.StepM.html
+++ b/devel/multiple-comparison/generated/arch.bootstrap.StepM.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StepM - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.StepM - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.compute.html b/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.compute.html
index 17cc2129a8..b695ad051f 100644
--- a/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.compute.html
+++ b/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.compute.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MCS.compute - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.MCS.compute - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.excluded.html b/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.excluded.html
index 041af708d4..78d734a827 100644
--- a/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.excluded.html
+++ b/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.excluded.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MCS.excluded - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.MCS.excluded - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.included.html b/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.included.html
index ab61217b7a..0c473822ac 100644
--- a/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.included.html
+++ b/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.included.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MCS.included - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.MCS.included - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.pvalues.html b/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.pvalues.html
index 5c0d8a443d..7f6d9b4adf 100644
--- a/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.pvalues.html
+++ b/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.pvalues.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MCS.pvalues - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.MCS.pvalues - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.reset.html b/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.reset.html
index a710c3ad3f..6e46ec3a2c 100644
--- a/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.reset.html
+++ b/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.reset.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MCS.reset - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.MCS.reset - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.seed.html b/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.seed.html
index d56eeab022..e34436693e 100644
--- a/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.seed.html
+++ b/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.seed.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MCS.seed - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.MCS.seed - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.better_models.html b/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.better_models.html
index ecfef1db41..85a888369b 100644
--- a/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.better_models.html
+++ b/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.better_models.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.SPA.better_models - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.SPA.better_models - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.compute.html b/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.compute.html
index 257f537104..15b9483946 100644
--- a/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.compute.html
+++ b/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.compute.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.SPA.compute - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.SPA.compute - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.critical_values.html b/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.critical_values.html
index f96090fb1d..0a33031420 100644
--- a/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.critical_values.html
+++ b/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.critical_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.SPA.critical_values - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.SPA.critical_values - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.pvalues.html b/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.pvalues.html
index e6d3034522..78148708cc 100644
--- a/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.pvalues.html
+++ b/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.pvalues.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.SPA.pvalues - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.SPA.pvalues - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.reset.html b/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.reset.html
index 948bfd83cc..45b341354d 100644
--- a/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.reset.html
+++ b/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.reset.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.SPA.reset - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.SPA.reset - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.seed.html b/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.seed.html
index f4e58bb6ba..17a59413eb 100644
--- a/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.seed.html
+++ b/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.seed.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.SPA.seed - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.SPA.seed - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.subset.html b/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.subset.html
index a88625c9f5..feedf6ee8b 100644
--- a/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.subset.html
+++ b/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.subset.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.SPA.subset - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.SPA.subset - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/multiple-comparison/generated/generated/arch.bootstrap.StepM.compute.html b/devel/multiple-comparison/generated/generated/arch.bootstrap.StepM.compute.html
index bb8c000dd5..1259a54647 100644
--- a/devel/multiple-comparison/generated/generated/arch.bootstrap.StepM.compute.html
+++ b/devel/multiple-comparison/generated/generated/arch.bootstrap.StepM.compute.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StepM.compute - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.StepM.compute - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/multiple-comparison/generated/generated/arch.bootstrap.StepM.reset.html b/devel/multiple-comparison/generated/generated/arch.bootstrap.StepM.reset.html
index 23edbe1524..389b3b1812 100644
--- a/devel/multiple-comparison/generated/generated/arch.bootstrap.StepM.reset.html
+++ b/devel/multiple-comparison/generated/generated/arch.bootstrap.StepM.reset.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StepM.reset - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.StepM.reset - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/multiple-comparison/generated/generated/arch.bootstrap.StepM.seed.html b/devel/multiple-comparison/generated/generated/arch.bootstrap.StepM.seed.html
index 8ffde612bd..a99cc95354 100644
--- a/devel/multiple-comparison/generated/generated/arch.bootstrap.StepM.seed.html
+++ b/devel/multiple-comparison/generated/generated/arch.bootstrap.StepM.seed.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StepM.seed - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.StepM.seed - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/multiple-comparison/generated/generated/arch.bootstrap.StepM.superior_models.html b/devel/multiple-comparison/generated/generated/arch.bootstrap.StepM.superior_models.html
index e127cd5c98..984fe32ff9 100644
--- a/devel/multiple-comparison/generated/generated/arch.bootstrap.StepM.superior_models.html
+++ b/devel/multiple-comparison/generated/generated/arch.bootstrap.StepM.superior_models.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StepM.superior_models - arch v6.1.1 (+15)</title>
+        <title>arch.bootstrap.StepM.superior_models - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/multiple-comparison/multiple-comparison-reference.html b/devel/multiple-comparison/multiple-comparison-reference.html
index c6d80d3caf..f8b7267044 100644
--- a/devel/multiple-comparison/multiple-comparison-reference.html
+++ b/devel/multiple-comparison/multiple-comparison-reference.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Module Reference - arch v6.1.1 (+15)</title>
+        <title>Module Reference - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/multiple-comparison/multiple-comparison_examples.html b/devel/multiple-comparison/multiple-comparison_examples.html
index eca485b3aa..a38af5c6e4 100644
--- a/devel/multiple-comparison/multiple-comparison_examples.html
+++ b/devel/multiple-comparison/multiple-comparison_examples.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Multiple Comparisons - arch v6.1.1 (+15)</title>
+        <title>Multiple Comparisons - arch 6.2.0</title>
       
     
 
@@ -93,7 +93,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -103,7 +103,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -190,10 +190,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/multiple-comparison/multiple-comparisons.html b/devel/multiple-comparison/multiple-comparisons.html
index 5ba097454c..fea126781a 100644
--- a/devel/multiple-comparison/multiple-comparisons.html
+++ b/devel/multiple-comparison/multiple-comparisons.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Multiple Comparison Procedures - arch v6.1.1 (+15)</title>
+        <title>Multiple Comparison Procedures - arch 6.2.0</title>
       
     
 
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/objects.inv b/devel/objects.inv
index ac194e7b9b3bb1fec520e67dbce956c1a0291a8c..16ddf846eca075813d7746d27341eecf47cc8d4e 100644
GIT binary patch
delta 16
XcmeBp%KZHa^8_nqBRzwSepa6VJ-G(g

delta 33
ocmezVgt_A>^8_mqLp?*il+-dqQ|)w1(-gxLGgC{`jkZ>w0M3~Ud;kCd

diff --git a/devel/py-modindex.html b/devel/py-modindex.html
index 394fd509bc..992f937e62 100644
--- a/devel/py-modindex.html
+++ b/devel/py-modindex.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>None - arch v6.1.1 (+15)</title>
+        <title>None - arch 6.2.0</title>
       
     
 
@@ -90,7 +90,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -100,7 +100,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -187,10 +187,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/searchindex.js b/devel/searchindex.js
index e4c029e678..0c91ea43e1 100644
--- a/devel/searchindex.js
+++ b/devel/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"titles": ["API Reference", "References", "Bootstrapping", "Bootstrap Examples", "Confidence Intervals", "arch.bootstrap.CircularBlockBootstrap", "arch.bootstrap.IIDBootstrap", "arch.bootstrap.IndependentSamplesBootstrap", "arch.bootstrap.MovingBlockBootstrap", "arch.bootstrap.StationaryBootstrap", "arch.bootstrap.optimal_block_length", "arch.bootstrap.CircularBlockBootstrap.apply", "arch.bootstrap.CircularBlockBootstrap.bootstrap", "arch.bootstrap.CircularBlockBootstrap.clone", "arch.bootstrap.CircularBlockBootstrap.conf_int", "arch.bootstrap.CircularBlockBootstrap.cov", "arch.bootstrap.CircularBlockBootstrap.generator", "arch.bootstrap.CircularBlockBootstrap.get_state", "arch.bootstrap.CircularBlockBootstrap.index", "arch.bootstrap.CircularBlockBootstrap.random_state", "arch.bootstrap.CircularBlockBootstrap.reset", "arch.bootstrap.CircularBlockBootstrap.seed", "arch.bootstrap.CircularBlockBootstrap.set_state", "arch.bootstrap.CircularBlockBootstrap.state", "arch.bootstrap.CircularBlockBootstrap.update_indices", "arch.bootstrap.CircularBlockBootstrap.var", "arch.bootstrap.IIDBootstrap.apply", "arch.bootstrap.IIDBootstrap.bootstrap", "arch.bootstrap.IIDBootstrap.clone", "arch.bootstrap.IIDBootstrap.conf_int", "arch.bootstrap.IIDBootstrap.cov", "arch.bootstrap.IIDBootstrap.generator", "arch.bootstrap.IIDBootstrap.get_state", "arch.bootstrap.IIDBootstrap.index", "arch.bootstrap.IIDBootstrap.random_state", "arch.bootstrap.IIDBootstrap.reset", "arch.bootstrap.IIDBootstrap.seed", "arch.bootstrap.IIDBootstrap.set_state", "arch.bootstrap.IIDBootstrap.state", "arch.bootstrap.IIDBootstrap.update_indices", "arch.bootstrap.IIDBootstrap.var", "arch.bootstrap.IndependentSamplesBootstrap.apply", "arch.bootstrap.IndependentSamplesBootstrap.bootstrap", "arch.bootstrap.IndependentSamplesBootstrap.clone", "arch.bootstrap.IndependentSamplesBootstrap.conf_int", "arch.bootstrap.IndependentSamplesBootstrap.cov", 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424, 438, 494, 496, 511, 513, 527, 529, 676, 678, 692, 694, 781, 783, 797, 799, 812, 840, 895, 897, 898, 899], "exact": [4, 89, 266, 420, 421], "conceptu": 4, "mani": [4, 259, 419, 424, 895, 898], "replic": [4, 11, 12, 14, 15, 25, 26, 27, 29, 30, 40, 41, 42, 44, 45, 55, 56, 57, 59, 60, 70, 71, 72, 74, 75, 85, 87, 238, 239, 240, 259, 264], "order": [4, 5, 6, 7, 8, 9, 87, 271, 273, 275, 420, 425, 426, 431, 433, 435, 502, 518, 534, 647, 650, 683, 699, 726, 729, 756, 759, 769, 772, 788, 864, 867, 891, 895], "empir": [4, 897], "while": [4, 258, 259, 260, 266, 420, 424, 891, 895, 897, 899], "littl": [4, 421], "highli": [4, 419, 421, 898], "persist": [4, 419, 421], "iid": [4, 899], "good": [4, 259, 421, 899], "choic": [4, 277, 354, 368, 402, 424, 495, 512, 528, 677, 693, 782, 798, 898], "instead": [4, 5, 6, 8, 9, 94, 421, 436, 441, 443, 444, 652, 731, 761, 774, 869, 895, 899], "block": [4, 5, 8, 9, 10, 88, 90, 238, 239, 240, 259, 266, 269, 274, 341, 421, 891, 899], "side": [4, 101, 133, 134, 269, 271, 274, 276, 277, 421], "95": [4, 14, 29, 44, 59, 74, 420, 891, 895, 896, 898, 899], "default": [4, 14, 15, 20, 25, 29, 30, 35, 40, 44, 45, 50, 55, 59, 60, 65, 70, 74, 75, 80, 85, 91, 95, 97, 238, 239, 240, 259, 262, 263, 264, 265, 266, 267, 268, 417, 421, 424, 425, 432, 433, 435, 442, 461, 471, 478, 488, 496, 513, 529, 544, 554, 561, 571, 578, 588, 595, 606, 613, 623, 630, 640, 654, 660, 670, 678, 694, 709, 719, 739, 749, 783, 799, 812, 813, 840, 841, 879, 889, 891, 895, 897, 898, 899], "hat": [4, 10, 120, 130, 144, 154, 164, 174, 184, 194, 204, 214, 224, 234, 269, 271, 274, 277, 362, 368, 381, 398, 424], "theta": [4, 274, 440, 708], "_b": 4, "star": [4, 274], "left": [4, 10, 100, 104, 108, 120, 130, 144, 154, 164, 174, 184, 194, 204, 214, 224, 234, 269, 271, 274, 276, 277, 424, 425, 431, 435, 437, 440, 646, 725, 755, 768, 898], "_": [4, 269, 271, 274, 277, 362, 368, 381, 424, 438, 895], "right": [4, 10, 100, 104, 108, 120, 130, 144, 154, 164, 174, 184, 194, 204, 214, 224, 234, 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"slow": [4, 239, 240], "inner": [4, 14, 29, 44, 59, 74, 100, 104, 105, 106, 107, 108, 109, 110, 111, 277], "outer": 4, "In": [4, 89, 238, 239, 240, 259, 417, 420, 424, 425, 431, 433, 435, 437, 440, 494, 496, 511, 513, 527, 529, 676, 678, 692, 694, 781, 783, 797, 799, 812, 813, 840, 841, 891, 892, 895, 896, 898, 899], "necessari": [4, 89, 895], "done": 4, "moment": [4, 95, 650, 729, 759, 763, 772, 776, 867], "delta": [4, 269, 271, 274, 398, 420, 425, 457], "detail": [4, 238, 239, 240, 259, 269, 271, 274, 277, 650, 729, 759, 772, 825, 867, 897], "descript": [4, 273, 427, 428, 438], "mathemat": 4, "formula": 4, "beyond": [4, 899], "intent": [4, 421], "document": [4, 236, 424], "sharpe_ratio_s": 4, "e1": 4, "e2": 4, "vstack": 4, "vcv": 4, "dot": [4, 89, 259, 459, 477, 492, 509, 525, 543, 560, 577, 594, 612, 629, 644, 659, 674, 690, 708, 723, 738, 753, 766, 779, 795, 861, 878], "avar": 4, "std_err_func": [4, 14, 29, 44, 59, 74], "match": [4, 10, 91, 97, 496, 513, 529, 678, 694, 783, 799, 812, 840, 895], "patter": 4, "arg": [4, 5, 6, 7, 8, 9, 11, 13, 14, 15, 25, 26, 28, 29, 30, 40, 41, 43, 44, 45, 55, 56, 58, 59, 60, 70, 71, 73, 74, 75, 85], "kwarg": [4, 5, 6, 7, 8, 9, 11, 13, 14, 15, 25, 26, 28, 29, 30, 40, 41, 43, 44, 45, 55, 56, 58, 59, 60, 70, 71, 73, 74, 75, 85], "improv": [4, 95, 96, 258, 263, 421, 452, 898], "jackknif": [4, 95], "term": [4, 265, 269, 271, 274, 276, 277, 279, 288, 289, 292, 301, 302, 305, 312, 313, 316, 325, 326, 329, 339, 340, 344, 351, 352, 368, 420, 424, 425, 440, 898], "offer": [4, 898], "accuraci": 4, "condit": [4, 95, 266, 421, 424, 425, 427, 431, 433, 435, 443, 445, 446, 447, 450, 453, 458, 471, 476, 488, 497, 502, 518, 534, 542, 554, 559, 571, 576, 588, 593, 606, 611, 623, 628, 640, 646, 658, 670, 683, 699, 707, 719, 725, 737, 749, 755, 768, 788, 809, 811, 819, 821, 823, 824, 826, 833, 835, 849, 856, 863, 873, 877, 889, 892, 895, 897, 898], "satisfi": [4, 266, 425, 492, 509, 525, 644, 674, 690, 723, 753, 766, 779, 795, 861], "special": [4, 437, 901], "case": [4, 89, 100, 104, 105, 106, 107, 108, 109, 110, 111, 259, 420, 437, 891], "base": [4, 89, 95, 260, 264, 265, 267, 277, 282, 295, 307, 318, 332, 346, 354, 368, 402, 420, 424, 452, 453, 496, 513, 529, 678, 694, 763, 776, 783, 894, 897, 899], "frac": [4, 10, 100, 104, 105, 106, 107, 108, 109, 110, 111, 120, 130, 144, 154, 164, 174, 184, 194, 204, 214, 224, 234, 271, 277, 424, 433, 437, 646, 725, 755, 768], "z_": [4, 277, 420], "skew": [4, 94, 421, 443, 755, 891, 898], "like": [4, 10, 88, 89, 258, 420, 424, 427, 428, 429, 438, 439, 445, 496, 513, 529, 678, 694, 783, 799, 812, 840, 891, 896, 898, 899], "leav": [4, 95], "out": [4, 94, 95, 424, 892, 897, 899], "block_siz": [5, 8, 9, 238, 239, 240], "arraylik": [5, 6, 7, 8, 9, 13, 28, 43, 58], "random_st": [5, 6, 7, 8, 9, 95, 96, 436, 441, 443, 444, 451, 461, 478, 496, 513, 529, 544, 561, 578, 595, 613, 630, 660, 678, 694, 709, 739, 783, 799, 812, 840, 879, 899], "sourc": [5, 6, 7, 8, 9, 10, 13, 24, 26, 27, 28, 29, 30, 32, 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6, 7, 8, 9, 20, 23, 35, 38, 50, 53, 65, 68, 80, 83, 95, 96, 100, 101, 104, 105, 106, 107, 108, 109, 110, 111, 238, 239, 240, 262, 263, 264, 265, 266, 267, 271, 277, 354, 362, 368, 377, 381, 402, 424, 425, 427, 428, 429, 434, 436, 438, 439, 441, 443, 444, 445, 450, 461, 472, 478, 489, 494, 496, 502, 511, 513, 518, 527, 529, 544, 555, 561, 572, 578, 589, 595, 607, 613, 624, 630, 641, 660, 671, 676, 678, 683, 692, 694, 699, 709, 720, 739, 750, 781, 783, 797, 799, 809, 812, 813, 819, 833, 840, 841, 849, 854, 879, 890, 891, 895, 897], "fresh": [5, 6, 8, 9, 13, 28, 43, 58, 73], "system": [5, 6, 8, 9, 271, 436, 441, 443, 444], "deprec": [5, 6, 8, 9, 92, 93, 96, 436, 441, 443, 444, 652, 731, 761, 774, 869], "ha": [5, 6, 7, 8, 9, 20, 35, 50, 65, 80, 95, 96, 259, 267, 419, 421, 424, 430, 432, 442, 725, 812, 840, 873, 895, 897, 898, 899], "been": [5, 6, 7, 8, 9, 20, 35, 50, 65, 80, 95, 96, 267, 421, 437, 812, 840, 854, 873, 895, 899], "tupl": [5, 6, 7, 8, 9, 12, 17, 18, 22, 23, 27, 32, 33, 37, 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159, 161, 162, 163, 164, 165, 166, 167, 168, 169, 171, 172, 173, 174, 175, 176, 177, 178, 179, 181, 182, 183, 184, 185, 186, 187, 188, 189, 191, 192, 193, 194, 195, 196, 197, 198, 199, 201, 202, 203, 204, 205, 206, 207, 208, 209, 211, 212, 213, 214, 215, 216, 217, 218, 219, 221, 222, 223, 224, 225, 226, 227, 228, 229, 231, 232, 233, 234, 235, 242, 243, 244, 247, 249, 250, 257, 268, 272, 276, 277, 280, 293, 306, 317, 324, 331, 345, 354, 362, 366, 368, 371, 377, 381, 385, 389, 395, 398, 402, 406, 411, 421, 424, 454, 455, 456, 461, 464, 467, 469, 471, 472, 473, 474, 475, 478, 481, 484, 486, 488, 489, 490, 494, 495, 496, 500, 501, 502, 503, 507, 511, 512, 513, 516, 517, 518, 519, 523, 527, 528, 529, 532, 533, 534, 535, 539, 540, 541, 544, 547, 550, 552, 554, 555, 556, 557, 558, 561, 564, 567, 569, 571, 572, 573, 574, 575, 578, 581, 584, 586, 588, 589, 590, 591, 592, 595, 598, 601, 604, 606, 607, 608, 609, 610, 613, 616, 619, 621, 623, 624, 625, 626, 627, 630, 633, 636, 638, 640, 641, 642, 643, 646, 647, 649, 650, 651, 653, 654, 655, 656, 657, 660, 663, 666, 668, 670, 671, 672, 676, 677, 678, 681, 682, 683, 684, 688, 692, 693, 694, 697, 698, 699, 700, 704, 705, 706, 709, 712, 715, 717, 719, 720, 721, 722, 725, 726, 728, 729, 730, 732, 733, 734, 735, 736, 739, 742, 745, 747, 749, 750, 751, 752, 755, 756, 758, 759, 760, 762, 763, 764, 765, 768, 769, 771, 772, 773, 775, 776, 777, 781, 782, 783, 786, 787, 788, 789, 793, 797, 798, 799, 802, 803, 805, 809, 811, 812, 813, 819, 822, 825, 833, 835, 836, 840, 841, 846, 849, 857, 859, 860, 864, 866, 867, 868, 870, 871, 874, 875, 876, 879, 882, 885, 887, 889, 890, 891, 897, 899, 900], "enter": [5, 6, 7, 8, 9, 12, 27, 42, 57, 72, 892], "dict": [5, 6, 7, 8, 9, 11, 12, 14, 15, 17, 18, 22, 23, 25, 26, 27, 29, 30, 32, 33, 37, 38, 39, 40, 41, 42, 44, 45, 47, 48, 52, 53, 54, 55, 56, 57, 59, 60, 62, 63, 67, 68, 70, 71, 72, 74, 75, 77, 78, 82, 83, 85, 279, 292, 305, 316, 329, 344, 494, 496, 511, 513, 527, 529, 676, 678, 692, 694, 781, 783, 797, 799, 812, 840, 900], "note": [5, 6, 7, 8, 9, 10, 11, 14, 26, 29, 41, 44, 56, 59, 71, 74, 100, 101, 104, 105, 106, 107, 108, 109, 110, 111, 120, 130, 144, 154, 164, 174, 184, 194, 204, 214, 224, 234, 238, 239, 240, 247, 248, 258, 262, 263, 264, 265, 266, 267, 269, 271, 274, 276, 277, 362, 368, 377, 381, 398, 421, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 437, 438, 439, 440, 442, 443, 445, 452, 459, 461, 477, 478, 492, 494, 495, 496, 509, 511, 512, 513, 525, 527, 528, 529, 543, 544, 560, 561, 577, 578, 594, 595, 612, 613, 629, 630, 644, 646, 650, 654, 659, 660, 674, 676, 677, 678, 690, 692, 693, 694, 708, 709, 723, 725, 729, 733, 738, 739, 753, 755, 759, 763, 766, 768, 772, 776, 779, 781, 782, 783, 795, 797, 798, 799, 812, 840, 861, 863, 867, 871, 872, 873, 878, 879, 891, 895, 897, 899], "support": [5, 6, 7, 8, 9, 91, 95, 96, 97, 277, 288, 301, 312, 325, 339, 351, 366, 385, 417, 424, 461, 470, 478, 487, 494, 496, 511, 513, 527, 529, 544, 553, 561, 570, 578, 587, 595, 605, 613, 622, 630, 639, 660, 669, 676, 678, 692, 694, 709, 718, 739, 748, 781, 783, 797, 799, 812, 813, 819, 840, 841, 849, 879, 888, 891, 892, 895, 899], "date": [5, 6, 7, 8, 9, 91, 97, 421, 424, 496, 513, 529, 678, 694, 783, 799, 812, 813, 840, 841, 891, 895, 896, 897, 898, 899], "accessibli": [5, 6, 7, 8, 9], "attribut": [5, 6, 7, 8, 9, 424, 897, 899], "To": [5, 6, 7, 8, 9, 420, 422], "you": [5, 6, 7, 8, 9, 895, 899], "after": [5, 6, 7, 8, 9, 95, 96, 424, 496, 513, 529, 678, 694, 783, 799, 897], "reserv": [5, 6, 7, 8, 9, 898], "instanc": [5, 7, 8, 9, 13, 16, 19, 28, 31, 34, 43, 46, 49, 58, 61, 64, 73, 76, 79, 87, 89, 95, 96, 115, 125, 139, 149, 159, 169, 179, 189, 199, 209, 219, 229, 272, 273, 354, 366, 385, 395, 402, 411, 436, 441, 443, 444, 461, 478, 496, 513, 529, 544, 561, 578, 595, 613, 630, 660, 678, 694, 709, 739, 783, 799, 812, 815, 822, 840, 843, 857, 879, 898], "optim": [5, 8, 9, 10, 95, 100, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 117, 120, 121, 122, 123, 127, 130, 131, 136, 137, 141, 144, 145, 146, 147, 151, 154, 155, 156, 157, 161, 164, 165, 166, 167, 171, 174, 175, 176, 177, 181, 184, 185, 186, 187, 191, 194, 195, 196, 197, 201, 204, 205, 206, 207, 211, 214, 215, 216, 217, 221, 224, 225, 226, 227, 231, 234, 235, 268, 271, 277, 354, 368, 402, 450, 490, 492, 494, 507, 509, 511, 523, 525, 527, 642, 644, 654, 672, 674, 676, 688, 690, 692, 721, 723, 733, 751, 753, 763, 764, 766, 776, 777, 779, 781, 793, 795, 797, 837, 846, 859, 861, 871, 891, 896, 897, 898, 899], "exp": [5, 8, 10], "access": [5, 6, 7, 8, 9, 248, 259, 421, 898], "wai": [5, 6, 7, 8, 9, 898], "retain": [5, 6, 7, 8, 9], "wa": [5, 6, 7, 8, 9, 91, 94, 95, 96, 97, 421, 447, 450, 496, 513, 529, 678, 694, 783, 799, 895], "avail": [5, 6, 7, 8, 9, 259, 262, 265, 421, 424, 452, 461, 478, 496, 513, 529, 544, 561, 578, 595, 613, 630, 660, 678, 694, 709, 739, 783, 799, 812, 840, 879, 897, 898, 899], "syntax": [5, 6, 7, 8, 9, 496, 513, 529, 678, 694, 783, 799, 812, 840], "z": [5, 6, 7, 8, 9, 87, 96, 100, 104, 105, 106, 107, 108, 109, 110, 111, 263, 277, 368, 421, 650, 729, 759, 772, 867], "bs_x": [5, 6, 7, 8, 9], "bs_y": [5, 6, 7, 8, 9], "bs_z": [5, 6, 7, 8, 9], "1234": [5, 6, 7, 8, 9, 438], "properti": [5, 6, 7, 8, 9, 16, 18, 19, 23, 31, 33, 34, 38, 46, 48, 49, 53, 61, 63, 64, 68, 76, 78, 79, 83, 91, 96, 97, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 238, 239, 240, 242, 243, 244, 250, 257, 262, 263, 264, 265, 266, 267, 270, 272, 273, 275, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 351, 352, 353, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 367, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 386, 387, 388, 389, 390, 391, 392, 393, 394, 396, 397, 398, 399, 401, 403, 404, 405, 406, 407, 408, 409, 410, 412, 413, 414, 416, 420, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 453, 457, 460, 462, 463, 466, 468, 470, 472, 479, 480, 483, 485, 487, 489, 493, 496, 497, 498, 499, 504, 505, 506, 510, 513, 514, 515, 520, 521, 522, 526, 529, 530, 531, 536, 537, 538, 545, 546, 549, 551, 553, 555, 562, 563, 566, 568, 570, 572, 579, 580, 583, 585, 587, 589, 596, 597, 600, 602, 603, 605, 607, 614, 615, 618, 620, 622, 624, 631, 632, 635, 637, 639, 641, 645, 648, 652, 661, 662, 665, 667, 669, 671, 675, 678, 679, 680, 685, 686, 687, 691, 694, 695, 696, 701, 702, 703, 710, 711, 714, 716, 718, 720, 724, 727, 731, 740, 741, 744, 746, 748, 750, 754, 757, 761, 767, 770, 774, 780, 783, 784, 785, 790, 791, 792, 796, 799, 800, 801, 806, 807, 808, 810, 811, 812, 814, 815, 816, 817, 818, 820, 821, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 834, 835, 837, 838, 839, 840, 842, 843, 844, 845, 846, 847, 848, 850, 851, 852, 853, 854, 855, 856, 858, 862, 865, 869, 880, 881, 884, 886, 888, 890, 892, 893, 898, 900], "uniform": 6, "integ": [6, 94, 96, 100, 104, 105, 106, 107, 108, 109, 110, 111, 116, 126, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 238, 239, 240, 246, 252, 256, 259, 277, 354, 368, 402, 421, 424, 450, 465, 482, 496, 513, 529, 548, 565, 582, 599, 617, 634, 664, 678, 694, 713, 743, 783, 799, 812, 813, 840, 841, 883, 891], "equival": [6, 259, 368, 427, 438, 496, 513, 529, 678, 694, 783, 799, 812, 840, 891, 899], "structur": [7, 89, 267, 417, 420, 421, 424, 427, 428, 438, 895], "unequ": 7, "cross": [7, 273, 276, 277, 368, 420], "section": [7, 273, 276, 277, 368, 420], "relat": [7, 95, 96, 258, 433, 435, 891, 898], "inappropri": 7, "without": [8, 14, 29, 44, 59, 74, 89, 427, 428, 429, 438, 439, 445, 891], "circularblockbootstrap": [8, 9, 10, 58, 73, 90], "expon": 9, "arraylike1d": [10, 461], "arraylike2d": 10, "dimension": [10, 15, 25, 30, 40, 45, 55, 60, 70, 75, 85, 89, 895, 896], "oper": [10, 433], "b_sb": 10, "b_cb": 10, "algorithm": [10, 262, 267, 901], "its": [10, 20, 35, 50, 65, 80, 251, 258, 259, 368, 420, 421, 898], "correct": [10, 14, 29, 44, 59, 74, 259, 495, 512, 528, 677, 693, 782, 798], "tune": 10, "k_n": 10, "consecut": 10, "insid": [10, 895], "conserv": 10, "band": 10, "pm": 10, "log_": 10, "maximum": [10, 262, 263, 267, 271, 276, 281, 294, 392, 424, 440, 891, 895, 896, 898, 899], "lceil": 10, "rceil": 10, "_i": 10, "2g": 10, "d_": [10, 269, 274], "sum_": [10, 269, 271, 274, 277, 398, 424, 425, 426, 427, 428, 431, 433, 435, 437, 438, 440], "k": [10, 238, 239, 240, 259, 264, 265, 267, 271, 354, 368, 402, 425, 427, 428, 431, 435, 438, 439, 502, 518, 683, 699, 899], "h": [10, 95, 100, 104, 105, 106, 107, 108, 109, 110, 111, 237, 239, 262, 263, 264, 269, 271, 274, 424, 496, 513, 529, 678, 694, 783, 799, 812, 840, 895, 897, 899], "gamma_": [10, 425, 431, 435], "c_": 10, "x_k": 10, "bar": [10, 421, 438], "x_": [10, 95, 271, 895], "remain": [10, 895], "constant": [10, 95, 117, 120, 127, 130, 141, 144, 151, 154, 161, 164, 171, 174, 181, 184, 191, 194, 201, 204, 211, 214, 221, 224, 231, 234, 262, 263, 264, 265, 267, 269, 271, 274, 276, 277, 417, 420, 421, 424, 427, 428, 429, 430, 438, 439, 496, 502, 513, 518, 529, 534, 678, 683, 694, 699, 755, 783, 788, 799, 813, 841, 891, 896, 899], "c_i": 10, "taken": 10, "andrew": [10, 95, 108, 264, 266, 267, 268, 419], "patton": 10, "matlab": 10, "program": 10, "do": [10, 12, 27, 42, 57, 72, 269, 274, 424, 438, 496, 513, 529, 678, 694, 783, 799, 812, 813, 840, 841, 872, 897, 900], "autocovari": [10, 277], "rather": [10, 96, 258, 259, 263, 267, 424, 897, 898], "dimitri": 10, "halbert": 10, "white": [10, 237, 239, 258, 277, 354, 368, 402, 421, 896, 898], "2004": [10, 264, 267], "automat": [10, 91, 95, 96, 97, 262, 263, 264, 265, 267, 268, 276, 281, 294, 417, 419, 421, 427, 428, 429, 438, 439, 445, 891], "select": [10, 95, 96, 121, 131, 145, 155, 165, 175, 185, 195, 205, 215, 225, 235, 240, 262, 263, 264, 267, 268, 271, 273, 276, 281, 294, 392, 417, 419, 421, 466, 468, 483, 485, 549, 551, 566, 568, 583, 585, 600, 602, 618, 620, 635, 637, 665, 667, 714, 716, 744, 746, 884, 886], "review": [10, 264, 443], "23": [10, 237, 239, 421, 896], "53": [10, 420, 896], "70": [10, 259], "doi": [10, 236], "1081": 10, "etc": [10, 424, 898], "120028836": 10, "2009": [10, 420, 424, 897], "28": [10, 421, 424, 891, 895, 896, 897, 898], "372": 10, "375": 10, "1080": 10, "07474930802459016": 10, "func": [11, 14, 15, 25, 26, 29, 30, 40, 41, 44, 45, 55, 56, 59, 60, 70, 71, 74, 75, 85, 891, 896, 897, 898], "callabl": [11, 14, 15, 25, 26, 29, 30, 40, 41, 44, 45, 55, 56, 59, 60, 70, 71, 74, 75, 85, 95, 258, 452, 461, 465, 478, 482, 496, 513, 529, 544, 548, 561, 565, 578, 582, 595, 599, 613, 617, 630, 634, 653, 660, 664, 678, 694, 709, 713, 732, 739, 743, 762, 775, 783, 799, 812, 840, 870, 879, 883], "ndarrai": [11, 12, 14, 15, 17, 18, 21, 22, 23, 24, 25, 26, 27, 29, 30, 32, 33, 36, 37, 38, 39, 40, 41, 42, 44, 45, 47, 48, 51, 52, 53, 54, 55, 56, 57, 59, 60, 62, 63, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 77, 78, 81, 82, 83, 84, 85, 95, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 118, 128, 134, 135, 142, 152, 162, 172, 182, 192, 202, 212, 222, 232, 238, 239, 240, 246, 247, 252, 253, 256, 262, 263, 264, 265, 266, 267, 268, 269, 271, 274, 276, 277, 290, 303, 314, 327, 342, 353, 427, 428, 429, 434, 438, 439, 445, 446, 447, 450, 454, 455, 456, 458, 459, 465, 467, 469, 471, 473, 474, 475, 476, 477, 478, 482, 484, 486, 488, 491, 492, 494, 495, 496, 501, 502, 503, 505, 506, 508, 509, 511, 512, 513, 517, 518, 519, 521, 522, 524, 525, 527, 528, 529, 533, 534, 535, 537, 538, 539, 540, 541, 542, 543, 544, 548, 550, 552, 554, 556, 557, 558, 559, 560, 561, 565, 567, 569, 571, 573, 574, 575, 576, 577, 578, 582, 584, 586, 588, 590, 591, 592, 593, 594, 595, 599, 601, 604, 606, 608, 609, 610, 611, 612, 613, 617, 619, 621, 623, 625, 626, 627, 628, 629, 630, 634, 636, 638, 640, 642, 643, 644, 646, 647, 650, 651, 653, 654, 655, 656, 657, 658, 659, 660, 664, 666, 668, 670, 673, 674, 676, 677, 678, 682, 683, 684, 686, 687, 689, 690, 692, 693, 694, 698, 699, 700, 702, 703, 704, 705, 706, 707, 708, 709, 713, 715, 717, 719, 721, 722, 723, 725, 726, 729, 730, 732, 733, 734, 735, 736, 737, 738, 739, 743, 745, 747, 749, 751, 752, 753, 755, 756, 759, 760, 762, 763, 764, 765, 766, 768, 769, 772, 773, 775, 776, 778, 779, 781, 782, 783, 787, 788, 789, 791, 792, 794, 795, 797, 798, 799, 803, 804, 805, 807, 811, 812, 813, 820, 821, 828, 829, 830, 831, 835, 840, 841, 851, 856, 859, 860, 861, 863, 864, 867, 868, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 883, 885, 887, 889, 891, 895], "rep": [11, 12, 14, 15, 25, 26, 27, 29, 30, 40, 41, 42, 44, 45, 55, 56, 57, 59, 60, 70, 71, 72, 74, 75, 85, 238, 239, 240, 259], "str": [11, 12, 14, 15, 17, 18, 22, 23, 25, 26, 27, 29, 30, 32, 33, 37, 38, 39, 40, 41, 42, 44, 45, 47, 48, 52, 53, 54, 55, 56, 57, 59, 60, 62, 63, 67, 68, 70, 71, 72, 74, 75, 77, 78, 82, 83, 85, 101, 119, 129, 143, 153, 163, 173, 183, 193, 203, 213, 223, 233, 259, 270, 272, 273, 275, 277, 278, 279, 283, 288, 289, 291, 292, 296, 302, 304, 305, 308, 312, 313, 315, 316, 319, 324, 325, 326, 328, 329, 333, 339, 340, 343, 344, 347, 351, 352, 354, 357, 368, 371, 374, 387, 393, 394, 395, 401, 402, 403, 408, 409, 410, 411, 416, 447, 450, 462, 464, 479, 481, 494, 495, 496, 498, 500, 511, 512, 513, 514, 516, 527, 528, 529, 530, 532, 545, 547, 562, 564, 579, 581, 596, 598, 614, 616, 631, 633, 648, 649, 661, 663, 676, 677, 678, 679, 681, 692, 693, 694, 695, 697, 710, 712, 727, 728, 740, 742, 757, 758, 770, 771, 781, 782, 783, 784, 786, 797, 798, 799, 800, 802, 812, 813, 819, 840, 841, 849, 865, 866, 880, 882, 900], "extra": [11, 14, 15, 25, 26, 29, 30, 40, 41, 44, 45, 55, 56, 59, 60, 70, 71, 74, 75, 85, 239, 240], "conflict": [11, 26, 41, 56, 71], "nparam": [11, 26, 41, 56, 71], "row": [11, 14, 26, 29, 41, 44, 56, 59, 71, 74, 424, 496, 513, 529, 678, 694, 783, 799, 812, 836, 840, 895], "correspond": [11, 14, 26, 29, 41, 44, 56, 59, 71, 74, 239, 250, 259, 471, 488, 494, 496, 511, 513, 527, 529, 554, 571, 588, 606, 623, 640, 670, 676, 678, 692, 694, 719, 749, 781, 783, 797, 799, 812, 840, 889, 897, 898], "iter": [11, 12, 24, 26, 27, 39, 41, 42, 54, 56, 57, 69, 71, 72, 84, 92, 452, 494, 511, 527, 676, 692, 781, 797, 891, 896, 897, 898], "up": [11, 14, 26, 29, 41, 44, 56, 59, 71, 74, 267, 421, 424, 437, 496, 513, 529, 678, 694, 783, 799, 812, 840, 895, 897, 899], "append": [11, 14, 26, 29, 41, 44, 56, 59, 71, 74, 259, 895, 897], "befor": [11, 14, 26, 29, 41, 44, 56, 59, 71, 74, 100, 104, 105, 106, 107, 108, 109, 110, 111, 248, 263, 421, 494, 511, 527, 676, 692, 781, 797, 854, 899], "randn": [11, 12, 14, 15, 25, 26, 27, 29, 30, 40, 41, 42, 44, 45, 55, 56, 57, 59, 60, 70, 71, 72, 74, 75, 85, 87, 89, 259, 428, 429, 438, 439, 445, 502, 518, 683, 699], "over": [12, 27, 42, 57, 72, 94, 899], "calcul": [12, 27, 42, 57, 72, 95, 113, 117, 118, 123, 127, 128, 137, 141, 142, 147, 151, 152, 157, 161, 162, 167, 171, 172, 177, 181, 182, 187, 191, 192, 197, 201, 202, 207, 211, 212, 217, 221, 222, 227, 231, 232, 264, 267, 646, 647, 725, 726, 755, 756, 768, 769, 864], "kei": [12, 27, 42, 57, 72, 389, 406, 417, 496, 513, 529, 678, 694, 783, 799, 812, 840, 895], "problem": [12, 27, 42, 57, 72, 89, 96, 421], "doe": [12, 27, 42, 57, 72, 90, 259, 277, 362, 381, 421, 471, 488, 494, 511, 527, 554, 571, 588, 606, 623, 640, 670, 676, 692, 719, 749, 781, 797, 873, 889, 891, 897], "output": [12, 27, 42, 57, 72, 88, 259, 494, 511, 527, 653, 676, 692, 732, 762, 775, 781, 797, 870, 891, 897, 898], "arang": [12, 27, 42, 57, 72, 89, 895, 897, 899], "posdata": [12, 27, 42, 57, 72], "kwdata": [12, 27, 42, 57, 72], "someth": [12, 27, 42, 57, 72], "prng": [13, 16, 28, 31, 43, 46, 58, 61, 73, 76], "norm": [14, 29, 44, 59, 74], "bc": [14, 29, 44, 59, 74, 755], "bca": [14, 29, 44, 59, 74, 95], "float": [14, 15, 17, 22, 23, 25, 29, 30, 32, 37, 38, 40, 44, 45, 47, 52, 53, 55, 59, 60, 62, 67, 68, 70, 74, 75, 77, 82, 83, 85, 99, 100, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 117, 120, 121, 122, 123, 127, 130, 131, 136, 137, 141, 144, 145, 146, 147, 151, 154, 155, 156, 157, 161, 164, 165, 166, 167, 171, 174, 175, 176, 177, 181, 184, 185, 186, 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735, 736, 737, 739, 743, 747, 749, 751, 752, 755, 756, 759, 760, 762, 764, 765, 768, 769, 772, 773, 775, 777, 778, 781, 782, 788, 793, 794, 797, 798, 804, 808, 810, 814, 819, 832, 834, 836, 842, 849, 852, 853, 854, 859, 860, 863, 864, 867, 868, 870, 872, 874, 875, 876, 877, 879, 883, 887, 889, 891, 895, 900], "tail": [14, 29, 44, 59, 74, 424, 443, 654, 895, 898], "bool": [14, 15, 20, 25, 29, 30, 35, 40, 44, 45, 50, 55, 59, 60, 65, 70, 74, 75, 80, 85, 99, 100, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 114, 116, 124, 126, 138, 140, 148, 150, 158, 160, 168, 170, 178, 180, 188, 190, 198, 200, 208, 210, 218, 220, 228, 230, 239, 240, 262, 263, 266, 270, 271, 272, 277, 330, 334, 336, 354, 368, 385, 402, 425, 427, 428, 429, 434, 438, 439, 440, 445, 446, 447, 448, 450, 457, 470, 487, 491, 494, 496, 508, 511, 513, 524, 527, 529, 553, 570, 587, 605, 622, 639, 646, 669, 673, 676, 678, 689, 692, 694, 718, 725, 748, 755, 768, 778, 781, 783, 794, 797, 799, 809, 812, 833, 840, 863, 888, 891], "fals": [14, 15, 20, 25, 29, 30, 35, 40, 44, 45, 50, 55, 59, 60, 65, 70, 74, 75, 80, 85, 88, 91, 95, 97, 99, 100, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 239, 240, 266, 271, 277, 354, 368, 385, 402, 421, 425, 427, 428, 429, 434, 438, 439, 440, 445, 448, 491, 494, 508, 511, 513, 524, 527, 529, 646, 673, 676, 678, 689, 692, 694, 725, 755, 768, 778, 781, 783, 794, 797, 799, 809, 812, 833, 840, 863, 891, 895, 897, 899], "nonparametr": [14, 29, 44, 59, 74, 89], "semi": [14, 29, 44, 59, 74, 89], "studentize_rep": [14, 29, 44, 59, 74], "One": [14, 29, 44, 59, 74, 421, 424], "debias": [14, 29, 44, 59, 74, 266, 277], "bia": [14, 29, 44, 59, 74, 274, 420], "coverag": [14, 29, 44, 59, 74], "flag": [14, 20, 29, 35, 44, 50, 59, 65, 74, 80, 100, 104, 105, 106, 107, 108, 109, 110, 111, 114, 116, 124, 126, 138, 140, 148, 150, 158, 160, 168, 170, 178, 180, 188, 190, 198, 200, 208, 210, 218, 220, 228, 230, 239, 240, 262, 271, 385, 427, 428, 429, 434, 438, 439, 440, 445, 470, 487, 491, 494, 508, 511, 524, 527, 553, 570, 587, 605, 622, 639, 646, 669, 673, 676, 689, 692, 718, 725, 748, 755, 768, 778, 781, 794, 797, 809, 833, 837, 863, 888, 891], "indic": [14, 20, 24, 29, 35, 39, 44, 48, 50, 54, 59, 65, 69, 74, 80, 84, 100, 104, 105, 106, 107, 108, 109, 110, 111, 114, 116, 124, 126, 138, 140, 148, 150, 158, 160, 168, 170, 178, 180, 188, 190, 198, 200, 208, 210, 218, 220, 228, 230, 239, 240, 242, 243, 247, 253, 257, 259, 262, 266, 271, 330, 334, 336, 385, 420, 421, 424, 427, 428, 429, 438, 439, 440, 445, 470, 487, 491, 494, 496, 502, 508, 511, 513, 518, 524, 527, 529, 553, 570, 587, 605, 622, 639, 646, 669, 673, 676, 678, 683, 689, 692, 694, 699, 718, 725, 748, 755, 768, 778, 781, 783, 794, 797, 799, 809, 812, 813, 819, 833, 840, 841, 849, 863, 888, 891, 898], "whether": [14, 15, 20, 25, 29, 30, 35, 40, 44, 45, 50, 55, 59, 60, 65, 70, 74, 75, 80, 85, 100, 104, 105, 106, 107, 108, 109, 110, 111, 114, 116, 124, 126, 138, 140, 148, 150, 158, 160, 168, 170, 178, 180, 188, 190, 198, 200, 208, 210, 218, 220, 228, 230, 258, 259, 262, 265, 266, 272, 277, 354, 368, 385, 402, 417, 419, 421, 427, 428, 429, 434, 438, 439, 440, 445, 447, 450, 491, 494, 496, 508, 511, 513, 524, 527, 529, 646, 673, 676, 678, 689, 692, 694, 725, 755, 768, 778, 781, 783, 794, 797, 799, 812, 840, 863, 891, 898], "previous": [14, 29, 44, 59, 74, 94, 896, 898, 899], "rerun": [14, 29, 44, 59, 74, 420], "ignor": [14, 29, 44, 59, 74, 421, 819, 849, 891, 895], "chang": [14, 29, 44, 59, 74, 93, 94, 95, 97, 259, 277, 420, 421, 452, 496, 513, 529, 678, 694, 783, 799, 812, 840, 872, 896, 897, 898, 899], "about": [14, 29, 44, 59, 74, 496, 513, 529, 678, 694, 783, 799, 846, 897, 898], "elimin": [14, 29, 44, 59, 74, 238], "need": [14, 29, 44, 59, 74, 89, 277, 420, 895, 897, 899], "bound": [14, 29, 44, 59, 74, 239, 250, 458, 461, 469, 471, 476, 478, 486, 488, 492, 509, 525, 542, 544, 552, 554, 559, 561, 569, 571, 576, 578, 586, 588, 593, 595, 604, 606, 611, 613, 621, 623, 628, 630, 638, 640, 650, 658, 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74], "ad": [14, 29, 44, 59, 74, 91, 92, 93, 94, 95, 96, 97, 898, 901], "recent": [15, 25, 30, 40, 45, 55, 60, 70, 75, 85, 88, 236, 239, 277], "interest": [15, 25, 30, 40, 45, 55, 60, 70, 75, 85, 898], "center": [15, 25, 30, 40, 45, 55, 60, 70, 75, 85, 88, 95, 99, 100, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 239, 259, 265, 898], "signatur": [15, 25, 30, 40, 45, 55, 60, 70, 75, 85], "axi": [15, 25, 30, 40, 45, 55, 60, 70, 75, 85, 496, 513, 529, 678, 694, 783, 799, 812, 840, 898, 899], "addit": [15, 25, 30, 40, 45, 55, 60, 70, 75, 85, 236, 265, 421, 465, 482, 548, 565, 582, 599, 617, 634, 664, 713, 743, 883, 897, 898], "elif": [15, 25, 30, 40, 45, 55, 60, 70, 75, 85, 897], "get": [16, 17, 19, 23, 31, 32, 34, 38, 46, 47, 49, 53, 61, 62, 64, 68, 76, 77, 79, 83, 280, 281, 288, 293, 294, 301, 306, 312, 317, 324, 325, 330, 331, 334, 336, 339, 345, 351, 393, 409, 417, 472, 489, 493, 504, 505, 510, 520, 521, 526, 536, 537, 555, 572, 589, 607, 624, 641, 671, 675, 685, 686, 691, 701, 702, 720, 750, 780, 790, 791, 796, 806, 890], "option": [16, 31, 46, 61, 76, 89, 95, 238, 239, 240, 259, 262, 277, 421, 428, 438, 446, 494, 496, 511, 513, 527, 529, 676, 678, 692, 694, 781, 783, 797, 799, 891, 892, 898, 901], "map": [17, 23, 32, 38, 47, 53, 62, 68, 77, 83], "current": [18, 33, 48, 63, 78, 95, 265, 873, 891, 896, 897, 898], "use_se": [20, 35, 50, 65, 80], "last": [20, 35, 50, 65, 80, 266, 269, 274, 424, 450, 494, 495, 511, 512, 527, 528, 676, 677, 692, 693, 781, 782, 797, 798, 895], "rese": [21, 36, 51, 66, 81], "otherwis": [23, 38, 53, 68, 83, 854], "updat": [24, 39, 54, 69, 84, 91, 94, 95, 97, 262, 265, 427, 452, 472, 489, 494, 511, 527, 555, 572, 589, 607, 624, 641, 671, 676, 692, 720, 750, 781, 797, 872, 890, 892], "overridden": [24, 39, 54, 69, 84, 421, 446], "new": [24, 39, 54, 69, 84, 95, 262, 427, 428, 429, 438, 439, 445, 496, 513, 529, 678, 694, 783, 799, 812, 840, 891], "least": [86, 266, 269, 274, 368, 421, 439], "total": 86, "aris": 86, "experiment": 86, "divid": [87, 100, 104, 105, 106, 107, 108, 109, 110, 111, 809, 833, 891, 898], "sharp_ratio": 87, "lowest": 87, "demonstr": [87, 496, 513, 529, 678, 694, 783, 799, 812, 840, 891], "rand": 87, "po": 87, "kw": 87, "xstar": 87, "alwai": [87, 247, 259, 421, 424, 899], "even": [87, 247, 420, 453, 898], "singleton": 87, "ystar": [87, 89], "zstar": 87, "challeng": 88, "simpli": [88, 94, 896], "tradit": 88, "financ": [88, 898], "choos": 88, "captur": [88, 452, 898, 901], "param_cov": [88, 450], "148534": 88, "508540": 88, "561637": 88, "729435": 88, "442891": 88, "273945": 88, "495087": 88, "049454": 88, "020830": 88, "those": [89, 258, 897, 899], "At": 89, "minimum": [89, 91, 96, 97, 262, 263, 267, 277], "thei": [89, 258, 259, 263, 424, 438, 897, 899, 901], "perform": [89, 91, 95, 97, 258, 259, 267, 273, 452], "For": [89, 236, 496, 513, 529, 678, 694, 783, 799, 812, 840, 895, 898, 899], "simplic": 89, "consid": [89, 258, 271, 276, 895], "ol": [89, 96, 259, 263, 271, 272, 273, 274, 277, 285, 298, 321, 354, 368, 402, 419, 421], "regress": [89, 95, 259, 262, 263, 265, 267, 269, 271, 272, 273, 274, 276, 277, 280, 282, 293, 295, 306, 307, 317, 318, 331, 332, 345, 346, 354, 362, 368, 377, 381, 391, 397, 398, 401, 402, 413, 416, 419, 420, 502, 518, 683, 699], "regressand": 89, "x_orig": 89, "linalg": 89, "pinv": 89, "ravel": 89, "resid": [89, 270, 272, 421, 447, 450, 454, 456, 458, 461, 465, 467, 469, 471, 473, 475, 476, 478, 482, 484, 486, 488, 539, 541, 542, 544, 548, 550, 552, 554, 556, 558, 559, 561, 565, 567, 569, 571, 573, 575, 576, 578, 582, 584, 586, 588, 590, 592, 593, 595, 599, 601, 604, 606, 608, 610, 611, 613, 617, 619, 621, 623, 625, 627, 628, 630, 634, 636, 638, 640, 642, 643, 646, 655, 657, 658, 660, 664, 666, 668, 670, 704, 706, 707, 709, 713, 715, 717, 719, 721, 722, 725, 734, 736, 737, 739, 743, 745, 747, 749, 751, 752, 755, 764, 765, 768, 859, 860, 863, 873, 874, 876, 877, 879, 883, 885, 887, 889, 895, 896, 898, 899], "y_star": 89, "1d": [89, 899], "functool": 89, "wrapper": [89, 258], "fix": [89, 90, 91, 94, 95, 96, 97, 421, 424, 425, 432, 434, 442, 447, 893, 894, 901], "ols_parti": 89, "effect": [89, 899], "conduct": 89, "account": [89, 258, 265, 282, 295, 307, 318, 332, 346, 354, 362, 368, 381, 402, 895], "ols_semi_v2": 89, "ultim": 89, "meaningfulli": 89, "simpler": 89, "rel": [89, 442], "expens": [89, 872], "rare": 89, "encount": 89, "actual": [89, 259, 424, 897], "cousin": 89, "fulli": [89, 274, 419], "model": [89, 91, 93, 94, 95, 97, 236, 237, 238, 239, 240, 241, 242, 243, 244, 247, 253, 254, 257, 260, 267, 271, 272, 273, 274, 276, 280, 285, 293, 298, 306, 317, 331, 345, 354, 361, 362, 363, 366, 368, 372, 375, 376, 377, 380, 381, 382, 385, 402, 417, 420, 421, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 452, 453, 455, 457, 458, 460, 461, 463, 464, 465, 467, 469, 471, 474, 476, 478, 480, 481, 482, 484, 486, 488, 491, 494, 495, 496, 498, 500, 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"asymptot": [89, 262, 265, 277], "desir": [89, 899], "present": 89, "rng": [89, 461, 465, 478, 482, 496, 513, 529, 544, 548, 561, 565, 578, 582, 595, 599, 613, 617, 630, 634, 660, 664, 678, 694, 709, 713, 739, 743, 783, 799, 812, 840, 879, 883, 895, 899], "facilit": [89, 822, 857], "manner": 89, "continu": [89, 236], "assum": [89, 95, 276, 420, 421, 424, 494, 511, 527, 646, 676, 692, 725, 755, 768, 781, 797, 873, 895, 897, 898, 899], "mle": [89, 491, 508, 524, 673, 689, 778, 794], "ols_para": 89, "beta": [89, 259, 269, 271, 274, 276, 277, 420, 424, 433, 891, 895, 896, 898, 899], "sigma2": [89, 455, 458, 469, 474, 476, 486, 540, 542, 552, 557, 559, 569, 574, 576, 586, 591, 593, 604, 609, 611, 621, 626, 628, 638, 646, 656, 658, 668, 705, 707, 717, 725, 735, 737, 747, 755, 768, 863, 873, 875, 877, 887, 896, 898], "r_": [89, 897], "plain": 89, "specifi": [89, 91, 94, 95, 97, 420, 421, 424, 427, 433, 435, 438, 446, 447, 453, 495, 512, 528, 677, 693, 782, 798, 809, 833, 891, 892, 894, 895, 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See Notes for requirements"], [11, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.CircularBlockBootstrap.bootstrap": [[12, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.CircularBlockBootstrap.clone": [[13, 2, 2, 0, "args", "Positional arguments to bootstrap"], [13, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [13, 2, 2, 0, "seed", "The seed value to pass to the closed generator"]], "arch.bootstrap.CircularBlockBootstrap.conf_int": [[14, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func and std_err_func, when appropriate"], [14, 2, 2, 0, "func", "Function the computes parameter values.  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See Notes for requirements"], [41, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.IndependentSamplesBootstrap.bootstrap": [[42, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.IndependentSamplesBootstrap.clone": [[43, 2, 2, 0, "args", "Positional arguments to bootstrap"], [43, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [43, 2, 2, 0, "seed", "The seed value to pass to the closed generator"]], "arch.bootstrap.IndependentSamplesBootstrap.conf_int": [[44, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func and std_err_func, when appropriate"], [44, 2, 2, 0, "func", "Function the computes parameter values.  See Notes for requirements"], [44, 2, 2, 0, "method", "One of 'basic', 'percentile', 'studentized', 'norm' (identical to 'var', 'cov'), 'bc' (identical to 'debiased', 'bias-corrected'), or 'bca'"], [44, 2, 2, 0, "reps", "Number of bootstrap replications"], [44, 2, 2, 0, "reuse", "Flag indicating whether to reuse previously computed bootstrap results.  This allows alternative methods to be compared without rerunning the bootstrap simulation.  Reuse is ignored if reps is not the same across multiple runs, func changes across calls, or method is 'studentized'."], [44, 2, 2, 0, "sampling", "Type of sampling to use: 'nonparametric', 'semi-parametric' (or 'semi') or 'parametric'.  The default is 'nonparametric'.  See notes about the changes to func required when using 'semi' or 'parametric'."], [44, 2, 2, 0, "size", "Coverage of confidence interval"], [44, 2, 2, 0, "std_err_func", "Function to use when standardizing estimated parameters when using the studentized bootstrap.  Providing an analytical function eliminates the need for a nested bootstrap"], [44, 2, 2, 0, "studentize_reps", "Number of bootstraps to use in the inner bootstrap when using the studentized bootstrap.  Ignored when std_err_func is provided"], [44, 2, 2, 0, "tail", "One of 'two', 'upper' or 'lower'."]], "arch.bootstrap.IndependentSamplesBootstrap.cov": [[45, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [45, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [45, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."], [45, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.IndependentSamplesBootstrap.reset": [[50, 2, 2, 0, "use_seed", "Flag indicating whether to use the last seed if provided.  If False or if no seed has been set, the bootstrap will be reset to the initial state.  Default is True"]], "arch.bootstrap.IndependentSamplesBootstrap.seed": [[51, 2, 2, 0, "value", "Value to use as the seed."]], "arch.bootstrap.IndependentSamplesBootstrap.set_state": [[52, 2, 2, 0, "state", "Dictionary or tuple containing the state."]], "arch.bootstrap.IndependentSamplesBootstrap.var": [[55, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [55, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [55, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."], [55, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.MCS.__init__": [[238, 2, 2, 0, "block_size", "Length of window to use in the bootstrap.  If not provided, sqrt(T) is used.  In general, this should be provided and chosen to be appropriate for the data."], [238, 2, 2, 0, "bootstrap", "Bootstrap to use.  Options are 'stationary' or 'sb': Stationary bootstrap (Default) 'circular' or 'cbb': Circular block bootstrap 'moving block' or 'mbb': Moving block bootstrap"], [238, 2, 2, 0, "losses", "T by k array containing losses from a set of models"], [238, 2, 2, 0, "method", "MCS test and elimination implementation method, either 'max' or 'R'. Default is 'R'."], [238, 2, 2, 0, "reps", "Number of bootstrap replications to uses.  Default is 1000."], [238, 2, 2, 0, "seed", "Seed value to use when creating the bootstrap used in the comparison. If an integer or None, the NumPy default_rng is used with the seed value.  If a Generator or a RandomState, the argument is used."], [238, 2, 2, 0, "size", "Value in (0,1) to use as the test size when implementing the mcs. Default value is 0.05."]], "arch.bootstrap.MCS": [[241, 3, 1, 0, "compute", "Compute the set of models in the confidence set."], [242, 5, 1, 0, "excluded", "List of model indices that are excluded from the MCS"], [243, 5, 1, 0, "included", "List of model indices that are included in the MCS"], [244, 5, 1, 0, "pvalues", "Model p-values for inclusion in the MCS"], [245, 3, 1, 0, "reset", "Reset the bootstrap to it's initial state."], [246, 3, 1, 0, "seed", "Seed the bootstrap's random number generator"]], "arch.bootstrap.MCS.seed": [[246, 2, 2, 0, "value", "Integer to use as the seed"]], "arch.bootstrap.MovingBlockBootstrap.__init__": [[8, 2, 2, 0, "args", "Positional arguments to bootstrap"], [8, 2, 2, 0, "block_size", "Size of block to use"], [8, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [8, 2, 2, 0, "random_state", "RandomState to use to ensure reproducable results. Cannot be used with seed"], [8, 2, 2, 0, "seed", "Seed to use to ensure reproducable results. If an int, passes the value to value to np.random.default_rng. If None, a fresh Generator is constructed with system-provided entropy."]], "arch.bootstrap.MovingBlockBootstrap": [[56, 3, 1, 0, "apply", "Applies a function to bootstrap replicated data"], [57, 3, 1, 0, "bootstrap", "Iterator for use when bootstrapping"], [58, 3, 1, 0, "clone", "Clones the bootstrap using different data with a fresh prng."], [59, 3, 1, 0, "conf_int", "Function the computes parameter values.  See Notes for requirements"], [60, 3, 1, 0, "cov", "Compute parameter covariance using bootstrap"], [8, 4, 1, 0, "data", "Two-element tuple with the pos_data in the first position and kw_data in the second (pos_data, kw_data)"], [61, 5, 1, 0, "generator", "Set or get the instance PRNG"], [62, 3, 1, 0, "get_state", "Gets the state of the bootstrap's random number generator"], [63, 5, 1, 0, "index", "The current index of the bootstrap"], [8, 4, 1, 0, "kw_data", "Dictionary containing the keyword arguments"], [8, 4, 1, 0, "pos_data", "Tuple containing the positional arguments (in the order entered)"], [64, 5, 1, 0, "random_state", "Set or get the instance random state"], [65, 3, 1, 0, "reset", "Resets the bootstrap to either its initial state or the last seed."], [66, 3, 1, 0, "seed", "Reseeds the bootstrap's random number generator"], [67, 3, 1, 0, "set_state", "Sets the state of the bootstrap's random number generator"], [68, 5, 1, 0, "state", "Set or get the generator's state"], [69, 3, 1, 0, "update_indices", "Update indices for the next iteration of the bootstrap.  This must be overridden when creating new bootstraps."], [70, 3, 1, 0, "var", "Compute parameter variance using bootstrap"]], "arch.bootstrap.MovingBlockBootstrap.apply": [[56, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func.  Must not conflict with keyword arguments used to initialize bootstrap"], [56, 2, 2, 0, "func", "Function the computes parameter values.  See Notes for requirements"], [56, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.MovingBlockBootstrap.bootstrap": [[57, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.MovingBlockBootstrap.clone": [[58, 2, 2, 0, "args", "Positional arguments to bootstrap"], [58, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [58, 2, 2, 0, "seed", "The seed value to pass to the closed generator"]], "arch.bootstrap.MovingBlockBootstrap.conf_int": [[59, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func and std_err_func, when appropriate"], [59, 2, 2, 0, "func", "Function the computes parameter values.  See Notes for requirements"], [59, 2, 2, 0, "method", "One of 'basic', 'percentile', 'studentized', 'norm' (identical to 'var', 'cov'), 'bc' (identical to 'debiased', 'bias-corrected'), or 'bca'"], [59, 2, 2, 0, "reps", "Number of bootstrap replications"], [59, 2, 2, 0, "reuse", "Flag indicating whether to reuse previously computed bootstrap results.  This allows alternative methods to be compared without rerunning the bootstrap simulation.  Reuse is ignored if reps is not the same across multiple runs, func changes across calls, or method is 'studentized'."], [59, 2, 2, 0, "sampling", "Type of sampling to use: 'nonparametric', 'semi-parametric' (or 'semi') or 'parametric'.  The default is 'nonparametric'.  See notes about the changes to func required when using 'semi' or 'parametric'."], [59, 2, 2, 0, "size", "Coverage of confidence interval"], [59, 2, 2, 0, "std_err_func", "Function to use when standardizing estimated parameters when using the studentized bootstrap.  Providing an analytical function eliminates the need for a nested bootstrap"], [59, 2, 2, 0, "studentize_reps", "Number of bootstraps to use in the inner bootstrap when using the studentized bootstrap.  Ignored when std_err_func is provided"], [59, 2, 2, 0, "tail", "One of 'two', 'upper' or 'lower'."]], "arch.bootstrap.MovingBlockBootstrap.cov": [[60, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [60, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [60, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."], [60, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.MovingBlockBootstrap.reset": [[65, 2, 2, 0, "use_seed", "Flag indicating whether to use the last seed if provided.  If False or if no seed has been set, the bootstrap will be reset to the initial state.  Default is True"]], "arch.bootstrap.MovingBlockBootstrap.seed": [[66, 2, 2, 0, "value", "Value to use as the seed."]], "arch.bootstrap.MovingBlockBootstrap.set_state": [[67, 2, 2, 0, "state", "Dictionary or tuple containing the state."]], "arch.bootstrap.MovingBlockBootstrap.var": [[70, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [70, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [70, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."], [70, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.SPA.__init__": [[239, 2, 2, 0, "benchmark", "T element array of benchmark model losses"], [239, 2, 2, 0, "block_size", "Length of window to use in the bootstrap.  If not provided, sqrt(T) is used.  In general, this should be provided and chosen to be appropriate for the data."], [239, 2, 2, 0, "bootstrap", "Bootstrap to use.  Options are 'stationary' or 'sb': Stationary bootstrap (Default) 'circular' or 'cbb': Circular block bootstrap 'moving block' or 'mbb': Moving block bootstrap"], [239, 2, 2, 0, "models", "T  by k element array of alternative model losses"], [239, 2, 2, 0, "nested", "Flag indicating to use a nested bootstrap to compute variances for studentization.  Default is False.  Note that this can be slow since the procedure requires k extra bootstraps."], [239, 2, 2, 0, "reps", "Number of bootstrap replications to uses.  Default is 1000."], [239, 2, 2, 0, "seed", "Seed value to use when creating the bootstrap used in the comparison. If an integer or None, the NumPy default_rng is used with the seed value.  If a Generator or a RandomState, the argument is used."], [239, 2, 2, 0, "studentize", "Flag indicating to studentize loss differentials. Default is True"]], "arch.bootstrap.SPA": [[247, 3, 1, 0, "better_models", "Returns set of models rejected as being equal-or-worse than the benchmark"], [248, 3, 1, 0, "compute", "Compute the bootstrap pvalue."], [249, 3, 1, 0, "critical_values", "Returns data-dependent critical values"], [250, 5, 1, 0, "pvalues", "P-values corresponding to the lower, consistent and upper p-values."], [251, 3, 1, 0, "reset", "Reset the bootstrap to its initial state."], [252, 3, 1, 0, "seed", "Seed the bootstrap's random number generator"], [253, 3, 1, 0, "subset", "Sets a list of active models to run the SPA on.  Primarily for internal use."]], "arch.bootstrap.SPA.better_models": [[247, 2, 2, 0, "pvalue", "P-value in (0,1) to use when computing superior models"], [247, 2, 2, 0, "pvalue_type", "String in 'lower', 'consistent', or 'upper' indicating which critical value to use."]], "arch.bootstrap.SPA.critical_values": [[249, 2, 2, 0, "pvalue", "P-value in (0,1) to use when computing the critical values."]], "arch.bootstrap.SPA.seed": [[252, 2, 2, 0, "value", "Integer to use as the seed"]], "arch.bootstrap.SPA.subset": [[253, 2, 2, 0, "selector", "Boolean array indicating which columns to use when computing the p-values.  This is primarily for use by StepM."]], "arch.bootstrap.StationaryBootstrap.__init__": [[9, 2, 2, 0, "args", "Positional arguments to bootstrap"], [9, 2, 2, 0, "block_size", "Average size of block to use"], [9, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [9, 2, 2, 0, "random_state", "RandomState to use to ensure reproducable results. Cannot be used with seed"], [9, 2, 2, 0, "seed", "Seed to use to ensure reproducable results. If an int, passes the value to value to np.random.default_rng. If None, a fresh Generator is constructed with system-provided entropy."]], "arch.bootstrap.StationaryBootstrap": [[71, 3, 1, 0, "apply", "Applies a function to bootstrap replicated data"], [72, 3, 1, 0, "bootstrap", "Iterator for use when bootstrapping"], [73, 3, 1, 0, "clone", "Clones the bootstrap using different data with a fresh prng."], [74, 3, 1, 0, "conf_int", "Function the computes parameter values.  See Notes for requirements"], [75, 3, 1, 0, "cov", "Compute parameter covariance using bootstrap"], [9, 4, 1, 0, "data", "Two-element tuple with the pos_data in the first position and kw_data in the second (pos_data, kw_data)"], [76, 5, 1, 0, "generator", "Set or get the instance PRNG"], [77, 3, 1, 0, "get_state", "Gets the state of the bootstrap's random number generator"], [78, 5, 1, 0, "index", "The current index of the bootstrap"], [9, 4, 1, 0, "kw_data", "Dictionary containing the keyword arguments"], [9, 4, 1, 0, "pos_data", "Tuple containing the positional arguments (in the order entered)"], [79, 5, 1, 0, "random_state", "Set or get the instance random state"], [80, 3, 1, 0, "reset", "Resets the bootstrap to either its initial state or the last seed."], [81, 3, 1, 0, "seed", "Reseeds the bootstrap's random number generator"], [82, 3, 1, 0, "set_state", "Sets the state of the bootstrap's random number generator"], [83, 5, 1, 0, "state", "Set or get the generator's state"], [84, 3, 1, 0, "update_indices", "Update indices for the next iteration of the bootstrap.  This must be overridden when creating new bootstraps."], [85, 3, 1, 0, "var", "Compute parameter variance using bootstrap"]], "arch.bootstrap.StationaryBootstrap.apply": [[71, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func.  Must not conflict with keyword arguments used to initialize bootstrap"], [71, 2, 2, 0, "func", "Function the computes parameter values.  See Notes for requirements"], [71, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.StationaryBootstrap.bootstrap": [[72, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.StationaryBootstrap.clone": [[73, 2, 2, 0, "args", "Positional arguments to bootstrap"], [73, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [73, 2, 2, 0, "seed", "The seed value to pass to the closed generator"]], "arch.bootstrap.StationaryBootstrap.conf_int": [[74, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func and std_err_func, when appropriate"], [74, 2, 2, 0, "func", "Function the computes parameter values.  See Notes for requirements"], [74, 2, 2, 0, "method", "One of 'basic', 'percentile', 'studentized', 'norm' (identical to 'var', 'cov'), 'bc' (identical to 'debiased', 'bias-corrected'), or 'bca'"], [74, 2, 2, 0, "reps", "Number of bootstrap replications"], [74, 2, 2, 0, "reuse", "Flag indicating whether to reuse previously computed bootstrap results.  This allows alternative methods to be compared without rerunning the bootstrap simulation.  Reuse is ignored if reps is not the same across multiple runs, func changes across calls, or method is 'studentized'."], [74, 2, 2, 0, "sampling", "Type of sampling to use: 'nonparametric', 'semi-parametric' (or 'semi') or 'parametric'.  The default is 'nonparametric'.  See notes about the changes to func required when using 'semi' or 'parametric'."], [74, 2, 2, 0, "size", "Coverage of confidence interval"], [74, 2, 2, 0, "std_err_func", "Function to use when standardizing estimated parameters when using the studentized bootstrap.  Providing an analytical function eliminates the need for a nested bootstrap"], [74, 2, 2, 0, "studentize_reps", "Number of bootstraps to use in the inner bootstrap when using the studentized bootstrap.  Ignored when std_err_func is provided"], [74, 2, 2, 0, "tail", "One of 'two', 'upper' or 'lower'."]], "arch.bootstrap.StationaryBootstrap.cov": [[75, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [75, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [75, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."], [75, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.StationaryBootstrap.reset": [[80, 2, 2, 0, "use_seed", "Flag indicating whether to use the last seed if provided.  If False or if no seed has been set, the bootstrap will be reset to the initial state.  Default is True"]], "arch.bootstrap.StationaryBootstrap.seed": [[81, 2, 2, 0, "value", "Value to use as the seed."]], "arch.bootstrap.StationaryBootstrap.set_state": [[82, 2, 2, 0, "state", "Dictionary or tuple containing the state."]], "arch.bootstrap.StationaryBootstrap.var": [[85, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [85, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [85, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."], [85, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.StepM.__init__": [[240, 2, 2, 0, "benchmark", "T element array of benchmark model losses"], [240, 2, 2, 0, "block_size", "Length of window to use in the bootstrap.  If not provided, sqrt(T) is used.  In general, this should be provided and chosen to be appropriate for the data."], [240, 2, 2, 0, "bootstrap", "Bootstrap to use.  Options are 'stationary' or 'sb': Stationary bootstrap (Default) 'circular' or 'cbb': Circular block bootstrap 'moving block' or 'mbb': Moving block bootstrap"], [240, 2, 2, 0, "models", "T by k element array of alternative model losses"], [240, 2, 2, 0, "nested", "Flag indicating to use a nested bootstrap to compute variances for studentization.  Default is False.  Note that this can be slow since the procedure requires k extra bootstraps."], [240, 2, 2, 0, "reps", "Number of bootstrap replications to uses.  Default is 1000."], [240, 2, 2, 0, "seed", "Seed value to use when creating the bootstrap used in the comparison. If an integer or None, the NumPy default_rng is used with the seed value.  If a Generator or a RandomState, the argument is used."], [240, 2, 2, 0, "size", "Value in (0,1) to use as the test size when implementing the comparison. Default value is 0.05."], [240, 2, 2, 0, "studentize", "Flag indicating to studentize loss differentials. Default is True"]], "arch.bootstrap.StepM": [[254, 3, 1, 0, "compute", "Compute the set of superior models."], [255, 3, 1, 0, "reset", "Reset the bootstrap to it's initial state."], [256, 3, 1, 0, "seed", "Seed the bootstrap's random number generator"], [257, 5, 1, 0, "superior_models", "List of the indices or column names of the superior models"]], "arch.bootstrap.StepM.seed": [[256, 2, 2, 0, "value", "Integer to use as the seed"]], "arch.bootstrap.optimal_block_length": [[10, 2, 2, 0, "x", "A one-dimensional or two-dimensional array-like.  Operates columns by column if 2-dimensional."]], "arch.covariance": [[98, 0, 0, 1, "kernel", ""]], "arch.covariance.kernel": [[99, 1, 1, 0, "Andrews", "Alternative name of the QuadraticSpectral covariance estimator."], [100, 1, 1, 0, "Bartlett", "Bartlett's (Newey-West) kernel covariance estimation."], [101, 1, 1, 0, "CovarianceEstimate", "Covariance estimate using a long-run covariance estimator"], [102, 1, 1, 0, "Gallant", "Alternative name for Parzen covariance estimator."], [103, 1, 1, 0, "NeweyWest", "Alternative name for Bartlett covariance estimator."], [104, 1, 1, 0, "Parzen", "Parzen's kernel covariance estimation."], [105, 1, 1, 0, "ParzenCauchy", "Parzen's Cauchy kernel covariance estimation."], [106, 1, 1, 0, "ParzenGeometric", "Parzen's Geometric kernel covariance estimation."], [107, 1, 1, 0, "ParzenRiesz", "Parzen-Reisz kernel covariance estimation."], [108, 1, 1, 0, "QuadraticSpectral", "Quadratic-Spectral (Andrews') kernel covariance estimation."], [109, 1, 1, 0, "TukeyHamming", "Tukey-Hamming kernel covariance estimation."], [110, 1, 1, 0, "TukeyHanning", "Tukey-Hanning kernel covariance estimation."], [111, 1, 1, 0, "TukeyParzen", "Tukey-Parzen kernel covariance estimation."]], "arch.covariance.kernel.Andrews": [[112, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [113, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [114, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [115, 5, 1, 0, "cov", "The estimated covariances."], [116, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [117, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [118, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [119, 5, 1, 0, "name", "The covariance estimator's name."], [120, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [121, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.Bartlett.__init__": [[100, 2, 2, 0, "bandwidth", "The kernel's bandwidth.  If None, optimal bandwidth is estimated."], [100, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [100, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."], [100, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [100, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."], [100, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.Bartlett": [[122, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [123, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [124, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [125, 5, 1, 0, "cov", "The estimated covariances."], [126, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [127, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [128, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [129, 5, 1, 0, "name", "The covariance estimator's name."], [130, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [131, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.CovarianceEstimate.__init__": [[101, 2, 2, 0, "columns", "Column labels to use if covariance estimates are returned as DataFrames."], [101, 2, 2, 0, "long_run", "The long-run covariance estimate. If not provided, computed from short_run and one_sided_strict."], [101, 2, 2, 0, "one_sided_strict", "The one-sided-strict covariance estimate. If not provided, computed from short_run and one_sided_strict."], [101, 2, 2, 0, "short_run", "The short-run covariance estimate."]], "arch.covariance.kernel.CovarianceEstimate": [[132, 5, 1, 0, "long_run", "The long-run covariance estimate."], [133, 5, 1, 0, "one_sided", "The one-sided covariance estimate."], [134, 5, 1, 0, "one_sided_strict", "The one-sided strict covariance estimate."], [135, 5, 1, 0, "short_run", "The short-run covariance estimate."]], "arch.covariance.kernel.Gallant": [[136, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [137, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [138, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [139, 5, 1, 0, "cov", "The estimated covariances."], [140, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [141, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [142, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [143, 5, 1, 0, "name", "The covariance estimator's name."], [144, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [145, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.NeweyWest": [[146, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [147, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [148, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [149, 5, 1, 0, "cov", "The estimated covariances."], [150, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [151, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [152, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [153, 5, 1, 0, "name", "The covariance estimator's name."], [154, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [155, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.Parzen.__init__": [[104, 2, 2, 0, "bandwidth", "The kernel's bandwidth.  If None, optimal bandwidth is estimated."], [104, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [104, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."], [104, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [104, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."], [104, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.Parzen": [[156, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [157, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [158, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [159, 5, 1, 0, "cov", "The estimated covariances."], [160, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [161, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [162, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [163, 5, 1, 0, "name", "The covariance estimator's name."], [164, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [165, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.ParzenCauchy.__init__": [[105, 2, 2, 0, "bandwidth", "The kernel's bandwidth.  If None, optimal bandwidth is estimated."], [105, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [105, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."], [105, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [105, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."], [105, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.ParzenCauchy": [[166, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [167, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [168, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [169, 5, 1, 0, "cov", "The estimated covariances."], [170, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [171, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [172, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [173, 5, 1, 0, "name", "The covariance estimator's name."], [174, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [175, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.ParzenGeometric.__init__": [[106, 2, 2, 0, "bandwidth", "The kernel's bandwidth.  If None, optimal bandwidth is estimated."], [106, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [106, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."], [106, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [106, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."], [106, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.ParzenGeometric": [[176, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [177, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [178, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [179, 5, 1, 0, "cov", "The estimated covariances."], [180, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [181, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [182, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [183, 5, 1, 0, "name", "The covariance estimator's name."], [184, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [185, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.ParzenRiesz.__init__": [[107, 2, 2, 0, "bandwidth", "The kernel's bandwidth.  If None, optimal bandwidth is estimated."], [107, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [107, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."], [107, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [107, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."], [107, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.ParzenRiesz": [[186, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [187, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [188, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [189, 5, 1, 0, "cov", "The estimated covariances."], [190, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [191, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [192, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [193, 5, 1, 0, "name", "The covariance estimator's name."], [194, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [195, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.QuadraticSpectral.__init__": [[108, 2, 2, 0, "bandwidth", "The kernel's bandwidth.  If None, optimal bandwidth is estimated."], [108, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [108, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."], [108, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [108, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."], [108, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.QuadraticSpectral": [[196, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [197, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [198, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [199, 5, 1, 0, "cov", "The estimated covariances."], [200, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [201, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [202, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [203, 5, 1, 0, "name", "The covariance estimator's name."], [204, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [205, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.TukeyHamming.__init__": [[109, 2, 2, 0, "bandwidth", "The kernel's bandwidth.  If None, optimal bandwidth is estimated."], [109, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [109, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."], [109, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [109, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."], [109, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.TukeyHamming": [[206, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [207, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [208, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [209, 5, 1, 0, "cov", "The estimated covariances."], [210, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [211, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [212, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [213, 5, 1, 0, "name", "The covariance estimator's name."], [214, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [215, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.TukeyHanning.__init__": [[110, 2, 2, 0, "bandwidth", "The kernel's bandwidth.  If None, optimal bandwidth is estimated."], [110, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [110, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."], [110, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [110, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."], [110, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.TukeyHanning": [[216, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [217, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [218, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [219, 5, 1, 0, "cov", "The estimated covariances."], [220, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [221, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [222, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [223, 5, 1, 0, "name", "The covariance estimator's name."], [224, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [225, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.TukeyParzen.__init__": [[111, 2, 2, 0, "bandwidth", "The kernel's bandwidth.  If None, optimal bandwidth is estimated."], [111, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [111, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."], [111, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [111, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."], [111, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.TukeyParzen": [[226, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [227, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [228, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [229, 5, 1, 0, "cov", "The estimated covariances."], [230, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [231, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [232, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [233, 5, 1, 0, "name", "The covariance estimator's name."], [234, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [235, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.unitroot": [[262, 1, 1, 0, "ADF", "Augmented Dickey-Fuller unit root test"], [263, 1, 1, 0, "DFGLS", "Elliott, Rothenberg and Stock's (1) GLS detrended Dickey-Fuller"], [264, 1, 1, 0, "KPSS", "Kwiatkowski, Phillips, Schmidt and Shin (KPSS) stationarity test"], [265, 1, 1, 0, "PhillipsPerron", "Phillips-Perron unit root test"], [266, 1, 1, 0, "VarianceRatio", "Variance Ratio test of a random walk."], [267, 1, 1, 0, "ZivotAndrews", "Zivot-Andrews structural-break unit-root test"], [268, 6, 1, 0, "auto_bandwidth", "Automatic bandwidth selection of Andrews (1991) and Newey & West (1994)."], [261, 0, 0, 1, "cointegration", ""]], "arch.unitroot.ADF.__init__": [[262, 2, 2, 0, "lags", "The number of lags to use in the ADF regression.  If omitted or None, method is used to automatically select the lag length with no more than max_lags are included."], [262, 2, 2, 0, "low_memory", "Flag indicating whether to use a low memory implementation of the lag selection algorithm. The low memory algorithm is slower than the standard algorithm but will use 2-4% of the memory required for the standard algorithm. This options allows automatic lag selection to be used in very long time series. If None, use automatic selection of algorithm."], [262, 2, 2, 0, "max_lags", "The maximum number of lags to use when selecting lag length"], [262, 2, 2, 0, "method", "The method to use when selecting the lag length"], [262, 2, 2, 0, "trend", "The trend component to include in the test"], [262, 2, 2, 0, "y", "The data to test for a unit root"]], "arch.unitroot.ADF": [[278, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [279, 5, 1, 0, "critical_values", "Dictionary containing critical values specific to the test, number of observations and included deterministic trend terms."], [280, 5, 1, 0, "lags", "Sets or gets the number of lags used in the model. When bootstrap use DF-type regressions, lags is the number of lags in the regression model.  When bootstrap use long-run variance estimators, lags is the number of lags used in the long-run variance estimator."], [281, 5, 1, 0, "max_lags", "Sets or gets the maximum lags used when automatically selecting lag length"], [282, 5, 1, 0, "nobs", "The number of observations used when computing the test statistic. Accounts for loss of data due to lags for regression-based bootstrap."], [283, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [284, 5, 1, 0, "pvalue", "Returns the p-value for the test statistic"], [285, 5, 1, 0, "regression", "Returns the OLS regression results from the ADF model estimated"], [286, 5, 1, 0, "stat", "The test statistic for a unit root"], [287, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [288, 5, 1, 0, "trend", "Sets or gets the deterministic trend term used in the test. See valid_trends for a list of supported trends"], [289, 5, 1, 0, "valid_trends", "List of valid trend terms."], [290, 5, 1, 0, "y", "Returns the data used in the test statistic"]], "arch.unitroot.DFGLS.__init__": [[263, 2, 2, 0, "lags", "The number of lags to use in the ADF regression.  If omitted or None, method is used to automatically select the lag length with no more than max_lags are included."], [263, 2, 2, 0, "max_lags", "The maximum number of lags to use when selecting lag length. When using automatic lag length selection, the lag is selected using OLS detrending rather than GLS detrending (2)."], [263, 2, 2, 0, "method", "The method to use when selecting the lag length"], [263, 2, 2, 0, "trend", "The trend component to include in the test"], [263, 2, 2, 0, "y", "The data to test for a unit root"]], "arch.unitroot.DFGLS": [[291, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [292, 5, 1, 0, "critical_values", "Dictionary containing critical values specific to the test, number of observations and included deterministic trend terms."], [293, 5, 1, 0, "lags", "Sets or gets the number of lags used in the model. When bootstrap use DF-type regressions, lags is the number of lags in the regression model.  When bootstrap use long-run variance estimators, lags is the number of lags used in the long-run variance estimator."], [294, 5, 1, 0, "max_lags", "Sets or gets the maximum lags used when automatically selecting lag length"], [295, 5, 1, 0, "nobs", "The number of observations used when computing the test statistic. Accounts for loss of data due to lags for regression-based bootstrap."], [296, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [297, 5, 1, 0, "pvalue", "Returns the p-value for the test statistic"], [298, 5, 1, 0, "regression", "Returns the OLS regression results from the ADF model estimated"], [299, 5, 1, 0, "stat", "The test statistic for a unit root"], [300, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [301, 5, 1, 0, "trend", "Sets or gets the deterministic trend term used in the test. See valid_trends for a list of supported trends"], [302, 5, 1, 0, "valid_trends", "List of valid trend terms."], [303, 5, 1, 0, "y", "Returns the data used in the test statistic"]], "arch.unitroot.KPSS.__init__": [[264, 2, 2, 0, "lags", "The number of lags to use in the Newey-West estimator of the long-run covariance.  If omitted or None, the number of lags is calculated with the data-dependent method of Hobijn et al. (1998). See also Andrews (1991), Newey & West (1994), and Schwert (1989). Set lags=-1 to use the old method that only depends on the sample size, 12 * (nobs/100) ** (1/4)."], [264, 2, 2, 0, "trend", "\"c\" - Include a constant (Default) \"ct\" - Include a constant and linear time trend"], [264, 2, 2, 0, "y", "The data to test for stationarity"]], "arch.unitroot.KPSS": [[304, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [305, 5, 1, 0, "critical_values", "Dictionary containing critical values specific to the test, number of observations and included deterministic trend terms."], [306, 5, 1, 0, "lags", "Sets or gets the number of lags used in the model. When bootstrap use DF-type regressions, lags is the number of lags in the regression model.  When bootstrap use long-run variance estimators, lags is the number of lags used in the long-run variance estimator."], [307, 5, 1, 0, "nobs", "The number of observations used when computing the test statistic. Accounts for loss of data due to lags for regression-based bootstrap."], [308, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [309, 5, 1, 0, "pvalue", "Returns the p-value for the test statistic"], [310, 5, 1, 0, "stat", "The test statistic for a unit root"], [311, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [312, 5, 1, 0, "trend", "Sets or gets the deterministic trend term used in the test. See valid_trends for a list of supported trends"], [313, 5, 1, 0, "valid_trends", "List of valid trend terms."], [314, 5, 1, 0, "y", "Returns the data used in the test statistic"]], "arch.unitroot.PhillipsPerron.__init__": [[265, 2, 2, 0, "lags", "The number of lags to use in the Newey-West estimator of the long-run covariance.  If omitted or None, the lag length is set automatically to 12 * (nobs/100) ** (1/4)"], [265, 2, 2, 0, "test_type", "The test to use when computing the test statistic. \"tau\" is based on the t-stat and \"rho\" uses a test based on nobs times the re-centered regression coefficient"], [265, 2, 2, 0, "trend", "The trend component to include in the test"], [265, 2, 2, 0, "y", "The data to test for a unit root"]], "arch.unitroot.PhillipsPerron": [[315, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [316, 5, 1, 0, "critical_values", "Dictionary containing critical values specific to the test, number of observations and included deterministic trend terms."], [317, 5, 1, 0, "lags", "Sets or gets the number of lags used in the model. When bootstrap use DF-type regressions, lags is the number of lags in the regression model.  When bootstrap use long-run variance estimators, lags is the number of lags used in the long-run variance estimator."], [318, 5, 1, 0, "nobs", "The number of observations used when computing the test statistic. Accounts for loss of data due to lags for regression-based bootstrap."], [319, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [320, 5, 1, 0, "pvalue", "Returns the p-value for the test statistic"], [321, 5, 1, 0, "regression", "Returns OLS regression results for the specification used in the test"], [322, 5, 1, 0, "stat", "The test statistic for a unit root"], [323, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [324, 5, 1, 0, "test_type", "Gets or sets the test type returned by stat. Valid values are \"tau\" or \"rho\""], [325, 5, 1, 0, "trend", "Sets or gets the deterministic trend term used in the test. See valid_trends for a list of supported trends"], [326, 5, 1, 0, "valid_trends", "List of valid trend terms."], [327, 5, 1, 0, "y", "Returns the data used in the test statistic"]], "arch.unitroot.VarianceRatio.__init__": [[266, 2, 2, 0, "debiased", "Indicates whether to use a debiased version of the test. Default is True. Only applicable if overlap is True."], [266, 2, 2, 0, "lags", "The number of periods to used in the multi-period variance, which is the numerator of the test statistic.  Must be at least 2"], [266, 2, 2, 0, "overlap", "Indicates whether to use all overlapping blocks.  Default is True.  If False, the number of observations in y minus 1 must be an exact multiple of lags.  If this condition is not satisfied, some values at the end of y will be discarded."], [266, 2, 2, 0, "robust", "Indicates whether to use heteroskedasticity robust inference. Default is True."], [266, 2, 2, 0, "trend", "\"c\" allows for a non-zero drift in the random walk, while \"n\" requires that the increments to y are mean 0"], [266, 2, 2, 0, "y", "The data to test for a random walk"]], "arch.unitroot.VarianceRatio": [[328, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [329, 5, 1, 0, "critical_values", "Dictionary containing critical values specific to the test, number of observations and included deterministic trend terms."], [330, 5, 1, 0, "debiased", "Sets of gets the indicator to use debiased variances in the ratio"], [331, 5, 1, 0, "lags", "Sets or gets the number of lags used in the model. When bootstrap use DF-type regressions, lags is the number of lags in the regression model.  When bootstrap use long-run variance estimators, lags is the number of lags used in the long-run variance estimator."], [332, 5, 1, 0, "nobs", "The number of observations used when computing the test statistic. Accounts for loss of data due to lags for regression-based bootstrap."], [333, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [334, 5, 1, 0, "overlap", "Sets of gets the indicator to use overlapping returns in the long-period variance estimator"], [335, 5, 1, 0, "pvalue", "Returns the p-value for the test statistic"], [336, 5, 1, 0, "robust", "Sets of gets the indicator to use a heteroskedasticity robust variance estimator"], [337, 5, 1, 0, "stat", "The test statistic for a unit root"], [338, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [339, 5, 1, 0, "trend", "Sets or gets the deterministic trend term used in the test. See valid_trends for a list of supported trends"], [340, 5, 1, 0, "valid_trends", "List of valid trend terms."], [341, 5, 1, 0, "vr", "The ratio of the long block lags-period variance to the 1-period variance"], [342, 5, 1, 0, "y", "Returns the data used in the test statistic"]], "arch.unitroot.ZivotAndrews.__init__": [[267, 2, 2, 0, "lags", "The number of lags to use in the ADF regression.  If omitted or None, method is used to automatically select the lag length with no more than max_lags are included."], [267, 2, 2, 0, "max_lags", "The maximum number of lags to use when selecting lag length"], [267, 2, 2, 0, "method", "The method to use when selecting the lag length"], [267, 2, 2, 0, "trend", "The trend component to include in the test"], [267, 2, 2, 0, "trim", "percentage of series at begin/end to exclude from break-period calculation in range [0, 0.333] (default=0.15)"], [267, 2, 2, 0, "y", "data series"]], "arch.unitroot.ZivotAndrews": [[343, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [344, 5, 1, 0, "critical_values", "Dictionary containing critical values specific to the test, number of observations and included deterministic trend terms."], [345, 5, 1, 0, "lags", "Sets or gets the number of lags used in the model. When bootstrap use DF-type regressions, lags is the number of lags in the regression model.  When bootstrap use long-run variance estimators, lags is the number of lags used in the long-run variance estimator."], [346, 5, 1, 0, "nobs", "The number of observations used when computing the test statistic. Accounts for loss of data due to lags for regression-based bootstrap."], [347, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [348, 5, 1, 0, "pvalue", "Returns the p-value for the test statistic"], [349, 5, 1, 0, "stat", "The test statistic for a unit root"], [350, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [351, 5, 1, 0, "trend", "Sets or gets the deterministic trend term used in the test. See valid_trends for a list of supported trends"], [352, 5, 1, 0, "valid_trends", "List of valid trend terms."], [353, 5, 1, 0, "y", "Returns the data used in the test statistic"]], "arch.unitroot.auto_bandwidth": [[268, 2, 2, 0, "kernel", "The kernel function to use for selecting the bandwidth"], [268, 2, 2, 0, "y", "Data on which to apply the bandwidth selection"]], "arch.unitroot.cointegration": [[269, 1, 1, 0, "CanonicalCointegratingReg", "Canonical Cointegrating Regression cointegrating vector estimation."], [270, 1, 1, 0, "CointegrationAnalysisResults", "summary()"], [271, 1, 1, 0, "DynamicOLS", "Dynamic OLS (DOLS) cointegrating vector estimation"], [272, 1, 1, 0, "DynamicOLSResults", "Estimation results for Dynamic OLS models"], [273, 1, 1, 0, "EngleGrangerTestResults", "Results class for Engle-Granger cointegration tests."], [274, 1, 1, 0, "FullyModifiedOLS", "Fully Modified OLS cointegrating vector estimation."], [275, 1, 1, 0, "PhillipsOuliarisTestResults", "plot([axes, title])"], [276, 6, 1, 0, "engle_granger", "Test for cointegration within a set of time series."], [277, 6, 1, 0, "phillips_ouliaris", "Test for cointegration within a set of time series."]], "arch.unitroot.cointegration.CanonicalCointegratingReg.__init__": [[269, 2, 2, 0, "trend", "Trend to include in the cointegrating regression. Trends are:"], [269, 2, 2, 0, "x", "The right-hand-side variables in the cointegrating regression."], [269, 2, 2, 0, "x_trend", "Trends that affects affect the x-data but do not appear in the cointegrating regression. x_trend must be at least as large as trend, so that if trend is \"ct\", x_trend must be either \"ct\" or \"ctt\"."], [269, 2, 2, 0, "y", "The left-hand-side variable in the cointegrating regression."]], "arch.unitroot.cointegration.CanonicalCointegratingReg": [[354, 3, 1, 0, "fit", "Estimate the cointegrating vector."]], "arch.unitroot.cointegration.CanonicalCointegratingReg.fit": [[354, 2, 2, 0, "bandwidth", "The bandwidth to use. If not provided, the optimal bandwidth is estimated from the data. Setting the bandwidth to 0 and using \"unadjusted\" produces the classic OLS covariance estimator. Setting the bandwidth to 0 and using \"robust\" produces White's covariance estimator."], [354, 2, 2, 0, "df_adjust", "Whether the adjust the parameter covariance to account for the number of parameters estimated in the regression. If true, the parameter covariance estimator is multiplied by T/(T-k) where k is the number of regressors in the model."], [354, 2, 2, 0, "diff", "Use differenced data to estimate the residuals."], [354, 2, 2, 0, "force_int", "Whether the force the estimated optimal bandwidth to be an integer."], [354, 2, 2, 0, "kernel", "The string name of any of any known kernel-based long-run covariance estimators. Common choices are \"bartlett\" for the Bartlett kernel (Newey-West), \"parzen\" for the Parzen kernel and \"quadratic-spectral\" for the Quadratic Spectral kernel."]], "arch.unitroot.cointegration.CointegrationAnalysisResults": [[355, 5, 1, 0, "bandwidth", "The bandwidth used in the parameter covariance estimation"], [356, 5, 1, 0, "cov", "The estimated parameter covariance of the cointegrating vector"], [357, 5, 1, 0, "kernel", "The kernel used to estimate the covariance"], [358, 5, 1, 0, "long_run_variance", "Long-run variance estimate used in the parameter covariance estimator"], [359, 5, 1, 0, "params", "The estimated parameters of the cointegrating vector"], [360, 5, 1, 0, "pvalues", "P-value of the parameters in the cointegrating vector"], [361, 5, 1, 0, "resid", "The model residuals"], [362, 5, 1, 0, "residual_variance", "The variance of the regression residual."], [363, 5, 1, 0, "rsquared", "The model R\u00b2"], [364, 5, 1, 0, "rsquared_adj", "The degree-of-freedom adjusted R\u00b2"], [365, 5, 1, 0, "std_errors", "Standard errors  of the parameters in the cointegrating vector"], [366, 3, 1, 0, "summary", "Summary of the model, containing estimated parameters and std. errors"], [367, 5, 1, 0, "tvalues", "T-statistics of the parameters in the cointegrating vector"]], "arch.unitroot.cointegration.DynamicOLS.__init__": [[271, 2, 2, 0, "common", "Flag indicating that lags and leads should be restricted to the same value. When common is None, lags must equal leads and max_lag must equal max_lead."], [271, 2, 2, 0, "lags", "The number of lags to include in the model.  If None, the optimal number of lags is chosen using method."], [271, 2, 2, 0, "leads", "The number of leads to include in the model.  If None, the optimal number of leads is chosen using method."], [271, 2, 2, 0, "max_lag", "The maximum lag to consider. See Notes for value used when None."], [271, 2, 2, 0, "max_lead", "The maximum lead to consider. See Notes for value used when None."], [271, 2, 2, 0, "method", "The method used to select lag length when lags or leads is None."], [271, 2, 2, 0, "trend", "Trend to include in the cointegrating regression. Trends are:"], [271, 2, 2, 0, "x", "The right-hand-side variables in the cointegrating regression."], [271, 2, 2, 0, "y", "The left-hand-side variable in the cointegrating regression."]], "arch.unitroot.cointegration.DynamicOLS": [[368, 3, 1, 0, "fit", "Estimate the Dynamic OLS regression"]], "arch.unitroot.cointegration.DynamicOLS.fit": [[368, 2, 2, 0, "bandwidth", "The bandwidth to use. If not provided, the optimal bandwidth is estimated from the data. Setting the bandwidth to 0 and using \"unadjusted\" produces the classic OLS covariance estimator. Setting the bandwidth to 0 and using \"robust\" produces White's covariance estimator."], [368, 2, 2, 0, "cov_type", "Either \"unadjusted\" (or is equivalent \"homoskedastic\") or \"robust\" (or its equivalent \"kernel\")."], [368, 2, 2, 0, "df_adjust", "Whether the adjust the parameter covariance to account for the number of parameters estimated in the regression. If true, the parameter covariance estimator is multiplied by T/(T-k) where k is the number of regressors in the model."], [368, 2, 2, 0, "force_int", "Whether the force the estimated optimal bandwidth to be an integer."], [368, 2, 2, 0, "kernel", "The string name of any of any known kernel-based long-run covariance estimators. Common choices are \"bartlett\" for the Bartlett kernel (Newey-West), \"parzen\" for the Parzen kernel and \"quadratic-spectral\" for the Quadratic Spectral kernel."]], "arch.unitroot.cointegration.DynamicOLSResults.__init__": [[272, 2, 2, 0, "cov", "The estimated parameter covariance."], [272, 2, 2, 0, "cov_type", "The type of the parameter covariance estimator used."], [272, 2, 2, 0, "df_adjust", "Whether to degree of freedom adjust the estimator."], [272, 2, 2, 0, "kernel_est", "The covariance estimator instance used to estimate the parameter covariance."], [272, 2, 2, 0, "lags", "The number of lags included in the model."], [272, 2, 2, 0, "leads", "The number of leads included in the model."], [272, 2, 2, 0, "params", "The estimated model parameters."], [272, 2, 2, 0, "reg_results", "Regression results from fitting statsmodels OLS."], [272, 2, 2, 0, "resid", "The model residuals."]], "arch.unitroot.cointegration.DynamicOLSResults": [[369, 5, 1, 0, "bandwidth", "The bandwidth used in the parameter covariance estimation"], [370, 5, 1, 0, "cov", "The estimated parameter covariance of the cointegrating vector"], [371, 5, 1, 0, "cov_type", "The type of parameter covariance estimator used"], [372, 5, 1, 0, "full_cov", "Parameter covariance of the all model parameters, incl. leads and lags"], [373, 5, 1, 0, "full_params", "The complete set of parameters, including leads and lags"], [374, 5, 1, 0, "kernel", "The kernel used to estimate the covariance"], [375, 5, 1, 0, "lags", "The number of lags included in the model"], [376, 5, 1, 0, "leads", "The number of leads included in the model"], [377, 5, 1, 0, "long_run_variance", "The long-run variance of the regression residual."], [378, 5, 1, 0, "params", "The estimated parameters of the cointegrating vector"], [379, 5, 1, 0, "pvalues", "P-value of the parameters in the cointegrating vector"], [380, 5, 1, 0, "resid", "The model residuals"], [381, 5, 1, 0, "residual_variance", "The variance of the regression residual."], [382, 5, 1, 0, "rsquared", "The model R\u00b2"], [383, 5, 1, 0, "rsquared_adj", "The degree-of-freedom adjusted R\u00b2"], [384, 5, 1, 0, "std_errors", "Standard errors  of the parameters in the cointegrating vector"], [385, 3, 1, 0, "summary", "Summary of the model, containing estimated parameters and std. errors"], [386, 5, 1, 0, "tvalues", "T-statistics of the parameters in the cointegrating vector"]], "arch.unitroot.cointegration.DynamicOLSResults.summary": [[385, 2, 2, 0, "full", "Flag indicating whether to include all estimated parameters (True) or only the parameters of the cointegrating vector"]], "arch.unitroot.cointegration.EngleGrangerTestResults.__init__": [[273, 2, 2, 0, "adf", "The ADF instance used to perform the test and lag selection."], [273, 2, 2, 0, "alternative", "The alternative hypothesis."], [273, 2, 2, 0, "crit_vals", "The critical values of the Engle-Granger specific to the sample size and model dimension."], [273, 2, 2, 0, "null", "The null hypothesis."], [273, 2, 2, 0, "order", "The number of stochastic trends in the null distribution."], [273, 2, 2, 0, "pvalue", "The pvalue of the Engle-Granger test statistic."], [273, 2, 2, 0, "stat", "The Engle-Granger test statistic."], [273, 2, 2, 0, "trend", "The model's trend description."], [273, 2, 2, 0, "xsection", "The OLS results used in the cross-sectional regression."]], "arch.unitroot.cointegration.EngleGrangerTestResults": [[387, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [388, 5, 1, 0, "cointegrating_vector", "The estimated cointegrating vector."], [389, 5, 1, 0, "critical_values", "Critical Values"], [390, 5, 1, 0, "distribution_order", "The number of stochastic trends under the null hypothesis."], [391, 5, 1, 0, "lags", "The number of lags used in the Augmented Dickey-Fuller regression."], [392, 5, 1, 0, "max_lags", "The maximum number of lags used in the lag-length selection."], [393, 5, 1, 0, "name", "Sets or gets the name of the cointegration test"], [394, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [395, 3, 1, 0, "plot", "Plot the cointegration residuals."], [396, 5, 1, 0, "pvalue", "The p-value of the test statistic."], [397, 5, 1, 0, "resid", "The residual from the cointegrating regression."], [398, 5, 1, 0, "rho", "The estimated coefficient in the Dickey-Fuller Test"], [399, 5, 1, 0, "stat", "The test statistic."], [400, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [401, 5, 1, 0, "trend", "The trend used in the cointegrating regression"]], "arch.unitroot.cointegration.EngleGrangerTestResults.plot": [[395, 2, 2, 0, "axes", "matplotlib axes instance to hold the figure."], [395, 2, 2, 0, "title", "Title for the figure."]], "arch.unitroot.cointegration.FullyModifiedOLS.__init__": [[274, 2, 2, 0, "trend", "Trend to include in the cointegrating regression. Trends are:"], [274, 2, 2, 0, "x", "The right-hand-side variables in the cointegrating regression."], [274, 2, 2, 0, "x_trend", "Trends that affects affect the x-data but do not appear in the cointegrating regression. x_trend must be at least as large as trend, so that if trend is \"ct\", x_trend must be either \"ct\" or \"ctt\"."], [274, 2, 2, 0, "y", "The left-hand-side variable in the cointegrating regression."]], "arch.unitroot.cointegration.FullyModifiedOLS": [[402, 3, 1, 0, "fit", "Estimate the cointegrating vector."]], "arch.unitroot.cointegration.FullyModifiedOLS.fit": [[402, 2, 2, 0, "bandwidth", "The bandwidth to use. If not provided, the optimal bandwidth is estimated from the data. Setting the bandwidth to 0 and using \"unadjusted\" produces the classic OLS covariance estimator. Setting the bandwidth to 0 and using \"robust\" produces White's covariance estimator."], [402, 2, 2, 0, "df_adjust", "Whether the adjust the parameter covariance to account for the number of parameters estimated in the regression. If true, the parameter covariance estimator is multiplied by T/(T-k) where k is the number of regressors in the model."], [402, 2, 2, 0, "diff", "Use differenced data to estimate the residuals."], [402, 2, 2, 0, "force_int", "Whether the force the estimated optimal bandwidth to be an integer."], [402, 2, 2, 0, "kernel", "The string name of any of any known kernel-based long-run covariance estimators. Common choices are \"bartlett\" for the Bartlett kernel (Newey-West), \"parzen\" for the Parzen kernel and \"quadratic-spectral\" for the Quadratic Spectral kernel."]], "arch.unitroot.cointegration.PhillipsOuliarisTestResults": [[403, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [404, 5, 1, 0, "bandwidth", "Bandwidth used by the long-run covariance estimator"], [405, 5, 1, 0, "cointegrating_vector", "The estimated cointegrating vector."], [406, 5, 1, 0, "critical_values", "Critical Values"], [407, 5, 1, 0, "distribution_order", "The number of stochastic trends under the null hypothesis."], [408, 5, 1, 0, "kernel", "Name of the long-run covariance estimator"], [409, 5, 1, 0, "name", "Sets or gets the name of the cointegration test"], [410, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [411, 3, 1, 0, "plot", "Plot the cointegration residuals."], [412, 5, 1, 0, "pvalue", "The p-value of the test statistic."], [413, 5, 1, 0, "resid", "The residual from the cointegrating regression."], [414, 5, 1, 0, "stat", "The test statistic."], [415, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [416, 5, 1, 0, "trend", "The trend used in the cointegrating regression"]], "arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot": [[411, 2, 2, 0, "axes", "matplotlib axes instance to hold the figure."], [411, 2, 2, 0, "title", "Title for the figure."]], "arch.unitroot.cointegration.engle_granger": [[276, 2, 2, 0, "lags", "The number of lagged differences to include in the Augmented Dickey-Fuller test used on the residuals of the"], [276, 2, 2, 0, "max_lags", "The maximum number of lags to consider when using automatic lag-length in the Augmented Dickey-Fuller regression."], [276, 2, 2, 0, "method", "The method used to select the number of lags included in the Augmented Dickey-Fuller regression."], [276, 2, 2, 0, "trend", "Trend to include in the cointegrating regression. Trends are:"], [276, 2, 2, 0, "x", "The right-hand-side variables in the cointegrating regression."], [276, 2, 2, 0, "y", "The left-hand-side variable in the cointegrating regression."]], "arch.unitroot.cointegration.phillips_ouliaris": [[277, 2, 2, 0, "bandwidth", "The bandwidth to use. If not provided, the optimal bandwidth is estimated from the data. Setting the bandwidth to 0 and using \"unadjusted\" produces the classic OLS covariance estimator. Setting the bandwidth to 0 and using \"robust\" produces White's covariance estimator."], [277, 2, 2, 0, "force_int", "Whether the force the estimated optimal bandwidth to be an integer."], [277, 2, 2, 0, "kernel", "The string name of any of any known kernel-based long-run covariance estimators. Common choices are \"bartlett\" for the Bartlett kernel (Newey-West), \"parzen\" for the Parzen kernel and \"quadratic-spectral\" for the Quadratic Spectral kernel."], [277, 2, 2, 0, "test_type", "The test statistic to compute. Supported options are:"], [277, 2, 2, 0, "trend", "Trend to include in the cointegrating regression. Trends are:"], [277, 2, 2, 0, "x", "The right-hand-side variables in the cointegrating regression."], [277, 2, 2, 0, "y", "The left-hand-side variable in the cointegrating regression."]], "arch.univariate": [[425, 1, 1, 0, "APARCH", "Asymmetric Power ARCH (APARCH) volatility process"], [426, 1, 1, 0, "ARCH", "ARCH process"], [427, 1, 1, 0, "ARCHInMean", "(G)ARCH-in-mean model and simulation"], [428, 1, 1, 0, "ARX", "Autoregressive model with optional exogenous regressors estimation and simulation"], [429, 1, 1, 0, "ConstantMean", "Constant mean model estimation and simulation."], [430, 1, 1, 0, "ConstantVariance", "Constant volatility process"], [431, 1, 1, 0, "EGARCH", "EGARCH model estimation"], [432, 1, 1, 0, "EWMAVariance", "Exponentially Weighted Moving-Average (RiskMetrics) Variance process"], [433, 1, 1, 0, "FIGARCH", "FIGARCH model"], [434, 1, 1, 0, "FixedVariance", "Fixed volatility process"], [435, 1, 1, 0, "GARCH", "GARCH and related model estimation"], [436, 1, 1, 0, "GeneralizedError", "Generalized Error distribution for use with ARCH models"], [437, 1, 1, 0, "HARCH", "Heterogeneous ARCH process"], [438, 1, 1, 0, "HARX", "Heterogeneous Autoregression (HAR), with optional exogenous regressors, model estimation and simulation"], [439, 1, 1, 0, "LS", "Least squares model estimation and simulation"], [440, 1, 1, 0, "MIDASHyperbolic", "MIDAS Hyperbolic ARCH process"], [441, 1, 1, 0, "Normal", "Standard normal distribution for use with ARCH models"], [442, 1, 1, 0, "RiskMetrics2006", "RiskMetrics 2006 Variance process"], [443, 1, 1, 0, "SkewStudent", "Standardized Skewed Student's distribution for use with ARCH models"], [444, 1, 1, 0, "StudentsT", "Standardized Student's distribution for use with ARCH models"], [445, 1, 1, 0, "ZeroMean", "Model with zero conditional mean estimation and simulation"], [891, 6, 1, 0, "arch_model", "Initialization of common ARCH model specifications"]], "arch.univariate.APARCH.__init__": [[425, 2, 2, 0, "common_asym", "Restrict all asymmetry terms to share the same asymmetry parameter. If False (default), then there are no restrictions on the o asymmetry parameters."], [425, 2, 2, 0, "delta", "Value to use for a fixed delta in the APARCH model. If not provided, the value of delta is jointly estimated with other model parameters. User provided delta is restricted to lie in (0.05, 4.0)."], [425, 2, 2, 0, "o", "Order of the asymmetric innovation. Must satisfy o<=p."], [425, 2, 2, 0, "p", "Order of the symmetric innovation. Must satisfy p>=o."], [425, 2, 2, 0, "q", "Order of the lagged (transformed) conditional variance"]], "arch.univariate.APARCH": [[454, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [455, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [456, 3, 1, 0, "bounds", "Returns bounds for parameters"], [457, 5, 1, 0, "common_asym", "The value of delta in the model. NaN is delta is estimated."], [458, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [459, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [460, 5, 1, 0, "delta", "The value of delta in the model. NaN is delta is estimated."], [461, 3, 1, 0, "forecast", "Forecast volatility from the model"], [462, 5, 1, 0, "name", "The name of the volatility process"], [463, 5, 1, 0, "num_params", "The number of parameters in the model"], [464, 3, 1, 0, "parameter_names", "Names of model parameters"], [465, 3, 1, 0, "simulate", "Simulate data from the model"], [466, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [467, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [468, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [469, 3, 1, 0, "update", "Compute the variance for a single observation"], [470, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [471, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [472, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.APARCH.backcast": [[454, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.APARCH.backcast_transform": [[455, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.APARCH.bounds": [[456, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.APARCH.compute_variance": [[458, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [458, 2, 2, 0, "parameters", "Model parameters"], [458, 2, 2, 0, "resids", "Vector of mean zero residuals"], [458, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [458, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.APARCH.forecast": [[461, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [461, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [461, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [461, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [461, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [461, 2, 2, 0, "resids", "Residuals to use in the recursion"], [461, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [461, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [461, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [461, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.APARCH.simulate": [[465, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [465, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [465, 2, 2, 0, "nobs", "Number of data points to simulate"], [465, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [465, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.APARCH.starting_values": [[467, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.APARCH.update": [[469, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [469, 2, 2, 0, "index", "The numerical index of the variance to compute"], [469, 2, 2, 0, "parameters", "The variance model parameters"], [469, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [469, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [469, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.APARCH.variance_bounds": [[471, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [471, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.ARCH.__init__": [[426, 2, 2, 0, "p", "Order of the symmetric innovation"]], "arch.univariate.ARCH": [[473, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [474, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [475, 3, 1, 0, "bounds", "Returns bounds for parameters"], [476, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [477, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [478, 3, 1, 0, "forecast", "Forecast volatility from the model"], [479, 5, 1, 0, "name", "The name of the volatility process"], [480, 5, 1, 0, "num_params", "The number of parameters in the model"], [481, 3, 1, 0, "parameter_names", "Names of model parameters"], [482, 3, 1, 0, "simulate", "Simulate data from the model"], [483, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [484, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [485, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [486, 3, 1, 0, "update", "Compute the variance for a single observation"], [487, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [488, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [489, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.ARCH.backcast": [[473, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.ARCH.backcast_transform": [[474, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.ARCH.bounds": [[475, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.ARCH.compute_variance": [[476, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [476, 2, 2, 0, "parameters", "Model parameters"], [476, 2, 2, 0, "resids", "Vector of mean zero residuals"], [476, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [476, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.ARCH.forecast": [[478, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [478, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [478, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [478, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [478, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [478, 2, 2, 0, "resids", "Residuals to use in the recursion"], [478, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [478, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [478, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [478, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.ARCH.simulate": [[482, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [482, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [482, 2, 2, 0, "nobs", "Number of data points to simulate"], [482, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [482, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.ARCH.starting_values": [[484, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.ARCH.update": [[486, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [486, 2, 2, 0, "index", "The numerical index of the variance to compute"], [486, 2, 2, 0, "parameters", "The variance model parameters"], [486, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [486, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [486, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.ARCH.variance_bounds": [[488, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [488, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.ARCHInMean.__init__": [[427, 2, 2, 0, "constant", "Flag whether the model should include a constant"], [427, 2, 2, 0, "distribution", "Error distribution to use in the model"], [427, 2, 2, 0, "form", "The form of the conditional variance that appears in the mean equation. The string names use the log of the conditional variance (\"log\"), the square-root of the conditional variance (\"vol\") or the conditional variance. When specified using a float, interpreted as \\sigma_t^{form} so that 1 is equivalent to \"vol\" and 2 is equivalent to \"var\". When using a number, must be different from 0."], [427, 2, 2, 0, "hold_back", "Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."], [427, 2, 2, 0, "lags", "Description of lag structure of the HAR.  Scalar included all lags between 1 and the value.  A 1-d array includes the AR lags lags[0], lags[1], ..."], [427, 2, 2, 0, "rescale", "Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."], [427, 2, 2, 0, "volatility", "Volatility process to use in the model. volatility.updateable must return True."], [427, 2, 2, 0, "x", "nobs by k element array containing exogenous regressors"], [427, 2, 2, 0, "y", "nobs element vector containing the dependent variable"]], "arch.univariate.ARCHInMean": [[490, 3, 1, 0, "bounds", "Construct bounds for parameters to use in non-linear optimization"], [491, 3, 1, 0, "compute_param_cov", "Computes parameter covariances using numerical derivatives."], [492, 3, 1, 0, "constraints", "Construct linear constraint arrays  for use in non-linear optimization"], [493, 5, 1, 0, "distribution", "Set or gets the error distribution"], [494, 3, 1, 0, "fit", "Estimate model parameters"], [495, 3, 1, 0, "fix", "Allows an ARCHModelFixedResult to be constructed from fixed parameters."], [496, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [497, 5, 1, 0, "form", "The form of the conditional variance in the mean"], [498, 5, 1, 0, "name", "The name of the model."], [499, 5, 1, 0, "num_params", "Returns the number of parameters"], [500, 3, 1, 0, "parameter_names", "List of parameters names"], [501, 3, 1, 0, "resids", "Compute model residuals"], [502, 3, 1, 0, "simulate", "Simulates data from a linear regression, AR or HAR models"], [503, 3, 1, 0, "starting_values", "Returns starting values for the mean model, often the same as the values returned from fit"], [504, 5, 1, 0, "volatility", "Set or gets the volatility process"], [505, 5, 1, 0, "x", "Gets the value of the exogenous regressors in the model"], [506, 5, 1, 0, "y", "Returns the dependent variable"]], "arch.univariate.ARCHInMean.compute_param_cov": [[491, 2, 2, 0, "backcast", "Value to use for pre-sample observations"], [491, 2, 2, 0, "params", "Model parameters"], [491, 2, 2, 0, "robust", "Flag indicating whether to use robust standard errors (True) or classic MLE (False)"]], "arch.univariate.ARCHInMean.fit": [[494, 2, 2, 0, "backcast", "Value to use as backcast. Should be measure \\sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."], [494, 2, 2, 0, "cov_type", "Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."], [494, 2, 2, 0, "disp", "Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is \"off\" and True is \"final\""], [494, 2, 2, 0, "first_obs", "First observation to use when estimating model"], [494, 2, 2, 0, "last_obs", "Last observation to use when estimating model"], [494, 2, 2, 0, "options", "Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."], [494, 2, 2, 0, "show_warning", "Flag indicating whether convergence warnings should be shown."], [494, 2, 2, 0, "starting_values", "Array of starting values to use.  If not provided, starting values are constructed by the model components."], [494, 2, 2, 0, "tol", "Tolerance for termination."], [494, 2, 2, 0, "update_freq", "Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."]], "arch.univariate.ARCHInMean.fix": [[495, 2, 2, 0, "first_obs", "First observation to use when fixing model"], [495, 2, 2, 0, "last_obs", "Last observation to use when fixing model"], [495, 2, 2, 0, "params", "User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."]], "arch.univariate.ARCHInMean.forecast": [[496, 2, 2, 0, "align", "Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."], [496, 2, 2, 0, "horizon", "Number of steps to forecast"], [496, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [496, 2, 2, 0, "params", "Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."], [496, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [496, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."], [496, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [496, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [496, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."], [496, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"]], "arch.univariate.ARCHInMean.resids": [[501, 2, 2, 0, "params", "Model parameters"], [501, 2, 2, 0, "regressors", "Alternative regressor values to use when computing model residuals"], [501, 2, 2, 0, "y", "Alternative values to use when computing model residuals"]], "arch.univariate.ARCHInMean.simulate": [[502, 2, 2, 0, "burn", "Number of values to simulate to initialize the model and remove dependence on initial values."], [502, 2, 2, 0, "initial_value", "Either a scalar value or max(lags) array set of initial values to use when initializing the model.  If omitted, 0.0 is used."], [502, 2, 2, 0, "initial_value_vol", "An array or scalar to use when initializing the volatility process."], [502, 2, 2, 0, "nobs", "Length of series to simulate"], [502, 2, 2, 0, "params", "Parameters to use when simulating the model.  Parameter order is [mean volatility distribution] where the parameters of the mean model are ordered [constant lag[0] lag[1] ... lag[p] ex[0] ... ex[k-1]] where lag[j] indicates the coefficient on the jth lag in the model and ex[j] is the coefficient on the jth exogenous variable."], [502, 2, 2, 0, "x", "nobs + burn by k array of exogenous variables to include in the simulation."]], "arch.univariate.ARX.__init__": [[428, 2, 2, 0, "constant", "Flag whether the model should include a constant"], [428, 2, 2, 0, "distribution", "Error distribution to use in the model"], [428, 2, 2, 0, "hold_back", "Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."], [428, 2, 2, 0, "lags", "Description of lag structure of the HAR.  Scalar included all lags between 1 and the value.  A 1-d array includes the AR lags lags[0], lags[1], ..."], [428, 2, 2, 0, "rescale", "Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."], [428, 2, 2, 0, "volatility", "Volatility process to use in the model"], [428, 2, 2, 0, "x", "nobs by k element array containing exogenous regressors"], [428, 2, 2, 0, "y", "nobs element vector containing the dependent variable"]], "arch.univariate.ARX": [[507, 3, 1, 0, "bounds", "Construct bounds for parameters to use in non-linear optimization"], [508, 3, 1, 0, "compute_param_cov", "Computes parameter covariances using numerical derivatives."], [509, 3, 1, 0, "constraints", "Construct linear constraint arrays  for use in non-linear optimization"], [510, 5, 1, 0, "distribution", "Set or gets the error distribution"], [511, 3, 1, 0, "fit", "Estimate model parameters"], [512, 3, 1, 0, "fix", "Allows an ARCHModelFixedResult to be constructed from fixed parameters."], [513, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [514, 5, 1, 0, "name", "The name of the model."], [515, 5, 1, 0, "num_params", "Returns the number of parameters"], [516, 3, 1, 0, "parameter_names", "List of parameters names"], [517, 3, 1, 0, "resids", "Compute model residuals"], [518, 3, 1, 0, "simulate", "Simulates data from a linear regression, AR or HAR models"], [519, 3, 1, 0, "starting_values", "Returns starting values for the mean model, often the same as the values returned from fit"], [520, 5, 1, 0, "volatility", "Set or gets the volatility process"], [521, 5, 1, 0, "x", "Gets the value of the exogenous regressors in the model"], [522, 5, 1, 0, "y", "Returns the dependent variable"]], "arch.univariate.ARX.compute_param_cov": [[508, 2, 2, 0, "backcast", "Value to use for pre-sample observations"], [508, 2, 2, 0, "params", "Model parameters"], [508, 2, 2, 0, "robust", "Flag indicating whether to use robust standard errors (True) or classic MLE (False)"]], "arch.univariate.ARX.fit": [[511, 2, 2, 0, "backcast", "Value to use as backcast. Should be measure \\sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."], [511, 2, 2, 0, "cov_type", "Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."], [511, 2, 2, 0, "disp", "Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is \"off\" and True is \"final\""], [511, 2, 2, 0, "first_obs", "First observation to use when estimating model"], [511, 2, 2, 0, "last_obs", "Last observation to use when estimating model"], [511, 2, 2, 0, "options", "Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."], [511, 2, 2, 0, "show_warning", "Flag indicating whether convergence warnings should be shown."], [511, 2, 2, 0, "starting_values", "Array of starting values to use.  If not provided, starting values are constructed by the model components."], [511, 2, 2, 0, "tol", "Tolerance for termination."], [511, 2, 2, 0, "update_freq", "Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."]], "arch.univariate.ARX.fix": [[512, 2, 2, 0, "first_obs", "First observation to use when fixing model"], [512, 2, 2, 0, "last_obs", "Last observation to use when fixing model"], [512, 2, 2, 0, "params", "User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."]], "arch.univariate.ARX.forecast": [[513, 2, 2, 0, "align", "Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."], [513, 2, 2, 0, "horizon", "Number of steps to forecast"], [513, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [513, 2, 2, 0, "params", "Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."], [513, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [513, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."], [513, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [513, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [513, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."], [513, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"]], "arch.univariate.ARX.resids": [[517, 2, 2, 0, "params", "Model parameters"], [517, 2, 2, 0, "regressors", "Alternative regressor values to use when computing model residuals"], [517, 2, 2, 0, "y", "Alternative values to use when computing model residuals"]], "arch.univariate.ARX.simulate": [[518, 2, 2, 0, "burn", "Number of values to simulate to initialize the model and remove dependence on initial values."], [518, 2, 2, 0, "initial_value", "Either a scalar value or max(lags) array set of initial values to use when initializing the model.  If omitted, 0.0 is used."], [518, 2, 2, 0, "initial_value_vol", "An array or scalar to use when initializing the volatility process."], [518, 2, 2, 0, "nobs", "Length of series to simulate"], [518, 2, 2, 0, "params", "Parameters to use when simulating the model.  Parameter order is [mean volatility distribution] where the parameters of the mean model are ordered [constant lag[0] lag[1] ... lag[p] ex[0] ... ex[k-1]] where lag[j] indicates the coefficient on the jth lag in the model and ex[j] is the coefficient on the jth exogenous variable."], [518, 2, 2, 0, "x", "nobs + burn by k array of exogenous variables to include in the simulation."]], "arch.univariate.ConstantMean.__init__": [[429, 2, 2, 0, "distribution", "Error distribution to use in the model"], [429, 2, 2, 0, "hold_back", "Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."], [429, 2, 2, 0, "rescale", "Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."], [429, 2, 2, 0, "volatility", "Volatility process to use in the model"], [429, 2, 2, 0, "y", "nobs element vector containing the dependent variable"]], "arch.univariate.ConstantMean": [[523, 3, 1, 0, "bounds", "Construct bounds for parameters to use in non-linear optimization"], [524, 3, 1, 0, "compute_param_cov", "Computes parameter covariances using numerical derivatives."], [525, 3, 1, 0, "constraints", "Construct linear constraint arrays  for use in non-linear optimization"], [526, 5, 1, 0, "distribution", "Set or gets the error distribution"], [527, 3, 1, 0, "fit", "Estimate model parameters"], [528, 3, 1, 0, "fix", "Allows an ARCHModelFixedResult to be constructed from fixed parameters."], [529, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [530, 5, 1, 0, "name", "The name of the model."], [531, 5, 1, 0, "num_params", "Returns the number of parameters"], [532, 3, 1, 0, "parameter_names", "List of parameters names"], [533, 3, 1, 0, "resids", "Compute model residuals"], [534, 3, 1, 0, "simulate", "Simulated data from a constant mean model"], [535, 3, 1, 0, "starting_values", "Returns starting values for the mean model, often the same as the values returned from fit"], [536, 5, 1, 0, "volatility", "Set or gets the volatility process"], [537, 5, 1, 0, "x", "Gets the value of the exogenous regressors in the model"], [538, 5, 1, 0, "y", "Returns the dependent variable"]], "arch.univariate.ConstantMean.compute_param_cov": [[524, 2, 2, 0, "backcast", "Value to use for pre-sample observations"], [524, 2, 2, 0, "params", "Model parameters"], [524, 2, 2, 0, "robust", "Flag indicating whether to use robust standard errors (True) or classic MLE (False)"]], "arch.univariate.ConstantMean.fit": [[527, 2, 2, 0, "backcast", "Value to use as backcast. Should be measure \\sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."], [527, 2, 2, 0, "cov_type", "Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."], [527, 2, 2, 0, "disp", "Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is \"off\" and True is \"final\""], [527, 2, 2, 0, "first_obs", "First observation to use when estimating model"], [527, 2, 2, 0, "last_obs", "Last observation to use when estimating model"], [527, 2, 2, 0, "options", "Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."], [527, 2, 2, 0, "show_warning", "Flag indicating whether convergence warnings should be shown."], [527, 2, 2, 0, "starting_values", "Array of starting values to use.  If not provided, starting values are constructed by the model components."], [527, 2, 2, 0, "tol", "Tolerance for termination."], [527, 2, 2, 0, "update_freq", "Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."]], "arch.univariate.ConstantMean.fix": [[528, 2, 2, 0, "first_obs", "First observation to use when fixing model"], [528, 2, 2, 0, "last_obs", "Last observation to use when fixing model"], [528, 2, 2, 0, "params", "User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."]], "arch.univariate.ConstantMean.forecast": [[529, 2, 2, 0, "align", "Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."], [529, 2, 2, 0, "horizon", "Number of steps to forecast"], [529, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [529, 2, 2, 0, "params", "Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."], [529, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [529, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."], [529, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [529, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [529, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."], [529, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"]], "arch.univariate.ConstantMean.resids": [[533, 2, 2, 0, "params", "Model parameters"], [533, 2, 2, 0, "regressors", "Alternative regressor values to use when computing model residuals"], [533, 2, 2, 0, "y", "Alternative values to use when computing model residuals"]], "arch.univariate.ConstantMean.simulate": [[534, 2, 2, 0, "burn", "Number of values to simulate to initialize the model and remove dependence on initial values."], [534, 2, 2, 0, "initial_value", "This value is not used."], [534, 2, 2, 0, "initial_value_vol", "An array or scalar to use when initializing the volatility process."], [534, 2, 2, 0, "nobs", "Length of series to simulate"], [534, 2, 2, 0, "params", "Parameters to use when simulating the model.  Parameter order is [mean volatility distribution]. There is one parameter in the mean model, mu."], [534, 2, 2, 0, "x", "This value is not used."]], "arch.univariate.ConstantVariance": [[539, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [540, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [541, 3, 1, 0, "bounds", "Returns bounds for parameters"], [542, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [543, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [544, 3, 1, 0, "forecast", "Forecast volatility from the model"], [545, 5, 1, 0, "name", "The name of the volatility process"], [546, 5, 1, 0, "num_params", "The number of parameters in the model"], [547, 3, 1, 0, "parameter_names", "Names of model parameters"], [548, 3, 1, 0, "simulate", "Simulate data from the model"], [549, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [550, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [551, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [552, 3, 1, 0, "update", "Compute the variance for a single observation"], [553, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [554, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [555, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.ConstantVariance.backcast": [[539, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.ConstantVariance.backcast_transform": [[540, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.ConstantVariance.bounds": [[541, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.ConstantVariance.compute_variance": [[542, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [542, 2, 2, 0, "parameters", "Model parameters"], [542, 2, 2, 0, "resids", "Vector of mean zero residuals"], [542, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [542, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.ConstantVariance.forecast": [[544, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [544, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [544, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [544, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [544, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [544, 2, 2, 0, "resids", "Residuals to use in the recursion"], [544, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [544, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [544, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [544, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.ConstantVariance.simulate": [[548, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [548, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [548, 2, 2, 0, "nobs", "Number of data points to simulate"], [548, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [548, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.ConstantVariance.starting_values": [[550, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.ConstantVariance.update": [[552, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [552, 2, 2, 0, "index", "The numerical index of the variance to compute"], [552, 2, 2, 0, "parameters", "The variance model parameters"], [552, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [552, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [552, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.ConstantVariance.variance_bounds": [[554, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [554, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.EGARCH.__init__": [[431, 2, 2, 0, "o", "Order of the asymmetric innovation"], [431, 2, 2, 0, "p", "Order of the symmetric innovation"], [431, 2, 2, 0, "q", "Order of the lagged (transformed) conditional variance"]], "arch.univariate.EGARCH": [[556, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [557, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [558, 3, 1, 0, "bounds", "Returns bounds for parameters"], [559, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [560, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [561, 3, 1, 0, "forecast", "Forecast volatility from the model"], [562, 5, 1, 0, "name", "The name of the volatility process"], [563, 5, 1, 0, "num_params", "The number of parameters in the model"], [564, 3, 1, 0, "parameter_names", "Names of model parameters"], [565, 3, 1, 0, "simulate", "Simulate data from the model"], [566, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [567, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [568, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [569, 3, 1, 0, "update", "Compute the variance for a single observation"], [570, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [571, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [572, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.EGARCH.backcast": [[556, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.EGARCH.backcast_transform": [[557, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.EGARCH.bounds": [[558, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.EGARCH.compute_variance": [[559, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [559, 2, 2, 0, "parameters", "Model parameters"], [559, 2, 2, 0, "resids", "Vector of mean zero residuals"], [559, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [559, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.EGARCH.forecast": [[561, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [561, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [561, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [561, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [561, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [561, 2, 2, 0, "resids", "Residuals to use in the recursion"], [561, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [561, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [561, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [561, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.EGARCH.simulate": [[565, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [565, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [565, 2, 2, 0, "nobs", "Number of data points to simulate"], [565, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [565, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.EGARCH.starting_values": [[567, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.EGARCH.update": [[569, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [569, 2, 2, 0, "index", "The numerical index of the variance to compute"], [569, 2, 2, 0, "parameters", "The variance model parameters"], [569, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [569, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [569, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.EGARCH.variance_bounds": [[571, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [571, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.EWMAVariance.__init__": [[432, 2, 2, 0, "lam", "Smoothing parameter. Default is 0.94. Set to None to estimate lam jointly with other model parameters"]], "arch.univariate.EWMAVariance": [[573, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [574, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [575, 3, 1, 0, "bounds", "Returns bounds for parameters"], [576, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [577, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [578, 3, 1, 0, "forecast", "Forecast volatility from the model"], [579, 5, 1, 0, "name", "The name of the volatility process"], [580, 5, 1, 0, "num_params", "The number of parameters in the model"], [581, 3, 1, 0, "parameter_names", "Names of model parameters"], [582, 3, 1, 0, "simulate", "Simulate data from the model"], [583, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [584, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [585, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [586, 3, 1, 0, "update", "Compute the variance for a single observation"], [587, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [588, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [589, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.EWMAVariance.backcast": [[573, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.EWMAVariance.backcast_transform": [[574, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.EWMAVariance.bounds": [[575, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.EWMAVariance.compute_variance": [[576, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [576, 2, 2, 0, "parameters", "Model parameters"], [576, 2, 2, 0, "resids", "Vector of mean zero residuals"], [576, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [576, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.EWMAVariance.forecast": [[578, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [578, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [578, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [578, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [578, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [578, 2, 2, 0, "resids", "Residuals to use in the recursion"], [578, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [578, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [578, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [578, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.EWMAVariance.simulate": [[582, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [582, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [582, 2, 2, 0, "nobs", "Number of data points to simulate"], [582, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [582, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.EWMAVariance.starting_values": [[584, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.EWMAVariance.update": [[586, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [586, 2, 2, 0, "index", "The numerical index of the variance to compute"], [586, 2, 2, 0, "parameters", "The variance model parameters"], [586, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [586, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [586, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.EWMAVariance.variance_bounds": [[588, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [588, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.FIGARCH.__init__": [[433, 2, 2, 0, "p", "Order of the symmetric innovation"], [433, 2, 2, 0, "power", "Power to use with the innovations, abs(e) ** power.  Default is 2.0, which produces FIGARCH and related models. Using 1.0 produces FIAVARCH and related models.  Other powers can be specified, although these should be strictly positive, and usually larger than 0.25."], [433, 2, 2, 0, "q", "Order of the lagged (transformed) conditional variance"], [433, 2, 2, 0, "truncation", "Truncation point to use in ARCH(\\infty) representation. Default is 1000."]], "arch.univariate.FIGARCH": [[590, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [591, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [592, 3, 1, 0, "bounds", "Returns bounds for parameters"], [593, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [594, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [595, 3, 1, 0, "forecast", "Forecast volatility from the model"], [596, 5, 1, 0, "name", "The name of the volatility process"], [597, 5, 1, 0, "num_params", "The number of parameters in the model"], [598, 3, 1, 0, "parameter_names", "Names of model parameters"], [599, 3, 1, 0, "simulate", "Simulate data from the model"], [600, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [601, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [602, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [603, 5, 1, 0, "truncation", "Truncation lag for the ARCH-infinity approximation"], [604, 3, 1, 0, "update", "Compute the variance for a single observation"], [605, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [606, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [607, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.FIGARCH.backcast": [[590, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.FIGARCH.backcast_transform": [[591, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.FIGARCH.bounds": [[592, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.FIGARCH.compute_variance": [[593, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [593, 2, 2, 0, "parameters", "Model parameters"], [593, 2, 2, 0, "resids", "Vector of mean zero residuals"], [593, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [593, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.FIGARCH.forecast": [[595, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [595, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [595, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [595, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [595, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [595, 2, 2, 0, "resids", "Residuals to use in the recursion"], [595, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [595, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [595, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [595, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.FIGARCH.simulate": [[599, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [599, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [599, 2, 2, 0, "nobs", "Number of data points to simulate"], [599, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [599, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.FIGARCH.starting_values": [[601, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.FIGARCH.update": [[604, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [604, 2, 2, 0, "index", "The numerical index of the variance to compute"], [604, 2, 2, 0, "parameters", "The variance model parameters"], [604, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [604, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [604, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.FIGARCH.variance_bounds": [[606, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [606, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.FixedVariance.__init__": [[434, 2, 2, 0, "unit_scale", "Flag whether to enforce a unit scale.  If False, a scale parameter will be estimated so that the model variance will be proportional to variance. If True, the model variance is set of variance"], [434, 2, 2, 0, "variance", "Array containing the variances to use.  Should have the same shape as the data used in the model."]], "arch.univariate.FixedVariance": [[608, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [609, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [610, 3, 1, 0, "bounds", "Returns bounds for parameters"], [611, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [612, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [613, 3, 1, 0, "forecast", "Forecast volatility from the model"], [614, 5, 1, 0, "name", "The name of the volatility process"], [615, 5, 1, 0, "num_params", "The number of parameters in the model"], [616, 3, 1, 0, "parameter_names", "Names of model parameters"], [617, 3, 1, 0, "simulate", "Simulate data from the model"], [618, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [619, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [620, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [621, 3, 1, 0, "update", "Compute the variance for a single observation"], [622, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [623, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [624, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.FixedVariance.backcast": [[608, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.FixedVariance.backcast_transform": [[609, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.FixedVariance.bounds": [[610, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.FixedVariance.compute_variance": [[611, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [611, 2, 2, 0, "parameters", "Model parameters"], [611, 2, 2, 0, "resids", "Vector of mean zero residuals"], [611, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [611, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.FixedVariance.forecast": [[613, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [613, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [613, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [613, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [613, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [613, 2, 2, 0, "resids", "Residuals to use in the recursion"], [613, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [613, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [613, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [613, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.FixedVariance.simulate": [[617, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [617, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [617, 2, 2, 0, "nobs", "Number of data points to simulate"], [617, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [617, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.FixedVariance.starting_values": [[619, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.FixedVariance.update": [[621, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [621, 2, 2, 0, "index", "The numerical index of the variance to compute"], [621, 2, 2, 0, "parameters", "The variance model parameters"], [621, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [621, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [621, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.FixedVariance.variance_bounds": [[623, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [623, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.GARCH.__init__": [[435, 2, 2, 0, "o", "Order of the asymmetric innovation"], [435, 2, 2, 0, "p", "Order of the symmetric innovation"], [435, 2, 2, 0, "power", "Power to use with the innovations, abs(e) ** power.  Default is 2.0, which produces ARCH and related models. Using 1.0 produces AVARCH and related models.  Other powers can be specified, although these should be strictly positive, and usually larger than 0.25."], [435, 2, 2, 0, "q", "Order of the lagged (transformed) conditional variance"]], "arch.univariate.GARCH": [[625, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [626, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [627, 3, 1, 0, "bounds", "Returns bounds for parameters"], [628, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [629, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [630, 3, 1, 0, "forecast", "Forecast volatility from the model"], [631, 5, 1, 0, "name", "The name of the volatility process"], [632, 5, 1, 0, "num_params", "The number of parameters in the model"], [633, 3, 1, 0, "parameter_names", "Names of model parameters"], [634, 3, 1, 0, "simulate", "Simulate data from the model"], [635, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [636, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [637, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [638, 3, 1, 0, "update", "Compute the variance for a single observation"], [639, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [640, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [641, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.GARCH.backcast": [[625, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.GARCH.backcast_transform": [[626, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.GARCH.bounds": [[627, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.GARCH.compute_variance": [[628, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [628, 2, 2, 0, "parameters", "Model parameters"], [628, 2, 2, 0, "resids", "Vector of mean zero residuals"], [628, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [628, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.GARCH.forecast": [[630, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [630, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [630, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [630, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [630, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [630, 2, 2, 0, "resids", "Residuals to use in the recursion"], [630, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [630, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [630, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [630, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.GARCH.simulate": [[634, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [634, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [634, 2, 2, 0, "nobs", "Number of data points to simulate"], [634, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [634, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.GARCH.starting_values": [[636, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.GARCH.update": [[638, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [638, 2, 2, 0, "index", "The numerical index of the variance to compute"], [638, 2, 2, 0, "parameters", "The variance model parameters"], [638, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [638, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [638, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.GARCH.variance_bounds": [[640, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [640, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.GeneralizedError.__init__": [[436, 2, 2, 0, "random_state", "Deprecated since version 5.0: random_state is deprecated. Use seed instead."], [436, 2, 2, 0, "seed", "Random number generator instance or int to use. Set to ensure reproducibility. If using an int, the argument is passed to np.random.default_rng.  If not provided, default_rng is used with system-provided entropy."]], "arch.univariate.GeneralizedError": [[642, 3, 1, 0, "bounds", "Parameter bounds for use in optimization."], [643, 3, 1, 0, "cdf", "Cumulative distribution function"], [644, 3, 1, 0, "constraints", "Construct arrays to use in constrained optimization."], [645, 5, 1, 0, "generator", "The NumPy Generator or RandomState attached to the distribution"], [646, 3, 1, 0, "loglikelihood", "Computes the log-likelihood of assuming residuals are have a Generalized Error Distribution, conditional on the variance."], [647, 3, 1, 0, "moment", "Moment of order n"], [648, 5, 1, 0, "name", "The name of the distribution"], [649, 3, 1, 0, "parameter_names", "Names of distribution shape parameters"], [650, 3, 1, 0, "partial_moment", "Order n lower partial moment from -inf to z"], [651, 3, 1, 0, "ppf", "Inverse cumulative density function (ICDF)"], [652, 5, 1, 0, "random_state", "The NumPy RandomState attached to the distribution"], [653, 3, 1, 0, "simulate", "Simulates i.i.d. draws from the distribution"], [654, 3, 1, 0, "starting_values", "Construct starting values for use in optimization."]], "arch.univariate.GeneralizedError.bounds": [[642, 2, 2, 0, "resids", "Residuals to use when computing the bounds"]], "arch.univariate.GeneralizedError.cdf": [[643, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [643, 2, 2, 0, "resids", "Values at which to evaluate the cdf"]], "arch.univariate.GeneralizedError.loglikelihood": [[646, 2, 2, 0, "individual", "Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"], [646, 2, 2, 0, "parameters", "Shape parameter of the GED distribution"], [646, 2, 2, 0, "resids", "The residuals to use in the log-likelihood calculation"], [646, 2, 2, 0, "sigma2", "Conditional variances of resids"]], "arch.univariate.GeneralizedError.moment": [[647, 2, 2, 0, "n", "Order of moment"], [647, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."]], "arch.univariate.GeneralizedError.partial_moment": [[650, 2, 2, 0, "n", "Order of partial moment"], [650, 2, 2, 0, "parameters", "Distribution parameters.  Use None for parameterless distributions."], [650, 2, 2, 0, "z", "Upper bound for partial moment integral"]], "arch.univariate.GeneralizedError.ppf": [[651, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [651, 2, 2, 0, "pits", "Probability-integral-transformed values in the interval (0, 1)."]], "arch.univariate.GeneralizedError.simulate": [[653, 2, 2, 0, "parameters", "Distribution parameters"]], "arch.univariate.GeneralizedError.starting_values": [[654, 2, 2, 0, "std_resid", "Estimated standardized residuals to use in computing starting values for the shape parameter"]], "arch.univariate.HARCH.__init__": [[437, 2, 2, 0, "lags", "List of lags to include in the model, or if scalar, includes all lags up the value"]], "arch.univariate.HARCH": [[655, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [656, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [657, 3, 1, 0, "bounds", "Returns bounds for parameters"], [658, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [659, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [660, 3, 1, 0, "forecast", "Forecast volatility from the model"], [661, 5, 1, 0, "name", "The name of the volatility process"], [662, 5, 1, 0, "num_params", "The number of parameters in the model"], [663, 3, 1, 0, "parameter_names", "Names of model parameters"], [664, 3, 1, 0, "simulate", "Simulate data from the model"], [665, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [666, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [667, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [668, 3, 1, 0, "update", "Compute the variance for a single observation"], [669, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [670, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [671, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.HARCH.backcast": [[655, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.HARCH.backcast_transform": [[656, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.HARCH.bounds": [[657, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.HARCH.compute_variance": [[658, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [658, 2, 2, 0, "parameters", "Model parameters"], [658, 2, 2, 0, "resids", "Vector of mean zero residuals"], [658, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [658, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.HARCH.forecast": [[660, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [660, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [660, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [660, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [660, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [660, 2, 2, 0, "resids", "Residuals to use in the recursion"], [660, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [660, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [660, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [660, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.HARCH.simulate": [[664, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [664, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [664, 2, 2, 0, "nobs", "Number of data points to simulate"], [664, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [664, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.HARCH.starting_values": [[666, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.HARCH.update": [[668, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [668, 2, 2, 0, "index", "The numerical index of the variance to compute"], [668, 2, 2, 0, "parameters", "The variance model parameters"], [668, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [668, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [668, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.HARCH.variance_bounds": [[670, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [670, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.HARX.__init__": [[438, 2, 2, 0, "constant", "Flag whether the model should include a constant"], [438, 2, 2, 0, "distribution", "Error distribution to use in the model"], [438, 2, 2, 0, "hold_back", "Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."], [438, 2, 2, 0, "lags", "Description of lag structure of the HAR."], [438, 2, 2, 0, "rescale", "Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."], [438, 2, 2, 0, "use_rotated", "Flag indicating to use the alternative rotated form of the HAR where HAR lags do not overlap"], [438, 2, 2, 0, "volatility", "Volatility process to use in the model"], [438, 2, 2, 0, "x", "nobs by k element array containing exogenous regressors"], [438, 2, 2, 0, "y", "nobs element vector containing the dependent variable"]], "arch.univariate.HARX": [[672, 3, 1, 0, "bounds", "Construct bounds for parameters to use in non-linear optimization"], [673, 3, 1, 0, "compute_param_cov", "Computes parameter covariances using numerical derivatives."], [674, 3, 1, 0, "constraints", "Construct linear constraint arrays  for use in non-linear optimization"], [675, 5, 1, 0, "distribution", "Set or gets the error distribution"], [676, 3, 1, 0, "fit", "Estimate model parameters"], [677, 3, 1, 0, "fix", "Allows an ARCHModelFixedResult to be constructed from fixed parameters."], [678, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [679, 5, 1, 0, "name", "The name of the model."], [680, 5, 1, 0, "num_params", "Returns the number of parameters"], [681, 3, 1, 0, "parameter_names", "List of parameters names"], [682, 3, 1, 0, "resids", "Compute model residuals"], [683, 3, 1, 0, "simulate", "Simulates data from a linear regression, AR or HAR models"], [684, 3, 1, 0, "starting_values", "Returns starting values for the mean model, often the same as the values returned from fit"], [685, 5, 1, 0, "volatility", "Set or gets the volatility process"], [686, 5, 1, 0, "x", "Gets the value of the exogenous regressors in the model"], [687, 5, 1, 0, "y", "Returns the dependent variable"]], "arch.univariate.HARX.compute_param_cov": [[673, 2, 2, 0, "backcast", "Value to use for pre-sample observations"], [673, 2, 2, 0, "params", "Model parameters"], [673, 2, 2, 0, "robust", "Flag indicating whether to use robust standard errors (True) or classic MLE (False)"]], "arch.univariate.HARX.fit": [[676, 2, 2, 0, "backcast", "Value to use as backcast. Should be measure \\sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."], [676, 2, 2, 0, "cov_type", "Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."], [676, 2, 2, 0, "disp", "Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is \"off\" and True is \"final\""], [676, 2, 2, 0, "first_obs", "First observation to use when estimating model"], [676, 2, 2, 0, "last_obs", "Last observation to use when estimating model"], [676, 2, 2, 0, "options", "Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."], [676, 2, 2, 0, "show_warning", "Flag indicating whether convergence warnings should be shown."], [676, 2, 2, 0, "starting_values", "Array of starting values to use.  If not provided, starting values are constructed by the model components."], [676, 2, 2, 0, "tol", "Tolerance for termination."], [676, 2, 2, 0, "update_freq", "Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."]], "arch.univariate.HARX.fix": [[677, 2, 2, 0, "first_obs", "First observation to use when fixing model"], [677, 2, 2, 0, "last_obs", "Last observation to use when fixing model"], [677, 2, 2, 0, "params", "User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."]], "arch.univariate.HARX.forecast": [[678, 2, 2, 0, "align", "Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."], [678, 2, 2, 0, "horizon", "Number of steps to forecast"], [678, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [678, 2, 2, 0, "params", "Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."], [678, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [678, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."], [678, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [678, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [678, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."], [678, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"]], "arch.univariate.HARX.resids": [[682, 2, 2, 0, "params", "Model parameters"], [682, 2, 2, 0, "regressors", "Alternative regressor values to use when computing model residuals"], [682, 2, 2, 0, "y", "Alternative values to use when computing model residuals"]], "arch.univariate.HARX.simulate": [[683, 2, 2, 0, "burn", "Number of values to simulate to initialize the model and remove dependence on initial values."], [683, 2, 2, 0, "initial_value", "Either a scalar value or max(lags) array set of initial values to use when initializing the model.  If omitted, 0.0 is used."], [683, 2, 2, 0, "initial_value_vol", "An array or scalar to use when initializing the volatility process."], [683, 2, 2, 0, "nobs", "Length of series to simulate"], [683, 2, 2, 0, "params", "Parameters to use when simulating the model.  Parameter order is [mean volatility distribution] where the parameters of the mean model are ordered [constant lag[0] lag[1] ... lag[p] ex[0] ... ex[k-1]] where lag[j] indicates the coefficient on the jth lag in the model and ex[j] is the coefficient on the jth exogenous variable."], [683, 2, 2, 0, "x", "nobs + burn by k array of exogenous variables to include in the simulation."]], "arch.univariate.LS.__init__": [[439, 2, 2, 0, "constant", "Flag whether the model should include a constant"], [439, 2, 2, 0, "distribution", "Error distribution to use in the model"], [439, 2, 2, 0, "hold_back", "Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."], [439, 2, 2, 0, "rescale", "Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."], [439, 2, 2, 0, "volatility", "Volatility process to use in the model"], [439, 2, 2, 0, "y", "nobs by k element array containing exogenous regressors"]], "arch.univariate.LS": [[688, 3, 1, 0, "bounds", "Construct bounds for parameters to use in non-linear optimization"], [689, 3, 1, 0, "compute_param_cov", "Computes parameter covariances using numerical derivatives."], [690, 3, 1, 0, "constraints", "Construct linear constraint arrays  for use in non-linear optimization"], [691, 5, 1, 0, "distribution", "Set or gets the error distribution"], [692, 3, 1, 0, "fit", "Estimate model parameters"], [693, 3, 1, 0, "fix", "Allows an ARCHModelFixedResult to be constructed from fixed parameters."], [694, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [695, 5, 1, 0, "name", "The name of the model."], [696, 5, 1, 0, "num_params", "Returns the number of parameters"], [697, 3, 1, 0, "parameter_names", "List of parameters names"], [698, 3, 1, 0, "resids", "Compute model residuals"], [699, 3, 1, 0, "simulate", "Simulates data from a linear regression, AR or HAR models"], [700, 3, 1, 0, "starting_values", "Returns starting values for the mean model, often the same as the values returned from fit"], [701, 5, 1, 0, "volatility", "Set or gets the volatility process"], [702, 5, 1, 0, "x", "Gets the value of the exogenous regressors in the model"], [703, 5, 1, 0, "y", "Returns the dependent variable"]], "arch.univariate.LS.compute_param_cov": [[689, 2, 2, 0, "backcast", "Value to use for pre-sample observations"], [689, 2, 2, 0, "params", "Model parameters"], [689, 2, 2, 0, "robust", "Flag indicating whether to use robust standard errors (True) or classic MLE (False)"]], "arch.univariate.LS.fit": [[692, 2, 2, 0, "backcast", "Value to use as backcast. Should be measure \\sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."], [692, 2, 2, 0, "cov_type", "Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."], [692, 2, 2, 0, "disp", "Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is \"off\" and True is \"final\""], [692, 2, 2, 0, "first_obs", "First observation to use when estimating model"], [692, 2, 2, 0, "last_obs", "Last observation to use when estimating model"], [692, 2, 2, 0, "options", "Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."], [692, 2, 2, 0, "show_warning", "Flag indicating whether convergence warnings should be shown."], [692, 2, 2, 0, "starting_values", "Array of starting values to use.  If not provided, starting values are constructed by the model components."], [692, 2, 2, 0, "tol", "Tolerance for termination."], [692, 2, 2, 0, "update_freq", "Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."]], "arch.univariate.LS.fix": [[693, 2, 2, 0, "first_obs", "First observation to use when fixing model"], [693, 2, 2, 0, "last_obs", "Last observation to use when fixing model"], [693, 2, 2, 0, "params", "User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."]], "arch.univariate.LS.forecast": [[694, 2, 2, 0, "align", "Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."], [694, 2, 2, 0, "horizon", "Number of steps to forecast"], [694, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [694, 2, 2, 0, "params", "Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."], [694, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [694, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."], [694, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [694, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [694, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."], [694, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"]], "arch.univariate.LS.resids": [[698, 2, 2, 0, "params", "Model parameters"], [698, 2, 2, 0, "regressors", "Alternative regressor values to use when computing model residuals"], [698, 2, 2, 0, "y", "Alternative values to use when computing model residuals"]], "arch.univariate.LS.simulate": [[699, 2, 2, 0, "burn", "Number of values to simulate to initialize the model and remove dependence on initial values."], [699, 2, 2, 0, "initial_value", "Either a scalar value or max(lags) array set of initial values to use when initializing the model.  If omitted, 0.0 is used."], [699, 2, 2, 0, "initial_value_vol", "An array or scalar to use when initializing the volatility process."], [699, 2, 2, 0, "nobs", "Length of series to simulate"], [699, 2, 2, 0, "params", "Parameters to use when simulating the model.  Parameter order is [mean volatility distribution] where the parameters of the mean model are ordered [constant lag[0] lag[1] ... lag[p] ex[0] ... ex[k-1]] where lag[j] indicates the coefficient on the jth lag in the model and ex[j] is the coefficient on the jth exogenous variable."], [699, 2, 2, 0, "x", "nobs + burn by k array of exogenous variables to include in the simulation."]], "arch.univariate.MIDASHyperbolic.__init__": [[440, 2, 2, 0, "asym", "Flag indicating whether to include an asymmetric term"], [440, 2, 2, 0, "m", "Length of maximum lag to include in the model"]], "arch.univariate.MIDASHyperbolic": [[704, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [705, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [706, 3, 1, 0, "bounds", "Returns bounds for parameters"], [707, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [708, 3, 1, 0, "constraints", "Constraints"], [709, 3, 1, 0, "forecast", "Forecast volatility from the model"], [710, 5, 1, 0, "name", "The name of the volatility process"], [711, 5, 1, 0, "num_params", "The number of parameters in the model"], [712, 3, 1, 0, "parameter_names", "Names of model parameters"], [713, 3, 1, 0, "simulate", "Simulate data from the model"], [714, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [715, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [716, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [717, 3, 1, 0, "update", "Compute the variance for a single observation"], [718, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [719, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [720, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.MIDASHyperbolic.backcast": [[704, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.MIDASHyperbolic.backcast_transform": [[705, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.MIDASHyperbolic.bounds": [[706, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.MIDASHyperbolic.compute_variance": [[707, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [707, 2, 2, 0, "parameters", "Model parameters"], [707, 2, 2, 0, "resids", "Vector of mean zero residuals"], [707, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [707, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.MIDASHyperbolic.forecast": [[709, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [709, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [709, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [709, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [709, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [709, 2, 2, 0, "resids", "Residuals to use in the recursion"], [709, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [709, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [709, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [709, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.MIDASHyperbolic.simulate": [[713, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [713, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [713, 2, 2, 0, "nobs", "Number of data points to simulate"], [713, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [713, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.MIDASHyperbolic.starting_values": [[715, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.MIDASHyperbolic.update": [[717, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [717, 2, 2, 0, "index", "The numerical index of the variance to compute"], [717, 2, 2, 0, "parameters", "The variance model parameters"], [717, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [717, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [717, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.MIDASHyperbolic.variance_bounds": [[719, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [719, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.Normal.__init__": [[441, 2, 2, 0, "random_state", "Deprecated since version 5.0: random_state is deprecated. Use seed instead."], [441, 2, 2, 0, "seed", "Random number generator instance or int to use. Set to ensure reproducibility. If using an int, the argument is passed to np.random.default_rng.  If not provided, default_rng is used with system-provided entropy."]], "arch.univariate.Normal": [[721, 3, 1, 0, "bounds", "Parameter bounds for use in optimization."], [722, 3, 1, 0, "cdf", "Cumulative distribution function"], [723, 3, 1, 0, "constraints", "Construct arrays to use in constrained optimization."], [724, 5, 1, 0, "generator", "The NumPy Generator or RandomState attached to the distribution"], [725, 3, 1, 0, "loglikelihood", "Computes the log-likelihood of assuming residuals are normally distributed, conditional on the variance"], [726, 3, 1, 0, "moment", "Moment of order n"], [727, 5, 1, 0, "name", "The name of the distribution"], [728, 3, 1, 0, "parameter_names", "Names of distribution shape parameters"], [729, 3, 1, 0, "partial_moment", "Order n lower partial moment from -inf to z"], [730, 3, 1, 0, "ppf", "Inverse cumulative density function (ICDF)"], [731, 5, 1, 0, "random_state", "The NumPy RandomState attached to the distribution"], [732, 3, 1, 0, "simulate", "Simulates i.i.d. draws from the distribution"], [733, 3, 1, 0, "starting_values", "Construct starting values for use in optimization."]], "arch.univariate.Normal.bounds": [[721, 2, 2, 0, "resids", "Residuals to use when computing the bounds"]], "arch.univariate.Normal.cdf": [[722, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [722, 2, 2, 0, "resids", "Values at which to evaluate the cdf"]], "arch.univariate.Normal.loglikelihood": [[725, 2, 2, 0, "individual", "Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"], [725, 2, 2, 0, "parameters", "The normal likelihood has no shape parameters. Empty since the standard normal has no shape parameters."], [725, 2, 2, 0, "resids", "The residuals to use in the log-likelihood calculation"], [725, 2, 2, 0, "sigma2", "Conditional variances of resids"]], "arch.univariate.Normal.moment": [[726, 2, 2, 0, "n", "Order of moment"], [726, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."]], "arch.univariate.Normal.partial_moment": [[729, 2, 2, 0, "n", "Order of partial moment"], [729, 2, 2, 0, "parameters", "Distribution parameters.  Use None for parameterless distributions."], [729, 2, 2, 0, "z", "Upper bound for partial moment integral"]], "arch.univariate.Normal.ppf": [[730, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [730, 2, 2, 0, "pits", "Probability-integral-transformed values in the interval (0, 1)."]], "arch.univariate.Normal.simulate": [[732, 2, 2, 0, "parameters", "Distribution parameters"]], "arch.univariate.Normal.starting_values": [[733, 2, 2, 0, "std_resid", "Estimated standardized residuals to use in computing starting values for the shape parameter"]], "arch.univariate.RiskMetrics2006.__init__": [[442, 2, 2, 0, "kmax", "Number of components. Default is 14."], [442, 2, 2, 0, "rho", "Relative scale of adjacent cycles. Default is sqrt(2)"], [442, 2, 2, 0, "tau0", "Length of long cycle. Default is 1560."], [442, 2, 2, 0, "tau1", "Length of short cycle. Default is 4."]], "arch.univariate.RiskMetrics2006": [[734, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [735, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [736, 3, 1, 0, "bounds", "Returns bounds for parameters"], [737, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [738, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [739, 3, 1, 0, "forecast", "Forecast volatility from the model"], [740, 5, 1, 0, "name", "The name of the volatility process"], [741, 5, 1, 0, "num_params", "The number of parameters in the model"], [742, 3, 1, 0, "parameter_names", "Names of model parameters"], [743, 3, 1, 0, "simulate", "Simulate data from the model"], [744, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [745, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [746, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [747, 3, 1, 0, "update", "Compute the variance for a single observation"], [748, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [749, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [750, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.RiskMetrics2006.backcast": [[734, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.RiskMetrics2006.backcast_transform": [[735, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.RiskMetrics2006.bounds": [[736, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.RiskMetrics2006.compute_variance": [[737, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [737, 2, 2, 0, "parameters", "Model parameters"], [737, 2, 2, 0, "resids", "Vector of mean zero residuals"], [737, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [737, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.RiskMetrics2006.forecast": [[739, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [739, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [739, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [739, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [739, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [739, 2, 2, 0, "resids", "Residuals to use in the recursion"], [739, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [739, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [739, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [739, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.RiskMetrics2006.simulate": [[743, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [743, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [743, 2, 2, 0, "nobs", "Number of data points to simulate"], [743, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [743, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.RiskMetrics2006.starting_values": [[745, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.RiskMetrics2006.update": [[747, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [747, 2, 2, 0, "index", "The numerical index of the variance to compute"], [747, 2, 2, 0, "parameters", "The variance model parameters"], [747, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [747, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [747, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.RiskMetrics2006.variance_bounds": [[749, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [749, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.SkewStudent.__init__": [[443, 2, 2, 0, "random_state", "Deprecated since version 5.0: random_state is deprecated. Use seed instead."], [443, 2, 2, 0, "seed", "Random number generator instance or int to use. Set to ensure reproducibility. If using an int, the argument is passed to np.random.default_rng.  If not provided, default_rng is used with system-provided entropy."]], "arch.univariate.SkewStudent": [[751, 3, 1, 0, "bounds", "Parameter bounds for use in optimization."], [752, 3, 1, 0, "cdf", "Cumulative distribution function"], [753, 3, 1, 0, "constraints", "Construct arrays to use in constrained optimization."], [754, 5, 1, 0, "generator", "The NumPy Generator or RandomState attached to the distribution"], [755, 3, 1, 0, "loglikelihood", "Computes the log-likelihood of assuming residuals are have a standardized (to have unit variance) Skew Student's t distribution, conditional on the variance."], [756, 3, 1, 0, "moment", "Moment of order n"], [757, 5, 1, 0, "name", "The name of the distribution"], [758, 3, 1, 0, "parameter_names", "Names of distribution shape parameters"], [759, 3, 1, 0, "partial_moment", "Order n lower partial moment from -inf to z"], [760, 3, 1, 0, "ppf", "Inverse cumulative density function (ICDF)"], [761, 5, 1, 0, "random_state", "The NumPy RandomState attached to the distribution"], [762, 3, 1, 0, "simulate", "Simulates i.i.d. draws from the distribution"], [763, 3, 1, 0, "starting_values", "Construct starting values for use in optimization."]], "arch.univariate.SkewStudent.bounds": [[751, 2, 2, 0, "resids", "Residuals to use when computing the bounds"]], "arch.univariate.SkewStudent.cdf": [[752, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [752, 2, 2, 0, "resids", "Values at which to evaluate the cdf"]], "arch.univariate.SkewStudent.loglikelihood": [[755, 2, 2, 0, "individual", "Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"], [755, 2, 2, 0, "parameters", "Shape parameter of the skew-t distribution"], [755, 2, 2, 0, "resids", "The residuals to use in the log-likelihood calculation"], [755, 2, 2, 0, "sigma2", "Conditional variances of resids"]], "arch.univariate.SkewStudent.moment": [[756, 2, 2, 0, "n", "Order of moment"], [756, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."]], "arch.univariate.SkewStudent.partial_moment": [[759, 2, 2, 0, "n", "Order of partial moment"], [759, 2, 2, 0, "parameters", "Distribution parameters.  Use None for parameterless distributions."], [759, 2, 2, 0, "z", "Upper bound for partial moment integral"]], "arch.univariate.SkewStudent.ppf": [[760, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [760, 2, 2, 0, "pits", "Probability-integral-transformed values in the interval (0, 1)."]], "arch.univariate.SkewStudent.simulate": [[762, 2, 2, 0, "parameters", "Distribution parameters"]], "arch.univariate.SkewStudent.starting_values": [[763, 2, 2, 0, "std_resid", "Estimated standardized residuals to use in computing starting values for the shape parameter"]], "arch.univariate.StudentsT.__init__": [[444, 2, 2, 0, "random_state", "Deprecated since version 5.0: random_state is deprecated. Use seed instead."], [444, 2, 2, 0, "seed", "Random number generator instance or int to use. Set to ensure reproducibility. If using an int, the argument is passed to np.random.default_rng.  If not provided, default_rng is used with system-provided entropy."]], "arch.univariate.StudentsT": [[764, 3, 1, 0, "bounds", "Parameter bounds for use in optimization."], [765, 3, 1, 0, "cdf", "Cumulative distribution function"], [766, 3, 1, 0, "constraints", "Construct arrays to use in constrained optimization."], [767, 5, 1, 0, "generator", "The NumPy Generator or RandomState attached to the distribution"], [768, 3, 1, 0, "loglikelihood", "Computes the log-likelihood of assuming residuals are have a standardized (to have unit variance) Student's t distribution, conditional on the variance."], [769, 3, 1, 0, "moment", "Moment of order n"], [770, 5, 1, 0, "name", "The name of the distribution"], [771, 3, 1, 0, "parameter_names", "Names of distribution shape parameters"], [772, 3, 1, 0, "partial_moment", "Order n lower partial moment from -inf to z"], [773, 3, 1, 0, "ppf", "Inverse cumulative density function (ICDF)"], [774, 5, 1, 0, "random_state", "The NumPy RandomState attached to the distribution"], [775, 3, 1, 0, "simulate", "Simulates i.i.d. draws from the distribution"], [776, 3, 1, 0, "starting_values", "Construct starting values for use in optimization."]], "arch.univariate.StudentsT.bounds": [[764, 2, 2, 0, "resids", "Residuals to use when computing the bounds"]], "arch.univariate.StudentsT.cdf": [[765, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [765, 2, 2, 0, "resids", "Values at which to evaluate the cdf"]], "arch.univariate.StudentsT.loglikelihood": [[768, 2, 2, 0, "individual", "Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"], [768, 2, 2, 0, "parameters", "Shape parameter of the t distribution"], [768, 2, 2, 0, "resids", "The residuals to use in the log-likelihood calculation"], [768, 2, 2, 0, "sigma2", "Conditional variances of resids"]], "arch.univariate.StudentsT.moment": [[769, 2, 2, 0, "n", "Order of moment"], [769, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."]], "arch.univariate.StudentsT.partial_moment": [[772, 2, 2, 0, "n", "Order of partial moment"], [772, 2, 2, 0, "parameters", "Distribution parameters.  Use None for parameterless distributions."], [772, 2, 2, 0, "z", "Upper bound for partial moment integral"]], "arch.univariate.StudentsT.ppf": [[773, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [773, 2, 2, 0, "pits", "Probability-integral-transformed values in the interval (0, 1)."]], "arch.univariate.StudentsT.simulate": [[775, 2, 2, 0, "parameters", "Distribution parameters"]], "arch.univariate.StudentsT.starting_values": [[776, 2, 2, 0, "std_resid", "Estimated standardized residuals to use in computing starting values for the shape parameter"]], "arch.univariate.ZeroMean.__init__": [[445, 2, 2, 0, "distribution", "Error distribution to use in the model"], [445, 2, 2, 0, "hold_back", "Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."], [445, 2, 2, 0, "rescale", "Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."], [445, 2, 2, 0, "volatility", "Volatility process to use in the model"], [445, 2, 2, 0, "y", "nobs element vector containing the dependent variable"]], "arch.univariate.ZeroMean": [[777, 3, 1, 0, "bounds", "Construct bounds for parameters to use in non-linear optimization"], [778, 3, 1, 0, "compute_param_cov", "Computes parameter covariances using numerical derivatives."], [779, 3, 1, 0, "constraints", "Construct linear constraint arrays  for use in non-linear optimization"], [780, 5, 1, 0, "distribution", "Set or gets the error distribution"], [781, 3, 1, 0, "fit", "Estimate model parameters"], [782, 3, 1, 0, "fix", "Allows an ARCHModelFixedResult to be constructed from fixed parameters."], [783, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [784, 5, 1, 0, "name", "The name of the model."], [785, 5, 1, 0, "num_params", "Returns the number of parameters"], [786, 3, 1, 0, "parameter_names", "List of parameters names"], [787, 3, 1, 0, "resids", "Compute model residuals"], [788, 3, 1, 0, "simulate", "Simulated data from a zero mean model"], [789, 3, 1, 0, "starting_values", "Returns starting values for the mean model, often the same as the values returned from fit"], [790, 5, 1, 0, "volatility", "Set or gets the volatility process"], [791, 5, 1, 0, "x", "Gets the value of the exogenous regressors in the model"], [792, 5, 1, 0, "y", "Returns the dependent variable"]], "arch.univariate.ZeroMean.compute_param_cov": [[778, 2, 2, 0, "backcast", "Value to use for pre-sample observations"], [778, 2, 2, 0, "params", "Model parameters"], [778, 2, 2, 0, "robust", "Flag indicating whether to use robust standard errors (True) or classic MLE (False)"]], "arch.univariate.ZeroMean.fit": [[781, 2, 2, 0, "backcast", "Value to use as backcast. Should be measure \\sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."], [781, 2, 2, 0, "cov_type", "Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."], [781, 2, 2, 0, "disp", "Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is \"off\" and True is \"final\""], [781, 2, 2, 0, "first_obs", "First observation to use when estimating model"], [781, 2, 2, 0, "last_obs", "Last observation to use when estimating model"], [781, 2, 2, 0, "options", "Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."], [781, 2, 2, 0, "show_warning", "Flag indicating whether convergence warnings should be shown."], [781, 2, 2, 0, "starting_values", "Array of starting values to use.  If not provided, starting values are constructed by the model components."], [781, 2, 2, 0, "tol", "Tolerance for termination."], [781, 2, 2, 0, "update_freq", "Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."]], "arch.univariate.ZeroMean.fix": [[782, 2, 2, 0, "first_obs", "First observation to use when fixing model"], [782, 2, 2, 0, "last_obs", "Last observation to use when fixing model"], [782, 2, 2, 0, "params", "User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."]], "arch.univariate.ZeroMean.forecast": [[783, 2, 2, 0, "align", "Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."], [783, 2, 2, 0, "horizon", "Number of steps to forecast"], [783, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [783, 2, 2, 0, "params", "Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."], [783, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [783, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."], [783, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [783, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [783, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."], [783, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"]], "arch.univariate.ZeroMean.resids": [[787, 2, 2, 0, "params", "Model parameters"], [787, 2, 2, 0, "regressors", "Alternative regressor values to use when computing model residuals"], [787, 2, 2, 0, "y", "Alternative values to use when computing model residuals"]], "arch.univariate.ZeroMean.simulate": [[788, 2, 2, 0, "burn", "Number of values to simulate to initialize the model and remove dependence on initial values."], [788, 2, 2, 0, "initial_value", "This value is not used."], [788, 2, 2, 0, "initial_value_vol", "An array or scalar to use when initializing the volatility process."], [788, 2, 2, 0, "nobs", "Length of series to simulate"], [788, 2, 2, 0, "params", "Parameters to use when simulating the model.  Parameter order is [volatility distribution]. There are no mean parameters."], [788, 2, 2, 0, "x", "This value is not used."]], "arch.univariate.arch_model": [[891, 2, 2, 0, "dist", "Name of the error distribution.  Currently supported options are:"], [891, 2, 2, 0, "hold_back", "Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."], [891, 2, 2, 0, "lags", "Either a scalar integer value indicating lag length or a list of integers specifying lag locations."], [891, 2, 2, 0, "mean", "Name of the mean model.  Currently supported options are: 'Constant', 'Zero', 'LS', 'AR', 'ARX', 'HAR' and  'HARX'"], [891, 2, 2, 0, "o", "Lag order of the asymmetric innovation"], [891, 2, 2, 0, "p", "Lag order of the symmetric innovation"], [891, 2, 2, 0, "power", "Power to use with GARCH and related models"], [891, 2, 2, 0, "q", "Lag order of lagged volatility or equivalent"], [891, 2, 2, 0, "rescale", "Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."], [891, 2, 2, 0, "vol", "Name of the volatility model.  Currently supported options are: 'GARCH' (default), 'ARCH', 'EGARCH', 'FIGARCH', 'APARCH' and 'HARCH'"], [891, 2, 2, 0, "x", "Exogenous regressors.  Ignored if model does not permit exogenous regressors."], [891, 2, 2, 0, "y", "The dependent variable"]], "arch.univariate.base": [[446, 1, 1, 0, "ARCHModel", "Abstract base class for mean models in ARCH processes.  Specifies the conditional mean process."], [447, 1, 1, 0, "ARCHModelFixedResult", "Results for fixed parameters for an ARCHModel model"], [448, 1, 1, 0, "ARCHModelForecast", "Container for forecasts from an ARCH Model"], [449, 1, 1, 0, "ARCHModelForecastSimulation", "Container for a simulation or bootstrap-based forecasts from an ARCH Model"], [450, 1, 1, 0, "ARCHModelResult", "Results from estimation of an ARCHModel model"]], "arch.univariate.base.ARCHModel": [[793, 3, 1, 0, "bounds", "Construct bounds for parameters to use in non-linear optimization"], [794, 3, 1, 0, "compute_param_cov", "Computes parameter covariances using numerical derivatives."], [795, 3, 1, 0, "constraints", "Construct linear constraint arrays  for use in non-linear optimization"], [796, 5, 1, 0, "distribution", "Set or gets the error distribution"], [797, 3, 1, 0, "fit", "Estimate model parameters"], [798, 3, 1, 0, "fix", "Allows an ARCHModelFixedResult to be constructed from fixed parameters."], [799, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [800, 5, 1, 0, "name", "The name of the model."], [801, 5, 1, 0, "num_params", "Number of parameters in the model"], [802, 3, 1, 0, "parameter_names", "List of parameters names"], [803, 3, 1, 0, "resids", "Compute model residuals"], [804, 3, 1, 0, "simulate", ""], [805, 3, 1, 0, "starting_values", "Returns starting values for the mean model, often the same as the values returned from fit"], [806, 5, 1, 0, "volatility", "Set or gets the volatility process"], [807, 5, 1, 0, "y", "Returns the dependent variable"]], "arch.univariate.base.ARCHModel.compute_param_cov": [[794, 2, 2, 0, "backcast", "Value to use for pre-sample observations"], [794, 2, 2, 0, "params", "Model parameters"], [794, 2, 2, 0, "robust", "Flag indicating whether to use robust standard errors (True) or classic MLE (False)"]], "arch.univariate.base.ARCHModel.fit": [[797, 2, 2, 0, "backcast", "Value to use as backcast. Should be measure \\sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."], [797, 2, 2, 0, "cov_type", "Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."], [797, 2, 2, 0, "disp", "Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is \"off\" and True is \"final\""], [797, 2, 2, 0, "first_obs", "First observation to use when estimating model"], [797, 2, 2, 0, "last_obs", "Last observation to use when estimating model"], [797, 2, 2, 0, "options", "Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."], [797, 2, 2, 0, "show_warning", "Flag indicating whether convergence warnings should be shown."], [797, 2, 2, 0, "starting_values", "Array of starting values to use.  If not provided, starting values are constructed by the model components."], [797, 2, 2, 0, "tol", "Tolerance for termination."], [797, 2, 2, 0, "update_freq", "Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."]], "arch.univariate.base.ARCHModel.fix": [[798, 2, 2, 0, "first_obs", "First observation to use when fixing model"], [798, 2, 2, 0, "last_obs", "Last observation to use when fixing model"], [798, 2, 2, 0, "params", "User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."]], "arch.univariate.base.ARCHModel.forecast": [[799, 2, 2, 0, "align", "Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."], [799, 2, 2, 0, "horizon", "Number of steps to forecast"], [799, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [799, 2, 2, 0, "params", "Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."], [799, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [799, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."], [799, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [799, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [799, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."], [799, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"]], "arch.univariate.base.ARCHModel.resids": [[803, 2, 2, 0, "params", "Model parameters"], [803, 2, 2, 0, "regressors", "Alternative regressor values to use when computing model residuals"], [803, 2, 2, 0, "y", "Alternative values to use when computing model residuals"]], "arch.univariate.base.ARCHModelFixedResult.__init__": [[447, 2, 2, 0, "dep_var", "Dependent variable"], [447, 2, 2, 0, "is_pandas", "Whether the original input was pandas"], [447, 2, 2, 0, "loglikelihood", "Loglikelihood at specified parameters"], [447, 2, 2, 0, "model", "The model object used to estimate the parameters"], [447, 2, 2, 0, "names", "Model parameter names"], [447, 2, 2, 0, "params", "Estimated parameters"], [447, 2, 2, 0, "resid", "Residuals from model.  Residuals have same shape as original data and contain nan-values in locations not used in estimation"], [447, 2, 2, 0, "volatility", "Conditional volatility from model"]], "arch.univariate.base.ARCHModelFixedResult": [[808, 5, 1, 0, "aic", "Akaike Information Criteria"], [809, 3, 1, 0, "arch_lm_test", "ARCH LM test for conditional heteroskedasticity"], [810, 5, 1, 0, "bic", "Schwarz/Bayesian Information Criteria"], [811, 5, 1, 0, "conditional_volatility", "Estimated conditional volatility"], [812, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [813, 3, 1, 0, "hedgehog_plot", "Plot forecasts from estimated model"], [814, 5, 1, 0, "loglikelihood", "Model loglikelihood"], [815, 5, 1, 0, "model", "Model instance used to produce the fit"], [816, 5, 1, 0, "nobs", "Number of data points used to estimate model"], [817, 5, 1, 0, "num_params", "Number of parameters in model"], [818, 5, 1, 0, "params", "Model Parameters"], [819, 3, 1, 0, "plot", "Plot standardized residuals and conditional volatility"], [820, 5, 1, 0, "resid", "Model residuals"], [821, 5, 1, 0, "std_resid", "Residuals standardized by conditional volatility"], [822, 3, 1, 0, "summary", "Constructs a summary of the results from a fit model."]], "arch.univariate.base.ARCHModelFixedResult.arch_lm_test": [[809, 2, 2, 0, "lags", "Number of lags to include in the model.  If not specified,"], [809, 2, 2, 0, "standardized", "Flag indicating to test the model residuals divided by their conditional standard deviations.  If False, directly tests the estimated residuals."]], "arch.univariate.base.ARCHModelFixedResult.forecast": [[812, 2, 2, 0, "align", "Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."], [812, 2, 2, 0, "horizon", "Number of steps to forecast"], [812, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [812, 2, 2, 0, "params", "Alternative parameters to use.  If not provided, the parameters estimated when fitting the model are used.  Must be identical in shape to the parameters computed by fitting the model."], [812, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [812, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast."], [812, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [812, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [812, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."], [812, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"]], "arch.univariate.base.ARCHModelFixedResult.hedgehog_plot": [[813, 2, 2, 0, "horizon", "Number of steps to forecast"], [813, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [813, 2, 2, 0, "params", "Alternative parameters to use.  If not provided, the parameters computed by fitting the model are used.  Must be 1-d and identical in shape to the parameters computed by fitting the model."], [813, 2, 2, 0, "plot_type", "Quantity to plot, the forecast volatility or the forecast mean"], [813, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [813, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index.  Strings must be convertible to a date time, such as in '1945-01-01'.  If not provided, the start is set to the earliest forecastable date."], [813, 2, 2, 0, "step", "Non-negative number of forecasts to skip between spines"]], "arch.univariate.base.ARCHModelFixedResult.plot": [[819, 2, 2, 0, "annualize", "String containing frequency of data that indicates plot should contain annualized volatility.  Supported values are 'D' (daily), 'W' (weekly) and 'M' (monthly), which scale variance by 252, 52, and 12, respectively."], [819, 2, 2, 0, "scale", "Value to use when scaling returns to annualize.  If scale is provided, annualize is ignored and the value in scale is used."]], "arch.univariate.base.ARCHModelForecast.__init__": [[448, 2, 2, 0, "align", ""], [448, 2, 2, 0, "index", ""], [448, 2, 2, 0, "mean", ""], [448, 2, 2, 0, "residual_variance", ""], [448, 2, 2, 0, "simulated_paths", ""], [448, 2, 2, 0, "simulated_residual_variances", ""], [448, 2, 2, 0, "simulated_residuals", ""], [448, 2, 2, 0, "simulated_variances", ""], [448, 2, 2, 0, "variance", ""]], "arch.univariate.base.ARCHModelForecast": [[823, 5, 1, 0, "mean", "Forecast values for the conditional mean of the process"], [824, 5, 1, 0, "residual_variance", "Forecast values for the conditional variance of the residuals"], [825, 5, 1, 0, "simulations", "Detailed simulation results if using a simulation-based method"], [826, 5, 1, 0, "variance", "Forecast values for the conditional variance of the process"]], "arch.univariate.base.ARCHModelForecastSimulation.__init__": [[449, 2, 2, 0, "index", ""], [449, 2, 2, 0, "residual_variances", ""], [449, 2, 2, 0, "residuals", ""], [449, 2, 2, 0, "values", ""], [449, 2, 2, 0, "variances", ""]], "arch.univariate.base.ARCHModelForecastSimulation": [[827, 5, 1, 0, "index", "The index aligned to dimension 0 of the simulation paths"], [828, 5, 1, 0, "residual_variances", "Simulated variance of the residuals"], [829, 5, 1, 0, "residuals", "Simulated residuals used to produce the values"], [830, 5, 1, 0, "values", "The values of the process"], [831, 5, 1, 0, "variances", "Simulated variances of the values"]], "arch.univariate.base.ARCHModelResult.__init__": [[450, 2, 2, 0, "cov_type", "String describing the covariance estimator used"], [450, 2, 2, 0, "dep_var", "Dependent variable"], [450, 2, 2, 0, "fit_start", "Integer index of the first observation used to fit the model"], [450, 2, 2, 0, "fit_stop", "Integer index of the last observation used to fit the model using slice notation fit_start:fit_stop"], [450, 2, 2, 0, "is_pandas", "Whether the original input was pandas"], [450, 2, 2, 0, "loglikelihood", "Loglikelihood at estimated parameters"], [450, 2, 2, 0, "model", "The model object used to estimate the parameters"], [450, 2, 2, 0, "names", "Model parameter names"], [450, 2, 2, 0, "optim_output", "Result of log-likelihood optimization"], [450, 2, 2, 0, "param_cov", "Estimated variance-covariance matrix of params.  If none, calls method to compute variance from model when parameter covariance is first used from result"], [450, 2, 2, 0, "params", "Estimated parameters"], [450, 2, 2, 0, "r2", "Model R-squared"], [450, 2, 2, 0, "resid", "Residuals from model.  Residuals have same shape as original data and contain nan-values in locations not used in estimation"], [450, 2, 2, 0, "volatility", "Conditional volatility from model"]], "arch.univariate.base.ARCHModelResult": [[832, 5, 1, 0, "aic", "Akaike Information Criteria"], [833, 3, 1, 0, "arch_lm_test", "ARCH LM test for conditional heteroskedasticity"], [834, 5, 1, 0, "bic", "Schwarz/Bayesian Information Criteria"], [835, 5, 1, 0, "conditional_volatility", "Estimated conditional volatility"], [836, 3, 1, 0, "conf_int", "Parameter confidence intervals"], [837, 5, 1, 0, "convergence_flag", "scipy.optimize.minimize result flag"], [838, 5, 1, 0, "fit_start", "Start of sample used to estimate parameters"], [839, 5, 1, 0, "fit_stop", "End of sample used to estimate parameters"], [840, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [841, 3, 1, 0, "hedgehog_plot", "Plot forecasts from estimated model"], [842, 5, 1, 0, "loglikelihood", "Model loglikelihood"], [843, 5, 1, 0, "model", "Model instance used to produce the fit"], [844, 5, 1, 0, "nobs", "Number of data points used to estimate model"], [845, 5, 1, 0, "num_params", "Number of parameters in model"], [846, 5, 1, 0, "optimization_result", "Information about the convergence of the loglikelihood optimization"], [847, 5, 1, 0, "param_cov", "Parameter covariance"], [848, 5, 1, 0, "params", "Model Parameters"], [849, 3, 1, 0, "plot", "Plot standardized residuals and conditional volatility"], [850, 5, 1, 0, "pvalues", "Array of p-values for the t-statistics"], [851, 5, 1, 0, "resid", "Model residuals"], [852, 5, 1, 0, "rsquared", "R-squared"], [853, 5, 1, 0, "rsquared_adj", "Degree of freedom adjusted R-squared"], [854, 5, 1, 0, "scale", "The scale applied to the original data before estimating the model."], [855, 5, 1, 0, "std_err", "Array of parameter standard errors"], [856, 5, 1, 0, "std_resid", "Residuals standardized by conditional volatility"], [857, 3, 1, 0, "summary", "Constructs a summary of the results from a fit model."], [858, 5, 1, 0, "tvalues", "Array of t-statistics testing the null that the coefficient are 0"]], "arch.univariate.base.ARCHModelResult.arch_lm_test": [[833, 2, 2, 0, "lags", "Number of lags to include in the model.  If not specified,"], [833, 2, 2, 0, "standardized", "Flag indicating to test the model residuals divided by their conditional standard deviations.  If False, directly tests the estimated residuals."]], "arch.univariate.base.ARCHModelResult.conf_int": [[836, 2, 2, 0, "alpha", "Size (prob.) to use when constructing the confidence interval."]], "arch.univariate.base.ARCHModelResult.forecast": [[840, 2, 2, 0, "align", "Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."], [840, 2, 2, 0, "horizon", "Number of steps to forecast"], [840, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [840, 2, 2, 0, "params", "Alternative parameters to use.  If not provided, the parameters estimated when fitting the model are used.  Must be identical in shape to the parameters computed by fitting the model."], [840, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [840, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast."], [840, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [840, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [840, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."], [840, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"]], "arch.univariate.base.ARCHModelResult.hedgehog_plot": [[841, 2, 2, 0, "horizon", "Number of steps to forecast"], [841, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [841, 2, 2, 0, "params", "Alternative parameters to use.  If not provided, the parameters computed by fitting the model are used.  Must be 1-d and identical in shape to the parameters computed by fitting the model."], [841, 2, 2, 0, "plot_type", "Quantity to plot, the forecast volatility or the forecast mean"], [841, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [841, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index.  Strings must be convertible to a date time, such as in '1945-01-01'.  If not provided, the start is set to the earliest forecastable date."], [841, 2, 2, 0, "step", "Non-negative number of forecasts to skip between spines"]], "arch.univariate.base.ARCHModelResult.plot": [[849, 2, 2, 0, "annualize", "String containing frequency of data that indicates plot should contain annualized volatility.  Supported values are 'D' (daily), 'W' (weekly) and 'M' (monthly), which scale variance by 252, 52, and 12, respectively."], [849, 2, 2, 0, "scale", "Value to use when scaling returns to annualize.  If scale is provided, annualize is ignored and the value in scale is used."]], "arch.univariate.distribution": [[451, 1, 1, 0, "Distribution", "Template for subclassing only"]], "arch.univariate.distribution.Distribution": [[859, 3, 1, 0, "bounds", "Parameter bounds for use in optimization."], [860, 3, 1, 0, "cdf", "Cumulative distribution function"], [861, 3, 1, 0, "constraints", "Construct arrays to use in constrained optimization."], [862, 5, 1, 0, "generator", "The NumPy Generator or RandomState attached to the distribution"], [863, 3, 1, 0, "loglikelihood", "Loglikelihood evaluation."], [864, 3, 1, 0, "moment", "Moment of order n"], [865, 5, 1, 0, "name", "The name of the distribution"], [866, 3, 1, 0, "parameter_names", "Names of distribution shape parameters"], [867, 3, 1, 0, "partial_moment", "Order n lower partial moment from -inf to z"], [868, 3, 1, 0, "ppf", "Inverse cumulative density function (ICDF)"], [869, 5, 1, 0, "random_state", "The NumPy RandomState attached to the distribution"], [870, 3, 1, 0, "simulate", "Simulates i.i.d. draws from the distribution"], [871, 3, 1, 0, "starting_values", "Construct starting values for use in optimization."]], "arch.univariate.distribution.Distribution.bounds": [[859, 2, 2, 0, "resids", "Residuals to use when computing the bounds"]], "arch.univariate.distribution.Distribution.cdf": [[860, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [860, 2, 2, 0, "resids", "Values at which to evaluate the cdf"]], "arch.univariate.distribution.Distribution.loglikelihood": [[863, 2, 2, 0, "individual", "Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"], [863, 2, 2, 0, "parameters", "Distribution shape parameters"], [863, 2, 2, 0, "resids", "nobs array of model residuals"], [863, 2, 2, 0, "sigma2", "nobs array of conditional variances"]], "arch.univariate.distribution.Distribution.moment": [[864, 2, 2, 0, "n", "Order of moment"], [864, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."]], "arch.univariate.distribution.Distribution.partial_moment": [[867, 2, 2, 0, "n", "Order of partial moment"], [867, 2, 2, 0, "parameters", "Distribution parameters.  Use None for parameterless distributions."], [867, 2, 2, 0, "z", "Upper bound for partial moment integral"]], "arch.univariate.distribution.Distribution.ppf": [[868, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [868, 2, 2, 0, "pits", "Probability-integral-transformed values in the interval (0, 1)."]], "arch.univariate.distribution.Distribution.simulate": [[870, 2, 2, 0, "parameters", "Distribution parameters"]], "arch.univariate.distribution.Distribution.starting_values": [[871, 2, 2, 0, "std_resid", "Estimated standardized residuals to use in computing starting values for the shape parameter"]], "arch.univariate.recursions_python": [[452, 1, 1, 0, "VolatilityUpdater", "Base class that all volatility updaters must inherit from."]], "arch.univariate.recursions_python.VolatilityUpdater": [[872, 3, 1, 0, "initialize_update", "Initialize the recursion prior to calling update"], [873, 3, 1, 0, "update", "Update the current variance at location t"]], "arch.univariate.recursions_python.VolatilityUpdater.initialize_update": [[872, 2, 2, 0, "backcast", "The backcast value(s)."], [872, 2, 2, 0, "nobs", "The number of observations in the sample."], [872, 2, 2, 0, "parameters", "The model parameters."]], "arch.univariate.recursions_python.VolatilityUpdater.update": [[873, 2, 2, 0, "parameters", "Model parameters"], [873, 2, 2, 0, "resids", "Residuals to use in the recursion"], [873, 2, 2, 0, "sigma2", "Conditional variances with same shape as resids"], [873, 2, 2, 0, "t", "The index of the value of sigma2 to update. Assumes but does not check that update has been called recursively for 0,1,...,t-1."], [873, 2, 2, 0, "var_bounds", "nobs by 2-element array of upper and lower bounds for conditional variances for each time period"]], "arch.univariate.volatility": [[453, 1, 1, 0, "VolatilityProcess", "Abstract base class for ARCH models.  Allows the conditional mean model to be specified separately from the conditional variance, even though parameters are estimated jointly."]], "arch.univariate.volatility.VolatilityProcess": [[874, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [875, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [876, 3, 1, 0, "bounds", "Returns bounds for parameters"], [877, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [878, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [879, 3, 1, 0, "forecast", "Forecast volatility from the model"], [880, 5, 1, 0, "name", "The name of the volatility process"], [881, 5, 1, 0, "num_params", "The number of parameters in the model"], [882, 3, 1, 0, "parameter_names", "Names of model parameters"], [883, 3, 1, 0, "simulate", "Simulate data from the model"], [884, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [885, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [886, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [887, 3, 1, 0, "update", "Compute the variance for a single observation"], [888, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [889, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [890, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.volatility.VolatilityProcess.backcast": [[874, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.volatility.VolatilityProcess.backcast_transform": [[875, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.volatility.VolatilityProcess.bounds": [[876, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.volatility.VolatilityProcess.compute_variance": [[877, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [877, 2, 2, 0, "parameters", "Model parameters"], [877, 2, 2, 0, "resids", "Vector of mean zero residuals"], [877, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [877, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.volatility.VolatilityProcess.forecast": [[879, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [879, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [879, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [879, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [879, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [879, 2, 2, 0, "resids", "Residuals to use in the recursion"], [879, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [879, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [879, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [879, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.volatility.VolatilityProcess.simulate": [[883, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [883, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [883, 2, 2, 0, "nobs", "Number of data points to simulate"], [883, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [883, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.volatility.VolatilityProcess.starting_values": [[885, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.volatility.VolatilityProcess.update": [[887, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [887, 2, 2, 0, "index", "The numerical index of the variance to compute"], [887, 2, 2, 0, "parameters", "The variance model parameters"], [887, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [887, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [887, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.volatility.VolatilityProcess.variance_bounds": [[889, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [889, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.utility": [[900, 0, 0, 1, "testing", ""]], "arch.utility.testing": [[900, 1, 1, 0, "WaldTestStatistic", "Test statistic holder for Wald-type tests"]], "arch.utility.testing.WaldTestStatistic.__init__": [[900, 2, 2, 0, "alternative", "A statement of the test's alternative hypothesis"], [900, 2, 2, 0, "df", "Degree of freedom."], [900, 2, 2, 0, "name", "Name of test"], [900, 2, 2, 0, "null", "A statement of the test's null hypothesis"], [900, 2, 2, 0, "stat", "The test statistic"]], "arch.utility.testing.WaldTestStatistic": [[900, 5, 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"arch.unitroot.cointegration.DynamicOLSResults.std_errors", "arch.unitroot.cointegration.DynamicOLSResults.summary", "arch.unitroot.cointegration.DynamicOLSResults.tvalues", "arch.unitroot.cointegration.EngleGrangerTestResults.alternative_hypothesis", "arch.unitroot.cointegration.EngleGrangerTestResults.cointegrating_vector", "arch.unitroot.cointegration.EngleGrangerTestResults.critical_values", "arch.unitroot.cointegration.EngleGrangerTestResults.distribution_order", "arch.unitroot.cointegration.EngleGrangerTestResults.lags", "arch.unitroot.cointegration.EngleGrangerTestResults.max_lags", "arch.unitroot.cointegration.EngleGrangerTestResults.name", "arch.unitroot.cointegration.EngleGrangerTestResults.null_hypothesis", "arch.unitroot.cointegration.EngleGrangerTestResults.plot", "arch.unitroot.cointegration.EngleGrangerTestResults.pvalue", "arch.unitroot.cointegration.EngleGrangerTestResults.resid", "arch.unitroot.cointegration.EngleGrangerTestResults.rho", 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694, 707, 737, 783, 799, 812, 813, 840, 841, 872, 877, 891, 894, 895, 896, 897, 898, 899, 901], "call": [3, 11, 14, 15, 25, 26, 29, 30, 40, 41, 44, 45, 55, 56, 59, 60, 70, 71, 74, 75, 85, 89, 95, 96, 248, 258, 424, 450, 872, 873, 896, 897, 898, 901], "full": [3, 385], "param": [3, 4, 11, 14, 15, 25, 26, 29, 30, 40, 41, 44, 45, 55, 56, 59, 60, 70, 71, 74, 75, 85, 88, 89, 259, 270, 272, 424, 447, 450, 491, 495, 496, 501, 502, 508, 512, 513, 517, 518, 524, 528, 529, 533, 534, 673, 677, 678, 682, 683, 689, 693, 694, 698, 699, 778, 782, 783, 787, 788, 794, 798, 799, 803, 804, 812, 813, 840, 841, 895, 897, 898], "7": [3, 8, 259, 264, 267, 420, 421, 891, 895, 896, 897, 898, 899], "919351": 3, "18": [3, 236, 421, 496, 513, 529, 678, 694, 783, 799, 895, 897, 898], "455084": 3, "429115": 3, "dtype": [3, 88, 259, 420, 897, 898, 899], "float64": [3, 88, 259, 420, 897, 898, 899], "accept": [3, 89, 94, 96, 496, 513, 529, 678, 694, 783, 799, 812, 840, 895], "keyword": [3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 25, 26, 27, 28, 29, 30, 40, 41, 42, 43, 44, 45, 48, 55, 56, 57, 58, 59, 60, 70, 71, 72, 73, 74, 75, 85, 87, 88, 89, 95, 96], "argument": [3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 25, 26, 27, 28, 29, 30, 40, 41, 42, 43, 44, 45, 48, 55, 56, 57, 58, 59, 60, 70, 71, 72, 73, 74, 75, 85, 87, 88, 89, 95, 96, 238, 239, 240, 436, 441, 443, 444, 653, 732, 762, 775, 870, 898], "seed": [3, 5, 6, 7, 8, 9, 13, 16, 20, 28, 31, 35, 43, 46, 50, 58, 61, 65, 73, 76, 80, 96, 238, 239, 240, 259, 436, 441, 443, 444, 451, 898, 899], "randomst": [3, 5, 6, 7, 8, 9, 13, 16, 19, 23, 28, 31, 34, 38, 43, 46, 49, 53, 58, 61, 64, 68, 73, 76, 79, 83, 89, 95, 96, 238, 239, 240, 436, 438, 441, 443, 444, 451, 461, 478, 496, 513, 529, 544, 561, 578, 595, 613, 630, 645, 652, 660, 678, 694, 709, 724, 731, 739, 754, 761, 767, 774, 783, 799, 812, 840, 862, 869, 879, 895, 898, 899], "int": [3, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 17, 21, 22, 23, 25, 26, 27, 28, 29, 30, 32, 36, 37, 38, 40, 41, 42, 43, 44, 45, 47, 51, 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259, 436, 441, 443, 444], "creat": [3, 5, 6, 7, 8, 9, 24, 39, 54, 69, 84, 96, 238, 239, 240, 898], "core": [3, 424, 898], "pseudo": [3, 16, 31, 46, 61, 76, 89], "across": [3, 7, 14, 29, 44, 59, 74, 259, 421, 424], "multipl": [3, 14, 29, 44, 59, 74, 87, 93, 236, 237, 240, 266, 419, 421, 458, 476, 542, 559, 576, 593, 611, 628, 658, 707, 737, 877], "chosen": [3, 4, 10, 238, 239, 240, 271], "must": [3, 4, 5, 6, 7, 8, 9, 11, 14, 15, 24, 25, 26, 29, 30, 39, 40, 41, 44, 45, 54, 55, 56, 59, 60, 69, 70, 71, 74, 75, 84, 85, 89, 100, 104, 105, 106, 107, 108, 109, 110, 111, 248, 266, 269, 271, 274, 423, 425, 427, 452, 461, 478, 493, 495, 496, 504, 510, 512, 513, 520, 526, 528, 529, 536, 544, 561, 578, 595, 613, 630, 660, 675, 677, 678, 685, 691, 693, 694, 701, 709, 739, 780, 782, 783, 790, 796, 798, 799, 806, 812, 813, 840, 841, 879, 892, 895, 901], "appropri": [3, 4, 7, 14, 29, 44, 59, 74, 86, 238, 239, 240, 455, 474, 540, 557, 574, 591, 609, 626, 656, 705, 735, 875], "squar": [3, 4, 259, 368, 421, 424, 427, 439, 450, 471, 488, 496, 513, 529, 554, 571, 588, 606, 623, 640, 670, 678, 694, 719, 749, 783, 799, 811, 812, 813, 835, 840, 841, 852, 853, 889, 891, 895, 896, 898, 899], "ar": [3, 4, 7, 10, 11, 12, 14, 15, 25, 26, 27, 29, 30, 40, 41, 42, 44, 45, 55, 56, 57, 59, 60, 70, 71, 72, 74, 75, 85, 89, 90, 91, 95, 96, 97, 100, 101, 104, 105, 106, 107, 108, 109, 110, 111, 114, 124, 138, 148, 158, 168, 178, 188, 198, 208, 218, 228, 236, 238, 239, 240, 242, 243, 247, 258, 259, 260, 262, 263, 265, 266, 267, 269, 271, 274, 276, 277, 321, 324, 354, 368, 402, 417, 419, 420, 421, 424, 425, 427, 428, 431, 433, 435, 437, 438, 442, 446, 452, 453, 459, 461, 471, 477, 478, 488, 494, 495, 496, 502, 511, 512, 513, 518, 527, 528, 529, 543, 544, 554, 560, 561, 571, 577, 578, 588, 594, 595, 606, 612, 613, 623, 629, 630, 640, 646, 659, 660, 670, 676, 677, 678, 683, 692, 693, 694, 699, 708, 709, 719, 725, 738, 739, 749, 755, 768, 781, 782, 783, 788, 797, 798, 799, 811, 812, 813, 819, 835, 840, 841, 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768, 769, 771, 772, 773, 775, 776, 786, 787, 788, 793, 794, 795, 797, 798, 799, 802, 803, 804, 805, 809, 812, 813, 819, 822, 836, 857, 859, 860, 861, 863, 864, 866, 867, 868, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 882, 883, 885, 887, 889, 891, 900], "length": [5, 7, 8, 9, 10, 90, 91, 95, 96, 97, 238, 239, 240, 262, 263, 265, 267, 271, 276, 281, 294, 392, 420, 421, 424, 427, 428, 429, 437, 438, 439, 440, 442, 445, 496, 502, 513, 518, 529, 534, 678, 683, 694, 699, 783, 788, 799, 812, 840, 891, 897], "wrap": [5, 8, 9, 10, 90], "around": [5, 8, 9, 10, 90, 258, 420], "posit": [5, 6, 7, 8, 9, 12, 13, 27, 28, 42, 43, 48, 57, 58, 72, 73, 87, 118, 128, 142, 152, 162, 172, 182, 192, 202, 212, 222, 232, 259, 265, 266, 421, 424, 433, 435, 496, 513, 529, 678, 694, 783, 799, 811, 812, 835, 840, 895], "ensur": [5, 6, 7, 8, 9, 89, 277, 436, 441, 443, 444, 471, 488, 554, 571, 588, 606, 623, 640, 670, 719, 749, 889], "reproduc": [5, 6, 7, 8, 9, 89, 95, 259, 436, 441, 443, 444, 898], "If": [5, 6, 7, 8, 9, 20, 23, 35, 38, 50, 53, 65, 68, 80, 83, 95, 96, 100, 101, 104, 105, 106, 107, 108, 109, 110, 111, 238, 239, 240, 262, 263, 264, 265, 266, 267, 271, 277, 354, 362, 368, 377, 381, 402, 424, 425, 427, 428, 429, 434, 436, 438, 439, 441, 443, 444, 445, 450, 461, 472, 478, 489, 494, 496, 502, 511, 513, 518, 527, 529, 544, 555, 561, 572, 578, 589, 595, 607, 613, 624, 630, 641, 660, 671, 676, 678, 683, 692, 694, 699, 709, 720, 739, 750, 781, 783, 797, 799, 809, 812, 813, 819, 833, 840, 841, 849, 854, 879, 890, 891, 895, 897], "fresh": [5, 6, 8, 9, 13, 28, 43, 58, 73], "system": [5, 6, 8, 9, 271, 436, 441, 443, 444], "deprec": [5, 6, 8, 9, 92, 93, 96, 436, 441, 443, 444, 652, 731, 761, 774, 869], "ha": [5, 6, 7, 8, 9, 20, 35, 50, 65, 80, 95, 96, 259, 267, 419, 421, 424, 430, 432, 442, 725, 812, 840, 873, 895, 897, 898, 899], "been": [5, 6, 7, 8, 9, 20, 35, 50, 65, 80, 95, 96, 267, 421, 437, 812, 840, 854, 873, 895, 899], "tupl": [5, 6, 7, 8, 9, 12, 17, 18, 22, 23, 27, 32, 33, 37, 38, 39, 42, 47, 48, 52, 53, 54, 57, 62, 63, 67, 68, 72, 77, 78, 82, 83, 87, 456, 459, 465, 475, 477, 478, 482, 490, 492, 496, 507, 509, 513, 523, 525, 529, 541, 543, 544, 548, 558, 560, 561, 565, 575, 577, 578, 582, 592, 594, 595, 599, 610, 612, 613, 617, 627, 629, 630, 634, 642, 644, 653, 657, 659, 660, 664, 672, 674, 678, 688, 690, 694, 706, 708, 709, 713, 721, 723, 732, 736, 738, 739, 743, 751, 753, 762, 764, 766, 775, 777, 779, 783, 793, 795, 799, 812, 840, 859, 861, 870, 876, 878, 879, 883], "pos_data": [5, 6, 7, 8, 9], "kw_data": [5, 6, 7, 8, 9], "type": [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 19, 23, 25, 26, 27, 28, 29, 30, 31, 32, 34, 38, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 53, 55, 56, 57, 58, 59, 60, 61, 62, 64, 68, 70, 71, 72, 73, 74, 75, 76, 77, 79, 83, 85, 86, 95, 96, 112, 113, 114, 115, 117, 118, 119, 120, 121, 122, 123, 124, 125, 127, 128, 129, 130, 131, 136, 137, 138, 139, 141, 142, 143, 144, 145, 146, 147, 148, 149, 151, 152, 153, 154, 155, 156, 157, 158, 159, 161, 162, 163, 164, 165, 166, 167, 168, 169, 171, 172, 173, 174, 175, 176, 177, 178, 179, 181, 182, 183, 184, 185, 186, 187, 188, 189, 191, 192, 193, 194, 195, 196, 197, 198, 199, 201, 202, 203, 204, 205, 206, 207, 208, 209, 211, 212, 213, 214, 215, 216, 217, 218, 219, 221, 222, 223, 224, 225, 226, 227, 228, 229, 231, 232, 233, 234, 235, 242, 243, 244, 247, 249, 250, 257, 268, 272, 276, 277, 280, 293, 306, 317, 324, 331, 345, 354, 362, 366, 368, 371, 377, 381, 385, 389, 395, 398, 402, 406, 411, 421, 424, 454, 455, 456, 461, 464, 467, 469, 471, 472, 473, 474, 475, 478, 481, 484, 486, 488, 489, 490, 494, 495, 496, 500, 501, 502, 503, 507, 511, 512, 513, 516, 517, 518, 519, 523, 527, 528, 529, 532, 533, 534, 535, 539, 540, 541, 544, 547, 550, 552, 554, 555, 556, 557, 558, 561, 564, 567, 569, 571, 572, 573, 574, 575, 578, 581, 584, 586, 588, 589, 590, 591, 592, 595, 598, 601, 604, 606, 607, 608, 609, 610, 613, 616, 619, 621, 623, 624, 625, 626, 627, 630, 633, 636, 638, 640, 641, 642, 643, 646, 647, 649, 650, 651, 653, 654, 655, 656, 657, 660, 663, 666, 668, 670, 671, 672, 676, 677, 678, 681, 682, 683, 684, 688, 692, 693, 694, 697, 698, 699, 700, 704, 705, 706, 709, 712, 715, 717, 719, 720, 721, 722, 725, 726, 728, 729, 730, 732, 733, 734, 735, 736, 739, 742, 745, 747, 749, 750, 751, 752, 755, 756, 758, 759, 760, 762, 763, 764, 765, 768, 769, 771, 772, 773, 775, 776, 777, 781, 782, 783, 786, 787, 788, 789, 793, 797, 798, 799, 802, 803, 805, 809, 811, 812, 813, 819, 822, 825, 833, 835, 836, 840, 841, 846, 849, 857, 859, 860, 864, 866, 867, 868, 870, 871, 874, 875, 876, 879, 882, 885, 887, 889, 890, 891, 897, 899, 900], "enter": [5, 6, 7, 8, 9, 12, 27, 42, 57, 72, 892], "dict": [5, 6, 7, 8, 9, 11, 12, 14, 15, 17, 18, 22, 23, 25, 26, 27, 29, 30, 32, 33, 37, 38, 39, 40, 41, 42, 44, 45, 47, 48, 52, 53, 54, 55, 56, 57, 59, 60, 62, 63, 67, 68, 70, 71, 72, 74, 75, 77, 78, 82, 83, 85, 279, 292, 305, 316, 329, 344, 494, 496, 511, 513, 527, 529, 676, 678, 692, 694, 781, 783, 797, 799, 812, 840, 900], "note": [5, 6, 7, 8, 9, 10, 11, 14, 26, 29, 41, 44, 56, 59, 71, 74, 100, 101, 104, 105, 106, 107, 108, 109, 110, 111, 120, 130, 144, 154, 164, 174, 184, 194, 204, 214, 224, 234, 238, 239, 240, 247, 248, 258, 262, 263, 264, 265, 266, 267, 269, 271, 274, 276, 277, 362, 368, 377, 381, 398, 421, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 437, 438, 439, 440, 442, 443, 445, 452, 459, 461, 477, 478, 492, 494, 495, 496, 509, 511, 512, 513, 525, 527, 528, 529, 543, 544, 560, 561, 577, 578, 594, 595, 612, 613, 629, 630, 644, 646, 650, 654, 659, 660, 674, 676, 677, 678, 690, 692, 693, 694, 708, 709, 723, 725, 729, 733, 738, 739, 753, 755, 759, 763, 766, 768, 772, 776, 779, 781, 782, 783, 795, 797, 798, 799, 812, 840, 861, 863, 867, 871, 872, 873, 878, 879, 891, 895, 897, 899], "support": [5, 6, 7, 8, 9, 91, 95, 96, 97, 277, 288, 301, 312, 325, 339, 351, 366, 385, 417, 424, 461, 470, 478, 487, 494, 496, 511, 513, 527, 529, 544, 553, 561, 570, 578, 587, 595, 605, 613, 622, 630, 639, 660, 669, 676, 678, 692, 694, 709, 718, 739, 748, 781, 783, 797, 799, 812, 813, 819, 840, 841, 849, 879, 888, 891, 892, 895, 899], "date": [5, 6, 7, 8, 9, 91, 97, 421, 424, 496, 513, 529, 678, 694, 783, 799, 812, 813, 840, 841, 891, 895, 896, 897, 898, 899], "accessibli": [5, 6, 7, 8, 9], "attribut": [5, 6, 7, 8, 9, 424, 897, 899], "To": [5, 6, 7, 8, 9, 420, 422], "you": [5, 6, 7, 8, 9, 895, 899], "after": [5, 6, 7, 8, 9, 95, 96, 424, 496, 513, 529, 678, 694, 783, 799, 897], "reserv": [5, 6, 7, 8, 9, 898], "instanc": [5, 7, 8, 9, 13, 16, 19, 28, 31, 34, 43, 46, 49, 58, 61, 64, 73, 76, 79, 87, 89, 95, 96, 115, 125, 139, 149, 159, 169, 179, 189, 199, 209, 219, 229, 272, 273, 354, 366, 385, 395, 402, 411, 436, 441, 443, 444, 461, 478, 496, 513, 529, 544, 561, 578, 595, 613, 630, 660, 678, 694, 709, 739, 783, 799, 812, 815, 822, 840, 843, 857, 879, 898], "optim": [5, 8, 9, 10, 95, 100, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 117, 120, 121, 122, 123, 127, 130, 131, 136, 137, 141, 144, 145, 146, 147, 151, 154, 155, 156, 157, 161, 164, 165, 166, 167, 171, 174, 175, 176, 177, 181, 184, 185, 186, 187, 191, 194, 195, 196, 197, 201, 204, 205, 206, 207, 211, 214, 215, 216, 217, 221, 224, 225, 226, 227, 231, 234, 235, 268, 271, 277, 354, 368, 402, 450, 490, 492, 494, 507, 509, 511, 523, 525, 527, 642, 644, 654, 672, 674, 676, 688, 690, 692, 721, 723, 733, 751, 753, 763, 764, 766, 776, 777, 779, 781, 793, 795, 797, 837, 846, 859, 861, 871, 891, 896, 897, 898, 899], "exp": [5, 8, 10], "access": [5, 6, 7, 8, 9, 248, 259, 421, 898], "wai": [5, 6, 7, 8, 9, 898], "retain": [5, 6, 7, 8, 9], "wa": [5, 6, 7, 8, 9, 91, 94, 95, 96, 97, 421, 447, 450, 496, 513, 529, 678, 694, 783, 799, 895], "avail": [5, 6, 7, 8, 9, 259, 262, 265, 421, 424, 452, 461, 478, 496, 513, 529, 544, 561, 578, 595, 613, 630, 660, 678, 694, 709, 739, 783, 799, 812, 840, 879, 897, 898, 899], "syntax": [5, 6, 7, 8, 9, 496, 513, 529, 678, 694, 783, 799, 812, 840], "z": [5, 6, 7, 8, 9, 87, 96, 100, 104, 105, 106, 107, 108, 109, 110, 111, 263, 277, 368, 421, 650, 729, 759, 772, 867], "bs_x": [5, 6, 7, 8, 9], "bs_y": [5, 6, 7, 8, 9], "bs_z": [5, 6, 7, 8, 9], "1234": [5, 6, 7, 8, 9, 438], "properti": [5, 6, 7, 8, 9, 16, 18, 19, 23, 31, 33, 34, 38, 46, 48, 49, 53, 61, 63, 64, 68, 76, 78, 79, 83, 91, 96, 97, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 238, 239, 240, 242, 243, 244, 250, 257, 262, 263, 264, 265, 266, 267, 270, 272, 273, 275, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 351, 352, 353, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 367, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 386, 387, 388, 389, 390, 391, 392, 393, 394, 396, 397, 398, 399, 401, 403, 404, 405, 406, 407, 408, 409, 410, 412, 413, 414, 416, 420, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 453, 457, 460, 462, 463, 466, 468, 470, 472, 479, 480, 483, 485, 487, 489, 493, 496, 497, 498, 499, 504, 505, 506, 510, 513, 514, 515, 520, 521, 522, 526, 529, 530, 531, 536, 537, 538, 545, 546, 549, 551, 553, 555, 562, 563, 566, 568, 570, 572, 579, 580, 583, 585, 587, 589, 596, 597, 600, 602, 603, 605, 607, 614, 615, 618, 620, 622, 624, 631, 632, 635, 637, 639, 641, 645, 648, 652, 661, 662, 665, 667, 669, 671, 675, 678, 679, 680, 685, 686, 687, 691, 694, 695, 696, 701, 702, 703, 710, 711, 714, 716, 718, 720, 724, 727, 731, 740, 741, 744, 746, 748, 750, 754, 757, 761, 767, 770, 774, 780, 783, 784, 785, 790, 791, 792, 796, 799, 800, 801, 806, 807, 808, 810, 811, 812, 814, 815, 816, 817, 818, 820, 821, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 834, 835, 837, 838, 839, 840, 842, 843, 844, 845, 846, 847, 848, 850, 851, 852, 853, 854, 855, 856, 858, 862, 865, 869, 880, 881, 884, 886, 888, 890, 892, 893, 898, 900], "uniform": 6, "integ": [6, 94, 96, 100, 104, 105, 106, 107, 108, 109, 110, 111, 116, 126, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 238, 239, 240, 246, 252, 256, 259, 277, 354, 368, 402, 421, 424, 450, 465, 482, 496, 513, 529, 548, 565, 582, 599, 617, 634, 664, 678, 694, 713, 743, 783, 799, 812, 813, 840, 841, 883, 891], "equival": [6, 259, 368, 427, 438, 496, 513, 529, 678, 694, 783, 799, 812, 840, 891, 899], "structur": [7, 89, 267, 417, 420, 421, 424, 427, 428, 438, 895], "unequ": 7, "cross": [7, 273, 276, 277, 368, 420], "section": [7, 273, 276, 277, 368, 420], "relat": [7, 95, 96, 258, 433, 435, 891, 898], "inappropri": 7, "without": [8, 14, 29, 44, 59, 74, 89, 427, 428, 429, 438, 439, 445, 891], "circularblockbootstrap": [8, 9, 10, 58, 73, 90], "expon": 9, "arraylike1d": [10, 461], "arraylike2d": 10, "dimension": [10, 15, 25, 30, 40, 45, 55, 60, 70, 75, 85, 89, 895, 896], "oper": [10, 433], "b_sb": 10, "b_cb": 10, "algorithm": [10, 262, 267, 901], "its": [10, 20, 35, 50, 65, 80, 251, 258, 259, 368, 420, 421, 898], "correct": [10, 14, 29, 44, 59, 74, 259, 495, 512, 528, 677, 693, 782, 798], "tune": 10, "k_n": 10, "consecut": 10, "insid": [10, 895], "conserv": 10, "band": 10, "pm": 10, "log_": 10, "maximum": [10, 262, 263, 267, 271, 276, 281, 294, 392, 424, 440, 891, 895, 896, 898, 899], "lceil": 10, "rceil": 10, "_i": 10, "2g": 10, "d_": [10, 269, 274], "sum_": [10, 269, 271, 274, 277, 398, 424, 425, 426, 427, 428, 431, 433, 435, 437, 438, 440], "k": [10, 238, 239, 240, 259, 264, 265, 267, 271, 354, 368, 402, 425, 427, 428, 431, 435, 438, 439, 502, 518, 683, 699, 899], "h": [10, 95, 100, 104, 105, 106, 107, 108, 109, 110, 111, 237, 239, 262, 263, 264, 269, 271, 274, 424, 496, 513, 529, 678, 694, 783, 799, 812, 840, 895, 897, 899], "gamma_": [10, 425, 431, 435], "c_": 10, "x_k": 10, "bar": [10, 421, 438], "x_": [10, 95, 271, 895], "remain": [10, 895], "constant": [10, 95, 117, 120, 127, 130, 141, 144, 151, 154, 161, 164, 171, 174, 181, 184, 191, 194, 201, 204, 211, 214, 221, 224, 231, 234, 262, 263, 264, 265, 267, 269, 271, 274, 276, 277, 417, 420, 421, 424, 427, 428, 429, 430, 438, 439, 496, 502, 513, 518, 529, 534, 678, 683, 694, 699, 755, 783, 788, 799, 813, 841, 891, 896, 899], "c_i": 10, "taken": 10, "andrew": [10, 95, 108, 264, 266, 267, 268, 419], "patton": 10, "matlab": 10, "program": 10, "do": [10, 12, 27, 42, 57, 72, 269, 274, 424, 438, 496, 513, 529, 678, 694, 783, 799, 812, 813, 840, 841, 872, 897, 900], "autocovari": [10, 277], "rather": [10, 96, 258, 259, 263, 267, 424, 897, 898], "dimitri": 10, "halbert": 10, "white": [10, 237, 239, 258, 277, 354, 368, 402, 421, 896, 898], "2004": [10, 264, 267], "automat": [10, 91, 95, 96, 97, 262, 263, 264, 265, 267, 268, 276, 281, 294, 417, 419, 421, 427, 428, 429, 438, 439, 445, 891], "select": [10, 95, 96, 121, 131, 145, 155, 165, 175, 185, 195, 205, 215, 225, 235, 240, 262, 263, 264, 267, 268, 271, 273, 276, 281, 294, 392, 417, 419, 421, 466, 468, 483, 485, 549, 551, 566, 568, 583, 585, 600, 602, 618, 620, 635, 637, 665, 667, 714, 716, 744, 746, 884, 886], "review": [10, 264, 443], "23": [10, 237, 239, 421, 896], "53": [10, 420, 896], "70": [10, 259], "doi": [10, 236], "1081": 10, "etc": [10, 424, 898], "120028836": 10, "2009": [10, 420, 424, 897], "28": [10, 421, 424, 891, 895, 896, 897, 898], "372": 10, "375": 10, "1080": 10, "07474930802459016": 10, "func": [11, 14, 15, 25, 26, 29, 30, 40, 41, 44, 45, 55, 56, 59, 60, 70, 71, 74, 75, 85, 891, 896, 897, 898], "callabl": [11, 14, 15, 25, 26, 29, 30, 40, 41, 44, 45, 55, 56, 59, 60, 70, 71, 74, 75, 85, 95, 258, 452, 461, 465, 478, 482, 496, 513, 529, 544, 548, 561, 565, 578, 582, 595, 599, 613, 617, 630, 634, 653, 660, 664, 678, 694, 709, 713, 732, 739, 743, 762, 775, 783, 799, 812, 840, 870, 879, 883], "ndarrai": [11, 12, 14, 15, 17, 18, 21, 22, 23, 24, 25, 26, 27, 29, 30, 32, 33, 36, 37, 38, 39, 40, 41, 42, 44, 45, 47, 48, 51, 52, 53, 54, 55, 56, 57, 59, 60, 62, 63, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 77, 78, 81, 82, 83, 84, 85, 95, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 118, 128, 134, 135, 142, 152, 162, 172, 182, 192, 202, 212, 222, 232, 238, 239, 240, 246, 247, 252, 253, 256, 262, 263, 264, 265, 266, 267, 268, 269, 271, 274, 276, 277, 290, 303, 314, 327, 342, 353, 427, 428, 429, 434, 438, 439, 445, 446, 447, 450, 454, 455, 456, 458, 459, 465, 467, 469, 471, 473, 474, 475, 476, 477, 478, 482, 484, 486, 488, 491, 492, 494, 495, 496, 501, 502, 503, 505, 506, 508, 509, 511, 512, 513, 517, 518, 519, 521, 522, 524, 525, 527, 528, 529, 533, 534, 535, 537, 538, 539, 540, 541, 542, 543, 544, 548, 550, 552, 554, 556, 557, 558, 559, 560, 561, 565, 567, 569, 571, 573, 574, 575, 576, 577, 578, 582, 584, 586, 588, 590, 591, 592, 593, 594, 595, 599, 601, 604, 606, 608, 609, 610, 611, 612, 613, 617, 619, 621, 623, 625, 626, 627, 628, 629, 630, 634, 636, 638, 640, 642, 643, 644, 646, 647, 650, 651, 653, 654, 655, 656, 657, 658, 659, 660, 664, 666, 668, 670, 673, 674, 676, 677, 678, 682, 683, 684, 686, 687, 689, 690, 692, 693, 694, 698, 699, 700, 702, 703, 704, 705, 706, 707, 708, 709, 713, 715, 717, 719, 721, 722, 723, 725, 726, 729, 730, 732, 733, 734, 735, 736, 737, 738, 739, 743, 745, 747, 749, 751, 752, 753, 755, 756, 759, 760, 762, 763, 764, 765, 766, 768, 769, 772, 773, 775, 776, 778, 779, 781, 782, 783, 787, 788, 789, 791, 792, 794, 795, 797, 798, 799, 803, 804, 805, 807, 811, 812, 813, 820, 821, 828, 829, 830, 831, 835, 840, 841, 851, 856, 859, 860, 861, 863, 864, 867, 868, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 883, 885, 887, 889, 891, 895], "rep": [11, 12, 14, 15, 25, 26, 27, 29, 30, 40, 41, 42, 44, 45, 55, 56, 57, 59, 60, 70, 71, 72, 74, 75, 85, 238, 239, 240, 259], "str": [11, 12, 14, 15, 17, 18, 22, 23, 25, 26, 27, 29, 30, 32, 33, 37, 38, 39, 40, 41, 42, 44, 45, 47, 48, 52, 53, 54, 55, 56, 57, 59, 60, 62, 63, 67, 68, 70, 71, 72, 74, 75, 77, 78, 82, 83, 85, 101, 119, 129, 143, 153, 163, 173, 183, 193, 203, 213, 223, 233, 259, 270, 272, 273, 275, 277, 278, 279, 283, 288, 289, 291, 292, 296, 302, 304, 305, 308, 312, 313, 315, 316, 319, 324, 325, 326, 328, 329, 333, 339, 340, 343, 344, 347, 351, 352, 354, 357, 368, 371, 374, 387, 393, 394, 395, 401, 402, 403, 408, 409, 410, 411, 416, 447, 450, 462, 464, 479, 481, 494, 495, 496, 498, 500, 511, 512, 513, 514, 516, 527, 528, 529, 530, 532, 545, 547, 562, 564, 579, 581, 596, 598, 614, 616, 631, 633, 648, 649, 661, 663, 676, 677, 678, 679, 681, 692, 693, 694, 695, 697, 710, 712, 727, 728, 740, 742, 757, 758, 770, 771, 781, 782, 783, 784, 786, 797, 798, 799, 800, 802, 812, 813, 819, 840, 841, 849, 865, 866, 880, 882, 900], "extra": [11, 14, 15, 25, 26, 29, 30, 40, 41, 44, 45, 55, 56, 59, 60, 70, 71, 74, 75, 85, 239, 240], "conflict": [11, 26, 41, 56, 71], "nparam": [11, 26, 41, 56, 71], "row": [11, 14, 26, 29, 41, 44, 56, 59, 71, 74, 424, 496, 513, 529, 678, 694, 783, 799, 812, 836, 840, 895], "correspond": [11, 14, 26, 29, 41, 44, 56, 59, 71, 74, 239, 250, 259, 471, 488, 494, 496, 511, 513, 527, 529, 554, 571, 588, 606, 623, 640, 670, 676, 678, 692, 694, 719, 749, 781, 783, 797, 799, 812, 840, 889, 897, 898], "iter": [11, 12, 24, 26, 27, 39, 41, 42, 54, 56, 57, 69, 71, 72, 84, 92, 452, 494, 511, 527, 676, 692, 781, 797, 891, 896, 897, 898], "up": [11, 14, 26, 29, 41, 44, 56, 59, 71, 74, 267, 421, 424, 437, 496, 513, 529, 678, 694, 783, 799, 812, 840, 895, 897, 899], "append": [11, 14, 26, 29, 41, 44, 56, 59, 71, 74, 259, 895, 897], "befor": [11, 14, 26, 29, 41, 44, 56, 59, 71, 74, 100, 104, 105, 106, 107, 108, 109, 110, 111, 248, 263, 421, 494, 511, 527, 676, 692, 781, 797, 854, 899], "randn": [11, 12, 14, 15, 25, 26, 27, 29, 30, 40, 41, 42, 44, 45, 55, 56, 57, 59, 60, 70, 71, 72, 74, 75, 85, 87, 89, 259, 428, 429, 438, 439, 445, 502, 518, 683, 699], "over": [12, 27, 42, 57, 72, 94, 899], "calcul": [12, 27, 42, 57, 72, 95, 113, 117, 118, 123, 127, 128, 137, 141, 142, 147, 151, 152, 157, 161, 162, 167, 171, 172, 177, 181, 182, 187, 191, 192, 197, 201, 202, 207, 211, 212, 217, 221, 222, 227, 231, 232, 264, 267, 646, 647, 725, 726, 755, 756, 768, 769, 864], "kei": [12, 27, 42, 57, 72, 389, 406, 417, 496, 513, 529, 678, 694, 783, 799, 812, 840, 895], "problem": [12, 27, 42, 57, 72, 89, 96, 421], "doe": [12, 27, 42, 57, 72, 90, 259, 277, 362, 381, 421, 471, 488, 494, 511, 527, 554, 571, 588, 606, 623, 640, 670, 676, 692, 719, 749, 781, 797, 873, 889, 891, 897], "output": [12, 27, 42, 57, 72, 88, 259, 494, 511, 527, 653, 676, 692, 732, 762, 775, 781, 797, 870, 891, 897, 898], "arang": [12, 27, 42, 57, 72, 89, 895, 897, 899], "posdata": [12, 27, 42, 57, 72], "kwdata": [12, 27, 42, 57, 72], "someth": [12, 27, 42, 57, 72], "prng": [13, 16, 28, 31, 43, 46, 58, 61, 73, 76], "norm": [14, 29, 44, 59, 74], "bc": [14, 29, 44, 59, 74, 755], "bca": [14, 29, 44, 59, 74, 95], "float": [14, 15, 17, 22, 23, 25, 29, 30, 32, 37, 38, 40, 44, 45, 47, 52, 53, 55, 59, 60, 62, 67, 68, 70, 74, 75, 77, 82, 83, 85, 99, 100, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 117, 120, 121, 122, 123, 127, 130, 131, 136, 137, 141, 144, 145, 146, 147, 151, 154, 155, 156, 157, 161, 164, 165, 166, 167, 171, 174, 175, 176, 177, 181, 184, 185, 186, 187, 191, 194, 195, 196, 197, 201, 204, 205, 206, 207, 211, 214, 215, 216, 217, 221, 224, 225, 226, 227, 231, 234, 235, 238, 240, 247, 249, 267, 268, 270, 273, 275, 279, 284, 286, 292, 297, 299, 305, 309, 310, 316, 320, 322, 329, 335, 337, 341, 344, 348, 349, 354, 355, 358, 362, 363, 364, 369, 377, 381, 382, 383, 396, 398, 399, 402, 404, 412, 414, 425, 427, 432, 433, 435, 442, 447, 450, 454, 455, 456, 458, 460, 461, 465, 469, 471, 473, 474, 475, 476, 478, 482, 486, 488, 490, 491, 494, 495, 497, 502, 507, 508, 511, 512, 518, 523, 524, 527, 528, 534, 539, 540, 541, 542, 544, 548, 552, 554, 556, 557, 558, 559, 561, 565, 569, 571, 573, 574, 575, 576, 578, 582, 586, 588, 590, 591, 592, 593, 595, 599, 604, 606, 608, 609, 610, 611, 613, 617, 621, 623, 625, 626, 627, 628, 630, 634, 638, 640, 642, 643, 646, 647, 650, 651, 653, 655, 656, 657, 658, 660, 664, 668, 670, 672, 673, 676, 677, 683, 688, 689, 692, 693, 699, 704, 705, 706, 707, 709, 713, 717, 719, 721, 722, 725, 726, 729, 730, 732, 734, 735, 736, 737, 739, 743, 747, 749, 751, 752, 755, 756, 759, 760, 762, 764, 765, 768, 769, 772, 773, 775, 777, 778, 781, 782, 788, 793, 794, 797, 798, 804, 808, 810, 814, 819, 832, 834, 836, 842, 849, 852, 853, 854, 859, 860, 863, 864, 867, 868, 870, 872, 874, 875, 876, 877, 879, 883, 887, 889, 891, 895, 900], "tail": [14, 29, 44, 59, 74, 424, 443, 654, 895, 898], "bool": [14, 15, 20, 25, 29, 30, 35, 40, 44, 45, 50, 55, 59, 60, 65, 70, 74, 75, 80, 85, 99, 100, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 114, 116, 124, 126, 138, 140, 148, 150, 158, 160, 168, 170, 178, 180, 188, 190, 198, 200, 208, 210, 218, 220, 228, 230, 239, 240, 262, 263, 266, 270, 271, 272, 277, 330, 334, 336, 354, 368, 385, 402, 425, 427, 428, 429, 434, 438, 439, 440, 445, 446, 447, 448, 450, 457, 470, 487, 491, 494, 496, 508, 511, 513, 524, 527, 529, 553, 570, 587, 605, 622, 639, 646, 669, 673, 676, 678, 689, 692, 694, 718, 725, 748, 755, 768, 778, 781, 783, 794, 797, 799, 809, 812, 833, 840, 863, 888, 891], "fals": [14, 15, 20, 25, 29, 30, 35, 40, 44, 45, 50, 55, 59, 60, 65, 70, 74, 75, 80, 85, 88, 91, 95, 97, 99, 100, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 239, 240, 266, 271, 277, 354, 368, 385, 402, 421, 425, 427, 428, 429, 434, 438, 439, 440, 445, 448, 491, 494, 508, 511, 513, 524, 527, 529, 646, 673, 676, 678, 689, 692, 694, 725, 755, 768, 778, 781, 783, 794, 797, 799, 809, 812, 833, 840, 863, 891, 895, 897, 899], "nonparametr": [14, 29, 44, 59, 74, 89], "semi": [14, 29, 44, 59, 74, 89], "studentize_rep": [14, 29, 44, 59, 74], "One": [14, 29, 44, 59, 74, 421, 424], "debias": [14, 29, 44, 59, 74, 266, 277], "bia": [14, 29, 44, 59, 74, 274, 420], "coverag": [14, 29, 44, 59, 74], "flag": [14, 20, 29, 35, 44, 50, 59, 65, 74, 80, 100, 104, 105, 106, 107, 108, 109, 110, 111, 114, 116, 124, 126, 138, 140, 148, 150, 158, 160, 168, 170, 178, 180, 188, 190, 198, 200, 208, 210, 218, 220, 228, 230, 239, 240, 262, 271, 385, 427, 428, 429, 434, 438, 439, 440, 445, 470, 487, 491, 494, 508, 511, 524, 527, 553, 570, 587, 605, 622, 639, 646, 669, 673, 676, 689, 692, 718, 725, 748, 755, 768, 778, 781, 794, 797, 809, 833, 837, 863, 888, 891], "indic": [14, 20, 24, 29, 35, 39, 44, 48, 50, 54, 59, 65, 69, 74, 80, 84, 100, 104, 105, 106, 107, 108, 109, 110, 111, 114, 116, 124, 126, 138, 140, 148, 150, 158, 160, 168, 170, 178, 180, 188, 190, 198, 200, 208, 210, 218, 220, 228, 230, 239, 240, 242, 243, 247, 253, 257, 259, 262, 266, 271, 330, 334, 336, 385, 420, 421, 424, 427, 428, 429, 438, 439, 440, 445, 470, 487, 491, 494, 496, 502, 508, 511, 513, 518, 524, 527, 529, 553, 570, 587, 605, 622, 639, 646, 669, 673, 676, 678, 683, 689, 692, 694, 699, 718, 725, 748, 755, 768, 778, 781, 783, 794, 797, 799, 809, 812, 813, 819, 833, 840, 841, 849, 863, 888, 891, 898], "whether": [14, 15, 20, 25, 29, 30, 35, 40, 44, 45, 50, 55, 59, 60, 65, 70, 74, 75, 80, 85, 100, 104, 105, 106, 107, 108, 109, 110, 111, 114, 116, 124, 126, 138, 140, 148, 150, 158, 160, 168, 170, 178, 180, 188, 190, 198, 200, 208, 210, 218, 220, 228, 230, 258, 259, 262, 265, 266, 272, 277, 354, 368, 385, 402, 417, 419, 421, 427, 428, 429, 434, 438, 439, 440, 445, 447, 450, 491, 494, 496, 508, 511, 513, 524, 527, 529, 646, 673, 676, 678, 689, 692, 694, 725, 755, 768, 778, 781, 783, 794, 797, 799, 812, 840, 863, 891, 898], "previous": [14, 29, 44, 59, 74, 94, 896, 898, 899], "rerun": [14, 29, 44, 59, 74, 420], "ignor": [14, 29, 44, 59, 74, 421, 819, 849, 891, 895], "chang": [14, 29, 44, 59, 74, 93, 94, 95, 97, 259, 277, 420, 421, 452, 496, 513, 529, 678, 694, 783, 799, 812, 840, 872, 896, 897, 898, 899], "about": [14, 29, 44, 59, 74, 496, 513, 529, 678, 694, 783, 799, 846, 897, 898], "elimin": [14, 29, 44, 59, 74, 238], "need": [14, 29, 44, 59, 74, 89, 277, 420, 895, 897, 899], "bound": [14, 29, 44, 59, 74, 239, 250, 458, 461, 469, 471, 476, 478, 486, 488, 492, 509, 525, 542, 544, 552, 554, 559, 561, 569, 571, 576, 578, 586, 588, 593, 595, 604, 606, 611, 613, 621, 623, 628, 630, 638, 640, 650, 658, 660, 668, 670, 674, 690, 707, 709, 717, 719, 729, 737, 739, 747, 749, 759, 772, 779, 795, 867, 873, 877, 879, 887, 889], "inf": [14, 29, 44, 59, 74, 650, 729, 759, 772, 867], "similarli": [14, 29, 44, 59, 74], "between": [14, 29, 44, 59, 74, 95, 258, 259, 417, 421, 427, 428, 438, 763, 776, 813, 841, 898, 899], "direct": [14, 29, 44, 59, 74, 440, 898], "normal": [14, 29, 44, 59, 74, 89, 96, 259, 424, 440, 654, 891, 895, 896, 897, 898, 899], "approxim": [14, 29, 44, 59, 74, 262, 265, 267, 271, 454, 455, 456, 467, 471, 473, 474, 475, 484, 488, 539, 540, 541, 550, 554, 556, 557, 558, 567, 571, 573, 574, 575, 584, 588, 590, 591, 592, 601, 603, 606, 608, 609, 610, 619, 623, 625, 626, 627, 636, 640, 655, 656, 657, 666, 670, 704, 705, 706, 715, 719, 734, 735, 736, 745, 749, 874, 875, 876, 885, 889], "scale": [14, 29, 44, 59, 74, 95, 259, 427, 428, 429, 434, 438, 439, 442, 445, 455, 474, 540, 557, 574, 591, 609, 626, 656, 705, 735, 819, 849, 875, 891, 896, 898, 899], "acceler": [14, 29, 44, 59, 74], "ad": [14, 29, 44, 59, 74, 91, 92, 93, 94, 95, 96, 97, 898, 901], "recent": [15, 25, 30, 40, 45, 55, 60, 70, 75, 85, 88, 236, 239, 277], "interest": [15, 25, 30, 40, 45, 55, 60, 70, 75, 85, 898], "center": [15, 25, 30, 40, 45, 55, 60, 70, 75, 85, 88, 95, 99, 100, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 239, 259, 265, 898], "signatur": [15, 25, 30, 40, 45, 55, 60, 70, 75, 85], "axi": [15, 25, 30, 40, 45, 55, 60, 70, 75, 85, 496, 513, 529, 678, 694, 783, 799, 812, 840, 898, 899], "addit": [15, 25, 30, 40, 45, 55, 60, 70, 75, 85, 236, 265, 421, 465, 482, 548, 565, 582, 599, 617, 634, 664, 713, 743, 883, 897, 898], "elif": [15, 25, 30, 40, 45, 55, 60, 70, 75, 85, 897], "get": [16, 17, 19, 23, 31, 32, 34, 38, 46, 47, 49, 53, 61, 62, 64, 68, 76, 77, 79, 83, 280, 281, 288, 293, 294, 301, 306, 312, 317, 324, 325, 330, 331, 334, 336, 339, 345, 351, 393, 409, 417, 472, 489, 493, 504, 505, 510, 520, 521, 526, 536, 537, 555, 572, 589, 607, 624, 641, 671, 675, 685, 686, 691, 701, 702, 720, 750, 780, 790, 791, 796, 806, 890], "option": [16, 31, 46, 61, 76, 89, 95, 238, 239, 240, 259, 262, 277, 421, 428, 438, 446, 494, 496, 511, 513, 527, 529, 676, 678, 692, 694, 781, 783, 797, 799, 891, 892, 898, 901], "map": [17, 23, 32, 38, 47, 53, 62, 68, 77, 83], "current": [18, 33, 48, 63, 78, 95, 265, 873, 891, 896, 897, 898], "use_se": [20, 35, 50, 65, 80], "last": [20, 35, 50, 65, 80, 266, 269, 274, 424, 450, 494, 495, 511, 512, 527, 528, 676, 677, 692, 693, 781, 782, 797, 798, 895], "rese": [21, 36, 51, 66, 81], "otherwis": [23, 38, 53, 68, 83, 854], "updat": [24, 39, 54, 69, 84, 91, 94, 95, 97, 262, 265, 427, 452, 472, 489, 494, 511, 527, 555, 572, 589, 607, 624, 641, 671, 676, 692, 720, 750, 781, 797, 872, 890, 892], "overridden": [24, 39, 54, 69, 84, 421, 446], "new": [24, 39, 54, 69, 84, 95, 262, 427, 428, 429, 438, 439, 445, 496, 513, 529, 678, 694, 783, 799, 812, 840, 891], "least": [86, 266, 269, 274, 368, 421, 439], "total": 86, "aris": 86, "experiment": 86, 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See Notes for requirements"], [11, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.CircularBlockBootstrap.bootstrap": [[12, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.CircularBlockBootstrap.clone": [[13, 2, 2, 0, "args", "Positional arguments to bootstrap"], [13, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [13, 2, 2, 0, "seed", "The seed value to pass to the closed generator"]], "arch.bootstrap.CircularBlockBootstrap.conf_int": [[14, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func and std_err_func, when appropriate"], [14, 2, 2, 0, "func", "Function the computes parameter values.  See Notes for requirements"], [14, 2, 2, 0, "method", "One of 'basic', 'percentile', 'studentized', 'norm' (identical to 'var', 'cov'), 'bc' (identical to 'debiased', 'bias-corrected'), or 'bca'"], [14, 2, 2, 0, "reps", "Number of bootstrap replications"], [14, 2, 2, 0, "reuse", "Flag indicating whether to reuse previously computed bootstrap results.  This allows alternative methods to be compared without rerunning the bootstrap simulation.  Reuse is ignored if reps is not the same across multiple runs, func changes across calls, or method is 'studentized'."], [14, 2, 2, 0, "sampling", "Type of sampling to use: 'nonparametric', 'semi-parametric' (or 'semi') or 'parametric'.  The default is 'nonparametric'.  See notes about the changes to func required when using 'semi' or 'parametric'."], [14, 2, 2, 0, "size", "Coverage of confidence interval"], [14, 2, 2, 0, "std_err_func", "Function to use when standardizing estimated parameters when using the studentized bootstrap.  Providing an analytical function eliminates the need for a nested bootstrap"], [14, 2, 2, 0, "studentize_reps", "Number of bootstraps to use in the inner bootstrap when using the studentized bootstrap.  Ignored when std_err_func is provided"], [14, 2, 2, 0, "tail", "One of 'two', 'upper' or 'lower'."]], "arch.bootstrap.CircularBlockBootstrap.cov": [[15, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [15, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [15, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."], [15, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.CircularBlockBootstrap.reset": [[20, 2, 2, 0, "use_seed", "Flag indicating whether to use the last seed if provided.  If False or if no seed has been set, the bootstrap will be reset to the initial state.  Default is True"]], "arch.bootstrap.CircularBlockBootstrap.seed": [[21, 2, 2, 0, "value", "Value to use as the seed."]], "arch.bootstrap.CircularBlockBootstrap.set_state": [[22, 2, 2, 0, "state", "Dictionary or tuple containing the state."]], "arch.bootstrap.CircularBlockBootstrap.var": [[25, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [25, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [25, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."], [25, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.IIDBootstrap.__init__": [[6, 2, 2, 0, "args", "Positional arguments to bootstrap"], [6, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [6, 2, 2, 0, "random_state", "RandomState to use to ensure reproducable results. Cannot be used with seed"], [6, 2, 2, 0, "seed", "Seed to use to ensure reproducable results. If an int, passes the value to value to np.random.default_rng. If None, a fresh Generator is constructed with system-provided entropy."]], "arch.bootstrap.IIDBootstrap": [[26, 3, 1, 0, "apply", "Applies a function to bootstrap replicated data"], [27, 3, 1, 0, "bootstrap", "Iterator for use when bootstrapping"], [28, 3, 1, 0, "clone", "Clones the bootstrap using different data with a fresh prng."], [29, 3, 1, 0, "conf_int", "Function the computes parameter values.  See Notes for requirements"], [30, 3, 1, 0, "cov", "Compute parameter covariance using bootstrap"], [6, 4, 1, 0, "data", "Two-element tuple with the pos_data in the first position and kw_data in the second (pos_data, kw_data)"], [31, 5, 1, 0, "generator", "Set or get the instance PRNG"], [32, 3, 1, 0, "get_state", "Gets the state of the bootstrap's random number generator"], [33, 5, 1, 0, "index", "The current index of the bootstrap"], [6, 4, 1, 0, "kw_data", "Dictionary containing the keyword arguments"], [6, 4, 1, 0, "pos_data", "Tuple containing the positional arguments (in the order entered)"], [34, 5, 1, 0, "random_state", "Set or get the instance random state"], [35, 3, 1, 0, "reset", "Resets the bootstrap to either its initial state or the last seed."], [36, 3, 1, 0, "seed", "Reseeds the bootstrap's random number generator"], [37, 3, 1, 0, "set_state", "Sets the state of the bootstrap's random number generator"], [38, 5, 1, 0, "state", "Set or get the generator's state"], [39, 3, 1, 0, "update_indices", "Update indices for the next iteration of the bootstrap.  This must be overridden when creating new bootstraps."], [40, 3, 1, 0, "var", "Compute parameter variance using bootstrap"]], "arch.bootstrap.IIDBootstrap.apply": [[26, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func.  Must not conflict with keyword arguments used to initialize bootstrap"], [26, 2, 2, 0, "func", "Function the computes parameter values.  See Notes for requirements"], [26, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.IIDBootstrap.bootstrap": [[27, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.IIDBootstrap.clone": [[28, 2, 2, 0, "args", "Positional arguments to bootstrap"], [28, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [28, 2, 2, 0, "seed", "The seed value to pass to the closed generator"]], "arch.bootstrap.IIDBootstrap.conf_int": [[29, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func and std_err_func, when appropriate"], [29, 2, 2, 0, "func", "Function the computes parameter values.  See Notes for requirements"], [29, 2, 2, 0, "method", "One of 'basic', 'percentile', 'studentized', 'norm' (identical to 'var', 'cov'), 'bc' (identical to 'debiased', 'bias-corrected'), or 'bca'"], [29, 2, 2, 0, "reps", "Number of bootstrap replications"], [29, 2, 2, 0, "reuse", "Flag indicating whether to reuse previously computed bootstrap results.  This allows alternative methods to be compared without rerunning the bootstrap simulation.  Reuse is ignored if reps is not the same across multiple runs, func changes across calls, or method is 'studentized'."], [29, 2, 2, 0, "sampling", "Type of sampling to use: 'nonparametric', 'semi-parametric' (or 'semi') or 'parametric'.  The default is 'nonparametric'.  See notes about the changes to func required when using 'semi' or 'parametric'."], [29, 2, 2, 0, "size", "Coverage of confidence interval"], [29, 2, 2, 0, "std_err_func", "Function to use when standardizing estimated parameters when using the studentized bootstrap.  Providing an analytical function eliminates the need for a nested bootstrap"], [29, 2, 2, 0, "studentize_reps", "Number of bootstraps to use in the inner bootstrap when using the studentized bootstrap.  Ignored when std_err_func is provided"], [29, 2, 2, 0, "tail", "One of 'two', 'upper' or 'lower'."]], "arch.bootstrap.IIDBootstrap.cov": [[30, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [30, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [30, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."], [30, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.IIDBootstrap.reset": [[35, 2, 2, 0, "use_seed", "Flag indicating whether to use the last seed if provided.  If False or if no seed has been set, the bootstrap will be reset to the initial state.  Default is True"]], "arch.bootstrap.IIDBootstrap.seed": [[36, 2, 2, 0, "value", "Value to use as the seed."]], "arch.bootstrap.IIDBootstrap.set_state": [[37, 2, 2, 0, "state", "Dictionary or tuple containing the state."]], "arch.bootstrap.IIDBootstrap.var": [[40, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [40, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [40, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."], [40, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.IndependentSamplesBootstrap.__init__": [[7, 2, 2, 0, "args", "Positional arguments to bootstrap"], [7, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"]], "arch.bootstrap.IndependentSamplesBootstrap": [[41, 3, 1, 0, "apply", "Applies a function to bootstrap replicated data"], [42, 3, 1, 0, "bootstrap", "Iterator for use when bootstrapping"], [43, 3, 1, 0, "clone", "Clones the bootstrap using different data with a fresh prng."], [44, 3, 1, 0, "conf_int", "Function the computes parameter values.  See Notes for requirements"], [45, 3, 1, 0, "cov", "Compute parameter covariance using bootstrap"], [7, 4, 1, 0, "data", "Two-element tuple with the pos_data in the first position and kw_data in the second (pos_data, kw_data)"], [46, 5, 1, 0, "generator", "Set or get the instance PRNG"], [47, 3, 1, 0, "get_state", "Gets the state of the bootstrap's random number generator"], [48, 5, 1, 0, "index", "Returns the current index of the bootstrap"], [7, 4, 1, 0, "kw_data", "Dictionary containing the keyword arguments"], [7, 4, 1, 0, "pos_data", "Tuple containing the positional arguments (in the order entered)"], [49, 5, 1, 0, "random_state", "Set or get the instance random state"], [50, 3, 1, 0, "reset", "Resets the bootstrap to either its initial state or the last seed."], [51, 3, 1, 0, "seed", "Reseeds the bootstrap's random number generator"], [52, 3, 1, 0, "set_state", "Sets the state of the bootstrap's random number generator"], [53, 5, 1, 0, "state", "Set or get the generator's state"], [54, 3, 1, 0, "update_indices", "Update indices for the next iteration of the bootstrap.  This must be overridden when creating new bootstraps."], [55, 3, 1, 0, "var", "Compute parameter variance using bootstrap"]], "arch.bootstrap.IndependentSamplesBootstrap.apply": [[41, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func.  Must not conflict with keyword arguments used to initialize bootstrap"], [41, 2, 2, 0, "func", "Function the computes parameter values.  See Notes for requirements"], [41, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.IndependentSamplesBootstrap.bootstrap": [[42, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.IndependentSamplesBootstrap.clone": [[43, 2, 2, 0, "args", "Positional arguments to bootstrap"], [43, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [43, 2, 2, 0, "seed", "The seed value to pass to the closed generator"]], "arch.bootstrap.IndependentSamplesBootstrap.conf_int": [[44, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func and std_err_func, when appropriate"], [44, 2, 2, 0, "func", "Function the computes parameter values.  See Notes for requirements"], [44, 2, 2, 0, "method", "One of 'basic', 'percentile', 'studentized', 'norm' (identical to 'var', 'cov'), 'bc' (identical to 'debiased', 'bias-corrected'), or 'bca'"], [44, 2, 2, 0, "reps", "Number of bootstrap replications"], [44, 2, 2, 0, "reuse", "Flag indicating whether to reuse previously computed bootstrap results.  This allows alternative methods to be compared without rerunning the bootstrap simulation.  Reuse is ignored if reps is not the same across multiple runs, func changes across calls, or method is 'studentized'."], [44, 2, 2, 0, "sampling", "Type of sampling to use: 'nonparametric', 'semi-parametric' (or 'semi') or 'parametric'.  The default is 'nonparametric'.  See notes about the changes to func required when using 'semi' or 'parametric'."], [44, 2, 2, 0, "size", "Coverage of confidence interval"], [44, 2, 2, 0, "std_err_func", "Function to use when standardizing estimated parameters when using the studentized bootstrap.  Providing an analytical function eliminates the need for a nested bootstrap"], [44, 2, 2, 0, "studentize_reps", "Number of bootstraps to use in the inner bootstrap when using the studentized bootstrap.  Ignored when std_err_func is provided"], [44, 2, 2, 0, "tail", "One of 'two', 'upper' or 'lower'."]], "arch.bootstrap.IndependentSamplesBootstrap.cov": [[45, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [45, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [45, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."], [45, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.IndependentSamplesBootstrap.reset": [[50, 2, 2, 0, "use_seed", "Flag indicating whether to use the last seed if provided.  If False or if no seed has been set, the bootstrap will be reset to the initial state.  Default is True"]], "arch.bootstrap.IndependentSamplesBootstrap.seed": [[51, 2, 2, 0, "value", "Value to use as the seed."]], "arch.bootstrap.IndependentSamplesBootstrap.set_state": [[52, 2, 2, 0, "state", "Dictionary or tuple containing the state."]], "arch.bootstrap.IndependentSamplesBootstrap.var": [[55, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [55, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [55, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."], [55, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.MCS.__init__": [[238, 2, 2, 0, "block_size", "Length of window to use in the bootstrap.  If not provided, sqrt(T) is used.  In general, this should be provided and chosen to be appropriate for the data."], [238, 2, 2, 0, "bootstrap", "Bootstrap to use.  Options are 'stationary' or 'sb': Stationary bootstrap (Default) 'circular' or 'cbb': Circular block bootstrap 'moving block' or 'mbb': Moving block bootstrap"], [238, 2, 2, 0, "losses", "T by k array containing losses from a set of models"], [238, 2, 2, 0, "method", "MCS test and elimination implementation method, either 'max' or 'R'. Default is 'R'."], [238, 2, 2, 0, "reps", "Number of bootstrap replications to uses.  Default is 1000."], [238, 2, 2, 0, "seed", "Seed value to use when creating the bootstrap used in the comparison. If an integer or None, the NumPy default_rng is used with the seed value.  If a Generator or a RandomState, the argument is used."], [238, 2, 2, 0, "size", "Value in (0,1) to use as the test size when implementing the mcs. Default value is 0.05."]], "arch.bootstrap.MCS": [[241, 3, 1, 0, "compute", "Compute the set of models in the confidence set."], [242, 5, 1, 0, "excluded", "List of model indices that are excluded from the MCS"], [243, 5, 1, 0, "included", "List of model indices that are included in the MCS"], [244, 5, 1, 0, "pvalues", "Model p-values for inclusion in the MCS"], [245, 3, 1, 0, "reset", "Reset the bootstrap to it's initial state."], [246, 3, 1, 0, "seed", "Seed the bootstrap's random number generator"]], "arch.bootstrap.MCS.seed": [[246, 2, 2, 0, "value", "Integer to use as the seed"]], "arch.bootstrap.MovingBlockBootstrap.__init__": [[8, 2, 2, 0, "args", "Positional arguments to bootstrap"], [8, 2, 2, 0, "block_size", "Size of block to use"], [8, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [8, 2, 2, 0, "random_state", "RandomState to use to ensure reproducable results. Cannot be used with seed"], [8, 2, 2, 0, "seed", "Seed to use to ensure reproducable results. If an int, passes the value to value to np.random.default_rng. If None, a fresh Generator is constructed with system-provided entropy."]], "arch.bootstrap.MovingBlockBootstrap": [[56, 3, 1, 0, "apply", "Applies a function to bootstrap replicated data"], [57, 3, 1, 0, "bootstrap", "Iterator for use when bootstrapping"], [58, 3, 1, 0, "clone", "Clones the bootstrap using different data with a fresh prng."], [59, 3, 1, 0, "conf_int", "Function the computes parameter values.  See Notes for requirements"], [60, 3, 1, 0, "cov", "Compute parameter covariance using bootstrap"], [8, 4, 1, 0, "data", "Two-element tuple with the pos_data in the first position and kw_data in the second (pos_data, kw_data)"], [61, 5, 1, 0, "generator", "Set or get the instance PRNG"], [62, 3, 1, 0, "get_state", "Gets the state of the bootstrap's random number generator"], [63, 5, 1, 0, "index", "The current index of the bootstrap"], [8, 4, 1, 0, "kw_data", "Dictionary containing the keyword arguments"], [8, 4, 1, 0, "pos_data", "Tuple containing the positional arguments (in the order entered)"], [64, 5, 1, 0, "random_state", "Set or get the instance random state"], [65, 3, 1, 0, "reset", "Resets the bootstrap to either its initial state or the last seed."], [66, 3, 1, 0, "seed", "Reseeds the bootstrap's random number generator"], [67, 3, 1, 0, "set_state", "Sets the state of the bootstrap's random number generator"], [68, 5, 1, 0, "state", "Set or get the generator's state"], [69, 3, 1, 0, "update_indices", "Update indices for the next iteration of the bootstrap.  This must be overridden when creating new bootstraps."], [70, 3, 1, 0, "var", "Compute parameter variance using bootstrap"]], "arch.bootstrap.MovingBlockBootstrap.apply": [[56, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func.  Must not conflict with keyword arguments used to initialize bootstrap"], [56, 2, 2, 0, "func", "Function the computes parameter values.  See Notes for requirements"], [56, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.MovingBlockBootstrap.bootstrap": [[57, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.MovingBlockBootstrap.clone": [[58, 2, 2, 0, "args", "Positional arguments to bootstrap"], [58, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [58, 2, 2, 0, "seed", "The seed value to pass to the closed generator"]], "arch.bootstrap.MovingBlockBootstrap.conf_int": [[59, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func and std_err_func, when appropriate"], [59, 2, 2, 0, "func", "Function the computes parameter values.  See Notes for requirements"], [59, 2, 2, 0, "method", "One of 'basic', 'percentile', 'studentized', 'norm' (identical to 'var', 'cov'), 'bc' (identical to 'debiased', 'bias-corrected'), or 'bca'"], [59, 2, 2, 0, "reps", "Number of bootstrap replications"], [59, 2, 2, 0, "reuse", "Flag indicating whether to reuse previously computed bootstrap results.  This allows alternative methods to be compared without rerunning the bootstrap simulation.  Reuse is ignored if reps is not the same across multiple runs, func changes across calls, or method is 'studentized'."], [59, 2, 2, 0, "sampling", "Type of sampling to use: 'nonparametric', 'semi-parametric' (or 'semi') or 'parametric'.  The default is 'nonparametric'.  See notes about the changes to func required when using 'semi' or 'parametric'."], [59, 2, 2, 0, "size", "Coverage of confidence interval"], [59, 2, 2, 0, "std_err_func", "Function to use when standardizing estimated parameters when using the studentized bootstrap.  Providing an analytical function eliminates the need for a nested bootstrap"], [59, 2, 2, 0, "studentize_reps", "Number of bootstraps to use in the inner bootstrap when using the studentized bootstrap.  Ignored when std_err_func is provided"], [59, 2, 2, 0, "tail", "One of 'two', 'upper' or 'lower'."]], "arch.bootstrap.MovingBlockBootstrap.cov": [[60, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [60, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [60, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."], [60, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.MovingBlockBootstrap.reset": [[65, 2, 2, 0, "use_seed", "Flag indicating whether to use the last seed if provided.  If False or if no seed has been set, the bootstrap will be reset to the initial state.  Default is True"]], "arch.bootstrap.MovingBlockBootstrap.seed": [[66, 2, 2, 0, "value", "Value to use as the seed."]], "arch.bootstrap.MovingBlockBootstrap.set_state": [[67, 2, 2, 0, "state", "Dictionary or tuple containing the state."]], "arch.bootstrap.MovingBlockBootstrap.var": [[70, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [70, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [70, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."], [70, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.SPA.__init__": [[239, 2, 2, 0, "benchmark", "T element array of benchmark model losses"], [239, 2, 2, 0, "block_size", "Length of window to use in the bootstrap.  If not provided, sqrt(T) is used.  In general, this should be provided and chosen to be appropriate for the data."], [239, 2, 2, 0, "bootstrap", "Bootstrap to use.  Options are 'stationary' or 'sb': Stationary bootstrap (Default) 'circular' or 'cbb': Circular block bootstrap 'moving block' or 'mbb': Moving block bootstrap"], [239, 2, 2, 0, "models", "T  by k element array of alternative model losses"], [239, 2, 2, 0, "nested", "Flag indicating to use a nested bootstrap to compute variances for studentization.  Default is False.  Note that this can be slow since the procedure requires k extra bootstraps."], [239, 2, 2, 0, "reps", "Number of bootstrap replications to uses.  Default is 1000."], [239, 2, 2, 0, "seed", "Seed value to use when creating the bootstrap used in the comparison. If an integer or None, the NumPy default_rng is used with the seed value.  If a Generator or a RandomState, the argument is used."], [239, 2, 2, 0, "studentize", "Flag indicating to studentize loss differentials. Default is True"]], "arch.bootstrap.SPA": [[247, 3, 1, 0, "better_models", "Returns set of models rejected as being equal-or-worse than the benchmark"], [248, 3, 1, 0, "compute", "Compute the bootstrap pvalue."], [249, 3, 1, 0, "critical_values", "Returns data-dependent critical values"], [250, 5, 1, 0, "pvalues", "P-values corresponding to the lower, consistent and upper p-values."], [251, 3, 1, 0, "reset", "Reset the bootstrap to its initial state."], [252, 3, 1, 0, "seed", "Seed the bootstrap's random number generator"], [253, 3, 1, 0, "subset", "Sets a list of active models to run the SPA on.  Primarily for internal use."]], "arch.bootstrap.SPA.better_models": [[247, 2, 2, 0, "pvalue", "P-value in (0,1) to use when computing superior models"], [247, 2, 2, 0, "pvalue_type", "String in 'lower', 'consistent', or 'upper' indicating which critical value to use."]], "arch.bootstrap.SPA.critical_values": [[249, 2, 2, 0, "pvalue", "P-value in (0,1) to use when computing the critical values."]], "arch.bootstrap.SPA.seed": [[252, 2, 2, 0, "value", "Integer to use as the seed"]], "arch.bootstrap.SPA.subset": [[253, 2, 2, 0, "selector", "Boolean array indicating which columns to use when computing the p-values.  This is primarily for use by StepM."]], "arch.bootstrap.StationaryBootstrap.__init__": [[9, 2, 2, 0, "args", "Positional arguments to bootstrap"], [9, 2, 2, 0, "block_size", "Average size of block to use"], [9, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [9, 2, 2, 0, "random_state", "RandomState to use to ensure reproducable results. Cannot be used with seed"], [9, 2, 2, 0, "seed", "Seed to use to ensure reproducable results. If an int, passes the value to value to np.random.default_rng. If None, a fresh Generator is constructed with system-provided entropy."]], "arch.bootstrap.StationaryBootstrap": [[71, 3, 1, 0, "apply", "Applies a function to bootstrap replicated data"], [72, 3, 1, 0, "bootstrap", "Iterator for use when bootstrapping"], [73, 3, 1, 0, "clone", "Clones the bootstrap using different data with a fresh prng."], [74, 3, 1, 0, "conf_int", "Function the computes parameter values.  See Notes for requirements"], [75, 3, 1, 0, "cov", "Compute parameter covariance using bootstrap"], [9, 4, 1, 0, "data", "Two-element tuple with the pos_data in the first position and kw_data in the second (pos_data, kw_data)"], [76, 5, 1, 0, "generator", "Set or get the instance PRNG"], [77, 3, 1, 0, "get_state", "Gets the state of the bootstrap's random number generator"], [78, 5, 1, 0, "index", "The current index of the bootstrap"], [9, 4, 1, 0, "kw_data", "Dictionary containing the keyword arguments"], [9, 4, 1, 0, "pos_data", "Tuple containing the positional arguments (in the order entered)"], [79, 5, 1, 0, "random_state", "Set or get the instance random state"], [80, 3, 1, 0, "reset", "Resets the bootstrap to either its initial state or the last seed."], [81, 3, 1, 0, "seed", "Reseeds the bootstrap's random number generator"], [82, 3, 1, 0, "set_state", "Sets the state of the bootstrap's random number generator"], [83, 5, 1, 0, "state", "Set or get the generator's state"], [84, 3, 1, 0, "update_indices", "Update indices for the next iteration of the bootstrap.  This must be overridden when creating new bootstraps."], [85, 3, 1, 0, "var", "Compute parameter variance using bootstrap"]], "arch.bootstrap.StationaryBootstrap.apply": [[71, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func.  Must not conflict with keyword arguments used to initialize bootstrap"], [71, 2, 2, 0, "func", "Function the computes parameter values.  See Notes for requirements"], [71, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.StationaryBootstrap.bootstrap": [[72, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.StationaryBootstrap.clone": [[73, 2, 2, 0, "args", "Positional arguments to bootstrap"], [73, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [73, 2, 2, 0, "seed", "The seed value to pass to the closed generator"]], "arch.bootstrap.StationaryBootstrap.conf_int": [[74, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func and std_err_func, when appropriate"], [74, 2, 2, 0, "func", "Function the computes parameter values.  See Notes for requirements"], [74, 2, 2, 0, "method", "One of 'basic', 'percentile', 'studentized', 'norm' (identical to 'var', 'cov'), 'bc' (identical to 'debiased', 'bias-corrected'), or 'bca'"], [74, 2, 2, 0, "reps", "Number of bootstrap replications"], [74, 2, 2, 0, "reuse", "Flag indicating whether to reuse previously computed bootstrap results.  This allows alternative methods to be compared without rerunning the bootstrap simulation.  Reuse is ignored if reps is not the same across multiple runs, func changes across calls, or method is 'studentized'."], [74, 2, 2, 0, "sampling", "Type of sampling to use: 'nonparametric', 'semi-parametric' (or 'semi') or 'parametric'.  The default is 'nonparametric'.  See notes about the changes to func required when using 'semi' or 'parametric'."], [74, 2, 2, 0, "size", "Coverage of confidence interval"], [74, 2, 2, 0, "std_err_func", "Function to use when standardizing estimated parameters when using the studentized bootstrap.  Providing an analytical function eliminates the need for a nested bootstrap"], [74, 2, 2, 0, "studentize_reps", "Number of bootstraps to use in the inner bootstrap when using the studentized bootstrap.  Ignored when std_err_func is provided"], [74, 2, 2, 0, "tail", "One of 'two', 'upper' or 'lower'."]], "arch.bootstrap.StationaryBootstrap.cov": [[75, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [75, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [75, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."], [75, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.StationaryBootstrap.reset": [[80, 2, 2, 0, "use_seed", "Flag indicating whether to use the last seed if provided.  If False or if no seed has been set, the bootstrap will be reset to the initial state.  Default is True"]], "arch.bootstrap.StationaryBootstrap.seed": [[81, 2, 2, 0, "value", "Value to use as the seed."]], "arch.bootstrap.StationaryBootstrap.set_state": [[82, 2, 2, 0, "state", "Dictionary or tuple containing the state."]], "arch.bootstrap.StationaryBootstrap.var": [[85, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [85, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [85, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."], [85, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.StepM.__init__": [[240, 2, 2, 0, "benchmark", "T element array of benchmark model losses"], [240, 2, 2, 0, "block_size", "Length of window to use in the bootstrap.  If not provided, sqrt(T) is used.  In general, this should be provided and chosen to be appropriate for the data."], [240, 2, 2, 0, "bootstrap", "Bootstrap to use.  Options are 'stationary' or 'sb': Stationary bootstrap (Default) 'circular' or 'cbb': Circular block bootstrap 'moving block' or 'mbb': Moving block bootstrap"], [240, 2, 2, 0, "models", "T by k element array of alternative model losses"], [240, 2, 2, 0, "nested", "Flag indicating to use a nested bootstrap to compute variances for studentization.  Default is False.  Note that this can be slow since the procedure requires k extra bootstraps."], [240, 2, 2, 0, "reps", "Number of bootstrap replications to uses.  Default is 1000."], [240, 2, 2, 0, "seed", "Seed value to use when creating the bootstrap used in the comparison. If an integer or None, the NumPy default_rng is used with the seed value.  If a Generator or a RandomState, the argument is used."], [240, 2, 2, 0, "size", "Value in (0,1) to use as the test size when implementing the comparison. Default value is 0.05."], [240, 2, 2, 0, "studentize", "Flag indicating to studentize loss differentials. Default is True"]], "arch.bootstrap.StepM": [[254, 3, 1, 0, "compute", "Compute the set of superior models."], [255, 3, 1, 0, "reset", "Reset the bootstrap to it's initial state."], [256, 3, 1, 0, "seed", "Seed the bootstrap's random number generator"], [257, 5, 1, 0, "superior_models", "List of the indices or column names of the superior models"]], "arch.bootstrap.StepM.seed": [[256, 2, 2, 0, "value", "Integer to use as the seed"]], "arch.bootstrap.optimal_block_length": [[10, 2, 2, 0, "x", "A one-dimensional or two-dimensional array-like.  Operates columns by column if 2-dimensional."]], "arch.covariance": [[98, 0, 0, 1, "kernel", ""]], "arch.covariance.kernel": [[99, 1, 1, 0, "Andrews", "Alternative name of the QuadraticSpectral covariance estimator."], [100, 1, 1, 0, "Bartlett", "Bartlett's (Newey-West) kernel covariance estimation."], [101, 1, 1, 0, "CovarianceEstimate", "Covariance estimate using a long-run covariance estimator"], [102, 1, 1, 0, "Gallant", "Alternative name for Parzen covariance estimator."], [103, 1, 1, 0, "NeweyWest", "Alternative name for Bartlett covariance estimator."], [104, 1, 1, 0, "Parzen", "Parzen's kernel covariance estimation."], [105, 1, 1, 0, "ParzenCauchy", "Parzen's Cauchy kernel covariance estimation."], [106, 1, 1, 0, "ParzenGeometric", "Parzen's Geometric kernel covariance estimation."], [107, 1, 1, 0, "ParzenRiesz", "Parzen-Reisz kernel covariance estimation."], [108, 1, 1, 0, "QuadraticSpectral", "Quadratic-Spectral (Andrews') kernel covariance estimation."], [109, 1, 1, 0, "TukeyHamming", "Tukey-Hamming kernel covariance estimation."], [110, 1, 1, 0, "TukeyHanning", "Tukey-Hanning kernel covariance estimation."], [111, 1, 1, 0, "TukeyParzen", "Tukey-Parzen kernel covariance estimation."]], "arch.covariance.kernel.Andrews": [[112, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [113, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [114, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [115, 5, 1, 0, "cov", "The estimated covariances."], [116, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [117, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [118, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [119, 5, 1, 0, "name", "The covariance estimator's name."], [120, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [121, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.Bartlett.__init__": [[100, 2, 2, 0, "bandwidth", "The kernel's bandwidth.  If None, optimal bandwidth is estimated."], [100, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [100, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."], [100, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [100, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."], [100, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.Bartlett": [[122, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [123, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [124, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [125, 5, 1, 0, "cov", "The estimated covariances."], [126, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [127, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [128, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [129, 5, 1, 0, "name", "The covariance estimator's name."], [130, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [131, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.CovarianceEstimate.__init__": [[101, 2, 2, 0, "columns", "Column labels to use if covariance estimates are returned as DataFrames."], [101, 2, 2, 0, "long_run", "The long-run covariance estimate. If not provided, computed from short_run and one_sided_strict."], [101, 2, 2, 0, "one_sided_strict", "The one-sided-strict covariance estimate. If not provided, computed from short_run and one_sided_strict."], [101, 2, 2, 0, "short_run", "The short-run covariance estimate."]], "arch.covariance.kernel.CovarianceEstimate": [[132, 5, 1, 0, "long_run", "The long-run covariance estimate."], [133, 5, 1, 0, "one_sided", "The one-sided covariance estimate."], [134, 5, 1, 0, "one_sided_strict", "The one-sided strict covariance estimate."], [135, 5, 1, 0, "short_run", "The short-run covariance estimate."]], "arch.covariance.kernel.Gallant": [[136, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [137, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [138, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [139, 5, 1, 0, "cov", "The estimated covariances."], [140, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [141, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [142, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [143, 5, 1, 0, "name", "The covariance estimator's name."], [144, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [145, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.NeweyWest": [[146, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [147, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [148, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [149, 5, 1, 0, "cov", "The estimated covariances."], [150, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [151, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [152, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [153, 5, 1, 0, "name", "The covariance estimator's name."], [154, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [155, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.Parzen.__init__": [[104, 2, 2, 0, "bandwidth", "The kernel's bandwidth.  If None, optimal bandwidth is estimated."], [104, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [104, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."], [104, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [104, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."], [104, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.Parzen": [[156, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [157, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [158, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [159, 5, 1, 0, "cov", "The estimated covariances."], [160, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [161, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [162, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [163, 5, 1, 0, "name", "The covariance estimator's name."], [164, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [165, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.ParzenCauchy.__init__": [[105, 2, 2, 0, "bandwidth", "The kernel's bandwidth.  If None, optimal bandwidth is estimated."], [105, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [105, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."], [105, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [105, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."], [105, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.ParzenCauchy": [[166, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [167, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [168, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [169, 5, 1, 0, "cov", "The estimated covariances."], [170, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [171, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [172, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [173, 5, 1, 0, "name", "The covariance estimator's name."], [174, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [175, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.ParzenGeometric.__init__": [[106, 2, 2, 0, "bandwidth", "The kernel's bandwidth.  If None, optimal bandwidth is estimated."], [106, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [106, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."], [106, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [106, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."], [106, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.ParzenGeometric": [[176, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [177, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [178, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [179, 5, 1, 0, "cov", "The estimated covariances."], [180, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [181, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [182, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [183, 5, 1, 0, "name", "The covariance estimator's name."], [184, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [185, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.ParzenRiesz.__init__": [[107, 2, 2, 0, "bandwidth", "The kernel's bandwidth.  If None, optimal bandwidth is estimated."], [107, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [107, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."], [107, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [107, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."], [107, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.ParzenRiesz": [[186, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [187, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [188, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [189, 5, 1, 0, "cov", "The estimated covariances."], [190, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [191, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [192, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [193, 5, 1, 0, "name", "The covariance estimator's name."], [194, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [195, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.QuadraticSpectral.__init__": [[108, 2, 2, 0, "bandwidth", "The kernel's bandwidth.  If None, optimal bandwidth is estimated."], [108, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [108, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."], [108, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [108, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."], [108, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.QuadraticSpectral": [[196, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [197, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [198, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [199, 5, 1, 0, "cov", "The estimated covariances."], [200, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [201, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [202, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [203, 5, 1, 0, "name", "The covariance estimator's name."], [204, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [205, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.TukeyHamming.__init__": [[109, 2, 2, 0, "bandwidth", "The kernel's bandwidth.  If None, optimal bandwidth is estimated."], [109, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [109, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."], [109, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [109, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."], [109, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.TukeyHamming": [[206, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [207, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [208, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [209, 5, 1, 0, "cov", "The estimated covariances."], [210, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [211, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [212, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [213, 5, 1, 0, "name", "The covariance estimator's name."], [214, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [215, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.TukeyHanning.__init__": [[110, 2, 2, 0, "bandwidth", "The kernel's bandwidth.  If None, optimal bandwidth is estimated."], [110, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [110, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."], [110, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [110, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."], [110, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.TukeyHanning": [[216, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [217, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [218, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [219, 5, 1, 0, "cov", "The estimated covariances."], [220, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [221, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [222, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [223, 5, 1, 0, "name", "The covariance estimator's name."], [224, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [225, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.TukeyParzen.__init__": [[111, 2, 2, 0, "bandwidth", "The kernel's bandwidth.  If None, optimal bandwidth is estimated."], [111, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [111, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."], [111, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [111, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."], [111, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.TukeyParzen": [[226, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [227, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [228, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [229, 5, 1, 0, "cov", "The estimated covariances."], [230, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [231, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [232, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [233, 5, 1, 0, "name", "The covariance estimator's name."], [234, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [235, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.unitroot": [[262, 1, 1, 0, "ADF", "Augmented Dickey-Fuller unit root test"], [263, 1, 1, 0, "DFGLS", "Elliott, Rothenberg and Stock's (1) GLS detrended Dickey-Fuller"], [264, 1, 1, 0, "KPSS", "Kwiatkowski, Phillips, Schmidt and Shin (KPSS) stationarity test"], [265, 1, 1, 0, "PhillipsPerron", "Phillips-Perron unit root test"], [266, 1, 1, 0, "VarianceRatio", "Variance Ratio test of a random walk."], [267, 1, 1, 0, "ZivotAndrews", "Zivot-Andrews structural-break unit-root test"], [268, 6, 1, 0, "auto_bandwidth", "Automatic bandwidth selection of Andrews (1991) and Newey & West (1994)."], [261, 0, 0, 1, "cointegration", ""]], "arch.unitroot.ADF.__init__": [[262, 2, 2, 0, "lags", "The number of lags to use in the ADF regression.  If omitted or None, method is used to automatically select the lag length with no more than max_lags are included."], [262, 2, 2, 0, "low_memory", "Flag indicating whether to use a low memory implementation of the lag selection algorithm. The low memory algorithm is slower than the standard algorithm but will use 2-4% of the memory required for the standard algorithm. This options allows automatic lag selection to be used in very long time series. If None, use automatic selection of algorithm."], [262, 2, 2, 0, "max_lags", "The maximum number of lags to use when selecting lag length"], [262, 2, 2, 0, "method", "The method to use when selecting the lag length"], [262, 2, 2, 0, "trend", "The trend component to include in the test"], [262, 2, 2, 0, "y", "The data to test for a unit root"]], "arch.unitroot.ADF": [[278, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [279, 5, 1, 0, "critical_values", "Dictionary containing critical values specific to the test, number of observations and included deterministic trend terms."], [280, 5, 1, 0, "lags", "Sets or gets the number of lags used in the model. When bootstrap use DF-type regressions, lags is the number of lags in the regression model.  When bootstrap use long-run variance estimators, lags is the number of lags used in the long-run variance estimator."], [281, 5, 1, 0, "max_lags", "Sets or gets the maximum lags used when automatically selecting lag length"], [282, 5, 1, 0, "nobs", "The number of observations used when computing the test statistic. Accounts for loss of data due to lags for regression-based bootstrap."], [283, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [284, 5, 1, 0, "pvalue", "Returns the p-value for the test statistic"], [285, 5, 1, 0, "regression", "Returns the OLS regression results from the ADF model estimated"], [286, 5, 1, 0, "stat", "The test statistic for a unit root"], [287, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [288, 5, 1, 0, "trend", "Sets or gets the deterministic trend term used in the test. See valid_trends for a list of supported trends"], [289, 5, 1, 0, "valid_trends", "List of valid trend terms."], [290, 5, 1, 0, "y", "Returns the data used in the test statistic"]], "arch.unitroot.DFGLS.__init__": [[263, 2, 2, 0, "lags", "The number of lags to use in the ADF regression.  If omitted or None, method is used to automatically select the lag length with no more than max_lags are included."], [263, 2, 2, 0, "max_lags", "The maximum number of lags to use when selecting lag length. When using automatic lag length selection, the lag is selected using OLS detrending rather than GLS detrending (2)."], [263, 2, 2, 0, "method", "The method to use when selecting the lag length"], [263, 2, 2, 0, "trend", "The trend component to include in the test"], [263, 2, 2, 0, "y", "The data to test for a unit root"]], "arch.unitroot.DFGLS": [[291, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [292, 5, 1, 0, "critical_values", "Dictionary containing critical values specific to the test, number of observations and included deterministic trend terms."], [293, 5, 1, 0, "lags", "Sets or gets the number of lags used in the model. When bootstrap use DF-type regressions, lags is the number of lags in the regression model.  When bootstrap use long-run variance estimators, lags is the number of lags used in the long-run variance estimator."], [294, 5, 1, 0, "max_lags", "Sets or gets the maximum lags used when automatically selecting lag length"], [295, 5, 1, 0, "nobs", "The number of observations used when computing the test statistic. Accounts for loss of data due to lags for regression-based bootstrap."], [296, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [297, 5, 1, 0, "pvalue", "Returns the p-value for the test statistic"], [298, 5, 1, 0, "regression", "Returns the OLS regression results from the ADF model estimated"], [299, 5, 1, 0, "stat", "The test statistic for a unit root"], [300, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [301, 5, 1, 0, "trend", "Sets or gets the deterministic trend term used in the test. See valid_trends for a list of supported trends"], [302, 5, 1, 0, "valid_trends", "List of valid trend terms."], [303, 5, 1, 0, "y", "Returns the data used in the test statistic"]], "arch.unitroot.KPSS.__init__": [[264, 2, 2, 0, "lags", "The number of lags to use in the Newey-West estimator of the long-run covariance.  If omitted or None, the number of lags is calculated with the data-dependent method of Hobijn et al. (1998). See also Andrews (1991), Newey & West (1994), and Schwert (1989). Set lags=-1 to use the old method that only depends on the sample size, 12 * (nobs/100) ** (1/4)."], [264, 2, 2, 0, "trend", "\"c\" - Include a constant (Default) \"ct\" - Include a constant and linear time trend"], [264, 2, 2, 0, "y", "The data to test for stationarity"]], "arch.unitroot.KPSS": [[304, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [305, 5, 1, 0, "critical_values", "Dictionary containing critical values specific to the test, number of observations and included deterministic trend terms."], [306, 5, 1, 0, "lags", "Sets or gets the number of lags used in the model. When bootstrap use DF-type regressions, lags is the number of lags in the regression model.  When bootstrap use long-run variance estimators, lags is the number of lags used in the long-run variance estimator."], [307, 5, 1, 0, "nobs", "The number of observations used when computing the test statistic. Accounts for loss of data due to lags for regression-based bootstrap."], [308, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [309, 5, 1, 0, "pvalue", "Returns the p-value for the test statistic"], [310, 5, 1, 0, "stat", "The test statistic for a unit root"], [311, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [312, 5, 1, 0, "trend", "Sets or gets the deterministic trend term used in the test. See valid_trends for a list of supported trends"], [313, 5, 1, 0, "valid_trends", "List of valid trend terms."], [314, 5, 1, 0, "y", "Returns the data used in the test statistic"]], "arch.unitroot.PhillipsPerron.__init__": [[265, 2, 2, 0, "lags", "The number of lags to use in the Newey-West estimator of the long-run covariance.  If omitted or None, the lag length is set automatically to 12 * (nobs/100) ** (1/4)"], [265, 2, 2, 0, "test_type", "The test to use when computing the test statistic. \"tau\" is based on the t-stat and \"rho\" uses a test based on nobs times the re-centered regression coefficient"], [265, 2, 2, 0, "trend", "The trend component to include in the test"], [265, 2, 2, 0, "y", "The data to test for a unit root"]], "arch.unitroot.PhillipsPerron": [[315, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [316, 5, 1, 0, "critical_values", "Dictionary containing critical values specific to the test, number of observations and included deterministic trend terms."], [317, 5, 1, 0, "lags", "Sets or gets the number of lags used in the model. When bootstrap use DF-type regressions, lags is the number of lags in the regression model.  When bootstrap use long-run variance estimators, lags is the number of lags used in the long-run variance estimator."], [318, 5, 1, 0, "nobs", "The number of observations used when computing the test statistic. Accounts for loss of data due to lags for regression-based bootstrap."], [319, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [320, 5, 1, 0, "pvalue", "Returns the p-value for the test statistic"], [321, 5, 1, 0, "regression", "Returns OLS regression results for the specification used in the test"], [322, 5, 1, 0, "stat", "The test statistic for a unit root"], [323, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [324, 5, 1, 0, "test_type", "Gets or sets the test type returned by stat. Valid values are \"tau\" or \"rho\""], [325, 5, 1, 0, "trend", "Sets or gets the deterministic trend term used in the test. See valid_trends for a list of supported trends"], [326, 5, 1, 0, "valid_trends", "List of valid trend terms."], [327, 5, 1, 0, "y", "Returns the data used in the test statistic"]], "arch.unitroot.VarianceRatio.__init__": [[266, 2, 2, 0, "debiased", "Indicates whether to use a debiased version of the test. Default is True. Only applicable if overlap is True."], [266, 2, 2, 0, "lags", "The number of periods to used in the multi-period variance, which is the numerator of the test statistic.  Must be at least 2"], [266, 2, 2, 0, "overlap", "Indicates whether to use all overlapping blocks.  Default is True.  If False, the number of observations in y minus 1 must be an exact multiple of lags.  If this condition is not satisfied, some values at the end of y will be discarded."], [266, 2, 2, 0, "robust", "Indicates whether to use heteroskedasticity robust inference. Default is True."], [266, 2, 2, 0, "trend", "\"c\" allows for a non-zero drift in the random walk, while \"n\" requires that the increments to y are mean 0"], [266, 2, 2, 0, "y", "The data to test for a random walk"]], "arch.unitroot.VarianceRatio": [[328, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [329, 5, 1, 0, "critical_values", "Dictionary containing critical values specific to the test, number of observations and included deterministic trend terms."], [330, 5, 1, 0, "debiased", "Sets of gets the indicator to use debiased variances in the ratio"], [331, 5, 1, 0, "lags", "Sets or gets the number of lags used in the model. When bootstrap use DF-type regressions, lags is the number of lags in the regression model.  When bootstrap use long-run variance estimators, lags is the number of lags used in the long-run variance estimator."], [332, 5, 1, 0, "nobs", "The number of observations used when computing the test statistic. Accounts for loss of data due to lags for regression-based bootstrap."], [333, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [334, 5, 1, 0, "overlap", "Sets of gets the indicator to use overlapping returns in the long-period variance estimator"], [335, 5, 1, 0, "pvalue", "Returns the p-value for the test statistic"], [336, 5, 1, 0, "robust", "Sets of gets the indicator to use a heteroskedasticity robust variance estimator"], [337, 5, 1, 0, "stat", "The test statistic for a unit root"], [338, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [339, 5, 1, 0, "trend", "Sets or gets the deterministic trend term used in the test. See valid_trends for a list of supported trends"], [340, 5, 1, 0, "valid_trends", "List of valid trend terms."], [341, 5, 1, 0, "vr", "The ratio of the long block lags-period variance to the 1-period variance"], [342, 5, 1, 0, "y", "Returns the data used in the test statistic"]], "arch.unitroot.ZivotAndrews.__init__": [[267, 2, 2, 0, "lags", "The number of lags to use in the ADF regression.  If omitted or None, method is used to automatically select the lag length with no more than max_lags are included."], [267, 2, 2, 0, "max_lags", "The maximum number of lags to use when selecting lag length"], [267, 2, 2, 0, "method", "The method to use when selecting the lag length"], [267, 2, 2, 0, "trend", "The trend component to include in the test"], [267, 2, 2, 0, "trim", "percentage of series at begin/end to exclude from break-period calculation in range [0, 0.333] (default=0.15)"], [267, 2, 2, 0, "y", "data series"]], "arch.unitroot.ZivotAndrews": [[343, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [344, 5, 1, 0, "critical_values", "Dictionary containing critical values specific to the test, number of observations and included deterministic trend terms."], [345, 5, 1, 0, "lags", "Sets or gets the number of lags used in the model. When bootstrap use DF-type regressions, lags is the number of lags in the regression model.  When bootstrap use long-run variance estimators, lags is the number of lags used in the long-run variance estimator."], [346, 5, 1, 0, "nobs", "The number of observations used when computing the test statistic. Accounts for loss of data due to lags for regression-based bootstrap."], [347, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [348, 5, 1, 0, "pvalue", "Returns the p-value for the test statistic"], [349, 5, 1, 0, "stat", "The test statistic for a unit root"], [350, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [351, 5, 1, 0, "trend", "Sets or gets the deterministic trend term used in the test. See valid_trends for a list of supported trends"], [352, 5, 1, 0, "valid_trends", "List of valid trend terms."], [353, 5, 1, 0, "y", "Returns the data used in the test statistic"]], "arch.unitroot.auto_bandwidth": [[268, 2, 2, 0, "kernel", "The kernel function to use for selecting the bandwidth"], [268, 2, 2, 0, "y", "Data on which to apply the bandwidth selection"]], "arch.unitroot.cointegration": [[269, 1, 1, 0, "CanonicalCointegratingReg", "Canonical Cointegrating Regression cointegrating vector estimation."], [270, 1, 1, 0, "CointegrationAnalysisResults", "summary()"], [271, 1, 1, 0, "DynamicOLS", "Dynamic OLS (DOLS) cointegrating vector estimation"], [272, 1, 1, 0, "DynamicOLSResults", "Estimation results for Dynamic OLS models"], [273, 1, 1, 0, "EngleGrangerTestResults", "Results class for Engle-Granger cointegration tests."], [274, 1, 1, 0, "FullyModifiedOLS", "Fully Modified OLS cointegrating vector estimation."], [275, 1, 1, 0, "PhillipsOuliarisTestResults", "plot([axes, title])"], [276, 6, 1, 0, "engle_granger", "Test for cointegration within a set of time series."], [277, 6, 1, 0, "phillips_ouliaris", "Test for cointegration within a set of time series."]], "arch.unitroot.cointegration.CanonicalCointegratingReg.__init__": [[269, 2, 2, 0, "trend", "Trend to include in the cointegrating regression. Trends are:"], [269, 2, 2, 0, "x", "The right-hand-side variables in the cointegrating regression."], [269, 2, 2, 0, "x_trend", "Trends that affects affect the x-data but do not appear in the cointegrating regression. x_trend must be at least as large as trend, so that if trend is \"ct\", x_trend must be either \"ct\" or \"ctt\"."], [269, 2, 2, 0, "y", "The left-hand-side variable in the cointegrating regression."]], "arch.unitroot.cointegration.CanonicalCointegratingReg": [[354, 3, 1, 0, "fit", "Estimate the cointegrating vector."]], "arch.unitroot.cointegration.CanonicalCointegratingReg.fit": [[354, 2, 2, 0, "bandwidth", "The bandwidth to use. If not provided, the optimal bandwidth is estimated from the data. Setting the bandwidth to 0 and using \"unadjusted\" produces the classic OLS covariance estimator. Setting the bandwidth to 0 and using \"robust\" produces White's covariance estimator."], [354, 2, 2, 0, "df_adjust", "Whether the adjust the parameter covariance to account for the number of parameters estimated in the regression. If true, the parameter covariance estimator is multiplied by T/(T-k) where k is the number of regressors in the model."], [354, 2, 2, 0, "diff", "Use differenced data to estimate the residuals."], [354, 2, 2, 0, "force_int", "Whether the force the estimated optimal bandwidth to be an integer."], [354, 2, 2, 0, "kernel", "The string name of any of any known kernel-based long-run covariance estimators. Common choices are \"bartlett\" for the Bartlett kernel (Newey-West), \"parzen\" for the Parzen kernel and \"quadratic-spectral\" for the Quadratic Spectral kernel."]], "arch.unitroot.cointegration.CointegrationAnalysisResults": [[355, 5, 1, 0, "bandwidth", "The bandwidth used in the parameter covariance estimation"], [356, 5, 1, 0, "cov", "The estimated parameter covariance of the cointegrating vector"], [357, 5, 1, 0, "kernel", "The kernel used to estimate the covariance"], [358, 5, 1, 0, "long_run_variance", "Long-run variance estimate used in the parameter covariance estimator"], [359, 5, 1, 0, "params", "The estimated parameters of the cointegrating vector"], [360, 5, 1, 0, "pvalues", "P-value of the parameters in the cointegrating vector"], [361, 5, 1, 0, "resid", "The model residuals"], [362, 5, 1, 0, "residual_variance", "The variance of the regression residual."], [363, 5, 1, 0, "rsquared", "The model R\u00b2"], [364, 5, 1, 0, "rsquared_adj", "The degree-of-freedom adjusted R\u00b2"], [365, 5, 1, 0, "std_errors", "Standard errors  of the parameters in the cointegrating vector"], [366, 3, 1, 0, "summary", "Summary of the model, containing estimated parameters and std. errors"], [367, 5, 1, 0, "tvalues", "T-statistics of the parameters in the cointegrating vector"]], "arch.unitroot.cointegration.DynamicOLS.__init__": [[271, 2, 2, 0, "common", "Flag indicating that lags and leads should be restricted to the same value. When common is None, lags must equal leads and max_lag must equal max_lead."], [271, 2, 2, 0, "lags", "The number of lags to include in the model.  If None, the optimal number of lags is chosen using method."], [271, 2, 2, 0, "leads", "The number of leads to include in the model.  If None, the optimal number of leads is chosen using method."], [271, 2, 2, 0, "max_lag", "The maximum lag to consider. See Notes for value used when None."], [271, 2, 2, 0, "max_lead", "The maximum lead to consider. See Notes for value used when None."], [271, 2, 2, 0, "method", "The method used to select lag length when lags or leads is None."], [271, 2, 2, 0, "trend", "Trend to include in the cointegrating regression. Trends are:"], [271, 2, 2, 0, "x", "The right-hand-side variables in the cointegrating regression."], [271, 2, 2, 0, "y", "The left-hand-side variable in the cointegrating regression."]], "arch.unitroot.cointegration.DynamicOLS": [[368, 3, 1, 0, "fit", "Estimate the Dynamic OLS regression"]], "arch.unitroot.cointegration.DynamicOLS.fit": [[368, 2, 2, 0, "bandwidth", "The bandwidth to use. If not provided, the optimal bandwidth is estimated from the data. Setting the bandwidth to 0 and using \"unadjusted\" produces the classic OLS covariance estimator. Setting the bandwidth to 0 and using \"robust\" produces White's covariance estimator."], [368, 2, 2, 0, "cov_type", "Either \"unadjusted\" (or is equivalent \"homoskedastic\") or \"robust\" (or its equivalent \"kernel\")."], [368, 2, 2, 0, "df_adjust", "Whether the adjust the parameter covariance to account for the number of parameters estimated in the regression. If true, the parameter covariance estimator is multiplied by T/(T-k) where k is the number of regressors in the model."], [368, 2, 2, 0, "force_int", "Whether the force the estimated optimal bandwidth to be an integer."], [368, 2, 2, 0, "kernel", "The string name of any of any known kernel-based long-run covariance estimators. Common choices are \"bartlett\" for the Bartlett kernel (Newey-West), \"parzen\" for the Parzen kernel and \"quadratic-spectral\" for the Quadratic Spectral kernel."]], "arch.unitroot.cointegration.DynamicOLSResults.__init__": [[272, 2, 2, 0, "cov", "The estimated parameter covariance."], [272, 2, 2, 0, "cov_type", "The type of the parameter covariance estimator used."], [272, 2, 2, 0, "df_adjust", "Whether to degree of freedom adjust the estimator."], [272, 2, 2, 0, "kernel_est", "The covariance estimator instance used to estimate the parameter covariance."], [272, 2, 2, 0, "lags", "The number of lags included in the model."], [272, 2, 2, 0, "leads", "The number of leads included in the model."], [272, 2, 2, 0, "params", "The estimated model parameters."], [272, 2, 2, 0, "reg_results", "Regression results from fitting statsmodels OLS."], [272, 2, 2, 0, "resid", "The model residuals."]], "arch.unitroot.cointegration.DynamicOLSResults": [[369, 5, 1, 0, "bandwidth", "The bandwidth used in the parameter covariance estimation"], [370, 5, 1, 0, "cov", "The estimated parameter covariance of the cointegrating vector"], [371, 5, 1, 0, "cov_type", "The type of parameter covariance estimator used"], [372, 5, 1, 0, "full_cov", "Parameter covariance of the all model parameters, incl. leads and lags"], [373, 5, 1, 0, "full_params", "The complete set of parameters, including leads and lags"], [374, 5, 1, 0, "kernel", "The kernel used to estimate the covariance"], [375, 5, 1, 0, "lags", "The number of lags included in the model"], [376, 5, 1, 0, "leads", "The number of leads included in the model"], [377, 5, 1, 0, "long_run_variance", "The long-run variance of the regression residual."], [378, 5, 1, 0, "params", "The estimated parameters of the cointegrating vector"], [379, 5, 1, 0, "pvalues", "P-value of the parameters in the cointegrating vector"], [380, 5, 1, 0, "resid", "The model residuals"], [381, 5, 1, 0, "residual_variance", "The variance of the regression residual."], [382, 5, 1, 0, "rsquared", "The model R\u00b2"], [383, 5, 1, 0, "rsquared_adj", "The degree-of-freedom adjusted R\u00b2"], [384, 5, 1, 0, "std_errors", "Standard errors  of the parameters in the cointegrating vector"], [385, 3, 1, 0, "summary", "Summary of the model, containing estimated parameters and std. errors"], [386, 5, 1, 0, "tvalues", "T-statistics of the parameters in the cointegrating vector"]], "arch.unitroot.cointegration.DynamicOLSResults.summary": [[385, 2, 2, 0, "full", "Flag indicating whether to include all estimated parameters (True) or only the parameters of the cointegrating vector"]], "arch.unitroot.cointegration.EngleGrangerTestResults.__init__": [[273, 2, 2, 0, "adf", "The ADF instance used to perform the test and lag selection."], [273, 2, 2, 0, "alternative", "The alternative hypothesis."], [273, 2, 2, 0, "crit_vals", "The critical values of the Engle-Granger specific to the sample size and model dimension."], [273, 2, 2, 0, "null", "The null hypothesis."], [273, 2, 2, 0, "order", "The number of stochastic trends in the null distribution."], [273, 2, 2, 0, "pvalue", "The pvalue of the Engle-Granger test statistic."], [273, 2, 2, 0, "stat", "The Engle-Granger test statistic."], [273, 2, 2, 0, "trend", "The model's trend description."], [273, 2, 2, 0, "xsection", "The OLS results used in the cross-sectional regression."]], "arch.unitroot.cointegration.EngleGrangerTestResults": [[387, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [388, 5, 1, 0, "cointegrating_vector", "The estimated cointegrating vector."], [389, 5, 1, 0, "critical_values", "Critical Values"], [390, 5, 1, 0, "distribution_order", "The number of stochastic trends under the null hypothesis."], [391, 5, 1, 0, "lags", "The number of lags used in the Augmented Dickey-Fuller regression."], [392, 5, 1, 0, "max_lags", "The maximum number of lags used in the lag-length selection."], [393, 5, 1, 0, "name", "Sets or gets the name of the cointegration test"], [394, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [395, 3, 1, 0, "plot", "Plot the cointegration residuals."], [396, 5, 1, 0, "pvalue", "The p-value of the test statistic."], [397, 5, 1, 0, "resid", "The residual from the cointegrating regression."], [398, 5, 1, 0, "rho", "The estimated coefficient in the Dickey-Fuller Test"], [399, 5, 1, 0, "stat", "The test statistic."], [400, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [401, 5, 1, 0, "trend", "The trend used in the cointegrating regression"]], "arch.unitroot.cointegration.EngleGrangerTestResults.plot": [[395, 2, 2, 0, "axes", "matplotlib axes instance to hold the figure."], [395, 2, 2, 0, "title", "Title for the figure."]], "arch.unitroot.cointegration.FullyModifiedOLS.__init__": [[274, 2, 2, 0, "trend", "Trend to include in the cointegrating regression. Trends are:"], [274, 2, 2, 0, "x", "The right-hand-side variables in the cointegrating regression."], [274, 2, 2, 0, "x_trend", "Trends that affects affect the x-data but do not appear in the cointegrating regression. x_trend must be at least as large as trend, so that if trend is \"ct\", x_trend must be either \"ct\" or \"ctt\"."], [274, 2, 2, 0, "y", "The left-hand-side variable in the cointegrating regression."]], "arch.unitroot.cointegration.FullyModifiedOLS": [[402, 3, 1, 0, "fit", "Estimate the cointegrating vector."]], "arch.unitroot.cointegration.FullyModifiedOLS.fit": [[402, 2, 2, 0, "bandwidth", "The bandwidth to use. If not provided, the optimal bandwidth is estimated from the data. Setting the bandwidth to 0 and using \"unadjusted\" produces the classic OLS covariance estimator. Setting the bandwidth to 0 and using \"robust\" produces White's covariance estimator."], [402, 2, 2, 0, "df_adjust", "Whether the adjust the parameter covariance to account for the number of parameters estimated in the regression. If true, the parameter covariance estimator is multiplied by T/(T-k) where k is the number of regressors in the model."], [402, 2, 2, 0, "diff", "Use differenced data to estimate the residuals."], [402, 2, 2, 0, "force_int", "Whether the force the estimated optimal bandwidth to be an integer."], [402, 2, 2, 0, "kernel", "The string name of any of any known kernel-based long-run covariance estimators. Common choices are \"bartlett\" for the Bartlett kernel (Newey-West), \"parzen\" for the Parzen kernel and \"quadratic-spectral\" for the Quadratic Spectral kernel."]], "arch.unitroot.cointegration.PhillipsOuliarisTestResults": [[403, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [404, 5, 1, 0, "bandwidth", "Bandwidth used by the long-run covariance estimator"], [405, 5, 1, 0, "cointegrating_vector", "The estimated cointegrating vector."], [406, 5, 1, 0, "critical_values", "Critical Values"], [407, 5, 1, 0, "distribution_order", "The number of stochastic trends under the null hypothesis."], [408, 5, 1, 0, "kernel", "Name of the long-run covariance estimator"], [409, 5, 1, 0, "name", "Sets or gets the name of the cointegration test"], [410, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [411, 3, 1, 0, "plot", "Plot the cointegration residuals."], [412, 5, 1, 0, "pvalue", "The p-value of the test statistic."], [413, 5, 1, 0, "resid", "The residual from the cointegrating regression."], [414, 5, 1, 0, "stat", "The test statistic."], [415, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [416, 5, 1, 0, "trend", "The trend used in the cointegrating regression"]], "arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot": [[411, 2, 2, 0, "axes", "matplotlib axes instance to hold the figure."], [411, 2, 2, 0, "title", "Title for the figure."]], "arch.unitroot.cointegration.engle_granger": [[276, 2, 2, 0, "lags", "The number of lagged differences to include in the Augmented Dickey-Fuller test used on the residuals of the"], [276, 2, 2, 0, "max_lags", "The maximum number of lags to consider when using automatic lag-length in the Augmented Dickey-Fuller regression."], [276, 2, 2, 0, "method", "The method used to select the number of lags included in the Augmented Dickey-Fuller regression."], [276, 2, 2, 0, "trend", "Trend to include in the cointegrating regression. Trends are:"], [276, 2, 2, 0, "x", "The right-hand-side variables in the cointegrating regression."], [276, 2, 2, 0, "y", "The left-hand-side variable in the cointegrating regression."]], "arch.unitroot.cointegration.phillips_ouliaris": [[277, 2, 2, 0, "bandwidth", "The bandwidth to use. If not provided, the optimal bandwidth is estimated from the data. Setting the bandwidth to 0 and using \"unadjusted\" produces the classic OLS covariance estimator. Setting the bandwidth to 0 and using \"robust\" produces White's covariance estimator."], [277, 2, 2, 0, "force_int", "Whether the force the estimated optimal bandwidth to be an integer."], [277, 2, 2, 0, "kernel", "The string name of any of any known kernel-based long-run covariance estimators. Common choices are \"bartlett\" for the Bartlett kernel (Newey-West), \"parzen\" for the Parzen kernel and \"quadratic-spectral\" for the Quadratic Spectral kernel."], [277, 2, 2, 0, "test_type", "The test statistic to compute. Supported options are:"], [277, 2, 2, 0, "trend", "Trend to include in the cointegrating regression. Trends are:"], [277, 2, 2, 0, "x", "The right-hand-side variables in the cointegrating regression."], [277, 2, 2, 0, "y", "The left-hand-side variable in the cointegrating regression."]], "arch.univariate": [[425, 1, 1, 0, "APARCH", "Asymmetric Power ARCH (APARCH) volatility process"], [426, 1, 1, 0, "ARCH", "ARCH process"], [427, 1, 1, 0, "ARCHInMean", "(G)ARCH-in-mean model and simulation"], [428, 1, 1, 0, "ARX", "Autoregressive model with optional exogenous regressors estimation and simulation"], [429, 1, 1, 0, "ConstantMean", "Constant mean model estimation and simulation."], [430, 1, 1, 0, "ConstantVariance", "Constant volatility process"], [431, 1, 1, 0, "EGARCH", "EGARCH model estimation"], [432, 1, 1, 0, "EWMAVariance", "Exponentially Weighted Moving-Average (RiskMetrics) Variance process"], [433, 1, 1, 0, "FIGARCH", "FIGARCH model"], [434, 1, 1, 0, "FixedVariance", "Fixed volatility process"], [435, 1, 1, 0, "GARCH", "GARCH and related model estimation"], [436, 1, 1, 0, "GeneralizedError", "Generalized Error distribution for use with ARCH models"], [437, 1, 1, 0, "HARCH", "Heterogeneous ARCH process"], [438, 1, 1, 0, "HARX", "Heterogeneous Autoregression (HAR), with optional exogenous regressors, model estimation and simulation"], [439, 1, 1, 0, "LS", "Least squares model estimation and simulation"], [440, 1, 1, 0, "MIDASHyperbolic", "MIDAS Hyperbolic ARCH process"], [441, 1, 1, 0, "Normal", "Standard normal distribution for use with ARCH models"], [442, 1, 1, 0, "RiskMetrics2006", "RiskMetrics 2006 Variance process"], [443, 1, 1, 0, "SkewStudent", "Standardized Skewed Student's distribution for use with ARCH models"], [444, 1, 1, 0, "StudentsT", "Standardized Student's distribution for use with ARCH models"], [445, 1, 1, 0, "ZeroMean", "Model with zero conditional mean estimation and simulation"], [891, 6, 1, 0, "arch_model", "Initialization of common ARCH model specifications"]], "arch.univariate.APARCH.__init__": [[425, 2, 2, 0, "common_asym", "Restrict all asymmetry terms to share the same asymmetry parameter. If False (default), then there are no restrictions on the o asymmetry parameters."], [425, 2, 2, 0, "delta", "Value to use for a fixed delta in the APARCH model. If not provided, the value of delta is jointly estimated with other model parameters. User provided delta is restricted to lie in (0.05, 4.0)."], [425, 2, 2, 0, "o", "Order of the asymmetric innovation. Must satisfy o<=p."], [425, 2, 2, 0, "p", "Order of the symmetric innovation. Must satisfy p>=o."], [425, 2, 2, 0, "q", "Order of the lagged (transformed) conditional variance"]], "arch.univariate.APARCH": [[454, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [455, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [456, 3, 1, 0, "bounds", "Returns bounds for parameters"], [457, 5, 1, 0, "common_asym", "The value of delta in the model. NaN is delta is estimated."], [458, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [459, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [460, 5, 1, 0, "delta", "The value of delta in the model. NaN is delta is estimated."], [461, 3, 1, 0, "forecast", "Forecast volatility from the model"], [462, 5, 1, 0, "name", "The name of the volatility process"], [463, 5, 1, 0, "num_params", "The number of parameters in the model"], [464, 3, 1, 0, "parameter_names", "Names of model parameters"], [465, 3, 1, 0, "simulate", "Simulate data from the model"], [466, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [467, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [468, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [469, 3, 1, 0, "update", "Compute the variance for a single observation"], [470, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [471, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [472, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.APARCH.backcast": [[454, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.APARCH.backcast_transform": [[455, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.APARCH.bounds": [[456, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.APARCH.compute_variance": [[458, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [458, 2, 2, 0, "parameters", "Model parameters"], [458, 2, 2, 0, "resids", "Vector of mean zero residuals"], [458, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [458, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.APARCH.forecast": [[461, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [461, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [461, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [461, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [461, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [461, 2, 2, 0, "resids", "Residuals to use in the recursion"], [461, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [461, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [461, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [461, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.APARCH.simulate": [[465, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [465, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [465, 2, 2, 0, "nobs", "Number of data points to simulate"], [465, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [465, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.APARCH.starting_values": [[467, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.APARCH.update": [[469, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [469, 2, 2, 0, "index", "The numerical index of the variance to compute"], [469, 2, 2, 0, "parameters", "The variance model parameters"], [469, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [469, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [469, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.APARCH.variance_bounds": [[471, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [471, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.ARCH.__init__": [[426, 2, 2, 0, "p", "Order of the symmetric innovation"]], "arch.univariate.ARCH": [[473, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [474, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [475, 3, 1, 0, "bounds", "Returns bounds for parameters"], [476, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [477, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [478, 3, 1, 0, "forecast", "Forecast volatility from the model"], [479, 5, 1, 0, "name", "The name of the volatility process"], [480, 5, 1, 0, "num_params", "The number of parameters in the model"], [481, 3, 1, 0, "parameter_names", "Names of model parameters"], [482, 3, 1, 0, "simulate", "Simulate data from the model"], [483, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [484, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [485, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [486, 3, 1, 0, "update", "Compute the variance for a single observation"], [487, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [488, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [489, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.ARCH.backcast": [[473, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.ARCH.backcast_transform": [[474, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.ARCH.bounds": [[475, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.ARCH.compute_variance": [[476, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [476, 2, 2, 0, "parameters", "Model parameters"], [476, 2, 2, 0, "resids", "Vector of mean zero residuals"], [476, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [476, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.ARCH.forecast": [[478, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [478, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [478, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [478, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [478, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [478, 2, 2, 0, "resids", "Residuals to use in the recursion"], [478, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [478, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [478, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [478, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.ARCH.simulate": [[482, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [482, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [482, 2, 2, 0, "nobs", "Number of data points to simulate"], [482, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [482, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.ARCH.starting_values": [[484, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.ARCH.update": [[486, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [486, 2, 2, 0, "index", "The numerical index of the variance to compute"], [486, 2, 2, 0, "parameters", "The variance model parameters"], [486, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [486, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [486, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.ARCH.variance_bounds": [[488, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [488, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.ARCHInMean.__init__": [[427, 2, 2, 0, "constant", "Flag whether the model should include a constant"], [427, 2, 2, 0, "distribution", "Error distribution to use in the model"], [427, 2, 2, 0, "form", "The form of the conditional variance that appears in the mean equation. The string names use the log of the conditional variance (\"log\"), the square-root of the conditional variance (\"vol\") or the conditional variance. When specified using a float, interpreted as \\sigma_t^{form} so that 1 is equivalent to \"vol\" and 2 is equivalent to \"var\". When using a number, must be different from 0."], [427, 2, 2, 0, "hold_back", "Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."], [427, 2, 2, 0, "lags", "Description of lag structure of the HAR.  Scalar included all lags between 1 and the value.  A 1-d array includes the AR lags lags[0], lags[1], ..."], [427, 2, 2, 0, "rescale", "Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."], [427, 2, 2, 0, "volatility", "Volatility process to use in the model. volatility.updateable must return True."], [427, 2, 2, 0, "x", "nobs by k element array containing exogenous regressors"], [427, 2, 2, 0, "y", "nobs element vector containing the dependent variable"]], "arch.univariate.ARCHInMean": [[490, 3, 1, 0, "bounds", "Construct bounds for parameters to use in non-linear optimization"], [491, 3, 1, 0, "compute_param_cov", "Computes parameter covariances using numerical derivatives."], [492, 3, 1, 0, "constraints", "Construct linear constraint arrays  for use in non-linear optimization"], [493, 5, 1, 0, "distribution", "Set or gets the error distribution"], [494, 3, 1, 0, "fit", "Estimate model parameters"], [495, 3, 1, 0, "fix", "Allows an ARCHModelFixedResult to be constructed from fixed parameters."], [496, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [497, 5, 1, 0, "form", "The form of the conditional variance in the mean"], [498, 5, 1, 0, "name", "The name of the model."], [499, 5, 1, 0, "num_params", "Returns the number of parameters"], [500, 3, 1, 0, "parameter_names", "List of parameters names"], [501, 3, 1, 0, "resids", "Compute model residuals"], [502, 3, 1, 0, "simulate", "Simulates data from a linear regression, AR or HAR models"], [503, 3, 1, 0, "starting_values", "Returns starting values for the mean model, often the same as the values returned from fit"], [504, 5, 1, 0, "volatility", "Set or gets the volatility process"], [505, 5, 1, 0, "x", "Gets the value of the exogenous regressors in the model"], [506, 5, 1, 0, "y", "Returns the dependent variable"]], "arch.univariate.ARCHInMean.compute_param_cov": [[491, 2, 2, 0, "backcast", "Value to use for pre-sample observations"], [491, 2, 2, 0, "params", "Model parameters"], [491, 2, 2, 0, "robust", "Flag indicating whether to use robust standard errors (True) or classic MLE (False)"]], "arch.univariate.ARCHInMean.fit": [[494, 2, 2, 0, "backcast", "Value to use as backcast. Should be measure \\sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."], [494, 2, 2, 0, "cov_type", "Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."], [494, 2, 2, 0, "disp", "Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is \"off\" and True is \"final\""], [494, 2, 2, 0, "first_obs", "First observation to use when estimating model"], [494, 2, 2, 0, "last_obs", "Last observation to use when estimating model"], [494, 2, 2, 0, "options", "Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."], [494, 2, 2, 0, "show_warning", "Flag indicating whether convergence warnings should be shown."], [494, 2, 2, 0, "starting_values", "Array of starting values to use.  If not provided, starting values are constructed by the model components."], [494, 2, 2, 0, "tol", "Tolerance for termination."], [494, 2, 2, 0, "update_freq", "Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."]], "arch.univariate.ARCHInMean.fix": [[495, 2, 2, 0, "first_obs", "First observation to use when fixing model"], [495, 2, 2, 0, "last_obs", "Last observation to use when fixing model"], [495, 2, 2, 0, "params", "User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."]], "arch.univariate.ARCHInMean.forecast": [[496, 2, 2, 0, "align", "Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."], [496, 2, 2, 0, "horizon", "Number of steps to forecast"], [496, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [496, 2, 2, 0, "params", "Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."], [496, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [496, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."], [496, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [496, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [496, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."], [496, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"]], "arch.univariate.ARCHInMean.resids": [[501, 2, 2, 0, "params", "Model parameters"], [501, 2, 2, 0, "regressors", "Alternative regressor values to use when computing model residuals"], [501, 2, 2, 0, "y", "Alternative values to use when computing model residuals"]], "arch.univariate.ARCHInMean.simulate": [[502, 2, 2, 0, "burn", "Number of values to simulate to initialize the model and remove dependence on initial values."], [502, 2, 2, 0, "initial_value", "Either a scalar value or max(lags) array set of initial values to use when initializing the model.  If omitted, 0.0 is used."], [502, 2, 2, 0, "initial_value_vol", "An array or scalar to use when initializing the volatility process."], [502, 2, 2, 0, "nobs", "Length of series to simulate"], [502, 2, 2, 0, "params", "Parameters to use when simulating the model.  Parameter order is [mean volatility distribution] where the parameters of the mean model are ordered [constant lag[0] lag[1] ... lag[p] ex[0] ... ex[k-1]] where lag[j] indicates the coefficient on the jth lag in the model and ex[j] is the coefficient on the jth exogenous variable."], [502, 2, 2, 0, "x", "nobs + burn by k array of exogenous variables to include in the simulation."]], "arch.univariate.ARX.__init__": [[428, 2, 2, 0, "constant", "Flag whether the model should include a constant"], [428, 2, 2, 0, "distribution", "Error distribution to use in the model"], [428, 2, 2, 0, "hold_back", "Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."], [428, 2, 2, 0, "lags", "Description of lag structure of the HAR.  Scalar included all lags between 1 and the value.  A 1-d array includes the AR lags lags[0], lags[1], ..."], [428, 2, 2, 0, "rescale", "Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."], [428, 2, 2, 0, "volatility", "Volatility process to use in the model"], [428, 2, 2, 0, "x", "nobs by k element array containing exogenous regressors"], [428, 2, 2, 0, "y", "nobs element vector containing the dependent variable"]], "arch.univariate.ARX": [[507, 3, 1, 0, "bounds", "Construct bounds for parameters to use in non-linear optimization"], [508, 3, 1, 0, "compute_param_cov", "Computes parameter covariances using numerical derivatives."], [509, 3, 1, 0, "constraints", "Construct linear constraint arrays  for use in non-linear optimization"], [510, 5, 1, 0, "distribution", "Set or gets the error distribution"], [511, 3, 1, 0, "fit", "Estimate model parameters"], [512, 3, 1, 0, "fix", "Allows an ARCHModelFixedResult to be constructed from fixed parameters."], [513, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [514, 5, 1, 0, "name", "The name of the model."], [515, 5, 1, 0, "num_params", "Returns the number of parameters"], [516, 3, 1, 0, "parameter_names", "List of parameters names"], [517, 3, 1, 0, "resids", "Compute model residuals"], [518, 3, 1, 0, "simulate", "Simulates data from a linear regression, AR or HAR models"], [519, 3, 1, 0, "starting_values", "Returns starting values for the mean model, often the same as the values returned from fit"], [520, 5, 1, 0, "volatility", "Set or gets the volatility process"], [521, 5, 1, 0, "x", "Gets the value of the exogenous regressors in the model"], [522, 5, 1, 0, "y", "Returns the dependent variable"]], "arch.univariate.ARX.compute_param_cov": [[508, 2, 2, 0, "backcast", "Value to use for pre-sample observations"], [508, 2, 2, 0, "params", "Model parameters"], [508, 2, 2, 0, "robust", "Flag indicating whether to use robust standard errors (True) or classic MLE (False)"]], "arch.univariate.ARX.fit": [[511, 2, 2, 0, "backcast", "Value to use as backcast. Should be measure \\sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."], [511, 2, 2, 0, "cov_type", "Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."], [511, 2, 2, 0, "disp", "Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is \"off\" and True is \"final\""], [511, 2, 2, 0, "first_obs", "First observation to use when estimating model"], [511, 2, 2, 0, "last_obs", "Last observation to use when estimating model"], [511, 2, 2, 0, "options", "Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."], [511, 2, 2, 0, "show_warning", "Flag indicating whether convergence warnings should be shown."], [511, 2, 2, 0, "starting_values", "Array of starting values to use.  If not provided, starting values are constructed by the model components."], [511, 2, 2, 0, "tol", "Tolerance for termination."], [511, 2, 2, 0, "update_freq", "Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."]], "arch.univariate.ARX.fix": [[512, 2, 2, 0, "first_obs", "First observation to use when fixing model"], [512, 2, 2, 0, "last_obs", "Last observation to use when fixing model"], [512, 2, 2, 0, "params", "User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."]], "arch.univariate.ARX.forecast": [[513, 2, 2, 0, "align", "Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."], [513, 2, 2, 0, "horizon", "Number of steps to forecast"], [513, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [513, 2, 2, 0, "params", "Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."], [513, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [513, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."], [513, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [513, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [513, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."], [513, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"]], "arch.univariate.ARX.resids": [[517, 2, 2, 0, "params", "Model parameters"], [517, 2, 2, 0, "regressors", "Alternative regressor values to use when computing model residuals"], [517, 2, 2, 0, "y", "Alternative values to use when computing model residuals"]], "arch.univariate.ARX.simulate": [[518, 2, 2, 0, "burn", "Number of values to simulate to initialize the model and remove dependence on initial values."], [518, 2, 2, 0, "initial_value", "Either a scalar value or max(lags) array set of initial values to use when initializing the model.  If omitted, 0.0 is used."], [518, 2, 2, 0, "initial_value_vol", "An array or scalar to use when initializing the volatility process."], [518, 2, 2, 0, "nobs", "Length of series to simulate"], [518, 2, 2, 0, "params", "Parameters to use when simulating the model.  Parameter order is [mean volatility distribution] where the parameters of the mean model are ordered [constant lag[0] lag[1] ... lag[p] ex[0] ... ex[k-1]] where lag[j] indicates the coefficient on the jth lag in the model and ex[j] is the coefficient on the jth exogenous variable."], [518, 2, 2, 0, "x", "nobs + burn by k array of exogenous variables to include in the simulation."]], "arch.univariate.ConstantMean.__init__": [[429, 2, 2, 0, "distribution", "Error distribution to use in the model"], [429, 2, 2, 0, "hold_back", "Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."], [429, 2, 2, 0, "rescale", "Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."], [429, 2, 2, 0, "volatility", "Volatility process to use in the model"], [429, 2, 2, 0, "y", "nobs element vector containing the dependent variable"]], "arch.univariate.ConstantMean": [[523, 3, 1, 0, "bounds", "Construct bounds for parameters to use in non-linear optimization"], [524, 3, 1, 0, "compute_param_cov", "Computes parameter covariances using numerical derivatives."], [525, 3, 1, 0, "constraints", "Construct linear constraint arrays  for use in non-linear optimization"], [526, 5, 1, 0, "distribution", "Set or gets the error distribution"], [527, 3, 1, 0, "fit", "Estimate model parameters"], [528, 3, 1, 0, "fix", "Allows an ARCHModelFixedResult to be constructed from fixed parameters."], [529, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [530, 5, 1, 0, "name", "The name of the model."], [531, 5, 1, 0, "num_params", "Returns the number of parameters"], [532, 3, 1, 0, "parameter_names", "List of parameters names"], [533, 3, 1, 0, "resids", "Compute model residuals"], [534, 3, 1, 0, "simulate", "Simulated data from a constant mean model"], [535, 3, 1, 0, "starting_values", "Returns starting values for the mean model, often the same as the values returned from fit"], [536, 5, 1, 0, "volatility", "Set or gets the volatility process"], [537, 5, 1, 0, "x", "Gets the value of the exogenous regressors in the model"], [538, 5, 1, 0, "y", "Returns the dependent variable"]], "arch.univariate.ConstantMean.compute_param_cov": [[524, 2, 2, 0, "backcast", "Value to use for pre-sample observations"], [524, 2, 2, 0, "params", "Model parameters"], [524, 2, 2, 0, "robust", "Flag indicating whether to use robust standard errors (True) or classic MLE (False)"]], "arch.univariate.ConstantMean.fit": [[527, 2, 2, 0, "backcast", "Value to use as backcast. Should be measure \\sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."], [527, 2, 2, 0, "cov_type", "Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."], [527, 2, 2, 0, "disp", "Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is \"off\" and True is \"final\""], [527, 2, 2, 0, "first_obs", "First observation to use when estimating model"], [527, 2, 2, 0, "last_obs", "Last observation to use when estimating model"], [527, 2, 2, 0, "options", "Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."], [527, 2, 2, 0, "show_warning", "Flag indicating whether convergence warnings should be shown."], [527, 2, 2, 0, "starting_values", "Array of starting values to use.  If not provided, starting values are constructed by the model components."], [527, 2, 2, 0, "tol", "Tolerance for termination."], [527, 2, 2, 0, "update_freq", "Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."]], "arch.univariate.ConstantMean.fix": [[528, 2, 2, 0, "first_obs", "First observation to use when fixing model"], [528, 2, 2, 0, "last_obs", "Last observation to use when fixing model"], [528, 2, 2, 0, "params", "User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."]], "arch.univariate.ConstantMean.forecast": [[529, 2, 2, 0, "align", "Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."], [529, 2, 2, 0, "horizon", "Number of steps to forecast"], [529, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [529, 2, 2, 0, "params", "Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."], [529, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [529, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."], [529, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [529, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [529, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."], [529, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"]], "arch.univariate.ConstantMean.resids": [[533, 2, 2, 0, "params", "Model parameters"], [533, 2, 2, 0, "regressors", "Alternative regressor values to use when computing model residuals"], [533, 2, 2, 0, "y", "Alternative values to use when computing model residuals"]], "arch.univariate.ConstantMean.simulate": [[534, 2, 2, 0, "burn", "Number of values to simulate to initialize the model and remove dependence on initial values."], [534, 2, 2, 0, "initial_value", "This value is not used."], [534, 2, 2, 0, "initial_value_vol", "An array or scalar to use when initializing the volatility process."], [534, 2, 2, 0, "nobs", "Length of series to simulate"], [534, 2, 2, 0, "params", "Parameters to use when simulating the model.  Parameter order is [mean volatility distribution]. There is one parameter in the mean model, mu."], [534, 2, 2, 0, "x", "This value is not used."]], "arch.univariate.ConstantVariance": [[539, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [540, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [541, 3, 1, 0, "bounds", "Returns bounds for parameters"], [542, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [543, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [544, 3, 1, 0, "forecast", "Forecast volatility from the model"], [545, 5, 1, 0, "name", "The name of the volatility process"], [546, 5, 1, 0, "num_params", "The number of parameters in the model"], [547, 3, 1, 0, "parameter_names", "Names of model parameters"], [548, 3, 1, 0, "simulate", "Simulate data from the model"], [549, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [550, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [551, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [552, 3, 1, 0, "update", "Compute the variance for a single observation"], [553, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [554, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [555, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.ConstantVariance.backcast": [[539, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.ConstantVariance.backcast_transform": [[540, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.ConstantVariance.bounds": [[541, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.ConstantVariance.compute_variance": [[542, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [542, 2, 2, 0, "parameters", "Model parameters"], [542, 2, 2, 0, "resids", "Vector of mean zero residuals"], [542, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [542, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.ConstantVariance.forecast": [[544, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [544, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [544, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [544, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [544, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [544, 2, 2, 0, "resids", "Residuals to use in the recursion"], [544, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [544, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [544, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [544, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.ConstantVariance.simulate": [[548, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [548, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [548, 2, 2, 0, "nobs", "Number of data points to simulate"], [548, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [548, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.ConstantVariance.starting_values": [[550, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.ConstantVariance.update": [[552, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [552, 2, 2, 0, "index", "The numerical index of the variance to compute"], [552, 2, 2, 0, "parameters", "The variance model parameters"], [552, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [552, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [552, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.ConstantVariance.variance_bounds": [[554, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [554, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.EGARCH.__init__": [[431, 2, 2, 0, "o", "Order of the asymmetric innovation"], [431, 2, 2, 0, "p", "Order of the symmetric innovation"], [431, 2, 2, 0, "q", "Order of the lagged (transformed) conditional variance"]], "arch.univariate.EGARCH": [[556, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [557, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [558, 3, 1, 0, "bounds", "Returns bounds for parameters"], [559, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [560, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [561, 3, 1, 0, "forecast", "Forecast volatility from the model"], [562, 5, 1, 0, "name", "The name of the volatility process"], [563, 5, 1, 0, "num_params", "The number of parameters in the model"], [564, 3, 1, 0, "parameter_names", "Names of model parameters"], [565, 3, 1, 0, "simulate", "Simulate data from the model"], [566, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [567, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [568, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [569, 3, 1, 0, "update", "Compute the variance for a single observation"], [570, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [571, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [572, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.EGARCH.backcast": [[556, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.EGARCH.backcast_transform": [[557, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.EGARCH.bounds": [[558, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.EGARCH.compute_variance": [[559, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [559, 2, 2, 0, "parameters", "Model parameters"], [559, 2, 2, 0, "resids", "Vector of mean zero residuals"], [559, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [559, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.EGARCH.forecast": [[561, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [561, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [561, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [561, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [561, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [561, 2, 2, 0, "resids", "Residuals to use in the recursion"], [561, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [561, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [561, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [561, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.EGARCH.simulate": [[565, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [565, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [565, 2, 2, 0, "nobs", "Number of data points to simulate"], [565, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [565, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.EGARCH.starting_values": [[567, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.EGARCH.update": [[569, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [569, 2, 2, 0, "index", "The numerical index of the variance to compute"], [569, 2, 2, 0, "parameters", "The variance model parameters"], [569, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [569, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [569, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.EGARCH.variance_bounds": [[571, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [571, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.EWMAVariance.__init__": [[432, 2, 2, 0, "lam", "Smoothing parameter. Default is 0.94. Set to None to estimate lam jointly with other model parameters"]], "arch.univariate.EWMAVariance": [[573, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [574, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [575, 3, 1, 0, "bounds", "Returns bounds for parameters"], [576, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [577, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [578, 3, 1, 0, "forecast", "Forecast volatility from the model"], [579, 5, 1, 0, "name", "The name of the volatility process"], [580, 5, 1, 0, "num_params", "The number of parameters in the model"], [581, 3, 1, 0, "parameter_names", "Names of model parameters"], [582, 3, 1, 0, "simulate", "Simulate data from the model"], [583, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [584, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [585, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [586, 3, 1, 0, "update", "Compute the variance for a single observation"], [587, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [588, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [589, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.EWMAVariance.backcast": [[573, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.EWMAVariance.backcast_transform": [[574, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.EWMAVariance.bounds": [[575, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.EWMAVariance.compute_variance": [[576, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [576, 2, 2, 0, "parameters", "Model parameters"], [576, 2, 2, 0, "resids", "Vector of mean zero residuals"], [576, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [576, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.EWMAVariance.forecast": [[578, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [578, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [578, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [578, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [578, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [578, 2, 2, 0, "resids", "Residuals to use in the recursion"], [578, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [578, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [578, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [578, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.EWMAVariance.simulate": [[582, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [582, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [582, 2, 2, 0, "nobs", "Number of data points to simulate"], [582, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [582, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.EWMAVariance.starting_values": [[584, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.EWMAVariance.update": [[586, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [586, 2, 2, 0, "index", "The numerical index of the variance to compute"], [586, 2, 2, 0, "parameters", "The variance model parameters"], [586, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [586, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [586, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.EWMAVariance.variance_bounds": [[588, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [588, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.FIGARCH.__init__": [[433, 2, 2, 0, "p", "Order of the symmetric innovation"], [433, 2, 2, 0, "power", "Power to use with the innovations, abs(e) ** power.  Default is 2.0, which produces FIGARCH and related models. Using 1.0 produces FIAVARCH and related models.  Other powers can be specified, although these should be strictly positive, and usually larger than 0.25."], [433, 2, 2, 0, "q", "Order of the lagged (transformed) conditional variance"], [433, 2, 2, 0, "truncation", "Truncation point to use in ARCH(\\infty) representation. Default is 1000."]], "arch.univariate.FIGARCH": [[590, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [591, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [592, 3, 1, 0, "bounds", "Returns bounds for parameters"], [593, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [594, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [595, 3, 1, 0, "forecast", "Forecast volatility from the model"], [596, 5, 1, 0, "name", "The name of the volatility process"], [597, 5, 1, 0, "num_params", "The number of parameters in the model"], [598, 3, 1, 0, "parameter_names", "Names of model parameters"], [599, 3, 1, 0, "simulate", "Simulate data from the model"], [600, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [601, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [602, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [603, 5, 1, 0, "truncation", "Truncation lag for the ARCH-infinity approximation"], [604, 3, 1, 0, "update", "Compute the variance for a single observation"], [605, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [606, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [607, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.FIGARCH.backcast": [[590, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.FIGARCH.backcast_transform": [[591, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.FIGARCH.bounds": [[592, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.FIGARCH.compute_variance": [[593, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [593, 2, 2, 0, "parameters", "Model parameters"], [593, 2, 2, 0, "resids", "Vector of mean zero residuals"], [593, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [593, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.FIGARCH.forecast": [[595, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [595, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [595, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [595, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [595, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [595, 2, 2, 0, "resids", "Residuals to use in the recursion"], [595, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [595, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [595, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [595, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.FIGARCH.simulate": [[599, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [599, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [599, 2, 2, 0, "nobs", "Number of data points to simulate"], [599, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [599, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.FIGARCH.starting_values": [[601, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.FIGARCH.update": [[604, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [604, 2, 2, 0, "index", "The numerical index of the variance to compute"], [604, 2, 2, 0, "parameters", "The variance model parameters"], [604, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [604, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [604, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.FIGARCH.variance_bounds": [[606, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [606, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.FixedVariance.__init__": [[434, 2, 2, 0, "unit_scale", "Flag whether to enforce a unit scale.  If False, a scale parameter will be estimated so that the model variance will be proportional to variance. If True, the model variance is set of variance"], [434, 2, 2, 0, "variance", "Array containing the variances to use.  Should have the same shape as the data used in the model."]], "arch.univariate.FixedVariance": [[608, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [609, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [610, 3, 1, 0, "bounds", "Returns bounds for parameters"], [611, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [612, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [613, 3, 1, 0, "forecast", "Forecast volatility from the model"], [614, 5, 1, 0, "name", "The name of the volatility process"], [615, 5, 1, 0, "num_params", "The number of parameters in the model"], [616, 3, 1, 0, "parameter_names", "Names of model parameters"], [617, 3, 1, 0, "simulate", "Simulate data from the model"], [618, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [619, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [620, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [621, 3, 1, 0, "update", "Compute the variance for a single observation"], [622, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [623, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [624, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.FixedVariance.backcast": [[608, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.FixedVariance.backcast_transform": [[609, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.FixedVariance.bounds": [[610, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.FixedVariance.compute_variance": [[611, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [611, 2, 2, 0, "parameters", "Model parameters"], [611, 2, 2, 0, "resids", "Vector of mean zero residuals"], [611, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [611, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.FixedVariance.forecast": [[613, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [613, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [613, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [613, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [613, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [613, 2, 2, 0, "resids", "Residuals to use in the recursion"], [613, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [613, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [613, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [613, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.FixedVariance.simulate": [[617, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [617, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [617, 2, 2, 0, "nobs", "Number of data points to simulate"], [617, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [617, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.FixedVariance.starting_values": [[619, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.FixedVariance.update": [[621, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [621, 2, 2, 0, "index", "The numerical index of the variance to compute"], [621, 2, 2, 0, "parameters", "The variance model parameters"], [621, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [621, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [621, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.FixedVariance.variance_bounds": [[623, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [623, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.GARCH.__init__": [[435, 2, 2, 0, "o", "Order of the asymmetric innovation"], [435, 2, 2, 0, "p", "Order of the symmetric innovation"], [435, 2, 2, 0, "power", "Power to use with the innovations, abs(e) ** power.  Default is 2.0, which produces ARCH and related models. Using 1.0 produces AVARCH and related models.  Other powers can be specified, although these should be strictly positive, and usually larger than 0.25."], [435, 2, 2, 0, "q", "Order of the lagged (transformed) conditional variance"]], "arch.univariate.GARCH": [[625, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [626, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [627, 3, 1, 0, "bounds", "Returns bounds for parameters"], [628, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [629, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [630, 3, 1, 0, "forecast", "Forecast volatility from the model"], [631, 5, 1, 0, "name", "The name of the volatility process"], [632, 5, 1, 0, "num_params", "The number of parameters in the model"], [633, 3, 1, 0, "parameter_names", "Names of model parameters"], [634, 3, 1, 0, "simulate", "Simulate data from the model"], [635, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [636, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [637, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [638, 3, 1, 0, "update", "Compute the variance for a single observation"], [639, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [640, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [641, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.GARCH.backcast": [[625, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.GARCH.backcast_transform": [[626, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.GARCH.bounds": [[627, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.GARCH.compute_variance": [[628, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [628, 2, 2, 0, "parameters", "Model parameters"], [628, 2, 2, 0, "resids", "Vector of mean zero residuals"], [628, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [628, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.GARCH.forecast": [[630, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [630, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [630, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [630, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [630, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [630, 2, 2, 0, "resids", "Residuals to use in the recursion"], [630, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [630, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [630, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [630, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.GARCH.simulate": [[634, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [634, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [634, 2, 2, 0, "nobs", "Number of data points to simulate"], [634, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [634, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.GARCH.starting_values": [[636, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.GARCH.update": [[638, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [638, 2, 2, 0, "index", "The numerical index of the variance to compute"], [638, 2, 2, 0, "parameters", "The variance model parameters"], [638, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [638, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [638, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.GARCH.variance_bounds": [[640, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [640, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.GeneralizedError.__init__": [[436, 2, 2, 0, "random_state", "Deprecated since version 5.0: random_state is deprecated. Use seed instead."], [436, 2, 2, 0, "seed", "Random number generator instance or int to use. Set to ensure reproducibility. If using an int, the argument is passed to np.random.default_rng.  If not provided, default_rng is used with system-provided entropy."]], "arch.univariate.GeneralizedError": [[642, 3, 1, 0, "bounds", "Parameter bounds for use in optimization."], [643, 3, 1, 0, "cdf", "Cumulative distribution function"], [644, 3, 1, 0, "constraints", "Construct arrays to use in constrained optimization."], [645, 5, 1, 0, "generator", "The NumPy Generator or RandomState attached to the distribution"], [646, 3, 1, 0, "loglikelihood", "Computes the log-likelihood of assuming residuals are have a Generalized Error Distribution, conditional on the variance."], [647, 3, 1, 0, "moment", "Moment of order n"], [648, 5, 1, 0, "name", "The name of the distribution"], [649, 3, 1, 0, "parameter_names", "Names of distribution shape parameters"], [650, 3, 1, 0, "partial_moment", "Order n lower partial moment from -inf to z"], [651, 3, 1, 0, "ppf", "Inverse cumulative density function (ICDF)"], [652, 5, 1, 0, "random_state", "The NumPy RandomState attached to the distribution"], [653, 3, 1, 0, "simulate", "Simulates i.i.d. draws from the distribution"], [654, 3, 1, 0, "starting_values", "Construct starting values for use in optimization."]], "arch.univariate.GeneralizedError.bounds": [[642, 2, 2, 0, "resids", "Residuals to use when computing the bounds"]], "arch.univariate.GeneralizedError.cdf": [[643, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [643, 2, 2, 0, "resids", "Values at which to evaluate the cdf"]], "arch.univariate.GeneralizedError.loglikelihood": [[646, 2, 2, 0, "individual", "Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"], [646, 2, 2, 0, "parameters", "Shape parameter of the GED distribution"], [646, 2, 2, 0, "resids", "The residuals to use in the log-likelihood calculation"], [646, 2, 2, 0, "sigma2", "Conditional variances of resids"]], "arch.univariate.GeneralizedError.moment": [[647, 2, 2, 0, "n", "Order of moment"], [647, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."]], "arch.univariate.GeneralizedError.partial_moment": [[650, 2, 2, 0, "n", "Order of partial moment"], [650, 2, 2, 0, "parameters", "Distribution parameters.  Use None for parameterless distributions."], [650, 2, 2, 0, "z", "Upper bound for partial moment integral"]], "arch.univariate.GeneralizedError.ppf": [[651, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [651, 2, 2, 0, "pits", "Probability-integral-transformed values in the interval (0, 1)."]], "arch.univariate.GeneralizedError.simulate": [[653, 2, 2, 0, "parameters", "Distribution parameters"]], "arch.univariate.GeneralizedError.starting_values": [[654, 2, 2, 0, "std_resid", "Estimated standardized residuals to use in computing starting values for the shape parameter"]], "arch.univariate.HARCH.__init__": [[437, 2, 2, 0, "lags", "List of lags to include in the model, or if scalar, includes all lags up the value"]], "arch.univariate.HARCH": [[655, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [656, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [657, 3, 1, 0, "bounds", "Returns bounds for parameters"], [658, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [659, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [660, 3, 1, 0, "forecast", "Forecast volatility from the model"], [661, 5, 1, 0, "name", "The name of the volatility process"], [662, 5, 1, 0, "num_params", "The number of parameters in the model"], [663, 3, 1, 0, "parameter_names", "Names of model parameters"], [664, 3, 1, 0, "simulate", "Simulate data from the model"], [665, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [666, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [667, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [668, 3, 1, 0, "update", "Compute the variance for a single observation"], [669, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [670, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [671, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.HARCH.backcast": [[655, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.HARCH.backcast_transform": [[656, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.HARCH.bounds": [[657, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.HARCH.compute_variance": [[658, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [658, 2, 2, 0, "parameters", "Model parameters"], [658, 2, 2, 0, "resids", "Vector of mean zero residuals"], [658, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [658, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.HARCH.forecast": [[660, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [660, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [660, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [660, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [660, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [660, 2, 2, 0, "resids", "Residuals to use in the recursion"], [660, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [660, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [660, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [660, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.HARCH.simulate": [[664, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [664, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [664, 2, 2, 0, "nobs", "Number of data points to simulate"], [664, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [664, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.HARCH.starting_values": [[666, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.HARCH.update": [[668, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [668, 2, 2, 0, "index", "The numerical index of the variance to compute"], [668, 2, 2, 0, "parameters", "The variance model parameters"], [668, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [668, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [668, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.HARCH.variance_bounds": [[670, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [670, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.HARX.__init__": [[438, 2, 2, 0, "constant", "Flag whether the model should include a constant"], [438, 2, 2, 0, "distribution", "Error distribution to use in the model"], [438, 2, 2, 0, "hold_back", "Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."], [438, 2, 2, 0, "lags", "Description of lag structure of the HAR."], [438, 2, 2, 0, "rescale", "Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."], [438, 2, 2, 0, "use_rotated", "Flag indicating to use the alternative rotated form of the HAR where HAR lags do not overlap"], [438, 2, 2, 0, "volatility", "Volatility process to use in the model"], [438, 2, 2, 0, "x", "nobs by k element array containing exogenous regressors"], [438, 2, 2, 0, "y", "nobs element vector containing the dependent variable"]], "arch.univariate.HARX": [[672, 3, 1, 0, "bounds", "Construct bounds for parameters to use in non-linear optimization"], [673, 3, 1, 0, "compute_param_cov", "Computes parameter covariances using numerical derivatives."], [674, 3, 1, 0, "constraints", "Construct linear constraint arrays  for use in non-linear optimization"], [675, 5, 1, 0, "distribution", "Set or gets the error distribution"], [676, 3, 1, 0, "fit", "Estimate model parameters"], [677, 3, 1, 0, "fix", "Allows an ARCHModelFixedResult to be constructed from fixed parameters."], [678, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [679, 5, 1, 0, "name", "The name of the model."], [680, 5, 1, 0, "num_params", "Returns the number of parameters"], [681, 3, 1, 0, "parameter_names", "List of parameters names"], [682, 3, 1, 0, "resids", "Compute model residuals"], [683, 3, 1, 0, "simulate", "Simulates data from a linear regression, AR or HAR models"], [684, 3, 1, 0, "starting_values", "Returns starting values for the mean model, often the same as the values returned from fit"], [685, 5, 1, 0, "volatility", "Set or gets the volatility process"], [686, 5, 1, 0, "x", "Gets the value of the exogenous regressors in the model"], [687, 5, 1, 0, "y", "Returns the dependent variable"]], "arch.univariate.HARX.compute_param_cov": [[673, 2, 2, 0, "backcast", "Value to use for pre-sample observations"], [673, 2, 2, 0, "params", "Model parameters"], [673, 2, 2, 0, "robust", "Flag indicating whether to use robust standard errors (True) or classic MLE (False)"]], "arch.univariate.HARX.fit": [[676, 2, 2, 0, "backcast", "Value to use as backcast. Should be measure \\sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."], [676, 2, 2, 0, "cov_type", "Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."], [676, 2, 2, 0, "disp", "Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is \"off\" and True is \"final\""], [676, 2, 2, 0, "first_obs", "First observation to use when estimating model"], [676, 2, 2, 0, "last_obs", "Last observation to use when estimating model"], [676, 2, 2, 0, "options", "Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."], [676, 2, 2, 0, "show_warning", "Flag indicating whether convergence warnings should be shown."], [676, 2, 2, 0, "starting_values", "Array of starting values to use.  If not provided, starting values are constructed by the model components."], [676, 2, 2, 0, "tol", "Tolerance for termination."], [676, 2, 2, 0, "update_freq", "Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."]], "arch.univariate.HARX.fix": [[677, 2, 2, 0, "first_obs", "First observation to use when fixing model"], [677, 2, 2, 0, "last_obs", "Last observation to use when fixing model"], [677, 2, 2, 0, "params", "User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."]], "arch.univariate.HARX.forecast": [[678, 2, 2, 0, "align", "Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."], [678, 2, 2, 0, "horizon", "Number of steps to forecast"], [678, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [678, 2, 2, 0, "params", "Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."], [678, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [678, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."], [678, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [678, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [678, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."], [678, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"]], "arch.univariate.HARX.resids": [[682, 2, 2, 0, "params", "Model parameters"], [682, 2, 2, 0, "regressors", "Alternative regressor values to use when computing model residuals"], [682, 2, 2, 0, "y", "Alternative values to use when computing model residuals"]], "arch.univariate.HARX.simulate": [[683, 2, 2, 0, "burn", "Number of values to simulate to initialize the model and remove dependence on initial values."], [683, 2, 2, 0, "initial_value", "Either a scalar value or max(lags) array set of initial values to use when initializing the model.  If omitted, 0.0 is used."], [683, 2, 2, 0, "initial_value_vol", "An array or scalar to use when initializing the volatility process."], [683, 2, 2, 0, "nobs", "Length of series to simulate"], [683, 2, 2, 0, "params", "Parameters to use when simulating the model.  Parameter order is [mean volatility distribution] where the parameters of the mean model are ordered [constant lag[0] lag[1] ... lag[p] ex[0] ... ex[k-1]] where lag[j] indicates the coefficient on the jth lag in the model and ex[j] is the coefficient on the jth exogenous variable."], [683, 2, 2, 0, "x", "nobs + burn by k array of exogenous variables to include in the simulation."]], "arch.univariate.LS.__init__": [[439, 2, 2, 0, "constant", "Flag whether the model should include a constant"], [439, 2, 2, 0, "distribution", "Error distribution to use in the model"], [439, 2, 2, 0, "hold_back", "Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."], [439, 2, 2, 0, "rescale", "Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."], [439, 2, 2, 0, "volatility", "Volatility process to use in the model"], [439, 2, 2, 0, "y", "nobs by k element array containing exogenous regressors"]], "arch.univariate.LS": [[688, 3, 1, 0, "bounds", "Construct bounds for parameters to use in non-linear optimization"], [689, 3, 1, 0, "compute_param_cov", "Computes parameter covariances using numerical derivatives."], [690, 3, 1, 0, "constraints", "Construct linear constraint arrays  for use in non-linear optimization"], [691, 5, 1, 0, "distribution", "Set or gets the error distribution"], [692, 3, 1, 0, "fit", "Estimate model parameters"], [693, 3, 1, 0, "fix", "Allows an ARCHModelFixedResult to be constructed from fixed parameters."], [694, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [695, 5, 1, 0, "name", "The name of the model."], [696, 5, 1, 0, "num_params", "Returns the number of parameters"], [697, 3, 1, 0, "parameter_names", "List of parameters names"], [698, 3, 1, 0, "resids", "Compute model residuals"], [699, 3, 1, 0, "simulate", "Simulates data from a linear regression, AR or HAR models"], [700, 3, 1, 0, "starting_values", "Returns starting values for the mean model, often the same as the values returned from fit"], [701, 5, 1, 0, "volatility", "Set or gets the volatility process"], [702, 5, 1, 0, "x", "Gets the value of the exogenous regressors in the model"], [703, 5, 1, 0, "y", "Returns the dependent variable"]], "arch.univariate.LS.compute_param_cov": [[689, 2, 2, 0, "backcast", "Value to use for pre-sample observations"], [689, 2, 2, 0, "params", "Model parameters"], [689, 2, 2, 0, "robust", "Flag indicating whether to use robust standard errors (True) or classic MLE (False)"]], "arch.univariate.LS.fit": [[692, 2, 2, 0, "backcast", "Value to use as backcast. Should be measure \\sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."], [692, 2, 2, 0, "cov_type", "Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."], [692, 2, 2, 0, "disp", "Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is \"off\" and True is \"final\""], [692, 2, 2, 0, "first_obs", "First observation to use when estimating model"], [692, 2, 2, 0, "last_obs", "Last observation to use when estimating model"], [692, 2, 2, 0, "options", "Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."], [692, 2, 2, 0, "show_warning", "Flag indicating whether convergence warnings should be shown."], [692, 2, 2, 0, "starting_values", "Array of starting values to use.  If not provided, starting values are constructed by the model components."], [692, 2, 2, 0, "tol", "Tolerance for termination."], [692, 2, 2, 0, "update_freq", "Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."]], "arch.univariate.LS.fix": [[693, 2, 2, 0, "first_obs", "First observation to use when fixing model"], [693, 2, 2, 0, "last_obs", "Last observation to use when fixing model"], [693, 2, 2, 0, "params", "User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."]], "arch.univariate.LS.forecast": [[694, 2, 2, 0, "align", "Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."], [694, 2, 2, 0, "horizon", "Number of steps to forecast"], [694, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [694, 2, 2, 0, "params", "Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."], [694, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [694, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."], [694, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [694, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [694, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."], [694, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"]], "arch.univariate.LS.resids": [[698, 2, 2, 0, "params", "Model parameters"], [698, 2, 2, 0, "regressors", "Alternative regressor values to use when computing model residuals"], [698, 2, 2, 0, "y", "Alternative values to use when computing model residuals"]], "arch.univariate.LS.simulate": [[699, 2, 2, 0, "burn", "Number of values to simulate to initialize the model and remove dependence on initial values."], [699, 2, 2, 0, "initial_value", "Either a scalar value or max(lags) array set of initial values to use when initializing the model.  If omitted, 0.0 is used."], [699, 2, 2, 0, "initial_value_vol", "An array or scalar to use when initializing the volatility process."], [699, 2, 2, 0, "nobs", "Length of series to simulate"], [699, 2, 2, 0, "params", "Parameters to use when simulating the model.  Parameter order is [mean volatility distribution] where the parameters of the mean model are ordered [constant lag[0] lag[1] ... lag[p] ex[0] ... ex[k-1]] where lag[j] indicates the coefficient on the jth lag in the model and ex[j] is the coefficient on the jth exogenous variable."], [699, 2, 2, 0, "x", "nobs + burn by k array of exogenous variables to include in the simulation."]], "arch.univariate.MIDASHyperbolic.__init__": [[440, 2, 2, 0, "asym", "Flag indicating whether to include an asymmetric term"], [440, 2, 2, 0, "m", "Length of maximum lag to include in the model"]], "arch.univariate.MIDASHyperbolic": [[704, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [705, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [706, 3, 1, 0, "bounds", "Returns bounds for parameters"], [707, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [708, 3, 1, 0, "constraints", "Constraints"], [709, 3, 1, 0, "forecast", "Forecast volatility from the model"], [710, 5, 1, 0, "name", "The name of the volatility process"], [711, 5, 1, 0, "num_params", "The number of parameters in the model"], [712, 3, 1, 0, "parameter_names", "Names of model parameters"], [713, 3, 1, 0, "simulate", "Simulate data from the model"], [714, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [715, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [716, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [717, 3, 1, 0, "update", "Compute the variance for a single observation"], [718, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [719, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [720, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.MIDASHyperbolic.backcast": [[704, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.MIDASHyperbolic.backcast_transform": [[705, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.MIDASHyperbolic.bounds": [[706, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.MIDASHyperbolic.compute_variance": [[707, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [707, 2, 2, 0, "parameters", "Model parameters"], [707, 2, 2, 0, "resids", "Vector of mean zero residuals"], [707, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [707, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.MIDASHyperbolic.forecast": [[709, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [709, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [709, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [709, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [709, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [709, 2, 2, 0, "resids", "Residuals to use in the recursion"], [709, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [709, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [709, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [709, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.MIDASHyperbolic.simulate": [[713, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [713, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [713, 2, 2, 0, "nobs", "Number of data points to simulate"], [713, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [713, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.MIDASHyperbolic.starting_values": [[715, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.MIDASHyperbolic.update": [[717, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [717, 2, 2, 0, "index", "The numerical index of the variance to compute"], [717, 2, 2, 0, "parameters", "The variance model parameters"], [717, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [717, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [717, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.MIDASHyperbolic.variance_bounds": [[719, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [719, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.Normal.__init__": [[441, 2, 2, 0, "random_state", "Deprecated since version 5.0: random_state is deprecated. Use seed instead."], [441, 2, 2, 0, "seed", "Random number generator instance or int to use. Set to ensure reproducibility. If using an int, the argument is passed to np.random.default_rng.  If not provided, default_rng is used with system-provided entropy."]], "arch.univariate.Normal": [[721, 3, 1, 0, "bounds", "Parameter bounds for use in optimization."], [722, 3, 1, 0, "cdf", "Cumulative distribution function"], [723, 3, 1, 0, "constraints", "Construct arrays to use in constrained optimization."], [724, 5, 1, 0, "generator", "The NumPy Generator or RandomState attached to the distribution"], [725, 3, 1, 0, "loglikelihood", "Computes the log-likelihood of assuming residuals are normally distributed, conditional on the variance"], [726, 3, 1, 0, "moment", "Moment of order n"], [727, 5, 1, 0, "name", "The name of the distribution"], [728, 3, 1, 0, "parameter_names", "Names of distribution shape parameters"], [729, 3, 1, 0, "partial_moment", "Order n lower partial moment from -inf to z"], [730, 3, 1, 0, "ppf", "Inverse cumulative density function (ICDF)"], [731, 5, 1, 0, "random_state", "The NumPy RandomState attached to the distribution"], [732, 3, 1, 0, "simulate", "Simulates i.i.d. draws from the distribution"], [733, 3, 1, 0, "starting_values", "Construct starting values for use in optimization."]], "arch.univariate.Normal.bounds": [[721, 2, 2, 0, "resids", "Residuals to use when computing the bounds"]], "arch.univariate.Normal.cdf": [[722, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [722, 2, 2, 0, "resids", "Values at which to evaluate the cdf"]], "arch.univariate.Normal.loglikelihood": [[725, 2, 2, 0, "individual", "Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"], [725, 2, 2, 0, "parameters", "The normal likelihood has no shape parameters. Empty since the standard normal has no shape parameters."], [725, 2, 2, 0, "resids", "The residuals to use in the log-likelihood calculation"], [725, 2, 2, 0, "sigma2", "Conditional variances of resids"]], "arch.univariate.Normal.moment": [[726, 2, 2, 0, "n", "Order of moment"], [726, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."]], "arch.univariate.Normal.partial_moment": [[729, 2, 2, 0, "n", "Order of partial moment"], [729, 2, 2, 0, "parameters", "Distribution parameters.  Use None for parameterless distributions."], [729, 2, 2, 0, "z", "Upper bound for partial moment integral"]], "arch.univariate.Normal.ppf": [[730, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [730, 2, 2, 0, "pits", "Probability-integral-transformed values in the interval (0, 1)."]], "arch.univariate.Normal.simulate": [[732, 2, 2, 0, "parameters", "Distribution parameters"]], "arch.univariate.Normal.starting_values": [[733, 2, 2, 0, "std_resid", "Estimated standardized residuals to use in computing starting values for the shape parameter"]], "arch.univariate.RiskMetrics2006.__init__": [[442, 2, 2, 0, "kmax", "Number of components. Default is 14."], [442, 2, 2, 0, "rho", "Relative scale of adjacent cycles. Default is sqrt(2)"], [442, 2, 2, 0, "tau0", "Length of long cycle. Default is 1560."], [442, 2, 2, 0, "tau1", "Length of short cycle. Default is 4."]], "arch.univariate.RiskMetrics2006": [[734, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [735, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [736, 3, 1, 0, "bounds", "Returns bounds for parameters"], [737, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [738, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [739, 3, 1, 0, "forecast", "Forecast volatility from the model"], [740, 5, 1, 0, "name", "The name of the volatility process"], [741, 5, 1, 0, "num_params", "The number of parameters in the model"], [742, 3, 1, 0, "parameter_names", "Names of model parameters"], [743, 3, 1, 0, "simulate", "Simulate data from the model"], [744, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [745, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [746, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [747, 3, 1, 0, "update", "Compute the variance for a single observation"], [748, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [749, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [750, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.RiskMetrics2006.backcast": [[734, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.RiskMetrics2006.backcast_transform": [[735, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.RiskMetrics2006.bounds": [[736, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.RiskMetrics2006.compute_variance": [[737, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [737, 2, 2, 0, "parameters", "Model parameters"], [737, 2, 2, 0, "resids", "Vector of mean zero residuals"], [737, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [737, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.RiskMetrics2006.forecast": [[739, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [739, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [739, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [739, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [739, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [739, 2, 2, 0, "resids", "Residuals to use in the recursion"], [739, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [739, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [739, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [739, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.RiskMetrics2006.simulate": [[743, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [743, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [743, 2, 2, 0, "nobs", "Number of data points to simulate"], [743, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [743, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.RiskMetrics2006.starting_values": [[745, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.RiskMetrics2006.update": [[747, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [747, 2, 2, 0, "index", "The numerical index of the variance to compute"], [747, 2, 2, 0, "parameters", "The variance model parameters"], [747, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [747, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [747, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.RiskMetrics2006.variance_bounds": [[749, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [749, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.SkewStudent.__init__": [[443, 2, 2, 0, "random_state", "Deprecated since version 5.0: random_state is deprecated. Use seed instead."], [443, 2, 2, 0, "seed", "Random number generator instance or int to use. Set to ensure reproducibility. If using an int, the argument is passed to np.random.default_rng.  If not provided, default_rng is used with system-provided entropy."]], "arch.univariate.SkewStudent": [[751, 3, 1, 0, "bounds", "Parameter bounds for use in optimization."], [752, 3, 1, 0, "cdf", "Cumulative distribution function"], [753, 3, 1, 0, "constraints", "Construct arrays to use in constrained optimization."], [754, 5, 1, 0, "generator", "The NumPy Generator or RandomState attached to the distribution"], [755, 3, 1, 0, "loglikelihood", "Computes the log-likelihood of assuming residuals are have a standardized (to have unit variance) Skew Student's t distribution, conditional on the variance."], [756, 3, 1, 0, "moment", "Moment of order n"], [757, 5, 1, 0, "name", "The name of the distribution"], [758, 3, 1, 0, "parameter_names", "Names of distribution shape parameters"], [759, 3, 1, 0, "partial_moment", "Order n lower partial moment from -inf to z"], [760, 3, 1, 0, "ppf", "Inverse cumulative density function (ICDF)"], [761, 5, 1, 0, "random_state", "The NumPy RandomState attached to the distribution"], [762, 3, 1, 0, "simulate", "Simulates i.i.d. draws from the distribution"], [763, 3, 1, 0, "starting_values", "Construct starting values for use in optimization."]], "arch.univariate.SkewStudent.bounds": [[751, 2, 2, 0, "resids", "Residuals to use when computing the bounds"]], "arch.univariate.SkewStudent.cdf": [[752, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [752, 2, 2, 0, "resids", "Values at which to evaluate the cdf"]], "arch.univariate.SkewStudent.loglikelihood": [[755, 2, 2, 0, "individual", "Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"], [755, 2, 2, 0, "parameters", "Shape parameter of the skew-t distribution"], [755, 2, 2, 0, "resids", "The residuals to use in the log-likelihood calculation"], [755, 2, 2, 0, "sigma2", "Conditional variances of resids"]], "arch.univariate.SkewStudent.moment": [[756, 2, 2, 0, "n", "Order of moment"], [756, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."]], "arch.univariate.SkewStudent.partial_moment": [[759, 2, 2, 0, "n", "Order of partial moment"], [759, 2, 2, 0, "parameters", "Distribution parameters.  Use None for parameterless distributions."], [759, 2, 2, 0, "z", "Upper bound for partial moment integral"]], "arch.univariate.SkewStudent.ppf": [[760, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [760, 2, 2, 0, "pits", "Probability-integral-transformed values in the interval (0, 1)."]], "arch.univariate.SkewStudent.simulate": [[762, 2, 2, 0, "parameters", "Distribution parameters"]], "arch.univariate.SkewStudent.starting_values": [[763, 2, 2, 0, "std_resid", "Estimated standardized residuals to use in computing starting values for the shape parameter"]], "arch.univariate.StudentsT.__init__": [[444, 2, 2, 0, "random_state", "Deprecated since version 5.0: random_state is deprecated. Use seed instead."], [444, 2, 2, 0, "seed", "Random number generator instance or int to use. Set to ensure reproducibility. If using an int, the argument is passed to np.random.default_rng.  If not provided, default_rng is used with system-provided entropy."]], "arch.univariate.StudentsT": [[764, 3, 1, 0, "bounds", "Parameter bounds for use in optimization."], [765, 3, 1, 0, "cdf", "Cumulative distribution function"], [766, 3, 1, 0, "constraints", "Construct arrays to use in constrained optimization."], [767, 5, 1, 0, "generator", "The NumPy Generator or RandomState attached to the distribution"], [768, 3, 1, 0, "loglikelihood", "Computes the log-likelihood of assuming residuals are have a standardized (to have unit variance) Student's t distribution, conditional on the variance."], [769, 3, 1, 0, "moment", "Moment of order n"], [770, 5, 1, 0, "name", "The name of the distribution"], [771, 3, 1, 0, "parameter_names", "Names of distribution shape parameters"], [772, 3, 1, 0, "partial_moment", "Order n lower partial moment from -inf to z"], [773, 3, 1, 0, "ppf", "Inverse cumulative density function (ICDF)"], [774, 5, 1, 0, "random_state", "The NumPy RandomState attached to the distribution"], [775, 3, 1, 0, "simulate", "Simulates i.i.d. draws from the distribution"], [776, 3, 1, 0, "starting_values", "Construct starting values for use in optimization."]], "arch.univariate.StudentsT.bounds": [[764, 2, 2, 0, "resids", "Residuals to use when computing the bounds"]], "arch.univariate.StudentsT.cdf": [[765, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [765, 2, 2, 0, "resids", "Values at which to evaluate the cdf"]], "arch.univariate.StudentsT.loglikelihood": [[768, 2, 2, 0, "individual", "Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"], [768, 2, 2, 0, "parameters", "Shape parameter of the t distribution"], [768, 2, 2, 0, "resids", "The residuals to use in the log-likelihood calculation"], [768, 2, 2, 0, "sigma2", "Conditional variances of resids"]], "arch.univariate.StudentsT.moment": [[769, 2, 2, 0, "n", "Order of moment"], [769, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."]], "arch.univariate.StudentsT.partial_moment": [[772, 2, 2, 0, "n", "Order of partial moment"], [772, 2, 2, 0, "parameters", "Distribution parameters.  Use None for parameterless distributions."], [772, 2, 2, 0, "z", "Upper bound for partial moment integral"]], "arch.univariate.StudentsT.ppf": [[773, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [773, 2, 2, 0, "pits", "Probability-integral-transformed values in the interval (0, 1)."]], "arch.univariate.StudentsT.simulate": [[775, 2, 2, 0, "parameters", "Distribution parameters"]], "arch.univariate.StudentsT.starting_values": [[776, 2, 2, 0, "std_resid", "Estimated standardized residuals to use in computing starting values for the shape parameter"]], "arch.univariate.ZeroMean.__init__": [[445, 2, 2, 0, "distribution", "Error distribution to use in the model"], [445, 2, 2, 0, "hold_back", "Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."], [445, 2, 2, 0, "rescale", "Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."], [445, 2, 2, 0, "volatility", "Volatility process to use in the model"], [445, 2, 2, 0, "y", "nobs element vector containing the dependent variable"]], "arch.univariate.ZeroMean": [[777, 3, 1, 0, "bounds", "Construct bounds for parameters to use in non-linear optimization"], [778, 3, 1, 0, "compute_param_cov", "Computes parameter covariances using numerical derivatives."], [779, 3, 1, 0, "constraints", "Construct linear constraint arrays  for use in non-linear optimization"], [780, 5, 1, 0, "distribution", "Set or gets the error distribution"], [781, 3, 1, 0, "fit", "Estimate model parameters"], [782, 3, 1, 0, "fix", "Allows an ARCHModelFixedResult to be constructed from fixed parameters."], [783, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [784, 5, 1, 0, "name", "The name of the model."], [785, 5, 1, 0, "num_params", "Returns the number of parameters"], [786, 3, 1, 0, "parameter_names", "List of parameters names"], [787, 3, 1, 0, "resids", "Compute model residuals"], [788, 3, 1, 0, "simulate", "Simulated data from a zero mean model"], [789, 3, 1, 0, "starting_values", "Returns starting values for the mean model, often the same as the values returned from fit"], [790, 5, 1, 0, "volatility", "Set or gets the volatility process"], [791, 5, 1, 0, "x", "Gets the value of the exogenous regressors in the model"], [792, 5, 1, 0, "y", "Returns the dependent variable"]], "arch.univariate.ZeroMean.compute_param_cov": [[778, 2, 2, 0, "backcast", "Value to use for pre-sample observations"], [778, 2, 2, 0, "params", "Model parameters"], [778, 2, 2, 0, "robust", "Flag indicating whether to use robust standard errors (True) or classic MLE (False)"]], "arch.univariate.ZeroMean.fit": [[781, 2, 2, 0, "backcast", "Value to use as backcast. Should be measure \\sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."], [781, 2, 2, 0, "cov_type", "Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."], [781, 2, 2, 0, "disp", "Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is \"off\" and True is \"final\""], [781, 2, 2, 0, "first_obs", "First observation to use when estimating model"], [781, 2, 2, 0, "last_obs", "Last observation to use when estimating model"], [781, 2, 2, 0, "options", "Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."], [781, 2, 2, 0, "show_warning", "Flag indicating whether convergence warnings should be shown."], [781, 2, 2, 0, "starting_values", "Array of starting values to use.  If not provided, starting values are constructed by the model components."], [781, 2, 2, 0, "tol", "Tolerance for termination."], [781, 2, 2, 0, "update_freq", "Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."]], "arch.univariate.ZeroMean.fix": [[782, 2, 2, 0, "first_obs", "First observation to use when fixing model"], [782, 2, 2, 0, "last_obs", "Last observation to use when fixing model"], [782, 2, 2, 0, "params", "User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."]], "arch.univariate.ZeroMean.forecast": [[783, 2, 2, 0, "align", "Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."], [783, 2, 2, 0, "horizon", "Number of steps to forecast"], [783, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [783, 2, 2, 0, "params", "Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."], [783, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [783, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."], [783, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [783, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [783, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."], [783, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"]], "arch.univariate.ZeroMean.resids": [[787, 2, 2, 0, "params", "Model parameters"], [787, 2, 2, 0, "regressors", "Alternative regressor values to use when computing model residuals"], [787, 2, 2, 0, "y", "Alternative values to use when computing model residuals"]], "arch.univariate.ZeroMean.simulate": [[788, 2, 2, 0, "burn", "Number of values to simulate to initialize the model and remove dependence on initial values."], [788, 2, 2, 0, "initial_value", "This value is not used."], [788, 2, 2, 0, "initial_value_vol", "An array or scalar to use when initializing the volatility process."], [788, 2, 2, 0, "nobs", "Length of series to simulate"], [788, 2, 2, 0, "params", "Parameters to use when simulating the model.  Parameter order is [volatility distribution]. There are no mean parameters."], [788, 2, 2, 0, "x", "This value is not used."]], "arch.univariate.arch_model": [[891, 2, 2, 0, "dist", "Name of the error distribution.  Currently supported options are:"], [891, 2, 2, 0, "hold_back", "Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."], [891, 2, 2, 0, "lags", "Either a scalar integer value indicating lag length or a list of integers specifying lag locations."], [891, 2, 2, 0, "mean", "Name of the mean model.  Currently supported options are: 'Constant', 'Zero', 'LS', 'AR', 'ARX', 'HAR' and  'HARX'"], [891, 2, 2, 0, "o", "Lag order of the asymmetric innovation"], [891, 2, 2, 0, "p", "Lag order of the symmetric innovation"], [891, 2, 2, 0, "power", "Power to use with GARCH and related models"], [891, 2, 2, 0, "q", "Lag order of lagged volatility or equivalent"], [891, 2, 2, 0, "rescale", "Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."], [891, 2, 2, 0, "vol", "Name of the volatility model.  Currently supported options are: 'GARCH' (default), 'ARCH', 'EGARCH', 'FIGARCH', 'APARCH' and 'HARCH'"], [891, 2, 2, 0, "x", "Exogenous regressors.  Ignored if model does not permit exogenous regressors."], [891, 2, 2, 0, "y", "The dependent variable"]], "arch.univariate.base": [[446, 1, 1, 0, "ARCHModel", "Abstract base class for mean models in ARCH processes.  Specifies the conditional mean process."], [447, 1, 1, 0, "ARCHModelFixedResult", "Results for fixed parameters for an ARCHModel model"], [448, 1, 1, 0, "ARCHModelForecast", "Container for forecasts from an ARCH Model"], [449, 1, 1, 0, "ARCHModelForecastSimulation", "Container for a simulation or bootstrap-based forecasts from an ARCH Model"], [450, 1, 1, 0, "ARCHModelResult", "Results from estimation of an ARCHModel model"]], "arch.univariate.base.ARCHModel": [[793, 3, 1, 0, "bounds", "Construct bounds for parameters to use in non-linear optimization"], [794, 3, 1, 0, "compute_param_cov", "Computes parameter covariances using numerical derivatives."], [795, 3, 1, 0, "constraints", "Construct linear constraint arrays  for use in non-linear optimization"], [796, 5, 1, 0, "distribution", "Set or gets the error distribution"], [797, 3, 1, 0, "fit", "Estimate model parameters"], [798, 3, 1, 0, "fix", "Allows an ARCHModelFixedResult to be constructed from fixed parameters."], [799, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [800, 5, 1, 0, "name", "The name of the model."], [801, 5, 1, 0, "num_params", "Number of parameters in the model"], [802, 3, 1, 0, "parameter_names", "List of parameters names"], [803, 3, 1, 0, "resids", "Compute model residuals"], [804, 3, 1, 0, "simulate", ""], [805, 3, 1, 0, "starting_values", "Returns starting values for the mean model, often the same as the values returned from fit"], [806, 5, 1, 0, "volatility", "Set or gets the volatility process"], [807, 5, 1, 0, "y", "Returns the dependent variable"]], "arch.univariate.base.ARCHModel.compute_param_cov": [[794, 2, 2, 0, "backcast", "Value to use for pre-sample observations"], [794, 2, 2, 0, "params", "Model parameters"], [794, 2, 2, 0, "robust", "Flag indicating whether to use robust standard errors (True) or classic MLE (False)"]], "arch.univariate.base.ARCHModel.fit": [[797, 2, 2, 0, "backcast", "Value to use as backcast. Should be measure \\sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."], [797, 2, 2, 0, "cov_type", "Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."], [797, 2, 2, 0, "disp", "Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is \"off\" and True is \"final\""], [797, 2, 2, 0, "first_obs", "First observation to use when estimating model"], [797, 2, 2, 0, "last_obs", "Last observation to use when estimating model"], [797, 2, 2, 0, "options", "Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."], [797, 2, 2, 0, "show_warning", "Flag indicating whether convergence warnings should be shown."], [797, 2, 2, 0, "starting_values", "Array of starting values to use.  If not provided, starting values are constructed by the model components."], [797, 2, 2, 0, "tol", "Tolerance for termination."], [797, 2, 2, 0, "update_freq", "Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."]], "arch.univariate.base.ARCHModel.fix": [[798, 2, 2, 0, "first_obs", "First observation to use when fixing model"], [798, 2, 2, 0, "last_obs", "Last observation to use when fixing model"], [798, 2, 2, 0, "params", "User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."]], "arch.univariate.base.ARCHModel.forecast": [[799, 2, 2, 0, "align", "Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."], [799, 2, 2, 0, "horizon", "Number of steps to forecast"], [799, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [799, 2, 2, 0, "params", "Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."], [799, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [799, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."], [799, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [799, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [799, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."], [799, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"]], "arch.univariate.base.ARCHModel.resids": [[803, 2, 2, 0, "params", "Model parameters"], [803, 2, 2, 0, "regressors", "Alternative regressor values to use when computing model residuals"], [803, 2, 2, 0, "y", "Alternative values to use when computing model residuals"]], "arch.univariate.base.ARCHModelFixedResult.__init__": [[447, 2, 2, 0, "dep_var", "Dependent variable"], [447, 2, 2, 0, "is_pandas", "Whether the original input was pandas"], [447, 2, 2, 0, "loglikelihood", "Loglikelihood at specified parameters"], [447, 2, 2, 0, "model", "The model object used to estimate the parameters"], [447, 2, 2, 0, "names", "Model parameter names"], [447, 2, 2, 0, "params", "Estimated parameters"], [447, 2, 2, 0, "resid", "Residuals from model.  Residuals have same shape as original data and contain nan-values in locations not used in estimation"], [447, 2, 2, 0, "volatility", "Conditional volatility from model"]], "arch.univariate.base.ARCHModelFixedResult": [[808, 5, 1, 0, "aic", "Akaike Information Criteria"], [809, 3, 1, 0, "arch_lm_test", "ARCH LM test for conditional heteroskedasticity"], [810, 5, 1, 0, "bic", "Schwarz/Bayesian Information Criteria"], [811, 5, 1, 0, "conditional_volatility", "Estimated conditional volatility"], [812, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [813, 3, 1, 0, "hedgehog_plot", "Plot forecasts from estimated model"], [814, 5, 1, 0, "loglikelihood", "Model loglikelihood"], [815, 5, 1, 0, "model", "Model instance used to produce the fit"], [816, 5, 1, 0, "nobs", "Number of data points used to estimate model"], [817, 5, 1, 0, "num_params", "Number of parameters in model"], [818, 5, 1, 0, "params", "Model Parameters"], [819, 3, 1, 0, "plot", "Plot standardized residuals and conditional volatility"], [820, 5, 1, 0, "resid", "Model residuals"], [821, 5, 1, 0, "std_resid", "Residuals standardized by conditional volatility"], [822, 3, 1, 0, "summary", "Constructs a summary of the results from a fit model."]], "arch.univariate.base.ARCHModelFixedResult.arch_lm_test": [[809, 2, 2, 0, "lags", "Number of lags to include in the model.  If not specified,"], [809, 2, 2, 0, "standardized", "Flag indicating to test the model residuals divided by their conditional standard deviations.  If False, directly tests the estimated residuals."]], "arch.univariate.base.ARCHModelFixedResult.forecast": [[812, 2, 2, 0, "align", "Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."], [812, 2, 2, 0, "horizon", "Number of steps to forecast"], [812, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [812, 2, 2, 0, "params", "Alternative parameters to use.  If not provided, the parameters estimated when fitting the model are used.  Must be identical in shape to the parameters computed by fitting the model."], [812, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [812, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast."], [812, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [812, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [812, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."], [812, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"]], "arch.univariate.base.ARCHModelFixedResult.hedgehog_plot": [[813, 2, 2, 0, "horizon", "Number of steps to forecast"], [813, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [813, 2, 2, 0, "params", "Alternative parameters to use.  If not provided, the parameters computed by fitting the model are used.  Must be 1-d and identical in shape to the parameters computed by fitting the model."], [813, 2, 2, 0, "plot_type", "Quantity to plot, the forecast volatility or the forecast mean"], [813, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [813, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index.  Strings must be convertible to a date time, such as in '1945-01-01'.  If not provided, the start is set to the earliest forecastable date."], [813, 2, 2, 0, "step", "Non-negative number of forecasts to skip between spines"]], "arch.univariate.base.ARCHModelFixedResult.plot": [[819, 2, 2, 0, "annualize", "String containing frequency of data that indicates plot should contain annualized volatility.  Supported values are 'D' (daily), 'W' (weekly) and 'M' (monthly), which scale variance by 252, 52, and 12, respectively."], [819, 2, 2, 0, "scale", "Value to use when scaling returns to annualize.  If scale is provided, annualize is ignored and the value in scale is used."]], "arch.univariate.base.ARCHModelForecast.__init__": [[448, 2, 2, 0, "align", ""], [448, 2, 2, 0, "index", ""], [448, 2, 2, 0, "mean", ""], [448, 2, 2, 0, "residual_variance", ""], [448, 2, 2, 0, "simulated_paths", ""], [448, 2, 2, 0, "simulated_residual_variances", ""], [448, 2, 2, 0, "simulated_residuals", ""], [448, 2, 2, 0, "simulated_variances", ""], [448, 2, 2, 0, "variance", ""]], "arch.univariate.base.ARCHModelForecast": [[823, 5, 1, 0, "mean", "Forecast values for the conditional mean of the process"], [824, 5, 1, 0, "residual_variance", "Forecast values for the conditional variance of the residuals"], [825, 5, 1, 0, "simulations", "Detailed simulation results if using a simulation-based method"], [826, 5, 1, 0, "variance", "Forecast values for the conditional variance of the process"]], "arch.univariate.base.ARCHModelForecastSimulation.__init__": [[449, 2, 2, 0, "index", ""], [449, 2, 2, 0, "residual_variances", ""], [449, 2, 2, 0, "residuals", ""], [449, 2, 2, 0, "values", ""], [449, 2, 2, 0, "variances", ""]], "arch.univariate.base.ARCHModelForecastSimulation": [[827, 5, 1, 0, "index", "The index aligned to dimension 0 of the simulation paths"], [828, 5, 1, 0, "residual_variances", "Simulated variance of the residuals"], [829, 5, 1, 0, "residuals", "Simulated residuals used to produce the values"], [830, 5, 1, 0, "values", "The values of the process"], [831, 5, 1, 0, "variances", "Simulated variances of the values"]], "arch.univariate.base.ARCHModelResult.__init__": [[450, 2, 2, 0, "cov_type", "String describing the covariance estimator used"], [450, 2, 2, 0, "dep_var", "Dependent variable"], [450, 2, 2, 0, "fit_start", "Integer index of the first observation used to fit the model"], [450, 2, 2, 0, "fit_stop", "Integer index of the last observation used to fit the model using slice notation fit_start:fit_stop"], [450, 2, 2, 0, "is_pandas", "Whether the original input was pandas"], [450, 2, 2, 0, "loglikelihood", "Loglikelihood at estimated parameters"], [450, 2, 2, 0, "model", "The model object used to estimate the parameters"], [450, 2, 2, 0, "names", "Model parameter names"], [450, 2, 2, 0, "optim_output", "Result of log-likelihood optimization"], [450, 2, 2, 0, "param_cov", "Estimated variance-covariance matrix of params.  If none, calls method to compute variance from model when parameter covariance is first used from result"], [450, 2, 2, 0, "params", "Estimated parameters"], [450, 2, 2, 0, "r2", "Model R-squared"], [450, 2, 2, 0, "resid", "Residuals from model.  Residuals have same shape as original data and contain nan-values in locations not used in estimation"], [450, 2, 2, 0, "volatility", "Conditional volatility from model"]], "arch.univariate.base.ARCHModelResult": [[832, 5, 1, 0, "aic", "Akaike Information Criteria"], [833, 3, 1, 0, "arch_lm_test", "ARCH LM test for conditional heteroskedasticity"], [834, 5, 1, 0, "bic", "Schwarz/Bayesian Information Criteria"], [835, 5, 1, 0, "conditional_volatility", "Estimated conditional volatility"], [836, 3, 1, 0, "conf_int", "Parameter confidence intervals"], [837, 5, 1, 0, "convergence_flag", "scipy.optimize.minimize result flag"], [838, 5, 1, 0, "fit_start", "Start of sample used to estimate parameters"], [839, 5, 1, 0, "fit_stop", "End of sample used to estimate parameters"], [840, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [841, 3, 1, 0, "hedgehog_plot", "Plot forecasts from estimated model"], [842, 5, 1, 0, "loglikelihood", "Model loglikelihood"], [843, 5, 1, 0, "model", "Model instance used to produce the fit"], [844, 5, 1, 0, "nobs", "Number of data points used to estimate model"], [845, 5, 1, 0, "num_params", "Number of parameters in model"], [846, 5, 1, 0, "optimization_result", "Information about the convergence of the loglikelihood optimization"], [847, 5, 1, 0, "param_cov", "Parameter covariance"], [848, 5, 1, 0, "params", "Model Parameters"], [849, 3, 1, 0, "plot", "Plot standardized residuals and conditional volatility"], [850, 5, 1, 0, "pvalues", "Array of p-values for the t-statistics"], [851, 5, 1, 0, "resid", "Model residuals"], [852, 5, 1, 0, "rsquared", "R-squared"], [853, 5, 1, 0, "rsquared_adj", "Degree of freedom adjusted R-squared"], [854, 5, 1, 0, "scale", "The scale applied to the original data before estimating the model."], [855, 5, 1, 0, "std_err", "Array of parameter standard errors"], [856, 5, 1, 0, "std_resid", "Residuals standardized by conditional volatility"], [857, 3, 1, 0, "summary", "Constructs a summary of the results from a fit model."], [858, 5, 1, 0, "tvalues", "Array of t-statistics testing the null that the coefficient are 0"]], "arch.univariate.base.ARCHModelResult.arch_lm_test": [[833, 2, 2, 0, "lags", "Number of lags to include in the model.  If not specified,"], [833, 2, 2, 0, "standardized", "Flag indicating to test the model residuals divided by their conditional standard deviations.  If False, directly tests the estimated residuals."]], "arch.univariate.base.ARCHModelResult.conf_int": [[836, 2, 2, 0, "alpha", "Size (prob.) to use when constructing the confidence interval."]], "arch.univariate.base.ARCHModelResult.forecast": [[840, 2, 2, 0, "align", "Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."], [840, 2, 2, 0, "horizon", "Number of steps to forecast"], [840, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [840, 2, 2, 0, "params", "Alternative parameters to use.  If not provided, the parameters estimated when fitting the model are used.  Must be identical in shape to the parameters computed by fitting the model."], [840, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [840, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast."], [840, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [840, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [840, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."], [840, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"]], "arch.univariate.base.ARCHModelResult.hedgehog_plot": [[841, 2, 2, 0, "horizon", "Number of steps to forecast"], [841, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [841, 2, 2, 0, "params", "Alternative parameters to use.  If not provided, the parameters computed by fitting the model are used.  Must be 1-d and identical in shape to the parameters computed by fitting the model."], [841, 2, 2, 0, "plot_type", "Quantity to plot, the forecast volatility or the forecast mean"], [841, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [841, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index.  Strings must be convertible to a date time, such as in '1945-01-01'.  If not provided, the start is set to the earliest forecastable date."], [841, 2, 2, 0, "step", "Non-negative number of forecasts to skip between spines"]], "arch.univariate.base.ARCHModelResult.plot": [[849, 2, 2, 0, "annualize", "String containing frequency of data that indicates plot should contain annualized volatility.  Supported values are 'D' (daily), 'W' (weekly) and 'M' (monthly), which scale variance by 252, 52, and 12, respectively."], [849, 2, 2, 0, "scale", "Value to use when scaling returns to annualize.  If scale is provided, annualize is ignored and the value in scale is used."]], "arch.univariate.distribution": [[451, 1, 1, 0, "Distribution", "Template for subclassing only"]], "arch.univariate.distribution.Distribution": [[859, 3, 1, 0, "bounds", "Parameter bounds for use in optimization."], [860, 3, 1, 0, "cdf", "Cumulative distribution function"], [861, 3, 1, 0, "constraints", "Construct arrays to use in constrained optimization."], [862, 5, 1, 0, "generator", "The NumPy Generator or RandomState attached to the distribution"], [863, 3, 1, 0, "loglikelihood", "Loglikelihood evaluation."], [864, 3, 1, 0, "moment", "Moment of order n"], [865, 5, 1, 0, "name", "The name of the distribution"], [866, 3, 1, 0, "parameter_names", "Names of distribution shape parameters"], [867, 3, 1, 0, "partial_moment", "Order n lower partial moment from -inf to z"], [868, 3, 1, 0, "ppf", "Inverse cumulative density function (ICDF)"], [869, 5, 1, 0, "random_state", "The NumPy RandomState attached to the distribution"], [870, 3, 1, 0, "simulate", "Simulates i.i.d. draws from the distribution"], [871, 3, 1, 0, "starting_values", "Construct starting values for use in optimization."]], "arch.univariate.distribution.Distribution.bounds": [[859, 2, 2, 0, "resids", "Residuals to use when computing the bounds"]], "arch.univariate.distribution.Distribution.cdf": [[860, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [860, 2, 2, 0, "resids", "Values at which to evaluate the cdf"]], "arch.univariate.distribution.Distribution.loglikelihood": [[863, 2, 2, 0, "individual", "Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"], [863, 2, 2, 0, "parameters", "Distribution shape parameters"], [863, 2, 2, 0, "resids", "nobs array of model residuals"], [863, 2, 2, 0, "sigma2", "nobs array of conditional variances"]], "arch.univariate.distribution.Distribution.moment": [[864, 2, 2, 0, "n", "Order of moment"], [864, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."]], "arch.univariate.distribution.Distribution.partial_moment": [[867, 2, 2, 0, "n", "Order of partial moment"], [867, 2, 2, 0, "parameters", "Distribution parameters.  Use None for parameterless distributions."], [867, 2, 2, 0, "z", "Upper bound for partial moment integral"]], "arch.univariate.distribution.Distribution.ppf": [[868, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [868, 2, 2, 0, "pits", "Probability-integral-transformed values in the interval (0, 1)."]], "arch.univariate.distribution.Distribution.simulate": [[870, 2, 2, 0, "parameters", "Distribution parameters"]], "arch.univariate.distribution.Distribution.starting_values": [[871, 2, 2, 0, "std_resid", "Estimated standardized residuals to use in computing starting values for the shape parameter"]], "arch.univariate.recursions_python": [[452, 1, 1, 0, "VolatilityUpdater", "Base class that all volatility updaters must inherit from."]], "arch.univariate.recursions_python.VolatilityUpdater": [[872, 3, 1, 0, "initialize_update", "Initialize the recursion prior to calling update"], [873, 3, 1, 0, "update", "Update the current variance at location t"]], "arch.univariate.recursions_python.VolatilityUpdater.initialize_update": [[872, 2, 2, 0, "backcast", "The backcast value(s)."], [872, 2, 2, 0, "nobs", "The number of observations in the sample."], [872, 2, 2, 0, "parameters", "The model parameters."]], "arch.univariate.recursions_python.VolatilityUpdater.update": [[873, 2, 2, 0, "parameters", "Model parameters"], [873, 2, 2, 0, "resids", "Residuals to use in the recursion"], [873, 2, 2, 0, "sigma2", "Conditional variances with same shape as resids"], [873, 2, 2, 0, "t", "The index of the value of sigma2 to update. Assumes but does not check that update has been called recursively for 0,1,...,t-1."], [873, 2, 2, 0, "var_bounds", "nobs by 2-element array of upper and lower bounds for conditional variances for each time period"]], "arch.univariate.volatility": [[453, 1, 1, 0, "VolatilityProcess", "Abstract base class for ARCH models.  Allows the conditional mean model to be specified separately from the conditional variance, even though parameters are estimated jointly."]], "arch.univariate.volatility.VolatilityProcess": [[874, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [875, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [876, 3, 1, 0, "bounds", "Returns bounds for parameters"], [877, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [878, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [879, 3, 1, 0, "forecast", "Forecast volatility from the model"], [880, 5, 1, 0, "name", "The name of the volatility process"], [881, 5, 1, 0, "num_params", "The number of parameters in the model"], [882, 3, 1, 0, "parameter_names", "Names of model parameters"], [883, 3, 1, 0, "simulate", "Simulate data from the model"], [884, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [885, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [886, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [887, 3, 1, 0, "update", "Compute the variance for a single observation"], [888, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [889, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [890, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.volatility.VolatilityProcess.backcast": [[874, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.volatility.VolatilityProcess.backcast_transform": [[875, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.volatility.VolatilityProcess.bounds": [[876, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.volatility.VolatilityProcess.compute_variance": [[877, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [877, 2, 2, 0, "parameters", "Model parameters"], [877, 2, 2, 0, "resids", "Vector of mean zero residuals"], [877, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [877, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.volatility.VolatilityProcess.forecast": [[879, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [879, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [879, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [879, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [879, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [879, 2, 2, 0, "resids", "Residuals to use in the recursion"], [879, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [879, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [879, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [879, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.volatility.VolatilityProcess.simulate": [[883, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [883, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [883, 2, 2, 0, "nobs", "Number of data points to simulate"], [883, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [883, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.volatility.VolatilityProcess.starting_values": [[885, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.volatility.VolatilityProcess.update": [[887, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [887, 2, 2, 0, "index", "The numerical index of the variance to compute"], [887, 2, 2, 0, "parameters", "The variance model parameters"], [887, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [887, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [887, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.volatility.VolatilityProcess.variance_bounds": [[889, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [889, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.utility": [[900, 0, 0, 1, "testing", ""]], "arch.utility.testing": [[900, 1, 1, 0, "WaldTestStatistic", "Test statistic holder for Wald-type tests"]], "arch.utility.testing.WaldTestStatistic.__init__": [[900, 2, 2, 0, "alternative", "A statement of the test's alternative hypothesis"], [900, 2, 2, 0, "df", "Degree of freedom."], [900, 2, 2, 0, "name", "Name of test"], [900, 2, 2, 0, "null", "A statement of the test's null hypothesis"], [900, 2, 2, 0, "stat", "The test statistic"]], "arch.utility.testing.WaldTestStatistic": [[900, 5, 1, 0, "critical_values", "Critical values test for common test sizes"], [900, 5, 1, 0, "null", "Null hypothesis"], [900, 5, 1, 0, "pval", "P-value of test statistic"], [900, 5, 1, 0, "stat", "Test statistic"]]}, "objtypes": {"0": "py:module", "1": "py:class", "2": "py:parameter", "3": "py:method", "4": "py:attribute", "5": "py:property", "6": "py:function"}, "objnames": {"0": ["py", "module", "Python module"], "1": ["py", "class", "Python class"], "2": ["py", "parameter", "Python parameter"], "3": ["py", "method", "Python method"], "4": ["py", "attribute", "Python attribute"], "5": ["py", "property", "Python property"], "6": ["py", "function", "Python function"]}, "titleterms": {"api": 0, "refer": [0, 1, 237, 258], "volatil": [0, 96, 453, 504, 520, 536, 685, 701, 790, 806, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 894, 896, 897, 898, 899, 901], "model": [0, 3, 96, 258, 259, 815, 843, 891, 892, 893, 894, 895, 896, 898], "high": 0, "level": [0, 87], "mean": [0, 823, 892, 896, 898], "specif": 0, "process": [0, 896, 898, 901], "shock": 0, "distribut": [0, 86, 423, 451, 493, 510, 526, 675, 691, 780, 796, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 898], "unit": [0, 96, 418, 419, 421], "root": [0, 96, 418, 419, 421], "test": [0, 258, 259, 261, 418, 419, 420, 421, 900], "cointegr": [0, 261, 269, 270, 271, 272, 273, 274, 275, 276, 277, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 419, 420], "relationship": 0, "estim": [0, 3, 87, 88, 98, 261, 896], "bootstrap": [0, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 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diff --git a/devel/sitemap.xml b/devel/sitemap.xml
index 6011311975..6f587e57c2 100644
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unds.html</loc></url><url><loc>https://bashtage.github.io/arch/univariate/generated/generated/arch.univariate.ARX.compute_param_cov.html</loc></url><url><loc>https://bashtage.github.io/arch/univariate/generated/generated/arch.univariate.ARX.constraints.html</loc></url><url><loc>https://bashtage.github.io/arch/univariate/generated/generated/arch.univariate.ARX.distribution.html</loc></url><url><loc>https://bashtage.github.io/arch/univariate/generated/generated/arch.univariate.ARX.fit.html</loc></url><url><loc>https://bashtage.github.io/arch/univariate/generated/generated/arch.univariate.ARX.fix.html</loc></url><url><loc>https://bashtage.github.io/arch/univariate/generated/generated/arch.univariate.ARX.forecast.html</loc></url><url><loc>https://bashtage.github.io/arch/univariate/generated/generated/arch.univariate.ARX.name.html</loc></url><url><loc>https://bashtage.github.io/arch/univariate/generated/generated/arch.univariate.ARX.num_params.html</loc></url><url><loc>https://bashtage.github.io/arch/univariate/generated/generated/arch.univariate.ARX.parameter_names.html</loc></url><url><loc>https://bashtage.github.io/arch/univariate/generated/generated/arch.univariate.ARX.resids.html</loc></url><url><loc>https://bashtage.github.io/arch/univariate/generated/generated/arch.univariate.ARX.simulate.html</loc></url><url><loc>https://bashtage.github.io/arch/univariate/generated/generated/arch.univariate.ARX.starting_values.html</loc></url><url><loc>https://bashtage.github.io/arch/univariate/generated/generated/arch.univariate.ARX.volatility.html</loc></url><url><loc>https://bashtage.github.io/arch/univariate/generated/generated/arch.univariate.ARX.x.html</loc></url><url><loc>https://bashtage.github.io/arch/univariate/generated/generated/arch.univariate.ARX.y.html</loc></url><url><loc>https://bashtage.github.io/arch/univariate/generated/generated/arch.univariate.ConstantMean.bounds.html</loc></url><url><loc>https://bashtage.github.io/arch/univariate/generated/generated/arch.univariate.ConstantMean.compute_param_cov.html</loc></url><url><loc>https://bashtage.github.io/arch/univariate/generated/generated/arch.univariate.ConstantMean.constraints.html</loc></url><url><loc>https://bashtage.github.io/arch/univariate/generated/generated/arch.univariate.ConstantMean.distribution.html</loc></url><url><loc>https://bashtage.github.io/arch/univariate/generated/generated/arch.univariate.ConstantMean.fit.html</loc></url><url><loc>https://bashtage.github.io/arch/univariate/generated/generated/arch.univariate.ConstantMean.fix.html</loc></url><url><loc>https://bashtage.github.io/arch/univariate/generated/generated/arch.univariate.ConstantMean.forecast.html</loc></url><url><loc>https://bashtage.github.io/arch/univariate/generated/generated/arch.univariate.ConstantMean.name.html</loc></url><url><loc>https://bashtage.github.io/arch/univariate/generated/generated/arch.univariate.ConstantMean.num_params.html</loc></url><url><loc>https://bashtage.github.io/arch/univariate/generated/generated/arch.univariate.ConstantMean.parameter_names.html</loc></url><url><loc>https://bashtage.github.io/arch/univariate/generated/generated/arch.univariate.ConstantMean.resids.html</loc></url><url><loc>https://bashtage.github.io/arch/univariate/generated/generated/arch.univariate.ConstantMean.simulate.html</loc></url><url><loc>https://bashtage.github.io/arch/univariate/generated/generated/arch.univariate.ConstantMean.starting_values.html</loc></url><url><loc>https://bashtage.github.io/arch/univariate/generated/generated/arch.univariate.ConstantMean.volatility.html</loc></url><url><loc>https://bashtage.github.io/arch/univariate/generated/generated/arch.univariate.ConstantMean.x.html</loc></url><url><loc>https://bashtage.github.io/arch/univariate/generated/generated/arch.univariate.ConstantMean.y.html</loc></url><url><loc>https://bashtage.github.io/arch/univariate/generated/generated/arch.univariate.ConstantVariance.backcast.html</loc></url><url><loc>https://bashtage.github.io/arch/univariate/generated/generated/arch.univariate.ConstantVariance.backcast_transform.html</loc></url><url><loc>https://bashtage.github.io/arch/univariate/generated/generated/arch.univariate.ConstantVariance.bounds.html</loc></url><url><loc>https://bashtage.github.io/arch/univariate/generated/generated/arch.univariate.ConstantVariance.compute_variance.html</loc></url><url><loc>https://bashtage.github.io/arch/univariate/generated/generated/arch.univariate.ConstantVariance.constraints.html</loc></url><url><loc>https://bashtage.github.io/arch/univariate/generated/generated/arch.univariate.ConstantVariance.forecast.html</loc></url><url><loc>https://bashtage.github.io/arch/univariate/generated/generated/arch.univariate.ConstantVariance.name.html</loc></url><url><loc>https://bashtage.github.io/arch/univariate/generated/generated/arch.univariate.ConstantVariance.num_params.html</loc></url><url><loc>https://bashtage.github.io/arch/univariate/generated/generated/arch.univariate.ConstantVariance.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erated/arch.unitroot.PhillipsPerron.regression.html</loc></url><url><loc>https://bashtage.github.io/arch/unitroot/generated/generated/arch.unitroot.PhillipsPerron.stat.html</loc></url><url><loc>https://bashtage.github.io/arch/unitroot/generated/generated/arch.unitroot.PhillipsPerron.summary.html</loc></url><url><loc>https://bashtage.github.io/arch/unitroot/generated/generated/arch.unitroot.PhillipsPerron.test_type.html</loc></url><url><loc>https://bashtage.github.io/arch/unitroot/generated/generated/arch.unitroot.PhillipsPerron.trend.html</loc></url><url><loc>https://bashtage.github.io/arch/unitroot/generated/generated/arch.unitroot.PhillipsPerron.valid_trends.html</loc></url><url><loc>https://bashtage.github.io/arch/unitroot/generated/generated/arch.unitroot.PhillipsPerron.y.html</loc></url><url><loc>https://bashtage.github.io/arch/unitroot/generated/generated/arch.unitroot.VarianceRatio.alternative_hypothesis.html</loc></url><url><loc>https://bashtage.github.io/arch/unitroot/generated/generated/arch.unitroot.VarianceRatio.critical_values.html</loc></url><url><loc>https://bashtage.github.io/arch/unitroot/generated/generated/arch.unitroot.VarianceRatio.debiased.html</loc></url><url><loc>https://bashtage.github.io/arch/unitroot/generated/generated/arch.unitroot.VarianceRatio.lags.html</loc></url><url><loc>https://bashtage.github.io/arch/unitroot/generated/generated/arch.unitroot.VarianceRatio.nobs.html</loc></url><url><loc>https://bashtage.github.io/arch/unitroot/generated/generated/arch.unitroot.VarianceRatio.null_hypothesis.html</loc></url><url><loc>https://bashtage.github.io/arch/unitroot/generated/generated/arch.unitroot.VarianceRatio.overlap.html</loc></url><url><loc>https://bashtage.github.io/arch/unitroot/generated/generated/arch.unitroot.VarianceRatio.pvalue.html</loc></url><url><loc>https://bashtage.github.io/arch/unitroot/generated/generated/arch.unitroot.VarianceRatio.robust.html</loc></url><url><loc>https://bashtage.github.io/arch/unitroot/generated/generated/arch.unitroot.VarianceRatio.stat.html</loc></url><url><loc>https://bashtage.github.io/arch/unitroot/generated/generated/arch.unitroot.VarianceRatio.summary.html</loc></url><url><loc>https://bashtage.github.io/arch/unitroot/generated/generated/arch.unitroot.VarianceRatio.trend.html</loc></url><url><loc>https://bashtage.github.io/arch/unitroot/generated/generated/arch.unitroot.VarianceRatio.valid_trends.html</loc></url><url><loc>https://bashtage.github.io/arch/unitroot/generated/generated/arch.unitroot.VarianceRatio.vr.html</loc></url><url><loc>https://bashtage.github.io/arch/unitroot/generated/generated/arch.unitroot.VarianceRatio.y.html</loc></url><url><loc>https://bashtage.github.io/arch/unitroot/generated/generated/arch.unitroot.ZivotAndrews.alternative_hypothesis.html</loc></url><url><loc>https://bashtage.github.io/arch/unitroot/generated/generated/arch.unitroot.ZivotAndrews.critical_values.html</loc></url><url><loc>https://bashtage.github.io/arch/unitroot/generated/generated/arch.unitroot.ZivotAndrews.lags.html</loc></url><url><loc>https://bashtage.github.io/arch/unitroot/generated/generated/arch.unitroot.ZivotAndrews.nobs.html</loc></url><url><loc>https://bashtage.github.io/arch/unitroot/generated/generated/arch.unitroot.ZivotAndrews.null_hypothesis.html</loc></url><url><loc>https://bashtage.github.io/arch/unitroot/generated/generated/arch.unitroot.ZivotAndrews.pvalue.html</loc></url><url><loc>https://bashtage.github.io/arch/unitroot/generated/generated/arch.unitroot.ZivotAndrews.stat.html</loc></url><url><loc>https://bashtage.github.io/arch/unitroot/generated/generated/arch.unitroot.ZivotAndrews.summary.html</loc></url><url><loc>https://bashtage.github.io/arch/unitroot/generated/generated/arch.unitroot.ZivotAndrews.trend.html</loc></url><url><loc>https://bashtage.github.io/arch/unitroot/generated/generated/arch.unitroot.ZivotAndrews.valid_trends.html</loc></url><url><loc>https://bashtage.github.io/arch/unitroot/generated/generated/arch.unitroot.ZivotAndrews.y.html</loc></url><url><loc>https://bashtage.github.io/arch/unitroot/generated/generated/arch.unitroot.cointegration.CanonicalCointegratingReg.fit.html</loc></url><url><loc>https://bashtage.github.io/arch/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.bandwidth.html</loc></url><url><loc>https://bashtage.github.io/arch/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.cov.html</loc></url><url><loc>https://bashtage.github.io/arch/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.kernel.html</loc></url><url><loc>https://bashtage.github.io/arch/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.long_run_variance.html</loc></url><url><loc>https://bashtage.github.io/arch/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.params.html</loc></url><url><loc>https://bashtage.github.io/arch/unitroot/generated/generat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index 4551f69c09..e673fd59a0 100644
--- a/devel/unitroot/cointegration.html
+++ b/devel/unitroot/cointegration.html
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+    arch 6.2.0
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diff --git a/devel/unitroot/generated/arch.unitroot.DFGLS.html b/devel/unitroot/generated/arch.unitroot.DFGLS.html
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@@ -92,7 +92,7 @@
 
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+    arch 6.2.0
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diff --git a/devel/unitroot/generated/arch.unitroot.KPSS.html b/devel/unitroot/generated/arch.unitroot.KPSS.html
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--- a/devel/unitroot/generated/arch.unitroot.KPSS.html
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diff --git a/devel/unitroot/generated/arch.unitroot.PhillipsPerron.html b/devel/unitroot/generated/arch.unitroot.PhillipsPerron.html
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/arch.unitroot.VarianceRatio.html b/devel/unitroot/generated/arch.unitroot.VarianceRatio.html
index 211b16d5eb..d83762fb6f 100644
--- a/devel/unitroot/generated/arch.unitroot.VarianceRatio.html
+++ b/devel/unitroot/generated/arch.unitroot.VarianceRatio.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.VarianceRatio - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.VarianceRatio - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/arch.unitroot.ZivotAndrews.html b/devel/unitroot/generated/arch.unitroot.ZivotAndrews.html
index f64dfcb7ad..4137497161 100644
--- a/devel/unitroot/generated/arch.unitroot.ZivotAndrews.html
+++ b/devel/unitroot/generated/arch.unitroot.ZivotAndrews.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.ZivotAndrews - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.ZivotAndrews - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/unitroot/generated/arch.unitroot.auto_bandwidth.html b/devel/unitroot/generated/arch.unitroot.auto_bandwidth.html
index 662d750dc9..1824b4c3ce 100644
--- a/devel/unitroot/generated/arch.unitroot.auto_bandwidth.html
+++ b/devel/unitroot/generated/arch.unitroot.auto_bandwidth.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.auto_bandwidth - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.auto_bandwidth - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/unitroot/generated/arch.unitroot.cointegration.CanonicalCointegratingReg.html b/devel/unitroot/generated/arch.unitroot.cointegration.CanonicalCointegratingReg.html
index 51447f1258..64a8e433a2 100644
--- a/devel/unitroot/generated/arch.unitroot.cointegration.CanonicalCointegratingReg.html
+++ b/devel/unitroot/generated/arch.unitroot.cointegration.CanonicalCointegratingReg.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.CanonicalCointegratingReg - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.cointegration.CanonicalCointegratingReg - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/unitroot/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.html b/devel/unitroot/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.html
index 9d0ca41e5f..7e4364b337 100644
--- a/devel/unitroot/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.html
+++ b/devel/unitroot/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.CointegrationAnalysisResults - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.cointegration.CointegrationAnalysisResults - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/unitroot/generated/arch.unitroot.cointegration.DynamicOLS.html b/devel/unitroot/generated/arch.unitroot.cointegration.DynamicOLS.html
index ace1debaec..b5067bc27b 100644
--- a/devel/unitroot/generated/arch.unitroot.cointegration.DynamicOLS.html
+++ b/devel/unitroot/generated/arch.unitroot.cointegration.DynamicOLS.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLS - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.cointegration.DynamicOLS - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/unitroot/generated/arch.unitroot.cointegration.DynamicOLSResults.html b/devel/unitroot/generated/arch.unitroot.cointegration.DynamicOLSResults.html
index 4c67b3501f..70dbc0b9fe 100644
--- a/devel/unitroot/generated/arch.unitroot.cointegration.DynamicOLSResults.html
+++ b/devel/unitroot/generated/arch.unitroot.cointegration.DynamicOLSResults.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/unitroot/generated/arch.unitroot.cointegration.EngleGrangerTestResults.html b/devel/unitroot/generated/arch.unitroot.cointegration.EngleGrangerTestResults.html
index ff72029847..e659df71c0 100644
--- a/devel/unitroot/generated/arch.unitroot.cointegration.EngleGrangerTestResults.html
+++ b/devel/unitroot/generated/arch.unitroot.cointegration.EngleGrangerTestResults.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.EngleGrangerTestResults - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.cointegration.EngleGrangerTestResults - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/unitroot/generated/arch.unitroot.cointegration.FullyModifiedOLS.html b/devel/unitroot/generated/arch.unitroot.cointegration.FullyModifiedOLS.html
index 774f765c96..dfeaf950e3 100644
--- a/devel/unitroot/generated/arch.unitroot.cointegration.FullyModifiedOLS.html
+++ b/devel/unitroot/generated/arch.unitroot.cointegration.FullyModifiedOLS.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.FullyModifiedOLS - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.cointegration.FullyModifiedOLS - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/unitroot/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.html b/devel/unitroot/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.html
index 2bf38a6b17..a799eeb502 100644
--- a/devel/unitroot/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.html
+++ b/devel/unitroot/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index a102171907..6a9e74cb71 100644
--- a/devel/unitroot/generated/arch.unitroot.cointegration.engle_granger.html
+++ b/devel/unitroot/generated/arch.unitroot.cointegration.engle_granger.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.unitroot.cointegration.engle_granger - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index 9a531477fd..718de8eb14 100644
--- a/devel/unitroot/generated/arch.unitroot.cointegration.phillips_ouliaris.html
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.phillips_ouliaris - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.cointegration.phillips_ouliaris - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index 9d305b4cde..cd8a3e9427 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index 37b40fbbc5..ff44c33366 100644
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+++ b/devel/unitroot/generated/generated/arch.unitroot.ADF.critical_values.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index 0eb5099fbb..bb2200090b 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index 2781b4289f..2f3b0e7a6f 100644
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+++ b/devel/unitroot/generated/generated/arch.unitroot.ADF.max_lags.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index 71b4ca3c92..2a7158a62d 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index ece589ae35..75d444d14f 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index 9edd14d6a5..a6d347950f 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.ADF.regression.html b/devel/unitroot/generated/generated/arch.unitroot.ADF.regression.html
index 77bba4c935..78916525ae 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index 33d101daee..8c7b9c27ec 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.ADF.stat - arch v6.1.1 (+15)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.ADF.summary.html b/devel/unitroot/generated/generated/arch.unitroot.ADF.summary.html
index 231806d7ee..47f0740e32 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.ADF.summary.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.ADF.summary.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.ADF.summary - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.ADF.summary - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.ADF.trend.html b/devel/unitroot/generated/generated/arch.unitroot.ADF.trend.html
index 7b99d67653..4d29ae3aec 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.ADF.trend.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.ADF.trend.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.ADF.trend - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.ADF.trend - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/unitroot/generated/generated/arch.unitroot.ADF.valid_trends.html b/devel/unitroot/generated/generated/arch.unitroot.ADF.valid_trends.html
index eedda7fd82..10ecfa842e 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.ADF.valid_trends.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.ADF.valid_trends.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.ADF.valid_trends - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.ADF.valid_trends - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/unitroot/generated/generated/arch.unitroot.ADF.y.html b/devel/unitroot/generated/generated/arch.unitroot.ADF.y.html
index acc058e03a..8d0d51eff7 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.ADF.y.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.ADF.y.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.ADF.y - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.ADF.y - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/unitroot/generated/generated/arch.unitroot.DFGLS.alternative_hypothesis.html b/devel/unitroot/generated/generated/arch.unitroot.DFGLS.alternative_hypothesis.html
index e017350576..4c63cc0d1b 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.DFGLS.alternative_hypothesis.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.DFGLS.alternative_hypothesis.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.DFGLS.alternative_hypothesis - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.DFGLS.alternative_hypothesis - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/unitroot/generated/generated/arch.unitroot.DFGLS.critical_values.html b/devel/unitroot/generated/generated/arch.unitroot.DFGLS.critical_values.html
index 2cee59fa87..1976cd0516 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.DFGLS.critical_values.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.DFGLS.critical_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.DFGLS.critical_values - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.DFGLS.critical_values - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/unitroot/generated/generated/arch.unitroot.DFGLS.lags.html b/devel/unitroot/generated/generated/arch.unitroot.DFGLS.lags.html
index 2f1b760307..e5cd7f87d1 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.DFGLS.lags.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.DFGLS.lags.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.DFGLS.lags - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.DFGLS.lags - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/unitroot/generated/generated/arch.unitroot.DFGLS.max_lags.html b/devel/unitroot/generated/generated/arch.unitroot.DFGLS.max_lags.html
index a5b7bbdb1b..4e89296900 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.DFGLS.max_lags.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.DFGLS.max_lags.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.DFGLS.max_lags - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.DFGLS.max_lags - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/unitroot/generated/generated/arch.unitroot.DFGLS.nobs.html b/devel/unitroot/generated/generated/arch.unitroot.DFGLS.nobs.html
index f92b4956e3..1a1ccd8de9 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.DFGLS.nobs.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.DFGLS.nobs.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.DFGLS.nobs - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.DFGLS.nobs - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/unitroot/generated/generated/arch.unitroot.DFGLS.null_hypothesis.html b/devel/unitroot/generated/generated/arch.unitroot.DFGLS.null_hypothesis.html
index a099bc80e3..d3c4ab12a2 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.DFGLS.null_hypothesis.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.DFGLS.null_hypothesis.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.DFGLS.null_hypothesis - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.DFGLS.null_hypothesis - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
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index 6b715b8f5e..fef81004f6 100644
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+++ b/devel/unitroot/generated/generated/arch.unitroot.DFGLS.pvalue.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.unitroot.DFGLS.pvalue - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+    arch 6.2.0
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index 82b0c5e36c..7feed7cb29 100644
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+++ b/devel/unitroot/generated/generated/arch.unitroot.DFGLS.regression.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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+++ b/devel/unitroot/generated/generated/arch.unitroot.DFGLS.stat.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.DFGLS.stat - arch v6.1.1 (+15)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.DFGLS.summary.html b/devel/unitroot/generated/generated/arch.unitroot.DFGLS.summary.html
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+++ b/devel/unitroot/generated/generated/arch.unitroot.DFGLS.summary.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.DFGLS.trend.html b/devel/unitroot/generated/generated/arch.unitroot.DFGLS.trend.html
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.DFGLS.trend - arch v6.1.1 (+15)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.DFGLS.valid_trends.html b/devel/unitroot/generated/generated/arch.unitroot.DFGLS.valid_trends.html
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.DFGLS.valid_trends - arch v6.1.1 (+15)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.DFGLS.y - arch v6.1.1 (+15)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index 1d4c0a96ac..466ad27f68 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.KPSS.alternative_hypothesis - arch v6.1.1 (+15)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.KPSS.critical_values.html b/devel/unitroot/generated/generated/arch.unitroot.KPSS.critical_values.html
index 2cea49eac1..15220e6ac9 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.KPSS.critical_values - arch v6.1.1 (+15)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.KPSS.lags.html b/devel/unitroot/generated/generated/arch.unitroot.KPSS.lags.html
index 69fc65c352..a46726489e 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.KPSS.lags - arch v6.1.1 (+15)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index a5d8f45b60..6788abb87a 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.KPSS.nobs - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.KPSS.nobs - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.KPSS.null_hypothesis.html b/devel/unitroot/generated/generated/arch.unitroot.KPSS.null_hypothesis.html
index 40179326ff..0209fa70e4 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.KPSS.null_hypothesis.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.KPSS.null_hypothesis.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.KPSS.null_hypothesis - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.KPSS.null_hypothesis - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.KPSS.pvalue.html b/devel/unitroot/generated/generated/arch.unitroot.KPSS.pvalue.html
index 6b9d6c39b1..ce552437ad 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.KPSS.pvalue.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.KPSS.pvalue.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.KPSS.pvalue - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.KPSS.pvalue - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.KPSS.stat.html b/devel/unitroot/generated/generated/arch.unitroot.KPSS.stat.html
index 4a022e38f2..a0e7e4aa54 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.KPSS.stat.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.KPSS.stat.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.KPSS.stat - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.KPSS.stat - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.KPSS.summary.html b/devel/unitroot/generated/generated/arch.unitroot.KPSS.summary.html
index 6fe373c423..93ab4b31dc 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.KPSS.summary.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.KPSS.summary.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.KPSS.summary - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.KPSS.summary - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.KPSS.trend.html b/devel/unitroot/generated/generated/arch.unitroot.KPSS.trend.html
index b15f26ea30..4a26902bb9 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.KPSS.trend.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.KPSS.trend.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.KPSS.trend - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.KPSS.trend - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.KPSS.valid_trends.html b/devel/unitroot/generated/generated/arch.unitroot.KPSS.valid_trends.html
index b6962e8396..9da6249b18 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.KPSS.valid_trends.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.KPSS.valid_trends.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.KPSS.valid_trends - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.KPSS.valid_trends - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.KPSS.y.html b/devel/unitroot/generated/generated/arch.unitroot.KPSS.y.html
index d57e30f2ba..e2d2d37b86 100644
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+++ b/devel/unitroot/generated/generated/arch.unitroot.KPSS.y.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.KPSS.y - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.KPSS.y - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.alternative_hypothesis.html b/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.alternative_hypothesis.html
index 85ed9ebb37..59e94cc4ec 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.alternative_hypothesis.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.alternative_hypothesis.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.PhillipsPerron.alternative_hypothesis - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.PhillipsPerron.alternative_hypothesis - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.critical_values.html b/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.critical_values.html
index 425ac46ef1..6a1e9c0936 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.critical_values.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.critical_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.PhillipsPerron.critical_values - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.PhillipsPerron.critical_values - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.lags.html b/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.lags.html
index 4572f7c019..493d419915 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.lags.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.lags.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.PhillipsPerron.lags - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.PhillipsPerron.lags - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.nobs.html b/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.nobs.html
index 91ef6f18a8..8c6bd864c1 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.nobs.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.nobs.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.PhillipsPerron.nobs - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.PhillipsPerron.nobs - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.null_hypothesis.html b/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.null_hypothesis.html
index 919531acd2..93ece0f91c 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.null_hypothesis.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.null_hypothesis.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.PhillipsPerron.null_hypothesis - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.PhillipsPerron.null_hypothesis - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.pvalue.html b/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.pvalue.html
index 559b06b722..8519d69542 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.pvalue.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.pvalue.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.PhillipsPerron.pvalue - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.PhillipsPerron.pvalue - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.regression.html b/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.regression.html
index 4d74c6b30f..481dba9af4 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.regression.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.regression.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.PhillipsPerron.regression - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.PhillipsPerron.regression - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.stat.html b/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.stat.html
index 72f9a1b007..405b2cc24a 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.stat.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.stat.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.PhillipsPerron.stat - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.PhillipsPerron.stat - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.summary.html b/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.summary.html
index 58f4913216..7d8943a935 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.summary.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.summary.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.PhillipsPerron.summary - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.PhillipsPerron.summary - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.test_type.html b/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.test_type.html
index 79fcd658c9..6c8d70fb89 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.test_type.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.test_type.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.PhillipsPerron.test_type - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.PhillipsPerron.test_type - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.trend.html b/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.trend.html
index 2c5ae66ac9..02fa9d5a46 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.trend.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.trend.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.PhillipsPerron.trend - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.PhillipsPerron.trend - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.valid_trends.html b/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.valid_trends.html
index ac0673b7de..9172c4b6fc 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.valid_trends.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.valid_trends.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.PhillipsPerron.valid_trends - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.PhillipsPerron.valid_trends - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.y.html b/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.y.html
index 565bbf94f2..fa49dc6b4e 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.y.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.y.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.PhillipsPerron.y - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.PhillipsPerron.y - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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+++ b/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.alternative_hypothesis.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.VarianceRatio.alternative_hypothesis - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.VarianceRatio.alternative_hypothesis - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index 8979024214..17a425f709 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.critical_values.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.critical_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.VarianceRatio.critical_values - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.VarianceRatio.critical_values - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.debiased.html b/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.debiased.html
index 0884835d7d..50f97fc5fc 100644
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+++ b/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.debiased.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.VarianceRatio.debiased - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.VarianceRatio.debiased - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.lags.html b/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.lags.html
index 8749963029..112f98f9a4 100644
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+++ b/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.lags.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.VarianceRatio.lags - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.VarianceRatio.lags - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.nobs.html b/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.nobs.html
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.VarianceRatio.nobs - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.VarianceRatio.nobs - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.null_hypothesis.html b/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.null_hypothesis.html
index c0f0175717..8156c64bab 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.VarianceRatio.null_hypothesis - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.VarianceRatio.null_hypothesis - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.overlap.html b/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.overlap.html
index 06efd33255..be8cf2ea09 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.VarianceRatio.overlap - arch v6.1.1 (+15)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.pvalue.html b/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.pvalue.html
index 0fdfd58bc6..fa2412d76e 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.VarianceRatio.pvalue - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.VarianceRatio.pvalue - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.robust.html b/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.robust.html
index 1576aefe1a..f2627d3182 100644
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+++ b/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.robust.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.VarianceRatio.robust - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.VarianceRatio.robust - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.stat.html b/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.stat.html
index 612c33a466..80e7bc2052 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.VarianceRatio.stat - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.VarianceRatio.stat - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.summary.html b/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.summary.html
index 026e658b63..4c8df59ec0 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.VarianceRatio.summary - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.VarianceRatio.summary - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.trend.html b/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.trend.html
index 68abe7cc4d..b3a2c4da25 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.trend.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.trend.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.VarianceRatio.trend - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.VarianceRatio.trend - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.valid_trends.html b/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.valid_trends.html
index 08abf1f801..0527ab05cf 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.valid_trends.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.valid_trends.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.VarianceRatio.valid_trends - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.VarianceRatio.valid_trends - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.vr.html b/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.vr.html
index a2e767061c..1e9751d761 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.vr.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.vr.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.VarianceRatio.vr - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.VarianceRatio.vr - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.y.html b/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.y.html
index ddd3d48d28..50f5da7c13 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.y.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.y.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.VarianceRatio.y - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.VarianceRatio.y - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.ZivotAndrews.alternative_hypothesis.html b/devel/unitroot/generated/generated/arch.unitroot.ZivotAndrews.alternative_hypothesis.html
index 230a1eefc2..1df21c360f 100644
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+++ b/devel/unitroot/generated/generated/arch.unitroot.ZivotAndrews.alternative_hypothesis.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.ZivotAndrews.alternative_hypothesis - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.ZivotAndrews.alternative_hypothesis - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.ZivotAndrews.critical_values.html b/devel/unitroot/generated/generated/arch.unitroot.ZivotAndrews.critical_values.html
index 43888cd832..fa185bbbf2 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.ZivotAndrews.critical_values.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.ZivotAndrews.critical_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.ZivotAndrews.critical_values - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.ZivotAndrews.critical_values - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index 4d09ca03d1..7b1c63abe2 100644
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@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.unitroot.ZivotAndrews.lags - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index 3d3f77c391..c37127edf5 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.ZivotAndrews.nobs.html
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.ZivotAndrews.nobs - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.ZivotAndrews.nobs - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/unitroot/generated/generated/arch.unitroot.ZivotAndrews.null_hypothesis.html b/devel/unitroot/generated/generated/arch.unitroot.ZivotAndrews.null_hypothesis.html
index f4685142f1..dcb5e3fe2f 100644
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+++ b/devel/unitroot/generated/generated/arch.unitroot.ZivotAndrews.null_hypothesis.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.ZivotAndrews.null_hypothesis - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.ZivotAndrews.null_hypothesis - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.ZivotAndrews.pvalue.html b/devel/unitroot/generated/generated/arch.unitroot.ZivotAndrews.pvalue.html
index 168f924a33..7218124d3e 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.ZivotAndrews.pvalue.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.ZivotAndrews.pvalue.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.ZivotAndrews.pvalue - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.ZivotAndrews.pvalue - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index ee04460c78..f97dfd3f94 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.ZivotAndrews.stat.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.ZivotAndrews.stat.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.unitroot.ZivotAndrews.stat - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index f89d4abef7..460a814cac 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.ZivotAndrews.summary.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.ZivotAndrews.summary.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.unitroot.ZivotAndrews.summary - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index 6ab465363c..ad442deec5 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.ZivotAndrews.trend.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.ZivotAndrews.trend.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.unitroot.ZivotAndrews.trend - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index be11a958ee..a551dd87db 100644
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+++ b/devel/unitroot/generated/generated/arch.unitroot.ZivotAndrews.valid_trends.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.unitroot.ZivotAndrews.valid_trends - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.ZivotAndrews.y.html b/devel/unitroot/generated/generated/arch.unitroot.ZivotAndrews.y.html
index 3b96acfca4..5a9e89a12d 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.CanonicalCointegratingReg.fit.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.CanonicalCointegratingReg.fit.html
index 903e21d44c..221d025a9e 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.CanonicalCointegratingReg.fit - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.cointegration.CanonicalCointegratingReg.fit - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index d771bfdc6e..d86755546f 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index ec6950ec98..0513076f3c 100644
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@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.unitroot.cointegration.CointegrationAnalysisResults.cov - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.kernel.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.kernel.html
index 8930d60c60..384cf1f530 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.kernel.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.kernel.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.unitroot.cointegration.CointegrationAnalysisResults.kernel - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.long_run_variance.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.long_run_variance.html
index a8b38a952e..4779f8ac5e 100644
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+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.long_run_variance.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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@@ -15,7 +15,7 @@
     
   
       
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@@ -15,7 +15,7 @@
     
   
       
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -15,7 +15,7 @@
     
   
       
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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index a7193b7d3a..50e9362bd0 100644
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+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.bandwidth.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.bandwidth - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.bandwidth - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.cov.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.cov.html
index 9c56013506..2ec535773f 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.cov.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.cov.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.cov - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.cov - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.cov_type.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.cov_type.html
index b9f57eda4f..88c2fd2e4a 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.cov_type.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.cov_type.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.cov_type - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.cov_type - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.full_cov.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.full_cov.html
index 114d56c58e..4de1b91781 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.full_cov.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.full_cov.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.full_cov - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.full_cov - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.full_params.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.full_params.html
index bfc7b3e627..ebfdd41de7 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.full_params.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.full_params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.full_params - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.full_params - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.kernel.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.kernel.html
index fea830a966..818bcad6c0 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.kernel.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.kernel.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.kernel - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.kernel - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.lags.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.lags.html
index b39038110f..af8056367a 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.lags.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.lags.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.lags - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.lags - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.leads.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.leads.html
index f62b4a453a..e4850dfffd 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.leads.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.leads.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.leads - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.leads - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.long_run_variance.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.long_run_variance.html
index e317a31c22..13c0fb89c5 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.long_run_variance.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.long_run_variance.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.long_run_variance - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.long_run_variance - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.params.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.params.html
index a57381e78d..595c07dac8 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.params.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.params - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.params - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.pvalues.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.pvalues.html
index 1ce28b138e..3dbcd2f5a6 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.pvalues.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.pvalues.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.pvalues - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.pvalues - arch 6.2.0</title>
       
     
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+            arch 6.2.0
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+    arch 6.2.0
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index 9b1c10b4a5..04086b0571 100644
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+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.resid.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.unitroot.cointegration.DynamicOLSResults.resid - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+            arch 6.2.0
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.unitroot.cointegration.DynamicOLSResults.residual_variance - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.rsquared.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.rsquared - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.rsquared - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
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index 25ecc8f915..4de93fa42a 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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index 9695365696..32ddcfdadf 100644
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@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.unitroot.cointegration.DynamicOLSResults.tvalues - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.alternative_hypothesis.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.alternative_hypothesis.html
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+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.alternative_hypothesis.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.cointegrating_vector.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.cointegrating_vector.html
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+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.cointegrating_vector.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.critical_values.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.critical_values.html
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+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.critical_values.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.unitroot.cointegration.EngleGrangerTestResults.critical_values - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.distribution_order.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.distribution_order.html
index ca25f2ac40..77574bd1ea 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.distribution_order.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.distribution_order.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.EngleGrangerTestResults.distribution_order - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.cointegration.EngleGrangerTestResults.distribution_order - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.lags.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.lags.html
index 9ebdc897d9..b8ebd533bf 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.lags.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.lags.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.EngleGrangerTestResults.lags - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.cointegration.EngleGrangerTestResults.lags - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.max_lags.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.max_lags.html
index a6b4b8a086..c3f19b06f0 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.max_lags.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.max_lags.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.EngleGrangerTestResults.max_lags - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.cointegration.EngleGrangerTestResults.max_lags - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.name.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.name.html
index 9ba27f8b3d..42c56df34f 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.name.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.EngleGrangerTestResults.name - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.cointegration.EngleGrangerTestResults.name - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.null_hypothesis.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.null_hypothesis.html
index 3a6b2b1b27..36dc009775 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.null_hypothesis.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.null_hypothesis.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.EngleGrangerTestResults.null_hypothesis - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.cointegration.EngleGrangerTestResults.null_hypothesis - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.plot.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.plot.html
index 738e90205c..7561886b74 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.plot.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.plot.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.EngleGrangerTestResults.plot - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.cointegration.EngleGrangerTestResults.plot - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.pvalue.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.pvalue.html
index 5c75d7a6bd..23d4caef3b 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.pvalue.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.pvalue.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.EngleGrangerTestResults.pvalue - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.cointegration.EngleGrangerTestResults.pvalue - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.resid.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.resid.html
index b133ba87b5..4efb7d155f 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.resid.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.resid.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.EngleGrangerTestResults.resid - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.cointegration.EngleGrangerTestResults.resid - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.rho.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.rho.html
index 0827f9f3e9..beffbf750a 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.rho.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.rho.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.EngleGrangerTestResults.rho - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.cointegration.EngleGrangerTestResults.rho - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.stat.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.stat.html
index 2d306bff51..7171a01d63 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.stat.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.stat.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.unitroot.cointegration.EngleGrangerTestResults.stat - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+    arch 6.2.0
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.summary.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.summary.html
index 4c7b71c09d..406ebf0b45 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.summary.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.summary.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.EngleGrangerTestResults.summary - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.cointegration.EngleGrangerTestResults.summary - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+            arch 6.2.0
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.trend.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.trend.html
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--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.trend.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.trend.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.EngleGrangerTestResults.trend - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.cointegration.EngleGrangerTestResults.trend - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.FullyModifiedOLS.fit - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.cointegration.FullyModifiedOLS.fit - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+            arch 6.2.0
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.alternative_hypothesis - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.bandwidth.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.bandwidth.html
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@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.bandwidth - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.cointegrating_vector - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
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-    arch v6.1.1 (+15)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.kernel.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.kernel.html
index 13c490de65..baca52c175 100644
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+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.kernel.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.name.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.name.html
index 8d9d417633..01b4ca6185 100644
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+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.name - arch v6.1.1 (+15)</title>
+        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.name - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.null_hypothesis.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.null_hypothesis - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot.html
index 7e58f1dcca..95279f6580 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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@@ -189,10 +189,10 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.pvalue.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.pvalue.html
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+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.pvalue.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.pvalue - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.resid.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.resid.html
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.stat.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.stat.html
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.summary.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.summary.html
index 1849bed46d..2a623bf5fe 100644
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+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.summary.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.trend.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.trend.html
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
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diff --git a/devel/unitroot/introduction.html b/devel/unitroot/introduction.html
index f0e9daa49f..f54237c474 100644
--- a/devel/unitroot/introduction.html
+++ b/devel/unitroot/introduction.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Introduction - arch v6.1.1 (+15)</title>
+        <title>Introduction - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
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diff --git a/devel/unitroot/tests.html b/devel/unitroot/tests.html
index 40592b1639..42687e918d 100644
--- a/devel/unitroot/tests.html
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@@ -92,7 +92,7 @@
 
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-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
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@@ -189,10 +189,10 @@
 
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-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/unitroot/unitroot.html b/devel/unitroot/unitroot.html
index 0d3f72e593..b5329a0bb9 100644
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@@ -15,7 +15,7 @@
     
   
       
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+        <title>Unit Root Testing - arch 6.2.0</title>
       
     
 
@@ -87,7 +87,7 @@
 
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+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -97,7 +97,7 @@
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+            arch 6.2.0
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@@ -184,10 +184,10 @@
 
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+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+    arch 6.2.0
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--- a/devel/unitroot/unitroot_cointegration_examples.html
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@@ -15,7 +15,7 @@
     
   
       
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+        <title>Cointegration Testing - arch 6.2.0</title>
       
     
 
@@ -93,7 +93,7 @@
 
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diff --git a/devel/unitroot/unitroot_examples.html b/devel/unitroot/unitroot_examples.html
index c1b9cfbe53..41e39106c8 100644
--- a/devel/unitroot/unitroot_examples.html
+++ b/devel/unitroot/unitroot_examples.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>Unit Root Testing - arch 6.2.0</title>
       
     
 
@@ -93,7 +93,7 @@
 
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+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -103,7 +103,7 @@
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+            arch 6.2.0
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@@ -190,10 +190,10 @@
 
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-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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diff --git a/devel/univariate/background.html b/devel/univariate/background.html
index 75357e3cb5..5bc8439cde 100644
--- a/devel/univariate/background.html
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@@ -15,7 +15,7 @@
     
   
       
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@@ -87,7 +87,7 @@
 
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+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -97,7 +97,7 @@
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+            arch 6.2.0
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@@ -184,10 +184,10 @@
 
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-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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--- a/devel/univariate/distribution.html
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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diff --git a/devel/univariate/forecasting.html b/devel/univariate/forecasting.html
index 263dfff3ca..b759a07dd0 100644
--- a/devel/univariate/forecasting.html
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/arch.univariate.APARCH.html b/devel/univariate/generated/arch.univariate.APARCH.html
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--- a/devel/univariate/generated/arch.univariate.APARCH.html
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@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.univariate.APARCH - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
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--- a/devel/univariate/generated/arch.univariate.ARCH.html
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
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diff --git a/devel/univariate/generated/arch.univariate.ARCHInMean.html b/devel/univariate/generated/arch.univariate.ARCHInMean.html
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--- a/devel/univariate/generated/arch.univariate.ARCHInMean.html
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/arch.univariate.ARX.html b/devel/univariate/generated/arch.univariate.ARX.html
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/arch.univariate.ConstantMean.html b/devel/univariate/generated/arch.univariate.ConstantMean.html
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--- a/devel/univariate/generated/arch.univariate.ConstantMean.html
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+    arch 6.2.0
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--- a/devel/univariate/generated/arch.univariate.EGARCH.html
+++ b/devel/univariate/generated/arch.univariate.EGARCH.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.univariate.EGARCH - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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--- a/devel/univariate/generated/arch.univariate.EWMAVariance.html
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+    arch 6.2.0
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--- a/devel/univariate/generated/arch.univariate.FIGARCH.html
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@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.univariate.FIGARCH - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/arch.univariate.FixedVariance.html b/devel/univariate/generated/arch.univariate.FixedVariance.html
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--- a/devel/univariate/generated/arch.univariate.FixedVariance.html
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/arch.univariate.GARCH.html b/devel/univariate/generated/arch.univariate.GARCH.html
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/arch.univariate.GeneralizedError.html b/devel/univariate/generated/arch.univariate.GeneralizedError.html
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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--- a/devel/univariate/generated/arch.univariate.HARX.html
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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--- a/devel/univariate/generated/arch.univariate.LS.html
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/arch.univariate.Normal.html b/devel/univariate/generated/arch.univariate.Normal.html
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--- a/devel/univariate/generated/arch.univariate.Normal.html
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+    arch 6.2.0
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--- a/devel/univariate/generated/arch.univariate.RiskMetrics2006.html
+++ b/devel/univariate/generated/arch.univariate.RiskMetrics2006.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+    arch 6.2.0
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index df96596e0d..b7424c494f 100644
--- a/devel/univariate/generated/arch.univariate.SkewStudent.html
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+    arch 6.2.0
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--- a/devel/univariate/generated/arch.univariate.ZeroMean.html
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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--- a/devel/univariate/generated/arch.univariate.base.ARCHModel.html
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/arch.univariate.base.ARCHModelResult.html b/devel/univariate/generated/arch.univariate.base.ARCHModelResult.html
index ec54213d41..a8c87d1208 100644
--- a/devel/univariate/generated/arch.univariate.base.ARCHModelResult.html
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+    arch 6.2.0
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--- a/devel/univariate/generated/arch.univariate.distribution.Distribution.html
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -15,7 +15,7 @@
     
   
       
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.ARCH.backcast_transform.html b/devel/univariate/generated/generated/arch.univariate.ARCH.backcast_transform.html
index d33ba5910d..8de964d7dc 100644
--- a/devel/univariate/generated/generated/arch.univariate.ARCH.backcast_transform.html
+++ b/devel/univariate/generated/generated/arch.univariate.ARCH.backcast_transform.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCH.backcast_transform - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.ARCH.backcast_transform - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.ARCH.bounds.html b/devel/univariate/generated/generated/arch.univariate.ARCH.bounds.html
index a672e62322..2d95647b5f 100644
--- a/devel/univariate/generated/generated/arch.univariate.ARCH.bounds.html
+++ b/devel/univariate/generated/generated/arch.univariate.ARCH.bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCH.bounds - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.ARCH.bounds - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.ARCH.compute_variance.html b/devel/univariate/generated/generated/arch.univariate.ARCH.compute_variance.html
index 4ca9b8c67c..ea36a492de 100644
--- a/devel/univariate/generated/generated/arch.univariate.ARCH.compute_variance.html
+++ b/devel/univariate/generated/generated/arch.univariate.ARCH.compute_variance.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCH.compute_variance - arch v6.1.1 (+15)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.ARCH.constraints.html b/devel/univariate/generated/generated/arch.univariate.ARCH.constraints.html
index a023085ccc..1ba9dd40a7 100644
--- a/devel/univariate/generated/generated/arch.univariate.ARCH.constraints.html
+++ b/devel/univariate/generated/generated/arch.univariate.ARCH.constraints.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCH.constraints - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.ARCH.constraints - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.ARCH.forecast.html b/devel/univariate/generated/generated/arch.univariate.ARCH.forecast.html
index b9e89d815b..1773849834 100644
--- a/devel/univariate/generated/generated/arch.univariate.ARCH.forecast.html
+++ b/devel/univariate/generated/generated/arch.univariate.ARCH.forecast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCH.forecast - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.ARCH.forecast - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.ARCH.name.html b/devel/univariate/generated/generated/arch.univariate.ARCH.name.html
index 34769e4562..b6ee91ce67 100644
--- a/devel/univariate/generated/generated/arch.univariate.ARCH.name.html
+++ b/devel/univariate/generated/generated/arch.univariate.ARCH.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCH.name - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.ARCH.name - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.ARCH.num_params.html b/devel/univariate/generated/generated/arch.univariate.ARCH.num_params.html
index af30d0426d..3a4faa8ce0 100644
--- a/devel/univariate/generated/generated/arch.univariate.ARCH.num_params.html
+++ b/devel/univariate/generated/generated/arch.univariate.ARCH.num_params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCH.num_params - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.ARCH.num_params - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.ARCH.parameter_names.html b/devel/univariate/generated/generated/arch.univariate.ARCH.parameter_names.html
index d396395660..98eb5b7a60 100644
--- a/devel/univariate/generated/generated/arch.univariate.ARCH.parameter_names.html
+++ b/devel/univariate/generated/generated/arch.univariate.ARCH.parameter_names.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCH.parameter_names - arch v6.1.1 (+15)</title>
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.ARCH.simulate.html b/devel/univariate/generated/generated/arch.univariate.ARCH.simulate.html
index 169b57fa60..e158ec3a4e 100644
--- a/devel/univariate/generated/generated/arch.univariate.ARCH.simulate.html
+++ b/devel/univariate/generated/generated/arch.univariate.ARCH.simulate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCH.simulate - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.ARCH.simulate - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.ARCH.start.html b/devel/univariate/generated/generated/arch.univariate.ARCH.start.html
index f6c7a6b574..5d2a782ad3 100644
--- a/devel/univariate/generated/generated/arch.univariate.ARCH.start.html
+++ b/devel/univariate/generated/generated/arch.univariate.ARCH.start.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCH.start - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.ARCH.start - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.ARCH.starting_values.html b/devel/univariate/generated/generated/arch.univariate.ARCH.starting_values.html
index 3e7c3b6350..15a7c08c0a 100644
--- a/devel/univariate/generated/generated/arch.univariate.ARCH.starting_values.html
+++ b/devel/univariate/generated/generated/arch.univariate.ARCH.starting_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCH.starting_values - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.ARCH.starting_values - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+    arch 6.2.0
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index ec9bd88972..17c4d9bf30 100644
--- a/devel/univariate/generated/generated/arch.univariate.ARCH.stop.html
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+    arch 6.2.0
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index bd6d43cae6..4b31bfbf3c 100644
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index ccb8222790..a8330836af 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCHInMean.fit - arch v6.1.1 (+15)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
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+++ b/devel/univariate/generated/generated/arch.univariate.ARCHInMean.fix.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+            arch 6.2.0
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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index dcd241b25c..30fb67ec3a 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.ARCHInMean.forecast.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+++ b/devel/univariate/generated/generated/arch.univariate.ARCHInMean.form.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCHInMean.form - arch v6.1.1 (+15)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCHInMean.name - arch v6.1.1 (+15)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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+++ b/devel/univariate/generated/generated/arch.univariate.ARCHInMean.parameter_names.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCHInMean.parameter_names - arch v6.1.1 (+15)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCHInMean.resids - arch v6.1.1 (+15)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCHInMean.starting_values - arch v6.1.1 (+15)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+    arch 6.2.0
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index 3ab86af856..8a3eec0e74 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.ARCHInMean.y.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index ce01fdce06..5219e6bfb0 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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+++ b/devel/univariate/generated/generated/arch.univariate.ARX.compute_param_cov.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.ARX.fit.html b/devel/univariate/generated/generated/arch.univariate.ARX.fit.html
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
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diff --git a/devel/univariate/generated/generated/arch.univariate.ARX.num_params.html b/devel/univariate/generated/generated/arch.univariate.ARX.num_params.html
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
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index 60e3e72b92..60c48a9f9c 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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index 28d86e4a6e..6b2c77aa4d 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+            arch 6.2.0
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+    arch 6.2.0
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@@ -15,7 +15,7 @@
     
   
       
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index fe480329ec..a86c08b5a5 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index a65aebd8d5..c06b7218ce 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantMean.fit.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantMean.fit.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantMean.fit - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.ConstantMean.fit - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantMean.fix.html b/devel/univariate/generated/generated/arch.univariate.ConstantMean.fix.html
index 02d238108d..6c71464752 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantMean.fix.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantMean.fix.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantMean.fix - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.ConstantMean.fix - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantMean.forecast.html b/devel/univariate/generated/generated/arch.univariate.ConstantMean.forecast.html
index 73bd33efc9..54107a628b 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantMean.forecast.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantMean.forecast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantMean.forecast - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.ConstantMean.forecast - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
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@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantMean.name.html b/devel/univariate/generated/generated/arch.univariate.ConstantMean.name.html
index 615fbf8d0a..9117467b5e 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantMean.name.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantMean.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantMean.name - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.ConstantMean.name - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantMean.num_params.html b/devel/univariate/generated/generated/arch.univariate.ConstantMean.num_params.html
index 1acc123d12..2f20f53cfd 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantMean.num_params.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantMean.num_params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantMean.num_params - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.ConstantMean.num_params - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantMean.parameter_names.html b/devel/univariate/generated/generated/arch.univariate.ConstantMean.parameter_names.html
index a8ba70f02f..37de387786 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantMean.parameter_names.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantMean.parameter_names.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantMean.parameter_names - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.ConstantMean.parameter_names - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantMean.resids.html b/devel/univariate/generated/generated/arch.univariate.ConstantMean.resids.html
index 67ba2697b3..74bc3d18e0 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantMean.resids.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantMean.resids.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantMean.resids - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.ConstantMean.resids - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantMean.simulate.html b/devel/univariate/generated/generated/arch.univariate.ConstantMean.simulate.html
index 35687629e2..c40da2aed7 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantMean.simulate.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantMean.simulate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantMean.simulate - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.ConstantMean.simulate - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantMean.starting_values.html b/devel/univariate/generated/generated/arch.univariate.ConstantMean.starting_values.html
index 8b72486080..8a09522447 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantMean.starting_values.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantMean.starting_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantMean.starting_values - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.ConstantMean.starting_values - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantMean.volatility.html b/devel/univariate/generated/generated/arch.univariate.ConstantMean.volatility.html
index a20131d8af..b711993b24 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantMean.volatility.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantMean.volatility.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantMean.volatility - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.ConstantMean.volatility - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantMean.x.html b/devel/univariate/generated/generated/arch.univariate.ConstantMean.x.html
index bfd32c794c..5030d97b09 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantMean.x.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantMean.x.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantMean.x - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.ConstantMean.x - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantMean.y.html b/devel/univariate/generated/generated/arch.univariate.ConstantMean.y.html
index dbe981a6d7..3d9aa48b9a 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantMean.y.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantMean.y.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantMean.y - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.ConstantMean.y - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.backcast.html b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.backcast.html
index b2d51b54b7..e4db24c027 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.backcast.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.backcast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantVariance.backcast - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.ConstantVariance.backcast - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.backcast_transform.html b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.backcast_transform.html
index 2497216701..c0a6e5fd0c 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.backcast_transform.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.backcast_transform.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantVariance.backcast_transform - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.ConstantVariance.backcast_transform - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.bounds.html b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.bounds.html
index e801a7134b..59dd65852e 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.bounds.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantVariance.bounds - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.ConstantVariance.bounds - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.compute_variance.html b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.compute_variance.html
index 75f5776419..41dca9068b 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.compute_variance.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.compute_variance.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantVariance.compute_variance - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.ConstantVariance.compute_variance - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.constraints.html b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.constraints.html
index b69b16bac5..07c26e50f6 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.constraints.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.constraints.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantVariance.constraints - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.ConstantVariance.constraints - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.forecast.html b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.forecast.html
index b3fb52476a..7e0c7729cf 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.forecast.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.forecast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantVariance.forecast - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.ConstantVariance.forecast - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.name.html b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.name.html
index 304d379ed3..b3985a237e 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.name.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantVariance.name - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.ConstantVariance.name - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.num_params.html b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.num_params.html
index 15f7cc4ec1..0452939f37 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.num_params.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.num_params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantVariance.num_params - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.ConstantVariance.num_params - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.parameter_names.html b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.parameter_names.html
index f8daefc42a..74bdb9066c 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.parameter_names.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.parameter_names.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantVariance.parameter_names - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.ConstantVariance.parameter_names - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.simulate.html b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.simulate.html
index 1795e6b99d..0f31520c46 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.simulate.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.simulate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantVariance.simulate - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.ConstantVariance.simulate - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.start.html b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.start.html
index eed0bc7e2d..7cca8654e1 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.start.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.start.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantVariance.start - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.ConstantVariance.start - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.starting_values.html b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.starting_values.html
index 86eefea472..5e83f4e096 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.starting_values.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.starting_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantVariance.starting_values - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.ConstantVariance.starting_values - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.stop.html b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.stop.html
index b386ac26b2..0a66e909be 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.stop.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.stop.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantVariance.stop - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.ConstantVariance.stop - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.update.html b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.update.html
index ba36b86ed9..cdff481c22 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.update.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.update.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantVariance.update - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.ConstantVariance.update - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.updateable.html b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.updateable.html
index a655bd790d..9aaa1d0e0b 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.updateable.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.updateable.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantVariance.updateable - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.ConstantVariance.updateable - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.variance_bounds.html b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.variance_bounds.html
index 45c9b6ac9b..38856c8fad 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.variance_bounds.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.variance_bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantVariance.variance_bounds - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.ConstantVariance.variance_bounds - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.volatility_updater.html b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.volatility_updater.html
index 69900a3d0d..017931e436 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.volatility_updater.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.volatility_updater.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantVariance.volatility_updater - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.ConstantVariance.volatility_updater - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.EGARCH.backcast.html b/devel/univariate/generated/generated/arch.univariate.EGARCH.backcast.html
index 5c3b8067f2..1bbca8288a 100644
--- a/devel/univariate/generated/generated/arch.univariate.EGARCH.backcast.html
+++ b/devel/univariate/generated/generated/arch.univariate.EGARCH.backcast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EGARCH.backcast - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.EGARCH.backcast - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.EGARCH.backcast_transform.html b/devel/univariate/generated/generated/arch.univariate.EGARCH.backcast_transform.html
index e104860387..ea6ae53600 100644
--- a/devel/univariate/generated/generated/arch.univariate.EGARCH.backcast_transform.html
+++ b/devel/univariate/generated/generated/arch.univariate.EGARCH.backcast_transform.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EGARCH.backcast_transform - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.EGARCH.backcast_transform - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.EGARCH.bounds.html b/devel/univariate/generated/generated/arch.univariate.EGARCH.bounds.html
index 7c98d91739..c4ed8b6e6e 100644
--- a/devel/univariate/generated/generated/arch.univariate.EGARCH.bounds.html
+++ b/devel/univariate/generated/generated/arch.univariate.EGARCH.bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EGARCH.bounds - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.EGARCH.bounds - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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diff --git a/devel/univariate/generated/generated/arch.univariate.EGARCH.compute_variance.html b/devel/univariate/generated/generated/arch.univariate.EGARCH.compute_variance.html
index 570d86ce5b..bd587b9045 100644
--- a/devel/univariate/generated/generated/arch.univariate.EGARCH.compute_variance.html
+++ b/devel/univariate/generated/generated/arch.univariate.EGARCH.compute_variance.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.univariate.EGARCH.compute_variance - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index 720ee5344b..e7c782d6fe 100644
--- a/devel/univariate/generated/generated/arch.univariate.EGARCH.constraints.html
+++ b/devel/univariate/generated/generated/arch.univariate.EGARCH.constraints.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index 6dc6e0a957..50e3b3ef2c 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index b4a9d227ce..7517816efc 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.EGARCH.num_params.html b/devel/univariate/generated/generated/arch.univariate.EGARCH.num_params.html
index c628cec4c3..678325dbbc 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.EGARCH.num_params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EGARCH.num_params - arch v6.1.1 (+15)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.EGARCH.parameter_names.html b/devel/univariate/generated/generated/arch.univariate.EGARCH.parameter_names.html
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EGARCH.parameter_names - arch v6.1.1 (+15)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.EGARCH.simulate.html b/devel/univariate/generated/generated/arch.univariate.EGARCH.simulate.html
index 4395d21fa4..8a5879ed4b 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EGARCH.simulate - arch v6.1.1 (+15)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.EGARCH.start.html b/devel/univariate/generated/generated/arch.univariate.EGARCH.start.html
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EGARCH.start - arch v6.1.1 (+15)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.EGARCH.starting_values.html b/devel/univariate/generated/generated/arch.univariate.EGARCH.starting_values.html
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+++ b/devel/univariate/generated/generated/arch.univariate.EGARCH.starting_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EGARCH.starting_values - arch v6.1.1 (+15)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.EGARCH.stop.html b/devel/univariate/generated/generated/arch.univariate.EGARCH.stop.html
index a62221613d..09fcae9e51 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.EGARCH.update.html b/devel/univariate/generated/generated/arch.univariate.EGARCH.update.html
index 4698674d9c..41a45111c5 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EGARCH.update - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.EGARCH.update - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.EGARCH.updateable.html b/devel/univariate/generated/generated/arch.univariate.EGARCH.updateable.html
index 26dfba5830..f375daa4ed 100644
--- a/devel/univariate/generated/generated/arch.univariate.EGARCH.updateable.html
+++ b/devel/univariate/generated/generated/arch.univariate.EGARCH.updateable.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EGARCH.updateable - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.EGARCH.updateable - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.EGARCH.variance_bounds.html b/devel/univariate/generated/generated/arch.univariate.EGARCH.variance_bounds.html
index 04c8e9fc3a..80daa47a93 100644
--- a/devel/univariate/generated/generated/arch.univariate.EGARCH.variance_bounds.html
+++ b/devel/univariate/generated/generated/arch.univariate.EGARCH.variance_bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EGARCH.variance_bounds - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.EGARCH.variance_bounds - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.EGARCH.volatility_updater.html b/devel/univariate/generated/generated/arch.univariate.EGARCH.volatility_updater.html
index 0d012f47b1..8b98fc9ad3 100644
--- a/devel/univariate/generated/generated/arch.univariate.EGARCH.volatility_updater.html
+++ b/devel/univariate/generated/generated/arch.univariate.EGARCH.volatility_updater.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EGARCH.volatility_updater - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.EGARCH.volatility_updater - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.EWMAVariance.backcast.html b/devel/univariate/generated/generated/arch.univariate.EWMAVariance.backcast.html
index 958a0cd749..8592b581e1 100644
--- a/devel/univariate/generated/generated/arch.univariate.EWMAVariance.backcast.html
+++ b/devel/univariate/generated/generated/arch.univariate.EWMAVariance.backcast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EWMAVariance.backcast - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.EWMAVariance.backcast - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.EWMAVariance.backcast_transform.html b/devel/univariate/generated/generated/arch.univariate.EWMAVariance.backcast_transform.html
index 483bc31417..dc5a578813 100644
--- a/devel/univariate/generated/generated/arch.univariate.EWMAVariance.backcast_transform.html
+++ b/devel/univariate/generated/generated/arch.univariate.EWMAVariance.backcast_transform.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EWMAVariance.backcast_transform - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.EWMAVariance.backcast_transform - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.EWMAVariance.bounds.html b/devel/univariate/generated/generated/arch.univariate.EWMAVariance.bounds.html
index 2cfbdc89d4..a6e6ea2f5b 100644
--- a/devel/univariate/generated/generated/arch.univariate.EWMAVariance.bounds.html
+++ b/devel/univariate/generated/generated/arch.univariate.EWMAVariance.bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EWMAVariance.bounds - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.EWMAVariance.bounds - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.EWMAVariance.compute_variance.html b/devel/univariate/generated/generated/arch.univariate.EWMAVariance.compute_variance.html
index 73edf429e0..461e1be41c 100644
--- a/devel/univariate/generated/generated/arch.univariate.EWMAVariance.compute_variance.html
+++ b/devel/univariate/generated/generated/arch.univariate.EWMAVariance.compute_variance.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EWMAVariance.compute_variance - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.EWMAVariance.compute_variance - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.EWMAVariance.constraints.html b/devel/univariate/generated/generated/arch.univariate.EWMAVariance.constraints.html
index ee11d0060f..597bc64dd1 100644
--- a/devel/univariate/generated/generated/arch.univariate.EWMAVariance.constraints.html
+++ b/devel/univariate/generated/generated/arch.univariate.EWMAVariance.constraints.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EWMAVariance.constraints - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.EWMAVariance.constraints - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.EWMAVariance.forecast.html b/devel/univariate/generated/generated/arch.univariate.EWMAVariance.forecast.html
index 1634dc3fe0..7eaa47a732 100644
--- a/devel/univariate/generated/generated/arch.univariate.EWMAVariance.forecast.html
+++ b/devel/univariate/generated/generated/arch.univariate.EWMAVariance.forecast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EWMAVariance.forecast - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.EWMAVariance.forecast - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.EWMAVariance.name.html b/devel/univariate/generated/generated/arch.univariate.EWMAVariance.name.html
index e0b8395797..d50f042fbc 100644
--- a/devel/univariate/generated/generated/arch.univariate.EWMAVariance.name.html
+++ b/devel/univariate/generated/generated/arch.univariate.EWMAVariance.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EWMAVariance.name - arch v6.1.1 (+15)</title>
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.EWMAVariance.num_params.html b/devel/univariate/generated/generated/arch.univariate.EWMAVariance.num_params.html
index 56367c7a5e..2ce64a20bd 100644
--- a/devel/univariate/generated/generated/arch.univariate.EWMAVariance.num_params.html
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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index 5999f8d9e9..e5d784387e 100644
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@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.univariate.EWMAVariance.parameter_names - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index 2cce5f99d8..8bb57ad2e9 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.EWMAVariance.simulate.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.univariate.EWMAVariance.simulate - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.EWMAVariance.stop.html b/devel/univariate/generated/generated/arch.univariate.EWMAVariance.stop.html
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EWMAVariance.updateable - arch v6.1.1 (+15)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index 383ad7ea6b..85854e2aef 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EWMAVariance.variance_bounds - arch v6.1.1 (+15)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.FIGARCH.backcast_transform.html b/devel/univariate/generated/generated/arch.univariate.FIGARCH.backcast_transform.html
index 3e79c34668..a825e029cf 100644
--- a/devel/univariate/generated/generated/arch.univariate.FIGARCH.backcast_transform.html
+++ b/devel/univariate/generated/generated/arch.univariate.FIGARCH.backcast_transform.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FIGARCH.backcast_transform - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.FIGARCH.backcast_transform - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.FIGARCH.bounds.html b/devel/univariate/generated/generated/arch.univariate.FIGARCH.bounds.html
index 97c04690e3..db745a7eca 100644
--- a/devel/univariate/generated/generated/arch.univariate.FIGARCH.bounds.html
+++ b/devel/univariate/generated/generated/arch.univariate.FIGARCH.bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FIGARCH.bounds - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.FIGARCH.bounds - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.FIGARCH.compute_variance.html b/devel/univariate/generated/generated/arch.univariate.FIGARCH.compute_variance.html
index d0a4aa0404..3a8bbdf3dd 100644
--- a/devel/univariate/generated/generated/arch.univariate.FIGARCH.compute_variance.html
+++ b/devel/univariate/generated/generated/arch.univariate.FIGARCH.compute_variance.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FIGARCH.compute_variance - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.FIGARCH.compute_variance - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.FIGARCH.constraints.html b/devel/univariate/generated/generated/arch.univariate.FIGARCH.constraints.html
index bbb8d1a11a..a8c1fe84a0 100644
--- a/devel/univariate/generated/generated/arch.univariate.FIGARCH.constraints.html
+++ b/devel/univariate/generated/generated/arch.univariate.FIGARCH.constraints.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FIGARCH.constraints - arch v6.1.1 (+15)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.FIGARCH.forecast.html b/devel/univariate/generated/generated/arch.univariate.FIGARCH.forecast.html
index c312a06088..5cd6c27ee5 100644
--- a/devel/univariate/generated/generated/arch.univariate.FIGARCH.forecast.html
+++ b/devel/univariate/generated/generated/arch.univariate.FIGARCH.forecast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FIGARCH.forecast - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.FIGARCH.forecast - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.FIGARCH.name.html b/devel/univariate/generated/generated/arch.univariate.FIGARCH.name.html
index ffa173a923..a60d030dea 100644
--- a/devel/univariate/generated/generated/arch.univariate.FIGARCH.name.html
+++ b/devel/univariate/generated/generated/arch.univariate.FIGARCH.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FIGARCH.name - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.FIGARCH.name - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.FIGARCH.num_params.html b/devel/univariate/generated/generated/arch.univariate.FIGARCH.num_params.html
index 2ed1f16d4a..b53481871d 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.FIGARCH.num_params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FIGARCH.num_params - arch v6.1.1 (+15)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.FIGARCH.parameter_names.html b/devel/univariate/generated/generated/arch.univariate.FIGARCH.parameter_names.html
index dee6027b53..e7a996850c 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.FIGARCH.parameter_names.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FIGARCH.parameter_names - arch v6.1.1 (+15)</title>
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.FIGARCH.simulate.html b/devel/univariate/generated/generated/arch.univariate.FIGARCH.simulate.html
index e5da39d5c1..35e677d66c 100644
--- a/devel/univariate/generated/generated/arch.univariate.FIGARCH.simulate.html
+++ b/devel/univariate/generated/generated/arch.univariate.FIGARCH.simulate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FIGARCH.simulate - arch v6.1.1 (+15)</title>
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.FIGARCH.start.html b/devel/univariate/generated/generated/arch.univariate.FIGARCH.start.html
index c1930f8951..d234dafd33 100644
--- a/devel/univariate/generated/generated/arch.univariate.FIGARCH.start.html
+++ b/devel/univariate/generated/generated/arch.univariate.FIGARCH.start.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FIGARCH.start - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.FIGARCH.start - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.FIGARCH.starting_values.html b/devel/univariate/generated/generated/arch.univariate.FIGARCH.starting_values.html
index 46f73f5752..9785f5a3dc 100644
--- a/devel/univariate/generated/generated/arch.univariate.FIGARCH.starting_values.html
+++ b/devel/univariate/generated/generated/arch.univariate.FIGARCH.starting_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FIGARCH.starting_values - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.FIGARCH.starting_values - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.FIGARCH.stop.html b/devel/univariate/generated/generated/arch.univariate.FIGARCH.stop.html
index 35a8fc7786..1ebb48cb23 100644
--- a/devel/univariate/generated/generated/arch.univariate.FIGARCH.stop.html
+++ b/devel/univariate/generated/generated/arch.univariate.FIGARCH.stop.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.univariate.FIGARCH.stop - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.FIGARCH.truncation.html b/devel/univariate/generated/generated/arch.univariate.FIGARCH.truncation.html
index f55c74e7bc..bbb2f6b3cf 100644
--- a/devel/univariate/generated/generated/arch.univariate.FIGARCH.truncation.html
+++ b/devel/univariate/generated/generated/arch.univariate.FIGARCH.truncation.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FIGARCH.truncation - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.FIGARCH.truncation - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.FIGARCH.update.html b/devel/univariate/generated/generated/arch.univariate.FIGARCH.update.html
index cf49861858..63cece2a3a 100644
--- a/devel/univariate/generated/generated/arch.univariate.FIGARCH.update.html
+++ b/devel/univariate/generated/generated/arch.univariate.FIGARCH.update.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FIGARCH.update - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.FIGARCH.update - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.FIGARCH.updateable.html b/devel/univariate/generated/generated/arch.univariate.FIGARCH.updateable.html
index becea982af..c507fb0c0c 100644
--- a/devel/univariate/generated/generated/arch.univariate.FIGARCH.updateable.html
+++ b/devel/univariate/generated/generated/arch.univariate.FIGARCH.updateable.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FIGARCH.updateable - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.FIGARCH.updateable - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.FIGARCH.variance_bounds.html b/devel/univariate/generated/generated/arch.univariate.FIGARCH.variance_bounds.html
index 8e060ebe42..91bff38a1e 100644
--- a/devel/univariate/generated/generated/arch.univariate.FIGARCH.variance_bounds.html
+++ b/devel/univariate/generated/generated/arch.univariate.FIGARCH.variance_bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FIGARCH.variance_bounds - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.FIGARCH.variance_bounds - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.FIGARCH.volatility_updater.html b/devel/univariate/generated/generated/arch.univariate.FIGARCH.volatility_updater.html
index 87ba75bf16..6c53b81b2a 100644
--- a/devel/univariate/generated/generated/arch.univariate.FIGARCH.volatility_updater.html
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FIGARCH.volatility_updater - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.FIGARCH.volatility_updater - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.FixedVariance.backcast.html b/devel/univariate/generated/generated/arch.univariate.FixedVariance.backcast.html
index a52ff7d611..ae3c857fb1 100644
--- a/devel/univariate/generated/generated/arch.univariate.FixedVariance.backcast.html
+++ b/devel/univariate/generated/generated/arch.univariate.FixedVariance.backcast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FixedVariance.backcast - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.FixedVariance.backcast - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.FixedVariance.backcast_transform.html b/devel/univariate/generated/generated/arch.univariate.FixedVariance.backcast_transform.html
index c57d9f1a30..e93d32819b 100644
--- a/devel/univariate/generated/generated/arch.univariate.FixedVariance.backcast_transform.html
+++ b/devel/univariate/generated/generated/arch.univariate.FixedVariance.backcast_transform.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FixedVariance.backcast_transform - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.FixedVariance.backcast_transform - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.FixedVariance.bounds.html b/devel/univariate/generated/generated/arch.univariate.FixedVariance.bounds.html
index ff755e1aab..070adce29c 100644
--- a/devel/univariate/generated/generated/arch.univariate.FixedVariance.bounds.html
+++ b/devel/univariate/generated/generated/arch.univariate.FixedVariance.bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FixedVariance.bounds - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.FixedVariance.bounds - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.FixedVariance.compute_variance.html b/devel/univariate/generated/generated/arch.univariate.FixedVariance.compute_variance.html
index 9f413a33c5..d1107f9a7d 100644
--- a/devel/univariate/generated/generated/arch.univariate.FixedVariance.compute_variance.html
+++ b/devel/univariate/generated/generated/arch.univariate.FixedVariance.compute_variance.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FixedVariance.compute_variance - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.FixedVariance.compute_variance - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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index 98eb6a2195..c6e7d649d5 100644
--- a/devel/univariate/generated/generated/arch.univariate.FixedVariance.constraints.html
+++ b/devel/univariate/generated/generated/arch.univariate.FixedVariance.constraints.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.univariate.FixedVariance.constraints - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+    arch 6.2.0
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+++ b/devel/univariate/generated/generated/arch.univariate.FixedVariance.forecast.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index 153d445115..a161c904dd 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.FixedVariance.parameter_names.html b/devel/univariate/generated/generated/arch.univariate.FixedVariance.parameter_names.html
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FixedVariance.parameter_names - arch v6.1.1 (+15)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.FixedVariance.simulate.html b/devel/univariate/generated/generated/arch.univariate.FixedVariance.simulate.html
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+++ b/devel/univariate/generated/generated/arch.univariate.FixedVariance.simulate.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.FixedVariance.starting_values.html b/devel/univariate/generated/generated/arch.univariate.FixedVariance.starting_values.html
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FixedVariance.starting_values - arch v6.1.1 (+15)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index 43f4302cb4..7bb60b5d2b 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FixedVariance.update - arch v6.1.1 (+15)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
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diff --git a/devel/univariate/generated/generated/arch.univariate.FixedVariance.updateable.html b/devel/univariate/generated/generated/arch.univariate.FixedVariance.updateable.html
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FixedVariance.updateable - arch v6.1.1 (+15)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index e35336f80c..18126251d6 100644
--- a/devel/univariate/generated/generated/arch.univariate.FixedVariance.variance_bounds.html
+++ b/devel/univariate/generated/generated/arch.univariate.FixedVariance.variance_bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FixedVariance.variance_bounds - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.FixedVariance.variance_bounds - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.FixedVariance.volatility_updater.html b/devel/univariate/generated/generated/arch.univariate.FixedVariance.volatility_updater.html
index 509d4920fd..042d9c5b9a 100644
--- a/devel/univariate/generated/generated/arch.univariate.FixedVariance.volatility_updater.html
+++ b/devel/univariate/generated/generated/arch.univariate.FixedVariance.volatility_updater.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FixedVariance.volatility_updater - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.FixedVariance.volatility_updater - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.GARCH.backcast.html b/devel/univariate/generated/generated/arch.univariate.GARCH.backcast.html
index 07b592e54d..94b2687f14 100644
--- a/devel/univariate/generated/generated/arch.univariate.GARCH.backcast.html
+++ b/devel/univariate/generated/generated/arch.univariate.GARCH.backcast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GARCH.backcast - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.GARCH.backcast - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.GARCH.backcast_transform.html b/devel/univariate/generated/generated/arch.univariate.GARCH.backcast_transform.html
index 85030db462..5e3f3378a5 100644
--- a/devel/univariate/generated/generated/arch.univariate.GARCH.backcast_transform.html
+++ b/devel/univariate/generated/generated/arch.univariate.GARCH.backcast_transform.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GARCH.backcast_transform - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.GARCH.backcast_transform - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.GARCH.bounds.html b/devel/univariate/generated/generated/arch.univariate.GARCH.bounds.html
index 223f019069..5b3d95bde5 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.GARCH.bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GARCH.bounds - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.GARCH.bounds - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.GARCH.compute_variance.html b/devel/univariate/generated/generated/arch.univariate.GARCH.compute_variance.html
index efb138a611..297390ba93 100644
--- a/devel/univariate/generated/generated/arch.univariate.GARCH.compute_variance.html
+++ b/devel/univariate/generated/generated/arch.univariate.GARCH.compute_variance.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GARCH.compute_variance - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.GARCH.compute_variance - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.GARCH.constraints.html b/devel/univariate/generated/generated/arch.univariate.GARCH.constraints.html
index 2134145619..900fe4bab3 100644
--- a/devel/univariate/generated/generated/arch.univariate.GARCH.constraints.html
+++ b/devel/univariate/generated/generated/arch.univariate.GARCH.constraints.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GARCH.constraints - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.GARCH.constraints - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.GARCH.forecast.html b/devel/univariate/generated/generated/arch.univariate.GARCH.forecast.html
index f7b6589739..6efb4d45ff 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.GARCH.forecast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GARCH.forecast - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.GARCH.forecast - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.GARCH.name.html b/devel/univariate/generated/generated/arch.univariate.GARCH.name.html
index 993cd19129..b13c0b72c2 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.GARCH.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GARCH.name - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.GARCH.name - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.GARCH.num_params.html b/devel/univariate/generated/generated/arch.univariate.GARCH.num_params.html
index 45fc2280aa..47a3c23482 100644
--- a/devel/univariate/generated/generated/arch.univariate.GARCH.num_params.html
+++ b/devel/univariate/generated/generated/arch.univariate.GARCH.num_params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GARCH.num_params - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.GARCH.num_params - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.GARCH.parameter_names.html b/devel/univariate/generated/generated/arch.univariate.GARCH.parameter_names.html
index a183d2f720..32e30919a7 100644
--- a/devel/univariate/generated/generated/arch.univariate.GARCH.parameter_names.html
+++ b/devel/univariate/generated/generated/arch.univariate.GARCH.parameter_names.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GARCH.parameter_names - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.GARCH.parameter_names - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.GARCH.simulate.html b/devel/univariate/generated/generated/arch.univariate.GARCH.simulate.html
index e03a17901a..2ae91ed40c 100644
--- a/devel/univariate/generated/generated/arch.univariate.GARCH.simulate.html
+++ b/devel/univariate/generated/generated/arch.univariate.GARCH.simulate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GARCH.simulate - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.GARCH.simulate - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.GARCH.start.html b/devel/univariate/generated/generated/arch.univariate.GARCH.start.html
index bc7a73896e..e6c642dea7 100644
--- a/devel/univariate/generated/generated/arch.univariate.GARCH.start.html
+++ b/devel/univariate/generated/generated/arch.univariate.GARCH.start.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GARCH.start - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.GARCH.start - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.GARCH.starting_values.html b/devel/univariate/generated/generated/arch.univariate.GARCH.starting_values.html
index d8ef7348ab..8e65a11771 100644
--- a/devel/univariate/generated/generated/arch.univariate.GARCH.starting_values.html
+++ b/devel/univariate/generated/generated/arch.univariate.GARCH.starting_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GARCH.starting_values - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.GARCH.starting_values - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.GARCH.stop.html b/devel/univariate/generated/generated/arch.univariate.GARCH.stop.html
index 5fde4f082c..fa07498b98 100644
--- a/devel/univariate/generated/generated/arch.univariate.GARCH.stop.html
+++ b/devel/univariate/generated/generated/arch.univariate.GARCH.stop.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GARCH.stop - arch v6.1.1 (+15)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.GARCH.update.html b/devel/univariate/generated/generated/arch.univariate.GARCH.update.html
index b0af1272ef..eb43698aaa 100644
--- a/devel/univariate/generated/generated/arch.univariate.GARCH.update.html
+++ b/devel/univariate/generated/generated/arch.univariate.GARCH.update.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GARCH.update - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.GARCH.update - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.GARCH.updateable.html b/devel/univariate/generated/generated/arch.univariate.GARCH.updateable.html
index f80eeb00d3..4f71c9a48a 100644
--- a/devel/univariate/generated/generated/arch.univariate.GARCH.updateable.html
+++ b/devel/univariate/generated/generated/arch.univariate.GARCH.updateable.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GARCH.updateable - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.GARCH.updateable - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.GARCH.variance_bounds.html b/devel/univariate/generated/generated/arch.univariate.GARCH.variance_bounds.html
index 9493a136c1..f406e718e6 100644
--- a/devel/univariate/generated/generated/arch.univariate.GARCH.variance_bounds.html
+++ b/devel/univariate/generated/generated/arch.univariate.GARCH.variance_bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GARCH.variance_bounds - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.GARCH.variance_bounds - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.GARCH.volatility_updater.html b/devel/univariate/generated/generated/arch.univariate.GARCH.volatility_updater.html
index 11fe3e1357..f91841f20a 100644
--- a/devel/univariate/generated/generated/arch.univariate.GARCH.volatility_updater.html
+++ b/devel/univariate/generated/generated/arch.univariate.GARCH.volatility_updater.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GARCH.volatility_updater - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.GARCH.volatility_updater - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.GeneralizedError.bounds.html b/devel/univariate/generated/generated/arch.univariate.GeneralizedError.bounds.html
index 70fdd8ca8b..97ea40d405 100644
--- a/devel/univariate/generated/generated/arch.univariate.GeneralizedError.bounds.html
+++ b/devel/univariate/generated/generated/arch.univariate.GeneralizedError.bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GeneralizedError.bounds - arch v6.1.1 (+15)</title>
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.GeneralizedError.cdf.html b/devel/univariate/generated/generated/arch.univariate.GeneralizedError.cdf.html
index 8be3dd5b96..d198f0c0bc 100644
--- a/devel/univariate/generated/generated/arch.univariate.GeneralizedError.cdf.html
+++ b/devel/univariate/generated/generated/arch.univariate.GeneralizedError.cdf.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GeneralizedError.cdf - arch v6.1.1 (+15)</title>
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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index 71df9bea6d..db91fa1275 100644
--- a/devel/univariate/generated/generated/arch.univariate.GeneralizedError.constraints.html
+++ b/devel/univariate/generated/generated/arch.univariate.GeneralizedError.constraints.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.univariate.GeneralizedError.constraints - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+    arch 6.2.0
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index e230d48326..f9efc04f80 100644
--- a/devel/univariate/generated/generated/arch.univariate.GeneralizedError.generator.html
+++ b/devel/univariate/generated/generated/arch.univariate.GeneralizedError.generator.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.univariate.GeneralizedError.generator - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index bab540e1de..8970871397 100644
--- a/devel/univariate/generated/generated/arch.univariate.GeneralizedError.loglikelihood.html
+++ b/devel/univariate/generated/generated/arch.univariate.GeneralizedError.loglikelihood.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.univariate.GeneralizedError.loglikelihood - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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--- a/devel/univariate/generated/generated/arch.univariate.GeneralizedError.moment.html
+++ b/devel/univariate/generated/generated/arch.univariate.GeneralizedError.moment.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GeneralizedError.moment - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.GeneralizedError.moment - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GeneralizedError.name - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.GeneralizedError.name - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.GeneralizedError.parameter_names.html b/devel/univariate/generated/generated/arch.univariate.GeneralizedError.parameter_names.html
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--- a/devel/univariate/generated/generated/arch.univariate.GeneralizedError.parameter_names.html
+++ b/devel/univariate/generated/generated/arch.univariate.GeneralizedError.parameter_names.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GeneralizedError.parameter_names - arch v6.1.1 (+15)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.GeneralizedError.partial_moment.html b/devel/univariate/generated/generated/arch.univariate.GeneralizedError.partial_moment.html
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GeneralizedError.partial_moment - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.GeneralizedError.partial_moment - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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+++ b/devel/univariate/generated/generated/arch.univariate.GeneralizedError.ppf.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GeneralizedError.ppf - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.GeneralizedError.ppf - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.GeneralizedError.random_state.html b/devel/univariate/generated/generated/arch.univariate.GeneralizedError.random_state.html
index 112e3c9c22..0b98c890dc 100644
--- a/devel/univariate/generated/generated/arch.univariate.GeneralizedError.random_state.html
+++ b/devel/univariate/generated/generated/arch.univariate.GeneralizedError.random_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GeneralizedError.random_state - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.GeneralizedError.random_state - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.GeneralizedError.simulate.html b/devel/univariate/generated/generated/arch.univariate.GeneralizedError.simulate.html
index 351c65c47c..d588763fc2 100644
--- a/devel/univariate/generated/generated/arch.univariate.GeneralizedError.simulate.html
+++ b/devel/univariate/generated/generated/arch.univariate.GeneralizedError.simulate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GeneralizedError.simulate - arch v6.1.1 (+15)</title>
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.GeneralizedError.starting_values.html b/devel/univariate/generated/generated/arch.univariate.GeneralizedError.starting_values.html
index ab6a2db789..c86fa3e8a3 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GeneralizedError.starting_values - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.GeneralizedError.starting_values - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+    arch 6.2.0
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index 108eeecdf8..176e599d39 100644
--- a/devel/univariate/generated/generated/arch.univariate.HARCH.backcast.html
+++ b/devel/univariate/generated/generated/arch.univariate.HARCH.backcast.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.HARCH.backcast_transform.html b/devel/univariate/generated/generated/arch.univariate.HARCH.backcast_transform.html
index 58b0d07594..817e0156b0 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.HARCH.backcast_transform.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index 23f0c2ac1a..8f2ab46010 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index 031ea3e0b4..5c58010fec 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.HARCH.num_params.html b/devel/univariate/generated/generated/arch.univariate.HARCH.num_params.html
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
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index a528403518..04d57718db 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
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index 42d72c862c..e5018dc6c3 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
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+++ b/devel/univariate/generated/generated/arch.univariate.HARCH.start.html
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+            arch 6.2.0
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+++ b/devel/univariate/generated/generated/arch.univariate.HARCH.starting_values.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+    arch 6.2.0
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index d8bcd4ea3d..e24dbbba35 100644
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+            arch 6.2.0
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index a93ec9dca9..2dd8eee2d2 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index a2c1774ce0..741016f5d1 100644
--- a/devel/univariate/generated/generated/arch.univariate.HARX.fit.html
+++ b/devel/univariate/generated/generated/arch.univariate.HARX.fit.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index 4f9dbb7edd..fa8b4aa074 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.HARX.fix.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index cc4e654a1e..a85df31260 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.HARX.forecast.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index f6056336b3..e116db3efa 100644
--- a/devel/univariate/generated/generated/arch.univariate.HARX.name.html
+++ b/devel/univariate/generated/generated/arch.univariate.HARX.name.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.HARX.num_params.html b/devel/univariate/generated/generated/arch.univariate.HARX.num_params.html
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.HARX.parameter_names.html b/devel/univariate/generated/generated/arch.univariate.HARX.parameter_names.html
index d86861d42b..d6dc3d8fbc 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.HARX.resids.html b/devel/univariate/generated/generated/arch.univariate.HARX.resids.html
index 95bf195058..dd8ae19eea 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.HARX.simulate.html b/devel/univariate/generated/generated/arch.univariate.HARX.simulate.html
index 1af1de3450..be6705e37b 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.HARX.starting_values.html b/devel/univariate/generated/generated/arch.univariate.HARX.starting_values.html
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+++ b/devel/univariate/generated/generated/arch.univariate.HARX.starting_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.HARX.starting_values - arch v6.1.1 (+15)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index 73b698fc72..cd39113447 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.HARX.volatility.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.HARX.x.html b/devel/univariate/generated/generated/arch.univariate.HARX.x.html
index 9877770dcf..2f2577580b 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.HARX.x.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.HARX.x - arch v6.1.1 (+15)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.HARX.y.html b/devel/univariate/generated/generated/arch.univariate.HARX.y.html
index b07ad65841..aa10da2b41 100644
--- a/devel/univariate/generated/generated/arch.univariate.HARX.y.html
+++ b/devel/univariate/generated/generated/arch.univariate.HARX.y.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index bb84384b8e..6182ede962 100644
--- a/devel/univariate/generated/generated/arch.univariate.LS.bounds.html
+++ b/devel/univariate/generated/generated/arch.univariate.LS.bounds.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.univariate.LS.bounds - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index d7e7d7ce35..49fd472888 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.LS.compute_param_cov.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index ee3536fdca..dd54eabf2f 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index 3c23a02397..03051eeaea 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.LS.fit.html b/devel/univariate/generated/generated/arch.univariate.LS.fit.html
index b611f8726d..40e0477912 100644
--- a/devel/univariate/generated/generated/arch.univariate.LS.fit.html
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index 2d64fb6268..3e6c88e035 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index d26378d7a5..fa96225e8b 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.LS.forecast - arch v6.1.1 (+15)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index 6fa6413559..45bddc2801 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.LS.name.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.LS.num_params.html b/devel/univariate/generated/generated/arch.univariate.LS.num_params.html
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index 41121979ec..7da9086113 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.LS.parameter_names.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.univariate.LS.parameter_names - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index c02ec6404b..8ed4e4c972 100644
--- a/devel/univariate/generated/generated/arch.univariate.LS.resids.html
+++ b/devel/univariate/generated/generated/arch.univariate.LS.resids.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.LS.resids - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.LS.resids - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.LS.simulate.html b/devel/univariate/generated/generated/arch.univariate.LS.simulate.html
index 88384fad14..61a5494c73 100644
--- a/devel/univariate/generated/generated/arch.univariate.LS.simulate.html
+++ b/devel/univariate/generated/generated/arch.univariate.LS.simulate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.LS.simulate - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.LS.simulate - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.LS.starting_values.html b/devel/univariate/generated/generated/arch.univariate.LS.starting_values.html
index 56f62b5494..5358e7e531 100644
--- a/devel/univariate/generated/generated/arch.univariate.LS.starting_values.html
+++ b/devel/univariate/generated/generated/arch.univariate.LS.starting_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.LS.starting_values - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.LS.starting_values - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.LS.volatility.html b/devel/univariate/generated/generated/arch.univariate.LS.volatility.html
index 37b25d04fa..317e627eed 100644
--- a/devel/univariate/generated/generated/arch.univariate.LS.volatility.html
+++ b/devel/univariate/generated/generated/arch.univariate.LS.volatility.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.LS.volatility - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.LS.volatility - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.LS.x.html b/devel/univariate/generated/generated/arch.univariate.LS.x.html
index 78800d5383..77e30e5eb1 100644
--- a/devel/univariate/generated/generated/arch.univariate.LS.x.html
+++ b/devel/univariate/generated/generated/arch.univariate.LS.x.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.LS.x - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.LS.x - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.LS.y.html b/devel/univariate/generated/generated/arch.univariate.LS.y.html
index 9cc4b46380..f084caa069 100644
--- a/devel/univariate/generated/generated/arch.univariate.LS.y.html
+++ b/devel/univariate/generated/generated/arch.univariate.LS.y.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.LS.y - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.LS.y - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.backcast.html b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.backcast.html
index 1643010b33..5d0eac276a 100644
--- a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.backcast.html
+++ b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.backcast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.MIDASHyperbolic.backcast - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.MIDASHyperbolic.backcast - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.backcast_transform.html b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.backcast_transform.html
index 92ce8ea30b..9cc377d529 100644
--- a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.backcast_transform.html
+++ b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.backcast_transform.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.MIDASHyperbolic.backcast_transform - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.MIDASHyperbolic.backcast_transform - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.bounds.html b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.bounds.html
index 35c393c137..e8462dfe4e 100644
--- a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.bounds.html
+++ b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.MIDASHyperbolic.bounds - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.MIDASHyperbolic.bounds - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.compute_variance.html b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.compute_variance.html
index 97252206ad..618edff648 100644
--- a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.compute_variance.html
+++ b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.compute_variance.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.MIDASHyperbolic.compute_variance - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.MIDASHyperbolic.compute_variance - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.constraints.html b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.constraints.html
index a425ba795c..2b20d74d42 100644
--- a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.constraints.html
+++ b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.constraints.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.MIDASHyperbolic.constraints - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.MIDASHyperbolic.constraints - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.forecast.html b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.forecast.html
index 05c0811497..9d196686ec 100644
--- a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.forecast.html
+++ b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.forecast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.MIDASHyperbolic.forecast - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.MIDASHyperbolic.forecast - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.name.html b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.name.html
index e57c37e2a0..c01f49567c 100644
--- a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.name.html
+++ b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.MIDASHyperbolic.name - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.MIDASHyperbolic.name - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.num_params.html b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.num_params.html
index c34df3a2ba..4c19577920 100644
--- a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.num_params.html
+++ b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.num_params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.MIDASHyperbolic.num_params - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.MIDASHyperbolic.num_params - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.parameter_names.html b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.parameter_names.html
index 30a89b86d0..7742cec487 100644
--- a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.parameter_names.html
+++ b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.parameter_names.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.MIDASHyperbolic.parameter_names - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.MIDASHyperbolic.parameter_names - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.simulate.html b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.simulate.html
index 263446119f..898b206cc8 100644
--- a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.simulate.html
+++ b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.simulate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.MIDASHyperbolic.simulate - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.MIDASHyperbolic.simulate - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.start.html b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.start.html
index 438b1c9597..22be8e1407 100644
--- a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.start.html
+++ b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.start.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.MIDASHyperbolic.start - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.MIDASHyperbolic.start - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.starting_values.html b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.starting_values.html
index 8d771811fc..dda74beb72 100644
--- a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.starting_values.html
+++ b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.starting_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.MIDASHyperbolic.starting_values - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.MIDASHyperbolic.starting_values - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.stop.html b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.stop.html
index 13ecab71ec..3c656e23a2 100644
--- a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.stop.html
+++ b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.stop.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.MIDASHyperbolic.stop - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.MIDASHyperbolic.stop - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.update.html b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.update.html
index 07dc3eefe3..59087b6897 100644
--- a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.update.html
+++ b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.update.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.MIDASHyperbolic.update - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.MIDASHyperbolic.update - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
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index 9c58eb3211..a9fa0c1a59 100644
--- a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.updateable.html
+++ b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.updateable.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.MIDASHyperbolic.updateable - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.MIDASHyperbolic.updateable - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.variance_bounds.html b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.variance_bounds.html
index d8e4da72b8..bb1774a37f 100644
--- a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.variance_bounds.html
+++ b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.variance_bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.MIDASHyperbolic.variance_bounds - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.MIDASHyperbolic.variance_bounds - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.volatility_updater.html b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.volatility_updater.html
index 8cbc0dd90a..85a7979061 100644
--- a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.volatility_updater.html
+++ b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.volatility_updater.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.MIDASHyperbolic.volatility_updater - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.MIDASHyperbolic.volatility_updater - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.Normal.bounds.html b/devel/univariate/generated/generated/arch.univariate.Normal.bounds.html
index 4efae190c5..78fdead86e 100644
--- a/devel/univariate/generated/generated/arch.univariate.Normal.bounds.html
+++ b/devel/univariate/generated/generated/arch.univariate.Normal.bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.Normal.bounds - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.Normal.bounds - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.Normal.cdf.html b/devel/univariate/generated/generated/arch.univariate.Normal.cdf.html
index f1cd05450d..35c1ef5e42 100644
--- a/devel/univariate/generated/generated/arch.univariate.Normal.cdf.html
+++ b/devel/univariate/generated/generated/arch.univariate.Normal.cdf.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.Normal.cdf - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.Normal.cdf - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.Normal.constraints.html b/devel/univariate/generated/generated/arch.univariate.Normal.constraints.html
index 2e2c96ebeb..d8c3abdf93 100644
--- a/devel/univariate/generated/generated/arch.univariate.Normal.constraints.html
+++ b/devel/univariate/generated/generated/arch.univariate.Normal.constraints.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.Normal.constraints - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.Normal.constraints - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.Normal.generator.html b/devel/univariate/generated/generated/arch.univariate.Normal.generator.html
index d387df5159..b29c2708c7 100644
--- a/devel/univariate/generated/generated/arch.univariate.Normal.generator.html
+++ b/devel/univariate/generated/generated/arch.univariate.Normal.generator.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.Normal.generator - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.Normal.generator - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.Normal.loglikelihood.html b/devel/univariate/generated/generated/arch.univariate.Normal.loglikelihood.html
index f28ffc719e..631afb1547 100644
--- a/devel/univariate/generated/generated/arch.univariate.Normal.loglikelihood.html
+++ b/devel/univariate/generated/generated/arch.univariate.Normal.loglikelihood.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.Normal.loglikelihood - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.Normal.loglikelihood - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.Normal.moment.html b/devel/univariate/generated/generated/arch.univariate.Normal.moment.html
index ede8961f40..7e017e7d99 100644
--- a/devel/univariate/generated/generated/arch.univariate.Normal.moment.html
+++ b/devel/univariate/generated/generated/arch.univariate.Normal.moment.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.Normal.moment - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.Normal.moment - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.Normal.name.html b/devel/univariate/generated/generated/arch.univariate.Normal.name.html
index 1b15025e3f..0c17037913 100644
--- a/devel/univariate/generated/generated/arch.univariate.Normal.name.html
+++ b/devel/univariate/generated/generated/arch.univariate.Normal.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.Normal.name - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.Normal.name - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.Normal.parameter_names.html b/devel/univariate/generated/generated/arch.univariate.Normal.parameter_names.html
index 638cb6c294..bf701077ec 100644
--- a/devel/univariate/generated/generated/arch.univariate.Normal.parameter_names.html
+++ b/devel/univariate/generated/generated/arch.univariate.Normal.parameter_names.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.Normal.parameter_names - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.Normal.parameter_names - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.Normal.partial_moment.html b/devel/univariate/generated/generated/arch.univariate.Normal.partial_moment.html
index aa955980fd..64bc06ddb2 100644
--- a/devel/univariate/generated/generated/arch.univariate.Normal.partial_moment.html
+++ b/devel/univariate/generated/generated/arch.univariate.Normal.partial_moment.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.Normal.partial_moment - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.Normal.partial_moment - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.Normal.ppf.html b/devel/univariate/generated/generated/arch.univariate.Normal.ppf.html
index d551fff666..61ac50fe99 100644
--- a/devel/univariate/generated/generated/arch.univariate.Normal.ppf.html
+++ b/devel/univariate/generated/generated/arch.univariate.Normal.ppf.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.Normal.ppf - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.Normal.ppf - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.Normal.random_state.html b/devel/univariate/generated/generated/arch.univariate.Normal.random_state.html
index 7c6b5814d2..1311e0771d 100644
--- a/devel/univariate/generated/generated/arch.univariate.Normal.random_state.html
+++ b/devel/univariate/generated/generated/arch.univariate.Normal.random_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.Normal.random_state - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.Normal.random_state - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.Normal.simulate.html b/devel/univariate/generated/generated/arch.univariate.Normal.simulate.html
index ede8b99815..866e451dbf 100644
--- a/devel/univariate/generated/generated/arch.univariate.Normal.simulate.html
+++ b/devel/univariate/generated/generated/arch.univariate.Normal.simulate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.Normal.simulate - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.Normal.simulate - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.Normal.starting_values.html b/devel/univariate/generated/generated/arch.univariate.Normal.starting_values.html
index 71bd4ed36d..b3986b77a5 100644
--- a/devel/univariate/generated/generated/arch.univariate.Normal.starting_values.html
+++ b/devel/univariate/generated/generated/arch.univariate.Normal.starting_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.Normal.starting_values - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.Normal.starting_values - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.RiskMetrics2006.backcast.html b/devel/univariate/generated/generated/arch.univariate.RiskMetrics2006.backcast.html
index bbde94195b..00de4ac831 100644
--- a/devel/univariate/generated/generated/arch.univariate.RiskMetrics2006.backcast.html
+++ b/devel/univariate/generated/generated/arch.univariate.RiskMetrics2006.backcast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.RiskMetrics2006.backcast - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.RiskMetrics2006.backcast - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.RiskMetrics2006.backcast_transform.html b/devel/univariate/generated/generated/arch.univariate.RiskMetrics2006.backcast_transform.html
index 15bf4a6465..bc18f191d3 100644
--- a/devel/univariate/generated/generated/arch.univariate.RiskMetrics2006.backcast_transform.html
+++ b/devel/univariate/generated/generated/arch.univariate.RiskMetrics2006.backcast_transform.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.RiskMetrics2006.backcast_transform - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.RiskMetrics2006.backcast_transform - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.RiskMetrics2006.bounds.html b/devel/univariate/generated/generated/arch.univariate.RiskMetrics2006.bounds.html
index 3d4af394ff..589e6166aa 100644
--- a/devel/univariate/generated/generated/arch.univariate.RiskMetrics2006.bounds.html
+++ b/devel/univariate/generated/generated/arch.univariate.RiskMetrics2006.bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.RiskMetrics2006.bounds - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.RiskMetrics2006.bounds - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.RiskMetrics2006.compute_variance.html b/devel/univariate/generated/generated/arch.univariate.RiskMetrics2006.compute_variance.html
index c96c9993fa..be5ea67213 100644
--- a/devel/univariate/generated/generated/arch.univariate.RiskMetrics2006.compute_variance.html
+++ b/devel/univariate/generated/generated/arch.univariate.RiskMetrics2006.compute_variance.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.RiskMetrics2006.compute_variance - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.RiskMetrics2006.compute_variance - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.RiskMetrics2006.constraints.html b/devel/univariate/generated/generated/arch.univariate.RiskMetrics2006.constraints.html
index 4b2f55e21f..90712f5bbb 100644
--- a/devel/univariate/generated/generated/arch.univariate.RiskMetrics2006.constraints.html
+++ b/devel/univariate/generated/generated/arch.univariate.RiskMetrics2006.constraints.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.RiskMetrics2006.constraints - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.RiskMetrics2006.constraints - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.RiskMetrics2006.forecast.html b/devel/univariate/generated/generated/arch.univariate.RiskMetrics2006.forecast.html
index f61f12aa3b..9fb5b5e1e2 100644
--- a/devel/univariate/generated/generated/arch.univariate.RiskMetrics2006.forecast.html
+++ b/devel/univariate/generated/generated/arch.univariate.RiskMetrics2006.forecast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.RiskMetrics2006.forecast - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.RiskMetrics2006.forecast - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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@@ -15,7 +15,7 @@
     
   
       
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -92,7 +92,7 @@
 
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+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.RiskMetrics2006.volatility_updater.html b/devel/univariate/generated/generated/arch.univariate.RiskMetrics2006.volatility_updater.html
index 9d3d9f4133..b662c02677 100644
--- a/devel/univariate/generated/generated/arch.univariate.RiskMetrics2006.volatility_updater.html
+++ b/devel/univariate/generated/generated/arch.univariate.RiskMetrics2006.volatility_updater.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.RiskMetrics2006.volatility_updater - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.RiskMetrics2006.volatility_updater - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.SkewStudent.bounds.html b/devel/univariate/generated/generated/arch.univariate.SkewStudent.bounds.html
index 2c95a7ff04..8142695923 100644
--- a/devel/univariate/generated/generated/arch.univariate.SkewStudent.bounds.html
+++ b/devel/univariate/generated/generated/arch.univariate.SkewStudent.bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.SkewStudent.bounds - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.SkewStudent.bounds - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.SkewStudent.cdf.html b/devel/univariate/generated/generated/arch.univariate.SkewStudent.cdf.html
index 5720bef586..730a823b45 100644
--- a/devel/univariate/generated/generated/arch.univariate.SkewStudent.cdf.html
+++ b/devel/univariate/generated/generated/arch.univariate.SkewStudent.cdf.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.SkewStudent.cdf - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.SkewStudent.cdf - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.SkewStudent.constraints.html b/devel/univariate/generated/generated/arch.univariate.SkewStudent.constraints.html
index fcfd032bec..b1707c4191 100644
--- a/devel/univariate/generated/generated/arch.univariate.SkewStudent.constraints.html
+++ b/devel/univariate/generated/generated/arch.univariate.SkewStudent.constraints.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.SkewStudent.constraints - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.SkewStudent.constraints - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.SkewStudent.generator.html b/devel/univariate/generated/generated/arch.univariate.SkewStudent.generator.html
index 33a3339f2c..c32e5cca4e 100644
--- a/devel/univariate/generated/generated/arch.univariate.SkewStudent.generator.html
+++ b/devel/univariate/generated/generated/arch.univariate.SkewStudent.generator.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.SkewStudent.generator - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.SkewStudent.generator - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.SkewStudent.loglikelihood.html b/devel/univariate/generated/generated/arch.univariate.SkewStudent.loglikelihood.html
index 8c8703325f..31afe46626 100644
--- a/devel/univariate/generated/generated/arch.univariate.SkewStudent.loglikelihood.html
+++ b/devel/univariate/generated/generated/arch.univariate.SkewStudent.loglikelihood.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.SkewStudent.loglikelihood - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.SkewStudent.loglikelihood - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.SkewStudent.moment.html b/devel/univariate/generated/generated/arch.univariate.SkewStudent.moment.html
index 27e0ddf4f5..e515d23ac3 100644
--- a/devel/univariate/generated/generated/arch.univariate.SkewStudent.moment.html
+++ b/devel/univariate/generated/generated/arch.univariate.SkewStudent.moment.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.SkewStudent.moment - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.SkewStudent.moment - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.SkewStudent.name.html b/devel/univariate/generated/generated/arch.univariate.SkewStudent.name.html
index 8176e18182..41ec067123 100644
--- a/devel/univariate/generated/generated/arch.univariate.SkewStudent.name.html
+++ b/devel/univariate/generated/generated/arch.univariate.SkewStudent.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.SkewStudent.name - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.SkewStudent.name - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.SkewStudent.parameter_names.html b/devel/univariate/generated/generated/arch.univariate.SkewStudent.parameter_names.html
index 1a8a314cb6..836b5b487e 100644
--- a/devel/univariate/generated/generated/arch.univariate.SkewStudent.parameter_names.html
+++ b/devel/univariate/generated/generated/arch.univariate.SkewStudent.parameter_names.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.SkewStudent.parameter_names - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.SkewStudent.parameter_names - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.SkewStudent.partial_moment.html b/devel/univariate/generated/generated/arch.univariate.SkewStudent.partial_moment.html
index 9a3244ca31..e1d30ee98f 100644
--- a/devel/univariate/generated/generated/arch.univariate.SkewStudent.partial_moment.html
+++ b/devel/univariate/generated/generated/arch.univariate.SkewStudent.partial_moment.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.SkewStudent.partial_moment - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.SkewStudent.partial_moment - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.SkewStudent.ppf.html b/devel/univariate/generated/generated/arch.univariate.SkewStudent.ppf.html
index ec568e14a2..1f2e7b536c 100644
--- a/devel/univariate/generated/generated/arch.univariate.SkewStudent.ppf.html
+++ b/devel/univariate/generated/generated/arch.univariate.SkewStudent.ppf.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.SkewStudent.ppf - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.SkewStudent.ppf - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.SkewStudent.random_state.html b/devel/univariate/generated/generated/arch.univariate.SkewStudent.random_state.html
index 49bf0cee3d..7b413f92c0 100644
--- a/devel/univariate/generated/generated/arch.univariate.SkewStudent.random_state.html
+++ b/devel/univariate/generated/generated/arch.univariate.SkewStudent.random_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.SkewStudent.random_state - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.SkewStudent.random_state - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.SkewStudent.simulate.html b/devel/univariate/generated/generated/arch.univariate.SkewStudent.simulate.html
index c3709c0745..2b68873aec 100644
--- a/devel/univariate/generated/generated/arch.univariate.SkewStudent.simulate.html
+++ b/devel/univariate/generated/generated/arch.univariate.SkewStudent.simulate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.SkewStudent.simulate - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.SkewStudent.simulate - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.SkewStudent.starting_values.html b/devel/univariate/generated/generated/arch.univariate.SkewStudent.starting_values.html
index 3495864071..2729c810cd 100644
--- a/devel/univariate/generated/generated/arch.univariate.SkewStudent.starting_values.html
+++ b/devel/univariate/generated/generated/arch.univariate.SkewStudent.starting_values.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.univariate.SkewStudent.starting_values - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index f79e7ca1ca..f69988069b 100644
--- a/devel/univariate/generated/generated/arch.univariate.StudentsT.bounds.html
+++ b/devel/univariate/generated/generated/arch.univariate.StudentsT.bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.StudentsT.bounds - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.StudentsT.bounds - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index eb75f0bf81..55afa08d18 100644
--- a/devel/univariate/generated/generated/arch.univariate.StudentsT.cdf.html
+++ b/devel/univariate/generated/generated/arch.univariate.StudentsT.cdf.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.StudentsT.cdf - arch v6.1.1 (+15)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index 44203536f3..443037fd76 100644
--- a/devel/univariate/generated/generated/arch.univariate.StudentsT.constraints.html
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.StudentsT.generator.html b/devel/univariate/generated/generated/arch.univariate.StudentsT.generator.html
index ed6ca29475..c6601c86bb 100644
--- a/devel/univariate/generated/generated/arch.univariate.StudentsT.generator.html
+++ b/devel/univariate/generated/generated/arch.univariate.StudentsT.generator.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.StudentsT.loglikelihood.html b/devel/univariate/generated/generated/arch.univariate.StudentsT.loglikelihood.html
index 58534f0ad3..9724dd1f0a 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.StudentsT.loglikelihood.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.StudentsT.moment.html b/devel/univariate/generated/generated/arch.univariate.StudentsT.moment.html
index f21405ee96..1cc755cff0 100644
--- a/devel/univariate/generated/generated/arch.univariate.StudentsT.moment.html
+++ b/devel/univariate/generated/generated/arch.univariate.StudentsT.moment.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.StudentsT.moment - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.StudentsT.moment - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index 168dbad74f..fe27a34cb5 100644
--- a/devel/univariate/generated/generated/arch.univariate.StudentsT.name.html
+++ b/devel/univariate/generated/generated/arch.univariate.StudentsT.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.StudentsT.name - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.StudentsT.name - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
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@@ -15,7 +15,7 @@
     
   
       
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@@ -15,7 +15,7 @@
     
   
       
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -92,7 +92,7 @@
 
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+            arch 6.2.0
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
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@@ -92,7 +92,7 @@
 
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-            arch v6.1.1 (+15)
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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index dabfc6d418..9c120ed98e 100644
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -92,7 +92,7 @@
 
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@@ -189,10 +189,10 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.ZeroMean.fix.html b/devel/univariate/generated/generated/arch.univariate.ZeroMean.fix.html
index 6bacc64bcd..f28d3a21cf 100644
--- a/devel/univariate/generated/generated/arch.univariate.ZeroMean.fix.html
+++ b/devel/univariate/generated/generated/arch.univariate.ZeroMean.fix.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ZeroMean.fix - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.ZeroMean.fix - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index 7f500518f4..cd335f9437 100644
--- a/devel/univariate/generated/generated/arch.univariate.ZeroMean.forecast.html
+++ b/devel/univariate/generated/generated/arch.univariate.ZeroMean.forecast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ZeroMean.forecast - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.ZeroMean.forecast - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index 7979fa059b..20340770ee 100644
--- a/devel/univariate/generated/generated/arch.univariate.ZeroMean.name.html
+++ b/devel/univariate/generated/generated/arch.univariate.ZeroMean.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ZeroMean.name - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.ZeroMean.name - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.ZeroMean.num_params.html b/devel/univariate/generated/generated/arch.univariate.ZeroMean.num_params.html
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--- a/devel/univariate/generated/generated/arch.univariate.ZeroMean.num_params.html
+++ b/devel/univariate/generated/generated/arch.univariate.ZeroMean.num_params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ZeroMean.num_params - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.ZeroMean.num_params - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.ZeroMean.parameter_names.html b/devel/univariate/generated/generated/arch.univariate.ZeroMean.parameter_names.html
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+++ b/devel/univariate/generated/generated/arch.univariate.ZeroMean.parameter_names.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ZeroMean.parameter_names - arch v6.1.1 (+15)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.ZeroMean.resids.html b/devel/univariate/generated/generated/arch.univariate.ZeroMean.resids.html
index 29dd0cc951..848eab4e44 100644
--- a/devel/univariate/generated/generated/arch.univariate.ZeroMean.resids.html
+++ b/devel/univariate/generated/generated/arch.univariate.ZeroMean.resids.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ZeroMean.resids - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.ZeroMean.resids - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.ZeroMean.simulate.html b/devel/univariate/generated/generated/arch.univariate.ZeroMean.simulate.html
index bdb43764d1..7184206db3 100644
--- a/devel/univariate/generated/generated/arch.univariate.ZeroMean.simulate.html
+++ b/devel/univariate/generated/generated/arch.univariate.ZeroMean.simulate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ZeroMean.simulate - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.ZeroMean.simulate - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.ZeroMean.starting_values.html b/devel/univariate/generated/generated/arch.univariate.ZeroMean.starting_values.html
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ZeroMean.starting_values - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.ZeroMean.starting_values - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index 81a6b5b0f0..de730100b5 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.ZeroMean.volatility.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ZeroMean.volatility - arch v6.1.1 (+15)</title>
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.ZeroMean.x.html b/devel/univariate/generated/generated/arch.univariate.ZeroMean.x.html
index ad88b81efb..20b197c938 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.ZeroMean.x.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ZeroMean.x - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.ZeroMean.x - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.ZeroMean.y.html b/devel/univariate/generated/generated/arch.univariate.ZeroMean.y.html
index 0b81eb7819..0e875a07fb 100644
--- a/devel/univariate/generated/generated/arch.univariate.ZeroMean.y.html
+++ b/devel/univariate/generated/generated/arch.univariate.ZeroMean.y.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ZeroMean.y - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.ZeroMean.y - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -15,7 +15,7 @@
     
   
       
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -92,7 +92,7 @@
 
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModel.resids.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModel.resids.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+            arch 6.2.0
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+    arch 6.2.0
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--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModel.simulate.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModel.simulate.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+    arch 6.2.0
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+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModel.starting_values.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+            arch 6.2.0
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index bd7145a7be..45177665c4 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModel.y - arch v6.1.1 (+15)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index 2b2c8308a0..db2689db0b 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index c33b405c89..1f6810e634 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index 18b6dccb9a..566f3dd213 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index 34f1a2b4bc..b5cf886194 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.forecast.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.forecast.html
index a8de25dd7b..304e0a53b5 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.forecast.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.forecast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelFixedResult.forecast - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.base.ARCHModelFixedResult.forecast - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.html
index 36ffd2ebcc..937e44cc4a 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelFixedResult.hedgehog_plot - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.base.ARCHModelFixedResult.hedgehog_plot - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+    arch 6.2.0
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index b5cc293488..7b66d9064b 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.loglikelihood.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.loglikelihood.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.univariate.base.ARCHModelFixedResult.loglikelihood - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+            arch 6.2.0
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+    arch 6.2.0
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+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.model.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.univariate.base.ARCHModelFixedResult.model - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index c382233fd9..67e3e0b32d 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.nobs.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.univariate.base.ARCHModelFixedResult.nobs - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+            arch 6.2.0
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index 00a7bbecba..366cb7e09c 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.params.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.params.html
index c9360cbce2..81f234fd2f 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index 3650868ec2..def1c0b52e 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index 05f5dfa8a6..ca70dd8c37 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index fe1d18cd00..987d80eba7 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.summary.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.summary.html
index 9320da3c7d..d743666c50 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.summary.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelForecast.mean.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelForecast.mean.html
index 340b1a08f4..e0ebe5bc87 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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@@ -15,7 +15,7 @@
     
   
       
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -15,7 +15,7 @@
     
   
       
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -15,7 +15,7 @@
     
   
       
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--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.bic.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.bic.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.univariate.base.ARCHModelResult.bic - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+            arch 6.2.0
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+    arch 6.2.0
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index 6765276d6c..6bd4d4d99c 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.conditional_volatility.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.univariate.base.ARCHModelResult.conditional_volatility - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+            arch 6.2.0
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index 42657137c6..af1c6a75a1 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.conf_int.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.conf_int.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.univariate.base.ARCHModelResult.conf_int - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+            arch 6.2.0
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index d4a40a04eb..959328a89a 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.convergence_flag.html
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index ad5592f3ef..578e9d10bb 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.fit_stop.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.fit_stop.html
index cd06187cfa..8e3d3c7f63 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.fit_stop.html
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index db34ec5e4f..8dbc1860f9 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index afe732c655..3ab1fbf25e 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index 8eaef82d7c..976e648ccd 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.model.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.model.html
index 6f9e87c2ff..ce093e299b 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.model.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.model - arch v6.1.1 (+15)</title>
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index d8bd00cb57..53020aecf7 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.nobs.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index 721e2758b2..1e80d473db 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.num_params.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.num_params.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.univariate.base.ARCHModelResult.num_params - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index 2d6f3a8fdc..16d6173cca 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.optimization_result.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.optimization_result.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.optimization_result - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.base.ARCHModelResult.optimization_result - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index ea73a9c57c..ad7bdbab4a 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.param_cov.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.param_cov.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.param_cov - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.base.ARCHModelResult.param_cov - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.params.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.params.html
index 9d419eb009..fbce67cffe 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.params.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.params - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.base.ARCHModelResult.params - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.plot.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.plot.html
index 5d5a9b1a98..b5da419783 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.plot.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.plot.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.plot - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.base.ARCHModelResult.plot - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.pvalues.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.pvalues.html
index 96cd44bc4d..10c7025f12 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.pvalues.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.pvalues.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.pvalues - arch v6.1.1 (+15)</title>
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.resid.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.resid.html
index ecfebd8fc4..10b34ea6fa 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.resid.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index ffba11e4e7..a082c1f952 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.rsquared.html
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@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.univariate.base.ARCHModelResult.rsquared - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.rsquared_adj.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.rsquared_adj.html
index 1a1b017bd3..96542d5d1e 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.rsquared_adj.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.rsquared_adj.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.rsquared_adj - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.base.ARCHModelResult.rsquared_adj - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.scale.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.scale.html
index 874eb9f8de..9f251106c0 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.scale.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.scale.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.scale - arch v6.1.1 (+15)</title>
+        <title>arch.univariate.base.ARCHModelResult.scale - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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index 5abfc770ae..d811089d24 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.std_err.html
@@ -15,7 +15,7 @@
     
   
       
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+    arch 6.2.0
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@@ -15,7 +15,7 @@
     
   
       
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+    arch 6.2.0
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    arch 6.2.0
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -92,7 +92,7 @@
 
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@@ -15,7 +15,7 @@
     
   
       
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+    arch 6.2.0
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+++ b/devel/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.bounds.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.univariate.volatility.VolatilityProcess.bounds - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+            arch 6.2.0
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.univariate.volatility.VolatilityProcess.compute_variance - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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+++ b/devel/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.constraints.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index b436826eb4..ce5b12420c 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-            arch v6.1.1 (+15)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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index 20bd413030..bf3de39103 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-            arch v6.1.1 (+15)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
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index b428a82aaf..6839c39ddd 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    <a href="../../../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch v6.1.1 (+15)
+    arch 6.2.0
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@@ -92,7 +92,7 @@
 
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@@ -92,7 +92,7 @@
 
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-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
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index 206649af7a..b748dd1def 100644
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@@ -92,7 +92,7 @@
 
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-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
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index 2aa7ae6fc3..7ae63647ab 100644
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@@ -87,7 +87,7 @@
 
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index 700f0a8550..76b9ff7a30 100644
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@@ -87,7 +87,7 @@
 
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-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
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index 5a7b89e60e..cdaea64a96 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -93,7 +93,7 @@
 
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+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -15,7 +15,7 @@
     
   
       
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@@ -93,7 +93,7 @@
 
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@@ -837,7 +837,7 @@ <h3 id="Initial-Mean-Model-Estimation">Initial Mean Model Estimation<a class="he
 Method:            Maximum Likelihood   BIC:                           4571.50
                                         No. Observations:                 1237
 Date:                Tue, Sep 26 2023   Df Residuals:                     1233
-Time:                        11:05:24   Df Model:                            4
+Time:                        14:48:05   Df Model:                            4
                                    Mean Model
 ================================================================================
                       coef    std err          t      P&gt;|t|     95.0% Conf. Int.
@@ -882,7 +882,7 @@ <h3 id="Initial-Volatility-Model-Estimation">Initial Volatility Model Estimation
 Method:            Maximum Likelihood   BIC:                           3902.35
                                         No. Observations:                 1237
 Date:                Tue, Sep 26 2023   Df Residuals:                     1237
-Time:                        11:05:24   Df Model:                            0
+Time:                        14:48:05   Df Model:                            0
                               Volatility Model
 ===========================================================================
                  coef    std err          t      P&gt;|t|     95.0% Conf. Int.
@@ -955,7 +955,7 @@ <h3 id="Re-estimating-the-mean-with-a-FixedVariance">Re-estimating the mean with
 Method:            Maximum Likelihood   BIC:                           3907.50
                                         No. Observations:                 1237
 Date:                Tue, Sep 26 2023   Df Residuals:                     1233
-Time:                        11:05:25   Df Model:                            4
+Time:                        14:48:06   Df Model:                            4
                                     Mean Model
 ==================================================================================
                       coef    std err          t      P&gt;|t|       95.0% Conf. Int.
@@ -1011,7 +1011,7 @@ <h3 id="Zig-Zag-estimation">Zig-Zag estimation<a class="headerlink" href="#Zig-Z
 Method:                     Maximum Likelihood   BIC:                           3899.96
                                                  No. Observations:                 1237
 Date:                         Tue, Sep 26 2023   Df Residuals:                     1233
-Time:                                 11:05:25   Df Model:                            4
+Time:                                 14:48:06   Df Model:                            4
                                     Mean Model
 =================================================================================
                       coef    std err          t      P&gt;|t|      95.0% Conf. Int.
@@ -1051,7 +1051,7 @@ <h3 id="Direct-Estimation">Direct Estimation<a class="headerlink" href="#Direct-
 Method:            Maximum Likelihood   BIC:                           3922.19
                                         No. Observations:                 1237
 Date:                Tue, Sep 26 2023   Df Residuals:                     1233
-Time:                        11:05:26   Df Model:                            4
+Time:                        14:48:06   Df Model:                            4
                                    Mean Model
 ================================================================================
                       coef    std err          t      P&gt;|t|     95.0% Conf. Int.
diff --git a/devel/univariate/univariate_using_fixed_variance.ipynb b/devel/univariate/univariate_using_fixed_variance.ipynb
index 3e6d50ed17..de250d35d7 100644
--- a/devel/univariate/univariate_using_fixed_variance.ipynb
+++ b/devel/univariate/univariate_using_fixed_variance.ipynb
@@ -23,10 +23,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-09-26T11:05:22.756216Z",
-     "iopub.status.busy": "2023-09-26T11:05:22.755761Z",
-     "iopub.status.idle": "2023-09-26T11:05:23.908555Z",
-     "shell.execute_reply": "2023-09-26T11:05:23.907877Z"
+     "iopub.execute_input": "2023-09-26T14:48:03.977999Z",
+     "iopub.status.busy": "2023-09-26T14:48:03.977573Z",
+     "iopub.status.idle": "2023-09-26T14:48:05.058914Z",
+     "shell.execute_reply": "2023-09-26T14:48:05.058289Z"
     }
    },
    "outputs": [],
@@ -56,10 +56,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-09-26T11:05:23.913566Z",
-     "iopub.status.busy": "2023-09-26T11:05:23.912311Z",
-     "iopub.status.idle": "2023-09-26T11:05:23.916858Z",
-     "shell.execute_reply": "2023-09-26T11:05:23.916309Z"
+     "iopub.execute_input": "2023-09-26T14:48:05.063791Z",
+     "iopub.status.busy": "2023-09-26T14:48:05.062484Z",
+     "iopub.status.idle": "2023-09-26T14:48:05.067043Z",
+     "shell.execute_reply": "2023-09-26T14:48:05.066511Z"
     }
    },
    "outputs": [],
@@ -82,10 +82,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-09-26T11:05:23.920989Z",
-     "iopub.status.busy": "2023-09-26T11:05:23.919896Z",
-     "iopub.status.idle": "2023-09-26T11:05:24.429406Z",
-     "shell.execute_reply": "2023-09-26T11:05:24.428745Z"
+     "iopub.execute_input": "2023-09-26T14:48:05.071124Z",
+     "iopub.status.busy": "2023-09-26T14:48:05.070048Z",
+     "iopub.status.idle": "2023-09-26T14:48:05.550556Z",
+     "shell.execute_reply": "2023-09-26T14:48:05.549801Z"
     }
    },
    "outputs": [
@@ -122,10 +122,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-09-26T11:05:24.468832Z",
-     "iopub.status.busy": "2023-09-26T11:05:24.468531Z",
-     "iopub.status.idle": "2023-09-26T11:05:24.485780Z",
-     "shell.execute_reply": "2023-09-26T11:05:24.485147Z"
+     "iopub.execute_input": "2023-09-26T14:48:05.587172Z",
+     "iopub.status.busy": "2023-09-26T14:48:05.586433Z",
+     "iopub.status.idle": "2023-09-26T14:48:05.602355Z",
+     "shell.execute_reply": "2023-09-26T14:48:05.601797Z"
     }
    },
    "outputs": [
@@ -142,7 +142,7 @@
       "Method:            Maximum Likelihood   BIC:                           4571.50\n",
       "                                        No. Observations:                 1237\n",
       "Date:                Tue, Sep 26 2023   Df Residuals:                     1233\n",
-      "Time:                        11:05:24   Df Model:                            4\n",
+      "Time:                        14:48:05   Df Model:                            4\n",
       "                                   Mean Model                                   \n",
       "================================================================================\n",
       "                      coef    std err          t      P>|t|     95.0% Conf. Int.\n",
@@ -184,10 +184,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-09-26T11:05:24.489146Z",
-     "iopub.status.busy": "2023-09-26T11:05:24.488913Z",
-     "iopub.status.idle": "2023-09-26T11:05:24.579436Z",
-     "shell.execute_reply": "2023-09-26T11:05:24.578783Z"
+     "iopub.execute_input": "2023-09-26T14:48:05.607450Z",
+     "iopub.status.busy": "2023-09-26T14:48:05.605852Z",
+     "iopub.status.idle": "2023-09-26T14:48:05.677839Z",
+     "shell.execute_reply": "2023-09-26T14:48:05.677284Z"
     }
    },
    "outputs": [
@@ -204,7 +204,7 @@
       "Method:            Maximum Likelihood   BIC:                           3902.35\n",
       "                                        No. Observations:                 1237\n",
       "Date:                Tue, Sep 26 2023   Df Residuals:                     1237\n",
-      "Time:                        11:05:24   Df Model:                            0\n",
+      "Time:                        14:48:05   Df Model:                            0\n",
       "                              Volatility Model                             \n",
       "===========================================================================\n",
       "                 coef    std err          t      P>|t|     95.0% Conf. Int.\n",
@@ -230,10 +230,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-09-26T11:05:24.582852Z",
-     "iopub.status.busy": "2023-09-26T11:05:24.582624Z",
-     "iopub.status.idle": "2023-09-26T11:05:25.640208Z",
-     "shell.execute_reply": "2023-09-26T11:05:25.639524Z"
+     "iopub.execute_input": "2023-09-26T14:48:05.683171Z",
+     "iopub.status.busy": "2023-09-26T14:48:05.681634Z",
+     "iopub.status.idle": "2023-09-26T14:48:06.642207Z",
+     "shell.execute_reply": "2023-09-26T14:48:06.641602Z"
     }
    },
    "outputs": [
@@ -268,10 +268,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-09-26T11:05:25.645047Z",
-     "iopub.status.busy": "2023-09-26T11:05:25.643868Z",
-     "iopub.status.idle": "2023-09-26T11:05:25.683942Z",
-     "shell.execute_reply": "2023-09-26T11:05:25.683284Z"
+     "iopub.execute_input": "2023-09-26T14:48:06.646772Z",
+     "iopub.status.busy": "2023-09-26T14:48:06.645579Z",
+     "iopub.status.idle": "2023-09-26T14:48:06.671400Z",
+     "shell.execute_reply": "2023-09-26T14:48:06.670840Z"
     }
    },
    "outputs": [
@@ -301,7 +301,7 @@
       "Method:            Maximum Likelihood   BIC:                           3907.50\n",
       "                                        No. Observations:                 1237\n",
       "Date:                Tue, Sep 26 2023   Df Residuals:                     1233\n",
-      "Time:                        11:05:25   Df Model:                            4\n",
+      "Time:                        14:48:06   Df Model:                            4\n",
       "                                    Mean Model                                    \n",
       "==================================================================================\n",
       "                      coef    std err          t      P>|t|       95.0% Conf. Int.\n",
@@ -344,10 +344,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-09-26T11:05:25.689618Z",
-     "iopub.status.busy": "2023-09-26T11:05:25.687939Z",
-     "iopub.status.idle": "2023-09-26T11:05:25.961247Z",
-     "shell.execute_reply": "2023-09-26T11:05:25.960494Z"
+     "iopub.execute_input": "2023-09-26T14:48:06.676451Z",
+     "iopub.status.busy": "2023-09-26T14:48:06.674914Z",
+     "iopub.status.idle": "2023-09-26T14:48:06.887404Z",
+     "shell.execute_reply": "2023-09-26T14:48:06.886824Z"
     }
    },
    "outputs": [
@@ -357,21 +357,15 @@
      "text": [
       "0\n",
       "1\n",
-      "2\n"
+      "2\n",
+      "3\n",
+      "4\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "3\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "4\n",
       "                    HAR - Fixed Variance (Unit Scale) Model Results                    \n",
       "=======================================================================================\n",
       "Dep. Variable:                       VIX Index   R-squared:                       0.876\n",
@@ -381,7 +375,7 @@
       "Method:                     Maximum Likelihood   BIC:                           3899.96\n",
       "                                                 No. Observations:                 1237\n",
       "Date:                         Tue, Sep 26 2023   Df Residuals:                     1233\n",
-      "Time:                                 11:05:25   Df Model:                            4\n",
+      "Time:                                 14:48:06   Df Model:                            4\n",
       "                                    Mean Model                                   \n",
       "=================================================================================\n",
       "                      coef    std err          t      P>|t|      95.0% Conf. Int.\n",
@@ -422,10 +416,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-09-26T11:05:25.964655Z",
-     "iopub.status.busy": "2023-09-26T11:05:25.964329Z",
-     "iopub.status.idle": "2023-09-26T11:05:26.079467Z",
-     "shell.execute_reply": "2023-09-26T11:05:26.078848Z"
+     "iopub.execute_input": "2023-09-26T14:48:06.892584Z",
+     "iopub.status.busy": "2023-09-26T14:48:06.891040Z",
+     "iopub.status.idle": "2023-09-26T14:48:07.003671Z",
+     "shell.execute_reply": "2023-09-26T14:48:07.003086Z"
     },
     "tags": []
    },
@@ -443,7 +437,7 @@
       "Method:            Maximum Likelihood   BIC:                           3922.19\n",
       "                                        No. Observations:                 1237\n",
       "Date:                Tue, Sep 26 2023   Df Residuals:                     1233\n",
-      "Time:                        11:05:26   Df Model:                            4\n",
+      "Time:                        14:48:06   Df Model:                            4\n",
       "                                   Mean Model                                   \n",
       "================================================================================\n",
       "                      coef    std err          t      P>|t|     95.0% Conf. Int.\n",
diff --git a/devel/univariate/univariate_volatility_forecasting.html b/devel/univariate/univariate_volatility_forecasting.html
index 9c821accb7..2b5cde364f 100644
--- a/devel/univariate/univariate_volatility_forecasting.html
+++ b/devel/univariate/univariate_volatility_forecasting.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Volatility Forecasting - arch v6.1.1 (+15)</title>
+        <title>Volatility Forecasting - arch 6.2.0</title>
       
     
 
@@ -93,7 +93,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
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@@ -103,7 +103,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -190,10 +190,10 @@
 
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-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/univariate/univariate_volatility_modeling.html b/devel/univariate/univariate_volatility_modeling.html
index 44cfeda5d2..7b708aeb6f 100644
--- a/devel/univariate/univariate_volatility_modeling.html
+++ b/devel/univariate/univariate_volatility_modeling.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>ARCH Modeling - arch v6.1.1 (+15)</title>
+        <title>ARCH Modeling - arch 6.2.0</title>
       
     
 
@@ -93,7 +93,7 @@
 
 <header class="md-header" data-md-component="header">
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-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -103,7 +103,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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@@ -190,10 +190,10 @@
 
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-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/devel/univariate/univariate_volatility_scenarios.html b/devel/univariate/univariate_volatility_scenarios.html
index cfb5d9017c..3c69078922 100644
--- a/devel/univariate/univariate_volatility_scenarios.html
+++ b/devel/univariate/univariate_volatility_scenarios.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Volatility Scenarios - arch v6.1.1 (+15)</title>
+        <title>Volatility Scenarios - arch 6.2.0</title>
       
     
 
@@ -93,7 +93,7 @@
 
 <header class="md-header" data-md-component="header">
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-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -103,7 +103,7 @@
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-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -190,10 +190,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/utility.html b/devel/univariate/utility.html
index a07a475b69..f9fa0d8e82 100644
--- a/devel/univariate/utility.html
+++ b/devel/univariate/utility.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Utilities - arch v6.1.1 (+15)</title>
+        <title>Utilities - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
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     <div class="md-nav__source">
diff --git a/devel/univariate/volatility.html b/devel/univariate/volatility.html
index 246f5b8c46..b6bbd45884 100644
--- a/devel/univariate/volatility.html
+++ b/devel/univariate/volatility.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Volatility Processes - arch v6.1.1 (+15)</title>
+        <title>Volatility Processes - arch 6.2.0</title>
       
     
 
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-header__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.1.1 (+15)
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v6.1.1 (+15)" class="md-nav__button md-logo" aria-label="arch v6.1.1 (+15)" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.1.1 (+15)
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
diff --git a/genindex.html b/genindex.html
index cda306c4a6..e16a125d71 100644
--- a/genindex.html
+++ b/genindex.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>None - arch 6.1.0</title>
+        <title>None - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL(".",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -394,7 +394,7 @@
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -441,7 +441,7 @@
     <script id="__config" type="application/json">{"base": ".", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/index.html b/index.html
index 522de4c81c..88623d78cb 100644
--- a/index.html
+++ b/index.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch 6.1.0</title>
+        <title>arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL(".",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="#" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="#" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -197,10 +197,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="#" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="#" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -413,7 +413,7 @@
 Documentation for <a class="reference external" href="https://bashtage.github.io/arch/devel/">recent developments</a>
 is located at <a class="reference external" href="https://bashtage.github.io/arch/devel/">devel</a>.</p>
 </div>
-<h1 id="introduction">Introduction<a class="headerlink" href="#introduction" title="Permalink to this heading">¶</a></h1>
+<h1 id="introduction">Introduction<a class="headerlink" href="#introduction" title="Link to this heading">¶</a></h1>
 <p>The ARCH toolbox contains routines for:</p>
 <ul class="simple">
 <li><p>Univariate volatility models;</p></li>
@@ -427,7 +427,7 @@ <h1 id="introduction">Introduction<a class="headerlink" href="#introduction" tit
 routines relevant for the analysis of financial data.</p>
 <div class="toctree-wrapper compound">
 </div>
-<h1 id="citation">Citation<a class="headerlink" href="#citation" title="Permalink to this heading">¶</a></h1>
+<h1 id="citation">Citation<a class="headerlink" href="#citation" title="Link to this heading">¶</a></h1>
 <p>This package should be cited using Zenodo. For example, for the 4.13 release,</p>
 <aside class="footnote-list brackets">
 <aside class="footnote brackets" id="id1" role="doc-footnote">
@@ -437,7 +437,7 @@ <h1 id="citation">Citation<a class="headerlink" href="#citation" title="Permalin
 </aside>
 </aside>
 <a class="reference external image-reference" href="https://doi.org/10.5281/zenodo.593254"><img alt="https://zenodo.org/badge/doi/10.5281/zenodo.593254.svg" src="https://zenodo.org/badge/doi/10.5281/zenodo.593254.svg" /></a>
-<h1 id="index">Index<a class="headerlink" href="#index" title="Permalink to this heading">¶</a></h1>
+<h1 id="index">Index<a class="headerlink" href="#index" title="Link to this heading">¶</a></h1>
 <ul class="simple">
 <li><p><a class="reference internal" href="genindex.html"><span class="std std-ref">Index</span></a></p></li>
 <li><p><a class="reference internal" href="py-modindex.html"><span class="std std-ref">Module Index</span></a></p></li>
@@ -494,7 +494,7 @@ <h1 id="index">Index<a class="headerlink" href="#index" title="Permalink to this
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -541,7 +541,7 @@ <h1 id="index">Index<a class="headerlink" href="#index" title="Permalink to this
     <script id="__config" type="application/json">{"base": ".", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/multiple-comparison/background.html b/multiple-comparison/background.html
index a20f32f151..a8978d15e5 100644
--- a/multiple-comparison/background.html
+++ b/multiple-comparison/background.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>References - arch 6.1.0</title>
+        <title>References - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -441,7 +441,7 @@
                   
 
 
-<h1 id="references">References<a class="headerlink" href="#references" title="Permalink to this heading">¶</a></h1>
+<h1 id="references">References<a class="headerlink" href="#references" title="Link to this heading">¶</a></h1>
 <p>Articles used in the creation of this module include</p>
 <div role="list" class="citation-list">
 <div class="citation" id="hansen" role="doc-biblioentry">
@@ -528,7 +528,7 @@ <h1 id="references">References<a class="headerlink" href="#references" title="Pe
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -575,7 +575,7 @@ <h1 id="references">References<a class="headerlink" href="#references" title="Pe
     <script id="__config" type="application/json">{"base": "..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/multiple-comparison/generated/arch.bootstrap.MCS.html b/multiple-comparison/generated/arch.bootstrap.MCS.html
index 2a2d177f0e..73a192e6aa 100644
--- a/multiple-comparison/generated/arch.bootstrap.MCS.html
+++ b/multiple-comparison/generated/arch.bootstrap.MCS.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MCS - arch 6.1.0</title>
+        <title>arch.bootstrap.MCS - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -939,34 +939,34 @@
                   
 
 
-<h1 id="arch-bootstrap-mcs">arch.bootstrap.MCS<a class="headerlink" href="#arch-bootstrap-mcs" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-mcs">arch.bootstrap.MCS<a class="headerlink" href="#arch-bootstrap-mcs" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.MCS">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.bootstrap.</span></span><span class="sig-name descname"><span class="pre">MCS</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MCS.__init__.losses" title="arch.bootstrap.MCS.__init__.losses (Python parameter) — T by k array containing losses from a set of models"><span class="n"><span class="pre">losses</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MCS.__init__.size" title="arch.bootstrap.MCS.__init__.size (Python parameter) — Value in (0,1) to use as the test size when implementing the mcs. Default value is 0.05."><span class="n"><span class="pre">size</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MCS.__init__.reps" title="arch.bootstrap.MCS.__init__.reps (Python parameter) — Number of bootstrap replications to uses.  Default is 1000."><span class="n"><span class="pre">reps</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MCS.__init__.block_size" title="arch.bootstrap.MCS.__init__.block_size (Python parameter) — Length of window to use in the bootstrap.  If not provided, sqrt(T) is used.  In general, this should be provided and chosen to be appropriate for the data."><span class="n"><span class="pre">block_size</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MCS.__init__.method" title="arch.bootstrap.MCS.__init__.method (Python parameter) — MCS test and elimination implementation method, either 'max' or 'R'. Default is 'R'."><span class="n"><span class="pre">method</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;R&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MCS.__init__.bootstrap" title="arch.bootstrap.MCS.__init__.bootstrap (Python parameter) — Bootstrap to use.  Options are 'stationary' or 'sb': Stationary bootstrap (Default) 'circular' or 'cbb': Circular block bootstrap 'moving block' or 'mbb': Moving block bootstrap"><span class="n"><span class="pre">bootstrap</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;stationary&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MCS.__init__.seed" title="arch.bootstrap.MCS.__init__.seed (Python parameter) — Seed value to use when creating the bootstrap used in the comparison. If an integer or None, the NumPy default_rng is used with the seed value.  If a Generator or a RandomState, the argument is used."><span class="n"><span class="pre">seed</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/bootstrap/multiple_comparison.html#MCS"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.MCS" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.bootstrap.</span></span><span class="sig-name descname"><span class="pre">MCS</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MCS.__init__.losses" title="arch.bootstrap.MCS.__init__.losses (Python parameter) — T by k array containing losses from a set of models"><span class="n"><span class="pre">losses</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MCS.__init__.size" title="arch.bootstrap.MCS.__init__.size (Python parameter) — Value in (0,1) to use as the test size when implementing the mcs. Default value is 0.05."><span class="n"><span class="pre">size</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MCS.__init__.reps" title="arch.bootstrap.MCS.__init__.reps (Python parameter) — Number of bootstrap replications to uses.  Default is 1000."><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MCS.__init__.block_size" title="arch.bootstrap.MCS.__init__.block_size (Python parameter) — Length of window to use in the bootstrap.  If not provided, sqrt(T) is used.  In general, this should be provided and chosen to be appropriate for the data."><span class="n"><span class="pre">block_size</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MCS.__init__.method" title="arch.bootstrap.MCS.__init__.method (Python parameter) — MCS test and elimination implementation method, either 'max' or 'R'. Default is 'R'."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'R'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'max'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;R&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MCS.__init__.bootstrap" title="arch.bootstrap.MCS.__init__.bootstrap (Python parameter) — Bootstrap to use.  Options are 'stationary' or 'sb': Stationary bootstrap (Default) 'circular' or 'cbb': Circular block bootstrap 'moving block' or 'mbb': Moving block bootstrap"><span class="n"><span class="pre">bootstrap</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'stationary'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'sb'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'circular'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'cbb'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'moving</span> <span class="pre">block'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'mbb'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;stationary&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MCS.__init__.seed" title="arch.bootstrap.MCS.__init__.seed (Python parameter) — Seed value to use when creating the bootstrap used in the comparison. If an integer or None, the NumPy default_rng is used with the seed value.  If a Generator or a RandomState, the argument is used."><span class="n"><span class="pre">seed</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><span class="pre">Generator</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/bootstrap/multiple_comparison.html#MCS"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.MCS" title="Link to this definition">¶</a></dt>
 <dd><p>Model Confidence Set (MCS) of Hansen, Lunde and Nason.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.MCS-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.MCS-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.MCS.__init__.losses"><span class="sig-name descname">losses</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, DataFrame}</span></span><a class="headerlink" href="#arch.bootstrap.MCS.__init__.losses" title="Permalink to this definition">¶</a></dt><dd><p>T by k array containing losses from a set of models</p>
+<dt id="arch.bootstrap.MCS.__init__.losses"><span class="n sig-name">losses</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a></span></span><a class="headerlink" href="#arch.bootstrap.MCS.__init__.losses" title="Permalink to this definition">¶</a></dt><dd><p>T by k array containing losses from a set of models</p>
 </dd>
-<dt id="arch.bootstrap.MCS.__init__.size"><span class="sig-name descname">size</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>, optional</span></span><a class="headerlink" href="#arch.bootstrap.MCS.__init__.size" title="Permalink to this definition">¶</a></dt><dd><p>Value in (0,1) to use as the test size when implementing the
+<dt id="arch.bootstrap.MCS.__init__.size"><span class="n sig-name">size</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><a class="headerlink" href="#arch.bootstrap.MCS.__init__.size" title="Permalink to this definition">¶</a></dt><dd><p>Value in (0,1) to use as the test size when implementing the
 mcs. Default value is 0.05.</p>
 </dd>
-<dt id="arch.bootstrap.MCS.__init__.block_size"><span class="sig-name descname">block_size</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.bootstrap.MCS.__init__.block_size" title="Permalink to this definition">¶</a></dt><dd><p>Length of window to use in the bootstrap.  If not provided, sqrt(T)
+<dt id="arch.bootstrap.MCS.__init__.block_size"><span class="n sig-name">block_size</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.MCS.__init__.block_size" title="Permalink to this definition">¶</a></dt><dd><p>Length of window to use in the bootstrap.  If not provided, sqrt(T)
 is used.  In general, this should be provided and chosen to be
 appropriate for the data.</p>
 </dd>
-<dt id="arch.bootstrap.MCS.__init__.method"><span class="sig-name descname">method</span><span class="p"> : </span><span class="desctype"><span class="desctype">{'max', 'R'}, optional</span></span><a class="headerlink" href="#arch.bootstrap.MCS.__init__.method" title="Permalink to this definition">¶</a></dt><dd><p>MCS test and elimination implementation method, either ‘max’ or ‘R’.
+<dt id="arch.bootstrap.MCS.__init__.method"><span class="n sig-name">method</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'R'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'max'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;R&#39;</span></code><a class="headerlink" href="#arch.bootstrap.MCS.__init__.method" title="Permalink to this definition">¶</a></dt><dd><p>MCS test and elimination implementation method, either ‘max’ or ‘R’.
 Default is ‘R’.</p>
 </dd>
-<dt id="arch.bootstrap.MCS.__init__.reps"><span class="sig-name descname">reps</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.bootstrap.MCS.__init__.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications to uses.  Default is 1000.</p>
+<dt id="arch.bootstrap.MCS.__init__.reps"><span class="n sig-name">reps</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.bootstrap.MCS.__init__.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications to uses.  Default is 1000.</p>
 </dd>
-<dt id="arch.bootstrap.MCS.__init__.bootstrap"><span class="sig-name descname">bootstrap</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, optional</span></span><a class="headerlink" href="#arch.bootstrap.MCS.__init__.bootstrap" title="Permalink to this definition">¶</a></dt><dd><p>Bootstrap to use.  Options are
+<dt id="arch.bootstrap.MCS.__init__.bootstrap"><span class="n sig-name">bootstrap</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'stationary'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'sb'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'circular'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'cbb'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'moving block'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'mbb'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;stationary&#39;</span></code><a class="headerlink" href="#arch.bootstrap.MCS.__init__.bootstrap" title="Permalink to this definition">¶</a></dt><dd><p>Bootstrap to use.  Options are
 ‘stationary’ or ‘sb’: Stationary bootstrap (Default)
 ‘circular’ or ‘cbb’: Circular block bootstrap
 ‘moving block’ or ‘mbb’: Moving block bootstrap</p>
 </dd>
-<dt id="arch.bootstrap.MCS.__init__.seed"><span class="sig-name descname">seed</span><span class="p"> : </span><span class="desctype"><span class="desctype">{int, Generator, RandomState}, optional</span></span><a class="headerlink" href="#arch.bootstrap.MCS.__init__.seed" title="Permalink to this definition">¶</a></dt><dd><p>Seed value to use when creating the bootstrap used in the comparison.
+<dt id="arch.bootstrap.MCS.__init__.seed"><span class="n sig-name">seed</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)">Generator</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)">RandomState</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.MCS.__init__.seed" title="Permalink to this definition">¶</a></dt><dd><p>Seed value to use when creating the bootstrap used in the comparison.
 If an integer or None, the NumPy default_rng is used with the seed
 value.  If a Generator or a RandomState, the argument is used.</p>
 </dd>
@@ -1080,7 +1080,7 @@ <h1 id="arch-bootstrap-mcs">arch.bootstrap.MCS<a class="headerlink" href="#arch-
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1127,7 +1127,7 @@ <h1 id="arch-bootstrap-mcs">arch.bootstrap.MCS<a class="headerlink" href="#arch-
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diff --git a/multiple-comparison/generated/arch.bootstrap.SPA.html b/multiple-comparison/generated/arch.bootstrap.SPA.html
index e876a0a0b0..69ae25b0cc 100644
--- a/multiple-comparison/generated/arch.bootstrap.SPA.html
+++ b/multiple-comparison/generated/arch.bootstrap.SPA.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.SPA - arch 6.1.0</title>
+        <title>arch.bootstrap.SPA - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css?v=64f7a1ef" />
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       <img src="../../_static/bw-logo.svg" alt="logo">
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@@ -975,37 +975,37 @@
                   
 
 
-<h1 id="arch-bootstrap-spa">arch.bootstrap.SPA<a class="headerlink" href="#arch-bootstrap-spa" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-spa">arch.bootstrap.SPA<a class="headerlink" href="#arch-bootstrap-spa" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.SPA">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.bootstrap.</span></span><span class="sig-name descname"><span class="pre">SPA</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.SPA.__init__.benchmark" title="arch.bootstrap.SPA.__init__.benchmark (Python parameter) — T element array of benchmark model losses"><span class="n"><span class="pre">benchmark</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.SPA.__init__.models" title="arch.bootstrap.SPA.__init__.models (Python parameter) — T  by k element array of alternative model losses"><span class="n"><span class="pre">models</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.SPA.__init__.block_size" title="arch.bootstrap.SPA.__init__.block_size (Python parameter) — Length of window to use in the bootstrap.  If not provided, sqrt(T) is used.  In general, this should be provided and chosen to be appropriate for the data."><span class="n"><span class="pre">block_size</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.SPA.__init__.reps" title="arch.bootstrap.SPA.__init__.reps (Python parameter) — Number of bootstrap replications to uses.  Default is 1000."><span class="n"><span class="pre">reps</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.SPA.__init__.bootstrap" title="arch.bootstrap.SPA.__init__.bootstrap (Python parameter) — Bootstrap to use.  Options are 'stationary' or 'sb': Stationary bootstrap (Default) 'circular' or 'cbb': Circular block bootstrap 'moving block' or 'mbb': Moving block bootstrap"><span class="n"><span class="pre">bootstrap</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;stationary&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.SPA.__init__.studentize" title="arch.bootstrap.SPA.__init__.studentize (Python parameter) — Flag indicating to studentize loss differentials. Default is True"><span class="n"><span class="pre">studentize</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.SPA.__init__.nested" title="arch.bootstrap.SPA.__init__.nested (Python parameter) — Flag indicating to use a nested bootstrap to compute variances for studentization.  Default is False.  Note that this can be slow since the procedure requires k extra bootstraps."><span class="n"><span class="pre">nested</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.SPA.__init__.seed" title="arch.bootstrap.SPA.__init__.seed (Python parameter) — Seed value to use when creating the bootstrap used in the comparison. If an integer or None, the NumPy default_rng is used with the seed value.  If a Generator or a RandomState, the argument is used."><span class="n"><span class="pre">seed</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/bootstrap/multiple_comparison.html#SPA"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.SPA" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.bootstrap.</span></span><span class="sig-name descname"><span class="pre">SPA</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.SPA.__init__.benchmark" title="arch.bootstrap.SPA.__init__.benchmark (Python parameter) — T element array of benchmark model losses"><span class="n"><span class="pre">benchmark</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.SPA.__init__.models" title="arch.bootstrap.SPA.__init__.models (Python parameter) — T  by k element array of alternative model losses"><span class="n"><span class="pre">models</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.SPA.__init__.block_size" title="arch.bootstrap.SPA.__init__.block_size (Python parameter) — Length of window to use in the bootstrap.  If not provided, sqrt(T) is used.  In general, this should be provided and chosen to be appropriate for the data."><span class="n"><span class="pre">block_size</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.SPA.__init__.reps" title="arch.bootstrap.SPA.__init__.reps (Python parameter) — Number of bootstrap replications to uses.  Default is 1000."><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.SPA.__init__.bootstrap" title="arch.bootstrap.SPA.__init__.bootstrap (Python parameter) — Bootstrap to use.  Options are 'stationary' or 'sb': Stationary bootstrap (Default) 'circular' or 'cbb': Circular block bootstrap 'moving block' or 'mbb': Moving block bootstrap"><span class="n"><span class="pre">bootstrap</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'stationary'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'sb'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'circular'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'cbb'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'moving</span> <span class="pre">block'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'mbb'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;stationary&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.SPA.__init__.studentize" title="arch.bootstrap.SPA.__init__.studentize (Python parameter) — Flag indicating to studentize loss differentials. Default is True"><span class="n"><span class="pre">studentize</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.SPA.__init__.nested" title="arch.bootstrap.SPA.__init__.nested (Python parameter) — Flag indicating to use a nested bootstrap to compute variances for studentization.  Default is False.  Note that this can be slow since the procedure requires k extra bootstraps."><span class="n"><span class="pre">nested</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.SPA.__init__.seed" title="arch.bootstrap.SPA.__init__.seed (Python parameter) — Seed value to use when creating the bootstrap used in the comparison. If an integer or None, the NumPy default_rng is used with the seed value.  If a Generator or a RandomState, the argument is used."><span class="n"><span class="pre">seed</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><span class="pre">Generator</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/bootstrap/multiple_comparison.html#SPA"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.SPA" title="Link to this definition">¶</a></dt>
 <dd><p>Test of Superior Predictive Ability (SPA) of White and Hansen.</p>
 <p>The SPA is also known as the Reality Check or Bootstrap Data Snooper.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.SPA-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.SPA-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.SPA.__init__.benchmark"><span class="sig-name descname">benchmark</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, Series}</span></span><a class="headerlink" href="#arch.bootstrap.SPA.__init__.benchmark" title="Permalink to this definition">¶</a></dt><dd><p>T element array of benchmark model <em>losses</em></p>
+<dt id="arch.bootstrap.SPA.__init__.benchmark"><span class="n sig-name">benchmark</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.bootstrap.SPA.__init__.benchmark" title="Permalink to this definition">¶</a></dt><dd><p>T element array of benchmark model <em>losses</em></p>
 </dd>
-<dt id="arch.bootstrap.SPA.__init__.models"><span class="sig-name descname">models</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, DataFrame}</span></span><a class="headerlink" href="#arch.bootstrap.SPA.__init__.models" title="Permalink to this definition">¶</a></dt><dd><p>T  by k element array of alternative model <em>losses</em></p>
+<dt id="arch.bootstrap.SPA.__init__.models"><span class="n sig-name">models</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.bootstrap.SPA.__init__.models" title="Permalink to this definition">¶</a></dt><dd><p>T  by k element array of alternative model <em>losses</em></p>
 </dd>
-<dt id="arch.bootstrap.SPA.__init__.block_size"><span class="sig-name descname">block_size</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.bootstrap.SPA.__init__.block_size" title="Permalink to this definition">¶</a></dt><dd><p>Length of window to use in the bootstrap.  If not provided, sqrt(T)
+<dt id="arch.bootstrap.SPA.__init__.block_size"><span class="n sig-name">block_size</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.SPA.__init__.block_size" title="Permalink to this definition">¶</a></dt><dd><p>Length of window to use in the bootstrap.  If not provided, sqrt(T)
 is used.  In general, this should be provided and chosen to be
 appropriate for the data.</p>
 </dd>
-<dt id="arch.bootstrap.SPA.__init__.reps"><span class="sig-name descname">reps</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.bootstrap.SPA.__init__.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications to uses.  Default is 1000.</p>
+<dt id="arch.bootstrap.SPA.__init__.reps"><span class="n sig-name">reps</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.bootstrap.SPA.__init__.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications to uses.  Default is 1000.</p>
 </dd>
-<dt id="arch.bootstrap.SPA.__init__.bootstrap"><span class="sig-name descname">bootstrap</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, optional</span></span><a class="headerlink" href="#arch.bootstrap.SPA.__init__.bootstrap" title="Permalink to this definition">¶</a></dt><dd><p>Bootstrap to use.  Options are
+<dt id="arch.bootstrap.SPA.__init__.bootstrap"><span class="n sig-name">bootstrap</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'stationary'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'sb'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'circular'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'cbb'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'moving block'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'mbb'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;stationary&#39;</span></code><a class="headerlink" href="#arch.bootstrap.SPA.__init__.bootstrap" title="Permalink to this definition">¶</a></dt><dd><p>Bootstrap to use.  Options are
 ‘stationary’ or ‘sb’: Stationary bootstrap (Default)
 ‘circular’ or ‘cbb’: Circular block bootstrap
 ‘moving block’ or ‘mbb’: Moving block bootstrap</p>
 </dd>
-<dt id="arch.bootstrap.SPA.__init__.studentize"><span class="sig-name descname">studentize</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><a class="headerlink" href="#arch.bootstrap.SPA.__init__.studentize" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating to studentize loss differentials. Default is True</p>
+<dt id="arch.bootstrap.SPA.__init__.studentize"><span class="n sig-name">studentize</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.bootstrap.SPA.__init__.studentize" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating to studentize loss differentials. Default is True</p>
 </dd>
-<dt id="arch.bootstrap.SPA.__init__.nested"><span class="sig-name descname">nested</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><a class="headerlink" href="#arch.bootstrap.SPA.__init__.nested" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating to use a nested bootstrap to compute variances for
+<dt id="arch.bootstrap.SPA.__init__.nested"><span class="n sig-name">nested</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.bootstrap.SPA.__init__.nested" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating to use a nested bootstrap to compute variances for
 studentization.  Default is False.  Note that this can be slow since
 the procedure requires k extra bootstraps.</p>
 </dd>
-<dt id="arch.bootstrap.SPA.__init__.seed"><span class="sig-name descname">seed</span><span class="p"> : </span><span class="desctype"><span class="desctype">{int, Generator, RandomState}, optional</span></span><a class="headerlink" href="#arch.bootstrap.SPA.__init__.seed" title="Permalink to this definition">¶</a></dt><dd><p>Seed value to use when creating the bootstrap used in the comparison.
+<dt id="arch.bootstrap.SPA.__init__.seed"><span class="n sig-name">seed</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)">Generator</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)">RandomState</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.SPA.__init__.seed" title="Permalink to this definition">¶</a></dt><dd><p>Seed value to use when creating the bootstrap used in the comparison.
 If an integer or None, the NumPy default_rng is used with the seed
 value.  If a Generator or a RandomState, the argument is used.</p>
 </dd>
@@ -1139,7 +1139,7 @@ <h1 id="arch-bootstrap-spa">arch.bootstrap.SPA<a class="headerlink" href="#arch-
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1186,7 +1186,7 @@ <h1 id="arch-bootstrap-spa">arch.bootstrap.SPA<a class="headerlink" href="#arch-
     <script id="__config" type="application/json">{"base": "../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/multiple-comparison/generated/arch.bootstrap.StepM.html b/multiple-comparison/generated/arch.bootstrap.StepM.html
index be2a56e79b..bcb6a238fd 100644
--- a/multiple-comparison/generated/arch.bootstrap.StepM.html
+++ b/multiple-comparison/generated/arch.bootstrap.StepM.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StepM - arch 6.1.0</title>
+        <title>arch.bootstrap.StepM - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -923,39 +923,39 @@
                   
 
 
-<h1 id="arch-bootstrap-stepm">arch.bootstrap.StepM<a class="headerlink" href="#arch-bootstrap-stepm" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-stepm">arch.bootstrap.StepM<a class="headerlink" href="#arch-bootstrap-stepm" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.StepM">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.bootstrap.</span></span><span class="sig-name descname"><span class="pre">StepM</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StepM.__init__.benchmark" title="arch.bootstrap.StepM.__init__.benchmark (Python parameter) — T element array of benchmark model losses"><span class="n"><span class="pre">benchmark</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StepM.__init__.models" title="arch.bootstrap.StepM.__init__.models (Python parameter) — T by k element array of alternative model losses"><span class="n"><span class="pre">models</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StepM.__init__.size" title="arch.bootstrap.StepM.__init__.size (Python parameter) — Value in (0,1) to use as the test size when implementing the comparison. Default value is 0.05."><span class="n"><span class="pre">size</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.05</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StepM.__init__.block_size" title="arch.bootstrap.StepM.__init__.block_size (Python parameter) — Length of window to use in the bootstrap.  If not provided, sqrt(T) is used.  In general, this should be provided and chosen to be appropriate for the data."><span class="n"><span class="pre">block_size</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StepM.__init__.reps" title="arch.bootstrap.StepM.__init__.reps (Python parameter) — Number of bootstrap replications to uses.  Default is 1000."><span class="n"><span class="pre">reps</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StepM.__init__.bootstrap" title="arch.bootstrap.StepM.__init__.bootstrap (Python parameter) — Bootstrap to use.  Options are 'stationary' or 'sb': Stationary bootstrap (Default) 'circular' or 'cbb': Circular block bootstrap 'moving block' or 'mbb': Moving block bootstrap"><span class="n"><span class="pre">bootstrap</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;stationary&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StepM.__init__.studentize" title="arch.bootstrap.StepM.__init__.studentize (Python parameter) — Flag indicating to studentize loss differentials. Default is True"><span class="n"><span class="pre">studentize</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StepM.__init__.nested" title="arch.bootstrap.StepM.__init__.nested (Python parameter) — Flag indicating to use a nested bootstrap to compute variances for studentization.  Default is False.  Note that this can be slow since the procedure requires k extra bootstraps."><span class="n"><span class="pre">nested</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StepM.__init__.seed" title="arch.bootstrap.StepM.__init__.seed (Python parameter) — Seed value to use when creating the bootstrap used in the comparison. If an integer or None, the NumPy default_rng is used with the seed value.  If a Generator or a RandomState, the argument is used."><span class="n"><span class="pre">seed</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/bootstrap/multiple_comparison.html#StepM"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.StepM" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.bootstrap.</span></span><span class="sig-name descname"><span class="pre">StepM</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StepM.__init__.benchmark" title="arch.bootstrap.StepM.__init__.benchmark (Python parameter) — T element array of benchmark model losses"><span class="n"><span class="pre">benchmark</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StepM.__init__.models" title="arch.bootstrap.StepM.__init__.models (Python parameter) — T by k element array of alternative model losses"><span class="n"><span class="pre">models</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StepM.__init__.size" title="arch.bootstrap.StepM.__init__.size (Python parameter) — Value in (0,1) to use as the test size when implementing the comparison. Default value is 0.05."><span class="n"><span class="pre">size</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.05</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StepM.__init__.block_size" title="arch.bootstrap.StepM.__init__.block_size (Python parameter) — Length of window to use in the bootstrap.  If not provided, sqrt(T) is used.  In general, this should be provided and chosen to be appropriate for the data."><span class="n"><span class="pre">block_size</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StepM.__init__.reps" title="arch.bootstrap.StepM.__init__.reps (Python parameter) — Number of bootstrap replications to uses.  Default is 1000."><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StepM.__init__.bootstrap" title="arch.bootstrap.StepM.__init__.bootstrap (Python parameter) — Bootstrap to use.  Options are 'stationary' or 'sb': Stationary bootstrap (Default) 'circular' or 'cbb': Circular block bootstrap 'moving block' or 'mbb': Moving block bootstrap"><span class="n"><span class="pre">bootstrap</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'stationary'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'sb'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'circular'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'cbb'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'moving</span> <span class="pre">block'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'mbb'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;stationary&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StepM.__init__.studentize" title="arch.bootstrap.StepM.__init__.studentize (Python parameter) — Flag indicating to studentize loss differentials. Default is True"><span class="n"><span class="pre">studentize</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StepM.__init__.nested" title="arch.bootstrap.StepM.__init__.nested (Python parameter) — Flag indicating to use a nested bootstrap to compute variances for studentization.  Default is False.  Note that this can be slow since the procedure requires k extra bootstraps."><span class="n"><span class="pre">nested</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StepM.__init__.seed" title="arch.bootstrap.StepM.__init__.seed (Python parameter) — Seed value to use when creating the bootstrap used in the comparison. If an integer or None, the NumPy default_rng is used with the seed value.  If a Generator or a RandomState, the argument is used."><span class="n"><span class="pre">seed</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><span class="pre">Generator</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/bootstrap/multiple_comparison.html#StepM"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.StepM" title="Link to this definition">¶</a></dt>
 <dd><p>StepM multiple comparison procedure of Romano and Wolf.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.StepM-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.StepM-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.StepM.__init__.benchmark"><span class="sig-name descname">benchmark</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, Series}</span></span><a class="headerlink" href="#arch.bootstrap.StepM.__init__.benchmark" title="Permalink to this definition">¶</a></dt><dd><p>T element array of benchmark model <em>losses</em></p>
+<dt id="arch.bootstrap.StepM.__init__.benchmark"><span class="n sig-name">benchmark</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.bootstrap.StepM.__init__.benchmark" title="Permalink to this definition">¶</a></dt><dd><p>T element array of benchmark model <em>losses</em></p>
 </dd>
-<dt id="arch.bootstrap.StepM.__init__.models"><span class="sig-name descname">models</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, DataFrame}</span></span><a class="headerlink" href="#arch.bootstrap.StepM.__init__.models" title="Permalink to this definition">¶</a></dt><dd><p>T by k element array of alternative model <em>losses</em></p>
+<dt id="arch.bootstrap.StepM.__init__.models"><span class="n sig-name">models</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.bootstrap.StepM.__init__.models" title="Permalink to this definition">¶</a></dt><dd><p>T by k element array of alternative model <em>losses</em></p>
 </dd>
-<dt id="arch.bootstrap.StepM.__init__.size"><span class="sig-name descname">size</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>, optional</span></span><a class="headerlink" href="#arch.bootstrap.StepM.__init__.size" title="Permalink to this definition">¶</a></dt><dd><p>Value in (0,1) to use as the test size when implementing the
+<dt id="arch.bootstrap.StepM.__init__.size"><span class="n sig-name">size</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.05</span></code><a class="headerlink" href="#arch.bootstrap.StepM.__init__.size" title="Permalink to this definition">¶</a></dt><dd><p>Value in (0,1) to use as the test size when implementing the
 comparison. Default value is 0.05.</p>
 </dd>
-<dt id="arch.bootstrap.StepM.__init__.block_size"><span class="sig-name descname">block_size</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.bootstrap.StepM.__init__.block_size" title="Permalink to this definition">¶</a></dt><dd><p>Length of window to use in the bootstrap.  If not provided, sqrt(T)
+<dt id="arch.bootstrap.StepM.__init__.block_size"><span class="n sig-name">block_size</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.StepM.__init__.block_size" title="Permalink to this definition">¶</a></dt><dd><p>Length of window to use in the bootstrap.  If not provided, sqrt(T)
 is used.  In general, this should be provided and chosen to be
 appropriate for the data.</p>
 </dd>
-<dt id="arch.bootstrap.StepM.__init__.reps"><span class="sig-name descname">reps</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.bootstrap.StepM.__init__.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications to uses.  Default is 1000.</p>
+<dt id="arch.bootstrap.StepM.__init__.reps"><span class="n sig-name">reps</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.bootstrap.StepM.__init__.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications to uses.  Default is 1000.</p>
 </dd>
-<dt id="arch.bootstrap.StepM.__init__.bootstrap"><span class="sig-name descname">bootstrap</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, optional</span></span><a class="headerlink" href="#arch.bootstrap.StepM.__init__.bootstrap" title="Permalink to this definition">¶</a></dt><dd><p>Bootstrap to use.  Options are
+<dt id="arch.bootstrap.StepM.__init__.bootstrap"><span class="n sig-name">bootstrap</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'stationary'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'sb'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'circular'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'cbb'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'moving block'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'mbb'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;stationary&#39;</span></code><a class="headerlink" href="#arch.bootstrap.StepM.__init__.bootstrap" title="Permalink to this definition">¶</a></dt><dd><p>Bootstrap to use.  Options are
 ‘stationary’ or ‘sb’: Stationary bootstrap (Default)
 ‘circular’ or ‘cbb’: Circular block bootstrap
 ‘moving block’ or ‘mbb’: Moving block bootstrap</p>
 </dd>
-<dt id="arch.bootstrap.StepM.__init__.studentize"><span class="sig-name descname">studentize</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, optional</span></span><a class="headerlink" href="#arch.bootstrap.StepM.__init__.studentize" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating to studentize loss differentials. Default is True</p>
+<dt id="arch.bootstrap.StepM.__init__.studentize"><span class="n sig-name">studentize</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.bootstrap.StepM.__init__.studentize" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating to studentize loss differentials. Default is True</p>
 </dd>
-<dt id="arch.bootstrap.StepM.__init__.nested"><span class="sig-name descname">nested</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, optional</span></span><a class="headerlink" href="#arch.bootstrap.StepM.__init__.nested" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating to use a nested bootstrap to compute variances for
+<dt id="arch.bootstrap.StepM.__init__.nested"><span class="n sig-name">nested</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.bootstrap.StepM.__init__.nested" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating to use a nested bootstrap to compute variances for
 studentization.  Default is False.  Note that this can be slow since
 the procedure requires k extra bootstraps.</p>
 </dd>
-<dt id="arch.bootstrap.StepM.__init__.seed"><span class="sig-name descname">seed</span><span class="p"> : </span><span class="desctype"><span class="desctype">{int, Generator, RandomState}, optional</span></span><a class="headerlink" href="#arch.bootstrap.StepM.__init__.seed" title="Permalink to this definition">¶</a></dt><dd><p>Seed value to use when creating the bootstrap used in the comparison.
+<dt id="arch.bootstrap.StepM.__init__.seed"><span class="n sig-name">seed</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)">Generator</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)">RandomState</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.StepM.__init__.seed" title="Permalink to this definition">¶</a></dt><dd><p>Seed value to use when creating the bootstrap used in the comparison.
 If an integer or None, the NumPy default_rng is used with the seed
 value.  If a Generator or a RandomState, the argument is used.</p>
 </dd>
@@ -1070,7 +1070,7 @@ <h1 id="arch-bootstrap-stepm">arch.bootstrap.StepM<a class="headerlink" href="#a
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1117,7 +1117,7 @@ <h1 id="arch-bootstrap-stepm">arch.bootstrap.StepM<a class="headerlink" href="#a
     <script id="__config" type="application/json">{"base": "../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/multiple-comparison/generated/generated/arch.bootstrap.MCS.compute.html b/multiple-comparison/generated/generated/arch.bootstrap.MCS.compute.html
index ac46723b2f..b695ad051f 100644
--- a/multiple-comparison/generated/generated/arch.bootstrap.MCS.compute.html
+++ b/multiple-comparison/generated/generated/arch.bootstrap.MCS.compute.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MCS.compute - arch 6.1.0</title>
+        <title>arch.bootstrap.MCS.compute - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -492,19 +492,6 @@
     <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="arch.bootstrap.MCS.compute (Python method) — Compute the set of models in the confidence set." class="md-ellipsis">MCS.<wbr>compute</span>
   </a>
   
-    <nav class="md-nav" aria-label="MCS.compute">
-      <ul class="md-nav__list">
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.MCS.compute-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
-</li>
-        
-      </ul>
-    </nav>
-  
 </li>
       
     </ul>
@@ -755,19 +742,6 @@
     <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="arch.bootstrap.MCS.compute (Python method) — Compute the set of models in the confidence set." class="md-ellipsis">MCS.<wbr>compute</span>
   </a>
   
-    <nav class="md-nav" aria-label="MCS.compute">
-      <ul class="md-nav__list">
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.MCS.compute-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
-</li>
-        
-      </ul>
-    </nav>
-  
 </li>
       
     </ul>
@@ -784,16 +758,11 @@
                   
 
 
-<h1 id="arch-bootstrap-mcs-compute">arch.bootstrap.MCS.compute<a class="headerlink" href="#arch-bootstrap-mcs-compute" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-mcs-compute">arch.bootstrap.MCS.compute<a class="headerlink" href="#arch-bootstrap-mcs-compute" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.bootstrap.MCS.compute">
-<span class="sig-prename descclassname"><span class="pre">MCS.</span></span><span class="sig-name descname"><span class="pre">compute</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/bootstrap/multiple_comparison.html#MCS.compute"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.MCS.compute" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">MCS.</span></span><span class="sig-name descname"><span class="pre">compute</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/bootstrap/multiple_comparison.html#MCS.compute"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.MCS.compute" title="Link to this definition">¶</a></dt>
 <dd><p>Compute the set of models in the confidence set.</p>
-<dl class="field-list simple">
-<dt class="field-odd" id="arch.bootstrap.MCS.compute-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.MCS.compute-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code></a></p>
-</dd>
-</dl>
 </dd></dl>
 
 
@@ -863,7 +832,7 @@ <h1 id="arch-bootstrap-mcs-compute">arch.bootstrap.MCS.compute<a class="headerli
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -910,7 +879,7 @@ <h1 id="arch-bootstrap-mcs-compute">arch.bootstrap.MCS.compute<a class="headerli
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/multiple-comparison/generated/generated/arch.bootstrap.MCS.excluded.html b/multiple-comparison/generated/generated/arch.bootstrap.MCS.excluded.html
index 4642284957..78d734a827 100644
--- a/multiple-comparison/generated/generated/arch.bootstrap.MCS.excluded.html
+++ b/multiple-comparison/generated/generated/arch.bootstrap.MCS.excluded.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MCS.excluded - arch 6.1.0</title>
+        <title>arch.bootstrap.MCS.excluded - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -798,17 +798,17 @@
                   
 
 
-<h1 id="arch-bootstrap-mcs-excluded">arch.bootstrap.MCS.excluded<a class="headerlink" href="#arch-bootstrap-mcs-excluded" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-mcs-excluded">arch.bootstrap.MCS.excluded<a class="headerlink" href="#arch-bootstrap-mcs-excluded" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.bootstrap.MCS.excluded">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">MCS.</span></span><span class="sig-name descname"><span class="pre">excluded</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.11)"><span class="pre">collections.abc.Hashable</span></a><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#arch.bootstrap.MCS.excluded" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">MCS.</span></span><span class="sig-name descname"><span class="pre">excluded</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.11)"><span class="pre">Hashable</span></a><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#arch.bootstrap.MCS.excluded" title="Link to this definition">¶</a></dt>
 <dd><p>List of model indices that are excluded from the MCS</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.MCS.excluded-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.MCS.excluded-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p><strong>excluded</strong> – List of column indices or names of the excluded models</p>
 </dd>
 <dt class="field-even" id="arch.bootstrap.MCS.excluded-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.MCS.excluded-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -880,7 +880,7 @@ <h1 id="arch-bootstrap-mcs-excluded">arch.bootstrap.MCS.excluded<a class="header
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -927,7 +927,7 @@ <h1 id="arch-bootstrap-mcs-excluded">arch.bootstrap.MCS.excluded<a class="header
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/multiple-comparison/generated/generated/arch.bootstrap.MCS.included.html b/multiple-comparison/generated/generated/arch.bootstrap.MCS.included.html
index f3fde5b4d6..0c473822ac 100644
--- a/multiple-comparison/generated/generated/arch.bootstrap.MCS.included.html
+++ b/multiple-comparison/generated/generated/arch.bootstrap.MCS.included.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MCS.included - arch 6.1.0</title>
+        <title>arch.bootstrap.MCS.included - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -798,17 +798,17 @@
                   
 
 
-<h1 id="arch-bootstrap-mcs-included">arch.bootstrap.MCS.included<a class="headerlink" href="#arch-bootstrap-mcs-included" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-mcs-included">arch.bootstrap.MCS.included<a class="headerlink" href="#arch-bootstrap-mcs-included" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.bootstrap.MCS.included">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">MCS.</span></span><span class="sig-name descname"><span class="pre">included</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.11)"><span class="pre">collections.abc.Hashable</span></a><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#arch.bootstrap.MCS.included" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">MCS.</span></span><span class="sig-name descname"><span class="pre">included</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.11)"><span class="pre">Hashable</span></a><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#arch.bootstrap.MCS.included" title="Link to this definition">¶</a></dt>
 <dd><p>List of model indices that are included in the MCS</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.MCS.included-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.MCS.included-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p><strong>included</strong> – List of column indices or names of the included models</p>
 </dd>
 <dt class="field-even" id="arch.bootstrap.MCS.included-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.MCS.included-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -880,7 +880,7 @@ <h1 id="arch-bootstrap-mcs-included">arch.bootstrap.MCS.included<a class="header
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -927,7 +927,7 @@ <h1 id="arch-bootstrap-mcs-included">arch.bootstrap.MCS.included<a class="header
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/multiple-comparison/generated/generated/arch.bootstrap.MCS.pvalues.html b/multiple-comparison/generated/generated/arch.bootstrap.MCS.pvalues.html
index a8b9e9a34c..7f6d9b4adf 100644
--- a/multiple-comparison/generated/generated/arch.bootstrap.MCS.pvalues.html
+++ b/multiple-comparison/generated/generated/arch.bootstrap.MCS.pvalues.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MCS.pvalues - arch 6.1.0</title>
+        <title>arch.bootstrap.MCS.pvalues - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -798,10 +798,10 @@
                   
 
 
-<h1 id="arch-bootstrap-mcs-pvalues">arch.bootstrap.MCS.pvalues<a class="headerlink" href="#arch-bootstrap-mcs-pvalues" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-mcs-pvalues">arch.bootstrap.MCS.pvalues<a class="headerlink" href="#arch-bootstrap-mcs-pvalues" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.bootstrap.MCS.pvalues">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">MCS.</span></span><span class="sig-name descname"><span class="pre">pvalues</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.0.1)"><span class="pre">DataFrame</span></a></span><a class="headerlink" href="#arch.bootstrap.MCS.pvalues" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">MCS.</span></span><span class="sig-name descname"><span class="pre">pvalues</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a></span><a class="headerlink" href="#arch.bootstrap.MCS.pvalues" title="Link to this definition">¶</a></dt>
 <dd><p>Model p-values for inclusion in the MCS</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.MCS.pvalues-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.MCS.pvalues-returns" title="Permalink to this headline">¶</a></dt>
@@ -809,7 +809,7 @@ <h1 id="arch-bootstrap-mcs-pvalues">arch.bootstrap.MCS.pvalues<a class="headerli
 containing the smallest size where the model is in the MCS.</p>
 </dd>
 <dt class="field-even" id="arch.bootstrap.MCS.pvalues-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.MCS.pvalues-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p>DataFrame</p>
+<dd class="field-even"><p><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -881,7 +881,7 @@ <h1 id="arch-bootstrap-mcs-pvalues">arch.bootstrap.MCS.pvalues<a class="headerli
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -928,7 +928,7 @@ <h1 id="arch-bootstrap-mcs-pvalues">arch.bootstrap.MCS.pvalues<a class="headerli
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/multiple-comparison/generated/generated/arch.bootstrap.MCS.reset.html b/multiple-comparison/generated/generated/arch.bootstrap.MCS.reset.html
index ffc965e9c6..6e46ec3a2c 100644
--- a/multiple-comparison/generated/generated/arch.bootstrap.MCS.reset.html
+++ b/multiple-comparison/generated/generated/arch.bootstrap.MCS.reset.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MCS.reset - arch 6.1.0</title>
+        <title>arch.bootstrap.MCS.reset - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -507,19 +507,6 @@
     <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="arch.bootstrap.MCS.reset (Python method) — Reset the bootstrap to it&#39;s initial state." class="md-ellipsis">MCS.<wbr>reset</span>
   </a>
   
-    <nav class="md-nav" aria-label="MCS.reset">
-      <ul class="md-nav__list">
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.MCS.reset-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
-</li>
-        
-      </ul>
-    </nav>
-  
 </li>
       
     </ul>
@@ -755,19 +742,6 @@
     <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="arch.bootstrap.MCS.reset (Python method) — Reset the bootstrap to it&#39;s initial state." class="md-ellipsis">MCS.<wbr>reset</span>
   </a>
   
-    <nav class="md-nav" aria-label="MCS.reset">
-      <ul class="md-nav__list">
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.MCS.reset-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
-</li>
-        
-      </ul>
-    </nav>
-  
 </li>
       
     </ul>
@@ -784,16 +758,11 @@
                   
 
 
-<h1 id="arch-bootstrap-mcs-reset">arch.bootstrap.MCS.reset<a class="headerlink" href="#arch-bootstrap-mcs-reset" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-mcs-reset">arch.bootstrap.MCS.reset<a class="headerlink" href="#arch-bootstrap-mcs-reset" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.bootstrap.MCS.reset">
-<span class="sig-prename descclassname"><span class="pre">MCS.</span></span><span class="sig-name descname"><span class="pre">reset</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#arch.bootstrap.MCS.reset" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">MCS.</span></span><span class="sig-name descname"><span class="pre">reset</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.MCS.reset" title="Link to this definition">¶</a></dt>
 <dd><p>Reset the bootstrap to it’s initial state.</p>
-<dl class="field-list simple">
-<dt class="field-odd" id="arch.bootstrap.MCS.reset-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.MCS.reset-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code></a></p>
-</dd>
-</dl>
 </dd></dl>
 
 
@@ -863,7 +832,7 @@ <h1 id="arch-bootstrap-mcs-reset">arch.bootstrap.MCS.reset<a class="headerlink"
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -910,7 +879,7 @@ <h1 id="arch-bootstrap-mcs-reset">arch.bootstrap.MCS.reset<a class="headerlink"
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/multiple-comparison/generated/generated/arch.bootstrap.MCS.seed.html b/multiple-comparison/generated/generated/arch.bootstrap.MCS.seed.html
index 2f7f898ce1..e34436693e 100644
--- a/multiple-comparison/generated/generated/arch.bootstrap.MCS.seed.html
+++ b/multiple-comparison/generated/generated/arch.bootstrap.MCS.seed.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MCS.seed - arch 6.1.0</title>
+        <title>arch.bootstrap.MCS.seed - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -543,13 +543,6 @@
       </ul>
     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.MCS.seed-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
       </ul>
@@ -796,13 +789,6 @@
       </ul>
     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.MCS.seed-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
       </ul>
@@ -824,21 +810,18 @@
                   
 
 
-<h1 id="arch-bootstrap-mcs-seed">arch.bootstrap.MCS.seed<a class="headerlink" href="#arch-bootstrap-mcs-seed" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-mcs-seed">arch.bootstrap.MCS.seed<a class="headerlink" href="#arch-bootstrap-mcs-seed" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.bootstrap.MCS.seed">
-<span class="sig-prename descclassname"><span class="pre">MCS.</span></span><span class="sig-name descname"><span class="pre">seed</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MCS.seed.value" title="arch.bootstrap.MCS.seed.value (Python parameter) — Integer to use as the seed"><span class="n"><span class="pre">value</span></span></a></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.bootstrap.MCS.seed" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">MCS.</span></span><span class="sig-name descname"><span class="pre">seed</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MCS.seed.value" title="arch.bootstrap.MCS.seed.value (Python parameter) — Integer to use as the seed"><span class="n"><span class="pre">value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.MCS.seed" title="Link to this definition">¶</a></dt>
 <dd><p>Seed the bootstrap’s random number generator</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.MCS.seed-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.MCS.seed-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.MCS.seed.value"><span class="sig-name descname">value</span><span class="p"> : </span><span class="desctype"><span class="desctype">{int, List[<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>], ndarray[<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>]}</span></span><a class="headerlink" href="#arch.bootstrap.MCS.seed.value" title="Permalink to this definition">¶</a></dt><dd><p>Integer to use as the seed</p>
+<dt id="arch.bootstrap.MCS.seed.value"><span class="n sig-name">value</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.bootstrap.MCS.seed.value" title="Permalink to this definition">¶</a></dt><dd><p>Integer to use as the seed</p>
 </dd>
 </dl>
 </dd>
-<dt class="field-even" id="arch.bootstrap.MCS.seed-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.MCS.seed-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code></a></p>
-</dd>
 </dl>
 </dd></dl>
 
@@ -909,7 +892,7 @@ <h1 id="arch-bootstrap-mcs-seed">arch.bootstrap.MCS.seed<a class="headerlink" hr
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -956,7 +939,7 @@ <h1 id="arch-bootstrap-mcs-seed">arch.bootstrap.MCS.seed<a class="headerlink" hr
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/multiple-comparison/generated/generated/arch.bootstrap.SPA.better_models.html b/multiple-comparison/generated/generated/arch.bootstrap.SPA.better_models.html
index 54e0e8af5b..85a888369b 100644
--- a/multiple-comparison/generated/generated/arch.bootstrap.SPA.better_models.html
+++ b/multiple-comparison/generated/generated/arch.bootstrap.SPA.better_models.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.SPA.better_models - arch 6.1.0</title>
+        <title>arch.bootstrap.SPA.better_models - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -867,18 +867,18 @@
                   
 
 
-<h1 id="arch-bootstrap-spa-better-models">arch.bootstrap.SPA.better_models<a class="headerlink" href="#arch-bootstrap-spa-better-models" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-spa-better-models">arch.bootstrap.SPA.better_models<a class="headerlink" href="#arch-bootstrap-spa-better-models" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.bootstrap.SPA.better_models">
-<span class="sig-prename descclassname"><span class="pre">SPA.</span></span><span class="sig-name descname"><span class="pre">better_models</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.SPA.better_models.pvalue" title="arch.bootstrap.SPA.better_models.pvalue (Python parameter) — P-value in (0,1) to use when computing superior models"><span class="n"><span class="pre">pvalue</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.05</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.SPA.better_models.pvalue_type" title="arch.bootstrap.SPA.better_models.pvalue_type (Python parameter) — String in 'lower', 'consistent', or 'upper' indicating which critical value to use."><span class="n"><span class="pre">pvalue_type</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;consistent&#39;</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/bootstrap/multiple_comparison.html#SPA.better_models"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.SPA.better_models" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.SPA.better_models">
+<span class="sig-prename descclassname"><span class="pre">SPA.</span></span><span class="sig-name descname"><span class="pre">better_models</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.SPA.better_models.pvalue" title="arch.bootstrap.SPA.better_models.pvalue (Python parameter) — P-value in (0,1) to use when computing superior models"><span class="n"><span class="pre">pvalue</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.05</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.SPA.better_models.pvalue_type" title="arch.bootstrap.SPA.better_models.pvalue_type (Python parameter) — String in 'lower', 'consistent', or 'upper' indicating which critical value to use."><span class="n"><span class="pre">pvalue_type</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'lower'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'consistent'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'upper'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;consistent&#39;</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.11)"><span class="pre">Hashable</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/bootstrap/multiple_comparison.html#SPA.better_models"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.SPA.better_models" title="Link to this definition">¶</a></dt>
 <dd><p>Returns set of models rejected as being equal-or-worse than the
 benchmark</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.SPA.better_models-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.SPA.better_models-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.SPA.better_models.pvalue"><span class="sig-name descname">pvalue</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>, optional</span></span><a class="headerlink" href="#arch.bootstrap.SPA.better_models.pvalue" title="Permalink to this definition">¶</a></dt><dd><p>P-value in (0,1) to use when computing superior models</p>
+<dt id="arch.bootstrap.SPA.better_models.pvalue"><span class="n sig-name">pvalue</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.05</span></code><a class="headerlink" href="#arch.bootstrap.SPA.better_models.pvalue" title="Permalink to this definition">¶</a></dt><dd><p>P-value in (0,1) to use when computing superior models</p>
 </dd>
-<dt id="arch.bootstrap.SPA.better_models.pvalue_type"><span class="sig-name descname">pvalue_type</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, optional</span></span><a class="headerlink" href="#arch.bootstrap.SPA.better_models.pvalue_type" title="Permalink to this definition">¶</a></dt><dd><p>String in ‘lower’, ‘consistent’, or ‘upper’ indicating which
+<dt id="arch.bootstrap.SPA.better_models.pvalue_type"><span class="n sig-name">pvalue_type</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'lower'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'consistent'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'upper'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;consistent&#39;</span></code><a class="headerlink" href="#arch.bootstrap.SPA.better_models.pvalue_type" title="Permalink to this definition">¶</a></dt><dd><p>String in ‘lower’, ‘consistent’, or ‘upper’ indicating which
 critical value to use.</p>
 </dd>
 </dl>
@@ -888,7 +888,7 @@ <h1 id="arch-bootstrap-spa-better-models">arch.bootstrap.SPA.better_models<a cla
 names are returned if models is a DataFrame.</p>
 </dd>
 <dt class="field-odd" id="arch.bootstrap.SPA.better_models-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.SPA.better_models-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span></code></a></p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -963,7 +963,7 @@ <h1 id="arch-bootstrap-spa-better-models">arch.bootstrap.SPA.better_models<a cla
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1010,7 +1010,7 @@ <h1 id="arch-bootstrap-spa-better-models">arch.bootstrap.SPA.better_models<a cla
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/multiple-comparison/generated/generated/arch.bootstrap.SPA.compute.html b/multiple-comparison/generated/generated/arch.bootstrap.SPA.compute.html
index 1de5013726..15b9483946 100644
--- a/multiple-comparison/generated/generated/arch.bootstrap.SPA.compute.html
+++ b/multiple-comparison/generated/generated/arch.bootstrap.SPA.compute.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.SPA.compute - arch 6.1.0</title>
+        <title>arch.bootstrap.SPA.compute - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -477,19 +477,6 @@
     <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="arch.bootstrap.SPA.compute (Python method) — Compute the bootstrap pvalue." class="md-ellipsis">SPA.<wbr>compute</span>
   </a>
   
-    <nav class="md-nav" aria-label="SPA.compute">
-      <ul class="md-nav__list">
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.SPA.compute-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
-</li>
-        
-      </ul>
-    </nav>
-  
 </li>
       
     </ul>
@@ -770,19 +757,6 @@
     <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="arch.bootstrap.SPA.compute (Python method) — Compute the bootstrap pvalue." class="md-ellipsis">SPA.<wbr>compute</span>
   </a>
   
-    <nav class="md-nav" aria-label="SPA.compute">
-      <ul class="md-nav__list">
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.SPA.compute-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
-</li>
-        
-      </ul>
-    </nav>
-  
 </li>
       
     </ul>
@@ -799,16 +773,11 @@
                   
 
 
-<h1 id="arch-bootstrap-spa-compute">arch.bootstrap.SPA.compute<a class="headerlink" href="#arch-bootstrap-spa-compute" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-spa-compute">arch.bootstrap.SPA.compute<a class="headerlink" href="#arch-bootstrap-spa-compute" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.bootstrap.SPA.compute">
-<span class="sig-prename descclassname"><span class="pre">SPA.</span></span><span class="sig-name descname"><span class="pre">compute</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/bootstrap/multiple_comparison.html#SPA.compute"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.SPA.compute" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">SPA.</span></span><span class="sig-name descname"><span class="pre">compute</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/bootstrap/multiple_comparison.html#SPA.compute"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.SPA.compute" title="Link to this definition">¶</a></dt>
 <dd><p>Compute the bootstrap pvalue.</p>
-<dl class="field-list simple">
-<dt class="field-odd" id="arch.bootstrap.SPA.compute-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.SPA.compute-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code></a></p>
-</dd>
-</dl>
 <p class="rubric">Notes</p>
 <p>Must be called before accessing the pvalue.</p>
 </dd></dl>
@@ -880,7 +849,7 @@ <h1 id="arch-bootstrap-spa-compute">arch.bootstrap.SPA.compute<a class="headerli
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -927,7 +896,7 @@ <h1 id="arch-bootstrap-spa-compute">arch.bootstrap.SPA.compute<a class="headerli
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/multiple-comparison/generated/generated/arch.bootstrap.SPA.critical_values.html b/multiple-comparison/generated/generated/arch.bootstrap.SPA.critical_values.html
index 50dcd4049c..0a33031420 100644
--- a/multiple-comparison/generated/generated/arch.bootstrap.SPA.critical_values.html
+++ b/multiple-comparison/generated/generated/arch.bootstrap.SPA.critical_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.SPA.critical_values - arch 6.1.0</title>
+        <title>arch.bootstrap.SPA.critical_values - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -853,15 +853,15 @@
                   
 
 
-<h1 id="arch-bootstrap-spa-critical-values">arch.bootstrap.SPA.critical_values<a class="headerlink" href="#arch-bootstrap-spa-critical-values" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-spa-critical-values">arch.bootstrap.SPA.critical_values<a class="headerlink" href="#arch-bootstrap-spa-critical-values" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.bootstrap.SPA.critical_values">
-<span class="sig-prename descclassname"><span class="pre">SPA.</span></span><span class="sig-name descname"><span class="pre">critical_values</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.SPA.critical_values.pvalue" title="arch.bootstrap.SPA.critical_values.pvalue (Python parameter) — P-value in (0,1) to use when computing the critical values."><span class="n"><span class="pre">pvalue</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.05</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/bootstrap/multiple_comparison.html#SPA.critical_values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.SPA.critical_values" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">SPA.</span></span><span class="sig-name descname"><span class="pre">critical_values</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.SPA.critical_values.pvalue" title="arch.bootstrap.SPA.critical_values.pvalue (Python parameter) — P-value in (0,1) to use when computing the critical values."><span class="n"><span class="pre">pvalue</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.05</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/bootstrap/multiple_comparison.html#SPA.critical_values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.SPA.critical_values" title="Link to this definition">¶</a></dt>
 <dd><p>Returns data-dependent critical values</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.SPA.critical_values-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.SPA.critical_values-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.SPA.critical_values.pvalue"><span class="sig-name descname">pvalue</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>, optional</span></span><a class="headerlink" href="#arch.bootstrap.SPA.critical_values.pvalue" title="Permalink to this definition">¶</a></dt><dd><p>P-value in (0,1) to use when computing the critical values.</p>
+<dt id="arch.bootstrap.SPA.critical_values.pvalue"><span class="n sig-name">pvalue</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.05</span></code><a class="headerlink" href="#arch.bootstrap.SPA.critical_values.pvalue" title="Permalink to this definition">¶</a></dt><dd><p>P-value in (0,1) to use when computing the critical values.</p>
 </dd>
 </dl>
 </dd>
@@ -870,7 +870,7 @@ <h1 id="arch-bootstrap-spa-critical-values">arch.bootstrap.SPA.critical_values<a
 upper methodologies</p>
 </dd>
 <dt class="field-odd" id="arch.bootstrap.SPA.critical_values-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.SPA.critical_values-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>Series</p>
+<dd class="field-odd"><p><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.Series</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -942,7 +942,7 @@ <h1 id="arch-bootstrap-spa-critical-values">arch.bootstrap.SPA.critical_values<a
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -989,7 +989,7 @@ <h1 id="arch-bootstrap-spa-critical-values">arch.bootstrap.SPA.critical_values<a
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/multiple-comparison/generated/generated/arch.bootstrap.SPA.pvalues.html b/multiple-comparison/generated/generated/arch.bootstrap.SPA.pvalues.html
index 1a2aaf4aba..78148708cc 100644
--- a/multiple-comparison/generated/generated/arch.bootstrap.SPA.pvalues.html
+++ b/multiple-comparison/generated/generated/arch.bootstrap.SPA.pvalues.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.SPA.pvalues - arch 6.1.0</title>
+        <title>arch.bootstrap.SPA.pvalues - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -813,10 +813,10 @@
                   
 
 
-<h1 id="arch-bootstrap-spa-pvalues">arch.bootstrap.SPA.pvalues<a class="headerlink" href="#arch-bootstrap-spa-pvalues" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-spa-pvalues">arch.bootstrap.SPA.pvalues<a class="headerlink" href="#arch-bootstrap-spa-pvalues" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.bootstrap.SPA.pvalues">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">SPA.</span></span><span class="sig-name descname"><span class="pre">pvalues</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.0.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.bootstrap.SPA.pvalues" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">SPA.</span></span><span class="sig-name descname"><span class="pre">pvalues</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.bootstrap.SPA.pvalues" title="Link to this definition">¶</a></dt>
 <dd><p>P-values corresponding to the lower, consistent and
 upper p-values.</p>
 <dl class="field-list simple">
@@ -825,7 +825,7 @@ <h1 id="arch-bootstrap-spa-pvalues">arch.bootstrap.SPA.pvalues<a class="headerli
 estimator, and the upper bound.</p>
 </dd>
 <dt class="field-even" id="arch.bootstrap.SPA.pvalues-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.SPA.pvalues-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p>Series</p>
+<dd class="field-even"><p><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.Series</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -897,7 +897,7 @@ <h1 id="arch-bootstrap-spa-pvalues">arch.bootstrap.SPA.pvalues<a class="headerli
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -944,7 +944,7 @@ <h1 id="arch-bootstrap-spa-pvalues">arch.bootstrap.SPA.pvalues<a class="headerli
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/multiple-comparison/generated/generated/arch.bootstrap.SPA.reset.html b/multiple-comparison/generated/generated/arch.bootstrap.SPA.reset.html
index 9bf79a11eb..45b341354d 100644
--- a/multiple-comparison/generated/generated/arch.bootstrap.SPA.reset.html
+++ b/multiple-comparison/generated/generated/arch.bootstrap.SPA.reset.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.SPA.reset - arch 6.1.0</title>
+        <title>arch.bootstrap.SPA.reset - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -507,19 +507,6 @@
     <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="arch.bootstrap.SPA.reset (Python method) — Reset the bootstrap to its initial state." class="md-ellipsis">SPA.<wbr>reset</span>
   </a>
   
-    <nav class="md-nav" aria-label="SPA.reset">
-      <ul class="md-nav__list">
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.SPA.reset-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
-</li>
-        
-      </ul>
-    </nav>
-  
 </li>
       
     </ul>
@@ -770,19 +757,6 @@
     <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="arch.bootstrap.SPA.reset (Python method) — Reset the bootstrap to its initial state." class="md-ellipsis">SPA.<wbr>reset</span>
   </a>
   
-    <nav class="md-nav" aria-label="SPA.reset">
-      <ul class="md-nav__list">
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.SPA.reset-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
-</li>
-        
-      </ul>
-    </nav>
-  
 </li>
       
     </ul>
@@ -799,16 +773,11 @@
                   
 
 
-<h1 id="arch-bootstrap-spa-reset">arch.bootstrap.SPA.reset<a class="headerlink" href="#arch-bootstrap-spa-reset" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-spa-reset">arch.bootstrap.SPA.reset<a class="headerlink" href="#arch-bootstrap-spa-reset" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.bootstrap.SPA.reset">
-<span class="sig-prename descclassname"><span class="pre">SPA.</span></span><span class="sig-name descname"><span class="pre">reset</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/bootstrap/multiple_comparison.html#SPA.reset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.SPA.reset" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">SPA.</span></span><span class="sig-name descname"><span class="pre">reset</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/bootstrap/multiple_comparison.html#SPA.reset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.SPA.reset" title="Link to this definition">¶</a></dt>
 <dd><p>Reset the bootstrap to its initial state.</p>
-<dl class="field-list simple">
-<dt class="field-odd" id="arch.bootstrap.SPA.reset-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.SPA.reset-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code></a></p>
-</dd>
-</dl>
 </dd></dl>
 
 
@@ -878,7 +847,7 @@ <h1 id="arch-bootstrap-spa-reset">arch.bootstrap.SPA.reset<a class="headerlink"
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -925,7 +894,7 @@ <h1 id="arch-bootstrap-spa-reset">arch.bootstrap.SPA.reset<a class="headerlink"
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/multiple-comparison/generated/generated/arch.bootstrap.SPA.seed.html b/multiple-comparison/generated/generated/arch.bootstrap.SPA.seed.html
index e8e2abe057..17a59413eb 100644
--- a/multiple-comparison/generated/generated/arch.bootstrap.SPA.seed.html
+++ b/multiple-comparison/generated/generated/arch.bootstrap.SPA.seed.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.SPA.seed - arch 6.1.0</title>
+        <title>arch.bootstrap.SPA.seed - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -543,13 +543,6 @@
       </ul>
     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.SPA.seed-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
       </ul>
@@ -811,13 +804,6 @@
       </ul>
     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.SPA.seed-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
       </ul>
@@ -839,21 +825,18 @@
                   
 
 
-<h1 id="arch-bootstrap-spa-seed">arch.bootstrap.SPA.seed<a class="headerlink" href="#arch-bootstrap-spa-seed" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-spa-seed">arch.bootstrap.SPA.seed<a class="headerlink" href="#arch-bootstrap-spa-seed" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.bootstrap.SPA.seed">
-<span class="sig-prename descclassname"><span class="pre">SPA.</span></span><span class="sig-name descname"><span class="pre">seed</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.SPA.seed.value" title="arch.bootstrap.SPA.seed.value (Python parameter) — Integer to use as the seed"><span class="n"><span class="pre">value</span></span></a></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.bootstrap.SPA.seed" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">SPA.</span></span><span class="sig-name descname"><span class="pre">seed</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.SPA.seed.value" title="arch.bootstrap.SPA.seed.value (Python parameter) — Integer to use as the seed"><span class="n"><span class="pre">value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.SPA.seed" title="Link to this definition">¶</a></dt>
 <dd><p>Seed the bootstrap’s random number generator</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.SPA.seed-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.SPA.seed-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.SPA.seed.value"><span class="sig-name descname">value</span><span class="p"> : </span><span class="desctype"><span class="desctype">{int, List[<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>], ndarray[<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>]}</span></span><a class="headerlink" href="#arch.bootstrap.SPA.seed.value" title="Permalink to this definition">¶</a></dt><dd><p>Integer to use as the seed</p>
+<dt id="arch.bootstrap.SPA.seed.value"><span class="n sig-name">value</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.bootstrap.SPA.seed.value" title="Permalink to this definition">¶</a></dt><dd><p>Integer to use as the seed</p>
 </dd>
 </dl>
 </dd>
-<dt class="field-even" id="arch.bootstrap.SPA.seed-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.SPA.seed-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code></a></p>
-</dd>
 </dl>
 </dd></dl>
 
@@ -924,7 +907,7 @@ <h1 id="arch-bootstrap-spa-seed">arch.bootstrap.SPA.seed<a class="headerlink" hr
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -971,7 +954,7 @@ <h1 id="arch-bootstrap-spa-seed">arch.bootstrap.SPA.seed<a class="headerlink" hr
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/multiple-comparison/generated/generated/arch.bootstrap.SPA.subset.html b/multiple-comparison/generated/generated/arch.bootstrap.SPA.subset.html
index e12e455e6e..feedf6ee8b 100644
--- a/multiple-comparison/generated/generated/arch.bootstrap.SPA.subset.html
+++ b/multiple-comparison/generated/generated/arch.bootstrap.SPA.subset.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.SPA.subset - arch 6.1.0</title>
+        <title>arch.bootstrap.SPA.subset - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -558,13 +558,6 @@
       </ul>
     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.SPA.subset-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
       </ul>
@@ -811,13 +804,6 @@
       </ul>
     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.SPA.subset-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
       </ul>
@@ -839,23 +825,20 @@
                   
 
 
-<h1 id="arch-bootstrap-spa-subset">arch.bootstrap.SPA.subset<a class="headerlink" href="#arch-bootstrap-spa-subset" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-spa-subset">arch.bootstrap.SPA.subset<a class="headerlink" href="#arch-bootstrap-spa-subset" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.bootstrap.SPA.subset">
-<span class="sig-prename descclassname"><span class="pre">SPA.</span></span><span class="sig-name descname"><span class="pre">subset</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.SPA.subset.selector" title="arch.bootstrap.SPA.subset.selector (Python parameter) — Boolean array indicating which columns to use when computing the p-values.  This is primarily for use by StepM."><span class="n"><span class="pre">selector</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/bootstrap/multiple_comparison.html#SPA.subset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.SPA.subset" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">SPA.</span></span><span class="sig-name descname"><span class="pre">subset</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.SPA.subset.selector" title="arch.bootstrap.SPA.subset.selector (Python parameter) — Boolean array indicating which columns to use when computing the p-values.  This is primarily for use by StepM."><span class="n"><span class="pre">selector</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/bootstrap/multiple_comparison.html#SPA.subset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.SPA.subset" title="Link to this definition">¶</a></dt>
 <dd><p>Sets a list of active models to run the SPA on.  Primarily for
 internal use.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.SPA.subset-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.SPA.subset-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.SPA.subset.selector"><span class="sig-name descname">selector</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.bootstrap.SPA.subset.selector" title="Permalink to this definition">¶</a></dt><dd><p>Boolean array indicating which columns to use when computing the
+<dt id="arch.bootstrap.SPA.subset.selector"><span class="n sig-name">selector</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.bootstrap.SPA.subset.selector" title="Permalink to this definition">¶</a></dt><dd><p>Boolean array indicating which columns to use when computing the
 p-values.  This is primarily for use by StepM.</p>
 </dd>
 </dl>
 </dd>
-<dt class="field-even" id="arch.bootstrap.SPA.subset-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.SPA.subset-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code></a></p>
-</dd>
 </dl>
 </dd></dl>
 
@@ -926,7 +909,7 @@ <h1 id="arch-bootstrap-spa-subset">arch.bootstrap.SPA.subset<a class="headerlink
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -973,7 +956,7 @@ <h1 id="arch-bootstrap-spa-subset">arch.bootstrap.SPA.subset<a class="headerlink
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/multiple-comparison/generated/generated/arch.bootstrap.StepM.compute.html b/multiple-comparison/generated/generated/arch.bootstrap.StepM.compute.html
index 1d56e5053c..1259a54647 100644
--- a/multiple-comparison/generated/generated/arch.bootstrap.StepM.compute.html
+++ b/multiple-comparison/generated/generated/arch.bootstrap.StepM.compute.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StepM.compute - arch 6.1.0</title>
+        <title>arch.bootstrap.StepM.compute - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -477,19 +477,6 @@
     <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="arch.bootstrap.StepM.compute (Python method) — Compute the set of superior models." class="md-ellipsis">Step<wbr>M.<wbr>compute</span>
   </a>
   
-    <nav class="md-nav" aria-label="StepM.compute">
-      <ul class="md-nav__list">
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.StepM.compute-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
-</li>
-        
-      </ul>
-    </nav>
-  
 </li>
       
     </ul>
@@ -725,19 +712,6 @@
     <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="arch.bootstrap.StepM.compute (Python method) — Compute the set of superior models." class="md-ellipsis">Step<wbr>M.<wbr>compute</span>
   </a>
   
-    <nav class="md-nav" aria-label="StepM.compute">
-      <ul class="md-nav__list">
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.StepM.compute-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
-</li>
-        
-      </ul>
-    </nav>
-  
 </li>
       
     </ul>
@@ -754,16 +728,11 @@
                   
 
 
-<h1 id="arch-bootstrap-stepm-compute">arch.bootstrap.StepM.compute<a class="headerlink" href="#arch-bootstrap-stepm-compute" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-stepm-compute">arch.bootstrap.StepM.compute<a class="headerlink" href="#arch-bootstrap-stepm-compute" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.bootstrap.StepM.compute">
-<span class="sig-prename descclassname"><span class="pre">StepM.</span></span><span class="sig-name descname"><span class="pre">compute</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/bootstrap/multiple_comparison.html#StepM.compute"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.StepM.compute" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">StepM.</span></span><span class="sig-name descname"><span class="pre">compute</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/bootstrap/multiple_comparison.html#StepM.compute"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.StepM.compute" title="Link to this definition">¶</a></dt>
 <dd><p>Compute the set of superior models.</p>
-<dl class="field-list simple">
-<dt class="field-odd" id="arch.bootstrap.StepM.compute-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.StepM.compute-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code></a></p>
-</dd>
-</dl>
 </dd></dl>
 
 
@@ -833,7 +802,7 @@ <h1 id="arch-bootstrap-stepm-compute">arch.bootstrap.StepM.compute<a class="head
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -880,7 +849,7 @@ <h1 id="arch-bootstrap-stepm-compute">arch.bootstrap.StepM.compute<a class="head
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/multiple-comparison/generated/generated/arch.bootstrap.StepM.reset.html b/multiple-comparison/generated/generated/arch.bootstrap.StepM.reset.html
index 8ea837bc7a..389b3b1812 100644
--- a/multiple-comparison/generated/generated/arch.bootstrap.StepM.reset.html
+++ b/multiple-comparison/generated/generated/arch.bootstrap.StepM.reset.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StepM.reset - arch 6.1.0</title>
+        <title>arch.bootstrap.StepM.reset - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -492,19 +492,6 @@
     <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="arch.bootstrap.StepM.reset (Python method) — Reset the bootstrap to it&#39;s initial state." class="md-ellipsis">Step<wbr>M.<wbr>reset</span>
   </a>
   
-    <nav class="md-nav" aria-label="StepM.reset">
-      <ul class="md-nav__list">
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.StepM.reset-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
-</li>
-        
-      </ul>
-    </nav>
-  
 </li>
       
     </ul>
@@ -725,19 +712,6 @@
     <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="arch.bootstrap.StepM.reset (Python method) — Reset the bootstrap to it&#39;s initial state." class="md-ellipsis">Step<wbr>M.<wbr>reset</span>
   </a>
   
-    <nav class="md-nav" aria-label="StepM.reset">
-      <ul class="md-nav__list">
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.StepM.reset-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
-</li>
-        
-      </ul>
-    </nav>
-  
 </li>
       
     </ul>
@@ -754,16 +728,11 @@
                   
 
 
-<h1 id="arch-bootstrap-stepm-reset">arch.bootstrap.StepM.reset<a class="headerlink" href="#arch-bootstrap-stepm-reset" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-stepm-reset">arch.bootstrap.StepM.reset<a class="headerlink" href="#arch-bootstrap-stepm-reset" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.bootstrap.StepM.reset">
-<span class="sig-prename descclassname"><span class="pre">StepM.</span></span><span class="sig-name descname"><span class="pre">reset</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#arch.bootstrap.StepM.reset" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">StepM.</span></span><span class="sig-name descname"><span class="pre">reset</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.StepM.reset" title="Link to this definition">¶</a></dt>
 <dd><p>Reset the bootstrap to it’s initial state.</p>
-<dl class="field-list simple">
-<dt class="field-odd" id="arch.bootstrap.StepM.reset-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.StepM.reset-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code></a></p>
-</dd>
-</dl>
 </dd></dl>
 
 
@@ -833,7 +802,7 @@ <h1 id="arch-bootstrap-stepm-reset">arch.bootstrap.StepM.reset<a class="headerli
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -880,7 +849,7 @@ <h1 id="arch-bootstrap-stepm-reset">arch.bootstrap.StepM.reset<a class="headerli
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/multiple-comparison/generated/generated/arch.bootstrap.StepM.seed.html b/multiple-comparison/generated/generated/arch.bootstrap.StepM.seed.html
index 06b1ce55e0..a99cc95354 100644
--- a/multiple-comparison/generated/generated/arch.bootstrap.StepM.seed.html
+++ b/multiple-comparison/generated/generated/arch.bootstrap.StepM.seed.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StepM.seed - arch 6.1.0</title>
+        <title>arch.bootstrap.StepM.seed - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -528,13 +528,6 @@
       </ul>
     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.StepM.seed-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
       </ul>
@@ -766,13 +759,6 @@
       </ul>
     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.bootstrap.StepM.seed-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
       </ul>
@@ -794,21 +780,18 @@
                   
 
 
-<h1 id="arch-bootstrap-stepm-seed">arch.bootstrap.StepM.seed<a class="headerlink" href="#arch-bootstrap-stepm-seed" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-stepm-seed">arch.bootstrap.StepM.seed<a class="headerlink" href="#arch-bootstrap-stepm-seed" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.bootstrap.StepM.seed">
-<span class="sig-prename descclassname"><span class="pre">StepM.</span></span><span class="sig-name descname"><span class="pre">seed</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StepM.seed.value" title="arch.bootstrap.StepM.seed.value (Python parameter) — Integer to use as the seed"><span class="n"><span class="pre">value</span></span></a></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.bootstrap.StepM.seed" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">StepM.</span></span><span class="sig-name descname"><span class="pre">seed</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StepM.seed.value" title="arch.bootstrap.StepM.seed.value (Python parameter) — Integer to use as the seed"><span class="n"><span class="pre">value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.StepM.seed" title="Link to this definition">¶</a></dt>
 <dd><p>Seed the bootstrap’s random number generator</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.StepM.seed-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.StepM.seed-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.StepM.seed.value"><span class="sig-name descname">value</span><span class="p"> : </span><span class="desctype"><span class="desctype">{int, List[<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>], ndarray[<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>]}</span></span><a class="headerlink" href="#arch.bootstrap.StepM.seed.value" title="Permalink to this definition">¶</a></dt><dd><p>Integer to use as the seed</p>
+<dt id="arch.bootstrap.StepM.seed.value"><span class="n sig-name">value</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.bootstrap.StepM.seed.value" title="Permalink to this definition">¶</a></dt><dd><p>Integer to use as the seed</p>
 </dd>
 </dl>
 </dd>
-<dt class="field-even" id="arch.bootstrap.StepM.seed-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.StepM.seed-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code></a></p>
-</dd>
 </dl>
 </dd></dl>
 
@@ -879,7 +862,7 @@ <h1 id="arch-bootstrap-stepm-seed">arch.bootstrap.StepM.seed<a class="headerlink
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -926,7 +909,7 @@ <h1 id="arch-bootstrap-stepm-seed">arch.bootstrap.StepM.seed<a class="headerlink
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/multiple-comparison/generated/generated/arch.bootstrap.StepM.superior_models.html b/multiple-comparison/generated/generated/arch.bootstrap.StepM.superior_models.html
index 1a3d3e8f6c..984fe32ff9 100644
--- a/multiple-comparison/generated/generated/arch.bootstrap.StepM.superior_models.html
+++ b/multiple-comparison/generated/generated/arch.bootstrap.StepM.superior_models.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StepM.superior_models - arch 6.1.0</title>
+        <title>arch.bootstrap.StepM.superior_models - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -768,10 +768,10 @@
                   
 
 
-<h1 id="arch-bootstrap-stepm-superior-models">arch.bootstrap.StepM.superior_models<a class="headerlink" href="#arch-bootstrap-stepm-superior-models" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-bootstrap-stepm-superior-models">arch.bootstrap.StepM.superior_models<a class="headerlink" href="#arch-bootstrap-stepm-superior-models" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.bootstrap.StepM.superior_models">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">StepM.</span></span><span class="sig-name descname"><span class="pre">superior_models</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.11)"><span class="pre">collections.abc.Hashable</span></a><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#arch.bootstrap.StepM.superior_models" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">StepM.</span></span><span class="sig-name descname"><span class="pre">superior_models</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.11)"><span class="pre">Hashable</span></a><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#arch.bootstrap.StepM.superior_models" title="Link to this definition">¶</a></dt>
 <dd><p>List of the indices or column names of the superior models</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.StepM.superior_models-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.StepM.superior_models-returns" title="Permalink to this headline">¶</a></dt>
@@ -779,7 +779,7 @@ <h1 id="arch-bootstrap-stepm-superior-models">arch.bootstrap.StepM.superior_mode
 array or contains column names if models is a DataFrame.</p>
 </dd>
 <dt class="field-even" id="arch.bootstrap.StepM.superior_models-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.StepM.superior_models-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -851,7 +851,7 @@ <h1 id="arch-bootstrap-stepm-superior-models">arch.bootstrap.StepM.superior_mode
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -898,7 +898,7 @@ <h1 id="arch-bootstrap-stepm-superior-models">arch.bootstrap.StepM.superior_mode
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/multiple-comparison/multiple-comparison-reference.html b/multiple-comparison/multiple-comparison-reference.html
index 855799d4a2..f8b7267044 100644
--- a/multiple-comparison/multiple-comparison-reference.html
+++ b/multiple-comparison/multiple-comparison-reference.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Module Reference - arch 6.1.0</title>
+        <title>Module Reference - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -749,8 +749,8 @@
                   
 
 
-<h1 id="module-arch.bootstrap.multiple_comparison"><span id="module-reference"></span>Module Reference<a class="headerlink" href="#module-arch.bootstrap.multiple_comparison" title="Permalink to this heading">¶</a></h1>
-<h2 id="test-of-superior-predictive-ability-spa-reality-check">Test of Superior Predictive Ability (SPA), Reality Check<a class="headerlink" href="#test-of-superior-predictive-ability-spa-reality-check" title="Permalink to this heading">¶</a></h2>
+<h1 id="module-arch.bootstrap.multiple_comparison"><span id="module-reference"></span>Module Reference<a class="headerlink" href="#module-arch.bootstrap.multiple_comparison" title="Link to this heading">¶</a></h1>
+<h2 id="test-of-superior-predictive-ability-spa-reality-check">Test of Superior Predictive Ability (SPA), Reality Check<a class="headerlink" href="#test-of-superior-predictive-ability-spa-reality-check" title="Link to this heading">¶</a></h2>
 <p>The test of Superior Predictive Ability (Hansen 2005), or SPA, is an improved
 version of the Reality Check (White 2000).  It tests whether the best
 forecasting performance from a set of models is better than that of the
@@ -772,7 +772,7 @@ <h2 id="test-of-superior-predictive-ability-spa-reality-check">Test of Superior
 </tr>
 </tbody>
 </table>
-<h2 id="stepwise-multiple-testing-stepm">Stepwise Multiple Testing (StepM)<a class="headerlink" href="#stepwise-multiple-testing-stepm" title="Permalink to this heading">¶</a></h2>
+<h2 id="stepwise-multiple-testing-stepm">Stepwise Multiple Testing (StepM)<a class="headerlink" href="#stepwise-multiple-testing-stepm" title="Link to this heading">¶</a></h2>
 <p>The Stepwise Multiple Testing procedure (Romano &amp; Wolf (2005)) is closely
 related to the SPA, except that it returns a set of models that are superior
 to the benchmark model, rather than the p-value from the null.  They are
@@ -786,7 +786,7 @@ <h2 id="stepwise-multiple-testing-stepm">Stepwise Multiple Testing (StepM)<a cla
 </tr>
 </tbody>
 </table>
-<h2 id="model-confidence-set-mcs">Model Confidence Set (MCS)<a class="headerlink" href="#model-confidence-set-mcs" title="Permalink to this heading">¶</a></h2>
+<h2 id="model-confidence-set-mcs">Model Confidence Set (MCS)<a class="headerlink" href="#model-confidence-set-mcs" title="Link to this heading">¶</a></h2>
 <p>The Model Confidence Set (Hansen, Lunde &amp; Nason (2011)) differs from other
 multiple comparison procedures in that there is no benchmark.  The MCS attempts
 to identify the set of models which produce the same expected loss, while
@@ -867,7 +867,7 @@ <h2 id="model-confidence-set-mcs">Model Confidence Set (MCS)<a class="headerlink
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -914,10 +914,10 @@ <h2 id="model-confidence-set-mcs">Model Confidence Set (MCS)<a class="headerlink
     <script id="__config" type="application/json">{"base": "..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
         <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+        <script id="MathJax-script" src="../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
     
   </body>
 </html>
\ No newline at end of file
diff --git a/multiple-comparison/multiple-comparison_examples.html b/multiple-comparison/multiple-comparison_examples.html
index 8cf85cc009..a38af5c6e4 100644
--- a/multiple-comparison/multiple-comparison_examples.html
+++ b/multiple-comparison/multiple-comparison_examples.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Multiple Comparisons - arch 6.1.0</title>
+        <title>Multiple Comparisons - arch 6.2.0</title>
       
     
 
@@ -47,9 +47,9 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css" />
-        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css?v=2aa19091" />
+        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -93,7 +93,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -103,7 +103,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -190,10 +190,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -562,7 +562,7 @@
                   
 
 
-<h1 id="Multiple-Comparisons">Multiple Comparisons<a class="headerlink" href="#Multiple-Comparisons" title="Permalink to this heading">¶</a></h1>
+<h1 id="Multiple-Comparisons">Multiple Comparisons<a class="headerlink" href="#Multiple-Comparisons" title="Link to this heading">¶</a></h1>
 <p><em>This setup code is required to run in an IPython notebook</em></p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[1]:
@@ -600,13 +600,13 @@ <h1 id="Multiple-Comparisons">Multiple Comparisons<a class="headerlink" href="#M
 <li><p><code class="docutils literal notranslate"><span class="pre">MCS</span></code> - The model confidence set which computes the set of models which with performance indistinguishable from others in the set.</p></li>
 </ul>
 <p>All procedures take <strong>losses</strong> as inputs. That is, smaller values are preferred to larger values. This is common when evaluating forecasting models where the loss function is usually defined as a positive function of the forecast error that is increasing in the absolute error. Leading examples are Mean Square Error (MSE) and Mean Absolute Deviation (MAD).</p>
-<h2 id="The-test-of-Superior-Predictive-Ability-(SPA)">The test of Superior Predictive Ability (SPA)<a class="headerlink" href="#The-test-of-Superior-Predictive-Ability-(SPA)" title="Permalink to this heading">¶</a></h2>
+<h2 id="The-test-of-Superior-Predictive-Ability-(SPA)">The test of Superior Predictive Ability (SPA)<a class="headerlink" href="#The-test-of-Superior-Predictive-Ability-(SPA)" title="Link to this heading">¶</a></h2>
 <p>This procedure requires a <span class="math notranslate nohighlight">\(t\)</span>-element array of benchmark losses and a <span class="math notranslate nohighlight">\(t\)</span> by <span class="math notranslate nohighlight">\(k\)</span>-element array of model losses. The null hypothesis is that no model is better than the benchmark, or</p>
 <div class="math notranslate nohighlight">
 \[H_0: \max_i E[L_i] \geq E[L_{bm}]\]</div>
 <p>where <span class="math notranslate nohighlight">\(L_i\)</span> is the loss from model <span class="math notranslate nohighlight">\(i\)</span> and <span class="math notranslate nohighlight">\(L_{bm}\)</span> is the loss from the benchmark model.</p>
 <p>This procedure is normally used when there are many competing forecasting models such as in the study of technical trading rules. The example below will make use of a set of models which are all equivalently good to a benchmark model and will serve as a <em>size study</em>.</p>
-<h3 id="Study-Design">Study Design<a class="headerlink" href="#Study-Design" title="Permalink to this heading">¶</a></h3>
+<h3 id="Study-Design">Study Design<a class="headerlink" href="#Study-Design" title="Link to this heading">¶</a></h3>
 <p>The study will make use of a measurement error in predictors to produce a large set of correlated variables that all have equal expected MSE. The benchmark will have identical measurement error and so all models have the same expected loss, although will have different forecasts.</p>
 <p>The first block computed the series to be forecast.</p>
 <div class="nbinput nblast docutils container">
@@ -760,7 +760,7 @@ <h3 id="Study-Design">Study Design<a class="headerlink" href="#Study-Design" tit
 <img alt="../_images/multiple-comparison_multiple-comparison_examples_17_0.png" src="../_images/multiple-comparison_multiple-comparison_examples_17_0.png" />
 </div>
 </div>
-<h3 id="Power">Power<a class="headerlink" href="#Power" title="Permalink to this heading">¶</a></h3>
+<h3 id="Power">Power<a class="headerlink" href="#Power" title="Link to this heading">¶</a></h3>
 <p>The SPA also has power to reject then the null is violated. The simulation will be modified so that the amount of measurement error differs across models, and so that some models are actually better than the benchmark. The p-values should be small indicating rejection of the null.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[8]:
@@ -813,7 +813,7 @@ <h3 id="Power">Power<a class="headerlink" href="#Power" title="Permalink to this
 <img alt="../_images/multiple-comparison_multiple-comparison_examples_22_0.png" src="../_images/multiple-comparison_multiple-comparison_examples_22_0.png" />
 </div>
 </div>
-<h2 id="Stepwise-Multiple-Testing-(StepM)">Stepwise Multiple Testing (StepM)<a class="headerlink" href="#Stepwise-Multiple-Testing-(StepM)" title="Permalink to this heading">¶</a></h2>
+<h2 id="Stepwise-Multiple-Testing-(StepM)">Stepwise Multiple Testing (StepM)<a class="headerlink" href="#Stepwise-Multiple-Testing-(StepM)" title="Link to this heading">¶</a></h2>
 <p>Stepwise Multiple Testing is similar to the SPA and has the same null. The primary difference is that it identifies the set of models which are better than the benchmark, rather than just asking the basic question if any model is better.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[10]:
@@ -858,7 +858,7 @@ <h2 id="Stepwise-Multiple-Testing-(StepM)">Stepwise Multiple Testing (StepM)<a c
 <img alt="../_images/multiple-comparison_multiple-comparison_examples_26_0.png" src="../_images/multiple-comparison_multiple-comparison_examples_26_0.png" />
 </div>
 </div>
-<h2 id="The-Model-Confidence-Set">The Model Confidence Set<a class="headerlink" href="#The-Model-Confidence-Set" title="Permalink to this heading">¶</a></h2>
+<h2 id="The-Model-Confidence-Set">The Model Confidence Set<a class="headerlink" href="#The-Model-Confidence-Set" title="Link to this heading">¶</a></h2>
 <p>The model confidence set takes a set of <strong>losses</strong> as its input and finds the set which are not statistically different from each other while controlling the familywise error rate. The primary output is a set of p-values, where models with a pvalue above the size are in the MCS. Small p-values indicate that the model is easily rejected from the set that includes the best.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[12]:
@@ -1007,7 +1007,7 @@ <h2 id="The-Model-Confidence-Set">The Model Confidence Set<a class="headerlink"
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1054,10 +1054,10 @@ <h2 id="The-Model-Confidence-Set">The Model Confidence Set<a class="headerlink"
     <script id="__config" type="application/json">{"base": "..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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         <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+        <script id="MathJax-script" src="../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
     
   </body>
 </html>
\ No newline at end of file
diff --git a/multiple-comparison/multiple-comparisons.html b/multiple-comparison/multiple-comparisons.html
index 0ba8992c6d..fea126781a 100644
--- a/multiple-comparison/multiple-comparisons.html
+++ b/multiple-comparison/multiple-comparisons.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Multiple Comparison Procedures - arch 6.1.0</title>
+        <title>Multiple Comparison Procedures - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -492,7 +492,7 @@
                   
 
 
-<h1 id="multiple-comparison-procedures">Multiple Comparison Procedures<a class="headerlink" href="#multiple-comparison-procedures" title="Permalink to this heading">¶</a></h1>
+<h1 id="multiple-comparison-procedures">Multiple Comparison Procedures<a class="headerlink" href="#multiple-comparison-procedures" title="Link to this heading">¶</a></h1>
 <p>This module contains a set of bootstrap-based multiple comparison procedures.
 These are designed to allow multiple models to be compared while controlling
 a the <a class="reference external" href="https://www.google.com/search?q=familywise+error+rate">Familywise Error Rate</a>,
@@ -571,7 +571,7 @@ <h1 id="multiple-comparison-procedures">Multiple Comparison Procedures<a class="
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -618,7 +618,7 @@ <h1 id="multiple-comparison-procedures">Multiple Comparison Procedures<a class="
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   </body>
diff --git a/objects.inv b/objects.inv
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"arch.unitroot.DFGLS.trend", "arch.unitroot.DFGLS.valid_trends", "arch.unitroot.DFGLS.y", "arch.unitroot.KPSS.alternative_hypothesis", "arch.unitroot.KPSS.critical_values", "arch.unitroot.KPSS.lags", "arch.unitroot.KPSS.nobs", "arch.unitroot.KPSS.null_hypothesis", "arch.unitroot.KPSS.pvalue", "arch.unitroot.KPSS.stat", "arch.unitroot.KPSS.summary", "arch.unitroot.KPSS.trend", "arch.unitroot.KPSS.valid_trends", "arch.unitroot.KPSS.y", "arch.unitroot.PhillipsPerron.alternative_hypothesis", "arch.unitroot.PhillipsPerron.critical_values", "arch.unitroot.PhillipsPerron.lags", "arch.unitroot.PhillipsPerron.nobs", "arch.unitroot.PhillipsPerron.null_hypothesis", "arch.unitroot.PhillipsPerron.pvalue", "arch.unitroot.PhillipsPerron.regression", "arch.unitroot.PhillipsPerron.stat", "arch.unitroot.PhillipsPerron.summary", "arch.unitroot.PhillipsPerron.test_type", "arch.unitroot.PhillipsPerron.trend", "arch.unitroot.PhillipsPerron.valid_trends", "arch.unitroot.PhillipsPerron.y", 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"arch.univariate.ConstantVariance.parameter_names", "arch.univariate.ConstantVariance.simulate", "arch.univariate.ConstantVariance.start", "arch.univariate.ConstantVariance.starting_values", "arch.univariate.ConstantVariance.stop", "arch.univariate.ConstantVariance.update", "arch.univariate.ConstantVariance.updateable", "arch.univariate.ConstantVariance.variance_bounds", "arch.univariate.ConstantVariance.volatility_updater", "arch.univariate.EGARCH.backcast", "arch.univariate.EGARCH.backcast_transform", "arch.univariate.EGARCH.bounds", "arch.univariate.EGARCH.compute_variance", "arch.univariate.EGARCH.constraints", "arch.univariate.EGARCH.forecast", "arch.univariate.EGARCH.name", "arch.univariate.EGARCH.num_params", "arch.univariate.EGARCH.parameter_names", "arch.univariate.EGARCH.simulate", "arch.univariate.EGARCH.start", "arch.univariate.EGARCH.starting_values", "arch.univariate.EGARCH.stop", "arch.univariate.EGARCH.update", "arch.univariate.EGARCH.updateable", 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783, 799, 812, 813, 840, 841, 872, 877, 891, 894, 895, 896, 897, 898, 899, 901], "call": [3, 11, 14, 15, 25, 26, 29, 30, 40, 41, 44, 45, 55, 56, 59, 60, 70, 71, 74, 75, 85, 89, 95, 96, 248, 258, 424, 450, 872, 873, 896, 897, 898, 901], "full": [3, 385], "param": [3, 4, 11, 14, 15, 25, 26, 29, 30, 40, 41, 44, 45, 55, 56, 59, 60, 70, 71, 74, 75, 85, 88, 89, 259, 270, 272, 424, 447, 450, 491, 495, 496, 501, 502, 508, 512, 513, 517, 518, 524, 528, 529, 533, 534, 673, 677, 678, 682, 683, 689, 693, 694, 698, 699, 778, 782, 783, 787, 788, 794, 798, 799, 803, 804, 812, 813, 840, 841, 895, 897, 898], "7": [3, 8, 259, 264, 267, 420, 421, 891, 895, 896, 897, 898, 899], "919351": 3, "18": [3, 236, 421, 496, 513, 529, 678, 694, 783, 799, 812, 840, 895, 897, 898], "455084": 3, "429115": 3, "dtype": [3, 88, 259, 420, 895, 897, 898, 899], "float64": [3, 88, 259, 420, 895, 897, 898, 899], "accept": [3, 89, 94, 96, 496, 513, 529, 678, 694, 783, 799, 812, 840, 895], "keyword": [3, 4, 5, 6, 7, 8, 9, 11, 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894, 899], "nest": [4, 14, 29, 44, 59, 74, 239, 240], "version": [4, 5, 6, 8, 9, 11, 14, 26, 29, 41, 44, 56, 59, 71, 74, 89, 236, 258, 266, 421, 436, 441, 443, 444, 496, 513, 529, 652, 678, 694, 731, 761, 774, 783, 799, 812, 840, 869, 897, 898], "produc": [4, 12, 16, 27, 31, 42, 46, 57, 61, 72, 76, 89, 95, 96, 258, 259, 277, 354, 368, 402, 424, 427, 428, 429, 433, 435, 438, 439, 445, 452, 461, 471, 478, 488, 496, 513, 529, 544, 554, 561, 571, 578, 588, 595, 606, 613, 623, 630, 640, 660, 670, 678, 694, 709, 719, 739, 749, 763, 776, 783, 799, 812, 813, 815, 819, 829, 840, 841, 843, 849, 879, 889, 891, 893, 894, 897, 898, 899], "higher": [4, 87, 259, 271, 895], "refin": 4, "circumst": [4, 89, 898, 899], "although": [4, 89, 259, 420, 421, 424, 433, 435, 895, 898], "slow": [4, 239, 240], "inner": [4, 14, 29, 44, 59, 74, 100, 104, 105, 106, 107, 108, 109, 110, 111, 277], "outer": 4, "In": [4, 89, 238, 239, 240, 259, 417, 420, 424, 425, 431, 433, 435, 437, 440, 494, 496, 511, 513, 527, 529, 676, 678, 692, 694, 781, 783, 797, 799, 812, 813, 840, 841, 891, 892, 895, 896, 897, 898, 899], "necessari": [4, 89, 895], "done": 4, "moment": [4, 95, 650, 729, 759, 763, 772, 776, 867], "delta": [4, 269, 271, 274, 398, 420, 425, 457], "detail": [4, 238, 239, 240, 259, 269, 271, 274, 277, 650, 729, 759, 772, 825, 867, 897], "descript": [4, 273, 427, 428, 438], "mathemat": 4, "formula": 4, "beyond": [4, 899], "intent": [4, 421], "document": [4, 236, 424], "sharpe_ratio_s": 4, "e1": 4, "e2": 4, "vstack": 4, "vcv": 4, "dot": [4, 89, 259, 459, 477, 492, 509, 525, 543, 560, 577, 594, 612, 629, 644, 659, 674, 690, 708, 723, 738, 753, 766, 779, 795, 861, 878], "avar": 4, "std_err_func": [4, 14, 29, 44, 59, 74], "match": [4, 10, 496, 513, 529, 678, 694, 783, 799, 812, 840, 895], "patter": 4, "arg": [4, 5, 6, 7, 8, 9, 11, 13, 14, 15, 25, 26, 28, 29, 30, 40, 41, 43, 44, 45, 55, 56, 58, 59, 60, 70, 71, 73, 74, 75, 85], "kwarg": [4, 5, 6, 7, 8, 9, 11, 13, 14, 15, 25, 26, 28, 29, 30, 40, 41, 43, 44, 45, 55, 56, 58, 59, 60, 70, 71, 73, 74, 75, 85], "improv": [4, 95, 96, 258, 263, 421, 452, 898], "jackknif": [4, 95], "term": [4, 265, 269, 271, 274, 276, 277, 279, 288, 289, 292, 301, 302, 305, 312, 313, 316, 325, 326, 329, 339, 340, 344, 351, 352, 368, 420, 424, 425, 440, 898], "offer": [4, 898], "accuraci": 4, "condit": [4, 95, 266, 421, 424, 425, 427, 431, 433, 435, 443, 445, 446, 447, 450, 453, 458, 471, 476, 488, 497, 502, 518, 534, 542, 554, 559, 571, 576, 588, 593, 606, 611, 623, 628, 640, 646, 658, 670, 683, 699, 707, 719, 725, 737, 749, 755, 768, 788, 809, 811, 819, 821, 823, 824, 826, 833, 835, 849, 856, 863, 873, 877, 889, 892, 895, 897, 898], "satisfi": [4, 266, 425, 492, 509, 525, 644, 674, 690, 723, 753, 766, 779, 795, 861], "special": [4, 437, 901], "case": [4, 89, 100, 104, 105, 106, 107, 108, 109, 110, 111, 259, 420, 437, 891], "base": [4, 89, 95, 260, 264, 265, 267, 277, 282, 295, 307, 318, 332, 346, 354, 368, 402, 420, 424, 452, 453, 496, 513, 529, 678, 694, 763, 776, 783, 894, 897, 899], "frac": [4, 10, 100, 104, 105, 106, 107, 108, 109, 110, 111, 120, 130, 144, 154, 164, 174, 184, 194, 204, 214, 224, 234, 271, 277, 424, 433, 437, 646, 725, 755, 768], "z_": [4, 277, 420], "skew": [4, 94, 421, 443, 755, 891, 898], "like": [4, 10, 88, 89, 258, 420, 424, 427, 428, 429, 438, 439, 445, 496, 513, 529, 678, 694, 783, 799, 812, 840, 891, 896, 898, 899], "leav": [4, 95], "out": [4, 94, 95, 424, 892, 897, 899], "block_siz": [5, 8, 9, 238, 239, 240], "random_st": [5, 6, 7, 8, 9, 95, 96, 436, 441, 443, 444, 451, 461, 478, 496, 513, 529, 544, 561, 578, 595, 613, 630, 660, 678, 694, 709, 739, 783, 799, 812, 840, 879, 899], "sourc": [5, 6, 7, 8, 9, 10, 13, 24, 26, 27, 28, 29, 30, 32, 35, 36, 37, 39, 40, 50, 54, 69, 84, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 238, 239, 240, 241, 247, 248, 249, 251, 253, 254, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 354, 368, 385, 400, 402, 415, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 456, 458, 459, 464, 465, 467, 484, 496, 501, 503, 532, 533, 534, 539, 540, 541, 542, 543, 547, 548, 550, 556, 557, 558, 559, 560, 564, 565, 567, 571, 575, 576, 577, 581, 582, 584, 590, 591, 592, 593, 594, 598, 599, 601, 606, 608, 610, 611, 612, 616, 617, 619, 625, 626, 627, 628, 629, 633, 634, 636, 640, 642, 643, 644, 646, 647, 649, 650, 651, 653, 654, 657, 658, 659, 663, 664, 666, 678, 681, 682, 683, 706, 707, 708, 712, 713, 715, 721, 722, 723, 725, 726, 728, 729, 730, 732, 733, 734, 735, 736, 737, 738, 742, 743, 745, 749, 751, 752, 753, 755, 756, 758, 759, 760, 762, 763, 764, 765, 766, 768, 769, 771, 772, 773, 775, 776, 786, 787, 788, 793, 794, 795, 797, 798, 799, 802, 803, 804, 805, 809, 812, 813, 819, 822, 836, 857, 859, 860, 861, 863, 864, 866, 867, 868, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 882, 883, 885, 887, 889, 891, 900], "length": [5, 7, 8, 9, 10, 90, 95, 96, 238, 239, 240, 262, 263, 265, 267, 271, 276, 281, 294, 392, 420, 421, 424, 427, 428, 429, 437, 438, 439, 440, 442, 445, 496, 502, 513, 518, 529, 534, 678, 683, 694, 699, 783, 788, 799, 812, 840, 891, 897], "wrap": [5, 8, 9, 10, 90], "around": [5, 8, 9, 10, 90, 258, 420], "posit": [5, 6, 7, 8, 9, 12, 13, 27, 28, 42, 43, 48, 57, 58, 72, 73, 87, 118, 128, 142, 152, 162, 172, 182, 192, 202, 212, 222, 232, 259, 265, 266, 421, 424, 433, 435, 496, 513, 529, 678, 694, 783, 799, 811, 812, 835, 840, 895], "option": [5, 6, 8, 9, 16, 31, 46, 61, 76, 89, 95, 238, 239, 240, 247, 249, 259, 262, 263, 264, 265, 266, 267, 277, 421, 425, 427, 428, 429, 432, 433, 434, 435, 436, 438, 439, 441, 442, 443, 444, 445, 446, 448, 461, 465, 471, 478, 482, 488, 491, 494, 496, 501, 502, 508, 511, 513, 517, 518, 524, 527, 529, 533, 534, 544, 548, 554, 561, 565, 571, 578, 582, 588, 595, 599, 606, 613, 617, 623, 630, 634, 640, 646, 647, 650, 651, 660, 664, 670, 673, 676, 678, 682, 683, 689, 692, 694, 698, 699, 709, 713, 719, 725, 726, 729, 730, 739, 743, 749, 755, 756, 759, 760, 768, 769, 772, 773, 778, 781, 783, 787, 788, 794, 797, 799, 803, 809, 812, 813, 819, 833, 836, 840, 841, 849, 863, 864, 867, 868, 879, 883, 889, 891, 892, 898, 901], "ensur": [5, 6, 7, 8, 9, 89, 277, 436, 441, 443, 444, 471, 488, 554, 571, 588, 606, 623, 640, 670, 719, 749, 889], "reproduc": [5, 6, 7, 8, 9, 89, 95, 259, 436, 441, 443, 444, 898], "If": [5, 6, 7, 8, 9, 20, 23, 35, 38, 50, 53, 65, 68, 80, 83, 95, 96, 100, 101, 104, 105, 106, 107, 108, 109, 110, 111, 238, 239, 240, 262, 263, 264, 265, 266, 267, 271, 277, 354, 362, 368, 377, 381, 402, 424, 425, 427, 428, 429, 434, 436, 438, 439, 441, 443, 444, 445, 450, 461, 472, 478, 489, 494, 496, 502, 511, 513, 518, 527, 529, 544, 555, 561, 572, 578, 589, 595, 607, 613, 624, 630, 641, 660, 671, 676, 678, 683, 692, 694, 699, 709, 720, 739, 750, 781, 783, 797, 799, 809, 812, 813, 819, 833, 840, 841, 849, 854, 879, 890, 891, 895, 897], "fresh": [5, 6, 8, 9, 13, 28, 43, 58, 73], "system": [5, 6, 8, 9, 271, 436, 441, 443, 444], "deprec": [5, 6, 8, 9, 92, 93, 96, 436, 441, 443, 444, 652, 731, 761, 774, 869, 899], "ha": [5, 6, 7, 8, 9, 20, 35, 50, 65, 80, 95, 96, 259, 267, 419, 421, 424, 430, 432, 442, 725, 873, 895, 897, 898, 899], "been": [5, 6, 7, 8, 9, 20, 35, 50, 65, 80, 95, 96, 267, 421, 437, 854, 873, 895, 899], "tupl": [5, 6, 7, 8, 9, 12, 18, 22, 23, 27, 33, 37, 38, 39, 42, 48, 52, 53, 54, 57, 63, 67, 68, 72, 78, 82, 83, 87, 456, 459, 465, 475, 477, 482, 490, 492, 496, 507, 509, 513, 523, 525, 529, 541, 543, 548, 558, 560, 565, 575, 577, 582, 592, 594, 599, 610, 612, 617, 627, 629, 634, 642, 644, 657, 659, 664, 672, 674, 678, 688, 690, 694, 706, 708, 713, 721, 723, 736, 738, 743, 751, 753, 764, 766, 777, 779, 783, 793, 795, 799, 812, 840, 859, 861, 876, 878, 883], "pos_data": [5, 6, 7, 8, 9], "kw_data": [5, 6, 7, 8, 9], "type": [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 79, 80, 81, 82, 83, 84, 85, 86, 95, 96, 112, 113, 114, 115, 117, 118, 119, 120, 121, 122, 123, 124, 125, 127, 128, 129, 130, 131, 136, 137, 138, 139, 141, 142, 143, 144, 145, 146, 147, 148, 149, 151, 152, 153, 154, 155, 156, 157, 158, 159, 161, 162, 163, 164, 165, 166, 167, 168, 169, 171, 172, 173, 174, 175, 176, 177, 178, 179, 181, 182, 183, 184, 185, 186, 187, 188, 189, 191, 192, 193, 194, 195, 196, 197, 198, 199, 201, 202, 203, 204, 205, 206, 207, 208, 209, 211, 212, 213, 214, 215, 216, 217, 218, 219, 221, 222, 223, 224, 225, 226, 227, 228, 229, 231, 232, 233, 234, 235, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 268, 272, 276, 277, 280, 287, 293, 300, 306, 311, 317, 323, 324, 331, 338, 345, 350, 354, 362, 366, 368, 371, 377, 381, 385, 389, 395, 398, 400, 402, 406, 411, 415, 421, 424, 454, 455, 456, 458, 459, 461, 464, 465, 467, 469, 471, 472, 473, 474, 475, 476, 477, 478, 481, 482, 484, 486, 488, 489, 490, 491, 492, 494, 495, 496, 500, 501, 502, 503, 507, 508, 509, 511, 512, 513, 516, 517, 518, 519, 523, 524, 525, 527, 528, 529, 532, 533, 534, 535, 539, 540, 541, 542, 543, 544, 547, 548, 550, 552, 554, 555, 556, 557, 558, 559, 560, 561, 564, 565, 567, 569, 571, 572, 573, 574, 575, 576, 577, 578, 581, 582, 584, 586, 588, 589, 590, 591, 592, 593, 594, 595, 598, 599, 601, 604, 606, 607, 608, 609, 610, 611, 612, 613, 616, 617, 619, 621, 623, 624, 625, 626, 627, 628, 629, 630, 633, 634, 636, 638, 640, 641, 642, 643, 644, 646, 647, 649, 650, 651, 653, 654, 655, 656, 657, 658, 659, 660, 663, 664, 666, 668, 670, 671, 672, 673, 674, 676, 677, 678, 681, 682, 683, 684, 688, 689, 690, 692, 693, 694, 697, 698, 699, 700, 704, 705, 706, 707, 708, 709, 712, 713, 715, 717, 719, 720, 721, 722, 723, 725, 726, 728, 729, 730, 732, 733, 734, 735, 736, 737, 738, 739, 742, 743, 745, 747, 749, 750, 751, 752, 753, 755, 756, 758, 759, 760, 762, 763, 764, 765, 766, 768, 769, 771, 772, 773, 775, 776, 777, 778, 779, 781, 782, 783, 786, 787, 788, 789, 793, 794, 795, 797, 798, 799, 802, 803, 804, 805, 809, 811, 812, 813, 819, 822, 825, 833, 835, 836, 840, 841, 846, 849, 857, 859, 860, 861, 863, 864, 866, 867, 868, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 882, 883, 885, 887, 889, 890, 891, 897, 899, 900], "enter": [5, 6, 7, 8, 9, 12, 27, 42, 57, 72, 892], "dict": [5, 6, 7, 8, 9, 11, 14, 15, 17, 18, 22, 23, 25, 26, 29, 30, 32, 33, 37, 38, 39, 40, 41, 44, 45, 47, 48, 52, 53, 54, 55, 56, 59, 60, 62, 63, 67, 68, 70, 71, 74, 75, 77, 78, 82, 83, 85, 279, 292, 305, 316, 329, 344, 494, 496, 511, 513, 527, 529, 676, 678, 692, 694, 781, 783, 797, 799, 812, 840, 900], "note": [5, 6, 7, 8, 9, 10, 11, 14, 26, 29, 41, 44, 56, 59, 71, 74, 100, 101, 104, 105, 106, 107, 108, 109, 110, 111, 120, 130, 144, 154, 164, 174, 184, 194, 204, 214, 224, 234, 238, 239, 240, 247, 248, 258, 262, 263, 264, 265, 266, 267, 269, 271, 274, 276, 277, 362, 368, 377, 381, 398, 421, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 437, 438, 439, 440, 442, 443, 445, 452, 459, 461, 477, 478, 492, 494, 495, 496, 509, 511, 512, 513, 525, 527, 528, 529, 543, 544, 560, 561, 577, 578, 594, 595, 612, 613, 629, 630, 644, 646, 650, 654, 659, 660, 674, 676, 677, 678, 690, 692, 693, 694, 708, 709, 723, 725, 729, 733, 738, 739, 753, 755, 759, 763, 766, 768, 772, 776, 779, 781, 782, 783, 795, 797, 798, 799, 812, 840, 861, 863, 867, 871, 872, 873, 878, 879, 891, 895, 897, 899], "support": [5, 6, 7, 8, 9, 91, 95, 96, 97, 277, 288, 301, 312, 325, 339, 351, 366, 385, 417, 424, 461, 470, 478, 487, 494, 496, 511, 513, 527, 529, 544, 553, 561, 570, 578, 587, 595, 605, 613, 622, 630, 639, 660, 669, 676, 678, 692, 694, 709, 718, 739, 748, 781, 783, 797, 799, 812, 813, 819, 840, 841, 849, 879, 888, 891, 892, 895, 899], "date": [5, 6, 7, 8, 9, 91, 97, 421, 424, 496, 513, 529, 678, 694, 783, 799, 812, 813, 840, 841, 891, 895, 896, 897, 898, 899], "accessibli": [5, 6, 7, 8, 9], "attribut": [5, 6, 7, 8, 9, 424, 897, 899], "To": [5, 6, 7, 8, 9, 420, 422], "you": [5, 6, 7, 8, 9, 895, 897, 899], "after": [5, 6, 7, 8, 9, 95, 96, 424, 496, 513, 529, 678, 694, 783, 799, 812, 840, 897], "reserv": [5, 6, 7, 8, 9, 898], "instanc": [5, 7, 8, 9, 13, 16, 19, 28, 31, 34, 43, 46, 49, 58, 61, 64, 73, 76, 79, 87, 89, 95, 96, 115, 125, 139, 149, 159, 169, 179, 189, 199, 209, 219, 229, 272, 273, 354, 366, 385, 395, 402, 411, 436, 441, 443, 444, 461, 478, 496, 513, 529, 544, 561, 578, 595, 613, 630, 660, 678, 694, 709, 739, 783, 799, 812, 815, 822, 840, 843, 857, 879, 898], "optim": [5, 8, 9, 10, 95, 100, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 117, 120, 121, 122, 123, 127, 130, 131, 136, 137, 141, 144, 145, 146, 147, 151, 154, 155, 156, 157, 161, 164, 165, 166, 167, 171, 174, 175, 176, 177, 181, 184, 185, 186, 187, 191, 194, 195, 196, 197, 201, 204, 205, 206, 207, 211, 214, 215, 216, 217, 221, 224, 225, 226, 227, 231, 234, 235, 268, 271, 277, 354, 368, 402, 450, 490, 492, 494, 507, 509, 511, 523, 525, 527, 642, 644, 654, 672, 674, 676, 688, 690, 692, 721, 723, 733, 751, 753, 763, 764, 766, 776, 777, 779, 781, 793, 795, 797, 837, 846, 859, 861, 871, 891, 896, 897, 898, 899], "exp": [5, 8, 10], "access": [5, 6, 7, 8, 9, 248, 259, 421, 898], "wai": [5, 6, 7, 8, 9, 898], "retain": [5, 6, 7, 8, 9], "wa": [5, 6, 7, 8, 9, 91, 94, 95, 96, 97, 421, 447, 450, 496, 513, 529, 678, 694, 783, 799, 812, 840, 895], "avail": [5, 6, 7, 8, 9, 259, 262, 265, 421, 424, 452, 461, 478, 496, 513, 529, 544, 561, 578, 595, 613, 630, 660, 678, 694, 709, 739, 783, 799, 812, 840, 879, 897, 898, 899], "syntax": [5, 6, 7, 8, 9, 496, 513, 529, 678, 694, 783, 799, 812, 840], "z": [5, 6, 7, 8, 9, 87, 96, 100, 104, 105, 106, 107, 108, 109, 110, 111, 263, 277, 368, 421, 650, 729, 759, 772, 867], "bs_x": [5, 6, 7, 8, 9], "bs_y": [5, 6, 7, 8, 9], "bs_z": [5, 6, 7, 8, 9], "1234": [5, 6, 7, 8, 9, 438], "properti": [5, 6, 7, 8, 9, 16, 18, 19, 23, 31, 33, 34, 38, 46, 48, 49, 53, 61, 63, 64, 68, 76, 78, 79, 83, 91, 96, 97, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 238, 239, 240, 242, 243, 244, 250, 257, 262, 263, 264, 265, 266, 267, 270, 272, 273, 275, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 351, 352, 353, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 367, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 386, 387, 388, 389, 390, 391, 392, 393, 394, 396, 397, 398, 399, 401, 403, 404, 405, 406, 407, 408, 409, 410, 412, 413, 414, 416, 420, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 453, 457, 460, 462, 463, 466, 468, 470, 472, 479, 480, 483, 485, 487, 489, 493, 496, 497, 498, 499, 504, 505, 506, 510, 513, 514, 515, 520, 521, 522, 526, 529, 530, 531, 536, 537, 538, 545, 546, 549, 551, 553, 555, 562, 563, 566, 568, 570, 572, 579, 580, 583, 585, 587, 589, 596, 597, 600, 602, 603, 605, 607, 614, 615, 618, 620, 622, 624, 631, 632, 635, 637, 639, 641, 645, 648, 652, 661, 662, 665, 667, 669, 671, 675, 678, 679, 680, 685, 686, 687, 691, 694, 695, 696, 701, 702, 703, 710, 711, 714, 716, 718, 720, 724, 727, 731, 740, 741, 744, 746, 748, 750, 754, 757, 761, 767, 770, 774, 780, 783, 784, 785, 790, 791, 792, 796, 799, 800, 801, 806, 807, 808, 810, 811, 812, 814, 815, 816, 817, 818, 820, 821, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 834, 835, 837, 838, 839, 840, 842, 843, 844, 845, 846, 847, 848, 850, 851, 852, 853, 854, 855, 856, 858, 862, 865, 869, 880, 881, 884, 886, 888, 890, 892, 893, 898, 900], "uniform": 6, "integ": [6, 94, 96, 100, 104, 105, 106, 107, 108, 109, 110, 111, 116, 126, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 238, 239, 240, 246, 252, 256, 259, 277, 354, 368, 402, 421, 424, 450, 465, 482, 496, 513, 529, 548, 565, 582, 599, 617, 634, 664, 678, 694, 713, 743, 783, 799, 812, 813, 840, 841, 883, 891], "equival": [6, 259, 368, 427, 438, 496, 513, 529, 678, 694, 783, 799, 812, 840, 891, 899], "structur": [7, 89, 267, 417, 420, 421, 424, 427, 428, 438, 895], "unequ": 7, "cross": [7, 273, 276, 277, 368, 420], "section": [7, 273, 276, 277, 368, 420], "relat": [7, 95, 96, 258, 433, 435, 891, 898], "inappropri": 7, "without": [8, 14, 29, 44, 59, 74, 89, 427, 428, 429, 438, 439, 445, 891, 897], "circularblockbootstrap": [8, 9, 10, 90], "expon": 9, "array_lik": [10, 100, 104, 105, 106, 107, 108, 109, 110, 111, 267, 269, 271, 274, 276, 277, 496, 502, 513, 518, 529, 534, 678, 683, 694, 699, 783, 799, 812, 840], "dimension": [10, 15, 25, 30, 40, 45, 55, 60, 70, 75, 85, 89, 895, 896], "oper": [10, 433], "b_sb": 10, "b_cb": 10, "algorithm": [10, 262, 267, 901], "its": [10, 20, 35, 50, 65, 80, 251, 258, 259, 368, 420, 421, 898], "correct": [10, 14, 29, 44, 59, 74, 259, 495, 512, 528, 677, 693, 782, 798], "tune": 10, "k_n": 10, "consecut": 10, "insid": [10, 895], "conserv": 10, "band": 10, "pm": 10, "log_": 10, "maximum": [10, 262, 263, 267, 271, 276, 281, 294, 392, 424, 440, 891, 895, 896, 898, 899], "lceil": 10, "rceil": 10, "_i": 10, "2g": 10, "d_": [10, 269, 274], "sum_": [10, 269, 271, 274, 277, 398, 424, 425, 426, 427, 428, 431, 433, 435, 437, 438, 440], "k": [10, 238, 239, 240, 259, 264, 265, 267, 271, 354, 368, 402, 425, 427, 428, 431, 435, 438, 439, 502, 518, 683, 699, 899], "h": [10, 95, 100, 104, 105, 106, 107, 108, 109, 110, 111, 237, 239, 262, 263, 264, 269, 271, 274, 424, 496, 513, 529, 678, 694, 783, 799, 812, 840, 895, 897, 899], "gamma_": [10, 425, 431, 435], "c_": 10, "x_k": 10, "bar": [10, 421, 438], "x_": [10, 95, 271, 895], "remain": [10, 895], "constant": [10, 95, 117, 120, 127, 130, 141, 144, 151, 154, 161, 164, 171, 174, 181, 184, 191, 194, 201, 204, 211, 214, 221, 224, 231, 234, 262, 263, 264, 265, 267, 269, 271, 274, 276, 277, 417, 420, 421, 424, 427, 428, 429, 430, 438, 439, 496, 502, 513, 518, 529, 534, 678, 683, 694, 699, 755, 783, 788, 799, 813, 841, 891, 896, 899], "c_i": 10, "taken": 10, "andrew": [10, 95, 108, 264, 266, 267, 268, 419], "patton": 10, "matlab": 10, "program": 10, "do": [10, 12, 27, 42, 57, 72, 269, 274, 424, 438, 496, 513, 529, 678, 694, 783, 799, 812, 813, 840, 841, 872, 897, 900], "autocovari": [10, 277], "rather": [10, 96, 258, 259, 263, 267, 424, 897, 898], "dimitri": 10, "halbert": 10, "white": [10, 237, 239, 258, 277, 354, 368, 402, 421, 896, 898], "2004": [10, 264, 267], "automat": [10, 91, 95, 96, 97, 262, 263, 264, 265, 267, 268, 276, 281, 294, 417, 419, 421, 427, 428, 429, 438, 439, 445, 891], "select": [10, 95, 96, 121, 131, 145, 155, 165, 175, 185, 195, 205, 215, 225, 235, 240, 262, 263, 264, 267, 268, 271, 273, 276, 281, 294, 392, 417, 419, 421, 466, 468, 483, 485, 549, 551, 566, 568, 583, 585, 600, 602, 618, 620, 635, 637, 665, 667, 714, 716, 744, 746, 884, 886], "review": [10, 264, 443], "23": [10, 237, 239, 421, 896], "53": [10, 420, 896], "70": [10, 259], "doi": [10, 236], "1081": 10, "etc": [10, 424, 898], "120028836": 10, "2009": [10, 420, 424, 897], "28": [10, 421, 424, 891, 895, 896, 897], "372": 10, "375": 10, "1080": 10, "07474930802459016": 10, "func": [11, 14, 15, 25, 26, 29, 30, 40, 41, 44, 45, 55, 56, 59, 60, 70, 71, 74, 75, 85, 891, 896, 897, 898], "rep": [11, 12, 14, 15, 25, 26, 27, 29, 30, 40, 41, 42, 44, 45, 55, 56, 57, 59, 60, 70, 71, 72, 74, 75, 85, 238, 239, 240, 259], "callabl": [11, 14, 15, 25, 26, 29, 30, 40, 41, 44, 45, 55, 56, 59, 60, 70, 71, 74, 75, 85, 95, 258, 452, 461, 465, 478, 482, 496, 513, 529, 544, 548, 561, 565, 578, 582, 595, 599, 613, 617, 630, 634, 653, 660, 664, 678, 694, 709, 713, 732, 739, 743, 762, 775, 783, 799, 812, 840, 870, 879, 883], "extra": [11, 14, 15, 25, 26, 29, 30, 40, 41, 44, 45, 55, 56, 59, 60, 70, 71, 74, 75, 85, 239, 240], "conflict": [11, 26, 41, 56, 71], "nparam": [11, 26, 41, 56, 71], "row": [11, 14, 26, 29, 41, 44, 56, 59, 71, 74, 424, 496, 513, 529, 678, 694, 783, 799, 812, 836, 840, 895], "correspond": [11, 14, 26, 29, 41, 44, 56, 59, 71, 74, 239, 250, 259, 471, 488, 494, 496, 511, 513, 527, 529, 554, 571, 588, 606, 623, 640, 670, 676, 678, 692, 694, 719, 749, 781, 783, 797, 799, 812, 840, 889, 897, 898], "iter": [11, 12, 24, 26, 27, 39, 41, 42, 54, 56, 57, 69, 71, 72, 84, 92, 452, 494, 511, 527, 676, 692, 781, 797, 891, 896, 897, 898], "ndarrai": [11, 14, 15, 18, 21, 23, 24, 25, 26, 29, 30, 33, 36, 38, 39, 40, 41, 44, 45, 48, 51, 53, 54, 55, 56, 59, 60, 63, 66, 68, 69, 70, 71, 74, 75, 78, 81, 83, 84, 85, 95, 101, 118, 128, 132, 133, 134, 135, 142, 152, 162, 172, 182, 192, 202, 212, 222, 232, 238, 239, 240, 246, 252, 253, 256, 262, 263, 264, 265, 266, 268, 290, 303, 314, 327, 342, 353, 427, 428, 429, 438, 439, 445, 447, 448, 450, 454, 455, 456, 458, 459, 461, 465, 467, 469, 471, 473, 474, 475, 476, 477, 478, 482, 484, 486, 488, 491, 492, 494, 495, 496, 501, 502, 503, 505, 506, 508, 509, 511, 512, 513, 517, 518, 519, 521, 522, 524, 525, 527, 528, 529, 533, 534, 535, 537, 538, 539, 540, 541, 542, 543, 544, 548, 550, 552, 554, 556, 557, 558, 559, 560, 561, 565, 567, 569, 571, 573, 574, 575, 576, 577, 578, 582, 584, 586, 588, 590, 591, 592, 593, 594, 595, 599, 601, 604, 606, 608, 609, 610, 611, 612, 613, 617, 619, 621, 623, 625, 626, 627, 628, 629, 630, 634, 636, 638, 640, 642, 643, 644, 646, 647, 650, 651, 653, 654, 655, 656, 657, 658, 659, 660, 664, 666, 668, 670, 673, 674, 676, 677, 678, 682, 683, 684, 686, 687, 689, 690, 692, 693, 694, 698, 699, 700, 702, 703, 704, 705, 706, 707, 708, 709, 713, 715, 717, 719, 721, 722, 723, 725, 726, 729, 730, 732, 733, 734, 735, 736, 737, 738, 739, 743, 745, 747, 749, 751, 752, 753, 755, 756, 759, 760, 762, 763, 764, 765, 766, 768, 769, 772, 773, 775, 776, 778, 779, 781, 782, 783, 787, 788, 789, 791, 792, 794, 795, 797, 798, 799, 803, 805, 807, 811, 812, 813, 820, 821, 828, 829, 830, 831, 835, 840, 841, 851, 856, 859, 860, 861, 863, 864, 867, 868, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 883, 885, 887, 889, 891, 895], "up": [11, 14, 26, 29, 41, 44, 56, 59, 71, 74, 267, 421, 424, 437, 496, 513, 529, 678, 694, 783, 799, 812, 840, 895, 897, 899], "append": [11, 14, 26, 29, 41, 44, 56, 59, 71, 74, 259, 895, 897], "befor": [11, 14, 26, 29, 41, 44, 56, 59, 71, 74, 100, 104, 105, 106, 107, 108, 109, 110, 111, 248, 263, 421, 494, 511, 527, 676, 692, 781, 797, 854, 897, 899], "randn": [11, 12, 14, 15, 25, 26, 27, 29, 30, 40, 41, 42, 44, 45, 55, 56, 57, 59, 60, 70, 71, 72, 74, 75, 85, 87, 89, 259, 428, 429, 438, 439, 445, 502, 518, 683, 699], "over": [12, 27, 42, 57, 72, 94, 899], "calcul": [12, 27, 42, 57, 72, 95, 113, 117, 118, 123, 127, 128, 137, 141, 142, 147, 151, 152, 157, 161, 162, 167, 171, 172, 177, 181, 182, 187, 191, 192, 197, 201, 202, 207, 211, 212, 217, 221, 222, 227, 231, 232, 264, 267, 646, 647, 725, 726, 755, 756, 768, 769, 864], "kei": [12, 27, 42, 57, 72, 389, 406, 417, 496, 513, 529, 678, 694, 783, 799, 812, 840, 895], "problem": [12, 27, 42, 57, 72, 89, 96, 421], "doe": [12, 27, 42, 57, 72, 90, 259, 277, 362, 381, 421, 471, 488, 494, 511, 527, 554, 571, 588, 606, 623, 640, 670, 676, 692, 719, 749, 781, 797, 873, 889, 891, 897], "output": [12, 27, 42, 57, 72, 88, 259, 494, 511, 527, 653, 676, 692, 732, 762, 775, 781, 797, 870, 891, 897, 898], "arang": [12, 27, 42, 57, 72, 89, 895, 897, 899], "posdata": [12, 27, 42, 57, 72], "kwdata": [12, 27, 42, 57, 72], "someth": [12, 27, 42, 57, 72], "prng": [13, 16, 28, 31, 43, 46, 58, 61, 73, 76], "tail": [14, 29, 44, 59, 74, 424, 443, 654, 895, 898], "fals": [14, 15, 20, 25, 29, 30, 35, 40, 44, 45, 50, 55, 59, 60, 65, 70, 74, 75, 80, 85, 88, 95, 99, 100, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 239, 240, 266, 271, 277, 354, 368, 385, 402, 421, 425, 427, 428, 429, 434, 438, 439, 440, 445, 448, 491, 494, 508, 511, 524, 527, 646, 673, 676, 689, 692, 725, 755, 768, 778, 781, 794, 797, 809, 833, 863, 891, 895, 897, 899], "nonparametr": [14, 29, 44, 59, 74, 89], "studentize_rep": [14, 29, 44, 59, 74], "str": [14, 18, 23, 29, 33, 38, 39, 44, 48, 53, 54, 59, 63, 68, 74, 78, 83, 101, 119, 129, 143, 153, 163, 173, 183, 193, 203, 213, 223, 233, 238, 239, 240, 247, 259, 268, 272, 273, 277, 278, 279, 283, 288, 289, 291, 292, 296, 302, 304, 305, 308, 312, 313, 315, 316, 319, 324, 325, 326, 328, 329, 333, 339, 340, 343, 344, 347, 351, 352, 354, 357, 368, 371, 374, 387, 393, 394, 395, 401, 402, 403, 408, 409, 410, 411, 416, 447, 450, 462, 464, 479, 481, 494, 495, 496, 498, 500, 511, 512, 513, 514, 516, 527, 528, 529, 530, 532, 545, 547, 562, 564, 579, 581, 596, 598, 614, 616, 631, 633, 648, 649, 661, 663, 676, 677, 678, 679, 681, 692, 693, 694, 695, 697, 710, 712, 727, 728, 740, 742, 757, 758, 770, 771, 781, 782, 783, 784, 786, 797, 798, 799, 800, 802, 812, 813, 819, 840, 841, 849, 865, 866, 880, 882, 891, 900], "One": [14, 29, 44, 59, 74, 421, 424], "norm": [14, 29, 44, 59, 74], "bc": [14, 29, 44, 59, 74, 755], "debias": [14, 29, 44, 59, 74, 266, 277], "bia": [14, 29, 44, 59, 74, 274, 420], "bca": [14, 29, 44, 59, 74, 95], "float": [14, 23, 29, 38, 44, 53, 59, 68, 74, 83, 100, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 117, 120, 121, 122, 123, 127, 130, 131, 136, 137, 141, 144, 145, 146, 147, 151, 154, 155, 156, 157, 161, 164, 165, 166, 167, 171, 174, 175, 176, 177, 181, 184, 185, 186, 187, 191, 194, 195, 196, 197, 201, 204, 205, 206, 207, 211, 214, 215, 216, 217, 221, 224, 225, 226, 227, 231, 234, 235, 238, 240, 247, 249, 267, 268, 273, 279, 284, 286, 292, 297, 299, 305, 309, 310, 316, 320, 322, 329, 335, 337, 341, 344, 348, 349, 355, 358, 362, 363, 364, 369, 377, 381, 382, 383, 396, 398, 399, 404, 412, 414, 425, 427, 432, 433, 435, 442, 447, 450, 454, 455, 456, 458, 460, 461, 465, 469, 471, 473, 474, 475, 476, 478, 482, 486, 488, 490, 491, 494, 497, 502, 507, 508, 511, 518, 523, 524, 527, 534, 539, 540, 541, 542, 544, 548, 552, 554, 556, 557, 558, 559, 561, 565, 569, 571, 573, 574, 575, 576, 578, 582, 586, 588, 590, 591, 592, 593, 595, 599, 604, 606, 608, 609, 610, 611, 613, 617, 621, 623, 625, 626, 627, 628, 630, 634, 638, 640, 646, 647, 650, 651, 655, 656, 657, 658, 660, 664, 668, 670, 672, 673, 676, 683, 688, 689, 692, 699, 704, 705, 706, 707, 709, 713, 717, 719, 725, 726, 729, 730, 735, 736, 737, 739, 743, 747, 749, 755, 756, 759, 760, 768, 769, 772, 773, 777, 778, 781, 788, 793, 794, 797, 808, 810, 814, 819, 832, 834, 836, 842, 849, 852, 853, 854, 863, 864, 867, 868, 872, 874, 875, 876, 877, 879, 883, 887, 889, 891, 895, 900], "coverag": [14, 29, 44, 59, 74], "bool": [14, 15, 20, 25, 29, 30, 35, 40, 44, 45, 50, 55, 59, 60, 65, 70, 74, 75, 80, 85, 100, 104, 105, 106, 107, 108, 109, 110, 111, 114, 116, 124, 126, 138, 140, 148, 150, 158, 160, 168, 170, 178, 180, 188, 190, 198, 200, 208, 210, 218, 220, 228, 230, 239, 240, 262, 266, 271, 272, 277, 330, 334, 336, 354, 368, 385, 402, 425, 427, 428, 429, 434, 438, 439, 440, 445, 447, 450, 457, 470, 487, 491, 494, 496, 508, 511, 513, 524, 527, 529, 553, 570, 587, 605, 622, 639, 646, 669, 673, 676, 678, 689, 692, 694, 718, 725, 748, 755, 768, 778, 781, 783, 794, 797, 799, 809, 812, 833, 840, 863, 888, 891], "flag": [14, 20, 29, 35, 44, 50, 59, 65, 74, 80, 100, 104, 105, 106, 107, 108, 109, 110, 111, 114, 116, 124, 126, 138, 140, 148, 150, 158, 160, 168, 170, 178, 180, 188, 190, 198, 200, 208, 210, 218, 220, 228, 230, 239, 240, 262, 271, 385, 427, 428, 429, 434, 438, 439, 440, 445, 470, 487, 491, 494, 508, 511, 524, 527, 553, 570, 587, 605, 622, 639, 646, 669, 673, 676, 689, 692, 718, 725, 748, 755, 768, 778, 781, 794, 797, 809, 833, 837, 863, 888, 891], "indic": [14, 20, 24, 29, 35, 39, 44, 48, 50, 54, 59, 65, 69, 74, 80, 84, 100, 104, 105, 106, 107, 108, 109, 110, 111, 114, 116, 124, 126, 138, 140, 148, 150, 158, 160, 168, 170, 178, 180, 188, 190, 198, 200, 208, 210, 218, 220, 228, 230, 239, 240, 242, 243, 247, 253, 257, 259, 262, 266, 271, 330, 334, 336, 385, 420, 421, 424, 427, 428, 429, 438, 439, 440, 445, 470, 487, 491, 494, 496, 502, 508, 511, 513, 518, 524, 527, 529, 553, 570, 587, 605, 622, 639, 646, 669, 673, 676, 678, 683, 689, 692, 694, 699, 718, 725, 748, 755, 768, 778, 781, 783, 794, 797, 799, 809, 812, 813, 819, 833, 840, 841, 849, 863, 888, 891, 898], "whether": [14, 15, 20, 25, 29, 30, 35, 40, 44, 45, 50, 55, 59, 60, 65, 70, 74, 75, 80, 85, 100, 104, 105, 106, 107, 108, 109, 110, 111, 114, 116, 124, 126, 138, 140, 148, 150, 158, 160, 168, 170, 178, 180, 188, 190, 198, 200, 208, 210, 218, 220, 228, 230, 258, 259, 262, 265, 266, 272, 277, 354, 368, 385, 402, 417, 419, 421, 427, 428, 429, 434, 438, 439, 440, 445, 447, 450, 491, 494, 496, 508, 511, 513, 524, 527, 529, 646, 673, 676, 678, 689, 692, 694, 725, 755, 768, 778, 781, 783, 794, 797, 799, 812, 840, 863, 891, 898], "previous": [14, 29, 44, 59, 74, 94, 896, 898, 899], "rerun": [14, 29, 44, 59, 74, 420], "ignor": [14, 29, 44, 59, 74, 421, 819, 849, 891, 895], "chang": [14, 29, 44, 59, 74, 93, 94, 95, 259, 277, 420, 421, 452, 496, 513, 529, 678, 694, 783, 799, 812, 840, 872, 896, 898, 899], "semi": [14, 29, 44, 59, 74, 89], "about": [14, 29, 44, 59, 74, 496, 513, 529, 678, 694, 783, 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Cannot be used with seed"], [5, 2, 2, 0, "seed", "Seed to use to ensure reproducable results. If an int, passes the value to value to np.random.default_rng. If None, a fresh Generator is constructed with system-provided entropy."]], "arch.bootstrap.CircularBlockBootstrap": [[11, 3, 1, 0, "apply", "Applies a function to bootstrap replicated data"], [12, 3, 1, 0, "bootstrap", "Iterator for use when bootstrapping"], [13, 3, 1, 0, "clone", "Clones the bootstrap using different data with a fresh prng."], [14, 3, 1, 0, "conf_int", "Function the computes parameter values.  See Notes for requirements"], [15, 3, 1, 0, "cov", "Compute parameter covariance using bootstrap"], [5, 4, 1, 0, "data", "Two-element tuple with the pos_data in the first position and kw_data in the second (pos_data, kw_data)"], [16, 5, 1, 0, "generator", "Set or get the instance PRNG"], [17, 3, 1, 0, "get_state", "Gets the state of the bootstrap's random number generator"], [18, 5, 1, 0, "index", "The current index of the bootstrap"], [5, 4, 1, 0, "kw_data", "Dictionary containing the keyword arguments"], [5, 4, 1, 0, "pos_data", "Tuple containing the positional arguments (in the order entered)"], [19, 5, 1, 0, "random_state", "Set or get the instance random state"], [20, 3, 1, 0, "reset", "Resets the bootstrap to either its initial state or the last seed."], [21, 3, 1, 0, "seed", "Reseeds the bootstrap's random number generator"], [22, 3, 1, 0, "set_state", "Sets the state of the bootstrap's random number generator"], [23, 5, 1, 0, "state", "Set or get the generator's state"], [24, 3, 1, 0, "update_indices", "Update indices for the next iteration of the bootstrap.  This must be overridden when creating new bootstraps."], [25, 3, 1, 0, "var", "Compute parameter variance using bootstrap"]], "arch.bootstrap.CircularBlockBootstrap.apply": [[11, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func.  Must not conflict with keyword arguments used to initialize bootstrap"], [11, 2, 2, 0, "func", "Function the computes parameter values.  See Notes for requirements"], [11, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.CircularBlockBootstrap.bootstrap": [[12, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.CircularBlockBootstrap.clone": [[13, 2, 2, 0, "args", "Positional arguments to bootstrap"], [13, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [13, 2, 2, 0, "seed", "The seed value to pass to the closed generator"]], "arch.bootstrap.CircularBlockBootstrap.conf_int": [[14, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func and std_err_func, when appropriate"], [14, 2, 2, 0, "func", "Function the computes parameter values.  See Notes for requirements"], [14, 2, 2, 0, "method", "One of 'basic', 'percentile', 'studentized', 'norm' (identical to 'var', 'cov'), 'bc' (identical to 'debiased', 'bias-corrected'), or 'bca'"], [14, 2, 2, 0, "reps", "Number of bootstrap replications"], [14, 2, 2, 0, "reuse", "Flag indicating whether to reuse previously computed bootstrap results.  This allows alternative methods to be compared without rerunning the bootstrap simulation.  Reuse is ignored if reps is not the same across multiple runs, func changes across calls, or method is 'studentized'."], [14, 2, 2, 0, "sampling", "Type of sampling to use: 'nonparametric', 'semi-parametric' (or 'semi') or 'parametric'.  The default is 'nonparametric'.  See notes about the changes to func required when using 'semi' or 'parametric'."], [14, 2, 2, 0, "size", "Coverage of confidence interval"], [14, 2, 2, 0, "std_err_func", "Function to use when standardizing estimated parameters when using the studentized bootstrap.  Providing an analytical function eliminates the need for a nested bootstrap"], [14, 2, 2, 0, "studentize_reps", "Number of bootstraps to use in the inner bootstrap when using the studentized bootstrap.  Ignored when std_err_func is provided"], [14, 2, 2, 0, "tail", "One of 'two', 'upper' or 'lower'."]], "arch.bootstrap.CircularBlockBootstrap.cov": [[15, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [15, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [15, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."], [15, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.CircularBlockBootstrap.reset": [[20, 2, 2, 0, "use_seed", "Flag indicating whether to use the last seed if provided.  If False or if no seed has been set, the bootstrap will be reset to the initial state.  Default is True"]], "arch.bootstrap.CircularBlockBootstrap.seed": [[21, 2, 2, 0, "value", "Value to use as the seed."]], "arch.bootstrap.CircularBlockBootstrap.set_state": [[22, 2, 2, 0, "state", "Dictionary or tuple containing the state."]], "arch.bootstrap.CircularBlockBootstrap.var": [[25, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [25, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [25, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."], [25, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.IIDBootstrap.__init__": [[6, 2, 2, 0, "args", "Positional arguments to bootstrap"], [6, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [6, 2, 2, 0, "random_state", "RandomState to use to ensure reproducable results. Cannot be used with seed"], [6, 2, 2, 0, "seed", "Seed to use to ensure reproducable results. If an int, passes the value to value to np.random.default_rng. If None, a fresh Generator is constructed with system-provided entropy."]], "arch.bootstrap.IIDBootstrap": [[26, 3, 1, 0, "apply", "Applies a function to bootstrap replicated data"], [27, 3, 1, 0, "bootstrap", "Iterator for use when bootstrapping"], [28, 3, 1, 0, "clone", "Clones the bootstrap using different data with a fresh prng."], [29, 3, 1, 0, "conf_int", "Function the computes parameter values.  See Notes for requirements"], [30, 3, 1, 0, "cov", "Compute parameter covariance using bootstrap"], [6, 4, 1, 0, "data", "Two-element tuple with the pos_data in the first position and kw_data in the second (pos_data, kw_data)"], [31, 5, 1, 0, "generator", "Set or get the instance PRNG"], [32, 3, 1, 0, "get_state", "Gets the state of the bootstrap's random number generator"], [33, 5, 1, 0, "index", "The current index of the bootstrap"], [6, 4, 1, 0, "kw_data", "Dictionary containing the keyword arguments"], [6, 4, 1, 0, "pos_data", "Tuple containing the positional arguments (in the order entered)"], [34, 5, 1, 0, "random_state", "Set or get the instance random state"], [35, 3, 1, 0, "reset", "Resets the bootstrap to either its initial state or the last seed."], [36, 3, 1, 0, "seed", "Reseeds the bootstrap's random number generator"], [37, 3, 1, 0, "set_state", "Sets the state of the bootstrap's random number generator"], [38, 5, 1, 0, "state", "Set or get the generator's state"], [39, 3, 1, 0, "update_indices", "Update indices for the next iteration of the bootstrap.  This must be overridden when creating new bootstraps."], [40, 3, 1, 0, "var", "Compute parameter variance using bootstrap"]], "arch.bootstrap.IIDBootstrap.apply": [[26, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func.  Must not conflict with keyword arguments used to initialize bootstrap"], [26, 2, 2, 0, "func", "Function the computes parameter values.  See Notes for requirements"], [26, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.IIDBootstrap.bootstrap": [[27, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.IIDBootstrap.clone": [[28, 2, 2, 0, "args", "Positional arguments to bootstrap"], [28, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [28, 2, 2, 0, "seed", "The seed value to pass to the closed generator"]], "arch.bootstrap.IIDBootstrap.conf_int": [[29, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func and std_err_func, when appropriate"], [29, 2, 2, 0, "func", "Function the computes parameter values.  See Notes for requirements"], [29, 2, 2, 0, "method", "One of 'basic', 'percentile', 'studentized', 'norm' (identical to 'var', 'cov'), 'bc' (identical to 'debiased', 'bias-corrected'), or 'bca'"], [29, 2, 2, 0, "reps", "Number of bootstrap replications"], [29, 2, 2, 0, "reuse", "Flag indicating whether to reuse previously computed bootstrap results.  This allows alternative methods to be compared without rerunning the bootstrap simulation.  Reuse is ignored if reps is not the same across multiple runs, func changes across calls, or method is 'studentized'."], [29, 2, 2, 0, "sampling", "Type of sampling to use: 'nonparametric', 'semi-parametric' (or 'semi') or 'parametric'.  The default is 'nonparametric'.  See notes about the changes to func required when using 'semi' or 'parametric'."], [29, 2, 2, 0, "size", "Coverage of confidence interval"], [29, 2, 2, 0, "std_err_func", "Function to use when standardizing estimated parameters when using the studentized bootstrap.  Providing an analytical function eliminates the need for a nested bootstrap"], [29, 2, 2, 0, "studentize_reps", "Number of bootstraps to use in the inner bootstrap when using the studentized bootstrap.  Ignored when std_err_func is provided"], [29, 2, 2, 0, "tail", "One of 'two', 'upper' or 'lower'."]], "arch.bootstrap.IIDBootstrap.cov": [[30, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [30, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [30, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."], [30, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.IIDBootstrap.reset": [[35, 2, 2, 0, "use_seed", "Flag indicating whether to use the last seed if provided.  If False or if no seed has been set, the bootstrap will be reset to the initial state.  Default is True"]], "arch.bootstrap.IIDBootstrap.seed": [[36, 2, 2, 0, "value", "Value to use as the seed."]], "arch.bootstrap.IIDBootstrap.set_state": [[37, 2, 2, 0, "state", "Dictionary or tuple containing the state."]], "arch.bootstrap.IIDBootstrap.var": [[40, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [40, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [40, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."], [40, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.IndependentSamplesBootstrap.__init__": [[7, 2, 2, 0, "args", "Positional arguments to bootstrap"], [7, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"]], "arch.bootstrap.IndependentSamplesBootstrap": [[41, 3, 1, 0, "apply", "Applies a function to bootstrap replicated data"], [42, 3, 1, 0, "bootstrap", "Iterator for use when bootstrapping"], [43, 3, 1, 0, "clone", "Clones the bootstrap using different data with a fresh prng."], [44, 3, 1, 0, "conf_int", "Function the computes parameter values.  See Notes for requirements"], [45, 3, 1, 0, "cov", "Compute parameter covariance using bootstrap"], [7, 4, 1, 0, "data", "Two-element tuple with the pos_data in the first position and kw_data in the second (pos_data, kw_data)"], [46, 5, 1, 0, "generator", "Set or get the instance PRNG"], [47, 3, 1, 0, "get_state", "Gets the state of the bootstrap's random number generator"], [48, 5, 1, 0, "index", "Returns the current index of the bootstrap"], [7, 4, 1, 0, "kw_data", "Dictionary containing the keyword arguments"], [7, 4, 1, 0, "pos_data", "Tuple containing the positional arguments (in the order entered)"], [49, 5, 1, 0, "random_state", "Set or get the instance random state"], [50, 3, 1, 0, "reset", "Resets the bootstrap to either its initial state or the last seed."], [51, 3, 1, 0, "seed", "Reseeds the bootstrap's random number generator"], [52, 3, 1, 0, "set_state", "Sets the state of the bootstrap's random number generator"], [53, 5, 1, 0, "state", "Set or get the generator's state"], [54, 3, 1, 0, "update_indices", "Update indices for the next iteration of the bootstrap.  This must be overridden when creating new bootstraps."], [55, 3, 1, 0, "var", "Compute parameter variance using bootstrap"]], "arch.bootstrap.IndependentSamplesBootstrap.apply": [[41, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func.  Must not conflict with keyword arguments used to initialize bootstrap"], [41, 2, 2, 0, "func", "Function the computes parameter values.  See Notes for requirements"], [41, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.IndependentSamplesBootstrap.bootstrap": [[42, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.IndependentSamplesBootstrap.clone": [[43, 2, 2, 0, "args", "Positional arguments to bootstrap"], [43, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [43, 2, 2, 0, "seed", "The seed value to pass to the closed generator"]], "arch.bootstrap.IndependentSamplesBootstrap.conf_int": [[44, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func and std_err_func, when appropriate"], [44, 2, 2, 0, "func", "Function the computes parameter values.  See Notes for requirements"], [44, 2, 2, 0, "method", "One of 'basic', 'percentile', 'studentized', 'norm' (identical to 'var', 'cov'), 'bc' (identical to 'debiased', 'bias-corrected'), or 'bca'"], [44, 2, 2, 0, "reps", "Number of bootstrap replications"], [44, 2, 2, 0, "reuse", "Flag indicating whether to reuse previously computed bootstrap results.  This allows alternative methods to be compared without rerunning the bootstrap simulation.  Reuse is ignored if reps is not the same across multiple runs, func changes across calls, or method is 'studentized'."], [44, 2, 2, 0, "sampling", "Type of sampling to use: 'nonparametric', 'semi-parametric' (or 'semi') or 'parametric'.  The default is 'nonparametric'.  See notes about the changes to func required when using 'semi' or 'parametric'."], [44, 2, 2, 0, "size", "Coverage of confidence interval"], [44, 2, 2, 0, "std_err_func", "Function to use when standardizing estimated parameters when using the studentized bootstrap.  Providing an analytical function eliminates the need for a nested bootstrap"], [44, 2, 2, 0, "studentize_reps", "Number of bootstraps to use in the inner bootstrap when using the studentized bootstrap.  Ignored when std_err_func is provided"], [44, 2, 2, 0, "tail", "One of 'two', 'upper' or 'lower'."]], "arch.bootstrap.IndependentSamplesBootstrap.cov": [[45, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [45, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [45, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."], [45, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.IndependentSamplesBootstrap.reset": [[50, 2, 2, 0, "use_seed", "Flag indicating whether to use the last seed if provided.  If False or if no seed has been set, the bootstrap will be reset to the initial state.  Default is True"]], "arch.bootstrap.IndependentSamplesBootstrap.seed": [[51, 2, 2, 0, "value", "Value to use as the seed."]], "arch.bootstrap.IndependentSamplesBootstrap.set_state": [[52, 2, 2, 0, "state", "Dictionary or tuple containing the state."]], "arch.bootstrap.IndependentSamplesBootstrap.var": [[55, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [55, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [55, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."], [55, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.MCS.__init__": [[238, 2, 2, 0, "block_size", "Length of window to use in the bootstrap.  If not provided, sqrt(T) is used.  In general, this should be provided and chosen to be appropriate for the data."], [238, 2, 2, 0, "bootstrap", "Bootstrap to use.  Options are 'stationary' or 'sb': Stationary bootstrap (Default) 'circular' or 'cbb': Circular block bootstrap 'moving block' or 'mbb': Moving block bootstrap"], [238, 2, 2, 0, "losses", "T by k array containing losses from a set of models"], [238, 2, 2, 0, "method", "MCS test and elimination implementation method, either 'max' or 'R'. Default is 'R'."], [238, 2, 2, 0, "reps", "Number of bootstrap replications to uses.  Default is 1000."], [238, 2, 2, 0, "seed", "Seed value to use when creating the bootstrap used in the comparison. If an integer or None, the NumPy default_rng is used with the seed value.  If a Generator or a RandomState, the argument is used."], [238, 2, 2, 0, "size", "Value in (0,1) to use as the test size when implementing the mcs. Default value is 0.05."]], "arch.bootstrap.MCS": [[241, 3, 1, 0, "compute", "Compute the set of models in the confidence set."], [242, 5, 1, 0, "excluded", "List of model indices that are excluded from the MCS"], [243, 5, 1, 0, "included", "List of model indices that are included in the MCS"], [244, 5, 1, 0, "pvalues", "Model p-values for inclusion in the MCS"], [245, 3, 1, 0, "reset", "Reset the bootstrap to it's initial state."], [246, 3, 1, 0, "seed", "Seed the bootstrap's random number generator"]], "arch.bootstrap.MCS.seed": [[246, 2, 2, 0, "value", "Integer to use as the seed"]], "arch.bootstrap.MovingBlockBootstrap.__init__": [[8, 2, 2, 0, "args", "Positional arguments to bootstrap"], [8, 2, 2, 0, "block_size", "Size of block to use"], [8, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [8, 2, 2, 0, "random_state", "RandomState to use to ensure reproducable results. Cannot be used with seed"], [8, 2, 2, 0, "seed", "Seed to use to ensure reproducable results. If an int, passes the value to value to np.random.default_rng. If None, a fresh Generator is constructed with system-provided entropy."]], "arch.bootstrap.MovingBlockBootstrap": [[56, 3, 1, 0, "apply", "Applies a function to bootstrap replicated data"], [57, 3, 1, 0, "bootstrap", "Iterator for use when bootstrapping"], [58, 3, 1, 0, "clone", "Clones the bootstrap using different data with a fresh prng."], [59, 3, 1, 0, "conf_int", "Function the computes parameter values.  See Notes for requirements"], [60, 3, 1, 0, "cov", "Compute parameter covariance using bootstrap"], [8, 4, 1, 0, "data", "Two-element tuple with the pos_data in the first position and kw_data in the second (pos_data, kw_data)"], [61, 5, 1, 0, "generator", "Set or get the instance PRNG"], [62, 3, 1, 0, "get_state", "Gets the state of the bootstrap's random number generator"], [63, 5, 1, 0, "index", "The current index of the bootstrap"], [8, 4, 1, 0, "kw_data", "Dictionary containing the keyword arguments"], [8, 4, 1, 0, "pos_data", "Tuple containing the positional arguments (in the order entered)"], [64, 5, 1, 0, "random_state", "Set or get the instance random state"], [65, 3, 1, 0, "reset", "Resets the bootstrap to either its initial state or the last seed."], [66, 3, 1, 0, "seed", "Reseeds the bootstrap's random number generator"], [67, 3, 1, 0, "set_state", "Sets the state of the bootstrap's random number generator"], [68, 5, 1, 0, "state", "Set or get the generator's state"], [69, 3, 1, 0, "update_indices", "Update indices for the next iteration of the bootstrap.  This must be overridden when creating new bootstraps."], [70, 3, 1, 0, "var", "Compute parameter variance using bootstrap"]], "arch.bootstrap.MovingBlockBootstrap.apply": [[56, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func.  Must not conflict with keyword arguments used to initialize bootstrap"], [56, 2, 2, 0, "func", "Function the computes parameter values.  See Notes for requirements"], [56, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.MovingBlockBootstrap.bootstrap": [[57, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.MovingBlockBootstrap.clone": [[58, 2, 2, 0, "args", "Positional arguments to bootstrap"], [58, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [58, 2, 2, 0, "seed", "The seed value to pass to the closed generator"]], "arch.bootstrap.MovingBlockBootstrap.conf_int": [[59, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func and std_err_func, when appropriate"], [59, 2, 2, 0, "func", "Function the computes parameter values.  See Notes for requirements"], [59, 2, 2, 0, "method", "One of 'basic', 'percentile', 'studentized', 'norm' (identical to 'var', 'cov'), 'bc' (identical to 'debiased', 'bias-corrected'), or 'bca'"], [59, 2, 2, 0, "reps", "Number of bootstrap replications"], [59, 2, 2, 0, "reuse", "Flag indicating whether to reuse previously computed bootstrap results.  This allows alternative methods to be compared without rerunning the bootstrap simulation.  Reuse is ignored if reps is not the same across multiple runs, func changes across calls, or method is 'studentized'."], [59, 2, 2, 0, "sampling", "Type of sampling to use: 'nonparametric', 'semi-parametric' (or 'semi') or 'parametric'.  The default is 'nonparametric'.  See notes about the changes to func required when using 'semi' or 'parametric'."], [59, 2, 2, 0, "size", "Coverage of confidence interval"], [59, 2, 2, 0, "std_err_func", "Function to use when standardizing estimated parameters when using the studentized bootstrap.  Providing an analytical function eliminates the need for a nested bootstrap"], [59, 2, 2, 0, "studentize_reps", "Number of bootstraps to use in the inner bootstrap when using the studentized bootstrap.  Ignored when std_err_func is provided"], [59, 2, 2, 0, "tail", "One of 'two', 'upper' or 'lower'."]], "arch.bootstrap.MovingBlockBootstrap.cov": [[60, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [60, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [60, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."], [60, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.MovingBlockBootstrap.reset": [[65, 2, 2, 0, "use_seed", "Flag indicating whether to use the last seed if provided.  If False or if no seed has been set, the bootstrap will be reset to the initial state.  Default is True"]], "arch.bootstrap.MovingBlockBootstrap.seed": [[66, 2, 2, 0, "value", "Value to use as the seed."]], "arch.bootstrap.MovingBlockBootstrap.set_state": [[67, 2, 2, 0, "state", "Dictionary or tuple containing the state."]], "arch.bootstrap.MovingBlockBootstrap.var": [[70, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [70, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [70, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."], [70, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.SPA.__init__": [[239, 2, 2, 0, "benchmark", "T element array of benchmark model losses"], [239, 2, 2, 0, "block_size", "Length of window to use in the bootstrap.  If not provided, sqrt(T) is used.  In general, this should be provided and chosen to be appropriate for the data."], [239, 2, 2, 0, "bootstrap", "Bootstrap to use.  Options are 'stationary' or 'sb': Stationary bootstrap (Default) 'circular' or 'cbb': Circular block bootstrap 'moving block' or 'mbb': Moving block bootstrap"], [239, 2, 2, 0, "models", "T  by k element array of alternative model losses"], [239, 2, 2, 0, "nested", "Flag indicating to use a nested bootstrap to compute variances for studentization.  Default is False.  Note that this can be slow since the procedure requires k extra bootstraps."], [239, 2, 2, 0, "reps", "Number of bootstrap replications to uses.  Default is 1000."], [239, 2, 2, 0, "seed", "Seed value to use when creating the bootstrap used in the comparison. If an integer or None, the NumPy default_rng is used with the seed value.  If a Generator or a RandomState, the argument is used."], [239, 2, 2, 0, "studentize", "Flag indicating to studentize loss differentials. Default is True"]], "arch.bootstrap.SPA": [[247, 3, 1, 0, "better_models", "Returns set of models rejected as being equal-or-worse than the benchmark"], [248, 3, 1, 0, "compute", "Compute the bootstrap pvalue."], [249, 3, 1, 0, "critical_values", "Returns data-dependent critical values"], [250, 5, 1, 0, "pvalues", "P-values corresponding to the lower, consistent and upper p-values."], [251, 3, 1, 0, "reset", "Reset the bootstrap to its initial state."], [252, 3, 1, 0, "seed", "Seed the bootstrap's random number generator"], [253, 3, 1, 0, "subset", "Sets a list of active models to run the SPA on.  Primarily for internal use."]], "arch.bootstrap.SPA.better_models": [[247, 2, 2, 0, "pvalue", "P-value in (0,1) to use when computing superior models"], [247, 2, 2, 0, "pvalue_type", "String in 'lower', 'consistent', or 'upper' indicating which critical value to use."]], "arch.bootstrap.SPA.critical_values": [[249, 2, 2, 0, "pvalue", "P-value in (0,1) to use when computing the critical values."]], "arch.bootstrap.SPA.seed": [[252, 2, 2, 0, "value", "Integer to use as the seed"]], "arch.bootstrap.SPA.subset": [[253, 2, 2, 0, "selector", "Boolean array indicating which columns to use when computing the p-values.  This is primarily for use by StepM."]], "arch.bootstrap.StationaryBootstrap.__init__": [[9, 2, 2, 0, "args", "Positional arguments to bootstrap"], [9, 2, 2, 0, "block_size", "Average size of block to use"], [9, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [9, 2, 2, 0, "random_state", "RandomState to use to ensure reproducable results. Cannot be used with seed"], [9, 2, 2, 0, "seed", "Seed to use to ensure reproducable results. If an int, passes the value to value to np.random.default_rng. If None, a fresh Generator is constructed with system-provided entropy."]], "arch.bootstrap.StationaryBootstrap": [[71, 3, 1, 0, "apply", "Applies a function to bootstrap replicated data"], [72, 3, 1, 0, "bootstrap", "Iterator for use when bootstrapping"], [73, 3, 1, 0, "clone", "Clones the bootstrap using different data with a fresh prng."], [74, 3, 1, 0, "conf_int", "Function the computes parameter values.  See Notes for requirements"], [75, 3, 1, 0, "cov", "Compute parameter covariance using bootstrap"], [9, 4, 1, 0, "data", "Two-element tuple with the pos_data in the first position and kw_data in the second (pos_data, kw_data)"], [76, 5, 1, 0, "generator", "Set or get the instance PRNG"], [77, 3, 1, 0, "get_state", "Gets the state of the bootstrap's random number generator"], [78, 5, 1, 0, "index", "The current index of the bootstrap"], [9, 4, 1, 0, "kw_data", "Dictionary containing the keyword arguments"], [9, 4, 1, 0, "pos_data", "Tuple containing the positional arguments (in the order entered)"], [79, 5, 1, 0, "random_state", "Set or get the instance random state"], [80, 3, 1, 0, "reset", "Resets the bootstrap to either its initial state or the last seed."], [81, 3, 1, 0, "seed", "Reseeds the bootstrap's random number generator"], [82, 3, 1, 0, "set_state", "Sets the state of the bootstrap's random number generator"], [83, 5, 1, 0, "state", "Set or get the generator's state"], [84, 3, 1, 0, "update_indices", "Update indices for the next iteration of the bootstrap.  This must be overridden when creating new bootstraps."], [85, 3, 1, 0, "var", "Compute parameter variance using bootstrap"]], "arch.bootstrap.StationaryBootstrap.apply": [[71, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func.  Must not conflict with keyword arguments used to initialize bootstrap"], [71, 2, 2, 0, "func", "Function the computes parameter values.  See Notes for requirements"], [71, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.StationaryBootstrap.bootstrap": [[72, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.StationaryBootstrap.clone": [[73, 2, 2, 0, "args", "Positional arguments to bootstrap"], [73, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [73, 2, 2, 0, "seed", "The seed value to pass to the closed generator"]], "arch.bootstrap.StationaryBootstrap.conf_int": [[74, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func and std_err_func, when appropriate"], [74, 2, 2, 0, "func", "Function the computes parameter values.  See Notes for requirements"], [74, 2, 2, 0, "method", "One of 'basic', 'percentile', 'studentized', 'norm' (identical to 'var', 'cov'), 'bc' (identical to 'debiased', 'bias-corrected'), or 'bca'"], [74, 2, 2, 0, "reps", "Number of bootstrap replications"], [74, 2, 2, 0, "reuse", "Flag indicating whether to reuse previously computed bootstrap results.  This allows alternative methods to be compared without rerunning the bootstrap simulation.  Reuse is ignored if reps is not the same across multiple runs, func changes across calls, or method is 'studentized'."], [74, 2, 2, 0, "sampling", "Type of sampling to use: 'nonparametric', 'semi-parametric' (or 'semi') or 'parametric'.  The default is 'nonparametric'.  See notes about the changes to func required when using 'semi' or 'parametric'."], [74, 2, 2, 0, "size", "Coverage of confidence interval"], [74, 2, 2, 0, "std_err_func", "Function to use when standardizing estimated parameters when using the studentized bootstrap.  Providing an analytical function eliminates the need for a nested bootstrap"], [74, 2, 2, 0, "studentize_reps", "Number of bootstraps to use in the inner bootstrap when using the studentized bootstrap.  Ignored when std_err_func is provided"], [74, 2, 2, 0, "tail", "One of 'two', 'upper' or 'lower'."]], "arch.bootstrap.StationaryBootstrap.cov": [[75, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [75, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [75, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."], [75, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.StationaryBootstrap.reset": [[80, 2, 2, 0, "use_seed", "Flag indicating whether to use the last seed if provided.  If False or if no seed has been set, the bootstrap will be reset to the initial state.  Default is True"]], "arch.bootstrap.StationaryBootstrap.seed": [[81, 2, 2, 0, "value", "Value to use as the seed."]], "arch.bootstrap.StationaryBootstrap.set_state": [[82, 2, 2, 0, "state", "Dictionary or tuple containing the state."]], "arch.bootstrap.StationaryBootstrap.var": [[85, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [85, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [85, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."], [85, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.StepM.__init__": [[240, 2, 2, 0, "benchmark", "T element array of benchmark model losses"], [240, 2, 2, 0, "block_size", "Length of window to use in the bootstrap.  If not provided, sqrt(T) is used.  In general, this should be provided and chosen to be appropriate for the data."], [240, 2, 2, 0, "bootstrap", "Bootstrap to use.  Options are 'stationary' or 'sb': Stationary bootstrap (Default) 'circular' or 'cbb': Circular block bootstrap 'moving block' or 'mbb': Moving block bootstrap"], [240, 2, 2, 0, "models", "T by k element array of alternative model losses"], [240, 2, 2, 0, "nested", "Flag indicating to use a nested bootstrap to compute variances for studentization.  Default is False.  Note that this can be slow since the procedure requires k extra bootstraps."], [240, 2, 2, 0, "reps", "Number of bootstrap replications to uses.  Default is 1000."], [240, 2, 2, 0, "seed", "Seed value to use when creating the bootstrap used in the comparison. If an integer or None, the NumPy default_rng is used with the seed value.  If a Generator or a RandomState, the argument is used."], [240, 2, 2, 0, "size", "Value in (0,1) to use as the test size when implementing the comparison. Default value is 0.05."], [240, 2, 2, 0, "studentize", "Flag indicating to studentize loss differentials. Default is True"]], "arch.bootstrap.StepM": [[254, 3, 1, 0, "compute", "Compute the set of superior models."], [255, 3, 1, 0, "reset", "Reset the bootstrap to it's initial state."], [256, 3, 1, 0, "seed", "Seed the bootstrap's random number generator"], [257, 5, 1, 0, "superior_models", "List of the indices or column names of the superior models"]], "arch.bootstrap.StepM.seed": [[256, 2, 2, 0, "value", "Integer to use as the seed"]], "arch.bootstrap.optimal_block_length": [[10, 2, 2, 0, "x", "A one-dimensional or two-dimensional array-like.  Operates columns by column if 2-dimensional."]], "arch.covariance": [[98, 0, 0, 1, "kernel", ""]], "arch.covariance.kernel": [[99, 1, 1, 0, "Andrews", "Alternative name of the QuadraticSpectral covariance estimator."], [100, 1, 1, 0, "Bartlett", "Bartlett's (Newey-West) kernel covariance estimation."], [101, 1, 1, 0, "CovarianceEstimate", "Covariance estimate using a long-run covariance estimator"], [102, 1, 1, 0, "Gallant", "Alternative name for Parzen covariance estimator."], [103, 1, 1, 0, "NeweyWest", "Alternative name for Bartlett covariance estimator."], [104, 1, 1, 0, "Parzen", "Parzen's kernel covariance estimation."], [105, 1, 1, 0, "ParzenCauchy", "Parzen's Cauchy kernel covariance estimation."], [106, 1, 1, 0, "ParzenGeometric", "Parzen's Geometric kernel covariance estimation."], [107, 1, 1, 0, "ParzenRiesz", "Parzen-Reisz kernel covariance estimation."], [108, 1, 1, 0, "QuadraticSpectral", "Quadratic-Spectral (Andrews') kernel covariance estimation."], [109, 1, 1, 0, "TukeyHamming", "Tukey-Hamming kernel covariance estimation."], [110, 1, 1, 0, "TukeyHanning", "Tukey-Hanning kernel covariance estimation."], [111, 1, 1, 0, "TukeyParzen", "Tukey-Parzen kernel covariance estimation."]], "arch.covariance.kernel.Andrews": [[112, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [113, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [114, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [115, 5, 1, 0, "cov", "The estimated covariances."], [116, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [117, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [118, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [119, 5, 1, 0, "name", "The covariance estimator's name."], [120, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [121, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.Bartlett.__init__": [[100, 2, 2, 0, "bandwidth", "The kernel's bandwidth.  If None, optimal bandwidth is estimated."], [100, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [100, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."], [100, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [100, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."], [100, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.Bartlett": [[122, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [123, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [124, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [125, 5, 1, 0, "cov", "The estimated covariances."], [126, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [127, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [128, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [129, 5, 1, 0, "name", "The covariance estimator's name."], [130, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [131, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.CovarianceEstimate.__init__": [[101, 2, 2, 0, "columns", "Column labels to use if covariance estimates are returned as DataFrames."], [101, 2, 2, 0, "long_run", "The long-run covariance estimate. If not provided, computed from short_run and one_sided_strict."], [101, 2, 2, 0, "one_sided_strict", "The one-sided-strict covariance estimate. If not provided, computed from short_run and one_sided_strict."], [101, 2, 2, 0, "short_run", "The short-run covariance estimate."]], "arch.covariance.kernel.CovarianceEstimate": [[132, 5, 1, 0, "long_run", "The long-run covariance estimate."], [133, 5, 1, 0, "one_sided", "The one-sided covariance estimate."], [134, 5, 1, 0, "one_sided_strict", "The one-sided strict covariance estimate."], [135, 5, 1, 0, "short_run", "The short-run covariance estimate."]], "arch.covariance.kernel.Gallant": [[136, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [137, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [138, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [139, 5, 1, 0, "cov", "The estimated covariances."], [140, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [141, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [142, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [143, 5, 1, 0, "name", "The covariance estimator's name."], [144, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [145, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.NeweyWest": [[146, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [147, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [148, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [149, 5, 1, 0, "cov", "The estimated covariances."], [150, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [151, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [152, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [153, 5, 1, 0, "name", "The covariance estimator's name."], [154, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [155, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.Parzen.__init__": [[104, 2, 2, 0, "bandwidth", "The kernel's bandwidth.  If None, optimal bandwidth is estimated."], [104, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [104, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."], [104, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [104, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."], [104, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.Parzen": [[156, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [157, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [158, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [159, 5, 1, 0, "cov", "The estimated covariances."], [160, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [161, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [162, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [163, 5, 1, 0, "name", "The covariance estimator's name."], [164, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [165, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.ParzenCauchy.__init__": [[105, 2, 2, 0, "bandwidth", "The kernel's bandwidth.  If None, optimal bandwidth is estimated."], [105, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [105, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."], [105, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [105, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."], [105, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.ParzenCauchy": [[166, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [167, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [168, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [169, 5, 1, 0, "cov", "The estimated covariances."], [170, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [171, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [172, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [173, 5, 1, 0, "name", "The covariance estimator's name."], [174, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [175, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.ParzenGeometric.__init__": [[106, 2, 2, 0, "bandwidth", "The kernel's bandwidth.  If None, optimal bandwidth is estimated."], [106, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [106, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."], [106, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [106, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."], [106, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.ParzenGeometric": [[176, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [177, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [178, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [179, 5, 1, 0, "cov", "The estimated covariances."], [180, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [181, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [182, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [183, 5, 1, 0, "name", "The covariance estimator's name."], [184, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [185, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.ParzenRiesz.__init__": [[107, 2, 2, 0, "bandwidth", "The kernel's bandwidth.  If None, optimal bandwidth is estimated."], [107, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [107, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."], [107, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [107, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."], [107, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.ParzenRiesz": [[186, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [187, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [188, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [189, 5, 1, 0, "cov", "The estimated covariances."], [190, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [191, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [192, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [193, 5, 1, 0, "name", "The covariance estimator's name."], [194, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [195, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.QuadraticSpectral.__init__": [[108, 2, 2, 0, "bandwidth", "The kernel's bandwidth.  If None, optimal bandwidth is estimated."], [108, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [108, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."], [108, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [108, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."], [108, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.QuadraticSpectral": [[196, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [197, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [198, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [199, 5, 1, 0, "cov", "The estimated covariances."], [200, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [201, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [202, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [203, 5, 1, 0, "name", "The covariance estimator's name."], [204, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [205, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.TukeyHamming.__init__": [[109, 2, 2, 0, "bandwidth", "The kernel's bandwidth.  If None, optimal bandwidth is estimated."], [109, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [109, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."], [109, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [109, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."], [109, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.TukeyHamming": [[206, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [207, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [208, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [209, 5, 1, 0, "cov", "The estimated covariances."], [210, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [211, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [212, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [213, 5, 1, 0, "name", "The covariance estimator's name."], [214, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [215, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.TukeyHanning.__init__": [[110, 2, 2, 0, "bandwidth", "The kernel's bandwidth.  If None, optimal bandwidth is estimated."], [110, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [110, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."], [110, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [110, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."], [110, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.TukeyHanning": [[216, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [217, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [218, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [219, 5, 1, 0, "cov", "The estimated covariances."], [220, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [221, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [222, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [223, 5, 1, 0, "name", "The covariance estimator's name."], [224, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [225, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.TukeyParzen.__init__": [[111, 2, 2, 0, "bandwidth", "The kernel's bandwidth.  If None, optimal bandwidth is estimated."], [111, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [111, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."], [111, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [111, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."], [111, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.TukeyParzen": [[226, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [227, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [228, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [229, 5, 1, 0, "cov", "The estimated covariances."], [230, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [231, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [232, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [233, 5, 1, 0, "name", "The covariance estimator's name."], [234, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [235, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.unitroot": [[262, 1, 1, 0, "ADF", "Augmented Dickey-Fuller unit root test"], [263, 1, 1, 0, "DFGLS", "Elliott, Rothenberg and Stock's (1) GLS detrended Dickey-Fuller"], [264, 1, 1, 0, "KPSS", "Kwiatkowski, Phillips, Schmidt and Shin (KPSS) stationarity test"], [265, 1, 1, 0, "PhillipsPerron", "Phillips-Perron unit root test"], [266, 1, 1, 0, "VarianceRatio", "Variance Ratio test of a random walk."], [267, 1, 1, 0, "ZivotAndrews", "Zivot-Andrews structural-break unit-root test"], [268, 6, 1, 0, "auto_bandwidth", "Automatic bandwidth selection of Andrews (1991) and Newey & West (1994)."], [261, 0, 0, 1, "cointegration", ""]], "arch.unitroot.ADF.__init__": [[262, 2, 2, 0, "lags", "The number of lags to use in the ADF regression.  If omitted or None, method is used to automatically select the lag length with no more than max_lags are included."], [262, 2, 2, 0, "low_memory", "Flag indicating whether to use a low memory implementation of the lag selection algorithm. The low memory algorithm is slower than the standard algorithm but will use 2-4% of the memory required for the standard algorithm. This options allows automatic lag selection to be used in very long time series. If None, use automatic selection of algorithm."], [262, 2, 2, 0, "max_lags", "The maximum number of lags to use when selecting lag length"], [262, 2, 2, 0, "method", "The method to use when selecting the lag length"], [262, 2, 2, 0, "trend", "The trend component to include in the test"], [262, 2, 2, 0, "y", "The data to test for a unit root"]], "arch.unitroot.ADF": [[278, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [279, 5, 1, 0, "critical_values", "Dictionary containing critical values specific to the test, number of observations and included deterministic trend terms."], [280, 5, 1, 0, "lags", "Sets or gets the number of lags used in the model. When bootstrap use DF-type regressions, lags is the number of lags in the regression model.  When bootstrap use long-run variance estimators, lags is the number of lags used in the long-run variance estimator."], [281, 5, 1, 0, "max_lags", "Sets or gets the maximum lags used when automatically selecting lag length"], [282, 5, 1, 0, "nobs", "The number of observations used when computing the test statistic. Accounts for loss of data due to lags for regression-based bootstrap."], [283, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [284, 5, 1, 0, "pvalue", "Returns the p-value for the test statistic"], [285, 5, 1, 0, "regression", "Returns the OLS regression results from the ADF model estimated"], [286, 5, 1, 0, "stat", "The test statistic for a unit root"], [287, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [288, 5, 1, 0, "trend", "Sets or gets the deterministic trend term used in the test. See valid_trends for a list of supported trends"], [289, 5, 1, 0, "valid_trends", "List of valid trend terms."], [290, 5, 1, 0, "y", "Returns the data used in the test statistic"]], "arch.unitroot.DFGLS.__init__": [[263, 2, 2, 0, "lags", "The number of lags to use in the ADF regression.  If omitted or None, method is used to automatically select the lag length with no more than max_lags are included."], [263, 2, 2, 0, "max_lags", "The maximum number of lags to use when selecting lag length. When using automatic lag length selection, the lag is selected using OLS detrending rather than GLS detrending (2)."], [263, 2, 2, 0, "method", "The method to use when selecting the lag length"], [263, 2, 2, 0, "trend", "The trend component to include in the test"], [263, 2, 2, 0, "y", "The data to test for a unit root"]], "arch.unitroot.DFGLS": [[291, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [292, 5, 1, 0, "critical_values", "Dictionary containing critical values specific to the test, number of observations and included deterministic trend terms."], [293, 5, 1, 0, "lags", "Sets or gets the number of lags used in the model. When bootstrap use DF-type regressions, lags is the number of lags in the regression model.  When bootstrap use long-run variance estimators, lags is the number of lags used in the long-run variance estimator."], [294, 5, 1, 0, "max_lags", "Sets or gets the maximum lags used when automatically selecting lag length"], [295, 5, 1, 0, "nobs", "The number of observations used when computing the test statistic. Accounts for loss of data due to lags for regression-based bootstrap."], [296, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [297, 5, 1, 0, "pvalue", "Returns the p-value for the test statistic"], [298, 5, 1, 0, "regression", "Returns the OLS regression results from the ADF model estimated"], [299, 5, 1, 0, "stat", "The test statistic for a unit root"], [300, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [301, 5, 1, 0, "trend", "Sets or gets the deterministic trend term used in the test. See valid_trends for a list of supported trends"], [302, 5, 1, 0, "valid_trends", "List of valid trend terms."], [303, 5, 1, 0, "y", "Returns the data used in the test statistic"]], "arch.unitroot.KPSS.__init__": [[264, 2, 2, 0, "lags", "The number of lags to use in the Newey-West estimator of the long-run covariance.  If omitted or None, the number of lags is calculated with the data-dependent method of Hobijn et al. (1998). See also Andrews (1991), Newey & West (1994), and Schwert (1989). Set lags=-1 to use the old method that only depends on the sample size, 12 * (nobs/100) ** (1/4)."], [264, 2, 2, 0, "trend", "\"c\" - Include a constant (Default) \"ct\" - Include a constant and linear time trend"], [264, 2, 2, 0, "y", "The data to test for stationarity"]], "arch.unitroot.KPSS": [[304, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [305, 5, 1, 0, "critical_values", "Dictionary containing critical values specific to the test, number of observations and included deterministic trend terms."], [306, 5, 1, 0, "lags", "Sets or gets the number of lags used in the model. When bootstrap use DF-type regressions, lags is the number of lags in the regression model.  When bootstrap use long-run variance estimators, lags is the number of lags used in the long-run variance estimator."], [307, 5, 1, 0, "nobs", "The number of observations used when computing the test statistic. Accounts for loss of data due to lags for regression-based bootstrap."], [308, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [309, 5, 1, 0, "pvalue", "Returns the p-value for the test statistic"], [310, 5, 1, 0, "stat", "The test statistic for a unit root"], [311, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [312, 5, 1, 0, "trend", "Sets or gets the deterministic trend term used in the test. See valid_trends for a list of supported trends"], [313, 5, 1, 0, "valid_trends", "List of valid trend terms."], [314, 5, 1, 0, "y", "Returns the data used in the test statistic"]], "arch.unitroot.PhillipsPerron.__init__": [[265, 2, 2, 0, "lags", "The number of lags to use in the Newey-West estimator of the long-run covariance.  If omitted or None, the lag length is set automatically to 12 * (nobs/100) ** (1/4)"], [265, 2, 2, 0, "test_type", "The test to use when computing the test statistic. \"tau\" is based on the t-stat and \"rho\" uses a test based on nobs times the re-centered regression coefficient"], [265, 2, 2, 0, "trend", "The trend component to include in the test"], [265, 2, 2, 0, "y", "The data to test for a unit root"]], "arch.unitroot.PhillipsPerron": [[315, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [316, 5, 1, 0, "critical_values", "Dictionary containing critical values specific to the test, number of observations and included deterministic trend terms."], [317, 5, 1, 0, "lags", "Sets or gets the number of lags used in the model. When bootstrap use DF-type regressions, lags is the number of lags in the regression model.  When bootstrap use long-run variance estimators, lags is the number of lags used in the long-run variance estimator."], [318, 5, 1, 0, "nobs", "The number of observations used when computing the test statistic. Accounts for loss of data due to lags for regression-based bootstrap."], [319, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [320, 5, 1, 0, "pvalue", "Returns the p-value for the test statistic"], [321, 5, 1, 0, "regression", "Returns OLS regression results for the specification used in the test"], [322, 5, 1, 0, "stat", "The test statistic for a unit root"], [323, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [324, 5, 1, 0, "test_type", "Gets or sets the test type returned by stat. Valid values are \"tau\" or \"rho\""], [325, 5, 1, 0, "trend", "Sets or gets the deterministic trend term used in the test. See valid_trends for a list of supported trends"], [326, 5, 1, 0, "valid_trends", "List of valid trend terms."], [327, 5, 1, 0, "y", "Returns the data used in the test statistic"]], "arch.unitroot.VarianceRatio.__init__": [[266, 2, 2, 0, "debiased", "Indicates whether to use a debiased version of the test. Default is True. Only applicable if overlap is True."], [266, 2, 2, 0, "lags", "The number of periods to used in the multi-period variance, which is the numerator of the test statistic.  Must be at least 2"], [266, 2, 2, 0, "overlap", "Indicates whether to use all overlapping blocks.  Default is True.  If False, the number of observations in y minus 1 must be an exact multiple of lags.  If this condition is not satisfied, some values at the end of y will be discarded."], [266, 2, 2, 0, "robust", "Indicates whether to use heteroskedasticity robust inference. Default is True."], [266, 2, 2, 0, "trend", "\"c\" allows for a non-zero drift in the random walk, while \"n\" requires that the increments to y are mean 0"], [266, 2, 2, 0, "y", "The data to test for a random walk"]], "arch.unitroot.VarianceRatio": [[328, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [329, 5, 1, 0, "critical_values", "Dictionary containing critical values specific to the test, number of observations and included deterministic trend terms."], [330, 5, 1, 0, "debiased", "Sets of gets the indicator to use debiased variances in the ratio"], [331, 5, 1, 0, "lags", "Sets or gets the number of lags used in the model. When bootstrap use DF-type regressions, lags is the number of lags in the regression model.  When bootstrap use long-run variance estimators, lags is the number of lags used in the long-run variance estimator."], [332, 5, 1, 0, "nobs", "The number of observations used when computing the test statistic. Accounts for loss of data due to lags for regression-based bootstrap."], [333, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [334, 5, 1, 0, "overlap", "Sets of gets the indicator to use overlapping returns in the long-period variance estimator"], [335, 5, 1, 0, "pvalue", "Returns the p-value for the test statistic"], [336, 5, 1, 0, "robust", "Sets of gets the indicator to use a heteroskedasticity robust variance estimator"], [337, 5, 1, 0, "stat", "The test statistic for a unit root"], [338, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [339, 5, 1, 0, "trend", "Sets or gets the deterministic trend term used in the test. See valid_trends for a list of supported trends"], [340, 5, 1, 0, "valid_trends", "List of valid trend terms."], [341, 5, 1, 0, "vr", "The ratio of the long block lags-period variance to the 1-period variance"], [342, 5, 1, 0, "y", "Returns the data used in the test statistic"]], "arch.unitroot.ZivotAndrews.__init__": [[267, 2, 2, 0, "lags", "The number of lags to use in the ADF regression.  If omitted or None, method is used to automatically select the lag length with no more than max_lags are included."], [267, 2, 2, 0, "max_lags", "The maximum number of lags to use when selecting lag length"], [267, 2, 2, 0, "method", "The method to use when selecting the lag length"], [267, 2, 2, 0, "trend", "The trend component to include in the test"], [267, 2, 2, 0, "trim", "percentage of series at begin/end to exclude from break-period calculation in range [0, 0.333] (default=0.15)"], [267, 2, 2, 0, "y", "data series"]], "arch.unitroot.ZivotAndrews": [[343, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [344, 5, 1, 0, "critical_values", "Dictionary containing critical values specific to the test, number of observations and included deterministic trend terms."], [345, 5, 1, 0, "lags", "Sets or gets the number of lags used in the model. When bootstrap use DF-type regressions, lags is the number of lags in the regression model.  When bootstrap use long-run variance estimators, lags is the number of lags used in the long-run variance estimator."], [346, 5, 1, 0, "nobs", "The number of observations used when computing the test statistic. Accounts for loss of data due to lags for regression-based bootstrap."], [347, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [348, 5, 1, 0, "pvalue", "Returns the p-value for the test statistic"], [349, 5, 1, 0, "stat", "The test statistic for a unit root"], [350, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [351, 5, 1, 0, "trend", "Sets or gets the deterministic trend term used in the test. See valid_trends for a list of supported trends"], [352, 5, 1, 0, "valid_trends", "List of valid trend terms."], [353, 5, 1, 0, "y", "Returns the data used in the test statistic"]], "arch.unitroot.auto_bandwidth": [[268, 2, 2, 0, "kernel", "The kernel function to use for selecting the bandwidth"], [268, 2, 2, 0, "y", "Data on which to apply the bandwidth selection"]], "arch.unitroot.cointegration": [[269, 1, 1, 0, "CanonicalCointegratingReg", "Canonical Cointegrating Regression cointegrating vector estimation."], [270, 1, 1, 0, "CointegrationAnalysisResults", "summary()"], [271, 1, 1, 0, "DynamicOLS", "Dynamic OLS (DOLS) cointegrating vector estimation"], [272, 1, 1, 0, "DynamicOLSResults", "Estimation results for Dynamic OLS models"], [273, 1, 1, 0, "EngleGrangerTestResults", "Results class for Engle-Granger cointegration tests."], [274, 1, 1, 0, "FullyModifiedOLS", "Fully Modified OLS cointegrating vector estimation."], [275, 1, 1, 0, "PhillipsOuliarisTestResults", "plot([axes, title])"], [276, 6, 1, 0, "engle_granger", "Test for cointegration within a set of time series."], [277, 6, 1, 0, "phillips_ouliaris", "Test for cointegration within a set of time series."]], "arch.unitroot.cointegration.CanonicalCointegratingReg.__init__": [[269, 2, 2, 0, "trend", "Trend to include in the cointegrating regression. Trends are:"], [269, 2, 2, 0, "x", "The right-hand-side variables in the cointegrating regression."], [269, 2, 2, 0, "x_trend", "Trends that affects affect the x-data but do not appear in the cointegrating regression. x_trend must be at least as large as trend, so that if trend is \"ct\", x_trend must be either \"ct\" or \"ctt\"."], [269, 2, 2, 0, "y", "The left-hand-side variable in the cointegrating regression."]], "arch.unitroot.cointegration.CanonicalCointegratingReg": [[354, 3, 1, 0, "fit", "Estimate the cointegrating vector."]], "arch.unitroot.cointegration.CanonicalCointegratingReg.fit": [[354, 2, 2, 0, "bandwidth", "The bandwidth to use. If not provided, the optimal bandwidth is estimated from the data. Setting the bandwidth to 0 and using \"unadjusted\" produces the classic OLS covariance estimator. Setting the bandwidth to 0 and using \"robust\" produces White's covariance estimator."], [354, 2, 2, 0, "df_adjust", "Whether the adjust the parameter covariance to account for the number of parameters estimated in the regression. If true, the parameter covariance estimator is multiplied by T/(T-k) where k is the number of regressors in the model."], [354, 2, 2, 0, "diff", "Use differenced data to estimate the residuals."], [354, 2, 2, 0, "force_int", "Whether the force the estimated optimal bandwidth to be an integer."], [354, 2, 2, 0, "kernel", "The string name of any of any known kernel-based long-run covariance estimators. Common choices are \"bartlett\" for the Bartlett kernel (Newey-West), \"parzen\" for the Parzen kernel and \"quadratic-spectral\" for the Quadratic Spectral kernel."]], "arch.unitroot.cointegration.CointegrationAnalysisResults": [[355, 5, 1, 0, "bandwidth", "The bandwidth used in the parameter covariance estimation"], [356, 5, 1, 0, "cov", "The estimated parameter covariance of the cointegrating vector"], [357, 5, 1, 0, "kernel", "The kernel used to estimate the covariance"], [358, 5, 1, 0, "long_run_variance", "Long-run variance estimate used in the parameter covariance estimator"], [359, 5, 1, 0, "params", "The estimated parameters of the cointegrating vector"], [360, 5, 1, 0, "pvalues", "P-value of the parameters in the cointegrating vector"], [361, 5, 1, 0, "resid", "The model residuals"], [362, 5, 1, 0, "residual_variance", "The variance of the regression residual."], [363, 5, 1, 0, "rsquared", "The model R\u00b2"], [364, 5, 1, 0, "rsquared_adj", "The degree-of-freedom adjusted R\u00b2"], [365, 5, 1, 0, "std_errors", "Standard errors  of the parameters in the cointegrating vector"], [366, 3, 1, 0, "summary", "Summary of the model, containing estimated parameters and std. errors"], [367, 5, 1, 0, "tvalues", "T-statistics of the parameters in the cointegrating vector"]], "arch.unitroot.cointegration.DynamicOLS.__init__": [[271, 2, 2, 0, "common", "Flag indicating that lags and leads should be restricted to the same value. When common is None, lags must equal leads and max_lag must equal max_lead."], [271, 2, 2, 0, "lags", "The number of lags to include in the model.  If None, the optimal number of lags is chosen using method."], [271, 2, 2, 0, "leads", "The number of leads to include in the model.  If None, the optimal number of leads is chosen using method."], [271, 2, 2, 0, "max_lag", "The maximum lag to consider. See Notes for value used when None."], [271, 2, 2, 0, "max_lead", "The maximum lead to consider. See Notes for value used when None."], [271, 2, 2, 0, "method", "The method used to select lag length when lags or leads is None."], [271, 2, 2, 0, "trend", "Trend to include in the cointegrating regression. Trends are:"], [271, 2, 2, 0, "x", "The right-hand-side variables in the cointegrating regression."], [271, 2, 2, 0, "y", "The left-hand-side variable in the cointegrating regression."]], "arch.unitroot.cointegration.DynamicOLS": [[368, 3, 1, 0, "fit", "Estimate the Dynamic OLS regression"]], "arch.unitroot.cointegration.DynamicOLS.fit": [[368, 2, 2, 0, "bandwidth", "The bandwidth to use. If not provided, the optimal bandwidth is estimated from the data. Setting the bandwidth to 0 and using \"unadjusted\" produces the classic OLS covariance estimator. Setting the bandwidth to 0 and using \"robust\" produces White's covariance estimator."], [368, 2, 2, 0, "cov_type", "Either \"unadjusted\" (or is equivalent \"homoskedastic\") or \"robust\" (or its equivalent \"kernel\")."], [368, 2, 2, 0, "df_adjust", "Whether the adjust the parameter covariance to account for the number of parameters estimated in the regression. If true, the parameter covariance estimator is multiplied by T/(T-k) where k is the number of regressors in the model."], [368, 2, 2, 0, "force_int", "Whether the force the estimated optimal bandwidth to be an integer."], [368, 2, 2, 0, "kernel", "The string name of any of any known kernel-based long-run covariance estimators. Common choices are \"bartlett\" for the Bartlett kernel (Newey-West), \"parzen\" for the Parzen kernel and \"quadratic-spectral\" for the Quadratic Spectral kernel."]], "arch.unitroot.cointegration.DynamicOLSResults.__init__": [[272, 2, 2, 0, "cov", "The estimated parameter covariance."], [272, 2, 2, 0, "cov_type", "The type of the parameter covariance estimator used."], [272, 2, 2, 0, "df_adjust", "Whether to degree of freedom adjust the estimator."], [272, 2, 2, 0, "kernel_est", "The covariance estimator instance used to estimate the parameter covariance."], [272, 2, 2, 0, "lags", "The number of lags included in the model."], [272, 2, 2, 0, "leads", "The number of leads included in the model."], [272, 2, 2, 0, "params", "The estimated model parameters."], [272, 2, 2, 0, "reg_results", "Regression results from fitting statsmodels OLS."], [272, 2, 2, 0, "resid", "The model residuals."]], "arch.unitroot.cointegration.DynamicOLSResults": [[369, 5, 1, 0, "bandwidth", "The bandwidth used in the parameter covariance estimation"], [370, 5, 1, 0, "cov", "The estimated parameter covariance of the cointegrating vector"], [371, 5, 1, 0, "cov_type", "The type of parameter covariance estimator used"], [372, 5, 1, 0, "full_cov", "Parameter covariance of the all model parameters, incl. leads and lags"], [373, 5, 1, 0, "full_params", "The complete set of parameters, including leads and lags"], [374, 5, 1, 0, "kernel", "The kernel used to estimate the covariance"], [375, 5, 1, 0, "lags", "The number of lags included in the model"], [376, 5, 1, 0, "leads", "The number of leads included in the model"], [377, 5, 1, 0, "long_run_variance", "The long-run variance of the regression residual."], [378, 5, 1, 0, "params", "The estimated parameters of the cointegrating vector"], [379, 5, 1, 0, "pvalues", "P-value of the parameters in the cointegrating vector"], [380, 5, 1, 0, "resid", "The model residuals"], [381, 5, 1, 0, "residual_variance", "The variance of the regression residual."], [382, 5, 1, 0, "rsquared", "The model R\u00b2"], [383, 5, 1, 0, "rsquared_adj", "The degree-of-freedom adjusted R\u00b2"], [384, 5, 1, 0, "std_errors", "Standard errors  of the parameters in the cointegrating vector"], [385, 3, 1, 0, "summary", "Summary of the model, containing estimated parameters and std. errors"], [386, 5, 1, 0, "tvalues", "T-statistics of the parameters in the cointegrating vector"]], "arch.unitroot.cointegration.DynamicOLSResults.summary": [[385, 2, 2, 0, "full", "Flag indicating whether to include all estimated parameters (True) or only the parameters of the cointegrating vector"]], "arch.unitroot.cointegration.EngleGrangerTestResults.__init__": [[273, 2, 2, 0, "adf", "The ADF instance used to perform the test and lag selection."], [273, 2, 2, 0, "alternative", "The alternative hypothesis."], [273, 2, 2, 0, "crit_vals", "The critical values of the Engle-Granger specific to the sample size and model dimension."], [273, 2, 2, 0, "null", "The null hypothesis."], [273, 2, 2, 0, "order", "The number of stochastic trends in the null distribution."], [273, 2, 2, 0, "pvalue", "The pvalue of the Engle-Granger test statistic."], [273, 2, 2, 0, "stat", "The Engle-Granger test statistic."], [273, 2, 2, 0, "trend", "The model's trend description."], [273, 2, 2, 0, "xsection", "The OLS results used in the cross-sectional regression."]], "arch.unitroot.cointegration.EngleGrangerTestResults": [[387, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [388, 5, 1, 0, "cointegrating_vector", "The estimated cointegrating vector."], [389, 5, 1, 0, "critical_values", "Critical Values"], [390, 5, 1, 0, "distribution_order", "The number of stochastic trends under the null hypothesis."], [391, 5, 1, 0, "lags", "The number of lags used in the Augmented Dickey-Fuller regression."], [392, 5, 1, 0, "max_lags", "The maximum number of lags used in the lag-length selection."], [393, 5, 1, 0, "name", "Sets or gets the name of the cointegration test"], [394, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [395, 3, 1, 0, "plot", "Plot the cointegration residuals."], [396, 5, 1, 0, "pvalue", "The p-value of the test statistic."], [397, 5, 1, 0, "resid", "The residual from the cointegrating regression."], [398, 5, 1, 0, "rho", "The estimated coefficient in the Dickey-Fuller Test"], [399, 5, 1, 0, "stat", "The test statistic."], [400, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [401, 5, 1, 0, "trend", "The trend used in the cointegrating regression"]], "arch.unitroot.cointegration.EngleGrangerTestResults.plot": [[395, 2, 2, 0, "axes", "matplotlib axes instance to hold the figure."], [395, 2, 2, 0, "title", "Title for the figure."]], "arch.unitroot.cointegration.FullyModifiedOLS.__init__": [[274, 2, 2, 0, "trend", "Trend to include in the cointegrating regression. Trends are:"], [274, 2, 2, 0, "x", "The right-hand-side variables in the cointegrating regression."], [274, 2, 2, 0, "x_trend", "Trends that affects affect the x-data but do not appear in the cointegrating regression. x_trend must be at least as large as trend, so that if trend is \"ct\", x_trend must be either \"ct\" or \"ctt\"."], [274, 2, 2, 0, "y", "The left-hand-side variable in the cointegrating regression."]], "arch.unitroot.cointegration.FullyModifiedOLS": [[402, 3, 1, 0, "fit", "Estimate the cointegrating vector."]], "arch.unitroot.cointegration.FullyModifiedOLS.fit": [[402, 2, 2, 0, "bandwidth", "The bandwidth to use. If not provided, the optimal bandwidth is estimated from the data. Setting the bandwidth to 0 and using \"unadjusted\" produces the classic OLS covariance estimator. Setting the bandwidth to 0 and using \"robust\" produces White's covariance estimator."], [402, 2, 2, 0, "df_adjust", "Whether the adjust the parameter covariance to account for the number of parameters estimated in the regression. If true, the parameter covariance estimator is multiplied by T/(T-k) where k is the number of regressors in the model."], [402, 2, 2, 0, "diff", "Use differenced data to estimate the residuals."], [402, 2, 2, 0, "force_int", "Whether the force the estimated optimal bandwidth to be an integer."], [402, 2, 2, 0, "kernel", "The string name of any of any known kernel-based long-run covariance estimators. Common choices are \"bartlett\" for the Bartlett kernel (Newey-West), \"parzen\" for the Parzen kernel and \"quadratic-spectral\" for the Quadratic Spectral kernel."]], "arch.unitroot.cointegration.PhillipsOuliarisTestResults": [[403, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [404, 5, 1, 0, "bandwidth", "Bandwidth used by the long-run covariance estimator"], [405, 5, 1, 0, "cointegrating_vector", "The estimated cointegrating vector."], [406, 5, 1, 0, "critical_values", "Critical Values"], [407, 5, 1, 0, "distribution_order", "The number of stochastic trends under the null hypothesis."], [408, 5, 1, 0, "kernel", "Name of the long-run covariance estimator"], [409, 5, 1, 0, "name", "Sets or gets the name of the cointegration test"], [410, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [411, 3, 1, 0, "plot", "Plot the cointegration residuals."], [412, 5, 1, 0, "pvalue", "The p-value of the test statistic."], [413, 5, 1, 0, "resid", "The residual from the cointegrating regression."], [414, 5, 1, 0, "stat", "The test statistic."], [415, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [416, 5, 1, 0, "trend", "The trend used in the cointegrating regression"]], "arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot": [[411, 2, 2, 0, "axes", "matplotlib axes instance to hold the figure."], [411, 2, 2, 0, "title", "Title for the figure."]], "arch.unitroot.cointegration.engle_granger": [[276, 2, 2, 0, "lags", "The number of lagged differences to include in the Augmented Dickey-Fuller test used on the residuals of the"], [276, 2, 2, 0, "max_lags", "The maximum number of lags to consider when using automatic lag-length in the Augmented Dickey-Fuller regression."], [276, 2, 2, 0, "method", "The method used to select the number of lags included in the Augmented Dickey-Fuller regression."], [276, 2, 2, 0, "trend", "Trend to include in the cointegrating regression. Trends are:"], [276, 2, 2, 0, "x", "The right-hand-side variables in the cointegrating regression."], [276, 2, 2, 0, "y", "The left-hand-side variable in the cointegrating regression."]], "arch.unitroot.cointegration.phillips_ouliaris": [[277, 2, 2, 0, "bandwidth", "The bandwidth to use. If not provided, the optimal bandwidth is estimated from the data. Setting the bandwidth to 0 and using \"unadjusted\" produces the classic OLS covariance estimator. Setting the bandwidth to 0 and using \"robust\" produces White's covariance estimator."], [277, 2, 2, 0, "force_int", "Whether the force the estimated optimal bandwidth to be an integer."], [277, 2, 2, 0, "kernel", "The string name of any of any known kernel-based long-run covariance estimators. Common choices are \"bartlett\" for the Bartlett kernel (Newey-West), \"parzen\" for the Parzen kernel and \"quadratic-spectral\" for the Quadratic Spectral kernel."], [277, 2, 2, 0, "test_type", "The test statistic to compute. Supported options are:"], [277, 2, 2, 0, "trend", "Trend to include in the cointegrating regression. Trends are:"], [277, 2, 2, 0, "x", "The right-hand-side variables in the cointegrating regression."], [277, 2, 2, 0, "y", "The left-hand-side variable in the cointegrating regression."]], "arch.univariate": [[425, 1, 1, 0, "APARCH", "Asymmetric Power ARCH (APARCH) volatility process"], [426, 1, 1, 0, "ARCH", "ARCH process"], [427, 1, 1, 0, "ARCHInMean", "(G)ARCH-in-mean model and simulation"], [428, 1, 1, 0, "ARX", "Autoregressive model with optional exogenous regressors estimation and simulation"], [429, 1, 1, 0, "ConstantMean", "Constant mean model estimation and simulation."], [430, 1, 1, 0, "ConstantVariance", "Constant volatility process"], [431, 1, 1, 0, "EGARCH", "EGARCH model estimation"], [432, 1, 1, 0, "EWMAVariance", "Exponentially Weighted Moving-Average (RiskMetrics) Variance process"], [433, 1, 1, 0, "FIGARCH", "FIGARCH model"], [434, 1, 1, 0, "FixedVariance", "Fixed volatility process"], [435, 1, 1, 0, "GARCH", "GARCH and related model estimation"], [436, 1, 1, 0, "GeneralizedError", "Generalized Error distribution for use with ARCH models"], [437, 1, 1, 0, "HARCH", "Heterogeneous ARCH process"], [438, 1, 1, 0, "HARX", "Heterogeneous Autoregression (HAR), with optional exogenous regressors, model estimation and simulation"], [439, 1, 1, 0, "LS", "Least squares model estimation and simulation"], [440, 1, 1, 0, "MIDASHyperbolic", "MIDAS Hyperbolic ARCH process"], [441, 1, 1, 0, "Normal", "Standard normal distribution for use with ARCH models"], [442, 1, 1, 0, "RiskMetrics2006", "RiskMetrics 2006 Variance process"], [443, 1, 1, 0, "SkewStudent", "Standardized Skewed Student's distribution for use with ARCH models"], [444, 1, 1, 0, "StudentsT", "Standardized Student's distribution for use with ARCH models"], [445, 1, 1, 0, "ZeroMean", "Model with zero conditional mean estimation and simulation"], [891, 6, 1, 0, "arch_model", "Initialization of common ARCH model specifications"]], "arch.univariate.APARCH.__init__": [[425, 2, 2, 0, "common_asym", "Restrict all asymmetry terms to share the same asymmetry parameter. If False (default), then there are no restrictions on the o asymmetry parameters."], [425, 2, 2, 0, "delta", "Value to use for a fixed delta in the APARCH model. If not provided, the value of delta is jointly estimated with other model parameters. User provided delta is restricted to lie in (0.05, 4.0)."], [425, 2, 2, 0, "o", "Order of the asymmetric innovation. Must satisfy o<=p."], [425, 2, 2, 0, "p", "Order of the symmetric innovation. Must satisfy p>=o."], [425, 2, 2, 0, "q", "Order of the lagged (transformed) conditional variance"]], "arch.univariate.APARCH": [[454, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [455, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [456, 3, 1, 0, "bounds", "Returns bounds for parameters"], [457, 5, 1, 0, "common_asym", "The value of delta in the model. NaN is delta is estimated."], [458, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [459, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [460, 5, 1, 0, "delta", "The value of delta in the model. NaN is delta is estimated."], [461, 3, 1, 0, "forecast", "Forecast volatility from the model"], [462, 5, 1, 0, "name", "The name of the volatility process"], [463, 5, 1, 0, "num_params", "The number of parameters in the model"], [464, 3, 1, 0, "parameter_names", "Names of model parameters"], [465, 3, 1, 0, "simulate", "Simulate data from the model"], [466, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [467, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [468, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [469, 3, 1, 0, "update", "Compute the variance for a single observation"], [470, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [471, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [472, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.APARCH.backcast": [[454, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.APARCH.backcast_transform": [[455, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.APARCH.bounds": [[456, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.APARCH.compute_variance": [[458, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [458, 2, 2, 0, "parameters", "Model parameters"], [458, 2, 2, 0, "resids", "Vector of mean zero residuals"], [458, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [458, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.APARCH.forecast": [[461, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [461, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [461, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [461, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [461, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [461, 2, 2, 0, "resids", "Residuals to use in the recursion"], [461, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [461, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [461, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [461, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.APARCH.simulate": [[465, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [465, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [465, 2, 2, 0, "nobs", "Number of data points to simulate"], [465, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [465, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.APARCH.starting_values": [[467, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.APARCH.update": [[469, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [469, 2, 2, 0, "index", "The numerical index of the variance to compute"], [469, 2, 2, 0, "parameters", "The variance model parameters"], [469, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [469, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [469, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.APARCH.variance_bounds": [[471, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [471, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.ARCH.__init__": [[426, 2, 2, 0, "p", "Order of the symmetric innovation"]], "arch.univariate.ARCH": [[473, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [474, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [475, 3, 1, 0, "bounds", "Returns bounds for parameters"], [476, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [477, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [478, 3, 1, 0, "forecast", "Forecast volatility from the model"], [479, 5, 1, 0, "name", "The name of the volatility process"], [480, 5, 1, 0, "num_params", "The number of parameters in the model"], [481, 3, 1, 0, "parameter_names", "Names of model parameters"], [482, 3, 1, 0, "simulate", "Simulate data from the model"], [483, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [484, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [485, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [486, 3, 1, 0, "update", "Compute the variance for a single observation"], [487, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [488, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [489, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.ARCH.backcast": [[473, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.ARCH.backcast_transform": [[474, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.ARCH.bounds": [[475, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.ARCH.compute_variance": [[476, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [476, 2, 2, 0, "parameters", "Model parameters"], [476, 2, 2, 0, "resids", "Vector of mean zero residuals"], [476, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [476, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.ARCH.forecast": [[478, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [478, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [478, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [478, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [478, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [478, 2, 2, 0, "resids", "Residuals to use in the recursion"], [478, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [478, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [478, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [478, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.ARCH.simulate": [[482, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [482, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [482, 2, 2, 0, "nobs", "Number of data points to simulate"], [482, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [482, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.ARCH.starting_values": [[484, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.ARCH.update": [[486, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [486, 2, 2, 0, "index", "The numerical index of the variance to compute"], [486, 2, 2, 0, "parameters", "The variance model parameters"], [486, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [486, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [486, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.ARCH.variance_bounds": [[488, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [488, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.ARCHInMean.__init__": [[427, 2, 2, 0, "constant", "Flag whether the model should include a constant"], [427, 2, 2, 0, "distribution", "Error distribution to use in the model"], [427, 2, 2, 0, "form", "The form of the conditional variance that appears in the mean equation. The string names use the log of the conditional variance (\"log\"), the square-root of the conditional variance (\"vol\") or the conditional variance. When specified using a float, interpreted as \\sigma_t^{form} so that 1 is equivalent to \"vol\" and 2 is equivalent to \"var\". When using a number, must be different from 0."], [427, 2, 2, 0, "hold_back", "Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."], [427, 2, 2, 0, "lags", "Description of lag structure of the HAR.  Scalar included all lags between 1 and the value.  A 1-d array includes the AR lags lags[0], lags[1], ..."], [427, 2, 2, 0, "rescale", "Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."], [427, 2, 2, 0, "volatility", "Volatility process to use in the model. volatility.updateable must return True."], [427, 2, 2, 0, "x", "nobs by k element array containing exogenous regressors"], [427, 2, 2, 0, "y", "nobs element vector containing the dependent variable"]], "arch.univariate.ARCHInMean": [[490, 3, 1, 0, "bounds", "Construct bounds for parameters to use in non-linear optimization"], [491, 3, 1, 0, "compute_param_cov", "Computes parameter covariances using numerical derivatives."], [492, 3, 1, 0, "constraints", "Construct linear constraint arrays  for use in non-linear optimization"], [493, 5, 1, 0, "distribution", "Set or gets the error distribution"], [494, 3, 1, 0, "fit", "Estimate model parameters"], [495, 3, 1, 0, "fix", "Allows an ARCHModelFixedResult to be constructed from fixed parameters."], [496, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [497, 5, 1, 0, "form", "The form of the conditional variance in the mean"], [498, 5, 1, 0, "name", "The name of the model."], [499, 5, 1, 0, "num_params", "Returns the number of parameters"], [500, 3, 1, 0, "parameter_names", "List of parameters names"], [501, 3, 1, 0, "resids", "Compute model residuals"], [502, 3, 1, 0, "simulate", "Simulates data from a linear regression, AR or HAR models"], [503, 3, 1, 0, "starting_values", "Returns starting values for the mean model, often the same as the values returned from fit"], [504, 5, 1, 0, "volatility", "Set or gets the volatility process"], [505, 5, 1, 0, "x", "Gets the value of the exogenous regressors in the model"], [506, 5, 1, 0, "y", "Returns the dependent variable"]], "arch.univariate.ARCHInMean.compute_param_cov": [[491, 2, 2, 0, "backcast", "Value to use for pre-sample observations"], [491, 2, 2, 0, "params", "Model parameters"], [491, 2, 2, 0, "robust", "Flag indicating whether to use robust standard errors (True) or classic MLE (False)"]], "arch.univariate.ARCHInMean.fit": [[494, 2, 2, 0, "backcast", "Value to use as backcast. Should be measure \\sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."], [494, 2, 2, 0, "cov_type", "Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."], [494, 2, 2, 0, "disp", "Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is \"off\" and True is \"final\""], [494, 2, 2, 0, "first_obs", "First observation to use when estimating model"], [494, 2, 2, 0, "last_obs", "Last observation to use when estimating model"], [494, 2, 2, 0, "options", "Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."], [494, 2, 2, 0, "show_warning", "Flag indicating whether convergence warnings should be shown."], [494, 2, 2, 0, "starting_values", "Array of starting values to use.  If not provided, starting values are constructed by the model components."], [494, 2, 2, 0, "tol", "Tolerance for termination."], [494, 2, 2, 0, "update_freq", "Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."]], "arch.univariate.ARCHInMean.fix": [[495, 2, 2, 0, "first_obs", "First observation to use when fixing model"], [495, 2, 2, 0, "last_obs", "Last observation to use when fixing model"], [495, 2, 2, 0, "params", "User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."]], "arch.univariate.ARCHInMean.forecast": [[496, 2, 2, 0, "align", "Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."], [496, 2, 2, 0, "horizon", "Number of steps to forecast"], [496, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [496, 2, 2, 0, "params", "Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."], [496, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [496, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."], [496, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [496, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [496, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."], [496, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"]], "arch.univariate.ARCHInMean.resids": [[501, 2, 2, 0, "params", "Model parameters"], [501, 2, 2, 0, "regressors", "Alternative regressor values to use when computing model residuals"], [501, 2, 2, 0, "y", "Alternative values to use when computing model residuals"]], "arch.univariate.ARCHInMean.simulate": [[502, 2, 2, 0, "burn", "Number of values to simulate to initialize the model and remove dependence on initial values."], [502, 2, 2, 0, "initial_value", "Either a scalar value or max(lags) array set of initial values to use when initializing the model.  If omitted, 0.0 is used."], [502, 2, 2, 0, "initial_value_vol", "An array or scalar to use when initializing the volatility process."], [502, 2, 2, 0, "nobs", "Length of series to simulate"], [502, 2, 2, 0, "params", "Parameters to use when simulating the model.  Parameter order is [mean volatility distribution] where the parameters of the mean model are ordered [constant lag[0] lag[1] ... lag[p] ex[0] ... ex[k-1]] where lag[j] indicates the coefficient on the jth lag in the model and ex[j] is the coefficient on the jth exogenous variable."], [502, 2, 2, 0, "x", "nobs + burn by k array of exogenous variables to include in the simulation."]], "arch.univariate.ARX.__init__": [[428, 2, 2, 0, "constant", "Flag whether the model should include a constant"], [428, 2, 2, 0, "distribution", "Error distribution to use in the model"], [428, 2, 2, 0, "hold_back", "Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."], [428, 2, 2, 0, "lags", "Description of lag structure of the HAR.  Scalar included all lags between 1 and the value.  A 1-d array includes the AR lags lags[0], lags[1], ..."], [428, 2, 2, 0, "rescale", "Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."], [428, 2, 2, 0, "volatility", "Volatility process to use in the model"], [428, 2, 2, 0, "x", "nobs by k element array containing exogenous regressors"], [428, 2, 2, 0, "y", "nobs element vector containing the dependent variable"]], "arch.univariate.ARX": [[507, 3, 1, 0, "bounds", "Construct bounds for parameters to use in non-linear optimization"], [508, 3, 1, 0, "compute_param_cov", "Computes parameter covariances using numerical derivatives."], [509, 3, 1, 0, "constraints", "Construct linear constraint arrays  for use in non-linear optimization"], [510, 5, 1, 0, "distribution", "Set or gets the error distribution"], [511, 3, 1, 0, "fit", "Estimate model parameters"], [512, 3, 1, 0, "fix", "Allows an ARCHModelFixedResult to be constructed from fixed parameters."], [513, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [514, 5, 1, 0, "name", "The name of the model."], [515, 5, 1, 0, "num_params", "Returns the number of parameters"], [516, 3, 1, 0, "parameter_names", "List of parameters names"], [517, 3, 1, 0, "resids", "Compute model residuals"], [518, 3, 1, 0, "simulate", "Simulates data from a linear regression, AR or HAR models"], [519, 3, 1, 0, "starting_values", "Returns starting values for the mean model, often the same as the values returned from fit"], [520, 5, 1, 0, "volatility", "Set or gets the volatility process"], [521, 5, 1, 0, "x", "Gets the value of the exogenous regressors in the model"], [522, 5, 1, 0, "y", "Returns the dependent variable"]], "arch.univariate.ARX.compute_param_cov": [[508, 2, 2, 0, "backcast", "Value to use for pre-sample observations"], [508, 2, 2, 0, "params", "Model parameters"], [508, 2, 2, 0, "robust", "Flag indicating whether to use robust standard errors (True) or classic MLE (False)"]], "arch.univariate.ARX.fit": [[511, 2, 2, 0, "backcast", "Value to use as backcast. Should be measure \\sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."], [511, 2, 2, 0, "cov_type", "Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."], [511, 2, 2, 0, "disp", "Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is \"off\" and True is \"final\""], [511, 2, 2, 0, "first_obs", "First observation to use when estimating model"], [511, 2, 2, 0, "last_obs", "Last observation to use when estimating model"], [511, 2, 2, 0, "options", "Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."], [511, 2, 2, 0, "show_warning", "Flag indicating whether convergence warnings should be shown."], [511, 2, 2, 0, "starting_values", "Array of starting values to use.  If not provided, starting values are constructed by the model components."], [511, 2, 2, 0, "tol", "Tolerance for termination."], [511, 2, 2, 0, "update_freq", "Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."]], "arch.univariate.ARX.fix": [[512, 2, 2, 0, "first_obs", "First observation to use when fixing model"], [512, 2, 2, 0, "last_obs", "Last observation to use when fixing model"], [512, 2, 2, 0, "params", "User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."]], "arch.univariate.ARX.forecast": [[513, 2, 2, 0, "align", "Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."], [513, 2, 2, 0, "horizon", "Number of steps to forecast"], [513, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [513, 2, 2, 0, "params", "Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."], [513, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [513, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."], [513, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [513, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [513, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."], [513, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"]], "arch.univariate.ARX.resids": [[517, 2, 2, 0, "params", "Model parameters"], [517, 2, 2, 0, "regressors", "Alternative regressor values to use when computing model residuals"], [517, 2, 2, 0, "y", "Alternative values to use when computing model residuals"]], "arch.univariate.ARX.simulate": [[518, 2, 2, 0, "burn", "Number of values to simulate to initialize the model and remove dependence on initial values."], [518, 2, 2, 0, "initial_value", "Either a scalar value or max(lags) array set of initial values to use when initializing the model.  If omitted, 0.0 is used."], [518, 2, 2, 0, "initial_value_vol", "An array or scalar to use when initializing the volatility process."], [518, 2, 2, 0, "nobs", "Length of series to simulate"], [518, 2, 2, 0, "params", "Parameters to use when simulating the model.  Parameter order is [mean volatility distribution] where the parameters of the mean model are ordered [constant lag[0] lag[1] ... lag[p] ex[0] ... ex[k-1]] where lag[j] indicates the coefficient on the jth lag in the model and ex[j] is the coefficient on the jth exogenous variable."], [518, 2, 2, 0, "x", "nobs + burn by k array of exogenous variables to include in the simulation."]], "arch.univariate.ConstantMean.__init__": [[429, 2, 2, 0, "distribution", "Error distribution to use in the model"], [429, 2, 2, 0, "hold_back", "Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."], [429, 2, 2, 0, "rescale", "Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."], [429, 2, 2, 0, "volatility", "Volatility process to use in the model"], [429, 2, 2, 0, "y", "nobs element vector containing the dependent variable"]], "arch.univariate.ConstantMean": [[523, 3, 1, 0, "bounds", "Construct bounds for parameters to use in non-linear optimization"], [524, 3, 1, 0, "compute_param_cov", "Computes parameter covariances using numerical derivatives."], [525, 3, 1, 0, "constraints", "Construct linear constraint arrays  for use in non-linear optimization"], [526, 5, 1, 0, "distribution", "Set or gets the error distribution"], [527, 3, 1, 0, "fit", "Estimate model parameters"], [528, 3, 1, 0, "fix", "Allows an ARCHModelFixedResult to be constructed from fixed parameters."], [529, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [530, 5, 1, 0, "name", "The name of the model."], [531, 5, 1, 0, "num_params", "Returns the number of parameters"], [532, 3, 1, 0, "parameter_names", "List of parameters names"], [533, 3, 1, 0, "resids", "Compute model residuals"], [534, 3, 1, 0, "simulate", "Simulated data from a constant mean model"], [535, 3, 1, 0, "starting_values", "Returns starting values for the mean model, often the same as the values returned from fit"], [536, 5, 1, 0, "volatility", "Set or gets the volatility process"], [537, 5, 1, 0, "x", "Gets the value of the exogenous regressors in the model"], [538, 5, 1, 0, "y", "Returns the dependent variable"]], "arch.univariate.ConstantMean.compute_param_cov": [[524, 2, 2, 0, "backcast", "Value to use for pre-sample observations"], [524, 2, 2, 0, "params", "Model parameters"], [524, 2, 2, 0, "robust", "Flag indicating whether to use robust standard errors (True) or classic MLE (False)"]], "arch.univariate.ConstantMean.fit": [[527, 2, 2, 0, "backcast", "Value to use as backcast. Should be measure \\sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."], [527, 2, 2, 0, "cov_type", "Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."], [527, 2, 2, 0, "disp", "Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is \"off\" and True is \"final\""], [527, 2, 2, 0, "first_obs", "First observation to use when estimating model"], [527, 2, 2, 0, "last_obs", "Last observation to use when estimating model"], [527, 2, 2, 0, "options", "Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."], [527, 2, 2, 0, "show_warning", "Flag indicating whether convergence warnings should be shown."], [527, 2, 2, 0, "starting_values", "Array of starting values to use.  If not provided, starting values are constructed by the model components."], [527, 2, 2, 0, "tol", "Tolerance for termination."], [527, 2, 2, 0, "update_freq", "Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."]], "arch.univariate.ConstantMean.fix": [[528, 2, 2, 0, "first_obs", "First observation to use when fixing model"], [528, 2, 2, 0, "last_obs", "Last observation to use when fixing model"], [528, 2, 2, 0, "params", "User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."]], "arch.univariate.ConstantMean.forecast": [[529, 2, 2, 0, "align", "Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."], [529, 2, 2, 0, "horizon", "Number of steps to forecast"], [529, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [529, 2, 2, 0, "params", "Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."], [529, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [529, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."], [529, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [529, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [529, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."], [529, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"]], "arch.univariate.ConstantMean.resids": [[533, 2, 2, 0, "params", "Model parameters"], [533, 2, 2, 0, "regressors", "Alternative regressor values to use when computing model residuals"], [533, 2, 2, 0, "y", "Alternative values to use when computing model residuals"]], "arch.univariate.ConstantMean.simulate": [[534, 2, 2, 0, "burn", "Number of values to simulate to initialize the model and remove dependence on initial values."], [534, 2, 2, 0, "initial_value", "This value is not used."], [534, 2, 2, 0, "initial_value_vol", "An array or scalar to use when initializing the volatility process."], [534, 2, 2, 0, "nobs", "Length of series to simulate"], [534, 2, 2, 0, "params", "Parameters to use when simulating the model.  Parameter order is [mean volatility distribution]. There is one parameter in the mean model, mu."], [534, 2, 2, 0, "x", "This value is not used."]], "arch.univariate.ConstantVariance": [[539, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [540, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [541, 3, 1, 0, "bounds", "Returns bounds for parameters"], [542, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [543, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [544, 3, 1, 0, "forecast", "Forecast volatility from the model"], [545, 5, 1, 0, "name", "The name of the volatility process"], [546, 5, 1, 0, "num_params", "The number of parameters in the model"], [547, 3, 1, 0, "parameter_names", "Names of model parameters"], [548, 3, 1, 0, "simulate", "Simulate data from the model"], [549, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [550, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [551, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [552, 3, 1, 0, "update", "Compute the variance for a single observation"], [553, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [554, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [555, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.ConstantVariance.backcast": [[539, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.ConstantVariance.backcast_transform": [[540, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.ConstantVariance.bounds": [[541, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.ConstantVariance.compute_variance": [[542, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [542, 2, 2, 0, "parameters", "Model parameters"], [542, 2, 2, 0, "resids", "Vector of mean zero residuals"], [542, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [542, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.ConstantVariance.forecast": [[544, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [544, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [544, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [544, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [544, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [544, 2, 2, 0, "resids", "Residuals to use in the recursion"], [544, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [544, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [544, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [544, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.ConstantVariance.simulate": [[548, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [548, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [548, 2, 2, 0, "nobs", "Number of data points to simulate"], [548, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [548, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.ConstantVariance.starting_values": [[550, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.ConstantVariance.update": [[552, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [552, 2, 2, 0, "index", "The numerical index of the variance to compute"], [552, 2, 2, 0, "parameters", "The variance model parameters"], [552, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [552, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [552, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.ConstantVariance.variance_bounds": [[554, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [554, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.EGARCH.__init__": [[431, 2, 2, 0, "o", "Order of the asymmetric innovation"], [431, 2, 2, 0, "p", "Order of the symmetric innovation"], [431, 2, 2, 0, "q", "Order of the lagged (transformed) conditional variance"]], "arch.univariate.EGARCH": [[556, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [557, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [558, 3, 1, 0, "bounds", "Returns bounds for parameters"], [559, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [560, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [561, 3, 1, 0, "forecast", "Forecast volatility from the model"], [562, 5, 1, 0, "name", "The name of the volatility process"], [563, 5, 1, 0, "num_params", "The number of parameters in the model"], [564, 3, 1, 0, "parameter_names", "Names of model parameters"], [565, 3, 1, 0, "simulate", "Simulate data from the model"], [566, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [567, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [568, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [569, 3, 1, 0, "update", "Compute the variance for a single observation"], [570, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [571, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [572, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.EGARCH.backcast": [[556, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.EGARCH.backcast_transform": [[557, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.EGARCH.bounds": [[558, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.EGARCH.compute_variance": [[559, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [559, 2, 2, 0, "parameters", "Model parameters"], [559, 2, 2, 0, "resids", "Vector of mean zero residuals"], [559, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [559, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.EGARCH.forecast": [[561, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [561, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [561, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [561, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [561, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [561, 2, 2, 0, "resids", "Residuals to use in the recursion"], [561, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [561, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [561, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [561, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.EGARCH.simulate": [[565, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [565, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [565, 2, 2, 0, "nobs", "Number of data points to simulate"], [565, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [565, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.EGARCH.starting_values": [[567, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.EGARCH.update": [[569, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [569, 2, 2, 0, "index", "The numerical index of the variance to compute"], [569, 2, 2, 0, "parameters", "The variance model parameters"], [569, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [569, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [569, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.EGARCH.variance_bounds": [[571, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [571, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.EWMAVariance.__init__": [[432, 2, 2, 0, "lam", "Smoothing parameter. Default is 0.94. Set to None to estimate lam jointly with other model parameters"]], "arch.univariate.EWMAVariance": [[573, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [574, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [575, 3, 1, 0, "bounds", "Returns bounds for parameters"], [576, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [577, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [578, 3, 1, 0, "forecast", "Forecast volatility from the model"], [579, 5, 1, 0, "name", "The name of the volatility process"], [580, 5, 1, 0, "num_params", "The number of parameters in the model"], [581, 3, 1, 0, "parameter_names", "Names of model parameters"], [582, 3, 1, 0, "simulate", "Simulate data from the model"], [583, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [584, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [585, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [586, 3, 1, 0, "update", "Compute the variance for a single observation"], [587, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [588, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [589, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.EWMAVariance.backcast": [[573, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.EWMAVariance.backcast_transform": [[574, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.EWMAVariance.bounds": [[575, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.EWMAVariance.compute_variance": [[576, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [576, 2, 2, 0, "parameters", "Model parameters"], [576, 2, 2, 0, "resids", "Vector of mean zero residuals"], [576, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [576, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.EWMAVariance.forecast": [[578, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [578, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [578, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [578, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [578, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [578, 2, 2, 0, "resids", "Residuals to use in the recursion"], [578, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [578, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [578, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [578, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.EWMAVariance.simulate": [[582, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [582, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [582, 2, 2, 0, "nobs", "Number of data points to simulate"], [582, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [582, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.EWMAVariance.starting_values": [[584, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.EWMAVariance.update": [[586, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [586, 2, 2, 0, "index", "The numerical index of the variance to compute"], [586, 2, 2, 0, "parameters", "The variance model parameters"], [586, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [586, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [586, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.EWMAVariance.variance_bounds": [[588, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [588, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.FIGARCH.__init__": [[433, 2, 2, 0, "p", "Order of the symmetric innovation"], [433, 2, 2, 0, "power", "Power to use with the innovations, abs(e) ** power.  Default is 2.0, which produces FIGARCH and related models. Using 1.0 produces FIAVARCH and related models.  Other powers can be specified, although these should be strictly positive, and usually larger than 0.25."], [433, 2, 2, 0, "q", "Order of the lagged (transformed) conditional variance"], [433, 2, 2, 0, "truncation", "Truncation point to use in ARCH(\\infty) representation. Default is 1000."]], "arch.univariate.FIGARCH": [[590, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [591, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [592, 3, 1, 0, "bounds", "Returns bounds for parameters"], [593, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [594, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [595, 3, 1, 0, "forecast", "Forecast volatility from the model"], [596, 5, 1, 0, "name", "The name of the volatility process"], [597, 5, 1, 0, "num_params", "The number of parameters in the model"], [598, 3, 1, 0, "parameter_names", "Names of model parameters"], [599, 3, 1, 0, "simulate", "Simulate data from the model"], [600, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [601, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [602, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [603, 5, 1, 0, "truncation", "Truncation lag for the ARCH-infinity approximation"], [604, 3, 1, 0, "update", "Compute the variance for a single observation"], [605, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [606, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [607, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.FIGARCH.backcast": [[590, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.FIGARCH.backcast_transform": [[591, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.FIGARCH.bounds": [[592, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.FIGARCH.compute_variance": [[593, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [593, 2, 2, 0, "parameters", "Model parameters"], [593, 2, 2, 0, "resids", "Vector of mean zero residuals"], [593, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [593, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.FIGARCH.forecast": [[595, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [595, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [595, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [595, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [595, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [595, 2, 2, 0, "resids", "Residuals to use in the recursion"], [595, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [595, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [595, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [595, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.FIGARCH.simulate": [[599, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [599, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [599, 2, 2, 0, "nobs", "Number of data points to simulate"], [599, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [599, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.FIGARCH.starting_values": [[601, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.FIGARCH.update": [[604, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [604, 2, 2, 0, "index", "The numerical index of the variance to compute"], [604, 2, 2, 0, "parameters", "The variance model parameters"], [604, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [604, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [604, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.FIGARCH.variance_bounds": [[606, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [606, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.FixedVariance.__init__": [[434, 2, 2, 0, "unit_scale", "Flag whether to enforce a unit scale.  If False, a scale parameter will be estimated so that the model variance will be proportional to variance. If True, the model variance is set of variance"], [434, 2, 2, 0, "variance", "Array containing the variances to use.  Should have the same shape as the data used in the model."]], "arch.univariate.FixedVariance": [[608, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [609, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [610, 3, 1, 0, "bounds", "Returns bounds for parameters"], [611, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [612, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [613, 3, 1, 0, "forecast", "Forecast volatility from the model"], [614, 5, 1, 0, "name", "The name of the volatility process"], [615, 5, 1, 0, "num_params", "The number of parameters in the model"], [616, 3, 1, 0, "parameter_names", "Names of model parameters"], [617, 3, 1, 0, "simulate", "Simulate data from the model"], [618, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [619, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [620, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [621, 3, 1, 0, "update", "Compute the variance for a single observation"], [622, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [623, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [624, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.FixedVariance.backcast": [[608, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.FixedVariance.backcast_transform": [[609, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.FixedVariance.bounds": [[610, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.FixedVariance.compute_variance": [[611, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [611, 2, 2, 0, "parameters", "Model parameters"], [611, 2, 2, 0, "resids", "Vector of mean zero residuals"], [611, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [611, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.FixedVariance.forecast": [[613, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [613, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [613, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [613, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [613, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [613, 2, 2, 0, "resids", "Residuals to use in the recursion"], [613, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [613, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [613, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [613, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.FixedVariance.simulate": [[617, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [617, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [617, 2, 2, 0, "nobs", "Number of data points to simulate"], [617, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [617, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.FixedVariance.starting_values": [[619, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.FixedVariance.update": [[621, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [621, 2, 2, 0, "index", "The numerical index of the variance to compute"], [621, 2, 2, 0, "parameters", "The variance model parameters"], [621, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [621, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [621, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.FixedVariance.variance_bounds": [[623, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [623, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.GARCH.__init__": [[435, 2, 2, 0, "o", "Order of the asymmetric innovation"], [435, 2, 2, 0, "p", "Order of the symmetric innovation"], [435, 2, 2, 0, "power", "Power to use with the innovations, abs(e) ** power.  Default is 2.0, which produces ARCH and related models. Using 1.0 produces AVARCH and related models.  Other powers can be specified, although these should be strictly positive, and usually larger than 0.25."], [435, 2, 2, 0, "q", "Order of the lagged (transformed) conditional variance"]], "arch.univariate.GARCH": [[625, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [626, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [627, 3, 1, 0, "bounds", "Returns bounds for parameters"], [628, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [629, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [630, 3, 1, 0, "forecast", "Forecast volatility from the model"], [631, 5, 1, 0, "name", "The name of the volatility process"], [632, 5, 1, 0, "num_params", "The number of parameters in the model"], [633, 3, 1, 0, "parameter_names", "Names of model parameters"], [634, 3, 1, 0, "simulate", "Simulate data from the model"], [635, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [636, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [637, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [638, 3, 1, 0, "update", "Compute the variance for a single observation"], [639, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [640, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [641, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.GARCH.backcast": [[625, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.GARCH.backcast_transform": [[626, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.GARCH.bounds": [[627, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.GARCH.compute_variance": [[628, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [628, 2, 2, 0, "parameters", "Model parameters"], [628, 2, 2, 0, "resids", "Vector of mean zero residuals"], [628, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [628, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.GARCH.forecast": [[630, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [630, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [630, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [630, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [630, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [630, 2, 2, 0, "resids", "Residuals to use in the recursion"], [630, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [630, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [630, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [630, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.GARCH.simulate": [[634, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [634, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [634, 2, 2, 0, "nobs", "Number of data points to simulate"], [634, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [634, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.GARCH.starting_values": [[636, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.GARCH.update": [[638, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [638, 2, 2, 0, "index", "The numerical index of the variance to compute"], [638, 2, 2, 0, "parameters", "The variance model parameters"], [638, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [638, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [638, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.GARCH.variance_bounds": [[640, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [640, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.GeneralizedError.__init__": [[436, 2, 2, 0, "random_state", "Deprecated since version 5.0: random_state is deprecated. Use seed instead."], [436, 2, 2, 0, "seed", "Random number generator instance or int to use. Set to ensure reproducibility. If using an int, the argument is passed to np.random.default_rng.  If not provided, default_rng is used with system-provided entropy."]], "arch.univariate.GeneralizedError": [[642, 3, 1, 0, "bounds", "Parameter bounds for use in optimization."], [643, 3, 1, 0, "cdf", "Cumulative distribution function"], [644, 3, 1, 0, "constraints", "Construct arrays to use in constrained optimization."], [645, 5, 1, 0, "generator", "The NumPy Generator or RandomState attached to the distribution"], [646, 3, 1, 0, "loglikelihood", "Computes the log-likelihood of assuming residuals are have a Generalized Error Distribution, conditional on the variance."], [647, 3, 1, 0, "moment", "Moment of order n"], [648, 5, 1, 0, "name", "The name of the distribution"], [649, 3, 1, 0, "parameter_names", "Names of distribution shape parameters"], [650, 3, 1, 0, "partial_moment", "Order n lower partial moment from -inf to z"], [651, 3, 1, 0, "ppf", "Inverse cumulative density function (ICDF)"], [652, 5, 1, 0, "random_state", "The NumPy RandomState attached to the distribution"], [653, 3, 1, 0, "simulate", "Simulates i.i.d. draws from the distribution"], [654, 3, 1, 0, "starting_values", "Construct starting values for use in optimization."]], "arch.univariate.GeneralizedError.bounds": [[642, 2, 2, 0, "resids", "Residuals to use when computing the bounds"]], "arch.univariate.GeneralizedError.cdf": [[643, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [643, 2, 2, 0, "resids", "Values at which to evaluate the cdf"]], "arch.univariate.GeneralizedError.loglikelihood": [[646, 2, 2, 0, "individual", "Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"], [646, 2, 2, 0, "parameters", "Shape parameter of the GED distribution"], [646, 2, 2, 0, "resids", "The residuals to use in the log-likelihood calculation"], [646, 2, 2, 0, "sigma2", "Conditional variances of resids"]], "arch.univariate.GeneralizedError.moment": [[647, 2, 2, 0, "n", "Order of moment"], [647, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."]], "arch.univariate.GeneralizedError.partial_moment": [[650, 2, 2, 0, "n", "Order of partial moment"], [650, 2, 2, 0, "parameters", "Distribution parameters.  Use None for parameterless distributions."], [650, 2, 2, 0, "z", "Upper bound for partial moment integral"]], "arch.univariate.GeneralizedError.ppf": [[651, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [651, 2, 2, 0, "pits", "Probability-integral-transformed values in the interval (0, 1)."]], "arch.univariate.GeneralizedError.simulate": [[653, 2, 2, 0, "parameters", "Distribution parameters"]], "arch.univariate.GeneralizedError.starting_values": [[654, 2, 2, 0, "std_resid", "Estimated standardized residuals to use in computing starting values for the shape parameter"]], "arch.univariate.HARCH.__init__": [[437, 2, 2, 0, "lags", "List of lags to include in the model, or if scalar, includes all lags up the value"]], "arch.univariate.HARCH": [[655, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [656, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [657, 3, 1, 0, "bounds", "Returns bounds for parameters"], [658, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [659, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [660, 3, 1, 0, "forecast", "Forecast volatility from the model"], [661, 5, 1, 0, "name", "The name of the volatility process"], [662, 5, 1, 0, "num_params", "The number of parameters in the model"], [663, 3, 1, 0, "parameter_names", "Names of model parameters"], [664, 3, 1, 0, "simulate", "Simulate data from the model"], [665, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [666, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [667, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [668, 3, 1, 0, "update", "Compute the variance for a single observation"], [669, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [670, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [671, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.HARCH.backcast": [[655, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.HARCH.backcast_transform": [[656, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.HARCH.bounds": [[657, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.HARCH.compute_variance": [[658, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [658, 2, 2, 0, "parameters", "Model parameters"], [658, 2, 2, 0, "resids", "Vector of mean zero residuals"], [658, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [658, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.HARCH.forecast": [[660, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [660, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [660, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [660, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [660, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [660, 2, 2, 0, "resids", "Residuals to use in the recursion"], [660, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [660, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [660, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [660, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.HARCH.simulate": [[664, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [664, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [664, 2, 2, 0, "nobs", "Number of data points to simulate"], [664, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [664, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.HARCH.starting_values": [[666, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.HARCH.update": [[668, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [668, 2, 2, 0, "index", "The numerical index of the variance to compute"], [668, 2, 2, 0, "parameters", "The variance model parameters"], [668, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [668, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [668, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.HARCH.variance_bounds": [[670, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [670, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.HARX.__init__": [[438, 2, 2, 0, "constant", "Flag whether the model should include a constant"], [438, 2, 2, 0, "distribution", "Error distribution to use in the model"], [438, 2, 2, 0, "hold_back", "Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."], [438, 2, 2, 0, "lags", "Description of lag structure of the HAR."], [438, 2, 2, 0, "rescale", "Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."], [438, 2, 2, 0, "use_rotated", "Flag indicating to use the alternative rotated form of the HAR where HAR lags do not overlap"], [438, 2, 2, 0, "volatility", "Volatility process to use in the model"], [438, 2, 2, 0, "x", "nobs by k element array containing exogenous regressors"], [438, 2, 2, 0, "y", "nobs element vector containing the dependent variable"]], "arch.univariate.HARX": [[672, 3, 1, 0, "bounds", "Construct bounds for parameters to use in non-linear optimization"], [673, 3, 1, 0, "compute_param_cov", "Computes parameter covariances using numerical derivatives."], [674, 3, 1, 0, "constraints", "Construct linear constraint arrays  for use in non-linear optimization"], [675, 5, 1, 0, "distribution", "Set or gets the error distribution"], [676, 3, 1, 0, "fit", "Estimate model parameters"], [677, 3, 1, 0, "fix", "Allows an ARCHModelFixedResult to be constructed from fixed parameters."], [678, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [679, 5, 1, 0, "name", "The name of the model."], [680, 5, 1, 0, "num_params", "Returns the number of parameters"], [681, 3, 1, 0, "parameter_names", "List of parameters names"], [682, 3, 1, 0, "resids", "Compute model residuals"], [683, 3, 1, 0, "simulate", "Simulates data from a linear regression, AR or HAR models"], [684, 3, 1, 0, "starting_values", "Returns starting values for the mean model, often the same as the values returned from fit"], [685, 5, 1, 0, "volatility", "Set or gets the volatility process"], [686, 5, 1, 0, "x", "Gets the value of the exogenous regressors in the model"], [687, 5, 1, 0, "y", "Returns the dependent variable"]], "arch.univariate.HARX.compute_param_cov": [[673, 2, 2, 0, "backcast", "Value to use for pre-sample observations"], [673, 2, 2, 0, "params", "Model parameters"], [673, 2, 2, 0, "robust", "Flag indicating whether to use robust standard errors (True) or classic MLE (False)"]], "arch.univariate.HARX.fit": [[676, 2, 2, 0, "backcast", "Value to use as backcast. Should be measure \\sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."], [676, 2, 2, 0, "cov_type", "Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."], [676, 2, 2, 0, "disp", "Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is \"off\" and True is \"final\""], [676, 2, 2, 0, "first_obs", "First observation to use when estimating model"], [676, 2, 2, 0, "last_obs", "Last observation to use when estimating model"], [676, 2, 2, 0, "options", "Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."], [676, 2, 2, 0, "show_warning", "Flag indicating whether convergence warnings should be shown."], [676, 2, 2, 0, "starting_values", "Array of starting values to use.  If not provided, starting values are constructed by the model components."], [676, 2, 2, 0, "tol", "Tolerance for termination."], [676, 2, 2, 0, "update_freq", "Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."]], "arch.univariate.HARX.fix": [[677, 2, 2, 0, "first_obs", "First observation to use when fixing model"], [677, 2, 2, 0, "last_obs", "Last observation to use when fixing model"], [677, 2, 2, 0, "params", "User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."]], "arch.univariate.HARX.forecast": [[678, 2, 2, 0, "align", "Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."], [678, 2, 2, 0, "horizon", "Number of steps to forecast"], [678, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [678, 2, 2, 0, "params", "Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."], [678, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [678, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."], [678, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [678, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [678, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."], [678, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"]], "arch.univariate.HARX.resids": [[682, 2, 2, 0, "params", "Model parameters"], [682, 2, 2, 0, "regressors", "Alternative regressor values to use when computing model residuals"], [682, 2, 2, 0, "y", "Alternative values to use when computing model residuals"]], "arch.univariate.HARX.simulate": [[683, 2, 2, 0, "burn", "Number of values to simulate to initialize the model and remove dependence on initial values."], [683, 2, 2, 0, "initial_value", "Either a scalar value or max(lags) array set of initial values to use when initializing the model.  If omitted, 0.0 is used."], [683, 2, 2, 0, "initial_value_vol", "An array or scalar to use when initializing the volatility process."], [683, 2, 2, 0, "nobs", "Length of series to simulate"], [683, 2, 2, 0, "params", "Parameters to use when simulating the model.  Parameter order is [mean volatility distribution] where the parameters of the mean model are ordered [constant lag[0] lag[1] ... lag[p] ex[0] ... ex[k-1]] where lag[j] indicates the coefficient on the jth lag in the model and ex[j] is the coefficient on the jth exogenous variable."], [683, 2, 2, 0, "x", "nobs + burn by k array of exogenous variables to include in the simulation."]], "arch.univariate.LS.__init__": [[439, 2, 2, 0, "constant", "Flag whether the model should include a constant"], [439, 2, 2, 0, "distribution", "Error distribution to use in the model"], [439, 2, 2, 0, "hold_back", "Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."], [439, 2, 2, 0, "rescale", "Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."], [439, 2, 2, 0, "volatility", "Volatility process to use in the model"], [439, 2, 2, 0, "y", "nobs by k element array containing exogenous regressors"]], "arch.univariate.LS": [[688, 3, 1, 0, "bounds", "Construct bounds for parameters to use in non-linear optimization"], [689, 3, 1, 0, "compute_param_cov", "Computes parameter covariances using numerical derivatives."], [690, 3, 1, 0, "constraints", "Construct linear constraint arrays  for use in non-linear optimization"], [691, 5, 1, 0, "distribution", "Set or gets the error distribution"], [692, 3, 1, 0, "fit", "Estimate model parameters"], [693, 3, 1, 0, "fix", "Allows an ARCHModelFixedResult to be constructed from fixed parameters."], [694, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [695, 5, 1, 0, "name", "The name of the model."], [696, 5, 1, 0, "num_params", "Returns the number of parameters"], [697, 3, 1, 0, "parameter_names", "List of parameters names"], [698, 3, 1, 0, "resids", "Compute model residuals"], [699, 3, 1, 0, "simulate", "Simulates data from a linear regression, AR or HAR models"], [700, 3, 1, 0, "starting_values", "Returns starting values for the mean model, often the same as the values returned from fit"], [701, 5, 1, 0, "volatility", "Set or gets the volatility process"], [702, 5, 1, 0, "x", "Gets the value of the exogenous regressors in the model"], [703, 5, 1, 0, "y", "Returns the dependent variable"]], "arch.univariate.LS.compute_param_cov": [[689, 2, 2, 0, "backcast", "Value to use for pre-sample observations"], [689, 2, 2, 0, "params", "Model parameters"], [689, 2, 2, 0, "robust", "Flag indicating whether to use robust standard errors (True) or classic MLE (False)"]], "arch.univariate.LS.fit": [[692, 2, 2, 0, "backcast", "Value to use as backcast. Should be measure \\sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."], [692, 2, 2, 0, "cov_type", "Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."], [692, 2, 2, 0, "disp", "Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is \"off\" and True is \"final\""], [692, 2, 2, 0, "first_obs", "First observation to use when estimating model"], [692, 2, 2, 0, "last_obs", "Last observation to use when estimating model"], [692, 2, 2, 0, "options", "Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."], [692, 2, 2, 0, "show_warning", "Flag indicating whether convergence warnings should be shown."], [692, 2, 2, 0, "starting_values", "Array of starting values to use.  If not provided, starting values are constructed by the model components."], [692, 2, 2, 0, "tol", "Tolerance for termination."], [692, 2, 2, 0, "update_freq", "Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."]], "arch.univariate.LS.fix": [[693, 2, 2, 0, "first_obs", "First observation to use when fixing model"], [693, 2, 2, 0, "last_obs", "Last observation to use when fixing model"], [693, 2, 2, 0, "params", "User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."]], "arch.univariate.LS.forecast": [[694, 2, 2, 0, "align", "Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."], [694, 2, 2, 0, "horizon", "Number of steps to forecast"], [694, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [694, 2, 2, 0, "params", "Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."], [694, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [694, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."], [694, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [694, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [694, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."], [694, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"]], "arch.univariate.LS.resids": [[698, 2, 2, 0, "params", "Model parameters"], [698, 2, 2, 0, "regressors", "Alternative regressor values to use when computing model residuals"], [698, 2, 2, 0, "y", "Alternative values to use when computing model residuals"]], "arch.univariate.LS.simulate": [[699, 2, 2, 0, "burn", "Number of values to simulate to initialize the model and remove dependence on initial values."], [699, 2, 2, 0, "initial_value", "Either a scalar value or max(lags) array set of initial values to use when initializing the model.  If omitted, 0.0 is used."], [699, 2, 2, 0, "initial_value_vol", "An array or scalar to use when initializing the volatility process."], [699, 2, 2, 0, "nobs", "Length of series to simulate"], [699, 2, 2, 0, "params", "Parameters to use when simulating the model.  Parameter order is [mean volatility distribution] where the parameters of the mean model are ordered [constant lag[0] lag[1] ... lag[p] ex[0] ... ex[k-1]] where lag[j] indicates the coefficient on the jth lag in the model and ex[j] is the coefficient on the jth exogenous variable."], [699, 2, 2, 0, "x", "nobs + burn by k array of exogenous variables to include in the simulation."]], "arch.univariate.MIDASHyperbolic.__init__": [[440, 2, 2, 0, "asym", "Flag indicating whether to include an asymmetric term"], [440, 2, 2, 0, "m", "Length of maximum lag to include in the model"]], "arch.univariate.MIDASHyperbolic": [[704, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [705, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [706, 3, 1, 0, "bounds", "Returns bounds for parameters"], [707, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [708, 3, 1, 0, "constraints", "Constraints"], [709, 3, 1, 0, "forecast", "Forecast volatility from the model"], [710, 5, 1, 0, "name", "The name of the volatility process"], [711, 5, 1, 0, "num_params", "The number of parameters in the model"], [712, 3, 1, 0, "parameter_names", "Names of model parameters"], [713, 3, 1, 0, "simulate", "Simulate data from the model"], [714, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [715, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [716, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [717, 3, 1, 0, "update", "Compute the variance for a single observation"], [718, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [719, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [720, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.MIDASHyperbolic.backcast": [[704, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.MIDASHyperbolic.backcast_transform": [[705, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.MIDASHyperbolic.bounds": [[706, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.MIDASHyperbolic.compute_variance": [[707, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [707, 2, 2, 0, "parameters", "Model parameters"], [707, 2, 2, 0, "resids", "Vector of mean zero residuals"], [707, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [707, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.MIDASHyperbolic.forecast": [[709, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [709, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [709, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [709, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [709, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [709, 2, 2, 0, "resids", "Residuals to use in the recursion"], [709, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [709, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [709, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [709, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.MIDASHyperbolic.simulate": [[713, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [713, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [713, 2, 2, 0, "nobs", "Number of data points to simulate"], [713, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [713, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.MIDASHyperbolic.starting_values": [[715, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.MIDASHyperbolic.update": [[717, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [717, 2, 2, 0, "index", "The numerical index of the variance to compute"], [717, 2, 2, 0, "parameters", "The variance model parameters"], [717, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [717, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [717, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.MIDASHyperbolic.variance_bounds": [[719, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [719, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.Normal.__init__": [[441, 2, 2, 0, "random_state", "Deprecated since version 5.0: random_state is deprecated. Use seed instead."], [441, 2, 2, 0, "seed", "Random number generator instance or int to use. Set to ensure reproducibility. If using an int, the argument is passed to np.random.default_rng.  If not provided, default_rng is used with system-provided entropy."]], "arch.univariate.Normal": [[721, 3, 1, 0, "bounds", "Parameter bounds for use in optimization."], [722, 3, 1, 0, "cdf", "Cumulative distribution function"], [723, 3, 1, 0, "constraints", "Construct arrays to use in constrained optimization."], [724, 5, 1, 0, "generator", "The NumPy Generator or RandomState attached to the distribution"], [725, 3, 1, 0, "loglikelihood", "Computes the log-likelihood of assuming residuals are normally distributed, conditional on the variance"], [726, 3, 1, 0, "moment", "Moment of order n"], [727, 5, 1, 0, "name", "The name of the distribution"], [728, 3, 1, 0, "parameter_names", "Names of distribution shape parameters"], [729, 3, 1, 0, "partial_moment", "Order n lower partial moment from -inf to z"], [730, 3, 1, 0, "ppf", "Inverse cumulative density function (ICDF)"], [731, 5, 1, 0, "random_state", "The NumPy RandomState attached to the distribution"], [732, 3, 1, 0, "simulate", "Simulates i.i.d. draws from the distribution"], [733, 3, 1, 0, "starting_values", "Construct starting values for use in optimization."]], "arch.univariate.Normal.bounds": [[721, 2, 2, 0, "resids", "Residuals to use when computing the bounds"]], "arch.univariate.Normal.cdf": [[722, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [722, 2, 2, 0, "resids", "Values at which to evaluate the cdf"]], "arch.univariate.Normal.loglikelihood": [[725, 2, 2, 0, "individual", "Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"], [725, 2, 2, 0, "parameters", "The normal likelihood has no shape parameters. Empty since the standard normal has no shape parameters."], [725, 2, 2, 0, "resids", "The residuals to use in the log-likelihood calculation"], [725, 2, 2, 0, "sigma2", "Conditional variances of resids"]], "arch.univariate.Normal.moment": [[726, 2, 2, 0, "n", "Order of moment"], [726, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."]], "arch.univariate.Normal.partial_moment": [[729, 2, 2, 0, "n", "Order of partial moment"], [729, 2, 2, 0, "parameters", "Distribution parameters.  Use None for parameterless distributions."], [729, 2, 2, 0, "z", "Upper bound for partial moment integral"]], "arch.univariate.Normal.ppf": [[730, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [730, 2, 2, 0, "pits", "Probability-integral-transformed values in the interval (0, 1)."]], "arch.univariate.Normal.simulate": [[732, 2, 2, 0, "parameters", "Distribution parameters"]], "arch.univariate.Normal.starting_values": [[733, 2, 2, 0, "std_resid", "Estimated standardized residuals to use in computing starting values for the shape parameter"]], "arch.univariate.RiskMetrics2006.__init__": [[442, 2, 2, 0, "kmax", "Number of components. Default is 14."], [442, 2, 2, 0, "rho", "Relative scale of adjacent cycles. Default is sqrt(2)"], [442, 2, 2, 0, "tau0", "Length of long cycle. Default is 1560."], [442, 2, 2, 0, "tau1", "Length of short cycle. Default is 4."]], "arch.univariate.RiskMetrics2006": [[734, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [735, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [736, 3, 1, 0, "bounds", "Returns bounds for parameters"], [737, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [738, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [739, 3, 1, 0, "forecast", "Forecast volatility from the model"], [740, 5, 1, 0, "name", "The name of the volatility process"], [741, 5, 1, 0, "num_params", "The number of parameters in the model"], [742, 3, 1, 0, "parameter_names", "Names of model parameters"], [743, 3, 1, 0, "simulate", "Simulate data from the model"], [744, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [745, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [746, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [747, 3, 1, 0, "update", "Compute the variance for a single observation"], [748, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [749, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [750, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.RiskMetrics2006.backcast": [[734, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.RiskMetrics2006.backcast_transform": [[735, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.RiskMetrics2006.bounds": [[736, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.RiskMetrics2006.compute_variance": [[737, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [737, 2, 2, 0, "parameters", "Model parameters"], [737, 2, 2, 0, "resids", "Vector of mean zero residuals"], [737, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [737, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.RiskMetrics2006.forecast": [[739, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [739, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [739, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [739, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [739, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [739, 2, 2, 0, "resids", "Residuals to use in the recursion"], [739, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [739, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [739, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [739, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.RiskMetrics2006.simulate": [[743, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [743, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [743, 2, 2, 0, "nobs", "Number of data points to simulate"], [743, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [743, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.RiskMetrics2006.starting_values": [[745, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.RiskMetrics2006.update": [[747, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [747, 2, 2, 0, "index", "The numerical index of the variance to compute"], [747, 2, 2, 0, "parameters", "The variance model parameters"], [747, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [747, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [747, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.RiskMetrics2006.variance_bounds": [[749, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [749, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.SkewStudent.__init__": [[443, 2, 2, 0, "random_state", "Deprecated since version 5.0: random_state is deprecated. Use seed instead."], [443, 2, 2, 0, "seed", "Random number generator instance or int to use. Set to ensure reproducibility. If using an int, the argument is passed to np.random.default_rng.  If not provided, default_rng is used with system-provided entropy."]], "arch.univariate.SkewStudent": [[751, 3, 1, 0, "bounds", "Parameter bounds for use in optimization."], [752, 3, 1, 0, "cdf", "Cumulative distribution function"], [753, 3, 1, 0, "constraints", "Construct arrays to use in constrained optimization."], [754, 5, 1, 0, "generator", "The NumPy Generator or RandomState attached to the distribution"], [755, 3, 1, 0, "loglikelihood", "Computes the log-likelihood of assuming residuals are have a standardized (to have unit variance) Skew Student's t distribution, conditional on the variance."], [756, 3, 1, 0, "moment", "Moment of order n"], [757, 5, 1, 0, "name", "The name of the distribution"], [758, 3, 1, 0, "parameter_names", "Names of distribution shape parameters"], [759, 3, 1, 0, "partial_moment", "Order n lower partial moment from -inf to z"], [760, 3, 1, 0, "ppf", "Inverse cumulative density function (ICDF)"], [761, 5, 1, 0, "random_state", "The NumPy RandomState attached to the distribution"], [762, 3, 1, 0, "simulate", "Simulates i.i.d. draws from the distribution"], [763, 3, 1, 0, "starting_values", "Construct starting values for use in optimization."]], "arch.univariate.SkewStudent.bounds": [[751, 2, 2, 0, "resids", "Residuals to use when computing the bounds"]], "arch.univariate.SkewStudent.cdf": [[752, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [752, 2, 2, 0, "resids", "Values at which to evaluate the cdf"]], "arch.univariate.SkewStudent.loglikelihood": [[755, 2, 2, 0, "individual", "Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"], [755, 2, 2, 0, "parameters", "Shape parameter of the skew-t distribution"], [755, 2, 2, 0, "resids", "The residuals to use in the log-likelihood calculation"], [755, 2, 2, 0, "sigma2", "Conditional variances of resids"]], "arch.univariate.SkewStudent.moment": [[756, 2, 2, 0, "n", "Order of moment"], [756, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."]], "arch.univariate.SkewStudent.partial_moment": [[759, 2, 2, 0, "n", "Order of partial moment"], [759, 2, 2, 0, "parameters", "Distribution parameters.  Use None for parameterless distributions."], [759, 2, 2, 0, "z", "Upper bound for partial moment integral"]], "arch.univariate.SkewStudent.ppf": [[760, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [760, 2, 2, 0, "pits", "Probability-integral-transformed values in the interval (0, 1)."]], "arch.univariate.SkewStudent.simulate": [[762, 2, 2, 0, "parameters", "Distribution parameters"]], "arch.univariate.SkewStudent.starting_values": [[763, 2, 2, 0, "std_resid", "Estimated standardized residuals to use in computing starting values for the shape parameter"]], "arch.univariate.StudentsT.__init__": [[444, 2, 2, 0, "random_state", "Deprecated since version 5.0: random_state is deprecated. Use seed instead."], [444, 2, 2, 0, "seed", "Random number generator instance or int to use. Set to ensure reproducibility. If using an int, the argument is passed to np.random.default_rng.  If not provided, default_rng is used with system-provided entropy."]], "arch.univariate.StudentsT": [[764, 3, 1, 0, "bounds", "Parameter bounds for use in optimization."], [765, 3, 1, 0, "cdf", "Cumulative distribution function"], [766, 3, 1, 0, "constraints", "Construct arrays to use in constrained optimization."], [767, 5, 1, 0, "generator", "The NumPy Generator or RandomState attached to the distribution"], [768, 3, 1, 0, "loglikelihood", "Computes the log-likelihood of assuming residuals are have a standardized (to have unit variance) Student's t distribution, conditional on the variance."], [769, 3, 1, 0, "moment", "Moment of order n"], [770, 5, 1, 0, "name", "The name of the distribution"], [771, 3, 1, 0, "parameter_names", "Names of distribution shape parameters"], [772, 3, 1, 0, "partial_moment", "Order n lower partial moment from -inf to z"], [773, 3, 1, 0, "ppf", "Inverse cumulative density function (ICDF)"], [774, 5, 1, 0, "random_state", "The NumPy RandomState attached to the distribution"], [775, 3, 1, 0, "simulate", "Simulates i.i.d. draws from the distribution"], [776, 3, 1, 0, "starting_values", "Construct starting values for use in optimization."]], "arch.univariate.StudentsT.bounds": [[764, 2, 2, 0, "resids", "Residuals to use when computing the bounds"]], "arch.univariate.StudentsT.cdf": [[765, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [765, 2, 2, 0, "resids", "Values at which to evaluate the cdf"]], "arch.univariate.StudentsT.loglikelihood": [[768, 2, 2, 0, "individual", "Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"], [768, 2, 2, 0, "parameters", "Shape parameter of the t distribution"], [768, 2, 2, 0, "resids", "The residuals to use in the log-likelihood calculation"], [768, 2, 2, 0, "sigma2", "Conditional variances of resids"]], "arch.univariate.StudentsT.moment": [[769, 2, 2, 0, "n", "Order of moment"], [769, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."]], "arch.univariate.StudentsT.partial_moment": [[772, 2, 2, 0, "n", "Order of partial moment"], [772, 2, 2, 0, "parameters", "Distribution parameters.  Use None for parameterless distributions."], [772, 2, 2, 0, "z", "Upper bound for partial moment integral"]], "arch.univariate.StudentsT.ppf": [[773, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [773, 2, 2, 0, "pits", "Probability-integral-transformed values in the interval (0, 1)."]], "arch.univariate.StudentsT.simulate": [[775, 2, 2, 0, "parameters", "Distribution parameters"]], "arch.univariate.StudentsT.starting_values": [[776, 2, 2, 0, "std_resid", "Estimated standardized residuals to use in computing starting values for the shape parameter"]], "arch.univariate.ZeroMean.__init__": [[445, 2, 2, 0, "distribution", "Error distribution to use in the model"], [445, 2, 2, 0, "hold_back", "Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."], [445, 2, 2, 0, "rescale", "Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."], [445, 2, 2, 0, "volatility", "Volatility process to use in the model"], [445, 2, 2, 0, "y", "nobs element vector containing the dependent variable"]], "arch.univariate.ZeroMean": [[777, 3, 1, 0, "bounds", "Construct bounds for parameters to use in non-linear optimization"], [778, 3, 1, 0, "compute_param_cov", "Computes parameter covariances using numerical derivatives."], [779, 3, 1, 0, "constraints", "Construct linear constraint arrays  for use in non-linear optimization"], [780, 5, 1, 0, "distribution", "Set or gets the error distribution"], [781, 3, 1, 0, "fit", "Estimate model parameters"], [782, 3, 1, 0, "fix", "Allows an ARCHModelFixedResult to be constructed from fixed parameters."], [783, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [784, 5, 1, 0, "name", "The name of the model."], [785, 5, 1, 0, "num_params", "Returns the number of parameters"], [786, 3, 1, 0, "parameter_names", "List of parameters names"], [787, 3, 1, 0, "resids", "Compute model residuals"], [788, 3, 1, 0, "simulate", "Simulated data from a zero mean model"], [789, 3, 1, 0, "starting_values", "Returns starting values for the mean model, often the same as the values returned from fit"], [790, 5, 1, 0, "volatility", "Set or gets the volatility process"], [791, 5, 1, 0, "x", "Gets the value of the exogenous regressors in the model"], [792, 5, 1, 0, "y", "Returns the dependent variable"]], "arch.univariate.ZeroMean.compute_param_cov": [[778, 2, 2, 0, "backcast", "Value to use for pre-sample observations"], [778, 2, 2, 0, "params", "Model parameters"], [778, 2, 2, 0, "robust", "Flag indicating whether to use robust standard errors (True) or classic MLE (False)"]], "arch.univariate.ZeroMean.fit": [[781, 2, 2, 0, "backcast", "Value to use as backcast. Should be measure \\sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."], [781, 2, 2, 0, "cov_type", "Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."], [781, 2, 2, 0, "disp", "Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is \"off\" and True is \"final\""], [781, 2, 2, 0, "first_obs", "First observation to use when estimating model"], [781, 2, 2, 0, "last_obs", "Last observation to use when estimating model"], [781, 2, 2, 0, "options", "Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."], [781, 2, 2, 0, "show_warning", "Flag indicating whether convergence warnings should be shown."], [781, 2, 2, 0, "starting_values", "Array of starting values to use.  If not provided, starting values are constructed by the model components."], [781, 2, 2, 0, "tol", "Tolerance for termination."], [781, 2, 2, 0, "update_freq", "Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."]], "arch.univariate.ZeroMean.fix": [[782, 2, 2, 0, "first_obs", "First observation to use when fixing model"], [782, 2, 2, 0, "last_obs", "Last observation to use when fixing model"], [782, 2, 2, 0, "params", "User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."]], "arch.univariate.ZeroMean.forecast": [[783, 2, 2, 0, "align", "Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."], [783, 2, 2, 0, "horizon", "Number of steps to forecast"], [783, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [783, 2, 2, 0, "params", "Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."], [783, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [783, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."], [783, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [783, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [783, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."], [783, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"]], "arch.univariate.ZeroMean.resids": [[787, 2, 2, 0, "params", "Model parameters"], [787, 2, 2, 0, "regressors", "Alternative regressor values to use when computing model residuals"], [787, 2, 2, 0, "y", "Alternative values to use when computing model residuals"]], "arch.univariate.ZeroMean.simulate": [[788, 2, 2, 0, "burn", "Number of values to simulate to initialize the model and remove dependence on initial values."], [788, 2, 2, 0, "initial_value", "This value is not used."], [788, 2, 2, 0, "initial_value_vol", "An array or scalar to use when initializing the volatility process."], [788, 2, 2, 0, "nobs", "Length of series to simulate"], [788, 2, 2, 0, "params", "Parameters to use when simulating the model.  Parameter order is [volatility distribution]. There are no mean parameters."], [788, 2, 2, 0, "x", "This value is not used."]], "arch.univariate.arch_model": [[891, 2, 2, 0, "dist", "Name of the error distribution.  Currently supported options are:"], [891, 2, 2, 0, "hold_back", "Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."], [891, 2, 2, 0, "lags", "Either a scalar integer value indicating lag length or a list of integers specifying lag locations."], [891, 2, 2, 0, "mean", "Name of the mean model.  Currently supported options are: 'Constant', 'Zero', 'LS', 'AR', 'ARX', 'HAR' and  'HARX'"], [891, 2, 2, 0, "o", "Lag order of the asymmetric innovation"], [891, 2, 2, 0, "p", "Lag order of the symmetric innovation"], [891, 2, 2, 0, "power", "Power to use with GARCH and related models"], [891, 2, 2, 0, "q", "Lag order of lagged volatility or equivalent"], [891, 2, 2, 0, "rescale", "Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."], [891, 2, 2, 0, "vol", "Name of the volatility model.  Currently supported options are: 'GARCH' (default), 'ARCH', 'EGARCH', 'FIGARCH', 'APARCH' and 'HARCH'"], [891, 2, 2, 0, "x", "Exogenous regressors.  Ignored if model does not permit exogenous regressors."], [891, 2, 2, 0, "y", "The dependent variable"]], "arch.univariate.base": [[446, 1, 1, 0, "ARCHModel", "Abstract base class for mean models in ARCH processes.  Specifies the conditional mean process."], [447, 1, 1, 0, "ARCHModelFixedResult", "Results for fixed parameters for an ARCHModel model"], [448, 1, 1, 0, "ARCHModelForecast", "Container for forecasts from an ARCH Model"], [449, 1, 1, 0, "ARCHModelForecastSimulation", "Container for a simulation or bootstrap-based forecasts from an ARCH Model"], [450, 1, 1, 0, "ARCHModelResult", "Results from estimation of an ARCHModel model"]], "arch.univariate.base.ARCHModel": [[793, 3, 1, 0, "bounds", "Construct bounds for parameters to use in non-linear optimization"], [794, 3, 1, 0, "compute_param_cov", "Computes parameter covariances using numerical derivatives."], [795, 3, 1, 0, "constraints", "Construct linear constraint arrays  for use in non-linear optimization"], [796, 5, 1, 0, "distribution", "Set or gets the error distribution"], [797, 3, 1, 0, "fit", "Estimate model parameters"], [798, 3, 1, 0, "fix", "Allows an ARCHModelFixedResult to be constructed from fixed parameters."], [799, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [800, 5, 1, 0, "name", "The name of the model."], [801, 5, 1, 0, "num_params", "Number of parameters in the model"], [802, 3, 1, 0, "parameter_names", "List of parameters names"], [803, 3, 1, 0, "resids", "Compute model residuals"], [804, 3, 1, 0, "simulate", "DataFrame"], [805, 3, 1, 0, "starting_values", "Returns starting values for the mean model, often the same as the values returned from fit"], [806, 5, 1, 0, "volatility", "Set or gets the volatility process"], [807, 5, 1, 0, "y", "Returns the dependent variable"]], "arch.univariate.base.ARCHModel.compute_param_cov": [[794, 2, 2, 0, "backcast", "Value to use for pre-sample observations"], [794, 2, 2, 0, "params", "Model parameters"], [794, 2, 2, 0, "robust", "Flag indicating whether to use robust standard errors (True) or classic MLE (False)"]], "arch.univariate.base.ARCHModel.fit": [[797, 2, 2, 0, "backcast", "Value to use as backcast. Should be measure \\sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."], [797, 2, 2, 0, "cov_type", "Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."], [797, 2, 2, 0, "disp", "Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is \"off\" and True is \"final\""], [797, 2, 2, 0, "first_obs", "First observation to use when estimating model"], [797, 2, 2, 0, "last_obs", "Last observation to use when estimating model"], [797, 2, 2, 0, "options", "Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."], [797, 2, 2, 0, "show_warning", "Flag indicating whether convergence warnings should be shown."], [797, 2, 2, 0, "starting_values", "Array of starting values to use.  If not provided, starting values are constructed by the model components."], [797, 2, 2, 0, "tol", "Tolerance for termination."], [797, 2, 2, 0, "update_freq", "Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."]], "arch.univariate.base.ARCHModel.fix": [[798, 2, 2, 0, "first_obs", "First observation to use when fixing model"], [798, 2, 2, 0, "last_obs", "Last observation to use when fixing model"], [798, 2, 2, 0, "params", "User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."]], "arch.univariate.base.ARCHModel.forecast": [[799, 2, 2, 0, "align", "Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."], [799, 2, 2, 0, "horizon", "Number of steps to forecast"], [799, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [799, 2, 2, 0, "params", "Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."], [799, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [799, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."], [799, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [799, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [799, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."], [799, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"]], "arch.univariate.base.ARCHModel.resids": [[803, 2, 2, 0, "params", "Model parameters"], [803, 2, 2, 0, "regressors", "Alternative regressor values to use when computing model residuals"], [803, 2, 2, 0, "y", "Alternative values to use when computing model residuals"]], "arch.univariate.base.ARCHModelFixedResult.__init__": [[447, 2, 2, 0, "dep_var", "Dependent variable"], [447, 2, 2, 0, "is_pandas", "Whether the original input was pandas"], [447, 2, 2, 0, "loglikelihood", "Loglikelihood at specified parameters"], [447, 2, 2, 0, "model", "The model object used to estimate the parameters"], [447, 2, 2, 0, "names", "Model parameter names"], [447, 2, 2, 0, "params", "Estimated parameters"], [447, 2, 2, 0, "resid", "Residuals from model.  Residuals have same shape as original data and contain nan-values in locations not used in estimation"], [447, 2, 2, 0, "volatility", "Conditional volatility from model"]], "arch.univariate.base.ARCHModelFixedResult": [[808, 5, 1, 0, "aic", "Akaike Information Criteria"], [809, 3, 1, 0, "arch_lm_test", "ARCH LM test for conditional heteroskedasticity"], [810, 5, 1, 0, "bic", "Schwarz/Bayesian Information Criteria"], [811, 5, 1, 0, "conditional_volatility", "Estimated conditional volatility"], [812, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [813, 3, 1, 0, "hedgehog_plot", "Plot forecasts from estimated model"], [814, 5, 1, 0, "loglikelihood", "Model loglikelihood"], [815, 5, 1, 0, "model", "Model instance used to produce the fit"], [816, 5, 1, 0, "nobs", "Number of data points used to estimate model"], [817, 5, 1, 0, "num_params", "Number of parameters in model"], [818, 5, 1, 0, "params", "Model Parameters"], [819, 3, 1, 0, "plot", "Plot standardized residuals and conditional volatility"], [820, 5, 1, 0, "resid", "Model residuals"], [821, 5, 1, 0, "std_resid", "Residuals standardized by conditional volatility"], [822, 3, 1, 0, "summary", "Constructs a summary of the results from a fit model."]], "arch.univariate.base.ARCHModelFixedResult.arch_lm_test": [[809, 2, 2, 0, "lags", "Number of lags to include in the model.  If not specified,"], [809, 2, 2, 0, "standardized", "Flag indicating to test the model residuals divided by their conditional standard deviations.  If False, directly tests the estimated residuals."]], "arch.univariate.base.ARCHModelFixedResult.forecast": [[812, 2, 2, 0, "align", "Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."], [812, 2, 2, 0, "horizon", "Number of steps to forecast"], [812, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [812, 2, 2, 0, "params", "Alternative parameters to use.  If not provided, the parameters estimated when fitting the model are used.  Must be identical in shape to the parameters computed by fitting the model."], [812, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [812, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."], [812, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [812, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [812, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."], [812, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"]], "arch.univariate.base.ARCHModelFixedResult.hedgehog_plot": [[813, 2, 2, 0, "horizon", "Number of steps to forecast"], [813, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [813, 2, 2, 0, "params", "Alternative parameters to use.  If not provided, the parameters computed by fitting the model are used.  Must be 1-d and identical in shape to the parameters computed by fitting the model."], [813, 2, 2, 0, "plot_type", "Quantity to plot, the forecast volatility or the forecast mean"], [813, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [813, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index.  Strings must be convertible to a date time, such as in '1945-01-01'.  If not provided, the start is set to the earliest forecastable date."], [813, 2, 2, 0, "step", "Non-negative number of forecasts to skip between spines"]], "arch.univariate.base.ARCHModelFixedResult.plot": [[819, 2, 2, 0, "annualize", "String containing frequency of data that indicates plot should contain annualized volatility.  Supported values are 'D' (daily), 'W' (weekly) and 'M' (monthly), which scale variance by 252, 52, and 12, respectively."], [819, 2, 2, 0, "scale", "Value to use when scaling returns to annualize.  If scale is provided, annualize is ignored and the value in scale is used."]], "arch.univariate.base.ARCHModelForecast.__init__": [[448, 2, 2, 0, "align", ""], [448, 2, 2, 0, "index", ""], [448, 2, 2, 0, "mean", ""], [448, 2, 2, 0, "residual_variance", ""], [448, 2, 2, 0, "simulated_paths", ""], [448, 2, 2, 0, "simulated_residual_variances", ""], [448, 2, 2, 0, "simulated_residuals", ""], [448, 2, 2, 0, "simulated_variances", ""], [448, 2, 2, 0, "variance", ""]], "arch.univariate.base.ARCHModelForecast": [[823, 5, 1, 0, "mean", "Forecast values for the conditional mean of the process"], [824, 5, 1, 0, "residual_variance", "Forecast values for the conditional variance of the residuals"], [825, 5, 1, 0, "simulations", "Detailed simulation results if using a simulation-based method"], [826, 5, 1, 0, "variance", "Forecast values for the conditional variance of the process"]], "arch.univariate.base.ARCHModelForecastSimulation.__init__": [[449, 2, 2, 0, "index", ""], [449, 2, 2, 0, "residual_variances", ""], [449, 2, 2, 0, "residuals", ""], [449, 2, 2, 0, "values", ""], [449, 2, 2, 0, "variances", ""]], "arch.univariate.base.ARCHModelForecastSimulation": [[827, 5, 1, 0, "index", "The index aligned to dimension 0 of the simulation paths"], [828, 5, 1, 0, "residual_variances", "Simulated variance of the residuals"], [829, 5, 1, 0, "residuals", "Simulated residuals used to produce the values"], [830, 5, 1, 0, "values", "The values of the process"], [831, 5, 1, 0, "variances", "Simulated variances of the values"]], "arch.univariate.base.ARCHModelResult.__init__": [[450, 2, 2, 0, "cov_type", "String describing the covariance estimator used"], [450, 2, 2, 0, "dep_var", "Dependent variable"], [450, 2, 2, 0, "fit_start", "Integer index of the first observation used to fit the model"], [450, 2, 2, 0, "fit_stop", "Integer index of the last observation used to fit the model using slice notation fit_start:fit_stop"], [450, 2, 2, 0, "is_pandas", "Whether the original input was pandas"], [450, 2, 2, 0, "loglikelihood", "Loglikelihood at estimated parameters"], [450, 2, 2, 0, "model", "The model object used to estimate the parameters"], [450, 2, 2, 0, "names", "Model parameter names"], [450, 2, 2, 0, "optim_output", "Result of log-likelihood optimization"], [450, 2, 2, 0, "param_cov", "Estimated variance-covariance matrix of params.  If none, calls method to compute variance from model when parameter covariance is first used from result"], [450, 2, 2, 0, "params", "Estimated parameters"], [450, 2, 2, 0, "r2", "Model R-squared"], [450, 2, 2, 0, "resid", "Residuals from model.  Residuals have same shape as original data and contain nan-values in locations not used in estimation"], [450, 2, 2, 0, "volatility", "Conditional volatility from model"]], "arch.univariate.base.ARCHModelResult": [[832, 5, 1, 0, "aic", "Akaike Information Criteria"], [833, 3, 1, 0, "arch_lm_test", "ARCH LM test for conditional heteroskedasticity"], [834, 5, 1, 0, "bic", "Schwarz/Bayesian Information Criteria"], [835, 5, 1, 0, "conditional_volatility", "Estimated conditional volatility"], [836, 3, 1, 0, "conf_int", "Parameter confidence intervals"], [837, 5, 1, 0, "convergence_flag", "scipy.optimize.minimize result flag"], [838, 5, 1, 0, "fit_start", "Start of sample used to estimate parameters"], [839, 5, 1, 0, "fit_stop", "End of sample used to estimate parameters"], [840, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [841, 3, 1, 0, "hedgehog_plot", "Plot forecasts from estimated model"], [842, 5, 1, 0, "loglikelihood", "Model loglikelihood"], [843, 5, 1, 0, "model", "Model instance used to produce the fit"], [844, 5, 1, 0, "nobs", "Number of data points used to estimate model"], [845, 5, 1, 0, "num_params", "Number of parameters in model"], [846, 5, 1, 0, "optimization_result", "Information about the convergence of the loglikelihood optimization"], [847, 5, 1, 0, "param_cov", "Parameter covariance"], [848, 5, 1, 0, "params", "Model Parameters"], [849, 3, 1, 0, "plot", "Plot standardized residuals and conditional volatility"], [850, 5, 1, 0, "pvalues", "Array of p-values for the t-statistics"], [851, 5, 1, 0, "resid", "Model residuals"], [852, 5, 1, 0, "rsquared", "R-squared"], [853, 5, 1, 0, "rsquared_adj", "Degree of freedom adjusted R-squared"], [854, 5, 1, 0, "scale", "The scale applied to the original data before estimating the model."], [855, 5, 1, 0, "std_err", "Array of parameter standard errors"], [856, 5, 1, 0, "std_resid", "Residuals standardized by conditional volatility"], [857, 3, 1, 0, "summary", "Constructs a summary of the results from a fit model."], [858, 5, 1, 0, "tvalues", "Array of t-statistics testing the null that the coefficient are 0"]], "arch.univariate.base.ARCHModelResult.arch_lm_test": [[833, 2, 2, 0, "lags", "Number of lags to include in the model.  If not specified,"], [833, 2, 2, 0, "standardized", "Flag indicating to test the model residuals divided by their conditional standard deviations.  If False, directly tests the estimated residuals."]], "arch.univariate.base.ARCHModelResult.conf_int": [[836, 2, 2, 0, "alpha", "Size (prob.) to use when constructing the confidence interval."]], "arch.univariate.base.ARCHModelResult.forecast": [[840, 2, 2, 0, "align", "Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."], [840, 2, 2, 0, "horizon", "Number of steps to forecast"], [840, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [840, 2, 2, 0, "params", "Alternative parameters to use.  If not provided, the parameters estimated when fitting the model are used.  Must be identical in shape to the parameters computed by fitting the model."], [840, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [840, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."], [840, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [840, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [840, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."], [840, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"]], "arch.univariate.base.ARCHModelResult.hedgehog_plot": [[841, 2, 2, 0, "horizon", "Number of steps to forecast"], [841, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [841, 2, 2, 0, "params", "Alternative parameters to use.  If not provided, the parameters computed by fitting the model are used.  Must be 1-d and identical in shape to the parameters computed by fitting the model."], [841, 2, 2, 0, "plot_type", "Quantity to plot, the forecast volatility or the forecast mean"], [841, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [841, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index.  Strings must be convertible to a date time, such as in '1945-01-01'.  If not provided, the start is set to the earliest forecastable date."], [841, 2, 2, 0, "step", "Non-negative number of forecasts to skip between spines"]], "arch.univariate.base.ARCHModelResult.plot": [[849, 2, 2, 0, "annualize", "String containing frequency of data that indicates plot should contain annualized volatility.  Supported values are 'D' (daily), 'W' (weekly) and 'M' (monthly), which scale variance by 252, 52, and 12, respectively."], [849, 2, 2, 0, "scale", "Value to use when scaling returns to annualize.  If scale is provided, annualize is ignored and the value in scale is used."]], "arch.univariate.distribution": [[451, 1, 1, 0, "Distribution", "Template for subclassing only"]], "arch.univariate.distribution.Distribution": [[859, 3, 1, 0, "bounds", "Parameter bounds for use in optimization."], [860, 3, 1, 0, "cdf", "Cumulative distribution function"], [861, 3, 1, 0, "constraints", "Construct arrays to use in constrained optimization."], [862, 5, 1, 0, "generator", "The NumPy Generator or RandomState attached to the distribution"], [863, 3, 1, 0, "loglikelihood", "Loglikelihood evaluation."], [864, 3, 1, 0, "moment", "Moment of order n"], [865, 5, 1, 0, "name", "The name of the distribution"], [866, 3, 1, 0, "parameter_names", "Names of distribution shape parameters"], [867, 3, 1, 0, "partial_moment", "Order n lower partial moment from -inf to z"], [868, 3, 1, 0, "ppf", "Inverse cumulative density function (ICDF)"], [869, 5, 1, 0, "random_state", "The NumPy RandomState attached to the distribution"], [870, 3, 1, 0, "simulate", "Simulates i.i.d. draws from the distribution"], [871, 3, 1, 0, "starting_values", "Construct starting values for use in optimization."]], "arch.univariate.distribution.Distribution.bounds": [[859, 2, 2, 0, "resids", "Residuals to use when computing the bounds"]], "arch.univariate.distribution.Distribution.cdf": [[860, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [860, 2, 2, 0, "resids", "Values at which to evaluate the cdf"]], "arch.univariate.distribution.Distribution.loglikelihood": [[863, 2, 2, 0, "individual", "Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"], [863, 2, 2, 0, "parameters", "Distribution shape parameters"], [863, 2, 2, 0, "resids", "nobs array of model residuals"], [863, 2, 2, 0, "sigma2", "nobs array of conditional variances"]], "arch.univariate.distribution.Distribution.moment": [[864, 2, 2, 0, "n", "Order of moment"], [864, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."]], "arch.univariate.distribution.Distribution.partial_moment": [[867, 2, 2, 0, "n", "Order of partial moment"], [867, 2, 2, 0, "parameters", "Distribution parameters.  Use None for parameterless distributions."], [867, 2, 2, 0, "z", "Upper bound for partial moment integral"]], "arch.univariate.distribution.Distribution.ppf": [[868, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [868, 2, 2, 0, "pits", "Probability-integral-transformed values in the interval (0, 1)."]], "arch.univariate.distribution.Distribution.simulate": [[870, 2, 2, 0, "parameters", "Distribution parameters"]], "arch.univariate.distribution.Distribution.starting_values": [[871, 2, 2, 0, "std_resid", "Estimated standardized residuals to use in computing starting values for the shape parameter"]], "arch.univariate.recursions_python": [[452, 1, 1, 0, "VolatilityUpdater", "Base class that all volatility updaters must inherit from."]], "arch.univariate.recursions_python.VolatilityUpdater": [[872, 3, 1, 0, "initialize_update", "Initialize the recursion prior to calling update"], [873, 3, 1, 0, "update", "Update the current variance at location t"]], "arch.univariate.recursions_python.VolatilityUpdater.initialize_update": [[872, 2, 2, 0, "backcast", "The backcast value(s)."], [872, 2, 2, 0, "nobs", "The number of observations in the sample."], [872, 2, 2, 0, "parameters", "The model parameters."]], "arch.univariate.recursions_python.VolatilityUpdater.update": [[873, 2, 2, 0, "parameters", "Model parameters"], [873, 2, 2, 0, "resids", "Residuals to use in the recursion"], [873, 2, 2, 0, "sigma2", "Conditional variances with same shape as resids"], [873, 2, 2, 0, "t", "The index of the value of sigma2 to update. Assumes but does not check that update has been called recursively for 0,1,...,t-1."], [873, 2, 2, 0, "var_bounds", "nobs by 2-element array of upper and lower bounds for conditional variances for each time period"]], "arch.univariate.volatility": [[453, 1, 1, 0, "VolatilityProcess", "Abstract base class for ARCH models.  Allows the conditional mean model to be specified separately from the conditional variance, even though parameters are estimated jointly."]], "arch.univariate.volatility.VolatilityProcess": [[874, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [875, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [876, 3, 1, 0, "bounds", "Returns bounds for parameters"], [877, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [878, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [879, 3, 1, 0, "forecast", "Forecast volatility from the model"], [880, 5, 1, 0, "name", "The name of the volatility process"], [881, 5, 1, 0, "num_params", "The number of parameters in the model"], [882, 3, 1, 0, "parameter_names", "Names of model parameters"], [883, 3, 1, 0, "simulate", "Simulate data from the model"], [884, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [885, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [886, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [887, 3, 1, 0, "update", "Compute the variance for a single observation"], [888, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [889, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [890, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.volatility.VolatilityProcess.backcast": [[874, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.volatility.VolatilityProcess.backcast_transform": [[875, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.volatility.VolatilityProcess.bounds": [[876, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.volatility.VolatilityProcess.compute_variance": [[877, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [877, 2, 2, 0, "parameters", "Model parameters"], [877, 2, 2, 0, "resids", "Vector of mean zero residuals"], [877, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [877, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.volatility.VolatilityProcess.forecast": [[879, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [879, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [879, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [879, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [879, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [879, 2, 2, 0, "resids", "Residuals to use in the recursion"], [879, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [879, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [879, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  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"slow": [4, 239, 240], "inner": [4, 14, 29, 44, 59, 74, 100, 104, 105, 106, 107, 108, 109, 110, 111, 277], "outer": 4, "In": [4, 89, 238, 239, 240, 259, 417, 420, 424, 425, 431, 433, 435, 437, 440, 494, 496, 511, 513, 527, 529, 676, 678, 692, 694, 781, 783, 797, 799, 812, 813, 840, 841, 891, 892, 895, 896, 898, 899], "necessari": [4, 89, 895], "done": 4, "moment": [4, 95, 650, 729, 759, 763, 772, 776, 867], "delta": [4, 269, 271, 274, 398, 420, 425, 457], "detail": [4, 238, 239, 240, 259, 269, 271, 274, 277, 650, 729, 759, 772, 825, 867, 897], "descript": [4, 273, 427, 428, 438], "mathemat": 4, "formula": 4, "beyond": [4, 899], "intent": [4, 421], "document": [4, 236, 424], "sharpe_ratio_s": 4, "e1": 4, "e2": 4, "vstack": 4, "vcv": 4, "dot": [4, 89, 259, 459, 477, 492, 509, 525, 543, 560, 577, 594, 612, 629, 644, 659, 674, 690, 708, 723, 738, 753, 766, 779, 795, 861, 878], "avar": 4, "std_err_func": [4, 14, 29, 44, 59, 74], "match": [4, 10, 91, 97, 496, 513, 529, 678, 694, 783, 799, 812, 840, 895], "patter": 4, "arg": [4, 5, 6, 7, 8, 9, 11, 13, 14, 15, 25, 26, 28, 29, 30, 40, 41, 43, 44, 45, 55, 56, 58, 59, 60, 70, 71, 73, 74, 75, 85], "kwarg": [4, 5, 6, 7, 8, 9, 11, 13, 14, 15, 25, 26, 28, 29, 30, 40, 41, 43, 44, 45, 55, 56, 58, 59, 60, 70, 71, 73, 74, 75, 85], "improv": [4, 95, 96, 258, 263, 421, 452, 898], "jackknif": [4, 95], "term": [4, 265, 269, 271, 274, 276, 277, 279, 288, 289, 292, 301, 302, 305, 312, 313, 316, 325, 326, 329, 339, 340, 344, 351, 352, 368, 420, 424, 425, 440, 898], "offer": [4, 898], "accuraci": 4, "condit": [4, 95, 266, 421, 424, 425, 427, 431, 433, 435, 443, 445, 446, 447, 450, 453, 458, 471, 476, 488, 497, 502, 518, 534, 542, 554, 559, 571, 576, 588, 593, 606, 611, 623, 628, 640, 646, 658, 670, 683, 699, 707, 719, 725, 737, 749, 755, 768, 788, 809, 811, 819, 821, 823, 824, 826, 833, 835, 849, 856, 863, 873, 877, 889, 892, 895, 897, 898], "satisfi": [4, 266, 425, 492, 509, 525, 644, 674, 690, 723, 753, 766, 779, 795, 861], "special": [4, 437, 901], "case": [4, 89, 100, 104, 105, 106, 107, 108, 109, 110, 111, 259, 420, 437, 891], "base": [4, 89, 95, 260, 264, 265, 267, 277, 282, 295, 307, 318, 332, 346, 354, 368, 402, 420, 424, 452, 453, 496, 513, 529, 678, 694, 763, 776, 783, 894, 897, 899], "frac": [4, 10, 100, 104, 105, 106, 107, 108, 109, 110, 111, 120, 130, 144, 154, 164, 174, 184, 194, 204, 214, 224, 234, 271, 277, 424, 433, 437, 646, 725, 755, 768], "z_": [4, 277, 420], "skew": [4, 94, 421, 443, 755, 891, 898], "like": [4, 10, 88, 89, 258, 420, 424, 427, 428, 429, 438, 439, 445, 496, 513, 529, 678, 694, 783, 799, 812, 840, 891, 896, 898, 899], "leav": [4, 95], "out": [4, 94, 95, 424, 892, 897, 899], "block_siz": [5, 8, 9, 238, 239, 240], "arraylik": [5, 6, 7, 8, 9, 13, 28, 43, 58], "random_st": [5, 6, 7, 8, 9, 95, 96, 436, 441, 443, 444, 451, 461, 478, 496, 513, 529, 544, 561, 578, 595, 613, 630, 660, 678, 694, 709, 739, 783, 799, 812, 840, 879, 899], "sourc": [5, 6, 7, 8, 9, 10, 13, 24, 26, 27, 28, 29, 30, 32, 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38, 39, 42, 47, 48, 52, 53, 54, 57, 62, 63, 67, 68, 72, 77, 78, 82, 83, 87, 456, 459, 465, 475, 477, 478, 482, 490, 492, 496, 507, 509, 513, 523, 525, 529, 541, 543, 544, 548, 558, 560, 561, 565, 575, 577, 578, 582, 592, 594, 595, 599, 610, 612, 613, 617, 627, 629, 630, 634, 642, 644, 653, 657, 659, 660, 664, 672, 674, 678, 688, 690, 694, 706, 708, 709, 713, 721, 723, 732, 736, 738, 739, 743, 751, 753, 762, 764, 766, 775, 777, 779, 783, 793, 795, 799, 812, 840, 859, 861, 870, 876, 878, 879, 883], "pos_data": [5, 6, 7, 8, 9], "kw_data": [5, 6, 7, 8, 9], "type": [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 19, 23, 25, 26, 27, 28, 29, 30, 31, 32, 34, 38, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 53, 55, 56, 57, 58, 59, 60, 61, 62, 64, 68, 70, 71, 72, 73, 74, 75, 76, 77, 79, 83, 85, 86, 95, 96, 112, 113, 114, 115, 117, 118, 119, 120, 121, 122, 123, 124, 125, 127, 128, 129, 130, 131, 136, 137, 138, 139, 141, 142, 143, 144, 145, 146, 147, 148, 149, 151, 152, 153, 154, 155, 156, 157, 158, 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643, 646, 647, 649, 650, 651, 653, 654, 655, 656, 657, 660, 663, 666, 668, 670, 671, 672, 676, 677, 678, 681, 682, 683, 684, 688, 692, 693, 694, 697, 698, 699, 700, 704, 705, 706, 709, 712, 715, 717, 719, 720, 721, 722, 725, 726, 728, 729, 730, 732, 733, 734, 735, 736, 739, 742, 745, 747, 749, 750, 751, 752, 755, 756, 758, 759, 760, 762, 763, 764, 765, 768, 769, 771, 772, 773, 775, 776, 777, 781, 782, 783, 786, 787, 788, 789, 793, 797, 798, 799, 802, 803, 805, 809, 811, 812, 813, 819, 822, 825, 833, 835, 836, 840, 841, 846, 849, 857, 859, 860, 864, 866, 867, 868, 870, 871, 874, 875, 876, 879, 882, 885, 887, 889, 890, 891, 897, 899, 900], "enter": [5, 6, 7, 8, 9, 12, 27, 42, 57, 72, 892], "dict": [5, 6, 7, 8, 9, 11, 12, 14, 15, 17, 18, 22, 23, 25, 26, 27, 29, 30, 32, 33, 37, 38, 39, 40, 41, 42, 44, 45, 47, 48, 52, 53, 54, 55, 56, 57, 59, 60, 62, 63, 67, 68, 70, 71, 72, 74, 75, 77, 78, 82, 83, 85, 279, 292, 305, 316, 329, 344, 494, 496, 511, 513, 527, 529, 676, 678, 692, 694, 781, 783, 797, 799, 812, 840, 900], "note": [5, 6, 7, 8, 9, 10, 11, 14, 26, 29, 41, 44, 56, 59, 71, 74, 100, 101, 104, 105, 106, 107, 108, 109, 110, 111, 120, 130, 144, 154, 164, 174, 184, 194, 204, 214, 224, 234, 238, 239, 240, 247, 248, 258, 262, 263, 264, 265, 266, 267, 269, 271, 274, 276, 277, 362, 368, 377, 381, 398, 421, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 437, 438, 439, 440, 442, 443, 445, 452, 459, 461, 477, 478, 492, 494, 495, 496, 509, 511, 512, 513, 525, 527, 528, 529, 543, 544, 560, 561, 577, 578, 594, 595, 612, 613, 629, 630, 644, 646, 650, 654, 659, 660, 674, 676, 677, 678, 690, 692, 693, 694, 708, 709, 723, 725, 729, 733, 738, 739, 753, 755, 759, 763, 766, 768, 772, 776, 779, 781, 782, 783, 795, 797, 798, 799, 812, 840, 861, 863, 867, 871, 872, 873, 878, 879, 891, 895, 897, 899], "support": [5, 6, 7, 8, 9, 91, 95, 96, 97, 277, 288, 301, 312, 325, 339, 351, 366, 385, 417, 424, 461, 470, 478, 487, 494, 496, 511, 513, 527, 529, 544, 553, 561, 570, 578, 587, 595, 605, 613, 622, 630, 639, 660, 669, 676, 678, 692, 694, 709, 718, 739, 748, 781, 783, 797, 799, 812, 813, 819, 840, 841, 849, 879, 888, 891, 892, 895, 899], "date": [5, 6, 7, 8, 9, 91, 97, 421, 424, 496, 513, 529, 678, 694, 783, 799, 812, 813, 840, 841, 891, 895, 896, 897, 898, 899], "accessibli": [5, 6, 7, 8, 9], "attribut": [5, 6, 7, 8, 9, 424, 897, 899], "To": [5, 6, 7, 8, 9, 420, 422], "you": [5, 6, 7, 8, 9, 895, 899], "after": [5, 6, 7, 8, 9, 95, 96, 424, 496, 513, 529, 678, 694, 783, 799, 897], "reserv": [5, 6, 7, 8, 9, 898], "instanc": [5, 7, 8, 9, 13, 16, 19, 28, 31, 34, 43, 46, 49, 58, 61, 64, 73, 76, 79, 87, 89, 95, 96, 115, 125, 139, 149, 159, 169, 179, 189, 199, 209, 219, 229, 272, 273, 354, 366, 385, 395, 402, 411, 436, 441, 443, 444, 461, 478, 496, 513, 529, 544, 561, 578, 595, 613, 630, 660, 678, 694, 709, 739, 783, 799, 812, 815, 822, 840, 843, 857, 879, 898], "optim": [5, 8, 9, 10, 95, 100, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 117, 120, 121, 122, 123, 127, 130, 131, 136, 137, 141, 144, 145, 146, 147, 151, 154, 155, 156, 157, 161, 164, 165, 166, 167, 171, 174, 175, 176, 177, 181, 184, 185, 186, 187, 191, 194, 195, 196, 197, 201, 204, 205, 206, 207, 211, 214, 215, 216, 217, 221, 224, 225, 226, 227, 231, 234, 235, 268, 271, 277, 354, 368, 402, 450, 490, 492, 494, 507, 509, 511, 523, 525, 527, 642, 644, 654, 672, 674, 676, 688, 690, 692, 721, 723, 733, 751, 753, 763, 764, 766, 776, 777, 779, 781, 793, 795, 797, 837, 846, 859, 861, 871, 891, 896, 897, 898, 899], "exp": [5, 8, 10], "access": [5, 6, 7, 8, 9, 248, 259, 421, 898], "wai": [5, 6, 7, 8, 9, 898], "retain": [5, 6, 7, 8, 9], "wa": [5, 6, 7, 8, 9, 91, 94, 95, 96, 97, 421, 447, 450, 496, 513, 529, 678, 694, 783, 799, 895], "avail": [5, 6, 7, 8, 9, 259, 262, 265, 421, 424, 452, 461, 478, 496, 513, 529, 544, 561, 578, 595, 613, 630, 660, 678, 694, 709, 739, 783, 799, 812, 840, 879, 897, 898, 899], "syntax": [5, 6, 7, 8, 9, 496, 513, 529, 678, 694, 783, 799, 812, 840], "z": [5, 6, 7, 8, 9, 87, 96, 100, 104, 105, 106, 107, 108, 109, 110, 111, 263, 277, 368, 421, 650, 729, 759, 772, 867], "bs_x": [5, 6, 7, 8, 9], "bs_y": [5, 6, 7, 8, 9], "bs_z": [5, 6, 7, 8, 9], "1234": [5, 6, 7, 8, 9, 438], "properti": [5, 6, 7, 8, 9, 16, 18, 19, 23, 31, 33, 34, 38, 46, 48, 49, 53, 61, 63, 64, 68, 76, 78, 79, 83, 91, 96, 97, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 238, 239, 240, 242, 243, 244, 250, 257, 262, 263, 264, 265, 266, 267, 270, 272, 273, 275, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 351, 352, 353, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 367, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 386, 387, 388, 389, 390, 391, 392, 393, 394, 396, 397, 398, 399, 401, 403, 404, 405, 406, 407, 408, 409, 410, 412, 413, 414, 416, 420, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 453, 457, 460, 462, 463, 466, 468, 470, 472, 479, 480, 483, 485, 487, 489, 493, 496, 497, 498, 499, 504, 505, 506, 510, 513, 514, 515, 520, 521, 522, 526, 529, 530, 531, 536, 537, 538, 545, 546, 549, 551, 553, 555, 562, 563, 566, 568, 570, 572, 579, 580, 583, 585, 587, 589, 596, 597, 600, 602, 603, 605, 607, 614, 615, 618, 620, 622, 624, 631, 632, 635, 637, 639, 641, 645, 648, 652, 661, 662, 665, 667, 669, 671, 675, 678, 679, 680, 685, 686, 687, 691, 694, 695, 696, 701, 702, 703, 710, 711, 714, 716, 718, 720, 724, 727, 731, 740, 741, 744, 746, 748, 750, 754, 757, 761, 767, 770, 774, 780, 783, 784, 785, 790, 791, 792, 796, 799, 800, 801, 806, 807, 808, 810, 811, 812, 814, 815, 816, 817, 818, 820, 821, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 834, 835, 837, 838, 839, 840, 842, 843, 844, 845, 846, 847, 848, 850, 851, 852, 853, 854, 855, 856, 858, 862, 865, 869, 880, 881, 884, 886, 888, 890, 892, 893, 898, 900], "uniform": 6, "integ": [6, 94, 96, 100, 104, 105, 106, 107, 108, 109, 110, 111, 116, 126, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 238, 239, 240, 246, 252, 256, 259, 277, 354, 368, 402, 421, 424, 450, 465, 482, 496, 513, 529, 548, 565, 582, 599, 617, 634, 664, 678, 694, 713, 743, 783, 799, 812, 813, 840, 841, 883, 891], "equival": [6, 259, 368, 427, 438, 496, 513, 529, 678, 694, 783, 799, 812, 840, 891, 899], "structur": [7, 89, 267, 417, 420, 421, 424, 427, 428, 438, 895], "unequ": 7, "cross": [7, 273, 276, 277, 368, 420], "section": [7, 273, 276, 277, 368, 420], "relat": [7, 95, 96, 258, 433, 435, 891, 898], "inappropri": 7, "without": [8, 14, 29, 44, 59, 74, 89, 427, 428, 429, 438, 439, 445, 891], "circularblockbootstrap": [8, 9, 10, 58, 73, 90], "expon": 9, "arraylike1d": [10, 461], "arraylike2d": 10, "dimension": [10, 15, 25, 30, 40, 45, 55, 60, 70, 75, 85, 89, 895, 896], "oper": [10, 433], "b_sb": 10, "b_cb": 10, "algorithm": [10, 262, 267, 901], "its": [10, 20, 35, 50, 65, 80, 251, 258, 259, 368, 420, 421, 898], "correct": [10, 14, 29, 44, 59, 74, 259, 495, 512, 528, 677, 693, 782, 798], "tune": 10, "k_n": 10, "consecut": 10, "insid": [10, 895], "conserv": 10, "band": 10, "pm": 10, "log_": 10, "maximum": [10, 262, 263, 267, 271, 276, 281, 294, 392, 424, 440, 891, 895, 896, 898, 899], "lceil": 10, "rceil": 10, "_i": 10, "2g": 10, "d_": [10, 269, 274], "sum_": [10, 269, 271, 274, 277, 398, 424, 425, 426, 427, 428, 431, 433, 435, 437, 438, 440], "k": [10, 238, 239, 240, 259, 264, 265, 267, 271, 354, 368, 402, 425, 427, 428, 431, 435, 438, 439, 502, 518, 683, 699, 899], "h": [10, 95, 100, 104, 105, 106, 107, 108, 109, 110, 111, 237, 239, 262, 263, 264, 269, 271, 274, 424, 496, 513, 529, 678, 694, 783, 799, 812, 840, 895, 897, 899], "gamma_": [10, 425, 431, 435], "c_": 10, "x_k": 10, "bar": [10, 421, 438], "x_": [10, 95, 271, 895], "remain": [10, 895], "constant": [10, 95, 117, 120, 127, 130, 141, 144, 151, 154, 161, 164, 171, 174, 181, 184, 191, 194, 201, 204, 211, 214, 221, 224, 231, 234, 262, 263, 264, 265, 267, 269, 271, 274, 276, 277, 417, 420, 421, 424, 427, 428, 429, 430, 438, 439, 496, 502, 513, 518, 529, 534, 678, 683, 694, 699, 755, 783, 788, 799, 813, 841, 891, 896, 899], "c_i": 10, "taken": 10, "andrew": [10, 95, 108, 264, 266, 267, 268, 419], "patton": 10, "matlab": 10, "program": 10, "do": [10, 12, 27, 42, 57, 72, 269, 274, 424, 438, 496, 513, 529, 678, 694, 783, 799, 812, 813, 840, 841, 872, 897, 900], "autocovari": [10, 277], "rather": [10, 96, 258, 259, 263, 267, 424, 897, 898], "dimitri": 10, "halbert": 10, "white": [10, 237, 239, 258, 277, 354, 368, 402, 421, 896, 898], "2004": [10, 264, 267], "automat": [10, 91, 95, 96, 97, 262, 263, 264, 265, 267, 268, 276, 281, 294, 417, 419, 421, 427, 428, 429, 438, 439, 445, 891], "select": [10, 95, 96, 121, 131, 145, 155, 165, 175, 185, 195, 205, 215, 225, 235, 240, 262, 263, 264, 267, 268, 271, 273, 276, 281, 294, 392, 417, 419, 421, 466, 468, 483, 485, 549, 551, 566, 568, 583, 585, 600, 602, 618, 620, 635, 637, 665, 667, 714, 716, 744, 746, 884, 886], "review": [10, 264, 443], "23": [10, 237, 239, 421, 896], "53": [10, 420, 896], "70": [10, 259], "doi": [10, 236], "1081": 10, "etc": [10, 424, 898], "120028836": 10, "2009": [10, 420, 424, 897], "28": [10, 421, 424, 891, 895, 896, 897, 898], "372": 10, "375": 10, "1080": 10, "07474930802459016": 10, "func": [11, 14, 15, 25, 26, 29, 30, 40, 41, 44, 45, 55, 56, 59, 60, 70, 71, 74, 75, 85, 891, 896, 897, 898], "callabl": [11, 14, 15, 25, 26, 29, 30, 40, 41, 44, 45, 55, 56, 59, 60, 70, 71, 74, 75, 85, 95, 258, 452, 461, 465, 478, 482, 496, 513, 529, 544, 548, 561, 565, 578, 582, 595, 599, 613, 617, 630, 634, 653, 660, 664, 678, 694, 709, 713, 732, 739, 743, 762, 775, 783, 799, 812, 840, 870, 879, 883], "ndarrai": [11, 12, 14, 15, 17, 18, 21, 22, 23, 24, 25, 26, 27, 29, 30, 32, 33, 36, 37, 38, 39, 40, 41, 42, 44, 45, 47, 48, 51, 52, 53, 54, 55, 56, 57, 59, 60, 62, 63, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 77, 78, 81, 82, 83, 84, 85, 95, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 118, 128, 134, 135, 142, 152, 162, 172, 182, 192, 202, 212, 222, 232, 238, 239, 240, 246, 247, 252, 253, 256, 262, 263, 264, 265, 266, 267, 268, 269, 271, 274, 276, 277, 290, 303, 314, 327, 342, 353, 427, 428, 429, 434, 438, 439, 445, 446, 447, 450, 454, 455, 456, 458, 459, 465, 467, 469, 471, 473, 474, 475, 476, 477, 478, 482, 484, 486, 488, 491, 492, 494, 495, 496, 501, 502, 503, 505, 506, 508, 509, 511, 512, 513, 517, 518, 519, 521, 522, 524, 525, 527, 528, 529, 533, 534, 535, 537, 538, 539, 540, 541, 542, 543, 544, 548, 550, 552, 554, 556, 557, 558, 559, 560, 561, 565, 567, 569, 571, 573, 574, 575, 576, 577, 578, 582, 584, 586, 588, 590, 591, 592, 593, 594, 595, 599, 601, 604, 606, 608, 609, 610, 611, 612, 613, 617, 619, 621, 623, 625, 626, 627, 628, 629, 630, 634, 636, 638, 640, 642, 643, 644, 646, 647, 650, 651, 653, 654, 655, 656, 657, 658, 659, 660, 664, 666, 668, 670, 673, 674, 676, 677, 678, 682, 683, 684, 686, 687, 689, 690, 692, 693, 694, 698, 699, 700, 702, 703, 704, 705, 706, 707, 708, 709, 713, 715, 717, 719, 721, 722, 723, 725, 726, 729, 730, 732, 733, 734, 735, 736, 737, 738, 739, 743, 745, 747, 749, 751, 752, 753, 755, 756, 759, 760, 762, 763, 764, 765, 766, 768, 769, 772, 773, 775, 776, 778, 779, 781, 782, 783, 787, 788, 789, 791, 792, 794, 795, 797, 798, 799, 803, 804, 805, 807, 811, 812, 813, 820, 821, 828, 829, 830, 831, 835, 840, 841, 851, 856, 859, 860, 861, 863, 864, 867, 868, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 883, 885, 887, 889, 891, 895], "rep": [11, 12, 14, 15, 25, 26, 27, 29, 30, 40, 41, 42, 44, 45, 55, 56, 57, 59, 60, 70, 71, 72, 74, 75, 85, 238, 239, 240, 259], "str": [11, 12, 14, 15, 17, 18, 22, 23, 25, 26, 27, 29, 30, 32, 33, 37, 38, 39, 40, 41, 42, 44, 45, 47, 48, 52, 53, 54, 55, 56, 57, 59, 60, 62, 63, 67, 68, 70, 71, 72, 74, 75, 77, 78, 82, 83, 85, 101, 119, 129, 143, 153, 163, 173, 183, 193, 203, 213, 223, 233, 259, 270, 272, 273, 275, 277, 278, 279, 283, 288, 289, 291, 292, 296, 302, 304, 305, 308, 312, 313, 315, 316, 319, 324, 325, 326, 328, 329, 333, 339, 340, 343, 344, 347, 351, 352, 354, 357, 368, 371, 374, 387, 393, 394, 395, 401, 402, 403, 408, 409, 410, 411, 416, 447, 450, 462, 464, 479, 481, 494, 495, 496, 498, 500, 511, 512, 513, 514, 516, 527, 528, 529, 530, 532, 545, 547, 562, 564, 579, 581, 596, 598, 614, 616, 631, 633, 648, 649, 661, 663, 676, 677, 678, 679, 681, 692, 693, 694, 695, 697, 710, 712, 727, 728, 740, 742, 757, 758, 770, 771, 781, 782, 783, 784, 786, 797, 798, 799, 800, 802, 812, 813, 819, 840, 841, 849, 865, 866, 880, 882, 900], "extra": [11, 14, 15, 25, 26, 29, 30, 40, 41, 44, 45, 55, 56, 59, 60, 70, 71, 74, 75, 85, 239, 240], "conflict": [11, 26, 41, 56, 71], "nparam": [11, 26, 41, 56, 71], "row": [11, 14, 26, 29, 41, 44, 56, 59, 71, 74, 424, 496, 513, 529, 678, 694, 783, 799, 812, 836, 840, 895], "correspond": [11, 14, 26, 29, 41, 44, 56, 59, 71, 74, 239, 250, 259, 471, 488, 494, 496, 511, 513, 527, 529, 554, 571, 588, 606, 623, 640, 670, 676, 678, 692, 694, 719, 749, 781, 783, 797, 799, 812, 840, 889, 897, 898], "iter": [11, 12, 24, 26, 27, 39, 41, 42, 54, 56, 57, 69, 71, 72, 84, 92, 452, 494, 511, 527, 676, 692, 781, 797, 891, 896, 897, 898], "up": [11, 14, 26, 29, 41, 44, 56, 59, 71, 74, 267, 421, 424, 437, 496, 513, 529, 678, 694, 783, 799, 812, 840, 895, 897, 899], "append": [11, 14, 26, 29, 41, 44, 56, 59, 71, 74, 259, 895, 897], "befor": [11, 14, 26, 29, 41, 44, 56, 59, 71, 74, 100, 104, 105, 106, 107, 108, 109, 110, 111, 248, 263, 421, 494, 511, 527, 676, 692, 781, 797, 854, 899], "randn": [11, 12, 14, 15, 25, 26, 27, 29, 30, 40, 41, 42, 44, 45, 55, 56, 57, 59, 60, 70, 71, 72, 74, 75, 85, 87, 89, 259, 428, 429, 438, 439, 445, 502, 518, 683, 699], "over": [12, 27, 42, 57, 72, 94, 899], "calcul": [12, 27, 42, 57, 72, 95, 113, 117, 118, 123, 127, 128, 137, 141, 142, 147, 151, 152, 157, 161, 162, 167, 171, 172, 177, 181, 182, 187, 191, 192, 197, 201, 202, 207, 211, 212, 217, 221, 222, 227, 231, 232, 264, 267, 646, 647, 725, 726, 755, 756, 768, 769, 864], "kei": [12, 27, 42, 57, 72, 389, 406, 417, 496, 513, 529, 678, 694, 783, 799, 812, 840, 895], "problem": [12, 27, 42, 57, 72, 89, 96, 421], "doe": [12, 27, 42, 57, 72, 90, 259, 277, 362, 381, 421, 471, 488, 494, 511, 527, 554, 571, 588, 606, 623, 640, 670, 676, 692, 719, 749, 781, 797, 873, 889, 891, 897], "output": [12, 27, 42, 57, 72, 88, 259, 494, 511, 527, 653, 676, 692, 732, 762, 775, 781, 797, 870, 891, 897, 898], "arang": [12, 27, 42, 57, 72, 89, 895, 897, 899], "posdata": [12, 27, 42, 57, 72], "kwdata": [12, 27, 42, 57, 72], "someth": [12, 27, 42, 57, 72], "prng": [13, 16, 28, 31, 43, 46, 58, 61, 73, 76], "norm": [14, 29, 44, 59, 74], "bc": [14, 29, 44, 59, 74, 755], "bca": [14, 29, 44, 59, 74, 95], "float": [14, 15, 17, 22, 23, 25, 29, 30, 32, 37, 38, 40, 44, 45, 47, 52, 53, 55, 59, 60, 62, 67, 68, 70, 74, 75, 77, 82, 83, 85, 99, 100, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 117, 120, 121, 122, 123, 127, 130, 131, 136, 137, 141, 144, 145, 146, 147, 151, 154, 155, 156, 157, 161, 164, 165, 166, 167, 171, 174, 175, 176, 177, 181, 184, 185, 186, 187, 191, 194, 195, 196, 197, 201, 204, 205, 206, 207, 211, 214, 215, 216, 217, 221, 224, 225, 226, 227, 231, 234, 235, 238, 240, 247, 249, 267, 268, 270, 273, 275, 279, 284, 286, 292, 297, 299, 305, 309, 310, 316, 320, 322, 329, 335, 337, 341, 344, 348, 349, 354, 355, 358, 362, 363, 364, 369, 377, 381, 382, 383, 396, 398, 399, 402, 404, 412, 414, 425, 427, 432, 433, 435, 442, 447, 450, 454, 455, 456, 458, 460, 461, 465, 469, 471, 473, 474, 475, 476, 478, 482, 486, 488, 490, 491, 494, 495, 497, 502, 507, 508, 511, 512, 518, 523, 524, 527, 528, 534, 539, 540, 541, 542, 544, 548, 552, 554, 556, 557, 558, 559, 561, 565, 569, 571, 573, 574, 575, 576, 578, 582, 586, 588, 590, 591, 592, 593, 595, 599, 604, 606, 608, 609, 610, 611, 613, 617, 621, 623, 625, 626, 627, 628, 630, 634, 638, 640, 642, 643, 646, 647, 650, 651, 653, 655, 656, 657, 658, 660, 664, 668, 670, 672, 673, 676, 677, 683, 688, 689, 692, 693, 699, 704, 705, 706, 707, 709, 713, 717, 719, 721, 722, 725, 726, 729, 730, 732, 734, 735, 736, 737, 739, 743, 747, 749, 751, 752, 755, 756, 759, 760, 762, 764, 765, 768, 769, 772, 773, 775, 777, 778, 781, 782, 788, 793, 794, 797, 798, 804, 808, 810, 814, 819, 832, 834, 836, 842, 849, 852, 853, 854, 859, 860, 863, 864, 867, 868, 870, 872, 874, 875, 876, 877, 879, 883, 887, 889, 891, 895, 900], "tail": [14, 29, 44, 59, 74, 424, 443, 654, 895, 898], "bool": [14, 15, 20, 25, 29, 30, 35, 40, 44, 45, 50, 55, 59, 60, 65, 70, 74, 75, 80, 85, 99, 100, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 114, 116, 124, 126, 138, 140, 148, 150, 158, 160, 168, 170, 178, 180, 188, 190, 198, 200, 208, 210, 218, 220, 228, 230, 239, 240, 262, 263, 266, 270, 271, 272, 277, 330, 334, 336, 354, 368, 385, 402, 425, 427, 428, 429, 434, 438, 439, 440, 445, 446, 447, 448, 450, 457, 470, 487, 491, 494, 496, 508, 511, 513, 524, 527, 529, 553, 570, 587, 605, 622, 639, 646, 669, 673, 676, 678, 689, 692, 694, 718, 725, 748, 755, 768, 778, 781, 783, 794, 797, 799, 809, 812, 833, 840, 863, 888, 891], "fals": [14, 15, 20, 25, 29, 30, 35, 40, 44, 45, 50, 55, 59, 60, 65, 70, 74, 75, 80, 85, 88, 91, 95, 97, 99, 100, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 239, 240, 266, 271, 277, 354, 368, 385, 402, 421, 425, 427, 428, 429, 434, 438, 439, 440, 445, 448, 491, 494, 508, 511, 513, 524, 527, 529, 646, 673, 676, 678, 689, 692, 694, 725, 755, 768, 778, 781, 783, 794, 797, 799, 809, 812, 833, 840, 863, 891, 895, 897, 899], "nonparametr": [14, 29, 44, 59, 74, 89], "semi": [14, 29, 44, 59, 74, 89], "studentize_rep": [14, 29, 44, 59, 74], "One": [14, 29, 44, 59, 74, 421, 424], "debias": [14, 29, 44, 59, 74, 266, 277], "bia": [14, 29, 44, 59, 74, 274, 420], "coverag": [14, 29, 44, 59, 74], "flag": [14, 20, 29, 35, 44, 50, 59, 65, 74, 80, 100, 104, 105, 106, 107, 108, 109, 110, 111, 114, 116, 124, 126, 138, 140, 148, 150, 158, 160, 168, 170, 178, 180, 188, 190, 198, 200, 208, 210, 218, 220, 228, 230, 239, 240, 262, 271, 385, 427, 428, 429, 434, 438, 439, 440, 445, 470, 487, 491, 494, 508, 511, 524, 527, 553, 570, 587, 605, 622, 639, 646, 669, 673, 676, 689, 692, 718, 725, 748, 755, 768, 778, 781, 794, 797, 809, 833, 837, 863, 888, 891], "indic": [14, 20, 24, 29, 35, 39, 44, 48, 50, 54, 59, 65, 69, 74, 80, 84, 100, 104, 105, 106, 107, 108, 109, 110, 111, 114, 116, 124, 126, 138, 140, 148, 150, 158, 160, 168, 170, 178, 180, 188, 190, 198, 200, 208, 210, 218, 220, 228, 230, 239, 240, 242, 243, 247, 253, 257, 259, 262, 266, 271, 330, 334, 336, 385, 420, 421, 424, 427, 428, 429, 438, 439, 440, 445, 470, 487, 491, 494, 496, 502, 508, 511, 513, 518, 524, 527, 529, 553, 570, 587, 605, 622, 639, 646, 669, 673, 676, 678, 683, 689, 692, 694, 699, 718, 725, 748, 755, 768, 778, 781, 783, 794, 797, 799, 809, 812, 813, 819, 833, 840, 841, 849, 863, 888, 891, 898], "whether": [14, 15, 20, 25, 29, 30, 35, 40, 44, 45, 50, 55, 59, 60, 65, 70, 74, 75, 80, 85, 100, 104, 105, 106, 107, 108, 109, 110, 111, 114, 116, 124, 126, 138, 140, 148, 150, 158, 160, 168, 170, 178, 180, 188, 190, 198, 200, 208, 210, 218, 220, 228, 230, 258, 259, 262, 265, 266, 272, 277, 354, 368, 385, 402, 417, 419, 421, 427, 428, 429, 434, 438, 439, 440, 445, 447, 450, 491, 494, 496, 508, 511, 513, 524, 527, 529, 646, 673, 676, 678, 689, 692, 694, 725, 755, 768, 778, 781, 783, 794, 797, 799, 812, 840, 863, 891, 898], "previous": [14, 29, 44, 59, 74, 94, 896, 898, 899], "rerun": [14, 29, 44, 59, 74, 420], "ignor": [14, 29, 44, 59, 74, 421, 819, 849, 891, 895], "chang": [14, 29, 44, 59, 74, 93, 94, 95, 97, 259, 277, 420, 421, 452, 496, 513, 529, 678, 694, 783, 799, 812, 840, 872, 896, 897, 898, 899], "about": [14, 29, 44, 59, 74, 496, 513, 529, 678, 694, 783, 799, 846, 897, 898], "elimin": [14, 29, 44, 59, 74, 238], "need": [14, 29, 44, 59, 74, 89, 277, 420, 895, 897, 899], "bound": [14, 29, 44, 59, 74, 239, 250, 458, 461, 469, 471, 476, 478, 486, 488, 492, 509, 525, 542, 544, 552, 554, 559, 561, 569, 571, 576, 578, 586, 588, 593, 595, 604, 606, 611, 613, 621, 623, 628, 630, 638, 640, 650, 658, 660, 668, 670, 674, 690, 707, 709, 717, 719, 729, 737, 739, 747, 749, 759, 772, 779, 795, 867, 873, 877, 879, 887, 889], "inf": [14, 29, 44, 59, 74, 650, 729, 759, 772, 867], "similarli": [14, 29, 44, 59, 74], "between": [14, 29, 44, 59, 74, 95, 258, 259, 417, 421, 427, 428, 438, 763, 776, 813, 841, 898, 899], "direct": [14, 29, 44, 59, 74, 440, 898], "normal": [14, 29, 44, 59, 74, 89, 96, 259, 424, 440, 654, 891, 895, 896, 897, 898, 899], "approxim": [14, 29, 44, 59, 74, 262, 265, 267, 271, 454, 455, 456, 467, 471, 473, 474, 475, 484, 488, 539, 540, 541, 550, 554, 556, 557, 558, 567, 571, 573, 574, 575, 584, 588, 590, 591, 592, 601, 603, 606, 608, 609, 610, 619, 623, 625, 626, 627, 636, 640, 655, 656, 657, 666, 670, 704, 705, 706, 715, 719, 734, 735, 736, 745, 749, 874, 875, 876, 885, 889], "scale": [14, 29, 44, 59, 74, 95, 259, 427, 428, 429, 434, 438, 439, 442, 445, 455, 474, 540, 557, 574, 591, 609, 626, 656, 705, 735, 819, 849, 875, 891, 896, 898, 899], "acceler": [14, 29, 44, 59, 74], "ad": [14, 29, 44, 59, 74, 91, 92, 93, 94, 95, 96, 97, 898, 901], "recent": [15, 25, 30, 40, 45, 55, 60, 70, 75, 85, 88, 236, 239, 277], "interest": [15, 25, 30, 40, 45, 55, 60, 70, 75, 85, 898], "center": [15, 25, 30, 40, 45, 55, 60, 70, 75, 85, 88, 95, 99, 100, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 239, 259, 265, 898], "signatur": [15, 25, 30, 40, 45, 55, 60, 70, 75, 85], "axi": [15, 25, 30, 40, 45, 55, 60, 70, 75, 85, 496, 513, 529, 678, 694, 783, 799, 812, 840, 898, 899], "addit": [15, 25, 30, 40, 45, 55, 60, 70, 75, 85, 236, 265, 421, 465, 482, 548, 565, 582, 599, 617, 634, 664, 713, 743, 883, 897, 898], "elif": [15, 25, 30, 40, 45, 55, 60, 70, 75, 85, 897], "get": [16, 17, 19, 23, 31, 32, 34, 38, 46, 47, 49, 53, 61, 62, 64, 68, 76, 77, 79, 83, 280, 281, 288, 293, 294, 301, 306, 312, 317, 324, 325, 330, 331, 334, 336, 339, 345, 351, 393, 409, 417, 472, 489, 493, 504, 505, 510, 520, 521, 526, 536, 537, 555, 572, 589, 607, 624, 641, 671, 675, 685, 686, 691, 701, 702, 720, 750, 780, 790, 791, 796, 806, 890], "option": [16, 31, 46, 61, 76, 89, 95, 238, 239, 240, 259, 262, 277, 421, 428, 438, 446, 494, 496, 511, 513, 527, 529, 676, 678, 692, 694, 781, 783, 797, 799, 891, 892, 898, 901], "map": [17, 23, 32, 38, 47, 53, 62, 68, 77, 83], "current": [18, 33, 48, 63, 78, 95, 265, 873, 891, 896, 897, 898], "use_se": [20, 35, 50, 65, 80], "last": [20, 35, 50, 65, 80, 266, 269, 274, 424, 450, 494, 495, 511, 512, 527, 528, 676, 677, 692, 693, 781, 782, 797, 798, 895], "rese": [21, 36, 51, 66, 81], "otherwis": [23, 38, 53, 68, 83, 854], "updat": [24, 39, 54, 69, 84, 91, 94, 95, 97, 262, 265, 427, 452, 472, 489, 494, 511, 527, 555, 572, 589, 607, 624, 641, 671, 676, 692, 720, 750, 781, 797, 872, 890, 892], "overridden": [24, 39, 54, 69, 84, 421, 446], "new": [24, 39, 54, 69, 84, 95, 262, 427, 428, 429, 438, 439, 445, 496, 513, 529, 678, 694, 783, 799, 812, 840, 891], "least": [86, 266, 269, 274, 368, 421, 439], "total": 86, "aris": 86, "experiment": 86, "divid": [87, 100, 104, 105, 106, 107, 108, 109, 110, 111, 809, 833, 891, 898], "sharp_ratio": 87, "lowest": 87, "demonstr": [87, 496, 513, 529, 678, 694, 783, 799, 812, 840, 891], "rand": 87, "po": 87, "kw": 87, "xstar": 87, "alwai": [87, 247, 259, 421, 424, 899], "even": [87, 247, 420, 453, 898], "singleton": 87, "ystar": [87, 89], "zstar": 87, "challeng": 88, "simpli": [88, 94, 896], "tradit": 88, "financ": [88, 898], "choos": 88, "captur": [88, 452, 898, 901], "param_cov": [88, 450], "148534": 88, "508540": 88, "561637": 88, "729435": 88, "442891": 88, "273945": 88, "495087": 88, "049454": 88, "020830": 88, "those": [89, 258, 897, 899], "At": 89, "minimum": [89, 91, 96, 97, 262, 263, 267, 277], "thei": [89, 258, 259, 263, 424, 438, 897, 899, 901], "perform": [89, 91, 95, 97, 258, 259, 267, 273, 452], "For": [89, 236, 496, 513, 529, 678, 694, 783, 799, 812, 840, 895, 898, 899], "simplic": 89, "consid": [89, 258, 271, 276, 895], "ol": [89, 96, 259, 263, 271, 272, 273, 274, 277, 285, 298, 321, 354, 368, 402, 419, 421], "regress": [89, 95, 259, 262, 263, 265, 267, 269, 271, 272, 273, 274, 276, 277, 280, 282, 293, 295, 306, 307, 317, 318, 331, 332, 345, 346, 354, 362, 368, 377, 381, 391, 397, 398, 401, 402, 413, 416, 419, 420, 502, 518, 683, 699], "regressand": 89, "x_orig": 89, "linalg": 89, "pinv": 89, "ravel": 89, "resid": [89, 270, 272, 421, 447, 450, 454, 456, 458, 461, 465, 467, 469, 471, 473, 475, 476, 478, 482, 484, 486, 488, 539, 541, 542, 544, 548, 550, 552, 554, 556, 558, 559, 561, 565, 567, 569, 571, 573, 575, 576, 578, 582, 584, 586, 588, 590, 592, 593, 595, 599, 601, 604, 606, 608, 610, 611, 613, 617, 619, 621, 623, 625, 627, 628, 630, 634, 636, 638, 640, 642, 643, 646, 655, 657, 658, 660, 664, 666, 668, 670, 704, 706, 707, 709, 713, 715, 717, 719, 721, 722, 725, 734, 736, 737, 739, 743, 745, 747, 749, 751, 752, 755, 764, 765, 768, 859, 860, 863, 873, 874, 876, 877, 879, 883, 885, 887, 889, 895, 896, 898, 899], "y_star": 89, "1d": [89, 899], "functool": 89, "wrapper": [89, 258], "fix": [89, 90, 91, 94, 95, 96, 97, 421, 424, 425, 432, 434, 442, 447, 893, 894, 901], "ols_parti": 89, "effect": [89, 899], "conduct": 89, "account": [89, 258, 265, 282, 295, 307, 318, 332, 346, 354, 362, 368, 381, 402, 895], "ols_semi_v2": 89, "ultim": 89, "meaningfulli": 89, "simpler": 89, "rel": [89, 442], "expens": [89, 872], "rare": 89, "encount": 89, "actual": [89, 259, 424, 897], "cousin": 89, "fulli": [89, 274, 419], "model": [89, 91, 93, 94, 95, 97, 236, 237, 238, 239, 240, 241, 242, 243, 244, 247, 253, 254, 257, 260, 267, 271, 272, 273, 274, 276, 280, 285, 293, 298, 306, 317, 331, 345, 354, 361, 362, 363, 366, 368, 372, 375, 376, 377, 380, 381, 382, 385, 402, 417, 420, 421, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 452, 453, 455, 457, 458, 460, 461, 463, 464, 465, 467, 469, 471, 474, 476, 478, 480, 481, 482, 484, 486, 488, 491, 494, 495, 496, 498, 500, 501, 502, 503, 505, 508, 511, 512, 513, 514, 516, 517, 518, 519, 521, 524, 527, 528, 529, 530, 532, 533, 534, 535, 537, 540, 542, 544, 546, 547, 548, 550, 552, 554, 557, 559, 561, 563, 564, 565, 567, 569, 571, 574, 576, 578, 580, 581, 582, 584, 586, 588, 591, 593, 595, 597, 598, 599, 601, 604, 606, 609, 611, 613, 615, 616, 617, 619, 621, 623, 626, 628, 630, 632, 633, 634, 636, 638, 640, 656, 658, 660, 662, 663, 664, 666, 668, 670, 673, 676, 677, 678, 679, 681, 682, 683, 684, 686, 689, 692, 693, 694, 695, 697, 698, 699, 700, 702, 705, 707, 709, 711, 712, 713, 715, 717, 719, 735, 737, 739, 741, 742, 743, 745, 747, 749, 778, 781, 782, 783, 784, 786, 787, 788, 789, 791, 794, 797, 798, 799, 800, 801, 802, 803, 805, 809, 812, 813, 814, 816, 817, 818, 820, 822, 833, 840, 841, 842, 844, 845, 848, 851, 854, 857, 863, 872, 873, 875, 877, 879, 881, 882, 883, 885, 887, 889, 897, 899, 901], "through": [89, 262, 265, 420], "integr": [89, 95, 271, 650, 651, 729, 730, 759, 760, 772, 773, 867, 868], 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See Notes for requirements"], [11, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.CircularBlockBootstrap.bootstrap": [[12, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.CircularBlockBootstrap.clone": [[13, 2, 2, 0, "args", "Positional arguments to bootstrap"], [13, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [13, 2, 2, 0, "seed", "The seed value to pass to the closed generator"]], "arch.bootstrap.CircularBlockBootstrap.conf_int": [[14, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func and std_err_func, when appropriate"], [14, 2, 2, 0, "func", "Function the computes parameter values.  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See Notes for requirements"], [45, 3, 1, 0, "cov", "Compute parameter covariance using bootstrap"], [7, 4, 1, 0, "data", "Two-element tuple with the pos_data in the first position and kw_data in the second (pos_data, kw_data)"], [46, 5, 1, 0, "generator", "Set or get the instance PRNG"], [47, 3, 1, 0, "get_state", "Gets the state of the bootstrap's random number generator"], [48, 5, 1, 0, "index", "Returns the current index of the bootstrap"], [7, 4, 1, 0, "kw_data", "Dictionary containing the keyword arguments"], [7, 4, 1, 0, "pos_data", "Tuple containing the positional arguments (in the order entered)"], [49, 5, 1, 0, "random_state", "Set or get the instance random state"], [50, 3, 1, 0, "reset", "Resets the bootstrap to either its initial state or the last seed."], [51, 3, 1, 0, "seed", "Reseeds the bootstrap's random number generator"], [52, 3, 1, 0, "set_state", "Sets the state of the bootstrap's random number generator"], [53, 5, 1, 0, "state", "Set or get the generator's state"], [54, 3, 1, 0, "update_indices", "Update indices for the next iteration of the bootstrap.  This must be overridden when creating new bootstraps."], [55, 3, 1, 0, "var", "Compute parameter variance using bootstrap"]], "arch.bootstrap.IndependentSamplesBootstrap.apply": [[41, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func.  Must not conflict with keyword arguments used to initialize bootstrap"], [41, 2, 2, 0, "func", "Function the computes parameter values.  See Notes for requirements"], [41, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.IndependentSamplesBootstrap.bootstrap": [[42, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.IndependentSamplesBootstrap.clone": [[43, 2, 2, 0, "args", "Positional arguments to bootstrap"], [43, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [43, 2, 2, 0, "seed", "The seed value to pass to the closed generator"]], "arch.bootstrap.IndependentSamplesBootstrap.conf_int": [[44, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func and std_err_func, when appropriate"], [44, 2, 2, 0, "func", "Function the computes parameter values.  See Notes for requirements"], [44, 2, 2, 0, "method", "One of 'basic', 'percentile', 'studentized', 'norm' (identical to 'var', 'cov'), 'bc' (identical to 'debiased', 'bias-corrected'), or 'bca'"], [44, 2, 2, 0, "reps", "Number of bootstrap replications"], [44, 2, 2, 0, "reuse", "Flag indicating whether to reuse previously computed bootstrap results.  This allows alternative methods to be compared without rerunning the bootstrap simulation.  Reuse is ignored if reps is not the same across multiple runs, func changes across calls, or method is 'studentized'."], [44, 2, 2, 0, "sampling", "Type of sampling to use: 'nonparametric', 'semi-parametric' (or 'semi') or 'parametric'.  The default is 'nonparametric'.  See notes about the changes to func required when using 'semi' or 'parametric'."], [44, 2, 2, 0, "size", "Coverage of confidence interval"], [44, 2, 2, 0, "std_err_func", "Function to use when standardizing estimated parameters when using the studentized bootstrap.  Providing an analytical function eliminates the need for a nested bootstrap"], [44, 2, 2, 0, "studentize_reps", "Number of bootstraps to use in the inner bootstrap when using the studentized bootstrap.  Ignored when std_err_func is provided"], [44, 2, 2, 0, "tail", "One of 'two', 'upper' or 'lower'."]], "arch.bootstrap.IndependentSamplesBootstrap.cov": [[45, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [45, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [45, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."], [45, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.IndependentSamplesBootstrap.reset": [[50, 2, 2, 0, "use_seed", "Flag indicating whether to use the last seed if provided.  If False or if no seed has been set, the bootstrap will be reset to the initial state.  Default is True"]], "arch.bootstrap.IndependentSamplesBootstrap.seed": [[51, 2, 2, 0, "value", "Value to use as the seed."]], "arch.bootstrap.IndependentSamplesBootstrap.set_state": [[52, 2, 2, 0, "state", "Dictionary or tuple containing the state."]], "arch.bootstrap.IndependentSamplesBootstrap.var": [[55, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [55, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [55, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."], [55, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.MCS.__init__": [[238, 2, 2, 0, "block_size", "Length of window to use in the bootstrap.  If not provided, sqrt(T) is used.  In general, this should be provided and chosen to be appropriate for the data."], [238, 2, 2, 0, "bootstrap", "Bootstrap to use.  Options are 'stationary' or 'sb': Stationary bootstrap (Default) 'circular' or 'cbb': Circular block bootstrap 'moving block' or 'mbb': Moving block bootstrap"], [238, 2, 2, 0, "losses", "T by k array containing losses from a set of models"], [238, 2, 2, 0, "method", "MCS test and elimination implementation method, either 'max' or 'R'. Default is 'R'."], [238, 2, 2, 0, "reps", "Number of bootstrap replications to uses.  Default is 1000."], [238, 2, 2, 0, "seed", "Seed value to use when creating the bootstrap used in the comparison. If an integer or None, the NumPy default_rng is used with the seed value.  If a Generator or a RandomState, the argument is used."], [238, 2, 2, 0, "size", "Value in (0,1) to use as the test size when implementing the mcs. Default value is 0.05."]], "arch.bootstrap.MCS": [[241, 3, 1, 0, "compute", "Compute the set of models in the confidence set."], [242, 5, 1, 0, "excluded", "List of model indices that are excluded from the MCS"], [243, 5, 1, 0, "included", "List of model indices that are included in the MCS"], [244, 5, 1, 0, "pvalues", "Model p-values for inclusion in the MCS"], [245, 3, 1, 0, "reset", "Reset the bootstrap to it's initial state."], [246, 3, 1, 0, "seed", "Seed the bootstrap's random number generator"]], "arch.bootstrap.MCS.seed": [[246, 2, 2, 0, "value", "Integer to use as the seed"]], "arch.bootstrap.MovingBlockBootstrap.__init__": [[8, 2, 2, 0, "args", "Positional arguments to bootstrap"], [8, 2, 2, 0, "block_size", "Size of block to use"], [8, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [8, 2, 2, 0, "random_state", "RandomState to use to ensure reproducable results. Cannot be used with seed"], [8, 2, 2, 0, "seed", "Seed to use to ensure reproducable results. If an int, passes the value to value to np.random.default_rng. If None, a fresh Generator is constructed with system-provided entropy."]], "arch.bootstrap.MovingBlockBootstrap": [[56, 3, 1, 0, "apply", "Applies a function to bootstrap replicated data"], [57, 3, 1, 0, "bootstrap", "Iterator for use when bootstrapping"], [58, 3, 1, 0, "clone", "Clones the bootstrap using different data with a fresh prng."], [59, 3, 1, 0, "conf_int", "Function the computes parameter values.  See Notes for requirements"], [60, 3, 1, 0, "cov", "Compute parameter covariance using bootstrap"], [8, 4, 1, 0, "data", "Two-element tuple with the pos_data in the first position and kw_data in the second (pos_data, kw_data)"], [61, 5, 1, 0, "generator", "Set or get the instance PRNG"], [62, 3, 1, 0, "get_state", "Gets the state of the bootstrap's random number generator"], [63, 5, 1, 0, "index", "The current index of the bootstrap"], [8, 4, 1, 0, "kw_data", "Dictionary containing the keyword arguments"], [8, 4, 1, 0, "pos_data", "Tuple containing the positional arguments (in the order entered)"], [64, 5, 1, 0, "random_state", "Set or get the instance random state"], [65, 3, 1, 0, "reset", "Resets the bootstrap to either its initial state or the last seed."], [66, 3, 1, 0, "seed", "Reseeds the bootstrap's random number generator"], [67, 3, 1, 0, "set_state", "Sets the state of the bootstrap's random number generator"], [68, 5, 1, 0, "state", "Set or get the generator's state"], [69, 3, 1, 0, "update_indices", "Update indices for the next iteration of the bootstrap.  This must be overridden when creating new bootstraps."], [70, 3, 1, 0, "var", "Compute parameter variance using bootstrap"]], "arch.bootstrap.MovingBlockBootstrap.apply": [[56, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func.  Must not conflict with keyword arguments used to initialize bootstrap"], [56, 2, 2, 0, "func", "Function the computes parameter values.  See Notes for requirements"], [56, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.MovingBlockBootstrap.bootstrap": [[57, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.MovingBlockBootstrap.clone": [[58, 2, 2, 0, "args", "Positional arguments to bootstrap"], [58, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [58, 2, 2, 0, "seed", "The seed value to pass to the closed generator"]], "arch.bootstrap.MovingBlockBootstrap.conf_int": [[59, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func and std_err_func, when appropriate"], [59, 2, 2, 0, "func", "Function the computes parameter values.  See Notes for requirements"], [59, 2, 2, 0, "method", "One of 'basic', 'percentile', 'studentized', 'norm' (identical to 'var', 'cov'), 'bc' (identical to 'debiased', 'bias-corrected'), or 'bca'"], [59, 2, 2, 0, "reps", "Number of bootstrap replications"], [59, 2, 2, 0, "reuse", "Flag indicating whether to reuse previously computed bootstrap results.  This allows alternative methods to be compared without rerunning the bootstrap simulation.  Reuse is ignored if reps is not the same across multiple runs, func changes across calls, or method is 'studentized'."], [59, 2, 2, 0, "sampling", "Type of sampling to use: 'nonparametric', 'semi-parametric' (or 'semi') or 'parametric'.  The default is 'nonparametric'.  See notes about the changes to func required when using 'semi' or 'parametric'."], [59, 2, 2, 0, "size", "Coverage of confidence interval"], [59, 2, 2, 0, "std_err_func", "Function to use when standardizing estimated parameters when using the studentized bootstrap.  Providing an analytical function eliminates the need for a nested bootstrap"], [59, 2, 2, 0, "studentize_reps", "Number of bootstraps to use in the inner bootstrap when using the studentized bootstrap.  Ignored when std_err_func is provided"], [59, 2, 2, 0, "tail", "One of 'two', 'upper' or 'lower'."]], "arch.bootstrap.MovingBlockBootstrap.cov": [[60, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [60, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [60, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."], [60, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.MovingBlockBootstrap.reset": [[65, 2, 2, 0, "use_seed", "Flag indicating whether to use the last seed if provided.  If False or if no seed has been set, the bootstrap will be reset to the initial state.  Default is True"]], "arch.bootstrap.MovingBlockBootstrap.seed": [[66, 2, 2, 0, "value", "Value to use as the seed."]], "arch.bootstrap.MovingBlockBootstrap.set_state": [[67, 2, 2, 0, "state", "Dictionary or tuple containing the state."]], "arch.bootstrap.MovingBlockBootstrap.var": [[70, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [70, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [70, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."], [70, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.SPA.__init__": [[239, 2, 2, 0, "benchmark", "T element array of benchmark model losses"], [239, 2, 2, 0, "block_size", "Length of window to use in the bootstrap.  If not provided, sqrt(T) is used.  In general, this should be provided and chosen to be appropriate for the data."], [239, 2, 2, 0, "bootstrap", "Bootstrap to use.  Options are 'stationary' or 'sb': Stationary bootstrap (Default) 'circular' or 'cbb': Circular block bootstrap 'moving block' or 'mbb': Moving block bootstrap"], [239, 2, 2, 0, "models", "T  by k element array of alternative model losses"], [239, 2, 2, 0, "nested", "Flag indicating to use a nested bootstrap to compute variances for studentization.  Default is False.  Note that this can be slow since the procedure requires k extra bootstraps."], [239, 2, 2, 0, "reps", "Number of bootstrap replications to uses.  Default is 1000."], [239, 2, 2, 0, "seed", "Seed value to use when creating the bootstrap used in the comparison. If an integer or None, the NumPy default_rng is used with the seed value.  If a Generator or a RandomState, the argument is used."], [239, 2, 2, 0, "studentize", "Flag indicating to studentize loss differentials. Default is True"]], "arch.bootstrap.SPA": [[247, 3, 1, 0, "better_models", "Returns set of models rejected as being equal-or-worse than the benchmark"], [248, 3, 1, 0, "compute", "Compute the bootstrap pvalue."], [249, 3, 1, 0, "critical_values", "Returns data-dependent critical values"], [250, 5, 1, 0, "pvalues", "P-values corresponding to the lower, consistent and upper p-values."], [251, 3, 1, 0, "reset", "Reset the bootstrap to its initial state."], [252, 3, 1, 0, "seed", "Seed the bootstrap's random number generator"], [253, 3, 1, 0, "subset", "Sets a list of active models to run the SPA on.  Primarily for internal use."]], "arch.bootstrap.SPA.better_models": [[247, 2, 2, 0, "pvalue", "P-value in (0,1) to use when computing superior models"], [247, 2, 2, 0, "pvalue_type", "String in 'lower', 'consistent', or 'upper' indicating which critical value to use."]], "arch.bootstrap.SPA.critical_values": [[249, 2, 2, 0, "pvalue", "P-value in (0,1) to use when computing the critical values."]], "arch.bootstrap.SPA.seed": [[252, 2, 2, 0, "value", "Integer to use as the seed"]], "arch.bootstrap.SPA.subset": [[253, 2, 2, 0, "selector", "Boolean array indicating which columns to use when computing the p-values.  This is primarily for use by StepM."]], "arch.bootstrap.StationaryBootstrap.__init__": [[9, 2, 2, 0, "args", "Positional arguments to bootstrap"], [9, 2, 2, 0, "block_size", "Average size of block to use"], [9, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [9, 2, 2, 0, "random_state", "RandomState to use to ensure reproducable results. Cannot be used with seed"], [9, 2, 2, 0, "seed", "Seed to use to ensure reproducable results. If an int, passes the value to value to np.random.default_rng. If None, a fresh Generator is constructed with system-provided entropy."]], "arch.bootstrap.StationaryBootstrap": [[71, 3, 1, 0, "apply", "Applies a function to bootstrap replicated data"], [72, 3, 1, 0, "bootstrap", "Iterator for use when bootstrapping"], [73, 3, 1, 0, "clone", "Clones the bootstrap using different data with a fresh prng."], [74, 3, 1, 0, "conf_int", "Function the computes parameter values.  See Notes for requirements"], [75, 3, 1, 0, "cov", "Compute parameter covariance using bootstrap"], [9, 4, 1, 0, "data", "Two-element tuple with the pos_data in the first position and kw_data in the second (pos_data, kw_data)"], [76, 5, 1, 0, "generator", "Set or get the instance PRNG"], [77, 3, 1, 0, "get_state", "Gets the state of the bootstrap's random number generator"], [78, 5, 1, 0, "index", "The current index of the bootstrap"], [9, 4, 1, 0, "kw_data", "Dictionary containing the keyword arguments"], [9, 4, 1, 0, "pos_data", "Tuple containing the positional arguments (in the order entered)"], [79, 5, 1, 0, "random_state", "Set or get the instance random state"], [80, 3, 1, 0, "reset", "Resets the bootstrap to either its initial state or the last seed."], [81, 3, 1, 0, "seed", "Reseeds the bootstrap's random number generator"], [82, 3, 1, 0, "set_state", "Sets the state of the bootstrap's random number generator"], [83, 5, 1, 0, "state", "Set or get the generator's state"], [84, 3, 1, 0, "update_indices", "Update indices for the next iteration of the bootstrap.  This must be overridden when creating new bootstraps."], [85, 3, 1, 0, "var", "Compute parameter variance using bootstrap"]], "arch.bootstrap.StationaryBootstrap.apply": [[71, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func.  Must not conflict with keyword arguments used to initialize bootstrap"], [71, 2, 2, 0, "func", "Function the computes parameter values.  See Notes for requirements"], [71, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.StationaryBootstrap.bootstrap": [[72, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.StationaryBootstrap.clone": [[73, 2, 2, 0, "args", "Positional arguments to bootstrap"], [73, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [73, 2, 2, 0, "seed", "The seed value to pass to the closed generator"]], "arch.bootstrap.StationaryBootstrap.conf_int": [[74, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func and std_err_func, when appropriate"], [74, 2, 2, 0, "func", "Function the computes parameter values.  See Notes for requirements"], [74, 2, 2, 0, "method", "One of 'basic', 'percentile', 'studentized', 'norm' (identical to 'var', 'cov'), 'bc' (identical to 'debiased', 'bias-corrected'), or 'bca'"], [74, 2, 2, 0, "reps", "Number of bootstrap replications"], [74, 2, 2, 0, "reuse", "Flag indicating whether to reuse previously computed bootstrap results.  This allows alternative methods to be compared without rerunning the bootstrap simulation.  Reuse is ignored if reps is not the same across multiple runs, func changes across calls, or method is 'studentized'."], [74, 2, 2, 0, "sampling", "Type of sampling to use: 'nonparametric', 'semi-parametric' (or 'semi') or 'parametric'.  The default is 'nonparametric'.  See notes about the changes to func required when using 'semi' or 'parametric'."], [74, 2, 2, 0, "size", "Coverage of confidence interval"], [74, 2, 2, 0, "std_err_func", "Function to use when standardizing estimated parameters when using the studentized bootstrap.  Providing an analytical function eliminates the need for a nested bootstrap"], [74, 2, 2, 0, "studentize_reps", "Number of bootstraps to use in the inner bootstrap when using the studentized bootstrap.  Ignored when std_err_func is provided"], [74, 2, 2, 0, "tail", "One of 'two', 'upper' or 'lower'."]], "arch.bootstrap.StationaryBootstrap.cov": [[75, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [75, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [75, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."], [75, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.StationaryBootstrap.reset": [[80, 2, 2, 0, "use_seed", "Flag indicating whether to use the last seed if provided.  If False or if no seed has been set, the bootstrap will be reset to the initial state.  Default is True"]], "arch.bootstrap.StationaryBootstrap.seed": [[81, 2, 2, 0, "value", "Value to use as the seed."]], "arch.bootstrap.StationaryBootstrap.set_state": [[82, 2, 2, 0, "state", "Dictionary or tuple containing the state."]], "arch.bootstrap.StationaryBootstrap.var": [[85, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [85, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [85, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."], [85, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.StepM.__init__": [[240, 2, 2, 0, "benchmark", "T element array of benchmark model losses"], [240, 2, 2, 0, "block_size", "Length of window to use in the bootstrap.  If not provided, sqrt(T) is used.  In general, this should be provided and chosen to be appropriate for the data."], [240, 2, 2, 0, "bootstrap", "Bootstrap to use.  Options are 'stationary' or 'sb': Stationary bootstrap (Default) 'circular' or 'cbb': Circular block bootstrap 'moving block' or 'mbb': Moving block bootstrap"], [240, 2, 2, 0, "models", "T by k element array of alternative model losses"], [240, 2, 2, 0, "nested", "Flag indicating to use a nested bootstrap to compute variances for studentization.  Default is False.  Note that this can be slow since the procedure requires k extra bootstraps."], [240, 2, 2, 0, "reps", "Number of bootstrap replications to uses.  Default is 1000."], [240, 2, 2, 0, "seed", "Seed value to use when creating the bootstrap used in the comparison. If an integer or None, the NumPy default_rng is used with the seed value.  If a Generator or a RandomState, the argument is used."], [240, 2, 2, 0, "size", "Value in (0,1) to use as the test size when implementing the comparison. Default value is 0.05."], [240, 2, 2, 0, "studentize", "Flag indicating to studentize loss differentials. Default is True"]], "arch.bootstrap.StepM": [[254, 3, 1, 0, "compute", "Compute the set of superior models."], [255, 3, 1, 0, "reset", "Reset the bootstrap to it's initial state."], [256, 3, 1, 0, "seed", "Seed the bootstrap's random number generator"], [257, 5, 1, 0, "superior_models", "List of the indices or column names of the superior models"]], "arch.bootstrap.StepM.seed": [[256, 2, 2, 0, "value", "Integer to use as the seed"]], "arch.bootstrap.optimal_block_length": [[10, 2, 2, 0, "x", "A one-dimensional or two-dimensional array-like.  Operates columns by column if 2-dimensional."]], "arch.covariance": [[98, 0, 0, 1, "kernel", ""]], "arch.covariance.kernel": [[99, 1, 1, 0, "Andrews", "Alternative name of the QuadraticSpectral covariance estimator."], [100, 1, 1, 0, "Bartlett", "Bartlett's (Newey-West) kernel covariance estimation."], [101, 1, 1, 0, "CovarianceEstimate", "Covariance estimate using a long-run covariance estimator"], [102, 1, 1, 0, "Gallant", "Alternative name for Parzen covariance estimator."], [103, 1, 1, 0, "NeweyWest", "Alternative name for Bartlett covariance estimator."], [104, 1, 1, 0, "Parzen", "Parzen's kernel covariance estimation."], [105, 1, 1, 0, "ParzenCauchy", "Parzen's Cauchy kernel covariance estimation."], [106, 1, 1, 0, "ParzenGeometric", "Parzen's Geometric kernel covariance estimation."], [107, 1, 1, 0, "ParzenRiesz", "Parzen-Reisz kernel covariance estimation."], [108, 1, 1, 0, "QuadraticSpectral", "Quadratic-Spectral (Andrews') kernel covariance estimation."], [109, 1, 1, 0, "TukeyHamming", "Tukey-Hamming kernel covariance estimation."], [110, 1, 1, 0, "TukeyHanning", "Tukey-Hanning kernel covariance estimation."], [111, 1, 1, 0, "TukeyParzen", "Tukey-Parzen kernel covariance estimation."]], "arch.covariance.kernel.Andrews": [[112, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [113, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [114, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [115, 5, 1, 0, "cov", "The estimated covariances."], [116, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [117, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [118, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [119, 5, 1, 0, "name", "The covariance estimator's name."], [120, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [121, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.Bartlett.__init__": [[100, 2, 2, 0, "bandwidth", "The kernel's bandwidth.  If None, optimal bandwidth is estimated."], [100, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [100, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."], [100, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [100, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."], [100, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.Bartlett": [[122, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [123, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [124, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [125, 5, 1, 0, "cov", "The estimated covariances."], [126, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [127, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [128, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [129, 5, 1, 0, "name", "The covariance estimator's name."], [130, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [131, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.CovarianceEstimate.__init__": [[101, 2, 2, 0, "columns", "Column labels to use if covariance estimates are returned as DataFrames."], [101, 2, 2, 0, "long_run", "The long-run covariance estimate. If not provided, computed from short_run and one_sided_strict."], [101, 2, 2, 0, "one_sided_strict", "The one-sided-strict covariance estimate. If not provided, computed from short_run and one_sided_strict."], [101, 2, 2, 0, "short_run", "The short-run covariance estimate."]], "arch.covariance.kernel.CovarianceEstimate": [[132, 5, 1, 0, "long_run", "The long-run covariance estimate."], [133, 5, 1, 0, "one_sided", "The one-sided covariance estimate."], [134, 5, 1, 0, "one_sided_strict", "The one-sided strict covariance estimate."], [135, 5, 1, 0, "short_run", "The short-run covariance estimate."]], "arch.covariance.kernel.Gallant": [[136, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [137, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [138, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [139, 5, 1, 0, "cov", "The estimated covariances."], [140, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [141, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [142, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [143, 5, 1, 0, "name", "The covariance estimator's name."], [144, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [145, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.NeweyWest": [[146, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [147, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [148, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [149, 5, 1, 0, "cov", "The estimated covariances."], [150, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [151, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [152, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [153, 5, 1, 0, "name", "The covariance estimator's name."], [154, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [155, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.Parzen.__init__": [[104, 2, 2, 0, "bandwidth", "The kernel's bandwidth.  If None, optimal bandwidth is estimated."], [104, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [104, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."], [104, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [104, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."], [104, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.Parzen": [[156, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [157, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [158, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [159, 5, 1, 0, "cov", "The estimated covariances."], [160, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [161, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [162, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [163, 5, 1, 0, "name", "The covariance estimator's name."], [164, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [165, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.ParzenCauchy.__init__": [[105, 2, 2, 0, "bandwidth", "The kernel's bandwidth.  If None, optimal bandwidth is estimated."], [105, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [105, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."], [105, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [105, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."], [105, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.ParzenCauchy": [[166, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [167, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [168, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [169, 5, 1, 0, "cov", "The estimated covariances."], [170, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [171, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [172, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [173, 5, 1, 0, "name", "The covariance estimator's name."], [174, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [175, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.ParzenGeometric.__init__": [[106, 2, 2, 0, "bandwidth", "The kernel's bandwidth.  If None, optimal bandwidth is estimated."], [106, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [106, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."], [106, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [106, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."], [106, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.ParzenGeometric": [[176, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [177, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [178, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [179, 5, 1, 0, "cov", "The estimated covariances."], [180, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [181, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [182, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [183, 5, 1, 0, "name", "The covariance estimator's name."], [184, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [185, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.ParzenRiesz.__init__": [[107, 2, 2, 0, "bandwidth", "The kernel's bandwidth.  If None, optimal bandwidth is estimated."], [107, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [107, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."], [107, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [107, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."], [107, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.ParzenRiesz": [[186, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [187, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [188, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [189, 5, 1, 0, "cov", "The estimated covariances."], [190, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [191, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [192, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [193, 5, 1, 0, "name", "The covariance estimator's name."], [194, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [195, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.QuadraticSpectral.__init__": [[108, 2, 2, 0, "bandwidth", "The kernel's bandwidth.  If None, optimal bandwidth is estimated."], [108, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [108, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."], [108, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [108, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."], [108, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.QuadraticSpectral": [[196, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [197, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [198, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [199, 5, 1, 0, "cov", "The estimated covariances."], [200, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [201, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [202, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [203, 5, 1, 0, "name", "The covariance estimator's name."], [204, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [205, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.TukeyHamming.__init__": [[109, 2, 2, 0, "bandwidth", "The kernel's bandwidth.  If None, optimal bandwidth is estimated."], [109, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [109, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."], [109, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [109, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."], [109, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.TukeyHamming": [[206, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [207, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [208, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [209, 5, 1, 0, "cov", "The estimated covariances."], [210, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [211, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [212, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [213, 5, 1, 0, "name", "The covariance estimator's name."], [214, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [215, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.TukeyHanning.__init__": [[110, 2, 2, 0, "bandwidth", "The kernel's bandwidth.  If None, optimal bandwidth is estimated."], [110, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [110, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."], [110, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [110, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."], [110, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.TukeyHanning": [[216, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [217, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [218, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [219, 5, 1, 0, "cov", "The estimated covariances."], [220, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [221, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [222, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [223, 5, 1, 0, "name", "The covariance estimator's name."], [224, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [225, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.TukeyParzen.__init__": [[111, 2, 2, 0, "bandwidth", "The kernel's bandwidth.  If None, optimal bandwidth is estimated."], [111, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [111, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."], [111, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [111, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."], [111, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.TukeyParzen": [[226, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [227, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [228, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [229, 5, 1, 0, "cov", "The estimated covariances."], [230, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [231, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [232, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [233, 5, 1, 0, "name", "The covariance estimator's name."], [234, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [235, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.unitroot": [[262, 1, 1, 0, "ADF", "Augmented Dickey-Fuller unit root test"], [263, 1, 1, 0, "DFGLS", "Elliott, Rothenberg and Stock's (1) GLS detrended Dickey-Fuller"], [264, 1, 1, 0, "KPSS", "Kwiatkowski, Phillips, Schmidt and Shin (KPSS) stationarity test"], [265, 1, 1, 0, "PhillipsPerron", "Phillips-Perron unit root test"], [266, 1, 1, 0, "VarianceRatio", "Variance Ratio test of a random walk."], [267, 1, 1, 0, "ZivotAndrews", "Zivot-Andrews structural-break unit-root test"], [268, 6, 1, 0, "auto_bandwidth", "Automatic bandwidth selection of Andrews (1991) and Newey & West (1994)."], [261, 0, 0, 1, "cointegration", ""]], "arch.unitroot.ADF.__init__": [[262, 2, 2, 0, "lags", "The number of lags to use in the ADF regression.  If omitted or None, method is used to automatically select the lag length with no more than max_lags are included."], [262, 2, 2, 0, "low_memory", "Flag indicating whether to use a low memory implementation of the lag selection algorithm. The low memory algorithm is slower than the standard algorithm but will use 2-4% of the memory required for the standard algorithm. This options allows automatic lag selection to be used in very long time series. If None, use automatic selection of algorithm."], [262, 2, 2, 0, "max_lags", "The maximum number of lags to use when selecting lag length"], [262, 2, 2, 0, "method", "The method to use when selecting the lag length"], [262, 2, 2, 0, "trend", "The trend component to include in the test"], [262, 2, 2, 0, "y", "The data to test for a unit root"]], "arch.unitroot.ADF": [[278, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [279, 5, 1, 0, "critical_values", "Dictionary containing critical values specific to the test, number of observations and included deterministic trend terms."], [280, 5, 1, 0, "lags", "Sets or gets the number of lags used in the model. When bootstrap use DF-type regressions, lags is the number of lags in the regression model.  When bootstrap use long-run variance estimators, lags is the number of lags used in the long-run variance estimator."], [281, 5, 1, 0, "max_lags", "Sets or gets the maximum lags used when automatically selecting lag length"], [282, 5, 1, 0, "nobs", "The number of observations used when computing the test statistic. Accounts for loss of data due to lags for regression-based bootstrap."], [283, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [284, 5, 1, 0, "pvalue", "Returns the p-value for the test statistic"], [285, 5, 1, 0, "regression", "Returns the OLS regression results from the ADF model estimated"], [286, 5, 1, 0, "stat", "The test statistic for a unit root"], [287, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [288, 5, 1, 0, "trend", "Sets or gets the deterministic trend term used in the test. See valid_trends for a list of supported trends"], [289, 5, 1, 0, "valid_trends", "List of valid trend terms."], [290, 5, 1, 0, "y", "Returns the data used in the test statistic"]], "arch.unitroot.DFGLS.__init__": [[263, 2, 2, 0, "lags", "The number of lags to use in the ADF regression.  If omitted or None, method is used to automatically select the lag length with no more than max_lags are included."], [263, 2, 2, 0, "max_lags", "The maximum number of lags to use when selecting lag length. When using automatic lag length selection, the lag is selected using OLS detrending rather than GLS detrending (2)."], [263, 2, 2, 0, "method", "The method to use when selecting the lag length"], [263, 2, 2, 0, "trend", "The trend component to include in the test"], [263, 2, 2, 0, "y", "The data to test for a unit root"]], "arch.unitroot.DFGLS": [[291, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [292, 5, 1, 0, "critical_values", "Dictionary containing critical values specific to the test, number of observations and included deterministic trend terms."], [293, 5, 1, 0, "lags", "Sets or gets the number of lags used in the model. When bootstrap use DF-type regressions, lags is the number of lags in the regression model.  When bootstrap use long-run variance estimators, lags is the number of lags used in the long-run variance estimator."], [294, 5, 1, 0, "max_lags", "Sets or gets the maximum lags used when automatically selecting lag length"], [295, 5, 1, 0, "nobs", "The number of observations used when computing the test statistic. Accounts for loss of data due to lags for regression-based bootstrap."], [296, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [297, 5, 1, 0, "pvalue", "Returns the p-value for the test statistic"], [298, 5, 1, 0, "regression", "Returns the OLS regression results from the ADF model estimated"], [299, 5, 1, 0, "stat", "The test statistic for a unit root"], [300, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [301, 5, 1, 0, "trend", "Sets or gets the deterministic trend term used in the test. See valid_trends for a list of supported trends"], [302, 5, 1, 0, "valid_trends", "List of valid trend terms."], [303, 5, 1, 0, "y", "Returns the data used in the test statistic"]], "arch.unitroot.KPSS.__init__": [[264, 2, 2, 0, "lags", "The number of lags to use in the Newey-West estimator of the long-run covariance.  If omitted or None, the number of lags is calculated with the data-dependent method of Hobijn et al. (1998). See also Andrews (1991), Newey & West (1994), and Schwert (1989). Set lags=-1 to use the old method that only depends on the sample size, 12 * (nobs/100) ** (1/4)."], [264, 2, 2, 0, "trend", "\"c\" - Include a constant (Default) \"ct\" - Include a constant and linear time trend"], [264, 2, 2, 0, "y", "The data to test for stationarity"]], "arch.unitroot.KPSS": [[304, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [305, 5, 1, 0, "critical_values", "Dictionary containing critical values specific to the test, number of observations and included deterministic trend terms."], [306, 5, 1, 0, "lags", "Sets or gets the number of lags used in the model. When bootstrap use DF-type regressions, lags is the number of lags in the regression model.  When bootstrap use long-run variance estimators, lags is the number of lags used in the long-run variance estimator."], [307, 5, 1, 0, "nobs", "The number of observations used when computing the test statistic. Accounts for loss of data due to lags for regression-based bootstrap."], [308, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [309, 5, 1, 0, "pvalue", "Returns the p-value for the test statistic"], [310, 5, 1, 0, "stat", "The test statistic for a unit root"], [311, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [312, 5, 1, 0, "trend", "Sets or gets the deterministic trend term used in the test. See valid_trends for a list of supported trends"], [313, 5, 1, 0, "valid_trends", "List of valid trend terms."], [314, 5, 1, 0, "y", "Returns the data used in the test statistic"]], "arch.unitroot.PhillipsPerron.__init__": [[265, 2, 2, 0, "lags", "The number of lags to use in the Newey-West estimator of the long-run covariance.  If omitted or None, the lag length is set automatically to 12 * (nobs/100) ** (1/4)"], [265, 2, 2, 0, "test_type", "The test to use when computing the test statistic. \"tau\" is based on the t-stat and \"rho\" uses a test based on nobs times the re-centered regression coefficient"], [265, 2, 2, 0, "trend", "The trend component to include in the test"], [265, 2, 2, 0, "y", "The data to test for a unit root"]], "arch.unitroot.PhillipsPerron": [[315, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [316, 5, 1, 0, "critical_values", "Dictionary containing critical values specific to the test, number of observations and included deterministic trend terms."], [317, 5, 1, 0, "lags", "Sets or gets the number of lags used in the model. When bootstrap use DF-type regressions, lags is the number of lags in the regression model.  When bootstrap use long-run variance estimators, lags is the number of lags used in the long-run variance estimator."], [318, 5, 1, 0, "nobs", "The number of observations used when computing the test statistic. Accounts for loss of data due to lags for regression-based bootstrap."], [319, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [320, 5, 1, 0, "pvalue", "Returns the p-value for the test statistic"], [321, 5, 1, 0, "regression", "Returns OLS regression results for the specification used in the test"], [322, 5, 1, 0, "stat", "The test statistic for a unit root"], [323, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [324, 5, 1, 0, "test_type", "Gets or sets the test type returned by stat. Valid values are \"tau\" or \"rho\""], [325, 5, 1, 0, "trend", "Sets or gets the deterministic trend term used in the test. See valid_trends for a list of supported trends"], [326, 5, 1, 0, "valid_trends", "List of valid trend terms."], [327, 5, 1, 0, "y", "Returns the data used in the test statistic"]], "arch.unitroot.VarianceRatio.__init__": [[266, 2, 2, 0, "debiased", "Indicates whether to use a debiased version of the test. Default is True. Only applicable if overlap is True."], [266, 2, 2, 0, "lags", "The number of periods to used in the multi-period variance, which is the numerator of the test statistic.  Must be at least 2"], [266, 2, 2, 0, "overlap", "Indicates whether to use all overlapping blocks.  Default is True.  If False, the number of observations in y minus 1 must be an exact multiple of lags.  If this condition is not satisfied, some values at the end of y will be discarded."], [266, 2, 2, 0, "robust", "Indicates whether to use heteroskedasticity robust inference. Default is True."], [266, 2, 2, 0, "trend", "\"c\" allows for a non-zero drift in the random walk, while \"n\" requires that the increments to y are mean 0"], [266, 2, 2, 0, "y", "The data to test for a random walk"]], "arch.unitroot.VarianceRatio": [[328, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [329, 5, 1, 0, "critical_values", "Dictionary containing critical values specific to the test, number of observations and included deterministic trend terms."], [330, 5, 1, 0, "debiased", "Sets of gets the indicator to use debiased variances in the ratio"], [331, 5, 1, 0, "lags", "Sets or gets the number of lags used in the model. When bootstrap use DF-type regressions, lags is the number of lags in the regression model.  When bootstrap use long-run variance estimators, lags is the number of lags used in the long-run variance estimator."], [332, 5, 1, 0, "nobs", "The number of observations used when computing the test statistic. Accounts for loss of data due to lags for regression-based bootstrap."], [333, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [334, 5, 1, 0, "overlap", "Sets of gets the indicator to use overlapping returns in the long-period variance estimator"], [335, 5, 1, 0, "pvalue", "Returns the p-value for the test statistic"], [336, 5, 1, 0, "robust", "Sets of gets the indicator to use a heteroskedasticity robust variance estimator"], [337, 5, 1, 0, "stat", "The test statistic for a unit root"], [338, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [339, 5, 1, 0, "trend", "Sets or gets the deterministic trend term used in the test. See valid_trends for a list of supported trends"], [340, 5, 1, 0, "valid_trends", "List of valid trend terms."], [341, 5, 1, 0, "vr", "The ratio of the long block lags-period variance to the 1-period variance"], [342, 5, 1, 0, "y", "Returns the data used in the test statistic"]], "arch.unitroot.ZivotAndrews.__init__": [[267, 2, 2, 0, "lags", "The number of lags to use in the ADF regression.  If omitted or None, method is used to automatically select the lag length with no more than max_lags are included."], [267, 2, 2, 0, "max_lags", "The maximum number of lags to use when selecting lag length"], [267, 2, 2, 0, "method", "The method to use when selecting the lag length"], [267, 2, 2, 0, "trend", "The trend component to include in the test"], [267, 2, 2, 0, "trim", "percentage of series at begin/end to exclude from break-period calculation in range [0, 0.333] (default=0.15)"], [267, 2, 2, 0, "y", "data series"]], "arch.unitroot.ZivotAndrews": [[343, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [344, 5, 1, 0, "critical_values", "Dictionary containing critical values specific to the test, number of observations and included deterministic trend terms."], [345, 5, 1, 0, "lags", "Sets or gets the number of lags used in the model. When bootstrap use DF-type regressions, lags is the number of lags in the regression model.  When bootstrap use long-run variance estimators, lags is the number of lags used in the long-run variance estimator."], [346, 5, 1, 0, "nobs", "The number of observations used when computing the test statistic. Accounts for loss of data due to lags for regression-based bootstrap."], [347, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [348, 5, 1, 0, "pvalue", "Returns the p-value for the test statistic"], [349, 5, 1, 0, "stat", "The test statistic for a unit root"], [350, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [351, 5, 1, 0, "trend", "Sets or gets the deterministic trend term used in the test. See valid_trends for a list of supported trends"], [352, 5, 1, 0, "valid_trends", "List of valid trend terms."], [353, 5, 1, 0, "y", "Returns the data used in the test statistic"]], "arch.unitroot.auto_bandwidth": [[268, 2, 2, 0, "kernel", "The kernel function to use for selecting the bandwidth"], [268, 2, 2, 0, "y", "Data on which to apply the bandwidth selection"]], "arch.unitroot.cointegration": [[269, 1, 1, 0, "CanonicalCointegratingReg", "Canonical Cointegrating Regression cointegrating vector estimation."], [270, 1, 1, 0, "CointegrationAnalysisResults", "summary()"], [271, 1, 1, 0, "DynamicOLS", "Dynamic OLS (DOLS) cointegrating vector estimation"], [272, 1, 1, 0, "DynamicOLSResults", "Estimation results for Dynamic OLS models"], [273, 1, 1, 0, "EngleGrangerTestResults", "Results class for Engle-Granger cointegration tests."], [274, 1, 1, 0, "FullyModifiedOLS", "Fully Modified OLS cointegrating vector estimation."], [275, 1, 1, 0, "PhillipsOuliarisTestResults", "plot([axes, title])"], [276, 6, 1, 0, "engle_granger", "Test for cointegration within a set of time series."], [277, 6, 1, 0, "phillips_ouliaris", "Test for cointegration within a set of time series."]], "arch.unitroot.cointegration.CanonicalCointegratingReg.__init__": [[269, 2, 2, 0, "trend", "Trend to include in the cointegrating regression. Trends are:"], [269, 2, 2, 0, "x", "The right-hand-side variables in the cointegrating regression."], [269, 2, 2, 0, "x_trend", "Trends that affects affect the x-data but do not appear in the cointegrating regression. x_trend must be at least as large as trend, so that if trend is \"ct\", x_trend must be either \"ct\" or \"ctt\"."], [269, 2, 2, 0, "y", "The left-hand-side variable in the cointegrating regression."]], "arch.unitroot.cointegration.CanonicalCointegratingReg": [[354, 3, 1, 0, "fit", "Estimate the cointegrating vector."]], "arch.unitroot.cointegration.CanonicalCointegratingReg.fit": [[354, 2, 2, 0, "bandwidth", "The bandwidth to use. If not provided, the optimal bandwidth is estimated from the data. Setting the bandwidth to 0 and using \"unadjusted\" produces the classic OLS covariance estimator. Setting the bandwidth to 0 and using \"robust\" produces White's covariance estimator."], [354, 2, 2, 0, "df_adjust", "Whether the adjust the parameter covariance to account for the number of parameters estimated in the regression. If true, the parameter covariance estimator is multiplied by T/(T-k) where k is the number of regressors in the model."], [354, 2, 2, 0, "diff", "Use differenced data to estimate the residuals."], [354, 2, 2, 0, "force_int", "Whether the force the estimated optimal bandwidth to be an integer."], [354, 2, 2, 0, "kernel", "The string name of any of any known kernel-based long-run covariance estimators. Common choices are \"bartlett\" for the Bartlett kernel (Newey-West), \"parzen\" for the Parzen kernel and \"quadratic-spectral\" for the Quadratic Spectral kernel."]], "arch.unitroot.cointegration.CointegrationAnalysisResults": [[355, 5, 1, 0, "bandwidth", "The bandwidth used in the parameter covariance estimation"], [356, 5, 1, 0, "cov", "The estimated parameter covariance of the cointegrating vector"], [357, 5, 1, 0, "kernel", "The kernel used to estimate the covariance"], [358, 5, 1, 0, "long_run_variance", "Long-run variance estimate used in the parameter covariance estimator"], [359, 5, 1, 0, "params", "The estimated parameters of the cointegrating vector"], [360, 5, 1, 0, "pvalues", "P-value of the parameters in the cointegrating vector"], [361, 5, 1, 0, "resid", "The model residuals"], [362, 5, 1, 0, "residual_variance", "The variance of the regression residual."], [363, 5, 1, 0, "rsquared", "The model R\u00b2"], [364, 5, 1, 0, "rsquared_adj", "The degree-of-freedom adjusted R\u00b2"], [365, 5, 1, 0, "std_errors", "Standard errors  of the parameters in the cointegrating vector"], [366, 3, 1, 0, "summary", "Summary of the model, containing estimated parameters and std. errors"], [367, 5, 1, 0, "tvalues", "T-statistics of the parameters in the cointegrating vector"]], "arch.unitroot.cointegration.DynamicOLS.__init__": [[271, 2, 2, 0, "common", "Flag indicating that lags and leads should be restricted to the same value. When common is None, lags must equal leads and max_lag must equal max_lead."], [271, 2, 2, 0, "lags", "The number of lags to include in the model.  If None, the optimal number of lags is chosen using method."], [271, 2, 2, 0, "leads", "The number of leads to include in the model.  If None, the optimal number of leads is chosen using method."], [271, 2, 2, 0, "max_lag", "The maximum lag to consider. See Notes for value used when None."], [271, 2, 2, 0, "max_lead", "The maximum lead to consider. See Notes for value used when None."], [271, 2, 2, 0, "method", "The method used to select lag length when lags or leads is None."], [271, 2, 2, 0, "trend", "Trend to include in the cointegrating regression. Trends are:"], [271, 2, 2, 0, "x", "The right-hand-side variables in the cointegrating regression."], [271, 2, 2, 0, "y", "The left-hand-side variable in the cointegrating regression."]], "arch.unitroot.cointegration.DynamicOLS": [[368, 3, 1, 0, "fit", "Estimate the Dynamic OLS regression"]], "arch.unitroot.cointegration.DynamicOLS.fit": [[368, 2, 2, 0, "bandwidth", "The bandwidth to use. If not provided, the optimal bandwidth is estimated from the data. Setting the bandwidth to 0 and using \"unadjusted\" produces the classic OLS covariance estimator. Setting the bandwidth to 0 and using \"robust\" produces White's covariance estimator."], [368, 2, 2, 0, "cov_type", "Either \"unadjusted\" (or is equivalent \"homoskedastic\") or \"robust\" (or its equivalent \"kernel\")."], [368, 2, 2, 0, "df_adjust", "Whether the adjust the parameter covariance to account for the number of parameters estimated in the regression. If true, the parameter covariance estimator is multiplied by T/(T-k) where k is the number of regressors in the model."], [368, 2, 2, 0, "force_int", "Whether the force the estimated optimal bandwidth to be an integer."], [368, 2, 2, 0, "kernel", "The string name of any of any known kernel-based long-run covariance estimators. Common choices are \"bartlett\" for the Bartlett kernel (Newey-West), \"parzen\" for the Parzen kernel and \"quadratic-spectral\" for the Quadratic Spectral kernel."]], "arch.unitroot.cointegration.DynamicOLSResults.__init__": [[272, 2, 2, 0, "cov", "The estimated parameter covariance."], [272, 2, 2, 0, "cov_type", "The type of the parameter covariance estimator used."], [272, 2, 2, 0, "df_adjust", "Whether to degree of freedom adjust the estimator."], [272, 2, 2, 0, "kernel_est", "The covariance estimator instance used to estimate the parameter covariance."], [272, 2, 2, 0, "lags", "The number of lags included in the model."], [272, 2, 2, 0, "leads", "The number of leads included in the model."], [272, 2, 2, 0, "params", "The estimated model parameters."], [272, 2, 2, 0, "reg_results", "Regression results from fitting statsmodels OLS."], [272, 2, 2, 0, "resid", "The model residuals."]], "arch.unitroot.cointegration.DynamicOLSResults": [[369, 5, 1, 0, "bandwidth", "The bandwidth used in the parameter covariance estimation"], [370, 5, 1, 0, "cov", "The estimated parameter covariance of the cointegrating vector"], [371, 5, 1, 0, "cov_type", "The type of parameter covariance estimator used"], [372, 5, 1, 0, "full_cov", "Parameter covariance of the all model parameters, incl. leads and lags"], [373, 5, 1, 0, "full_params", "The complete set of parameters, including leads and lags"], [374, 5, 1, 0, "kernel", "The kernel used to estimate the covariance"], [375, 5, 1, 0, "lags", "The number of lags included in the model"], [376, 5, 1, 0, "leads", "The number of leads included in the model"], [377, 5, 1, 0, "long_run_variance", "The long-run variance of the regression residual."], [378, 5, 1, 0, "params", "The estimated parameters of the cointegrating vector"], [379, 5, 1, 0, "pvalues", "P-value of the parameters in the cointegrating vector"], [380, 5, 1, 0, "resid", "The model residuals"], [381, 5, 1, 0, "residual_variance", "The variance of the regression residual."], [382, 5, 1, 0, "rsquared", "The model R\u00b2"], [383, 5, 1, 0, "rsquared_adj", "The degree-of-freedom adjusted R\u00b2"], [384, 5, 1, 0, "std_errors", "Standard errors  of the parameters in the cointegrating vector"], [385, 3, 1, 0, "summary", "Summary of the model, containing estimated parameters and std. errors"], [386, 5, 1, 0, "tvalues", "T-statistics of the parameters in the cointegrating vector"]], "arch.unitroot.cointegration.DynamicOLSResults.summary": [[385, 2, 2, 0, "full", "Flag indicating whether to include all estimated parameters (True) or only the parameters of the cointegrating vector"]], "arch.unitroot.cointegration.EngleGrangerTestResults.__init__": [[273, 2, 2, 0, "adf", "The ADF instance used to perform the test and lag selection."], [273, 2, 2, 0, "alternative", "The alternative hypothesis."], [273, 2, 2, 0, "crit_vals", "The critical values of the Engle-Granger specific to the sample size and model dimension."], [273, 2, 2, 0, "null", "The null hypothesis."], [273, 2, 2, 0, "order", "The number of stochastic trends in the null distribution."], [273, 2, 2, 0, "pvalue", "The pvalue of the Engle-Granger test statistic."], [273, 2, 2, 0, "stat", "The Engle-Granger test statistic."], [273, 2, 2, 0, "trend", "The model's trend description."], [273, 2, 2, 0, "xsection", "The OLS results used in the cross-sectional regression."]], "arch.unitroot.cointegration.EngleGrangerTestResults": [[387, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [388, 5, 1, 0, "cointegrating_vector", "The estimated cointegrating vector."], [389, 5, 1, 0, "critical_values", "Critical Values"], [390, 5, 1, 0, "distribution_order", "The number of stochastic trends under the null hypothesis."], [391, 5, 1, 0, "lags", "The number of lags used in the Augmented Dickey-Fuller regression."], [392, 5, 1, 0, "max_lags", "The maximum number of lags used in the lag-length selection."], [393, 5, 1, 0, "name", "Sets or gets the name of the cointegration test"], [394, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [395, 3, 1, 0, "plot", "Plot the cointegration residuals."], [396, 5, 1, 0, "pvalue", "The p-value of the test statistic."], [397, 5, 1, 0, "resid", "The residual from the cointegrating regression."], [398, 5, 1, 0, "rho", "The estimated coefficient in the Dickey-Fuller Test"], [399, 5, 1, 0, "stat", "The test statistic."], [400, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [401, 5, 1, 0, "trend", "The trend used in the cointegrating regression"]], "arch.unitroot.cointegration.EngleGrangerTestResults.plot": [[395, 2, 2, 0, "axes", "matplotlib axes instance to hold the figure."], [395, 2, 2, 0, "title", "Title for the figure."]], "arch.unitroot.cointegration.FullyModifiedOLS.__init__": [[274, 2, 2, 0, "trend", "Trend to include in the cointegrating regression. Trends are:"], [274, 2, 2, 0, "x", "The right-hand-side variables in the cointegrating regression."], [274, 2, 2, 0, "x_trend", "Trends that affects affect the x-data but do not appear in the cointegrating regression. x_trend must be at least as large as trend, so that if trend is \"ct\", x_trend must be either \"ct\" or \"ctt\"."], [274, 2, 2, 0, "y", "The left-hand-side variable in the cointegrating regression."]], "arch.unitroot.cointegration.FullyModifiedOLS": [[402, 3, 1, 0, "fit", "Estimate the cointegrating vector."]], "arch.unitroot.cointegration.FullyModifiedOLS.fit": [[402, 2, 2, 0, "bandwidth", "The bandwidth to use. If not provided, the optimal bandwidth is estimated from the data. Setting the bandwidth to 0 and using \"unadjusted\" produces the classic OLS covariance estimator. Setting the bandwidth to 0 and using \"robust\" produces White's covariance estimator."], [402, 2, 2, 0, "df_adjust", "Whether the adjust the parameter covariance to account for the number of parameters estimated in the regression. If true, the parameter covariance estimator is multiplied by T/(T-k) where k is the number of regressors in the model."], [402, 2, 2, 0, "diff", "Use differenced data to estimate the residuals."], [402, 2, 2, 0, "force_int", "Whether the force the estimated optimal bandwidth to be an integer."], [402, 2, 2, 0, "kernel", "The string name of any of any known kernel-based long-run covariance estimators. Common choices are \"bartlett\" for the Bartlett kernel (Newey-West), \"parzen\" for the Parzen kernel and \"quadratic-spectral\" for the Quadratic Spectral kernel."]], "arch.unitroot.cointegration.PhillipsOuliarisTestResults": [[403, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [404, 5, 1, 0, "bandwidth", "Bandwidth used by the long-run covariance estimator"], [405, 5, 1, 0, "cointegrating_vector", "The estimated cointegrating vector."], [406, 5, 1, 0, "critical_values", "Critical Values"], [407, 5, 1, 0, "distribution_order", "The number of stochastic trends under the null hypothesis."], [408, 5, 1, 0, "kernel", "Name of the long-run covariance estimator"], [409, 5, 1, 0, "name", "Sets or gets the name of the cointegration test"], [410, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [411, 3, 1, 0, "plot", "Plot the cointegration residuals."], [412, 5, 1, 0, "pvalue", "The p-value of the test statistic."], [413, 5, 1, 0, "resid", "The residual from the cointegrating regression."], [414, 5, 1, 0, "stat", "The test statistic."], [415, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [416, 5, 1, 0, "trend", "The trend used in the cointegrating regression"]], "arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot": [[411, 2, 2, 0, "axes", "matplotlib axes instance to hold the figure."], [411, 2, 2, 0, "title", "Title for the figure."]], "arch.unitroot.cointegration.engle_granger": [[276, 2, 2, 0, "lags", "The number of lagged differences to include in the Augmented Dickey-Fuller test used on the residuals of the"], [276, 2, 2, 0, "max_lags", "The maximum number of lags to consider when using automatic lag-length in the Augmented Dickey-Fuller regression."], [276, 2, 2, 0, "method", "The method used to select the number of lags included in the Augmented Dickey-Fuller regression."], [276, 2, 2, 0, "trend", "Trend to include in the cointegrating regression. Trends are:"], [276, 2, 2, 0, "x", "The right-hand-side variables in the cointegrating regression."], [276, 2, 2, 0, "y", "The left-hand-side variable in the cointegrating regression."]], "arch.unitroot.cointegration.phillips_ouliaris": [[277, 2, 2, 0, "bandwidth", "The bandwidth to use. If not provided, the optimal bandwidth is estimated from the data. Setting the bandwidth to 0 and using \"unadjusted\" produces the classic OLS covariance estimator. Setting the bandwidth to 0 and using \"robust\" produces White's covariance estimator."], [277, 2, 2, 0, "force_int", "Whether the force the estimated optimal bandwidth to be an integer."], [277, 2, 2, 0, "kernel", "The string name of any of any known kernel-based long-run covariance estimators. Common choices are \"bartlett\" for the Bartlett kernel (Newey-West), \"parzen\" for the Parzen kernel and \"quadratic-spectral\" for the Quadratic Spectral kernel."], [277, 2, 2, 0, "test_type", "The test statistic to compute. Supported options are:"], [277, 2, 2, 0, "trend", "Trend to include in the cointegrating regression. Trends are:"], [277, 2, 2, 0, "x", "The right-hand-side variables in the cointegrating regression."], [277, 2, 2, 0, "y", "The left-hand-side variable in the cointegrating regression."]], "arch.univariate": [[425, 1, 1, 0, "APARCH", "Asymmetric Power ARCH (APARCH) volatility process"], [426, 1, 1, 0, "ARCH", "ARCH process"], [427, 1, 1, 0, "ARCHInMean", "(G)ARCH-in-mean model and simulation"], [428, 1, 1, 0, "ARX", "Autoregressive model with optional exogenous regressors estimation and simulation"], [429, 1, 1, 0, "ConstantMean", "Constant mean model estimation and simulation."], [430, 1, 1, 0, "ConstantVariance", "Constant volatility process"], [431, 1, 1, 0, "EGARCH", "EGARCH model estimation"], [432, 1, 1, 0, "EWMAVariance", "Exponentially Weighted Moving-Average (RiskMetrics) Variance process"], [433, 1, 1, 0, "FIGARCH", "FIGARCH model"], [434, 1, 1, 0, "FixedVariance", "Fixed volatility process"], [435, 1, 1, 0, "GARCH", "GARCH and related model estimation"], [436, 1, 1, 0, "GeneralizedError", "Generalized Error distribution for use with ARCH models"], [437, 1, 1, 0, "HARCH", "Heterogeneous ARCH process"], [438, 1, 1, 0, "HARX", "Heterogeneous Autoregression (HAR), with optional exogenous regressors, model estimation and simulation"], [439, 1, 1, 0, "LS", "Least squares model estimation and simulation"], [440, 1, 1, 0, "MIDASHyperbolic", "MIDAS Hyperbolic ARCH process"], [441, 1, 1, 0, "Normal", "Standard normal distribution for use with ARCH models"], [442, 1, 1, 0, "RiskMetrics2006", "RiskMetrics 2006 Variance process"], [443, 1, 1, 0, "SkewStudent", "Standardized Skewed Student's distribution for use with ARCH models"], [444, 1, 1, 0, "StudentsT", "Standardized Student's distribution for use with ARCH models"], [445, 1, 1, 0, "ZeroMean", "Model with zero conditional mean estimation and simulation"], [891, 6, 1, 0, "arch_model", "Initialization of common ARCH model specifications"]], "arch.univariate.APARCH.__init__": [[425, 2, 2, 0, "common_asym", "Restrict all asymmetry terms to share the same asymmetry parameter. If False (default), then there are no restrictions on the o asymmetry parameters."], [425, 2, 2, 0, "delta", "Value to use for a fixed delta in the APARCH model. If not provided, the value of delta is jointly estimated with other model parameters. User provided delta is restricted to lie in (0.05, 4.0)."], [425, 2, 2, 0, "o", "Order of the asymmetric innovation. Must satisfy o<=p."], [425, 2, 2, 0, "p", "Order of the symmetric innovation. Must satisfy p>=o."], [425, 2, 2, 0, "q", "Order of the lagged (transformed) conditional variance"]], "arch.univariate.APARCH": [[454, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [455, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [456, 3, 1, 0, "bounds", "Returns bounds for parameters"], [457, 5, 1, 0, "common_asym", "The value of delta in the model. NaN is delta is estimated."], [458, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [459, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [460, 5, 1, 0, "delta", "The value of delta in the model. NaN is delta is estimated."], [461, 3, 1, 0, "forecast", "Forecast volatility from the model"], [462, 5, 1, 0, "name", "The name of the volatility process"], [463, 5, 1, 0, "num_params", "The number of parameters in the model"], [464, 3, 1, 0, "parameter_names", "Names of model parameters"], [465, 3, 1, 0, "simulate", "Simulate data from the model"], [466, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [467, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [468, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [469, 3, 1, 0, "update", "Compute the variance for a single observation"], [470, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [471, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [472, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.APARCH.backcast": [[454, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.APARCH.backcast_transform": [[455, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.APARCH.bounds": [[456, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.APARCH.compute_variance": [[458, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [458, 2, 2, 0, "parameters", "Model parameters"], [458, 2, 2, 0, "resids", "Vector of mean zero residuals"], [458, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [458, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.APARCH.forecast": [[461, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [461, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [461, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [461, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [461, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [461, 2, 2, 0, "resids", "Residuals to use in the recursion"], [461, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [461, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [461, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [461, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.APARCH.simulate": [[465, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [465, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [465, 2, 2, 0, "nobs", "Number of data points to simulate"], [465, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [465, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.APARCH.starting_values": [[467, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.APARCH.update": [[469, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [469, 2, 2, 0, "index", "The numerical index of the variance to compute"], [469, 2, 2, 0, "parameters", "The variance model parameters"], [469, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [469, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [469, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.APARCH.variance_bounds": [[471, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [471, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.ARCH.__init__": [[426, 2, 2, 0, "p", "Order of the symmetric innovation"]], "arch.univariate.ARCH": [[473, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [474, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [475, 3, 1, 0, "bounds", "Returns bounds for parameters"], [476, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [477, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [478, 3, 1, 0, "forecast", "Forecast volatility from the model"], [479, 5, 1, 0, "name", "The name of the volatility process"], [480, 5, 1, 0, "num_params", "The number of parameters in the model"], [481, 3, 1, 0, "parameter_names", "Names of model parameters"], [482, 3, 1, 0, "simulate", "Simulate data from the model"], [483, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [484, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [485, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [486, 3, 1, 0, "update", "Compute the variance for a single observation"], [487, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [488, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [489, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.ARCH.backcast": [[473, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.ARCH.backcast_transform": [[474, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.ARCH.bounds": [[475, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.ARCH.compute_variance": [[476, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [476, 2, 2, 0, "parameters", "Model parameters"], [476, 2, 2, 0, "resids", "Vector of mean zero residuals"], [476, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [476, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.ARCH.forecast": [[478, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [478, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [478, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [478, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [478, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [478, 2, 2, 0, "resids", "Residuals to use in the recursion"], [478, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [478, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [478, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [478, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.ARCH.simulate": [[482, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [482, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [482, 2, 2, 0, "nobs", "Number of data points to simulate"], [482, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [482, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.ARCH.starting_values": [[484, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.ARCH.update": [[486, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [486, 2, 2, 0, "index", "The numerical index of the variance to compute"], [486, 2, 2, 0, "parameters", "The variance model parameters"], [486, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [486, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [486, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.ARCH.variance_bounds": [[488, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [488, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.ARCHInMean.__init__": [[427, 2, 2, 0, "constant", "Flag whether the model should include a constant"], [427, 2, 2, 0, "distribution", "Error distribution to use in the model"], [427, 2, 2, 0, "form", "The form of the conditional variance that appears in the mean equation. The string names use the log of the conditional variance (\"log\"), the square-root of the conditional variance (\"vol\") or the conditional variance. When specified using a float, interpreted as \\sigma_t^{form} so that 1 is equivalent to \"vol\" and 2 is equivalent to \"var\". When using a number, must be different from 0."], [427, 2, 2, 0, "hold_back", "Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."], [427, 2, 2, 0, "lags", "Description of lag structure of the HAR.  Scalar included all lags between 1 and the value.  A 1-d array includes the AR lags lags[0], lags[1], ..."], [427, 2, 2, 0, "rescale", "Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."], [427, 2, 2, 0, "volatility", "Volatility process to use in the model. volatility.updateable must return True."], [427, 2, 2, 0, "x", "nobs by k element array containing exogenous regressors"], [427, 2, 2, 0, "y", "nobs element vector containing the dependent variable"]], "arch.univariate.ARCHInMean": [[490, 3, 1, 0, "bounds", "Construct bounds for parameters to use in non-linear optimization"], [491, 3, 1, 0, "compute_param_cov", "Computes parameter covariances using numerical derivatives."], [492, 3, 1, 0, "constraints", "Construct linear constraint arrays  for use in non-linear optimization"], [493, 5, 1, 0, "distribution", "Set or gets the error distribution"], [494, 3, 1, 0, "fit", "Estimate model parameters"], [495, 3, 1, 0, "fix", "Allows an ARCHModelFixedResult to be constructed from fixed parameters."], [496, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [497, 5, 1, 0, "form", "The form of the conditional variance in the mean"], [498, 5, 1, 0, "name", "The name of the model."], [499, 5, 1, 0, "num_params", "Returns the number of parameters"], [500, 3, 1, 0, "parameter_names", "List of parameters names"], [501, 3, 1, 0, "resids", "Compute model residuals"], [502, 3, 1, 0, "simulate", "Simulates data from a linear regression, AR or HAR models"], [503, 3, 1, 0, "starting_values", "Returns starting values for the mean model, often the same as the values returned from fit"], [504, 5, 1, 0, "volatility", "Set or gets the volatility process"], [505, 5, 1, 0, "x", "Gets the value of the exogenous regressors in the model"], [506, 5, 1, 0, "y", "Returns the dependent variable"]], "arch.univariate.ARCHInMean.compute_param_cov": [[491, 2, 2, 0, "backcast", "Value to use for pre-sample observations"], [491, 2, 2, 0, "params", "Model parameters"], [491, 2, 2, 0, "robust", "Flag indicating whether to use robust standard errors (True) or classic MLE (False)"]], "arch.univariate.ARCHInMean.fit": [[494, 2, 2, 0, "backcast", "Value to use as backcast. Should be measure \\sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."], [494, 2, 2, 0, "cov_type", "Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."], [494, 2, 2, 0, "disp", "Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is \"off\" and True is \"final\""], [494, 2, 2, 0, "first_obs", "First observation to use when estimating model"], [494, 2, 2, 0, "last_obs", "Last observation to use when estimating model"], [494, 2, 2, 0, "options", "Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."], [494, 2, 2, 0, "show_warning", "Flag indicating whether convergence warnings should be shown."], [494, 2, 2, 0, "starting_values", "Array of starting values to use.  If not provided, starting values are constructed by the model components."], [494, 2, 2, 0, "tol", "Tolerance for termination."], [494, 2, 2, 0, "update_freq", "Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."]], "arch.univariate.ARCHInMean.fix": [[495, 2, 2, 0, "first_obs", "First observation to use when fixing model"], [495, 2, 2, 0, "last_obs", "Last observation to use when fixing model"], [495, 2, 2, 0, "params", "User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."]], "arch.univariate.ARCHInMean.forecast": [[496, 2, 2, 0, "align", "Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."], [496, 2, 2, 0, "horizon", "Number of steps to forecast"], [496, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [496, 2, 2, 0, "params", "Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."], [496, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [496, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."], [496, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [496, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [496, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."], [496, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"]], "arch.univariate.ARCHInMean.resids": [[501, 2, 2, 0, "params", "Model parameters"], [501, 2, 2, 0, "regressors", "Alternative regressor values to use when computing model residuals"], [501, 2, 2, 0, "y", "Alternative values to use when computing model residuals"]], "arch.univariate.ARCHInMean.simulate": [[502, 2, 2, 0, "burn", "Number of values to simulate to initialize the model and remove dependence on initial values."], [502, 2, 2, 0, "initial_value", "Either a scalar value or max(lags) array set of initial values to use when initializing the model.  If omitted, 0.0 is used."], [502, 2, 2, 0, "initial_value_vol", "An array or scalar to use when initializing the volatility process."], [502, 2, 2, 0, "nobs", "Length of series to simulate"], [502, 2, 2, 0, "params", "Parameters to use when simulating the model.  Parameter order is [mean volatility distribution] where the parameters of the mean model are ordered [constant lag[0] lag[1] ... lag[p] ex[0] ... ex[k-1]] where lag[j] indicates the coefficient on the jth lag in the model and ex[j] is the coefficient on the jth exogenous variable."], [502, 2, 2, 0, "x", "nobs + burn by k array of exogenous variables to include in the simulation."]], "arch.univariate.ARX.__init__": [[428, 2, 2, 0, "constant", "Flag whether the model should include a constant"], [428, 2, 2, 0, "distribution", "Error distribution to use in the model"], [428, 2, 2, 0, "hold_back", "Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."], [428, 2, 2, 0, "lags", "Description of lag structure of the HAR.  Scalar included all lags between 1 and the value.  A 1-d array includes the AR lags lags[0], lags[1], ..."], [428, 2, 2, 0, "rescale", "Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."], [428, 2, 2, 0, "volatility", "Volatility process to use in the model"], [428, 2, 2, 0, "x", "nobs by k element array containing exogenous regressors"], [428, 2, 2, 0, "y", "nobs element vector containing the dependent variable"]], "arch.univariate.ARX": [[507, 3, 1, 0, "bounds", "Construct bounds for parameters to use in non-linear optimization"], [508, 3, 1, 0, "compute_param_cov", "Computes parameter covariances using numerical derivatives."], [509, 3, 1, 0, "constraints", "Construct linear constraint arrays  for use in non-linear optimization"], [510, 5, 1, 0, "distribution", "Set or gets the error distribution"], [511, 3, 1, 0, "fit", "Estimate model parameters"], [512, 3, 1, 0, "fix", "Allows an ARCHModelFixedResult to be constructed from fixed parameters."], [513, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [514, 5, 1, 0, "name", "The name of the model."], [515, 5, 1, 0, "num_params", "Returns the number of parameters"], [516, 3, 1, 0, "parameter_names", "List of parameters names"], [517, 3, 1, 0, "resids", "Compute model residuals"], [518, 3, 1, 0, "simulate", "Simulates data from a linear regression, AR or HAR models"], [519, 3, 1, 0, "starting_values", "Returns starting values for the mean model, often the same as the values returned from fit"], [520, 5, 1, 0, "volatility", "Set or gets the volatility process"], [521, 5, 1, 0, "x", "Gets the value of the exogenous regressors in the model"], [522, 5, 1, 0, "y", "Returns the dependent variable"]], "arch.univariate.ARX.compute_param_cov": [[508, 2, 2, 0, "backcast", "Value to use for pre-sample observations"], [508, 2, 2, 0, "params", "Model parameters"], [508, 2, 2, 0, "robust", "Flag indicating whether to use robust standard errors (True) or classic MLE (False)"]], "arch.univariate.ARX.fit": [[511, 2, 2, 0, "backcast", "Value to use as backcast. Should be measure \\sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."], [511, 2, 2, 0, "cov_type", "Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."], [511, 2, 2, 0, "disp", "Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is \"off\" and True is \"final\""], [511, 2, 2, 0, "first_obs", "First observation to use when estimating model"], [511, 2, 2, 0, "last_obs", "Last observation to use when estimating model"], [511, 2, 2, 0, "options", "Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."], [511, 2, 2, 0, "show_warning", "Flag indicating whether convergence warnings should be shown."], [511, 2, 2, 0, "starting_values", "Array of starting values to use.  If not provided, starting values are constructed by the model components."], [511, 2, 2, 0, "tol", "Tolerance for termination."], [511, 2, 2, 0, "update_freq", "Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."]], "arch.univariate.ARX.fix": [[512, 2, 2, 0, "first_obs", "First observation to use when fixing model"], [512, 2, 2, 0, "last_obs", "Last observation to use when fixing model"], [512, 2, 2, 0, "params", "User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."]], "arch.univariate.ARX.forecast": [[513, 2, 2, 0, "align", "Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."], [513, 2, 2, 0, "horizon", "Number of steps to forecast"], [513, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [513, 2, 2, 0, "params", "Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."], [513, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [513, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."], [513, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [513, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [513, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."], [513, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"]], "arch.univariate.ARX.resids": [[517, 2, 2, 0, "params", "Model parameters"], [517, 2, 2, 0, "regressors", "Alternative regressor values to use when computing model residuals"], [517, 2, 2, 0, "y", "Alternative values to use when computing model residuals"]], "arch.univariate.ARX.simulate": [[518, 2, 2, 0, "burn", "Number of values to simulate to initialize the model and remove dependence on initial values."], [518, 2, 2, 0, "initial_value", "Either a scalar value or max(lags) array set of initial values to use when initializing the model.  If omitted, 0.0 is used."], [518, 2, 2, 0, "initial_value_vol", "An array or scalar to use when initializing the volatility process."], [518, 2, 2, 0, "nobs", "Length of series to simulate"], [518, 2, 2, 0, "params", "Parameters to use when simulating the model.  Parameter order is [mean volatility distribution] where the parameters of the mean model are ordered [constant lag[0] lag[1] ... lag[p] ex[0] ... ex[k-1]] where lag[j] indicates the coefficient on the jth lag in the model and ex[j] is the coefficient on the jth exogenous variable."], [518, 2, 2, 0, "x", "nobs + burn by k array of exogenous variables to include in the simulation."]], "arch.univariate.ConstantMean.__init__": [[429, 2, 2, 0, "distribution", "Error distribution to use in the model"], [429, 2, 2, 0, "hold_back", "Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."], [429, 2, 2, 0, "rescale", "Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."], [429, 2, 2, 0, "volatility", "Volatility process to use in the model"], [429, 2, 2, 0, "y", "nobs element vector containing the dependent variable"]], "arch.univariate.ConstantMean": [[523, 3, 1, 0, "bounds", "Construct bounds for parameters to use in non-linear optimization"], [524, 3, 1, 0, "compute_param_cov", "Computes parameter covariances using numerical derivatives."], [525, 3, 1, 0, "constraints", "Construct linear constraint arrays  for use in non-linear optimization"], [526, 5, 1, 0, "distribution", "Set or gets the error distribution"], [527, 3, 1, 0, "fit", "Estimate model parameters"], [528, 3, 1, 0, "fix", "Allows an ARCHModelFixedResult to be constructed from fixed parameters."], [529, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [530, 5, 1, 0, "name", "The name of the model."], [531, 5, 1, 0, "num_params", "Returns the number of parameters"], [532, 3, 1, 0, "parameter_names", "List of parameters names"], [533, 3, 1, 0, "resids", "Compute model residuals"], [534, 3, 1, 0, "simulate", "Simulated data from a constant mean model"], [535, 3, 1, 0, "starting_values", "Returns starting values for the mean model, often the same as the values returned from fit"], [536, 5, 1, 0, "volatility", "Set or gets the volatility process"], [537, 5, 1, 0, "x", "Gets the value of the exogenous regressors in the model"], [538, 5, 1, 0, "y", "Returns the dependent variable"]], "arch.univariate.ConstantMean.compute_param_cov": [[524, 2, 2, 0, "backcast", "Value to use for pre-sample observations"], [524, 2, 2, 0, "params", "Model parameters"], [524, 2, 2, 0, "robust", "Flag indicating whether to use robust standard errors (True) or classic MLE (False)"]], "arch.univariate.ConstantMean.fit": [[527, 2, 2, 0, "backcast", "Value to use as backcast. Should be measure \\sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."], [527, 2, 2, 0, "cov_type", "Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."], [527, 2, 2, 0, "disp", "Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is \"off\" and True is \"final\""], [527, 2, 2, 0, "first_obs", "First observation to use when estimating model"], [527, 2, 2, 0, "last_obs", "Last observation to use when estimating model"], [527, 2, 2, 0, "options", "Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."], [527, 2, 2, 0, "show_warning", "Flag indicating whether convergence warnings should be shown."], [527, 2, 2, 0, "starting_values", "Array of starting values to use.  If not provided, starting values are constructed by the model components."], [527, 2, 2, 0, "tol", "Tolerance for termination."], [527, 2, 2, 0, "update_freq", "Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."]], "arch.univariate.ConstantMean.fix": [[528, 2, 2, 0, "first_obs", "First observation to use when fixing model"], [528, 2, 2, 0, "last_obs", "Last observation to use when fixing model"], [528, 2, 2, 0, "params", "User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."]], "arch.univariate.ConstantMean.forecast": [[529, 2, 2, 0, "align", "Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."], [529, 2, 2, 0, "horizon", "Number of steps to forecast"], [529, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [529, 2, 2, 0, "params", "Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."], [529, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [529, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."], [529, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [529, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [529, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."], [529, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"]], "arch.univariate.ConstantMean.resids": [[533, 2, 2, 0, "params", "Model parameters"], [533, 2, 2, 0, "regressors", "Alternative regressor values to use when computing model residuals"], [533, 2, 2, 0, "y", "Alternative values to use when computing model residuals"]], "arch.univariate.ConstantMean.simulate": [[534, 2, 2, 0, "burn", "Number of values to simulate to initialize the model and remove dependence on initial values."], [534, 2, 2, 0, "initial_value", "This value is not used."], [534, 2, 2, 0, "initial_value_vol", "An array or scalar to use when initializing the volatility process."], [534, 2, 2, 0, "nobs", "Length of series to simulate"], [534, 2, 2, 0, "params", "Parameters to use when simulating the model.  Parameter order is [mean volatility distribution]. There is one parameter in the mean model, mu."], [534, 2, 2, 0, "x", "This value is not used."]], "arch.univariate.ConstantVariance": [[539, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [540, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [541, 3, 1, 0, "bounds", "Returns bounds for parameters"], [542, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [543, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [544, 3, 1, 0, "forecast", "Forecast volatility from the model"], [545, 5, 1, 0, "name", "The name of the volatility process"], [546, 5, 1, 0, "num_params", "The number of parameters in the model"], [547, 3, 1, 0, "parameter_names", "Names of model parameters"], [548, 3, 1, 0, "simulate", "Simulate data from the model"], [549, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [550, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [551, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [552, 3, 1, 0, "update", "Compute the variance for a single observation"], [553, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [554, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [555, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.ConstantVariance.backcast": [[539, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.ConstantVariance.backcast_transform": [[540, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.ConstantVariance.bounds": [[541, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.ConstantVariance.compute_variance": [[542, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [542, 2, 2, 0, "parameters", "Model parameters"], [542, 2, 2, 0, "resids", "Vector of mean zero residuals"], [542, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [542, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.ConstantVariance.forecast": [[544, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [544, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [544, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [544, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [544, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [544, 2, 2, 0, "resids", "Residuals to use in the recursion"], [544, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [544, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [544, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [544, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.ConstantVariance.simulate": [[548, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [548, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [548, 2, 2, 0, "nobs", "Number of data points to simulate"], [548, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [548, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.ConstantVariance.starting_values": [[550, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.ConstantVariance.update": [[552, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [552, 2, 2, 0, "index", "The numerical index of the variance to compute"], [552, 2, 2, 0, "parameters", "The variance model parameters"], [552, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [552, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [552, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.ConstantVariance.variance_bounds": [[554, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [554, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.EGARCH.__init__": [[431, 2, 2, 0, "o", "Order of the asymmetric innovation"], [431, 2, 2, 0, "p", "Order of the symmetric innovation"], [431, 2, 2, 0, "q", "Order of the lagged (transformed) conditional variance"]], "arch.univariate.EGARCH": [[556, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [557, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [558, 3, 1, 0, "bounds", "Returns bounds for parameters"], [559, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [560, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [561, 3, 1, 0, "forecast", "Forecast volatility from the model"], [562, 5, 1, 0, "name", "The name of the volatility process"], [563, 5, 1, 0, "num_params", "The number of parameters in the model"], [564, 3, 1, 0, "parameter_names", "Names of model parameters"], [565, 3, 1, 0, "simulate", "Simulate data from the model"], [566, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [567, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [568, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [569, 3, 1, 0, "update", "Compute the variance for a single observation"], [570, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [571, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [572, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.EGARCH.backcast": [[556, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.EGARCH.backcast_transform": [[557, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.EGARCH.bounds": [[558, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.EGARCH.compute_variance": [[559, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [559, 2, 2, 0, "parameters", "Model parameters"], [559, 2, 2, 0, "resids", "Vector of mean zero residuals"], [559, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [559, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.EGARCH.forecast": [[561, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [561, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [561, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [561, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [561, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [561, 2, 2, 0, "resids", "Residuals to use in the recursion"], [561, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [561, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [561, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [561, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.EGARCH.simulate": [[565, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [565, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [565, 2, 2, 0, "nobs", "Number of data points to simulate"], [565, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [565, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.EGARCH.starting_values": [[567, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.EGARCH.update": [[569, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [569, 2, 2, 0, "index", "The numerical index of the variance to compute"], [569, 2, 2, 0, "parameters", "The variance model parameters"], [569, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [569, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [569, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.EGARCH.variance_bounds": [[571, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [571, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.EWMAVariance.__init__": [[432, 2, 2, 0, "lam", "Smoothing parameter. Default is 0.94. Set to None to estimate lam jointly with other model parameters"]], "arch.univariate.EWMAVariance": [[573, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [574, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [575, 3, 1, 0, "bounds", "Returns bounds for parameters"], [576, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [577, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [578, 3, 1, 0, "forecast", "Forecast volatility from the model"], [579, 5, 1, 0, "name", "The name of the volatility process"], [580, 5, 1, 0, "num_params", "The number of parameters in the model"], [581, 3, 1, 0, "parameter_names", "Names of model parameters"], [582, 3, 1, 0, "simulate", "Simulate data from the model"], [583, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [584, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [585, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [586, 3, 1, 0, "update", "Compute the variance for a single observation"], [587, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [588, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [589, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.EWMAVariance.backcast": [[573, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.EWMAVariance.backcast_transform": [[574, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.EWMAVariance.bounds": [[575, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.EWMAVariance.compute_variance": [[576, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [576, 2, 2, 0, "parameters", "Model parameters"], [576, 2, 2, 0, "resids", "Vector of mean zero residuals"], [576, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [576, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.EWMAVariance.forecast": [[578, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [578, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [578, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [578, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [578, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [578, 2, 2, 0, "resids", "Residuals to use in the recursion"], [578, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [578, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [578, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [578, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.EWMAVariance.simulate": [[582, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [582, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [582, 2, 2, 0, "nobs", "Number of data points to simulate"], [582, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [582, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.EWMAVariance.starting_values": [[584, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.EWMAVariance.update": [[586, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [586, 2, 2, 0, "index", "The numerical index of the variance to compute"], [586, 2, 2, 0, "parameters", "The variance model parameters"], [586, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [586, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [586, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.EWMAVariance.variance_bounds": [[588, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [588, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.FIGARCH.__init__": [[433, 2, 2, 0, "p", "Order of the symmetric innovation"], [433, 2, 2, 0, "power", "Power to use with the innovations, abs(e) ** power.  Default is 2.0, which produces FIGARCH and related models. Using 1.0 produces FIAVARCH and related models.  Other powers can be specified, although these should be strictly positive, and usually larger than 0.25."], [433, 2, 2, 0, "q", "Order of the lagged (transformed) conditional variance"], [433, 2, 2, 0, "truncation", "Truncation point to use in ARCH(\\infty) representation. Default is 1000."]], "arch.univariate.FIGARCH": [[590, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [591, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [592, 3, 1, 0, "bounds", "Returns bounds for parameters"], [593, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [594, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [595, 3, 1, 0, "forecast", "Forecast volatility from the model"], [596, 5, 1, 0, "name", "The name of the volatility process"], [597, 5, 1, 0, "num_params", "The number of parameters in the model"], [598, 3, 1, 0, "parameter_names", "Names of model parameters"], [599, 3, 1, 0, "simulate", "Simulate data from the model"], [600, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [601, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [602, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [603, 5, 1, 0, "truncation", "Truncation lag for the ARCH-infinity approximation"], [604, 3, 1, 0, "update", "Compute the variance for a single observation"], [605, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [606, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [607, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.FIGARCH.backcast": [[590, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.FIGARCH.backcast_transform": [[591, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.FIGARCH.bounds": [[592, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.FIGARCH.compute_variance": [[593, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [593, 2, 2, 0, "parameters", "Model parameters"], [593, 2, 2, 0, "resids", "Vector of mean zero residuals"], [593, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [593, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.FIGARCH.forecast": [[595, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [595, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [595, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [595, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [595, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [595, 2, 2, 0, "resids", "Residuals to use in the recursion"], [595, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [595, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [595, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [595, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.FIGARCH.simulate": [[599, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [599, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [599, 2, 2, 0, "nobs", "Number of data points to simulate"], [599, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [599, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.FIGARCH.starting_values": [[601, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.FIGARCH.update": [[604, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [604, 2, 2, 0, "index", "The numerical index of the variance to compute"], [604, 2, 2, 0, "parameters", "The variance model parameters"], [604, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [604, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [604, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.FIGARCH.variance_bounds": [[606, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [606, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.FixedVariance.__init__": [[434, 2, 2, 0, "unit_scale", "Flag whether to enforce a unit scale.  If False, a scale parameter will be estimated so that the model variance will be proportional to variance. If True, the model variance is set of variance"], [434, 2, 2, 0, "variance", "Array containing the variances to use.  Should have the same shape as the data used in the model."]], "arch.univariate.FixedVariance": [[608, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [609, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [610, 3, 1, 0, "bounds", "Returns bounds for parameters"], [611, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [612, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [613, 3, 1, 0, "forecast", "Forecast volatility from the model"], [614, 5, 1, 0, "name", "The name of the volatility process"], [615, 5, 1, 0, "num_params", "The number of parameters in the model"], [616, 3, 1, 0, "parameter_names", "Names of model parameters"], [617, 3, 1, 0, "simulate", "Simulate data from the model"], [618, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [619, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [620, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [621, 3, 1, 0, "update", "Compute the variance for a single observation"], [622, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [623, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [624, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.FixedVariance.backcast": [[608, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.FixedVariance.backcast_transform": [[609, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.FixedVariance.bounds": [[610, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.FixedVariance.compute_variance": [[611, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [611, 2, 2, 0, "parameters", "Model parameters"], [611, 2, 2, 0, "resids", "Vector of mean zero residuals"], [611, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [611, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.FixedVariance.forecast": [[613, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [613, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [613, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [613, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [613, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [613, 2, 2, 0, "resids", "Residuals to use in the recursion"], [613, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [613, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [613, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [613, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.FixedVariance.simulate": [[617, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [617, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [617, 2, 2, 0, "nobs", "Number of data points to simulate"], [617, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [617, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.FixedVariance.starting_values": [[619, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.FixedVariance.update": [[621, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [621, 2, 2, 0, "index", "The numerical index of the variance to compute"], [621, 2, 2, 0, "parameters", "The variance model parameters"], [621, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [621, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [621, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.FixedVariance.variance_bounds": [[623, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [623, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.GARCH.__init__": [[435, 2, 2, 0, "o", "Order of the asymmetric innovation"], [435, 2, 2, 0, "p", "Order of the symmetric innovation"], [435, 2, 2, 0, "power", "Power to use with the innovations, abs(e) ** power.  Default is 2.0, which produces ARCH and related models. Using 1.0 produces AVARCH and related models.  Other powers can be specified, although these should be strictly positive, and usually larger than 0.25."], [435, 2, 2, 0, "q", "Order of the lagged (transformed) conditional variance"]], "arch.univariate.GARCH": [[625, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [626, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [627, 3, 1, 0, "bounds", "Returns bounds for parameters"], [628, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [629, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [630, 3, 1, 0, "forecast", "Forecast volatility from the model"], [631, 5, 1, 0, "name", "The name of the volatility process"], [632, 5, 1, 0, "num_params", "The number of parameters in the model"], [633, 3, 1, 0, "parameter_names", "Names of model parameters"], [634, 3, 1, 0, "simulate", "Simulate data from the model"], [635, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [636, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [637, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [638, 3, 1, 0, "update", "Compute the variance for a single observation"], [639, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [640, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [641, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.GARCH.backcast": [[625, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.GARCH.backcast_transform": [[626, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.GARCH.bounds": [[627, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.GARCH.compute_variance": [[628, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [628, 2, 2, 0, "parameters", "Model parameters"], [628, 2, 2, 0, "resids", "Vector of mean zero residuals"], [628, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [628, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.GARCH.forecast": [[630, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [630, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [630, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [630, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [630, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [630, 2, 2, 0, "resids", "Residuals to use in the recursion"], [630, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [630, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [630, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [630, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.GARCH.simulate": [[634, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [634, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [634, 2, 2, 0, "nobs", "Number of data points to simulate"], [634, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [634, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.GARCH.starting_values": [[636, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.GARCH.update": [[638, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [638, 2, 2, 0, "index", "The numerical index of the variance to compute"], [638, 2, 2, 0, "parameters", "The variance model parameters"], [638, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [638, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [638, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.GARCH.variance_bounds": [[640, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [640, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.GeneralizedError.__init__": [[436, 2, 2, 0, "random_state", "Deprecated since version 5.0: random_state is deprecated. Use seed instead."], [436, 2, 2, 0, "seed", "Random number generator instance or int to use. Set to ensure reproducibility. If using an int, the argument is passed to np.random.default_rng.  If not provided, default_rng is used with system-provided entropy."]], "arch.univariate.GeneralizedError": [[642, 3, 1, 0, "bounds", "Parameter bounds for use in optimization."], [643, 3, 1, 0, "cdf", "Cumulative distribution function"], [644, 3, 1, 0, "constraints", "Construct arrays to use in constrained optimization."], [645, 5, 1, 0, "generator", "The NumPy Generator or RandomState attached to the distribution"], [646, 3, 1, 0, "loglikelihood", "Computes the log-likelihood of assuming residuals are have a Generalized Error Distribution, conditional on the variance."], [647, 3, 1, 0, "moment", "Moment of order n"], [648, 5, 1, 0, "name", "The name of the distribution"], [649, 3, 1, 0, "parameter_names", "Names of distribution shape parameters"], [650, 3, 1, 0, "partial_moment", "Order n lower partial moment from -inf to z"], [651, 3, 1, 0, "ppf", "Inverse cumulative density function (ICDF)"], [652, 5, 1, 0, "random_state", "The NumPy RandomState attached to the distribution"], [653, 3, 1, 0, "simulate", "Simulates i.i.d. draws from the distribution"], [654, 3, 1, 0, "starting_values", "Construct starting values for use in optimization."]], "arch.univariate.GeneralizedError.bounds": [[642, 2, 2, 0, "resids", "Residuals to use when computing the bounds"]], "arch.univariate.GeneralizedError.cdf": [[643, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [643, 2, 2, 0, "resids", "Values at which to evaluate the cdf"]], "arch.univariate.GeneralizedError.loglikelihood": [[646, 2, 2, 0, "individual", "Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"], [646, 2, 2, 0, "parameters", "Shape parameter of the GED distribution"], [646, 2, 2, 0, "resids", "The residuals to use in the log-likelihood calculation"], [646, 2, 2, 0, "sigma2", "Conditional variances of resids"]], "arch.univariate.GeneralizedError.moment": [[647, 2, 2, 0, "n", "Order of moment"], [647, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."]], "arch.univariate.GeneralizedError.partial_moment": [[650, 2, 2, 0, "n", "Order of partial moment"], [650, 2, 2, 0, "parameters", "Distribution parameters.  Use None for parameterless distributions."], [650, 2, 2, 0, "z", "Upper bound for partial moment integral"]], "arch.univariate.GeneralizedError.ppf": [[651, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [651, 2, 2, 0, "pits", "Probability-integral-transformed values in the interval (0, 1)."]], "arch.univariate.GeneralizedError.simulate": [[653, 2, 2, 0, "parameters", "Distribution parameters"]], "arch.univariate.GeneralizedError.starting_values": [[654, 2, 2, 0, "std_resid", "Estimated standardized residuals to use in computing starting values for the shape parameter"]], "arch.univariate.HARCH.__init__": [[437, 2, 2, 0, "lags", "List of lags to include in the model, or if scalar, includes all lags up the value"]], "arch.univariate.HARCH": [[655, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [656, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [657, 3, 1, 0, "bounds", "Returns bounds for parameters"], [658, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [659, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [660, 3, 1, 0, "forecast", "Forecast volatility from the model"], [661, 5, 1, 0, "name", "The name of the volatility process"], [662, 5, 1, 0, "num_params", "The number of parameters in the model"], [663, 3, 1, 0, "parameter_names", "Names of model parameters"], [664, 3, 1, 0, "simulate", "Simulate data from the model"], [665, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [666, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [667, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [668, 3, 1, 0, "update", "Compute the variance for a single observation"], [669, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [670, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [671, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.HARCH.backcast": [[655, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.HARCH.backcast_transform": [[656, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.HARCH.bounds": [[657, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.HARCH.compute_variance": [[658, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [658, 2, 2, 0, "parameters", "Model parameters"], [658, 2, 2, 0, "resids", "Vector of mean zero residuals"], [658, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [658, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.HARCH.forecast": [[660, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [660, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [660, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [660, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [660, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [660, 2, 2, 0, "resids", "Residuals to use in the recursion"], [660, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [660, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [660, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [660, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.HARCH.simulate": [[664, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [664, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [664, 2, 2, 0, "nobs", "Number of data points to simulate"], [664, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [664, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.HARCH.starting_values": [[666, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.HARCH.update": [[668, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [668, 2, 2, 0, "index", "The numerical index of the variance to compute"], [668, 2, 2, 0, "parameters", "The variance model parameters"], [668, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [668, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [668, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.HARCH.variance_bounds": [[670, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [670, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.HARX.__init__": [[438, 2, 2, 0, "constant", "Flag whether the model should include a constant"], [438, 2, 2, 0, "distribution", "Error distribution to use in the model"], [438, 2, 2, 0, "hold_back", "Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."], [438, 2, 2, 0, "lags", "Description of lag structure of the HAR."], [438, 2, 2, 0, "rescale", "Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."], [438, 2, 2, 0, "use_rotated", "Flag indicating to use the alternative rotated form of the HAR where HAR lags do not overlap"], [438, 2, 2, 0, "volatility", "Volatility process to use in the model"], [438, 2, 2, 0, "x", "nobs by k element array containing exogenous regressors"], [438, 2, 2, 0, "y", "nobs element vector containing the dependent variable"]], "arch.univariate.HARX": [[672, 3, 1, 0, "bounds", "Construct bounds for parameters to use in non-linear optimization"], [673, 3, 1, 0, "compute_param_cov", "Computes parameter covariances using numerical derivatives."], [674, 3, 1, 0, "constraints", "Construct linear constraint arrays  for use in non-linear optimization"], [675, 5, 1, 0, "distribution", "Set or gets the error distribution"], [676, 3, 1, 0, "fit", "Estimate model parameters"], [677, 3, 1, 0, "fix", "Allows an ARCHModelFixedResult to be constructed from fixed parameters."], [678, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [679, 5, 1, 0, "name", "The name of the model."], [680, 5, 1, 0, "num_params", "Returns the number of parameters"], [681, 3, 1, 0, "parameter_names", "List of parameters names"], [682, 3, 1, 0, "resids", "Compute model residuals"], [683, 3, 1, 0, "simulate", "Simulates data from a linear regression, AR or HAR models"], [684, 3, 1, 0, "starting_values", "Returns starting values for the mean model, often the same as the values returned from fit"], [685, 5, 1, 0, "volatility", "Set or gets the volatility process"], [686, 5, 1, 0, "x", "Gets the value of the exogenous regressors in the model"], [687, 5, 1, 0, "y", "Returns the dependent variable"]], "arch.univariate.HARX.compute_param_cov": [[673, 2, 2, 0, "backcast", "Value to use for pre-sample observations"], [673, 2, 2, 0, "params", "Model parameters"], [673, 2, 2, 0, "robust", "Flag indicating whether to use robust standard errors (True) or classic MLE (False)"]], "arch.univariate.HARX.fit": [[676, 2, 2, 0, "backcast", "Value to use as backcast. Should be measure \\sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."], [676, 2, 2, 0, "cov_type", "Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."], [676, 2, 2, 0, "disp", "Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is \"off\" and True is \"final\""], [676, 2, 2, 0, "first_obs", "First observation to use when estimating model"], [676, 2, 2, 0, "last_obs", "Last observation to use when estimating model"], [676, 2, 2, 0, "options", "Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."], [676, 2, 2, 0, "show_warning", "Flag indicating whether convergence warnings should be shown."], [676, 2, 2, 0, "starting_values", "Array of starting values to use.  If not provided, starting values are constructed by the model components."], [676, 2, 2, 0, "tol", "Tolerance for termination."], [676, 2, 2, 0, "update_freq", "Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."]], "arch.univariate.HARX.fix": [[677, 2, 2, 0, "first_obs", "First observation to use when fixing model"], [677, 2, 2, 0, "last_obs", "Last observation to use when fixing model"], [677, 2, 2, 0, "params", "User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."]], "arch.univariate.HARX.forecast": [[678, 2, 2, 0, "align", "Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."], [678, 2, 2, 0, "horizon", "Number of steps to forecast"], [678, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [678, 2, 2, 0, "params", "Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."], [678, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [678, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."], [678, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [678, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [678, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."], [678, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"]], "arch.univariate.HARX.resids": [[682, 2, 2, 0, "params", "Model parameters"], [682, 2, 2, 0, "regressors", "Alternative regressor values to use when computing model residuals"], [682, 2, 2, 0, "y", "Alternative values to use when computing model residuals"]], "arch.univariate.HARX.simulate": [[683, 2, 2, 0, "burn", "Number of values to simulate to initialize the model and remove dependence on initial values."], [683, 2, 2, 0, "initial_value", "Either a scalar value or max(lags) array set of initial values to use when initializing the model.  If omitted, 0.0 is used."], [683, 2, 2, 0, "initial_value_vol", "An array or scalar to use when initializing the volatility process."], [683, 2, 2, 0, "nobs", "Length of series to simulate"], [683, 2, 2, 0, "params", "Parameters to use when simulating the model.  Parameter order is [mean volatility distribution] where the parameters of the mean model are ordered [constant lag[0] lag[1] ... lag[p] ex[0] ... ex[k-1]] where lag[j] indicates the coefficient on the jth lag in the model and ex[j] is the coefficient on the jth exogenous variable."], [683, 2, 2, 0, "x", "nobs + burn by k array of exogenous variables to include in the simulation."]], "arch.univariate.LS.__init__": [[439, 2, 2, 0, "constant", "Flag whether the model should include a constant"], [439, 2, 2, 0, "distribution", "Error distribution to use in the model"], [439, 2, 2, 0, "hold_back", "Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."], [439, 2, 2, 0, "rescale", "Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."], [439, 2, 2, 0, "volatility", "Volatility process to use in the model"], [439, 2, 2, 0, "y", "nobs by k element array containing exogenous regressors"]], "arch.univariate.LS": [[688, 3, 1, 0, "bounds", "Construct bounds for parameters to use in non-linear optimization"], [689, 3, 1, 0, "compute_param_cov", "Computes parameter covariances using numerical derivatives."], [690, 3, 1, 0, "constraints", "Construct linear constraint arrays  for use in non-linear optimization"], [691, 5, 1, 0, "distribution", "Set or gets the error distribution"], [692, 3, 1, 0, "fit", "Estimate model parameters"], [693, 3, 1, 0, "fix", "Allows an ARCHModelFixedResult to be constructed from fixed parameters."], [694, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [695, 5, 1, 0, "name", "The name of the model."], [696, 5, 1, 0, "num_params", "Returns the number of parameters"], [697, 3, 1, 0, "parameter_names", "List of parameters names"], [698, 3, 1, 0, "resids", "Compute model residuals"], [699, 3, 1, 0, "simulate", "Simulates data from a linear regression, AR or HAR models"], [700, 3, 1, 0, "starting_values", "Returns starting values for the mean model, often the same as the values returned from fit"], [701, 5, 1, 0, "volatility", "Set or gets the volatility process"], [702, 5, 1, 0, "x", "Gets the value of the exogenous regressors in the model"], [703, 5, 1, 0, "y", "Returns the dependent variable"]], "arch.univariate.LS.compute_param_cov": [[689, 2, 2, 0, "backcast", "Value to use for pre-sample observations"], [689, 2, 2, 0, "params", "Model parameters"], [689, 2, 2, 0, "robust", "Flag indicating whether to use robust standard errors (True) or classic MLE (False)"]], "arch.univariate.LS.fit": [[692, 2, 2, 0, "backcast", "Value to use as backcast. Should be measure \\sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."], [692, 2, 2, 0, "cov_type", "Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."], [692, 2, 2, 0, "disp", "Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is \"off\" and True is \"final\""], [692, 2, 2, 0, "first_obs", "First observation to use when estimating model"], [692, 2, 2, 0, "last_obs", "Last observation to use when estimating model"], [692, 2, 2, 0, "options", "Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."], [692, 2, 2, 0, "show_warning", "Flag indicating whether convergence warnings should be shown."], [692, 2, 2, 0, "starting_values", "Array of starting values to use.  If not provided, starting values are constructed by the model components."], [692, 2, 2, 0, "tol", "Tolerance for termination."], [692, 2, 2, 0, "update_freq", "Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."]], "arch.univariate.LS.fix": [[693, 2, 2, 0, "first_obs", "First observation to use when fixing model"], [693, 2, 2, 0, "last_obs", "Last observation to use when fixing model"], [693, 2, 2, 0, "params", "User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."]], "arch.univariate.LS.forecast": [[694, 2, 2, 0, "align", "Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."], [694, 2, 2, 0, "horizon", "Number of steps to forecast"], [694, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [694, 2, 2, 0, "params", "Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."], [694, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [694, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."], [694, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [694, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [694, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."], [694, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"]], "arch.univariate.LS.resids": [[698, 2, 2, 0, "params", "Model parameters"], [698, 2, 2, 0, "regressors", "Alternative regressor values to use when computing model residuals"], [698, 2, 2, 0, "y", "Alternative values to use when computing model residuals"]], "arch.univariate.LS.simulate": [[699, 2, 2, 0, "burn", "Number of values to simulate to initialize the model and remove dependence on initial values."], [699, 2, 2, 0, "initial_value", "Either a scalar value or max(lags) array set of initial values to use when initializing the model.  If omitted, 0.0 is used."], [699, 2, 2, 0, "initial_value_vol", "An array or scalar to use when initializing the volatility process."], [699, 2, 2, 0, "nobs", "Length of series to simulate"], [699, 2, 2, 0, "params", "Parameters to use when simulating the model.  Parameter order is [mean volatility distribution] where the parameters of the mean model are ordered [constant lag[0] lag[1] ... lag[p] ex[0] ... ex[k-1]] where lag[j] indicates the coefficient on the jth lag in the model and ex[j] is the coefficient on the jth exogenous variable."], [699, 2, 2, 0, "x", "nobs + burn by k array of exogenous variables to include in the simulation."]], "arch.univariate.MIDASHyperbolic.__init__": [[440, 2, 2, 0, "asym", "Flag indicating whether to include an asymmetric term"], [440, 2, 2, 0, "m", "Length of maximum lag to include in the model"]], "arch.univariate.MIDASHyperbolic": [[704, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [705, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [706, 3, 1, 0, "bounds", "Returns bounds for parameters"], [707, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [708, 3, 1, 0, "constraints", "Constraints"], [709, 3, 1, 0, "forecast", "Forecast volatility from the model"], [710, 5, 1, 0, "name", "The name of the volatility process"], [711, 5, 1, 0, "num_params", "The number of parameters in the model"], [712, 3, 1, 0, "parameter_names", "Names of model parameters"], [713, 3, 1, 0, "simulate", "Simulate data from the model"], [714, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [715, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [716, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [717, 3, 1, 0, "update", "Compute the variance for a single observation"], [718, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [719, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [720, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.MIDASHyperbolic.backcast": [[704, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.MIDASHyperbolic.backcast_transform": [[705, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.MIDASHyperbolic.bounds": [[706, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.MIDASHyperbolic.compute_variance": [[707, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [707, 2, 2, 0, "parameters", "Model parameters"], [707, 2, 2, 0, "resids", "Vector of mean zero residuals"], [707, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [707, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.MIDASHyperbolic.forecast": [[709, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [709, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [709, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [709, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [709, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [709, 2, 2, 0, "resids", "Residuals to use in the recursion"], [709, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [709, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [709, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [709, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.MIDASHyperbolic.simulate": [[713, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [713, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [713, 2, 2, 0, "nobs", "Number of data points to simulate"], [713, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [713, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.MIDASHyperbolic.starting_values": [[715, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.MIDASHyperbolic.update": [[717, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [717, 2, 2, 0, "index", "The numerical index of the variance to compute"], [717, 2, 2, 0, "parameters", "The variance model parameters"], [717, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [717, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [717, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.MIDASHyperbolic.variance_bounds": [[719, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [719, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.Normal.__init__": [[441, 2, 2, 0, "random_state", "Deprecated since version 5.0: random_state is deprecated. Use seed instead."], [441, 2, 2, 0, "seed", "Random number generator instance or int to use. Set to ensure reproducibility. If using an int, the argument is passed to np.random.default_rng.  If not provided, default_rng is used with system-provided entropy."]], "arch.univariate.Normal": [[721, 3, 1, 0, "bounds", "Parameter bounds for use in optimization."], [722, 3, 1, 0, "cdf", "Cumulative distribution function"], [723, 3, 1, 0, "constraints", "Construct arrays to use in constrained optimization."], [724, 5, 1, 0, "generator", "The NumPy Generator or RandomState attached to the distribution"], [725, 3, 1, 0, "loglikelihood", "Computes the log-likelihood of assuming residuals are normally distributed, conditional on the variance"], [726, 3, 1, 0, "moment", "Moment of order n"], [727, 5, 1, 0, "name", "The name of the distribution"], [728, 3, 1, 0, "parameter_names", "Names of distribution shape parameters"], [729, 3, 1, 0, "partial_moment", "Order n lower partial moment from -inf to z"], [730, 3, 1, 0, "ppf", "Inverse cumulative density function (ICDF)"], [731, 5, 1, 0, "random_state", "The NumPy RandomState attached to the distribution"], [732, 3, 1, 0, "simulate", "Simulates i.i.d. draws from the distribution"], [733, 3, 1, 0, "starting_values", "Construct starting values for use in optimization."]], "arch.univariate.Normal.bounds": [[721, 2, 2, 0, "resids", "Residuals to use when computing the bounds"]], "arch.univariate.Normal.cdf": [[722, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [722, 2, 2, 0, "resids", "Values at which to evaluate the cdf"]], "arch.univariate.Normal.loglikelihood": [[725, 2, 2, 0, "individual", "Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"], [725, 2, 2, 0, "parameters", "The normal likelihood has no shape parameters. Empty since the standard normal has no shape parameters."], [725, 2, 2, 0, "resids", "The residuals to use in the log-likelihood calculation"], [725, 2, 2, 0, "sigma2", "Conditional variances of resids"]], "arch.univariate.Normal.moment": [[726, 2, 2, 0, "n", "Order of moment"], [726, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."]], "arch.univariate.Normal.partial_moment": [[729, 2, 2, 0, "n", "Order of partial moment"], [729, 2, 2, 0, "parameters", "Distribution parameters.  Use None for parameterless distributions."], [729, 2, 2, 0, "z", "Upper bound for partial moment integral"]], "arch.univariate.Normal.ppf": [[730, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [730, 2, 2, 0, "pits", "Probability-integral-transformed values in the interval (0, 1)."]], "arch.univariate.Normal.simulate": [[732, 2, 2, 0, "parameters", "Distribution parameters"]], "arch.univariate.Normal.starting_values": [[733, 2, 2, 0, "std_resid", "Estimated standardized residuals to use in computing starting values for the shape parameter"]], "arch.univariate.RiskMetrics2006.__init__": [[442, 2, 2, 0, "kmax", "Number of components. Default is 14."], [442, 2, 2, 0, "rho", "Relative scale of adjacent cycles. Default is sqrt(2)"], [442, 2, 2, 0, "tau0", "Length of long cycle. Default is 1560."], [442, 2, 2, 0, "tau1", "Length of short cycle. Default is 4."]], "arch.univariate.RiskMetrics2006": [[734, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [735, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [736, 3, 1, 0, "bounds", "Returns bounds for parameters"], [737, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [738, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [739, 3, 1, 0, "forecast", "Forecast volatility from the model"], [740, 5, 1, 0, "name", "The name of the volatility process"], [741, 5, 1, 0, "num_params", "The number of parameters in the model"], [742, 3, 1, 0, "parameter_names", "Names of model parameters"], [743, 3, 1, 0, "simulate", "Simulate data from the model"], [744, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [745, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [746, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [747, 3, 1, 0, "update", "Compute the variance for a single observation"], [748, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [749, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [750, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.RiskMetrics2006.backcast": [[734, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.RiskMetrics2006.backcast_transform": [[735, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.RiskMetrics2006.bounds": [[736, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.RiskMetrics2006.compute_variance": [[737, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [737, 2, 2, 0, "parameters", "Model parameters"], [737, 2, 2, 0, "resids", "Vector of mean zero residuals"], [737, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [737, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.RiskMetrics2006.forecast": [[739, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [739, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [739, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [739, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [739, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [739, 2, 2, 0, "resids", "Residuals to use in the recursion"], [739, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [739, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [739, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [739, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.RiskMetrics2006.simulate": [[743, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [743, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [743, 2, 2, 0, "nobs", "Number of data points to simulate"], [743, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [743, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.RiskMetrics2006.starting_values": [[745, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.RiskMetrics2006.update": [[747, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [747, 2, 2, 0, "index", "The numerical index of the variance to compute"], [747, 2, 2, 0, "parameters", "The variance model parameters"], [747, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [747, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [747, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.RiskMetrics2006.variance_bounds": [[749, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [749, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.SkewStudent.__init__": [[443, 2, 2, 0, "random_state", "Deprecated since version 5.0: random_state is deprecated. Use seed instead."], [443, 2, 2, 0, "seed", "Random number generator instance or int to use. Set to ensure reproducibility. If using an int, the argument is passed to np.random.default_rng.  If not provided, default_rng is used with system-provided entropy."]], "arch.univariate.SkewStudent": [[751, 3, 1, 0, "bounds", "Parameter bounds for use in optimization."], [752, 3, 1, 0, "cdf", "Cumulative distribution function"], [753, 3, 1, 0, "constraints", "Construct arrays to use in constrained optimization."], [754, 5, 1, 0, "generator", "The NumPy Generator or RandomState attached to the distribution"], [755, 3, 1, 0, "loglikelihood", "Computes the log-likelihood of assuming residuals are have a standardized (to have unit variance) Skew Student's t distribution, conditional on the variance."], [756, 3, 1, 0, "moment", "Moment of order n"], [757, 5, 1, 0, "name", "The name of the distribution"], [758, 3, 1, 0, "parameter_names", "Names of distribution shape parameters"], [759, 3, 1, 0, "partial_moment", "Order n lower partial moment from -inf to z"], [760, 3, 1, 0, "ppf", "Inverse cumulative density function (ICDF)"], [761, 5, 1, 0, "random_state", "The NumPy RandomState attached to the distribution"], [762, 3, 1, 0, "simulate", "Simulates i.i.d. draws from the distribution"], [763, 3, 1, 0, "starting_values", "Construct starting values for use in optimization."]], "arch.univariate.SkewStudent.bounds": [[751, 2, 2, 0, "resids", "Residuals to use when computing the bounds"]], "arch.univariate.SkewStudent.cdf": [[752, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [752, 2, 2, 0, "resids", "Values at which to evaluate the cdf"]], "arch.univariate.SkewStudent.loglikelihood": [[755, 2, 2, 0, "individual", "Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"], [755, 2, 2, 0, "parameters", "Shape parameter of the skew-t distribution"], [755, 2, 2, 0, "resids", "The residuals to use in the log-likelihood calculation"], [755, 2, 2, 0, "sigma2", "Conditional variances of resids"]], "arch.univariate.SkewStudent.moment": [[756, 2, 2, 0, "n", "Order of moment"], [756, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."]], "arch.univariate.SkewStudent.partial_moment": [[759, 2, 2, 0, "n", "Order of partial moment"], [759, 2, 2, 0, "parameters", "Distribution parameters.  Use None for parameterless distributions."], [759, 2, 2, 0, "z", "Upper bound for partial moment integral"]], "arch.univariate.SkewStudent.ppf": [[760, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [760, 2, 2, 0, "pits", "Probability-integral-transformed values in the interval (0, 1)."]], "arch.univariate.SkewStudent.simulate": [[762, 2, 2, 0, "parameters", "Distribution parameters"]], "arch.univariate.SkewStudent.starting_values": [[763, 2, 2, 0, "std_resid", "Estimated standardized residuals to use in computing starting values for the shape parameter"]], "arch.univariate.StudentsT.__init__": [[444, 2, 2, 0, "random_state", "Deprecated since version 5.0: random_state is deprecated. Use seed instead."], [444, 2, 2, 0, "seed", "Random number generator instance or int to use. Set to ensure reproducibility. If using an int, the argument is passed to np.random.default_rng.  If not provided, default_rng is used with system-provided entropy."]], "arch.univariate.StudentsT": [[764, 3, 1, 0, "bounds", "Parameter bounds for use in optimization."], [765, 3, 1, 0, "cdf", "Cumulative distribution function"], [766, 3, 1, 0, "constraints", "Construct arrays to use in constrained optimization."], [767, 5, 1, 0, "generator", "The NumPy Generator or RandomState attached to the distribution"], [768, 3, 1, 0, "loglikelihood", "Computes the log-likelihood of assuming residuals are have a standardized (to have unit variance) Student's t distribution, conditional on the variance."], [769, 3, 1, 0, "moment", "Moment of order n"], [770, 5, 1, 0, "name", "The name of the distribution"], [771, 3, 1, 0, "parameter_names", "Names of distribution shape parameters"], [772, 3, 1, 0, "partial_moment", "Order n lower partial moment from -inf to z"], [773, 3, 1, 0, "ppf", "Inverse cumulative density function (ICDF)"], [774, 5, 1, 0, "random_state", "The NumPy RandomState attached to the distribution"], [775, 3, 1, 0, "simulate", "Simulates i.i.d. draws from the distribution"], [776, 3, 1, 0, "starting_values", "Construct starting values for use in optimization."]], "arch.univariate.StudentsT.bounds": [[764, 2, 2, 0, "resids", "Residuals to use when computing the bounds"]], "arch.univariate.StudentsT.cdf": [[765, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [765, 2, 2, 0, "resids", "Values at which to evaluate the cdf"]], "arch.univariate.StudentsT.loglikelihood": [[768, 2, 2, 0, "individual", "Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"], [768, 2, 2, 0, "parameters", "Shape parameter of the t distribution"], [768, 2, 2, 0, "resids", "The residuals to use in the log-likelihood calculation"], [768, 2, 2, 0, "sigma2", "Conditional variances of resids"]], "arch.univariate.StudentsT.moment": [[769, 2, 2, 0, "n", "Order of moment"], [769, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."]], "arch.univariate.StudentsT.partial_moment": [[772, 2, 2, 0, "n", "Order of partial moment"], [772, 2, 2, 0, "parameters", "Distribution parameters.  Use None for parameterless distributions."], [772, 2, 2, 0, "z", "Upper bound for partial moment integral"]], "arch.univariate.StudentsT.ppf": [[773, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [773, 2, 2, 0, "pits", "Probability-integral-transformed values in the interval (0, 1)."]], "arch.univariate.StudentsT.simulate": [[775, 2, 2, 0, "parameters", "Distribution parameters"]], "arch.univariate.StudentsT.starting_values": [[776, 2, 2, 0, "std_resid", "Estimated standardized residuals to use in computing starting values for the shape parameter"]], "arch.univariate.ZeroMean.__init__": [[445, 2, 2, 0, "distribution", "Error distribution to use in the model"], [445, 2, 2, 0, "hold_back", "Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."], [445, 2, 2, 0, "rescale", "Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."], [445, 2, 2, 0, "volatility", "Volatility process to use in the model"], [445, 2, 2, 0, "y", "nobs element vector containing the dependent variable"]], "arch.univariate.ZeroMean": [[777, 3, 1, 0, "bounds", "Construct bounds for parameters to use in non-linear optimization"], [778, 3, 1, 0, "compute_param_cov", "Computes parameter covariances using numerical derivatives."], [779, 3, 1, 0, "constraints", "Construct linear constraint arrays  for use in non-linear optimization"], [780, 5, 1, 0, "distribution", "Set or gets the error distribution"], [781, 3, 1, 0, "fit", "Estimate model parameters"], [782, 3, 1, 0, "fix", "Allows an ARCHModelFixedResult to be constructed from fixed parameters."], [783, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [784, 5, 1, 0, "name", "The name of the model."], [785, 5, 1, 0, "num_params", "Returns the number of parameters"], [786, 3, 1, 0, "parameter_names", "List of parameters names"], [787, 3, 1, 0, "resids", "Compute model residuals"], [788, 3, 1, 0, "simulate", "Simulated data from a zero mean model"], [789, 3, 1, 0, "starting_values", "Returns starting values for the mean model, often the same as the values returned from fit"], [790, 5, 1, 0, "volatility", "Set or gets the volatility process"], [791, 5, 1, 0, "x", "Gets the value of the exogenous regressors in the model"], [792, 5, 1, 0, "y", "Returns the dependent variable"]], "arch.univariate.ZeroMean.compute_param_cov": [[778, 2, 2, 0, "backcast", "Value to use for pre-sample observations"], [778, 2, 2, 0, "params", "Model parameters"], [778, 2, 2, 0, "robust", "Flag indicating whether to use robust standard errors (True) or classic MLE (False)"]], "arch.univariate.ZeroMean.fit": [[781, 2, 2, 0, "backcast", "Value to use as backcast. Should be measure \\sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."], [781, 2, 2, 0, "cov_type", "Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."], [781, 2, 2, 0, "disp", "Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is \"off\" and True is \"final\""], [781, 2, 2, 0, "first_obs", "First observation to use when estimating model"], [781, 2, 2, 0, "last_obs", "Last observation to use when estimating model"], [781, 2, 2, 0, "options", "Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."], [781, 2, 2, 0, "show_warning", "Flag indicating whether convergence warnings should be shown."], [781, 2, 2, 0, "starting_values", "Array of starting values to use.  If not provided, starting values are constructed by the model components."], [781, 2, 2, 0, "tol", "Tolerance for termination."], [781, 2, 2, 0, "update_freq", "Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."]], "arch.univariate.ZeroMean.fix": [[782, 2, 2, 0, "first_obs", "First observation to use when fixing model"], [782, 2, 2, 0, "last_obs", "Last observation to use when fixing model"], [782, 2, 2, 0, "params", "User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."]], "arch.univariate.ZeroMean.forecast": [[783, 2, 2, 0, "align", "Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."], [783, 2, 2, 0, "horizon", "Number of steps to forecast"], [783, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [783, 2, 2, 0, "params", "Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."], [783, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [783, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."], [783, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [783, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [783, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."], [783, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"]], "arch.univariate.ZeroMean.resids": [[787, 2, 2, 0, "params", "Model parameters"], [787, 2, 2, 0, "regressors", "Alternative regressor values to use when computing model residuals"], [787, 2, 2, 0, "y", "Alternative values to use when computing model residuals"]], "arch.univariate.ZeroMean.simulate": [[788, 2, 2, 0, "burn", "Number of values to simulate to initialize the model and remove dependence on initial values."], [788, 2, 2, 0, "initial_value", "This value is not used."], [788, 2, 2, 0, "initial_value_vol", "An array or scalar to use when initializing the volatility process."], [788, 2, 2, 0, "nobs", "Length of series to simulate"], [788, 2, 2, 0, "params", "Parameters to use when simulating the model.  Parameter order is [volatility distribution]. There are no mean parameters."], [788, 2, 2, 0, "x", "This value is not used."]], "arch.univariate.arch_model": [[891, 2, 2, 0, "dist", "Name of the error distribution.  Currently supported options are:"], [891, 2, 2, 0, "hold_back", "Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."], [891, 2, 2, 0, "lags", "Either a scalar integer value indicating lag length or a list of integers specifying lag locations."], [891, 2, 2, 0, "mean", "Name of the mean model.  Currently supported options are: 'Constant', 'Zero', 'LS', 'AR', 'ARX', 'HAR' and  'HARX'"], [891, 2, 2, 0, "o", "Lag order of the asymmetric innovation"], [891, 2, 2, 0, "p", "Lag order of the symmetric innovation"], [891, 2, 2, 0, "power", "Power to use with GARCH and related models"], [891, 2, 2, 0, "q", "Lag order of lagged volatility or equivalent"], [891, 2, 2, 0, "rescale", "Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."], [891, 2, 2, 0, "vol", "Name of the volatility model.  Currently supported options are: 'GARCH' (default), 'ARCH', 'EGARCH', 'FIGARCH', 'APARCH' and 'HARCH'"], [891, 2, 2, 0, "x", "Exogenous regressors.  Ignored if model does not permit exogenous regressors."], [891, 2, 2, 0, "y", "The dependent variable"]], "arch.univariate.base": [[446, 1, 1, 0, "ARCHModel", "Abstract base class for mean models in ARCH processes.  Specifies the conditional mean process."], [447, 1, 1, 0, "ARCHModelFixedResult", "Results for fixed parameters for an ARCHModel model"], [448, 1, 1, 0, "ARCHModelForecast", "Container for forecasts from an ARCH Model"], [449, 1, 1, 0, "ARCHModelForecastSimulation", "Container for a simulation or bootstrap-based forecasts from an ARCH Model"], [450, 1, 1, 0, "ARCHModelResult", "Results from estimation of an ARCHModel model"]], "arch.univariate.base.ARCHModel": [[793, 3, 1, 0, "bounds", "Construct bounds for parameters to use in non-linear optimization"], [794, 3, 1, 0, "compute_param_cov", "Computes parameter covariances using numerical derivatives."], [795, 3, 1, 0, "constraints", "Construct linear constraint arrays  for use in non-linear optimization"], [796, 5, 1, 0, "distribution", "Set or gets the error distribution"], [797, 3, 1, 0, "fit", "Estimate model parameters"], [798, 3, 1, 0, "fix", "Allows an ARCHModelFixedResult to be constructed from fixed parameters."], [799, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [800, 5, 1, 0, "name", "The name of the model."], [801, 5, 1, 0, "num_params", "Number of parameters in the model"], [802, 3, 1, 0, "parameter_names", "List of parameters names"], [803, 3, 1, 0, "resids", "Compute model residuals"], [804, 3, 1, 0, "simulate", ""], [805, 3, 1, 0, "starting_values", "Returns starting values for the mean model, often the same as the values returned from fit"], [806, 5, 1, 0, "volatility", "Set or gets the volatility process"], [807, 5, 1, 0, "y", "Returns the dependent variable"]], "arch.univariate.base.ARCHModel.compute_param_cov": [[794, 2, 2, 0, "backcast", "Value to use for pre-sample observations"], [794, 2, 2, 0, "params", "Model parameters"], [794, 2, 2, 0, "robust", "Flag indicating whether to use robust standard errors (True) or classic MLE (False)"]], "arch.univariate.base.ARCHModel.fit": [[797, 2, 2, 0, "backcast", "Value to use as backcast. Should be measure \\sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."], [797, 2, 2, 0, "cov_type", "Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."], [797, 2, 2, 0, "disp", "Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is \"off\" and True is \"final\""], [797, 2, 2, 0, "first_obs", "First observation to use when estimating model"], [797, 2, 2, 0, "last_obs", "Last observation to use when estimating model"], [797, 2, 2, 0, "options", "Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."], [797, 2, 2, 0, "show_warning", "Flag indicating whether convergence warnings should be shown."], [797, 2, 2, 0, "starting_values", "Array of starting values to use.  If not provided, starting values are constructed by the model components."], [797, 2, 2, 0, "tol", "Tolerance for termination."], [797, 2, 2, 0, "update_freq", "Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."]], "arch.univariate.base.ARCHModel.fix": [[798, 2, 2, 0, "first_obs", "First observation to use when fixing model"], [798, 2, 2, 0, "last_obs", "Last observation to use when fixing model"], [798, 2, 2, 0, "params", "User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."]], "arch.univariate.base.ARCHModel.forecast": [[799, 2, 2, 0, "align", "Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."], [799, 2, 2, 0, "horizon", "Number of steps to forecast"], [799, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [799, 2, 2, 0, "params", "Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."], [799, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [799, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."], [799, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [799, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [799, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."], [799, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"]], "arch.univariate.base.ARCHModel.resids": [[803, 2, 2, 0, "params", "Model parameters"], [803, 2, 2, 0, "regressors", "Alternative regressor values to use when computing model residuals"], [803, 2, 2, 0, "y", "Alternative values to use when computing model residuals"]], "arch.univariate.base.ARCHModelFixedResult.__init__": [[447, 2, 2, 0, "dep_var", "Dependent variable"], [447, 2, 2, 0, "is_pandas", "Whether the original input was pandas"], [447, 2, 2, 0, "loglikelihood", "Loglikelihood at specified parameters"], [447, 2, 2, 0, "model", "The model object used to estimate the parameters"], [447, 2, 2, 0, "names", "Model parameter names"], [447, 2, 2, 0, "params", "Estimated parameters"], [447, 2, 2, 0, "resid", "Residuals from model.  Residuals have same shape as original data and contain nan-values in locations not used in estimation"], [447, 2, 2, 0, "volatility", "Conditional volatility from model"]], "arch.univariate.base.ARCHModelFixedResult": [[808, 5, 1, 0, "aic", "Akaike Information Criteria"], [809, 3, 1, 0, "arch_lm_test", "ARCH LM test for conditional heteroskedasticity"], [810, 5, 1, 0, "bic", "Schwarz/Bayesian Information Criteria"], [811, 5, 1, 0, "conditional_volatility", "Estimated conditional volatility"], [812, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [813, 3, 1, 0, "hedgehog_plot", "Plot forecasts from estimated model"], [814, 5, 1, 0, "loglikelihood", "Model loglikelihood"], [815, 5, 1, 0, "model", "Model instance used to produce the fit"], [816, 5, 1, 0, "nobs", "Number of data points used to estimate model"], [817, 5, 1, 0, "num_params", "Number of parameters in model"], [818, 5, 1, 0, "params", "Model Parameters"], [819, 3, 1, 0, "plot", "Plot standardized residuals and conditional volatility"], [820, 5, 1, 0, "resid", "Model residuals"], [821, 5, 1, 0, "std_resid", "Residuals standardized by conditional volatility"], [822, 3, 1, 0, "summary", "Constructs a summary of the results from a fit model."]], "arch.univariate.base.ARCHModelFixedResult.arch_lm_test": [[809, 2, 2, 0, "lags", "Number of lags to include in the model.  If not specified,"], [809, 2, 2, 0, "standardized", "Flag indicating to test the model residuals divided by their conditional standard deviations.  If False, directly tests the estimated residuals."]], "arch.univariate.base.ARCHModelFixedResult.forecast": [[812, 2, 2, 0, "align", "Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."], [812, 2, 2, 0, "horizon", "Number of steps to forecast"], [812, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [812, 2, 2, 0, "params", "Alternative parameters to use.  If not provided, the parameters estimated when fitting the model are used.  Must be identical in shape to the parameters computed by fitting the model."], [812, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [812, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast."], [812, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [812, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [812, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."], [812, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"]], "arch.univariate.base.ARCHModelFixedResult.hedgehog_plot": [[813, 2, 2, 0, "horizon", "Number of steps to forecast"], [813, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [813, 2, 2, 0, "params", "Alternative parameters to use.  If not provided, the parameters computed by fitting the model are used.  Must be 1-d and identical in shape to the parameters computed by fitting the model."], [813, 2, 2, 0, "plot_type", "Quantity to plot, the forecast volatility or the forecast mean"], [813, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [813, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index.  Strings must be convertible to a date time, such as in '1945-01-01'.  If not provided, the start is set to the earliest forecastable date."], [813, 2, 2, 0, "step", "Non-negative number of forecasts to skip between spines"]], "arch.univariate.base.ARCHModelFixedResult.plot": [[819, 2, 2, 0, "annualize", "String containing frequency of data that indicates plot should contain annualized volatility.  Supported values are 'D' (daily), 'W' (weekly) and 'M' (monthly), which scale variance by 252, 52, and 12, respectively."], [819, 2, 2, 0, "scale", "Value to use when scaling returns to annualize.  If scale is provided, annualize is ignored and the value in scale is used."]], "arch.univariate.base.ARCHModelForecast.__init__": [[448, 2, 2, 0, "align", ""], [448, 2, 2, 0, "index", ""], [448, 2, 2, 0, "mean", ""], [448, 2, 2, 0, "residual_variance", ""], [448, 2, 2, 0, "simulated_paths", ""], [448, 2, 2, 0, "simulated_residual_variances", ""], [448, 2, 2, 0, "simulated_residuals", ""], [448, 2, 2, 0, "simulated_variances", ""], [448, 2, 2, 0, "variance", ""]], "arch.univariate.base.ARCHModelForecast": [[823, 5, 1, 0, "mean", "Forecast values for the conditional mean of the process"], [824, 5, 1, 0, "residual_variance", "Forecast values for the conditional variance of the residuals"], [825, 5, 1, 0, "simulations", "Detailed simulation results if using a simulation-based method"], [826, 5, 1, 0, "variance", "Forecast values for the conditional variance of the process"]], "arch.univariate.base.ARCHModelForecastSimulation.__init__": [[449, 2, 2, 0, "index", ""], [449, 2, 2, 0, "residual_variances", ""], [449, 2, 2, 0, "residuals", ""], [449, 2, 2, 0, "values", ""], [449, 2, 2, 0, "variances", ""]], "arch.univariate.base.ARCHModelForecastSimulation": [[827, 5, 1, 0, "index", "The index aligned to dimension 0 of the simulation paths"], [828, 5, 1, 0, "residual_variances", "Simulated variance of the residuals"], [829, 5, 1, 0, "residuals", "Simulated residuals used to produce the values"], [830, 5, 1, 0, "values", "The values of the process"], [831, 5, 1, 0, "variances", "Simulated variances of the values"]], "arch.univariate.base.ARCHModelResult.__init__": [[450, 2, 2, 0, "cov_type", "String describing the covariance estimator used"], [450, 2, 2, 0, "dep_var", "Dependent variable"], [450, 2, 2, 0, "fit_start", "Integer index of the first observation used to fit the model"], [450, 2, 2, 0, "fit_stop", "Integer index of the last observation used to fit the model using slice notation fit_start:fit_stop"], [450, 2, 2, 0, "is_pandas", "Whether the original input was pandas"], [450, 2, 2, 0, "loglikelihood", "Loglikelihood at estimated parameters"], [450, 2, 2, 0, "model", "The model object used to estimate the parameters"], [450, 2, 2, 0, "names", "Model parameter names"], [450, 2, 2, 0, "optim_output", "Result of log-likelihood optimization"], [450, 2, 2, 0, "param_cov", "Estimated variance-covariance matrix of params.  If none, calls method to compute variance from model when parameter covariance is first used from result"], [450, 2, 2, 0, "params", "Estimated parameters"], [450, 2, 2, 0, "r2", "Model R-squared"], [450, 2, 2, 0, "resid", "Residuals from model.  Residuals have same shape as original data and contain nan-values in locations not used in estimation"], [450, 2, 2, 0, "volatility", "Conditional volatility from model"]], "arch.univariate.base.ARCHModelResult": [[832, 5, 1, 0, "aic", "Akaike Information Criteria"], [833, 3, 1, 0, "arch_lm_test", "ARCH LM test for conditional heteroskedasticity"], [834, 5, 1, 0, "bic", "Schwarz/Bayesian Information Criteria"], [835, 5, 1, 0, "conditional_volatility", "Estimated conditional volatility"], [836, 3, 1, 0, "conf_int", "Parameter confidence intervals"], [837, 5, 1, 0, "convergence_flag", "scipy.optimize.minimize result flag"], [838, 5, 1, 0, "fit_start", "Start of sample used to estimate parameters"], [839, 5, 1, 0, "fit_stop", "End of sample used to estimate parameters"], [840, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [841, 3, 1, 0, "hedgehog_plot", "Plot forecasts from estimated model"], [842, 5, 1, 0, "loglikelihood", "Model loglikelihood"], [843, 5, 1, 0, "model", "Model instance used to produce the fit"], [844, 5, 1, 0, "nobs", "Number of data points used to estimate model"], [845, 5, 1, 0, "num_params", "Number of parameters in model"], [846, 5, 1, 0, "optimization_result", "Information about the convergence of the loglikelihood optimization"], [847, 5, 1, 0, "param_cov", "Parameter covariance"], [848, 5, 1, 0, "params", "Model Parameters"], [849, 3, 1, 0, "plot", "Plot standardized residuals and conditional volatility"], [850, 5, 1, 0, "pvalues", "Array of p-values for the t-statistics"], [851, 5, 1, 0, "resid", "Model residuals"], [852, 5, 1, 0, "rsquared", "R-squared"], [853, 5, 1, 0, "rsquared_adj", "Degree of freedom adjusted R-squared"], [854, 5, 1, 0, "scale", "The scale applied to the original data before estimating the model."], [855, 5, 1, 0, "std_err", "Array of parameter standard errors"], [856, 5, 1, 0, "std_resid", "Residuals standardized by conditional volatility"], [857, 3, 1, 0, "summary", "Constructs a summary of the results from a fit model."], [858, 5, 1, 0, "tvalues", "Array of t-statistics testing the null that the coefficient are 0"]], "arch.univariate.base.ARCHModelResult.arch_lm_test": [[833, 2, 2, 0, "lags", "Number of lags to include in the model.  If not specified,"], [833, 2, 2, 0, "standardized", "Flag indicating to test the model residuals divided by their conditional standard deviations.  If False, directly tests the estimated residuals."]], "arch.univariate.base.ARCHModelResult.conf_int": [[836, 2, 2, 0, "alpha", "Size (prob.) to use when constructing the confidence interval."]], "arch.univariate.base.ARCHModelResult.forecast": [[840, 2, 2, 0, "align", "Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."], [840, 2, 2, 0, "horizon", "Number of steps to forecast"], [840, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [840, 2, 2, 0, "params", "Alternative parameters to use.  If not provided, the parameters estimated when fitting the model are used.  Must be identical in shape to the parameters computed by fitting the model."], [840, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [840, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast."], [840, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [840, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [840, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."], [840, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"]], "arch.univariate.base.ARCHModelResult.hedgehog_plot": [[841, 2, 2, 0, "horizon", "Number of steps to forecast"], [841, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [841, 2, 2, 0, "params", "Alternative parameters to use.  If not provided, the parameters computed by fitting the model are used.  Must be 1-d and identical in shape to the parameters computed by fitting the model."], [841, 2, 2, 0, "plot_type", "Quantity to plot, the forecast volatility or the forecast mean"], [841, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [841, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index.  Strings must be convertible to a date time, such as in '1945-01-01'.  If not provided, the start is set to the earliest forecastable date."], [841, 2, 2, 0, "step", "Non-negative number of forecasts to skip between spines"]], "arch.univariate.base.ARCHModelResult.plot": [[849, 2, 2, 0, "annualize", "String containing frequency of data that indicates plot should contain annualized volatility.  Supported values are 'D' (daily), 'W' (weekly) and 'M' (monthly), which scale variance by 252, 52, and 12, respectively."], [849, 2, 2, 0, "scale", "Value to use when scaling returns to annualize.  If scale is provided, annualize is ignored and the value in scale is used."]], "arch.univariate.distribution": [[451, 1, 1, 0, "Distribution", "Template for subclassing only"]], "arch.univariate.distribution.Distribution": [[859, 3, 1, 0, "bounds", "Parameter bounds for use in optimization."], [860, 3, 1, 0, "cdf", "Cumulative distribution function"], [861, 3, 1, 0, "constraints", "Construct arrays to use in constrained optimization."], [862, 5, 1, 0, "generator", "The NumPy Generator or RandomState attached to the distribution"], [863, 3, 1, 0, "loglikelihood", "Loglikelihood evaluation."], [864, 3, 1, 0, "moment", "Moment of order n"], [865, 5, 1, 0, "name", "The name of the distribution"], [866, 3, 1, 0, "parameter_names", "Names of distribution shape parameters"], [867, 3, 1, 0, "partial_moment", "Order n lower partial moment from -inf to z"], [868, 3, 1, 0, "ppf", "Inverse cumulative density function (ICDF)"], [869, 5, 1, 0, "random_state", "The NumPy RandomState attached to the distribution"], [870, 3, 1, 0, "simulate", "Simulates i.i.d. draws from the distribution"], [871, 3, 1, 0, "starting_values", "Construct starting values for use in optimization."]], "arch.univariate.distribution.Distribution.bounds": [[859, 2, 2, 0, "resids", "Residuals to use when computing the bounds"]], "arch.univariate.distribution.Distribution.cdf": [[860, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [860, 2, 2, 0, "resids", "Values at which to evaluate the cdf"]], "arch.univariate.distribution.Distribution.loglikelihood": [[863, 2, 2, 0, "individual", "Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"], [863, 2, 2, 0, "parameters", "Distribution shape parameters"], [863, 2, 2, 0, "resids", "nobs array of model residuals"], [863, 2, 2, 0, "sigma2", "nobs array of conditional variances"]], "arch.univariate.distribution.Distribution.moment": [[864, 2, 2, 0, "n", "Order of moment"], [864, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."]], "arch.univariate.distribution.Distribution.partial_moment": [[867, 2, 2, 0, "n", "Order of partial moment"], [867, 2, 2, 0, "parameters", "Distribution parameters.  Use None for parameterless distributions."], [867, 2, 2, 0, "z", "Upper bound for partial moment integral"]], "arch.univariate.distribution.Distribution.ppf": [[868, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [868, 2, 2, 0, "pits", "Probability-integral-transformed values in the interval (0, 1)."]], "arch.univariate.distribution.Distribution.simulate": [[870, 2, 2, 0, "parameters", "Distribution parameters"]], "arch.univariate.distribution.Distribution.starting_values": [[871, 2, 2, 0, "std_resid", "Estimated standardized residuals to use in computing starting values for the shape parameter"]], "arch.univariate.recursions_python": [[452, 1, 1, 0, "VolatilityUpdater", "Base class that all volatility updaters must inherit from."]], "arch.univariate.recursions_python.VolatilityUpdater": [[872, 3, 1, 0, "initialize_update", "Initialize the recursion prior to calling update"], [873, 3, 1, 0, "update", "Update the current variance at location t"]], "arch.univariate.recursions_python.VolatilityUpdater.initialize_update": [[872, 2, 2, 0, "backcast", "The backcast value(s)."], [872, 2, 2, 0, "nobs", "The number of observations in the sample."], [872, 2, 2, 0, "parameters", "The model parameters."]], "arch.univariate.recursions_python.VolatilityUpdater.update": [[873, 2, 2, 0, "parameters", "Model parameters"], [873, 2, 2, 0, "resids", "Residuals to use in the recursion"], [873, 2, 2, 0, "sigma2", "Conditional variances with same shape as resids"], [873, 2, 2, 0, "t", "The index of the value of sigma2 to update. Assumes but does not check that update has been called recursively for 0,1,...,t-1."], [873, 2, 2, 0, "var_bounds", "nobs by 2-element array of upper and lower bounds for conditional variances for each time period"]], "arch.univariate.volatility": [[453, 1, 1, 0, "VolatilityProcess", "Abstract base class for ARCH models.  Allows the conditional mean model to be specified separately from the conditional variance, even though parameters are estimated jointly."]], "arch.univariate.volatility.VolatilityProcess": [[874, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [875, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [876, 3, 1, 0, "bounds", "Returns bounds for parameters"], [877, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [878, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [879, 3, 1, 0, "forecast", "Forecast volatility from the model"], [880, 5, 1, 0, "name", "The name of the volatility process"], [881, 5, 1, 0, "num_params", "The number of parameters in the model"], [882, 3, 1, 0, "parameter_names", "Names of model parameters"], [883, 3, 1, 0, "simulate", "Simulate data from the model"], [884, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [885, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [886, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [887, 3, 1, 0, "update", "Compute the variance for a single observation"], [888, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [889, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [890, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.volatility.VolatilityProcess.backcast": [[874, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.volatility.VolatilityProcess.backcast_transform": [[875, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.volatility.VolatilityProcess.bounds": [[876, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.volatility.VolatilityProcess.compute_variance": [[877, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [877, 2, 2, 0, "parameters", "Model parameters"], [877, 2, 2, 0, "resids", "Vector of mean zero residuals"], [877, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [877, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.volatility.VolatilityProcess.forecast": [[879, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [879, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [879, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [879, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [879, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [879, 2, 2, 0, "resids", "Residuals to use in the recursion"], [879, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [879, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [879, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [879, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.volatility.VolatilityProcess.simulate": [[883, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [883, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [883, 2, 2, 0, "nobs", "Number of data points to simulate"], [883, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [883, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.volatility.VolatilityProcess.starting_values": [[885, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.volatility.VolatilityProcess.update": [[887, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [887, 2, 2, 0, "index", "The numerical index of the variance to compute"], [887, 2, 2, 0, "parameters", "The variance model parameters"], [887, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [887, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [887, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.volatility.VolatilityProcess.variance_bounds": [[889, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [889, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.utility": [[900, 0, 0, 1, "testing", ""]], "arch.utility.testing": [[900, 1, 1, 0, "WaldTestStatistic", "Test statistic holder for Wald-type tests"]], "arch.utility.testing.WaldTestStatistic.__init__": [[900, 2, 2, 0, "alternative", "A statement of the test's alternative hypothesis"], [900, 2, 2, 0, "df", "Degree of freedom."], [900, 2, 2, 0, "name", "Name of test"], [900, 2, 2, 0, "null", "A statement of the test's null hypothesis"], [900, 2, 2, 0, "stat", "The test statistic"]], "arch.utility.testing.WaldTestStatistic": [[900, 5, 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diff --git a/unitroot/cointegration.html b/unitroot/cointegration.html
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+++ b/unitroot/cointegration.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -97,7 +97,7 @@
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+    arch 6.2.0
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@@ -760,7 +760,7 @@
                   
 
 
-<h1 id="module-arch.unitroot.cointegration"><span id="cointegration-tests"></span>Cointegration Tests<a class="headerlink" href="#module-arch.unitroot.cointegration" title="Permalink to this heading">¶</a></h1>
+<h1 id="module-arch.unitroot.cointegration"><span id="cointegration-tests"></span>Cointegration Tests<a class="headerlink" href="#module-arch.unitroot.cointegration" title="Link to this heading">¶</a></h1>
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@@ -771,7 +771,7 @@ <h1 id="module-arch.unitroot.cointegration"><span id="cointegration-tests"></spa
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-<h1 id="cointegrating-vector-estimation">Cointegrating Vector Estimation<a class="headerlink" href="#cointegrating-vector-estimation" title="Permalink to this heading">¶</a></h1>
+<h1 id="cointegrating-vector-estimation">Cointegrating Vector Estimation<a class="headerlink" href="#cointegrating-vector-estimation" title="Link to this heading">¶</a></h1>
 <table class="autosummary longtable docutils data align-default">
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@@ -785,7 +785,7 @@ <h1 id="cointegrating-vector-estimation">Cointegrating Vector Estimation<a class
 </tr>
 </tbody>
 </table>
-<h2 id="results-classes">Results Classes<a class="headerlink" href="#results-classes" title="Permalink to this heading">¶</a></h2>
+<h2 id="results-classes">Results Classes<a class="headerlink" href="#results-classes" title="Link to this heading">¶</a></h2>
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@@ -869,7 +869,7 @@ <h2 id="results-classes">Results Classes<a class="headerlink" href="#results-cla
   
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-    7.0.1.
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index 1720fec4f2..b2184ab688 100644
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+++ b/unitroot/generated/arch.unitroot.ADF.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -47,8 +47,8 @@
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+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1099,21 +1099,21 @@
                   
 
 
-<h1 id="arch-unitroot-adf">arch.unitroot.ADF<a class="headerlink" href="#arch-unitroot-adf" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-adf">arch.unitroot.ADF<a class="headerlink" href="#arch-unitroot-adf" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.unitroot.ADF">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.unitroot.</span></span><span class="sig-name descname"><span class="pre">ADF</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.ADF.__init__.y" title="arch.unitroot.ADF.__init__.y (Python parameter) — The data to test for a unit root"><span class="n"><span class="pre">y</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.ADF.__init__.lags" title="arch.unitroot.ADF.__init__.lags (Python parameter) — The number of lags to use in the ADF regression.  If omitted or None, method is used to automatically select the lag length with no more than max_lags are included."><span class="n"><span class="pre">lags</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.ADF.__init__.trend" title="arch.unitroot.ADF.__init__.trend (Python parameter) — The trend component to include in the test"><span class="n"><span class="pre">trend</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.ADF.__init__.max_lags" title="arch.unitroot.ADF.__init__.max_lags (Python parameter) — The maximum number of lags to use when selecting lag length"><span class="n"><span class="pre">max_lags</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.ADF.__init__.method" title="arch.unitroot.ADF.__init__.method (Python parameter) — The method to use when selecting the lag length"><span class="n"><span class="pre">method</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;aic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.ADF.__init__.low_memory" title="arch.unitroot.ADF.__init__.low_memory (Python parameter) — Flag indicating whether to use a low memory implementation of the lag selection algorithm. The low memory algorithm is slower than the standard algorithm but will use 2-4% of the memory required for the standard algorithm. This options allows automatic lag selection to be used in very long time series. If None, use automatic selection of algorithm."><span class="n"><span class="pre">low_memory</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/unitroot/unitroot.html#ADF"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.ADF" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.unitroot.</span></span><span class="sig-name descname"><span class="pre">ADF</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.ADF.__init__.y" title="arch.unitroot.ADF.__init__.y (Python parameter) — The data to test for a unit root"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.ADF.__init__.lags" title="arch.unitroot.ADF.__init__.lags (Python parameter) — The number of lags to use in the ADF regression.  If omitted or None, method is used to automatically select the lag length with no more than max_lags are included."><span class="n"><span class="pre">lags</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.ADF.__init__.trend" title="arch.unitroot.ADF.__init__.trend (Python parameter) — The trend component to include in the test"><span class="n"><span class="pre">trend</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'n'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'c'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ct'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ctt'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.ADF.__init__.max_lags" title="arch.unitroot.ADF.__init__.max_lags (Python parameter) — The maximum number of lags to use when selecting lag length"><span class="n"><span class="pre">max_lags</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.ADF.__init__.method" title="arch.unitroot.ADF.__init__.method (Python parameter) — The method to use when selecting the lag length"><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'aic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'t-stat'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;aic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.ADF.__init__.low_memory" title="arch.unitroot.ADF.__init__.low_memory (Python parameter) — Flag indicating whether to use a low memory implementation of the lag selection algorithm. The low memory algorithm is slower than the standard algorithm but will use 2-4% of the memory required for the standard algorithm. This options allows automatic lag selection to be used in very long time series. If None, use automatic selection of algorithm."><span class="n"><span class="pre">low_memory</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/unitroot/unitroot.html#ADF"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.ADF" title="Link to this definition">¶</a></dt>
 <dd><p>Augmented Dickey-Fuller unit root test</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.unitroot.ADF-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.ADF-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.unitroot.ADF.__init__.y"><span class="sig-name descname">y</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, Series}</span></span><a class="headerlink" href="#arch.unitroot.ADF.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>The data to test for a unit root</p>
+<dt id="arch.unitroot.ADF.__init__.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.unitroot.ADF.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>The data to test for a unit root</p>
 </dd>
-<dt id="arch.unitroot.ADF.__init__.lags"><span class="sig-name descname">lags</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.unitroot.ADF.__init__.lags" title="Permalink to this definition">¶</a></dt><dd><p>The number of lags to use in the ADF regression.  If omitted or None,
+<dt id="arch.unitroot.ADF.__init__.lags"><span class="n sig-name">lags</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.unitroot.ADF.__init__.lags" title="Permalink to this definition">¶</a></dt><dd><p>The number of lags to use in the ADF regression.  If omitted or None,
 <cite>method</cite> is used to automatically select the lag length with no more
 than <cite>max_lags</cite> are included.</p>
 </dd>
-<dt id="arch.unitroot.ADF.__init__.trend"><span class="sig-name descname">trend</span><span class="p"> : </span><span class="desctype"><span class="desctype">{&quot;n&quot;, &quot;c&quot;, &quot;ct&quot;, &quot;ctt&quot;}, optional</span></span><a class="headerlink" href="#arch.unitroot.ADF.__init__.trend" title="Permalink to this definition">¶</a></dt><dd><p><p>The trend component to include in the test</p>
+<dt id="arch.unitroot.ADF.__init__.trend"><span class="n sig-name">trend</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'n'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'c'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'ct'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'ctt'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code><a class="headerlink" href="#arch.unitroot.ADF.__init__.trend" title="Permalink to this definition">¶</a></dt><dd><p><p>The trend component to include in the test</p>
 <ul class="simple">
 <li><p>”n” - No trend components</p></li>
 <li><p>”c” - Include a constant (Default)</p></li>
@@ -1122,9 +1122,9 @@ <h1 id="arch-unitroot-adf">arch.unitroot.ADF<a class="headerlink" href="#arch-un
 </ul>
 </p>
 </dd>
-<dt id="arch.unitroot.ADF.__init__.max_lags"><span class="sig-name descname">max_lags</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.unitroot.ADF.__init__.max_lags" title="Permalink to this definition">¶</a></dt><dd><p>The maximum number of lags to use when selecting lag length</p>
+<dt id="arch.unitroot.ADF.__init__.max_lags"><span class="n sig-name">max_lags</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.unitroot.ADF.__init__.max_lags" title="Permalink to this definition">¶</a></dt><dd><p>The maximum number of lags to use when selecting lag length</p>
 </dd>
-<dt id="arch.unitroot.ADF.__init__.method"><span class="sig-name descname">method</span><span class="p"> : </span><span class="desctype"><span class="desctype">{&quot;AIC&quot;, &quot;BIC&quot;, &quot;t-stat&quot;}, optional</span></span><a class="headerlink" href="#arch.unitroot.ADF.__init__.method" title="Permalink to this definition">¶</a></dt><dd><p><p>The method to use when selecting the lag length</p>
+<dt id="arch.unitroot.ADF.__init__.method"><span class="n sig-name">method</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'aic'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'bic'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'t-stat'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;aic&#39;</span></code><a class="headerlink" href="#arch.unitroot.ADF.__init__.method" title="Permalink to this definition">¶</a></dt><dd><p><p>The method to use when selecting the lag length</p>
 <ul class="simple">
 <li><p>”AIC” - Select the minimum of the Akaike IC</p></li>
 <li><p>”BIC” - Select the minimum of the Schwarz/Bayesian IC</p></li>
@@ -1132,7 +1132,7 @@ <h1 id="arch-unitroot-adf">arch.unitroot.ADF<a class="headerlink" href="#arch-un
 </ul>
 </p>
 </dd>
-<dt id="arch.unitroot.ADF.__init__.low_memory"><span class="sig-name descname">low_memory</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><a class="headerlink" href="#arch.unitroot.ADF.__init__.low_memory" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to use a low memory implementation of the
+<dt id="arch.unitroot.ADF.__init__.low_memory"><span class="n sig-name">low_memory</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.unitroot.ADF.__init__.low_memory" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to use a low memory implementation of the
 lag selection algorithm. The low memory algorithm is slower than
 the standard algorithm but will use 2-4% of the memory required for
 the standard algorithm. This options allows automatic lag selection
@@ -1315,7 +1315,7 @@ <h1 id="arch-unitroot-adf">arch.unitroot.ADF<a class="headerlink" href="#arch-un
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1362,7 +1362,7 @@ <h1 id="arch-unitroot-adf">arch.unitroot.ADF<a class="headerlink" href="#arch-un
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diff --git a/unitroot/generated/arch.unitroot.DFGLS.html b/unitroot/generated/arch.unitroot.DFGLS.html
index d9b63099f5..6af127d7a0 100644
--- a/unitroot/generated/arch.unitroot.DFGLS.html
+++ b/unitroot/generated/arch.unitroot.DFGLS.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.DFGLS - arch 6.1.0</title>
+        <title>arch.unitroot.DFGLS - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
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-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1085,32 +1085,32 @@
                   
 
 
-<h1 id="arch-unitroot-dfgls">arch.unitroot.DFGLS<a class="headerlink" href="#arch-unitroot-dfgls" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-dfgls">arch.unitroot.DFGLS<a class="headerlink" href="#arch-unitroot-dfgls" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.unitroot.DFGLS">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.unitroot.</span></span><span class="sig-name descname"><span class="pre">DFGLS</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.DFGLS.__init__.y" title="arch.unitroot.DFGLS.__init__.y (Python parameter) — The data to test for a unit root"><span class="n"><span class="pre">y</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.DFGLS.__init__.lags" title="arch.unitroot.DFGLS.__init__.lags (Python parameter) — The number of lags to use in the ADF regression.  If omitted or None, method is used to automatically select the lag length with no more than max_lags are included."><span class="n"><span class="pre">lags</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.DFGLS.__init__.trend" title="arch.unitroot.DFGLS.__init__.trend (Python parameter) — The trend component to include in the test"><span class="n"><span class="pre">trend</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.DFGLS.__init__.max_lags" title="arch.unitroot.DFGLS.__init__.max_lags (Python parameter) — The maximum number of lags to use when selecting lag length. When using automatic lag length selection, the lag is selected using OLS detrending rather than GLS detrending (2)."><span class="n"><span class="pre">max_lags</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.DFGLS.__init__.method" title="arch.unitroot.DFGLS.__init__.method (Python parameter) — The method to use when selecting the lag length"><span class="n"><span class="pre">method</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;aic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">low_memory</span></span><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/unitroot/unitroot.html#DFGLS"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.DFGLS" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.unitroot.</span></span><span class="sig-name descname"><span class="pre">DFGLS</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.DFGLS.__init__.y" title="arch.unitroot.DFGLS.__init__.y (Python parameter) — The data to test for a unit root"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.DFGLS.__init__.lags" title="arch.unitroot.DFGLS.__init__.lags (Python parameter) — The number of lags to use in the ADF regression.  If omitted or None, method is used to automatically select the lag length with no more than max_lags are included."><span class="n"><span class="pre">lags</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.DFGLS.__init__.trend" title="arch.unitroot.DFGLS.__init__.trend (Python parameter) — The trend component to include in the test"><span class="n"><span class="pre">trend</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'c'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ct'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.DFGLS.__init__.max_lags" title="arch.unitroot.DFGLS.__init__.max_lags (Python parameter) — The maximum number of lags to use when selecting lag length. When using automatic lag length selection, the lag is selected using OLS detrending rather than GLS detrending (2)."><span class="n"><span class="pre">max_lags</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.DFGLS.__init__.method" title="arch.unitroot.DFGLS.__init__.method (Python parameter) — The method to use when selecting the lag length"><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'aic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'t-stat'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;aic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">low_memory</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/unitroot/unitroot.html#DFGLS"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.DFGLS" title="Link to this definition">¶</a></dt>
 <dd><p>Elliott, Rothenberg and Stock’s (<a class="footnote-reference brackets" href="#id3" id="id1" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>) GLS detrended Dickey-Fuller</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.unitroot.DFGLS-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.DFGLS-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.unitroot.DFGLS.__init__.y"><span class="sig-name descname">y</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, Series}</span></span><a class="headerlink" href="#arch.unitroot.DFGLS.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>The data to test for a unit root</p>
+<dt id="arch.unitroot.DFGLS.__init__.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.unitroot.DFGLS.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>The data to test for a unit root</p>
 </dd>
-<dt id="arch.unitroot.DFGLS.__init__.lags"><span class="sig-name descname">lags</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.unitroot.DFGLS.__init__.lags" title="Permalink to this definition">¶</a></dt><dd><p>The number of lags to use in the ADF regression.  If omitted or None,
+<dt id="arch.unitroot.DFGLS.__init__.lags"><span class="n sig-name">lags</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.unitroot.DFGLS.__init__.lags" title="Permalink to this definition">¶</a></dt><dd><p>The number of lags to use in the ADF regression.  If omitted or None,
 <cite>method</cite> is used to automatically select the lag length with no more
 than <cite>max_lags</cite> are included.</p>
 </dd>
-<dt id="arch.unitroot.DFGLS.__init__.trend"><span class="sig-name descname">trend</span><span class="p"> : </span><span class="desctype"><span class="desctype">{&quot;c&quot;, &quot;ct&quot;}, optional</span></span><a class="headerlink" href="#arch.unitroot.DFGLS.__init__.trend" title="Permalink to this definition">¶</a></dt><dd><p><p>The trend component to include in the test</p>
+<dt id="arch.unitroot.DFGLS.__init__.trend"><span class="n sig-name">trend</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'c'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'ct'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code><a class="headerlink" href="#arch.unitroot.DFGLS.__init__.trend" title="Permalink to this definition">¶</a></dt><dd><p><p>The trend component to include in the test</p>
 <ul class="simple">
 <li><p>”c” - Include a constant (Default)</p></li>
 <li><p>”ct” - Include a constant and linear time trend</p></li>
 </ul>
 </p>
 </dd>
-<dt id="arch.unitroot.DFGLS.__init__.max_lags"><span class="sig-name descname">max_lags</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.unitroot.DFGLS.__init__.max_lags" title="Permalink to this definition">¶</a></dt><dd><p>The maximum number of lags to use when selecting lag length. When using
+<dt id="arch.unitroot.DFGLS.__init__.max_lags"><span class="n sig-name">max_lags</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.unitroot.DFGLS.__init__.max_lags" title="Permalink to this definition">¶</a></dt><dd><p>The maximum number of lags to use when selecting lag length. When using
 automatic lag length selection, the lag is selected using OLS
 detrending rather than GLS detrending (<a class="footnote-reference brackets" href="#id4" id="id2" role="doc-noteref"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></a>).</p>
 </dd>
-<dt id="arch.unitroot.DFGLS.__init__.method"><span class="sig-name descname">method</span><span class="p"> : </span><span class="desctype"><span class="desctype">{&quot;AIC&quot;, &quot;BIC&quot;, &quot;t-stat&quot;}, optional</span></span><a class="headerlink" href="#arch.unitroot.DFGLS.__init__.method" title="Permalink to this definition">¶</a></dt><dd><p><p>The method to use when selecting the lag length</p>
+<dt id="arch.unitroot.DFGLS.__init__.method"><span class="n sig-name">method</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'aic'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'bic'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'t-stat'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;aic&#39;</span></code><a class="headerlink" href="#arch.unitroot.DFGLS.__init__.method" title="Permalink to this definition">¶</a></dt><dd><p><p>The method to use when selecting the lag length</p>
 <ul class="simple">
 <li><p>”AIC” - Select the minimum of the Akaike IC</p></li>
 <li><p>”BIC” - Select the minimum of the Schwarz/Bayesian IC</p></li>
@@ -1282,7 +1282,7 @@ <h1 id="arch-unitroot-dfgls">arch.unitroot.DFGLS<a class="headerlink" href="#arc
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1329,7 +1329,7 @@ <h1 id="arch-unitroot-dfgls">arch.unitroot.DFGLS<a class="headerlink" href="#arc
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-        <script src="../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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diff --git a/unitroot/generated/arch.unitroot.KPSS.html b/unitroot/generated/arch.unitroot.KPSS.html
index 584af72e42..bea652053e 100644
--- a/unitroot/generated/arch.unitroot.KPSS.html
+++ b/unitroot/generated/arch.unitroot.KPSS.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.KPSS - arch 6.1.0</title>
+        <title>arch.unitroot.KPSS - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
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@@ -189,10 +189,10 @@
 
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-    arch 6.1.0
+    arch 6.2.0
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@@ -1013,24 +1013,24 @@
                   
 
 
-<h1 id="arch-unitroot-kpss">arch.unitroot.KPSS<a class="headerlink" href="#arch-unitroot-kpss" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-kpss">arch.unitroot.KPSS<a class="headerlink" href="#arch-unitroot-kpss" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
-<dt class="sig sig-object highlight py" id="arch.unitroot.KPSS">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.unitroot.</span></span><span class="sig-name descname"><span class="pre">KPSS</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.KPSS.__init__.y" title="arch.unitroot.KPSS.__init__.y (Python parameter) — The data to test for stationarity"><span class="n"><span class="pre">y</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.KPSS.__init__.lags" title="arch.unitroot.KPSS.__init__.lags (Python parameter) — The number of lags to use in the Newey-West estimator of the long-run covariance.  If omitted or None, the number of lags is calculated with the data-dependent method of Hobijn et al. (1998). See also Andrews (1991), Newey &amp; West (1994), and Schwert (1989). Set lags=-1 to use the old method that only depends on the sample size, 12 * (nobs/100) ** (1/4)."><span class="n"><span class="pre">lags</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.KPSS.__init__.trend" title="arch.unitroot.KPSS.__init__.trend (Python parameter) — &quot;c&quot; - Include a constant (Default) &quot;ct&quot; - Include a constant and linear time trend"><span class="n"><span class="pre">trend</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/unitroot/unitroot.html#KPSS"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.KPSS" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.unitroot.KPSS">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.unitroot.</span></span><span class="sig-name descname"><span class="pre">KPSS</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.KPSS.__init__.y" title="arch.unitroot.KPSS.__init__.y (Python parameter) — The data to test for stationarity"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.KPSS.__init__.lags" title="arch.unitroot.KPSS.__init__.lags (Python parameter) — The number of lags to use in the Newey-West estimator of the long-run covariance.  If omitted or None, the number of lags is calculated with the data-dependent method of Hobijn et al. (1998). See also Andrews (1991), Newey &amp; West (1994), and Schwert (1989). Set lags=-1 to use the old method that only depends on the sample size, 12 * (nobs/100) ** (1/4)."><span class="n"><span class="pre">lags</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.KPSS.__init__.trend" title="arch.unitroot.KPSS.__init__.trend (Python parameter) — &quot;c&quot; - Include a constant (Default) &quot;ct&quot; - Include a constant and linear time trend"><span class="n"><span class="pre">trend</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'c'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ct'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/unitroot/unitroot.html#KPSS"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.KPSS" title="Link to this definition">¶</a></dt>
 <dd><p>Kwiatkowski, Phillips, Schmidt and Shin (KPSS) stationarity test</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.unitroot.KPSS-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.KPSS-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.unitroot.KPSS.__init__.y"><span class="sig-name descname">y</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, Series}</span></span><a class="headerlink" href="#arch.unitroot.KPSS.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>The data to test for stationarity</p>
+<dt id="arch.unitroot.KPSS.__init__.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.unitroot.KPSS.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>The data to test for stationarity</p>
 </dd>
-<dt id="arch.unitroot.KPSS.__init__.lags"><span class="sig-name descname">lags</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.unitroot.KPSS.__init__.lags" title="Permalink to this definition">¶</a></dt><dd><p>The number of lags to use in the Newey-West estimator of the long-run
+<dt id="arch.unitroot.KPSS.__init__.lags"><span class="n sig-name">lags</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.unitroot.KPSS.__init__.lags" title="Permalink to this definition">¶</a></dt><dd><p>The number of lags to use in the Newey-West estimator of the long-run
 covariance.  If omitted or None, the number of lags is calculated
 with the data-dependent method of Hobijn et al. (1998). See also
 Andrews (1991), Newey &amp; West (1994), and Schwert (1989).
 Set lags=-1 to use the old method that only depends on the sample
 size, 12 * (nobs/100) ** (1/4).</p>
 </dd>
-<dt id="arch.unitroot.KPSS.__init__.trend"><span class="sig-name descname">trend</span><span class="p"> : </span><span class="desctype"><span class="desctype">{&quot;c&quot;, &quot;ct&quot;}, optional</span></span><a class="headerlink" href="#arch.unitroot.KPSS.__init__.trend" title="Permalink to this definition">¶</a></dt><dd><p><dl class="simple">
+<dt id="arch.unitroot.KPSS.__init__.trend"><span class="n sig-name">trend</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'c'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'ct'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code><a class="headerlink" href="#arch.unitroot.KPSS.__init__.trend" title="Permalink to this definition">¶</a></dt><dd><p><dl class="simple">
 <dt>The trend component to include in the ADF test</dt><dd><p>”c” - Include a constant (Default)
 “ct” - Include a constant and linear time trend</p>
 </dd>
@@ -1208,7 +1208,7 @@ <h1 id="arch-unitroot-kpss">arch.unitroot.KPSS<a class="headerlink" href="#arch-
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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diff --git a/unitroot/generated/arch.unitroot.PhillipsPerron.html b/unitroot/generated/arch.unitroot.PhillipsPerron.html
index 150b6659ae..10cce69c20 100644
--- a/unitroot/generated/arch.unitroot.PhillipsPerron.html
+++ b/unitroot/generated/arch.unitroot.PhillipsPerron.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.PhillipsPerron - arch 6.1.0</title>
+        <title>arch.unitroot.PhillipsPerron - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1071,21 +1071,21 @@
                   
 
 
-<h1 id="arch-unitroot-phillipsperron">arch.unitroot.PhillipsPerron<a class="headerlink" href="#arch-unitroot-phillipsperron" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-phillipsperron">arch.unitroot.PhillipsPerron<a class="headerlink" href="#arch-unitroot-phillipsperron" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.unitroot.PhillipsPerron">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.unitroot.</span></span><span class="sig-name descname"><span class="pre">PhillipsPerron</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.PhillipsPerron.__init__.y" title="arch.unitroot.PhillipsPerron.__init__.y (Python parameter) — The data to test for a unit root"><span class="n"><span class="pre">y</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.PhillipsPerron.__init__.lags" title="arch.unitroot.PhillipsPerron.__init__.lags (Python parameter) — The number of lags to use in the Newey-West estimator of the long-run covariance.  If omitted or None, the lag length is set automatically to 12 * (nobs/100) ** (1/4)"><span class="n"><span class="pre">lags</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.PhillipsPerron.__init__.trend" title="arch.unitroot.PhillipsPerron.__init__.trend (Python parameter) — The trend component to include in the test"><span class="n"><span class="pre">trend</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.PhillipsPerron.__init__.test_type" title="arch.unitroot.PhillipsPerron.__init__.test_type (Python parameter) — The test to use when computing the test statistic. &quot;tau&quot; is based on the t-stat and &quot;rho&quot; uses a test based on nobs times the re-centered regression coefficient"><span class="n"><span class="pre">test_type</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;tau&#39;</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/unitroot/unitroot.html#PhillipsPerron"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.PhillipsPerron" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.unitroot.</span></span><span class="sig-name descname"><span class="pre">PhillipsPerron</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.PhillipsPerron.__init__.y" title="arch.unitroot.PhillipsPerron.__init__.y (Python parameter) — The data to test for a unit root"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.PhillipsPerron.__init__.lags" title="arch.unitroot.PhillipsPerron.__init__.lags (Python parameter) — The number of lags to use in the Newey-West estimator of the long-run covariance.  If omitted or None, the lag length is set automatically to 12 * (nobs/100) ** (1/4)"><span class="n"><span class="pre">lags</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.PhillipsPerron.__init__.trend" title="arch.unitroot.PhillipsPerron.__init__.trend (Python parameter) — The trend component to include in the test"><span class="n"><span class="pre">trend</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'n'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'c'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ct'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.PhillipsPerron.__init__.test_type" title="arch.unitroot.PhillipsPerron.__init__.test_type (Python parameter) — The test to use when computing the test statistic. &quot;tau&quot; is based on the t-stat and &quot;rho&quot; uses a test based on nobs times the re-centered regression coefficient"><span class="n"><span class="pre">test_type</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'tau'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'rho'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;tau&#39;</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/unitroot/unitroot.html#PhillipsPerron"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.PhillipsPerron" title="Link to this definition">¶</a></dt>
 <dd><p>Phillips-Perron unit root test</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.unitroot.PhillipsPerron-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.PhillipsPerron-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.unitroot.PhillipsPerron.__init__.y"><span class="sig-name descname">y</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, Series}</span></span><a class="headerlink" href="#arch.unitroot.PhillipsPerron.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>The data to test for a unit root</p>
+<dt id="arch.unitroot.PhillipsPerron.__init__.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.unitroot.PhillipsPerron.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>The data to test for a unit root</p>
 </dd>
-<dt id="arch.unitroot.PhillipsPerron.__init__.lags"><span class="sig-name descname">lags</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.unitroot.PhillipsPerron.__init__.lags" title="Permalink to this definition">¶</a></dt><dd><p>The number of lags to use in the Newey-West estimator of the long-run
+<dt id="arch.unitroot.PhillipsPerron.__init__.lags"><span class="n sig-name">lags</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.unitroot.PhillipsPerron.__init__.lags" title="Permalink to this definition">¶</a></dt><dd><p>The number of lags to use in the Newey-West estimator of the long-run
 covariance.  If omitted or None, the lag length is set automatically to
 12 * (nobs/100) ** (1/4)</p>
 </dd>
-<dt id="arch.unitroot.PhillipsPerron.__init__.trend"><span class="sig-name descname">trend</span><span class="p"> : </span><span class="desctype"><span class="desctype">{&quot;n&quot;, &quot;c&quot;, &quot;ct&quot;}, optional</span></span><a class="headerlink" href="#arch.unitroot.PhillipsPerron.__init__.trend" title="Permalink to this definition">¶</a></dt><dd><p><p>The trend component to include in the test</p>
+<dt id="arch.unitroot.PhillipsPerron.__init__.trend"><span class="n sig-name">trend</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'n'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'c'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'ct'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code><a class="headerlink" href="#arch.unitroot.PhillipsPerron.__init__.trend" title="Permalink to this definition">¶</a></dt><dd><p><p>The trend component to include in the test</p>
 <ul class="simple">
 <li><p>”n” - No trend components</p></li>
 <li><p>”c” - Include a constant (Default)</p></li>
@@ -1093,7 +1093,7 @@ <h1 id="arch-unitroot-phillipsperron">arch.unitroot.PhillipsPerron<a class="head
 </ul>
 </p>
 </dd>
-<dt id="arch.unitroot.PhillipsPerron.__init__.test_type"><span class="sig-name descname">test_type</span><span class="p"> : </span><span class="desctype"><span class="desctype">{&quot;tau&quot;, &quot;rho&quot;}</span></span><a class="headerlink" href="#arch.unitroot.PhillipsPerron.__init__.test_type" title="Permalink to this definition">¶</a></dt><dd><p>The test to use when computing the test statistic. “tau” is based on
+<dt id="arch.unitroot.PhillipsPerron.__init__.test_type"><span class="n sig-name">test_type</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'tau'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'rho'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;tau&#39;</span></code><a class="headerlink" href="#arch.unitroot.PhillipsPerron.__init__.test_type" title="Permalink to this definition">¶</a></dt><dd><p>The test to use when computing the test statistic. “tau” is based on
 the t-stat and “rho” uses a test based on nobs times the re-centered
 regression coefficient</p>
 </dd>
@@ -1287,7 +1287,7 @@ <h1 id="arch-unitroot-phillipsperron">arch.unitroot.PhillipsPerron<a class="head
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1334,7 +1334,7 @@ <h1 id="arch-unitroot-phillipsperron">arch.unitroot.PhillipsPerron<a class="head
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diff --git a/unitroot/generated/arch.unitroot.VarianceRatio.html b/unitroot/generated/arch.unitroot.VarianceRatio.html
index 6923152f9a..d83762fb6f 100644
--- a/unitroot/generated/arch.unitroot.VarianceRatio.html
+++ b/unitroot/generated/arch.unitroot.VarianceRatio.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.VarianceRatio - arch 6.1.0</title>
+        <title>arch.unitroot.VarianceRatio - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1143,31 +1143,31 @@
                   
 
 
-<h1 id="arch-unitroot-varianceratio">arch.unitroot.VarianceRatio<a class="headerlink" href="#arch-unitroot-varianceratio" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-varianceratio">arch.unitroot.VarianceRatio<a class="headerlink" href="#arch-unitroot-varianceratio" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.unitroot.VarianceRatio">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.unitroot.</span></span><span class="sig-name descname"><span class="pre">VarianceRatio</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.VarianceRatio.__init__.y" title="arch.unitroot.VarianceRatio.__init__.y (Python parameter) — The data to test for a random walk"><span class="n"><span class="pre">y</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.VarianceRatio.__init__.lags" title="arch.unitroot.VarianceRatio.__init__.lags (Python parameter) — The number of periods to used in the multi-period variance, which is the numerator of the test statistic.  Must be at least 2"><span class="n"><span class="pre">lags</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">2</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.VarianceRatio.__init__.trend" title="arch.unitroot.VarianceRatio.__init__.trend (Python parameter) — &quot;c&quot; allows for a non-zero drift in the random walk, while &quot;n&quot; requires that the increments to y are mean 0"><span class="n"><span class="pre">trend</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.VarianceRatio.__init__.debiased" title="arch.unitroot.VarianceRatio.__init__.debiased (Python parameter) — Indicates whether to use a debiased version of the test. Default is True. Only applicable if overlap is True."><span class="n"><span class="pre">debiased</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.VarianceRatio.__init__.robust" title="arch.unitroot.VarianceRatio.__init__.robust (Python parameter) — Indicates whether to use heteroskedasticity robust inference. Default is True."><span class="n"><span class="pre">robust</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.VarianceRatio.__init__.overlap" title="arch.unitroot.VarianceRatio.__init__.overlap (Python parameter) — Indicates whether to use all overlapping blocks.  Default is True.  If False, the number of observations in y minus 1 must be an exact multiple of lags.  If this condition is not satisfied, some values at the end of y will be discarded."><span class="n"><span class="pre">overlap</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/unitroot/unitroot.html#VarianceRatio"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.VarianceRatio" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.unitroot.</span></span><span class="sig-name descname"><span class="pre">VarianceRatio</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.VarianceRatio.__init__.y" title="arch.unitroot.VarianceRatio.__init__.y (Python parameter) — The data to test for a random walk"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.VarianceRatio.__init__.lags" title="arch.unitroot.VarianceRatio.__init__.lags (Python parameter) — The number of periods to used in the multi-period variance, which is the numerator of the test statistic.  Must be at least 2"><span class="n"><span class="pre">lags</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">2</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.VarianceRatio.__init__.trend" title="arch.unitroot.VarianceRatio.__init__.trend (Python parameter) — &quot;c&quot; allows for a non-zero drift in the random walk, while &quot;n&quot; requires that the increments to y are mean 0"><span class="n"><span class="pre">trend</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'n'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'c'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.VarianceRatio.__init__.debiased" title="arch.unitroot.VarianceRatio.__init__.debiased (Python parameter) — Indicates whether to use a debiased version of the test. Default is True. Only applicable if overlap is True."><span class="n"><span class="pre">debiased</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.VarianceRatio.__init__.robust" title="arch.unitroot.VarianceRatio.__init__.robust (Python parameter) — Indicates whether to use heteroskedasticity robust inference. Default is True."><span class="n"><span class="pre">robust</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.VarianceRatio.__init__.overlap" title="arch.unitroot.VarianceRatio.__init__.overlap (Python parameter) — Indicates whether to use all overlapping blocks.  Default is True.  If False, the number of observations in y minus 1 must be an exact multiple of lags.  If this condition is not satisfied, some values at the end of y will be discarded."><span class="n"><span class="pre">overlap</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/unitroot/unitroot.html#VarianceRatio"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.VarianceRatio" title="Link to this definition">¶</a></dt>
 <dd><p>Variance Ratio test of a random walk.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.unitroot.VarianceRatio-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.VarianceRatio-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.unitroot.VarianceRatio.__init__.y"><span class="sig-name descname">y</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, Series}</span></span><a class="headerlink" href="#arch.unitroot.VarianceRatio.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>The data to test for a random walk</p>
+<dt id="arch.unitroot.VarianceRatio.__init__.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.unitroot.VarianceRatio.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>The data to test for a random walk</p>
 </dd>
-<dt id="arch.unitroot.VarianceRatio.__init__.lags"><span class="sig-name descname">lags</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.unitroot.VarianceRatio.__init__.lags" title="Permalink to this definition">¶</a></dt><dd><p>The number of periods to used in the multi-period variance, which is
+<dt id="arch.unitroot.VarianceRatio.__init__.lags"><span class="n sig-name">lags</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">2</span></code><a class="headerlink" href="#arch.unitroot.VarianceRatio.__init__.lags" title="Permalink to this definition">¶</a></dt><dd><p>The number of periods to used in the multi-period variance, which is
 the numerator of the test statistic.  Must be at least 2</p>
 </dd>
-<dt id="arch.unitroot.VarianceRatio.__init__.trend"><span class="sig-name descname">trend</span><span class="p"> : </span><span class="desctype"><span class="desctype">{&quot;n&quot;, &quot;c&quot;}, optional</span></span><a class="headerlink" href="#arch.unitroot.VarianceRatio.__init__.trend" title="Permalink to this definition">¶</a></dt><dd><p>“c” allows for a non-zero drift in the random walk, while “n” requires
+<dt id="arch.unitroot.VarianceRatio.__init__.trend"><span class="n sig-name">trend</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'n'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'c'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code><a class="headerlink" href="#arch.unitroot.VarianceRatio.__init__.trend" title="Permalink to this definition">¶</a></dt><dd><p>“c” allows for a non-zero drift in the random walk, while “n” requires
 that the increments to y are mean 0</p>
 </dd>
-<dt id="arch.unitroot.VarianceRatio.__init__.overlap"><span class="sig-name descname">overlap</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, optional</span></span><a class="headerlink" href="#arch.unitroot.VarianceRatio.__init__.overlap" title="Permalink to this definition">¶</a></dt><dd><p>Indicates whether to use all overlapping blocks.  Default is True.  If
+<dt id="arch.unitroot.VarianceRatio.__init__.overlap"><span class="n sig-name">overlap</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.unitroot.VarianceRatio.__init__.overlap" title="Permalink to this definition">¶</a></dt><dd><p>Indicates whether to use all overlapping blocks.  Default is True.  If
 False, the number of observations in y minus 1 must be an exact
 multiple of lags.  If this condition is not satisfied, some values at
 the end of y will be discarded.</p>
 </dd>
-<dt id="arch.unitroot.VarianceRatio.__init__.robust"><span class="sig-name descname">robust</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, optional</span></span><a class="headerlink" href="#arch.unitroot.VarianceRatio.__init__.robust" title="Permalink to this definition">¶</a></dt><dd><p>Indicates whether to use heteroskedasticity robust inference. Default
+<dt id="arch.unitroot.VarianceRatio.__init__.robust"><span class="n sig-name">robust</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.unitroot.VarianceRatio.__init__.robust" title="Permalink to this definition">¶</a></dt><dd><p>Indicates whether to use heteroskedasticity robust inference. Default
 is True.</p>
 </dd>
-<dt id="arch.unitroot.VarianceRatio.__init__.debiased"><span class="sig-name descname">debiased</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, optional</span></span><a class="headerlink" href="#arch.unitroot.VarianceRatio.__init__.debiased" title="Permalink to this definition">¶</a></dt><dd><p>Indicates whether to use a debiased version of the test. Default is
+<dt id="arch.unitroot.VarianceRatio.__init__.debiased"><span class="n sig-name">debiased</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.unitroot.VarianceRatio.__init__.debiased" title="Permalink to this definition">¶</a></dt><dd><p>Indicates whether to use a debiased version of the test. Default is
 True. Only applicable if overlap is True.</p>
 </dd>
 </dl>
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diff --git a/unitroot/generated/arch.unitroot.ZivotAndrews.html b/unitroot/generated/arch.unitroot.ZivotAndrews.html
index ea99ae082f..4137497161 100644
--- a/unitroot/generated/arch.unitroot.ZivotAndrews.html
+++ b/unitroot/generated/arch.unitroot.ZivotAndrews.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.ZivotAndrews - arch 6.1.0</title>
+        <title>arch.unitroot.ZivotAndrews - arch 6.2.0</title>
       
     
 
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@@ -92,7 +92,7 @@
 
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+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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+            arch 6.2.0
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+    arch 6.2.0
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-<h1 id="arch-unitroot-zivotandrews">arch.unitroot.ZivotAndrews<a class="headerlink" href="#arch-unitroot-zivotandrews" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-zivotandrews">arch.unitroot.ZivotAndrews<a class="headerlink" href="#arch-unitroot-zivotandrews" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.unitroot.ZivotAndrews">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.unitroot.</span></span><span class="sig-name descname"><span class="pre">ZivotAndrews</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.ZivotAndrews.__init__.y" title="arch.unitroot.ZivotAndrews.__init__.y (Python parameter) — data series"><span class="n"><span class="pre">y</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.ZivotAndrews.__init__.lags" title="arch.unitroot.ZivotAndrews.__init__.lags (Python parameter) — The number of lags to use in the ADF regression.  If omitted or None, method is used to automatically select the lag length with no more than max_lags are included."><span class="n"><span class="pre">lags</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.ZivotAndrews.__init__.trend" title="arch.unitroot.ZivotAndrews.__init__.trend (Python parameter) — The trend component to include in the test"><span class="n"><span class="pre">trend</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.ZivotAndrews.__init__.trim" title="arch.unitroot.ZivotAndrews.__init__.trim (Python parameter) — percentage of series at begin/end to exclude from break-period calculation in range [0, 0.333] (default=0.15)"><span class="n"><span class="pre">trim</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.15</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.ZivotAndrews.__init__.max_lags" title="arch.unitroot.ZivotAndrews.__init__.max_lags (Python parameter) — The maximum number of lags to use when selecting lag length"><span class="n"><span class="pre">max_lags</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.ZivotAndrews.__init__.method" title="arch.unitroot.ZivotAndrews.__init__.method (Python parameter) — The method to use when selecting the lag length"><span class="n"><span class="pre">method</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;aic&#39;</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/unitroot/unitroot.html#ZivotAndrews"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.ZivotAndrews" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.unitroot.</span></span><span class="sig-name descname"><span class="pre">ZivotAndrews</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.ZivotAndrews.__init__.y" title="arch.unitroot.ZivotAndrews.__init__.y (Python parameter) — data series"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.ZivotAndrews.__init__.lags" title="arch.unitroot.ZivotAndrews.__init__.lags (Python parameter) — The number of lags to use in the ADF regression.  If omitted or None, method is used to automatically select the lag length with no more than max_lags are included."><span class="n"><span class="pre">lags</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.ZivotAndrews.__init__.trend" title="arch.unitroot.ZivotAndrews.__init__.trend (Python parameter) — The trend component to include in the test"><span class="n"><span class="pre">trend</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'c'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ct'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'t'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.ZivotAndrews.__init__.trim" title="arch.unitroot.ZivotAndrews.__init__.trim (Python parameter) — percentage of series at begin/end to exclude from break-period calculation in range [0, 0.333] (default=0.15)"><span class="n"><span class="pre">trim</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.15</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.ZivotAndrews.__init__.max_lags" title="arch.unitroot.ZivotAndrews.__init__.max_lags (Python parameter) — The maximum number of lags to use when selecting lag length"><span class="n"><span class="pre">max_lags</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.ZivotAndrews.__init__.method" title="arch.unitroot.ZivotAndrews.__init__.method (Python parameter) — The method to use when selecting the lag length"><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'aic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'t-stat'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;aic&#39;</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/unitroot/unitroot.html#ZivotAndrews"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.ZivotAndrews" title="Link to this definition">¶</a></dt>
 <dd><p>Zivot-Andrews structural-break unit-root test</p>
 <p>The Zivot-Andrews test can be used to test for a unit root in a
 univariate process in the presence of serial correlation and a
@@ -1066,13 +1066,13 @@ <h1 id="arch-unitroot-zivotandrews">arch.unitroot.ZivotAndrews<a class="headerli
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.unitroot.ZivotAndrews-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.ZivotAndrews-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.unitroot.ZivotAndrews.__init__.y"><span class="sig-name descname">y</span><span class="p"> : </span><span class="desctype"><span class="desctype">array_like</span></span><a class="headerlink" href="#arch.unitroot.ZivotAndrews.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>data series</p>
+<dt id="arch.unitroot.ZivotAndrews.__init__.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.unitroot.ZivotAndrews.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>data series</p>
 </dd>
-<dt id="arch.unitroot.ZivotAndrews.__init__.lags"><span class="sig-name descname">lags</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.unitroot.ZivotAndrews.__init__.lags" title="Permalink to this definition">¶</a></dt><dd><p>The number of lags to use in the ADF regression.  If omitted or None,
+<dt id="arch.unitroot.ZivotAndrews.__init__.lags"><span class="n sig-name">lags</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.unitroot.ZivotAndrews.__init__.lags" title="Permalink to this definition">¶</a></dt><dd><p>The number of lags to use in the ADF regression.  If omitted or None,
 <cite>method</cite> is used to automatically select the lag length with no more
 than <cite>max_lags</cite> are included.</p>
 </dd>
-<dt id="arch.unitroot.ZivotAndrews.__init__.trend"><span class="sig-name descname">trend</span><span class="p"> : </span><span class="desctype"><span class="desctype">{&quot;c&quot;, &quot;t&quot;, &quot;ct&quot;}, optional</span></span><a class="headerlink" href="#arch.unitroot.ZivotAndrews.__init__.trend" title="Permalink to this definition">¶</a></dt><dd><p><p>The trend component to include in the test</p>
+<dt id="arch.unitroot.ZivotAndrews.__init__.trend"><span class="n sig-name">trend</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'c'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'ct'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'t'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code><a class="headerlink" href="#arch.unitroot.ZivotAndrews.__init__.trend" title="Permalink to this definition">¶</a></dt><dd><p><p>The trend component to include in the test</p>
 <ul class="simple">
 <li><p>”c” - Include a constant (Default)</p></li>
 <li><p>”t” - Include a linear time trend</p></li>
@@ -1080,12 +1080,12 @@ <h1 id="arch-unitroot-zivotandrews">arch.unitroot.ZivotAndrews<a class="headerli
 </ul>
 </p>
 </dd>
-<dt id="arch.unitroot.ZivotAndrews.__init__.trim"><span class="sig-name descname">trim</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><a class="headerlink" href="#arch.unitroot.ZivotAndrews.__init__.trim" title="Permalink to this definition">¶</a></dt><dd><p>percentage of series at begin/end to exclude from break-period
+<dt id="arch.unitroot.ZivotAndrews.__init__.trim"><span class="n sig-name">trim</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.15</span></code><a class="headerlink" href="#arch.unitroot.ZivotAndrews.__init__.trim" title="Permalink to this definition">¶</a></dt><dd><p>percentage of series at begin/end to exclude from break-period
 calculation in range [0, 0.333] (default=0.15)</p>
 </dd>
-<dt id="arch.unitroot.ZivotAndrews.__init__.max_lags"><span class="sig-name descname">max_lags</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.unitroot.ZivotAndrews.__init__.max_lags" title="Permalink to this definition">¶</a></dt><dd><p>The maximum number of lags to use when selecting lag length</p>
+<dt id="arch.unitroot.ZivotAndrews.__init__.max_lags"><span class="n sig-name">max_lags</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.unitroot.ZivotAndrews.__init__.max_lags" title="Permalink to this definition">¶</a></dt><dd><p>The maximum number of lags to use when selecting lag length</p>
 </dd>
-<dt id="arch.unitroot.ZivotAndrews.__init__.method"><span class="sig-name descname">method</span><span class="p"> : </span><span class="desctype"><span class="desctype">{&quot;AIC&quot;, &quot;BIC&quot;, &quot;t-stat&quot;}, optional</span></span><a class="headerlink" href="#arch.unitroot.ZivotAndrews.__init__.method" title="Permalink to this definition">¶</a></dt><dd><p><p>The method to use when selecting the lag length</p>
+<dt id="arch.unitroot.ZivotAndrews.__init__.method"><span class="n sig-name">method</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'aic'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'bic'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'t-stat'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;aic&#39;</span></code><a class="headerlink" href="#arch.unitroot.ZivotAndrews.__init__.method" title="Permalink to this definition">¶</a></dt><dd><p><p>The method to use when selecting the lag length</p>
 <ul class="simple">
 <li><p>”AIC” - Select the minimum of the Akaike IC</p></li>
 <li><p>”BIC” - Select the minimum of the Schwarz/Bayesian IC</p></li>
@@ -1240,7 +1240,7 @@ <h1 id="arch-unitroot-zivotandrews">arch.unitroot.ZivotAndrews<a class="headerli
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1287,7 +1287,7 @@ <h1 id="arch-unitroot-zivotandrews">arch.unitroot.ZivotAndrews<a class="headerli
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diff --git a/unitroot/generated/arch.unitroot.auto_bandwidth.html b/unitroot/generated/arch.unitroot.auto_bandwidth.html
index 4e6606d009..1824b4c3ce 100644
--- a/unitroot/generated/arch.unitroot.auto_bandwidth.html
+++ b/unitroot/generated/arch.unitroot.auto_bandwidth.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.auto_bandwidth - arch 6.1.0</title>
+        <title>arch.unitroot.auto_bandwidth - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -757,17 +757,17 @@
                   
 
 
-<h1 id="arch-unitroot-auto-bandwidth">arch.unitroot.auto_bandwidth<a class="headerlink" href="#arch-unitroot-auto-bandwidth" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-auto-bandwidth">arch.unitroot.auto_bandwidth<a class="headerlink" href="#arch-unitroot-auto-bandwidth" title="Link to this heading">¶</a></h1>
 <dl class="py function objdesc">
-<dt class="sig sig-object highlight py" id="arch.unitroot.auto_bandwidth">
-<span class="sig-prename descclassname"><span class="pre">arch.unitroot.</span></span><span class="sig-name descname"><span class="pre">auto_bandwidth</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.auto_bandwidth.y" title="arch.unitroot.auto_bandwidth.y (Python parameter) — Data on which to apply the bandwidth selection"><span class="n"><span class="pre">y</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.auto_bandwidth.kernel" title="arch.unitroot.auto_bandwidth.kernel (Python parameter) — The kernel function to use for selecting the bandwidth"><span class="n"><span class="pre">kernel</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;ba&#39;</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/unitroot/unitroot.html#auto_bandwidth"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.auto_bandwidth" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.unitroot.auto_bandwidth">
+<span class="sig-prename descclassname"><span class="pre">arch.unitroot.</span></span><span class="sig-name descname"><span class="pre">auto_bandwidth</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.auto_bandwidth.y" title="arch.unitroot.auto_bandwidth.y (Python parameter) — Data on which to apply the bandwidth selection"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.auto_bandwidth.kernel" title="arch.unitroot.auto_bandwidth.kernel (Python parameter) — The kernel function to use for selecting the bandwidth"><span class="n"><span class="pre">kernel</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'ba'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bartlett'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'nw'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'pa'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'parzen'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'gallant'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'qs'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'andrews'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;ba&#39;</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span></span><a class="reference internal" href="../../_modules/arch/unitroot/unitroot.html#auto_bandwidth"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.auto_bandwidth" title="Link to this definition">¶</a></dt>
 <dd><p>Automatic bandwidth selection of Andrews (1991) and Newey &amp; West (1994).</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.unitroot.auto_bandwidth-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.auto_bandwidth-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.unitroot.auto_bandwidth.y"><span class="sig-name descname">y</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, Series}</span></span><a class="headerlink" href="#arch.unitroot.auto_bandwidth.y" title="Permalink to this definition">¶</a></dt><dd><p>Data on which to apply the bandwidth selection</p>
+<dt id="arch.unitroot.auto_bandwidth.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.unitroot.auto_bandwidth.y" title="Permalink to this definition">¶</a></dt><dd><p>Data on which to apply the bandwidth selection</p>
 </dd>
-<dt id="arch.unitroot.auto_bandwidth.kernel"><span class="sig-name descname">kernel</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a></span></span><a class="headerlink" href="#arch.unitroot.auto_bandwidth.kernel" title="Permalink to this definition">¶</a></dt><dd><p><p>The kernel function to use for selecting the bandwidth</p>
+<dt id="arch.unitroot.auto_bandwidth.kernel"><span class="n sig-name">kernel</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'ba'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'bartlett'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'nw'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'pa'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'parzen'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'gallant'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'qs'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'andrews'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;ba&#39;</span></code><a class="headerlink" href="#arch.unitroot.auto_bandwidth.kernel" title="Permalink to this definition">¶</a></dt><dd><p><p>The kernel function to use for selecting the bandwidth</p>
 <ul class="simple">
 <li><p>”ba”, “bartlett”, “nw”: Bartlett kernel (default)</p></li>
 <li><p>”pa”, “parzen”, “gallant”: Parzen kernel</p></li>
@@ -781,7 +781,7 @@ <h1 id="arch-unitroot-auto-bandwidth">arch.unitroot.auto_bandwidth<a class="head
 <dd class="field-even"><p>The estimated optimal bandwidth.</p>
 </dd>
 <dt class="field-odd" id="arch.unitroot.auto_bandwidth-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.auto_bandwidth-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -853,7 +853,7 @@ <h1 id="arch-unitroot-auto-bandwidth">arch.unitroot.auto_bandwidth<a class="head
   
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diff --git a/unitroot/generated/arch.unitroot.cointegration.CanonicalCointegratingReg.html b/unitroot/generated/arch.unitroot.cointegration.CanonicalCointegratingReg.html
index 31a9b0e855..64a8e433a2 100644
--- a/unitroot/generated/arch.unitroot.cointegration.CanonicalCointegratingReg.html
+++ b/unitroot/generated/arch.unitroot.cointegration.CanonicalCointegratingReg.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.CanonicalCointegratingReg - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.CanonicalCointegratingReg - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
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-    <a href="../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -762,19 +762,19 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-canonicalcointegratingreg">arch.unitroot.cointegration.CanonicalCointegratingReg<a class="headerlink" href="#arch-unitroot-cointegration-canonicalcointegratingreg" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-canonicalcointegratingreg">arch.unitroot.cointegration.CanonicalCointegratingReg<a class="headerlink" href="#arch-unitroot-cointegration-canonicalcointegratingreg" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.unitroot.cointegration.CanonicalCointegratingReg">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.unitroot.cointegration.</span></span><span class="sig-name descname"><span class="pre">CanonicalCointegratingReg</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.y" title="arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.y (Python parameter) — The left-hand-side variable in the cointegrating regression."><span class="n"><span class="pre">y</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.x" title="arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.x (Python parameter) — The right-hand-side variables in the cointegrating regression."><span class="n"><span class="pre">x</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.trend" title="arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.trend (Python parameter) — Trend to include in the cointegrating regression. Trends are:"><span class="n"><span class="pre">trend</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.x_trend" title="arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.x_trend (Python parameter) — Trends that affects affect the x-data but do not appear in the cointegrating regression. x_trend must be at least as large as trend, so that if trend is &quot;ct&quot;, x_trend must be either &quot;ct&quot; or &quot;ctt&quot;."><span class="n"><span class="pre">x_trend</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/unitroot/cointegration.html#CanonicalCointegratingReg"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.cointegration.CanonicalCointegratingReg" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.unitroot.cointegration.</span></span><span class="sig-name descname"><span class="pre">CanonicalCointegratingReg</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.y" title="arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.y (Python parameter) — The left-hand-side variable in the cointegrating regression."><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.x" title="arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.x (Python parameter) — The right-hand-side variables in the cointegrating regression."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.trend" title="arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.trend (Python parameter) — Trend to include in the cointegrating regression. Trends are:"><span class="n"><span class="pre">trend</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'n'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'c'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ct'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ctt'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.x_trend" title="arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.x_trend (Python parameter) — Trends that affects affect the x-data but do not appear in the cointegrating regression. x_trend must be at least as large as trend, so that if trend is &quot;ct&quot;, x_trend must be either &quot;ct&quot; or &quot;ctt&quot;."><span class="n"><span class="pre">x_trend</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'n'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'c'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ct'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ctt'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/unitroot/cointegration.html#CanonicalCointegratingReg"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.cointegration.CanonicalCointegratingReg" title="Link to this definition">¶</a></dt>
 <dd><p>Canonical Cointegrating Regression cointegrating vector estimation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.unitroot.cointegration.CanonicalCointegratingReg-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.cointegration.CanonicalCointegratingReg-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.y"><span class="sig-name descname">y</span><span class="p"> : </span><span class="desctype"><span class="desctype">array_like</span></span><a class="headerlink" href="#arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>The left-hand-side variable in the cointegrating regression.</p>
+<dt id="arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>The left-hand-side variable in the cointegrating regression.</p>
 </dd>
-<dt id="arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.x"><span class="sig-name descname">x</span><span class="p"> : </span><span class="desctype"><span class="desctype">array_like</span></span><a class="headerlink" href="#arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>The right-hand-side variables in the cointegrating regression.</p>
+<dt id="arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>The right-hand-side variables in the cointegrating regression.</p>
 </dd>
-<dt id="arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.trend"><span class="sig-name descname">trend</span><span class="p"> : </span><span class="desctype"><span class="desctype">{&quot;n&quot;,&quot;c&quot;,&quot;ct&quot;,&quot;ctt&quot;}, default &quot;c&quot;</span></span><a class="headerlink" href="#arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.trend" title="Permalink to this definition">¶</a></dt><dd><p><p>Trend to include in the cointegrating regression. Trends are:</p>
+<dt id="arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.trend"><span class="n sig-name">trend</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'n'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'c'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'ct'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'ctt'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code><a class="headerlink" href="#arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.trend" title="Permalink to this definition">¶</a></dt><dd><p><p>Trend to include in the cointegrating regression. Trends are:</p>
 <ul class="simple">
 <li><p>”n”: No deterministic terms</p></li>
 <li><p>”c”: Constant</p></li>
@@ -783,7 +783,7 @@ <h1 id="arch-unitroot-cointegration-canonicalcointegratingreg">arch.unitroot.coi
 </ul>
 </p>
 </dd>
-<dt id="arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.x_trend"><span class="sig-name descname">x_trend</span><span class="p"> : </span><span class="desctype"><span class="desctype">{None,&quot;c&quot;,&quot;ct&quot;,&quot;ctt&quot;}, default None</span></span><a class="headerlink" href="#arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.x_trend" title="Permalink to this definition">¶</a></dt><dd><p>Trends that affects affect the x-data but do not appear in the
+<dt id="arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.x_trend"><span class="n sig-name">x_trend</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'n'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'c'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'ct'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'ctt'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.x_trend" title="Permalink to this definition">¶</a></dt><dd><p>Trends that affects affect the x-data but do not appear in the
 cointegrating regression. x_trend must be at least as large as
 trend, so that if trend is “ct”, x_trend must be either “ct” or
 “ctt”.</p>
@@ -908,7 +908,7 @@ <h1 id="arch-unitroot-cointegration-canonicalcointegratingreg">arch.unitroot.coi
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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+        <script id="MathJax-script" src="../../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
     
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diff --git a/unitroot/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.html b/unitroot/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.html
index 989e656ec2..7e4364b337 100644
--- a/unitroot/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.html
+++ b/unitroot/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.CointegrationAnalysisResults - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.CointegrationAnalysisResults - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1023,10 +1023,10 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-cointegrationanalysisresults">arch.unitroot.cointegration.CointegrationAnalysisResults<a class="headerlink" href="#arch-unitroot-cointegration-cointegrationanalysisresults" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-cointegrationanalysisresults">arch.unitroot.cointegration.CointegrationAnalysisResults<a class="headerlink" href="#arch-unitroot-cointegration-cointegrationanalysisresults" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.unitroot.cointegration.CointegrationAnalysisResults">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.unitroot.cointegration.</span></span><span class="sig-name descname"><span class="pre">CointegrationAnalysisResults</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">cov</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">resid</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">omega_112</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">kernel_est</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">num_x</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">trend</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">df_adjust</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">rsquared</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">rsquared_adj</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">estimator_type</span></span></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/unitroot/cointegration.html#CointegrationAnalysisResults"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.cointegration.CointegrationAnalysisResults" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.unitroot.cointegration.</span></span><span class="sig-name descname"><span class="pre">CointegrationAnalysisResults</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">params</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">cov</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">resid</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">omega_112</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">kernel_est</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">CovarianceEstimator</span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">num_x</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">trend</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'n'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'c'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ct'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ctt'</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">df_adjust</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">rsquared</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">rsquared_adj</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">estimator_type</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span></span></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/unitroot/cointegration.html#CointegrationAnalysisResults"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.cointegration.CointegrationAnalysisResults" title="Link to this definition">¶</a></dt>
 <dd><p class="rubric">Methods</p>
 <table class="autosummary longtable docutils data align-default">
 <tbody>
@@ -1145,7 +1145,7 @@ <h1 id="arch-unitroot-cointegration-cointegrationanalysisresults">arch.unitroot.
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1192,7 +1192,7 @@ <h1 id="arch-unitroot-cointegration-cointegrationanalysisresults">arch.unitroot.
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diff --git a/unitroot/generated/arch.unitroot.cointegration.DynamicOLS.html b/unitroot/generated/arch.unitroot.cointegration.DynamicOLS.html
index c271494061..b5067bc27b 100644
--- a/unitroot/generated/arch.unitroot.cointegration.DynamicOLS.html
+++ b/unitroot/generated/arch.unitroot.cointegration.DynamicOLS.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLS - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.DynamicOLS - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -832,19 +832,19 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-dynamicols">arch.unitroot.cointegration.DynamicOLS<a class="headerlink" href="#arch-unitroot-cointegration-dynamicols" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-dynamicols">arch.unitroot.cointegration.DynamicOLS<a class="headerlink" href="#arch-unitroot-cointegration-dynamicols" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.unitroot.cointegration.DynamicOLS">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.unitroot.cointegration.</span></span><span class="sig-name descname"><span class="pre">DynamicOLS</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLS.__init__.y" title="arch.unitroot.cointegration.DynamicOLS.__init__.y (Python parameter) — The left-hand-side variable in the cointegrating regression."><span class="n"><span class="pre">y</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLS.__init__.x" title="arch.unitroot.cointegration.DynamicOLS.__init__.x (Python parameter) — The right-hand-side variables in the cointegrating regression."><span class="n"><span class="pre">x</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLS.__init__.trend" title="arch.unitroot.cointegration.DynamicOLS.__init__.trend (Python parameter) — Trend to include in the cointegrating regression. Trends are:"><span class="n"><span class="pre">trend</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLS.__init__.lags" title="arch.unitroot.cointegration.DynamicOLS.__init__.lags (Python parameter) — The number of lags to include in the model.  If None, the optimal number of lags is chosen using method."><span class="n"><span class="pre">lags</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLS.__init__.leads" title="arch.unitroot.cointegration.DynamicOLS.__init__.leads (Python parameter) — The number of leads to include in the model.  If None, the optimal number of leads is chosen using method."><span class="n"><span class="pre">leads</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLS.__init__.common" title="arch.unitroot.cointegration.DynamicOLS.__init__.common (Python parameter) — Flag indicating that lags and leads should be restricted to the same value. When common is None, lags must equal leads and max_lag must equal max_lead."><span class="n"><span class="pre">common</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLS.__init__.max_lag" title="arch.unitroot.cointegration.DynamicOLS.__init__.max_lag (Python parameter) — The maximum lag to consider. See Notes for value used when None."><span class="n"><span class="pre">max_lag</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLS.__init__.max_lead" title="arch.unitroot.cointegration.DynamicOLS.__init__.max_lead (Python parameter) — The maximum lead to consider. See Notes for value used when None."><span class="n"><span class="pre">max_lead</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLS.__init__.method" title="arch.unitroot.cointegration.DynamicOLS.__init__.method (Python parameter) — The method used to select lag length when lags or leads is None."><span class="n"><span class="pre">method</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;bic&#39;</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/unitroot/cointegration.html#DynamicOLS"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLS" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.unitroot.cointegration.</span></span><span class="sig-name descname"><span class="pre">DynamicOLS</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLS.__init__.y" title="arch.unitroot.cointegration.DynamicOLS.__init__.y (Python parameter) — The left-hand-side variable in the cointegrating regression."><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLS.__init__.x" title="arch.unitroot.cointegration.DynamicOLS.__init__.x (Python parameter) — The right-hand-side variables in the cointegrating regression."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLS.__init__.trend" title="arch.unitroot.cointegration.DynamicOLS.__init__.trend (Python parameter) — Trend to include in the cointegrating regression. Trends are:"><span class="n"><span class="pre">trend</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'n'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'c'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ct'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ctt'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLS.__init__.lags" title="arch.unitroot.cointegration.DynamicOLS.__init__.lags (Python parameter) — The number of lags to include in the model.  If None, the optimal number of lags is chosen using method."><span class="n"><span class="pre">lags</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLS.__init__.leads" title="arch.unitroot.cointegration.DynamicOLS.__init__.leads (Python parameter) — The number of leads to include in the model.  If None, the optimal number of leads is chosen using method."><span class="n"><span class="pre">leads</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLS.__init__.common" title="arch.unitroot.cointegration.DynamicOLS.__init__.common (Python parameter) — Flag indicating that lags and leads should be restricted to the same value. When common is None, lags must equal leads and max_lag must equal max_lead."><span class="n"><span class="pre">common</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLS.__init__.max_lag" title="arch.unitroot.cointegration.DynamicOLS.__init__.max_lag (Python parameter) — The maximum lag to consider. See Notes for value used when None."><span class="n"><span class="pre">max_lag</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLS.__init__.max_lead" title="arch.unitroot.cointegration.DynamicOLS.__init__.max_lead (Python parameter) — The maximum lead to consider. See Notes for value used when None."><span class="n"><span class="pre">max_lead</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLS.__init__.method" title="arch.unitroot.cointegration.DynamicOLS.__init__.method (Python parameter) — The method used to select lag length when lags or leads is None."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'aic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'hqic'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;bic&#39;</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/unitroot/cointegration.html#DynamicOLS"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLS" title="Link to this definition">¶</a></dt>
 <dd><p>Dynamic OLS (DOLS) cointegrating vector estimation</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.unitroot.cointegration.DynamicOLS-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLS-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.unitroot.cointegration.DynamicOLS.__init__.y"><span class="sig-name descname">y</span><span class="p"> : </span><span class="desctype"><span class="desctype">array_like</span></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLS.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>The left-hand-side variable in the cointegrating regression.</p>
+<dt id="arch.unitroot.cointegration.DynamicOLS.__init__.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLS.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>The left-hand-side variable in the cointegrating regression.</p>
 </dd>
-<dt id="arch.unitroot.cointegration.DynamicOLS.__init__.x"><span class="sig-name descname">x</span><span class="p"> : </span><span class="desctype"><span class="desctype">array_like</span></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLS.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>The right-hand-side variables in the cointegrating regression.</p>
+<dt id="arch.unitroot.cointegration.DynamicOLS.__init__.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLS.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>The right-hand-side variables in the cointegrating regression.</p>
 </dd>
-<dt id="arch.unitroot.cointegration.DynamicOLS.__init__.trend"><span class="sig-name descname">trend</span><span class="p"> : </span><span class="desctype"><span class="desctype">{&quot;n&quot;,&quot;c&quot;,&quot;ct&quot;,&quot;ctt&quot;}, default &quot;c&quot;</span></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLS.__init__.trend" title="Permalink to this definition">¶</a></dt><dd><p><p>Trend to include in the cointegrating regression. Trends are:</p>
+<dt id="arch.unitroot.cointegration.DynamicOLS.__init__.trend"><span class="n sig-name">trend</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'n'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'c'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'ct'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'ctt'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLS.__init__.trend" title="Permalink to this definition">¶</a></dt><dd><p><p>Trend to include in the cointegrating regression. Trends are:</p>
 <ul class="simple">
 <li><p>”n”: No deterministic terms</p></li>
 <li><p>”c”: Constant</p></li>
@@ -853,21 +853,21 @@ <h1 id="arch-unitroot-cointegration-dynamicols">arch.unitroot.cointegration.Dyna
 </ul>
 </p>
 </dd>
-<dt id="arch.unitroot.cointegration.DynamicOLS.__init__.lags"><span class="sig-name descname">lags</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, default None</span></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLS.__init__.lags" title="Permalink to this definition">¶</a></dt><dd><p>The number of lags to include in the model.  If None, the optimal
+<dt id="arch.unitroot.cointegration.DynamicOLS.__init__.lags"><span class="n sig-name">lags</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLS.__init__.lags" title="Permalink to this definition">¶</a></dt><dd><p>The number of lags to include in the model.  If None, the optimal
 number of lags is chosen using method.</p>
 </dd>
-<dt id="arch.unitroot.cointegration.DynamicOLS.__init__.leads"><span class="sig-name descname">leads</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, default None</span></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLS.__init__.leads" title="Permalink to this definition">¶</a></dt><dd><p>The number of leads to include in the model.  If None, the optimal
+<dt id="arch.unitroot.cointegration.DynamicOLS.__init__.leads"><span class="n sig-name">leads</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLS.__init__.leads" title="Permalink to this definition">¶</a></dt><dd><p>The number of leads to include in the model.  If None, the optimal
 number of leads is chosen using method.</p>
 </dd>
-<dt id="arch.unitroot.cointegration.DynamicOLS.__init__.common"><span class="sig-name descname">common</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, default False</span></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLS.__init__.common" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating that lags and leads should be restricted to the same
+<dt id="arch.unitroot.cointegration.DynamicOLS.__init__.common"><span class="n sig-name">common</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLS.__init__.common" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating that lags and leads should be restricted to the same
 value. When common is None, lags must equal leads and max_lag must
 equal max_lead.</p>
 </dd>
-<dt id="arch.unitroot.cointegration.DynamicOLS.__init__.max_lag"><span class="sig-name descname">max_lag</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, default None</span></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLS.__init__.max_lag" title="Permalink to this definition">¶</a></dt><dd><p>The maximum lag to consider. See Notes for value used when None.</p>
+<dt id="arch.unitroot.cointegration.DynamicOLS.__init__.max_lag"><span class="n sig-name">max_lag</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLS.__init__.max_lag" title="Permalink to this definition">¶</a></dt><dd><p>The maximum lag to consider. See Notes for value used when None.</p>
 </dd>
-<dt id="arch.unitroot.cointegration.DynamicOLS.__init__.max_lead"><span class="sig-name descname">max_lead</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, default None</span></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLS.__init__.max_lead" title="Permalink to this definition">¶</a></dt><dd><p>The maximum lead to consider. See Notes for value used when None.</p>
+<dt id="arch.unitroot.cointegration.DynamicOLS.__init__.max_lead"><span class="n sig-name">max_lead</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLS.__init__.max_lead" title="Permalink to this definition">¶</a></dt><dd><p>The maximum lead to consider. See Notes for value used when None.</p>
 </dd>
-<dt id="arch.unitroot.cointegration.DynamicOLS.__init__.method"><span class="sig-name descname">method</span><span class="p"> : </span><span class="desctype"><span class="desctype">{&quot;aic&quot;,&quot;bic&quot;,&quot;hqic&quot;}, default &quot;bic&quot;</span></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLS.__init__.method" title="Permalink to this definition">¶</a></dt><dd><p><p>The method used to select lag length when lags or leads is None.</p>
+<dt id="arch.unitroot.cointegration.DynamicOLS.__init__.method"><span class="n sig-name">method</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'aic'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'bic'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'hqic'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;bic&#39;</span></code><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLS.__init__.method" title="Permalink to this definition">¶</a></dt><dd><p><p>The method used to select lag length when lags or leads is None.</p>
 <ul class="simple">
 <li><p>”aic” - Akaike Information Criterion</p></li>
 <li><p>”hqic” - Hannan-Quinn Information Criterion</p></li>
@@ -986,7 +986,7 @@ <h1 id="arch-unitroot-cointegration-dynamicols">arch.unitroot.cointegration.Dyna
   
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diff --git a/unitroot/generated/arch.unitroot.cointegration.DynamicOLSResults.html b/unitroot/generated/arch.unitroot.cointegration.DynamicOLSResults.html
index e771ff4456..70dbc0b9fe 100644
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+++ b/unitroot/generated/arch.unitroot.cointegration.DynamicOLSResults.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults - arch 6.1.0</title>
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@@ -92,7 +92,7 @@
 
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@@ -102,7 +102,7 @@
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-            arch 6.1.0
+            arch 6.2.0
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+    arch 6.2.0
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@@ -1291,32 +1291,32 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-dynamicolsresults">arch.unitroot.cointegration.DynamicOLSResults<a class="headerlink" href="#arch-unitroot-cointegration-dynamicolsresults" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-dynamicolsresults">arch.unitroot.cointegration.DynamicOLSResults<a class="headerlink" href="#arch-unitroot-cointegration-dynamicolsresults" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.unitroot.cointegration.DynamicOLSResults">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.unitroot.cointegration.</span></span><span class="sig-name descname"><span class="pre">DynamicOLSResults</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.params" title="arch.unitroot.cointegration.DynamicOLSResults.__init__.params (Python parameter) — The estimated model parameters."><span class="n"><span class="pre">params</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.cov" title="arch.unitroot.cointegration.DynamicOLSResults.__init__.cov (Python parameter) — The estimated parameter covariance."><span class="n"><span class="pre">cov</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.resid" title="arch.unitroot.cointegration.DynamicOLSResults.__init__.resid (Python parameter) — The model residuals."><span class="n"><span class="pre">resid</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.lags" title="arch.unitroot.cointegration.DynamicOLSResults.__init__.lags (Python parameter) — The number of lags included in the model."><span class="n"><span class="pre">lags</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.leads" title="arch.unitroot.cointegration.DynamicOLSResults.__init__.leads (Python parameter) — The number of leads included in the model."><span class="n"><span class="pre">leads</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.cov_type" title="arch.unitroot.cointegration.DynamicOLSResults.__init__.cov_type (Python parameter) — The type of the parameter covariance estimator used."><span class="n"><span class="pre">cov_type</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.kernel_est" title="arch.unitroot.cointegration.DynamicOLSResults.__init__.kernel_est (Python parameter) — The covariance estimator instance used to estimate the parameter covariance."><span class="n"><span class="pre">kernel_est</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">num_x</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">trend</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.reg_results" title="arch.unitroot.cointegration.DynamicOLSResults.__init__.reg_results (Python parameter) — Regression results from fitting statsmodels OLS."><span class="n"><span class="pre">reg_results</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.df_adjust" title="arch.unitroot.cointegration.DynamicOLSResults.__init__.df_adjust (Python parameter) — Whether to degree of freedom adjust the estimator."><span class="n"><span class="pre">df_adjust</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/unitroot/cointegration.html#DynamicOLSResults"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.unitroot.cointegration.</span></span><span class="sig-name descname"><span class="pre">DynamicOLSResults</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.params" title="arch.unitroot.cointegration.DynamicOLSResults.__init__.params (Python parameter) — The estimated model parameters."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.cov" title="arch.unitroot.cointegration.DynamicOLSResults.__init__.cov (Python parameter) — The estimated parameter covariance."><span class="n"><span class="pre">cov</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.resid" title="arch.unitroot.cointegration.DynamicOLSResults.__init__.resid (Python parameter) — The model residuals."><span class="n"><span class="pre">resid</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.lags" title="arch.unitroot.cointegration.DynamicOLSResults.__init__.lags (Python parameter) — The number of lags included in the model."><span class="n"><span class="pre">lags</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.leads" title="arch.unitroot.cointegration.DynamicOLSResults.__init__.leads (Python parameter) — The number of leads included in the model."><span class="n"><span class="pre">leads</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.cov_type" title="arch.unitroot.cointegration.DynamicOLSResults.__init__.cov_type (Python parameter) — The type of the parameter covariance estimator used."><span class="n"><span class="pre">cov_type</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.kernel_est" title="arch.unitroot.cointegration.DynamicOLSResults.__init__.kernel_est (Python parameter) — The covariance estimator instance used to estimate the parameter covariance."><span class="n"><span class="pre">kernel_est</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">CovarianceEstimator</span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">num_x</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">trend</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'n'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'c'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ct'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ctt'</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.reg_results" title="arch.unitroot.cointegration.DynamicOLSResults.__init__.reg_results (Python parameter) — Regression results from fitting statsmodels OLS."><span class="n"><span class="pre">reg_results</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.regression.linear_model.RegressionResults.html#statsmodels.regression.linear_model.RegressionResults" title="(in statsmodels 0.15.0 (+59) v0.15.0 (+59))"><span class="pre">RegressionResults</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.df_adjust" title="arch.unitroot.cointegration.DynamicOLSResults.__init__.df_adjust (Python parameter) — Whether to degree of freedom adjust the estimator."><span class="n"><span class="pre">df_adjust</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/unitroot/cointegration.html#DynamicOLSResults"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults" title="Link to this definition">¶</a></dt>
 <dd><p>Estimation results for Dynamic OLS models</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.unitroot.cointegration.DynamicOLSResults-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.unitroot.cointegration.DynamicOLSResults.__init__.params"><span class="sig-name descname">params</span><span class="p"> : </span><span class="desctype"><span class="desctype">Series</span></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.params" title="Permalink to this definition">¶</a></dt><dd><p>The estimated model parameters.</p>
+<dt id="arch.unitroot.cointegration.DynamicOLSResults.__init__.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.params" title="Permalink to this definition">¶</a></dt><dd><p>The estimated model parameters.</p>
 </dd>
-<dt id="arch.unitroot.cointegration.DynamicOLSResults.__init__.cov"><span class="sig-name descname">cov</span><span class="p"> : </span><span class="desctype"><span class="desctype">DataFrame</span></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.cov" title="Permalink to this definition">¶</a></dt><dd><p>The estimated parameter covariance.</p>
+<dt id="arch.unitroot.cointegration.DynamicOLSResults.__init__.cov"><span class="n sig-name">cov</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.cov" title="Permalink to this definition">¶</a></dt><dd><p>The estimated parameter covariance.</p>
 </dd>
-<dt id="arch.unitroot.cointegration.DynamicOLSResults.__init__.resid"><span class="sig-name descname">resid</span><span class="p"> : </span><span class="desctype"><span class="desctype">Series</span></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.resid" title="Permalink to this definition">¶</a></dt><dd><p>The model residuals.</p>
+<dt id="arch.unitroot.cointegration.DynamicOLSResults.__init__.resid"><span class="n sig-name">resid</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.resid" title="Permalink to this definition">¶</a></dt><dd><p>The model residuals.</p>
 </dd>
-<dt id="arch.unitroot.cointegration.DynamicOLSResults.__init__.lags"><span class="sig-name descname">lags</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.lags" title="Permalink to this definition">¶</a></dt><dd><p>The number of lags included in the model.</p>
+<dt id="arch.unitroot.cointegration.DynamicOLSResults.__init__.lags"><span class="n sig-name">lags</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.lags" title="Permalink to this definition">¶</a></dt><dd><p>The number of lags included in the model.</p>
 </dd>
-<dt id="arch.unitroot.cointegration.DynamicOLSResults.__init__.leads"><span class="sig-name descname">leads</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.leads" title="Permalink to this definition">¶</a></dt><dd><p>The number of leads included in the model.</p>
+<dt id="arch.unitroot.cointegration.DynamicOLSResults.__init__.leads"><span class="n sig-name">leads</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.leads" title="Permalink to this definition">¶</a></dt><dd><p>The number of leads included in the model.</p>
 </dd>
-<dt id="arch.unitroot.cointegration.DynamicOLSResults.__init__.cov_type"><span class="sig-name descname">cov_type</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.cov_type" title="Permalink to this definition">¶</a></dt><dd><p>The type of the parameter covariance estimator used.</p>
+<dt id="arch.unitroot.cointegration.DynamicOLSResults.__init__.cov_type"><span class="n sig-name">cov_type</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.cov_type" title="Permalink to this definition">¶</a></dt><dd><p>The type of the parameter covariance estimator used.</p>
 </dd>
-<dt id="arch.unitroot.cointegration.DynamicOLSResults.__init__.kernel_est"><span class="sig-name descname">kernel_est</span><span class="p"> : </span><span class="desctype"><span class="desctype">CovarianceEstimator</span></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.kernel_est" title="Permalink to this definition">¶</a></dt><dd><p>The covariance estimator instance used to estimate the parameter
+<dt id="arch.unitroot.cointegration.DynamicOLSResults.__init__.kernel_est"><span class="n sig-name">kernel_est</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype">CovarianceEstimator</span></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.kernel_est" title="Permalink to this definition">¶</a></dt><dd><p>The covariance estimator instance used to estimate the parameter
 covariance.</p>
 </dd>
-<dt id="arch.unitroot.cointegration.DynamicOLSResults.__init__.reg_results"><span class="sig-name descname">reg_results</span><span class="p"> : </span><span class="desctype"><span class="desctype">RegressionResults</span></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.reg_results" title="Permalink to this definition">¶</a></dt><dd><p>Regression results from fitting statsmodels OLS.</p>
+<dt id="arch.unitroot.cointegration.DynamicOLSResults.__init__.reg_results"><span class="n sig-name">reg_results</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.regression.linear_model.RegressionResults.html#statsmodels.regression.linear_model.RegressionResults" title="(in statsmodels 0.15.0 (+59) v0.15.0 (+59))">RegressionResults</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.reg_results" title="Permalink to this definition">¶</a></dt><dd><p>Regression results from fitting statsmodels OLS.</p>
 </dd>
-<dt id="arch.unitroot.cointegration.DynamicOLSResults.__init__.df_adjust"><span class="sig-name descname">df_adjust</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.df_adjust" title="Permalink to this definition">¶</a></dt><dd><p>Whether to degree of freedom adjust the estimator.</p>
+<dt id="arch.unitroot.cointegration.DynamicOLSResults.__init__.df_adjust"><span class="n sig-name">df_adjust</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.df_adjust" title="Permalink to this definition">¶</a></dt><dd><p>Whether to degree of freedom adjust the estimator.</p>
 </dd>
 </dl>
 </dd>
@@ -1454,7 +1454,7 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults">arch.unitroot.cointegrati
   
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diff --git a/unitroot/generated/arch.unitroot.cointegration.EngleGrangerTestResults.html b/unitroot/generated/arch.unitroot.cointegration.EngleGrangerTestResults.html
index 11fed1c551..e659df71c0 100644
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+++ b/unitroot/generated/arch.unitroot.cointegration.EngleGrangerTestResults.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.EngleGrangerTestResults - arch 6.1.0</title>
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@@ -47,8 +47,8 @@
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@@ -92,7 +92,7 @@
 
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+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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+            arch 6.2.0
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+    arch 6.2.0
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@@ -1225,32 +1225,32 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-englegrangertestresults">arch.unitroot.cointegration.EngleGrangerTestResults<a class="headerlink" href="#arch-unitroot-cointegration-englegrangertestresults" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-englegrangertestresults">arch.unitroot.cointegration.EngleGrangerTestResults<a class="headerlink" href="#arch-unitroot-cointegration-englegrangertestresults" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.unitroot.cointegration.EngleGrangerTestResults">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.unitroot.cointegration.</span></span><span class="sig-name descname"><span class="pre">EngleGrangerTestResults</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.stat" title="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.stat (Python parameter) — The Engle-Granger test statistic."><span class="n"><span class="pre">stat</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.pvalue" title="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.pvalue (Python parameter) — The pvalue of the Engle-Granger test statistic."><span class="n"><span class="pre">pvalue</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.crit_vals" title="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.crit_vals (Python parameter) — The critical values of the Engle-Granger specific to the sample size and model dimension."><span class="n"><span class="pre">crit_vals</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.null" title="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.null (Python parameter) — The null hypothesis."><span class="n"><span class="pre">null</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;No Cointegration&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.alternative" title="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.alternative (Python parameter) — The alternative hypothesis."><span class="n"><span class="pre">alternative</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;Cointegration&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.trend" title="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.trend (Python parameter) — The model's trend description."><span class="n"><span class="pre">trend</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.order" title="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.order (Python parameter) — The number of stochastic trends in the null distribution."><span class="n"><span class="pre">order</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">2</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.adf" title="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.adf (Python parameter) — The ADF instance used to perform the test and lag selection."><span class="n"><span class="pre">adf</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.xsection" title="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.xsection (Python parameter) — The OLS results used in the cross-sectional regression."><span class="n"><span class="pre">xsection</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/unitroot/_engle_granger.html#EngleGrangerTestResults"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.unitroot.cointegration.</span></span><span class="sig-name descname"><span class="pre">EngleGrangerTestResults</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.stat" title="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.stat (Python parameter) — The Engle-Granger test statistic."><span class="n"><span class="pre">stat</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.pvalue" title="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.pvalue (Python parameter) — The pvalue of the Engle-Granger test statistic."><span class="n"><span class="pre">pvalue</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.crit_vals" title="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.crit_vals (Python parameter) — The critical values of the Engle-Granger specific to the sample size and model dimension."><span class="n"><span class="pre">crit_vals</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.null" title="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.null (Python parameter) — The null hypothesis."><span class="n"><span class="pre">null</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;No Cointegration&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.alternative" title="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.alternative (Python parameter) — The alternative hypothesis."><span class="n"><span class="pre">alternative</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;Cointegration&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.trend" title="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.trend (Python parameter) — The model's trend description."><span class="n"><span class="pre">trend</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.order" title="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.order (Python parameter) — The number of stochastic trends in the null distribution."><span class="n"><span class="pre">order</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">2</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.adf" title="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.adf (Python parameter) — The ADF instance used to perform the test and lag selection."><span class="n"><span class="pre">adf</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.unitroot.ADF.html#arch.unitroot.ADF" title="arch.unitroot.unitroot.ADF (Python class)"><span class="pre">ADF</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.xsection" title="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.xsection (Python parameter) — The OLS results used in the cross-sectional regression."><span class="n"><span class="pre">xsection</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.regression.linear_model.RegressionResults.html#statsmodels.regression.linear_model.RegressionResults" title="(in statsmodels 0.15.0 (+59) v0.15.0 (+59))"><span class="pre">RegressionResults</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/unitroot/_engle_granger.html#EngleGrangerTestResults"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults" title="Link to this definition">¶</a></dt>
 <dd><p>Results class for Engle-Granger cointegration tests.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.unitroot.cointegration.EngleGrangerTestResults-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.stat"><span class="sig-name descname">stat</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.stat" title="Permalink to this definition">¶</a></dt><dd><p>The Engle-Granger test statistic.</p>
+<dt id="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.stat"><span class="n sig-name">stat</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.stat" title="Permalink to this definition">¶</a></dt><dd><p>The Engle-Granger test statistic.</p>
 </dd>
-<dt id="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.pvalue"><span class="sig-name descname">pvalue</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.pvalue" title="Permalink to this definition">¶</a></dt><dd><p>The pvalue of the Engle-Granger test statistic.</p>
+<dt id="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.pvalue"><span class="n sig-name">pvalue</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.pvalue" title="Permalink to this definition">¶</a></dt><dd><p>The pvalue of the Engle-Granger test statistic.</p>
 </dd>
-<dt id="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.crit_vals"><span class="sig-name descname">crit_vals</span><span class="p"> : </span><span class="desctype"><span class="desctype">Series</span></span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.crit_vals" title="Permalink to this definition">¶</a></dt><dd><p>The critical values of the Engle-Granger specific to the sample size
+<dt id="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.crit_vals"><span class="n sig-name">crit_vals</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.crit_vals" title="Permalink to this definition">¶</a></dt><dd><p>The critical values of the Engle-Granger specific to the sample size
 and model dimension.</p>
 </dd>
-<dt id="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.null"><span class="sig-name descname">null</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.null" title="Permalink to this definition">¶</a></dt><dd><p>The null hypothesis.</p>
+<dt id="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.null"><span class="n sig-name">null</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;No Cointegration&#39;</span></code><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.null" title="Permalink to this definition">¶</a></dt><dd><p>The null hypothesis.</p>
 </dd>
-<dt id="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.alternative"><span class="sig-name descname">alternative</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.alternative" title="Permalink to this definition">¶</a></dt><dd><p>The alternative hypothesis.</p>
+<dt id="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.alternative"><span class="n sig-name">alternative</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;Cointegration&#39;</span></code><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.alternative" title="Permalink to this definition">¶</a></dt><dd><p>The alternative hypothesis.</p>
 </dd>
-<dt id="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.trend"><span class="sig-name descname">trend</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.trend" title="Permalink to this definition">¶</a></dt><dd><p>The model’s trend description.</p>
+<dt id="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.trend"><span class="n sig-name">trend</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.trend" title="Permalink to this definition">¶</a></dt><dd><p>The model’s trend description.</p>
 </dd>
-<dt id="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.order"><span class="sig-name descname">order</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.order" title="Permalink to this definition">¶</a></dt><dd><p>The number of stochastic trends in the null distribution.</p>
+<dt id="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.order"><span class="n sig-name">order</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">2</span></code><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.order" title="Permalink to this definition">¶</a></dt><dd><p>The number of stochastic trends in the null distribution.</p>
 </dd>
-<dt id="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.adf"><span class="sig-name descname">adf</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference internal" href="arch.unitroot.ADF.html#arch.unitroot.ADF" title="arch.unitroot.ADF (Python class) — Augmented Dickey-Fuller unit root test">ADF</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.adf" title="Permalink to this definition">¶</a></dt><dd><p>The ADF instance used to perform the test and lag selection.</p>
+<dt id="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.adf"><span class="n sig-name">adf</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.unitroot.ADF.html#arch.unitroot.ADF" title="arch.unitroot.unitroot.ADF (Python class)">ADF</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.adf" title="Permalink to this definition">¶</a></dt><dd><p>The ADF instance used to perform the test and lag selection.</p>
 </dd>
-<dt id="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.xsection"><span class="sig-name descname">xsection</span><span class="p"> : </span><span class="desctype"><span class="desctype">RegressionResults</span></span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.xsection" title="Permalink to this definition">¶</a></dt><dd><p>The OLS results used in the cross-sectional regression.</p>
+<dt id="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.xsection"><span class="n sig-name">xsection</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.regression.linear_model.RegressionResults.html#statsmodels.regression.linear_model.RegressionResults" title="(in statsmodels 0.15.0 (+59) v0.15.0 (+59))">RegressionResults</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.xsection" title="Permalink to this definition">¶</a></dt><dd><p>The OLS results used in the cross-sectional regression.</p>
 </dd>
 </dl>
 </dd>
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-<h1 id="arch-unitroot-cointegration-fullymodifiedols">arch.unitroot.cointegration.FullyModifiedOLS<a class="headerlink" href="#arch-unitroot-cointegration-fullymodifiedols" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-fullymodifiedols">arch.unitroot.cointegration.FullyModifiedOLS<a class="headerlink" href="#arch-unitroot-cointegration-fullymodifiedols" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.unitroot.cointegration.FullyModifiedOLS">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.unitroot.cointegration.</span></span><span class="sig-name descname"><span class="pre">FullyModifiedOLS</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.FullyModifiedOLS.__init__.y" title="arch.unitroot.cointegration.FullyModifiedOLS.__init__.y (Python parameter) — The left-hand-side variable in the cointegrating regression."><span class="n"><span class="pre">y</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.FullyModifiedOLS.__init__.x" title="arch.unitroot.cointegration.FullyModifiedOLS.__init__.x (Python parameter) — The right-hand-side variables in the cointegrating regression."><span class="n"><span class="pre">x</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.FullyModifiedOLS.__init__.trend" title="arch.unitroot.cointegration.FullyModifiedOLS.__init__.trend (Python parameter) — Trend to include in the cointegrating regression. Trends are:"><span class="n"><span class="pre">trend</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.FullyModifiedOLS.__init__.x_trend" title="arch.unitroot.cointegration.FullyModifiedOLS.__init__.x_trend (Python parameter) — Trends that affects affect the x-data but do not appear in the cointegrating regression. x_trend must be at least as large as trend, so that if trend is &quot;ct&quot;, x_trend must be either &quot;ct&quot; or &quot;ctt&quot;."><span class="n"><span class="pre">x_trend</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/unitroot/cointegration.html#FullyModifiedOLS"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.cointegration.FullyModifiedOLS" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.unitroot.cointegration.</span></span><span class="sig-name descname"><span class="pre">FullyModifiedOLS</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.FullyModifiedOLS.__init__.y" title="arch.unitroot.cointegration.FullyModifiedOLS.__init__.y (Python parameter) — The left-hand-side variable in the cointegrating regression."><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.FullyModifiedOLS.__init__.x" title="arch.unitroot.cointegration.FullyModifiedOLS.__init__.x (Python parameter) — The right-hand-side variables in the cointegrating regression."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.FullyModifiedOLS.__init__.trend" title="arch.unitroot.cointegration.FullyModifiedOLS.__init__.trend (Python parameter) — Trend to include in the cointegrating regression. Trends are:"><span class="n"><span class="pre">trend</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'n'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'c'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ct'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ctt'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.FullyModifiedOLS.__init__.x_trend" title="arch.unitroot.cointegration.FullyModifiedOLS.__init__.x_trend (Python parameter) — Trends that affects affect the x-data but do not appear in the cointegrating regression. x_trend must be at least as large as trend, so that if trend is &quot;ct&quot;, x_trend must be either &quot;ct&quot; or &quot;ctt&quot;."><span class="n"><span class="pre">x_trend</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'n'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'c'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ct'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ctt'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/unitroot/cointegration.html#FullyModifiedOLS"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.cointegration.FullyModifiedOLS" title="Link to this definition">¶</a></dt>
 <dd><p>Fully Modified OLS cointegrating vector estimation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.unitroot.cointegration.FullyModifiedOLS-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.cointegration.FullyModifiedOLS-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.unitroot.cointegration.FullyModifiedOLS.__init__.y"><span class="sig-name descname">y</span><span class="p"> : </span><span class="desctype"><span class="desctype">array_like</span></span><a class="headerlink" href="#arch.unitroot.cointegration.FullyModifiedOLS.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>The left-hand-side variable in the cointegrating regression.</p>
+<dt id="arch.unitroot.cointegration.FullyModifiedOLS.__init__.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.FullyModifiedOLS.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>The left-hand-side variable in the cointegrating regression.</p>
 </dd>
-<dt id="arch.unitroot.cointegration.FullyModifiedOLS.__init__.x"><span class="sig-name descname">x</span><span class="p"> : </span><span class="desctype"><span class="desctype">array_like</span></span><a class="headerlink" href="#arch.unitroot.cointegration.FullyModifiedOLS.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>The right-hand-side variables in the cointegrating regression.</p>
+<dt id="arch.unitroot.cointegration.FullyModifiedOLS.__init__.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.FullyModifiedOLS.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>The right-hand-side variables in the cointegrating regression.</p>
 </dd>
-<dt id="arch.unitroot.cointegration.FullyModifiedOLS.__init__.trend"><span class="sig-name descname">trend</span><span class="p"> : </span><span class="desctype"><span class="desctype">{&quot;n&quot;,&quot;c&quot;,&quot;ct&quot;,&quot;ctt&quot;}, default &quot;c&quot;</span></span><a class="headerlink" href="#arch.unitroot.cointegration.FullyModifiedOLS.__init__.trend" title="Permalink to this definition">¶</a></dt><dd><p><p>Trend to include in the cointegrating regression. Trends are:</p>
+<dt id="arch.unitroot.cointegration.FullyModifiedOLS.__init__.trend"><span class="n sig-name">trend</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'n'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'c'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'ct'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'ctt'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code><a class="headerlink" href="#arch.unitroot.cointegration.FullyModifiedOLS.__init__.trend" title="Permalink to this definition">¶</a></dt><dd><p><p>Trend to include in the cointegrating regression. Trends are:</p>
 <ul class="simple">
 <li><p>”n”: No deterministic terms</p></li>
 <li><p>”c”: Constant</p></li>
@@ -783,7 +783,7 @@ <h1 id="arch-unitroot-cointegration-fullymodifiedols">arch.unitroot.cointegratio
 </ul>
 </p>
 </dd>
-<dt id="arch.unitroot.cointegration.FullyModifiedOLS.__init__.x_trend"><span class="sig-name descname">x_trend</span><span class="p"> : </span><span class="desctype"><span class="desctype">{None,&quot;c&quot;,&quot;ct&quot;,&quot;ctt&quot;}, default None</span></span><a class="headerlink" href="#arch.unitroot.cointegration.FullyModifiedOLS.__init__.x_trend" title="Permalink to this definition">¶</a></dt><dd><p>Trends that affects affect the x-data but do not appear in the
+<dt id="arch.unitroot.cointegration.FullyModifiedOLS.__init__.x_trend"><span class="n sig-name">x_trend</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'n'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'c'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'ct'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'ctt'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.unitroot.cointegration.FullyModifiedOLS.__init__.x_trend" title="Permalink to this definition">¶</a></dt><dd><p>Trends that affects affect the x-data but do not appear in the
 cointegrating regression. x_trend must be at least as large as
 trend, so that if trend is “ct”, x_trend must be either “ct” or
 “ctt”.</p>
@@ -915,7 +915,7 @@ <h1 id="arch-unitroot-cointegration-fullymodifiedols">arch.unitroot.cointegratio
   
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diff --git a/unitroot/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.html b/unitroot/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.html
index 77329b800a..a799eeb502 100644
--- a/unitroot/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.html
+++ b/unitroot/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults - arch 6.2.0</title>
       
     
 
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@@ -92,7 +92,7 @@
 
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-    <a href="../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch 6.1.0
+    arch 6.2.0
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@@ -1045,10 +1045,10 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-phillipsouliaristestresults">arch.unitroot.cointegration.PhillipsOuliarisTestResults<a class="headerlink" href="#arch-unitroot-cointegration-phillipsouliaristestresults" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-phillipsouliaristestresults">arch.unitroot.cointegration.PhillipsOuliarisTestResults<a class="headerlink" href="#arch-unitroot-cointegration-phillipsouliaristestresults" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.unitroot.cointegration.PhillipsOuliarisTestResults">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.unitroot.cointegration.</span></span><span class="sig-name descname"><span class="pre">PhillipsOuliarisTestResults</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">stat</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">pvalue</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">crit_vals</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">null</span></span><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;No Cointegration&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">alternative</span></span><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;Cointegration&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">trend</span></span><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">order</span></span><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">2</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">xsection</span></span><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">test_type</span></span><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;Za&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">kernel_est</span></span><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">rho</span></span><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.0</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/unitroot/_phillips_ouliaris.html#PhillipsOuliarisTestResults"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.unitroot.cointegration.</span></span><span class="sig-name descname"><span class="pre">PhillipsOuliarisTestResults</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">stat</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">pvalue</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">crit_vals</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">null</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;No Cointegration&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">alternative</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;Cointegration&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">trend</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">order</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">2</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">xsection</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.regression.linear_model.RegressionResults.html#statsmodels.regression.linear_model.RegressionResults" title="(in statsmodels 0.15.0 (+59) v0.15.0 (+59))"><span class="pre">RegressionResults</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">test_type</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;Za&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">kernel_est</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">CovarianceEstimator</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">rho</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.0</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/unitroot/_phillips_ouliaris.html#PhillipsOuliarisTestResults"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults" title="Link to this definition">¶</a></dt>
 <dd><p class="rubric">Methods</p>
 <table class="autosummary longtable docutils data align-default">
 <tbody>
@@ -1170,7 +1170,7 @@ <h1 id="arch-unitroot-cointegration-phillipsouliaristestresults">arch.unitroot.c
   
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diff --git a/unitroot/generated/arch.unitroot.cointegration.engle_granger.html b/unitroot/generated/arch.unitroot.cointegration.engle_granger.html
index 30383e9ba1..6a9e74cb71 100644
--- a/unitroot/generated/arch.unitroot.cointegration.engle_granger.html
+++ b/unitroot/generated/arch.unitroot.cointegration.engle_granger.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.engle_granger - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.engle_granger - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
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+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch 6.1.0
+    arch 6.2.0
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@@ -698,19 +698,19 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-engle-granger">arch.unitroot.cointegration.engle_granger<a class="headerlink" href="#arch-unitroot-cointegration-engle-granger" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-engle-granger">arch.unitroot.cointegration.engle_granger<a class="headerlink" href="#arch-unitroot-cointegration-engle-granger" title="Link to this heading">¶</a></h1>
 <dl class="py function objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.unitroot.cointegration.engle_granger">
-<span class="sig-prename descclassname"><span class="pre">arch.unitroot.cointegration.</span></span><span class="sig-name descname"><span class="pre">engle_granger</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.engle_granger.y" title="arch.unitroot.cointegration.engle_granger.y (Python parameter) — The left-hand-side variable in the cointegrating regression."><span class="n"><span class="pre">y</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.engle_granger.x" title="arch.unitroot.cointegration.engle_granger.x (Python parameter) — The right-hand-side variables in the cointegrating regression."><span class="n"><span class="pre">x</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.engle_granger.trend" title="arch.unitroot.cointegration.engle_granger.trend (Python parameter) — Trend to include in the cointegrating regression. Trends are:"><span class="n"><span class="pre">trend</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.engle_granger.lags" title="arch.unitroot.cointegration.engle_granger.lags (Python parameter) — The number of lagged differences to include in the Augmented Dickey-Fuller test used on the residuals of the"><span class="n"><span class="pre">lags</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.engle_granger.max_lags" title="arch.unitroot.cointegration.engle_granger.max_lags (Python parameter) — The maximum number of lags to consider when using automatic lag-length in the Augmented Dickey-Fuller regression."><span class="n"><span class="pre">max_lags</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.engle_granger.method" title="arch.unitroot.cointegration.engle_granger.method (Python parameter) — The method used to select the number of lags included in the Augmented Dickey-Fuller regression."><span class="n"><span class="pre">method</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;bic&#39;</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/unitroot/_engle_granger.html#engle_granger"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.cointegration.engle_granger" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">arch.unitroot.cointegration.</span></span><span class="sig-name descname"><span class="pre">engle_granger</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.engle_granger.y" title="arch.unitroot.cointegration.engle_granger.y (Python parameter) — The left-hand-side variable in the cointegrating regression."><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.engle_granger.x" title="arch.unitroot.cointegration.engle_granger.x (Python parameter) — The right-hand-side variables in the cointegrating regression."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.engle_granger.trend" title="arch.unitroot.cointegration.engle_granger.trend (Python parameter) — Trend to include in the cointegrating regression. Trends are:"><span class="n"><span class="pre">trend</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'n'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'c'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ct'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ctt'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.engle_granger.lags" title="arch.unitroot.cointegration.engle_granger.lags (Python parameter) — The number of lagged differences to include in the Augmented Dickey-Fuller test used on the residuals of the"><span class="n"><span class="pre">lags</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.engle_granger.max_lags" title="arch.unitroot.cointegration.engle_granger.max_lags (Python parameter) — The maximum number of lags to consider when using automatic lag-length in the Augmented Dickey-Fuller regression."><span class="n"><span class="pre">max_lags</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.engle_granger.method" title="arch.unitroot.cointegration.engle_granger.method (Python parameter) — The method used to select the number of lags included in the Augmented Dickey-Fuller regression."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'aic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'t-stat'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;bic&#39;</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="arch.unitroot.cointegration.EngleGrangerTestResults.html#arch.unitroot.cointegration.EngleGrangerTestResults" title="arch.unitroot._engle_granger.EngleGrangerTestResults (Python class)"><span class="pre">EngleGrangerTestResults</span></a></span></span></span><a class="reference internal" href="../../_modules/arch/unitroot/_engle_granger.html#engle_granger"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.cointegration.engle_granger" title="Link to this definition">¶</a></dt>
 <dd><p>Test for cointegration within a set of time series.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.unitroot.cointegration.engle_granger-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.cointegration.engle_granger-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.unitroot.cointegration.engle_granger.y"><span class="sig-name descname">y</span><span class="p"> : </span><span class="desctype"><span class="desctype">array_like</span></span><a class="headerlink" href="#arch.unitroot.cointegration.engle_granger.y" title="Permalink to this definition">¶</a></dt><dd><p>The left-hand-side variable in the cointegrating regression.</p>
+<dt id="arch.unitroot.cointegration.engle_granger.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.engle_granger.y" title="Permalink to this definition">¶</a></dt><dd><p>The left-hand-side variable in the cointegrating regression.</p>
 </dd>
-<dt id="arch.unitroot.cointegration.engle_granger.x"><span class="sig-name descname">x</span><span class="p"> : </span><span class="desctype"><span class="desctype">array_like</span></span><a class="headerlink" href="#arch.unitroot.cointegration.engle_granger.x" title="Permalink to this definition">¶</a></dt><dd><p>The right-hand-side variables in the cointegrating regression.</p>
+<dt id="arch.unitroot.cointegration.engle_granger.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.engle_granger.x" title="Permalink to this definition">¶</a></dt><dd><p>The right-hand-side variables in the cointegrating regression.</p>
 </dd>
-<dt id="arch.unitroot.cointegration.engle_granger.trend"><span class="sig-name descname">trend</span><span class="p"> : </span><span class="desctype"><span class="desctype">{&quot;n&quot;,&quot;c&quot;,&quot;ct&quot;,&quot;ctt&quot;}, default &quot;c&quot;</span></span><a class="headerlink" href="#arch.unitroot.cointegration.engle_granger.trend" title="Permalink to this definition">¶</a></dt><dd><p><p>Trend to include in the cointegrating regression. Trends are:</p>
+<dt id="arch.unitroot.cointegration.engle_granger.trend"><span class="n sig-name">trend</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'n'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'c'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'ct'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'ctt'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code><a class="headerlink" href="#arch.unitroot.cointegration.engle_granger.trend" title="Permalink to this definition">¶</a></dt><dd><p><p>Trend to include in the cointegrating regression. Trends are:</p>
 <ul class="simple">
 <li><p>”n”: No deterministic terms</p></li>
 <li><p>”c”: Constant</p></li>
@@ -719,13 +719,13 @@ <h1 id="arch-unitroot-cointegration-engle-granger">arch.unitroot.cointegration.e
 </ul>
 </p>
 </dd>
-<dt id="arch.unitroot.cointegration.engle_granger.lags"><span class="sig-name descname">lags</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, default None</span></span><a class="headerlink" href="#arch.unitroot.cointegration.engle_granger.lags" title="Permalink to this definition">¶</a></dt><dd><p>The number of lagged differences to include in the Augmented
+<dt id="arch.unitroot.cointegration.engle_granger.lags"><span class="n sig-name">lags</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.unitroot.cointegration.engle_granger.lags" title="Permalink to this definition">¶</a></dt><dd><p>The number of lagged differences to include in the Augmented
 Dickey-Fuller test used on the residuals of the</p>
 </dd>
-<dt id="arch.unitroot.cointegration.engle_granger.max_lags"><span class="sig-name descname">max_lags</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, default None</span></span><a class="headerlink" href="#arch.unitroot.cointegration.engle_granger.max_lags" title="Permalink to this definition">¶</a></dt><dd><p>The maximum number of lags to consider when using automatic
+<dt id="arch.unitroot.cointegration.engle_granger.max_lags"><span class="n sig-name">max_lags</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.unitroot.cointegration.engle_granger.max_lags" title="Permalink to this definition">¶</a></dt><dd><p>The maximum number of lags to consider when using automatic
 lag-length in the Augmented Dickey-Fuller regression.</p>
 </dd>
-<dt id="arch.unitroot.cointegration.engle_granger.method"><span class="sig-name descname">method</span><span class="p"> : </span><span class="desctype"><span class="desctype">{&quot;aic&quot;, &quot;bic&quot;, &quot;tstat&quot;}, default &quot;bic&quot;</span></span><a class="headerlink" href="#arch.unitroot.cointegration.engle_granger.method" title="Permalink to this definition">¶</a></dt><dd><p>The method used to select the number of lags included in the
+<dt id="arch.unitroot.cointegration.engle_granger.method"><span class="n sig-name">method</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'aic'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'bic'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'t-stat'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;bic&#39;</span></code><a class="headerlink" href="#arch.unitroot.cointegration.engle_granger.method" title="Permalink to this definition">¶</a></dt><dd><p>The method used to select the number of lags included in the
 Augmented Dickey-Fuller regression.</p>
 </dd>
 </dl>
@@ -734,7 +734,7 @@ <h1 id="arch-unitroot-cointegration-engle-granger">arch.unitroot.cointegration.e
 <dd class="field-even"><p>Results of the Engle-Granger test.</p>
 </dd>
 <dt class="field-odd" id="arch.unitroot.cointegration.engle_granger-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.cointegration.engle_granger-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference internal" href="arch.unitroot.cointegration.EngleGrangerTestResults.html#arch.unitroot.cointegration.EngleGrangerTestResults" title="arch.unitroot.cointegration.EngleGrangerTestResults (Python class) — Results class for Engle-Granger cointegration tests.">EngleGrangerTestResults</a></p>
+<dd class="field-odd"><p><a class="reference internal" href="arch.unitroot.cointegration.EngleGrangerTestResults.html#arch.unitroot.cointegration.EngleGrangerTestResults" title="arch.unitroot.cointegration.EngleGrangerTestResults (Python class) — Results class for Engle-Granger cointegration tests."><code class="xref py py-class docutils literal notranslate"><span class="pre">EngleGrangerTestResults</span></code></a></p>
 </dd>
 </dl>
 <div class="seealso admonition">
@@ -829,7 +829,7 @@ <h1 id="arch-unitroot-cointegration-engle-granger">arch.unitroot.cointegration.e
   
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diff --git a/unitroot/generated/arch.unitroot.cointegration.phillips_ouliaris.html b/unitroot/generated/arch.unitroot.cointegration.phillips_ouliaris.html
index ce6d2b42cf..718de8eb14 100644
--- a/unitroot/generated/arch.unitroot.cointegration.phillips_ouliaris.html
+++ b/unitroot/generated/arch.unitroot.cointegration.phillips_ouliaris.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.phillips_ouliaris - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.phillips_ouliaris - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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@@ -712,19 +712,19 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-phillips-ouliaris">arch.unitroot.cointegration.phillips_ouliaris<a class="headerlink" href="#arch-unitroot-cointegration-phillips-ouliaris" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-phillips-ouliaris">arch.unitroot.cointegration.phillips_ouliaris<a class="headerlink" href="#arch-unitroot-cointegration-phillips-ouliaris" title="Link to this heading">¶</a></h1>
 <dl class="py function objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.unitroot.cointegration.phillips_ouliaris">
-<span class="sig-prename descclassname"><span class="pre">arch.unitroot.cointegration.</span></span><span class="sig-name descname"><span class="pre">phillips_ouliaris</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.phillips_ouliaris.y" title="arch.unitroot.cointegration.phillips_ouliaris.y (Python parameter) — The left-hand-side variable in the cointegrating regression."><span class="n"><span class="pre">y</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.phillips_ouliaris.x" title="arch.unitroot.cointegration.phillips_ouliaris.x (Python parameter) — The right-hand-side variables in the cointegrating regression."><span class="n"><span class="pre">x</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.phillips_ouliaris.trend" title="arch.unitroot.cointegration.phillips_ouliaris.trend (Python parameter) — Trend to include in the cointegrating regression. Trends are:"><span class="n"><span class="pre">trend</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.phillips_ouliaris.test_type" title="arch.unitroot.cointegration.phillips_ouliaris.test_type (Python parameter) — The test statistic to compute. Supported options are:"><span class="n"><span class="pre">test_type</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;Zt&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.phillips_ouliaris.kernel" title="arch.unitroot.cointegration.phillips_ouliaris.kernel (Python parameter) — The string name of any of any known kernel-based long-run covariance estimators. Common choices are &quot;bartlett&quot; for the Bartlett kernel (Newey-West), &quot;parzen&quot; for the Parzen kernel and &quot;quadratic-spectral&quot; for the Quadratic Spectral kernel."><span class="n"><span class="pre">kernel</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;bartlett&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.phillips_ouliaris.bandwidth" title="arch.unitroot.cointegration.phillips_ouliaris.bandwidth (Python parameter) — The bandwidth to use. If not provided, the optimal bandwidth is estimated from the data. Setting the bandwidth to 0 and using &quot;unadjusted&quot; produces the classic OLS covariance estimator. Setting the bandwidth to 0 and using &quot;robust&quot; produces White's covariance estimator."><span class="n"><span class="pre">bandwidth</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.phillips_ouliaris.force_int" title="arch.unitroot.cointegration.phillips_ouliaris.force_int (Python parameter) — Whether the force the estimated optimal bandwidth to be an integer."><span class="n"><span class="pre">force_int</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/unitroot/_phillips_ouliaris.html#phillips_ouliaris"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.cointegration.phillips_ouliaris" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">arch.unitroot.cointegration.</span></span><span class="sig-name descname"><span class="pre">phillips_ouliaris</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.phillips_ouliaris.y" title="arch.unitroot.cointegration.phillips_ouliaris.y (Python parameter) — The left-hand-side variable in the cointegrating regression."><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.phillips_ouliaris.x" title="arch.unitroot.cointegration.phillips_ouliaris.x (Python parameter) — The right-hand-side variables in the cointegrating regression."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.phillips_ouliaris.trend" title="arch.unitroot.cointegration.phillips_ouliaris.trend (Python parameter) — Trend to include in the cointegrating regression. Trends are:"><span class="n"><span class="pre">trend</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'n'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'c'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ct'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ctt'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.phillips_ouliaris.test_type" title="arch.unitroot.cointegration.phillips_ouliaris.test_type (Python parameter) — The test statistic to compute. Supported options are:"><span class="n"><span class="pre">test_type</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'Za'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'Zt'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'Pu'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'Pz'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;Zt&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.phillips_ouliaris.kernel" title="arch.unitroot.cointegration.phillips_ouliaris.kernel (Python parameter) — The string name of any of any known kernel-based long-run covariance estimators. Common choices are &quot;bartlett&quot; for the Bartlett kernel (Newey-West), &quot;parzen&quot; for the Parzen kernel and &quot;quadratic-spectral&quot; for the Quadratic Spectral kernel."><span class="n"><span class="pre">kernel</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;bartlett&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.phillips_ouliaris.bandwidth" title="arch.unitroot.cointegration.phillips_ouliaris.bandwidth (Python parameter) — The bandwidth to use. If not provided, the optimal bandwidth is estimated from the data. Setting the bandwidth to 0 and using &quot;unadjusted&quot; produces the classic OLS covariance estimator. Setting the bandwidth to 0 and using &quot;robust&quot; produces White's covariance estimator."><span class="n"><span class="pre">bandwidth</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.phillips_ouliaris.force_int" title="arch.unitroot.cointegration.phillips_ouliaris.force_int (Python parameter) — Whether the force the estimated optimal bandwidth to be an integer."><span class="n"><span class="pre">force_int</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="arch.unitroot.cointegration.PhillipsOuliarisTestResults.html#arch.unitroot.cointegration.PhillipsOuliarisTestResults" title="arch.unitroot._phillips_ouliaris.PhillipsOuliarisTestResults (Python class)"><span class="pre">PhillipsOuliarisTestResults</span></a></span></span></span><a class="reference internal" href="../../_modules/arch/unitroot/_phillips_ouliaris.html#phillips_ouliaris"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.cointegration.phillips_ouliaris" title="Link to this definition">¶</a></dt>
 <dd><p>Test for cointegration within a set of time series.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.unitroot.cointegration.phillips_ouliaris-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.cointegration.phillips_ouliaris-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.unitroot.cointegration.phillips_ouliaris.y"><span class="sig-name descname">y</span><span class="p"> : </span><span class="desctype"><span class="desctype">array_like</span></span><a class="headerlink" href="#arch.unitroot.cointegration.phillips_ouliaris.y" title="Permalink to this definition">¶</a></dt><dd><p>The left-hand-side variable in the cointegrating regression.</p>
+<dt id="arch.unitroot.cointegration.phillips_ouliaris.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.phillips_ouliaris.y" title="Permalink to this definition">¶</a></dt><dd><p>The left-hand-side variable in the cointegrating regression.</p>
 </dd>
-<dt id="arch.unitroot.cointegration.phillips_ouliaris.x"><span class="sig-name descname">x</span><span class="p"> : </span><span class="desctype"><span class="desctype">array_like</span></span><a class="headerlink" href="#arch.unitroot.cointegration.phillips_ouliaris.x" title="Permalink to this definition">¶</a></dt><dd><p>The right-hand-side variables in the cointegrating regression.</p>
+<dt id="arch.unitroot.cointegration.phillips_ouliaris.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.phillips_ouliaris.x" title="Permalink to this definition">¶</a></dt><dd><p>The right-hand-side variables in the cointegrating regression.</p>
 </dd>
-<dt id="arch.unitroot.cointegration.phillips_ouliaris.trend"><span class="sig-name descname">trend</span><span class="p"> : </span><span class="desctype"><span class="desctype">{&quot;n&quot;,&quot;c&quot;,&quot;ct&quot;,&quot;ctt&quot;}, default &quot;c&quot;</span></span><a class="headerlink" href="#arch.unitroot.cointegration.phillips_ouliaris.trend" title="Permalink to this definition">¶</a></dt><dd><p><p>Trend to include in the cointegrating regression. Trends are:</p>
+<dt id="arch.unitroot.cointegration.phillips_ouliaris.trend"><span class="n sig-name">trend</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'n'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'c'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'ct'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'ctt'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code><a class="headerlink" href="#arch.unitroot.cointegration.phillips_ouliaris.trend" title="Permalink to this definition">¶</a></dt><dd><p><p>Trend to include in the cointegrating regression. Trends are:</p>
 <ul class="simple">
 <li><p>”n”: No deterministic terms</p></li>
 <li><p>”c”: Constant</p></li>
@@ -733,7 +733,7 @@ <h1 id="arch-unitroot-cointegration-phillips-ouliaris">arch.unitroot.cointegrati
 </ul>
 </p>
 </dd>
-<dt id="arch.unitroot.cointegration.phillips_ouliaris.test_type"><span class="sig-name descname">test_type</span><span class="p"> : </span><span class="desctype"><span class="desctype">{&quot;Za&quot;, &quot;Zt&quot;, &quot;Pu&quot;, &quot;Pz&quot;}, default &quot;Zt&quot;</span></span><a class="headerlink" href="#arch.unitroot.cointegration.phillips_ouliaris.test_type" title="Permalink to this definition">¶</a></dt><dd><p><p>The test statistic to compute. Supported options are:</p>
+<dt id="arch.unitroot.cointegration.phillips_ouliaris.test_type"><span class="n sig-name">test_type</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'Za'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'Zt'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'Pu'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'Pz'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;Zt&#39;</span></code><a class="headerlink" href="#arch.unitroot.cointegration.phillips_ouliaris.test_type" title="Permalink to this definition">¶</a></dt><dd><p><p>The test statistic to compute. Supported options are:</p>
 <ul class="simple">
 <li><p>”Za”: The Zα test based on the the debiased AR(1) coefficient.</p></li>
 <li><p>”Zt”: The Zt test based on the t-statistic from an AR(1).</p></li>
@@ -744,18 +744,18 @@ <h1 id="arch-unitroot-cointegration-phillips-ouliaris">arch.unitroot.cointegrati
 <p>See the notes for details on the test.</p>
 </p>
 </dd>
-<dt id="arch.unitroot.cointegration.phillips_ouliaris.kernel"><span class="sig-name descname">kernel</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, default &quot;bartlett&quot;</span></span><a class="headerlink" href="#arch.unitroot.cointegration.phillips_ouliaris.kernel" title="Permalink to this definition">¶</a></dt><dd><p>The string name of any of any known kernel-based long-run
+<dt id="arch.unitroot.cointegration.phillips_ouliaris.kernel"><span class="n sig-name">kernel</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;bartlett&#39;</span></code><a class="headerlink" href="#arch.unitroot.cointegration.phillips_ouliaris.kernel" title="Permalink to this definition">¶</a></dt><dd><p>The string name of any of any known kernel-based long-run
 covariance estimators. Common choices are “bartlett” for the
 Bartlett kernel (Newey-West), “parzen” for the Parzen kernel
 and “quadratic-spectral” for the Quadratic Spectral kernel.</p>
 </dd>
-<dt id="arch.unitroot.cointegration.phillips_ouliaris.bandwidth"><span class="sig-name descname">bandwidth</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, default None</span></span><a class="headerlink" href="#arch.unitroot.cointegration.phillips_ouliaris.bandwidth" title="Permalink to this definition">¶</a></dt><dd><p>The bandwidth to use. If not provided, the optimal bandwidth is
+<dt id="arch.unitroot.cointegration.phillips_ouliaris.bandwidth"><span class="n sig-name">bandwidth</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.unitroot.cointegration.phillips_ouliaris.bandwidth" title="Permalink to this definition">¶</a></dt><dd><p>The bandwidth to use. If not provided, the optimal bandwidth is
 estimated from the data. Setting the bandwidth to 0 and using
 “unadjusted” produces the classic OLS covariance estimator.
 Setting the bandwidth to 0 and using “robust” produces White’s
 covariance estimator.</p>
 </dd>
-<dt id="arch.unitroot.cointegration.phillips_ouliaris.force_int"><span class="sig-name descname">force_int</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, default False</span></span><a class="headerlink" href="#arch.unitroot.cointegration.phillips_ouliaris.force_int" title="Permalink to this definition">¶</a></dt><dd><p>Whether the force the estimated optimal bandwidth to be an integer.</p>
+<dt id="arch.unitroot.cointegration.phillips_ouliaris.force_int"><span class="n sig-name">force_int</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.unitroot.cointegration.phillips_ouliaris.force_int" title="Permalink to this definition">¶</a></dt><dd><p>Whether the force the estimated optimal bandwidth to be an integer.</p>
 </dd>
 </dl>
 </dd>
@@ -763,7 +763,7 @@ <h1 id="arch-unitroot-cointegration-phillips-ouliaris">arch.unitroot.cointegrati
 <dd class="field-even"><p>Results of the Phillips-Ouliaris test.</p>
 </dd>
 <dt class="field-odd" id="arch.unitroot.cointegration.phillips_ouliaris-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.cointegration.phillips_ouliaris-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference internal" href="arch.unitroot.cointegration.PhillipsOuliarisTestResults.html#arch.unitroot.cointegration.PhillipsOuliarisTestResults" title="arch.unitroot.cointegration.PhillipsOuliarisTestResults (Python class) — plot([axes, title])">PhillipsOuliarisTestResults</a></p>
+<dd class="field-odd"><p><a class="reference internal" href="arch.unitroot.cointegration.PhillipsOuliarisTestResults.html#arch.unitroot.cointegration.PhillipsOuliarisTestResults" title="arch.unitroot.cointegration.PhillipsOuliarisTestResults (Python class) — plot([axes, title])"><code class="xref py py-class docutils literal notranslate"><span class="pre">PhillipsOuliarisTestResults</span></code></a></p>
 </dd>
 </dl>
 <div class="seealso admonition">
@@ -912,7 +912,7 @@ <h1 id="arch-unitroot-cointegration-phillips-ouliaris">arch.unitroot.cointegrati
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -959,10 +959,10 @@ <h1 id="arch-unitroot-cointegration-phillips-ouliaris">arch.unitroot.cointegrati
     <script id="__config" type="application/json">{"base": "../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
         <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+        <script id="MathJax-script" src="../../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
     
   </body>
 </html>
\ No newline at end of file
diff --git a/unitroot/generated/generated/arch.unitroot.ADF.alternative_hypothesis.html b/unitroot/generated/generated/arch.unitroot.ADF.alternative_hypothesis.html
index 7b5972b9fa..cd8a3e9427 100644
--- a/unitroot/generated/generated/arch.unitroot.ADF.alternative_hypothesis.html
+++ b/unitroot/generated/generated/arch.unitroot.ADF.alternative_hypothesis.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.ADF.alternative_hypothesis - arch 6.1.0</title>
+        <title>arch.unitroot.ADF.alternative_hypothesis - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -883,10 +883,10 @@
                   
 
 
-<h1 id="arch-unitroot-adf-alternative-hypothesis">arch.unitroot.ADF.alternative_hypothesis<a class="headerlink" href="#arch-unitroot-adf-alternative-hypothesis" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-adf-alternative-hypothesis">arch.unitroot.ADF.alternative_hypothesis<a class="headerlink" href="#arch-unitroot-adf-alternative-hypothesis" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.ADF.alternative_hypothesis">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ADF.</span></span><span class="sig-name descname"><span class="pre">alternative_hypothesis</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.ADF.alternative_hypothesis" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ADF.</span></span><span class="sig-name descname"><span class="pre">alternative_hypothesis</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.ADF.alternative_hypothesis" title="Link to this definition">¶</a></dt>
 <dd><p>The alternative hypothesis</p>
 </dd></dl>
 
@@ -957,7 +957,7 @@ <h1 id="arch-unitroot-adf-alternative-hypothesis">arch.unitroot.ADF.alternative_
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1004,7 +1004,7 @@ <h1 id="arch-unitroot-adf-alternative-hypothesis">arch.unitroot.ADF.alternative_
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.ADF.critical_values.html b/unitroot/generated/generated/arch.unitroot.ADF.critical_values.html
index 609198e974..ff44c33366 100644
--- a/unitroot/generated/generated/arch.unitroot.ADF.critical_values.html
+++ b/unitroot/generated/generated/arch.unitroot.ADF.critical_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.ADF.critical_values - arch 6.1.0</title>
+        <title>arch.unitroot.ADF.critical_values - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -883,10 +883,10 @@
                   
 
 
-<h1 id="arch-unitroot-adf-critical-values">arch.unitroot.ADF.critical_values<a class="headerlink" href="#arch-unitroot-adf-critical-values" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-adf-critical-values">arch.unitroot.ADF.critical_values<a class="headerlink" href="#arch-unitroot-adf-critical-values" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.ADF.critical_values">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ADF.</span></span><span class="sig-name descname"><span class="pre">critical_values</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#arch.unitroot.ADF.critical_values" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ADF.</span></span><span class="sig-name descname"><span class="pre">critical_values</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#arch.unitroot.ADF.critical_values" title="Link to this definition">¶</a></dt>
 <dd><p>Dictionary containing critical values specific to the test, number of
 observations and included deterministic trend terms.</p>
 </dd></dl>
@@ -958,7 +958,7 @@ <h1 id="arch-unitroot-adf-critical-values">arch.unitroot.ADF.critical_values<a c
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1005,7 +1005,7 @@ <h1 id="arch-unitroot-adf-critical-values">arch.unitroot.ADF.critical_values<a c
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.ADF.lags.html b/unitroot/generated/generated/arch.unitroot.ADF.lags.html
index 7ee3dc864f..bb2200090b 100644
--- a/unitroot/generated/generated/arch.unitroot.ADF.lags.html
+++ b/unitroot/generated/generated/arch.unitroot.ADF.lags.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.ADF.lags - arch 6.1.0</title>
+        <title>arch.unitroot.ADF.lags - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -883,10 +883,10 @@
                   
 
 
-<h1 id="arch-unitroot-adf-lags">arch.unitroot.ADF.lags<a class="headerlink" href="#arch-unitroot-adf-lags" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-adf-lags">arch.unitroot.ADF.lags<a class="headerlink" href="#arch-unitroot-adf-lags" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.ADF.lags">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ADF.</span></span><span class="sig-name descname"><span class="pre">lags</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.unitroot.ADF.lags" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ADF.</span></span><span class="sig-name descname"><span class="pre">lags</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.unitroot.ADF.lags" title="Link to this definition">¶</a></dt>
 <dd><p>Sets or gets the number of lags used in the model.
 When bootstrap use DF-type regressions, lags is the number of lags in the
 regression model.  When bootstrap use long-run variance estimators, lags
@@ -960,7 +960,7 @@ <h1 id="arch-unitroot-adf-lags">arch.unitroot.ADF.lags<a class="headerlink" href
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1007,7 +1007,7 @@ <h1 id="arch-unitroot-adf-lags">arch.unitroot.ADF.lags<a class="headerlink" href
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.ADF.max_lags.html b/unitroot/generated/generated/arch.unitroot.ADF.max_lags.html
index d1081b0d88..2f3b0e7a6f 100644
--- a/unitroot/generated/generated/arch.unitroot.ADF.max_lags.html
+++ b/unitroot/generated/generated/arch.unitroot.ADF.max_lags.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.ADF.max_lags - arch 6.1.0</title>
+        <title>arch.unitroot.ADF.max_lags - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -883,10 +883,10 @@
                   
 
 
-<h1 id="arch-unitroot-adf-max-lags">arch.unitroot.ADF.max_lags<a class="headerlink" href="#arch-unitroot-adf-max-lags" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-adf-max-lags">arch.unitroot.ADF.max_lags<a class="headerlink" href="#arch-unitroot-adf-max-lags" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.ADF.max_lags">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ADF.</span></span><span class="sig-name descname"><span class="pre">max_lags</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.unitroot.ADF.max_lags" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ADF.</span></span><span class="sig-name descname"><span class="pre">max_lags</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.unitroot.ADF.max_lags" title="Link to this definition">¶</a></dt>
 <dd><p>Sets or gets the maximum lags used when automatically selecting lag
 length</p>
 </dd></dl>
@@ -958,7 +958,7 @@ <h1 id="arch-unitroot-adf-max-lags">arch.unitroot.ADF.max_lags<a class="headerli
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1005,7 +1005,7 @@ <h1 id="arch-unitroot-adf-max-lags">arch.unitroot.ADF.max_lags<a class="headerli
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.ADF.nobs.html b/unitroot/generated/generated/arch.unitroot.ADF.nobs.html
index 2596ffb89b..2a7158a62d 100644
--- a/unitroot/generated/generated/arch.unitroot.ADF.nobs.html
+++ b/unitroot/generated/generated/arch.unitroot.ADF.nobs.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.ADF.nobs - arch 6.1.0</title>
+        <title>arch.unitroot.ADF.nobs - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -883,10 +883,10 @@
                   
 
 
-<h1 id="arch-unitroot-adf-nobs">arch.unitroot.ADF.nobs<a class="headerlink" href="#arch-unitroot-adf-nobs" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-adf-nobs">arch.unitroot.ADF.nobs<a class="headerlink" href="#arch-unitroot-adf-nobs" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.ADF.nobs">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ADF.</span></span><span class="sig-name descname"><span class="pre">nobs</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.unitroot.ADF.nobs" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ADF.</span></span><span class="sig-name descname"><span class="pre">nobs</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.unitroot.ADF.nobs" title="Link to this definition">¶</a></dt>
 <dd><p>The number of observations used when computing the test statistic.
 Accounts for loss of data due to lags for regression-based bootstrap.</p>
 </dd></dl>
@@ -958,7 +958,7 @@ <h1 id="arch-unitroot-adf-nobs">arch.unitroot.ADF.nobs<a class="headerlink" href
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1005,7 +1005,7 @@ <h1 id="arch-unitroot-adf-nobs">arch.unitroot.ADF.nobs<a class="headerlink" href
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.ADF.null_hypothesis.html b/unitroot/generated/generated/arch.unitroot.ADF.null_hypothesis.html
index 451e0c54b4..75d444d14f 100644
--- a/unitroot/generated/generated/arch.unitroot.ADF.null_hypothesis.html
+++ b/unitroot/generated/generated/arch.unitroot.ADF.null_hypothesis.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.ADF.null_hypothesis - arch 6.1.0</title>
+        <title>arch.unitroot.ADF.null_hypothesis - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -883,10 +883,10 @@
                   
 
 
-<h1 id="arch-unitroot-adf-null-hypothesis">arch.unitroot.ADF.null_hypothesis<a class="headerlink" href="#arch-unitroot-adf-null-hypothesis" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-adf-null-hypothesis">arch.unitroot.ADF.null_hypothesis<a class="headerlink" href="#arch-unitroot-adf-null-hypothesis" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.ADF.null_hypothesis">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ADF.</span></span><span class="sig-name descname"><span class="pre">null_hypothesis</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.ADF.null_hypothesis" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ADF.</span></span><span class="sig-name descname"><span class="pre">null_hypothesis</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.ADF.null_hypothesis" title="Link to this definition">¶</a></dt>
 <dd><p>The null hypothesis</p>
 </dd></dl>
 
@@ -957,7 +957,7 @@ <h1 id="arch-unitroot-adf-null-hypothesis">arch.unitroot.ADF.null_hypothesis<a c
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1004,7 +1004,7 @@ <h1 id="arch-unitroot-adf-null-hypothesis">arch.unitroot.ADF.null_hypothesis<a c
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.ADF.pvalue.html b/unitroot/generated/generated/arch.unitroot.ADF.pvalue.html
index be5109c4dc..a6d347950f 100644
--- a/unitroot/generated/generated/arch.unitroot.ADF.pvalue.html
+++ b/unitroot/generated/generated/arch.unitroot.ADF.pvalue.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.ADF.pvalue - arch 6.1.0</title>
+        <title>arch.unitroot.ADF.pvalue - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -883,10 +883,10 @@
                   
 
 
-<h1 id="arch-unitroot-adf-pvalue">arch.unitroot.ADF.pvalue<a class="headerlink" href="#arch-unitroot-adf-pvalue" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-adf-pvalue">arch.unitroot.ADF.pvalue<a class="headerlink" href="#arch-unitroot-adf-pvalue" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.ADF.pvalue">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ADF.</span></span><span class="sig-name descname"><span class="pre">pvalue</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.ADF.pvalue" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ADF.</span></span><span class="sig-name descname"><span class="pre">pvalue</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.ADF.pvalue" title="Link to this definition">¶</a></dt>
 <dd><p>Returns the p-value for the test statistic</p>
 </dd></dl>
 
@@ -957,7 +957,7 @@ <h1 id="arch-unitroot-adf-pvalue">arch.unitroot.ADF.pvalue<a class="headerlink"
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1004,7 +1004,7 @@ <h1 id="arch-unitroot-adf-pvalue">arch.unitroot.ADF.pvalue<a class="headerlink"
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.ADF.regression.html b/unitroot/generated/generated/arch.unitroot.ADF.regression.html
index 1430df1252..78916525ae 100644
--- a/unitroot/generated/generated/arch.unitroot.ADF.regression.html
+++ b/unitroot/generated/generated/arch.unitroot.ADF.regression.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.ADF.regression - arch 6.1.0</title>
+        <title>arch.unitroot.ADF.regression - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -883,10 +883,10 @@
                   
 
 
-<h1 id="arch-unitroot-adf-regression">arch.unitroot.ADF.regression<a class="headerlink" href="#arch-unitroot-adf-regression" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-adf-regression">arch.unitroot.ADF.regression<a class="headerlink" href="#arch-unitroot-adf-regression" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.ADF.regression">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ADF.</span></span><span class="sig-name descname"><span class="pre">regression</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.regression.linear_model.RegressionResults.html#statsmodels.regression.linear_model.RegressionResults" title="(in statsmodels 0.15.0 (+8) v0.15.0 (+8))"><span class="pre">RegressionResults</span></a></span><a class="headerlink" href="#arch.unitroot.ADF.regression" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ADF.</span></span><span class="sig-name descname"><span class="pre">regression</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.regression.linear_model.RegressionResults.html#statsmodels.regression.linear_model.RegressionResults" title="(in statsmodels 0.15.0 (+59) v0.15.0 (+59))"><span class="pre">RegressionResults</span></a></span><a class="headerlink" href="#arch.unitroot.ADF.regression" title="Link to this definition">¶</a></dt>
 <dd><p>Returns the OLS regression results from the ADF model estimated</p>
 </dd></dl>
 
@@ -957,7 +957,7 @@ <h1 id="arch-unitroot-adf-regression">arch.unitroot.ADF.regression<a class="head
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1004,7 +1004,7 @@ <h1 id="arch-unitroot-adf-regression">arch.unitroot.ADF.regression<a class="head
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.ADF.stat.html b/unitroot/generated/generated/arch.unitroot.ADF.stat.html
index e4ccda1873..8c7b9c27ec 100644
--- a/unitroot/generated/generated/arch.unitroot.ADF.stat.html
+++ b/unitroot/generated/generated/arch.unitroot.ADF.stat.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.ADF.stat - arch 6.1.0</title>
+        <title>arch.unitroot.ADF.stat - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -883,10 +883,10 @@
                   
 
 
-<h1 id="arch-unitroot-adf-stat">arch.unitroot.ADF.stat<a class="headerlink" href="#arch-unitroot-adf-stat" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-adf-stat">arch.unitroot.ADF.stat<a class="headerlink" href="#arch-unitroot-adf-stat" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.ADF.stat">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ADF.</span></span><span class="sig-name descname"><span class="pre">stat</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.ADF.stat" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ADF.</span></span><span class="sig-name descname"><span class="pre">stat</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.ADF.stat" title="Link to this definition">¶</a></dt>
 <dd><p>The test statistic for a unit root</p>
 </dd></dl>
 
@@ -957,7 +957,7 @@ <h1 id="arch-unitroot-adf-stat">arch.unitroot.ADF.stat<a class="headerlink" href
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1004,7 +1004,7 @@ <h1 id="arch-unitroot-adf-stat">arch.unitroot.ADF.stat<a class="headerlink" href
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.ADF.summary.html b/unitroot/generated/generated/arch.unitroot.ADF.summary.html
index 5f205201a8..47f0740e32 100644
--- a/unitroot/generated/generated/arch.unitroot.ADF.summary.html
+++ b/unitroot/generated/generated/arch.unitroot.ADF.summary.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.ADF.summary - arch 6.1.0</title>
+        <title>arch.unitroot.ADF.summary - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -461,19 +461,6 @@
     <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="arch.unitroot.ADF.summary (Python method) — Summary of test, containing statistic, p-value and critical values" class="md-ellipsis">ADF.<wbr>summary</span>
   </a>
   
-    <nav class="md-nav" aria-label="ADF.summary">
-      <ul class="md-nav__list">
-        
-          <li class="md-nav__item">
-  <a href="#arch.unitroot.ADF.summary-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
-</li>
-        
-      </ul>
-    </nav>
-  
 </li>
       
     </ul>
@@ -880,19 +867,6 @@
     <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="arch.unitroot.ADF.summary (Python method) — Summary of test, containing statistic, p-value and critical values" class="md-ellipsis">ADF.<wbr>summary</span>
   </a>
   
-    <nav class="md-nav" aria-label="ADF.summary">
-      <ul class="md-nav__list">
-        
-          <li class="md-nav__item">
-  <a href="#arch.unitroot.ADF.summary-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
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-  
-</li>
-        
-      </ul>
-    </nav>
-  
 </li>
       
     </ul>
@@ -909,16 +883,11 @@
                   
 
 
-<h1 id="arch-unitroot-adf-summary">arch.unitroot.ADF.summary<a class="headerlink" href="#arch-unitroot-adf-summary" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-adf-summary">arch.unitroot.ADF.summary<a class="headerlink" href="#arch-unitroot-adf-summary" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.ADF.summary">
-<span class="sig-prename descclassname"><span class="pre">ADF.</span></span><span class="sig-name descname"><span class="pre">summary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#arch.unitroot.ADF.summary" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ADF.</span></span><span class="sig-name descname"><span class="pre">summary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.iolib.summary.Summary.html#statsmodels.iolib.summary.Summary" title="(in statsmodels 0.15.0 (+59) v0.15.0 (+59))"><span class="pre">Summary</span></a></span></span></span><a class="headerlink" href="#arch.unitroot.ADF.summary" title="Link to this definition">¶</a></dt>
 <dd><p>Summary of test, containing statistic, p-value and critical values</p>
-<dl class="field-list simple">
-<dt class="field-odd" id="arch.unitroot.ADF.summary-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.ADF.summary-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.iolib.summary.Summary.html#statsmodels.iolib.summary.Summary" title="(in statsmodels 0.15.0 (+8) v0.15.0 (+8))"><code class="xref py py-class docutils literal notranslate"><span class="pre">Summary</span></code></a></p>
-</dd>
-</dl>
 </dd></dl>
 
 
@@ -988,7 +957,7 @@ <h1 id="arch-unitroot-adf-summary">arch.unitroot.ADF.summary<a class="headerlink
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1035,7 +1004,7 @@ <h1 id="arch-unitroot-adf-summary">arch.unitroot.ADF.summary<a class="headerlink
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.ADF.trend.html b/unitroot/generated/generated/arch.unitroot.ADF.trend.html
index f8801a6cb6..4d29ae3aec 100644
--- a/unitroot/generated/generated/arch.unitroot.ADF.trend.html
+++ b/unitroot/generated/generated/arch.unitroot.ADF.trend.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.ADF.trend - arch 6.1.0</title>
+        <title>arch.unitroot.ADF.trend - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -883,10 +883,10 @@
                   
 
 
-<h1 id="arch-unitroot-adf-trend">arch.unitroot.ADF.trend<a class="headerlink" href="#arch-unitroot-adf-trend" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-adf-trend">arch.unitroot.ADF.trend<a class="headerlink" href="#arch-unitroot-adf-trend" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.ADF.trend">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ADF.</span></span><span class="sig-name descname"><span class="pre">trend</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.ADF.trend" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ADF.</span></span><span class="sig-name descname"><span class="pre">trend</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.ADF.trend" title="Link to this definition">¶</a></dt>
 <dd><p>Sets or gets the deterministic trend term used in the test. See
 valid_trends for a list of supported trends</p>
 </dd></dl>
@@ -958,7 +958,7 @@ <h1 id="arch-unitroot-adf-trend">arch.unitroot.ADF.trend<a class="headerlink" hr
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1005,7 +1005,7 @@ <h1 id="arch-unitroot-adf-trend">arch.unitroot.ADF.trend<a class="headerlink" hr
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.ADF.valid_trends.html b/unitroot/generated/generated/arch.unitroot.ADF.valid_trends.html
index 1dd7faccad..10ecfa842e 100644
--- a/unitroot/generated/generated/arch.unitroot.ADF.valid_trends.html
+++ b/unitroot/generated/generated/arch.unitroot.ADF.valid_trends.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.ADF.valid_trends - arch 6.1.0</title>
+        <title>arch.unitroot.ADF.valid_trends - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -883,10 +883,10 @@
                   
 
 
-<h1 id="arch-unitroot-adf-valid-trends">arch.unitroot.ADF.valid_trends<a class="headerlink" href="#arch-unitroot-adf-valid-trends" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-adf-valid-trends">arch.unitroot.ADF.valid_trends<a class="headerlink" href="#arch-unitroot-adf-valid-trends" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.ADF.valid_trends">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ADF.</span></span><span class="sig-name descname"><span class="pre">valid_trends</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#arch.unitroot.ADF.valid_trends" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ADF.</span></span><span class="sig-name descname"><span class="pre">valid_trends</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#arch.unitroot.ADF.valid_trends" title="Link to this definition">¶</a></dt>
 <dd><p>List of valid trend terms.</p>
 </dd></dl>
 
@@ -957,7 +957,7 @@ <h1 id="arch-unitroot-adf-valid-trends">arch.unitroot.ADF.valid_trends<a class="
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1004,7 +1004,7 @@ <h1 id="arch-unitroot-adf-valid-trends">arch.unitroot.ADF.valid_trends<a class="
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.ADF.y.html b/unitroot/generated/generated/arch.unitroot.ADF.y.html
index 3a9fb51370..8d0d51eff7 100644
--- a/unitroot/generated/generated/arch.unitroot.ADF.y.html
+++ b/unitroot/generated/generated/arch.unitroot.ADF.y.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.ADF.y - arch 6.1.0</title>
+        <title>arch.unitroot.ADF.y - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -883,10 +883,10 @@
                   
 
 
-<h1 id="arch-unitroot-adf-y">arch.unitroot.ADF.y<a class="headerlink" href="#arch-unitroot-adf-y" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-adf-y">arch.unitroot.ADF.y<a class="headerlink" href="#arch-unitroot-adf-y" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.ADF.y">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ADF.</span></span><span class="sig-name descname"><span class="pre">y</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.0.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.0.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.ADF.y" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ADF.</span></span><span class="sig-name descname"><span class="pre">y</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.ADF.y" title="Link to this definition">¶</a></dt>
 <dd><p>Returns the data used in the test statistic</p>
 </dd></dl>
 
@@ -957,7 +957,7 @@ <h1 id="arch-unitroot-adf-y">arch.unitroot.ADF.y<a class="headerlink" href="#arc
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1004,7 +1004,7 @@ <h1 id="arch-unitroot-adf-y">arch.unitroot.ADF.y<a class="headerlink" href="#arc
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.DFGLS.alternative_hypothesis.html b/unitroot/generated/generated/arch.unitroot.DFGLS.alternative_hypothesis.html
index 8de6b2c57f..4c63cc0d1b 100644
--- a/unitroot/generated/generated/arch.unitroot.DFGLS.alternative_hypothesis.html
+++ b/unitroot/generated/generated/arch.unitroot.DFGLS.alternative_hypothesis.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.DFGLS.alternative_hypothesis - arch 6.1.0</title>
+        <title>arch.unitroot.DFGLS.alternative_hypothesis - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -883,10 +883,10 @@
                   
 
 
-<h1 id="arch-unitroot-dfgls-alternative-hypothesis">arch.unitroot.DFGLS.alternative_hypothesis<a class="headerlink" href="#arch-unitroot-dfgls-alternative-hypothesis" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-dfgls-alternative-hypothesis">arch.unitroot.DFGLS.alternative_hypothesis<a class="headerlink" href="#arch-unitroot-dfgls-alternative-hypothesis" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.DFGLS.alternative_hypothesis">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DFGLS.</span></span><span class="sig-name descname"><span class="pre">alternative_hypothesis</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.DFGLS.alternative_hypothesis" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DFGLS.</span></span><span class="sig-name descname"><span class="pre">alternative_hypothesis</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.DFGLS.alternative_hypothesis" title="Link to this definition">¶</a></dt>
 <dd><p>The alternative hypothesis</p>
 </dd></dl>
 
@@ -957,7 +957,7 @@ <h1 id="arch-unitroot-dfgls-alternative-hypothesis">arch.unitroot.DFGLS.alternat
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1004,7 +1004,7 @@ <h1 id="arch-unitroot-dfgls-alternative-hypothesis">arch.unitroot.DFGLS.alternat
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.DFGLS.critical_values.html b/unitroot/generated/generated/arch.unitroot.DFGLS.critical_values.html
index 6395283068..1976cd0516 100644
--- a/unitroot/generated/generated/arch.unitroot.DFGLS.critical_values.html
+++ b/unitroot/generated/generated/arch.unitroot.DFGLS.critical_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.DFGLS.critical_values - arch 6.1.0</title>
+        <title>arch.unitroot.DFGLS.critical_values - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -883,10 +883,10 @@
                   
 
 
-<h1 id="arch-unitroot-dfgls-critical-values">arch.unitroot.DFGLS.critical_values<a class="headerlink" href="#arch-unitroot-dfgls-critical-values" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-dfgls-critical-values">arch.unitroot.DFGLS.critical_values<a class="headerlink" href="#arch-unitroot-dfgls-critical-values" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.DFGLS.critical_values">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DFGLS.</span></span><span class="sig-name descname"><span class="pre">critical_values</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#arch.unitroot.DFGLS.critical_values" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DFGLS.</span></span><span class="sig-name descname"><span class="pre">critical_values</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#arch.unitroot.DFGLS.critical_values" title="Link to this definition">¶</a></dt>
 <dd><p>Dictionary containing critical values specific to the test, number of
 observations and included deterministic trend terms.</p>
 </dd></dl>
@@ -958,7 +958,7 @@ <h1 id="arch-unitroot-dfgls-critical-values">arch.unitroot.DFGLS.critical_values
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1005,7 +1005,7 @@ <h1 id="arch-unitroot-dfgls-critical-values">arch.unitroot.DFGLS.critical_values
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.DFGLS.lags.html b/unitroot/generated/generated/arch.unitroot.DFGLS.lags.html
index f1c1062c7d..e5cd7f87d1 100644
--- a/unitroot/generated/generated/arch.unitroot.DFGLS.lags.html
+++ b/unitroot/generated/generated/arch.unitroot.DFGLS.lags.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.DFGLS.lags - arch 6.1.0</title>
+        <title>arch.unitroot.DFGLS.lags - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -883,10 +883,10 @@
                   
 
 
-<h1 id="arch-unitroot-dfgls-lags">arch.unitroot.DFGLS.lags<a class="headerlink" href="#arch-unitroot-dfgls-lags" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-dfgls-lags">arch.unitroot.DFGLS.lags<a class="headerlink" href="#arch-unitroot-dfgls-lags" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.DFGLS.lags">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DFGLS.</span></span><span class="sig-name descname"><span class="pre">lags</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.unitroot.DFGLS.lags" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DFGLS.</span></span><span class="sig-name descname"><span class="pre">lags</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.unitroot.DFGLS.lags" title="Link to this definition">¶</a></dt>
 <dd><p>Sets or gets the number of lags used in the model.
 When bootstrap use DF-type regressions, lags is the number of lags in the
 regression model.  When bootstrap use long-run variance estimators, lags
@@ -960,7 +960,7 @@ <h1 id="arch-unitroot-dfgls-lags">arch.unitroot.DFGLS.lags<a class="headerlink"
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1007,7 +1007,7 @@ <h1 id="arch-unitroot-dfgls-lags">arch.unitroot.DFGLS.lags<a class="headerlink"
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.DFGLS.max_lags.html b/unitroot/generated/generated/arch.unitroot.DFGLS.max_lags.html
index 9155ccfad3..4e89296900 100644
--- a/unitroot/generated/generated/arch.unitroot.DFGLS.max_lags.html
+++ b/unitroot/generated/generated/arch.unitroot.DFGLS.max_lags.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.DFGLS.max_lags - arch 6.1.0</title>
+        <title>arch.unitroot.DFGLS.max_lags - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -883,10 +883,10 @@
                   
 
 
-<h1 id="arch-unitroot-dfgls-max-lags">arch.unitroot.DFGLS.max_lags<a class="headerlink" href="#arch-unitroot-dfgls-max-lags" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-dfgls-max-lags">arch.unitroot.DFGLS.max_lags<a class="headerlink" href="#arch-unitroot-dfgls-max-lags" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.DFGLS.max_lags">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DFGLS.</span></span><span class="sig-name descname"><span class="pre">max_lags</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.unitroot.DFGLS.max_lags" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DFGLS.</span></span><span class="sig-name descname"><span class="pre">max_lags</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.unitroot.DFGLS.max_lags" title="Link to this definition">¶</a></dt>
 <dd><p>Sets or gets the maximum lags used when automatically selecting lag
 length</p>
 </dd></dl>
@@ -958,7 +958,7 @@ <h1 id="arch-unitroot-dfgls-max-lags">arch.unitroot.DFGLS.max_lags<a class="head
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1005,7 +1005,7 @@ <h1 id="arch-unitroot-dfgls-max-lags">arch.unitroot.DFGLS.max_lags<a class="head
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.DFGLS.nobs.html b/unitroot/generated/generated/arch.unitroot.DFGLS.nobs.html
index 8a4585bd3e..1a1ccd8de9 100644
--- a/unitroot/generated/generated/arch.unitroot.DFGLS.nobs.html
+++ b/unitroot/generated/generated/arch.unitroot.DFGLS.nobs.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.DFGLS.nobs - arch 6.1.0</title>
+        <title>arch.unitroot.DFGLS.nobs - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -883,10 +883,10 @@
                   
 
 
-<h1 id="arch-unitroot-dfgls-nobs">arch.unitroot.DFGLS.nobs<a class="headerlink" href="#arch-unitroot-dfgls-nobs" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-dfgls-nobs">arch.unitroot.DFGLS.nobs<a class="headerlink" href="#arch-unitroot-dfgls-nobs" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.DFGLS.nobs">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DFGLS.</span></span><span class="sig-name descname"><span class="pre">nobs</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.unitroot.DFGLS.nobs" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DFGLS.</span></span><span class="sig-name descname"><span class="pre">nobs</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.unitroot.DFGLS.nobs" title="Link to this definition">¶</a></dt>
 <dd><p>The number of observations used when computing the test statistic.
 Accounts for loss of data due to lags for regression-based bootstrap.</p>
 </dd></dl>
@@ -958,7 +958,7 @@ <h1 id="arch-unitroot-dfgls-nobs">arch.unitroot.DFGLS.nobs<a class="headerlink"
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1005,7 +1005,7 @@ <h1 id="arch-unitroot-dfgls-nobs">arch.unitroot.DFGLS.nobs<a class="headerlink"
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.DFGLS.null_hypothesis.html b/unitroot/generated/generated/arch.unitroot.DFGLS.null_hypothesis.html
index b617604f81..d3c4ab12a2 100644
--- a/unitroot/generated/generated/arch.unitroot.DFGLS.null_hypothesis.html
+++ b/unitroot/generated/generated/arch.unitroot.DFGLS.null_hypothesis.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.DFGLS.null_hypothesis - arch 6.1.0</title>
+        <title>arch.unitroot.DFGLS.null_hypothesis - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -883,10 +883,10 @@
                   
 
 
-<h1 id="arch-unitroot-dfgls-null-hypothesis">arch.unitroot.DFGLS.null_hypothesis<a class="headerlink" href="#arch-unitroot-dfgls-null-hypothesis" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-dfgls-null-hypothesis">arch.unitroot.DFGLS.null_hypothesis<a class="headerlink" href="#arch-unitroot-dfgls-null-hypothesis" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.DFGLS.null_hypothesis">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DFGLS.</span></span><span class="sig-name descname"><span class="pre">null_hypothesis</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.DFGLS.null_hypothesis" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DFGLS.</span></span><span class="sig-name descname"><span class="pre">null_hypothesis</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.DFGLS.null_hypothesis" title="Link to this definition">¶</a></dt>
 <dd><p>The null hypothesis</p>
 </dd></dl>
 
@@ -957,7 +957,7 @@ <h1 id="arch-unitroot-dfgls-null-hypothesis">arch.unitroot.DFGLS.null_hypothesis
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1004,7 +1004,7 @@ <h1 id="arch-unitroot-dfgls-null-hypothesis">arch.unitroot.DFGLS.null_hypothesis
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.DFGLS.pvalue.html b/unitroot/generated/generated/arch.unitroot.DFGLS.pvalue.html
index 0dc86e0d76..fef81004f6 100644
--- a/unitroot/generated/generated/arch.unitroot.DFGLS.pvalue.html
+++ b/unitroot/generated/generated/arch.unitroot.DFGLS.pvalue.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.DFGLS.pvalue - arch 6.1.0</title>
+        <title>arch.unitroot.DFGLS.pvalue - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -883,10 +883,10 @@
                   
 
 
-<h1 id="arch-unitroot-dfgls-pvalue">arch.unitroot.DFGLS.pvalue<a class="headerlink" href="#arch-unitroot-dfgls-pvalue" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-dfgls-pvalue">arch.unitroot.DFGLS.pvalue<a class="headerlink" href="#arch-unitroot-dfgls-pvalue" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.DFGLS.pvalue">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DFGLS.</span></span><span class="sig-name descname"><span class="pre">pvalue</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.DFGLS.pvalue" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DFGLS.</span></span><span class="sig-name descname"><span class="pre">pvalue</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.DFGLS.pvalue" title="Link to this definition">¶</a></dt>
 <dd><p>Returns the p-value for the test statistic</p>
 </dd></dl>
 
@@ -957,7 +957,7 @@ <h1 id="arch-unitroot-dfgls-pvalue">arch.unitroot.DFGLS.pvalue<a class="headerli
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1004,7 +1004,7 @@ <h1 id="arch-unitroot-dfgls-pvalue">arch.unitroot.DFGLS.pvalue<a class="headerli
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.DFGLS.regression.html b/unitroot/generated/generated/arch.unitroot.DFGLS.regression.html
index 5383157715..7feed7cb29 100644
--- a/unitroot/generated/generated/arch.unitroot.DFGLS.regression.html
+++ b/unitroot/generated/generated/arch.unitroot.DFGLS.regression.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.DFGLS.regression - arch 6.1.0</title>
+        <title>arch.unitroot.DFGLS.regression - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -883,10 +883,10 @@
                   
 
 
-<h1 id="arch-unitroot-dfgls-regression">arch.unitroot.DFGLS.regression<a class="headerlink" href="#arch-unitroot-dfgls-regression" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-dfgls-regression">arch.unitroot.DFGLS.regression<a class="headerlink" href="#arch-unitroot-dfgls-regression" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.DFGLS.regression">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DFGLS.</span></span><span class="sig-name descname"><span class="pre">regression</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.regression.linear_model.RegressionResults.html#statsmodels.regression.linear_model.RegressionResults" title="(in statsmodels 0.15.0 (+8) v0.15.0 (+8))"><span class="pre">RegressionResults</span></a></span><a class="headerlink" href="#arch.unitroot.DFGLS.regression" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DFGLS.</span></span><span class="sig-name descname"><span class="pre">regression</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.regression.linear_model.RegressionResults.html#statsmodels.regression.linear_model.RegressionResults" title="(in statsmodels 0.15.0 (+59) v0.15.0 (+59))"><span class="pre">RegressionResults</span></a></span><a class="headerlink" href="#arch.unitroot.DFGLS.regression" title="Link to this definition">¶</a></dt>
 <dd><p>Returns the OLS regression results from the ADF model estimated</p>
 </dd></dl>
 
@@ -957,7 +957,7 @@ <h1 id="arch-unitroot-dfgls-regression">arch.unitroot.DFGLS.regression<a class="
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1004,7 +1004,7 @@ <h1 id="arch-unitroot-dfgls-regression">arch.unitroot.DFGLS.regression<a class="
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.DFGLS.stat.html b/unitroot/generated/generated/arch.unitroot.DFGLS.stat.html
index 5319991a4b..8010dda3b3 100644
--- a/unitroot/generated/generated/arch.unitroot.DFGLS.stat.html
+++ b/unitroot/generated/generated/arch.unitroot.DFGLS.stat.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.DFGLS.stat - arch 6.1.0</title>
+        <title>arch.unitroot.DFGLS.stat - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -883,10 +883,10 @@
                   
 
 
-<h1 id="arch-unitroot-dfgls-stat">arch.unitroot.DFGLS.stat<a class="headerlink" href="#arch-unitroot-dfgls-stat" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-dfgls-stat">arch.unitroot.DFGLS.stat<a class="headerlink" href="#arch-unitroot-dfgls-stat" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.DFGLS.stat">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DFGLS.</span></span><span class="sig-name descname"><span class="pre">stat</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.DFGLS.stat" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DFGLS.</span></span><span class="sig-name descname"><span class="pre">stat</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.DFGLS.stat" title="Link to this definition">¶</a></dt>
 <dd><p>The test statistic for a unit root</p>
 </dd></dl>
 
@@ -957,7 +957,7 @@ <h1 id="arch-unitroot-dfgls-stat">arch.unitroot.DFGLS.stat<a class="headerlink"
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1004,7 +1004,7 @@ <h1 id="arch-unitroot-dfgls-stat">arch.unitroot.DFGLS.stat<a class="headerlink"
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.DFGLS.summary.html b/unitroot/generated/generated/arch.unitroot.DFGLS.summary.html
index da59a12426..d7ee315139 100644
--- a/unitroot/generated/generated/arch.unitroot.DFGLS.summary.html
+++ b/unitroot/generated/generated/arch.unitroot.DFGLS.summary.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.DFGLS.summary - arch 6.1.0</title>
+        <title>arch.unitroot.DFGLS.summary - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -476,19 +476,6 @@
     <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="arch.unitroot.DFGLS.summary (Python method) — Summary of test, containing statistic, p-value and critical values" class="md-ellipsis">DFGLS.<wbr>summary</span>
   </a>
   
-    <nav class="md-nav" aria-label="DFGLS.summary">
-      <ul class="md-nav__list">
-        
-          <li class="md-nav__item">
-  <a href="#arch.unitroot.DFGLS.summary-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
-</li>
-        
-      </ul>
-    </nav>
-  
 </li>
       
     </ul>
@@ -880,19 +867,6 @@
     <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="arch.unitroot.DFGLS.summary (Python method) — Summary of test, containing statistic, p-value and critical values" class="md-ellipsis">DFGLS.<wbr>summary</span>
   </a>
   
-    <nav class="md-nav" aria-label="DFGLS.summary">
-      <ul class="md-nav__list">
-        
-          <li class="md-nav__item">
-  <a href="#arch.unitroot.DFGLS.summary-return-type" class="md-nav__link">
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-  
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-        
-      </ul>
-    </nav>
-  
 </li>
       
     </ul>
@@ -909,16 +883,11 @@
                   
 
 
-<h1 id="arch-unitroot-dfgls-summary">arch.unitroot.DFGLS.summary<a class="headerlink" href="#arch-unitroot-dfgls-summary" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-dfgls-summary">arch.unitroot.DFGLS.summary<a class="headerlink" href="#arch-unitroot-dfgls-summary" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.DFGLS.summary">
-<span class="sig-prename descclassname"><span class="pre">DFGLS.</span></span><span class="sig-name descname"><span class="pre">summary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#arch.unitroot.DFGLS.summary" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">DFGLS.</span></span><span class="sig-name descname"><span class="pre">summary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.iolib.summary.Summary.html#statsmodels.iolib.summary.Summary" title="(in statsmodels 0.15.0 (+59) v0.15.0 (+59))"><span class="pre">Summary</span></a></span></span></span><a class="headerlink" href="#arch.unitroot.DFGLS.summary" title="Link to this definition">¶</a></dt>
 <dd><p>Summary of test, containing statistic, p-value and critical values</p>
-<dl class="field-list simple">
-<dt class="field-odd" id="arch.unitroot.DFGLS.summary-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.DFGLS.summary-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.iolib.summary.Summary.html#statsmodels.iolib.summary.Summary" title="(in statsmodels 0.15.0 (+8) v0.15.0 (+8))"><code class="xref py py-class docutils literal notranslate"><span class="pre">Summary</span></code></a></p>
-</dd>
-</dl>
 </dd></dl>
 
 
@@ -988,7 +957,7 @@ <h1 id="arch-unitroot-dfgls-summary">arch.unitroot.DFGLS.summary<a class="header
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
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@@ -1035,7 +1004,7 @@ <h1 id="arch-unitroot-dfgls-summary">arch.unitroot.DFGLS.summary<a class="header
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.DFGLS.trend.html b/unitroot/generated/generated/arch.unitroot.DFGLS.trend.html
index ac3198d294..f2d8ec4179 100644
--- a/unitroot/generated/generated/arch.unitroot.DFGLS.trend.html
+++ b/unitroot/generated/generated/arch.unitroot.DFGLS.trend.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.DFGLS.trend - arch 6.1.0</title>
+        <title>arch.unitroot.DFGLS.trend - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -883,10 +883,10 @@
                   
 
 
-<h1 id="arch-unitroot-dfgls-trend">arch.unitroot.DFGLS.trend<a class="headerlink" href="#arch-unitroot-dfgls-trend" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-dfgls-trend">arch.unitroot.DFGLS.trend<a class="headerlink" href="#arch-unitroot-dfgls-trend" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.DFGLS.trend">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DFGLS.</span></span><span class="sig-name descname"><span class="pre">trend</span></span><a class="headerlink" href="#arch.unitroot.DFGLS.trend" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DFGLS.</span></span><span class="sig-name descname"><span class="pre">trend</span></span><a class="headerlink" href="#arch.unitroot.DFGLS.trend" title="Link to this definition">¶</a></dt>
 <dd><p>Sets or gets the deterministic trend term used in the test. See
 valid_trends for a list of supported trends</p>
 </dd></dl>
@@ -958,7 +958,7 @@ <h1 id="arch-unitroot-dfgls-trend">arch.unitroot.DFGLS.trend<a class="headerlink
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1005,7 +1005,7 @@ <h1 id="arch-unitroot-dfgls-trend">arch.unitroot.DFGLS.trend<a class="headerlink
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.DFGLS.valid_trends.html b/unitroot/generated/generated/arch.unitroot.DFGLS.valid_trends.html
index d0cea67359..b6ec8c56fb 100644
--- a/unitroot/generated/generated/arch.unitroot.DFGLS.valid_trends.html
+++ b/unitroot/generated/generated/arch.unitroot.DFGLS.valid_trends.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.DFGLS.valid_trends - arch 6.1.0</title>
+        <title>arch.unitroot.DFGLS.valid_trends - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -883,10 +883,10 @@
                   
 
 
-<h1 id="arch-unitroot-dfgls-valid-trends">arch.unitroot.DFGLS.valid_trends<a class="headerlink" href="#arch-unitroot-dfgls-valid-trends" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-dfgls-valid-trends">arch.unitroot.DFGLS.valid_trends<a class="headerlink" href="#arch-unitroot-dfgls-valid-trends" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.DFGLS.valid_trends">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DFGLS.</span></span><span class="sig-name descname"><span class="pre">valid_trends</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#arch.unitroot.DFGLS.valid_trends" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DFGLS.</span></span><span class="sig-name descname"><span class="pre">valid_trends</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#arch.unitroot.DFGLS.valid_trends" title="Link to this definition">¶</a></dt>
 <dd><p>List of valid trend terms.</p>
 </dd></dl>
 
@@ -957,7 +957,7 @@ <h1 id="arch-unitroot-dfgls-valid-trends">arch.unitroot.DFGLS.valid_trends<a cla
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1004,7 +1004,7 @@ <h1 id="arch-unitroot-dfgls-valid-trends">arch.unitroot.DFGLS.valid_trends<a cla
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.DFGLS.y.html b/unitroot/generated/generated/arch.unitroot.DFGLS.y.html
index d22bc1df8f..fe0a583503 100644
--- a/unitroot/generated/generated/arch.unitroot.DFGLS.y.html
+++ b/unitroot/generated/generated/arch.unitroot.DFGLS.y.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.DFGLS.y - arch 6.1.0</title>
+        <title>arch.unitroot.DFGLS.y - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -883,10 +883,10 @@
                   
 
 
-<h1 id="arch-unitroot-dfgls-y">arch.unitroot.DFGLS.y<a class="headerlink" href="#arch-unitroot-dfgls-y" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-dfgls-y">arch.unitroot.DFGLS.y<a class="headerlink" href="#arch-unitroot-dfgls-y" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.DFGLS.y">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DFGLS.</span></span><span class="sig-name descname"><span class="pre">y</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.0.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.0.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.DFGLS.y" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DFGLS.</span></span><span class="sig-name descname"><span class="pre">y</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.DFGLS.y" title="Link to this definition">¶</a></dt>
 <dd><p>Returns the data used in the test statistic</p>
 </dd></dl>
 
@@ -957,7 +957,7 @@ <h1 id="arch-unitroot-dfgls-y">arch.unitroot.DFGLS.y<a class="headerlink" href="
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1004,7 +1004,7 @@ <h1 id="arch-unitroot-dfgls-y">arch.unitroot.DFGLS.y<a class="headerlink" href="
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.KPSS.alternative_hypothesis.html b/unitroot/generated/generated/arch.unitroot.KPSS.alternative_hypothesis.html
index 3ce398e180..466ad27f68 100644
--- a/unitroot/generated/generated/arch.unitroot.KPSS.alternative_hypothesis.html
+++ b/unitroot/generated/generated/arch.unitroot.KPSS.alternative_hypothesis.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.KPSS.alternative_hypothesis - arch 6.1.0</title>
+        <title>arch.unitroot.KPSS.alternative_hypothesis - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -853,10 +853,10 @@
                   
 
 
-<h1 id="arch-unitroot-kpss-alternative-hypothesis">arch.unitroot.KPSS.alternative_hypothesis<a class="headerlink" href="#arch-unitroot-kpss-alternative-hypothesis" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-kpss-alternative-hypothesis">arch.unitroot.KPSS.alternative_hypothesis<a class="headerlink" href="#arch-unitroot-kpss-alternative-hypothesis" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.KPSS.alternative_hypothesis">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">KPSS.</span></span><span class="sig-name descname"><span class="pre">alternative_hypothesis</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.KPSS.alternative_hypothesis" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">KPSS.</span></span><span class="sig-name descname"><span class="pre">alternative_hypothesis</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.KPSS.alternative_hypothesis" title="Link to this definition">¶</a></dt>
 <dd><p>The alternative hypothesis</p>
 </dd></dl>
 
@@ -927,7 +927,7 @@ <h1 id="arch-unitroot-kpss-alternative-hypothesis">arch.unitroot.KPSS.alternativ
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -974,7 +974,7 @@ <h1 id="arch-unitroot-kpss-alternative-hypothesis">arch.unitroot.KPSS.alternativ
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.KPSS.critical_values.html b/unitroot/generated/generated/arch.unitroot.KPSS.critical_values.html
index 0fb4587515..15220e6ac9 100644
--- a/unitroot/generated/generated/arch.unitroot.KPSS.critical_values.html
+++ b/unitroot/generated/generated/arch.unitroot.KPSS.critical_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.KPSS.critical_values - arch 6.1.0</title>
+        <title>arch.unitroot.KPSS.critical_values - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -853,10 +853,10 @@
                   
 
 
-<h1 id="arch-unitroot-kpss-critical-values">arch.unitroot.KPSS.critical_values<a class="headerlink" href="#arch-unitroot-kpss-critical-values" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-kpss-critical-values">arch.unitroot.KPSS.critical_values<a class="headerlink" href="#arch-unitroot-kpss-critical-values" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.KPSS.critical_values">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">KPSS.</span></span><span class="sig-name descname"><span class="pre">critical_values</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#arch.unitroot.KPSS.critical_values" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">KPSS.</span></span><span class="sig-name descname"><span class="pre">critical_values</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#arch.unitroot.KPSS.critical_values" title="Link to this definition">¶</a></dt>
 <dd><p>Dictionary containing critical values specific to the test, number of
 observations and included deterministic trend terms.</p>
 </dd></dl>
@@ -928,7 +928,7 @@ <h1 id="arch-unitroot-kpss-critical-values">arch.unitroot.KPSS.critical_values<a
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -975,7 +975,7 @@ <h1 id="arch-unitroot-kpss-critical-values">arch.unitroot.KPSS.critical_values<a
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.KPSS.lags.html b/unitroot/generated/generated/arch.unitroot.KPSS.lags.html
index 1409ea0552..a46726489e 100644
--- a/unitroot/generated/generated/arch.unitroot.KPSS.lags.html
+++ b/unitroot/generated/generated/arch.unitroot.KPSS.lags.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.KPSS.lags - arch 6.1.0</title>
+        <title>arch.unitroot.KPSS.lags - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -853,10 +853,10 @@
                   
 
 
-<h1 id="arch-unitroot-kpss-lags">arch.unitroot.KPSS.lags<a class="headerlink" href="#arch-unitroot-kpss-lags" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-kpss-lags">arch.unitroot.KPSS.lags<a class="headerlink" href="#arch-unitroot-kpss-lags" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.KPSS.lags">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">KPSS.</span></span><span class="sig-name descname"><span class="pre">lags</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.unitroot.KPSS.lags" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">KPSS.</span></span><span class="sig-name descname"><span class="pre">lags</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.unitroot.KPSS.lags" title="Link to this definition">¶</a></dt>
 <dd><p>Sets or gets the number of lags used in the model.
 When bootstrap use DF-type regressions, lags is the number of lags in the
 regression model.  When bootstrap use long-run variance estimators, lags
@@ -930,7 +930,7 @@ <h1 id="arch-unitroot-kpss-lags">arch.unitroot.KPSS.lags<a class="headerlink" hr
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -977,7 +977,7 @@ <h1 id="arch-unitroot-kpss-lags">arch.unitroot.KPSS.lags<a class="headerlink" hr
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.KPSS.nobs.html b/unitroot/generated/generated/arch.unitroot.KPSS.nobs.html
index 5eb2a1861b..6788abb87a 100644
--- a/unitroot/generated/generated/arch.unitroot.KPSS.nobs.html
+++ b/unitroot/generated/generated/arch.unitroot.KPSS.nobs.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.KPSS.nobs - arch 6.1.0</title>
+        <title>arch.unitroot.KPSS.nobs - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -853,10 +853,10 @@
                   
 
 
-<h1 id="arch-unitroot-kpss-nobs">arch.unitroot.KPSS.nobs<a class="headerlink" href="#arch-unitroot-kpss-nobs" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-kpss-nobs">arch.unitroot.KPSS.nobs<a class="headerlink" href="#arch-unitroot-kpss-nobs" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.KPSS.nobs">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">KPSS.</span></span><span class="sig-name descname"><span class="pre">nobs</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.unitroot.KPSS.nobs" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">KPSS.</span></span><span class="sig-name descname"><span class="pre">nobs</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.unitroot.KPSS.nobs" title="Link to this definition">¶</a></dt>
 <dd><p>The number of observations used when computing the test statistic.
 Accounts for loss of data due to lags for regression-based bootstrap.</p>
 </dd></dl>
@@ -928,7 +928,7 @@ <h1 id="arch-unitroot-kpss-nobs">arch.unitroot.KPSS.nobs<a class="headerlink" hr
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -975,7 +975,7 @@ <h1 id="arch-unitroot-kpss-nobs">arch.unitroot.KPSS.nobs<a class="headerlink" hr
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.KPSS.null_hypothesis.html b/unitroot/generated/generated/arch.unitroot.KPSS.null_hypothesis.html
index 6925d62230..0209fa70e4 100644
--- a/unitroot/generated/generated/arch.unitroot.KPSS.null_hypothesis.html
+++ b/unitroot/generated/generated/arch.unitroot.KPSS.null_hypothesis.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.KPSS.null_hypothesis - arch 6.1.0</title>
+        <title>arch.unitroot.KPSS.null_hypothesis - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -853,10 +853,10 @@
                   
 
 
-<h1 id="arch-unitroot-kpss-null-hypothesis">arch.unitroot.KPSS.null_hypothesis<a class="headerlink" href="#arch-unitroot-kpss-null-hypothesis" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-kpss-null-hypothesis">arch.unitroot.KPSS.null_hypothesis<a class="headerlink" href="#arch-unitroot-kpss-null-hypothesis" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.KPSS.null_hypothesis">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">KPSS.</span></span><span class="sig-name descname"><span class="pre">null_hypothesis</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.KPSS.null_hypothesis" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">KPSS.</span></span><span class="sig-name descname"><span class="pre">null_hypothesis</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.KPSS.null_hypothesis" title="Link to this definition">¶</a></dt>
 <dd><p>The null hypothesis</p>
 </dd></dl>
 
@@ -927,7 +927,7 @@ <h1 id="arch-unitroot-kpss-null-hypothesis">arch.unitroot.KPSS.null_hypothesis<a
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -974,7 +974,7 @@ <h1 id="arch-unitroot-kpss-null-hypothesis">arch.unitroot.KPSS.null_hypothesis<a
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.KPSS.pvalue.html b/unitroot/generated/generated/arch.unitroot.KPSS.pvalue.html
index 4d0a047f1a..ce552437ad 100644
--- a/unitroot/generated/generated/arch.unitroot.KPSS.pvalue.html
+++ b/unitroot/generated/generated/arch.unitroot.KPSS.pvalue.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.KPSS.pvalue - arch 6.1.0</title>
+        <title>arch.unitroot.KPSS.pvalue - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -853,10 +853,10 @@
                   
 
 
-<h1 id="arch-unitroot-kpss-pvalue">arch.unitroot.KPSS.pvalue<a class="headerlink" href="#arch-unitroot-kpss-pvalue" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-kpss-pvalue">arch.unitroot.KPSS.pvalue<a class="headerlink" href="#arch-unitroot-kpss-pvalue" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.KPSS.pvalue">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">KPSS.</span></span><span class="sig-name descname"><span class="pre">pvalue</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.KPSS.pvalue" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">KPSS.</span></span><span class="sig-name descname"><span class="pre">pvalue</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.KPSS.pvalue" title="Link to this definition">¶</a></dt>
 <dd><p>Returns the p-value for the test statistic</p>
 </dd></dl>
 
@@ -927,7 +927,7 @@ <h1 id="arch-unitroot-kpss-pvalue">arch.unitroot.KPSS.pvalue<a class="headerlink
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -974,7 +974,7 @@ <h1 id="arch-unitroot-kpss-pvalue">arch.unitroot.KPSS.pvalue<a class="headerlink
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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diff --git a/unitroot/generated/generated/arch.unitroot.KPSS.stat.html b/unitroot/generated/generated/arch.unitroot.KPSS.stat.html
index e868ed2509..a0e7e4aa54 100644
--- a/unitroot/generated/generated/arch.unitroot.KPSS.stat.html
+++ b/unitroot/generated/generated/arch.unitroot.KPSS.stat.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.KPSS.stat - arch 6.1.0</title>
+        <title>arch.unitroot.KPSS.stat - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -853,10 +853,10 @@
                   
 
 
-<h1 id="arch-unitroot-kpss-stat">arch.unitroot.KPSS.stat<a class="headerlink" href="#arch-unitroot-kpss-stat" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-kpss-stat">arch.unitroot.KPSS.stat<a class="headerlink" href="#arch-unitroot-kpss-stat" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.KPSS.stat">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">KPSS.</span></span><span class="sig-name descname"><span class="pre">stat</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.KPSS.stat" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">KPSS.</span></span><span class="sig-name descname"><span class="pre">stat</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.KPSS.stat" title="Link to this definition">¶</a></dt>
 <dd><p>The test statistic for a unit root</p>
 </dd></dl>
 
@@ -927,7 +927,7 @@ <h1 id="arch-unitroot-kpss-stat">arch.unitroot.KPSS.stat<a class="headerlink" hr
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -974,7 +974,7 @@ <h1 id="arch-unitroot-kpss-stat">arch.unitroot.KPSS.stat<a class="headerlink" hr
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.KPSS.summary.html b/unitroot/generated/generated/arch.unitroot.KPSS.summary.html
index 9124f3a4f1..93ab4b31dc 100644
--- a/unitroot/generated/generated/arch.unitroot.KPSS.summary.html
+++ b/unitroot/generated/generated/arch.unitroot.KPSS.summary.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.KPSS.summary - arch 6.1.0</title>
+        <title>arch.unitroot.KPSS.summary - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -536,19 +536,6 @@
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   </a>
   
-    <nav class="md-nav" aria-label="KPSS.summary">
-      <ul class="md-nav__list">
-        
-          <li class="md-nav__item">
-  <a href="#arch.unitroot.KPSS.summary-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
-</li>
-        
-      </ul>
-    </nav>
-  
 </li>
       
     </ul>
@@ -850,19 +837,6 @@
     <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="arch.unitroot.KPSS.summary (Python method) — Summary of test, containing statistic, p-value and critical values" class="md-ellipsis">KPSS.<wbr>summary</span>
   </a>
   
-    <nav class="md-nav" aria-label="KPSS.summary">
-      <ul class="md-nav__list">
-        
-          <li class="md-nav__item">
-  <a href="#arch.unitroot.KPSS.summary-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
-</li>
-        
-      </ul>
-    </nav>
-  
 </li>
       
     </ul>
@@ -879,16 +853,11 @@
                   
 
 
-<h1 id="arch-unitroot-kpss-summary">arch.unitroot.KPSS.summary<a class="headerlink" href="#arch-unitroot-kpss-summary" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-kpss-summary">arch.unitroot.KPSS.summary<a class="headerlink" href="#arch-unitroot-kpss-summary" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.KPSS.summary">
-<span class="sig-prename descclassname"><span class="pre">KPSS.</span></span><span class="sig-name descname"><span class="pre">summary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#arch.unitroot.KPSS.summary" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">KPSS.</span></span><span class="sig-name descname"><span class="pre">summary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.iolib.summary.Summary.html#statsmodels.iolib.summary.Summary" title="(in statsmodels 0.15.0 (+59) v0.15.0 (+59))"><span class="pre">Summary</span></a></span></span></span><a class="headerlink" href="#arch.unitroot.KPSS.summary" title="Link to this definition">¶</a></dt>
 <dd><p>Summary of test, containing statistic, p-value and critical values</p>
-<dl class="field-list simple">
-<dt class="field-odd" id="arch.unitroot.KPSS.summary-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.KPSS.summary-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.iolib.summary.Summary.html#statsmodels.iolib.summary.Summary" title="(in statsmodels 0.15.0 (+8) v0.15.0 (+8))"><code class="xref py py-class docutils literal notranslate"><span class="pre">Summary</span></code></a></p>
-</dd>
-</dl>
 </dd></dl>
 
 
@@ -958,7 +927,7 @@ <h1 id="arch-unitroot-kpss-summary">arch.unitroot.KPSS.summary<a class="headerli
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1005,7 +974,7 @@ <h1 id="arch-unitroot-kpss-summary">arch.unitroot.KPSS.summary<a class="headerli
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.KPSS.trend.html b/unitroot/generated/generated/arch.unitroot.KPSS.trend.html
index 1ec7e5826b..4a26902bb9 100644
--- a/unitroot/generated/generated/arch.unitroot.KPSS.trend.html
+++ b/unitroot/generated/generated/arch.unitroot.KPSS.trend.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.KPSS.trend - arch 6.1.0</title>
+        <title>arch.unitroot.KPSS.trend - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -853,10 +853,10 @@
                   
 
 
-<h1 id="arch-unitroot-kpss-trend">arch.unitroot.KPSS.trend<a class="headerlink" href="#arch-unitroot-kpss-trend" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-kpss-trend">arch.unitroot.KPSS.trend<a class="headerlink" href="#arch-unitroot-kpss-trend" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.KPSS.trend">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">KPSS.</span></span><span class="sig-name descname"><span class="pre">trend</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.KPSS.trend" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">KPSS.</span></span><span class="sig-name descname"><span class="pre">trend</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.KPSS.trend" title="Link to this definition">¶</a></dt>
 <dd><p>Sets or gets the deterministic trend term used in the test. See
 valid_trends for a list of supported trends</p>
 </dd></dl>
@@ -928,7 +928,7 @@ <h1 id="arch-unitroot-kpss-trend">arch.unitroot.KPSS.trend<a class="headerlink"
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -975,7 +975,7 @@ <h1 id="arch-unitroot-kpss-trend">arch.unitroot.KPSS.trend<a class="headerlink"
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.KPSS.valid_trends.html b/unitroot/generated/generated/arch.unitroot.KPSS.valid_trends.html
index 89cbde2641..9da6249b18 100644
--- a/unitroot/generated/generated/arch.unitroot.KPSS.valid_trends.html
+++ b/unitroot/generated/generated/arch.unitroot.KPSS.valid_trends.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.KPSS.valid_trends - arch 6.1.0</title>
+        <title>arch.unitroot.KPSS.valid_trends - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -853,10 +853,10 @@
                   
 
 
-<h1 id="arch-unitroot-kpss-valid-trends">arch.unitroot.KPSS.valid_trends<a class="headerlink" href="#arch-unitroot-kpss-valid-trends" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-kpss-valid-trends">arch.unitroot.KPSS.valid_trends<a class="headerlink" href="#arch-unitroot-kpss-valid-trends" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.KPSS.valid_trends">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">KPSS.</span></span><span class="sig-name descname"><span class="pre">valid_trends</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#arch.unitroot.KPSS.valid_trends" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">KPSS.</span></span><span class="sig-name descname"><span class="pre">valid_trends</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#arch.unitroot.KPSS.valid_trends" title="Link to this definition">¶</a></dt>
 <dd><p>List of valid trend terms.</p>
 </dd></dl>
 
@@ -927,7 +927,7 @@ <h1 id="arch-unitroot-kpss-valid-trends">arch.unitroot.KPSS.valid_trends<a class
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -974,7 +974,7 @@ <h1 id="arch-unitroot-kpss-valid-trends">arch.unitroot.KPSS.valid_trends<a class
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.KPSS.y.html b/unitroot/generated/generated/arch.unitroot.KPSS.y.html
index dec6d3cdc3..e2d2d37b86 100644
--- a/unitroot/generated/generated/arch.unitroot.KPSS.y.html
+++ b/unitroot/generated/generated/arch.unitroot.KPSS.y.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.KPSS.y - arch 6.1.0</title>
+        <title>arch.unitroot.KPSS.y - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -853,10 +853,10 @@
                   
 
 
-<h1 id="arch-unitroot-kpss-y">arch.unitroot.KPSS.y<a class="headerlink" href="#arch-unitroot-kpss-y" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-kpss-y">arch.unitroot.KPSS.y<a class="headerlink" href="#arch-unitroot-kpss-y" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.KPSS.y">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">KPSS.</span></span><span class="sig-name descname"><span class="pre">y</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.0.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.0.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.KPSS.y" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">KPSS.</span></span><span class="sig-name descname"><span class="pre">y</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.KPSS.y" title="Link to this definition">¶</a></dt>
 <dd><p>Returns the data used in the test statistic</p>
 </dd></dl>
 
@@ -927,7 +927,7 @@ <h1 id="arch-unitroot-kpss-y">arch.unitroot.KPSS.y<a class="headerlink" href="#a
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -974,7 +974,7 @@ <h1 id="arch-unitroot-kpss-y">arch.unitroot.KPSS.y<a class="headerlink" href="#a
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.PhillipsPerron.alternative_hypothesis.html b/unitroot/generated/generated/arch.unitroot.PhillipsPerron.alternative_hypothesis.html
index 0901ac4d9f..59e94cc4ec 100644
--- a/unitroot/generated/generated/arch.unitroot.PhillipsPerron.alternative_hypothesis.html
+++ b/unitroot/generated/generated/arch.unitroot.PhillipsPerron.alternative_hypothesis.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.PhillipsPerron.alternative_hypothesis - arch 6.1.0</title>
+        <title>arch.unitroot.PhillipsPerron.alternative_hypothesis - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -883,10 +883,10 @@
                   
 
 
-<h1 id="arch-unitroot-phillipsperron-alternative-hypothesis">arch.unitroot.PhillipsPerron.alternative_hypothesis<a class="headerlink" href="#arch-unitroot-phillipsperron-alternative-hypothesis" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-phillipsperron-alternative-hypothesis">arch.unitroot.PhillipsPerron.alternative_hypothesis<a class="headerlink" href="#arch-unitroot-phillipsperron-alternative-hypothesis" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.PhillipsPerron.alternative_hypothesis">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsPerron.</span></span><span class="sig-name descname"><span class="pre">alternative_hypothesis</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.PhillipsPerron.alternative_hypothesis" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsPerron.</span></span><span class="sig-name descname"><span class="pre">alternative_hypothesis</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.PhillipsPerron.alternative_hypothesis" title="Link to this definition">¶</a></dt>
 <dd><p>The alternative hypothesis</p>
 </dd></dl>
 
@@ -957,7 +957,7 @@ <h1 id="arch-unitroot-phillipsperron-alternative-hypothesis">arch.unitroot.Phill
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1004,7 +1004,7 @@ <h1 id="arch-unitroot-phillipsperron-alternative-hypothesis">arch.unitroot.Phill
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.PhillipsPerron.critical_values.html b/unitroot/generated/generated/arch.unitroot.PhillipsPerron.critical_values.html
index c4d8673f1b..6a1e9c0936 100644
--- a/unitroot/generated/generated/arch.unitroot.PhillipsPerron.critical_values.html
+++ b/unitroot/generated/generated/arch.unitroot.PhillipsPerron.critical_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.PhillipsPerron.critical_values - arch 6.1.0</title>
+        <title>arch.unitroot.PhillipsPerron.critical_values - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -883,10 +883,10 @@
                   
 
 
-<h1 id="arch-unitroot-phillipsperron-critical-values">arch.unitroot.PhillipsPerron.critical_values<a class="headerlink" href="#arch-unitroot-phillipsperron-critical-values" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-phillipsperron-critical-values">arch.unitroot.PhillipsPerron.critical_values<a class="headerlink" href="#arch-unitroot-phillipsperron-critical-values" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.PhillipsPerron.critical_values">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsPerron.</span></span><span class="sig-name descname"><span class="pre">critical_values</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#arch.unitroot.PhillipsPerron.critical_values" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsPerron.</span></span><span class="sig-name descname"><span class="pre">critical_values</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#arch.unitroot.PhillipsPerron.critical_values" title="Link to this definition">¶</a></dt>
 <dd><p>Dictionary containing critical values specific to the test, number of
 observations and included deterministic trend terms.</p>
 </dd></dl>
@@ -958,7 +958,7 @@ <h1 id="arch-unitroot-phillipsperron-critical-values">arch.unitroot.PhillipsPerr
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1005,7 +1005,7 @@ <h1 id="arch-unitroot-phillipsperron-critical-values">arch.unitroot.PhillipsPerr
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.PhillipsPerron.lags.html b/unitroot/generated/generated/arch.unitroot.PhillipsPerron.lags.html
index 0969300181..493d419915 100644
--- a/unitroot/generated/generated/arch.unitroot.PhillipsPerron.lags.html
+++ b/unitroot/generated/generated/arch.unitroot.PhillipsPerron.lags.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.PhillipsPerron.lags - arch 6.1.0</title>
+        <title>arch.unitroot.PhillipsPerron.lags - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -883,10 +883,10 @@
                   
 
 
-<h1 id="arch-unitroot-phillipsperron-lags">arch.unitroot.PhillipsPerron.lags<a class="headerlink" href="#arch-unitroot-phillipsperron-lags" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-phillipsperron-lags">arch.unitroot.PhillipsPerron.lags<a class="headerlink" href="#arch-unitroot-phillipsperron-lags" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.PhillipsPerron.lags">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsPerron.</span></span><span class="sig-name descname"><span class="pre">lags</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.unitroot.PhillipsPerron.lags" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsPerron.</span></span><span class="sig-name descname"><span class="pre">lags</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.unitroot.PhillipsPerron.lags" title="Link to this definition">¶</a></dt>
 <dd><p>Sets or gets the number of lags used in the model.
 When bootstrap use DF-type regressions, lags is the number of lags in the
 regression model.  When bootstrap use long-run variance estimators, lags
@@ -960,7 +960,7 @@ <h1 id="arch-unitroot-phillipsperron-lags">arch.unitroot.PhillipsPerron.lags<a c
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1007,7 +1007,7 @@ <h1 id="arch-unitroot-phillipsperron-lags">arch.unitroot.PhillipsPerron.lags<a c
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.PhillipsPerron.nobs.html b/unitroot/generated/generated/arch.unitroot.PhillipsPerron.nobs.html
index 3468836030..8c6bd864c1 100644
--- a/unitroot/generated/generated/arch.unitroot.PhillipsPerron.nobs.html
+++ b/unitroot/generated/generated/arch.unitroot.PhillipsPerron.nobs.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.PhillipsPerron.nobs - arch 6.1.0</title>
+        <title>arch.unitroot.PhillipsPerron.nobs - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -883,10 +883,10 @@
                   
 
 
-<h1 id="arch-unitroot-phillipsperron-nobs">arch.unitroot.PhillipsPerron.nobs<a class="headerlink" href="#arch-unitroot-phillipsperron-nobs" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-phillipsperron-nobs">arch.unitroot.PhillipsPerron.nobs<a class="headerlink" href="#arch-unitroot-phillipsperron-nobs" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.PhillipsPerron.nobs">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsPerron.</span></span><span class="sig-name descname"><span class="pre">nobs</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.unitroot.PhillipsPerron.nobs" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsPerron.</span></span><span class="sig-name descname"><span class="pre">nobs</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.unitroot.PhillipsPerron.nobs" title="Link to this definition">¶</a></dt>
 <dd><p>The number of observations used when computing the test statistic.
 Accounts for loss of data due to lags for regression-based bootstrap.</p>
 </dd></dl>
@@ -958,7 +958,7 @@ <h1 id="arch-unitroot-phillipsperron-nobs">arch.unitroot.PhillipsPerron.nobs<a c
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1005,7 +1005,7 @@ <h1 id="arch-unitroot-phillipsperron-nobs">arch.unitroot.PhillipsPerron.nobs<a c
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.PhillipsPerron.null_hypothesis.html b/unitroot/generated/generated/arch.unitroot.PhillipsPerron.null_hypothesis.html
index 7d1f615836..93ece0f91c 100644
--- a/unitroot/generated/generated/arch.unitroot.PhillipsPerron.null_hypothesis.html
+++ b/unitroot/generated/generated/arch.unitroot.PhillipsPerron.null_hypothesis.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.PhillipsPerron.null_hypothesis - arch 6.1.0</title>
+        <title>arch.unitroot.PhillipsPerron.null_hypothesis - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -883,10 +883,10 @@
                   
 
 
-<h1 id="arch-unitroot-phillipsperron-null-hypothesis">arch.unitroot.PhillipsPerron.null_hypothesis<a class="headerlink" href="#arch-unitroot-phillipsperron-null-hypothesis" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-phillipsperron-null-hypothesis">arch.unitroot.PhillipsPerron.null_hypothesis<a class="headerlink" href="#arch-unitroot-phillipsperron-null-hypothesis" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.PhillipsPerron.null_hypothesis">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsPerron.</span></span><span class="sig-name descname"><span class="pre">null_hypothesis</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.PhillipsPerron.null_hypothesis" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsPerron.</span></span><span class="sig-name descname"><span class="pre">null_hypothesis</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.PhillipsPerron.null_hypothesis" title="Link to this definition">¶</a></dt>
 <dd><p>The null hypothesis</p>
 </dd></dl>
 
@@ -957,7 +957,7 @@ <h1 id="arch-unitroot-phillipsperron-null-hypothesis">arch.unitroot.PhillipsPerr
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1004,7 +1004,7 @@ <h1 id="arch-unitroot-phillipsperron-null-hypothesis">arch.unitroot.PhillipsPerr
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.PhillipsPerron.pvalue.html b/unitroot/generated/generated/arch.unitroot.PhillipsPerron.pvalue.html
index 943786ae0b..8519d69542 100644
--- a/unitroot/generated/generated/arch.unitroot.PhillipsPerron.pvalue.html
+++ b/unitroot/generated/generated/arch.unitroot.PhillipsPerron.pvalue.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.PhillipsPerron.pvalue - arch 6.1.0</title>
+        <title>arch.unitroot.PhillipsPerron.pvalue - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -883,10 +883,10 @@
                   
 
 
-<h1 id="arch-unitroot-phillipsperron-pvalue">arch.unitroot.PhillipsPerron.pvalue<a class="headerlink" href="#arch-unitroot-phillipsperron-pvalue" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-phillipsperron-pvalue">arch.unitroot.PhillipsPerron.pvalue<a class="headerlink" href="#arch-unitroot-phillipsperron-pvalue" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.PhillipsPerron.pvalue">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsPerron.</span></span><span class="sig-name descname"><span class="pre">pvalue</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.PhillipsPerron.pvalue" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsPerron.</span></span><span class="sig-name descname"><span class="pre">pvalue</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.PhillipsPerron.pvalue" title="Link to this definition">¶</a></dt>
 <dd><p>Returns the p-value for the test statistic</p>
 </dd></dl>
 
@@ -957,7 +957,7 @@ <h1 id="arch-unitroot-phillipsperron-pvalue">arch.unitroot.PhillipsPerron.pvalue
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1004,7 +1004,7 @@ <h1 id="arch-unitroot-phillipsperron-pvalue">arch.unitroot.PhillipsPerron.pvalue
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.PhillipsPerron.regression.html b/unitroot/generated/generated/arch.unitroot.PhillipsPerron.regression.html
index 5ab8d0f3df..481dba9af4 100644
--- a/unitroot/generated/generated/arch.unitroot.PhillipsPerron.regression.html
+++ b/unitroot/generated/generated/arch.unitroot.PhillipsPerron.regression.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.PhillipsPerron.regression - arch 6.1.0</title>
+        <title>arch.unitroot.PhillipsPerron.regression - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -883,10 +883,10 @@
                   
 
 
-<h1 id="arch-unitroot-phillipsperron-regression">arch.unitroot.PhillipsPerron.regression<a class="headerlink" href="#arch-unitroot-phillipsperron-regression" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-phillipsperron-regression">arch.unitroot.PhillipsPerron.regression<a class="headerlink" href="#arch-unitroot-phillipsperron-regression" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.PhillipsPerron.regression">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsPerron.</span></span><span class="sig-name descname"><span class="pre">regression</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.regression.linear_model.RegressionResults.html#statsmodels.regression.linear_model.RegressionResults" title="(in statsmodels 0.15.0 (+8) v0.15.0 (+8))"><span class="pre">RegressionResults</span></a></span><a class="headerlink" href="#arch.unitroot.PhillipsPerron.regression" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsPerron.</span></span><span class="sig-name descname"><span class="pre">regression</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.regression.linear_model.RegressionResults.html#statsmodels.regression.linear_model.RegressionResults" title="(in statsmodels 0.15.0 (+59) v0.15.0 (+59))"><span class="pre">RegressionResults</span></a></span><a class="headerlink" href="#arch.unitroot.PhillipsPerron.regression" title="Link to this definition">¶</a></dt>
 <dd><p>Returns OLS regression results for the specification used in the test</p>
 <p>The results returned use a Newey-West covariance matrix with the same
 number of lags as are used in the test statistic.</p>
@@ -959,7 +959,7 @@ <h1 id="arch-unitroot-phillipsperron-regression">arch.unitroot.PhillipsPerron.re
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1006,7 +1006,7 @@ <h1 id="arch-unitroot-phillipsperron-regression">arch.unitroot.PhillipsPerron.re
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.PhillipsPerron.stat.html b/unitroot/generated/generated/arch.unitroot.PhillipsPerron.stat.html
index 50e22ac7b0..405b2cc24a 100644
--- a/unitroot/generated/generated/arch.unitroot.PhillipsPerron.stat.html
+++ b/unitroot/generated/generated/arch.unitroot.PhillipsPerron.stat.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.PhillipsPerron.stat - arch 6.1.0</title>
+        <title>arch.unitroot.PhillipsPerron.stat - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -883,10 +883,10 @@
                   
 
 
-<h1 id="arch-unitroot-phillipsperron-stat">arch.unitroot.PhillipsPerron.stat<a class="headerlink" href="#arch-unitroot-phillipsperron-stat" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-phillipsperron-stat">arch.unitroot.PhillipsPerron.stat<a class="headerlink" href="#arch-unitroot-phillipsperron-stat" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.PhillipsPerron.stat">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsPerron.</span></span><span class="sig-name descname"><span class="pre">stat</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.PhillipsPerron.stat" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsPerron.</span></span><span class="sig-name descname"><span class="pre">stat</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.PhillipsPerron.stat" title="Link to this definition">¶</a></dt>
 <dd><p>The test statistic for a unit root</p>
 </dd></dl>
 
@@ -957,7 +957,7 @@ <h1 id="arch-unitroot-phillipsperron-stat">arch.unitroot.PhillipsPerron.stat<a c
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1004,7 +1004,7 @@ <h1 id="arch-unitroot-phillipsperron-stat">arch.unitroot.PhillipsPerron.stat<a c
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.PhillipsPerron.summary.html b/unitroot/generated/generated/arch.unitroot.PhillipsPerron.summary.html
index 4e7cfe5929..7d8943a935 100644
--- a/unitroot/generated/generated/arch.unitroot.PhillipsPerron.summary.html
+++ b/unitroot/generated/generated/arch.unitroot.PhillipsPerron.summary.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.PhillipsPerron.summary - arch 6.1.0</title>
+        <title>arch.unitroot.PhillipsPerron.summary - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -491,19 +491,6 @@
     <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="arch.unitroot.PhillipsPerron.summary (Python method) — Summary of test, containing statistic, p-value and critical values" class="md-ellipsis">Phillips<wbr>Perron.<wbr>summary</span>
   </a>
   
-    <nav class="md-nav" aria-label="PhillipsPerron.summary">
-      <ul class="md-nav__list">
-        
-          <li class="md-nav__item">
-  <a href="#arch.unitroot.PhillipsPerron.summary-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
-</li>
-        
-      </ul>
-    </nav>
-  
 </li>
       
     </ul>
@@ -880,19 +867,6 @@
     <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="arch.unitroot.PhillipsPerron.summary (Python method) — Summary of test, containing statistic, p-value and critical values" class="md-ellipsis">Phillips<wbr>Perron.<wbr>summary</span>
   </a>
   
-    <nav class="md-nav" aria-label="PhillipsPerron.summary">
-      <ul class="md-nav__list">
-        
-          <li class="md-nav__item">
-  <a href="#arch.unitroot.PhillipsPerron.summary-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
-</li>
-        
-      </ul>
-    </nav>
-  
 </li>
       
     </ul>
@@ -909,16 +883,11 @@
                   
 
 
-<h1 id="arch-unitroot-phillipsperron-summary">arch.unitroot.PhillipsPerron.summary<a class="headerlink" href="#arch-unitroot-phillipsperron-summary" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-phillipsperron-summary">arch.unitroot.PhillipsPerron.summary<a class="headerlink" href="#arch-unitroot-phillipsperron-summary" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.PhillipsPerron.summary">
-<span class="sig-prename descclassname"><span class="pre">PhillipsPerron.</span></span><span class="sig-name descname"><span class="pre">summary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#arch.unitroot.PhillipsPerron.summary" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">PhillipsPerron.</span></span><span class="sig-name descname"><span class="pre">summary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.iolib.summary.Summary.html#statsmodels.iolib.summary.Summary" title="(in statsmodels 0.15.0 (+59) v0.15.0 (+59))"><span class="pre">Summary</span></a></span></span></span><a class="headerlink" href="#arch.unitroot.PhillipsPerron.summary" title="Link to this definition">¶</a></dt>
 <dd><p>Summary of test, containing statistic, p-value and critical values</p>
-<dl class="field-list simple">
-<dt class="field-odd" id="arch.unitroot.PhillipsPerron.summary-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.PhillipsPerron.summary-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.iolib.summary.Summary.html#statsmodels.iolib.summary.Summary" title="(in statsmodels 0.15.0 (+8) v0.15.0 (+8))"><code class="xref py py-class docutils literal notranslate"><span class="pre">Summary</span></code></a></p>
-</dd>
-</dl>
 </dd></dl>
 
 
@@ -988,7 +957,7 @@ <h1 id="arch-unitroot-phillipsperron-summary">arch.unitroot.PhillipsPerron.summa
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1035,7 +1004,7 @@ <h1 id="arch-unitroot-phillipsperron-summary">arch.unitroot.PhillipsPerron.summa
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.PhillipsPerron.test_type.html b/unitroot/generated/generated/arch.unitroot.PhillipsPerron.test_type.html
index fc0d770e69..6c8d70fb89 100644
--- a/unitroot/generated/generated/arch.unitroot.PhillipsPerron.test_type.html
+++ b/unitroot/generated/generated/arch.unitroot.PhillipsPerron.test_type.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.PhillipsPerron.test_type - arch 6.1.0</title>
+        <title>arch.unitroot.PhillipsPerron.test_type - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -883,10 +883,10 @@
                   
 
 
-<h1 id="arch-unitroot-phillipsperron-test-type">arch.unitroot.PhillipsPerron.test_type<a class="headerlink" href="#arch-unitroot-phillipsperron-test-type" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-phillipsperron-test-type">arch.unitroot.PhillipsPerron.test_type<a class="headerlink" href="#arch-unitroot-phillipsperron-test-type" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.PhillipsPerron.test_type">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsPerron.</span></span><span class="sig-name descname"><span class="pre">test_type</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.PhillipsPerron.test_type" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsPerron.</span></span><span class="sig-name descname"><span class="pre">test_type</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.PhillipsPerron.test_type" title="Link to this definition">¶</a></dt>
 <dd><p>Gets or sets the test type returned by stat.
 Valid values are “tau” or “rho”</p>
 </dd></dl>
@@ -958,7 +958,7 @@ <h1 id="arch-unitroot-phillipsperron-test-type">arch.unitroot.PhillipsPerron.tes
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1005,7 +1005,7 @@ <h1 id="arch-unitroot-phillipsperron-test-type">arch.unitroot.PhillipsPerron.tes
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.PhillipsPerron.trend.html b/unitroot/generated/generated/arch.unitroot.PhillipsPerron.trend.html
index 8fe308e24b..02fa9d5a46 100644
--- a/unitroot/generated/generated/arch.unitroot.PhillipsPerron.trend.html
+++ b/unitroot/generated/generated/arch.unitroot.PhillipsPerron.trend.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.PhillipsPerron.trend - arch 6.1.0</title>
+        <title>arch.unitroot.PhillipsPerron.trend - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -883,10 +883,10 @@
                   
 
 
-<h1 id="arch-unitroot-phillipsperron-trend">arch.unitroot.PhillipsPerron.trend<a class="headerlink" href="#arch-unitroot-phillipsperron-trend" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-phillipsperron-trend">arch.unitroot.PhillipsPerron.trend<a class="headerlink" href="#arch-unitroot-phillipsperron-trend" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.PhillipsPerron.trend">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsPerron.</span></span><span class="sig-name descname"><span class="pre">trend</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.PhillipsPerron.trend" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsPerron.</span></span><span class="sig-name descname"><span class="pre">trend</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.PhillipsPerron.trend" title="Link to this definition">¶</a></dt>
 <dd><p>Sets or gets the deterministic trend term used in the test. See
 valid_trends for a list of supported trends</p>
 </dd></dl>
@@ -958,7 +958,7 @@ <h1 id="arch-unitroot-phillipsperron-trend">arch.unitroot.PhillipsPerron.trend<a
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1005,7 +1005,7 @@ <h1 id="arch-unitroot-phillipsperron-trend">arch.unitroot.PhillipsPerron.trend<a
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.PhillipsPerron.valid_trends.html b/unitroot/generated/generated/arch.unitroot.PhillipsPerron.valid_trends.html
index 64d78c40c0..9172c4b6fc 100644
--- a/unitroot/generated/generated/arch.unitroot.PhillipsPerron.valid_trends.html
+++ b/unitroot/generated/generated/arch.unitroot.PhillipsPerron.valid_trends.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.PhillipsPerron.valid_trends - arch 6.1.0</title>
+        <title>arch.unitroot.PhillipsPerron.valid_trends - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -883,10 +883,10 @@
                   
 
 
-<h1 id="arch-unitroot-phillipsperron-valid-trends">arch.unitroot.PhillipsPerron.valid_trends<a class="headerlink" href="#arch-unitroot-phillipsperron-valid-trends" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-phillipsperron-valid-trends">arch.unitroot.PhillipsPerron.valid_trends<a class="headerlink" href="#arch-unitroot-phillipsperron-valid-trends" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.PhillipsPerron.valid_trends">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsPerron.</span></span><span class="sig-name descname"><span class="pre">valid_trends</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#arch.unitroot.PhillipsPerron.valid_trends" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsPerron.</span></span><span class="sig-name descname"><span class="pre">valid_trends</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#arch.unitroot.PhillipsPerron.valid_trends" title="Link to this definition">¶</a></dt>
 <dd><p>List of valid trend terms.</p>
 </dd></dl>
 
@@ -957,7 +957,7 @@ <h1 id="arch-unitroot-phillipsperron-valid-trends">arch.unitroot.PhillipsPerron.
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1004,7 +1004,7 @@ <h1 id="arch-unitroot-phillipsperron-valid-trends">arch.unitroot.PhillipsPerron.
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.PhillipsPerron.y.html b/unitroot/generated/generated/arch.unitroot.PhillipsPerron.y.html
index a33e054098..fa49dc6b4e 100644
--- a/unitroot/generated/generated/arch.unitroot.PhillipsPerron.y.html
+++ b/unitroot/generated/generated/arch.unitroot.PhillipsPerron.y.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.PhillipsPerron.y - arch 6.1.0</title>
+        <title>arch.unitroot.PhillipsPerron.y - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -883,10 +883,10 @@
                   
 
 
-<h1 id="arch-unitroot-phillipsperron-y">arch.unitroot.PhillipsPerron.y<a class="headerlink" href="#arch-unitroot-phillipsperron-y" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-phillipsperron-y">arch.unitroot.PhillipsPerron.y<a class="headerlink" href="#arch-unitroot-phillipsperron-y" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.PhillipsPerron.y">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsPerron.</span></span><span class="sig-name descname"><span class="pre">y</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.0.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.0.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.PhillipsPerron.y" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsPerron.</span></span><span class="sig-name descname"><span class="pre">y</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.PhillipsPerron.y" title="Link to this definition">¶</a></dt>
 <dd><p>Returns the data used in the test statistic</p>
 </dd></dl>
 
@@ -957,7 +957,7 @@ <h1 id="arch-unitroot-phillipsperron-y">arch.unitroot.PhillipsPerron.y<a class="
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1004,7 +1004,7 @@ <h1 id="arch-unitroot-phillipsperron-y">arch.unitroot.PhillipsPerron.y<a class="
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.VarianceRatio.alternative_hypothesis.html b/unitroot/generated/generated/arch.unitroot.VarianceRatio.alternative_hypothesis.html
index df16d3ea89..248add32d1 100644
--- a/unitroot/generated/generated/arch.unitroot.VarianceRatio.alternative_hypothesis.html
+++ b/unitroot/generated/generated/arch.unitroot.VarianceRatio.alternative_hypothesis.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.VarianceRatio.alternative_hypothesis - arch 6.1.0</title>
+        <title>arch.unitroot.VarianceRatio.alternative_hypothesis - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -913,10 +913,10 @@
                   
 
 
-<h1 id="arch-unitroot-varianceratio-alternative-hypothesis">arch.unitroot.VarianceRatio.alternative_hypothesis<a class="headerlink" href="#arch-unitroot-varianceratio-alternative-hypothesis" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-varianceratio-alternative-hypothesis">arch.unitroot.VarianceRatio.alternative_hypothesis<a class="headerlink" href="#arch-unitroot-varianceratio-alternative-hypothesis" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.VarianceRatio.alternative_hypothesis">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VarianceRatio.</span></span><span class="sig-name descname"><span class="pre">alternative_hypothesis</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.VarianceRatio.alternative_hypothesis" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VarianceRatio.</span></span><span class="sig-name descname"><span class="pre">alternative_hypothesis</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.VarianceRatio.alternative_hypothesis" title="Link to this definition">¶</a></dt>
 <dd><p>The alternative hypothesis</p>
 </dd></dl>
 
@@ -987,7 +987,7 @@ <h1 id="arch-unitroot-varianceratio-alternative-hypothesis">arch.unitroot.Varian
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1034,7 +1034,7 @@ <h1 id="arch-unitroot-varianceratio-alternative-hypothesis">arch.unitroot.Varian
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.VarianceRatio.critical_values.html b/unitroot/generated/generated/arch.unitroot.VarianceRatio.critical_values.html
index 87f5f6a087..17a425f709 100644
--- a/unitroot/generated/generated/arch.unitroot.VarianceRatio.critical_values.html
+++ b/unitroot/generated/generated/arch.unitroot.VarianceRatio.critical_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.VarianceRatio.critical_values - arch 6.1.0</title>
+        <title>arch.unitroot.VarianceRatio.critical_values - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -913,10 +913,10 @@
                   
 
 
-<h1 id="arch-unitroot-varianceratio-critical-values">arch.unitroot.VarianceRatio.critical_values<a class="headerlink" href="#arch-unitroot-varianceratio-critical-values" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-varianceratio-critical-values">arch.unitroot.VarianceRatio.critical_values<a class="headerlink" href="#arch-unitroot-varianceratio-critical-values" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.VarianceRatio.critical_values">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VarianceRatio.</span></span><span class="sig-name descname"><span class="pre">critical_values</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#arch.unitroot.VarianceRatio.critical_values" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VarianceRatio.</span></span><span class="sig-name descname"><span class="pre">critical_values</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#arch.unitroot.VarianceRatio.critical_values" title="Link to this definition">¶</a></dt>
 <dd><p>Dictionary containing critical values specific to the test, number of
 observations and included deterministic trend terms.</p>
 </dd></dl>
@@ -988,7 +988,7 @@ <h1 id="arch-unitroot-varianceratio-critical-values">arch.unitroot.VarianceRatio
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1035,7 +1035,7 @@ <h1 id="arch-unitroot-varianceratio-critical-values">arch.unitroot.VarianceRatio
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.VarianceRatio.debiased.html b/unitroot/generated/generated/arch.unitroot.VarianceRatio.debiased.html
index ef8c72ef0d..50f97fc5fc 100644
--- a/unitroot/generated/generated/arch.unitroot.VarianceRatio.debiased.html
+++ b/unitroot/generated/generated/arch.unitroot.VarianceRatio.debiased.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.VarianceRatio.debiased - arch 6.1.0</title>
+        <title>arch.unitroot.VarianceRatio.debiased - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -913,10 +913,10 @@
                   
 
 
-<h1 id="arch-unitroot-varianceratio-debiased">arch.unitroot.VarianceRatio.debiased<a class="headerlink" href="#arch-unitroot-varianceratio-debiased" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-varianceratio-debiased">arch.unitroot.VarianceRatio.debiased<a class="headerlink" href="#arch-unitroot-varianceratio-debiased" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.VarianceRatio.debiased">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VarianceRatio.</span></span><span class="sig-name descname"><span class="pre">debiased</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.unitroot.VarianceRatio.debiased" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VarianceRatio.</span></span><span class="sig-name descname"><span class="pre">debiased</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.unitroot.VarianceRatio.debiased" title="Link to this definition">¶</a></dt>
 <dd><p>Sets of gets the indicator to use debiased variances in the ratio</p>
 </dd></dl>
 
@@ -987,7 +987,7 @@ <h1 id="arch-unitroot-varianceratio-debiased">arch.unitroot.VarianceRatio.debias
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1034,7 +1034,7 @@ <h1 id="arch-unitroot-varianceratio-debiased">arch.unitroot.VarianceRatio.debias
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.VarianceRatio.lags.html b/unitroot/generated/generated/arch.unitroot.VarianceRatio.lags.html
index 93a6eb4380..112f98f9a4 100644
--- a/unitroot/generated/generated/arch.unitroot.VarianceRatio.lags.html
+++ b/unitroot/generated/generated/arch.unitroot.VarianceRatio.lags.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.VarianceRatio.lags - arch 6.1.0</title>
+        <title>arch.unitroot.VarianceRatio.lags - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -913,10 +913,10 @@
                   
 
 
-<h1 id="arch-unitroot-varianceratio-lags">arch.unitroot.VarianceRatio.lags<a class="headerlink" href="#arch-unitroot-varianceratio-lags" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-varianceratio-lags">arch.unitroot.VarianceRatio.lags<a class="headerlink" href="#arch-unitroot-varianceratio-lags" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.VarianceRatio.lags">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VarianceRatio.</span></span><span class="sig-name descname"><span class="pre">lags</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.unitroot.VarianceRatio.lags" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VarianceRatio.</span></span><span class="sig-name descname"><span class="pre">lags</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.unitroot.VarianceRatio.lags" title="Link to this definition">¶</a></dt>
 <dd><p>Sets or gets the number of lags used in the model.
 When bootstrap use DF-type regressions, lags is the number of lags in the
 regression model.  When bootstrap use long-run variance estimators, lags
@@ -990,7 +990,7 @@ <h1 id="arch-unitroot-varianceratio-lags">arch.unitroot.VarianceRatio.lags<a cla
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1037,7 +1037,7 @@ <h1 id="arch-unitroot-varianceratio-lags">arch.unitroot.VarianceRatio.lags<a cla
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.VarianceRatio.nobs.html b/unitroot/generated/generated/arch.unitroot.VarianceRatio.nobs.html
index 3304b384a1..8c38584847 100644
--- a/unitroot/generated/generated/arch.unitroot.VarianceRatio.nobs.html
+++ b/unitroot/generated/generated/arch.unitroot.VarianceRatio.nobs.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.VarianceRatio.nobs - arch 6.1.0</title>
+        <title>arch.unitroot.VarianceRatio.nobs - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -913,10 +913,10 @@
                   
 
 
-<h1 id="arch-unitroot-varianceratio-nobs">arch.unitroot.VarianceRatio.nobs<a class="headerlink" href="#arch-unitroot-varianceratio-nobs" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-varianceratio-nobs">arch.unitroot.VarianceRatio.nobs<a class="headerlink" href="#arch-unitroot-varianceratio-nobs" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.VarianceRatio.nobs">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VarianceRatio.</span></span><span class="sig-name descname"><span class="pre">nobs</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.unitroot.VarianceRatio.nobs" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VarianceRatio.</span></span><span class="sig-name descname"><span class="pre">nobs</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.unitroot.VarianceRatio.nobs" title="Link to this definition">¶</a></dt>
 <dd><p>The number of observations used when computing the test statistic.
 Accounts for loss of data due to lags for regression-based bootstrap.</p>
 </dd></dl>
@@ -988,7 +988,7 @@ <h1 id="arch-unitroot-varianceratio-nobs">arch.unitroot.VarianceRatio.nobs<a cla
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1035,7 +1035,7 @@ <h1 id="arch-unitroot-varianceratio-nobs">arch.unitroot.VarianceRatio.nobs<a cla
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.VarianceRatio.null_hypothesis.html b/unitroot/generated/generated/arch.unitroot.VarianceRatio.null_hypothesis.html
index 216b86c2ea..8156c64bab 100644
--- a/unitroot/generated/generated/arch.unitroot.VarianceRatio.null_hypothesis.html
+++ b/unitroot/generated/generated/arch.unitroot.VarianceRatio.null_hypothesis.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.VarianceRatio.null_hypothesis - arch 6.1.0</title>
+        <title>arch.unitroot.VarianceRatio.null_hypothesis - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -913,10 +913,10 @@
                   
 
 
-<h1 id="arch-unitroot-varianceratio-null-hypothesis">arch.unitroot.VarianceRatio.null_hypothesis<a class="headerlink" href="#arch-unitroot-varianceratio-null-hypothesis" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-varianceratio-null-hypothesis">arch.unitroot.VarianceRatio.null_hypothesis<a class="headerlink" href="#arch-unitroot-varianceratio-null-hypothesis" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.VarianceRatio.null_hypothesis">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VarianceRatio.</span></span><span class="sig-name descname"><span class="pre">null_hypothesis</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.VarianceRatio.null_hypothesis" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VarianceRatio.</span></span><span class="sig-name descname"><span class="pre">null_hypothesis</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.VarianceRatio.null_hypothesis" title="Link to this definition">¶</a></dt>
 <dd><p>The null hypothesis</p>
 </dd></dl>
 
@@ -987,7 +987,7 @@ <h1 id="arch-unitroot-varianceratio-null-hypothesis">arch.unitroot.VarianceRatio
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1034,7 +1034,7 @@ <h1 id="arch-unitroot-varianceratio-null-hypothesis">arch.unitroot.VarianceRatio
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.VarianceRatio.overlap.html b/unitroot/generated/generated/arch.unitroot.VarianceRatio.overlap.html
index cedb1bc590..be8cf2ea09 100644
--- a/unitroot/generated/generated/arch.unitroot.VarianceRatio.overlap.html
+++ b/unitroot/generated/generated/arch.unitroot.VarianceRatio.overlap.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.VarianceRatio.overlap - arch 6.1.0</title>
+        <title>arch.unitroot.VarianceRatio.overlap - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -913,10 +913,10 @@
                   
 
 
-<h1 id="arch-unitroot-varianceratio-overlap">arch.unitroot.VarianceRatio.overlap<a class="headerlink" href="#arch-unitroot-varianceratio-overlap" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-varianceratio-overlap">arch.unitroot.VarianceRatio.overlap<a class="headerlink" href="#arch-unitroot-varianceratio-overlap" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.VarianceRatio.overlap">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VarianceRatio.</span></span><span class="sig-name descname"><span class="pre">overlap</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.unitroot.VarianceRatio.overlap" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VarianceRatio.</span></span><span class="sig-name descname"><span class="pre">overlap</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.unitroot.VarianceRatio.overlap" title="Link to this definition">¶</a></dt>
 <dd><p>Sets of gets the indicator to use overlapping returns in the
 long-period variance estimator</p>
 </dd></dl>
@@ -988,7 +988,7 @@ <h1 id="arch-unitroot-varianceratio-overlap">arch.unitroot.VarianceRatio.overlap
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1035,7 +1035,7 @@ <h1 id="arch-unitroot-varianceratio-overlap">arch.unitroot.VarianceRatio.overlap
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.VarianceRatio.pvalue.html b/unitroot/generated/generated/arch.unitroot.VarianceRatio.pvalue.html
index c0b50957fa..fa2412d76e 100644
--- a/unitroot/generated/generated/arch.unitroot.VarianceRatio.pvalue.html
+++ b/unitroot/generated/generated/arch.unitroot.VarianceRatio.pvalue.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.VarianceRatio.pvalue - arch 6.1.0</title>
+        <title>arch.unitroot.VarianceRatio.pvalue - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -913,10 +913,10 @@
                   
 
 
-<h1 id="arch-unitroot-varianceratio-pvalue">arch.unitroot.VarianceRatio.pvalue<a class="headerlink" href="#arch-unitroot-varianceratio-pvalue" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-varianceratio-pvalue">arch.unitroot.VarianceRatio.pvalue<a class="headerlink" href="#arch-unitroot-varianceratio-pvalue" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.VarianceRatio.pvalue">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VarianceRatio.</span></span><span class="sig-name descname"><span class="pre">pvalue</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.VarianceRatio.pvalue" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VarianceRatio.</span></span><span class="sig-name descname"><span class="pre">pvalue</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.VarianceRatio.pvalue" title="Link to this definition">¶</a></dt>
 <dd><p>Returns the p-value for the test statistic</p>
 </dd></dl>
 
@@ -987,7 +987,7 @@ <h1 id="arch-unitroot-varianceratio-pvalue">arch.unitroot.VarianceRatio.pvalue<a
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1034,7 +1034,7 @@ <h1 id="arch-unitroot-varianceratio-pvalue">arch.unitroot.VarianceRatio.pvalue<a
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.VarianceRatio.robust.html b/unitroot/generated/generated/arch.unitroot.VarianceRatio.robust.html
index 7794b18c19..f2627d3182 100644
--- a/unitroot/generated/generated/arch.unitroot.VarianceRatio.robust.html
+++ b/unitroot/generated/generated/arch.unitroot.VarianceRatio.robust.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.VarianceRatio.robust - arch 6.1.0</title>
+        <title>arch.unitroot.VarianceRatio.robust - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -913,10 +913,10 @@
                   
 
 
-<h1 id="arch-unitroot-varianceratio-robust">arch.unitroot.VarianceRatio.robust<a class="headerlink" href="#arch-unitroot-varianceratio-robust" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-varianceratio-robust">arch.unitroot.VarianceRatio.robust<a class="headerlink" href="#arch-unitroot-varianceratio-robust" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.VarianceRatio.robust">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VarianceRatio.</span></span><span class="sig-name descname"><span class="pre">robust</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.unitroot.VarianceRatio.robust" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VarianceRatio.</span></span><span class="sig-name descname"><span class="pre">robust</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.unitroot.VarianceRatio.robust" title="Link to this definition">¶</a></dt>
 <dd><p>Sets of gets the indicator to use a heteroskedasticity robust
 variance estimator</p>
 </dd></dl>
@@ -988,7 +988,7 @@ <h1 id="arch-unitroot-varianceratio-robust">arch.unitroot.VarianceRatio.robust<a
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1035,7 +1035,7 @@ <h1 id="arch-unitroot-varianceratio-robust">arch.unitroot.VarianceRatio.robust<a
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.VarianceRatio.stat.html b/unitroot/generated/generated/arch.unitroot.VarianceRatio.stat.html
index 03ddd8663a..80e7bc2052 100644
--- a/unitroot/generated/generated/arch.unitroot.VarianceRatio.stat.html
+++ b/unitroot/generated/generated/arch.unitroot.VarianceRatio.stat.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.VarianceRatio.stat - arch 6.1.0</title>
+        <title>arch.unitroot.VarianceRatio.stat - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -913,10 +913,10 @@
                   
 
 
-<h1 id="arch-unitroot-varianceratio-stat">arch.unitroot.VarianceRatio.stat<a class="headerlink" href="#arch-unitroot-varianceratio-stat" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-varianceratio-stat">arch.unitroot.VarianceRatio.stat<a class="headerlink" href="#arch-unitroot-varianceratio-stat" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.VarianceRatio.stat">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VarianceRatio.</span></span><span class="sig-name descname"><span class="pre">stat</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.VarianceRatio.stat" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VarianceRatio.</span></span><span class="sig-name descname"><span class="pre">stat</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.VarianceRatio.stat" title="Link to this definition">¶</a></dt>
 <dd><p>The test statistic for a unit root</p>
 </dd></dl>
 
@@ -987,7 +987,7 @@ <h1 id="arch-unitroot-varianceratio-stat">arch.unitroot.VarianceRatio.stat<a cla
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1034,7 +1034,7 @@ <h1 id="arch-unitroot-varianceratio-stat">arch.unitroot.VarianceRatio.stat<a cla
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.VarianceRatio.summary.html b/unitroot/generated/generated/arch.unitroot.VarianceRatio.summary.html
index bf48f3390a..4c8df59ec0 100644
--- a/unitroot/generated/generated/arch.unitroot.VarianceRatio.summary.html
+++ b/unitroot/generated/generated/arch.unitroot.VarianceRatio.summary.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.VarianceRatio.summary - arch 6.1.0</title>
+        <title>arch.unitroot.VarianceRatio.summary - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -521,19 +521,6 @@
     <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="arch.unitroot.VarianceRatio.summary (Python method) — Summary of test, containing statistic, p-value and critical values" class="md-ellipsis">Variance<wbr>Ratio.<wbr>summary</span>
   </a>
   
-    <nav class="md-nav" aria-label="VarianceRatio.summary">
-      <ul class="md-nav__list">
-        
-          <li class="md-nav__item">
-  <a href="#arch.unitroot.VarianceRatio.summary-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
-</li>
-        
-      </ul>
-    </nav>
-  
 </li>
       
     </ul>
@@ -910,19 +897,6 @@
     <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="arch.unitroot.VarianceRatio.summary (Python method) — Summary of test, containing statistic, p-value and critical values" class="md-ellipsis">Variance<wbr>Ratio.<wbr>summary</span>
   </a>
   
-    <nav class="md-nav" aria-label="VarianceRatio.summary">
-      <ul class="md-nav__list">
-        
-          <li class="md-nav__item">
-  <a href="#arch.unitroot.VarianceRatio.summary-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
-</li>
-        
-      </ul>
-    </nav>
-  
 </li>
       
     </ul>
@@ -939,16 +913,11 @@
                   
 
 
-<h1 id="arch-unitroot-varianceratio-summary">arch.unitroot.VarianceRatio.summary<a class="headerlink" href="#arch-unitroot-varianceratio-summary" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-varianceratio-summary">arch.unitroot.VarianceRatio.summary<a class="headerlink" href="#arch-unitroot-varianceratio-summary" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.VarianceRatio.summary">
-<span class="sig-prename descclassname"><span class="pre">VarianceRatio.</span></span><span class="sig-name descname"><span class="pre">summary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#arch.unitroot.VarianceRatio.summary" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">VarianceRatio.</span></span><span class="sig-name descname"><span class="pre">summary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.iolib.summary.Summary.html#statsmodels.iolib.summary.Summary" title="(in statsmodels 0.15.0 (+59) v0.15.0 (+59))"><span class="pre">Summary</span></a></span></span></span><a class="headerlink" href="#arch.unitroot.VarianceRatio.summary" title="Link to this definition">¶</a></dt>
 <dd><p>Summary of test, containing statistic, p-value and critical values</p>
-<dl class="field-list simple">
-<dt class="field-odd" id="arch.unitroot.VarianceRatio.summary-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.VarianceRatio.summary-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.iolib.summary.Summary.html#statsmodels.iolib.summary.Summary" title="(in statsmodels 0.15.0 (+8) v0.15.0 (+8))"><code class="xref py py-class docutils literal notranslate"><span class="pre">Summary</span></code></a></p>
-</dd>
-</dl>
 </dd></dl>
 
 
@@ -1018,7 +987,7 @@ <h1 id="arch-unitroot-varianceratio-summary">arch.unitroot.VarianceRatio.summary
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1065,7 +1034,7 @@ <h1 id="arch-unitroot-varianceratio-summary">arch.unitroot.VarianceRatio.summary
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.VarianceRatio.trend.html b/unitroot/generated/generated/arch.unitroot.VarianceRatio.trend.html
index 385a165fcc..b3a2c4da25 100644
--- a/unitroot/generated/generated/arch.unitroot.VarianceRatio.trend.html
+++ b/unitroot/generated/generated/arch.unitroot.VarianceRatio.trend.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.VarianceRatio.trend - arch 6.1.0</title>
+        <title>arch.unitroot.VarianceRatio.trend - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -913,10 +913,10 @@
                   
 
 
-<h1 id="arch-unitroot-varianceratio-trend">arch.unitroot.VarianceRatio.trend<a class="headerlink" href="#arch-unitroot-varianceratio-trend" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-varianceratio-trend">arch.unitroot.VarianceRatio.trend<a class="headerlink" href="#arch-unitroot-varianceratio-trend" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.VarianceRatio.trend">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VarianceRatio.</span></span><span class="sig-name descname"><span class="pre">trend</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.VarianceRatio.trend" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VarianceRatio.</span></span><span class="sig-name descname"><span class="pre">trend</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.VarianceRatio.trend" title="Link to this definition">¶</a></dt>
 <dd><p>Sets or gets the deterministic trend term used in the test. See
 valid_trends for a list of supported trends</p>
 </dd></dl>
@@ -988,7 +988,7 @@ <h1 id="arch-unitroot-varianceratio-trend">arch.unitroot.VarianceRatio.trend<a c
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1035,7 +1035,7 @@ <h1 id="arch-unitroot-varianceratio-trend">arch.unitroot.VarianceRatio.trend<a c
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.VarianceRatio.valid_trends.html b/unitroot/generated/generated/arch.unitroot.VarianceRatio.valid_trends.html
index 4ecca4db72..0527ab05cf 100644
--- a/unitroot/generated/generated/arch.unitroot.VarianceRatio.valid_trends.html
+++ b/unitroot/generated/generated/arch.unitroot.VarianceRatio.valid_trends.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.VarianceRatio.valid_trends - arch 6.1.0</title>
+        <title>arch.unitroot.VarianceRatio.valid_trends - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -913,10 +913,10 @@
                   
 
 
-<h1 id="arch-unitroot-varianceratio-valid-trends">arch.unitroot.VarianceRatio.valid_trends<a class="headerlink" href="#arch-unitroot-varianceratio-valid-trends" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-varianceratio-valid-trends">arch.unitroot.VarianceRatio.valid_trends<a class="headerlink" href="#arch-unitroot-varianceratio-valid-trends" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.VarianceRatio.valid_trends">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VarianceRatio.</span></span><span class="sig-name descname"><span class="pre">valid_trends</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#arch.unitroot.VarianceRatio.valid_trends" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VarianceRatio.</span></span><span class="sig-name descname"><span class="pre">valid_trends</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#arch.unitroot.VarianceRatio.valid_trends" title="Link to this definition">¶</a></dt>
 <dd><p>List of valid trend terms.</p>
 </dd></dl>
 
@@ -987,7 +987,7 @@ <h1 id="arch-unitroot-varianceratio-valid-trends">arch.unitroot.VarianceRatio.va
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1034,7 +1034,7 @@ <h1 id="arch-unitroot-varianceratio-valid-trends">arch.unitroot.VarianceRatio.va
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.VarianceRatio.vr.html b/unitroot/generated/generated/arch.unitroot.VarianceRatio.vr.html
index 0bc3209324..1e9751d761 100644
--- a/unitroot/generated/generated/arch.unitroot.VarianceRatio.vr.html
+++ b/unitroot/generated/generated/arch.unitroot.VarianceRatio.vr.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.VarianceRatio.vr - arch 6.1.0</title>
+        <title>arch.unitroot.VarianceRatio.vr - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -913,10 +913,10 @@
                   
 
 
-<h1 id="arch-unitroot-varianceratio-vr">arch.unitroot.VarianceRatio.vr<a class="headerlink" href="#arch-unitroot-varianceratio-vr" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-varianceratio-vr">arch.unitroot.VarianceRatio.vr<a class="headerlink" href="#arch-unitroot-varianceratio-vr" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.VarianceRatio.vr">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VarianceRatio.</span></span><span class="sig-name descname"><span class="pre">vr</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.VarianceRatio.vr" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VarianceRatio.</span></span><span class="sig-name descname"><span class="pre">vr</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.VarianceRatio.vr" title="Link to this definition">¶</a></dt>
 <dd><p>The ratio of the long block lags-period variance
 to the 1-period variance</p>
 </dd></dl>
@@ -988,7 +988,7 @@ <h1 id="arch-unitroot-varianceratio-vr">arch.unitroot.VarianceRatio.vr<a class="
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1035,7 +1035,7 @@ <h1 id="arch-unitroot-varianceratio-vr">arch.unitroot.VarianceRatio.vr<a class="
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.VarianceRatio.y.html b/unitroot/generated/generated/arch.unitroot.VarianceRatio.y.html
index d5a9441312..50f5da7c13 100644
--- a/unitroot/generated/generated/arch.unitroot.VarianceRatio.y.html
+++ b/unitroot/generated/generated/arch.unitroot.VarianceRatio.y.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.VarianceRatio.y - arch 6.1.0</title>
+        <title>arch.unitroot.VarianceRatio.y - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -913,10 +913,10 @@
                   
 
 
-<h1 id="arch-unitroot-varianceratio-y">arch.unitroot.VarianceRatio.y<a class="headerlink" href="#arch-unitroot-varianceratio-y" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-varianceratio-y">arch.unitroot.VarianceRatio.y<a class="headerlink" href="#arch-unitroot-varianceratio-y" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.VarianceRatio.y">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VarianceRatio.</span></span><span class="sig-name descname"><span class="pre">y</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.0.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.0.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.VarianceRatio.y" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VarianceRatio.</span></span><span class="sig-name descname"><span class="pre">y</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.VarianceRatio.y" title="Link to this definition">¶</a></dt>
 <dd><p>Returns the data used in the test statistic</p>
 </dd></dl>
 
@@ -987,7 +987,7 @@ <h1 id="arch-unitroot-varianceratio-y">arch.unitroot.VarianceRatio.y<a class="he
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1034,7 +1034,7 @@ <h1 id="arch-unitroot-varianceratio-y">arch.unitroot.VarianceRatio.y<a class="he
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.ZivotAndrews.alternative_hypothesis.html b/unitroot/generated/generated/arch.unitroot.ZivotAndrews.alternative_hypothesis.html
index 993f99d28b..1df21c360f 100644
--- a/unitroot/generated/generated/arch.unitroot.ZivotAndrews.alternative_hypothesis.html
+++ b/unitroot/generated/generated/arch.unitroot.ZivotAndrews.alternative_hypothesis.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.ZivotAndrews.alternative_hypothesis - arch 6.1.0</title>
+        <title>arch.unitroot.ZivotAndrews.alternative_hypothesis - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -853,10 +853,10 @@
                   
 
 
-<h1 id="arch-unitroot-zivotandrews-alternative-hypothesis">arch.unitroot.ZivotAndrews.alternative_hypothesis<a class="headerlink" href="#arch-unitroot-zivotandrews-alternative-hypothesis" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-zivotandrews-alternative-hypothesis">arch.unitroot.ZivotAndrews.alternative_hypothesis<a class="headerlink" href="#arch-unitroot-zivotandrews-alternative-hypothesis" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.ZivotAndrews.alternative_hypothesis">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ZivotAndrews.</span></span><span class="sig-name descname"><span class="pre">alternative_hypothesis</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.ZivotAndrews.alternative_hypothesis" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ZivotAndrews.</span></span><span class="sig-name descname"><span class="pre">alternative_hypothesis</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.ZivotAndrews.alternative_hypothesis" title="Link to this definition">¶</a></dt>
 <dd><p>The alternative hypothesis</p>
 </dd></dl>
 
@@ -927,7 +927,7 @@ <h1 id="arch-unitroot-zivotandrews-alternative-hypothesis">arch.unitroot.ZivotAn
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -974,7 +974,7 @@ <h1 id="arch-unitroot-zivotandrews-alternative-hypothesis">arch.unitroot.ZivotAn
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.ZivotAndrews.critical_values.html b/unitroot/generated/generated/arch.unitroot.ZivotAndrews.critical_values.html
index 486f2c106f..fa185bbbf2 100644
--- a/unitroot/generated/generated/arch.unitroot.ZivotAndrews.critical_values.html
+++ b/unitroot/generated/generated/arch.unitroot.ZivotAndrews.critical_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.ZivotAndrews.critical_values - arch 6.1.0</title>
+        <title>arch.unitroot.ZivotAndrews.critical_values - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -853,10 +853,10 @@
                   
 
 
-<h1 id="arch-unitroot-zivotandrews-critical-values">arch.unitroot.ZivotAndrews.critical_values<a class="headerlink" href="#arch-unitroot-zivotandrews-critical-values" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-zivotandrews-critical-values">arch.unitroot.ZivotAndrews.critical_values<a class="headerlink" href="#arch-unitroot-zivotandrews-critical-values" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.ZivotAndrews.critical_values">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ZivotAndrews.</span></span><span class="sig-name descname"><span class="pre">critical_values</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#arch.unitroot.ZivotAndrews.critical_values" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ZivotAndrews.</span></span><span class="sig-name descname"><span class="pre">critical_values</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#arch.unitroot.ZivotAndrews.critical_values" title="Link to this definition">¶</a></dt>
 <dd><p>Dictionary containing critical values specific to the test, number of
 observations and included deterministic trend terms.</p>
 </dd></dl>
@@ -928,7 +928,7 @@ <h1 id="arch-unitroot-zivotandrews-critical-values">arch.unitroot.ZivotAndrews.c
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -975,7 +975,7 @@ <h1 id="arch-unitroot-zivotandrews-critical-values">arch.unitroot.ZivotAndrews.c
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.ZivotAndrews.lags.html b/unitroot/generated/generated/arch.unitroot.ZivotAndrews.lags.html
index 3b33448e79..7b1c63abe2 100644
--- a/unitroot/generated/generated/arch.unitroot.ZivotAndrews.lags.html
+++ b/unitroot/generated/generated/arch.unitroot.ZivotAndrews.lags.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.ZivotAndrews.lags - arch 6.1.0</title>
+        <title>arch.unitroot.ZivotAndrews.lags - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -853,10 +853,10 @@
                   
 
 
-<h1 id="arch-unitroot-zivotandrews-lags">arch.unitroot.ZivotAndrews.lags<a class="headerlink" href="#arch-unitroot-zivotandrews-lags" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-zivotandrews-lags">arch.unitroot.ZivotAndrews.lags<a class="headerlink" href="#arch-unitroot-zivotandrews-lags" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.ZivotAndrews.lags">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ZivotAndrews.</span></span><span class="sig-name descname"><span class="pre">lags</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.unitroot.ZivotAndrews.lags" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ZivotAndrews.</span></span><span class="sig-name descname"><span class="pre">lags</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.unitroot.ZivotAndrews.lags" title="Link to this definition">¶</a></dt>
 <dd><p>Sets or gets the number of lags used in the model.
 When bootstrap use DF-type regressions, lags is the number of lags in the
 regression model.  When bootstrap use long-run variance estimators, lags
@@ -930,7 +930,7 @@ <h1 id="arch-unitroot-zivotandrews-lags">arch.unitroot.ZivotAndrews.lags<a class
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -977,7 +977,7 @@ <h1 id="arch-unitroot-zivotandrews-lags">arch.unitroot.ZivotAndrews.lags<a class
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.ZivotAndrews.nobs.html b/unitroot/generated/generated/arch.unitroot.ZivotAndrews.nobs.html
index 75c6a663cb..c37127edf5 100644
--- a/unitroot/generated/generated/arch.unitroot.ZivotAndrews.nobs.html
+++ b/unitroot/generated/generated/arch.unitroot.ZivotAndrews.nobs.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.ZivotAndrews.nobs - arch 6.1.0</title>
+        <title>arch.unitroot.ZivotAndrews.nobs - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -853,10 +853,10 @@
                   
 
 
-<h1 id="arch-unitroot-zivotandrews-nobs">arch.unitroot.ZivotAndrews.nobs<a class="headerlink" href="#arch-unitroot-zivotandrews-nobs" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-zivotandrews-nobs">arch.unitroot.ZivotAndrews.nobs<a class="headerlink" href="#arch-unitroot-zivotandrews-nobs" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.ZivotAndrews.nobs">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ZivotAndrews.</span></span><span class="sig-name descname"><span class="pre">nobs</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.unitroot.ZivotAndrews.nobs" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ZivotAndrews.</span></span><span class="sig-name descname"><span class="pre">nobs</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.unitroot.ZivotAndrews.nobs" title="Link to this definition">¶</a></dt>
 <dd><p>The number of observations used when computing the test statistic.
 Accounts for loss of data due to lags for regression-based bootstrap.</p>
 </dd></dl>
@@ -928,7 +928,7 @@ <h1 id="arch-unitroot-zivotandrews-nobs">arch.unitroot.ZivotAndrews.nobs<a class
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -975,7 +975,7 @@ <h1 id="arch-unitroot-zivotandrews-nobs">arch.unitroot.ZivotAndrews.nobs<a class
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.ZivotAndrews.null_hypothesis.html b/unitroot/generated/generated/arch.unitroot.ZivotAndrews.null_hypothesis.html
index eb3298a5d7..dcb5e3fe2f 100644
--- a/unitroot/generated/generated/arch.unitroot.ZivotAndrews.null_hypothesis.html
+++ b/unitroot/generated/generated/arch.unitroot.ZivotAndrews.null_hypothesis.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.ZivotAndrews.null_hypothesis - arch 6.1.0</title>
+        <title>arch.unitroot.ZivotAndrews.null_hypothesis - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -853,10 +853,10 @@
                   
 
 
-<h1 id="arch-unitroot-zivotandrews-null-hypothesis">arch.unitroot.ZivotAndrews.null_hypothesis<a class="headerlink" href="#arch-unitroot-zivotandrews-null-hypothesis" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-zivotandrews-null-hypothesis">arch.unitroot.ZivotAndrews.null_hypothesis<a class="headerlink" href="#arch-unitroot-zivotandrews-null-hypothesis" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.ZivotAndrews.null_hypothesis">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ZivotAndrews.</span></span><span class="sig-name descname"><span class="pre">null_hypothesis</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.ZivotAndrews.null_hypothesis" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ZivotAndrews.</span></span><span class="sig-name descname"><span class="pre">null_hypothesis</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.ZivotAndrews.null_hypothesis" title="Link to this definition">¶</a></dt>
 <dd><p>The null hypothesis</p>
 </dd></dl>
 
@@ -927,7 +927,7 @@ <h1 id="arch-unitroot-zivotandrews-null-hypothesis">arch.unitroot.ZivotAndrews.n
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -974,7 +974,7 @@ <h1 id="arch-unitroot-zivotandrews-null-hypothesis">arch.unitroot.ZivotAndrews.n
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.ZivotAndrews.pvalue.html b/unitroot/generated/generated/arch.unitroot.ZivotAndrews.pvalue.html
index 0bcf553ce3..7218124d3e 100644
--- a/unitroot/generated/generated/arch.unitroot.ZivotAndrews.pvalue.html
+++ b/unitroot/generated/generated/arch.unitroot.ZivotAndrews.pvalue.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.ZivotAndrews.pvalue - arch 6.1.0</title>
+        <title>arch.unitroot.ZivotAndrews.pvalue - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -853,10 +853,10 @@
                   
 
 
-<h1 id="arch-unitroot-zivotandrews-pvalue">arch.unitroot.ZivotAndrews.pvalue<a class="headerlink" href="#arch-unitroot-zivotandrews-pvalue" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-zivotandrews-pvalue">arch.unitroot.ZivotAndrews.pvalue<a class="headerlink" href="#arch-unitroot-zivotandrews-pvalue" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.ZivotAndrews.pvalue">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ZivotAndrews.</span></span><span class="sig-name descname"><span class="pre">pvalue</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.ZivotAndrews.pvalue" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ZivotAndrews.</span></span><span class="sig-name descname"><span class="pre">pvalue</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.ZivotAndrews.pvalue" title="Link to this definition">¶</a></dt>
 <dd><p>Returns the p-value for the test statistic</p>
 </dd></dl>
 
@@ -927,7 +927,7 @@ <h1 id="arch-unitroot-zivotandrews-pvalue">arch.unitroot.ZivotAndrews.pvalue<a c
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -974,7 +974,7 @@ <h1 id="arch-unitroot-zivotandrews-pvalue">arch.unitroot.ZivotAndrews.pvalue<a c
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.ZivotAndrews.stat.html b/unitroot/generated/generated/arch.unitroot.ZivotAndrews.stat.html
index 50ac6d71ba..f97dfd3f94 100644
--- a/unitroot/generated/generated/arch.unitroot.ZivotAndrews.stat.html
+++ b/unitroot/generated/generated/arch.unitroot.ZivotAndrews.stat.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.ZivotAndrews.stat - arch 6.1.0</title>
+        <title>arch.unitroot.ZivotAndrews.stat - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -853,10 +853,10 @@
                   
 
 
-<h1 id="arch-unitroot-zivotandrews-stat">arch.unitroot.ZivotAndrews.stat<a class="headerlink" href="#arch-unitroot-zivotandrews-stat" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-zivotandrews-stat">arch.unitroot.ZivotAndrews.stat<a class="headerlink" href="#arch-unitroot-zivotandrews-stat" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.ZivotAndrews.stat">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ZivotAndrews.</span></span><span class="sig-name descname"><span class="pre">stat</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.ZivotAndrews.stat" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ZivotAndrews.</span></span><span class="sig-name descname"><span class="pre">stat</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.ZivotAndrews.stat" title="Link to this definition">¶</a></dt>
 <dd><p>The test statistic for a unit root</p>
 </dd></dl>
 
@@ -927,7 +927,7 @@ <h1 id="arch-unitroot-zivotandrews-stat">arch.unitroot.ZivotAndrews.stat<a class
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -974,7 +974,7 @@ <h1 id="arch-unitroot-zivotandrews-stat">arch.unitroot.ZivotAndrews.stat<a class
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.ZivotAndrews.summary.html b/unitroot/generated/generated/arch.unitroot.ZivotAndrews.summary.html
index 6e4ec89c30..460a814cac 100644
--- a/unitroot/generated/generated/arch.unitroot.ZivotAndrews.summary.html
+++ b/unitroot/generated/generated/arch.unitroot.ZivotAndrews.summary.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.ZivotAndrews.summary - arch 6.1.0</title>
+        <title>arch.unitroot.ZivotAndrews.summary - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -506,19 +506,6 @@
     <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="arch.unitroot.ZivotAndrews.summary (Python method) — Summary of test, containing statistic, p-value and critical values" class="md-ellipsis">Zivot<wbr>Andrews.<wbr>summary</span>
   </a>
   
-    <nav class="md-nav" aria-label="ZivotAndrews.summary">
-      <ul class="md-nav__list">
-        
-          <li class="md-nav__item">
-  <a href="#arch.unitroot.ZivotAndrews.summary-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
-</li>
-        
-      </ul>
-    </nav>
-  
 </li>
       
     </ul>
@@ -850,19 +837,6 @@
     <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="arch.unitroot.ZivotAndrews.summary (Python method) — Summary of test, containing statistic, p-value and critical values" class="md-ellipsis">Zivot<wbr>Andrews.<wbr>summary</span>
   </a>
   
-    <nav class="md-nav" aria-label="ZivotAndrews.summary">
-      <ul class="md-nav__list">
-        
-          <li class="md-nav__item">
-  <a href="#arch.unitroot.ZivotAndrews.summary-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
-</li>
-        
-      </ul>
-    </nav>
-  
 </li>
       
     </ul>
@@ -879,16 +853,11 @@
                   
 
 
-<h1 id="arch-unitroot-zivotandrews-summary">arch.unitroot.ZivotAndrews.summary<a class="headerlink" href="#arch-unitroot-zivotandrews-summary" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-zivotandrews-summary">arch.unitroot.ZivotAndrews.summary<a class="headerlink" href="#arch-unitroot-zivotandrews-summary" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.ZivotAndrews.summary">
-<span class="sig-prename descclassname"><span class="pre">ZivotAndrews.</span></span><span class="sig-name descname"><span class="pre">summary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#arch.unitroot.ZivotAndrews.summary" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ZivotAndrews.</span></span><span class="sig-name descname"><span class="pre">summary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.iolib.summary.Summary.html#statsmodels.iolib.summary.Summary" title="(in statsmodels 0.15.0 (+59) v0.15.0 (+59))"><span class="pre">Summary</span></a></span></span></span><a class="headerlink" href="#arch.unitroot.ZivotAndrews.summary" title="Link to this definition">¶</a></dt>
 <dd><p>Summary of test, containing statistic, p-value and critical values</p>
-<dl class="field-list simple">
-<dt class="field-odd" id="arch.unitroot.ZivotAndrews.summary-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.ZivotAndrews.summary-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.iolib.summary.Summary.html#statsmodels.iolib.summary.Summary" title="(in statsmodels 0.15.0 (+8) v0.15.0 (+8))"><code class="xref py py-class docutils literal notranslate"><span class="pre">Summary</span></code></a></p>
-</dd>
-</dl>
 </dd></dl>
 
 
@@ -958,7 +927,7 @@ <h1 id="arch-unitroot-zivotandrews-summary">arch.unitroot.ZivotAndrews.summary<a
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1005,7 +974,7 @@ <h1 id="arch-unitroot-zivotandrews-summary">arch.unitroot.ZivotAndrews.summary<a
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
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diff --git a/unitroot/generated/generated/arch.unitroot.ZivotAndrews.trend.html b/unitroot/generated/generated/arch.unitroot.ZivotAndrews.trend.html
index f0686539fa..ad442deec5 100644
--- a/unitroot/generated/generated/arch.unitroot.ZivotAndrews.trend.html
+++ b/unitroot/generated/generated/arch.unitroot.ZivotAndrews.trend.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.ZivotAndrews.trend - arch 6.1.0</title>
+        <title>arch.unitroot.ZivotAndrews.trend - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -853,10 +853,10 @@
                   
 
 
-<h1 id="arch-unitroot-zivotandrews-trend">arch.unitroot.ZivotAndrews.trend<a class="headerlink" href="#arch-unitroot-zivotandrews-trend" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-zivotandrews-trend">arch.unitroot.ZivotAndrews.trend<a class="headerlink" href="#arch-unitroot-zivotandrews-trend" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.ZivotAndrews.trend">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ZivotAndrews.</span></span><span class="sig-name descname"><span class="pre">trend</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.ZivotAndrews.trend" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ZivotAndrews.</span></span><span class="sig-name descname"><span class="pre">trend</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.ZivotAndrews.trend" title="Link to this definition">¶</a></dt>
 <dd><p>Sets or gets the deterministic trend term used in the test. See
 valid_trends for a list of supported trends</p>
 </dd></dl>
@@ -928,7 +928,7 @@ <h1 id="arch-unitroot-zivotandrews-trend">arch.unitroot.ZivotAndrews.trend<a cla
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -975,7 +975,7 @@ <h1 id="arch-unitroot-zivotandrews-trend">arch.unitroot.ZivotAndrews.trend<a cla
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.ZivotAndrews.valid_trends.html b/unitroot/generated/generated/arch.unitroot.ZivotAndrews.valid_trends.html
index 19dc6dbd48..a551dd87db 100644
--- a/unitroot/generated/generated/arch.unitroot.ZivotAndrews.valid_trends.html
+++ b/unitroot/generated/generated/arch.unitroot.ZivotAndrews.valid_trends.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.ZivotAndrews.valid_trends - arch 6.1.0</title>
+        <title>arch.unitroot.ZivotAndrews.valid_trends - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -853,10 +853,10 @@
                   
 
 
-<h1 id="arch-unitroot-zivotandrews-valid-trends">arch.unitroot.ZivotAndrews.valid_trends<a class="headerlink" href="#arch-unitroot-zivotandrews-valid-trends" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-zivotandrews-valid-trends">arch.unitroot.ZivotAndrews.valid_trends<a class="headerlink" href="#arch-unitroot-zivotandrews-valid-trends" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.ZivotAndrews.valid_trends">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ZivotAndrews.</span></span><span class="sig-name descname"><span class="pre">valid_trends</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#arch.unitroot.ZivotAndrews.valid_trends" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ZivotAndrews.</span></span><span class="sig-name descname"><span class="pre">valid_trends</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#arch.unitroot.ZivotAndrews.valid_trends" title="Link to this definition">¶</a></dt>
 <dd><p>List of valid trend terms.</p>
 </dd></dl>
 
@@ -927,7 +927,7 @@ <h1 id="arch-unitroot-zivotandrews-valid-trends">arch.unitroot.ZivotAndrews.vali
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -974,7 +974,7 @@ <h1 id="arch-unitroot-zivotandrews-valid-trends">arch.unitroot.ZivotAndrews.vali
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.ZivotAndrews.y.html b/unitroot/generated/generated/arch.unitroot.ZivotAndrews.y.html
index 38ba86f065..5a9e89a12d 100644
--- a/unitroot/generated/generated/arch.unitroot.ZivotAndrews.y.html
+++ b/unitroot/generated/generated/arch.unitroot.ZivotAndrews.y.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.ZivotAndrews.y - arch 6.1.0</title>
+        <title>arch.unitroot.ZivotAndrews.y - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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@@ -853,10 +853,10 @@
                   
 
 
-<h1 id="arch-unitroot-zivotandrews-y">arch.unitroot.ZivotAndrews.y<a class="headerlink" href="#arch-unitroot-zivotandrews-y" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-zivotandrews-y">arch.unitroot.ZivotAndrews.y<a class="headerlink" href="#arch-unitroot-zivotandrews-y" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.ZivotAndrews.y">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ZivotAndrews.</span></span><span class="sig-name descname"><span class="pre">y</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.0.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.0.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.ZivotAndrews.y" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ZivotAndrews.</span></span><span class="sig-name descname"><span class="pre">y</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.ZivotAndrews.y" title="Link to this definition">¶</a></dt>
 <dd><p>Returns the data used in the test statistic</p>
 </dd></dl>
 
@@ -927,7 +927,7 @@ <h1 id="arch-unitroot-zivotandrews-y">arch.unitroot.ZivotAndrews.y<a class="head
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -974,7 +974,7 @@ <h1 id="arch-unitroot-zivotandrews-y">arch.unitroot.ZivotAndrews.y<a class="head
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.CanonicalCointegratingReg.fit.html b/unitroot/generated/generated/arch.unitroot.cointegration.CanonicalCointegratingReg.fit.html
index 1fd601596e..221d025a9e 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.CanonicalCointegratingReg.fit.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.CanonicalCointegratingReg.fit.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.CanonicalCointegratingReg.fit - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.CanonicalCointegratingReg.fit - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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+    arch 6.2.0
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     <div class="md-nav__source">
@@ -794,30 +794,30 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-canonicalcointegratingreg-fit">arch.unitroot.cointegration.CanonicalCointegratingReg.fit<a class="headerlink" href="#arch-unitroot-cointegration-canonicalcointegratingreg-fit" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-canonicalcointegratingreg-fit">arch.unitroot.cointegration.CanonicalCointegratingReg.fit<a class="headerlink" href="#arch-unitroot-cointegration-canonicalcointegratingreg-fit" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.unitroot.cointegration.CanonicalCointegratingReg.fit">
-<span class="sig-prename descclassname"><span class="pre">CanonicalCointegratingReg.</span></span><span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.CanonicalCointegratingReg.fit.kernel" title="arch.unitroot.cointegration.CanonicalCointegratingReg.fit.kernel (Python parameter) — The string name of any of any known kernel-based long-run covariance estimators. Common choices are &quot;bartlett&quot; for the Bartlett kernel (Newey-West), &quot;parzen&quot; for the Parzen kernel and &quot;quadratic-spectral&quot; for the Quadratic Spectral kernel."><span class="n"><span class="pre">kernel</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;bartlett&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.CanonicalCointegratingReg.fit.bandwidth" title="arch.unitroot.cointegration.CanonicalCointegratingReg.fit.bandwidth (Python parameter) — The bandwidth to use. If not provided, the optimal bandwidth is estimated from the data. Setting the bandwidth to 0 and using &quot;unadjusted&quot; produces the classic OLS covariance estimator. Setting the bandwidth to 0 and using &quot;robust&quot; produces White's covariance estimator."><span class="n"><span class="pre">bandwidth</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.CanonicalCointegratingReg.fit.force_int" title="arch.unitroot.cointegration.CanonicalCointegratingReg.fit.force_int (Python parameter) — Whether the force the estimated optimal bandwidth to be an integer."><span class="n"><span class="pre">force_int</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.CanonicalCointegratingReg.fit.diff" title="arch.unitroot.cointegration.CanonicalCointegratingReg.fit.diff (Python parameter) — Use differenced data to estimate the residuals."><span class="n"><span class="pre">diff</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.CanonicalCointegratingReg.fit.df_adjust" title="arch.unitroot.cointegration.CanonicalCointegratingReg.fit.df_adjust (Python parameter) — Whether the adjust the parameter covariance to account for the number of parameters estimated in the regression. If true, the parameter covariance estimator is multiplied by T/(T-k) where k is the number of regressors in the model."><span class="n"><span class="pre">df_adjust</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/unitroot/cointegration.html#CanonicalCointegratingReg.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.cointegration.CanonicalCointegratingReg.fit" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">CanonicalCointegratingReg.</span></span><span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.CanonicalCointegratingReg.fit.kernel" title="arch.unitroot.cointegration.CanonicalCointegratingReg.fit.kernel (Python parameter) — The string name of any of any known kernel-based long-run covariance estimators. Common choices are &quot;bartlett&quot; for the Bartlett kernel (Newey-West), &quot;parzen&quot; for the Parzen kernel and &quot;quadratic-spectral&quot; for the Quadratic Spectral kernel."><span class="n"><span class="pre">kernel</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;bartlett&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.CanonicalCointegratingReg.fit.bandwidth" title="arch.unitroot.cointegration.CanonicalCointegratingReg.fit.bandwidth (Python parameter) — The bandwidth to use. If not provided, the optimal bandwidth is estimated from the data. Setting the bandwidth to 0 and using &quot;unadjusted&quot; produces the classic OLS covariance estimator. Setting the bandwidth to 0 and using &quot;robust&quot; produces White's covariance estimator."><span class="n"><span class="pre">bandwidth</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.CanonicalCointegratingReg.fit.force_int" title="arch.unitroot.cointegration.CanonicalCointegratingReg.fit.force_int (Python parameter) — Whether the force the estimated optimal bandwidth to be an integer."><span class="n"><span class="pre">force_int</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.CanonicalCointegratingReg.fit.diff" title="arch.unitroot.cointegration.CanonicalCointegratingReg.fit.diff (Python parameter) — Use differenced data to estimate the residuals."><span class="n"><span class="pre">diff</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.CanonicalCointegratingReg.fit.df_adjust" title="arch.unitroot.cointegration.CanonicalCointegratingReg.fit.df_adjust (Python parameter) — Whether the adjust the parameter covariance to account for the number of parameters estimated in the regression. If true, the parameter covariance estimator is multiplied by T/(T-k) where k is the number of regressors in the model."><span class="n"><span class="pre">df_adjust</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.unitroot.cointegration.CointegrationAnalysisResults.html#arch.unitroot.cointegration.CointegrationAnalysisResults" title="arch.unitroot.cointegration.CointegrationAnalysisResults (Python class) — summary()"><span class="pre">CointegrationAnalysisResults</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/unitroot/cointegration.html#CanonicalCointegratingReg.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.cointegration.CanonicalCointegratingReg.fit" title="Link to this definition">¶</a></dt>
 <dd><p>Estimate the cointegrating vector.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.unitroot.cointegration.CanonicalCointegratingReg.fit-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.cointegration.CanonicalCointegratingReg.fit-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.unitroot.cointegration.CanonicalCointegratingReg.fit.diff"><span class="sig-name descname">diff</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, default False</span></span><a class="headerlink" href="#arch.unitroot.cointegration.CanonicalCointegratingReg.fit.diff" title="Permalink to this definition">¶</a></dt><dd><p>Use differenced data to estimate the residuals.</p>
+<dt id="arch.unitroot.cointegration.CanonicalCointegratingReg.fit.diff"><span class="n sig-name">diff</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.unitroot.cointegration.CanonicalCointegratingReg.fit.diff" title="Permalink to this definition">¶</a></dt><dd><p>Use differenced data to estimate the residuals.</p>
 </dd>
-<dt id="arch.unitroot.cointegration.CanonicalCointegratingReg.fit.kernel"><span class="sig-name descname">kernel</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, default &quot;bartlett&quot;</span></span><a class="headerlink" href="#arch.unitroot.cointegration.CanonicalCointegratingReg.fit.kernel" title="Permalink to this definition">¶</a></dt><dd><p>The string name of any of any known kernel-based long-run
+<dt id="arch.unitroot.cointegration.CanonicalCointegratingReg.fit.kernel"><span class="n sig-name">kernel</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;bartlett&#39;</span></code><a class="headerlink" href="#arch.unitroot.cointegration.CanonicalCointegratingReg.fit.kernel" title="Permalink to this definition">¶</a></dt><dd><p>The string name of any of any known kernel-based long-run
 covariance estimators. Common choices are “bartlett” for the
 Bartlett kernel (Newey-West), “parzen” for the Parzen kernel
 and “quadratic-spectral” for the Quadratic Spectral kernel.</p>
 </dd>
-<dt id="arch.unitroot.cointegration.CanonicalCointegratingReg.fit.bandwidth"><span class="sig-name descname">bandwidth</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, default None</span></span><a class="headerlink" href="#arch.unitroot.cointegration.CanonicalCointegratingReg.fit.bandwidth" title="Permalink to this definition">¶</a></dt><dd><p>The bandwidth to use. If not provided, the optimal bandwidth is
+<dt id="arch.unitroot.cointegration.CanonicalCointegratingReg.fit.bandwidth"><span class="n sig-name">bandwidth</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.unitroot.cointegration.CanonicalCointegratingReg.fit.bandwidth" title="Permalink to this definition">¶</a></dt><dd><p>The bandwidth to use. If not provided, the optimal bandwidth is
 estimated from the data. Setting the bandwidth to 0 and using
 “unadjusted” produces the classic OLS covariance estimator.
 Setting the bandwidth to 0 and using “robust” produces White’s
 covariance estimator.</p>
 </dd>
-<dt id="arch.unitroot.cointegration.CanonicalCointegratingReg.fit.force_int"><span class="sig-name descname">force_int</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, default False</span></span><a class="headerlink" href="#arch.unitroot.cointegration.CanonicalCointegratingReg.fit.force_int" title="Permalink to this definition">¶</a></dt><dd><p>Whether the force the estimated optimal bandwidth to be an integer.</p>
+<dt id="arch.unitroot.cointegration.CanonicalCointegratingReg.fit.force_int"><span class="n sig-name">force_int</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.unitroot.cointegration.CanonicalCointegratingReg.fit.force_int" title="Permalink to this definition">¶</a></dt><dd><p>Whether the force the estimated optimal bandwidth to be an integer.</p>
 </dd>
-<dt id="arch.unitroot.cointegration.CanonicalCointegratingReg.fit.df_adjust"><span class="sig-name descname">df_adjust</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, default False</span></span><a class="headerlink" href="#arch.unitroot.cointegration.CanonicalCointegratingReg.fit.df_adjust" title="Permalink to this definition">¶</a></dt><dd><p>Whether the adjust the parameter covariance to account for the
+<dt id="arch.unitroot.cointegration.CanonicalCointegratingReg.fit.df_adjust"><span class="n sig-name">df_adjust</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.unitroot.cointegration.CanonicalCointegratingReg.fit.df_adjust" title="Permalink to this definition">¶</a></dt><dd><p>Whether the adjust the parameter covariance to account for the
 number of parameters estimated in the regression. If true, the
 parameter covariance estimator is multiplied by T/(T-k) where
 k is the number of regressors in the model.</p>
@@ -828,7 +828,7 @@ <h1 id="arch-unitroot-cointegration-canonicalcointegratingreg-fit">arch.unitroot
 <dd class="field-even"><p>The estimation results instance.</p>
 </dd>
 <dt class="field-odd" id="arch.unitroot.cointegration.CanonicalCointegratingReg.fit-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.cointegration.CanonicalCointegratingReg.fit-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference internal" href="../arch.unitroot.cointegration.CointegrationAnalysisResults.html#arch.unitroot.cointegration.CointegrationAnalysisResults" title="arch.unitroot.cointegration.CointegrationAnalysisResults (Python class) — summary()">CointegrationAnalysisResults</a></p>
+<dd class="field-odd"><p><a class="reference internal" href="../arch.unitroot.cointegration.CointegrationAnalysisResults.html#arch.unitroot.cointegration.CointegrationAnalysisResults" title="arch.unitroot.cointegration.CointegrationAnalysisResults (Python class) — summary()"><code class="xref py py-class docutils literal notranslate"><span class="pre">CointegrationAnalysisResults</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -900,7 +900,7 @@ <h1 id="arch-unitroot-cointegration-canonicalcointegratingreg-fit">arch.unitroot
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -947,7 +947,7 @@ <h1 id="arch-unitroot-cointegration-canonicalcointegratingreg-fit">arch.unitroot
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.bandwidth.html b/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.bandwidth.html
index 6c02f2e3bb..d86755546f 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.bandwidth.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.bandwidth.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.CointegrationAnalysisResults.bandwidth - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.CointegrationAnalysisResults.bandwidth - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -923,10 +923,10 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-bandwidth">arch.unitroot.cointegration.CointegrationAnalysisResults.bandwidth<a class="headerlink" href="#arch-unitroot-cointegration-cointegrationanalysisresults-bandwidth" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-bandwidth">arch.unitroot.cointegration.CointegrationAnalysisResults.bandwidth<a class="headerlink" href="#arch-unitroot-cointegration-cointegrationanalysisresults-bandwidth" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.CointegrationAnalysisResults.bandwidth">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CointegrationAnalysisResults.</span></span><span class="sig-name descname"><span class="pre">bandwidth</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.CointegrationAnalysisResults.bandwidth" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CointegrationAnalysisResults.</span></span><span class="sig-name descname"><span class="pre">bandwidth</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.CointegrationAnalysisResults.bandwidth" title="Link to this definition">¶</a></dt>
 <dd><p>The bandwidth used in the parameter covariance estimation</p>
 </dd></dl>
 
@@ -997,7 +997,7 @@ <h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-bandwidth">arch
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1044,7 +1044,7 @@ <h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-bandwidth">arch
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.cov.html b/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.cov.html
index fd92462632..0513076f3c 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.cov.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.cov.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.CointegrationAnalysisResults.cov - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.CointegrationAnalysisResults.cov - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -923,10 +923,10 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-cov">arch.unitroot.cointegration.CointegrationAnalysisResults.cov<a class="headerlink" href="#arch-unitroot-cointegration-cointegrationanalysisresults-cov" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-cov">arch.unitroot.cointegration.CointegrationAnalysisResults.cov<a class="headerlink" href="#arch-unitroot-cointegration-cointegrationanalysisresults-cov" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.CointegrationAnalysisResults.cov">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CointegrationAnalysisResults.</span></span><span class="sig-name descname"><span class="pre">cov</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.0.1)"><span class="pre">DataFrame</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.CointegrationAnalysisResults.cov" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CointegrationAnalysisResults.</span></span><span class="sig-name descname"><span class="pre">cov</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.CointegrationAnalysisResults.cov" title="Link to this definition">¶</a></dt>
 <dd><p>The estimated parameter covariance of the cointegrating vector</p>
 </dd></dl>
 
@@ -997,7 +997,7 @@ <h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-cov">arch.unitr
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1044,7 +1044,7 @@ <h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-cov">arch.unitr
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.kernel.html b/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.kernel.html
index 5b27cfc1c6..384cf1f530 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.kernel.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.kernel.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.CointegrationAnalysisResults.kernel - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.CointegrationAnalysisResults.kernel - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -923,10 +923,10 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-kernel">arch.unitroot.cointegration.CointegrationAnalysisResults.kernel<a class="headerlink" href="#arch-unitroot-cointegration-cointegrationanalysisresults-kernel" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-kernel">arch.unitroot.cointegration.CointegrationAnalysisResults.kernel<a class="headerlink" href="#arch-unitroot-cointegration-cointegrationanalysisresults-kernel" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.CointegrationAnalysisResults.kernel">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CointegrationAnalysisResults.</span></span><span class="sig-name descname"><span class="pre">kernel</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.CointegrationAnalysisResults.kernel" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CointegrationAnalysisResults.</span></span><span class="sig-name descname"><span class="pre">kernel</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.CointegrationAnalysisResults.kernel" title="Link to this definition">¶</a></dt>
 <dd><p>The kernel used to estimate the covariance</p>
 </dd></dl>
 
@@ -997,7 +997,7 @@ <h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-kernel">arch.un
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1044,7 +1044,7 @@ <h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-kernel">arch.un
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.long_run_variance.html b/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.long_run_variance.html
index 69d6b8f0f3..4779f8ac5e 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.long_run_variance.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.long_run_variance.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.CointegrationAnalysisResults.long_run_variance - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.CointegrationAnalysisResults.long_run_variance - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -923,10 +923,10 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-long-run-variance">arch.unitroot.cointegration.CointegrationAnalysisResults.long_run_variance<a class="headerlink" href="#arch-unitroot-cointegration-cointegrationanalysisresults-long-run-variance" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-long-run-variance">arch.unitroot.cointegration.CointegrationAnalysisResults.long_run_variance<a class="headerlink" href="#arch-unitroot-cointegration-cointegrationanalysisresults-long-run-variance" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.CointegrationAnalysisResults.long_run_variance">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CointegrationAnalysisResults.</span></span><span class="sig-name descname"><span class="pre">long_run_variance</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.CointegrationAnalysisResults.long_run_variance" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CointegrationAnalysisResults.</span></span><span class="sig-name descname"><span class="pre">long_run_variance</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.CointegrationAnalysisResults.long_run_variance" title="Link to this definition">¶</a></dt>
 <dd><p>Long-run variance estimate used in the parameter covariance estimator</p>
 </dd></dl>
 
@@ -997,7 +997,7 @@ <h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-long-run-varian
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1044,7 +1044,7 @@ <h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-long-run-varian
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.params.html b/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.params.html
index fa8ce87a87..bb7ccd2e95 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.params.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.CointegrationAnalysisResults.params - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.CointegrationAnalysisResults.params - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -923,10 +923,10 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-params">arch.unitroot.cointegration.CointegrationAnalysisResults.params<a class="headerlink" href="#arch-unitroot-cointegration-cointegrationanalysisresults-params" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-params">arch.unitroot.cointegration.CointegrationAnalysisResults.params<a class="headerlink" href="#arch-unitroot-cointegration-cointegrationanalysisresults-params" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.CointegrationAnalysisResults.params">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CointegrationAnalysisResults.</span></span><span class="sig-name descname"><span class="pre">params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.0.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.CointegrationAnalysisResults.params" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CointegrationAnalysisResults.</span></span><span class="sig-name descname"><span class="pre">params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.CointegrationAnalysisResults.params" title="Link to this definition">¶</a></dt>
 <dd><p>The estimated parameters of the cointegrating vector</p>
 </dd></dl>
 
@@ -997,7 +997,7 @@ <h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-params">arch.un
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1044,7 +1044,7 @@ <h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-params">arch.un
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.pvalues.html b/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.pvalues.html
index 4dad42ce65..8f82245bd2 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.pvalues.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.pvalues.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.CointegrationAnalysisResults.pvalues - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.CointegrationAnalysisResults.pvalues - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -923,10 +923,10 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-pvalues">arch.unitroot.cointegration.CointegrationAnalysisResults.pvalues<a class="headerlink" href="#arch-unitroot-cointegration-cointegrationanalysisresults-pvalues" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-pvalues">arch.unitroot.cointegration.CointegrationAnalysisResults.pvalues<a class="headerlink" href="#arch-unitroot-cointegration-cointegrationanalysisresults-pvalues" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.CointegrationAnalysisResults.pvalues">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CointegrationAnalysisResults.</span></span><span class="sig-name descname"><span class="pre">pvalues</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.0.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.CointegrationAnalysisResults.pvalues" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CointegrationAnalysisResults.</span></span><span class="sig-name descname"><span class="pre">pvalues</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.CointegrationAnalysisResults.pvalues" title="Link to this definition">¶</a></dt>
 <dd><p>P-value of the parameters in the cointegrating vector</p>
 </dd></dl>
 
@@ -997,7 +997,7 @@ <h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-pvalues">arch.u
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1044,7 +1044,7 @@ <h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-pvalues">arch.u
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.resid.html b/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.resid.html
index 0eca14cad2..f7e16bac5e 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.resid.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.resid.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.CointegrationAnalysisResults.resid - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.CointegrationAnalysisResults.resid - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -923,10 +923,10 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-resid">arch.unitroot.cointegration.CointegrationAnalysisResults.resid<a class="headerlink" href="#arch-unitroot-cointegration-cointegrationanalysisresults-resid" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-resid">arch.unitroot.cointegration.CointegrationAnalysisResults.resid<a class="headerlink" href="#arch-unitroot-cointegration-cointegrationanalysisresults-resid" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.CointegrationAnalysisResults.resid">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CointegrationAnalysisResults.</span></span><span class="sig-name descname"><span class="pre">resid</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.0.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.CointegrationAnalysisResults.resid" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CointegrationAnalysisResults.</span></span><span class="sig-name descname"><span class="pre">resid</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.CointegrationAnalysisResults.resid" title="Link to this definition">¶</a></dt>
 <dd><p>The model residuals</p>
 </dd></dl>
 
@@ -997,7 +997,7 @@ <h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-resid">arch.uni
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1044,7 +1044,7 @@ <h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-resid">arch.uni
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.residual_variance.html b/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.residual_variance.html
index b04dae27ca..0c2e821deb 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.residual_variance.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.residual_variance.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.CointegrationAnalysisResults.residual_variance - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.CointegrationAnalysisResults.residual_variance - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -963,17 +963,17 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-residual-variance">arch.unitroot.cointegration.CointegrationAnalysisResults.residual_variance<a class="headerlink" href="#arch-unitroot-cointegration-cointegrationanalysisresults-residual-variance" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-residual-variance">arch.unitroot.cointegration.CointegrationAnalysisResults.residual_variance<a class="headerlink" href="#arch-unitroot-cointegration-cointegrationanalysisresults-residual-variance" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.CointegrationAnalysisResults.residual_variance">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CointegrationAnalysisResults.</span></span><span class="sig-name descname"><span class="pre">residual_variance</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.CointegrationAnalysisResults.residual_variance" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CointegrationAnalysisResults.</span></span><span class="sig-name descname"><span class="pre">residual_variance</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.CointegrationAnalysisResults.residual_variance" title="Link to this definition">¶</a></dt>
 <dd><p>The variance of the regression residual.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.unitroot.cointegration.CointegrationAnalysisResults.residual_variance-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.cointegration.CointegrationAnalysisResults.residual_variance-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The estimated residual variance.</p>
 </dd>
 <dt class="field-even" id="arch.unitroot.cointegration.CointegrationAnalysisResults.residual_variance-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.cointegration.CointegrationAnalysisResults.residual_variance-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -1053,7 +1053,7 @@ <h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-residual-varian
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1100,10 +1100,10 @@ <h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-residual-varian
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+        <script id="MathJax-script" src="../../../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
     
   </body>
 </html>
\ No newline at end of file
diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.rsquared.html b/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.rsquared.html
index c4da88f1c8..229d20871f 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.rsquared.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.rsquared.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.CointegrationAnalysisResults.rsquared - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.CointegrationAnalysisResults.rsquared - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -923,10 +923,10 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-rsquared">arch.unitroot.cointegration.CointegrationAnalysisResults.rsquared<a class="headerlink" href="#arch-unitroot-cointegration-cointegrationanalysisresults-rsquared" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-rsquared">arch.unitroot.cointegration.CointegrationAnalysisResults.rsquared<a class="headerlink" href="#arch-unitroot-cointegration-cointegrationanalysisresults-rsquared" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.CointegrationAnalysisResults.rsquared">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CointegrationAnalysisResults.</span></span><span class="sig-name descname"><span class="pre">rsquared</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.CointegrationAnalysisResults.rsquared" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CointegrationAnalysisResults.</span></span><span class="sig-name descname"><span class="pre">rsquared</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.CointegrationAnalysisResults.rsquared" title="Link to this definition">¶</a></dt>
 <dd><p>The model R²</p>
 </dd></dl>
 
@@ -997,7 +997,7 @@ <h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-rsquared">arch.
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1044,7 +1044,7 @@ <h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-rsquared">arch.
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.rsquared_adj.html b/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.rsquared_adj.html
index 41747bd0d2..2f2da167aa 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.rsquared_adj.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.rsquared_adj.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.CointegrationAnalysisResults.rsquared_adj - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.CointegrationAnalysisResults.rsquared_adj - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -923,10 +923,10 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-rsquared-adj">arch.unitroot.cointegration.CointegrationAnalysisResults.rsquared_adj<a class="headerlink" href="#arch-unitroot-cointegration-cointegrationanalysisresults-rsquared-adj" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-rsquared-adj">arch.unitroot.cointegration.CointegrationAnalysisResults.rsquared_adj<a class="headerlink" href="#arch-unitroot-cointegration-cointegrationanalysisresults-rsquared-adj" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.CointegrationAnalysisResults.rsquared_adj">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CointegrationAnalysisResults.</span></span><span class="sig-name descname"><span class="pre">rsquared_adj</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.CointegrationAnalysisResults.rsquared_adj" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CointegrationAnalysisResults.</span></span><span class="sig-name descname"><span class="pre">rsquared_adj</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.CointegrationAnalysisResults.rsquared_adj" title="Link to this definition">¶</a></dt>
 <dd><p>The degree-of-freedom adjusted R²</p>
 </dd></dl>
 
@@ -997,7 +997,7 @@ <h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-rsquared-adj">a
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1044,7 +1044,7 @@ <h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-rsquared-adj">a
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.std_errors.html b/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.std_errors.html
index 17fd54c9de..39a2e2f944 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.std_errors.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.std_errors.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.CointegrationAnalysisResults.std_errors - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.CointegrationAnalysisResults.std_errors - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -923,10 +923,10 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-std-errors">arch.unitroot.cointegration.CointegrationAnalysisResults.std_errors<a class="headerlink" href="#arch-unitroot-cointegration-cointegrationanalysisresults-std-errors" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-std-errors">arch.unitroot.cointegration.CointegrationAnalysisResults.std_errors<a class="headerlink" href="#arch-unitroot-cointegration-cointegrationanalysisresults-std-errors" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.CointegrationAnalysisResults.std_errors">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CointegrationAnalysisResults.</span></span><span class="sig-name descname"><span class="pre">std_errors</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.0.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.CointegrationAnalysisResults.std_errors" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CointegrationAnalysisResults.</span></span><span class="sig-name descname"><span class="pre">std_errors</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.CointegrationAnalysisResults.std_errors" title="Link to this definition">¶</a></dt>
 <dd><p>Standard errors  of the parameters in the cointegrating vector</p>
 </dd></dl>
 
@@ -997,7 +997,7 @@ <h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-std-errors">arc
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1044,7 +1044,7 @@ <h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-std-errors">arc
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.summary.html b/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.summary.html
index 70fb8cb034..3757958391 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.summary.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.summary.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.CointegrationAnalysisResults.summary - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.CointegrationAnalysisResults.summary - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -963,10 +963,10 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-summary">arch.unitroot.cointegration.CointegrationAnalysisResults.summary<a class="headerlink" href="#arch-unitroot-cointegration-cointegrationanalysisresults-summary" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-summary">arch.unitroot.cointegration.CointegrationAnalysisResults.summary<a class="headerlink" href="#arch-unitroot-cointegration-cointegrationanalysisresults-summary" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.CointegrationAnalysisResults.summary">
-<span class="sig-prename descclassname"><span class="pre">CointegrationAnalysisResults.</span></span><span class="sig-name descname"><span class="pre">summary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#arch.unitroot.cointegration.CointegrationAnalysisResults.summary" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">CointegrationAnalysisResults.</span></span><span class="sig-name descname"><span class="pre">summary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.iolib.summary.Summary.html#statsmodels.iolib.summary.Summary" title="(in statsmodels 0.15.0 (+59) v0.15.0 (+59))"><span class="pre">Summary</span></a></span></span></span><a class="headerlink" href="#arch.unitroot.cointegration.CointegrationAnalysisResults.summary" title="Link to this definition">¶</a></dt>
 <dd><p>Summary of the model, containing estimated parameters and std. errors</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.unitroot.cointegration.CointegrationAnalysisResults.summary-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.cointegration.CointegrationAnalysisResults.summary-returns" title="Permalink to this headline">¶</a></dt>
@@ -974,7 +974,7 @@ <h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-summary">arch.u
 or latex.</p>
 </dd>
 <dt class="field-even" id="arch.unitroot.cointegration.CointegrationAnalysisResults.summary-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.cointegration.CointegrationAnalysisResults.summary-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p>Summary</p>
+<dd class="field-even"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">Summary</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1046,7 +1046,7 @@ <h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-summary">arch.u
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1093,7 +1093,7 @@ <h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-summary">arch.u
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diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.tvalues.html b/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.tvalues.html
index f805e661bc..e6371a7b7d 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.tvalues.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.tvalues.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.CointegrationAnalysisResults.tvalues - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.CointegrationAnalysisResults.tvalues - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -923,10 +923,10 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-tvalues">arch.unitroot.cointegration.CointegrationAnalysisResults.tvalues<a class="headerlink" href="#arch-unitroot-cointegration-cointegrationanalysisresults-tvalues" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-tvalues">arch.unitroot.cointegration.CointegrationAnalysisResults.tvalues<a class="headerlink" href="#arch-unitroot-cointegration-cointegrationanalysisresults-tvalues" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.CointegrationAnalysisResults.tvalues">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CointegrationAnalysisResults.</span></span><span class="sig-name descname"><span class="pre">tvalues</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.0.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.CointegrationAnalysisResults.tvalues" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CointegrationAnalysisResults.</span></span><span class="sig-name descname"><span class="pre">tvalues</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.CointegrationAnalysisResults.tvalues" title="Link to this definition">¶</a></dt>
 <dd><p>T-statistics of the parameters in the cointegrating vector</p>
 </dd></dl>
 
@@ -997,7 +997,7 @@ <h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-tvalues">arch.u
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLS.fit.html b/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLS.fit.html
index 97644a039f..625e4d605d 100644
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+++ b/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLS.fit.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -47,8 +47,8 @@
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -794,31 +794,31 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-dynamicols-fit">arch.unitroot.cointegration.DynamicOLS.fit<a class="headerlink" href="#arch-unitroot-cointegration-dynamicols-fit" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-dynamicols-fit">arch.unitroot.cointegration.DynamicOLS.fit<a class="headerlink" href="#arch-unitroot-cointegration-dynamicols-fit" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.unitroot.cointegration.DynamicOLS.fit">
-<span class="sig-prename descclassname"><span class="pre">DynamicOLS.</span></span><span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLS.fit.cov_type" title="arch.unitroot.cointegration.DynamicOLS.fit.cov_type (Python parameter) — Either &quot;unadjusted&quot; (or is equivalent &quot;homoskedastic&quot;) or &quot;robust&quot; (or its equivalent &quot;kernel&quot;)."><span class="n"><span class="pre">cov_type</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;unadjusted&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLS.fit.kernel" title="arch.unitroot.cointegration.DynamicOLS.fit.kernel (Python parameter) — The string name of any of any known kernel-based long-run covariance estimators. Common choices are &quot;bartlett&quot; for the Bartlett kernel (Newey-West), &quot;parzen&quot; for the Parzen kernel and &quot;quadratic-spectral&quot; for the Quadratic Spectral kernel."><span class="n"><span class="pre">kernel</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;bartlett&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLS.fit.bandwidth" title="arch.unitroot.cointegration.DynamicOLS.fit.bandwidth (Python parameter) — The bandwidth to use. If not provided, the optimal bandwidth is estimated from the data. Setting the bandwidth to 0 and using &quot;unadjusted&quot; produces the classic OLS covariance estimator. Setting the bandwidth to 0 and using &quot;robust&quot; produces White's covariance estimator."><span class="n"><span class="pre">bandwidth</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLS.fit.force_int" title="arch.unitroot.cointegration.DynamicOLS.fit.force_int (Python parameter) — Whether the force the estimated optimal bandwidth to be an integer."><span class="n"><span class="pre">force_int</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLS.fit.df_adjust" title="arch.unitroot.cointegration.DynamicOLS.fit.df_adjust (Python parameter) — Whether the adjust the parameter covariance to account for the number of parameters estimated in the regression. If true, the parameter covariance estimator is multiplied by T/(T-k) where k is the number of regressors in the model."><span class="n"><span class="pre">df_adjust</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/unitroot/cointegration.html#DynamicOLS.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLS.fit" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">DynamicOLS.</span></span><span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLS.fit.cov_type" title="arch.unitroot.cointegration.DynamicOLS.fit.cov_type (Python parameter) — Either &quot;unadjusted&quot; (or is equivalent &quot;homoskedastic&quot;) or &quot;robust&quot; (or its equivalent &quot;kernel&quot;)."><span class="n"><span class="pre">cov_type</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'unadjusted'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'homoskedastic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'robust'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'kernel'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;unadjusted&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLS.fit.kernel" title="arch.unitroot.cointegration.DynamicOLS.fit.kernel (Python parameter) — The string name of any of any known kernel-based long-run covariance estimators. Common choices are &quot;bartlett&quot; for the Bartlett kernel (Newey-West), &quot;parzen&quot; for the Parzen kernel and &quot;quadratic-spectral&quot; for the Quadratic Spectral kernel."><span class="n"><span class="pre">kernel</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;bartlett&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLS.fit.bandwidth" title="arch.unitroot.cointegration.DynamicOLS.fit.bandwidth (Python parameter) — The bandwidth to use. If not provided, the optimal bandwidth is estimated from the data. Setting the bandwidth to 0 and using &quot;unadjusted&quot; produces the classic OLS covariance estimator. Setting the bandwidth to 0 and using &quot;robust&quot; produces White's covariance estimator."><span class="n"><span class="pre">bandwidth</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLS.fit.force_int" title="arch.unitroot.cointegration.DynamicOLS.fit.force_int (Python parameter) — Whether the force the estimated optimal bandwidth to be an integer."><span class="n"><span class="pre">force_int</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLS.fit.df_adjust" title="arch.unitroot.cointegration.DynamicOLS.fit.df_adjust (Python parameter) — Whether the adjust the parameter covariance to account for the number of parameters estimated in the regression. If true, the parameter covariance estimator is multiplied by T/(T-k) where k is the number of regressors in the model."><span class="n"><span class="pre">df_adjust</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.unitroot.cointegration.DynamicOLSResults.html#arch.unitroot.cointegration.DynamicOLSResults" title="arch.unitroot.cointegration.DynamicOLSResults (Python class) — Estimation results for Dynamic OLS models"><span class="pre">DynamicOLSResults</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/unitroot/cointegration.html#DynamicOLS.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLS.fit" title="Link to this definition">¶</a></dt>
 <dd><p>Estimate the Dynamic OLS regression</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.unitroot.cointegration.DynamicOLS.fit-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLS.fit-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.unitroot.cointegration.DynamicOLS.fit.cov_type"><span class="sig-name descname">cov_type</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, default &quot;unadjusted&quot;</span></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLS.fit.cov_type" title="Permalink to this definition">¶</a></dt><dd><p>Either “unadjusted” (or is equivalent “homoskedastic”) or “robust”
+<dt id="arch.unitroot.cointegration.DynamicOLS.fit.cov_type"><span class="n sig-name">cov_type</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'unadjusted'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'homoskedastic'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'robust'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'kernel'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;unadjusted&#39;</span></code><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLS.fit.cov_type" title="Permalink to this definition">¶</a></dt><dd><p>Either “unadjusted” (or is equivalent “homoskedastic”) or “robust”
 (or its equivalent “kernel”).</p>
 </dd>
-<dt id="arch.unitroot.cointegration.DynamicOLS.fit.kernel"><span class="sig-name descname">kernel</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, default &quot;bartlett&quot;</span></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLS.fit.kernel" title="Permalink to this definition">¶</a></dt><dd><p>The string name of any of any known kernel-based long-run
+<dt id="arch.unitroot.cointegration.DynamicOLS.fit.kernel"><span class="n sig-name">kernel</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;bartlett&#39;</span></code><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLS.fit.kernel" title="Permalink to this definition">¶</a></dt><dd><p>The string name of any of any known kernel-based long-run
 covariance estimators. Common choices are “bartlett” for the
 Bartlett kernel (Newey-West), “parzen” for the Parzen kernel
 and “quadratic-spectral” for the Quadratic Spectral kernel.</p>
 </dd>
-<dt id="arch.unitroot.cointegration.DynamicOLS.fit.bandwidth"><span class="sig-name descname">bandwidth</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, default None</span></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLS.fit.bandwidth" title="Permalink to this definition">¶</a></dt><dd><p>The bandwidth to use. If not provided, the optimal bandwidth is
+<dt id="arch.unitroot.cointegration.DynamicOLS.fit.bandwidth"><span class="n sig-name">bandwidth</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLS.fit.bandwidth" title="Permalink to this definition">¶</a></dt><dd><p>The bandwidth to use. If not provided, the optimal bandwidth is
 estimated from the data. Setting the bandwidth to 0 and using
 “unadjusted” produces the classic OLS covariance estimator.
 Setting the bandwidth to 0 and using “robust” produces White’s
 covariance estimator.</p>
 </dd>
-<dt id="arch.unitroot.cointegration.DynamicOLS.fit.force_int"><span class="sig-name descname">force_int</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, default False</span></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLS.fit.force_int" title="Permalink to this definition">¶</a></dt><dd><p>Whether the force the estimated optimal bandwidth to be an integer.</p>
+<dt id="arch.unitroot.cointegration.DynamicOLS.fit.force_int"><span class="n sig-name">force_int</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLS.fit.force_int" title="Permalink to this definition">¶</a></dt><dd><p>Whether the force the estimated optimal bandwidth to be an integer.</p>
 </dd>
-<dt id="arch.unitroot.cointegration.DynamicOLS.fit.df_adjust"><span class="sig-name descname">df_adjust</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, default False</span></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLS.fit.df_adjust" title="Permalink to this definition">¶</a></dt><dd><p>Whether the adjust the parameter covariance to account for the
+<dt id="arch.unitroot.cointegration.DynamicOLS.fit.df_adjust"><span class="n sig-name">df_adjust</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLS.fit.df_adjust" title="Permalink to this definition">¶</a></dt><dd><p>Whether the adjust the parameter covariance to account for the
 number of parameters estimated in the regression. If true, the
 parameter covariance estimator is multiplied by T/(T-k) where
 k is the number of regressors in the model.</p>
@@ -829,7 +829,7 @@ <h1 id="arch-unitroot-cointegration-dynamicols-fit">arch.unitroot.cointegration.
 <dd class="field-even"><p>The estimation results.</p>
 </dd>
 <dt class="field-odd" id="arch.unitroot.cointegration.DynamicOLS.fit-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLS.fit-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference internal" href="../arch.unitroot.cointegration.DynamicOLSResults.html#arch.unitroot.cointegration.DynamicOLSResults" title="arch.unitroot.cointegration.DynamicOLSResults (Python class) — Estimation results for Dynamic OLS models">DynamicOLSResults</a></p>
+<dd class="field-odd"><p><a class="reference internal" href="../arch.unitroot.cointegration.DynamicOLSResults.html#arch.unitroot.cointegration.DynamicOLSResults" title="arch.unitroot.cointegration.DynamicOLSResults (Python class) — Estimation results for Dynamic OLS models"><code class="xref py py-class docutils literal notranslate"><span class="pre">DynamicOLSResults</span></code></a></p>
 </dd>
 </dl>
 <div class="seealso admonition">
@@ -837,7 +837,7 @@ <h1 id="arch-unitroot-cointegration-dynamicols-fit">arch.unitroot.cointegration.
 <dl class="simple">
 <dt><a class="reference internal" href="../arch.unitroot.cointegration.engle_granger.html#arch.unitroot.cointegration.engle_granger" title="arch.unitroot.cointegration.engle_granger (Python function) — Test for cointegration within a set of time series."><code class="xref py py-obj docutils literal notranslate"><span class="pre">arch.unitroot.cointegration.engle_granger</span></code></a></dt><dd><p>Cointegration testing using the Engle-Granger methodology</p>
 </dd>
-<dt><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.regression.linear_model.OLS.html#statsmodels.regression.linear_model.OLS" title="(in statsmodels 0.15.0 (+8) v0.15.0 (+8))"><code class="xref py py-obj docutils literal notranslate"><span class="pre">statsmodels.regression.linear_model.OLS</span></code></a></dt><dd><p>Ordinal Least Squares regression.</p>
+<dt><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.regression.linear_model.OLS.html#statsmodels.regression.linear_model.OLS" title="(in statsmodels 0.15.0 (+59) v0.15.0 (+59))"><code class="xref py py-obj docutils literal notranslate"><span class="pre">statsmodels.regression.linear_model.OLS</span></code></a></dt><dd><p>Ordinal Least Squares regression.</p>
 </dd>
 </dl>
 </div>
@@ -926,7 +926,7 @@ <h1 id="arch-unitroot-cointegration-dynamicols-fit">arch.unitroot.cointegration.
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -973,10 +973,10 @@ <h1 id="arch-unitroot-cointegration-dynamicols-fit">arch.unitroot.cointegration.
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+        <script id="MathJax-script" src="../../../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
     
   </body>
 </html>
\ No newline at end of file
diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.bandwidth.html b/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.bandwidth.html
index 0ad54b98d8..50e9362bd0 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.bandwidth.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.bandwidth.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.bandwidth - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.bandwidth - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -998,10 +998,10 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-dynamicolsresults-bandwidth">arch.unitroot.cointegration.DynamicOLSResults.bandwidth<a class="headerlink" href="#arch-unitroot-cointegration-dynamicolsresults-bandwidth" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-dynamicolsresults-bandwidth">arch.unitroot.cointegration.DynamicOLSResults.bandwidth<a class="headerlink" href="#arch-unitroot-cointegration-dynamicolsresults-bandwidth" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.DynamicOLSResults.bandwidth">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DynamicOLSResults.</span></span><span class="sig-name descname"><span class="pre">bandwidth</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.bandwidth" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DynamicOLSResults.</span></span><span class="sig-name descname"><span class="pre">bandwidth</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.bandwidth" title="Link to this definition">¶</a></dt>
 <dd><p>The bandwidth used in the parameter covariance estimation</p>
 </dd></dl>
 
@@ -1072,7 +1072,7 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults-bandwidth">arch.unitroot.c
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1119,7 +1119,7 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults-bandwidth">arch.unitroot.c
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
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   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.cov.html b/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.cov.html
index dd97187de9..2ec535773f 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.cov.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.cov.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.cov - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.cov - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -998,10 +998,10 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-dynamicolsresults-cov">arch.unitroot.cointegration.DynamicOLSResults.cov<a class="headerlink" href="#arch-unitroot-cointegration-dynamicolsresults-cov" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-dynamicolsresults-cov">arch.unitroot.cointegration.DynamicOLSResults.cov<a class="headerlink" href="#arch-unitroot-cointegration-dynamicolsresults-cov" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.DynamicOLSResults.cov">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DynamicOLSResults.</span></span><span class="sig-name descname"><span class="pre">cov</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.0.1)"><span class="pre">DataFrame</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.cov" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DynamicOLSResults.</span></span><span class="sig-name descname"><span class="pre">cov</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.cov" title="Link to this definition">¶</a></dt>
 <dd><p>The estimated parameter covariance of the cointegrating vector</p>
 </dd></dl>
 
@@ -1072,7 +1072,7 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults-cov">arch.unitroot.cointeg
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1119,7 +1119,7 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults-cov">arch.unitroot.cointeg
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.cov_type.html b/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.cov_type.html
index c1c11467fb..88c2fd2e4a 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.cov_type.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.cov_type.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.cov_type - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.cov_type - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -998,10 +998,10 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-dynamicolsresults-cov-type">arch.unitroot.cointegration.DynamicOLSResults.cov_type<a class="headerlink" href="#arch-unitroot-cointegration-dynamicolsresults-cov-type" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-dynamicolsresults-cov-type">arch.unitroot.cointegration.DynamicOLSResults.cov_type<a class="headerlink" href="#arch-unitroot-cointegration-dynamicolsresults-cov-type" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.DynamicOLSResults.cov_type">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DynamicOLSResults.</span></span><span class="sig-name descname"><span class="pre">cov_type</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.cov_type" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DynamicOLSResults.</span></span><span class="sig-name descname"><span class="pre">cov_type</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.cov_type" title="Link to this definition">¶</a></dt>
 <dd><p>The type of parameter covariance estimator used</p>
 </dd></dl>
 
@@ -1072,7 +1072,7 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults-cov-type">arch.unitroot.co
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1119,7 +1119,7 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults-cov-type">arch.unitroot.co
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.full_cov.html b/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.full_cov.html
index 28a3ec959c..4de1b91781 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.full_cov.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.full_cov.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.full_cov - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.full_cov - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -998,10 +998,10 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-dynamicolsresults-full-cov">arch.unitroot.cointegration.DynamicOLSResults.full_cov<a class="headerlink" href="#arch-unitroot-cointegration-dynamicolsresults-full-cov" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-dynamicolsresults-full-cov">arch.unitroot.cointegration.DynamicOLSResults.full_cov<a class="headerlink" href="#arch-unitroot-cointegration-dynamicolsresults-full-cov" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.DynamicOLSResults.full_cov">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DynamicOLSResults.</span></span><span class="sig-name descname"><span class="pre">full_cov</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.0.1)"><span class="pre">DataFrame</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.full_cov" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DynamicOLSResults.</span></span><span class="sig-name descname"><span class="pre">full_cov</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.full_cov" title="Link to this definition">¶</a></dt>
 <dd><p>Parameter covariance of the all model parameters, incl. leads and lags</p>
 </dd></dl>
 
@@ -1072,7 +1072,7 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults-full-cov">arch.unitroot.co
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1119,7 +1119,7 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults-full-cov">arch.unitroot.co
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.full_params.html b/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.full_params.html
index 3164d695e0..ebfdd41de7 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.full_params.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.full_params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.full_params - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.full_params - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -998,10 +998,10 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-dynamicolsresults-full-params">arch.unitroot.cointegration.DynamicOLSResults.full_params<a class="headerlink" href="#arch-unitroot-cointegration-dynamicolsresults-full-params" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-dynamicolsresults-full-params">arch.unitroot.cointegration.DynamicOLSResults.full_params<a class="headerlink" href="#arch-unitroot-cointegration-dynamicolsresults-full-params" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.DynamicOLSResults.full_params">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DynamicOLSResults.</span></span><span class="sig-name descname"><span class="pre">full_params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.0.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.full_params" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DynamicOLSResults.</span></span><span class="sig-name descname"><span class="pre">full_params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.full_params" title="Link to this definition">¶</a></dt>
 <dd><p>The complete set of parameters, including leads and lags</p>
 </dd></dl>
 
@@ -1072,7 +1072,7 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults-full-params">arch.unitroot
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1119,7 +1119,7 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults-full-params">arch.unitroot
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.kernel.html b/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.kernel.html
index a34161acb5..818bcad6c0 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.kernel.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.kernel.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.kernel - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.kernel - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -998,10 +998,10 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-dynamicolsresults-kernel">arch.unitroot.cointegration.DynamicOLSResults.kernel<a class="headerlink" href="#arch-unitroot-cointegration-dynamicolsresults-kernel" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-dynamicolsresults-kernel">arch.unitroot.cointegration.DynamicOLSResults.kernel<a class="headerlink" href="#arch-unitroot-cointegration-dynamicolsresults-kernel" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.DynamicOLSResults.kernel">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DynamicOLSResults.</span></span><span class="sig-name descname"><span class="pre">kernel</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.kernel" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DynamicOLSResults.</span></span><span class="sig-name descname"><span class="pre">kernel</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.kernel" title="Link to this definition">¶</a></dt>
 <dd><p>The kernel used to estimate the covariance</p>
 </dd></dl>
 
@@ -1072,7 +1072,7 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults-kernel">arch.unitroot.coin
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1119,7 +1119,7 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults-kernel">arch.unitroot.coin
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.lags.html b/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.lags.html
index 1cd4ab3b06..af8056367a 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.lags.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.lags.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.lags - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.lags - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -998,10 +998,10 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-dynamicolsresults-lags">arch.unitroot.cointegration.DynamicOLSResults.lags<a class="headerlink" href="#arch-unitroot-cointegration-dynamicolsresults-lags" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-dynamicolsresults-lags">arch.unitroot.cointegration.DynamicOLSResults.lags<a class="headerlink" href="#arch-unitroot-cointegration-dynamicolsresults-lags" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.DynamicOLSResults.lags">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DynamicOLSResults.</span></span><span class="sig-name descname"><span class="pre">lags</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.lags" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DynamicOLSResults.</span></span><span class="sig-name descname"><span class="pre">lags</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.lags" title="Link to this definition">¶</a></dt>
 <dd><p>The number of lags included in the model</p>
 </dd></dl>
 
@@ -1072,7 +1072,7 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults-lags">arch.unitroot.cointe
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1119,7 +1119,7 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults-lags">arch.unitroot.cointe
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.leads.html b/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.leads.html
index 24a8132f33..e4850dfffd 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.leads.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.leads.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.leads - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.leads - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -998,10 +998,10 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-dynamicolsresults-leads">arch.unitroot.cointegration.DynamicOLSResults.leads<a class="headerlink" href="#arch-unitroot-cointegration-dynamicolsresults-leads" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-dynamicolsresults-leads">arch.unitroot.cointegration.DynamicOLSResults.leads<a class="headerlink" href="#arch-unitroot-cointegration-dynamicolsresults-leads" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.DynamicOLSResults.leads">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DynamicOLSResults.</span></span><span class="sig-name descname"><span class="pre">leads</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.leads" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DynamicOLSResults.</span></span><span class="sig-name descname"><span class="pre">leads</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.leads" title="Link to this definition">¶</a></dt>
 <dd><p>The number of leads included in the model</p>
 </dd></dl>
 
@@ -1072,7 +1072,7 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults-leads">arch.unitroot.coint
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1119,7 +1119,7 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults-leads">arch.unitroot.coint
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.long_run_variance.html b/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.long_run_variance.html
index 80533b33b2..13c0fb89c5 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.long_run_variance.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.long_run_variance.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.long_run_variance - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.long_run_variance - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1038,17 +1038,17 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-dynamicolsresults-long-run-variance">arch.unitroot.cointegration.DynamicOLSResults.long_run_variance<a class="headerlink" href="#arch-unitroot-cointegration-dynamicolsresults-long-run-variance" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-dynamicolsresults-long-run-variance">arch.unitroot.cointegration.DynamicOLSResults.long_run_variance<a class="headerlink" href="#arch-unitroot-cointegration-dynamicolsresults-long-run-variance" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.DynamicOLSResults.long_run_variance">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DynamicOLSResults.</span></span><span class="sig-name descname"><span class="pre">long_run_variance</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.long_run_variance" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DynamicOLSResults.</span></span><span class="sig-name descname"><span class="pre">long_run_variance</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.long_run_variance" title="Link to this definition">¶</a></dt>
 <dd><p>The long-run variance of the regression residual.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.unitroot.cointegration.DynamicOLSResults.long_run_variance-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.long_run_variance-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The estimated long-run variance of the residual.</p>
 </dd>
 <dt class="field-even" id="arch.unitroot.cointegration.DynamicOLSResults.long_run_variance-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.long_run_variance-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -1126,7 +1126,7 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults-long-run-variance">arch.un
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1173,7 +1173,7 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults-long-run-variance">arch.un
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.params.html b/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.params.html
index cc8178614b..595c07dac8 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.params.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.params - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.params - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -998,10 +998,10 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-dynamicolsresults-params">arch.unitroot.cointegration.DynamicOLSResults.params<a class="headerlink" href="#arch-unitroot-cointegration-dynamicolsresults-params" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-dynamicolsresults-params">arch.unitroot.cointegration.DynamicOLSResults.params<a class="headerlink" href="#arch-unitroot-cointegration-dynamicolsresults-params" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.DynamicOLSResults.params">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DynamicOLSResults.</span></span><span class="sig-name descname"><span class="pre">params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.0.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.params" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DynamicOLSResults.</span></span><span class="sig-name descname"><span class="pre">params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.params" title="Link to this definition">¶</a></dt>
 <dd><p>The estimated parameters of the cointegrating vector</p>
 </dd></dl>
 
@@ -1072,7 +1072,7 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults-params">arch.unitroot.coin
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1119,7 +1119,7 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults-params">arch.unitroot.coin
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.pvalues.html b/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.pvalues.html
index 16ab915841..3dbcd2f5a6 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.pvalues.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.pvalues.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.pvalues - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.pvalues - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -998,10 +998,10 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-dynamicolsresults-pvalues">arch.unitroot.cointegration.DynamicOLSResults.pvalues<a class="headerlink" href="#arch-unitroot-cointegration-dynamicolsresults-pvalues" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-dynamicolsresults-pvalues">arch.unitroot.cointegration.DynamicOLSResults.pvalues<a class="headerlink" href="#arch-unitroot-cointegration-dynamicolsresults-pvalues" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.DynamicOLSResults.pvalues">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DynamicOLSResults.</span></span><span class="sig-name descname"><span class="pre">pvalues</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.0.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.pvalues" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DynamicOLSResults.</span></span><span class="sig-name descname"><span class="pre">pvalues</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.pvalues" title="Link to this definition">¶</a></dt>
 <dd><p>P-value of the parameters in the cointegrating vector</p>
 </dd></dl>
 
@@ -1072,7 +1072,7 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults-pvalues">arch.unitroot.coi
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1119,7 +1119,7 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults-pvalues">arch.unitroot.coi
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.resid.html b/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.resid.html
index 2a5d9c0055..04086b0571 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.resid.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.resid.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.resid - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.resid - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -998,10 +998,10 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-dynamicolsresults-resid">arch.unitroot.cointegration.DynamicOLSResults.resid<a class="headerlink" href="#arch-unitroot-cointegration-dynamicolsresults-resid" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-dynamicolsresults-resid">arch.unitroot.cointegration.DynamicOLSResults.resid<a class="headerlink" href="#arch-unitroot-cointegration-dynamicolsresults-resid" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.DynamicOLSResults.resid">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DynamicOLSResults.</span></span><span class="sig-name descname"><span class="pre">resid</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.0.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.resid" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DynamicOLSResults.</span></span><span class="sig-name descname"><span class="pre">resid</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.resid" title="Link to this definition">¶</a></dt>
 <dd><p>The model residuals</p>
 </dd></dl>
 
@@ -1072,7 +1072,7 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults-resid">arch.unitroot.coint
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1119,7 +1119,7 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults-resid">arch.unitroot.coint
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.residual_variance.html b/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.residual_variance.html
index 12ab9aa584..8b3320c8db 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.residual_variance.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.residual_variance.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.residual_variance - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.residual_variance - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1038,17 +1038,17 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-dynamicolsresults-residual-variance">arch.unitroot.cointegration.DynamicOLSResults.residual_variance<a class="headerlink" href="#arch-unitroot-cointegration-dynamicolsresults-residual-variance" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-dynamicolsresults-residual-variance">arch.unitroot.cointegration.DynamicOLSResults.residual_variance<a class="headerlink" href="#arch-unitroot-cointegration-dynamicolsresults-residual-variance" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.DynamicOLSResults.residual_variance">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DynamicOLSResults.</span></span><span class="sig-name descname"><span class="pre">residual_variance</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.residual_variance" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DynamicOLSResults.</span></span><span class="sig-name descname"><span class="pre">residual_variance</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.residual_variance" title="Link to this definition">¶</a></dt>
 <dd><p>The variance of the regression residual.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.unitroot.cointegration.DynamicOLSResults.residual_variance-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.residual_variance-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The estimated residual variance.</p>
 </dd>
 <dt class="field-even" id="arch.unitroot.cointegration.DynamicOLSResults.residual_variance-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.residual_variance-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -1128,7 +1128,7 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults-residual-variance">arch.un
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1175,10 +1175,10 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults-residual-variance">arch.un
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+        <script id="MathJax-script" src="../../../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
     
   </body>
 </html>
\ No newline at end of file
diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.rsquared.html b/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.rsquared.html
index 57f5b44358..c73bb85c8c 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.rsquared.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.rsquared.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.rsquared - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.rsquared - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -998,10 +998,10 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-dynamicolsresults-rsquared">arch.unitroot.cointegration.DynamicOLSResults.rsquared<a class="headerlink" href="#arch-unitroot-cointegration-dynamicolsresults-rsquared" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-dynamicolsresults-rsquared">arch.unitroot.cointegration.DynamicOLSResults.rsquared<a class="headerlink" href="#arch-unitroot-cointegration-dynamicolsresults-rsquared" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.DynamicOLSResults.rsquared">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DynamicOLSResults.</span></span><span class="sig-name descname"><span class="pre">rsquared</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.rsquared" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DynamicOLSResults.</span></span><span class="sig-name descname"><span class="pre">rsquared</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.rsquared" title="Link to this definition">¶</a></dt>
 <dd><p>The model R²</p>
 </dd></dl>
 
@@ -1072,7 +1072,7 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults-rsquared">arch.unitroot.co
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1119,7 +1119,7 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults-rsquared">arch.unitroot.co
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.rsquared_adj.html b/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.rsquared_adj.html
index 2fdd3702e6..b44c3d95c6 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.rsquared_adj.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.rsquared_adj.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.rsquared_adj - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.rsquared_adj - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -998,10 +998,10 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-dynamicolsresults-rsquared-adj">arch.unitroot.cointegration.DynamicOLSResults.rsquared_adj<a class="headerlink" href="#arch-unitroot-cointegration-dynamicolsresults-rsquared-adj" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-dynamicolsresults-rsquared-adj">arch.unitroot.cointegration.DynamicOLSResults.rsquared_adj<a class="headerlink" href="#arch-unitroot-cointegration-dynamicolsresults-rsquared-adj" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.DynamicOLSResults.rsquared_adj">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DynamicOLSResults.</span></span><span class="sig-name descname"><span class="pre">rsquared_adj</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.rsquared_adj" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DynamicOLSResults.</span></span><span class="sig-name descname"><span class="pre">rsquared_adj</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.rsquared_adj" title="Link to this definition">¶</a></dt>
 <dd><p>The degree-of-freedom adjusted R²</p>
 </dd></dl>
 
@@ -1072,7 +1072,7 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults-rsquared-adj">arch.unitroo
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1119,7 +1119,7 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults-rsquared-adj">arch.unitroo
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.std_errors.html b/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.std_errors.html
index 0784024887..ca016743ba 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.std_errors.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.std_errors.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.std_errors - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.std_errors - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -998,10 +998,10 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-dynamicolsresults-std-errors">arch.unitroot.cointegration.DynamicOLSResults.std_errors<a class="headerlink" href="#arch-unitroot-cointegration-dynamicolsresults-std-errors" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-dynamicolsresults-std-errors">arch.unitroot.cointegration.DynamicOLSResults.std_errors<a class="headerlink" href="#arch-unitroot-cointegration-dynamicolsresults-std-errors" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.DynamicOLSResults.std_errors">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DynamicOLSResults.</span></span><span class="sig-name descname"><span class="pre">std_errors</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.0.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.std_errors" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DynamicOLSResults.</span></span><span class="sig-name descname"><span class="pre">std_errors</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.std_errors" title="Link to this definition">¶</a></dt>
 <dd><p>Standard errors  of the parameters in the cointegrating vector</p>
 </dd></dl>
 
@@ -1072,7 +1072,7 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults-std-errors">arch.unitroot.
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1119,7 +1119,7 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults-std-errors">arch.unitroot.
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.summary.html b/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.summary.html
index ef97cdff97..4de93fa42a 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.summary.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.summary.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.summary - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.summary - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1078,15 +1078,15 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-dynamicolsresults-summary">arch.unitroot.cointegration.DynamicOLSResults.summary<a class="headerlink" href="#arch-unitroot-cointegration-dynamicolsresults-summary" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-dynamicolsresults-summary">arch.unitroot.cointegration.DynamicOLSResults.summary<a class="headerlink" href="#arch-unitroot-cointegration-dynamicolsresults-summary" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.DynamicOLSResults.summary">
-<span class="sig-prename descclassname"><span class="pre">DynamicOLSResults.</span></span><span class="sig-name descname"><span class="pre">summary</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLSResults.summary.full" title="arch.unitroot.cointegration.DynamicOLSResults.summary.full (Python parameter) — Flag indicating whether to include all estimated parameters (True) or only the parameters of the cointegrating vector"><span class="n"><span class="pre">full</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/unitroot/cointegration.html#DynamicOLSResults.summary"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.summary" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">DynamicOLSResults.</span></span><span class="sig-name descname"><span class="pre">summary</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLSResults.summary.full" title="arch.unitroot.cointegration.DynamicOLSResults.summary.full (Python parameter) — Flag indicating whether to include all estimated parameters (True) or only the parameters of the cointegrating vector"><span class="n"><span class="pre">full</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.iolib.summary.Summary.html#statsmodels.iolib.summary.Summary" title="(in statsmodels 0.15.0 (+59) v0.15.0 (+59))"><span class="pre">Summary</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/unitroot/cointegration.html#DynamicOLSResults.summary"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.summary" title="Link to this definition">¶</a></dt>
 <dd><p>Summary of the model, containing estimated parameters and std. errors</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.unitroot.cointegration.DynamicOLSResults.summary-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.summary-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.unitroot.cointegration.DynamicOLSResults.summary.full"><span class="sig-name descname">full</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, default False</span></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.summary.full" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to include all estimated parameters
+<dt id="arch.unitroot.cointegration.DynamicOLSResults.summary.full"><span class="n sig-name">full</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.summary.full" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to include all estimated parameters
 (True) or only the parameters of the cointegrating vector</p>
 </dd>
 </dl>
@@ -1096,7 +1096,7 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults-summary">arch.unitroot.coi
 or latex.</p>
 </dd>
 <dt class="field-odd" id="arch.unitroot.cointegration.DynamicOLSResults.summary-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.summary-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>Summary</p>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">Summary</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1168,7 +1168,7 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults-summary">arch.unitroot.coi
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1215,7 +1215,7 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults-summary">arch.unitroot.coi
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.tvalues.html b/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.tvalues.html
index 0d50fc0da5..32ddcfdadf 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.tvalues.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.tvalues.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.tvalues - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.tvalues - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -998,10 +998,10 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-dynamicolsresults-tvalues">arch.unitroot.cointegration.DynamicOLSResults.tvalues<a class="headerlink" href="#arch-unitroot-cointegration-dynamicolsresults-tvalues" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-dynamicolsresults-tvalues">arch.unitroot.cointegration.DynamicOLSResults.tvalues<a class="headerlink" href="#arch-unitroot-cointegration-dynamicolsresults-tvalues" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.DynamicOLSResults.tvalues">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DynamicOLSResults.</span></span><span class="sig-name descname"><span class="pre">tvalues</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.0.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.tvalues" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DynamicOLSResults.</span></span><span class="sig-name descname"><span class="pre">tvalues</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.tvalues" title="Link to this definition">¶</a></dt>
 <dd><p>T-statistics of the parameters in the cointegrating vector</p>
 </dd></dl>
 
@@ -1072,7 +1072,7 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults-tvalues">arch.unitroot.coi
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1119,7 +1119,7 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults-tvalues">arch.unitroot.coi
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.alternative_hypothesis.html b/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.alternative_hypothesis.html
index b23b4aafa7..2234b560e0 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.alternative_hypothesis.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.alternative_hypothesis.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.EngleGrangerTestResults.alternative_hypothesis - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.EngleGrangerTestResults.alternative_hypothesis - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -953,10 +953,10 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-englegrangertestresults-alternative-hypothesis">arch.unitroot.cointegration.EngleGrangerTestResults.alternative_hypothesis<a class="headerlink" href="#arch-unitroot-cointegration-englegrangertestresults-alternative-hypothesis" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-englegrangertestresults-alternative-hypothesis">arch.unitroot.cointegration.EngleGrangerTestResults.alternative_hypothesis<a class="headerlink" href="#arch-unitroot-cointegration-englegrangertestresults-alternative-hypothesis" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.EngleGrangerTestResults.alternative_hypothesis">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">EngleGrangerTestResults.</span></span><span class="sig-name descname"><span class="pre">alternative_hypothesis</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.alternative_hypothesis" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">EngleGrangerTestResults.</span></span><span class="sig-name descname"><span class="pre">alternative_hypothesis</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.alternative_hypothesis" title="Link to this definition">¶</a></dt>
 <dd><p>The alternative hypothesis</p>
 </dd></dl>
 
@@ -1027,7 +1027,7 @@ <h1 id="arch-unitroot-cointegration-englegrangertestresults-alternative-hypothes
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1074,7 +1074,7 @@ <h1 id="arch-unitroot-cointegration-englegrangertestresults-alternative-hypothes
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.cointegrating_vector.html b/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.cointegrating_vector.html
index 88836e48d5..bb5f38ef96 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.cointegrating_vector.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.cointegrating_vector.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.EngleGrangerTestResults.cointegrating_vector - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.EngleGrangerTestResults.cointegrating_vector - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -953,10 +953,10 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-englegrangertestresults-cointegrating-vector">arch.unitroot.cointegration.EngleGrangerTestResults.cointegrating_vector<a class="headerlink" href="#arch-unitroot-cointegration-englegrangertestresults-cointegrating-vector" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-englegrangertestresults-cointegrating-vector">arch.unitroot.cointegration.EngleGrangerTestResults.cointegrating_vector<a class="headerlink" href="#arch-unitroot-cointegration-englegrangertestresults-cointegrating-vector" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.EngleGrangerTestResults.cointegrating_vector">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">EngleGrangerTestResults.</span></span><span class="sig-name descname"><span class="pre">cointegrating_vector</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.0.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.cointegrating_vector" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">EngleGrangerTestResults.</span></span><span class="sig-name descname"><span class="pre">cointegrating_vector</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.cointegrating_vector" title="Link to this definition">¶</a></dt>
 <dd><p>The estimated cointegrating vector.</p>
 </dd></dl>
 
@@ -1027,7 +1027,7 @@ <h1 id="arch-unitroot-cointegration-englegrangertestresults-cointegrating-vector
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1074,7 +1074,7 @@ <h1 id="arch-unitroot-cointegration-englegrangertestresults-cointegrating-vector
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.critical_values.html b/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.critical_values.html
index 400d7dc259..c3b1968bbe 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.critical_values.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.critical_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.EngleGrangerTestResults.critical_values - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.EngleGrangerTestResults.critical_values - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -993,10 +993,10 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-englegrangertestresults-critical-values">arch.unitroot.cointegration.EngleGrangerTestResults.critical_values<a class="headerlink" href="#arch-unitroot-cointegration-englegrangertestresults-critical-values" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-englegrangertestresults-critical-values">arch.unitroot.cointegration.EngleGrangerTestResults.critical_values<a class="headerlink" href="#arch-unitroot-cointegration-englegrangertestresults-critical-values" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.EngleGrangerTestResults.critical_values">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">EngleGrangerTestResults.</span></span><span class="sig-name descname"><span class="pre">critical_values</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.0.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.critical_values" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">EngleGrangerTestResults.</span></span><span class="sig-name descname"><span class="pre">critical_values</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.critical_values" title="Link to this definition">¶</a></dt>
 <dd><p>Critical Values</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.unitroot.cointegration.EngleGrangerTestResults.critical_values-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.critical_values-returns" title="Permalink to this headline">¶</a></dt>
@@ -1004,7 +1004,7 @@ <h1 id="arch-unitroot-cointegration-englegrangertestresults-critical-values">arc
 of the test statistic.</p>
 </dd>
 <dt class="field-even" id="arch.unitroot.cointegration.EngleGrangerTestResults.critical_values-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.critical_values-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p>Series</p>
+<dd class="field-even"><p><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.Series</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1076,7 +1076,7 @@ <h1 id="arch-unitroot-cointegration-englegrangertestresults-critical-values">arc
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1123,7 +1123,7 @@ <h1 id="arch-unitroot-cointegration-englegrangertestresults-critical-values">arc
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.distribution_order.html b/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.distribution_order.html
index f44092a0c6..77574bd1ea 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.distribution_order.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.distribution_order.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.EngleGrangerTestResults.distribution_order - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.EngleGrangerTestResults.distribution_order - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -953,10 +953,10 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-englegrangertestresults-distribution-order">arch.unitroot.cointegration.EngleGrangerTestResults.distribution_order<a class="headerlink" href="#arch-unitroot-cointegration-englegrangertestresults-distribution-order" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-englegrangertestresults-distribution-order">arch.unitroot.cointegration.EngleGrangerTestResults.distribution_order<a class="headerlink" href="#arch-unitroot-cointegration-englegrangertestresults-distribution-order" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.EngleGrangerTestResults.distribution_order">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">EngleGrangerTestResults.</span></span><span class="sig-name descname"><span class="pre">distribution_order</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.distribution_order" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">EngleGrangerTestResults.</span></span><span class="sig-name descname"><span class="pre">distribution_order</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.distribution_order" title="Link to this definition">¶</a></dt>
 <dd><p>The number of stochastic trends under the null hypothesis.</p>
 </dd></dl>
 
@@ -1027,7 +1027,7 @@ <h1 id="arch-unitroot-cointegration-englegrangertestresults-distribution-order">
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1074,7 +1074,7 @@ <h1 id="arch-unitroot-cointegration-englegrangertestresults-distribution-order">
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.lags.html b/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.lags.html
index 992e936a70..b8ebd533bf 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.lags.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.lags.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.EngleGrangerTestResults.lags - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.EngleGrangerTestResults.lags - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -953,10 +953,10 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-englegrangertestresults-lags">arch.unitroot.cointegration.EngleGrangerTestResults.lags<a class="headerlink" href="#arch-unitroot-cointegration-englegrangertestresults-lags" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-englegrangertestresults-lags">arch.unitroot.cointegration.EngleGrangerTestResults.lags<a class="headerlink" href="#arch-unitroot-cointegration-englegrangertestresults-lags" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.EngleGrangerTestResults.lags">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">EngleGrangerTestResults.</span></span><span class="sig-name descname"><span class="pre">lags</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.lags" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">EngleGrangerTestResults.</span></span><span class="sig-name descname"><span class="pre">lags</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.lags" title="Link to this definition">¶</a></dt>
 <dd><p>The number of lags used in the Augmented Dickey-Fuller regression.</p>
 </dd></dl>
 
@@ -1027,7 +1027,7 @@ <h1 id="arch-unitroot-cointegration-englegrangertestresults-lags">arch.unitroot.
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1074,7 +1074,7 @@ <h1 id="arch-unitroot-cointegration-englegrangertestresults-lags">arch.unitroot.
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.max_lags.html b/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.max_lags.html
index d1a6750b8e..c3f19b06f0 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.max_lags.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.max_lags.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.EngleGrangerTestResults.max_lags - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.EngleGrangerTestResults.max_lags - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -953,10 +953,10 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-englegrangertestresults-max-lags">arch.unitroot.cointegration.EngleGrangerTestResults.max_lags<a class="headerlink" href="#arch-unitroot-cointegration-englegrangertestresults-max-lags" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-englegrangertestresults-max-lags">arch.unitroot.cointegration.EngleGrangerTestResults.max_lags<a class="headerlink" href="#arch-unitroot-cointegration-englegrangertestresults-max-lags" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.EngleGrangerTestResults.max_lags">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">EngleGrangerTestResults.</span></span><span class="sig-name descname"><span class="pre">max_lags</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.max_lags" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">EngleGrangerTestResults.</span></span><span class="sig-name descname"><span class="pre">max_lags</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.max_lags" title="Link to this definition">¶</a></dt>
 <dd><p>The maximum number of lags used in the lag-length selection.</p>
 </dd></dl>
 
@@ -1027,7 +1027,7 @@ <h1 id="arch-unitroot-cointegration-englegrangertestresults-max-lags">arch.unitr
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1074,7 +1074,7 @@ <h1 id="arch-unitroot-cointegration-englegrangertestresults-max-lags">arch.unitr
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.name.html b/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.name.html
index 35aeb46c83..42c56df34f 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.name.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.EngleGrangerTestResults.name - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.EngleGrangerTestResults.name - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -953,10 +953,10 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-englegrangertestresults-name">arch.unitroot.cointegration.EngleGrangerTestResults.name<a class="headerlink" href="#arch-unitroot-cointegration-englegrangertestresults-name" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-englegrangertestresults-name">arch.unitroot.cointegration.EngleGrangerTestResults.name<a class="headerlink" href="#arch-unitroot-cointegration-englegrangertestresults-name" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.EngleGrangerTestResults.name">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">EngleGrangerTestResults.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.name" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">EngleGrangerTestResults.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.name" title="Link to this definition">¶</a></dt>
 <dd><p>Sets or gets the name of the cointegration test</p>
 </dd></dl>
 
@@ -1027,7 +1027,7 @@ <h1 id="arch-unitroot-cointegration-englegrangertestresults-name">arch.unitroot.
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1074,7 +1074,7 @@ <h1 id="arch-unitroot-cointegration-englegrangertestresults-name">arch.unitroot.
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.null_hypothesis.html b/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.null_hypothesis.html
index 6f5725e203..36dc009775 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.null_hypothesis.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.null_hypothesis.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.EngleGrangerTestResults.null_hypothesis - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.EngleGrangerTestResults.null_hypothesis - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -953,10 +953,10 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-englegrangertestresults-null-hypothesis">arch.unitroot.cointegration.EngleGrangerTestResults.null_hypothesis<a class="headerlink" href="#arch-unitroot-cointegration-englegrangertestresults-null-hypothesis" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-englegrangertestresults-null-hypothesis">arch.unitroot.cointegration.EngleGrangerTestResults.null_hypothesis<a class="headerlink" href="#arch-unitroot-cointegration-englegrangertestresults-null-hypothesis" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.EngleGrangerTestResults.null_hypothesis">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">EngleGrangerTestResults.</span></span><span class="sig-name descname"><span class="pre">null_hypothesis</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.null_hypothesis" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">EngleGrangerTestResults.</span></span><span class="sig-name descname"><span class="pre">null_hypothesis</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.null_hypothesis" title="Link to this definition">¶</a></dt>
 <dd><p>The null hypothesis</p>
 </dd></dl>
 
@@ -1027,7 +1027,7 @@ <h1 id="arch-unitroot-cointegration-englegrangertestresults-null-hypothesis">arc
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1074,7 +1074,7 @@ <h1 id="arch-unitroot-cointegration-englegrangertestresults-null-hypothesis">arc
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.plot.html b/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.plot.html
index 1c83898156..7561886b74 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.plot.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.plot.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.EngleGrangerTestResults.plot - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.EngleGrangerTestResults.plot - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1047,17 +1047,17 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-englegrangertestresults-plot">arch.unitroot.cointegration.EngleGrangerTestResults.plot<a class="headerlink" href="#arch-unitroot-cointegration-englegrangertestresults-plot" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-englegrangertestresults-plot">arch.unitroot.cointegration.EngleGrangerTestResults.plot<a class="headerlink" href="#arch-unitroot-cointegration-englegrangertestresults-plot" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.EngleGrangerTestResults.plot">
-<span class="sig-prename descclassname"><span class="pre">EngleGrangerTestResults.</span></span><span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.EngleGrangerTestResults.plot.axes" title="arch.unitroot.cointegration.EngleGrangerTestResults.plot.axes (Python parameter) — matplotlib axes instance to hold the figure."><span class="n"><span class="pre">axes</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.EngleGrangerTestResults.plot.title" title="arch.unitroot.cointegration.EngleGrangerTestResults.plot.title (Python parameter) — Title for the figure."><span class="n"><span class="pre">title</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.plot" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.unitroot.cointegration.EngleGrangerTestResults.plot">
+<span class="sig-prename descclassname"><span class="pre">EngleGrangerTestResults.</span></span><span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.EngleGrangerTestResults.plot.axes" title="arch.unitroot.cointegration.EngleGrangerTestResults.plot.axes (Python parameter) — matplotlib axes instance to hold the figure."><span class="n"><span class="pre">axes</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="(in Matplotlib v3.8.0)"><span class="pre">Axes</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.EngleGrangerTestResults.plot.title" title="arch.unitroot.cointegration.EngleGrangerTestResults.plot.title (Python parameter) — Title for the figure."><span class="n"><span class="pre">title</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="(in Matplotlib v3.8.0)"><span class="pre">Figure</span></a></span></span></span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.plot" title="Link to this definition">¶</a></dt>
 <dd><p>Plot the cointegration residuals.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.unitroot.cointegration.EngleGrangerTestResults.plot-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.plot-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.unitroot.cointegration.EngleGrangerTestResults.plot.axes"><span class="sig-name descname">axes</span><span class="p"> : </span><span class="desctype"><span class="desctype">Axes, default None</span></span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.plot.axes" title="Permalink to this definition">¶</a></dt><dd><p>matplotlib axes instance to hold the figure.</p>
+<dt id="arch.unitroot.cointegration.EngleGrangerTestResults.plot.axes"><span class="n sig-name">axes</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="(in Matplotlib v3.8.0)">Axes</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.plot.axes" title="Permalink to this definition">¶</a></dt><dd><p>matplotlib axes instance to hold the figure.</p>
 </dd>
-<dt id="arch.unitroot.cointegration.EngleGrangerTestResults.plot.title"><span class="sig-name descname">title</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, default None</span></span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.plot.title" title="Permalink to this definition">¶</a></dt><dd><p>Title for the figure.</p>
+<dt id="arch.unitroot.cointegration.EngleGrangerTestResults.plot.title"><span class="n sig-name">title</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.plot.title" title="Permalink to this definition">¶</a></dt><dd><p>Title for the figure.</p>
 </dd>
 </dl>
 </dd>
@@ -1065,7 +1065,7 @@ <h1 id="arch-unitroot-cointegration-englegrangertestresults-plot">arch.unitroot.
 <dd class="field-even"><p>The matplotlib Figure instance.</p>
 </dd>
 <dt class="field-odd" id="arch.unitroot.cointegration.EngleGrangerTestResults.plot-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.plot-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>Figure</p>
+<dd class="field-odd"><p><a class="reference external" href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="(in Matplotlib v3.8.0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">matplotlib.figure.Figure</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1137,7 +1137,7 @@ <h1 id="arch-unitroot-cointegration-englegrangertestresults-plot">arch.unitroot.
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1184,7 +1184,7 @@ <h1 id="arch-unitroot-cointegration-englegrangertestresults-plot">arch.unitroot.
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.pvalue.html b/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.pvalue.html
index 2968c93dd3..23d4caef3b 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.pvalue.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.pvalue.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.EngleGrangerTestResults.pvalue - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.EngleGrangerTestResults.pvalue - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -953,10 +953,10 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-englegrangertestresults-pvalue">arch.unitroot.cointegration.EngleGrangerTestResults.pvalue<a class="headerlink" href="#arch-unitroot-cointegration-englegrangertestresults-pvalue" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-englegrangertestresults-pvalue">arch.unitroot.cointegration.EngleGrangerTestResults.pvalue<a class="headerlink" href="#arch-unitroot-cointegration-englegrangertestresults-pvalue" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.EngleGrangerTestResults.pvalue">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">EngleGrangerTestResults.</span></span><span class="sig-name descname"><span class="pre">pvalue</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.pvalue" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">EngleGrangerTestResults.</span></span><span class="sig-name descname"><span class="pre">pvalue</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.pvalue" title="Link to this definition">¶</a></dt>
 <dd><p>The p-value of the test statistic.</p>
 </dd></dl>
 
@@ -1027,7 +1027,7 @@ <h1 id="arch-unitroot-cointegration-englegrangertestresults-pvalue">arch.unitroo
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1074,7 +1074,7 @@ <h1 id="arch-unitroot-cointegration-englegrangertestresults-pvalue">arch.unitroo
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.resid.html b/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.resid.html
index bc792ea992..4efb7d155f 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.resid.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.resid.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.EngleGrangerTestResults.resid - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.EngleGrangerTestResults.resid - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -953,10 +953,10 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-englegrangertestresults-resid">arch.unitroot.cointegration.EngleGrangerTestResults.resid<a class="headerlink" href="#arch-unitroot-cointegration-englegrangertestresults-resid" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-englegrangertestresults-resid">arch.unitroot.cointegration.EngleGrangerTestResults.resid<a class="headerlink" href="#arch-unitroot-cointegration-englegrangertestresults-resid" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.EngleGrangerTestResults.resid">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">EngleGrangerTestResults.</span></span><span class="sig-name descname"><span class="pre">resid</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.0.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.resid" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">EngleGrangerTestResults.</span></span><span class="sig-name descname"><span class="pre">resid</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.resid" title="Link to this definition">¶</a></dt>
 <dd><p>The residual from the cointegrating regression.</p>
 </dd></dl>
 
@@ -1027,7 +1027,7 @@ <h1 id="arch-unitroot-cointegration-englegrangertestresults-resid">arch.unitroot
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1074,7 +1074,7 @@ <h1 id="arch-unitroot-cointegration-englegrangertestresults-resid">arch.unitroot
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.rho.html b/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.rho.html
index 190d700aa6..beffbf750a 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.rho.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.rho.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.EngleGrangerTestResults.rho - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.EngleGrangerTestResults.rho - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -993,17 +993,17 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-englegrangertestresults-rho">arch.unitroot.cointegration.EngleGrangerTestResults.rho<a class="headerlink" href="#arch-unitroot-cointegration-englegrangertestresults-rho" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-englegrangertestresults-rho">arch.unitroot.cointegration.EngleGrangerTestResults.rho<a class="headerlink" href="#arch-unitroot-cointegration-englegrangertestresults-rho" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.EngleGrangerTestResults.rho">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">EngleGrangerTestResults.</span></span><span class="sig-name descname"><span class="pre">rho</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.rho" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">EngleGrangerTestResults.</span></span><span class="sig-name descname"><span class="pre">rho</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.rho" title="Link to this definition">¶</a></dt>
 <dd><p>The estimated coefficient in the Dickey-Fuller Test</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.unitroot.cointegration.EngleGrangerTestResults.rho-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.rho-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The coefficient.</p>
 </dd>
 <dt class="field-even" id="arch.unitroot.cointegration.EngleGrangerTestResults.rho-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.rho-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -1081,7 +1081,7 @@ <h1 id="arch-unitroot-cointegration-englegrangertestresults-rho">arch.unitroot.c
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1128,10 +1128,10 @@ <h1 id="arch-unitroot-cointegration-englegrangertestresults-rho">arch.unitroot.c
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
         <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+        <script id="MathJax-script" src="../../../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
     
   </body>
 </html>
\ No newline at end of file
diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.stat.html b/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.stat.html
index c7ee9d9a1f..7171a01d63 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.stat.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.stat.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.EngleGrangerTestResults.stat - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.EngleGrangerTestResults.stat - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -953,10 +953,10 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-englegrangertestresults-stat">arch.unitroot.cointegration.EngleGrangerTestResults.stat<a class="headerlink" href="#arch-unitroot-cointegration-englegrangertestresults-stat" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-englegrangertestresults-stat">arch.unitroot.cointegration.EngleGrangerTestResults.stat<a class="headerlink" href="#arch-unitroot-cointegration-englegrangertestresults-stat" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.EngleGrangerTestResults.stat">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">EngleGrangerTestResults.</span></span><span class="sig-name descname"><span class="pre">stat</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.stat" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">EngleGrangerTestResults.</span></span><span class="sig-name descname"><span class="pre">stat</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.stat" title="Link to this definition">¶</a></dt>
 <dd><p>The test statistic.</p>
 </dd></dl>
 
@@ -1027,7 +1027,7 @@ <h1 id="arch-unitroot-cointegration-englegrangertestresults-stat">arch.unitroot.
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1074,7 +1074,7 @@ <h1 id="arch-unitroot-cointegration-englegrangertestresults-stat">arch.unitroot.
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.summary.html b/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.summary.html
index 0ffd88ce50..406ebf0b45 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.summary.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.summary.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.EngleGrangerTestResults.summary - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.EngleGrangerTestResults.summary - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -599,19 +599,6 @@
     <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="arch.unitroot.cointegration.EngleGrangerTestResults.summary (Python method) — Summary of test, containing statistic, p-value and critical values" class="md-ellipsis">Engle<wbr>Granger<wbr>Test<wbr>Results.<wbr>summary</span>
   </a>
   
-    <nav class="md-nav" aria-label="EngleGrangerTestResults.summary">
-      <ul class="md-nav__list">
-        
-          <li class="md-nav__item">
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-    <span class="md-ellipsis">Return type</span>
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-  
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@@ -950,19 +937,6 @@
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-    <nav class="md-nav" aria-label="EngleGrangerTestResults.summary">
-      <ul class="md-nav__list">
-        
-          <li class="md-nav__item">
-  <a href="#arch.unitroot.cointegration.EngleGrangerTestResults.summary-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
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-  
-</li>
-        
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-  
 </li>
       
     </ul>
@@ -979,16 +953,11 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-englegrangertestresults-summary">arch.unitroot.cointegration.EngleGrangerTestResults.summary<a class="headerlink" href="#arch-unitroot-cointegration-englegrangertestresults-summary" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-englegrangertestresults-summary">arch.unitroot.cointegration.EngleGrangerTestResults.summary<a class="headerlink" href="#arch-unitroot-cointegration-englegrangertestresults-summary" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.EngleGrangerTestResults.summary">
-<span class="sig-prename descclassname"><span class="pre">EngleGrangerTestResults.</span></span><span class="sig-name descname"><span class="pre">summary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/unitroot/_engle_granger.html#EngleGrangerTestResults.summary"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.summary" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">EngleGrangerTestResults.</span></span><span class="sig-name descname"><span class="pre">summary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.iolib.summary.Summary.html#statsmodels.iolib.summary.Summary" title="(in statsmodels 0.15.0 (+59) v0.15.0 (+59))"><span class="pre">Summary</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/unitroot/_engle_granger.html#EngleGrangerTestResults.summary"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.summary" title="Link to this definition">¶</a></dt>
 <dd><p>Summary of test, containing statistic, p-value and critical values</p>
-<dl class="field-list simple">
-<dt class="field-odd" id="arch.unitroot.cointegration.EngleGrangerTestResults.summary-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.summary-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.iolib.summary.Summary.html#statsmodels.iolib.summary.Summary" title="(in statsmodels 0.15.0 (+8) v0.15.0 (+8))"><code class="xref py py-class docutils literal notranslate"><span class="pre">Summary</span></code></a></p>
-</dd>
-</dl>
 </dd></dl>
 
 
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-    7.0.1.
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diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.trend.html b/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.trend.html
index 57ca42439e..b05259a1b8 100644
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+++ b/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.trend.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.EngleGrangerTestResults.trend - arch 6.1.0</title>
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+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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+            arch 6.2.0
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -953,10 +953,10 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-englegrangertestresults-trend">arch.unitroot.cointegration.EngleGrangerTestResults.trend<a class="headerlink" href="#arch-unitroot-cointegration-englegrangertestresults-trend" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-englegrangertestresults-trend">arch.unitroot.cointegration.EngleGrangerTestResults.trend<a class="headerlink" href="#arch-unitroot-cointegration-englegrangertestresults-trend" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.EngleGrangerTestResults.trend">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">EngleGrangerTestResults.</span></span><span class="sig-name descname"><span class="pre">trend</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.trend" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">EngleGrangerTestResults.</span></span><span class="sig-name descname"><span class="pre">trend</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.trend" title="Link to this definition">¶</a></dt>
 <dd><p>The trend used in the cointegrating regression</p>
 </dd></dl>
 
@@ -1027,7 +1027,7 @@ <h1 id="arch-unitroot-cointegration-englegrangertestresults-trend">arch.unitroot
   
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     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.FullyModifiedOLS.fit.html b/unitroot/generated/generated/arch.unitroot.cointegration.FullyModifiedOLS.fit.html
index d67fa0b75a..6c89925918 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.FullyModifiedOLS.fit.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.FullyModifiedOLS.fit.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.FullyModifiedOLS.fit - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.FullyModifiedOLS.fit - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
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@@ -102,7 +102,7 @@
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+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -794,30 +794,30 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-fullymodifiedols-fit">arch.unitroot.cointegration.FullyModifiedOLS.fit<a class="headerlink" href="#arch-unitroot-cointegration-fullymodifiedols-fit" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-fullymodifiedols-fit">arch.unitroot.cointegration.FullyModifiedOLS.fit<a class="headerlink" href="#arch-unitroot-cointegration-fullymodifiedols-fit" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.unitroot.cointegration.FullyModifiedOLS.fit">
-<span class="sig-prename descclassname"><span class="pre">FullyModifiedOLS.</span></span><span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.FullyModifiedOLS.fit.kernel" title="arch.unitroot.cointegration.FullyModifiedOLS.fit.kernel (Python parameter) — The string name of any of any known kernel-based long-run covariance estimators. Common choices are &quot;bartlett&quot; for the Bartlett kernel (Newey-West), &quot;parzen&quot; for the Parzen kernel and &quot;quadratic-spectral&quot; for the Quadratic Spectral kernel."><span class="n"><span class="pre">kernel</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;bartlett&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.FullyModifiedOLS.fit.bandwidth" title="arch.unitroot.cointegration.FullyModifiedOLS.fit.bandwidth (Python parameter) — The bandwidth to use. If not provided, the optimal bandwidth is estimated from the data. Setting the bandwidth to 0 and using &quot;unadjusted&quot; produces the classic OLS covariance estimator. Setting the bandwidth to 0 and using &quot;robust&quot; produces White's covariance estimator."><span class="n"><span class="pre">bandwidth</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.FullyModifiedOLS.fit.force_int" title="arch.unitroot.cointegration.FullyModifiedOLS.fit.force_int (Python parameter) — Whether the force the estimated optimal bandwidth to be an integer."><span class="n"><span class="pre">force_int</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.FullyModifiedOLS.fit.diff" title="arch.unitroot.cointegration.FullyModifiedOLS.fit.diff (Python parameter) — Use differenced data to estimate the residuals."><span class="n"><span class="pre">diff</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.FullyModifiedOLS.fit.df_adjust" title="arch.unitroot.cointegration.FullyModifiedOLS.fit.df_adjust (Python parameter) — Whether the adjust the parameter covariance to account for the number of parameters estimated in the regression. If true, the parameter covariance estimator is multiplied by T/(T-k) where k is the number of regressors in the model."><span class="n"><span class="pre">df_adjust</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/unitroot/cointegration.html#FullyModifiedOLS.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.cointegration.FullyModifiedOLS.fit" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">FullyModifiedOLS.</span></span><span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.FullyModifiedOLS.fit.kernel" title="arch.unitroot.cointegration.FullyModifiedOLS.fit.kernel (Python parameter) — The string name of any of any known kernel-based long-run covariance estimators. Common choices are &quot;bartlett&quot; for the Bartlett kernel (Newey-West), &quot;parzen&quot; for the Parzen kernel and &quot;quadratic-spectral&quot; for the Quadratic Spectral kernel."><span class="n"><span class="pre">kernel</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;bartlett&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.FullyModifiedOLS.fit.bandwidth" title="arch.unitroot.cointegration.FullyModifiedOLS.fit.bandwidth (Python parameter) — The bandwidth to use. If not provided, the optimal bandwidth is estimated from the data. Setting the bandwidth to 0 and using &quot;unadjusted&quot; produces the classic OLS covariance estimator. Setting the bandwidth to 0 and using &quot;robust&quot; produces White's covariance estimator."><span class="n"><span class="pre">bandwidth</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.FullyModifiedOLS.fit.force_int" title="arch.unitroot.cointegration.FullyModifiedOLS.fit.force_int (Python parameter) — Whether the force the estimated optimal bandwidth to be an integer."><span class="n"><span class="pre">force_int</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.FullyModifiedOLS.fit.diff" title="arch.unitroot.cointegration.FullyModifiedOLS.fit.diff (Python parameter) — Use differenced data to estimate the residuals."><span class="n"><span class="pre">diff</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.FullyModifiedOLS.fit.df_adjust" title="arch.unitroot.cointegration.FullyModifiedOLS.fit.df_adjust (Python parameter) — Whether the adjust the parameter covariance to account for the number of parameters estimated in the regression. If true, the parameter covariance estimator is multiplied by T/(T-k) where k is the number of regressors in the model."><span class="n"><span class="pre">df_adjust</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.unitroot.cointegration.CointegrationAnalysisResults.html#arch.unitroot.cointegration.CointegrationAnalysisResults" title="arch.unitroot.cointegration.CointegrationAnalysisResults (Python class) — summary()"><span class="pre">CointegrationAnalysisResults</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/unitroot/cointegration.html#FullyModifiedOLS.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.cointegration.FullyModifiedOLS.fit" title="Link to this definition">¶</a></dt>
 <dd><p>Estimate the cointegrating vector.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.unitroot.cointegration.FullyModifiedOLS.fit-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.cointegration.FullyModifiedOLS.fit-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.unitroot.cointegration.FullyModifiedOLS.fit.diff"><span class="sig-name descname">diff</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, default False</span></span><a class="headerlink" href="#arch.unitroot.cointegration.FullyModifiedOLS.fit.diff" title="Permalink to this definition">¶</a></dt><dd><p>Use differenced data to estimate the residuals.</p>
+<dt id="arch.unitroot.cointegration.FullyModifiedOLS.fit.diff"><span class="n sig-name">diff</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.unitroot.cointegration.FullyModifiedOLS.fit.diff" title="Permalink to this definition">¶</a></dt><dd><p>Use differenced data to estimate the residuals.</p>
 </dd>
-<dt id="arch.unitroot.cointegration.FullyModifiedOLS.fit.kernel"><span class="sig-name descname">kernel</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, default &quot;bartlett&quot;</span></span><a class="headerlink" href="#arch.unitroot.cointegration.FullyModifiedOLS.fit.kernel" title="Permalink to this definition">¶</a></dt><dd><p>The string name of any of any known kernel-based long-run
+<dt id="arch.unitroot.cointegration.FullyModifiedOLS.fit.kernel"><span class="n sig-name">kernel</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;bartlett&#39;</span></code><a class="headerlink" href="#arch.unitroot.cointegration.FullyModifiedOLS.fit.kernel" title="Permalink to this definition">¶</a></dt><dd><p>The string name of any of any known kernel-based long-run
 covariance estimators. Common choices are “bartlett” for the
 Bartlett kernel (Newey-West), “parzen” for the Parzen kernel
 and “quadratic-spectral” for the Quadratic Spectral kernel.</p>
 </dd>
-<dt id="arch.unitroot.cointegration.FullyModifiedOLS.fit.bandwidth"><span class="sig-name descname">bandwidth</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, default None</span></span><a class="headerlink" href="#arch.unitroot.cointegration.FullyModifiedOLS.fit.bandwidth" title="Permalink to this definition">¶</a></dt><dd><p>The bandwidth to use. If not provided, the optimal bandwidth is
+<dt id="arch.unitroot.cointegration.FullyModifiedOLS.fit.bandwidth"><span class="n sig-name">bandwidth</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.unitroot.cointegration.FullyModifiedOLS.fit.bandwidth" title="Permalink to this definition">¶</a></dt><dd><p>The bandwidth to use. If not provided, the optimal bandwidth is
 estimated from the data. Setting the bandwidth to 0 and using
 “unadjusted” produces the classic OLS covariance estimator.
 Setting the bandwidth to 0 and using “robust” produces White’s
 covariance estimator.</p>
 </dd>
-<dt id="arch.unitroot.cointegration.FullyModifiedOLS.fit.force_int"><span class="sig-name descname">force_int</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, default False</span></span><a class="headerlink" href="#arch.unitroot.cointegration.FullyModifiedOLS.fit.force_int" title="Permalink to this definition">¶</a></dt><dd><p>Whether the force the estimated optimal bandwidth to be an integer.</p>
+<dt id="arch.unitroot.cointegration.FullyModifiedOLS.fit.force_int"><span class="n sig-name">force_int</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.unitroot.cointegration.FullyModifiedOLS.fit.force_int" title="Permalink to this definition">¶</a></dt><dd><p>Whether the force the estimated optimal bandwidth to be an integer.</p>
 </dd>
-<dt id="arch.unitroot.cointegration.FullyModifiedOLS.fit.df_adjust"><span class="sig-name descname">df_adjust</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, default False</span></span><a class="headerlink" href="#arch.unitroot.cointegration.FullyModifiedOLS.fit.df_adjust" title="Permalink to this definition">¶</a></dt><dd><p>Whether the adjust the parameter covariance to account for the
+<dt id="arch.unitroot.cointegration.FullyModifiedOLS.fit.df_adjust"><span class="n sig-name">df_adjust</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.unitroot.cointegration.FullyModifiedOLS.fit.df_adjust" title="Permalink to this definition">¶</a></dt><dd><p>Whether the adjust the parameter covariance to account for the
 number of parameters estimated in the regression. If true, the
 parameter covariance estimator is multiplied by T/(T-k) where
 k is the number of regressors in the model.</p>
@@ -828,7 +828,7 @@ <h1 id="arch-unitroot-cointegration-fullymodifiedols-fit">arch.unitroot.cointegr
 <dd class="field-even"><p>The estimation results instance.</p>
 </dd>
 <dt class="field-odd" id="arch.unitroot.cointegration.FullyModifiedOLS.fit-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.cointegration.FullyModifiedOLS.fit-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference internal" href="../arch.unitroot.cointegration.CointegrationAnalysisResults.html#arch.unitroot.cointegration.CointegrationAnalysisResults" title="arch.unitroot.cointegration.CointegrationAnalysisResults (Python class) — summary()">CointegrationAnalysisResults</a></p>
+<dd class="field-odd"><p><a class="reference internal" href="../arch.unitroot.cointegration.CointegrationAnalysisResults.html#arch.unitroot.cointegration.CointegrationAnalysisResults" title="arch.unitroot.cointegration.CointegrationAnalysisResults (Python class) — summary()"><code class="xref py py-class docutils literal notranslate"><span class="pre">CointegrationAnalysisResults</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -900,7 +900,7 @@ <h1 id="arch-unitroot-cointegration-fullymodifiedols-fit">arch.unitroot.cointegr
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.alternative_hypothesis.html b/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.alternative_hypothesis.html
index e7c529e1e0..e4384f4d30 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.alternative_hypothesis.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.alternative_hypothesis.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.alternative_hypothesis - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.alternative_hypothesis - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,10 +938,10 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-alternative-hypothesis">arch.unitroot.cointegration.PhillipsOuliarisTestResults.alternative_hypothesis<a class="headerlink" href="#arch-unitroot-cointegration-phillipsouliaristestresults-alternative-hypothesis" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-alternative-hypothesis">arch.unitroot.cointegration.PhillipsOuliarisTestResults.alternative_hypothesis<a class="headerlink" href="#arch-unitroot-cointegration-phillipsouliaristestresults-alternative-hypothesis" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.PhillipsOuliarisTestResults.alternative_hypothesis">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsOuliarisTestResults.</span></span><span class="sig-name descname"><span class="pre">alternative_hypothesis</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.alternative_hypothesis" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsOuliarisTestResults.</span></span><span class="sig-name descname"><span class="pre">alternative_hypothesis</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.alternative_hypothesis" title="Link to this definition">¶</a></dt>
 <dd><p>The alternative hypothesis</p>
 </dd></dl>
 
@@ -1012,7 +1012,7 @@ <h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-alternative-hypo
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1059,7 +1059,7 @@ <h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-alternative-hypo
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diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.bandwidth.html b/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.bandwidth.html
index dec1882a22..93278e2108 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.bandwidth.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.bandwidth.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.bandwidth - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.bandwidth - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -938,10 +938,10 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-bandwidth">arch.unitroot.cointegration.PhillipsOuliarisTestResults.bandwidth<a class="headerlink" href="#arch-unitroot-cointegration-phillipsouliaristestresults-bandwidth" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-bandwidth">arch.unitroot.cointegration.PhillipsOuliarisTestResults.bandwidth<a class="headerlink" href="#arch-unitroot-cointegration-phillipsouliaristestresults-bandwidth" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.PhillipsOuliarisTestResults.bandwidth">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsOuliarisTestResults.</span></span><span class="sig-name descname"><span class="pre">bandwidth</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.bandwidth" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsOuliarisTestResults.</span></span><span class="sig-name descname"><span class="pre">bandwidth</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.bandwidth" title="Link to this definition">¶</a></dt>
 <dd><p>Bandwidth used by the long-run covariance estimator</p>
 </dd></dl>
 
@@ -1012,7 +1012,7 @@ <h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-bandwidth">arch.
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1059,7 +1059,7 @@ <h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-bandwidth">arch.
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
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diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.cointegrating_vector.html b/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.cointegrating_vector.html
index 2b0fc04879..a448ecd084 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.cointegrating_vector.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.cointegrating_vector.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.cointegrating_vector - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.cointegrating_vector - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -938,10 +938,10 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-cointegrating-vector">arch.unitroot.cointegration.PhillipsOuliarisTestResults.cointegrating_vector<a class="headerlink" href="#arch-unitroot-cointegration-phillipsouliaristestresults-cointegrating-vector" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-cointegrating-vector">arch.unitroot.cointegration.PhillipsOuliarisTestResults.cointegrating_vector<a class="headerlink" href="#arch-unitroot-cointegration-phillipsouliaristestresults-cointegrating-vector" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.PhillipsOuliarisTestResults.cointegrating_vector">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsOuliarisTestResults.</span></span><span class="sig-name descname"><span class="pre">cointegrating_vector</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.0.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.cointegrating_vector" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsOuliarisTestResults.</span></span><span class="sig-name descname"><span class="pre">cointegrating_vector</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.cointegrating_vector" title="Link to this definition">¶</a></dt>
 <dd><p>The estimated cointegrating vector.</p>
 </dd></dl>
 
@@ -1012,7 +1012,7 @@ <h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-cointegrating-ve
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1059,7 +1059,7 @@ <h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-cointegrating-ve
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.critical_values.html b/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.critical_values.html
index 3875eda8cd..51150420d8 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.critical_values.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.critical_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.critical_values - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.critical_values - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -978,10 +978,10 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-critical-values">arch.unitroot.cointegration.PhillipsOuliarisTestResults.critical_values<a class="headerlink" href="#arch-unitroot-cointegration-phillipsouliaristestresults-critical-values" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-critical-values">arch.unitroot.cointegration.PhillipsOuliarisTestResults.critical_values<a class="headerlink" href="#arch-unitroot-cointegration-phillipsouliaristestresults-critical-values" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.PhillipsOuliarisTestResults.critical_values">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsOuliarisTestResults.</span></span><span class="sig-name descname"><span class="pre">critical_values</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.0.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.critical_values" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsOuliarisTestResults.</span></span><span class="sig-name descname"><span class="pre">critical_values</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.critical_values" title="Link to this definition">¶</a></dt>
 <dd><p>Critical Values</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.unitroot.cointegration.PhillipsOuliarisTestResults.critical_values-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.critical_values-returns" title="Permalink to this headline">¶</a></dt>
@@ -989,7 +989,7 @@ <h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-critical-values"
 of the test statistic.</p>
 </dd>
 <dt class="field-even" id="arch.unitroot.cointegration.PhillipsOuliarisTestResults.critical_values-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.critical_values-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p>Series</p>
+<dd class="field-even"><p><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.Series</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1061,7 +1061,7 @@ <h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-critical-values"
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1108,7 +1108,7 @@ <h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-critical-values"
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.distribution_order.html b/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.distribution_order.html
index 40ea7e3bdc..3f02d2d653 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.distribution_order.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.distribution_order.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.distribution_order - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.distribution_order - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,10 +938,10 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-distribution-order">arch.unitroot.cointegration.PhillipsOuliarisTestResults.distribution_order<a class="headerlink" href="#arch-unitroot-cointegration-phillipsouliaristestresults-distribution-order" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-distribution-order">arch.unitroot.cointegration.PhillipsOuliarisTestResults.distribution_order<a class="headerlink" href="#arch-unitroot-cointegration-phillipsouliaristestresults-distribution-order" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.PhillipsOuliarisTestResults.distribution_order">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsOuliarisTestResults.</span></span><span class="sig-name descname"><span class="pre">distribution_order</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.distribution_order" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsOuliarisTestResults.</span></span><span class="sig-name descname"><span class="pre">distribution_order</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.distribution_order" title="Link to this definition">¶</a></dt>
 <dd><p>The number of stochastic trends under the null hypothesis.</p>
 </dd></dl>
 
@@ -1012,7 +1012,7 @@ <h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-distribution-ord
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1059,7 +1059,7 @@ <h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-distribution-ord
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.kernel.html b/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.kernel.html
index 0d2da1cec4..baca52c175 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.kernel.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.kernel.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.kernel - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.kernel - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,10 +938,10 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-kernel">arch.unitroot.cointegration.PhillipsOuliarisTestResults.kernel<a class="headerlink" href="#arch-unitroot-cointegration-phillipsouliaristestresults-kernel" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-kernel">arch.unitroot.cointegration.PhillipsOuliarisTestResults.kernel<a class="headerlink" href="#arch-unitroot-cointegration-phillipsouliaristestresults-kernel" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.PhillipsOuliarisTestResults.kernel">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsOuliarisTestResults.</span></span><span class="sig-name descname"><span class="pre">kernel</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.kernel" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsOuliarisTestResults.</span></span><span class="sig-name descname"><span class="pre">kernel</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.kernel" title="Link to this definition">¶</a></dt>
 <dd><p>Name of the long-run covariance estimator</p>
 </dd></dl>
 
@@ -1012,7 +1012,7 @@ <h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-kernel">arch.uni
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1059,7 +1059,7 @@ <h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-kernel">arch.uni
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.name.html b/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.name.html
index 468ff87dd4..01b4ca6185 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.name.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.name - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.name - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,10 +938,10 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-name">arch.unitroot.cointegration.PhillipsOuliarisTestResults.name<a class="headerlink" href="#arch-unitroot-cointegration-phillipsouliaristestresults-name" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-name">arch.unitroot.cointegration.PhillipsOuliarisTestResults.name<a class="headerlink" href="#arch-unitroot-cointegration-phillipsouliaristestresults-name" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.PhillipsOuliarisTestResults.name">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsOuliarisTestResults.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.name" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsOuliarisTestResults.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.name" title="Link to this definition">¶</a></dt>
 <dd><p>Sets or gets the name of the cointegration test</p>
 </dd></dl>
 
@@ -1012,7 +1012,7 @@ <h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-name">arch.unitr
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1059,7 +1059,7 @@ <h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-name">arch.unitr
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.null_hypothesis.html b/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.null_hypothesis.html
index de2c8b4e3c..917703230d 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.null_hypothesis.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.null_hypothesis.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.null_hypothesis - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.null_hypothesis - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -938,10 +938,10 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-null-hypothesis">arch.unitroot.cointegration.PhillipsOuliarisTestResults.null_hypothesis<a class="headerlink" href="#arch-unitroot-cointegration-phillipsouliaristestresults-null-hypothesis" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-null-hypothesis">arch.unitroot.cointegration.PhillipsOuliarisTestResults.null_hypothesis<a class="headerlink" href="#arch-unitroot-cointegration-phillipsouliaristestresults-null-hypothesis" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.PhillipsOuliarisTestResults.null_hypothesis">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsOuliarisTestResults.</span></span><span class="sig-name descname"><span class="pre">null_hypothesis</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.null_hypothesis" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsOuliarisTestResults.</span></span><span class="sig-name descname"><span class="pre">null_hypothesis</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.null_hypothesis" title="Link to this definition">¶</a></dt>
 <dd><p>The null hypothesis</p>
 </dd></dl>
 
@@ -1012,7 +1012,7 @@ <h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-null-hypothesis"
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1059,7 +1059,7 @@ <h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-null-hypothesis"
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot.html b/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot.html
index 2a89b7ccd7..95279f6580 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1032,17 +1032,17 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-plot">arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot<a class="headerlink" href="#arch-unitroot-cointegration-phillipsouliaristestresults-plot" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-plot">arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot<a class="headerlink" href="#arch-unitroot-cointegration-phillipsouliaristestresults-plot" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot">
-<span class="sig-prename descclassname"><span class="pre">PhillipsOuliarisTestResults.</span></span><span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot.axes" title="arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot.axes (Python parameter) — matplotlib axes instance to hold the figure."><span class="n"><span class="pre">axes</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot.title" title="arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot.title (Python parameter) — Title for the figure."><span class="n"><span class="pre">title</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot">
+<span class="sig-prename descclassname"><span class="pre">PhillipsOuliarisTestResults.</span></span><span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot.axes" title="arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot.axes (Python parameter) — matplotlib axes instance to hold the figure."><span class="n"><span class="pre">axes</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="(in Matplotlib v3.8.0)"><span class="pre">Axes</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot.title" title="arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot.title (Python parameter) — Title for the figure."><span class="n"><span class="pre">title</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="(in Matplotlib v3.8.0)"><span class="pre">Figure</span></a></span></span></span><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot" title="Link to this definition">¶</a></dt>
 <dd><p>Plot the cointegration residuals.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot.axes"><span class="sig-name descname">axes</span><span class="p"> : </span><span class="desctype"><span class="desctype">Axes, default None</span></span><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot.axes" title="Permalink to this definition">¶</a></dt><dd><p>matplotlib axes instance to hold the figure.</p>
+<dt id="arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot.axes"><span class="n sig-name">axes</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="(in Matplotlib v3.8.0)">Axes</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot.axes" title="Permalink to this definition">¶</a></dt><dd><p>matplotlib axes instance to hold the figure.</p>
 </dd>
-<dt id="arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot.title"><span class="sig-name descname">title</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, default None</span></span><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot.title" title="Permalink to this definition">¶</a></dt><dd><p>Title for the figure.</p>
+<dt id="arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot.title"><span class="n sig-name">title</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot.title" title="Permalink to this definition">¶</a></dt><dd><p>Title for the figure.</p>
 </dd>
 </dl>
 </dd>
@@ -1050,7 +1050,7 @@ <h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-plot">arch.unitr
 <dd class="field-even"><p>The matplotlib Figure instance.</p>
 </dd>
 <dt class="field-odd" id="arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>Figure</p>
+<dd class="field-odd"><p><a class="reference external" href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="(in Matplotlib v3.8.0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">matplotlib.figure.Figure</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1122,7 +1122,7 @@ <h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-plot">arch.unitr
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1169,7 +1169,7 @@ <h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-plot">arch.unitr
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.pvalue.html b/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.pvalue.html
index 5424e22441..8592f2e65d 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.pvalue.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.pvalue.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.pvalue - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.pvalue - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,10 +938,10 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-pvalue">arch.unitroot.cointegration.PhillipsOuliarisTestResults.pvalue<a class="headerlink" href="#arch-unitroot-cointegration-phillipsouliaristestresults-pvalue" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-pvalue">arch.unitroot.cointegration.PhillipsOuliarisTestResults.pvalue<a class="headerlink" href="#arch-unitroot-cointegration-phillipsouliaristestresults-pvalue" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.PhillipsOuliarisTestResults.pvalue">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsOuliarisTestResults.</span></span><span class="sig-name descname"><span class="pre">pvalue</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.pvalue" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsOuliarisTestResults.</span></span><span class="sig-name descname"><span class="pre">pvalue</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.pvalue" title="Link to this definition">¶</a></dt>
 <dd><p>The p-value of the test statistic.</p>
 </dd></dl>
 
@@ -1012,7 +1012,7 @@ <h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-pvalue">arch.uni
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1059,7 +1059,7 @@ <h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-pvalue">arch.uni
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.resid.html b/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.resid.html
index af5b570438..22162cc20b 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.resid.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.resid.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.resid - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.resid - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,10 +938,10 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-resid">arch.unitroot.cointegration.PhillipsOuliarisTestResults.resid<a class="headerlink" href="#arch-unitroot-cointegration-phillipsouliaristestresults-resid" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-resid">arch.unitroot.cointegration.PhillipsOuliarisTestResults.resid<a class="headerlink" href="#arch-unitroot-cointegration-phillipsouliaristestresults-resid" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.PhillipsOuliarisTestResults.resid">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsOuliarisTestResults.</span></span><span class="sig-name descname"><span class="pre">resid</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.0.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.resid" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsOuliarisTestResults.</span></span><span class="sig-name descname"><span class="pre">resid</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.resid" title="Link to this definition">¶</a></dt>
 <dd><p>The residual from the cointegrating regression.</p>
 </dd></dl>
 
@@ -1012,7 +1012,7 @@ <h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-resid">arch.unit
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1059,7 +1059,7 @@ <h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-resid">arch.unit
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.stat.html b/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.stat.html
index f4729096c3..6c01251b95 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.stat.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.stat.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.stat - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.stat - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,10 +938,10 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-stat">arch.unitroot.cointegration.PhillipsOuliarisTestResults.stat<a class="headerlink" href="#arch-unitroot-cointegration-phillipsouliaristestresults-stat" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-stat">arch.unitroot.cointegration.PhillipsOuliarisTestResults.stat<a class="headerlink" href="#arch-unitroot-cointegration-phillipsouliaristestresults-stat" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.PhillipsOuliarisTestResults.stat">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsOuliarisTestResults.</span></span><span class="sig-name descname"><span class="pre">stat</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.stat" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsOuliarisTestResults.</span></span><span class="sig-name descname"><span class="pre">stat</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.stat" title="Link to this definition">¶</a></dt>
 <dd><p>The test statistic.</p>
 </dd></dl>
 
@@ -1012,7 +1012,7 @@ <h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-stat">arch.unitr
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1059,7 +1059,7 @@ <h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-stat">arch.unitr
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.summary.html b/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.summary.html
index 6834ac2aaa..2a623bf5fe 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.summary.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.summary.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.summary - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.summary - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -614,19 +614,6 @@
     <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="arch.unitroot.cointegration.PhillipsOuliarisTestResults.summary (Python method) — Summary of test, containing statistic, p-value and critical values" class="md-ellipsis">Phillips<wbr>Ouliaris<wbr>Test<wbr>Results.<wbr>summary</span>
   </a>
   
-    <nav class="md-nav" aria-label="PhillipsOuliarisTestResults.summary">
-      <ul class="md-nav__list">
-        
-          <li class="md-nav__item">
-  <a href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.summary-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
-</li>
-        
-      </ul>
-    </nav>
-  
 </li>
       
     </ul>
@@ -935,19 +922,6 @@
     <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="arch.unitroot.cointegration.PhillipsOuliarisTestResults.summary (Python method) — Summary of test, containing statistic, p-value and critical values" class="md-ellipsis">Phillips<wbr>Ouliaris<wbr>Test<wbr>Results.<wbr>summary</span>
   </a>
   
-    <nav class="md-nav" aria-label="PhillipsOuliarisTestResults.summary">
-      <ul class="md-nav__list">
-        
-          <li class="md-nav__item">
-  <a href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.summary-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
-</li>
-        
-      </ul>
-    </nav>
-  
 </li>
       
     </ul>
@@ -964,16 +938,11 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-summary">arch.unitroot.cointegration.PhillipsOuliarisTestResults.summary<a class="headerlink" href="#arch-unitroot-cointegration-phillipsouliaristestresults-summary" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-summary">arch.unitroot.cointegration.PhillipsOuliarisTestResults.summary<a class="headerlink" href="#arch-unitroot-cointegration-phillipsouliaristestresults-summary" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.PhillipsOuliarisTestResults.summary">
-<span class="sig-prename descclassname"><span class="pre">PhillipsOuliarisTestResults.</span></span><span class="sig-name descname"><span class="pre">summary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/unitroot/_phillips_ouliaris.html#PhillipsOuliarisTestResults.summary"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.summary" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">PhillipsOuliarisTestResults.</span></span><span class="sig-name descname"><span class="pre">summary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.iolib.summary.Summary.html#statsmodels.iolib.summary.Summary" title="(in statsmodels 0.15.0 (+59) v0.15.0 (+59))"><span class="pre">Summary</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/unitroot/_phillips_ouliaris.html#PhillipsOuliarisTestResults.summary"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.summary" title="Link to this definition">¶</a></dt>
 <dd><p>Summary of test, containing statistic, p-value and critical values</p>
-<dl class="field-list simple">
-<dt class="field-odd" id="arch.unitroot.cointegration.PhillipsOuliarisTestResults.summary-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.summary-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.iolib.summary.Summary.html#statsmodels.iolib.summary.Summary" title="(in statsmodels 0.15.0 (+8) v0.15.0 (+8))"><code class="xref py py-class docutils literal notranslate"><span class="pre">Summary</span></code></a></p>
-</dd>
-</dl>
 </dd></dl>
 
 
@@ -1043,7 +1012,7 @@ <h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-summary">arch.un
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1090,7 +1059,7 @@ <h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-summary">arch.un
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.trend.html b/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.trend.html
index 01a898fefe..2009e8d581 100644
--- a/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.trend.html
+++ b/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.trend.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.trend - arch 6.1.0</title>
+        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.trend - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -938,10 +938,10 @@
                   
 
 
-<h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-trend">arch.unitroot.cointegration.PhillipsOuliarisTestResults.trend<a class="headerlink" href="#arch-unitroot-cointegration-phillipsouliaristestresults-trend" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-trend">arch.unitroot.cointegration.PhillipsOuliarisTestResults.trend<a class="headerlink" href="#arch-unitroot-cointegration-phillipsouliaristestresults-trend" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.PhillipsOuliarisTestResults.trend">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsOuliarisTestResults.</span></span><span class="sig-name descname"><span class="pre">trend</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.trend" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsOuliarisTestResults.</span></span><span class="sig-name descname"><span class="pre">trend</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.trend" title="Link to this definition">¶</a></dt>
 <dd><p>The trend used in the cointegrating regression</p>
 </dd></dl>
 
@@ -1012,7 +1012,7 @@ <h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-trend">arch.unit
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1059,7 +1059,7 @@ <h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-trend">arch.unit
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/unitroot/introduction.html b/unitroot/introduction.html
index c8e4e00e98..f54237c474 100644
--- a/unitroot/introduction.html
+++ b/unitroot/introduction.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Introduction - arch 6.1.0</title>
+        <title>Introduction - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -493,7 +493,7 @@
                   
 
 
-<h1 id="introduction">Introduction<a class="headerlink" href="#introduction" title="Permalink to this heading">¶</a></h1>
+<h1 id="introduction">Introduction<a class="headerlink" href="#introduction" title="Link to this heading">¶</a></h1>
 <p>All tests expect a 1-d series as the first input.  The input can be any array that
 can <cite>squeeze</cite> into a 1-d array, a pandas <cite>Series</cite> or a pandas <cite>DataFrame</cite> that
 contains a single variable.</p>
@@ -514,7 +514,7 @@ <h1 id="introduction">Introduction<a class="headerlink" href="#introduction" tit
 </li>
 <li><p><cite>summary()</cite> - Returns a summary object that can be printed to get a formatted table</p></li>
 </ul>
-<h2 id="basic-example">Basic Example<a class="headerlink" href="#basic-example" title="Permalink to this heading">¶</a></h2>
+<h2 id="basic-example">Basic Example<a class="headerlink" href="#basic-example" title="Link to this heading">¶</a></h2>
 <p>This basic example show the use of the Augmented-Dickey fuller to test whether the default premium,
 defined as the difference between the yields of large portfolios of BAA and AAA bonds.  This example
 uses a constant and time trend.</p>
@@ -615,7 +615,7 @@ <h2 id="basic-example">Basic Example<a class="headerlink" href="#basic-example"
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -662,7 +662,7 @@ <h2 id="basic-example">Basic Example<a class="headerlink" href="#basic-example"
     <script id="__config" type="application/json">{"base": "..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/unitroot/tests.html b/unitroot/tests.html
index 90ae458a92..42687e918d 100644
--- a/unitroot/tests.html
+++ b/unitroot/tests.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>The Unit Root Tests - arch 6.1.0</title>
+        <title>The Unit Root Tests - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -721,7 +721,7 @@
                   
 
 
-<h1 id="module-arch.unitroot"><span id="the-unit-root-tests"></span>The Unit Root Tests<a class="headerlink" href="#module-arch.unitroot" title="Permalink to this heading">¶</a></h1>
+<h1 id="module-arch.unitroot"><span id="the-unit-root-tests"></span>The Unit Root Tests<a class="headerlink" href="#module-arch.unitroot" title="Link to this heading">¶</a></h1>
 <table class="autosummary longtable docutils data align-default">
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="generated/arch.unitroot.ADF.html#arch.unitroot.ADF" title="arch.unitroot.ADF (Python class) — Augmented Dickey-Fuller unit root test"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ADF</span></code></a>(y[, lags, trend, max_lags, method, ...])</p></td>
@@ -744,7 +744,7 @@ <h1 id="module-arch.unitroot"><span id="the-unit-root-tests"></span>The Unit Roo
 </tr>
 </tbody>
 </table>
-<h2 id="automatic-bandwidth-selection">Automatic Bandwidth Selection<a class="headerlink" href="#automatic-bandwidth-selection" title="Permalink to this heading">¶</a></h2>
+<h2 id="automatic-bandwidth-selection">Automatic Bandwidth Selection<a class="headerlink" href="#automatic-bandwidth-selection" title="Link to this heading">¶</a></h2>
 <table class="autosummary longtable docutils data align-default">
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="generated/arch.unitroot.auto_bandwidth.html#arch.unitroot.auto_bandwidth" title="arch.unitroot.auto_bandwidth (Python function) — Automatic bandwidth selection of Andrews (1991) and Newey &amp; West (1994)."><code class="xref py py-obj docutils literal notranslate"><span class="pre">auto_bandwidth</span></code></a>(y[, kernel])</p></td>
@@ -819,7 +819,7 @@ <h2 id="automatic-bandwidth-selection">Automatic Bandwidth Selection<a class="he
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -866,7 +866,7 @@ <h2 id="automatic-bandwidth-selection">Automatic Bandwidth Selection<a class="he
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-        <script src="../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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diff --git a/unitroot/unitroot.html b/unitroot/unitroot.html
index e143dd2592..b5329a0bb9 100644
--- a/unitroot/unitroot.html
+++ b/unitroot/unitroot.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Unit Root Testing - arch 6.1.0</title>
+        <title>Unit Root Testing - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -621,7 +621,7 @@
                   
 
 
-<h1 id="unit-root-testing">Unit Root Testing<a class="headerlink" href="#unit-root-testing" title="Permalink to this heading">¶</a></h1>
+<h1 id="unit-root-testing">Unit Root Testing<a class="headerlink" href="#unit-root-testing" title="Link to this heading">¶</a></h1>
 <p>Many time series are highly persistent, and determining whether the data appear
 to be stationary or contains a unit root is the first step in many analyses.
 This module contains a number of routines:</p>
@@ -644,7 +644,7 @@ <h1 id="unit-root-testing">Unit Root Testing<a class="headerlink" href="#unit-ro
 <li class="toctree-l1"><a class="reference internal" href="tests.html">Unit Root Testing</a></li>
 </ul>
 </div>
-<h1 id="cointegration-analysis">Cointegration Analysis<a class="headerlink" href="#cointegration-analysis" title="Permalink to this heading">¶</a></h1>
+<h1 id="cointegration-analysis">Cointegration Analysis<a class="headerlink" href="#cointegration-analysis" title="Link to this heading">¶</a></h1>
 <p>The module extended the single-series unit root testing to multiple
 series and cointegration testing and cointegrating vector estimation.</p>
 <ul class="simple">
@@ -736,7 +736,7 @@ <h1 id="cointegration-analysis">Cointegration Analysis<a class="headerlink" href
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -783,7 +783,7 @@ <h1 id="cointegration-analysis">Cointegration Analysis<a class="headerlink" href
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-        <script src="../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/unitroot/unitroot_cointegration_examples.html b/unitroot/unitroot_cointegration_examples.html
index e80d4bd4fd..81b7e86123 100644
--- a/unitroot/unitroot_cointegration_examples.html
+++ b/unitroot/unitroot_cointegration_examples.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Cointegration Testing - arch 6.1.0</title>
+        <title>Cointegration Testing - arch 6.2.0</title>
       
     
 
@@ -47,9 +47,9 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css" />
-        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css?v=2aa19091" />
+        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -93,7 +93,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -103,7 +103,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -190,10 +190,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -494,7 +494,7 @@
                   
 
 
-<h1 id="Cointegration-Testing">Cointegration Testing<a class="headerlink" href="#Cointegration-Testing" title="Permalink to this heading">¶</a></h1>
+<h1 id="Cointegration-Testing">Cointegration Testing<a class="headerlink" href="#Cointegration-Testing" title="Link to this heading">¶</a></h1>
 <p><em>This setup code is required to run in an IPython notebook</em></p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[1]:
@@ -786,7 +786,7 @@ <h1 id="Cointegration-Testing">Cointegration Testing<a class="headerlink" href="
 <img alt="../_images/unitroot_unitroot_cointegration_examples_22_0.png" src="../_images/unitroot_unitroot_cointegration_examples_22_0.png" />
 </div>
 </div>
-<h2 id="Phillips-Ouliaris">Phillips-Ouliaris<a class="headerlink" href="#Phillips-Ouliaris" title="Permalink to this heading">¶</a></h2>
+<h2 id="Phillips-Ouliaris">Phillips-Ouliaris<a class="headerlink" href="#Phillips-Ouliaris" title="Link to this heading">¶</a></h2>
 <p>The Phillips-Ouliaris tests consists four distinct tests. Two are similar to the Engle-Granger test, only using a Phillips &amp; Perron-like approach replaces the lags in the ADF test with a long-run variance estimator. The other two use variance-ratio like approaches to test. In both cases the test stabilizes when there is no cointegration and diverges due to singularity of the covariance matrix of the I(1) time series when there is cointegration.</p>
 <ul class="simple">
 <li><p><span class="math notranslate nohighlight">\(Z_t\)</span> - Like PP using the t-stat of the AR(1) coefficient in an AR(1) of the residual from the cross-sectional regression.</p></li>
@@ -1010,7 +1010,7 @@ <h2 id="Phillips-Ouliaris">Phillips-Ouliaris<a class="headerlink" href="#Phillip
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1057,10 +1057,10 @@ <h2 id="Phillips-Ouliaris">Phillips-Ouliaris<a class="headerlink" href="#Phillip
     <script id="__config" type="application/json">{"base": "..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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+        <script id="MathJax-script" src="../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
     
   </body>
 </html>
\ No newline at end of file
diff --git a/unitroot/unitroot_examples.html b/unitroot/unitroot_examples.html
index abd2ef41e5..41e39106c8 100644
--- a/unitroot/unitroot_examples.html
+++ b/unitroot/unitroot_examples.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Unit Root Testing - arch 6.1.0</title>
+        <title>Unit Root Testing - arch 6.2.0</title>
       
     
 
@@ -47,9 +47,9 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css" />
-        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css?v=2aa19091" />
+        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -93,7 +93,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -103,7 +103,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -190,10 +190,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -632,7 +632,7 @@
                   
 
 
-<h1 id="Unit-Root-Testing">Unit Root Testing<a class="headerlink" href="#Unit-Root-Testing" title="Permalink to this heading">¶</a></h1>
+<h1 id="Unit-Root-Testing">Unit Root Testing<a class="headerlink" href="#Unit-Root-Testing" title="Link to this heading">¶</a></h1>
 <p><em>This setup code is required to run in an IPython notebook</em></p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[1]:
@@ -654,7 +654,7 @@ <h1 id="Unit-Root-Testing">Unit Root Testing<a class="headerlink" href="#Unit-Ro
 </code></pre></div>
 </div>
 </div>
-<h2 id="Setup">Setup<a class="headerlink" href="#Setup" title="Permalink to this heading">¶</a></h2>
+<h2 id="Setup">Setup<a class="headerlink" href="#Setup" title="Link to this heading">¶</a></h2>
 <p>Most examples will make use of the Default premium, which is the difference between the yields of BAA and AAA rated corporate bonds. The data is downloaded from FRED using pandas.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[2]:
@@ -696,7 +696,7 @@ <h2 id="Setup">Setup<a class="headerlink" href="#Setup" title="Permalink to this
 <img alt="../_images/unitroot_unitroot_examples_7_0.png" src="../_images/unitroot_unitroot_examples_7_0.png" />
 </div>
 </div>
-<h2 id="Augmented-Dickey-Fuller-Testing">Augmented Dickey-Fuller Testing<a class="headerlink" href="#Augmented-Dickey-Fuller-Testing" title="Permalink to this heading">¶</a></h2>
+<h2 id="Augmented-Dickey-Fuller-Testing">Augmented Dickey-Fuller Testing<a class="headerlink" href="#Augmented-Dickey-Fuller-Testing" title="Link to this heading">¶</a></h2>
 <p>The Augmented Dickey-Fuller test is the most common unit root test used. It is a regression of the first difference of the variable on its lagged level as well as additional lags of the first difference. The null is that the series contains a unit root, and the (one-sided) alternative is that the series is stationary.</p>
 <p>By default, the number of lags is selected by minimizing the AIC across a range of lag lengths (which can be set using <code class="docutils literal notranslate"><span class="pre">max_lag</span></code> when initializing the model). Additionally, the basic test includes a constant in the ADF regression.</p>
 <p>These results indicate that the Default premium is stationary.</p>
@@ -731,7 +731,7 @@ <h2 id="Augmented-Dickey-Fuller-Testing">Augmented Dickey-Fuller Testing<a class
 </div>
 <p>The number of lags can be directly set using <code class="docutils literal notranslate"><span class="pre">lags</span></code>. Changing the number of lags makes no difference to the conclusion.</p>
 <p><strong>Note</strong>: The ADF assumes residuals are white noise, and that the number of lags is sufficient to pick up any dependence in the data.</p>
-<h3 id="Setting-the-number-of-lags">Setting the number of lags<a class="headerlink" href="#Setting-the-number-of-lags" title="Permalink to this heading">¶</a></h3>
+<h3 id="Setting-the-number-of-lags">Setting the number of lags<a class="headerlink" href="#Setting-the-number-of-lags" title="Link to this heading">¶</a></h3>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[5]:
 </code></pre></div>
@@ -759,7 +759,7 @@ <h3 id="Setting-the-number-of-lags">Setting the number of lags<a class="headerli
 Alternative Hypothesis: The process is weakly stationary.
 </pre></div></div>
 </div>
-<h3 id="Deterministic-terms">Deterministic terms<a class="headerlink" href="#Deterministic-terms" title="Permalink to this heading">¶</a></h3>
+<h3 id="Deterministic-terms">Deterministic terms<a class="headerlink" href="#Deterministic-terms" title="Link to this heading">¶</a></h3>
 <p>The deterministic terms can be altered using <code class="docutils literal notranslate"><span class="pre">trend</span></code>. The options are:</p>
 <ul class="simple">
 <li><p><code class="docutils literal notranslate"><span class="pre">'nc'</span></code> : No deterministic terms</p></li>
@@ -795,7 +795,7 @@ <h3 id="Deterministic-terms">Deterministic terms<a class="headerlink" href="#Det
 Alternative Hypothesis: The process is weakly stationary.
 </pre></div></div>
 </div>
-<h3 id="Regression-output">Regression output<a class="headerlink" href="#Regression-output" title="Permalink to this heading">¶</a></h3>
+<h3 id="Regression-output">Regression output<a class="headerlink" href="#Regression-output" title="Link to this heading">¶</a></h3>
 <p>The ADF uses a standard regression when computing results. These can be accesses using <code class="docutils literal notranslate"><span class="pre">regression</span></code>.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[7]:
@@ -884,7 +884,7 @@ <h3 id="Regression-output">Regression output<a class="headerlink" href="#Regress
 <img alt="../_images/unitroot_unitroot_examples_22_0.png" src="../_images/unitroot_unitroot_examples_22_0.png" />
 </div>
 </div>
-<h2 id="Dickey-Fuller-GLS-Testing">Dickey-Fuller GLS Testing<a class="headerlink" href="#Dickey-Fuller-GLS-Testing" title="Permalink to this heading">¶</a></h2>
+<h2 id="Dickey-Fuller-GLS-Testing">Dickey-Fuller GLS Testing<a class="headerlink" href="#Dickey-Fuller-GLS-Testing" title="Link to this heading">¶</a></h2>
 <p>The Dickey-Fuller GLS test is an improved version of the ADF which uses a GLS-detrending regression before running an ADF regression with no additional deterministic terms. This test is only available with a constant or constant and time trend (<code class="docutils literal notranslate"><span class="pre">trend='c'</span></code> or <code class="docutils literal notranslate"><span class="pre">trend='ct'</span></code>).</p>
 <p>The results of this test agree with the ADF results.</p>
 <div class="nbinput docutils container">
@@ -944,7 +944,7 @@ <h2 id="Dickey-Fuller-GLS-Testing">Dickey-Fuller GLS Testing<a class="headerlink
 Alternative Hypothesis: The process is weakly stationary.
 </pre></div></div>
 </div>
-<h2 id="Phillips-Perron-Testing">Phillips-Perron Testing<a class="headerlink" href="#Phillips-Perron-Testing" title="Permalink to this heading">¶</a></h2>
+<h2 id="Phillips-Perron-Testing">Phillips-Perron Testing<a class="headerlink" href="#Phillips-Perron-Testing" title="Link to this heading">¶</a></h2>
 <p>The Phillips-Perron test is similar to the ADF except that the regression run does not include lagged values of the first differences. Instead, the PP test fixed the t-statistic using a long run variance estimation, implemented using a Newey-West covariance estimator.</p>
 <p>By default, the number of lags is automatically set, although this can be overridden using <code class="docutils literal notranslate"><span class="pre">lags</span></code>.</p>
 <div class="nbinput docutils container">
@@ -1060,7 +1060,7 @@ <h2 id="Phillips-Perron-Testing">Phillips-Perron Testing<a class="headerlink" hr
 Alternative Hypothesis: The process is weakly stationary.
 </pre></div></div>
 </div>
-<h2 id="KPSS-Testing">KPSS Testing<a class="headerlink" href="#KPSS-Testing" title="Permalink to this heading">¶</a></h2>
+<h2 id="KPSS-Testing">KPSS Testing<a class="headerlink" href="#KPSS-Testing" title="Link to this heading">¶</a></h2>
 <p>The KPSS test differs from the three previous in that the null is a stationary process and the alternative is a unit root.</p>
 <p>Note that here the null is rejected which indicates that the series might be a unit root.</p>
 <div class="nbinput docutils container">
@@ -1120,7 +1120,7 @@ <h2 id="KPSS-Testing">KPSS Testing<a class="headerlink" href="#KPSS-Testing" tit
 Alternative Hypothesis: The process contains a unit root.
 </pre></div></div>
 </div>
-<h2 id="Zivot-Andrews-Test">Zivot-Andrews Test<a class="headerlink" href="#Zivot-Andrews-Test" title="Permalink to this heading">¶</a></h2>
+<h2 id="Zivot-Andrews-Test">Zivot-Andrews Test<a class="headerlink" href="#Zivot-Andrews-Test" title="Link to this heading">¶</a></h2>
 <p>The Zivot-Andrews test allows the possibility of a single structural break in the series. Here we test the default using the test.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[18]:
@@ -1151,7 +1151,7 @@ <h2 id="Zivot-Andrews-Test">Zivot-Andrews Test<a class="headerlink" href="#Zivot
 Alternative Hypothesis: The process is trend and break stationary.
 </pre></div></div>
 </div>
-<h2 id="Variance-Ratio-Testing">Variance Ratio Testing<a class="headerlink" href="#Variance-Ratio-Testing" title="Permalink to this heading">¶</a></h2>
+<h2 id="Variance-Ratio-Testing">Variance Ratio Testing<a class="headerlink" href="#Variance-Ratio-Testing" title="Link to this heading">¶</a></h2>
 <p>Variance ratio tests are not usually used as unit root tests, and are instead used for testing whether a financial return series is a pure random walk versus having some predictability. This example uses the excess return on the market from Ken French’s data.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[19]:
@@ -1318,7 +1318,7 @@ <h2 id="Variance-Ratio-Testing">Variance Ratio Testing<a class="headerlink" href
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1365,10 +1365,10 @@ <h2 id="Variance-Ratio-Testing">Variance Ratio Testing<a class="headerlink" href
     <script id="__config" type="application/json">{"base": "..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
         <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+        <script id="MathJax-script" src="../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
     
   </body>
 </html>
\ No newline at end of file
diff --git a/univariate/background.html b/univariate/background.html
index 7bb16e54ba..5bc8439cde 100644
--- a/univariate/background.html
+++ b/univariate/background.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Theoretical Background - arch 6.1.0</title>
+        <title>Theoretical Background - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -606,7 +606,7 @@
                   
 
 
-<h1 id="theoretical-background">Theoretical Background<a class="headerlink" href="#theoretical-background" title="Permalink to this heading">¶</a></h1>
+<h1 id="theoretical-background">Theoretical Background<a class="headerlink" href="#theoretical-background" title="Link to this heading">¶</a></h1>
 <p><em>To be completed</em></p>
 
 
@@ -675,7 +675,7 @@ <h1 id="theoretical-background">Theoretical Background<a class="headerlink" href
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -722,7 +722,7 @@ <h1 id="theoretical-background">Theoretical Background<a class="headerlink" href
     <script id="__config" type="application/json">{"base": "..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/distribution.html b/univariate/distribution.html
index 363efb724f..b35c7b80d1 100644
--- a/univariate/distribution.html
+++ b/univariate/distribution.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Distributions - arch 6.1.0</title>
+        <title>Distributions - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -842,7 +842,7 @@
                   
 
 
-<h1 id="distributions"><span id="id1"></span>Distributions<a class="headerlink" href="#distributions" title="Permalink to this heading">¶</a></h1>
+<h1 id="distributions"><span id="id1"></span>Distributions<a class="headerlink" href="#distributions" title="Link to this heading">¶</a></h1>
 <p>A distribution is the final component of an ARCH Model.</p>
 <table class="autosummary longtable docutils data align-default">
 <tbody>
@@ -860,7 +860,7 @@ <h1 id="distributions"><span id="id1"></span>Distributions<a class="headerlink"
 </tr>
 </tbody>
 </table>
-<h2 id="writing-new-distributions">Writing New Distributions<a class="headerlink" href="#writing-new-distributions" title="Permalink to this heading">¶</a></h2>
+<h2 id="writing-new-distributions">Writing New Distributions<a class="headerlink" href="#writing-new-distributions" title="Link to this heading">¶</a></h2>
 <p>All distributions must inherit from :class:Distribution and provide all public
 methods.</p>
 <table class="autosummary longtable docutils data align-default">
@@ -937,7 +937,7 @@ <h2 id="writing-new-distributions">Writing New Distributions<a class="headerlink
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -984,7 +984,7 @@ <h2 id="writing-new-distributions">Writing New Distributions<a class="headerlink
     <script id="__config" type="application/json">{"base": "..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/univariate/forecasting.html b/univariate/forecasting.html
index 73038b9ac6..b759a07dd0 100644
--- a/univariate/forecasting.html
+++ b/univariate/forecasting.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Forecasting - arch 6.1.0</title>
+        <title>Forecasting - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -846,7 +846,7 @@
                   
 
 
-<h1 id="forecasting">Forecasting<a class="headerlink" href="#forecasting" title="Permalink to this heading">¶</a></h1>
+<h1 id="forecasting">Forecasting<a class="headerlink" href="#forecasting" title="Link to this heading">¶</a></h1>
 <p>Multi-period forecasts can be easily produced for ARCH-type models using
 forward recursion, with some caveats.  In particular, models that are
 non-linear in the sense that they do not evolve using squares or residuals
@@ -896,7 +896,7 @@ <h1 id="forecasting">Forecasting<a class="headerlink" href="#forecasting" title=
 <span class="n">res</span> <span class="o">=</span> <span class="n">am</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">last_obs</span><span class="o">=</span><span class="n">split_date</span><span class="p">)</span>
 </code></pre></div>
 </div>
-<h2 id="analytical-forecasts">Analytical Forecasts<a class="headerlink" href="#analytical-forecasts" title="Permalink to this heading">¶</a></h2>
+<h2 id="analytical-forecasts">Analytical Forecasts<a class="headerlink" href="#analytical-forecasts" title="Link to this heading">¶</a></h2>
 <p>Analytical forecasts are available for most models that evolve in terms of
 the squares of the model residuals, e.g., GARCH, HARCH, etc. These forecasts
 exploit the relationship <span class="math notranslate nohighlight">\(E_t[\epsilon_{t+1}^2] = \sigma_{t+1}^2\)</span> to
@@ -911,7 +911,7 @@ <h2 id="analytical-forecasts">Analytical Forecasts<a class="headerlink" href="#a
 <span class="n">forecasts</span><span class="o">.</span><span class="n">variance</span><span class="p">[</span><span class="n">split_date</span><span class="p">:]</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
 </code></pre></div>
 </div>
-<h2 id="simulation-forecasts">Simulation Forecasts<a class="headerlink" href="#simulation-forecasts" title="Permalink to this heading">¶</a></h2>
+<h2 id="simulation-forecasts">Simulation Forecasts<a class="headerlink" href="#simulation-forecasts" title="Link to this heading">¶</a></h2>
 <p>Simulation-based forecasts use the model random number generator to simulate
 draws of the standardized residuals, <span class="math notranslate nohighlight">\(e_{t+h}\)</span>.  These are used to
 generate a pre-specified number of paths of the variances which are then
@@ -937,7 +937,7 @@ <h2 id="simulation-forecasts">Simulation Forecasts<a class="headerlink" href="#s
 \end{equation}</div><div class="highlight-python notranslate"><div class="highlight"><pre><span></span><code><span class="n">forecasts</span> <span class="o">=</span> <span class="n">res</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">horizon</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">start</span><span class="o">=</span><span class="n">split_date</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;simulation&#39;</span><span class="p">)</span>
 </code></pre></div>
 </div>
-<h2 id="bootstrap-forecasts">Bootstrap Forecasts<a class="headerlink" href="#bootstrap-forecasts" title="Permalink to this heading">¶</a></h2>
+<h2 id="bootstrap-forecasts">Bootstrap Forecasts<a class="headerlink" href="#bootstrap-forecasts" title="Link to this heading">¶</a></h2>
 <p>Bootstrap-based forecasts are virtually identical to simulation-based forecasts
 except that the standardized residuals are generated by the model.  These
 standardized residuals are generated using the observed data and the
@@ -949,7 +949,7 @@ <h2 id="bootstrap-forecasts">Bootstrap Forecasts<a class="headerlink" href="#boo
 the simulated shocks are drawn (i.i.d., with replacement) from
 <span class="math notranslate nohighlight">\(\hat{e}_{1}, \hat{e}_{2},\ldots, \hat{e}_{t}\)</span>.  so that only
 data available at time <span class="math notranslate nohighlight">\(t\)</span> are used to simulate the paths.</p>
-<h2 id="forecasting-options">Forecasting Options<a class="headerlink" href="#forecasting-options" title="Permalink to this heading">¶</a></h2>
+<h2 id="forecasting-options">Forecasting Options<a class="headerlink" href="#forecasting-options" title="Link to this heading">¶</a></h2>
 <p>The <a class="reference internal" href="generated/generated/arch.univariate.base.ARCHModelResult.forecast.html#arch.univariate.base.ARCHModelResult.forecast" title="arch.univariate.base.ARCHModelResult.forecast (Python method) — Construct forecasts from estimated model"><code class="xref py py-meth docutils literal notranslate"><span class="pre">forecast()</span></code></a>  method
 is attached to a model fit result.`</p>
 <ul class="simple">
@@ -973,7 +973,7 @@ <h2 id="forecasting-options">Forecasting Options<a class="headerlink" href="#for
 <li><p><code class="docutils literal notranslate"><span class="pre">simulations</span></code> - A non-negative integer indicating the number of
 simulation to use when <code class="docutils literal notranslate"><span class="pre">method</span></code> is ‘simulation’ or ‘bootstrap’</p></li>
 </ul>
-<h2 id="understanding-forecast-output">Understanding Forecast Output<a class="headerlink" href="#understanding-forecast-output" title="Permalink to this heading">¶</a></h2>
+<h2 id="understanding-forecast-output">Understanding Forecast Output<a class="headerlink" href="#understanding-forecast-output" title="Link to this heading">¶</a></h2>
 <p>Any call to <a class="reference internal" href="generated/generated/arch.univariate.base.ARCHModelResult.forecast.html#arch.univariate.base.ARCHModelResult.forecast" title="arch.univariate.base.ARCHModelResult.forecast (Python method) — Construct forecasts from estimated model"><code class="xref py py-meth docutils literal notranslate"><span class="pre">forecast()</span></code></a> returns a
 <a class="reference internal" href="generated/arch.univariate.base.ARCHModelForecast.html#arch.univariate.base.ARCHModelForecast" title="arch.univariate.base.ARCHModelForecast (Python class) — Container for forecasts from an ARCH Model"><code class="xref py py-class docutils literal notranslate"><span class="pre">ARCHModelForecast</span></code></a> object with has 3 core
 attributes and 1 which may be useful when using simulation- or bootstrap-based
@@ -1025,7 +1025,7 @@ <h2 id="understanding-forecast-output">Understanding Forecast Output<a class="he
 </div>
 <p>The output will always have as many rows as the data input.  Values
 that are not forecast are <code class="docutils literal notranslate"><span class="pre">nan</span></code> filled.</p>
-<h2 id="output-classes">Output Classes<a class="headerlink" href="#output-classes" title="Permalink to this heading">¶</a></h2>
+<h2 id="output-classes">Output Classes<a class="headerlink" href="#output-classes" title="Link to this heading">¶</a></h2>
 <table class="autosummary longtable docutils data align-default">
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="generated/arch.univariate.base.ARCHModelForecast.html#arch.univariate.base.ARCHModelForecast" title="arch.univariate.base.ARCHModelForecast (Python class) — Container for forecasts from an ARCH Model"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ARCHModelForecast</span></code></a>(index, start_index, mean, ...)</p></td>
@@ -1103,7 +1103,7 @@ <h2 id="output-classes">Output Classes<a class="headerlink" href="#output-classe
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1150,10 +1150,10 @@ <h2 id="output-classes">Output Classes<a class="headerlink" href="#output-classe
     <script id="__config" type="application/json">{"base": "..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+        <script id="MathJax-script" src="../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
     
   </body>
 </html>
\ No newline at end of file
diff --git a/univariate/generated/arch.univariate.APARCH.html b/univariate/generated/arch.univariate.APARCH.html
index 7a4def2c65..228732e24a 100644
--- a/univariate/generated/arch.univariate.APARCH.html
+++ b/univariate/generated/arch.univariate.APARCH.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.APARCH - arch 6.1.0</title>
+        <title>arch.univariate.APARCH - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1442,26 +1442,26 @@
                   
 
 
-<h1 id="arch-univariate-aparch">arch.univariate.APARCH<a class="headerlink" href="#arch-univariate-aparch" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-aparch">arch.univariate.APARCH<a class="headerlink" href="#arch-univariate-aparch" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.APARCH">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">APARCH</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.__init__.p" title="arch.univariate.APARCH.__init__.p (Python parameter) — Order of the symmetric innovation. Must satisfy p&gt;=o."><span class="n"><span class="pre">p</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.__init__.o" title="arch.univariate.APARCH.__init__.o (Python parameter) — Order of the asymmetric innovation. Must satisfy o&lt;=p."><span class="n"><span class="pre">o</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.__init__.q" title="arch.univariate.APARCH.__init__.q (Python parameter) — Order of the lagged (transformed) conditional variance"><span class="n"><span class="pre">q</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.__init__.delta" title="arch.univariate.APARCH.__init__.delta (Python parameter) — Value to use for a fixed delta in the APARCH model. If not provided, the value of delta is jointly estimated with other model parameters. User provided delta is restricted to lie in (0.05, 4.0)."><span class="n"><span class="pre">delta</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.__init__.common_asym" title="arch.univariate.APARCH.__init__.common_asym (Python parameter) — Restrict all asymmetry terms to share the same asymmetry parameter. If False (default), then there are no restrictions on the o asymmetry parameters."><span class="n"><span class="pre">common_asym</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/volatility.html#APARCH"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.APARCH" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">APARCH</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.__init__.p" title="arch.univariate.APARCH.__init__.p (Python parameter) — Order of the symmetric innovation. Must satisfy p&gt;=o."><span class="n"><span class="pre">p</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.__init__.o" title="arch.univariate.APARCH.__init__.o (Python parameter) — Order of the asymmetric innovation. Must satisfy o&lt;=p."><span class="n"><span class="pre">o</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.__init__.q" title="arch.univariate.APARCH.__init__.q (Python parameter) — Order of the lagged (transformed) conditional variance"><span class="n"><span class="pre">q</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.__init__.delta" title="arch.univariate.APARCH.__init__.delta (Python parameter) — Value to use for a fixed delta in the APARCH model. If not provided, the value of delta is jointly estimated with other model parameters. User provided delta is restricted to lie in (0.05, 4.0)."><span class="n"><span class="pre">delta</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.__init__.common_asym" title="arch.univariate.APARCH.__init__.common_asym (Python parameter) — Restrict all asymmetry terms to share the same asymmetry parameter. If False (default), then there are no restrictions on the o asymmetry parameters."><span class="n"><span class="pre">common_asym</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/volatility.html#APARCH"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.APARCH" title="Link to this definition">¶</a></dt>
 <dd><p>Asymmetric Power ARCH (APARCH) volatility process</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.APARCH-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.APARCH-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.APARCH.__init__.p"><span class="sig-name descname">p</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.APARCH.__init__.p" title="Permalink to this definition">¶</a></dt><dd><p>Order of the symmetric innovation. Must satisfy p&gt;=o.</p>
+<dt id="arch.univariate.APARCH.__init__.p"><span class="n sig-name">p</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code><a class="headerlink" href="#arch.univariate.APARCH.__init__.p" title="Permalink to this definition">¶</a></dt><dd><p>Order of the symmetric innovation. Must satisfy p&gt;=o.</p>
 </dd>
-<dt id="arch.univariate.APARCH.__init__.o"><span class="sig-name descname">o</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.APARCH.__init__.o" title="Permalink to this definition">¶</a></dt><dd><p>Order of the asymmetric innovation. Must satisfy o&lt;=p.</p>
+<dt id="arch.univariate.APARCH.__init__.o"><span class="n sig-name">o</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code><a class="headerlink" href="#arch.univariate.APARCH.__init__.o" title="Permalink to this definition">¶</a></dt><dd><p>Order of the asymmetric innovation. Must satisfy o&lt;=p.</p>
 </dd>
-<dt id="arch.univariate.APARCH.__init__.q"><span class="sig-name descname">q</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.APARCH.__init__.q" title="Permalink to this definition">¶</a></dt><dd><p>Order of the lagged (transformed) conditional variance</p>
+<dt id="arch.univariate.APARCH.__init__.q"><span class="n sig-name">q</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code><a class="headerlink" href="#arch.univariate.APARCH.__init__.q" title="Permalink to this definition">¶</a></dt><dd><p>Order of the lagged (transformed) conditional variance</p>
 </dd>
-<dt id="arch.univariate.APARCH.__init__.delta"><span class="sig-name descname">delta</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>, optional</span></span><a class="headerlink" href="#arch.univariate.APARCH.__init__.delta" title="Permalink to this definition">¶</a></dt><dd><p>Value to use for a fixed delta in the APARCH model. If
+<dt id="arch.univariate.APARCH.__init__.delta"><span class="n sig-name">delta</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.APARCH.__init__.delta" title="Permalink to this definition">¶</a></dt><dd><p>Value to use for a fixed delta in the APARCH model. If
 not provided, the value of delta is jointly estimated
 with other model parameters. User provided delta is restricted
 to lie in (0.05, 4.0).</p>
 </dd>
-<dt id="arch.univariate.APARCH.__init__.common_asym"><span class="sig-name descname">common_asym</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, optional</span></span><a class="headerlink" href="#arch.univariate.APARCH.__init__.common_asym" title="Permalink to this definition">¶</a></dt><dd><p>Restrict all asymmetry terms to share the same asymmetry
+<dt id="arch.univariate.APARCH.__init__.common_asym"><span class="n sig-name">common_asym</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.univariate.APARCH.__init__.common_asym" title="Permalink to this definition">¶</a></dt><dd><p>Restrict all asymmetry terms to share the same asymmetry
 parameter. If False (default), then there are no restrictions
 on the <code class="docutils literal notranslate"><span class="pre">o</span></code> asymmetry parameters.</p>
 </dd>
@@ -1630,7 +1630,7 @@ <h1 id="arch-univariate-aparch">arch.univariate.APARCH<a class="headerlink" href
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1677,10 +1677,10 @@ <h1 id="arch-univariate-aparch">arch.univariate.APARCH<a class="headerlink" href
     <script id="__config" type="application/json">{"base": "../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+        <script id="MathJax-script" src="../../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
     
   </body>
 </html>
\ No newline at end of file
diff --git a/univariate/generated/arch.univariate.ARCH.html b/univariate/generated/arch.univariate.ARCH.html
index ef2e768107..aab655584e 100644
--- a/univariate/generated/arch.univariate.ARCH.html
+++ b/univariate/generated/arch.univariate.ARCH.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCH - arch 6.1.0</title>
+        <title>arch.univariate.ARCH - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1342,15 +1342,15 @@
                   
 
 
-<h1 id="arch-univariate-arch">arch.univariate.ARCH<a class="headerlink" href="#arch-univariate-arch" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-arch">arch.univariate.ARCH<a class="headerlink" href="#arch-univariate-arch" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ARCH">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">ARCH</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.__init__.p" title="arch.univariate.ARCH.__init__.p (Python parameter) — Order of the symmetric innovation"><span class="n"><span class="pre">p</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/volatility.html#ARCH"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ARCH" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">ARCH</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.__init__.p" title="arch.univariate.ARCH.__init__.p (Python parameter) — Order of the symmetric innovation"><span class="n"><span class="pre">p</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/volatility.html#ARCH"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ARCH" title="Link to this definition">¶</a></dt>
 <dd><p>ARCH process</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ARCH-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCH-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ARCH.__init__.p"><span class="sig-name descname">p</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.ARCH.__init__.p" title="Permalink to this definition">¶</a></dt><dd><p>Order of the symmetric innovation</p>
+<dt id="arch.univariate.ARCH.__init__.p"><span class="n sig-name">p</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code><a class="headerlink" href="#arch.univariate.ARCH.__init__.p" title="Permalink to this definition">¶</a></dt><dd><p>Order of the symmetric innovation</p>
 </dd>
 </dl>
 </dd>
@@ -1498,7 +1498,7 @@ <h1 id="arch-univariate-arch">arch.univariate.ARCH<a class="headerlink" href="#a
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1545,10 +1545,10 @@ <h1 id="arch-univariate-arch">arch.univariate.ARCH<a class="headerlink" href="#a
     <script id="__config" type="application/json">{"base": "../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+        <script id="MathJax-script" src="../../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
     
   </body>
 </html>
\ No newline at end of file
diff --git a/univariate/generated/arch.univariate.ARCHInMean.html b/univariate/generated/arch.univariate.ARCHInMean.html
index 985fd802d9..91b599904a 100644
--- a/univariate/generated/arch.univariate.ARCHInMean.html
+++ b/univariate/generated/arch.univariate.ARCHInMean.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCHInMean - arch 6.1.0</title>
+        <title>arch.univariate.ARCHInMean - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1434,39 +1434,39 @@
                   
 
 
-<h1 id="arch-univariate-archinmean">arch.univariate.ARCHInMean<a class="headerlink" href="#arch-univariate-archinmean" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-archinmean">arch.univariate.ARCHInMean<a class="headerlink" href="#arch-univariate-archinmean" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ARCHInMean">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">ARCHInMean</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.__init__.y" title="arch.univariate.ARCHInMean.__init__.y (Python parameter) — nobs element vector containing the dependent variable"><span class="n"><span class="pre">y</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.__init__.x" title="arch.univariate.ARCHInMean.__init__.x (Python parameter) — nobs by k element array containing exogenous regressors"><span class="n"><span class="pre">x</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.__init__.lags" title="arch.univariate.ARCHInMean.__init__.lags (Python parameter) — Description of lag structure of the HAR.  Scalar included all lags between 1 and the value.  A 1-d array includes the AR lags lags[0], lags[1], ..."><span class="n"><span class="pre">lags</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.__init__.constant" title="arch.univariate.ARCHInMean.__init__.constant (Python parameter) — Flag whether the model should include a constant"><span class="n"><span class="pre">constant</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.__init__.hold_back" title="arch.univariate.ARCHInMean.__init__.hold_back (Python parameter) — Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."><span class="n"><span class="pre">hold_back</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.__init__.volatility" title="arch.univariate.ARCHInMean.__init__.volatility (Python parameter) — Volatility process to use in the model. volatility.updateable must return True."><span class="n"><span class="pre">volatility</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.__init__.distribution" title="arch.univariate.ARCHInMean.__init__.distribution (Python parameter) — Error distribution to use in the model"><span class="n"><span class="pre">distribution</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.__init__.rescale" title="arch.univariate.ARCHInMean.__init__.rescale (Python parameter) — Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."><span class="n"><span class="pre">rescale</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.__init__.form" title="arch.univariate.ARCHInMean.__init__.form (Python parameter) — The form of the conditional variance that appears in the mean equation. The string names use the log of the conditional variance (&quot;log&quot;), the square-root of the conditional variance (&quot;vol&quot;) or the conditional variance. When specified using a float, interpreted as \sigma_t^{form} so that 1 is equivalent to &quot;vol&quot; and 2 is equivalent to &quot;var&quot;. When using a number, must be different from 0."><span class="n"><span class="pre">form</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;vol&#39;</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/mean.html#ARCHInMean"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ARCHInMean" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">ARCHInMean</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.__init__.y" title="arch.univariate.ARCHInMean.__init__.y (Python parameter) — nobs element vector containing the dependent variable"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.__init__.x" title="arch.univariate.ARCHInMean.__init__.x (Python parameter) — nobs by k element array containing exogenous regressors"><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.__init__.lags" title="arch.univariate.ARCHInMean.__init__.lags (Python parameter) — Description of lag structure of the HAR.  Scalar included all lags between 1 and the value.  A 1-d array includes the AR lags lags[0], lags[1], ..."><span class="n"><span class="pre">lags</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.__init__.constant" title="arch.univariate.ARCHInMean.__init__.constant (Python parameter) — Flag whether the model should include a constant"><span class="n"><span class="pre">constant</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.__init__.hold_back" title="arch.univariate.ARCHInMean.__init__.hold_back (Python parameter) — Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."><span class="n"><span class="pre">hold_back</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.__init__.volatility" title="arch.univariate.ARCHInMean.__init__.volatility (Python parameter) — Volatility process to use in the model. volatility.updateable must return True."><span class="n"><span class="pre">volatility</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.volatility.VolatilityProcess.html#arch.univariate.volatility.VolatilityProcess" title="arch.univariate.volatility.VolatilityProcess (Python class) — Abstract base class for ARCH models.  Allows the conditional mean model to be specified separately from the conditional variance, even though parameters are estimated jointly."><span class="pre">VolatilityProcess</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.__init__.distribution" title="arch.univariate.ARCHInMean.__init__.distribution (Python parameter) — Error distribution to use in the model"><span class="n"><span class="pre">distribution</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.distribution.Distribution.html#arch.univariate.distribution.Distribution" title="arch.univariate.distribution.Distribution (Python class) — Template for subclassing only"><span class="pre">Distribution</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.__init__.rescale" title="arch.univariate.ARCHInMean.__init__.rescale (Python parameter) — Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."><span class="n"><span class="pre">rescale</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.__init__.form" title="arch.univariate.ARCHInMean.__init__.form (Python parameter) — The form of the conditional variance that appears in the mean equation. The string names use the log of the conditional variance (&quot;log&quot;), the square-root of the conditional variance (&quot;vol&quot;) or the conditional variance. When specified using a float, interpreted as \sigma_t^{form} so that 1 is equivalent to &quot;vol&quot; and 2 is equivalent to &quot;var&quot;. When using a number, must be different from 0."><span class="n"><span class="pre">form</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'log'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'vol'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'var'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;vol&#39;</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/mean.html#ARCHInMean"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ARCHInMean" title="Link to this definition">¶</a></dt>
 <dd><p>(G)ARCH-in-mean model and simulation</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ARCHInMean-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCHInMean-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ARCHInMean.__init__.y"><span class="sig-name descname">y</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, Series}</span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>nobs element vector containing the dependent variable</p>
+<dt id="arch.univariate.ARCHInMean.__init__.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>nobs element vector containing the dependent variable</p>
 </dd>
-<dt id="arch.univariate.ARCHInMean.__init__.x"><span class="sig-name descname">x</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, DataFrame}, optional</span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>nobs by k element array containing exogenous regressors</p>
+<dt id="arch.univariate.ARCHInMean.__init__.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>nobs by k element array containing exogenous regressors</p>
 </dd>
-<dt id="arch.univariate.ARCHInMean.__init__.lags"><span class="sig-name descname">lags</span><span class="p"> : </span><span class="desctype"><span class="desctype">{scalar, 1-d array}, optional</span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.__init__.lags" title="Permalink to this definition">¶</a></dt><dd><p>Description of lag structure of the HAR.  Scalar included all lags
+<dt id="arch.univariate.ARCHInMean.__init__.lags"><span class="n sig-name">lags</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.__init__.lags" title="Permalink to this definition">¶</a></dt><dd><p>Description of lag structure of the HAR.  Scalar included all lags
 between 1 and the value.  A 1-d array includes the AR lags lags[0],
 lags[1], …</p>
 </dd>
-<dt id="arch.univariate.ARCHInMean.__init__.constant"><span class="sig-name descname">constant</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.__init__.constant" title="Permalink to this definition">¶</a></dt><dd><p>Flag whether the model should include a constant</p>
+<dt id="arch.univariate.ARCHInMean.__init__.constant"><span class="n sig-name">constant</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.__init__.constant" title="Permalink to this definition">¶</a></dt><dd><p>Flag whether the model should include a constant</p>
 </dd>
-<dt id="arch.univariate.ARCHInMean.__init__.hold_back"><span class="sig-name descname">hold_back</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.__init__.hold_back" title="Permalink to this definition">¶</a></dt><dd><p>Number of observations at the start of the sample to exclude when
+<dt id="arch.univariate.ARCHInMean.__init__.hold_back"><span class="n sig-name">hold_back</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.__init__.hold_back" title="Permalink to this definition">¶</a></dt><dd><p>Number of observations at the start of the sample to exclude when
 estimating model parameters.  Used when comparing models with different
 lag lengths to estimate on the common sample.</p>
 </dd>
-<dt id="arch.univariate.ARCHInMean.__init__.volatility"><span class="sig-name descname">volatility</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference internal" href="arch.univariate.volatility.VolatilityProcess.html#arch.univariate.volatility.VolatilityProcess" title="arch.univariate.volatility.VolatilityProcess (Python class) — Abstract base class for ARCH models.  Allows the conditional mean model to be specified separately from the conditional variance, even though parameters are estimated jointly.">VolatilityProcess</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.__init__.volatility" title="Permalink to this definition">¶</a></dt><dd><p>Volatility process to use in the model. <code class="docutils literal notranslate"><span class="pre">volatility.updateable</span></code> must
+<dt id="arch.univariate.ARCHInMean.__init__.volatility"><span class="n sig-name">volatility</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.volatility.VolatilityProcess.html#arch.univariate.volatility.VolatilityProcess" title="arch.univariate.volatility.VolatilityProcess (Python class) — Abstract base class for ARCH models.  Allows the conditional mean model to be specified separately from the conditional variance, even though parameters are estimated jointly.">VolatilityProcess</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.__init__.volatility" title="Permalink to this definition">¶</a></dt><dd><p>Volatility process to use in the model. <code class="docutils literal notranslate"><span class="pre">volatility.updateable</span></code> must
 return <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p>
 </dd>
-<dt id="arch.univariate.ARCHInMean.__init__.distribution"><span class="sig-name descname">distribution</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference internal" href="arch.univariate.distribution.Distribution.html#arch.univariate.distribution.Distribution" title="arch.univariate.distribution.Distribution (Python class) — Template for subclassing only">Distribution</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.__init__.distribution" title="Permalink to this definition">¶</a></dt><dd><p>Error distribution to use in the model</p>
+<dt id="arch.univariate.ARCHInMean.__init__.distribution"><span class="n sig-name">distribution</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.distribution.Distribution.html#arch.univariate.distribution.Distribution" title="arch.univariate.distribution.Distribution (Python class) — Template for subclassing only">Distribution</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.__init__.distribution" title="Permalink to this definition">¶</a></dt><dd><p>Error distribution to use in the model</p>
 </dd>
-<dt id="arch.univariate.ARCHInMean.__init__.rescale"><span class="sig-name descname">rescale</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.__init__.rescale" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to automatically rescale data if the scale of the
+<dt id="arch.univariate.ARCHInMean.__init__.rescale"><span class="n sig-name">rescale</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.__init__.rescale" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to automatically rescale data if the scale of the
 data is likely to produce convergence issues when estimating model parameters.
 If False, the model is estimated on the data without transformation.  If True,
 than y is rescaled and the new scale is reported in the estimation results.</p>
 </dd>
-<dt id="arch.univariate.ARCHInMean.__init__.form"><span class="sig-name descname">form</span><span class="p"> : </span><span class="desctype"><span class="desctype">{&quot;log&quot;, &quot;vol&quot;, &quot;var&quot;, <a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, float}</span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.__init__.form" title="Permalink to this definition">¶</a></dt><dd><p>The form of the conditional variance that appears in the mean equation. The
+<dt id="arch.univariate.ARCHInMean.__init__.form"><span class="n sig-name">form</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'log'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'vol'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'var'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;vol&#39;</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.__init__.form" title="Permalink to this definition">¶</a></dt><dd><p>The form of the conditional variance that appears in the mean equation. The
 string names use the log of the conditional variance (“log”), the square-root
 of the conditional variance (“vol”) or the conditional variance. When
 specified using a float, interpreted as <span class="math notranslate nohighlight">\(\sigma_t^{form}\)</span> so that
@@ -1622,7 +1622,7 @@ <h1 id="arch-univariate-archinmean">arch.univariate.ARCHInMean<a class="headerli
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
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diff --git a/univariate/generated/arch.univariate.ARX.html b/univariate/generated/arch.univariate.ARX.html
index 9773b55220..20422b1eb8 100644
--- a/univariate/generated/arch.univariate.ARX.html
+++ b/univariate/generated/arch.univariate.ARX.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARX - arch 6.1.0</title>
+        <title>arch.univariate.ARX - arch 6.2.0</title>
       
     
 
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
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-            arch 6.1.0
+            arch 6.2.0
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+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch 6.1.0
+    arch 6.2.0
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@@ -1358,34 +1358,34 @@
                   
 
 
-<h1 id="arch-univariate-arx">arch.univariate.ARX<a class="headerlink" href="#arch-univariate-arx" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-arx">arch.univariate.ARX<a class="headerlink" href="#arch-univariate-arx" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ARX">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">ARX</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.__init__.y" title="arch.univariate.ARX.__init__.y (Python parameter) — nobs element vector containing the dependent variable"><span class="n"><span class="pre">y</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.__init__.x" title="arch.univariate.ARX.__init__.x (Python parameter) — nobs by k element array containing exogenous regressors"><span class="n"><span class="pre">x</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.__init__.lags" title="arch.univariate.ARX.__init__.lags (Python parameter) — Description of lag structure of the HAR.  Scalar included all lags between 1 and the value.  A 1-d array includes the AR lags lags[0], lags[1], ..."><span class="n"><span class="pre">lags</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.__init__.constant" title="arch.univariate.ARX.__init__.constant (Python parameter) — Flag whether the model should include a constant"><span class="n"><span class="pre">constant</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.__init__.hold_back" title="arch.univariate.ARX.__init__.hold_back (Python parameter) — Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."><span class="n"><span class="pre">hold_back</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.__init__.volatility" title="arch.univariate.ARX.__init__.volatility (Python parameter) — Volatility process to use in the model"><span class="n"><span class="pre">volatility</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.__init__.distribution" title="arch.univariate.ARX.__init__.distribution (Python parameter) — Error distribution to use in the model"><span class="n"><span class="pre">distribution</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.__init__.rescale" title="arch.univariate.ARX.__init__.rescale (Python parameter) — Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."><span class="n"><span class="pre">rescale</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/mean.html#ARX"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ARX" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">ARX</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.__init__.y" title="arch.univariate.ARX.__init__.y (Python parameter) — nobs element vector containing the dependent variable"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.__init__.x" title="arch.univariate.ARX.__init__.x (Python parameter) — nobs by k element array containing exogenous regressors"><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.__init__.lags" title="arch.univariate.ARX.__init__.lags (Python parameter) — Description of lag structure of the HAR.  Scalar included all lags between 1 and the value.  A 1-d array includes the AR lags lags[0], lags[1], ..."><span class="n"><span class="pre">lags</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.__init__.constant" title="arch.univariate.ARX.__init__.constant (Python parameter) — Flag whether the model should include a constant"><span class="n"><span class="pre">constant</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.__init__.hold_back" title="arch.univariate.ARX.__init__.hold_back (Python parameter) — Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."><span class="n"><span class="pre">hold_back</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.__init__.volatility" title="arch.univariate.ARX.__init__.volatility (Python parameter) — Volatility process to use in the model"><span class="n"><span class="pre">volatility</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.volatility.VolatilityProcess.html#arch.univariate.volatility.VolatilityProcess" title="arch.univariate.volatility.VolatilityProcess (Python class) — Abstract base class for ARCH models.  Allows the conditional mean model to be specified separately from the conditional variance, even though parameters are estimated jointly."><span class="pre">VolatilityProcess</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.__init__.distribution" title="arch.univariate.ARX.__init__.distribution (Python parameter) — Error distribution to use in the model"><span class="n"><span class="pre">distribution</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.distribution.Distribution.html#arch.univariate.distribution.Distribution" title="arch.univariate.distribution.Distribution (Python class) — Template for subclassing only"><span class="pre">Distribution</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.__init__.rescale" title="arch.univariate.ARX.__init__.rescale (Python parameter) — Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."><span class="n"><span class="pre">rescale</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/mean.html#ARX"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ARX" title="Link to this definition">¶</a></dt>
 <dd><p>Autoregressive model with optional exogenous regressors estimation and
 simulation</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ARX-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARX-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ARX.__init__.y"><span class="sig-name descname">y</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, Series}</span></span><a class="headerlink" href="#arch.univariate.ARX.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>nobs element vector containing the dependent variable</p>
+<dt id="arch.univariate.ARX.__init__.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARX.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>nobs element vector containing the dependent variable</p>
 </dd>
-<dt id="arch.univariate.ARX.__init__.x"><span class="sig-name descname">x</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, DataFrame}, optional</span></span><a class="headerlink" href="#arch.univariate.ARX.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>nobs by k element array containing exogenous regressors</p>
+<dt id="arch.univariate.ARX.__init__.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARX.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>nobs by k element array containing exogenous regressors</p>
 </dd>
-<dt id="arch.univariate.ARX.__init__.lags"><span class="sig-name descname">lags</span><span class="p"> : </span><span class="desctype"><span class="desctype">scalar, 1-d array, optional</span></span><a class="headerlink" href="#arch.univariate.ARX.__init__.lags" title="Permalink to this definition">¶</a></dt><dd><p>Description of lag structure of the HAR.  Scalar included all lags
+<dt id="arch.univariate.ARX.__init__.lags"><span class="n sig-name">lags</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARX.__init__.lags" title="Permalink to this definition">¶</a></dt><dd><p>Description of lag structure of the HAR.  Scalar included all lags
 between 1 and the value.  A 1-d array includes the AR lags lags[0],
 lags[1], …</p>
 </dd>
-<dt id="arch.univariate.ARX.__init__.constant"><span class="sig-name descname">constant</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ARX.__init__.constant" title="Permalink to this definition">¶</a></dt><dd><p>Flag whether the model should include a constant</p>
+<dt id="arch.univariate.ARX.__init__.constant"><span class="n sig-name">constant</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.univariate.ARX.__init__.constant" title="Permalink to this definition">¶</a></dt><dd><p>Flag whether the model should include a constant</p>
 </dd>
-<dt id="arch.univariate.ARX.__init__.hold_back"><span class="sig-name descname">hold_back</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.ARX.__init__.hold_back" title="Permalink to this definition">¶</a></dt><dd><p>Number of observations at the start of the sample to exclude when
+<dt id="arch.univariate.ARX.__init__.hold_back"><span class="n sig-name">hold_back</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARX.__init__.hold_back" title="Permalink to this definition">¶</a></dt><dd><p>Number of observations at the start of the sample to exclude when
 estimating model parameters.  Used when comparing models with different
 lag lengths to estimate on the common sample.</p>
 </dd>
-<dt id="arch.univariate.ARX.__init__.volatility"><span class="sig-name descname">volatility</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference internal" href="arch.univariate.volatility.VolatilityProcess.html#arch.univariate.volatility.VolatilityProcess" title="arch.univariate.volatility.VolatilityProcess (Python class) — Abstract base class for ARCH models.  Allows the conditional mean model to be specified separately from the conditional variance, even though parameters are estimated jointly.">VolatilityProcess</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ARX.__init__.volatility" title="Permalink to this definition">¶</a></dt><dd><p>Volatility process to use in the model</p>
+<dt id="arch.univariate.ARX.__init__.volatility"><span class="n sig-name">volatility</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.volatility.VolatilityProcess.html#arch.univariate.volatility.VolatilityProcess" title="arch.univariate.volatility.VolatilityProcess (Python class) — Abstract base class for ARCH models.  Allows the conditional mean model to be specified separately from the conditional variance, even though parameters are estimated jointly.">VolatilityProcess</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARX.__init__.volatility" title="Permalink to this definition">¶</a></dt><dd><p>Volatility process to use in the model</p>
 </dd>
-<dt id="arch.univariate.ARX.__init__.distribution"><span class="sig-name descname">distribution</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference internal" href="arch.univariate.distribution.Distribution.html#arch.univariate.distribution.Distribution" title="arch.univariate.distribution.Distribution (Python class) — Template for subclassing only">Distribution</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ARX.__init__.distribution" title="Permalink to this definition">¶</a></dt><dd><p>Error distribution to use in the model</p>
+<dt id="arch.univariate.ARX.__init__.distribution"><span class="n sig-name">distribution</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.distribution.Distribution.html#arch.univariate.distribution.Distribution" title="arch.univariate.distribution.Distribution (Python class) — Template for subclassing only">Distribution</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARX.__init__.distribution" title="Permalink to this definition">¶</a></dt><dd><p>Error distribution to use in the model</p>
 </dd>
-<dt id="arch.univariate.ARX.__init__.rescale"><span class="sig-name descname">rescale</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ARX.__init__.rescale" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to automatically rescale data if the scale of the
+<dt id="arch.univariate.ARX.__init__.rescale"><span class="n sig-name">rescale</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARX.__init__.rescale" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to automatically rescale data if the scale of the
 data is likely to produce convergence issues when estimating model parameters.
 If False, the model is estimated on the data without transformation.  If True,
 than y is rescaled and the new scale is reported in the estimation results.</p>
@@ -1539,7 +1539,7 @@ <h1 id="arch-univariate-arx">arch.univariate.ARX<a class="headerlink" href="#arc
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1586,10 +1586,10 @@ <h1 id="arch-univariate-arx">arch.univariate.ARX<a class="headerlink" href="#arc
     <script id="__config" type="application/json">{"base": "../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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+        <script src="../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+        <script id="MathJax-script" src="../../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
     
   </body>
 </html>
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diff --git a/univariate/generated/arch.univariate.ConstantMean.html b/univariate/generated/arch.univariate.ConstantMean.html
index 346acb6f8b..ef7c0beba5 100644
--- a/univariate/generated/arch.univariate.ConstantMean.html
+++ b/univariate/generated/arch.univariate.ConstantMean.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantMean - arch 6.1.0</title>
+        <title>arch.univariate.ConstantMean - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1316,25 +1316,25 @@
                   
 
 
-<h1 id="arch-univariate-constantmean">arch.univariate.ConstantMean<a class="headerlink" href="#arch-univariate-constantmean" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-constantmean">arch.univariate.ConstantMean<a class="headerlink" href="#arch-univariate-constantmean" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ConstantMean">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">ConstantMean</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.__init__.y" title="arch.univariate.ConstantMean.__init__.y (Python parameter) — nobs element vector containing the dependent variable"><span class="n"><span class="pre">y</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.__init__.hold_back" title="arch.univariate.ConstantMean.__init__.hold_back (Python parameter) — Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."><span class="n"><span class="pre">hold_back</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.__init__.volatility" title="arch.univariate.ConstantMean.__init__.volatility (Python parameter) — Volatility process to use in the model"><span class="n"><span class="pre">volatility</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.__init__.distribution" title="arch.univariate.ConstantMean.__init__.distribution (Python parameter) — Error distribution to use in the model"><span class="n"><span class="pre">distribution</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.__init__.rescale" title="arch.univariate.ConstantMean.__init__.rescale (Python parameter) — Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."><span class="n"><span class="pre">rescale</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/mean.html#ConstantMean"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ConstantMean" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">ConstantMean</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.__init__.y" title="arch.univariate.ConstantMean.__init__.y (Python parameter) — nobs element vector containing the dependent variable"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.__init__.hold_back" title="arch.univariate.ConstantMean.__init__.hold_back (Python parameter) — Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."><span class="n"><span class="pre">hold_back</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.__init__.volatility" title="arch.univariate.ConstantMean.__init__.volatility (Python parameter) — Volatility process to use in the model"><span class="n"><span class="pre">volatility</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.volatility.VolatilityProcess.html#arch.univariate.volatility.VolatilityProcess" title="arch.univariate.volatility.VolatilityProcess (Python class) — Abstract base class for ARCH models.  Allows the conditional mean model to be specified separately from the conditional variance, even though parameters are estimated jointly."><span class="pre">VolatilityProcess</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.__init__.distribution" title="arch.univariate.ConstantMean.__init__.distribution (Python parameter) — Error distribution to use in the model"><span class="n"><span class="pre">distribution</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.distribution.Distribution.html#arch.univariate.distribution.Distribution" title="arch.univariate.distribution.Distribution (Python class) — Template for subclassing only"><span class="pre">Distribution</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.__init__.rescale" title="arch.univariate.ConstantMean.__init__.rescale (Python parameter) — Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."><span class="n"><span class="pre">rescale</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/mean.html#ConstantMean"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ConstantMean" title="Link to this definition">¶</a></dt>
 <dd><p>Constant mean model estimation and simulation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ConstantMean-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantMean-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ConstantMean.__init__.y"><span class="sig-name descname">y</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, Series}</span></span><a class="headerlink" href="#arch.univariate.ConstantMean.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>nobs element vector containing the dependent variable</p>
+<dt id="arch.univariate.ConstantMean.__init__.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>nobs element vector containing the dependent variable</p>
 </dd>
-<dt id="arch.univariate.ConstantMean.__init__.hold_back"><span class="sig-name descname">hold_back</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.ConstantMean.__init__.hold_back" title="Permalink to this definition">¶</a></dt><dd><p>Number of observations at the start of the sample to exclude when
+<dt id="arch.univariate.ConstantMean.__init__.hold_back"><span class="n sig-name">hold_back</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.__init__.hold_back" title="Permalink to this definition">¶</a></dt><dd><p>Number of observations at the start of the sample to exclude when
 estimating model parameters.  Used when comparing models with different
 lag lengths to estimate on the common sample.</p>
 </dd>
-<dt id="arch.univariate.ConstantMean.__init__.volatility"><span class="sig-name descname">volatility</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference internal" href="arch.univariate.volatility.VolatilityProcess.html#arch.univariate.volatility.VolatilityProcess" title="arch.univariate.volatility.VolatilityProcess (Python class) — Abstract base class for ARCH models.  Allows the conditional mean model to be specified separately from the conditional variance, even though parameters are estimated jointly.">VolatilityProcess</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ConstantMean.__init__.volatility" title="Permalink to this definition">¶</a></dt><dd><p>Volatility process to use in the model</p>
+<dt id="arch.univariate.ConstantMean.__init__.volatility"><span class="n sig-name">volatility</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.volatility.VolatilityProcess.html#arch.univariate.volatility.VolatilityProcess" title="arch.univariate.volatility.VolatilityProcess (Python class) — Abstract base class for ARCH models.  Allows the conditional mean model to be specified separately from the conditional variance, even though parameters are estimated jointly.">VolatilityProcess</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.__init__.volatility" title="Permalink to this definition">¶</a></dt><dd><p>Volatility process to use in the model</p>
 </dd>
-<dt id="arch.univariate.ConstantMean.__init__.distribution"><span class="sig-name descname">distribution</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference internal" href="arch.univariate.distribution.Distribution.html#arch.univariate.distribution.Distribution" title="arch.univariate.distribution.Distribution (Python class) — Template for subclassing only">Distribution</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ConstantMean.__init__.distribution" title="Permalink to this definition">¶</a></dt><dd><p>Error distribution to use in the model</p>
+<dt id="arch.univariate.ConstantMean.__init__.distribution"><span class="n sig-name">distribution</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.distribution.Distribution.html#arch.univariate.distribution.Distribution" title="arch.univariate.distribution.Distribution (Python class) — Template for subclassing only">Distribution</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.__init__.distribution" title="Permalink to this definition">¶</a></dt><dd><p>Error distribution to use in the model</p>
 </dd>
-<dt id="arch.univariate.ConstantMean.__init__.rescale"><span class="sig-name descname">rescale</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ConstantMean.__init__.rescale" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to automatically rescale data if the scale of the
+<dt id="arch.univariate.ConstantMean.__init__.rescale"><span class="n sig-name">rescale</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.__init__.rescale" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to automatically rescale data if the scale of the
 data is likely to produce convergence issues when estimating model parameters.
 If False, the model is estimated on the data without transformation.  If True,
 than y is rescaled and the new scale is reported in the estimation results.</p>
@@ -1481,7 +1481,7 @@ <h1 id="arch-univariate-constantmean">arch.univariate.ConstantMean<a class="head
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1528,10 +1528,10 @@ <h1 id="arch-univariate-constantmean">arch.univariate.ConstantMean<a class="head
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diff --git a/univariate/generated/arch.univariate.ConstantVariance.html b/univariate/generated/arch.univariate.ConstantVariance.html
index 76b48f8a67..c6c71c824f 100644
--- a/univariate/generated/arch.univariate.ConstantVariance.html
+++ b/univariate/generated/arch.univariate.ConstantVariance.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantVariance - arch 6.1.0</title>
+        <title>arch.univariate.ConstantVariance - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1296,10 +1296,10 @@
                   
 
 
-<h1 id="arch-univariate-constantvariance">arch.univariate.ConstantVariance<a class="headerlink" href="#arch-univariate-constantvariance" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-constantvariance">arch.univariate.ConstantVariance<a class="headerlink" href="#arch-univariate-constantvariance" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ConstantVariance">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">ConstantVariance</span></span><a class="reference internal" href="../../_modules/arch/univariate/volatility.html#ConstantVariance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ConstantVariance" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">ConstantVariance</span></span><a class="reference internal" href="../../_modules/arch/univariate/volatility.html#ConstantVariance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ConstantVariance" title="Link to this definition">¶</a></dt>
 <dd><p>Constant volatility process</p>
 <p class="rubric">Notes</p>
 <p>Model has the same variance in all periods</p>
@@ -1433,7 +1433,7 @@ <h1 id="arch-univariate-constantvariance">arch.univariate.ConstantVariance<a cla
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1480,7 +1480,7 @@ <h1 id="arch-univariate-constantvariance">arch.univariate.ConstantVariance<a cla
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diff --git a/univariate/generated/arch.univariate.EGARCH.html b/univariate/generated/arch.univariate.EGARCH.html
index c5c6781576..26d9921219 100644
--- a/univariate/generated/arch.univariate.EGARCH.html
+++ b/univariate/generated/arch.univariate.EGARCH.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EGARCH - arch 6.1.0</title>
+        <title>arch.univariate.EGARCH - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1370,19 +1370,19 @@
                   
 
 
-<h1 id="arch-univariate-egarch">arch.univariate.EGARCH<a class="headerlink" href="#arch-univariate-egarch" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-egarch">arch.univariate.EGARCH<a class="headerlink" href="#arch-univariate-egarch" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.EGARCH">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">EGARCH</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.__init__.p" title="arch.univariate.EGARCH.__init__.p (Python parameter) — Order of the symmetric innovation"><span class="n"><span class="pre">p</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.__init__.o" title="arch.univariate.EGARCH.__init__.o (Python parameter) — Order of the asymmetric innovation"><span class="n"><span class="pre">o</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.__init__.q" title="arch.univariate.EGARCH.__init__.q (Python parameter) — Order of the lagged (transformed) conditional variance"><span class="n"><span class="pre">q</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/volatility.html#EGARCH"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.EGARCH" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">EGARCH</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.__init__.p" title="arch.univariate.EGARCH.__init__.p (Python parameter) — Order of the symmetric innovation"><span class="n"><span class="pre">p</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.__init__.o" title="arch.univariate.EGARCH.__init__.o (Python parameter) — Order of the asymmetric innovation"><span class="n"><span class="pre">o</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.__init__.q" title="arch.univariate.EGARCH.__init__.q (Python parameter) — Order of the lagged (transformed) conditional variance"><span class="n"><span class="pre">q</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/volatility.html#EGARCH"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.EGARCH" title="Link to this definition">¶</a></dt>
 <dd><p>EGARCH model estimation</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.EGARCH-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EGARCH-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.EGARCH.__init__.p"><span class="sig-name descname">p</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.EGARCH.__init__.p" title="Permalink to this definition">¶</a></dt><dd><p>Order of the symmetric innovation</p>
+<dt id="arch.univariate.EGARCH.__init__.p"><span class="n sig-name">p</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code><a class="headerlink" href="#arch.univariate.EGARCH.__init__.p" title="Permalink to this definition">¶</a></dt><dd><p>Order of the symmetric innovation</p>
 </dd>
-<dt id="arch.univariate.EGARCH.__init__.o"><span class="sig-name descname">o</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.EGARCH.__init__.o" title="Permalink to this definition">¶</a></dt><dd><p>Order of the asymmetric innovation</p>
+<dt id="arch.univariate.EGARCH.__init__.o"><span class="n sig-name">o</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code><a class="headerlink" href="#arch.univariate.EGARCH.__init__.o" title="Permalink to this definition">¶</a></dt><dd><p>Order of the asymmetric innovation</p>
 </dd>
-<dt id="arch.univariate.EGARCH.__init__.q"><span class="sig-name descname">q</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.EGARCH.__init__.q" title="Permalink to this definition">¶</a></dt><dd><p>Order of the lagged (transformed) conditional variance</p>
+<dt id="arch.univariate.EGARCH.__init__.q"><span class="n sig-name">q</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code><a class="headerlink" href="#arch.univariate.EGARCH.__init__.q" title="Permalink to this definition">¶</a></dt><dd><p>Order of the lagged (transformed) conditional variance</p>
 </dd>
 </dl>
 </dd>
@@ -1542,7 +1542,7 @@ <h1 id="arch-univariate-egarch">arch.univariate.EGARCH<a class="headerlink" href
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1589,10 +1589,10 @@ <h1 id="arch-univariate-egarch">arch.univariate.EGARCH<a class="headerlink" href
     <script id="__config" type="application/json">{"base": "../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
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-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+        <script id="MathJax-script" src="../../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
     
   </body>
 </html>
\ No newline at end of file
diff --git a/univariate/generated/arch.univariate.EWMAVariance.html b/univariate/generated/arch.univariate.EWMAVariance.html
index d23835fbc5..5261b06398 100644
--- a/univariate/generated/arch.univariate.EWMAVariance.html
+++ b/univariate/generated/arch.univariate.EWMAVariance.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EWMAVariance - arch 6.1.0</title>
+        <title>arch.univariate.EWMAVariance - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1397,15 +1397,15 @@
                   
 
 
-<h1 id="arch-univariate-ewmavariance">arch.univariate.EWMAVariance<a class="headerlink" href="#arch-univariate-ewmavariance" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-ewmavariance">arch.univariate.EWMAVariance<a class="headerlink" href="#arch-univariate-ewmavariance" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.EWMAVariance">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">EWMAVariance</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.__init__.lam" title="arch.univariate.EWMAVariance.__init__.lam (Python parameter) — Smoothing parameter. Default is 0.94. Set to None to estimate lam jointly with other model parameters"><span class="n"><span class="pre">lam</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.94</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/volatility.html#EWMAVariance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.EWMAVariance" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">EWMAVariance</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.__init__.lam" title="arch.univariate.EWMAVariance.__init__.lam (Python parameter) — Smoothing parameter. Default is 0.94. Set to None to estimate lam jointly with other model parameters"><span class="n"><span class="pre">lam</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.94</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/volatility.html#EWMAVariance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.EWMAVariance" title="Link to this definition">¶</a></dt>
 <dd><p>Exponentially Weighted Moving-Average (RiskMetrics) Variance process</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.EWMAVariance-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EWMAVariance-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.EWMAVariance.__init__.lam"><span class="sig-name descname">lam</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, None}, optional</span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.__init__.lam" title="Permalink to this definition">¶</a></dt><dd><p>Smoothing parameter. Default is 0.94. Set to None to estimate lam
+<dt id="arch.univariate.EWMAVariance.__init__.lam"><span class="n sig-name">lam</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.94</span></code><a class="headerlink" href="#arch.univariate.EWMAVariance.__init__.lam" title="Permalink to this definition">¶</a></dt><dd><p>Smoothing parameter. Default is 0.94. Set to None to estimate lam
 jointly with other model parameters</p>
 </dd>
 </dl>
@@ -1554,7 +1554,7 @@ <h1 id="arch-univariate-ewmavariance">arch.univariate.EWMAVariance<a class="head
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1601,10 +1601,10 @@ <h1 id="arch-univariate-ewmavariance">arch.univariate.EWMAVariance<a class="head
     <script id="__config" type="application/json">{"base": "../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+        <script id="MathJax-script" src="../../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
     
   </body>
 </html>
\ No newline at end of file
diff --git a/univariate/generated/arch.univariate.FIGARCH.html b/univariate/generated/arch.univariate.FIGARCH.html
index 0dc03c5315..4062df558e 100644
--- a/univariate/generated/arch.univariate.FIGARCH.html
+++ b/univariate/generated/arch.univariate.FIGARCH.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FIGARCH - arch 6.1.0</title>
+        <title>arch.univariate.FIGARCH - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1406,24 +1406,24 @@
                   
 
 
-<h1 id="arch-univariate-figarch">arch.univariate.FIGARCH<a class="headerlink" href="#arch-univariate-figarch" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-figarch">arch.univariate.FIGARCH<a class="headerlink" href="#arch-univariate-figarch" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.FIGARCH">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">FIGARCH</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.__init__.p" title="arch.univariate.FIGARCH.__init__.p (Python parameter) — Order of the symmetric innovation"><span class="n"><span class="pre">p</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.__init__.q" title="arch.univariate.FIGARCH.__init__.q (Python parameter) — Order of the lagged (transformed) conditional variance"><span class="n"><span class="pre">q</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.__init__.power" title="arch.univariate.FIGARCH.__init__.power (Python parameter) — Power to use with the innovations, abs(e) ** power.  Default is 2.0, which produces FIGARCH and related models. Using 1.0 produces FIAVARCH and related models.  Other powers can be specified, although these should be strictly positive, and usually larger than 0.25."><span class="n"><span class="pre">power</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">2.0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.__init__.truncation" title="arch.univariate.FIGARCH.__init__.truncation (Python parameter) — Truncation point to use in ARCH(\infty) representation. Default is 1000."><span class="n"><span class="pre">truncation</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/volatility.html#FIGARCH"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.FIGARCH" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.FIGARCH">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">FIGARCH</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.__init__.p" title="arch.univariate.FIGARCH.__init__.p (Python parameter) — Order of the symmetric innovation"><span class="n"><span class="pre">p</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.__init__.q" title="arch.univariate.FIGARCH.__init__.q (Python parameter) — Order of the lagged (transformed) conditional variance"><span class="n"><span class="pre">q</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.__init__.power" title="arch.univariate.FIGARCH.__init__.power (Python parameter) — Power to use with the innovations, abs(e) ** power.  Default is 2.0, which produces FIGARCH and related models. Using 1.0 produces FIAVARCH and related models.  Other powers can be specified, although these should be strictly positive, and usually larger than 0.25."><span class="n"><span class="pre">power</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">2.0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.__init__.truncation" title="arch.univariate.FIGARCH.__init__.truncation (Python parameter) — Truncation point to use in ARCH(\infty) representation. Default is 1000."><span class="n"><span class="pre">truncation</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/volatility.html#FIGARCH"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.FIGARCH" title="Link to this definition">¶</a></dt>
 <dd><p>FIGARCH model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.FIGARCH-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FIGARCH-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.FIGARCH.__init__.p"><span class="sig-name descname">p</span><span class="p"> : </span><span class="desctype"><span class="desctype">{0, 1}</span></span><a class="headerlink" href="#arch.univariate.FIGARCH.__init__.p" title="Permalink to this definition">¶</a></dt><dd><p>Order of the symmetric innovation</p>
+<dt id="arch.univariate.FIGARCH.__init__.p"><span class="n sig-name">p</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code><a class="headerlink" href="#arch.univariate.FIGARCH.__init__.p" title="Permalink to this definition">¶</a></dt><dd><p>Order of the symmetric innovation</p>
 </dd>
-<dt id="arch.univariate.FIGARCH.__init__.q"><span class="sig-name descname">q</span><span class="p"> : </span><span class="desctype"><span class="desctype">{0, 1}</span></span><a class="headerlink" href="#arch.univariate.FIGARCH.__init__.q" title="Permalink to this definition">¶</a></dt><dd><p>Order of the lagged (transformed) conditional variance</p>
+<dt id="arch.univariate.FIGARCH.__init__.q"><span class="n sig-name">q</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code><a class="headerlink" href="#arch.univariate.FIGARCH.__init__.q" title="Permalink to this definition">¶</a></dt><dd><p>Order of the lagged (transformed) conditional variance</p>
 </dd>
-<dt id="arch.univariate.FIGARCH.__init__.power"><span class="sig-name descname">power</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>, optional</span></span><a class="headerlink" href="#arch.univariate.FIGARCH.__init__.power" title="Permalink to this definition">¶</a></dt><dd><p>Power to use with the innovations, abs(e) ** power.  Default is 2.0,
+<dt id="arch.univariate.FIGARCH.__init__.power"><span class="n sig-name">power</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">2.0</span></code><a class="headerlink" href="#arch.univariate.FIGARCH.__init__.power" title="Permalink to this definition">¶</a></dt><dd><p>Power to use with the innovations, abs(e) ** power.  Default is 2.0,
 which produces FIGARCH and related models. Using 1.0 produces
 FIAVARCH and related models.  Other powers can be specified, although
 these should be strictly positive, and usually larger than 0.25.</p>
 </dd>
-<dt id="arch.univariate.FIGARCH.__init__.truncation"><span class="sig-name descname">truncation</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.univariate.FIGARCH.__init__.truncation" title="Permalink to this definition">¶</a></dt><dd><p>Truncation point to use in ARCH(<span class="math notranslate nohighlight">\(\infty\)</span>) representation.
+<dt id="arch.univariate.FIGARCH.__init__.truncation"><span class="n sig-name">truncation</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.univariate.FIGARCH.__init__.truncation" title="Permalink to this definition">¶</a></dt><dd><p>Truncation point to use in ARCH(<span class="math notranslate nohighlight">\(\infty\)</span>) representation.
 Default is 1000.</p>
 </dd>
 </dl>
@@ -1598,7 +1598,7 @@ <h1 id="arch-univariate-figarch">arch.univariate.FIGARCH<a class="headerlink" hr
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1645,10 +1645,10 @@ <h1 id="arch-univariate-figarch">arch.univariate.FIGARCH<a class="headerlink" hr
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-        <script src="../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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+        <script id="MathJax-script" src="../../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
     
   </body>
 </html>
\ No newline at end of file
diff --git a/univariate/generated/arch.univariate.FixedVariance.html b/univariate/generated/arch.univariate.FixedVariance.html
index 28efa33f62..974465a516 100644
--- a/univariate/generated/arch.univariate.FixedVariance.html
+++ b/univariate/generated/arch.univariate.FixedVariance.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FixedVariance - arch 6.1.0</title>
+        <title>arch.univariate.FixedVariance - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1396,18 +1396,18 @@
                   
 
 
-<h1 id="arch-univariate-fixedvariance">arch.univariate.FixedVariance<a class="headerlink" href="#arch-univariate-fixedvariance" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-fixedvariance">arch.univariate.FixedVariance<a class="headerlink" href="#arch-univariate-fixedvariance" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.FixedVariance">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">FixedVariance</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.__init__.variance" title="arch.univariate.FixedVariance.__init__.variance (Python parameter) — Array containing the variances to use.  Should have the same shape as the data used in the model."><span class="n"><span class="pre">variance</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.__init__.unit_scale" title="arch.univariate.FixedVariance.__init__.unit_scale (Python parameter) — Flag whether to enforce a unit scale.  If False, a scale parameter will be estimated so that the model variance will be proportional to variance. If True, the model variance is set of variance"><span class="n"><span class="pre">unit_scale</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/volatility.html#FixedVariance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.FixedVariance" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.FixedVariance">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">FixedVariance</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.__init__.variance" title="arch.univariate.FixedVariance.__init__.variance (Python parameter) — Array containing the variances to use.  Should have the same shape as the data used in the model."><span class="n"><span class="pre">variance</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.__init__.unit_scale" title="arch.univariate.FixedVariance.__init__.unit_scale (Python parameter) — Flag whether to enforce a unit scale.  If False, a scale parameter will be estimated so that the model variance will be proportional to variance. If True, the model variance is set of variance"><span class="n"><span class="pre">unit_scale</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/volatility.html#FixedVariance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.FixedVariance" title="Link to this definition">¶</a></dt>
 <dd><p>Fixed volatility process</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.FixedVariance-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FixedVariance-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.FixedVariance.__init__.variance"><span class="sig-name descname">variance</span><span class="p"> : </span><span class="desctype"><span class="desctype">{array, Series}</span></span><a class="headerlink" href="#arch.univariate.FixedVariance.__init__.variance" title="Permalink to this definition">¶</a></dt><dd><p>Array containing the variances to use.  Should have the same shape as the
+<dt id="arch.univariate.FixedVariance.__init__.variance"><span class="n sig-name">variance</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.FixedVariance.__init__.variance" title="Permalink to this definition">¶</a></dt><dd><p>Array containing the variances to use.  Should have the same shape as the
 data used in the model.</p>
 </dd>
-<dt id="arch.univariate.FixedVariance.__init__.unit_scale"><span class="sig-name descname">unit_scale</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, optional</span></span><a class="headerlink" href="#arch.univariate.FixedVariance.__init__.unit_scale" title="Permalink to this definition">¶</a></dt><dd><p>Flag whether to enforce a unit scale.  If False, a scale parameter will be
+<dt id="arch.univariate.FixedVariance.__init__.unit_scale"><span class="n sig-name">unit_scale</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.univariate.FixedVariance.__init__.unit_scale" title="Permalink to this definition">¶</a></dt><dd><p>Flag whether to enforce a unit scale.  If False, a scale parameter will be
 estimated so that the model variance will be proportional to <code class="docutils literal notranslate"><span class="pre">variance</span></code>.
 If True, the model variance is set of <code class="docutils literal notranslate"><span class="pre">variance</span></code></p>
 </dd>
@@ -1547,7 +1547,7 @@ <h1 id="arch-univariate-fixedvariance">arch.univariate.FixedVariance<a class="he
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1594,7 +1594,7 @@ <h1 id="arch-univariate-fixedvariance">arch.univariate.FixedVariance<a class="he
     <script id="__config" type="application/json">{"base": "../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/arch.univariate.GARCH.html b/univariate/generated/arch.univariate.GARCH.html
index a90e0512cb..e930f20744 100644
--- a/univariate/generated/arch.univariate.GARCH.html
+++ b/univariate/generated/arch.univariate.GARCH.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GARCH - arch 6.1.0</title>
+        <title>arch.univariate.GARCH - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1384,10 +1384,10 @@
                   
 
 
-<h1 id="arch-univariate-garch">arch.univariate.GARCH<a class="headerlink" href="#arch-univariate-garch" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-garch">arch.univariate.GARCH<a class="headerlink" href="#arch-univariate-garch" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.GARCH">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">GARCH</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.__init__.p" title="arch.univariate.GARCH.__init__.p (Python parameter) — Order of the symmetric innovation"><span class="n"><span class="pre">p</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.__init__.o" title="arch.univariate.GARCH.__init__.o (Python parameter) — Order of the asymmetric innovation"><span class="n"><span class="pre">o</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.__init__.q" title="arch.univariate.GARCH.__init__.q (Python parameter) — Order of the lagged (transformed) conditional variance"><span class="n"><span class="pre">q</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.__init__.power" title="arch.univariate.GARCH.__init__.power (Python parameter) — Power to use with the innovations, abs(e) ** power.  Default is 2.0, which produces ARCH and related models. Using 1.0 produces AVARCH and related models.  Other powers can be specified, although these should be strictly positive, and usually larger than 0.25."><span class="n"><span class="pre">power</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">2.0</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/volatility.html#GARCH"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.GARCH" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.GARCH">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">GARCH</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.__init__.p" title="arch.univariate.GARCH.__init__.p (Python parameter) — Order of the symmetric innovation"><span class="n"><span class="pre">p</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.__init__.o" title="arch.univariate.GARCH.__init__.o (Python parameter) — Order of the asymmetric innovation"><span class="n"><span class="pre">o</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.__init__.q" title="arch.univariate.GARCH.__init__.q (Python parameter) — Order of the lagged (transformed) conditional variance"><span class="n"><span class="pre">q</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.__init__.power" title="arch.univariate.GARCH.__init__.power (Python parameter) — Power to use with the innovations, abs(e) ** power.  Default is 2.0, which produces ARCH and related models. Using 1.0 produces AVARCH and related models.  Other powers can be specified, although these should be strictly positive, and usually larger than 0.25."><span class="n"><span class="pre">power</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">2.0</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/volatility.html#GARCH"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.GARCH" title="Link to this definition">¶</a></dt>
 <dd><p>GARCH and related model estimation</p>
 <dl class="simple">
 <dt>The following models can be specified using GARCH:</dt><dd><ul class="simple">
@@ -1404,13 +1404,13 @@ <h1 id="arch-univariate-garch">arch.univariate.GARCH<a class="headerlink" href="
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.GARCH-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GARCH-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.GARCH.__init__.p"><span class="sig-name descname">p</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.GARCH.__init__.p" title="Permalink to this definition">¶</a></dt><dd><p>Order of the symmetric innovation</p>
+<dt id="arch.univariate.GARCH.__init__.p"><span class="n sig-name">p</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code><a class="headerlink" href="#arch.univariate.GARCH.__init__.p" title="Permalink to this definition">¶</a></dt><dd><p>Order of the symmetric innovation</p>
 </dd>
-<dt id="arch.univariate.GARCH.__init__.o"><span class="sig-name descname">o</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.GARCH.__init__.o" title="Permalink to this definition">¶</a></dt><dd><p>Order of the asymmetric innovation</p>
+<dt id="arch.univariate.GARCH.__init__.o"><span class="n sig-name">o</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code><a class="headerlink" href="#arch.univariate.GARCH.__init__.o" title="Permalink to this definition">¶</a></dt><dd><p>Order of the asymmetric innovation</p>
 </dd>
-<dt id="arch.univariate.GARCH.__init__.q"><span class="sig-name descname">q</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.GARCH.__init__.q" title="Permalink to this definition">¶</a></dt><dd><p>Order of the lagged (transformed) conditional variance</p>
+<dt id="arch.univariate.GARCH.__init__.q"><span class="n sig-name">q</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code><a class="headerlink" href="#arch.univariate.GARCH.__init__.q" title="Permalink to this definition">¶</a></dt><dd><p>Order of the lagged (transformed) conditional variance</p>
 </dd>
-<dt id="arch.univariate.GARCH.__init__.power"><span class="sig-name descname">power</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>, optional</span></span><a class="headerlink" href="#arch.univariate.GARCH.__init__.power" title="Permalink to this definition">¶</a></dt><dd><p>Power to use with the innovations, abs(e) ** power.  Default is 2.0, which
+<dt id="arch.univariate.GARCH.__init__.power"><span class="n sig-name">power</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">2.0</span></code><a class="headerlink" href="#arch.univariate.GARCH.__init__.power" title="Permalink to this definition">¶</a></dt><dd><p>Power to use with the innovations, abs(e) ** power.  Default is 2.0, which
 produces ARCH and related models. Using 1.0 produces AVARCH and related
 models.  Other powers can be specified, although these should be strictly
 positive, and usually larger than 0.25.</p>
@@ -1572,7 +1572,7 @@ <h1 id="arch-univariate-garch">arch.univariate.GARCH<a class="headerlink" href="
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1619,10 +1619,10 @@ <h1 id="arch-univariate-garch">arch.univariate.GARCH<a class="headerlink" href="
     <script id="__config" type="application/json">{"base": "../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
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-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+        <script id="MathJax-script" src="../../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
     
   </body>
 </html>
\ No newline at end of file
diff --git a/univariate/generated/arch.univariate.GeneralizedError.html b/univariate/generated/arch.univariate.GeneralizedError.html
index bc520b30fb..062b45ff39 100644
--- a/univariate/generated/arch.univariate.GeneralizedError.html
+++ b/univariate/generated/arch.univariate.GeneralizedError.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GeneralizedError - arch 6.1.0</title>
+        <title>arch.univariate.GeneralizedError - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1178,20 +1178,20 @@
                   
 
 
-<h1 id="arch-univariate-generalizederror">arch.univariate.GeneralizedError<a class="headerlink" href="#arch-univariate-generalizederror" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-generalizederror">arch.univariate.GeneralizedError<a class="headerlink" href="#arch-univariate-generalizederror" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.GeneralizedError">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">GeneralizedError</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.__init__.random_state" title="arch.univariate.GeneralizedError.__init__.random_state (Python parameter) — Deprecated since version 5.0: random_state is deprecated. Use seed instead."><span class="n"><span class="pre">random_state</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.__init__.seed" title="arch.univariate.GeneralizedError.__init__.seed (Python parameter) — Random number generator instance or int to use. Set to ensure reproducibility. If using an int, the argument is passed to np.random.default_rng.  If not provided, default_rng is used with system-provided entropy."><span class="n"><span class="pre">seed</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/distribution.html#GeneralizedError"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.GeneralizedError" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">GeneralizedError</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.__init__.random_state" title="arch.univariate.GeneralizedError.__init__.random_state (Python parameter) — Deprecated since version 5.0: random_state is deprecated. Use seed instead."><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">RandomState</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.__init__.seed" title="arch.univariate.GeneralizedError.__init__.seed (Python parameter) — Random number generator instance or int to use. Set to ensure reproducibility. If using an int, the argument is passed to np.random.default_rng.  If not provided, default_rng is used with system-provided entropy."><span class="n"><span class="pre">seed</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">RandomState</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Generator</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/distribution.html#GeneralizedError"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.GeneralizedError" title="Link to this definition">¶</a></dt>
 <dd><p>Generalized Error distribution for use with ARCH models</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.GeneralizedError-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GeneralizedError-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.GeneralizedError.__init__.random_state"><span class="sig-name descname">random_state</span><span class="p"> : </span><span class="desctype"><span class="desctype">RandomState, optional</span></span><a class="headerlink" href="#arch.univariate.GeneralizedError.__init__.random_state" title="Permalink to this definition">¶</a></dt><dd><p><div class="deprecated">
-<p><span class="versionmodified deprecated">Deprecated since version 5.0: </span>random_state is deprecated. Use seed instead.</p>
+<dt id="arch.univariate.GeneralizedError.__init__.random_state"><span class="n sig-name">random_state</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype">RandomState<span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.GeneralizedError.__init__.random_state" title="Permalink to this definition">¶</a></dt><dd><p><div class="admonition deprecated">
+<p class="admonition-title"><span class="versionmodified deprecated">Deprecated since version 5.0: </span>random_state is deprecated. Use seed instead.</p>
 </div>
 </p>
 </dd>
-<dt id="arch.univariate.GeneralizedError.__init__.seed"><span class="sig-name descname">seed</span><span class="p"> : </span><span class="desctype"><span class="desctype">{int, Generator, RandomState}, optional</span></span><a class="headerlink" href="#arch.univariate.GeneralizedError.__init__.seed" title="Permalink to this definition">¶</a></dt><dd><p>Random number generator instance or int to use. Set to ensure
+<dt id="arch.univariate.GeneralizedError.__init__.seed"><span class="n sig-name">seed</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span>RandomState<span class="w"> </span><span class="p">|</span><span class="w"> </span>Generator</span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.GeneralizedError.__init__.seed" title="Permalink to this definition">¶</a></dt><dd><p>Random number generator instance or int to use. Set to ensure
 reproducibility. If using an int, the argument is passed to
 <code class="docutils literal notranslate"><span class="pre">np.random.default_rng</span></code>.  If not provided, <code class="docutils literal notranslate"><span class="pre">default_rng</span></code>
 is used with system-provided entropy.</p>
@@ -1317,7 +1317,7 @@ <h1 id="arch-univariate-generalizederror">arch.univariate.GeneralizedError<a cla
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1364,7 +1364,7 @@ <h1 id="arch-univariate-generalizederror">arch.univariate.GeneralizedError<a cla
     <script id="__config" type="application/json">{"base": "../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/arch.univariate.HARCH.html b/univariate/generated/arch.univariate.HARCH.html
index 69330f3f23..371d98c43e 100644
--- a/univariate/generated/arch.univariate.HARCH.html
+++ b/univariate/generated/arch.univariate.HARCH.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.HARCH - arch 6.1.0</title>
+        <title>arch.univariate.HARCH - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1342,15 +1342,15 @@
                   
 
 
-<h1 id="arch-univariate-harch">arch.univariate.HARCH<a class="headerlink" href="#arch-univariate-harch" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-harch">arch.univariate.HARCH<a class="headerlink" href="#arch-univariate-harch" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.HARCH">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">HARCH</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.__init__.lags" title="arch.univariate.HARCH.__init__.lags (Python parameter) — List of lags to include in the model, or if scalar, includes all lags up the value"><span class="n"><span class="pre">lags</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/volatility.html#HARCH"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.HARCH" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">HARCH</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.__init__.lags" title="arch.univariate.HARCH.__init__.lags (Python parameter) — List of lags to include in the model, or if scalar, includes all lags up the value"><span class="n"><span class="pre">lags</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/volatility.html#HARCH"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.HARCH" title="Link to this definition">¶</a></dt>
 <dd><p>Heterogeneous ARCH process</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.HARCH-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARCH-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.HARCH.__init__.lags"><span class="sig-name descname">lags</span><span class="p"> : </span><span class="desctype"><span class="desctype">{list, array, int}</span></span><a class="headerlink" href="#arch.univariate.HARCH.__init__.lags" title="Permalink to this definition">¶</a></dt><dd><p>List of lags to include in the model, or if scalar, includes all lags up the
+<dt id="arch.univariate.HARCH.__init__.lags"><span class="n sig-name">lags</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">]</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code><a class="headerlink" href="#arch.univariate.HARCH.__init__.lags" title="Permalink to this definition">¶</a></dt><dd><p>List of lags to include in the model, or if scalar, includes all lags up the
 value</p>
 </dd>
 </dl>
@@ -1510,7 +1510,7 @@ <h1 id="arch-univariate-harch">arch.univariate.HARCH<a class="headerlink" href="
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1557,10 +1557,10 @@ <h1 id="arch-univariate-harch">arch.univariate.HARCH<a class="headerlink" href="
     <script id="__config" type="application/json">{"base": "../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
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-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+        <script id="MathJax-script" src="../../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
     
   </body>
 </html>
\ No newline at end of file
diff --git a/univariate/generated/arch.univariate.HARX.html b/univariate/generated/arch.univariate.HARX.html
index c29278ad66..a2545cf20f 100644
--- a/univariate/generated/arch.univariate.HARX.html
+++ b/univariate/generated/arch.univariate.HARX.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.HARX - arch 6.1.0</title>
+        <title>arch.univariate.HARX - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
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@@ -1372,20 +1372,20 @@
                   
 
 
-<h1 id="arch-univariate-harx">arch.univariate.HARX<a class="headerlink" href="#arch-univariate-harx" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-harx">arch.univariate.HARX<a class="headerlink" href="#arch-univariate-harx" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.HARX">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">HARX</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.__init__.y" title="arch.univariate.HARX.__init__.y (Python parameter) — nobs element vector containing the dependent variable"><span class="n"><span class="pre">y</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.__init__.x" title="arch.univariate.HARX.__init__.x (Python parameter) — nobs by k element array containing exogenous regressors"><span class="n"><span class="pre">x</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.__init__.lags" title="arch.univariate.HARX.__init__.lags (Python parameter) — Description of lag structure of the HAR."><span class="n"><span class="pre">lags</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.__init__.constant" title="arch.univariate.HARX.__init__.constant (Python parameter) — Flag whether the model should include a constant"><span class="n"><span class="pre">constant</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.__init__.use_rotated" title="arch.univariate.HARX.__init__.use_rotated (Python parameter) — Flag indicating to use the alternative rotated form of the HAR where HAR lags do not overlap"><span class="n"><span class="pre">use_rotated</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.__init__.hold_back" title="arch.univariate.HARX.__init__.hold_back (Python parameter) — Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."><span class="n"><span class="pre">hold_back</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.__init__.volatility" title="arch.univariate.HARX.__init__.volatility (Python parameter) — Volatility process to use in the model"><span class="n"><span class="pre">volatility</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.__init__.distribution" title="arch.univariate.HARX.__init__.distribution (Python parameter) — Error distribution to use in the model"><span class="n"><span class="pre">distribution</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.__init__.rescale" title="arch.univariate.HARX.__init__.rescale (Python parameter) — Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."><span class="n"><span class="pre">rescale</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/mean.html#HARX"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.HARX" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">HARX</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.__init__.y" title="arch.univariate.HARX.__init__.y (Python parameter) — nobs element vector containing the dependent variable"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.__init__.x" title="arch.univariate.HARX.__init__.x (Python parameter) — nobs by k element array containing exogenous regressors"><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.__init__.lags" title="arch.univariate.HARX.__init__.lags (Python parameter) — Description of lag structure of the HAR."><span class="n"><span class="pre">lags</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.__init__.constant" title="arch.univariate.HARX.__init__.constant (Python parameter) — Flag whether the model should include a constant"><span class="n"><span class="pre">constant</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.__init__.use_rotated" title="arch.univariate.HARX.__init__.use_rotated (Python parameter) — Flag indicating to use the alternative rotated form of the HAR where HAR lags do not overlap"><span class="n"><span class="pre">use_rotated</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.__init__.hold_back" title="arch.univariate.HARX.__init__.hold_back (Python parameter) — Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."><span class="n"><span class="pre">hold_back</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.__init__.volatility" title="arch.univariate.HARX.__init__.volatility (Python parameter) — Volatility process to use in the model"><span class="n"><span class="pre">volatility</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.volatility.VolatilityProcess.html#arch.univariate.volatility.VolatilityProcess" title="arch.univariate.volatility.VolatilityProcess (Python class) — Abstract base class for ARCH models.  Allows the conditional mean model to be specified separately from the conditional variance, even though parameters are estimated jointly."><span class="pre">VolatilityProcess</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.__init__.distribution" title="arch.univariate.HARX.__init__.distribution (Python parameter) — Error distribution to use in the model"><span class="n"><span class="pre">distribution</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.distribution.Distribution.html#arch.univariate.distribution.Distribution" title="arch.univariate.distribution.Distribution (Python class) — Template for subclassing only"><span class="pre">Distribution</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.__init__.rescale" title="arch.univariate.HARX.__init__.rescale (Python parameter) — Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."><span class="n"><span class="pre">rescale</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/mean.html#HARX"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.HARX" title="Link to this definition">¶</a></dt>
 <dd><p>Heterogeneous Autoregression (HAR), with optional exogenous regressors,
 model estimation and simulation</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.HARX-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARX-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.HARX.__init__.y"><span class="sig-name descname">y</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, Series}</span></span><a class="headerlink" href="#arch.univariate.HARX.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>nobs element vector containing the dependent variable</p>
+<dt id="arch.univariate.HARX.__init__.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.HARX.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>nobs element vector containing the dependent variable</p>
 </dd>
-<dt id="arch.univariate.HARX.__init__.x"><span class="sig-name descname">x</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, DataFrame}, optional</span></span><a class="headerlink" href="#arch.univariate.HARX.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>nobs by k element array containing exogenous regressors</p>
+<dt id="arch.univariate.HARX.__init__.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.HARX.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>nobs by k element array containing exogenous regressors</p>
 </dd>
-<dt id="arch.univariate.HARX.__init__.lags"><span class="sig-name descname">lags</span><span class="p"> : </span><span class="desctype"><span class="desctype">{scalar, ndarray}, optional</span></span><a class="headerlink" href="#arch.univariate.HARX.__init__.lags" title="Permalink to this definition">¶</a></dt><dd><p><p>Description of lag structure of the HAR.</p>
+<dt id="arch.univariate.HARX.__init__.lags"><span class="n sig-name">lags</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">]</span><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.HARX.__init__.lags" title="Permalink to this definition">¶</a></dt><dd><p><p>Description of lag structure of the HAR.</p>
 <ul class="simple">
 <li><p>Scalar included all lags between 1 and the value.</p></li>
 <li><p>A 1-d n-element array includes the HAR lags 1:lags[0]+1,
@@ -1395,20 +1395,20 @@ <h1 id="arch-univariate-harx">arch.univariate.HARX<a class="headerlink" href="#a
 </ul>
 </p>
 </dd>
-<dt id="arch.univariate.HARX.__init__.constant"><span class="sig-name descname">constant</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, optional</span></span><a class="headerlink" href="#arch.univariate.HARX.__init__.constant" title="Permalink to this definition">¶</a></dt><dd><p>Flag whether the model should include a constant</p>
+<dt id="arch.univariate.HARX.__init__.constant"><span class="n sig-name">constant</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.univariate.HARX.__init__.constant" title="Permalink to this definition">¶</a></dt><dd><p>Flag whether the model should include a constant</p>
 </dd>
-<dt id="arch.univariate.HARX.__init__.use_rotated"><span class="sig-name descname">use_rotated</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, optional</span></span><a class="headerlink" href="#arch.univariate.HARX.__init__.use_rotated" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating to use the alternative rotated form of the HAR where
+<dt id="arch.univariate.HARX.__init__.use_rotated"><span class="n sig-name">use_rotated</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.univariate.HARX.__init__.use_rotated" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating to use the alternative rotated form of the HAR where
 HAR lags do not overlap</p>
 </dd>
-<dt id="arch.univariate.HARX.__init__.hold_back"><span class="sig-name descname">hold_back</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.HARX.__init__.hold_back" title="Permalink to this definition">¶</a></dt><dd><p>Number of observations at the start of the sample to exclude when
+<dt id="arch.univariate.HARX.__init__.hold_back"><span class="n sig-name">hold_back</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.HARX.__init__.hold_back" title="Permalink to this definition">¶</a></dt><dd><p>Number of observations at the start of the sample to exclude when
 estimating model parameters.  Used when comparing models with different
 lag lengths to estimate on the common sample.</p>
 </dd>
-<dt id="arch.univariate.HARX.__init__.volatility"><span class="sig-name descname">volatility</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference internal" href="arch.univariate.volatility.VolatilityProcess.html#arch.univariate.volatility.VolatilityProcess" title="arch.univariate.volatility.VolatilityProcess (Python class) — Abstract base class for ARCH models.  Allows the conditional mean model to be specified separately from the conditional variance, even though parameters are estimated jointly.">VolatilityProcess</a>, optional</span></span><a class="headerlink" href="#arch.univariate.HARX.__init__.volatility" title="Permalink to this definition">¶</a></dt><dd><p>Volatility process to use in the model</p>
+<dt id="arch.univariate.HARX.__init__.volatility"><span class="n sig-name">volatility</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.volatility.VolatilityProcess.html#arch.univariate.volatility.VolatilityProcess" title="arch.univariate.volatility.VolatilityProcess (Python class) — Abstract base class for ARCH models.  Allows the conditional mean model to be specified separately from the conditional variance, even though parameters are estimated jointly.">VolatilityProcess</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.HARX.__init__.volatility" title="Permalink to this definition">¶</a></dt><dd><p>Volatility process to use in the model</p>
 </dd>
-<dt id="arch.univariate.HARX.__init__.distribution"><span class="sig-name descname">distribution</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference internal" href="arch.univariate.distribution.Distribution.html#arch.univariate.distribution.Distribution" title="arch.univariate.distribution.Distribution (Python class) — Template for subclassing only">Distribution</a>, optional</span></span><a class="headerlink" href="#arch.univariate.HARX.__init__.distribution" title="Permalink to this definition">¶</a></dt><dd><p>Error distribution to use in the model</p>
+<dt id="arch.univariate.HARX.__init__.distribution"><span class="n sig-name">distribution</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.distribution.Distribution.html#arch.univariate.distribution.Distribution" title="arch.univariate.distribution.Distribution (Python class) — Template for subclassing only">Distribution</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.HARX.__init__.distribution" title="Permalink to this definition">¶</a></dt><dd><p>Error distribution to use in the model</p>
 </dd>
-<dt id="arch.univariate.HARX.__init__.rescale"><span class="sig-name descname">rescale</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, optional</span></span><a class="headerlink" href="#arch.univariate.HARX.__init__.rescale" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to automatically rescale data if the scale of the
+<dt id="arch.univariate.HARX.__init__.rescale"><span class="n sig-name">rescale</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.HARX.__init__.rescale" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to automatically rescale data if the scale of the
 data is likely to produce convergence issues when estimating model parameters.
 If False, the model is estimated on the data without transformation.  If True,
 than y is rescaled and the new scale is reported in the estimation results.</p>
@@ -1589,7 +1589,7 @@ <h1 id="arch-univariate-harx">arch.univariate.HARX<a class="headerlink" href="#a
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1636,10 +1636,10 @@ <h1 id="arch-univariate-harx">arch.univariate.HARX<a class="headerlink" href="#a
     <script id="__config" type="application/json">{"base": "../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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+        <script src="../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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+        <script id="MathJax-script" src="../../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
     
   </body>
 </html>
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diff --git a/univariate/generated/arch.univariate.LS.html b/univariate/generated/arch.univariate.LS.html
index 3d954a477c..d6666178a7 100644
--- a/univariate/generated/arch.univariate.LS.html
+++ b/univariate/generated/arch.univariate.LS.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.LS - arch 6.1.0</title>
+        <title>arch.univariate.LS - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1344,29 +1344,29 @@
                   
 
 
-<h1 id="arch-univariate-ls">arch.univariate.LS<a class="headerlink" href="#arch-univariate-ls" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-ls">arch.univariate.LS<a class="headerlink" href="#arch-univariate-ls" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.LS">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">LS</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.__init__.y" title="arch.univariate.LS.__init__.y (Python parameter) — nobs by k element array containing exogenous regressors"><span class="n"><span class="pre">y</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.__init__.constant" title="arch.univariate.LS.__init__.constant (Python parameter) — Flag whether the model should include a constant"><span class="n"><span class="pre">constant</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.__init__.hold_back" title="arch.univariate.LS.__init__.hold_back (Python parameter) — Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."><span class="n"><span class="pre">hold_back</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.__init__.volatility" title="arch.univariate.LS.__init__.volatility (Python parameter) — Volatility process to use in the model"><span class="n"><span class="pre">volatility</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.__init__.distribution" title="arch.univariate.LS.__init__.distribution (Python parameter) — Error distribution to use in the model"><span class="n"><span class="pre">distribution</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.__init__.rescale" title="arch.univariate.LS.__init__.rescale (Python parameter) — Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."><span class="n"><span class="pre">rescale</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/mean.html#LS"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.LS" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">LS</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.__init__.y" title="arch.univariate.LS.__init__.y (Python parameter) — nobs by k element array containing exogenous regressors"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.__init__.constant" title="arch.univariate.LS.__init__.constant (Python parameter) — Flag whether the model should include a constant"><span class="n"><span class="pre">constant</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.__init__.hold_back" title="arch.univariate.LS.__init__.hold_back (Python parameter) — Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."><span class="n"><span class="pre">hold_back</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.__init__.volatility" title="arch.univariate.LS.__init__.volatility (Python parameter) — Volatility process to use in the model"><span class="n"><span class="pre">volatility</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.volatility.VolatilityProcess.html#arch.univariate.volatility.VolatilityProcess" title="arch.univariate.volatility.VolatilityProcess (Python class) — Abstract base class for ARCH models.  Allows the conditional mean model to be specified separately from the conditional variance, even though parameters are estimated jointly."><span class="pre">VolatilityProcess</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.__init__.distribution" title="arch.univariate.LS.__init__.distribution (Python parameter) — Error distribution to use in the model"><span class="n"><span class="pre">distribution</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.distribution.Distribution.html#arch.univariate.distribution.Distribution" title="arch.univariate.distribution.Distribution (Python class) — Template for subclassing only"><span class="pre">Distribution</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.__init__.rescale" title="arch.univariate.LS.__init__.rescale (Python parameter) — Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."><span class="n"><span class="pre">rescale</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/mean.html#LS"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.LS" title="Link to this definition">¶</a></dt>
 <dd><p>Least squares model estimation and simulation</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.LS-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.LS-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.LS.__init__.y"><span class="sig-name descname">y</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, DataFrame}, optional</span></span><a class="headerlink" href="#arch.univariate.LS.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>nobs element vector containing the dependent variable</p>
+<dt id="arch.univariate.LS.__init__.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.LS.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>nobs element vector containing the dependent variable</p>
 </dd>
-<dt id="arch.univariate.LS.__init__.y"><span class="n sig-name">y</span><span class="o">=</span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.LS.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>nobs by k element array containing exogenous regressors</p>
+<dt id="arch.univariate.LS.__init__.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.LS.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>nobs by k element array containing exogenous regressors</p>
 </dd>
-<dt id="arch.univariate.LS.__init__.constant"><span class="sig-name descname">constant</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, optional</span></span><a class="headerlink" href="#arch.univariate.LS.__init__.constant" title="Permalink to this definition">¶</a></dt><dd><p>Flag whether the model should include a constant</p>
+<dt id="arch.univariate.LS.__init__.constant"><span class="n sig-name">constant</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.univariate.LS.__init__.constant" title="Permalink to this definition">¶</a></dt><dd><p>Flag whether the model should include a constant</p>
 </dd>
-<dt id="arch.univariate.LS.__init__.hold_back"><span class="sig-name descname">hold_back</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.LS.__init__.hold_back" title="Permalink to this definition">¶</a></dt><dd><p>Number of observations at the start of the sample to exclude when
+<dt id="arch.univariate.LS.__init__.hold_back"><span class="n sig-name">hold_back</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.LS.__init__.hold_back" title="Permalink to this definition">¶</a></dt><dd><p>Number of observations at the start of the sample to exclude when
 estimating model parameters.  Used when comparing models with different
 lag lengths to estimate on the common sample.</p>
 </dd>
-<dt id="arch.univariate.LS.__init__.volatility"><span class="sig-name descname">volatility</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference internal" href="arch.univariate.volatility.VolatilityProcess.html#arch.univariate.volatility.VolatilityProcess" title="arch.univariate.volatility.VolatilityProcess (Python class) — Abstract base class for ARCH models.  Allows the conditional mean model to be specified separately from the conditional variance, even though parameters are estimated jointly.">VolatilityProcess</a>, optional</span></span><a class="headerlink" href="#arch.univariate.LS.__init__.volatility" title="Permalink to this definition">¶</a></dt><dd><p>Volatility process to use in the model</p>
+<dt id="arch.univariate.LS.__init__.volatility"><span class="n sig-name">volatility</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.volatility.VolatilityProcess.html#arch.univariate.volatility.VolatilityProcess" title="arch.univariate.volatility.VolatilityProcess (Python class) — Abstract base class for ARCH models.  Allows the conditional mean model to be specified separately from the conditional variance, even though parameters are estimated jointly.">VolatilityProcess</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.LS.__init__.volatility" title="Permalink to this definition">¶</a></dt><dd><p>Volatility process to use in the model</p>
 </dd>
-<dt id="arch.univariate.LS.__init__.distribution"><span class="sig-name descname">distribution</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference internal" href="arch.univariate.distribution.Distribution.html#arch.univariate.distribution.Distribution" title="arch.univariate.distribution.Distribution (Python class) — Template for subclassing only">Distribution</a>, optional</span></span><a class="headerlink" href="#arch.univariate.LS.__init__.distribution" title="Permalink to this definition">¶</a></dt><dd><p>Error distribution to use in the model</p>
+<dt id="arch.univariate.LS.__init__.distribution"><span class="n sig-name">distribution</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.distribution.Distribution.html#arch.univariate.distribution.Distribution" title="arch.univariate.distribution.Distribution (Python class) — Template for subclassing only">Distribution</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.LS.__init__.distribution" title="Permalink to this definition">¶</a></dt><dd><p>Error distribution to use in the model</p>
 </dd>
-<dt id="arch.univariate.LS.__init__.rescale"><span class="sig-name descname">rescale</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, optional</span></span><a class="headerlink" href="#arch.univariate.LS.__init__.rescale" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to automatically rescale data if the scale of the
+<dt id="arch.univariate.LS.__init__.rescale"><span class="n sig-name">rescale</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.LS.__init__.rescale" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to automatically rescale data if the scale of the
 data is likely to produce convergence issues when estimating model parameters.
 If False, the model is estimated on the data without transformation.  If True,
 than y is rescaled and the new scale is reported in the estimation results.</p>
@@ -1514,7 +1514,7 @@ <h1 id="arch-univariate-ls">arch.univariate.LS<a class="headerlink" href="#arch-
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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diff --git a/univariate/generated/arch.univariate.MIDASHyperbolic.html b/univariate/generated/arch.univariate.MIDASHyperbolic.html
index add1890b7e..f00a89b921 100644
--- a/univariate/generated/arch.univariate.MIDASHyperbolic.html
+++ b/univariate/generated/arch.univariate.MIDASHyperbolic.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.MIDASHyperbolic - arch 6.1.0</title>
+        <title>arch.univariate.MIDASHyperbolic - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1356,17 +1356,17 @@
                   
 
 
-<h1 id="arch-univariate-midashyperbolic">arch.univariate.MIDASHyperbolic<a class="headerlink" href="#arch-univariate-midashyperbolic" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-midashyperbolic">arch.univariate.MIDASHyperbolic<a class="headerlink" href="#arch-univariate-midashyperbolic" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.MIDASHyperbolic">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">MIDASHyperbolic</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.__init__.m" title="arch.univariate.MIDASHyperbolic.__init__.m (Python parameter) — Length of maximum lag to include in the model"><span class="n"><span class="pre">m</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">22</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.__init__.asym" title="arch.univariate.MIDASHyperbolic.__init__.asym (Python parameter) — Flag indicating whether to include an asymmetric term"><span class="n"><span class="pre">asym</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/volatility.html#MIDASHyperbolic"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.MIDASHyperbolic" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.MIDASHyperbolic">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">MIDASHyperbolic</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.__init__.m" title="arch.univariate.MIDASHyperbolic.__init__.m (Python parameter) — Length of maximum lag to include in the model"><span class="n"><span class="pre">m</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">22</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.__init__.asym" title="arch.univariate.MIDASHyperbolic.__init__.asym (Python parameter) — Flag indicating whether to include an asymmetric term"><span class="n"><span class="pre">asym</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/volatility.html#MIDASHyperbolic"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.MIDASHyperbolic" title="Link to this definition">¶</a></dt>
 <dd><p>MIDAS Hyperbolic ARCH process</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.MIDASHyperbolic-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.MIDASHyperbolic.__init__.m"><span class="sig-name descname">m</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.__init__.m" title="Permalink to this definition">¶</a></dt><dd><p>Length of maximum lag to include in the model</p>
+<dt id="arch.univariate.MIDASHyperbolic.__init__.m"><span class="n sig-name">m</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">22</span></code><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.__init__.m" title="Permalink to this definition">¶</a></dt><dd><p>Length of maximum lag to include in the model</p>
 </dd>
-<dt id="arch.univariate.MIDASHyperbolic.__init__.asym"><span class="sig-name descname">asym</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.__init__.asym" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to include an asymmetric term</p>
+<dt id="arch.univariate.MIDASHyperbolic.__init__.asym"><span class="n sig-name">asym</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.__init__.asym" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to include an asymmetric term</p>
 </dd>
 </dl>
 </dd>
@@ -1540,7 +1540,7 @@ <h1 id="arch-univariate-midashyperbolic">arch.univariate.MIDASHyperbolic<a class
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1587,10 +1587,10 @@ <h1 id="arch-univariate-midashyperbolic">arch.univariate.MIDASHyperbolic<a class
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+        <script src="../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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+        <script id="MathJax-script" src="../../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
     
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diff --git a/univariate/generated/arch.univariate.Normal.html b/univariate/generated/arch.univariate.Normal.html
index adc90505a9..21b02d6ee0 100644
--- a/univariate/generated/arch.univariate.Normal.html
+++ b/univariate/generated/arch.univariate.Normal.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.Normal - arch 6.1.0</title>
+        <title>arch.univariate.Normal - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1178,20 +1178,20 @@
                   
 
 
-<h1 id="arch-univariate-normal">arch.univariate.Normal<a class="headerlink" href="#arch-univariate-normal" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-normal">arch.univariate.Normal<a class="headerlink" href="#arch-univariate-normal" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.Normal">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">Normal</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.__init__.random_state" title="arch.univariate.Normal.__init__.random_state (Python parameter) — Deprecated since version 5.0: random_state is deprecated. Use seed instead."><span class="n"><span class="pre">random_state</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.__init__.seed" title="arch.univariate.Normal.__init__.seed (Python parameter) — Random number generator instance or int to use. Set to ensure reproducibility. If using an int, the argument is passed to np.random.default_rng.  If not provided, default_rng is used with system-provided entropy."><span class="n"><span class="pre">seed</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/distribution.html#Normal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.Normal" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.Normal">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">Normal</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.__init__.random_state" title="arch.univariate.Normal.__init__.random_state (Python parameter) — Deprecated since version 5.0: random_state is deprecated. Use seed instead."><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">RandomState</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.__init__.seed" title="arch.univariate.Normal.__init__.seed (Python parameter) — Random number generator instance or int to use. Set to ensure reproducibility. If using an int, the argument is passed to np.random.default_rng.  If not provided, default_rng is used with system-provided entropy."><span class="n"><span class="pre">seed</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">RandomState</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Generator</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/distribution.html#Normal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.Normal" title="Link to this definition">¶</a></dt>
 <dd><p>Standard normal distribution for use with ARCH models</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.Normal-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.Normal-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.Normal.__init__.random_state"><span class="sig-name descname">random_state</span><span class="p"> : </span><span class="desctype"><span class="desctype">RandomState, optional</span></span><a class="headerlink" href="#arch.univariate.Normal.__init__.random_state" title="Permalink to this definition">¶</a></dt><dd><p><div class="deprecated">
-<p><span class="versionmodified deprecated">Deprecated since version 5.0: </span>random_state is deprecated. Use seed instead.</p>
+<dt id="arch.univariate.Normal.__init__.random_state"><span class="n sig-name">random_state</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype">RandomState<span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.Normal.__init__.random_state" title="Permalink to this definition">¶</a></dt><dd><p><div class="admonition deprecated">
+<p class="admonition-title"><span class="versionmodified deprecated">Deprecated since version 5.0: </span>random_state is deprecated. Use seed instead.</p>
 </div>
 </p>
 </dd>
-<dt id="arch.univariate.Normal.__init__.seed"><span class="sig-name descname">seed</span><span class="p"> : </span><span class="desctype"><span class="desctype">{int, Generator, RandomState}, optional</span></span><a class="headerlink" href="#arch.univariate.Normal.__init__.seed" title="Permalink to this definition">¶</a></dt><dd><p>Random number generator instance or int to use. Set to ensure
+<dt id="arch.univariate.Normal.__init__.seed"><span class="n sig-name">seed</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span>RandomState<span class="w"> </span><span class="p">|</span><span class="w"> </span>Generator</span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.Normal.__init__.seed" title="Permalink to this definition">¶</a></dt><dd><p>Random number generator instance or int to use. Set to ensure
 reproducibility. If using an int, the argument is passed to
 <code class="docutils literal notranslate"><span class="pre">np.random.default_rng</span></code>.  If not provided, <code class="docutils literal notranslate"><span class="pre">default_rng</span></code>
 is used with system-provided entropy.</p>
@@ -1317,7 +1317,7 @@ <h1 id="arch-univariate-normal">arch.univariate.Normal<a class="headerlink" href
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1364,7 +1364,7 @@ <h1 id="arch-univariate-normal">arch.univariate.Normal<a class="headerlink" href
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-        <script src="../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
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diff --git a/univariate/generated/arch.univariate.RiskMetrics2006.html b/univariate/generated/arch.univariate.RiskMetrics2006.html
index d199383993..2f5e0882e4 100644
--- a/univariate/generated/arch.univariate.RiskMetrics2006.html
+++ b/univariate/generated/arch.univariate.RiskMetrics2006.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.RiskMetrics2006 - arch 6.1.0</title>
+        <title>arch.univariate.RiskMetrics2006 - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
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-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1439,21 +1439,21 @@
                   
 
 
-<h1 id="arch-univariate-riskmetrics2006">arch.univariate.RiskMetrics2006<a class="headerlink" href="#arch-univariate-riskmetrics2006" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-riskmetrics2006">arch.univariate.RiskMetrics2006<a class="headerlink" href="#arch-univariate-riskmetrics2006" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.RiskMetrics2006">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">RiskMetrics2006</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.__init__.tau0" title="arch.univariate.RiskMetrics2006.__init__.tau0 (Python parameter) — Length of long cycle. Default is 1560."><span class="n"><span class="pre">tau0</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1560</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.__init__.tau1" title="arch.univariate.RiskMetrics2006.__init__.tau1 (Python parameter) — Length of short cycle. Default is 4."><span class="n"><span class="pre">tau1</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">4</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.__init__.kmax" title="arch.univariate.RiskMetrics2006.__init__.kmax (Python parameter) — Number of components. Default is 14."><span class="n"><span class="pre">kmax</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">14</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.__init__.rho" title="arch.univariate.RiskMetrics2006.__init__.rho (Python parameter) — Relative scale of adjacent cycles. Default is sqrt(2)"><span class="n"><span class="pre">rho</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">1.4142135623730951</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/volatility.html#RiskMetrics2006"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.RiskMetrics2006" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">RiskMetrics2006</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.__init__.tau0" title="arch.univariate.RiskMetrics2006.__init__.tau0 (Python parameter) — Length of long cycle. Default is 1560."><span class="n"><span class="pre">tau0</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1560</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.__init__.tau1" title="arch.univariate.RiskMetrics2006.__init__.tau1 (Python parameter) — Length of short cycle. Default is 4."><span class="n"><span class="pre">tau1</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">4</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.__init__.kmax" title="arch.univariate.RiskMetrics2006.__init__.kmax (Python parameter) — Number of components. Default is 14."><span class="n"><span class="pre">kmax</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">14</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.__init__.rho" title="arch.univariate.RiskMetrics2006.__init__.rho (Python parameter) — Relative scale of adjacent cycles. Default is sqrt(2)"><span class="n"><span class="pre">rho</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">1.4142135623730951</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/volatility.html#RiskMetrics2006"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.RiskMetrics2006" title="Link to this definition">¶</a></dt>
 <dd><p>RiskMetrics 2006 Variance process</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.RiskMetrics2006-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.RiskMetrics2006-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.RiskMetrics2006.__init__.tau0"><span class="sig-name descname">tau0</span><span class="p"> : </span><span class="desctype"><span class="desctype">{int, float}, optional</span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.__init__.tau0" title="Permalink to this definition">¶</a></dt><dd><p>Length of long cycle. Default is 1560.</p>
+<dt id="arch.univariate.RiskMetrics2006.__init__.tau0"><span class="n sig-name">tau0</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1560</span></code><a class="headerlink" href="#arch.univariate.RiskMetrics2006.__init__.tau0" title="Permalink to this definition">¶</a></dt><dd><p>Length of long cycle. Default is 1560.</p>
 </dd>
-<dt id="arch.univariate.RiskMetrics2006.__init__.tau1"><span class="sig-name descname">tau1</span><span class="p"> : </span><span class="desctype"><span class="desctype">{int, float}, optional</span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.__init__.tau1" title="Permalink to this definition">¶</a></dt><dd><p>Length of short cycle. Default is 4.</p>
+<dt id="arch.univariate.RiskMetrics2006.__init__.tau1"><span class="n sig-name">tau1</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">4</span></code><a class="headerlink" href="#arch.univariate.RiskMetrics2006.__init__.tau1" title="Permalink to this definition">¶</a></dt><dd><p>Length of short cycle. Default is 4.</p>
 </dd>
-<dt id="arch.univariate.RiskMetrics2006.__init__.kmax"><span class="sig-name descname">kmax</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.__init__.kmax" title="Permalink to this definition">¶</a></dt><dd><p>Number of components. Default is 14.</p>
+<dt id="arch.univariate.RiskMetrics2006.__init__.kmax"><span class="n sig-name">kmax</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">14</span></code><a class="headerlink" href="#arch.univariate.RiskMetrics2006.__init__.kmax" title="Permalink to this definition">¶</a></dt><dd><p>Number of components. Default is 14.</p>
 </dd>
-<dt id="arch.univariate.RiskMetrics2006.__init__.rho"><span class="sig-name descname">rho</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>, optional</span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.__init__.rho" title="Permalink to this definition">¶</a></dt><dd><p>Relative scale of adjacent cycles. Default is sqrt(2)</p>
+<dt id="arch.univariate.RiskMetrics2006.__init__.rho"><span class="n sig-name">rho</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">1.4142135623730951</span></code><a class="headerlink" href="#arch.univariate.RiskMetrics2006.__init__.rho" title="Permalink to this definition">¶</a></dt><dd><p>Relative scale of adjacent cycles. Default is sqrt(2)</p>
 </dd>
 </dl>
 </dd>
@@ -1599,7 +1599,7 @@ <h1 id="arch-univariate-riskmetrics2006">arch.univariate.RiskMetrics2006<a class
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1646,7 +1646,7 @@ <h1 id="arch-univariate-riskmetrics2006">arch.univariate.RiskMetrics2006<a class
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diff --git a/univariate/generated/arch.univariate.SkewStudent.html b/univariate/generated/arch.univariate.SkewStudent.html
index 7ff98506e3..b7424c494f 100644
--- a/univariate/generated/arch.univariate.SkewStudent.html
+++ b/univariate/generated/arch.univariate.SkewStudent.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.SkewStudent - arch 6.1.0</title>
+        <title>arch.univariate.SkewStudent - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1178,20 +1178,20 @@
                   
 
 
-<h1 id="arch-univariate-skewstudent">arch.univariate.SkewStudent<a class="headerlink" href="#arch-univariate-skewstudent" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-skewstudent">arch.univariate.SkewStudent<a class="headerlink" href="#arch-univariate-skewstudent" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.SkewStudent">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">SkewStudent</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.__init__.random_state" title="arch.univariate.SkewStudent.__init__.random_state (Python parameter) — Deprecated since version 5.0: random_state is deprecated. Use seed instead."><span class="n"><span class="pre">random_state</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.__init__.seed" title="arch.univariate.SkewStudent.__init__.seed (Python parameter) — Random number generator instance or int to use. Set to ensure reproducibility. If using an int, the argument is passed to np.random.default_rng.  If not provided, default_rng is used with system-provided entropy."><span class="n"><span class="pre">seed</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/distribution.html#SkewStudent"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.SkewStudent" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.SkewStudent">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">SkewStudent</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.__init__.random_state" title="arch.univariate.SkewStudent.__init__.random_state (Python parameter) — Deprecated since version 5.0: random_state is deprecated. Use seed instead."><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">RandomState</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.__init__.seed" title="arch.univariate.SkewStudent.__init__.seed (Python parameter) — Random number generator instance or int to use. Set to ensure reproducibility. If using an int, the argument is passed to np.random.default_rng.  If not provided, default_rng is used with system-provided entropy."><span class="n"><span class="pre">seed</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">RandomState</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Generator</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/distribution.html#SkewStudent"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.SkewStudent" title="Link to this definition">¶</a></dt>
 <dd><p>Standardized Skewed Student’s distribution for use with ARCH models</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.SkewStudent-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.SkewStudent-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.SkewStudent.__init__.random_state"><span class="sig-name descname">random_state</span><span class="p"> : </span><span class="desctype"><span class="desctype">RandomState, optional</span></span><a class="headerlink" href="#arch.univariate.SkewStudent.__init__.random_state" title="Permalink to this definition">¶</a></dt><dd><p><div class="deprecated">
-<p><span class="versionmodified deprecated">Deprecated since version 5.0: </span>random_state is deprecated. Use seed instead.</p>
+<dt id="arch.univariate.SkewStudent.__init__.random_state"><span class="n sig-name">random_state</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype">RandomState<span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.SkewStudent.__init__.random_state" title="Permalink to this definition">¶</a></dt><dd><p><div class="admonition deprecated">
+<p class="admonition-title"><span class="versionmodified deprecated">Deprecated since version 5.0: </span>random_state is deprecated. Use seed instead.</p>
 </div>
 </p>
 </dd>
-<dt id="arch.univariate.SkewStudent.__init__.seed"><span class="sig-name descname">seed</span><span class="p"> : </span><span class="desctype"><span class="desctype">{int, Generator, RandomState}, optional</span></span><a class="headerlink" href="#arch.univariate.SkewStudent.__init__.seed" title="Permalink to this definition">¶</a></dt><dd><p>Random number generator instance or int to use. Set to ensure
+<dt id="arch.univariate.SkewStudent.__init__.seed"><span class="n sig-name">seed</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span>RandomState<span class="w"> </span><span class="p">|</span><span class="w"> </span>Generator</span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.SkewStudent.__init__.seed" title="Permalink to this definition">¶</a></dt><dd><p>Random number generator instance or int to use. Set to ensure
 reproducibility. If using an int, the argument is passed to
 <code class="docutils literal notranslate"><span class="pre">np.random.default_rng</span></code>.  If not provided, <code class="docutils literal notranslate"><span class="pre">default_rng</span></code>
 is used with system-provided entropy.</p>
@@ -1332,7 +1332,7 @@ <h1 id="arch-univariate-skewstudent">arch.univariate.SkewStudent<a class="header
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1379,10 +1379,10 @@ <h1 id="arch-univariate-skewstudent">arch.univariate.SkewStudent<a class="header
     <script id="__config" type="application/json">{"base": "../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
         <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+        <script id="MathJax-script" src="../../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
     
   </body>
 </html>
\ No newline at end of file
diff --git a/univariate/generated/arch.univariate.StudentsT.html b/univariate/generated/arch.univariate.StudentsT.html
index ffa148d961..0b96f435cb 100644
--- a/univariate/generated/arch.univariate.StudentsT.html
+++ b/univariate/generated/arch.univariate.StudentsT.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.StudentsT - arch 6.1.0</title>
+        <title>arch.univariate.StudentsT - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1178,20 +1178,20 @@
                   
 
 
-<h1 id="arch-univariate-studentst">arch.univariate.StudentsT<a class="headerlink" href="#arch-univariate-studentst" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-studentst">arch.univariate.StudentsT<a class="headerlink" href="#arch-univariate-studentst" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.StudentsT">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">StudentsT</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.__init__.random_state" title="arch.univariate.StudentsT.__init__.random_state (Python parameter) — Deprecated since version 5.0: random_state is deprecated. Use seed instead."><span class="n"><span class="pre">random_state</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.__init__.seed" title="arch.univariate.StudentsT.__init__.seed (Python parameter) — Random number generator instance or int to use. Set to ensure reproducibility. If using an int, the argument is passed to np.random.default_rng.  If not provided, default_rng is used with system-provided entropy."><span class="n"><span class="pre">seed</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/distribution.html#StudentsT"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.StudentsT" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.StudentsT">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">StudentsT</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.__init__.random_state" title="arch.univariate.StudentsT.__init__.random_state (Python parameter) — Deprecated since version 5.0: random_state is deprecated. Use seed instead."><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">RandomState</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.__init__.seed" title="arch.univariate.StudentsT.__init__.seed (Python parameter) — Random number generator instance or int to use. Set to ensure reproducibility. If using an int, the argument is passed to np.random.default_rng.  If not provided, default_rng is used with system-provided entropy."><span class="n"><span class="pre">seed</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">RandomState</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Generator</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/distribution.html#StudentsT"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.StudentsT" title="Link to this definition">¶</a></dt>
 <dd><p>Standardized Student’s distribution for use with ARCH models</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.StudentsT-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.StudentsT-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.StudentsT.__init__.random_state"><span class="sig-name descname">random_state</span><span class="p"> : </span><span class="desctype"><span class="desctype">RandomState, optional</span></span><a class="headerlink" href="#arch.univariate.StudentsT.__init__.random_state" title="Permalink to this definition">¶</a></dt><dd><p><div class="deprecated">
-<p><span class="versionmodified deprecated">Deprecated since version 5.0: </span>random_state is deprecated. Use seed instead.</p>
+<dt id="arch.univariate.StudentsT.__init__.random_state"><span class="n sig-name">random_state</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype">RandomState<span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.StudentsT.__init__.random_state" title="Permalink to this definition">¶</a></dt><dd><p><div class="admonition deprecated">
+<p class="admonition-title"><span class="versionmodified deprecated">Deprecated since version 5.0: </span>random_state is deprecated. Use seed instead.</p>
 </div>
 </p>
 </dd>
-<dt id="arch.univariate.StudentsT.__init__.seed"><span class="sig-name descname">seed</span><span class="p"> : </span><span class="desctype"><span class="desctype">{int, Generator, RandomState}, optional</span></span><a class="headerlink" href="#arch.univariate.StudentsT.__init__.seed" title="Permalink to this definition">¶</a></dt><dd><p>Random number generator instance or int to use. Set to ensure
+<dt id="arch.univariate.StudentsT.__init__.seed"><span class="n sig-name">seed</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span>RandomState<span class="w"> </span><span class="p">|</span><span class="w"> </span>Generator</span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.StudentsT.__init__.seed" title="Permalink to this definition">¶</a></dt><dd><p>Random number generator instance or int to use. Set to ensure
 reproducibility. If using an int, the argument is passed to
 <code class="docutils literal notranslate"><span class="pre">np.random.default_rng</span></code>.  If not provided, <code class="docutils literal notranslate"><span class="pre">default_rng</span></code>
 is used with system-provided entropy.</p>
@@ -1317,7 +1317,7 @@ <h1 id="arch-univariate-studentst">arch.univariate.StudentsT<a class="headerlink
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1364,7 +1364,7 @@ <h1 id="arch-univariate-studentst">arch.univariate.StudentsT<a class="headerlink
     <script id="__config" type="application/json">{"base": "../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/arch.univariate.ZeroMean.html b/univariate/generated/arch.univariate.ZeroMean.html
index 00d38e6b68..d2402b6cbc 100644
--- a/univariate/generated/arch.univariate.ZeroMean.html
+++ b/univariate/generated/arch.univariate.ZeroMean.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ZeroMean - arch 6.1.0</title>
+        <title>arch.univariate.ZeroMean - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1316,25 +1316,25 @@
                   
 
 
-<h1 id="arch-univariate-zeromean">arch.univariate.ZeroMean<a class="headerlink" href="#arch-univariate-zeromean" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-zeromean">arch.univariate.ZeroMean<a class="headerlink" href="#arch-univariate-zeromean" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ZeroMean">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">ZeroMean</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.__init__.y" title="arch.univariate.ZeroMean.__init__.y (Python parameter) — nobs element vector containing the dependent variable"><span class="n"><span class="pre">y</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.__init__.hold_back" title="arch.univariate.ZeroMean.__init__.hold_back (Python parameter) — Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."><span class="n"><span class="pre">hold_back</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.__init__.volatility" title="arch.univariate.ZeroMean.__init__.volatility (Python parameter) — Volatility process to use in the model"><span class="n"><span class="pre">volatility</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.__init__.distribution" title="arch.univariate.ZeroMean.__init__.distribution (Python parameter) — Error distribution to use in the model"><span class="n"><span class="pre">distribution</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.__init__.rescale" title="arch.univariate.ZeroMean.__init__.rescale (Python parameter) — Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."><span class="n"><span class="pre">rescale</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/mean.html#ZeroMean"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ZeroMean" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">ZeroMean</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.__init__.y" title="arch.univariate.ZeroMean.__init__.y (Python parameter) — nobs element vector containing the dependent variable"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.__init__.hold_back" title="arch.univariate.ZeroMean.__init__.hold_back (Python parameter) — Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."><span class="n"><span class="pre">hold_back</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.__init__.volatility" title="arch.univariate.ZeroMean.__init__.volatility (Python parameter) — Volatility process to use in the model"><span class="n"><span class="pre">volatility</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.volatility.VolatilityProcess.html#arch.univariate.volatility.VolatilityProcess" title="arch.univariate.volatility.VolatilityProcess (Python class) — Abstract base class for ARCH models.  Allows the conditional mean model to be specified separately from the conditional variance, even though parameters are estimated jointly."><span class="pre">VolatilityProcess</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.__init__.distribution" title="arch.univariate.ZeroMean.__init__.distribution (Python parameter) — Error distribution to use in the model"><span class="n"><span class="pre">distribution</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.distribution.Distribution.html#arch.univariate.distribution.Distribution" title="arch.univariate.distribution.Distribution (Python class) — Template for subclassing only"><span class="pre">Distribution</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.__init__.rescale" title="arch.univariate.ZeroMean.__init__.rescale (Python parameter) — Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."><span class="n"><span class="pre">rescale</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/mean.html#ZeroMean"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ZeroMean" title="Link to this definition">¶</a></dt>
 <dd><p>Model with zero conditional mean estimation and simulation</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ZeroMean-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ZeroMean-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ZeroMean.__init__.y"><span class="sig-name descname">y</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, Series}</span></span><a class="headerlink" href="#arch.univariate.ZeroMean.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>nobs element vector containing the dependent variable</p>
+<dt id="arch.univariate.ZeroMean.__init__.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>nobs element vector containing the dependent variable</p>
 </dd>
-<dt id="arch.univariate.ZeroMean.__init__.hold_back"><span class="sig-name descname">hold_back</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.ZeroMean.__init__.hold_back" title="Permalink to this definition">¶</a></dt><dd><p>Number of observations at the start of the sample to exclude when
+<dt id="arch.univariate.ZeroMean.__init__.hold_back"><span class="n sig-name">hold_back</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.__init__.hold_back" title="Permalink to this definition">¶</a></dt><dd><p>Number of observations at the start of the sample to exclude when
 estimating model parameters.  Used when comparing models with different
 lag lengths to estimate on the common sample.</p>
 </dd>
-<dt id="arch.univariate.ZeroMean.__init__.volatility"><span class="sig-name descname">volatility</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference internal" href="arch.univariate.volatility.VolatilityProcess.html#arch.univariate.volatility.VolatilityProcess" title="arch.univariate.volatility.VolatilityProcess (Python class) — Abstract base class for ARCH models.  Allows the conditional mean model to be specified separately from the conditional variance, even though parameters are estimated jointly.">VolatilityProcess</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ZeroMean.__init__.volatility" title="Permalink to this definition">¶</a></dt><dd><p>Volatility process to use in the model</p>
+<dt id="arch.univariate.ZeroMean.__init__.volatility"><span class="n sig-name">volatility</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.volatility.VolatilityProcess.html#arch.univariate.volatility.VolatilityProcess" title="arch.univariate.volatility.VolatilityProcess (Python class) — Abstract base class for ARCH models.  Allows the conditional mean model to be specified separately from the conditional variance, even though parameters are estimated jointly.">VolatilityProcess</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.__init__.volatility" title="Permalink to this definition">¶</a></dt><dd><p>Volatility process to use in the model</p>
 </dd>
-<dt id="arch.univariate.ZeroMean.__init__.distribution"><span class="sig-name descname">distribution</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference internal" href="arch.univariate.distribution.Distribution.html#arch.univariate.distribution.Distribution" title="arch.univariate.distribution.Distribution (Python class) — Template for subclassing only">Distribution</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ZeroMean.__init__.distribution" title="Permalink to this definition">¶</a></dt><dd><p>Error distribution to use in the model</p>
+<dt id="arch.univariate.ZeroMean.__init__.distribution"><span class="n sig-name">distribution</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.distribution.Distribution.html#arch.univariate.distribution.Distribution" title="arch.univariate.distribution.Distribution (Python class) — Template for subclassing only">Distribution</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.__init__.distribution" title="Permalink to this definition">¶</a></dt><dd><p>Error distribution to use in the model</p>
 </dd>
-<dt id="arch.univariate.ZeroMean.__init__.rescale"><span class="sig-name descname">rescale</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ZeroMean.__init__.rescale" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to automatically rescale data if the scale of the
+<dt id="arch.univariate.ZeroMean.__init__.rescale"><span class="n sig-name">rescale</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.__init__.rescale" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to automatically rescale data if the scale of the
 data is likely to produce convergence issues when estimating model parameters.
 If False, the model is estimated on the data without transformation.  If True,
 than y is rescaled and the new scale is reported in the estimation results.</p>
@@ -1481,7 +1481,7 @@ <h1 id="arch-univariate-zeromean">arch.univariate.ZeroMean<a class="headerlink"
   
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diff --git a/univariate/generated/arch.univariate.base.ARCHModel.html b/univariate/generated/arch.univariate.base.ARCHModel.html
index 76842829e6..f2e3db984f 100644
--- a/univariate/generated/arch.univariate.base.ARCHModel.html
+++ b/univariate/generated/arch.univariate.base.ARCHModel.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModel - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModel - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1232,10 +1232,10 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodel">arch.univariate.base.ARCHModel<a class="headerlink" href="#arch-univariate-base-archmodel" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodel">arch.univariate.base.ARCHModel<a class="headerlink" href="#arch-univariate-base-archmodel" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.base.ARCHModel">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.base.</span></span><span class="sig-name descname"><span class="pre">ARCHModel</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">volatility</span></span><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">distribution</span></span><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">hold_back</span></span><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">rescale</span></span><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/base.html#ARCHModel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModel" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.base.</span></span><span class="sig-name descname"><span class="pre">ARCHModel</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">volatility</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.volatility.VolatilityProcess.html#arch.univariate.volatility.VolatilityProcess" title="arch.univariate.volatility.VolatilityProcess (Python class) — Abstract base class for ARCH models.  Allows the conditional mean model to be specified separately from the conditional variance, even though parameters are estimated jointly."><span class="pre">VolatilityProcess</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">distribution</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.distribution.Distribution.html#arch.univariate.distribution.Distribution" title="arch.univariate.distribution.Distribution (Python class) — Template for subclassing only"><span class="pre">Distribution</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">hold_back</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">rescale</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/base.html#ARCHModel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModel" title="Link to this definition">¶</a></dt>
 <dd><p>Abstract base class for mean models in ARCH processes.  Specifies the
 conditional mean process.</p>
 <p>All public methods that raise NotImplementedError should be overridden by
@@ -1268,13 +1268,8 @@ <h1 id="arch-univariate-base-archmodel">arch.univariate.base.ARCHModel<a class="
 <tr class="row-even"><td><p><a class="reference internal" href="generated/arch.univariate.base.ARCHModel.resids.html#arch.univariate.base.ARCHModel.resids" title="arch.univariate.base.ARCHModel.resids (Python method) — Compute model residuals"><code class="xref py py-obj docutils literal notranslate"><span class="pre">resids</span></code></a>(params[, y, regressors])</p></td>
 <td><p>Compute model residuals</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/arch.univariate.base.ARCHModel.simulate.html#arch.univariate.base.ARCHModel.simulate" title="arch.univariate.base.ARCHModel.simulate (Python method) — DataFrame"><code class="xref py py-obj docutils literal notranslate"><span class="pre">simulate</span></code></a>(params, nobs[, burn, ...])</p></td>
-<td><p><dl class="field-list simple">
-<dt class="field-odd">rtype<span class="colon">:</span></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.0.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataFrame</span></code></a></p>
-</dd>
-</dl>
-</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/arch.univariate.base.ARCHModel.simulate.html#arch.univariate.base.ARCHModel.simulate" title="arch.univariate.base.ARCHModel.simulate (Python method)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">simulate</span></code></a>(params, nobs[, burn, ...])</p></td>
+<td><p></p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="generated/arch.univariate.base.ARCHModel.starting_values.html#arch.univariate.base.ARCHModel.starting_values" title="arch.univariate.base.ARCHModel.starting_values (Python method) — Returns starting values for the mean model, often the same as the values returned from fit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">starting_values</span></code></a>()</p></td>
 <td><p>Returns starting values for the mean model, often the same as the values returned from fit</p></td>
@@ -1370,7 +1365,7 @@ <h1 id="arch-univariate-base-archmodel">arch.univariate.base.ARCHModel<a class="
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1417,7 +1412,7 @@ <h1 id="arch-univariate-base-archmodel">arch.univariate.base.ARCHModel<a class="
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diff --git a/univariate/generated/arch.univariate.base.ARCHModelFixedResult.html b/univariate/generated/arch.univariate.base.ARCHModelFixedResult.html
index 9ecd7fa15a..3222de6e8e 100644
--- a/univariate/generated/arch.univariate.base.ARCHModelFixedResult.html
+++ b/univariate/generated/arch.univariate.base.ARCHModelFixedResult.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelFixedResult - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModelFixedResult - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
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+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1261,30 +1261,30 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodelfixedresult">arch.univariate.base.ARCHModelFixedResult<a class="headerlink" href="#arch-univariate-base-archmodelfixedresult" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodelfixedresult">arch.univariate.base.ARCHModelFixedResult<a class="headerlink" href="#arch-univariate-base-archmodelfixedresult" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.base.ARCHModelFixedResult">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.base.</span></span><span class="sig-name descname"><span class="pre">ARCHModelFixedResult</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.__init__.params" title="arch.univariate.base.ARCHModelFixedResult.__init__.params (Python parameter) — Estimated parameters"><span class="n"><span class="pre">params</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.__init__.resid" title="arch.univariate.base.ARCHModelFixedResult.__init__.resid (Python parameter) — Residuals from model.  Residuals have same shape as original data and contain nan-values in locations not used in estimation"><span class="n"><span class="pre">resid</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.__init__.volatility" title="arch.univariate.base.ARCHModelFixedResult.__init__.volatility (Python parameter) — Conditional volatility from model"><span class="n"><span class="pre">volatility</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.__init__.dep_var" title="arch.univariate.base.ARCHModelFixedResult.__init__.dep_var (Python parameter) — Dependent variable"><span class="n"><span class="pre">dep_var</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.__init__.names" title="arch.univariate.base.ARCHModelFixedResult.__init__.names (Python parameter) — Model parameter names"><span class="n"><span class="pre">names</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.__init__.loglikelihood" title="arch.univariate.base.ARCHModelFixedResult.__init__.loglikelihood (Python parameter) — Loglikelihood at specified parameters"><span class="n"><span class="pre">loglikelihood</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.__init__.is_pandas" title="arch.univariate.base.ARCHModelFixedResult.__init__.is_pandas (Python parameter) — Whether the original input was pandas"><span class="n"><span class="pre">is_pandas</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.__init__.model" title="arch.univariate.base.ARCHModelFixedResult.__init__.model (Python parameter) — The model object used to estimate the parameters"><span class="n"><span class="pre">model</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/base.html#ARCHModelFixedResult"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.base.</span></span><span class="sig-name descname"><span class="pre">ARCHModelFixedResult</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.__init__.params" title="arch.univariate.base.ARCHModelFixedResult.__init__.params (Python parameter) — Estimated parameters"><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.__init__.resid" title="arch.univariate.base.ARCHModelFixedResult.__init__.resid (Python parameter) — Residuals from model.  Residuals have same shape as original data and contain nan-values in locations not used in estimation"><span class="n"><span class="pre">resid</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.__init__.volatility" title="arch.univariate.base.ARCHModelFixedResult.__init__.volatility (Python parameter) — Conditional volatility from model"><span class="n"><span class="pre">volatility</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.__init__.dep_var" title="arch.univariate.base.ARCHModelFixedResult.__init__.dep_var (Python parameter) — Dependent variable"><span class="n"><span class="pre">dep_var</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">pandas.Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.__init__.names" title="arch.univariate.base.ARCHModelFixedResult.__init__.names (Python parameter) — Model parameter names"><span class="n"><span class="pre">names</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.__init__.loglikelihood" title="arch.univariate.base.ARCHModelFixedResult.__init__.loglikelihood (Python parameter) — Loglikelihood at specified parameters"><span class="n"><span class="pre">loglikelihood</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.__init__.is_pandas" title="arch.univariate.base.ARCHModelFixedResult.__init__.is_pandas (Python parameter) — Whether the original input was pandas"><span class="n"><span class="pre">is_pandas</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.__init__.model" title="arch.univariate.base.ARCHModelFixedResult.__init__.model (Python parameter) — The model object used to estimate the parameters"><span class="n"><span class="pre">model</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.base.ARCHModel.html#arch.univariate.base.ARCHModel" title="arch.univariate.base.ARCHModel (Python class) — Abstract base class for mean models in ARCH processes.  Specifies the conditional mean process."><span class="pre">ARCHModel</span></a></span></span></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/base.html#ARCHModelFixedResult"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult" title="Link to this definition">¶</a></dt>
 <dd><p>Results for fixed parameters for an ARCHModel model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.base.ARCHModelFixedResult-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.base.ARCHModelFixedResult.__init__.params"><span class="sig-name descname">params</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.__init__.params" title="Permalink to this definition">¶</a></dt><dd><p>Estimated parameters</p>
+<dt id="arch.univariate.base.ARCHModelFixedResult.__init__.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.__init__.params" title="Permalink to this definition">¶</a></dt><dd><p>Estimated parameters</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelFixedResult.__init__.resid"><span class="sig-name descname">resid</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.__init__.resid" title="Permalink to this definition">¶</a></dt><dd><p>Residuals from model.  Residuals have same shape as original data and
+<dt id="arch.univariate.base.ARCHModelFixedResult.__init__.resid"><span class="n sig-name">resid</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.__init__.resid" title="Permalink to this definition">¶</a></dt><dd><p>Residuals from model.  Residuals have same shape as original data and
 contain nan-values in locations not used in estimation</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelFixedResult.__init__.volatility"><span class="sig-name descname">volatility</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.__init__.volatility" title="Permalink to this definition">¶</a></dt><dd><p>Conditional volatility from model</p>
+<dt id="arch.univariate.base.ARCHModelFixedResult.__init__.volatility"><span class="n sig-name">volatility</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.__init__.volatility" title="Permalink to this definition">¶</a></dt><dd><p>Conditional volatility from model</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelFixedResult.__init__.dep_var"><span class="sig-name descname">dep_var</span><span class="p"> : </span><span class="desctype"><span class="desctype">Series</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.__init__.dep_var" title="Permalink to this definition">¶</a></dt><dd><p>Dependent variable</p>
+<dt id="arch.univariate.base.ARCHModelFixedResult.__init__.dep_var"><span class="n sig-name">dep_var</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">pandas.Series</a></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.__init__.dep_var" title="Permalink to this definition">¶</a></dt><dd><p>Dependent variable</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelFixedResult.__init__.names"><span class="sig-name descname">names</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>)</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.__init__.names" title="Permalink to this definition">¶</a></dt><dd><p>Model parameter names</p>
+<dt id="arch.univariate.base.ARCHModelFixedResult.__init__.names"><span class="n sig-name">names</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.__init__.names" title="Permalink to this definition">¶</a></dt><dd><p>Model parameter names</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelFixedResult.__init__.loglikelihood"><span class="sig-name descname">loglikelihood</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.__init__.loglikelihood" title="Permalink to this definition">¶</a></dt><dd><p>Loglikelihood at specified parameters</p>
+<dt id="arch.univariate.base.ARCHModelFixedResult.__init__.loglikelihood"><span class="n sig-name">loglikelihood</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.__init__.loglikelihood" title="Permalink to this definition">¶</a></dt><dd><p>Loglikelihood at specified parameters</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelFixedResult.__init__.is_pandas"><span class="sig-name descname">is_pandas</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.__init__.is_pandas" title="Permalink to this definition">¶</a></dt><dd><p>Whether the original input was pandas</p>
+<dt id="arch.univariate.base.ARCHModelFixedResult.__init__.is_pandas"><span class="n sig-name">is_pandas</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.__init__.is_pandas" title="Permalink to this definition">¶</a></dt><dd><p>Whether the original input was pandas</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelFixedResult.__init__.model"><span class="sig-name descname">model</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference internal" href="arch.univariate.base.ARCHModel.html#arch.univariate.base.ARCHModel" title="arch.univariate.base.ARCHModel (Python class) — Abstract base class for mean models in ARCH processes.  Specifies the conditional mean process.">ARCHModel</a></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.__init__.model" title="Permalink to this definition">¶</a></dt><dd><p>The model object used to estimate the parameters</p>
+<dt id="arch.univariate.base.ARCHModelFixedResult.__init__.model"><span class="n sig-name">model</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.base.ARCHModel.html#arch.univariate.base.ARCHModel" title="arch.univariate.base.ARCHModel (Python class) — Abstract base class for mean models in ARCH processes.  Specifies the conditional mean process.">ARCHModel</a></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.__init__.model" title="Permalink to this definition">¶</a></dt><dd><p>The model object used to estimate the parameters</p>
 </dd>
 </dl>
 </dd>
@@ -1413,7 +1413,7 @@ <h1 id="arch-univariate-base-archmodelfixedresult">arch.univariate.base.ARCHMode
   
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     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
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diff --git a/univariate/generated/arch.univariate.base.ARCHModelForecast.html b/univariate/generated/arch.univariate.base.ARCHModelForecast.html
index 8e54e715d5..f4421b9e9c 100644
--- a/univariate/generated/arch.univariate.base.ARCHModelForecast.html
+++ b/univariate/generated/arch.univariate.base.ARCHModelForecast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelForecast - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModelForecast - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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+    arch 6.2.0
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@@ -1073,31 +1073,31 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodelforecast">arch.univariate.base.ARCHModelForecast<a class="headerlink" href="#arch-univariate-base-archmodelforecast" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodelforecast">arch.univariate.base.ARCHModelForecast<a class="headerlink" href="#arch-univariate-base-archmodelforecast" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.base.ARCHModelForecast">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.base.</span></span><span class="sig-name descname"><span class="pre">ARCHModelForecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelForecast.__init__.index" title="arch.univariate.base.ARCHModelForecast.__init__.index (Python parameter)"><span class="n"><span class="pre">index</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">start_index</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelForecast.__init__.mean" title="arch.univariate.base.ARCHModelForecast.__init__.mean (Python parameter)"><span class="n"><span class="pre">mean</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelForecast.__init__.variance" title="arch.univariate.base.ARCHModelForecast.__init__.variance (Python parameter)"><span class="n"><span class="pre">variance</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelForecast.__init__.residual_variance" title="arch.univariate.base.ARCHModelForecast.__init__.residual_variance (Python parameter)"><span class="n"><span class="pre">residual_variance</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelForecast.__init__.simulated_paths" title="arch.univariate.base.ARCHModelForecast.__init__.simulated_paths (Python parameter)"><span class="n"><span class="pre">simulated_paths</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelForecast.__init__.simulated_variances" title="arch.univariate.base.ARCHModelForecast.__init__.simulated_variances (Python parameter)"><span class="n"><span class="pre">simulated_variances</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelForecast.__init__.simulated_residual_variances" title="arch.univariate.base.ARCHModelForecast.__init__.simulated_residual_variances (Python parameter)"><span class="n"><span class="pre">simulated_residual_variances</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelForecast.__init__.simulated_residuals" title="arch.univariate.base.ARCHModelForecast.__init__.simulated_residuals (Python parameter)"><span class="n"><span class="pre">simulated_residuals</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelForecast.__init__.align" title="arch.univariate.base.ARCHModelForecast.__init__.align (Python parameter)"><span class="n"><span class="pre">align</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;origin&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">reindex</span></span><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/base.html#ARCHModelForecast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModelForecast" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.base.</span></span><span class="sig-name descname"><span class="pre">ARCHModelForecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelForecast.__init__.index" title="arch.univariate.base.ARCHModelForecast.__init__.index (Python parameter)"><span class="n"><span class="pre">index</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><span class="pre">Label</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">pd.Index</span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">start_index</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelForecast.__init__.mean" title="arch.univariate.base.ARCHModelForecast.__init__.mean (Python parameter)"><span class="n"><span class="pre">mean</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">Float64Array</span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelForecast.__init__.variance" title="arch.univariate.base.ARCHModelForecast.__init__.variance (Python parameter)"><span class="n"><span class="pre">variance</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">Float64Array</span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelForecast.__init__.residual_variance" title="arch.univariate.base.ARCHModelForecast.__init__.residual_variance (Python parameter)"><span class="n"><span class="pre">residual_variance</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">Float64Array</span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelForecast.__init__.simulated_paths" title="arch.univariate.base.ARCHModelForecast.__init__.simulated_paths (Python parameter)"><span class="n"><span class="pre">simulated_paths</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">Float64Array</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelForecast.__init__.simulated_variances" title="arch.univariate.base.ARCHModelForecast.__init__.simulated_variances (Python parameter)"><span class="n"><span class="pre">simulated_variances</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">Float64Array</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelForecast.__init__.simulated_residual_variances" title="arch.univariate.base.ARCHModelForecast.__init__.simulated_residual_variances (Python parameter)"><span class="n"><span class="pre">simulated_residual_variances</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">Float64Array</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelForecast.__init__.simulated_residuals" title="arch.univariate.base.ARCHModelForecast.__init__.simulated_residuals (Python parameter)"><span class="n"><span class="pre">simulated_residuals</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">Float64Array</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelForecast.__init__.align" title="arch.univariate.base.ARCHModelForecast.__init__.align (Python parameter)"><span class="n"><span class="pre">align</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'origin'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'target'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;origin&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">reindex</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/base.html#ARCHModelForecast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModelForecast" title="Link to this definition">¶</a></dt>
 <dd><p>Container for forecasts from an ARCH Model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.base.ARCHModelForecast-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModelForecast-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.base.ARCHModelForecast.__init__.index"><span class="sig-name descname">index</span><span class="p"> : </span><span class="desctype"><span class="desctype">{list, ndarray}</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelForecast.__init__.index" title="Permalink to this definition">¶</a></dt><dd><p></p>
+<dt id="arch.univariate.base.ARCHModelForecast.__init__.index"><span class="n sig-name">index</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a><span class="p">[</span>Label<span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span>pd.Index</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelForecast.__init__.index" title="Permalink to this definition">¶</a></dt><dd><p></p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelForecast.__init__.mean"><span class="sig-name descname">mean</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelForecast.__init__.mean" title="Permalink to this definition">¶</a></dt><dd><p></p>
+<dt id="arch.univariate.base.ARCHModelForecast.__init__.mean"><span class="n sig-name">mean</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype">Float64Array</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelForecast.__init__.mean" title="Permalink to this definition">¶</a></dt><dd><p></p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelForecast.__init__.variance"><span class="sig-name descname">variance</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelForecast.__init__.variance" title="Permalink to this definition">¶</a></dt><dd><p></p>
+<dt id="arch.univariate.base.ARCHModelForecast.__init__.variance"><span class="n sig-name">variance</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype">Float64Array</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelForecast.__init__.variance" title="Permalink to this definition">¶</a></dt><dd><p></p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelForecast.__init__.residual_variance"><span class="sig-name descname">residual_variance</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelForecast.__init__.residual_variance" title="Permalink to this definition">¶</a></dt><dd><p></p>
+<dt id="arch.univariate.base.ARCHModelForecast.__init__.residual_variance"><span class="n sig-name">residual_variance</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype">Float64Array</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelForecast.__init__.residual_variance" title="Permalink to this definition">¶</a></dt><dd><p></p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelForecast.__init__.simulated_paths"><span class="sig-name descname">simulated_paths</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray, optional</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelForecast.__init__.simulated_paths" title="Permalink to this definition">¶</a></dt><dd><p></p>
+<dt id="arch.univariate.base.ARCHModelForecast.__init__.simulated_paths"><span class="n sig-name">simulated_paths</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype">Float64Array<span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelForecast.__init__.simulated_paths" title="Permalink to this definition">¶</a></dt><dd><p></p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelForecast.__init__.simulated_variances"><span class="sig-name descname">simulated_variances</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray, optional</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelForecast.__init__.simulated_variances" title="Permalink to this definition">¶</a></dt><dd><p></p>
+<dt id="arch.univariate.base.ARCHModelForecast.__init__.simulated_variances"><span class="n sig-name">simulated_variances</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype">Float64Array<span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelForecast.__init__.simulated_variances" title="Permalink to this definition">¶</a></dt><dd><p></p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelForecast.__init__.simulated_residual_variances"><span class="sig-name descname">simulated_residual_variances</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray, optional</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelForecast.__init__.simulated_residual_variances" title="Permalink to this definition">¶</a></dt><dd><p></p>
+<dt id="arch.univariate.base.ARCHModelForecast.__init__.simulated_residual_variances"><span class="n sig-name">simulated_residual_variances</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype">Float64Array<span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelForecast.__init__.simulated_residual_variances" title="Permalink to this definition">¶</a></dt><dd><p></p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelForecast.__init__.simulated_residuals"><span class="sig-name descname">simulated_residuals</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray, optional</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelForecast.__init__.simulated_residuals" title="Permalink to this definition">¶</a></dt><dd><p></p>
+<dt id="arch.univariate.base.ARCHModelForecast.__init__.simulated_residuals"><span class="n sig-name">simulated_residuals</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype">Float64Array<span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelForecast.__init__.simulated_residuals" title="Permalink to this definition">¶</a></dt><dd><p></p>
 </dd>
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 </dd>
 </dl>
 </dd>
@@ -1193,7 +1193,7 @@ <h1 id="arch-univariate-base-archmodelforecast">arch.univariate.base.ARCHModelFo
   
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index 84343570c8..df8131727b 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelForecastSimulation - arch 6.1.0</title>
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@@ -47,8 +47,8 @@
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@@ -102,7 +102,7 @@
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-            arch 6.1.0
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@@ -189,10 +189,10 @@
 
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-    arch 6.1.0
+    arch 6.2.0
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@@ -1039,23 +1039,23 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodelforecastsimulation">arch.univariate.base.ARCHModelForecastSimulation<a class="headerlink" href="#arch-univariate-base-archmodelforecastsimulation" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodelforecastsimulation">arch.univariate.base.ARCHModelForecastSimulation<a class="headerlink" href="#arch-univariate-base-archmodelforecastsimulation" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.base.ARCHModelForecastSimulation">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.base.</span></span><span class="sig-name descname"><span class="pre">ARCHModelForecastSimulation</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelForecastSimulation.__init__.index" title="arch.univariate.base.ARCHModelForecastSimulation.__init__.index (Python parameter)"><span class="n"><span class="pre">index</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelForecastSimulation.__init__.values" title="arch.univariate.base.ARCHModelForecastSimulation.__init__.values (Python parameter)"><span class="n"><span class="pre">values</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelForecastSimulation.__init__.residuals" title="arch.univariate.base.ARCHModelForecastSimulation.__init__.residuals (Python parameter)"><span class="n"><span class="pre">residuals</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelForecastSimulation.__init__.variances" title="arch.univariate.base.ARCHModelForecastSimulation.__init__.variances (Python parameter)"><span class="n"><span class="pre">variances</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelForecastSimulation.__init__.residual_variances" title="arch.univariate.base.ARCHModelForecastSimulation.__init__.residual_variances (Python parameter)"><span class="n"><span class="pre">residual_variances</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/base.html#ARCHModelForecastSimulation"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModelForecastSimulation" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.base.</span></span><span class="sig-name descname"><span class="pre">ARCHModelForecastSimulation</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelForecastSimulation.__init__.index" title="arch.univariate.base.ARCHModelForecastSimulation.__init__.index (Python parameter)"><span class="n"><span class="pre">index</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><span class="pre">Label</span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">pd.Index</span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelForecastSimulation.__init__.values" title="arch.univariate.base.ARCHModelForecastSimulation.__init__.values (Python parameter)"><span class="n"><span class="pre">values</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">Float64Array</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelForecastSimulation.__init__.residuals" title="arch.univariate.base.ARCHModelForecastSimulation.__init__.residuals (Python parameter)"><span class="n"><span class="pre">residuals</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">Float64Array</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelForecastSimulation.__init__.variances" title="arch.univariate.base.ARCHModelForecastSimulation.__init__.variances (Python parameter)"><span class="n"><span class="pre">variances</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">Float64Array</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelForecastSimulation.__init__.residual_variances" title="arch.univariate.base.ARCHModelForecastSimulation.__init__.residual_variances (Python parameter)"><span class="n"><span class="pre">residual_variances</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">Float64Array</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/base.html#ARCHModelForecastSimulation"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModelForecastSimulation" title="Link to this definition">¶</a></dt>
 <dd><p>Container for a simulation or bootstrap-based forecasts from an ARCH Model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.base.ARCHModelForecastSimulation-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModelForecastSimulation-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.base.ARCHModelForecastSimulation.__init__.index"><span class="n sig-name">index</span><a class="headerlink" href="#arch.univariate.base.ARCHModelForecastSimulation.__init__.index" title="Permalink to this definition">¶</a></dt><dd><p></p>
+<dt id="arch.univariate.base.ARCHModelForecastSimulation.__init__.index"><span class="n sig-name">index</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a><span class="p">[</span>Label<span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span>pd.Index</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelForecastSimulation.__init__.index" title="Permalink to this definition">¶</a></dt><dd><p></p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelForecastSimulation.__init__.values"><span class="n sig-name">values</span><a class="headerlink" href="#arch.univariate.base.ARCHModelForecastSimulation.__init__.values" title="Permalink to this definition">¶</a></dt><dd><p></p>
+<dt id="arch.univariate.base.ARCHModelForecastSimulation.__init__.values"><span class="n sig-name">values</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype">Float64Array<span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelForecastSimulation.__init__.values" title="Permalink to this definition">¶</a></dt><dd><p></p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelForecastSimulation.__init__.residuals"><span class="n sig-name">residuals</span><a class="headerlink" href="#arch.univariate.base.ARCHModelForecastSimulation.__init__.residuals" title="Permalink to this definition">¶</a></dt><dd><p></p>
+<dt id="arch.univariate.base.ARCHModelForecastSimulation.__init__.residuals"><span class="n sig-name">residuals</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype">Float64Array<span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelForecastSimulation.__init__.residuals" title="Permalink to this definition">¶</a></dt><dd><p></p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelForecastSimulation.__init__.variances"><span class="n sig-name">variances</span><a class="headerlink" href="#arch.univariate.base.ARCHModelForecastSimulation.__init__.variances" title="Permalink to this definition">¶</a></dt><dd><p></p>
+<dt id="arch.univariate.base.ARCHModelForecastSimulation.__init__.variances"><span class="n sig-name">variances</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype">Float64Array<span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelForecastSimulation.__init__.variances" title="Permalink to this definition">¶</a></dt><dd><p></p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelForecastSimulation.__init__.residual_variances"><span class="n sig-name">residual_variances</span><a class="headerlink" href="#arch.univariate.base.ARCHModelForecastSimulation.__init__.residual_variances" title="Permalink to this definition">¶</a></dt><dd><p></p>
+<dt id="arch.univariate.base.ARCHModelForecastSimulation.__init__.residual_variances"><span class="n sig-name">residual_variances</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype">Float64Array<span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelForecastSimulation.__init__.residual_variances" title="Permalink to this definition">¶</a></dt><dd><p></p>
 </dd>
 </dl>
 </dd>
@@ -1154,7 +1154,7 @@ <h1 id="arch-univariate-base-archmodelforecastsimulation">arch.univariate.base.A
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
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@@ -1201,7 +1201,7 @@ <h1 id="arch-univariate-base-archmodelforecastsimulation">arch.univariate.base.A
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diff --git a/univariate/generated/arch.univariate.base.ARCHModelResult.html b/univariate/generated/arch.univariate.base.ARCHModelResult.html
index 8cfbde77c4..a8c87d1208 100644
--- a/univariate/generated/arch.univariate.base.ARCHModelResult.html
+++ b/univariate/generated/arch.univariate.base.ARCHModelResult.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModelResult - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
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-    <a href="../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1609,45 +1609,45 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodelresult">arch.univariate.base.ARCHModelResult<a class="headerlink" href="#arch-univariate-base-archmodelresult" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodelresult">arch.univariate.base.ARCHModelResult<a class="headerlink" href="#arch-univariate-base-archmodelresult" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.base.ARCHModelResult">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.base.</span></span><span class="sig-name descname"><span class="pre">ARCHModelResult</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.__init__.params" title="arch.univariate.base.ARCHModelResult.__init__.params (Python parameter) — Estimated parameters"><span class="n"><span class="pre">params</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.__init__.param_cov" title="arch.univariate.base.ARCHModelResult.__init__.param_cov (Python parameter) — Estimated variance-covariance matrix of params.  If none, calls method to compute variance from model when parameter covariance is first used from result"><span class="n"><span class="pre">param_cov</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.__init__.r2" title="arch.univariate.base.ARCHModelResult.__init__.r2 (Python parameter) — Model R-squared"><span class="n"><span class="pre">r2</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.__init__.resid" title="arch.univariate.base.ARCHModelResult.__init__.resid (Python parameter) — Residuals from model.  Residuals have same shape as original data and contain nan-values in locations not used in estimation"><span class="n"><span class="pre">resid</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.__init__.volatility" title="arch.univariate.base.ARCHModelResult.__init__.volatility (Python parameter) — Conditional volatility from model"><span class="n"><span class="pre">volatility</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.__init__.cov_type" title="arch.univariate.base.ARCHModelResult.__init__.cov_type (Python parameter) — String describing the covariance estimator used"><span class="n"><span class="pre">cov_type</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.__init__.dep_var" title="arch.univariate.base.ARCHModelResult.__init__.dep_var (Python parameter) — Dependent variable"><span class="n"><span class="pre">dep_var</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.__init__.names" title="arch.univariate.base.ARCHModelResult.__init__.names (Python parameter) — Model parameter names"><span class="n"><span class="pre">names</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.__init__.loglikelihood" title="arch.univariate.base.ARCHModelResult.__init__.loglikelihood (Python parameter) — Loglikelihood at estimated parameters"><span class="n"><span class="pre">loglikelihood</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.__init__.is_pandas" title="arch.univariate.base.ARCHModelResult.__init__.is_pandas (Python parameter) — Whether the original input was pandas"><span class="n"><span class="pre">is_pandas</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.__init__.optim_output" title="arch.univariate.base.ARCHModelResult.__init__.optim_output (Python parameter) — Result of log-likelihood optimization"><span class="n"><span class="pre">optim_output</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.__init__.fit_start" title="arch.univariate.base.ARCHModelResult.__init__.fit_start (Python parameter) — Integer index of the first observation used to fit the model"><span class="n"><span class="pre">fit_start</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.__init__.fit_stop" title="arch.univariate.base.ARCHModelResult.__init__.fit_stop (Python parameter) — Integer index of the last observation used to fit the model using slice notation fit_start:fit_stop"><span class="n"><span class="pre">fit_stop</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.__init__.model" title="arch.univariate.base.ARCHModelResult.__init__.model (Python parameter) — The model object used to estimate the parameters"><span class="n"><span class="pre">model</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/base.html#ARCHModelResult"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModelResult" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.base.</span></span><span class="sig-name descname"><span class="pre">ARCHModelResult</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.__init__.params" title="arch.univariate.base.ARCHModelResult.__init__.params (Python parameter) — Estimated parameters"><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.__init__.param_cov" title="arch.univariate.base.ARCHModelResult.__init__.param_cov (Python parameter) — Estimated variance-covariance matrix of params.  If none, calls method to compute variance from model when parameter covariance is first used from result"><span class="n"><span class="pre">param_cov</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.__init__.r2" title="arch.univariate.base.ARCHModelResult.__init__.r2 (Python parameter) — Model R-squared"><span class="n"><span class="pre">r2</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.__init__.resid" title="arch.univariate.base.ARCHModelResult.__init__.resid (Python parameter) — Residuals from model.  Residuals have same shape as original data and contain nan-values in locations not used in estimation"><span class="n"><span class="pre">resid</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.__init__.volatility" title="arch.univariate.base.ARCHModelResult.__init__.volatility (Python parameter) — Conditional volatility from model"><span class="n"><span class="pre">volatility</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.__init__.cov_type" title="arch.univariate.base.ARCHModelResult.__init__.cov_type (Python parameter) — String describing the covariance estimator used"><span class="n"><span class="pre">cov_type</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.__init__.dep_var" title="arch.univariate.base.ARCHModelResult.__init__.dep_var (Python parameter) — Dependent variable"><span class="n"><span class="pre">dep_var</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">pandas.Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.__init__.names" title="arch.univariate.base.ARCHModelResult.__init__.names (Python parameter) — Model parameter names"><span class="n"><span class="pre">names</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.__init__.loglikelihood" title="arch.univariate.base.ARCHModelResult.__init__.loglikelihood (Python parameter) — Loglikelihood at estimated parameters"><span class="n"><span class="pre">loglikelihood</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.__init__.is_pandas" title="arch.univariate.base.ARCHModelResult.__init__.is_pandas (Python parameter) — Whether the original input was pandas"><span class="n"><span class="pre">is_pandas</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.__init__.optim_output" title="arch.univariate.base.ARCHModelResult.__init__.optim_output (Python parameter) — Result of log-likelihood optimization"><span class="n"><span class="pre">optim_output</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.OptimizeResult.html#scipy.optimize.OptimizeResult" title="(in SciPy v1.11.2)"><span class="pre">OptimizeResult</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.__init__.fit_start" title="arch.univariate.base.ARCHModelResult.__init__.fit_start (Python parameter) — Integer index of the first observation used to fit the model"><span class="n"><span class="pre">fit_start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.__init__.fit_stop" title="arch.univariate.base.ARCHModelResult.__init__.fit_stop (Python parameter) — Integer index of the last observation used to fit the model using slice notation fit_start:fit_stop"><span class="n"><span class="pre">fit_stop</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.__init__.model" title="arch.univariate.base.ARCHModelResult.__init__.model (Python parameter) — The model object used to estimate the parameters"><span class="n"><span class="pre">model</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.base.ARCHModel.html#arch.univariate.base.ARCHModel" title="arch.univariate.base.ARCHModel (Python class) — Abstract base class for mean models in ARCH processes.  Specifies the conditional mean process."><span class="pre">ARCHModel</span></a></span></span></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/base.html#ARCHModelResult"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModelResult" title="Link to this definition">¶</a></dt>
 <dd><p>Results from estimation of an ARCHModel model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.base.ARCHModelResult-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.base.ARCHModelResult.__init__.params"><span class="sig-name descname">params</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.__init__.params" title="Permalink to this definition">¶</a></dt><dd><p>Estimated parameters</p>
+<dt id="arch.univariate.base.ARCHModelResult.__init__.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.__init__.params" title="Permalink to this definition">¶</a></dt><dd><p>Estimated parameters</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelResult.__init__.param_cov"><span class="sig-name descname">param_cov</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, None}</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.__init__.param_cov" title="Permalink to this definition">¶</a></dt><dd><p>Estimated variance-covariance matrix of params.  If none, calls method
+<dt id="arch.univariate.base.ARCHModelResult.__init__.param_cov"><span class="n sig-name">param_cov</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.__init__.param_cov" title="Permalink to this definition">¶</a></dt><dd><p>Estimated variance-covariance matrix of params.  If none, calls method
 to compute variance from model when parameter covariance is first used
 from result</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelResult.__init__.r2"><span class="sig-name descname">r2</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.__init__.r2" title="Permalink to this definition">¶</a></dt><dd><p>Model R-squared</p>
+<dt id="arch.univariate.base.ARCHModelResult.__init__.r2"><span class="n sig-name">r2</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.__init__.r2" title="Permalink to this definition">¶</a></dt><dd><p>Model R-squared</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelResult.__init__.resid"><span class="sig-name descname">resid</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.__init__.resid" title="Permalink to this definition">¶</a></dt><dd><p>Residuals from model.  Residuals have same shape as original data and
+<dt id="arch.univariate.base.ARCHModelResult.__init__.resid"><span class="n sig-name">resid</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.__init__.resid" title="Permalink to this definition">¶</a></dt><dd><p>Residuals from model.  Residuals have same shape as original data and
 contain nan-values in locations not used in estimation</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelResult.__init__.volatility"><span class="sig-name descname">volatility</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.__init__.volatility" title="Permalink to this definition">¶</a></dt><dd><p>Conditional volatility from model</p>
+<dt id="arch.univariate.base.ARCHModelResult.__init__.volatility"><span class="n sig-name">volatility</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.__init__.volatility" title="Permalink to this definition">¶</a></dt><dd><p>Conditional volatility from model</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelResult.__init__.cov_type"><span class="sig-name descname">cov_type</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.__init__.cov_type" title="Permalink to this definition">¶</a></dt><dd><p>String describing the covariance estimator used</p>
+<dt id="arch.univariate.base.ARCHModelResult.__init__.cov_type"><span class="n sig-name">cov_type</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.__init__.cov_type" title="Permalink to this definition">¶</a></dt><dd><p>String describing the covariance estimator used</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelResult.__init__.dep_var"><span class="sig-name descname">dep_var</span><span class="p"> : </span><span class="desctype"><span class="desctype">Series</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.__init__.dep_var" title="Permalink to this definition">¶</a></dt><dd><p>Dependent variable</p>
+<dt id="arch.univariate.base.ARCHModelResult.__init__.dep_var"><span class="n sig-name">dep_var</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">pandas.Series</a></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.__init__.dep_var" title="Permalink to this definition">¶</a></dt><dd><p>Dependent variable</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelResult.__init__.names"><span class="sig-name descname">names</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>)</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.__init__.names" title="Permalink to this definition">¶</a></dt><dd><p>Model parameter names</p>
+<dt id="arch.univariate.base.ARCHModelResult.__init__.names"><span class="n sig-name">names</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.__init__.names" title="Permalink to this definition">¶</a></dt><dd><p>Model parameter names</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelResult.__init__.loglikelihood"><span class="sig-name descname">loglikelihood</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.__init__.loglikelihood" title="Permalink to this definition">¶</a></dt><dd><p>Loglikelihood at estimated parameters</p>
+<dt id="arch.univariate.base.ARCHModelResult.__init__.loglikelihood"><span class="n sig-name">loglikelihood</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.__init__.loglikelihood" title="Permalink to this definition">¶</a></dt><dd><p>Loglikelihood at estimated parameters</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelResult.__init__.is_pandas"><span class="sig-name descname">is_pandas</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.__init__.is_pandas" title="Permalink to this definition">¶</a></dt><dd><p>Whether the original input was pandas</p>
+<dt id="arch.univariate.base.ARCHModelResult.__init__.is_pandas"><span class="n sig-name">is_pandas</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.__init__.is_pandas" title="Permalink to this definition">¶</a></dt><dd><p>Whether the original input was pandas</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelResult.__init__.optim_output"><span class="sig-name descname">optim_output</span><span class="p"> : </span><span class="desctype"><span class="desctype">OptimizeResult</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.__init__.optim_output" title="Permalink to this definition">¶</a></dt><dd><p>Result of log-likelihood optimization</p>
+<dt id="arch.univariate.base.ARCHModelResult.__init__.optim_output"><span class="n sig-name">optim_output</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.OptimizeResult.html#scipy.optimize.OptimizeResult" title="(in SciPy v1.11.2)">OptimizeResult</a></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.__init__.optim_output" title="Permalink to this definition">¶</a></dt><dd><p>Result of log-likelihood optimization</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelResult.__init__.fit_start"><span class="sig-name descname">fit_start</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.__init__.fit_start" title="Permalink to this definition">¶</a></dt><dd><p>Integer index of the first observation used to fit the model</p>
+<dt id="arch.univariate.base.ARCHModelResult.__init__.fit_start"><span class="n sig-name">fit_start</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.__init__.fit_start" title="Permalink to this definition">¶</a></dt><dd><p>Integer index of the first observation used to fit the model</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelResult.__init__.fit_stop"><span class="sig-name descname">fit_stop</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.__init__.fit_stop" title="Permalink to this definition">¶</a></dt><dd><p>Integer index of the last observation used to fit the model using
+<dt id="arch.univariate.base.ARCHModelResult.__init__.fit_stop"><span class="n sig-name">fit_stop</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.__init__.fit_stop" title="Permalink to this definition">¶</a></dt><dd><p>Integer index of the last observation used to fit the model using
 slice notation <cite>fit_start:fit_stop</cite></p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelResult.__init__.model"><span class="sig-name descname">model</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference internal" href="arch.univariate.base.ARCHModel.html#arch.univariate.base.ARCHModel" title="arch.univariate.base.ARCHModel (Python class) — Abstract base class for mean models in ARCH processes.  Specifies the conditional mean process.">ARCHModel</a></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.__init__.model" title="Permalink to this definition">¶</a></dt><dd><p>The model object used to estimate the parameters</p>
+<dt id="arch.univariate.base.ARCHModelResult.__init__.model"><span class="n sig-name">model</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.base.ARCHModel.html#arch.univariate.base.ARCHModel" title="arch.univariate.base.ARCHModel (Python class) — Abstract base class for mean models in ARCH processes.  Specifies the conditional mean process.">ARCHModel</a></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.__init__.model" title="Permalink to this definition">¶</a></dt><dd><p>The model object used to estimate the parameters</p>
 </dd>
 </dl>
 </dd>
@@ -1812,7 +1812,7 @@ <h1 id="arch-univariate-base-archmodelresult">arch.univariate.base.ARCHModelResu
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1859,7 +1859,7 @@ <h1 id="arch-univariate-base-archmodelresult">arch.univariate.base.ARCHModelResu
     <script id="__config" type="application/json">{"base": "../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
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diff --git a/univariate/generated/arch.univariate.distribution.Distribution.html b/univariate/generated/arch.univariate.distribution.Distribution.html
index d45705bc52..b4cf0c2272 100644
--- a/univariate/generated/arch.univariate.distribution.Distribution.html
+++ b/univariate/generated/arch.univariate.distribution.Distribution.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.distribution.Distribution - arch 6.1.0</title>
+        <title>arch.univariate.distribution.Distribution - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1158,10 +1158,10 @@
                   
 
 
-<h1 id="arch-univariate-distribution-distribution">arch.univariate.distribution.Distribution<a class="headerlink" href="#arch-univariate-distribution-distribution" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-distribution-distribution">arch.univariate.distribution.Distribution<a class="headerlink" href="#arch-univariate-distribution-distribution" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.distribution.Distribution">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.distribution.</span></span><span class="sig-name descname"><span class="pre">Distribution</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/distribution.html#Distribution"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.distribution.Distribution" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.distribution.</span></span><span class="sig-name descname"><span class="pre">Distribution</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">RandomState</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">RandomState</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Generator</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/distribution.html#Distribution"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.distribution.Distribution" title="Link to this definition">¶</a></dt>
 <dd><p>Template for subclassing only</p>
 <p class="rubric">Methods</p>
 <table class="autosummary longtable docutils data align-default">
@@ -1281,7 +1281,7 @@ <h1 id="arch-univariate-distribution-distribution">arch.univariate.distribution.
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1328,7 +1328,7 @@ <h1 id="arch-univariate-distribution-distribution">arch.univariate.distribution.
     <script id="__config" type="application/json">{"base": "../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/univariate/generated/arch.univariate.recursions_python.VolatilityUpdater.html b/univariate/generated/arch.univariate.recursions_python.VolatilityUpdater.html
index 0a8744a28e..daf34fb40c 100644
--- a/univariate/generated/arch.univariate.recursions_python.VolatilityUpdater.html
+++ b/univariate/generated/arch.univariate.recursions_python.VolatilityUpdater.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.recursions_python.VolatilityUpdater - arch 6.1.0</title>
+        <title>arch.univariate.recursions_python.VolatilityUpdater - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1021,10 +1021,10 @@
                   
 
 
-<h1 id="arch-univariate-recursions-python-volatilityupdater">arch.univariate.recursions_python.VolatilityUpdater<a class="headerlink" href="#arch-univariate-recursions-python-volatilityupdater" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-recursions-python-volatilityupdater">arch.univariate.recursions_python.VolatilityUpdater<a class="headerlink" href="#arch-univariate-recursions-python-volatilityupdater" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.recursions_python.VolatilityUpdater">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.recursions_python.</span></span><span class="sig-name descname"><span class="pre">VolatilityUpdater</span></span><a class="reference internal" href="../../_modules/arch/univariate/recursions_python.html#VolatilityUpdater"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.recursions_python.VolatilityUpdater" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.recursions_python.</span></span><span class="sig-name descname"><span class="pre">VolatilityUpdater</span></span><a class="reference internal" href="../../_modules/arch/univariate/recursions_python.html#VolatilityUpdater"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.recursions_python.VolatilityUpdater" title="Link to this definition">¶</a></dt>
 <dd><p>Base class that all volatility updaters must inherit from.</p>
 <p class="rubric">Notes</p>
 <p>See the implementation available for information on modifying <code class="docutils literal notranslate"><span class="pre">__init__</span></code>
@@ -1117,7 +1117,7 @@ <h1 id="arch-univariate-recursions-python-volatilityupdater">arch.univariate.rec
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1164,7 +1164,7 @@ <h1 id="arch-univariate-recursions-python-volatilityupdater">arch.univariate.rec
     <script id="__config" type="application/json">{"base": "../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/univariate/generated/arch.univariate.volatility.VolatilityProcess.html b/univariate/generated/arch.univariate.volatility.VolatilityProcess.html
index 6b7bc537f0..9ef885108c 100644
--- a/univariate/generated/arch.univariate.volatility.VolatilityProcess.html
+++ b/univariate/generated/arch.univariate.volatility.VolatilityProcess.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.volatility.VolatilityProcess - arch 6.1.0</title>
+        <title>arch.univariate.volatility.VolatilityProcess - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1351,10 +1351,10 @@
                   
 
 
-<h1 id="arch-univariate-volatility-volatilityprocess">arch.univariate.volatility.VolatilityProcess<a class="headerlink" href="#arch-univariate-volatility-volatilityprocess" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-volatility-volatilityprocess">arch.univariate.volatility.VolatilityProcess<a class="headerlink" href="#arch-univariate-volatility-volatilityprocess" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.volatility.VolatilityProcess">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.volatility.</span></span><span class="sig-name descname"><span class="pre">VolatilityProcess</span></span><a class="reference internal" href="../../_modules/arch/univariate/volatility.html#VolatilityProcess"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.volatility.</span></span><span class="sig-name descname"><span class="pre">VolatilityProcess</span></span><a class="reference internal" href="../../_modules/arch/univariate/volatility.html#VolatilityProcess"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess" title="Link to this definition">¶</a></dt>
 <dd><p>Abstract base class for ARCH models.  Allows the conditional mean model to be
 specified separately from the conditional variance, even though parameters
 are estimated jointly.</p>
@@ -1488,7 +1488,7 @@ <h1 id="arch-univariate-volatility-volatilityprocess">arch.univariate.volatility
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1535,7 +1535,7 @@ <h1 id="arch-univariate-volatility-volatilityprocess">arch.univariate.volatility
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diff --git a/univariate/generated/generated/arch.univariate.APARCH.backcast.html b/univariate/generated/generated/arch.univariate.APARCH.backcast.html
index c31f234ff2..f6c19a5cfd 100644
--- a/univariate/generated/generated/arch.univariate.APARCH.backcast.html
+++ b/univariate/generated/generated/arch.univariate.APARCH.backcast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.APARCH.backcast - arch 6.1.0</title>
+        <title>arch.univariate.APARCH.backcast - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
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           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1278,15 +1278,15 @@
                   
 
 
-<h1 id="arch-univariate-aparch-backcast">arch.univariate.APARCH.backcast<a class="headerlink" href="#arch-univariate-aparch-backcast" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-aparch-backcast">arch.univariate.APARCH.backcast<a class="headerlink" href="#arch-univariate-aparch-backcast" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.APARCH.backcast">
-<span class="sig-prename descclassname"><span class="pre">APARCH.</span></span><span class="sig-name descname"><span class="pre">backcast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.backcast.resids" title="arch.univariate.APARCH.backcast.resids (Python parameter) — Vector of (approximate) residuals"><span class="n"><span class="pre">resids</span></span></a></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.APARCH.backcast" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">APARCH.</span></span><span class="sig-name descname"><span class="pre">backcast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.backcast.resids" title="arch.univariate.APARCH.backcast.resids (Python parameter) — Vector of (approximate) residuals"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.univariate.APARCH.backcast" title="Link to this definition">¶</a></dt>
 <dd><p>Construct values for backcasting to start the recursion</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.APARCH.backcast-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.APARCH.backcast-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.APARCH.backcast.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.APARCH.backcast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of (approximate) residuals</p>
+<dt id="arch.univariate.APARCH.backcast.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.APARCH.backcast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of (approximate) residuals</p>
 </dd>
 </dl>
 </dd>
@@ -1294,7 +1294,7 @@ <h1 id="arch-univariate-aparch-backcast">arch.univariate.APARCH.backcast<a class
 <dd class="field-even"><p><strong>backcast</strong> – Value to use in backcasting in the volatility recursion</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.APARCH.backcast-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.APARCH.backcast-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1366,7 +1366,7 @@ <h1 id="arch-univariate-aparch-backcast">arch.univariate.APARCH.backcast<a class
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1413,7 +1413,7 @@ <h1 id="arch-univariate-aparch-backcast">arch.univariate.APARCH.backcast<a class
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+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.APARCH.backcast_transform.html b/univariate/generated/generated/arch.univariate.APARCH.backcast_transform.html
index 976d1eb9f2..ed6098e2a7 100644
--- a/univariate/generated/generated/arch.univariate.APARCH.backcast_transform.html
+++ b/univariate/generated/generated/arch.univariate.APARCH.backcast_transform.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.APARCH.backcast_transform - arch 6.1.0</title>
+        <title>arch.univariate.APARCH.backcast_transform - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1278,15 +1278,15 @@
                   
 
 
-<h1 id="arch-univariate-aparch-backcast-transform">arch.univariate.APARCH.backcast_transform<a class="headerlink" href="#arch-univariate-aparch-backcast-transform" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-aparch-backcast-transform">arch.univariate.APARCH.backcast_transform<a class="headerlink" href="#arch-univariate-aparch-backcast-transform" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.APARCH.backcast_transform">
-<span class="sig-prename descclassname"><span class="pre">APARCH.</span></span><span class="sig-name descname"><span class="pre">backcast_transform</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.backcast_transform.backcast" title="arch.univariate.APARCH.backcast_transform.backcast (Python parameter) — User-provided backcast that approximates sigma2[0]."><span class="n"><span class="pre">backcast</span></span></a></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.APARCH.backcast_transform" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.APARCH.backcast_transform">
+<span class="sig-prename descclassname"><span class="pre">APARCH.</span></span><span class="sig-name descname"><span class="pre">backcast_transform</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.backcast_transform.backcast" title="arch.univariate.APARCH.backcast_transform.backcast (Python parameter) — User-provided backcast that approximates sigma2[0]."><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.univariate.APARCH.backcast_transform" title="Link to this definition">¶</a></dt>
 <dd><p>Transformation to apply to user-provided backcast values</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.APARCH.backcast_transform-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.APARCH.backcast_transform-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.APARCH.backcast_transform.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}</span></span><a class="headerlink" href="#arch.univariate.APARCH.backcast_transform.backcast" title="Permalink to this definition">¶</a></dt><dd><p>User-provided <code class="docutils literal notranslate"><span class="pre">backcast</span></code> that approximates sigma2[0].</p>
+<dt id="arch.univariate.APARCH.backcast_transform.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.APARCH.backcast_transform.backcast" title="Permalink to this definition">¶</a></dt><dd><p>User-provided <code class="docutils literal notranslate"><span class="pre">backcast</span></code> that approximates sigma2[0].</p>
 </dd>
 </dl>
 </dd>
@@ -1294,7 +1294,7 @@ <h1 id="arch-univariate-aparch-backcast-transform">arch.univariate.APARCH.backca
 <dd class="field-even"><p><strong>backcast</strong> – Backcast transformed to the model-appropriate scale</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.APARCH.backcast_transform-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.APARCH.backcast_transform-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>{float, ndarray}</p>
+<dd class="field-odd"><p><code class="docutils literal notranslate"><span class="pre">{float,</span> <span class="pre">ndarray}</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1366,7 +1366,7 @@ <h1 id="arch-univariate-aparch-backcast-transform">arch.univariate.APARCH.backca
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1413,7 +1413,7 @@ <h1 id="arch-univariate-aparch-backcast-transform">arch.univariate.APARCH.backca
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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diff --git a/univariate/generated/generated/arch.univariate.APARCH.bounds.html b/univariate/generated/generated/arch.univariate.APARCH.bounds.html
index b1d3fdf365..ca06eaf621 100644
--- a/univariate/generated/generated/arch.univariate.APARCH.bounds.html
+++ b/univariate/generated/generated/arch.univariate.APARCH.bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.APARCH.bounds - arch 6.1.0</title>
+        <title>arch.univariate.APARCH.bounds - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+    arch 6.2.0
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@@ -1278,15 +1278,15 @@
                   
 
 
-<h1 id="arch-univariate-aparch-bounds">arch.univariate.APARCH.bounds<a class="headerlink" href="#arch-univariate-aparch-bounds" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-aparch-bounds">arch.univariate.APARCH.bounds<a class="headerlink" href="#arch-univariate-aparch-bounds" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.APARCH.bounds">
-<span class="sig-prename descclassname"><span class="pre">APARCH.</span></span><span class="sig-name descname"><span class="pre">bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.bounds.resids" title="arch.univariate.APARCH.bounds.resids (Python parameter) — Vector of (approximate) residuals"><span class="n"><span class="pre">resids</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#APARCH.bounds"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.APARCH.bounds" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">APARCH.</span></span><span class="sig-name descname"><span class="pre">bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.bounds.resids" title="arch.univariate.APARCH.bounds.resids (Python parameter) — Vector of (approximate) residuals"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#APARCH.bounds"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.APARCH.bounds" title="Link to this definition">¶</a></dt>
 <dd><p>Returns bounds for parameters</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.APARCH.bounds-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.APARCH.bounds-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.APARCH.bounds.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.APARCH.bounds.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of (approximate) residuals</p>
+<dt id="arch.univariate.APARCH.bounds.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.APARCH.bounds.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of (approximate) residuals</p>
 </dd>
 </dl>
 </dd>
@@ -1294,7 +1294,7 @@ <h1 id="arch-univariate-aparch-bounds">arch.univariate.APARCH.bounds<a class="he
 <dd class="field-even"><p><strong>bounds</strong> – List of bounds where each element is (lower, upper).</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.APARCH.bounds-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.APARCH.bounds-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a>[<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a>[<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>,<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>]]</p>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">list[tuple[float,float]]</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1366,7 +1366,7 @@ <h1 id="arch-univariate-aparch-bounds">arch.univariate.APARCH.bounds<a class="he
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1413,7 +1413,7 @@ <h1 id="arch-univariate-aparch-bounds">arch.univariate.APARCH.bounds<a class="he
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diff --git a/univariate/generated/generated/arch.univariate.APARCH.common_asym.html b/univariate/generated/generated/arch.univariate.APARCH.common_asym.html
index e23892c3f4..d6078e9790 100644
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+++ b/univariate/generated/generated/arch.univariate.APARCH.common_asym.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.APARCH.common_asym - arch 6.1.0</title>
+        <title>arch.univariate.APARCH.common_asym - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1198,10 +1198,10 @@
                   
 
 
-<h1 id="arch-univariate-aparch-common-asym">arch.univariate.APARCH.common_asym<a class="headerlink" href="#arch-univariate-aparch-common-asym" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-aparch-common-asym">arch.univariate.APARCH.common_asym<a class="headerlink" href="#arch-univariate-aparch-common-asym" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.APARCH.common_asym">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">APARCH.</span></span><span class="sig-name descname"><span class="pre">common_asym</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.univariate.APARCH.common_asym" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">APARCH.</span></span><span class="sig-name descname"><span class="pre">common_asym</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.univariate.APARCH.common_asym" title="Link to this definition">¶</a></dt>
 <dd><p>The value of delta in the model. NaN is delta is estimated.</p>
 </dd></dl>
 
@@ -1272,7 +1272,7 @@ <h1 id="arch-univariate-aparch-common-asym">arch.univariate.APARCH.common_asym<a
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1319,7 +1319,7 @@ <h1 id="arch-univariate-aparch-common-asym">arch.univariate.APARCH.common_asym<a
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.APARCH.compute_variance.html b/univariate/generated/generated/arch.univariate.APARCH.compute_variance.html
index 4012cb4f15..ce20c4cec9 100644
--- a/univariate/generated/generated/arch.univariate.APARCH.compute_variance.html
+++ b/univariate/generated/generated/arch.univariate.APARCH.compute_variance.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.APARCH.compute_variance - arch 6.1.0</title>
+        <title>arch.univariate.APARCH.compute_variance - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -702,13 +702,6 @@
       </ul>
     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.univariate.APARCH.compute_variance-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
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       </ul>
@@ -1292,13 +1285,6 @@
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     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.univariate.APARCH.compute_variance-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
       </ul>
@@ -1320,30 +1306,27 @@
                   
 
 
-<h1 id="arch-univariate-aparch-compute-variance">arch.univariate.APARCH.compute_variance<a class="headerlink" href="#arch-univariate-aparch-compute-variance" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-aparch-compute-variance">arch.univariate.APARCH.compute_variance<a class="headerlink" href="#arch-univariate-aparch-compute-variance" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.APARCH.compute_variance">
-<span class="sig-prename descclassname"><span class="pre">APARCH.</span></span><span class="sig-name descname"><span class="pre">compute_variance</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.compute_variance.parameters" title="arch.univariate.APARCH.compute_variance.parameters (Python parameter) — Model parameters"><span class="n"><span class="pre">parameters</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.compute_variance.resids" title="arch.univariate.APARCH.compute_variance.resids (Python parameter) — Vector of mean zero residuals"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.compute_variance.sigma2" title="arch.univariate.APARCH.compute_variance.sigma2 (Python parameter) — Array with same size as resids to store the conditional variance"><span class="n"><span class="pre">sigma2</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.compute_variance.backcast" title="arch.univariate.APARCH.compute_variance.backcast (Python parameter) — Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."><span class="n"><span class="pre">backcast</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.compute_variance.var_bounds" title="arch.univariate.APARCH.compute_variance.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#APARCH.compute_variance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.APARCH.compute_variance" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">APARCH.</span></span><span class="sig-name descname"><span class="pre">compute_variance</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.compute_variance.parameters" title="arch.univariate.APARCH.compute_variance.parameters (Python parameter) — Model parameters"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.compute_variance.resids" title="arch.univariate.APARCH.compute_variance.resids (Python parameter) — Vector of mean zero residuals"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.compute_variance.sigma2" title="arch.univariate.APARCH.compute_variance.sigma2 (Python parameter) — Array with same size as resids to store the conditional variance"><span class="n"><span class="pre">sigma2</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.compute_variance.backcast" title="arch.univariate.APARCH.compute_variance.backcast (Python parameter) — Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.compute_variance.var_bounds" title="arch.univariate.APARCH.compute_variance.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#APARCH.compute_variance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.APARCH.compute_variance" title="Link to this definition">¶</a></dt>
 <dd><p>Compute the variance for the ARCH model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.APARCH.compute_variance-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.APARCH.compute_variance-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.APARCH.compute_variance.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.APARCH.compute_variance.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
+<dt id="arch.univariate.APARCH.compute_variance.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.APARCH.compute_variance.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
 </dd>
-<dt id="arch.univariate.APARCH.compute_variance.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.APARCH.compute_variance.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of mean zero residuals</p>
+<dt id="arch.univariate.APARCH.compute_variance.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.APARCH.compute_variance.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of mean zero residuals</p>
 </dd>
-<dt id="arch.univariate.APARCH.compute_variance.sigma2"><span class="sig-name descname">sigma2</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.APARCH.compute_variance.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>Array with same size as resids to store the conditional variance</p>
+<dt id="arch.univariate.APARCH.compute_variance.sigma2"><span class="n sig-name">sigma2</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.APARCH.compute_variance.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>Array with same size as resids to store the conditional variance</p>
 </dd>
-<dt id="arch.univariate.APARCH.compute_variance.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}</span></span><a class="headerlink" href="#arch.univariate.APARCH.compute_variance.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing ARCH recursion. Can be an ndarray
+<dt id="arch.univariate.APARCH.compute_variance.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.APARCH.compute_variance.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing ARCH recursion. Can be an ndarray
 when the model contains multiple components.</p>
 </dd>
-<dt id="arch.univariate.APARCH.compute_variance.var_bounds"><span class="sig-name descname">var_bounds</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.APARCH.compute_variance.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
+<dt id="arch.univariate.APARCH.compute_variance.var_bounds"><span class="n sig-name">var_bounds</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.APARCH.compute_variance.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
 </dd>
 </dl>
 </dd>
-<dt class="field-even" id="arch.univariate.APARCH.compute_variance-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.APARCH.compute_variance-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a></p>
-</dd>
 </dl>
 </dd></dl>
 
@@ -1414,7 +1397,7 @@ <h1 id="arch-univariate-aparch-compute-variance">arch.univariate.APARCH.compute_
   
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     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
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diff --git a/univariate/generated/generated/arch.univariate.APARCH.constraints.html b/univariate/generated/generated/arch.univariate.APARCH.constraints.html
index 2a025a24a2..a9486143f6 100644
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+++ b/univariate/generated/generated/arch.univariate.APARCH.constraints.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.APARCH.constraints - arch 6.1.0</title>
+        <title>arch.univariate.APARCH.constraints - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
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+    arch 6.2.0
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@@ -671,13 +671,6 @@
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-          <li class="md-nav__item">
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-</li>
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           <li class="md-nav__item">
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@@ -1205,13 +1198,6 @@
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-          <li class="md-nav__item">
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@@ -1238,20 +1224,17 @@
                   
 
 
-<h1 id="arch-univariate-aparch-constraints">arch.univariate.APARCH.constraints<a class="headerlink" href="#arch-univariate-aparch-constraints" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-aparch-constraints">arch.univariate.APARCH.constraints<a class="headerlink" href="#arch-univariate-aparch-constraints" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.APARCH.constraints">
-<span class="sig-prename descclassname"><span class="pre">APARCH.</span></span><span class="sig-name descname"><span class="pre">constraints</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#APARCH.constraints"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.APARCH.constraints" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">APARCH.</span></span><span class="sig-name descname"><span class="pre">constraints</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#APARCH.constraints"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.APARCH.constraints" title="Link to this definition">¶</a></dt>
 <dd><p>Construct parameter constraints arrays for parameter estimation</p>
 <dl class="field-list simple">
-<dt class="field-odd" id="arch.univariate.APARCH.constraints-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.APARCH.constraints-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>]</p>
-</dd>
-<dt class="field-even" id="arch.univariate.APARCH.constraints-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.APARCH.constraints-returns" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><ul class="simple">
-<li><p><strong>A</strong> (<em>ndarray</em>) – Parameters loadings in constraint. Shape is number of constraints
+<dt class="field-odd" id="arch.univariate.APARCH.constraints-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.APARCH.constraints-returns" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p><strong>A</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – Parameters loadings in constraint. Shape is number of constraints
 by number of parameters</p></li>
-<li><p><strong>b</strong> (<em>ndarray</em>) – Constraint values, one for each constraint</p></li>
+<li><p><strong>b</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – Constraint values, one for each constraint</p></li>
 </ul>
 </p>
 </dd>
@@ -1328,7 +1311,7 @@ <h1 id="arch-univariate-aparch-constraints">arch.univariate.APARCH.constraints<a
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1375,7 +1358,7 @@ <h1 id="arch-univariate-aparch-constraints">arch.univariate.APARCH.constraints<a
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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diff --git a/univariate/generated/generated/arch.univariate.APARCH.delta.html b/univariate/generated/generated/arch.univariate.APARCH.delta.html
index b72e96f958..2d732a989b 100644
--- a/univariate/generated/generated/arch.univariate.APARCH.delta.html
+++ b/univariate/generated/generated/arch.univariate.APARCH.delta.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.APARCH.delta - arch 6.1.0</title>
+        <title>arch.univariate.APARCH.delta - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1198,10 +1198,10 @@
                   
 
 
-<h1 id="arch-univariate-aparch-delta">arch.univariate.APARCH.delta<a class="headerlink" href="#arch-univariate-aparch-delta" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-aparch-delta">arch.univariate.APARCH.delta<a class="headerlink" href="#arch-univariate-aparch-delta" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.APARCH.delta">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">APARCH.</span></span><span class="sig-name descname"><span class="pre">delta</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.univariate.APARCH.delta" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">APARCH.</span></span><span class="sig-name descname"><span class="pre">delta</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.univariate.APARCH.delta" title="Link to this definition">¶</a></dt>
 <dd><p>The value of delta in the model. NaN is delta is estimated.</p>
 </dd></dl>
 
@@ -1272,7 +1272,7 @@ <h1 id="arch-univariate-aparch-delta">arch.univariate.APARCH.delta<a class="head
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1319,7 +1319,7 @@ <h1 id="arch-univariate-aparch-delta">arch.univariate.APARCH.delta<a class="head
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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diff --git a/univariate/generated/generated/arch.univariate.APARCH.forecast.html b/univariate/generated/generated/arch.univariate.APARCH.forecast.html
index e91fec45a4..87589c53d6 100644
--- a/univariate/generated/generated/arch.univariate.APARCH.forecast.html
+++ b/univariate/generated/generated/arch.univariate.APARCH.forecast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.APARCH.forecast - arch 6.1.0</title>
+        <title>arch.univariate.APARCH.forecast - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1418,38 +1418,38 @@
                   
 
 
-<h1 id="arch-univariate-aparch-forecast">arch.univariate.APARCH.forecast<a class="headerlink" href="#arch-univariate-aparch-forecast" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-aparch-forecast">arch.univariate.APARCH.forecast<a class="headerlink" href="#arch-univariate-aparch-forecast" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.APARCH.forecast">
-<span class="sig-prename descclassname"><span class="pre">APARCH.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.forecast.parameters" title="arch.univariate.APARCH.forecast.parameters (Python parameter) — Parameters required to forecast the volatility model"><span class="n"><span class="pre">parameters</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.forecast.resids" title="arch.univariate.APARCH.forecast.resids (Python parameter) — Residuals to use in the recursion"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.forecast.backcast" title="arch.univariate.APARCH.forecast.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.forecast.var_bounds" title="arch.univariate.APARCH.forecast.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.forecast.start" title="arch.univariate.APARCH.forecast.start (Python parameter) — Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."><span class="n"><span class="pre">start</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.forecast.horizon" title="arch.univariate.APARCH.forecast.horizon (Python parameter) — Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."><span class="n"><span class="pre">horizon</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.forecast.method" title="arch.univariate.APARCH.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic."><span class="n"><span class="pre">method</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.forecast.simulations" title="arch.univariate.APARCH.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.forecast.rng" title="arch.univariate.APARCH.forecast.rng (Python parameter) — Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."><span class="n"><span class="pre">rng</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.forecast.random_state" title="arch.univariate.APARCH.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.APARCH.forecast" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">APARCH.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.forecast.parameters" title="arch.univariate.APARCH.forecast.parameters (Python parameter) — Parameters required to forecast the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">ArrayLike1D</span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.forecast.resids" title="arch.univariate.APARCH.forecast.resids (Python parameter) — Residuals to use in the recursion"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">Float64Array</span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.forecast.backcast" title="arch.univariate.APARCH.forecast.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">Float64Array</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.forecast.var_bounds" title="arch.univariate.APARCH.forecast.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">Float64Array</span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.forecast.start" title="arch.univariate.APARCH.forecast.start (Python parameter) — Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.forecast.horizon" title="arch.univariate.APARCH.forecast.horizon (Python parameter) — Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.forecast.method" title="arch.univariate.APARCH.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">ForecastingMethod</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.forecast.simulations" title="arch.univariate.APARCH.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.forecast.rng" title="arch.univariate.APARCH.forecast.rng (Python parameter) — Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">RNGType</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.forecast.random_state" title="arch.univariate.APARCH.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">RandomState</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><span class="pre">VarianceForecast</span></span></span></span><a class="headerlink" href="#arch.univariate.APARCH.forecast" title="Link to this definition">¶</a></dt>
 <dd><p>Forecast volatility from the model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.APARCH.forecast-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.APARCH.forecast-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.APARCH.forecast.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, Series}</span></span><a class="headerlink" href="#arch.univariate.APARCH.forecast.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast the volatility model</p>
+<dt id="arch.univariate.APARCH.forecast.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype">ArrayLike1D</span></span><a class="headerlink" href="#arch.univariate.APARCH.forecast.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast the volatility model</p>
 </dd>
-<dt id="arch.univariate.APARCH.forecast.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.APARCH.forecast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Residuals to use in the recursion</p>
+<dt id="arch.univariate.APARCH.forecast.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype">Float64Array</span></span><a class="headerlink" href="#arch.univariate.APARCH.forecast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Residuals to use in the recursion</p>
 </dd>
-<dt id="arch.univariate.APARCH.forecast.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><a class="headerlink" href="#arch.univariate.APARCH.forecast.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing the recursion</p>
+<dt id="arch.univariate.APARCH.forecast.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype">Float64Array<span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><a class="headerlink" href="#arch.univariate.APARCH.forecast.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing the recursion</p>
 </dd>
-<dt id="arch.univariate.APARCH.forecast.var_bounds"><span class="sig-name descname">var_bounds</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray, 2-d</span></span><a class="headerlink" href="#arch.univariate.APARCH.forecast.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
+<dt id="arch.univariate.APARCH.forecast.var_bounds"><span class="n sig-name">var_bounds</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype">Float64Array</span></span><a class="headerlink" href="#arch.univariate.APARCH.forecast.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
 </dd>
-<dt id="arch.univariate.APARCH.forecast.start"><span class="sig-name descname">start</span><span class="p"> : </span><span class="desctype"><span class="desctype">{None, int}</span></span><a class="headerlink" href="#arch.univariate.APARCH.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>Index of the first observation to use as the starting point for
+<dt id="arch.univariate.APARCH.forecast.start"><span class="n sig-name">start</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.APARCH.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>Index of the first observation to use as the starting point for
 the forecast.  Default is len(resids).</p>
 </dd>
-<dt id="arch.univariate.APARCH.forecast.horizon"><span class="sig-name descname">horizon</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.APARCH.forecast.horizon" title="Permalink to this definition">¶</a></dt><dd><p>Forecast horizon.  Must be 1 or larger.  Forecasts are produced
+<dt id="arch.univariate.APARCH.forecast.horizon"><span class="n sig-name">horizon</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code><a class="headerlink" href="#arch.univariate.APARCH.forecast.horizon" title="Permalink to this definition">¶</a></dt><dd><p>Forecast horizon.  Must be 1 or larger.  Forecasts are produced
 for horizons in [1, horizon].</p>
 </dd>
-<dt id="arch.univariate.APARCH.forecast.method"><span class="sig-name descname">method</span><span class="p"> : </span><span class="desctype"><span class="desctype">{'analytic', 'simulation', 'bootstrap'}</span></span><a class="headerlink" href="#arch.univariate.APARCH.forecast.method" title="Permalink to this definition">¶</a></dt><dd><p>Method to use when producing the forecast. The default is analytic.</p>
+<dt id="arch.univariate.APARCH.forecast.method"><span class="n sig-name">method</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype">ForecastingMethod</span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code><a class="headerlink" href="#arch.univariate.APARCH.forecast.method" title="Permalink to this definition">¶</a></dt><dd><p>Method to use when producing the forecast. The default is analytic.</p>
 </dd>
-<dt id="arch.univariate.APARCH.forecast.simulations"><span class="sig-name descname">simulations</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.APARCH.forecast.simulations" title="Permalink to this definition">¶</a></dt><dd><p>Number of simulations to run when computing the forecast using
+<dt id="arch.univariate.APARCH.forecast.simulations"><span class="n sig-name">simulations</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.univariate.APARCH.forecast.simulations" title="Permalink to this definition">¶</a></dt><dd><p>Number of simulations to run when computing the forecast using
 either simulation or bootstrap.</p>
 </dd>
-<dt id="arch.univariate.APARCH.forecast.rng"><span class="sig-name descname">rng</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable</span></span><a class="headerlink" href="#arch.univariate.APARCH.forecast.rng" title="Permalink to this definition">¶</a></dt><dd><p>Callable random number generator required if method is
+<dt id="arch.univariate.APARCH.forecast.rng"><span class="n sig-name">rng</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype">RNGType<span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.APARCH.forecast.rng" title="Permalink to this definition">¶</a></dt><dd><p>Callable random number generator required if method is
 ‘simulation’. Must take a single shape input and return random
 samples numbers with that shape.</p>
 </dd>
-<dt id="arch.univariate.APARCH.forecast.random_state"><span class="sig-name descname">random_state</span><span class="p"> : </span><span class="desctype"><span class="desctype">RandomState, optional</span></span><a class="headerlink" href="#arch.univariate.APARCH.forecast.random_state" title="Permalink to this definition">¶</a></dt><dd><p>NumPy RandomState instance to use when method is ‘bootstrap’</p>
+<dt id="arch.univariate.APARCH.forecast.random_state"><span class="n sig-name">random_state</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype">RandomState<span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.APARCH.forecast.random_state" title="Permalink to this definition">¶</a></dt><dd><p>NumPy RandomState instance to use when method is ‘bootstrap’</p>
 </dd>
 </dl>
 </dd>
@@ -1458,7 +1458,7 @@ <h1 id="arch-univariate-aparch-forecast">arch.univariate.APARCH.forecast<a class
 or bootstrap, the simulated paths.</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.APARCH.forecast-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.APARCH.forecast-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>VarianceForecast</p>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">arch.univariate.volatility.VarianceForecast</span></code></p>
 </dd>
 <dt class="field-even" id="arch.univariate.APARCH.forecast-raises">Raises<span class="colon">:</span><a class="headerlink" href="#arch.univariate.APARCH.forecast-raises" title="Permalink to this headline">¶</a></dt>
 <dd class="field-even"><ul class="simple">
@@ -1545,7 +1545,7 @@ <h1 id="arch-univariate-aparch-forecast">arch.univariate.APARCH.forecast<a class
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1592,7 +1592,7 @@ <h1 id="arch-univariate-aparch-forecast">arch.univariate.APARCH.forecast<a class
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.APARCH.name.html b/univariate/generated/generated/arch.univariate.APARCH.name.html
index 2359a9aa44..97062d0dde 100644
--- a/univariate/generated/generated/arch.univariate.APARCH.name.html
+++ b/univariate/generated/generated/arch.univariate.APARCH.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.APARCH.name - arch 6.1.0</title>
+        <title>arch.univariate.APARCH.name - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1198,10 +1198,10 @@
                   
 
 
-<h1 id="arch-univariate-aparch-name">arch.univariate.APARCH.name<a class="headerlink" href="#arch-univariate-aparch-name" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-aparch-name">arch.univariate.APARCH.name<a class="headerlink" href="#arch-univariate-aparch-name" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.APARCH.name">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">APARCH.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.univariate.APARCH.name" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">APARCH.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.univariate.APARCH.name" title="Link to this definition">¶</a></dt>
 <dd><p>The name of the volatility process</p>
 </dd></dl>
 
@@ -1272,7 +1272,7 @@ <h1 id="arch-univariate-aparch-name">arch.univariate.APARCH.name<a class="header
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1319,7 +1319,7 @@ <h1 id="arch-univariate-aparch-name">arch.univariate.APARCH.name<a class="header
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.APARCH.num_params.html b/univariate/generated/generated/arch.univariate.APARCH.num_params.html
index 4ee6c17836..395e8c9efb 100644
--- a/univariate/generated/generated/arch.univariate.APARCH.num_params.html
+++ b/univariate/generated/generated/arch.univariate.APARCH.num_params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.APARCH.num_params - arch 6.1.0</title>
+        <title>arch.univariate.APARCH.num_params - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1198,10 +1198,10 @@
                   
 
 
-<h1 id="arch-univariate-aparch-num-params">arch.univariate.APARCH.num_params<a class="headerlink" href="#arch-univariate-aparch-num-params" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-aparch-num-params">arch.univariate.APARCH.num_params<a class="headerlink" href="#arch-univariate-aparch-num-params" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.APARCH.num_params">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">APARCH.</span></span><span class="sig-name descname"><span class="pre">num_params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.APARCH.num_params" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">APARCH.</span></span><span class="sig-name descname"><span class="pre">num_params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.APARCH.num_params" title="Link to this definition">¶</a></dt>
 <dd><p>The number of parameters in the model</p>
 </dd></dl>
 
@@ -1272,7 +1272,7 @@ <h1 id="arch-univariate-aparch-num-params">arch.univariate.APARCH.num_params<a c
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1319,7 +1319,7 @@ <h1 id="arch-univariate-aparch-num-params">arch.univariate.APARCH.num_params<a c
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diff --git a/univariate/generated/generated/arch.univariate.APARCH.parameter_names.html b/univariate/generated/generated/arch.univariate.APARCH.parameter_names.html
index 56c2d2badc..fdfda6abd1 100644
--- a/univariate/generated/generated/arch.univariate.APARCH.parameter_names.html
+++ b/univariate/generated/generated/arch.univariate.APARCH.parameter_names.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.APARCH.parameter_names - arch 6.1.0</title>
+        <title>arch.univariate.APARCH.parameter_names - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1238,17 +1238,17 @@
                   
 
 
-<h1 id="arch-univariate-aparch-parameter-names">arch.univariate.APARCH.parameter_names<a class="headerlink" href="#arch-univariate-aparch-parameter-names" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-aparch-parameter-names">arch.univariate.APARCH.parameter_names<a class="headerlink" href="#arch-univariate-aparch-parameter-names" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.APARCH.parameter_names">
-<span class="sig-prename descclassname"><span class="pre">APARCH.</span></span><span class="sig-name descname"><span class="pre">parameter_names</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#APARCH.parameter_names"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.APARCH.parameter_names" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">APARCH.</span></span><span class="sig-name descname"><span class="pre">parameter_names</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#APARCH.parameter_names"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.APARCH.parameter_names" title="Link to this definition">¶</a></dt>
 <dd><p>Names of model parameters</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.APARCH.parameter_names-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.APARCH.parameter_names-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p><strong>names</strong> – Variables names</p>
 </dd>
 <dt class="field-even" id="arch.univariate.APARCH.parameter_names-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.APARCH.parameter_names-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>)</p>
+<dd class="field-even"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span> <span class="pre">(str)</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1320,7 +1320,7 @@ <h1 id="arch-univariate-aparch-parameter-names">arch.univariate.APARCH.parameter
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1367,7 +1367,7 @@ <h1 id="arch-univariate-aparch-parameter-names">arch.univariate.APARCH.parameter
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.APARCH.simulate.html b/univariate/generated/generated/arch.univariate.APARCH.simulate.html
index 37f17c1d0c..9e71c145f2 100644
--- a/univariate/generated/generated/arch.univariate.APARCH.simulate.html
+++ b/univariate/generated/generated/arch.univariate.APARCH.simulate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.APARCH.simulate - arch 6.1.0</title>
+        <title>arch.univariate.APARCH.simulate - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -762,13 +762,6 @@
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     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.univariate.APARCH.simulate-return-type" class="md-nav__link">
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-  </a>
-  
 </li>
         
           <li class="md-nav__item">
@@ -1299,13 +1292,6 @@
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     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.univariate.APARCH.simulate-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
           <li class="md-nav__item">
@@ -1334,36 +1320,33 @@
                   
 
 
-<h1 id="arch-univariate-aparch-simulate">arch.univariate.APARCH.simulate<a class="headerlink" href="#arch-univariate-aparch-simulate" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-aparch-simulate">arch.univariate.APARCH.simulate<a class="headerlink" href="#arch-univariate-aparch-simulate" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.APARCH.simulate">
-<span class="sig-prename descclassname"><span class="pre">APARCH.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.simulate.parameters" title="arch.univariate.APARCH.simulate.parameters (Python parameter) — Parameters required to simulate the volatility model"><span class="n"><span class="pre">parameters</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.simulate.nobs" title="arch.univariate.APARCH.simulate.nobs (Python parameter) — Number of data points to simulate"><span class="n"><span class="pre">nobs</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.simulate.rng" title="arch.univariate.APARCH.simulate.rng (Python parameter) — Callable function that takes a single integer input and returns a vector of random numbers"><span class="n"><span class="pre">rng</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.simulate.burn" title="arch.univariate.APARCH.simulate.burn (Python parameter) — Number of additional observations to generate when initializing the simulation"><span class="n"><span class="pre">burn</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.simulate.initial_value" title="arch.univariate.APARCH.simulate.initial_value (Python parameter) — Scalar or array of initial values to use when initializing the simulation"><span class="n"><span class="pre">initial_value</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#APARCH.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.APARCH.simulate" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.APARCH.simulate">
+<span class="sig-prename descclassname"><span class="pre">APARCH.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.simulate.parameters" title="arch.univariate.APARCH.simulate.parameters (Python parameter) — Parameters required to simulate the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.simulate.nobs" title="arch.univariate.APARCH.simulate.nobs (Python parameter) — Number of data points to simulate"><span class="n"><span class="pre">nobs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.simulate.rng" title="arch.univariate.APARCH.simulate.rng (Python parameter) — Callable function that takes a single integer input and returns a vector of random numbers"><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.simulate.burn" title="arch.univariate.APARCH.simulate.burn (Python parameter) — Number of additional observations to generate when initializing the simulation"><span class="n"><span class="pre">burn</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.simulate.initial_value" title="arch.univariate.APARCH.simulate.initial_value (Python parameter) — Scalar or array of initial values to use when initializing the simulation"><span class="n"><span class="pre">initial_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#APARCH.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.APARCH.simulate" title="Link to this definition">¶</a></dt>
 <dd><p>Simulate data from the model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.APARCH.simulate-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.APARCH.simulate-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.APARCH.simulate.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, Series}</span></span><a class="headerlink" href="#arch.univariate.APARCH.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to simulate the volatility model</p>
+<dt id="arch.univariate.APARCH.simulate.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.APARCH.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to simulate the volatility model</p>
 </dd>
-<dt id="arch.univariate.APARCH.simulate.nobs"><span class="sig-name descname">nobs</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.APARCH.simulate.nobs" title="Permalink to this definition">¶</a></dt><dd><p>Number of data points to simulate</p>
+<dt id="arch.univariate.APARCH.simulate.nobs"><span class="n sig-name">nobs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.APARCH.simulate.nobs" title="Permalink to this definition">¶</a></dt><dd><p>Number of data points to simulate</p>
 </dd>
-<dt id="arch.univariate.APARCH.simulate.rng"><span class="sig-name descname">rng</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable</span></span><a class="headerlink" href="#arch.univariate.APARCH.simulate.rng" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that takes a single integer input and returns
+<dt id="arch.univariate.APARCH.simulate.rng"><span class="n sig-name">rng</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)">Callable</a><span class="p">[</span><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">,</span><span class="w"> </span><span class="p">...</span><span class="p">]</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.univariate.APARCH.simulate.rng" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that takes a single integer input and returns
 a vector of random numbers</p>
 </dd>
-<dt id="arch.univariate.APARCH.simulate.burn"><span class="sig-name descname">burn</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.univariate.APARCH.simulate.burn" title="Permalink to this definition">¶</a></dt><dd><p>Number of additional observations to generate when initializing
+<dt id="arch.univariate.APARCH.simulate.burn"><span class="n sig-name">burn</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code><a class="headerlink" href="#arch.univariate.APARCH.simulate.burn" title="Permalink to this definition">¶</a></dt><dd><p>Number of additional observations to generate when initializing
 the simulation</p>
 </dd>
-<dt id="arch.univariate.APARCH.simulate.initial_value"><span class="sig-name descname">initial_value</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}, optional</span></span><a class="headerlink" href="#arch.univariate.APARCH.simulate.initial_value" title="Permalink to this definition">¶</a></dt><dd><p>Scalar or array of initial values to use when initializing the
+<dt id="arch.univariate.APARCH.simulate.initial_value"><span class="n sig-name">initial_value</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.APARCH.simulate.initial_value" title="Permalink to this definition">¶</a></dt><dd><p>Scalar or array of initial values to use when initializing the
 simulation</p>
 </dd>
 </dl>
 </dd>
-<dt class="field-even" id="arch.univariate.APARCH.simulate-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.APARCH.simulate-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>]</p>
-</dd>
-<dt class="field-odd" id="arch.univariate.APARCH.simulate-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.APARCH.simulate-returns" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><ul class="simple">
-<li><p><strong>resids</strong> (<em>ndarray</em>) – The simulated residuals</p></li>
-<li><p><strong>variance</strong> (<em>ndarray</em>) – The simulated variance</p></li>
+<dt class="field-even" id="arch.univariate.APARCH.simulate-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.APARCH.simulate-returns" title="Permalink to this headline">¶</a></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><strong>resids</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – The simulated residuals</p></li>
+<li><p><strong>variance</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – The simulated variance</p></li>
 </ul>
 </p>
 </dd>
@@ -1437,7 +1420,7 @@ <h1 id="arch-univariate-aparch-simulate">arch.univariate.APARCH.simulate<a class
   
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diff --git a/univariate/generated/generated/arch.univariate.APARCH.start.html b/univariate/generated/generated/arch.univariate.APARCH.start.html
index 317eb66710..547b2324bf 100644
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+++ b/univariate/generated/generated/arch.univariate.APARCH.start.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -47,8 +47,8 @@
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch 6.1.0
+    arch 6.2.0
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@@ -1198,10 +1198,10 @@
                   
 
 
-<h1 id="arch-univariate-aparch-start">arch.univariate.APARCH.start<a class="headerlink" href="#arch-univariate-aparch-start" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-aparch-start">arch.univariate.APARCH.start<a class="headerlink" href="#arch-univariate-aparch-start" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.APARCH.start">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">APARCH.</span></span><span class="sig-name descname"><span class="pre">start</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.APARCH.start" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">APARCH.</span></span><span class="sig-name descname"><span class="pre">start</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.APARCH.start" title="Link to this definition">¶</a></dt>
 <dd><p>Index to use to start variance subarray selection</p>
 </dd></dl>
 
@@ -1272,7 +1272,7 @@ <h1 id="arch-univariate-aparch-start">arch.univariate.APARCH.start<a class="head
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1319,7 +1319,7 @@ <h1 id="arch-univariate-aparch-start">arch.univariate.APARCH.start<a class="head
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diff --git a/univariate/generated/generated/arch.univariate.APARCH.starting_values.html b/univariate/generated/generated/arch.univariate.APARCH.starting_values.html
index 48cba0cc38..799c3fa2fc 100644
--- a/univariate/generated/generated/arch.univariate.APARCH.starting_values.html
+++ b/univariate/generated/generated/arch.univariate.APARCH.starting_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.APARCH.starting_values - arch 6.1.0</title>
+        <title>arch.univariate.APARCH.starting_values - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1278,15 +1278,15 @@
                   
 
 
-<h1 id="arch-univariate-aparch-starting-values">arch.univariate.APARCH.starting_values<a class="headerlink" href="#arch-univariate-aparch-starting-values" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-aparch-starting-values">arch.univariate.APARCH.starting_values<a class="headerlink" href="#arch-univariate-aparch-starting-values" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.APARCH.starting_values">
-<span class="sig-prename descclassname"><span class="pre">APARCH.</span></span><span class="sig-name descname"><span class="pre">starting_values</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.starting_values.resids" title="arch.univariate.APARCH.starting_values.resids (Python parameter) — Array of (approximate) residuals to use when computing starting values"><span class="n"><span class="pre">resids</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#APARCH.starting_values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.APARCH.starting_values" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">APARCH.</span></span><span class="sig-name descname"><span class="pre">starting_values</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.starting_values.resids" title="arch.univariate.APARCH.starting_values.resids (Python parameter) — Array of (approximate) residuals to use when computing starting values"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#APARCH.starting_values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.APARCH.starting_values" title="Link to this definition">¶</a></dt>
 <dd><p>Returns starting values for the ARCH model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.APARCH.starting_values-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.APARCH.starting_values-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.APARCH.starting_values.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.APARCH.starting_values.resids" title="Permalink to this definition">¶</a></dt><dd><p>Array of (approximate) residuals to use when computing starting
+<dt id="arch.univariate.APARCH.starting_values.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.APARCH.starting_values.resids" title="Permalink to this definition">¶</a></dt><dd><p>Array of (approximate) residuals to use when computing starting
 values</p>
 </dd>
 </dl>
@@ -1295,7 +1295,7 @@ <h1 id="arch-univariate-aparch-starting-values">arch.univariate.APARCH.starting_
 <dd class="field-even"><p><strong>sv</strong> – Array of starting values</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.APARCH.starting_values-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.APARCH.starting_values-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1367,7 +1367,7 @@ <h1 id="arch-univariate-aparch-starting-values">arch.univariate.APARCH.starting_
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
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@@ -1414,7 +1414,7 @@ <h1 id="arch-univariate-aparch-starting-values">arch.univariate.APARCH.starting_
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diff --git a/univariate/generated/generated/arch.univariate.APARCH.stop.html b/univariate/generated/generated/arch.univariate.APARCH.stop.html
index 995d01ddd0..d37ee14518 100644
--- a/univariate/generated/generated/arch.univariate.APARCH.stop.html
+++ b/univariate/generated/generated/arch.univariate.APARCH.stop.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.APARCH.stop - arch 6.1.0</title>
+        <title>arch.univariate.APARCH.stop - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1198,10 +1198,10 @@
                   
 
 
-<h1 id="arch-univariate-aparch-stop">arch.univariate.APARCH.stop<a class="headerlink" href="#arch-univariate-aparch-stop" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-aparch-stop">arch.univariate.APARCH.stop<a class="headerlink" href="#arch-univariate-aparch-stop" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.APARCH.stop">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">APARCH.</span></span><span class="sig-name descname"><span class="pre">stop</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.APARCH.stop" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">APARCH.</span></span><span class="sig-name descname"><span class="pre">stop</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.APARCH.stop" title="Link to this definition">¶</a></dt>
 <dd><p>Index to use to stop variance subarray selection</p>
 </dd></dl>
 
@@ -1272,7 +1272,7 @@ <h1 id="arch-univariate-aparch-stop">arch.univariate.APARCH.stop<a class="header
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1319,7 +1319,7 @@ <h1 id="arch-univariate-aparch-stop">arch.univariate.APARCH.stop<a class="header
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diff --git a/univariate/generated/generated/arch.univariate.APARCH.update.html b/univariate/generated/generated/arch.univariate.APARCH.update.html
index 51c53aa9d4..a8442b3cd1 100644
--- a/univariate/generated/generated/arch.univariate.APARCH.update.html
+++ b/univariate/generated/generated/arch.univariate.APARCH.update.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.APARCH.update - arch 6.1.0</title>
+        <title>arch.univariate.APARCH.update - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1348,28 +1348,28 @@
                   
 
 
-<h1 id="arch-univariate-aparch-update">arch.univariate.APARCH.update<a class="headerlink" href="#arch-univariate-aparch-update" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-aparch-update">arch.univariate.APARCH.update<a class="headerlink" href="#arch-univariate-aparch-update" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.APARCH.update">
-<span class="sig-prename descclassname"><span class="pre">APARCH.</span></span><span class="sig-name descname"><span class="pre">update</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.update.index" title="arch.univariate.APARCH.update.index (Python parameter) — The numerical index of the variance to compute"><span class="n"><span class="pre">index</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.update.parameters" title="arch.univariate.APARCH.update.parameters (Python parameter) — The variance model parameters"><span class="n"><span class="pre">parameters</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.update.resids" title="arch.univariate.APARCH.update.resids (Python parameter) — The residual array. Only uses resids[:index] when computing sigma2[index]"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.update.sigma2" title="arch.univariate.APARCH.update.sigma2 (Python parameter) — The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."><span class="n"><span class="pre">sigma2</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.update.backcast" title="arch.univariate.APARCH.update.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.update.var_bounds" title="arch.univariate.APARCH.update.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.APARCH.update" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">APARCH.</span></span><span class="sig-name descname"><span class="pre">update</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.update.index" title="arch.univariate.APARCH.update.index (Python parameter) — The numerical index of the variance to compute"><span class="n"><span class="pre">index</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.update.parameters" title="arch.univariate.APARCH.update.parameters (Python parameter) — The variance model parameters"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.update.resids" title="arch.univariate.APARCH.update.resids (Python parameter) — The residual array. Only uses resids[:index] when computing sigma2[index]"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.update.sigma2" title="arch.univariate.APARCH.update.sigma2 (Python parameter) — The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."><span class="n"><span class="pre">sigma2</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.update.backcast" title="arch.univariate.APARCH.update.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.update.var_bounds" title="arch.univariate.APARCH.update.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span></span><a class="headerlink" href="#arch.univariate.APARCH.update" title="Link to this definition">¶</a></dt>
 <dd><p>Compute the variance for a single observation</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.APARCH.update-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.APARCH.update-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.APARCH.update.index"><span class="sig-name descname">index</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.APARCH.update.index" title="Permalink to this definition">¶</a></dt><dd><p>The numerical index of the variance to compute</p>
+<dt id="arch.univariate.APARCH.update.index"><span class="n sig-name">index</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.APARCH.update.index" title="Permalink to this definition">¶</a></dt><dd><p>The numerical index of the variance to compute</p>
 </dd>
-<dt id="arch.univariate.APARCH.update.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.APARCH.update.parameters" title="Permalink to this definition">¶</a></dt><dd><p>The variance model parameters</p>
+<dt id="arch.univariate.APARCH.update.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.APARCH.update.parameters" title="Permalink to this definition">¶</a></dt><dd><p>The variance model parameters</p>
 </dd>
-<dt id="arch.univariate.APARCH.update.resids"><span class="n sig-name">resids</span><a class="headerlink" href="#arch.univariate.APARCH.update.resids" title="Permalink to this definition">¶</a></dt><dd><p>The residual array. Only uses <code class="docutils literal notranslate"><span class="pre">resids[:index]</span></code> when computing
+<dt id="arch.univariate.APARCH.update.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.APARCH.update.resids" title="Permalink to this definition">¶</a></dt><dd><p>The residual array. Only uses <code class="docutils literal notranslate"><span class="pre">resids[:index]</span></code> when computing
 <code class="docutils literal notranslate"><span class="pre">sigma2[index]</span></code></p>
 </dd>
-<dt id="arch.univariate.APARCH.update.sigma2"><span class="sig-name descname">sigma2</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.APARCH.update.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>The array containing the variances. Only uses <code class="docutils literal notranslate"><span class="pre">sigma2[:index]</span></code>
+<dt id="arch.univariate.APARCH.update.sigma2"><span class="n sig-name">sigma2</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.APARCH.update.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>The array containing the variances. Only uses <code class="docutils literal notranslate"><span class="pre">sigma2[:index]</span></code>
 when computing <code class="docutils literal notranslate"><span class="pre">sigma2[index]</span></code>. The computed value is stored
 in <code class="docutils literal notranslate"><span class="pre">sigma2[index]</span></code>.</p>
 </dd>
-<dt id="arch.univariate.APARCH.update.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}</span></span><a class="headerlink" href="#arch.univariate.APARCH.update.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing the recursion</p>
+<dt id="arch.univariate.APARCH.update.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.APARCH.update.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing the recursion</p>
 </dd>
-<dt id="arch.univariate.APARCH.update.var_bounds"><span class="sig-name descname">var_bounds</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.APARCH.update.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
+<dt id="arch.univariate.APARCH.update.var_bounds"><span class="n sig-name">var_bounds</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.APARCH.update.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
 </dd>
 </dl>
 </dd>
@@ -1377,7 +1377,7 @@ <h1 id="arch-univariate-aparch-update">arch.univariate.APARCH.update<a class="he
 <dd class="field-even"><p>The variance computed for location <code class="docutils literal notranslate"><span class="pre">index</span></code></p>
 </dd>
 <dt class="field-odd" id="arch.univariate.APARCH.update-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.APARCH.update-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1449,7 +1449,7 @@ <h1 id="arch-univariate-aparch-update">arch.univariate.APARCH.update<a class="he
   
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@@ -1496,7 +1496,7 @@ <h1 id="arch-univariate-aparch-update">arch.univariate.APARCH.update<a class="he
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diff --git a/univariate/generated/generated/arch.univariate.APARCH.updateable.html b/univariate/generated/generated/arch.univariate.APARCH.updateable.html
index f1a9ce3b44..7ed1f15acf 100644
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+++ b/univariate/generated/generated/arch.univariate.APARCH.updateable.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.APARCH.updateable - arch 6.1.0</title>
+        <title>arch.univariate.APARCH.updateable - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1198,10 +1198,10 @@
                   
 
 
-<h1 id="arch-univariate-aparch-updateable">arch.univariate.APARCH.updateable<a class="headerlink" href="#arch-univariate-aparch-updateable" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-aparch-updateable">arch.univariate.APARCH.updateable<a class="headerlink" href="#arch-univariate-aparch-updateable" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.APARCH.updateable">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">APARCH.</span></span><span class="sig-name descname"><span class="pre">updateable</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.univariate.APARCH.updateable" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">APARCH.</span></span><span class="sig-name descname"><span class="pre">updateable</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.univariate.APARCH.updateable" title="Link to this definition">¶</a></dt>
 <dd><p>Flag indicating that the volatility process supports update</p>
 </dd></dl>
 
@@ -1272,7 +1272,7 @@ <h1 id="arch-univariate-aparch-updateable">arch.univariate.APARCH.updateable<a c
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1319,7 +1319,7 @@ <h1 id="arch-univariate-aparch-updateable">arch.univariate.APARCH.updateable<a c
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diff --git a/univariate/generated/generated/arch.univariate.APARCH.variance_bounds.html b/univariate/generated/generated/arch.univariate.APARCH.variance_bounds.html
index fc522cf418..318b0ce351 100644
--- a/univariate/generated/generated/arch.univariate.APARCH.variance_bounds.html
+++ b/univariate/generated/generated/arch.univariate.APARCH.variance_bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.APARCH.variance_bounds - arch 6.1.0</title>
+        <title>arch.univariate.APARCH.variance_bounds - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1292,20 +1292,20 @@
                   
 
 
-<h1 id="arch-univariate-aparch-variance-bounds">arch.univariate.APARCH.variance_bounds<a class="headerlink" href="#arch-univariate-aparch-variance-bounds" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-aparch-variance-bounds">arch.univariate.APARCH.variance_bounds<a class="headerlink" href="#arch-univariate-aparch-variance-bounds" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.APARCH.variance_bounds">
-<span class="sig-prename descclassname"><span class="pre">APARCH.</span></span><span class="sig-name descname"><span class="pre">variance_bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.variance_bounds.resids" title="arch.univariate.APARCH.variance_bounds.resids (Python parameter) — Approximate residuals to use to compute the lower and upper bounds on the conditional variance"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.variance_bounds.power" title="arch.univariate.APARCH.variance_bounds.power (Python parameter) — Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."><span class="n"><span class="pre">power</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">2.0</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.APARCH.variance_bounds" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.APARCH.variance_bounds">
+<span class="sig-prename descclassname"><span class="pre">APARCH.</span></span><span class="sig-name descname"><span class="pre">variance_bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.variance_bounds.resids" title="arch.univariate.APARCH.variance_bounds.resids (Python parameter) — Approximate residuals to use to compute the lower and upper bounds on the conditional variance"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.variance_bounds.power" title="arch.univariate.APARCH.variance_bounds.power (Python parameter) — Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."><span class="n"><span class="pre">power</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">2.0</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.univariate.APARCH.variance_bounds" title="Link to this definition">¶</a></dt>
 <dd><p>Construct loose bounds for conditional variances.</p>
 <p>These bounds are used in parameter estimation to ensure
 that the log-likelihood does not produce NaN values.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.APARCH.variance_bounds-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.APARCH.variance_bounds-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.APARCH.variance_bounds.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.APARCH.variance_bounds.resids" title="Permalink to this definition">¶</a></dt><dd><p>Approximate residuals to use to compute the lower and upper bounds
+<dt id="arch.univariate.APARCH.variance_bounds.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.APARCH.variance_bounds.resids" title="Permalink to this definition">¶</a></dt><dd><p>Approximate residuals to use to compute the lower and upper bounds
 on the conditional variance</p>
 </dd>
-<dt id="arch.univariate.APARCH.variance_bounds.power"><span class="sig-name descname">power</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>, optional</span></span><a class="headerlink" href="#arch.univariate.APARCH.variance_bounds.power" title="Permalink to this definition">¶</a></dt><dd><p>Power used in the model. 2.0, the default corresponds to standard
+<dt id="arch.univariate.APARCH.variance_bounds.power"><span class="n sig-name">power</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">2.0</span></code><a class="headerlink" href="#arch.univariate.APARCH.variance_bounds.power" title="Permalink to this definition">¶</a></dt><dd><p>Power used in the model. 2.0, the default corresponds to standard
 ARCH models that evolve in squares.</p>
 </dd>
 </dl>
@@ -1315,7 +1315,7 @@ <h1 id="arch-univariate-aparch-variance-bounds">arch.univariate.APARCH.variance_
 number of elements as resids</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.APARCH.variance_bounds-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.APARCH.variance_bounds-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1387,7 +1387,7 @@ <h1 id="arch-univariate-aparch-variance-bounds">arch.univariate.APARCH.variance_
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1434,7 +1434,7 @@ <h1 id="arch-univariate-aparch-variance-bounds">arch.univariate.APARCH.variance_
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.APARCH.volatility_updater.html b/univariate/generated/generated/arch.univariate.APARCH.volatility_updater.html
index f4e2bbaf81..5bc1a06341 100644
--- a/univariate/generated/generated/arch.univariate.APARCH.volatility_updater.html
+++ b/univariate/generated/generated/arch.univariate.APARCH.volatility_updater.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.APARCH.volatility_updater - arch 6.1.0</title>
+        <title>arch.univariate.APARCH.volatility_updater - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1252,17 +1252,17 @@
                   
 
 
-<h1 id="arch-univariate-aparch-volatility-updater">arch.univariate.APARCH.volatility_updater<a class="headerlink" href="#arch-univariate-aparch-volatility-updater" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-aparch-volatility-updater">arch.univariate.APARCH.volatility_updater<a class="headerlink" href="#arch-univariate-aparch-volatility-updater" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.APARCH.volatility_updater">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">APARCH.</span></span><span class="sig-name descname"><span class="pre">volatility_updater</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.univariate.recursions_python.VolatilityUpdater.html#arch.univariate.recursions_python.VolatilityUpdater" title="arch.univariate.recursions_python.VolatilityUpdater (Python class) — Base class that all volatility updaters must inherit from."><span class="pre">VolatilityUpdater</span></a></span><a class="headerlink" href="#arch.univariate.APARCH.volatility_updater" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">APARCH.</span></span><span class="sig-name descname"><span class="pre">volatility_updater</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><span class="pre">VolatilityUpdater</span></span><a class="headerlink" href="#arch.univariate.APARCH.volatility_updater" title="Link to this definition">¶</a></dt>
 <dd><p>Get the volatility updater associated with the volatility process</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.APARCH.volatility_updater-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.APARCH.volatility_updater-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The updater class</p>
 </dd>
 <dt class="field-even" id="arch.univariate.APARCH.volatility_updater-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.APARCH.volatility_updater-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference internal" href="../arch.univariate.recursions_python.VolatilityUpdater.html#arch.univariate.recursions_python.VolatilityUpdater" title="arch.univariate.recursions_python.VolatilityUpdater (Python class) — Base class that all volatility updaters must inherit from.">VolatilityUpdater</a></p>
+<dd class="field-even"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">VolatilityUpdater</span></code></p>
 </dd>
 <dt class="field-odd" id="arch.univariate.APARCH.volatility_updater-raises">Raises<span class="colon">:</span><a class="headerlink" href="#arch.univariate.APARCH.volatility_updater-raises" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#NotImplementedError" title="(in Python v3.11)"><strong>NotImplementedError</strong></a> – If the process is not updateable</p>
@@ -1337,7 +1337,7 @@ <h1 id="arch-univariate-aparch-volatility-updater">arch.univariate.APARCH.volati
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1384,7 +1384,7 @@ <h1 id="arch-univariate-aparch-volatility-updater">arch.univariate.APARCH.volati
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.ARCH.backcast.html b/univariate/generated/generated/arch.univariate.ARCH.backcast.html
index 12996e24c7..b79649c9b9 100644
--- a/univariate/generated/generated/arch.univariate.ARCH.backcast.html
+++ b/univariate/generated/generated/arch.univariate.ARCH.backcast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCH.backcast - arch 6.1.0</title>
+        <title>arch.univariate.ARCH.backcast - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1248,15 +1248,15 @@
                   
 
 
-<h1 id="arch-univariate-arch-backcast">arch.univariate.ARCH.backcast<a class="headerlink" href="#arch-univariate-arch-backcast" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-arch-backcast">arch.univariate.ARCH.backcast<a class="headerlink" href="#arch-univariate-arch-backcast" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ARCH.backcast">
-<span class="sig-prename descclassname"><span class="pre">ARCH.</span></span><span class="sig-name descname"><span class="pre">backcast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.backcast.resids" title="arch.univariate.ARCH.backcast.resids (Python parameter) — Vector of (approximate) residuals"><span class="n"><span class="pre">resids</span></span></a></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.ARCH.backcast" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ARCH.</span></span><span class="sig-name descname"><span class="pre">backcast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.backcast.resids" title="arch.univariate.ARCH.backcast.resids (Python parameter) — Vector of (approximate) residuals"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.univariate.ARCH.backcast" title="Link to this definition">¶</a></dt>
 <dd><p>Construct values for backcasting to start the recursion</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ARCH.backcast-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCH.backcast-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ARCH.backcast.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.ARCH.backcast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of (approximate) residuals</p>
+<dt id="arch.univariate.ARCH.backcast.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.ARCH.backcast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of (approximate) residuals</p>
 </dd>
 </dl>
 </dd>
@@ -1264,7 +1264,7 @@ <h1 id="arch-univariate-arch-backcast">arch.univariate.ARCH.backcast<a class="he
 <dd class="field-even"><p><strong>backcast</strong> – Value to use in backcasting in the volatility recursion</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.ARCH.backcast-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCH.backcast-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1336,7 +1336,7 @@ <h1 id="arch-univariate-arch-backcast">arch.univariate.ARCH.backcast<a class="he
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1383,7 +1383,7 @@ <h1 id="arch-univariate-arch-backcast">arch.univariate.ARCH.backcast<a class="he
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.ARCH.backcast_transform.html b/univariate/generated/generated/arch.univariate.ARCH.backcast_transform.html
index 749a9ae6df..8de964d7dc 100644
--- a/univariate/generated/generated/arch.univariate.ARCH.backcast_transform.html
+++ b/univariate/generated/generated/arch.univariate.ARCH.backcast_transform.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCH.backcast_transform - arch 6.1.0</title>
+        <title>arch.univariate.ARCH.backcast_transform - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1248,15 +1248,15 @@
                   
 
 
-<h1 id="arch-univariate-arch-backcast-transform">arch.univariate.ARCH.backcast_transform<a class="headerlink" href="#arch-univariate-arch-backcast-transform" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-arch-backcast-transform">arch.univariate.ARCH.backcast_transform<a class="headerlink" href="#arch-univariate-arch-backcast-transform" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.ARCH.backcast_transform">
-<span class="sig-prename descclassname"><span class="pre">ARCH.</span></span><span class="sig-name descname"><span class="pre">backcast_transform</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.backcast_transform.backcast" title="arch.univariate.ARCH.backcast_transform.backcast (Python parameter) — User-provided backcast that approximates sigma2[0]."><span class="n"><span class="pre">backcast</span></span></a></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.ARCH.backcast_transform" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ARCH.backcast_transform">
+<span class="sig-prename descclassname"><span class="pre">ARCH.</span></span><span class="sig-name descname"><span class="pre">backcast_transform</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.backcast_transform.backcast" title="arch.univariate.ARCH.backcast_transform.backcast (Python parameter) — User-provided backcast that approximates sigma2[0]."><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.univariate.ARCH.backcast_transform" title="Link to this definition">¶</a></dt>
 <dd><p>Transformation to apply to user-provided backcast values</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ARCH.backcast_transform-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCH.backcast_transform-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ARCH.backcast_transform.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}</span></span><a class="headerlink" href="#arch.univariate.ARCH.backcast_transform.backcast" title="Permalink to this definition">¶</a></dt><dd><p>User-provided <code class="docutils literal notranslate"><span class="pre">backcast</span></code> that approximates sigma2[0].</p>
+<dt id="arch.univariate.ARCH.backcast_transform.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.ARCH.backcast_transform.backcast" title="Permalink to this definition">¶</a></dt><dd><p>User-provided <code class="docutils literal notranslate"><span class="pre">backcast</span></code> that approximates sigma2[0].</p>
 </dd>
 </dl>
 </dd>
@@ -1264,7 +1264,7 @@ <h1 id="arch-univariate-arch-backcast-transform">arch.univariate.ARCH.backcast_t
 <dd class="field-even"><p><strong>backcast</strong> – Backcast transformed to the model-appropriate scale</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.ARCH.backcast_transform-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCH.backcast_transform-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>{float, ndarray}</p>
+<dd class="field-odd"><p><code class="docutils literal notranslate"><span class="pre">{float,</span> <span class="pre">ndarray}</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1336,7 +1336,7 @@ <h1 id="arch-univariate-arch-backcast-transform">arch.univariate.ARCH.backcast_t
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1383,7 +1383,7 @@ <h1 id="arch-univariate-arch-backcast-transform">arch.univariate.ARCH.backcast_t
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.ARCH.bounds.html b/univariate/generated/generated/arch.univariate.ARCH.bounds.html
index 0c38d9c771..2d95647b5f 100644
--- a/univariate/generated/generated/arch.univariate.ARCH.bounds.html
+++ b/univariate/generated/generated/arch.univariate.ARCH.bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCH.bounds - arch 6.1.0</title>
+        <title>arch.univariate.ARCH.bounds - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1248,15 +1248,15 @@
                   
 
 
-<h1 id="arch-univariate-arch-bounds">arch.univariate.ARCH.bounds<a class="headerlink" href="#arch-univariate-arch-bounds" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-arch-bounds">arch.univariate.ARCH.bounds<a class="headerlink" href="#arch-univariate-arch-bounds" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ARCH.bounds">
-<span class="sig-prename descclassname"><span class="pre">ARCH.</span></span><span class="sig-name descname"><span class="pre">bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.bounds.resids" title="arch.univariate.ARCH.bounds.resids (Python parameter) — Vector of (approximate) residuals"><span class="n"><span class="pre">resids</span></span></a></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.ARCH.bounds" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ARCH.</span></span><span class="sig-name descname"><span class="pre">bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.bounds.resids" title="arch.univariate.ARCH.bounds.resids (Python parameter) — Vector of (approximate) residuals"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#arch.univariate.ARCH.bounds" title="Link to this definition">¶</a></dt>
 <dd><p>Returns bounds for parameters</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ARCH.bounds-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCH.bounds-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ARCH.bounds.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.ARCH.bounds.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of (approximate) residuals</p>
+<dt id="arch.univariate.ARCH.bounds.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.ARCH.bounds.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of (approximate) residuals</p>
 </dd>
 </dl>
 </dd>
@@ -1264,7 +1264,7 @@ <h1 id="arch-univariate-arch-bounds">arch.univariate.ARCH.bounds<a class="header
 <dd class="field-even"><p><strong>bounds</strong> – List of bounds where each element is (lower, upper).</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.ARCH.bounds-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCH.bounds-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a>[<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a>[<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>,<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>]]</p>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">list[tuple[float,float]]</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1336,7 +1336,7 @@ <h1 id="arch-univariate-arch-bounds">arch.univariate.ARCH.bounds<a class="header
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
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@@ -1383,7 +1383,7 @@ <h1 id="arch-univariate-arch-bounds">arch.univariate.ARCH.bounds<a class="header
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.ARCH.compute_variance.html b/univariate/generated/generated/arch.univariate.ARCH.compute_variance.html
index eb507e6b4f..ea36a492de 100644
--- a/univariate/generated/generated/arch.univariate.ARCH.compute_variance.html
+++ b/univariate/generated/generated/arch.univariate.ARCH.compute_variance.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCH.compute_variance - arch 6.1.0</title>
+        <title>arch.univariate.ARCH.compute_variance - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -687,13 +687,6 @@
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-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.univariate.ARCH.compute_variance-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
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-  
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@@ -1262,13 +1255,6 @@
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-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.univariate.ARCH.compute_variance-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
       </ul>
@@ -1290,30 +1276,27 @@
                   
 
 
-<h1 id="arch-univariate-arch-compute-variance">arch.univariate.ARCH.compute_variance<a class="headerlink" href="#arch-univariate-arch-compute-variance" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-arch-compute-variance">arch.univariate.ARCH.compute_variance<a class="headerlink" href="#arch-univariate-arch-compute-variance" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ARCH.compute_variance">
-<span class="sig-prename descclassname"><span class="pre">ARCH.</span></span><span class="sig-name descname"><span class="pre">compute_variance</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.compute_variance.parameters" title="arch.univariate.ARCH.compute_variance.parameters (Python parameter) — Model parameters"><span class="n"><span class="pre">parameters</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.compute_variance.resids" title="arch.univariate.ARCH.compute_variance.resids (Python parameter) — Vector of mean zero residuals"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.compute_variance.sigma2" title="arch.univariate.ARCH.compute_variance.sigma2 (Python parameter) — Array with same size as resids to store the conditional variance"><span class="n"><span class="pre">sigma2</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.compute_variance.backcast" title="arch.univariate.ARCH.compute_variance.backcast (Python parameter) — Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."><span class="n"><span class="pre">backcast</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.compute_variance.var_bounds" title="arch.univariate.ARCH.compute_variance.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.ARCH.compute_variance" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ARCH.</span></span><span class="sig-name descname"><span class="pre">compute_variance</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.compute_variance.parameters" title="arch.univariate.ARCH.compute_variance.parameters (Python parameter) — Model parameters"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.compute_variance.resids" title="arch.univariate.ARCH.compute_variance.resids (Python parameter) — Vector of mean zero residuals"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.compute_variance.sigma2" title="arch.univariate.ARCH.compute_variance.sigma2 (Python parameter) — Array with same size as resids to store the conditional variance"><span class="n"><span class="pre">sigma2</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.compute_variance.backcast" title="arch.univariate.ARCH.compute_variance.backcast (Python parameter) — Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.compute_variance.var_bounds" title="arch.univariate.ARCH.compute_variance.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.univariate.ARCH.compute_variance" title="Link to this definition">¶</a></dt>
 <dd><p>Compute the variance for the ARCH model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ARCH.compute_variance-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCH.compute_variance-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ARCH.compute_variance.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.ARCH.compute_variance.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
+<dt id="arch.univariate.ARCH.compute_variance.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.ARCH.compute_variance.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
 </dd>
-<dt id="arch.univariate.ARCH.compute_variance.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.ARCH.compute_variance.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of mean zero residuals</p>
+<dt id="arch.univariate.ARCH.compute_variance.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.ARCH.compute_variance.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of mean zero residuals</p>
 </dd>
-<dt id="arch.univariate.ARCH.compute_variance.sigma2"><span class="sig-name descname">sigma2</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.ARCH.compute_variance.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>Array with same size as resids to store the conditional variance</p>
+<dt id="arch.univariate.ARCH.compute_variance.sigma2"><span class="n sig-name">sigma2</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.ARCH.compute_variance.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>Array with same size as resids to store the conditional variance</p>
 </dd>
-<dt id="arch.univariate.ARCH.compute_variance.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}</span></span><a class="headerlink" href="#arch.univariate.ARCH.compute_variance.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing ARCH recursion. Can be an ndarray
+<dt id="arch.univariate.ARCH.compute_variance.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.ARCH.compute_variance.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing ARCH recursion. Can be an ndarray
 when the model contains multiple components.</p>
 </dd>
-<dt id="arch.univariate.ARCH.compute_variance.var_bounds"><span class="sig-name descname">var_bounds</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.ARCH.compute_variance.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
+<dt id="arch.univariate.ARCH.compute_variance.var_bounds"><span class="n sig-name">var_bounds</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.ARCH.compute_variance.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
 </dd>
 </dl>
 </dd>
-<dt class="field-even" id="arch.univariate.ARCH.compute_variance-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCH.compute_variance-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a></p>
-</dd>
 </dl>
 </dd></dl>
 
@@ -1384,7 +1367,7 @@ <h1 id="arch-univariate-arch-compute-variance">arch.univariate.ARCH.compute_vari
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1431,7 +1414,7 @@ <h1 id="arch-univariate-arch-compute-variance">arch.univariate.ARCH.compute_vari
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diff --git a/univariate/generated/generated/arch.univariate.ARCH.constraints.html b/univariate/generated/generated/arch.univariate.ARCH.constraints.html
index dace391b2a..1ba9dd40a7 100644
--- a/univariate/generated/generated/arch.univariate.ARCH.constraints.html
+++ b/univariate/generated/generated/arch.univariate.ARCH.constraints.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCH.constraints - arch 6.1.0</title>
+        <title>arch.univariate.ARCH.constraints - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
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+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -656,13 +656,6 @@
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@@ -1175,13 +1168,6 @@
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       <ul class="md-nav__list">
         
-          <li class="md-nav__item">
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@@ -1208,20 +1194,17 @@
                   
 
 
-<h1 id="arch-univariate-arch-constraints">arch.univariate.ARCH.constraints<a class="headerlink" href="#arch-univariate-arch-constraints" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-arch-constraints">arch.univariate.ARCH.constraints<a class="headerlink" href="#arch-univariate-arch-constraints" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ARCH.constraints">
-<span class="sig-prename descclassname"><span class="pre">ARCH.</span></span><span class="sig-name descname"><span class="pre">constraints</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.ARCH.constraints" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ARCH.</span></span><span class="sig-name descname"><span class="pre">constraints</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#arch.univariate.ARCH.constraints" title="Link to this definition">¶</a></dt>
 <dd><p>Construct parameter constraints arrays for parameter estimation</p>
 <dl class="field-list simple">
-<dt class="field-odd" id="arch.univariate.ARCH.constraints-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCH.constraints-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>]</p>
-</dd>
-<dt class="field-even" id="arch.univariate.ARCH.constraints-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCH.constraints-returns" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><ul class="simple">
-<li><p><strong>A</strong> (<em>ndarray</em>) – Parameters loadings in constraint. Shape is number of constraints
+<dt class="field-odd" id="arch.univariate.ARCH.constraints-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCH.constraints-returns" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p><strong>A</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – Parameters loadings in constraint. Shape is number of constraints
 by number of parameters</p></li>
-<li><p><strong>b</strong> (<em>ndarray</em>) – Constraint values, one for each constraint</p></li>
+<li><p><strong>b</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – Constraint values, one for each constraint</p></li>
 </ul>
 </p>
 </dd>
@@ -1298,7 +1281,7 @@ <h1 id="arch-univariate-arch-constraints">arch.univariate.ARCH.constraints<a cla
   
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@@ -1345,7 +1328,7 @@ <h1 id="arch-univariate-arch-constraints">arch.univariate.ARCH.constraints<a cla
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diff --git a/univariate/generated/generated/arch.univariate.ARCH.forecast.html b/univariate/generated/generated/arch.univariate.ARCH.forecast.html
index 23690edc48..1773849834 100644
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@@ -102,7 +102,7 @@
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+    arch 6.2.0
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@@ -1388,38 +1388,38 @@
                   
 
 
-<h1 id="arch-univariate-arch-forecast">arch.univariate.ARCH.forecast<a class="headerlink" href="#arch-univariate-arch-forecast" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-arch-forecast">arch.univariate.ARCH.forecast<a class="headerlink" href="#arch-univariate-arch-forecast" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ARCH.forecast">
-<span class="sig-prename descclassname"><span class="pre">ARCH.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.forecast.parameters" title="arch.univariate.ARCH.forecast.parameters (Python parameter) — Parameters required to forecast the volatility model"><span class="n"><span class="pre">parameters</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.forecast.resids" title="arch.univariate.ARCH.forecast.resids (Python parameter) — Residuals to use in the recursion"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.forecast.backcast" title="arch.univariate.ARCH.forecast.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.forecast.var_bounds" title="arch.univariate.ARCH.forecast.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.forecast.start" title="arch.univariate.ARCH.forecast.start (Python parameter) — Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."><span class="n"><span class="pre">start</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.forecast.horizon" title="arch.univariate.ARCH.forecast.horizon (Python parameter) — Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."><span class="n"><span class="pre">horizon</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.forecast.method" title="arch.univariate.ARCH.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic."><span class="n"><span class="pre">method</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.forecast.simulations" title="arch.univariate.ARCH.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.forecast.rng" title="arch.univariate.ARCH.forecast.rng (Python parameter) — Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."><span class="n"><span class="pre">rng</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.forecast.random_state" title="arch.univariate.ARCH.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.ARCH.forecast" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ARCH.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.forecast.parameters" title="arch.univariate.ARCH.forecast.parameters (Python parameter) — Parameters required to forecast the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.forecast.resids" title="arch.univariate.ARCH.forecast.resids (Python parameter) — Residuals to use in the recursion"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.forecast.backcast" title="arch.univariate.ARCH.forecast.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.forecast.var_bounds" title="arch.univariate.ARCH.forecast.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.forecast.start" title="arch.univariate.ARCH.forecast.start (Python parameter) — Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.forecast.horizon" title="arch.univariate.ARCH.forecast.horizon (Python parameter) — Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.forecast.method" title="arch.univariate.ARCH.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.forecast.simulations" title="arch.univariate.ARCH.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.forecast.rng" title="arch.univariate.ARCH.forecast.rng (Python parameter) — Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.forecast.random_state" title="arch.univariate.ARCH.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><span class="pre">VarianceForecast</span></span></span></span><a class="headerlink" href="#arch.univariate.ARCH.forecast" title="Link to this definition">¶</a></dt>
 <dd><p>Forecast volatility from the model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ARCH.forecast-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCH.forecast-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ARCH.forecast.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, Series}</span></span><a class="headerlink" href="#arch.univariate.ARCH.forecast.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast the volatility model</p>
+<dt id="arch.univariate.ARCH.forecast.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.ARCH.forecast.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast the volatility model</p>
 </dd>
-<dt id="arch.univariate.ARCH.forecast.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.ARCH.forecast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Residuals to use in the recursion</p>
+<dt id="arch.univariate.ARCH.forecast.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.ARCH.forecast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Residuals to use in the recursion</p>
 </dd>
-<dt id="arch.univariate.ARCH.forecast.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><a class="headerlink" href="#arch.univariate.ARCH.forecast.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing the recursion</p>
+<dt id="arch.univariate.ARCH.forecast.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><a class="headerlink" href="#arch.univariate.ARCH.forecast.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing the recursion</p>
 </dd>
-<dt id="arch.univariate.ARCH.forecast.var_bounds"><span class="sig-name descname">var_bounds</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray, 2-d</span></span><a class="headerlink" href="#arch.univariate.ARCH.forecast.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
+<dt id="arch.univariate.ARCH.forecast.var_bounds"><span class="n sig-name">var_bounds</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.ARCH.forecast.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
 </dd>
-<dt id="arch.univariate.ARCH.forecast.start"><span class="sig-name descname">start</span><span class="p"> : </span><span class="desctype"><span class="desctype">{None, int}</span></span><a class="headerlink" href="#arch.univariate.ARCH.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>Index of the first observation to use as the starting point for
+<dt id="arch.univariate.ARCH.forecast.start"><span class="n sig-name">start</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCH.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>Index of the first observation to use as the starting point for
 the forecast.  Default is len(resids).</p>
 </dd>
-<dt id="arch.univariate.ARCH.forecast.horizon"><span class="sig-name descname">horizon</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.ARCH.forecast.horizon" title="Permalink to this definition">¶</a></dt><dd><p>Forecast horizon.  Must be 1 or larger.  Forecasts are produced
+<dt id="arch.univariate.ARCH.forecast.horizon"><span class="n sig-name">horizon</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code><a class="headerlink" href="#arch.univariate.ARCH.forecast.horizon" title="Permalink to this definition">¶</a></dt><dd><p>Forecast horizon.  Must be 1 or larger.  Forecasts are produced
 for horizons in [1, horizon].</p>
 </dd>
-<dt id="arch.univariate.ARCH.forecast.method"><span class="sig-name descname">method</span><span class="p"> : </span><span class="desctype"><span class="desctype">{'analytic', 'simulation', 'bootstrap'}</span></span><a class="headerlink" href="#arch.univariate.ARCH.forecast.method" title="Permalink to this definition">¶</a></dt><dd><p>Method to use when producing the forecast. The default is analytic.</p>
+<dt id="arch.univariate.ARCH.forecast.method"><span class="n sig-name">method</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'analytic'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'simulation'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'bootstrap'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code><a class="headerlink" href="#arch.univariate.ARCH.forecast.method" title="Permalink to this definition">¶</a></dt><dd><p>Method to use when producing the forecast. The default is analytic.</p>
 </dd>
-<dt id="arch.univariate.ARCH.forecast.simulations"><span class="sig-name descname">simulations</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.ARCH.forecast.simulations" title="Permalink to this definition">¶</a></dt><dd><p>Number of simulations to run when computing the forecast using
+<dt id="arch.univariate.ARCH.forecast.simulations"><span class="n sig-name">simulations</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.univariate.ARCH.forecast.simulations" title="Permalink to this definition">¶</a></dt><dd><p>Number of simulations to run when computing the forecast using
 either simulation or bootstrap.</p>
 </dd>
-<dt id="arch.univariate.ARCH.forecast.rng"><span class="sig-name descname">rng</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable</span></span><a class="headerlink" href="#arch.univariate.ARCH.forecast.rng" title="Permalink to this definition">¶</a></dt><dd><p>Callable random number generator required if method is
+<dt id="arch.univariate.ARCH.forecast.rng"><span class="n sig-name">rng</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)">Callable</a><span class="p">[</span><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">,</span><span class="w"> </span><span class="p">...</span><span class="p">]</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCH.forecast.rng" title="Permalink to this definition">¶</a></dt><dd><p>Callable random number generator required if method is
 ‘simulation’. Must take a single shape input and return random
 samples numbers with that shape.</p>
 </dd>
-<dt id="arch.univariate.ARCH.forecast.random_state"><span class="sig-name descname">random_state</span><span class="p"> : </span><span class="desctype"><span class="desctype">RandomState, optional</span></span><a class="headerlink" href="#arch.univariate.ARCH.forecast.random_state" title="Permalink to this definition">¶</a></dt><dd><p>NumPy RandomState instance to use when method is ‘bootstrap’</p>
+<dt id="arch.univariate.ARCH.forecast.random_state"><span class="n sig-name">random_state</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)">RandomState</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCH.forecast.random_state" title="Permalink to this definition">¶</a></dt><dd><p>NumPy RandomState instance to use when method is ‘bootstrap’</p>
 </dd>
 </dl>
 </dd>
@@ -1428,7 +1428,7 @@ <h1 id="arch-univariate-arch-forecast">arch.univariate.ARCH.forecast<a class="he
 or bootstrap, the simulated paths.</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.ARCH.forecast-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCH.forecast-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>VarianceForecast</p>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">arch.univariate.volatility.VarianceForecast</span></code></p>
 </dd>
 <dt class="field-even" id="arch.univariate.ARCH.forecast-raises">Raises<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCH.forecast-raises" title="Permalink to this headline">¶</a></dt>
 <dd class="field-even"><ul class="simple">
@@ -1515,7 +1515,7 @@ <h1 id="arch-univariate-arch-forecast">arch.univariate.ARCH.forecast<a class="he
   
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diff --git a/univariate/generated/generated/arch.univariate.ARCH.name.html b/univariate/generated/generated/arch.univariate.ARCH.name.html
index 638fb10e1e..b6ee91ce67 100644
--- a/univariate/generated/generated/arch.univariate.ARCH.name.html
+++ b/univariate/generated/generated/arch.univariate.ARCH.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCH.name - arch 6.1.0</title>
+        <title>arch.univariate.ARCH.name - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch 6.1.0
+            arch 6.2.0
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+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1168,10 +1168,10 @@
                   
 
 
-<h1 id="arch-univariate-arch-name">arch.univariate.ARCH.name<a class="headerlink" href="#arch-univariate-arch-name" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-arch-name">arch.univariate.ARCH.name<a class="headerlink" href="#arch-univariate-arch-name" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ARCH.name">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCH.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.univariate.ARCH.name" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCH.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.univariate.ARCH.name" title="Link to this definition">¶</a></dt>
 <dd><p>The name of the volatility process</p>
 </dd></dl>
 
@@ -1242,7 +1242,7 @@ <h1 id="arch-univariate-arch-name">arch.univariate.ARCH.name<a class="headerlink
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1289,7 +1289,7 @@ <h1 id="arch-univariate-arch-name">arch.univariate.ARCH.name<a class="headerlink
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.ARCH.num_params.html b/univariate/generated/generated/arch.univariate.ARCH.num_params.html
index f43eb3f25d..3a4faa8ce0 100644
--- a/univariate/generated/generated/arch.univariate.ARCH.num_params.html
+++ b/univariate/generated/generated/arch.univariate.ARCH.num_params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCH.num_params - arch 6.1.0</title>
+        <title>arch.univariate.ARCH.num_params - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1168,10 +1168,10 @@
                   
 
 
-<h1 id="arch-univariate-arch-num-params">arch.univariate.ARCH.num_params<a class="headerlink" href="#arch-univariate-arch-num-params" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-arch-num-params">arch.univariate.ARCH.num_params<a class="headerlink" href="#arch-univariate-arch-num-params" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ARCH.num_params">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCH.</span></span><span class="sig-name descname"><span class="pre">num_params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.ARCH.num_params" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCH.</span></span><span class="sig-name descname"><span class="pre">num_params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.ARCH.num_params" title="Link to this definition">¶</a></dt>
 <dd><p>The number of parameters in the model</p>
 </dd></dl>
 
@@ -1242,7 +1242,7 @@ <h1 id="arch-univariate-arch-num-params">arch.univariate.ARCH.num_params<a class
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1289,7 +1289,7 @@ <h1 id="arch-univariate-arch-num-params">arch.univariate.ARCH.num_params<a class
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.ARCH.parameter_names.html b/univariate/generated/generated/arch.univariate.ARCH.parameter_names.html
index 8d2eec24c1..98eb5b7a60 100644
--- a/univariate/generated/generated/arch.univariate.ARCH.parameter_names.html
+++ b/univariate/generated/generated/arch.univariate.ARCH.parameter_names.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCH.parameter_names - arch 6.1.0</title>
+        <title>arch.univariate.ARCH.parameter_names - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1208,17 +1208,17 @@
                   
 
 
-<h1 id="arch-univariate-arch-parameter-names">arch.univariate.ARCH.parameter_names<a class="headerlink" href="#arch-univariate-arch-parameter-names" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-arch-parameter-names">arch.univariate.ARCH.parameter_names<a class="headerlink" href="#arch-univariate-arch-parameter-names" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ARCH.parameter_names">
-<span class="sig-prename descclassname"><span class="pre">ARCH.</span></span><span class="sig-name descname"><span class="pre">parameter_names</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.ARCH.parameter_names" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ARCH.</span></span><span class="sig-name descname"><span class="pre">parameter_names</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#arch.univariate.ARCH.parameter_names" title="Link to this definition">¶</a></dt>
 <dd><p>Names of model parameters</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ARCH.parameter_names-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCH.parameter_names-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p><strong>names</strong> – Variables names</p>
 </dd>
 <dt class="field-even" id="arch.univariate.ARCH.parameter_names-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCH.parameter_names-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>)</p>
+<dd class="field-even"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span> <span class="pre">(str)</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1290,7 +1290,7 @@ <h1 id="arch-univariate-arch-parameter-names">arch.univariate.ARCH.parameter_nam
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1337,7 +1337,7 @@ <h1 id="arch-univariate-arch-parameter-names">arch.univariate.ARCH.parameter_nam
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.ARCH.simulate.html b/univariate/generated/generated/arch.univariate.ARCH.simulate.html
index 42e9d8e87e..e158ec3a4e 100644
--- a/univariate/generated/generated/arch.univariate.ARCH.simulate.html
+++ b/univariate/generated/generated/arch.univariate.ARCH.simulate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCH.simulate - arch 6.1.0</title>
+        <title>arch.univariate.ARCH.simulate - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -747,13 +747,6 @@
       </ul>
     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.univariate.ARCH.simulate-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
           <li class="md-nav__item">
@@ -1269,13 +1262,6 @@
       </ul>
     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.univariate.ARCH.simulate-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
           <li class="md-nav__item">
@@ -1304,36 +1290,33 @@
                   
 
 
-<h1 id="arch-univariate-arch-simulate">arch.univariate.ARCH.simulate<a class="headerlink" href="#arch-univariate-arch-simulate" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-arch-simulate">arch.univariate.ARCH.simulate<a class="headerlink" href="#arch-univariate-arch-simulate" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.ARCH.simulate">
-<span class="sig-prename descclassname"><span class="pre">ARCH.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.simulate.parameters" title="arch.univariate.ARCH.simulate.parameters (Python parameter) — Parameters required to simulate the volatility model"><span class="n"><span class="pre">parameters</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.simulate.nobs" title="arch.univariate.ARCH.simulate.nobs (Python parameter) — Number of data points to simulate"><span class="n"><span class="pre">nobs</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.simulate.rng" title="arch.univariate.ARCH.simulate.rng (Python parameter) — Callable function that takes a single integer input and returns a vector of random numbers"><span class="n"><span class="pre">rng</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.simulate.burn" title="arch.univariate.ARCH.simulate.burn (Python parameter) — Number of additional observations to generate when initializing the simulation"><span class="n"><span class="pre">burn</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.simulate.initial_value" title="arch.univariate.ARCH.simulate.initial_value (Python parameter) — Scalar or array of initial values to use when initializing the simulation"><span class="n"><span class="pre">initial_value</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.ARCH.simulate" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ARCH.simulate">
+<span class="sig-prename descclassname"><span class="pre">ARCH.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.simulate.parameters" title="arch.univariate.ARCH.simulate.parameters (Python parameter) — Parameters required to simulate the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.simulate.nobs" title="arch.univariate.ARCH.simulate.nobs (Python parameter) — Number of data points to simulate"><span class="n"><span class="pre">nobs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.simulate.rng" title="arch.univariate.ARCH.simulate.rng (Python parameter) — Callable function that takes a single integer input and returns a vector of random numbers"><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.simulate.burn" title="arch.univariate.ARCH.simulate.burn (Python parameter) — Number of additional observations to generate when initializing the simulation"><span class="n"><span class="pre">burn</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.simulate.initial_value" title="arch.univariate.ARCH.simulate.initial_value (Python parameter) — Scalar or array of initial values to use when initializing the simulation"><span class="n"><span class="pre">initial_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#arch.univariate.ARCH.simulate" title="Link to this definition">¶</a></dt>
 <dd><p>Simulate data from the model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ARCH.simulate-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCH.simulate-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ARCH.simulate.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, Series}</span></span><a class="headerlink" href="#arch.univariate.ARCH.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to simulate the volatility model</p>
+<dt id="arch.univariate.ARCH.simulate.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.ARCH.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to simulate the volatility model</p>
 </dd>
-<dt id="arch.univariate.ARCH.simulate.nobs"><span class="sig-name descname">nobs</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.ARCH.simulate.nobs" title="Permalink to this definition">¶</a></dt><dd><p>Number of data points to simulate</p>
+<dt id="arch.univariate.ARCH.simulate.nobs"><span class="n sig-name">nobs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.ARCH.simulate.nobs" title="Permalink to this definition">¶</a></dt><dd><p>Number of data points to simulate</p>
 </dd>
-<dt id="arch.univariate.ARCH.simulate.rng"><span class="sig-name descname">rng</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable</span></span><a class="headerlink" href="#arch.univariate.ARCH.simulate.rng" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that takes a single integer input and returns
+<dt id="arch.univariate.ARCH.simulate.rng"><span class="n sig-name">rng</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)">Callable</a><span class="p">[</span><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">,</span><span class="w"> </span><span class="p">...</span><span class="p">]</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.univariate.ARCH.simulate.rng" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that takes a single integer input and returns
 a vector of random numbers</p>
 </dd>
-<dt id="arch.univariate.ARCH.simulate.burn"><span class="sig-name descname">burn</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ARCH.simulate.burn" title="Permalink to this definition">¶</a></dt><dd><p>Number of additional observations to generate when initializing
+<dt id="arch.univariate.ARCH.simulate.burn"><span class="n sig-name">burn</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code><a class="headerlink" href="#arch.univariate.ARCH.simulate.burn" title="Permalink to this definition">¶</a></dt><dd><p>Number of additional observations to generate when initializing
 the simulation</p>
 </dd>
-<dt id="arch.univariate.ARCH.simulate.initial_value"><span class="sig-name descname">initial_value</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}, optional</span></span><a class="headerlink" href="#arch.univariate.ARCH.simulate.initial_value" title="Permalink to this definition">¶</a></dt><dd><p>Scalar or array of initial values to use when initializing the
+<dt id="arch.univariate.ARCH.simulate.initial_value"><span class="n sig-name">initial_value</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCH.simulate.initial_value" title="Permalink to this definition">¶</a></dt><dd><p>Scalar or array of initial values to use when initializing the
 simulation</p>
 </dd>
 </dl>
 </dd>
-<dt class="field-even" id="arch.univariate.ARCH.simulate-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCH.simulate-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>]</p>
-</dd>
-<dt class="field-odd" id="arch.univariate.ARCH.simulate-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCH.simulate-returns" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><ul class="simple">
-<li><p><strong>resids</strong> (<em>ndarray</em>) – The simulated residuals</p></li>
-<li><p><strong>variance</strong> (<em>ndarray</em>) – The simulated variance</p></li>
+<dt class="field-even" id="arch.univariate.ARCH.simulate-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCH.simulate-returns" title="Permalink to this headline">¶</a></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><strong>resids</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – The simulated residuals</p></li>
+<li><p><strong>variance</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – The simulated variance</p></li>
 </ul>
 </p>
 </dd>
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diff --git a/univariate/generated/generated/arch.univariate.ARCH.start.html b/univariate/generated/generated/arch.univariate.ARCH.start.html
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@@ -102,7 +102,7 @@
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+    arch 6.2.0
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@@ -1168,10 +1168,10 @@
                   
 
 
-<h1 id="arch-univariate-arch-start">arch.univariate.ARCH.start<a class="headerlink" href="#arch-univariate-arch-start" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-arch-start">arch.univariate.ARCH.start<a class="headerlink" href="#arch-univariate-arch-start" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ARCH.start">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCH.</span></span><span class="sig-name descname"><span class="pre">start</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.ARCH.start" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCH.</span></span><span class="sig-name descname"><span class="pre">start</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.ARCH.start" title="Link to this definition">¶</a></dt>
 <dd><p>Index to use to start variance subarray selection</p>
 </dd></dl>
 
@@ -1242,7 +1242,7 @@ <h1 id="arch-univariate-arch-start">arch.univariate.ARCH.start<a class="headerli
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1289,7 +1289,7 @@ <h1 id="arch-univariate-arch-start">arch.univariate.ARCH.start<a class="headerli
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.ARCH.starting_values.html b/univariate/generated/generated/arch.univariate.ARCH.starting_values.html
index 769cdff2d4..15a7c08c0a 100644
--- a/univariate/generated/generated/arch.univariate.ARCH.starting_values.html
+++ b/univariate/generated/generated/arch.univariate.ARCH.starting_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCH.starting_values - arch 6.1.0</title>
+        <title>arch.univariate.ARCH.starting_values - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1248,15 +1248,15 @@
                   
 
 
-<h1 id="arch-univariate-arch-starting-values">arch.univariate.ARCH.starting_values<a class="headerlink" href="#arch-univariate-arch-starting-values" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-arch-starting-values">arch.univariate.ARCH.starting_values<a class="headerlink" href="#arch-univariate-arch-starting-values" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ARCH.starting_values">
-<span class="sig-prename descclassname"><span class="pre">ARCH.</span></span><span class="sig-name descname"><span class="pre">starting_values</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.starting_values.resids" title="arch.univariate.ARCH.starting_values.resids (Python parameter) — Array of (approximate) residuals to use when computing starting values"><span class="n"><span class="pre">resids</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#ARCH.starting_values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ARCH.starting_values" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ARCH.</span></span><span class="sig-name descname"><span class="pre">starting_values</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.starting_values.resids" title="arch.univariate.ARCH.starting_values.resids (Python parameter) — Array of (approximate) residuals to use when computing starting values"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#ARCH.starting_values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ARCH.starting_values" title="Link to this definition">¶</a></dt>
 <dd><p>Returns starting values for the ARCH model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ARCH.starting_values-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCH.starting_values-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ARCH.starting_values.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.ARCH.starting_values.resids" title="Permalink to this definition">¶</a></dt><dd><p>Array of (approximate) residuals to use when computing starting
+<dt id="arch.univariate.ARCH.starting_values.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.ARCH.starting_values.resids" title="Permalink to this definition">¶</a></dt><dd><p>Array of (approximate) residuals to use when computing starting
 values</p>
 </dd>
 </dl>
@@ -1265,7 +1265,7 @@ <h1 id="arch-univariate-arch-starting-values">arch.univariate.ARCH.starting_valu
 <dd class="field-even"><p><strong>sv</strong> – Array of starting values</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.ARCH.starting_values-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCH.starting_values-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1337,7 +1337,7 @@ <h1 id="arch-univariate-arch-starting-values">arch.univariate.ARCH.starting_valu
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1384,7 +1384,7 @@ <h1 id="arch-univariate-arch-starting-values">arch.univariate.ARCH.starting_valu
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.ARCH.stop.html b/univariate/generated/generated/arch.univariate.ARCH.stop.html
index a63e2eb7a3..17c4d9bf30 100644
--- a/univariate/generated/generated/arch.univariate.ARCH.stop.html
+++ b/univariate/generated/generated/arch.univariate.ARCH.stop.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCH.stop - arch 6.1.0</title>
+        <title>arch.univariate.ARCH.stop - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1168,10 +1168,10 @@
                   
 
 
-<h1 id="arch-univariate-arch-stop">arch.univariate.ARCH.stop<a class="headerlink" href="#arch-univariate-arch-stop" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-arch-stop">arch.univariate.ARCH.stop<a class="headerlink" href="#arch-univariate-arch-stop" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ARCH.stop">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCH.</span></span><span class="sig-name descname"><span class="pre">stop</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.ARCH.stop" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCH.</span></span><span class="sig-name descname"><span class="pre">stop</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.ARCH.stop" title="Link to this definition">¶</a></dt>
 <dd><p>Index to use to stop variance subarray selection</p>
 </dd></dl>
 
@@ -1242,7 +1242,7 @@ <h1 id="arch-univariate-arch-stop">arch.univariate.ARCH.stop<a class="headerlink
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1289,7 +1289,7 @@ <h1 id="arch-univariate-arch-stop">arch.univariate.ARCH.stop<a class="headerlink
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.ARCH.update.html b/univariate/generated/generated/arch.univariate.ARCH.update.html
index 54065f7b25..4b31bfbf3c 100644
--- a/univariate/generated/generated/arch.univariate.ARCH.update.html
+++ b/univariate/generated/generated/arch.univariate.ARCH.update.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCH.update - arch 6.1.0</title>
+        <title>arch.univariate.ARCH.update - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1318,28 +1318,28 @@
                   
 
 
-<h1 id="arch-univariate-arch-update">arch.univariate.ARCH.update<a class="headerlink" href="#arch-univariate-arch-update" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-arch-update">arch.univariate.ARCH.update<a class="headerlink" href="#arch-univariate-arch-update" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.ARCH.update">
-<span class="sig-prename descclassname"><span class="pre">ARCH.</span></span><span class="sig-name descname"><span class="pre">update</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.update.index" title="arch.univariate.ARCH.update.index (Python parameter) — The numerical index of the variance to compute"><span class="n"><span class="pre">index</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.update.parameters" title="arch.univariate.ARCH.update.parameters (Python parameter) — The variance model parameters"><span class="n"><span class="pre">parameters</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.update.resids" title="arch.univariate.ARCH.update.resids (Python parameter) — The residual array. Only uses resids[:index] when computing sigma2[index]"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.update.sigma2" title="arch.univariate.ARCH.update.sigma2 (Python parameter) — The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."><span class="n"><span class="pre">sigma2</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.update.backcast" title="arch.univariate.ARCH.update.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.update.var_bounds" title="arch.univariate.ARCH.update.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.ARCH.update" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ARCH.update">
+<span class="sig-prename descclassname"><span class="pre">ARCH.</span></span><span class="sig-name descname"><span class="pre">update</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.update.index" title="arch.univariate.ARCH.update.index (Python parameter) — The numerical index of the variance to compute"><span class="n"><span class="pre">index</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.update.parameters" title="arch.univariate.ARCH.update.parameters (Python parameter) — The variance model parameters"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.update.resids" title="arch.univariate.ARCH.update.resids (Python parameter) — The residual array. Only uses resids[:index] when computing sigma2[index]"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.update.sigma2" title="arch.univariate.ARCH.update.sigma2 (Python parameter) — The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."><span class="n"><span class="pre">sigma2</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.update.backcast" title="arch.univariate.ARCH.update.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.update.var_bounds" title="arch.univariate.ARCH.update.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span></span><a class="headerlink" href="#arch.univariate.ARCH.update" title="Link to this definition">¶</a></dt>
 <dd><p>Compute the variance for a single observation</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ARCH.update-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCH.update-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ARCH.update.index"><span class="sig-name descname">index</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.ARCH.update.index" title="Permalink to this definition">¶</a></dt><dd><p>The numerical index of the variance to compute</p>
+<dt id="arch.univariate.ARCH.update.index"><span class="n sig-name">index</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.ARCH.update.index" title="Permalink to this definition">¶</a></dt><dd><p>The numerical index of the variance to compute</p>
 </dd>
-<dt id="arch.univariate.ARCH.update.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.ARCH.update.parameters" title="Permalink to this definition">¶</a></dt><dd><p>The variance model parameters</p>
+<dt id="arch.univariate.ARCH.update.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.ARCH.update.parameters" title="Permalink to this definition">¶</a></dt><dd><p>The variance model parameters</p>
 </dd>
-<dt id="arch.univariate.ARCH.update.resids"><span class="n sig-name">resids</span><a class="headerlink" href="#arch.univariate.ARCH.update.resids" title="Permalink to this definition">¶</a></dt><dd><p>The residual array. Only uses <code class="docutils literal notranslate"><span class="pre">resids[:index]</span></code> when computing
+<dt id="arch.univariate.ARCH.update.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.ARCH.update.resids" title="Permalink to this definition">¶</a></dt><dd><p>The residual array. Only uses <code class="docutils literal notranslate"><span class="pre">resids[:index]</span></code> when computing
 <code class="docutils literal notranslate"><span class="pre">sigma2[index]</span></code></p>
 </dd>
-<dt id="arch.univariate.ARCH.update.sigma2"><span class="sig-name descname">sigma2</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.ARCH.update.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>The array containing the variances. Only uses <code class="docutils literal notranslate"><span class="pre">sigma2[:index]</span></code>
+<dt id="arch.univariate.ARCH.update.sigma2"><span class="n sig-name">sigma2</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.ARCH.update.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>The array containing the variances. Only uses <code class="docutils literal notranslate"><span class="pre">sigma2[:index]</span></code>
 when computing <code class="docutils literal notranslate"><span class="pre">sigma2[index]</span></code>. The computed value is stored
 in <code class="docutils literal notranslate"><span class="pre">sigma2[index]</span></code>.</p>
 </dd>
-<dt id="arch.univariate.ARCH.update.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}</span></span><a class="headerlink" href="#arch.univariate.ARCH.update.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing the recursion</p>
+<dt id="arch.univariate.ARCH.update.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.ARCH.update.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing the recursion</p>
 </dd>
-<dt id="arch.univariate.ARCH.update.var_bounds"><span class="sig-name descname">var_bounds</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.ARCH.update.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
+<dt id="arch.univariate.ARCH.update.var_bounds"><span class="n sig-name">var_bounds</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.ARCH.update.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
 </dd>
 </dl>
 </dd>
@@ -1347,7 +1347,7 @@ <h1 id="arch-univariate-arch-update">arch.univariate.ARCH.update<a class="header
 <dd class="field-even"><p>The variance computed for location <code class="docutils literal notranslate"><span class="pre">index</span></code></p>
 </dd>
 <dt class="field-odd" id="arch.univariate.ARCH.update-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCH.update-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1419,7 +1419,7 @@ <h1 id="arch-univariate-arch-update">arch.univariate.ARCH.update<a class="header
   
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     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
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diff --git a/univariate/generated/generated/arch.univariate.ARCH.updateable.html b/univariate/generated/generated/arch.univariate.ARCH.updateable.html
index edd6e72325..a8330836af 100644
--- a/univariate/generated/generated/arch.univariate.ARCH.updateable.html
+++ b/univariate/generated/generated/arch.univariate.ARCH.updateable.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCH.updateable - arch 6.1.0</title>
+        <title>arch.univariate.ARCH.updateable - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1168,10 +1168,10 @@
                   
 
 
-<h1 id="arch-univariate-arch-updateable">arch.univariate.ARCH.updateable<a class="headerlink" href="#arch-univariate-arch-updateable" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-arch-updateable">arch.univariate.ARCH.updateable<a class="headerlink" href="#arch-univariate-arch-updateable" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ARCH.updateable">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCH.</span></span><span class="sig-name descname"><span class="pre">updateable</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.univariate.ARCH.updateable" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCH.</span></span><span class="sig-name descname"><span class="pre">updateable</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.univariate.ARCH.updateable" title="Link to this definition">¶</a></dt>
 <dd><p>Flag indicating that the volatility process supports update</p>
 </dd></dl>
 
@@ -1242,7 +1242,7 @@ <h1 id="arch-univariate-arch-updateable">arch.univariate.ARCH.updateable<a class
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1289,7 +1289,7 @@ <h1 id="arch-univariate-arch-updateable">arch.univariate.ARCH.updateable<a class
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diff --git a/univariate/generated/generated/arch.univariate.ARCH.variance_bounds.html b/univariate/generated/generated/arch.univariate.ARCH.variance_bounds.html
index fa991d1386..e22a64b160 100644
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+++ b/univariate/generated/generated/arch.univariate.ARCH.variance_bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCH.variance_bounds - arch 6.1.0</title>
+        <title>arch.univariate.ARCH.variance_bounds - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1262,20 +1262,20 @@
                   
 
 
-<h1 id="arch-univariate-arch-variance-bounds">arch.univariate.ARCH.variance_bounds<a class="headerlink" href="#arch-univariate-arch-variance-bounds" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-arch-variance-bounds">arch.univariate.ARCH.variance_bounds<a class="headerlink" href="#arch-univariate-arch-variance-bounds" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ARCH.variance_bounds">
-<span class="sig-prename descclassname"><span class="pre">ARCH.</span></span><span class="sig-name descname"><span class="pre">variance_bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.variance_bounds.resids" title="arch.univariate.ARCH.variance_bounds.resids (Python parameter) — Approximate residuals to use to compute the lower and upper bounds on the conditional variance"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.variance_bounds.power" title="arch.univariate.ARCH.variance_bounds.power (Python parameter) — Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."><span class="n"><span class="pre">power</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">2.0</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.ARCH.variance_bounds" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ARCH.</span></span><span class="sig-name descname"><span class="pre">variance_bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.variance_bounds.resids" title="arch.univariate.ARCH.variance_bounds.resids (Python parameter) — Approximate residuals to use to compute the lower and upper bounds on the conditional variance"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.variance_bounds.power" title="arch.univariate.ARCH.variance_bounds.power (Python parameter) — Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."><span class="n"><span class="pre">power</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">2.0</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.univariate.ARCH.variance_bounds" title="Link to this definition">¶</a></dt>
 <dd><p>Construct loose bounds for conditional variances.</p>
 <p>These bounds are used in parameter estimation to ensure
 that the log-likelihood does not produce NaN values.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ARCH.variance_bounds-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCH.variance_bounds-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ARCH.variance_bounds.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.ARCH.variance_bounds.resids" title="Permalink to this definition">¶</a></dt><dd><p>Approximate residuals to use to compute the lower and upper bounds
+<dt id="arch.univariate.ARCH.variance_bounds.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.ARCH.variance_bounds.resids" title="Permalink to this definition">¶</a></dt><dd><p>Approximate residuals to use to compute the lower and upper bounds
 on the conditional variance</p>
 </dd>
-<dt id="arch.univariate.ARCH.variance_bounds.power"><span class="sig-name descname">power</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ARCH.variance_bounds.power" title="Permalink to this definition">¶</a></dt><dd><p>Power used in the model. 2.0, the default corresponds to standard
+<dt id="arch.univariate.ARCH.variance_bounds.power"><span class="n sig-name">power</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">2.0</span></code><a class="headerlink" href="#arch.univariate.ARCH.variance_bounds.power" title="Permalink to this definition">¶</a></dt><dd><p>Power used in the model. 2.0, the default corresponds to standard
 ARCH models that evolve in squares.</p>
 </dd>
 </dl>
@@ -1285,7 +1285,7 @@ <h1 id="arch-univariate-arch-variance-bounds">arch.univariate.ARCH.variance_boun
 number of elements as resids</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.ARCH.variance_bounds-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCH.variance_bounds-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1357,7 +1357,7 @@ <h1 id="arch-univariate-arch-variance-bounds">arch.univariate.ARCH.variance_boun
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1404,7 +1404,7 @@ <h1 id="arch-univariate-arch-variance-bounds">arch.univariate.ARCH.variance_boun
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.ARCH.volatility_updater.html b/univariate/generated/generated/arch.univariate.ARCH.volatility_updater.html
index 1c8be866cd..9d444a7dc0 100644
--- a/univariate/generated/generated/arch.univariate.ARCH.volatility_updater.html
+++ b/univariate/generated/generated/arch.univariate.ARCH.volatility_updater.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCH.volatility_updater - arch 6.1.0</title>
+        <title>arch.univariate.ARCH.volatility_updater - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1222,17 +1222,17 @@
                   
 
 
-<h1 id="arch-univariate-arch-volatility-updater">arch.univariate.ARCH.volatility_updater<a class="headerlink" href="#arch-univariate-arch-volatility-updater" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-arch-volatility-updater">arch.univariate.ARCH.volatility_updater<a class="headerlink" href="#arch-univariate-arch-volatility-updater" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ARCH.volatility_updater">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCH.</span></span><span class="sig-name descname"><span class="pre">volatility_updater</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.univariate.recursions_python.VolatilityUpdater.html#arch.univariate.recursions_python.VolatilityUpdater" title="arch.univariate.recursions_python.VolatilityUpdater (Python class) — Base class that all volatility updaters must inherit from."><span class="pre">VolatilityUpdater</span></a></span><a class="headerlink" href="#arch.univariate.ARCH.volatility_updater" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCH.</span></span><span class="sig-name descname"><span class="pre">volatility_updater</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><span class="pre">VolatilityUpdater</span></span><a class="headerlink" href="#arch.univariate.ARCH.volatility_updater" title="Link to this definition">¶</a></dt>
 <dd><p>Get the volatility updater associated with the volatility process</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ARCH.volatility_updater-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCH.volatility_updater-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The updater class</p>
 </dd>
 <dt class="field-even" id="arch.univariate.ARCH.volatility_updater-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCH.volatility_updater-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference internal" href="../arch.univariate.recursions_python.VolatilityUpdater.html#arch.univariate.recursions_python.VolatilityUpdater" title="arch.univariate.recursions_python.VolatilityUpdater (Python class) — Base class that all volatility updaters must inherit from.">VolatilityUpdater</a></p>
+<dd class="field-even"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">VolatilityUpdater</span></code></p>
 </dd>
 <dt class="field-odd" id="arch.univariate.ARCH.volatility_updater-raises">Raises<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCH.volatility_updater-raises" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#NotImplementedError" title="(in Python v3.11)"><strong>NotImplementedError</strong></a> – If the process is not updateable</p>
@@ -1307,7 +1307,7 @@ <h1 id="arch-univariate-arch-volatility-updater">arch.univariate.ARCH.volatility
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1354,7 +1354,7 @@ <h1 id="arch-univariate-arch-volatility-updater">arch.univariate.ARCH.volatility
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diff --git a/univariate/generated/generated/arch.univariate.ARCHInMean.bounds.html b/univariate/generated/generated/arch.univariate.ARCHInMean.bounds.html
index 65744f431f..0c55a4e1f9 100644
--- a/univariate/generated/generated/arch.univariate.ARCHInMean.bounds.html
+++ b/univariate/generated/generated/arch.univariate.ARCHInMean.bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCHInMean.bounds - arch 6.1.0</title>
+        <title>arch.univariate.ARCHInMean.bounds - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1188,17 +1188,17 @@
                   
 
 
-<h1 id="arch-univariate-archinmean-bounds">arch.univariate.ARCHInMean.bounds<a class="headerlink" href="#arch-univariate-archinmean-bounds" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-archinmean-bounds">arch.univariate.ARCHInMean.bounds<a class="headerlink" href="#arch-univariate-archinmean-bounds" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ARCHInMean.bounds">
-<span class="sig-prename descclassname"><span class="pre">ARCHInMean.</span></span><span class="sig-name descname"><span class="pre">bounds</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.ARCHInMean.bounds" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ARCHInMean.</span></span><span class="sig-name descname"><span class="pre">bounds</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.bounds" title="Link to this definition">¶</a></dt>
 <dd><p>Construct bounds for parameters to use in non-linear optimization</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ARCHInMean.bounds-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCHInMean.bounds-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p><strong>bounds</strong> – Bounds for parameters to use in estimation.</p>
 </dd>
 <dt class="field-even" id="arch.univariate.ARCHInMean.bounds-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCHInMean.bounds-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a> (2-tuple of <a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>)</p>
+<dd class="field-even"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span> <span class="pre">(2-tuple</span></code> of <code class="xref py py-class docutils literal notranslate"><span class="pre">float)</span></code></p>
 </dd>
 </dl>
 </dd></dl>
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diff --git a/univariate/generated/generated/arch.univariate.ARCHInMean.compute_param_cov.html b/univariate/generated/generated/arch.univariate.ARCHInMean.compute_param_cov.html
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@@ -1242,26 +1228,23 @@
                   
 
 
-<h1 id="arch-univariate-archinmean-compute-param-cov">arch.univariate.ARCHInMean.compute_param_cov<a class="headerlink" href="#arch-univariate-archinmean-compute-param-cov" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-archinmean-compute-param-cov">arch.univariate.ARCHInMean.compute_param_cov<a class="headerlink" href="#arch-univariate-archinmean-compute-param-cov" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.ARCHInMean.compute_param_cov">
-<span class="sig-prename descclassname"><span class="pre">ARCHInMean.</span></span><span class="sig-name descname"><span class="pre">compute_param_cov</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.compute_param_cov.params" title="arch.univariate.ARCHInMean.compute_param_cov.params (Python parameter) — Model parameters"><span class="n"><span class="pre">params</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.compute_param_cov.backcast" title="arch.univariate.ARCHInMean.compute_param_cov.backcast (Python parameter) — Value to use for pre-sample observations"><span class="n"><span class="pre">backcast</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.compute_param_cov.robust" title="arch.univariate.ARCHInMean.compute_param_cov.robust (Python parameter) — Flag indicating whether to use robust standard errors (True) or classic MLE (False)"><span class="n"><span class="pre">robust</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.ARCHInMean.compute_param_cov" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ARCHInMean.compute_param_cov">
+<span class="sig-prename descclassname"><span class="pre">ARCHInMean.</span></span><span class="sig-name descname"><span class="pre">compute_param_cov</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.compute_param_cov.params" title="arch.univariate.ARCHInMean.compute_param_cov.params (Python parameter) — Model parameters"><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.compute_param_cov.backcast" title="arch.univariate.ARCHInMean.compute_param_cov.backcast (Python parameter) — Value to use for pre-sample observations"><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.compute_param_cov.robust" title="arch.univariate.ARCHInMean.compute_param_cov.robust (Python parameter) — Flag indicating whether to use robust standard errors (True) or classic MLE (False)"><span class="n"><span class="pre">robust</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.compute_param_cov" title="Link to this definition">¶</a></dt>
 <dd><p>Computes parameter covariances using numerical derivatives.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ARCHInMean.compute_param_cov-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCHInMean.compute_param_cov-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ARCHInMean.compute_param_cov.params"><span class="sig-name descname">params</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.compute_param_cov.params" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
+<dt id="arch.univariate.ARCHInMean.compute_param_cov.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.compute_param_cov.params" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
 </dd>
-<dt id="arch.univariate.ARCHInMean.compute_param_cov.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.compute_param_cov.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use for pre-sample observations</p>
+<dt id="arch.univariate.ARCHInMean.compute_param_cov.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.compute_param_cov.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use for pre-sample observations</p>
 </dd>
-<dt id="arch.univariate.ARCHInMean.compute_param_cov.robust"><span class="sig-name descname">robust</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.compute_param_cov.robust" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to use robust standard errors (True) or
+<dt id="arch.univariate.ARCHInMean.compute_param_cov.robust"><span class="n sig-name">robust</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.compute_param_cov.robust" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to use robust standard errors (True) or
 classic MLE (False)</p>
 </dd>
 </dl>
 </dd>
-<dt class="field-even" id="arch.univariate.ARCHInMean.compute_param_cov-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCHInMean.compute_param_cov-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a></p>
-</dd>
 </dl>
 </dd></dl>
 
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-    7.0.1.
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+++ b/univariate/generated/generated/arch.univariate.ARCHInMean.constraints.html
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-    arch 6.1.0
+    arch 6.2.0
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@@ -628,13 +628,6 @@
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       <ul class="md-nav__list">
         
-          <li class="md-nav__item">
-  <a href="#arch.univariate.ARCHInMean.constraints-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
-</li>
-        
           <li class="md-nav__item">
   <a href="#arch.univariate.ARCHInMean.constraints-returns" class="md-nav__link">
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@@ -1155,13 +1148,6 @@
     <nav class="md-nav" aria-label="ARCHInMean.constraints">
       <ul class="md-nav__list">
         
-          <li class="md-nav__item">
-  <a href="#arch.univariate.ARCHInMean.constraints-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
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-  
-</li>
-        
           <li class="md-nav__item">
   <a href="#arch.univariate.ARCHInMean.constraints-returns" class="md-nav__link">
     <span class="md-ellipsis">Returns</span>
@@ -1188,19 +1174,16 @@
                   
 
 
-<h1 id="arch-univariate-archinmean-constraints">arch.univariate.ARCHInMean.constraints<a class="headerlink" href="#arch-univariate-archinmean-constraints" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-archinmean-constraints">arch.univariate.ARCHInMean.constraints<a class="headerlink" href="#arch-univariate-archinmean-constraints" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ARCHInMean.constraints">
-<span class="sig-prename descclassname"><span class="pre">ARCHInMean.</span></span><span class="sig-name descname"><span class="pre">constraints</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.ARCHInMean.constraints" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ARCHInMean.</span></span><span class="sig-name descname"><span class="pre">constraints</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.constraints" title="Link to this definition">¶</a></dt>
 <dd><p>Construct linear constraint arrays  for use in non-linear optimization</p>
 <dl class="field-list simple">
-<dt class="field-odd" id="arch.univariate.ARCHInMean.constraints-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCHInMean.constraints-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>]</p>
-</dd>
-<dt class="field-even" id="arch.univariate.ARCHInMean.constraints-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCHInMean.constraints-returns" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><ul class="simple">
-<li><p><strong>a</strong> (<em>ndarray</em>) – Number of constraints by number of parameters loading array</p></li>
-<li><p><strong>b</strong> (<em>ndarray</em>) – Number of constraints array of lower bounds</p></li>
+<dt class="field-odd" id="arch.univariate.ARCHInMean.constraints-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCHInMean.constraints-returns" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p><strong>a</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – Number of constraints by number of parameters loading array</p></li>
+<li><p><strong>b</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – Number of constraints array of lower bounds</p></li>
 </ul>
 </p>
 </dd>
@@ -1276,7 +1259,7 @@ <h1 id="arch-univariate-archinmean-constraints">arch.univariate.ARCHInMean.const
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1323,7 +1306,7 @@ <h1 id="arch-univariate-archinmean-constraints">arch.univariate.ARCHInMean.const
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diff --git a/univariate/generated/generated/arch.univariate.ARCHInMean.distribution.html b/univariate/generated/generated/arch.univariate.ARCHInMean.distribution.html
index 4886067da1..b078d41ef1 100644
--- a/univariate/generated/generated/arch.univariate.ARCHInMean.distribution.html
+++ b/univariate/generated/generated/arch.univariate.ARCHInMean.distribution.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCHInMean.distribution - arch 6.1.0</title>
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+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1148,10 +1148,10 @@
                   
 
 
-<h1 id="arch-univariate-archinmean-distribution">arch.univariate.ARCHInMean.distribution<a class="headerlink" href="#arch-univariate-archinmean-distribution" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-archinmean-distribution">arch.univariate.ARCHInMean.distribution<a class="headerlink" href="#arch-univariate-archinmean-distribution" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ARCHInMean.distribution">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHInMean.</span></span><span class="sig-name descname"><span class="pre">distribution</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.univariate.distribution.Distribution.html#arch.univariate.distribution.Distribution" title="arch.univariate.distribution.Distribution (Python class) — Template for subclassing only"><span class="pre">Distribution</span></a></span><a class="headerlink" href="#arch.univariate.ARCHInMean.distribution" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHInMean.</span></span><span class="sig-name descname"><span class="pre">distribution</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.univariate.distribution.Distribution.html#arch.univariate.distribution.Distribution" title="arch.univariate.distribution.Distribution (Python class) — Template for subclassing only"><span class="pre">Distribution</span></a></span><a class="headerlink" href="#arch.univariate.ARCHInMean.distribution" title="Link to this definition">¶</a></dt>
 <dd><p>Set or gets the error distribution</p>
 <p>Distributions must be a subclass of Distribution</p>
 </dd></dl>
@@ -1223,7 +1223,7 @@ <h1 id="arch-univariate-archinmean-distribution">arch.univariate.ARCHInMean.dist
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1270,7 +1270,7 @@ <h1 id="arch-univariate-archinmean-distribution">arch.univariate.ARCHInMean.dist
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diff --git a/univariate/generated/generated/arch.univariate.ARCHInMean.fit.html b/univariate/generated/generated/arch.univariate.ARCHInMean.fit.html
index d88b4ccbed..49decdffdc 100644
--- a/univariate/generated/generated/arch.univariate.ARCHInMean.fit.html
+++ b/univariate/generated/generated/arch.univariate.ARCHInMean.fit.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCHInMean.fit - arch 6.1.0</title>
+        <title>arch.univariate.ARCHInMean.fit - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1354,40 +1354,40 @@
                   
 
 
-<h1 id="arch-univariate-archinmean-fit">arch.univariate.ARCHInMean.fit<a class="headerlink" href="#arch-univariate-archinmean-fit" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-archinmean-fit">arch.univariate.ARCHInMean.fit<a class="headerlink" href="#arch-univariate-archinmean-fit" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ARCHInMean.fit">
-<span class="sig-prename descclassname"><span class="pre">ARCHInMean.</span></span><span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.fit.update_freq" title="arch.univariate.ARCHInMean.fit.update_freq (Python parameter) — Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."><span class="n"><span class="pre">update_freq</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.fit.disp" title="arch.univariate.ARCHInMean.fit.disp (Python parameter) — Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is &quot;off&quot; and True is &quot;final&quot;"><span class="n"><span class="pre">disp</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;final&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.fit.starting_values" title="arch.univariate.ARCHInMean.fit.starting_values (Python parameter) — Array of starting values to use.  If not provided, starting values are constructed by the model components."><span class="n"><span class="pre">starting_values</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.fit.cov_type" title="arch.univariate.ARCHInMean.fit.cov_type (Python parameter) — Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."><span class="n"><span class="pre">cov_type</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;robust&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.fit.show_warning" title="arch.univariate.ARCHInMean.fit.show_warning (Python parameter) — Flag indicating whether convergence warnings should be shown."><span class="n"><span class="pre">show_warning</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.fit.first_obs" title="arch.univariate.ARCHInMean.fit.first_obs (Python parameter) — First observation to use when estimating model"><span class="n"><span class="pre">first_obs</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.fit.last_obs" title="arch.univariate.ARCHInMean.fit.last_obs (Python parameter) — Last observation to use when estimating model"><span class="n"><span class="pre">last_obs</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.fit.tol" title="arch.univariate.ARCHInMean.fit.tol (Python parameter) — Tolerance for termination."><span class="n"><span class="pre">tol</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.fit.options" title="arch.univariate.ARCHInMean.fit.options (Python parameter) — Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."><span class="n"><span class="pre">options</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.fit.backcast" title="arch.univariate.ARCHInMean.fit.backcast (Python parameter) — Value to use as backcast. Should be measure \sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."><span class="n"><span class="pre">backcast</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.ARCHInMean.fit" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ARCHInMean.</span></span><span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.fit.update_freq" title="arch.univariate.ARCHInMean.fit.update_freq (Python parameter) — Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."><span class="n"><span class="pre">update_freq</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.fit.disp" title="arch.univariate.ARCHInMean.fit.disp (Python parameter) — Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is &quot;off&quot; and True is &quot;final&quot;"><span class="n"><span class="pre">disp</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'off'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'final'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;final&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.fit.starting_values" title="arch.univariate.ARCHInMean.fit.starting_values (Python parameter) — Array of starting values to use.  If not provided, starting values are constructed by the model components."><span class="n"><span class="pre">starting_values</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.fit.cov_type" title="arch.univariate.ARCHInMean.fit.cov_type (Python parameter) — Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."><span class="n"><span class="pre">cov_type</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'robust'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'classic'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;robust&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.fit.show_warning" title="arch.univariate.ARCHInMean.fit.show_warning (Python parameter) — Flag indicating whether convergence warnings should be shown."><span class="n"><span class="pre">show_warning</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.fit.first_obs" title="arch.univariate.ARCHInMean.fit.first_obs (Python parameter) — First observation to use when estimating model"><span class="n"><span class="pre">first_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.fit.last_obs" title="arch.univariate.ARCHInMean.fit.last_obs (Python parameter) — Last observation to use when estimating model"><span class="n"><span class="pre">last_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.fit.tol" title="arch.univariate.ARCHInMean.fit.tol (Python parameter) — Tolerance for termination."><span class="n"><span class="pre">tol</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.fit.options" title="arch.univariate.ARCHInMean.fit.options (Python parameter) — Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."><span class="n"><span class="pre">options</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.fit.backcast" title="arch.univariate.ARCHInMean.fit.backcast (Python parameter) — Value to use as backcast. Should be measure \sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelResult.html#arch.univariate.base.ARCHModelResult" title="arch.univariate.base.ARCHModelResult (Python class) — Results from estimation of an ARCHModel model"><span class="pre">ARCHModelResult</span></a></span></span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.fit" title="Link to this definition">¶</a></dt>
 <dd><p>Estimate model parameters</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ARCHInMean.fit-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCHInMean.fit-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ARCHInMean.fit.update_freq"><span class="sig-name descname">update_freq</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.fit.update_freq" title="Permalink to this definition">¶</a></dt><dd><p>Frequency of iteration updates.  Output is generated every
+<dt id="arch.univariate.ARCHInMean.fit.update_freq"><span class="n sig-name">update_freq</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.fit.update_freq" title="Permalink to this definition">¶</a></dt><dd><p>Frequency of iteration updates.  Output is generated every
 <cite>update_freq</cite> iterations. Set to 0 to disable iterative output.</p>
 </dd>
-<dt id="arch.univariate.ARCHInMean.fit.disp"><span class="sig-name descname">disp</span><span class="p"> : </span><span class="desctype"><span class="desctype">{bool, &quot;off&quot;, &quot;final&quot;}</span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.fit.disp" title="Permalink to this definition">¶</a></dt><dd><p>Either ‘final’ to print optimization result or ‘off’ to display
+<dt id="arch.univariate.ARCHInMean.fit.disp"><span class="n sig-name">disp</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'off'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'final'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;final&#39;</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.fit.disp" title="Permalink to this definition">¶</a></dt><dd><p>Either ‘final’ to print optimization result or ‘off’ to display
 nothing. If using a boolean, False is “off” and True is “final”</p>
 </dd>
-<dt id="arch.univariate.ARCHInMean.fit.starting_values"><span class="sig-name descname">starting_values</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray, optional</span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.fit.starting_values" title="Permalink to this definition">¶</a></dt><dd><p>Array of starting values to use.  If not provided, starting values
+<dt id="arch.univariate.ARCHInMean.fit.starting_values"><span class="n sig-name">starting_values</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.fit.starting_values" title="Permalink to this definition">¶</a></dt><dd><p>Array of starting values to use.  If not provided, starting values
 are constructed by the model components.</p>
 </dd>
-<dt id="arch.univariate.ARCHInMean.fit.cov_type"><span class="sig-name descname">cov_type</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.fit.cov_type" title="Permalink to this definition">¶</a></dt><dd><p>Estimation method of parameter covariance.  Supported options are
+<dt id="arch.univariate.ARCHInMean.fit.cov_type"><span class="n sig-name">cov_type</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'robust'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'classic'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;robust&#39;</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.fit.cov_type" title="Permalink to this definition">¶</a></dt><dd><p>Estimation method of parameter covariance.  Supported options are
 ‘robust’, which does not assume the Information Matrix Equality
 holds and ‘classic’ which does.  In the ARCH literature, ‘robust’
 corresponds to Bollerslev-Wooldridge covariance estimator.</p>
 </dd>
-<dt id="arch.univariate.ARCHInMean.fit.show_warning"><span class="sig-name descname">show_warning</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.fit.show_warning" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether convergence warnings should be shown.</p>
+<dt id="arch.univariate.ARCHInMean.fit.show_warning"><span class="n sig-name">show_warning</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.fit.show_warning" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether convergence warnings should be shown.</p>
 </dd>
-<dt id="arch.univariate.ARCHInMean.fit.first_obs"><span class="sig-name descname">first_obs</span><span class="p"> : </span><span class="desctype"><span class="desctype">{int, <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, datetime, Timestamp}</span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.fit.first_obs" title="Permalink to this definition">¶</a></dt><dd><p>First observation to use when estimating model</p>
+<dt id="arch.univariate.ARCHInMean.fit.first_obs"><span class="n sig-name">first_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.fit.first_obs" title="Permalink to this definition">¶</a></dt><dd><p>First observation to use when estimating model</p>
 </dd>
-<dt id="arch.univariate.ARCHInMean.fit.last_obs"><span class="sig-name descname">last_obs</span><span class="p"> : </span><span class="desctype"><span class="desctype">{int, <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, datetime, Timestamp}</span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.fit.last_obs" title="Permalink to this definition">¶</a></dt><dd><p>Last observation to use when estimating model</p>
+<dt id="arch.univariate.ARCHInMean.fit.last_obs"><span class="n sig-name">last_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.fit.last_obs" title="Permalink to this definition">¶</a></dt><dd><p>Last observation to use when estimating model</p>
 </dd>
-<dt id="arch.univariate.ARCHInMean.fit.tol"><span class="sig-name descname">tol</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.fit.tol" title="Permalink to this definition">¶</a></dt><dd><p>Tolerance for termination.</p>
+<dt id="arch.univariate.ARCHInMean.fit.tol"><span class="n sig-name">tol</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.fit.tol" title="Permalink to this definition">¶</a></dt><dd><p>Tolerance for termination.</p>
 </dd>
-<dt id="arch.univariate.ARCHInMean.fit.options"><span class="sig-name descname">options</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.fit.options" title="Permalink to this definition">¶</a></dt><dd><p>Options to pass to <cite>scipy.optimize.minimize</cite>.  Valid entries
+<dt id="arch.univariate.ARCHInMean.fit.options"><span class="n sig-name">options</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)">Any</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.fit.options" title="Permalink to this definition">¶</a></dt><dd><p>Options to pass to <cite>scipy.optimize.minimize</cite>.  Valid entries
 include ‘ftol’, ‘eps’, ‘disp’, and ‘maxiter’.</p>
 </dd>
-<dt id="arch.univariate.ARCHInMean.fit.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}, optional</span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.fit.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use as backcast. Should be measure <span class="math notranslate nohighlight">\(\sigma^2_0\)</span>
+<dt id="arch.univariate.ARCHInMean.fit.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.fit.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use as backcast. Should be measure <span class="math notranslate nohighlight">\(\sigma^2_0\)</span>
 since model-specific non-linear transformations are applied to
 value before computing the variance recursions.</p>
 </dd>
@@ -1397,7 +1397,7 @@ <h1 id="arch-univariate-archinmean-fit">arch.univariate.ARCHInMean.fit<a class="
 <dd class="field-even"><p><strong>results</strong> – Object containing model results</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.ARCHInMean.fit-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCHInMean.fit-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference internal" href="../arch.univariate.base.ARCHModelResult.html#arch.univariate.base.ARCHModelResult" title="arch.univariate.base.ARCHModelResult (Python class) — Results from estimation of an ARCHModel model">ARCHModelResult</a></p>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">ARCHModelResult</span></code></p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -1473,7 +1473,7 @@ <h1 id="arch-univariate-archinmean-fit">arch.univariate.ARCHInMean.fit<a class="
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1520,10 +1520,10 @@ <h1 id="arch-univariate-archinmean-fit">arch.univariate.ARCHInMean.fit<a class="
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+        <script id="MathJax-script" src="../../../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
     
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 </html>
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diff --git a/univariate/generated/generated/arch.univariate.ARCHInMean.fix.html b/univariate/generated/generated/arch.univariate.ARCHInMean.fix.html
index 1d7651a3f6..70e2520af8 100644
--- a/univariate/generated/generated/arch.univariate.ARCHInMean.fix.html
+++ b/univariate/generated/generated/arch.univariate.ARCHInMean.fix.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCHInMean.fix - arch 6.1.0</title>
+        <title>arch.univariate.ARCHInMean.fix - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1256,21 +1256,21 @@
                   
 
 
-<h1 id="arch-univariate-archinmean-fix">arch.univariate.ARCHInMean.fix<a class="headerlink" href="#arch-univariate-archinmean-fix" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-archinmean-fix">arch.univariate.ARCHInMean.fix<a class="headerlink" href="#arch-univariate-archinmean-fix" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.ARCHInMean.fix">
-<span class="sig-prename descclassname"><span class="pre">ARCHInMean.</span></span><span class="sig-name descname"><span class="pre">fix</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.fix.params" title="arch.univariate.ARCHInMean.fix.params (Python parameter) — User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."><span class="n"><span class="pre">params</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.fix.first_obs" title="arch.univariate.ARCHInMean.fix.first_obs (Python parameter) — First observation to use when fixing model"><span class="n"><span class="pre">first_obs</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.fix.last_obs" title="arch.univariate.ARCHInMean.fix.last_obs (Python parameter) — Last observation to use when fixing model"><span class="n"><span class="pre">last_obs</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.ARCHInMean.fix" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ARCHInMean.fix">
+<span class="sig-prename descclassname"><span class="pre">ARCHInMean.</span></span><span class="sig-name descname"><span class="pre">fix</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.fix.params" title="arch.univariate.ARCHInMean.fix.params (Python parameter) — User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.fix.first_obs" title="arch.univariate.ARCHInMean.fix.first_obs (Python parameter) — First observation to use when fixing model"><span class="n"><span class="pre">first_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.fix.last_obs" title="arch.univariate.ARCHInMean.fix.last_obs (Python parameter) — Last observation to use when fixing model"><span class="n"><span class="pre">last_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelFixedResult.html#arch.univariate.base.ARCHModelFixedResult" title="arch.univariate.base.ARCHModelFixedResult (Python class) — Results for fixed parameters for an ARCHModel model"><span class="pre">ARCHModelFixedResult</span></a></span></span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.fix" title="Link to this definition">¶</a></dt>
 <dd><p>Allows an ARCHModelFixedResult to be constructed from fixed parameters.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ARCHInMean.fix-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCHInMean.fix-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ARCHInMean.fix.params"><span class="sig-name descname">params</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, Series}</span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.fix.params" title="Permalink to this definition">¶</a></dt><dd><p>User specified parameters to use when generating the result. Must
+<dt id="arch.univariate.ARCHInMean.fix.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.fix.params" title="Permalink to this definition">¶</a></dt><dd><p>User specified parameters to use when generating the result. Must
 have the correct number of parameters for a given choice of mean
 model, volatility model and distribution.</p>
 </dd>
-<dt id="arch.univariate.ARCHInMean.fix.first_obs"><span class="sig-name descname">first_obs</span><span class="p"> : </span><span class="desctype"><span class="desctype">{int, <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, datetime, Timestamp}</span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.fix.first_obs" title="Permalink to this definition">¶</a></dt><dd><p>First observation to use when fixing model</p>
+<dt id="arch.univariate.ARCHInMean.fix.first_obs"><span class="n sig-name">first_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.fix.first_obs" title="Permalink to this definition">¶</a></dt><dd><p>First observation to use when fixing model</p>
 </dd>
-<dt id="arch.univariate.ARCHInMean.fix.last_obs"><span class="sig-name descname">last_obs</span><span class="p"> : </span><span class="desctype"><span class="desctype">{int, <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, datetime, Timestamp}</span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.fix.last_obs" title="Permalink to this definition">¶</a></dt><dd><p>Last observation to use when fixing model</p>
+<dt id="arch.univariate.ARCHInMean.fix.last_obs"><span class="n sig-name">last_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.fix.last_obs" title="Permalink to this definition">¶</a></dt><dd><p>Last observation to use when fixing model</p>
 </dd>
 </dl>
 </dd>
@@ -1278,7 +1278,7 @@ <h1 id="arch-univariate-archinmean-fix">arch.univariate.ARCHInMean.fix<a class="
 <dd class="field-even"><p><strong>results</strong> – Object containing model results</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.ARCHInMean.fix-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCHInMean.fix-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference internal" href="../arch.univariate.base.ARCHModelFixedResult.html#arch.univariate.base.ARCHModelFixedResult" title="arch.univariate.base.ARCHModelFixedResult (Python class) — Results for fixed parameters for an ARCHModel model">ARCHModelFixedResult</a></p>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">ARCHModelFixedResult</span></code></p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -1352,7 +1352,7 @@ <h1 id="arch-univariate-archinmean-fix">arch.univariate.ARCHInMean.fix<a class="
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1399,7 +1399,7 @@ <h1 id="arch-univariate-archinmean-fix">arch.univariate.ARCHInMean.fix<a class="
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diff --git a/univariate/generated/generated/arch.univariate.ARCHInMean.forecast.html b/univariate/generated/generated/arch.univariate.ARCHInMean.forecast.html
index 2800f7934b..30fb67ec3a 100644
--- a/univariate/generated/generated/arch.univariate.ARCHInMean.forecast.html
+++ b/univariate/generated/generated/arch.univariate.ARCHInMean.forecast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCHInMean.forecast - arch 6.1.0</title>
+        <title>arch.univariate.ARCHInMean.forecast - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1354,56 +1354,56 @@
                   
 
 
-<h1 id="arch-univariate-archinmean-forecast">arch.univariate.ARCHInMean.forecast<a class="headerlink" href="#arch-univariate-archinmean-forecast" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-archinmean-forecast">arch.univariate.ARCHInMean.forecast<a class="headerlink" href="#arch-univariate-archinmean-forecast" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ARCHInMean.forecast">
-<span class="sig-prename descclassname"><span class="pre">ARCHInMean.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.forecast.params" title="arch.univariate.ARCHInMean.forecast.params (Python parameter) — Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."><span class="n"><span class="pre">params</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.forecast.horizon" title="arch.univariate.ARCHInMean.forecast.horizon (Python parameter) — Number of steps to forecast"><span class="n"><span class="pre">horizon</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.forecast.start" title="arch.univariate.ARCHInMean.forecast.start (Python parameter) — An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."><span class="n"><span class="pre">start</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.forecast.align" title="arch.univariate.ARCHInMean.forecast.align (Python parameter) — Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."><span class="n"><span class="pre">align</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;origin&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.forecast.method" title="arch.univariate.ARCHInMean.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons &gt; 1."><span class="n"><span class="pre">method</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.forecast.simulations" title="arch.univariate.ARCHInMean.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.forecast.rng" title="arch.univariate.ARCHInMean.forecast.rng (Python parameter) — Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."><span class="n"><span class="pre">rng</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.forecast.random_state" title="arch.univariate.ARCHInMean.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.forecast.reindex" title="arch.univariate.ARCHInMean.forecast.reindex (Python parameter) — Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."><span class="n"><span class="pre">reindex</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.forecast.x" title="arch.univariate.ARCHInMean.forecast.x (Python parameter) — Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"><span class="n"><span class="pre">x</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/mean.html#ARCHInMean.forecast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ARCHInMean.forecast" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ARCHInMean.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.forecast.params" title="arch.univariate.ARCHInMean.forecast.params (Python parameter) — Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.forecast.horizon" title="arch.univariate.ARCHInMean.forecast.horizon (Python parameter) — Number of steps to forecast"><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.forecast.start" title="arch.univariate.ARCHInMean.forecast.start (Python parameter) — An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.forecast.align" title="arch.univariate.ARCHInMean.forecast.align (Python parameter) — Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."><span class="n"><span class="pre">align</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'origin'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'target'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;origin&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.forecast.method" title="arch.univariate.ARCHInMean.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons &gt; 1."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.forecast.simulations" title="arch.univariate.ARCHInMean.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.forecast.rng" title="arch.univariate.ARCHInMean.forecast.rng (Python parameter) — Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.forecast.random_state" title="arch.univariate.ARCHInMean.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.forecast.reindex" title="arch.univariate.ARCHInMean.forecast.reindex (Python parameter) — Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."><span class="n"><span class="pre">reindex</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.forecast.x" title="arch.univariate.ARCHInMean.forecast.x (Python parameter) — Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.11)"><span class="pre">Hashable</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelForecast.html#arch.univariate.base.ARCHModelForecast" title="arch.univariate.base.ARCHModelForecast (Python class) — Container for forecasts from an ARCH Model"><span class="pre">ARCHModelForecast</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/mean.html#ARCHInMean.forecast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ARCHInMean.forecast" title="Link to this definition">¶</a></dt>
 <dd><p>Construct forecasts from estimated model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ARCHInMean.forecast-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCHInMean.forecast-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ARCHInMean.forecast.params"><span class="sig-name descname">params</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, Series}</span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.forecast.params" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast. Must be identical in
+<dt id="arch.univariate.ARCHInMean.forecast.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.forecast.params" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast. Must be identical in
 shape to the parameters computed by fitting the model.</p>
 </dd>
-<dt id="arch.univariate.ARCHInMean.forecast.horizon"><span class="sig-name descname">horizon</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.forecast.horizon" title="Permalink to this definition">¶</a></dt><dd><p>Number of steps to forecast</p>
+<dt id="arch.univariate.ARCHInMean.forecast.horizon"><span class="n sig-name">horizon</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.forecast.horizon" title="Permalink to this definition">¶</a></dt><dd><p>Number of steps to forecast</p>
 </dd>
-<dt id="arch.univariate.ARCHInMean.forecast.start"><span class="sig-name descname">start</span><span class="p"> : </span><span class="desctype"><span class="desctype">{int, datetime, Timestamp, str}, optional</span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>An integer, datetime or str indicating the first observation to
+<dt id="arch.univariate.ARCHInMean.forecast.start"><span class="n sig-name">start</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>An integer, datetime or str indicating the first observation to
 produce the forecast for.  Datetimes can only be used with pandas
 inputs that have a datetime index. Strings must be convertible
 to a date time, such as in ‘1945-01-01’.</p>
 </dd>
-<dt id="arch.univariate.ARCHInMean.forecast.align"><span class="sig-name descname">align</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.forecast.align" title="Permalink to this definition">¶</a></dt><dd><p>Either ‘origin’ or ‘target’.  When set of ‘origin’, the t-th row
+<dt id="arch.univariate.ARCHInMean.forecast.align"><span class="n sig-name">align</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'origin'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'target'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;origin&#39;</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.forecast.align" title="Permalink to this definition">¶</a></dt><dd><p>Either ‘origin’ or ‘target’.  When set of ‘origin’, the t-th row
 of forecasts contains the forecasts for t+1, t+2, …, t+h. When
 set to ‘target’, the t-th row contains the 1-step ahead forecast
 from time t-1, the 2 step from time t-2, …, and the h-step from
 time t-h.  ‘target’ simplified computing forecast errors since the
 realization and h-step forecast are aligned.</p>
 </dd>
-<dt id="arch.univariate.ARCHInMean.forecast.method"><span class="sig-name descname">method</span><span class="p"> : </span><span class="desctype"><span class="desctype">{'analytic', 'simulation', 'bootstrap'}</span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.forecast.method" title="Permalink to this definition">¶</a></dt><dd><p>Method to use when producing the forecast. The default is analytic.
+<dt id="arch.univariate.ARCHInMean.forecast.method"><span class="n sig-name">method</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'analytic'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'simulation'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'bootstrap'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.forecast.method" title="Permalink to this definition">¶</a></dt><dd><p>Method to use when producing the forecast. The default is analytic.
 The method only affects the variance forecast generation.  Not all
 volatility models support all methods. In particular, volatility
 models that do not evolve in squares such as EGARCH or TARCH do not
 support the ‘analytic’ method for horizons &gt; 1.</p>
 </dd>
-<dt id="arch.univariate.ARCHInMean.forecast.simulations"><span class="sig-name descname">simulations</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.forecast.simulations" title="Permalink to this definition">¶</a></dt><dd><p>Number of simulations to run when computing the forecast using
+<dt id="arch.univariate.ARCHInMean.forecast.simulations"><span class="n sig-name">simulations</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.forecast.simulations" title="Permalink to this definition">¶</a></dt><dd><p>Number of simulations to run when computing the forecast using
 either simulation or bootstrap.</p>
 </dd>
-<dt id="arch.univariate.ARCHInMean.forecast.rng"><span class="sig-name descname">rng</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable, optional</span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.forecast.rng" title="Permalink to this definition">¶</a></dt><dd><p>Custom random number generator to use in simulation-based forecasts.
+<dt id="arch.univariate.ARCHInMean.forecast.rng"><span class="n sig-name">rng</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)">Callable</a><span class="p">[</span><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">,</span><span class="w"> </span><span class="p">...</span><span class="p">]</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.forecast.rng" title="Permalink to this definition">¶</a></dt><dd><p>Custom random number generator to use in simulation-based forecasts.
 Must produce random samples using the syntax <cite>rng(size)</cite> where size
 the 2-element tuple (simulations, horizon).</p>
 </dd>
-<dt id="arch.univariate.ARCHInMean.forecast.random_state"><span class="sig-name descname">random_state</span><span class="p"> : </span><span class="desctype"><span class="desctype">RandomState, optional</span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.forecast.random_state" title="Permalink to this definition">¶</a></dt><dd><p>NumPy RandomState instance to use when method is ‘bootstrap’</p>
+<dt id="arch.univariate.ARCHInMean.forecast.random_state"><span class="n sig-name">random_state</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)">RandomState</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.forecast.random_state" title="Permalink to this definition">¶</a></dt><dd><p>NumPy RandomState instance to use when method is ‘bootstrap’</p>
 </dd>
-<dt id="arch.univariate.ARCHInMean.forecast.reindex"><span class="sig-name descname">reindex</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.forecast.reindex" title="Permalink to this definition">¶</a></dt><dd><p><p>Whether to reindex the forecasts to have the same dimension as the series
+<dt id="arch.univariate.ARCHInMean.forecast.reindex"><span class="n sig-name">reindex</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.forecast.reindex" title="Permalink to this definition">¶</a></dt><dd><p><p>Whether to reindex the forecasts to have the same dimension as the series
 being forecast. Prior to 4.18 this was the default. As of 4.19 this is
 now optional. If not provided, a warning is raised about the future
 change in the default which will occur after September 2021.</p>
-<div class="versionadded">
-<p><span class="versionmodified added">New in version 4.19.</span></p>
+<div class="admonition versionadded">
+<p class="admonition-title"><span class="versionmodified added">New in version 4.19.</span></p>
 </div>
 </p>
 </dd>
-<dt id="arch.univariate.ARCHInMean.forecast.x"><span class="sig-name descname">x</span><span class="p"> : </span><span class="desctype"><span class="desctype">{dict[label, array_like], array_like}</span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.forecast.x" title="Permalink to this definition">¶</a></dt><dd><p><p>Values to use for exogenous regressors if any are included in the
+<dt id="arch.univariate.ARCHInMean.forecast.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.11)">Hashable</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.forecast.x" title="Permalink to this definition">¶</a></dt><dd><p><p>Values to use for exogenous regressors if any are included in the
 model. Three formats are accepted:</p>
 <ul class="simple">
 <li><p>2-d array-like: This format can be used when there is a single
@@ -1426,8 +1426,8 @@ <h1 id="arch-univariate-archinmean-forecast">arch.univariate.ARCHInMean.forecast
 <p>Due to the complexity required to accommodate all scenarios, please
 see the example notebook that demonstrates the valid formats for
 x.</p>
-<div class="versionadded">
-<p><span class="versionmodified added">New in version 4.19.</span></p>
+<div class="admonition versionadded">
+<p class="admonition-title"><span class="versionmodified added">New in version 4.19.</span></p>
 </div>
 </p>
 </dd>
@@ -1438,7 +1438,7 @@ <h1 id="arch-univariate-archinmean-forecast">arch.univariate.ARCHInMean.forecast
 <code class="docutils literal notranslate"><span class="pre">variance</span></code> and <code class="docutils literal notranslate"><span class="pre">residual_variance</span></code>.</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.ARCHInMean.forecast-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCHInMean.forecast-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference internal" href="../arch.univariate.base.ARCHModelForecast.html#arch.univariate.base.ARCHModelForecast" title="arch.univariate.base.ARCHModelForecast (Python class) — Container for forecasts from an ARCH Model">arch.univariate.base.ARCHModelForecast</a></p>
+<dd class="field-odd"><p><a class="reference internal" href="../arch.univariate.base.ARCHModelForecast.html#arch.univariate.base.ARCHModelForecast" title="arch.univariate.base.ARCHModelForecast (Python class) — Container for forecasts from an ARCH Model"><code class="xref py py-class docutils literal notranslate"><span class="pre">arch.univariate.base.ARCHModelForecast</span></code></a></p>
 </dd>
 </dl>
 <p class="example rubric">Examples</p>
@@ -1538,7 +1538,7 @@ <h1 id="arch-univariate-archinmean-forecast">arch.univariate.ARCHInMean.forecast
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1585,7 +1585,7 @@ <h1 id="arch-univariate-archinmean-forecast">arch.univariate.ARCHInMean.forecast
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.ARCHInMean.form.html b/univariate/generated/generated/arch.univariate.ARCHInMean.form.html
index 64889507bd..a240bd0aac 100644
--- a/univariate/generated/generated/arch.univariate.ARCHInMean.form.html
+++ b/univariate/generated/generated/arch.univariate.ARCHInMean.form.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCHInMean.form - arch 6.1.0</title>
+        <title>arch.univariate.ARCHInMean.form - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1148,10 +1148,10 @@
                   
 
 
-<h1 id="arch-univariate-archinmean-form">arch.univariate.ARCHInMean.form<a class="headerlink" href="#arch-univariate-archinmean-form" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-archinmean-form">arch.univariate.ARCHInMean.form<a class="headerlink" href="#arch-univariate-archinmean-form" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ARCHInMean.form">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHInMean.</span></span><span class="sig-name descname"><span class="pre">form</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'log'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'vol'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'var'</span></span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.form" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHInMean.</span></span><span class="sig-name descname"><span class="pre">form</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'log'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'vol'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'var'</span></span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.form" title="Link to this definition">¶</a></dt>
 <dd><p>The form of the conditional variance in the mean</p>
 </dd></dl>
 
@@ -1222,7 +1222,7 @@ <h1 id="arch-univariate-archinmean-form">arch.univariate.ARCHInMean.form<a class
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1269,7 +1269,7 @@ <h1 id="arch-univariate-archinmean-form">arch.univariate.ARCHInMean.form<a class
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.ARCHInMean.name.html b/univariate/generated/generated/arch.univariate.ARCHInMean.name.html
index 529b92b341..2072bf2c11 100644
--- a/univariate/generated/generated/arch.univariate.ARCHInMean.name.html
+++ b/univariate/generated/generated/arch.univariate.ARCHInMean.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCHInMean.name - arch 6.1.0</title>
+        <title>arch.univariate.ARCHInMean.name - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1148,10 +1148,10 @@
                   
 
 
-<h1 id="arch-univariate-archinmean-name">arch.univariate.ARCHInMean.name<a class="headerlink" href="#arch-univariate-archinmean-name" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-archinmean-name">arch.univariate.ARCHInMean.name<a class="headerlink" href="#arch-univariate-archinmean-name" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ARCHInMean.name">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHInMean.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.univariate.ARCHInMean.name" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHInMean.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.univariate.ARCHInMean.name" title="Link to this definition">¶</a></dt>
 <dd><p>The name of the model.</p>
 </dd></dl>
 
@@ -1222,7 +1222,7 @@ <h1 id="arch-univariate-archinmean-name">arch.univariate.ARCHInMean.name<a class
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1269,7 +1269,7 @@ <h1 id="arch-univariate-archinmean-name">arch.univariate.ARCHInMean.name<a class
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.ARCHInMean.num_params.html b/univariate/generated/generated/arch.univariate.ARCHInMean.num_params.html
index 694450870a..6d0b3059e0 100644
--- a/univariate/generated/generated/arch.univariate.ARCHInMean.num_params.html
+++ b/univariate/generated/generated/arch.univariate.ARCHInMean.num_params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCHInMean.num_params - arch 6.1.0</title>
+        <title>arch.univariate.ARCHInMean.num_params - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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@@ -1148,10 +1148,10 @@
                   
 
 
-<h1 id="arch-univariate-archinmean-num-params">arch.univariate.ARCHInMean.num_params<a class="headerlink" href="#arch-univariate-archinmean-num-params" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-archinmean-num-params">arch.univariate.ARCHInMean.num_params<a class="headerlink" href="#arch-univariate-archinmean-num-params" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ARCHInMean.num_params">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHInMean.</span></span><span class="sig-name descname"><span class="pre">num_params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.ARCHInMean.num_params" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHInMean.</span></span><span class="sig-name descname"><span class="pre">num_params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.ARCHInMean.num_params" title="Link to this definition">¶</a></dt>
 <dd><p>Returns the number of parameters</p>
 </dd></dl>
 
@@ -1222,7 +1222,7 @@ <h1 id="arch-univariate-archinmean-num-params">arch.univariate.ARCHInMean.num_pa
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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diff --git a/univariate/generated/generated/arch.univariate.ARCHInMean.parameter_names.html b/univariate/generated/generated/arch.univariate.ARCHInMean.parameter_names.html
index b6e928cb62..8260605540 100644
--- a/univariate/generated/generated/arch.univariate.ARCHInMean.parameter_names.html
+++ b/univariate/generated/generated/arch.univariate.ARCHInMean.parameter_names.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCHInMean.parameter_names - arch 6.1.0</title>
+        <title>arch.univariate.ARCHInMean.parameter_names - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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@@ -1188,17 +1188,17 @@
                   
 
 
-<h1 id="arch-univariate-archinmean-parameter-names">arch.univariate.ARCHInMean.parameter_names<a class="headerlink" href="#arch-univariate-archinmean-parameter-names" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-archinmean-parameter-names">arch.univariate.ARCHInMean.parameter_names<a class="headerlink" href="#arch-univariate-archinmean-parameter-names" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ARCHInMean.parameter_names">
-<span class="sig-prename descclassname"><span class="pre">ARCHInMean.</span></span><span class="sig-name descname"><span class="pre">parameter_names</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.ARCHInMean.parameter_names" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ARCHInMean.</span></span><span class="sig-name descname"><span class="pre">parameter_names</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.parameter_names" title="Link to this definition">¶</a></dt>
 <dd><p>List of parameters names</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ARCHInMean.parameter_names-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCHInMean.parameter_names-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p><strong>names</strong> – List of variable names for the mean model</p>
 </dd>
 <dt class="field-even" id="arch.univariate.ARCHInMean.parameter_names-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCHInMean.parameter_names-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>)</p>
+<dd class="field-even"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span> <span class="pre">(str)</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1270,7 +1270,7 @@ <h1 id="arch-univariate-archinmean-parameter-names">arch.univariate.ARCHInMean.p
   
     Created using
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-    7.0.1.
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@@ -1317,7 +1317,7 @@ <h1 id="arch-univariate-archinmean-parameter-names">arch.univariate.ARCHInMean.p
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diff --git a/univariate/generated/generated/arch.univariate.ARCHInMean.resids.html b/univariate/generated/generated/arch.univariate.ARCHInMean.resids.html
index 5682e86b99..04f3fe10c4 100644
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+++ b/univariate/generated/generated/arch.univariate.ARCHInMean.resids.html
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-        <title>arch.univariate.ARCHInMean.resids - arch 6.1.0</title>
+        <title>arch.univariate.ARCHInMean.resids - arch 6.2.0</title>
       
     
 
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1256,19 +1256,19 @@
                   
 
 
-<h1 id="arch-univariate-archinmean-resids">arch.univariate.ARCHInMean.resids<a class="headerlink" href="#arch-univariate-archinmean-resids" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-archinmean-resids">arch.univariate.ARCHInMean.resids<a class="headerlink" href="#arch-univariate-archinmean-resids" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.ARCHInMean.resids">
-<span class="sig-prename descclassname"><span class="pre">ARCHInMean.</span></span><span class="sig-name descname"><span class="pre">resids</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.resids.params" title="arch.univariate.ARCHInMean.resids.params (Python parameter) — Model parameters"><span class="n"><span class="pre">params</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.resids.y" title="arch.univariate.ARCHInMean.resids.y (Python parameter) — Alternative values to use when computing model residuals"><span class="n"><span class="pre">y</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.resids.regressors" title="arch.univariate.ARCHInMean.resids.regressors (Python parameter) — Alternative regressor values to use when computing model residuals"><span class="n"><span class="pre">regressors</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/mean.html#ARCHInMean.resids"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ARCHInMean.resids" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ARCHInMean.resids">
+<span class="sig-prename descclassname"><span class="pre">ARCHInMean.</span></span><span class="sig-name descname"><span class="pre">resids</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.resids.params" title="arch.univariate.ARCHInMean.resids.params (Python parameter) — Model parameters"><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.resids.y" title="arch.univariate.ARCHInMean.resids.y (Python parameter) — Alternative values to use when computing model residuals"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.resids.regressors" title="arch.univariate.ARCHInMean.resids.regressors (Python parameter) — Alternative regressor values to use when computing model residuals"><span class="n"><span class="pre">regressors</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/mean.html#ARCHInMean.resids"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ARCHInMean.resids" title="Link to this definition">¶</a></dt>
 <dd><p>Compute model residuals</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ARCHInMean.resids-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCHInMean.resids-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ARCHInMean.resids.params"><span class="sig-name descname">params</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.resids.params" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
+<dt id="arch.univariate.ARCHInMean.resids.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.resids.params" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
 </dd>
-<dt id="arch.univariate.ARCHInMean.resids.y"><span class="sig-name descname">y</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray, optional</span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.resids.y" title="Permalink to this definition">¶</a></dt><dd><p>Alternative values to use when computing model residuals</p>
+<dt id="arch.univariate.ARCHInMean.resids.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.resids.y" title="Permalink to this definition">¶</a></dt><dd><p>Alternative values to use when computing model residuals</p>
 </dd>
-<dt id="arch.univariate.ARCHInMean.resids.regressors"><span class="sig-name descname">regressors</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray, optional</span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.resids.regressors" title="Permalink to this definition">¶</a></dt><dd><p>Alternative regressor values to use when computing model residuals</p>
+<dt id="arch.univariate.ARCHInMean.resids.regressors"><span class="n sig-name">regressors</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.resids.regressors" title="Permalink to this definition">¶</a></dt><dd><p>Alternative regressor values to use when computing model residuals</p>
 </dd>
 </dl>
 </dd>
@@ -1276,7 +1276,7 @@ <h1 id="arch-univariate-archinmean-resids">arch.univariate.ARCHInMean.resids<a c
 <dd class="field-even"><p><strong>resids</strong> – Model residuals</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.ARCHInMean.resids-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCHInMean.resids-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1348,7 +1348,7 @@ <h1 id="arch-univariate-archinmean-resids">arch.univariate.ARCHInMean.resids<a c
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1395,7 +1395,7 @@ <h1 id="arch-univariate-archinmean-resids">arch.univariate.ARCHInMean.resids<a c
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diff --git a/univariate/generated/generated/arch.univariate.ARCHInMean.simulate.html b/univariate/generated/generated/arch.univariate.ARCHInMean.simulate.html
index b558c0379b..36f6314b54 100644
--- a/univariate/generated/generated/arch.univariate.ARCHInMean.simulate.html
+++ b/univariate/generated/generated/arch.univariate.ARCHInMean.simulate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCHInMean.simulate - arch 6.1.0</title>
+        <title>arch.univariate.ARCHInMean.simulate - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1298,33 +1298,33 @@
                   
 
 
-<h1 id="arch-univariate-archinmean-simulate">arch.univariate.ARCHInMean.simulate<a class="headerlink" href="#arch-univariate-archinmean-simulate" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-archinmean-simulate">arch.univariate.ARCHInMean.simulate<a class="headerlink" href="#arch-univariate-archinmean-simulate" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ARCHInMean.simulate">
-<span class="sig-prename descclassname"><span class="pre">ARCHInMean.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.simulate.params" title="arch.univariate.ARCHInMean.simulate.params (Python parameter) — Parameters to use when simulating the model.  Parameter order is [mean volatility distribution] where the parameters of the mean model are ordered [constant lag[0] lag[1] ... lag[p] ex[0] ... ex[k-1]] where lag[j] indicates the coefficient on the jth lag in the model and ex[j] is the coefficient on the jth exogenous variable."><span class="n"><span class="pre">params</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.simulate.nobs" title="arch.univariate.ARCHInMean.simulate.nobs (Python parameter) — Length of series to simulate"><span class="n"><span class="pre">nobs</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.simulate.burn" title="arch.univariate.ARCHInMean.simulate.burn (Python parameter) — Number of values to simulate to initialize the model and remove dependence on initial values."><span class="n"><span class="pre">burn</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.simulate.initial_value" title="arch.univariate.ARCHInMean.simulate.initial_value (Python parameter) — Either a scalar value or max(lags) array set of initial values to use when initializing the model.  If omitted, 0.0 is used."><span class="n"><span class="pre">initial_value</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.simulate.x" title="arch.univariate.ARCHInMean.simulate.x (Python parameter) — nobs + burn by k array of exogenous variables to include in the simulation."><span class="n"><span class="pre">x</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.simulate.initial_value_vol" title="arch.univariate.ARCHInMean.simulate.initial_value_vol (Python parameter) — An array or scalar to use when initializing the volatility process."><span class="n"><span class="pre">initial_value_vol</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.ARCHInMean.simulate" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ARCHInMean.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.simulate.params" title="arch.univariate.ARCHInMean.simulate.params (Python parameter) — Parameters to use when simulating the model.  Parameter order is [mean volatility distribution] where the parameters of the mean model are ordered [constant lag[0] lag[1] ... lag[p] ex[0] ... ex[k-1]] where lag[j] indicates the coefficient on the jth lag in the model and ex[j] is the coefficient on the jth exogenous variable."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.simulate.nobs" title="arch.univariate.ARCHInMean.simulate.nobs (Python parameter) — Length of series to simulate"><span class="n"><span class="pre">nobs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.simulate.burn" title="arch.univariate.ARCHInMean.simulate.burn (Python parameter) — Number of values to simulate to initialize the model and remove dependence on initial values."><span class="n"><span class="pre">burn</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.simulate.initial_value" title="arch.univariate.ARCHInMean.simulate.initial_value (Python parameter) — Either a scalar value or max(lags) array set of initial values to use when initializing the model.  If omitted, 0.0 is used."><span class="n"><span class="pre">initial_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.simulate.x" title="arch.univariate.ARCHInMean.simulate.x (Python parameter) — nobs + burn by k array of exogenous variables to include in the simulation."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.simulate.initial_value_vol" title="arch.univariate.ARCHInMean.simulate.initial_value_vol (Python parameter) — An array or scalar to use when initializing the volatility process."><span class="n"><span class="pre">initial_value_vol</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a></span></span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.simulate" title="Link to this definition">¶</a></dt>
 <dd><p>Simulates data from a linear regression, AR or HAR models</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ARCHInMean.simulate-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCHInMean.simulate-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ARCHInMean.simulate.params"><span class="sig-name descname">params</span><span class="p"> : </span><span class="desctype"><span class="desctype">array_like</span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.simulate.params" title="Permalink to this definition">¶</a></dt><dd><p>Parameters to use when simulating the model.  Parameter order is
+<dt id="arch.univariate.ARCHInMean.simulate.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.simulate.params" title="Permalink to this definition">¶</a></dt><dd><p>Parameters to use when simulating the model.  Parameter order is
 [mean volatility distribution] where the parameters of the mean
 model are ordered [constant lag[0] lag[1] … lag[p] ex[0] …
 ex[k-1]] where lag[j] indicates the coefficient on the jth lag in
 the model and ex[j] is the coefficient on the jth exogenous
 variable.</p>
 </dd>
-<dt id="arch.univariate.ARCHInMean.simulate.nobs"><span class="sig-name descname">nobs</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.simulate.nobs" title="Permalink to this definition">¶</a></dt><dd><p>Length of series to simulate</p>
+<dt id="arch.univariate.ARCHInMean.simulate.nobs"><span class="n sig-name">nobs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.simulate.nobs" title="Permalink to this definition">¶</a></dt><dd><p>Length of series to simulate</p>
 </dd>
-<dt id="arch.univariate.ARCHInMean.simulate.burn"><span class="sig-name descname">burn</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.simulate.burn" title="Permalink to this definition">¶</a></dt><dd><p>Number of values to simulate to initialize the model and remove
+<dt id="arch.univariate.ARCHInMean.simulate.burn"><span class="n sig-name">burn</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.simulate.burn" title="Permalink to this definition">¶</a></dt><dd><p>Number of values to simulate to initialize the model and remove
 dependence on initial values.</p>
 </dd>
-<dt id="arch.univariate.ARCHInMean.simulate.initial_value"><span class="sig-name descname">initial_value</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, float}, optional</span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.simulate.initial_value" title="Permalink to this definition">¶</a></dt><dd><p>Either a scalar value or <cite>max(lags)</cite> array set of initial values to
+<dt id="arch.univariate.ARCHInMean.simulate.initial_value"><span class="n sig-name">initial_value</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.simulate.initial_value" title="Permalink to this definition">¶</a></dt><dd><p>Either a scalar value or <cite>max(lags)</cite> array set of initial values to
 use when initializing the model.  If omitted, 0.0 is used.</p>
 </dd>
-<dt id="arch.univariate.ARCHInMean.simulate.x"><span class="sig-name descname">x</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, DataFrame}, optional</span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.simulate.x" title="Permalink to this definition">¶</a></dt><dd><p>nobs + burn by k array of exogenous variables to include in the
+<dt id="arch.univariate.ARCHInMean.simulate.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.simulate.x" title="Permalink to this definition">¶</a></dt><dd><p>nobs + burn by k array of exogenous variables to include in the
 simulation.</p>
 </dd>
-<dt id="arch.univariate.ARCHInMean.simulate.initial_value_vol"><span class="sig-name descname">initial_value_vol</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, float}, optional</span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.simulate.initial_value_vol" title="Permalink to this definition">¶</a></dt><dd><p>An array or scalar to use when initializing the volatility process.</p>
+<dt id="arch.univariate.ARCHInMean.simulate.initial_value_vol"><span class="n sig-name">initial_value_vol</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.simulate.initial_value_vol" title="Permalink to this definition">¶</a></dt><dd><p>An array or scalar to use when initializing the volatility process.</p>
 </dd>
 </dl>
 </dd>
@@ -1334,7 +1334,7 @@ <h1 id="arch-univariate-archinmean-simulate">arch.univariate.ARCHInMean.simulate
 containing the errors used in the simulation</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.ARCHInMean.simulate-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCHInMean.simulate-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>DataFrame</p>
+<dd class="field-odd"><p><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code></a></p>
 </dd>
 </dl>
 <p class="example rubric">Examples</p>
@@ -1427,7 +1427,7 @@ <h1 id="arch-univariate-archinmean-simulate">arch.univariate.ARCHInMean.simulate
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1474,7 +1474,7 @@ <h1 id="arch-univariate-archinmean-simulate">arch.univariate.ARCHInMean.simulate
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.ARCHInMean.starting_values.html b/univariate/generated/generated/arch.univariate.ARCHInMean.starting_values.html
index e3bd4eaf34..53f06662c7 100644
--- a/univariate/generated/generated/arch.univariate.ARCHInMean.starting_values.html
+++ b/univariate/generated/generated/arch.univariate.ARCHInMean.starting_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCHInMean.starting_values - arch 6.1.0</title>
+        <title>arch.univariate.ARCHInMean.starting_values - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1188,10 +1188,10 @@
                   
 
 
-<h1 id="arch-univariate-archinmean-starting-values">arch.univariate.ARCHInMean.starting_values<a class="headerlink" href="#arch-univariate-archinmean-starting-values" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-archinmean-starting-values">arch.univariate.ARCHInMean.starting_values<a class="headerlink" href="#arch-univariate-archinmean-starting-values" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ARCHInMean.starting_values">
-<span class="sig-prename descclassname"><span class="pre">ARCHInMean.</span></span><span class="sig-name descname"><span class="pre">starting_values</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/mean.html#ARCHInMean.starting_values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ARCHInMean.starting_values" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ARCHInMean.</span></span><span class="sig-name descname"><span class="pre">starting_values</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/mean.html#ARCHInMean.starting_values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ARCHInMean.starting_values" title="Link to this definition">¶</a></dt>
 <dd><p>Returns starting values for the mean model, often the same as the
 values returned from fit</p>
 <dl class="field-list simple">
@@ -1199,7 +1199,7 @@ <h1 id="arch-univariate-archinmean-starting-values">arch.univariate.ARCHInMean.s
 <dd class="field-odd"><p><strong>sv</strong> – Starting values</p>
 </dd>
 <dt class="field-even" id="arch.univariate.ARCHInMean.starting_values-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCHInMean.starting_values-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p>ndarray</p>
+<dd class="field-even"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1271,7 +1271,7 @@ <h1 id="arch-univariate-archinmean-starting-values">arch.univariate.ARCHInMean.s
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1318,7 +1318,7 @@ <h1 id="arch-univariate-archinmean-starting-values">arch.univariate.ARCHInMean.s
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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diff --git a/univariate/generated/generated/arch.univariate.ARCHInMean.volatility.html b/univariate/generated/generated/arch.univariate.ARCHInMean.volatility.html
index 2c3fa91276..48c6842870 100644
--- a/univariate/generated/generated/arch.univariate.ARCHInMean.volatility.html
+++ b/univariate/generated/generated/arch.univariate.ARCHInMean.volatility.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCHInMean.volatility - arch 6.1.0</title>
+        <title>arch.univariate.ARCHInMean.volatility - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1148,10 +1148,10 @@
                   
 
 
-<h1 id="arch-univariate-archinmean-volatility">arch.univariate.ARCHInMean.volatility<a class="headerlink" href="#arch-univariate-archinmean-volatility" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-archinmean-volatility">arch.univariate.ARCHInMean.volatility<a class="headerlink" href="#arch-univariate-archinmean-volatility" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ARCHInMean.volatility">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHInMean.</span></span><span class="sig-name descname"><span class="pre">volatility</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.univariate.volatility.VolatilityProcess.html#arch.univariate.volatility.VolatilityProcess" title="arch.univariate.volatility.VolatilityProcess (Python class) — Abstract base class for ARCH models.  Allows the conditional mean model to be specified separately from the conditional variance, even though parameters are estimated jointly."><span class="pre">VolatilityProcess</span></a></span><a class="headerlink" href="#arch.univariate.ARCHInMean.volatility" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHInMean.</span></span><span class="sig-name descname"><span class="pre">volatility</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.univariate.volatility.VolatilityProcess.html#arch.univariate.volatility.VolatilityProcess" title="arch.univariate.volatility.VolatilityProcess (Python class) — Abstract base class for ARCH models.  Allows the conditional mean model to be specified separately from the conditional variance, even though parameters are estimated jointly."><span class="pre">VolatilityProcess</span></a></span><a class="headerlink" href="#arch.univariate.ARCHInMean.volatility" title="Link to this definition">¶</a></dt>
 <dd><p>Set or gets the volatility process</p>
 <p>Volatility processes must be a subclass of VolatilityProcess</p>
 </dd></dl>
@@ -1223,7 +1223,7 @@ <h1 id="arch-univariate-archinmean-volatility">arch.univariate.ARCHInMean.volati
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1270,7 +1270,7 @@ <h1 id="arch-univariate-archinmean-volatility">arch.univariate.ARCHInMean.volati
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diff --git a/univariate/generated/generated/arch.univariate.ARCHInMean.x.html b/univariate/generated/generated/arch.univariate.ARCHInMean.x.html
index be6f36ab83..8f58d51034 100644
--- a/univariate/generated/generated/arch.univariate.ARCHInMean.x.html
+++ b/univariate/generated/generated/arch.univariate.ARCHInMean.x.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCHInMean.x - arch 6.1.0</title>
+        <title>arch.univariate.ARCHInMean.x - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1148,10 +1148,10 @@
                   
 
 
-<h1 id="arch-univariate-archinmean-x">arch.univariate.ARCHInMean.x<a class="headerlink" href="#arch-univariate-archinmean-x" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-archinmean-x">arch.univariate.ARCHInMean.x<a class="headerlink" href="#arch-univariate-archinmean-x" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ARCHInMean.x">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHInMean.</span></span><span class="sig-name descname"><span class="pre">x</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.0.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.ARCHInMean.x" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHInMean.</span></span><span class="sig-name descname"><span class="pre">x</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.ARCHInMean.x" title="Link to this definition">¶</a></dt>
 <dd><p>Gets the value of the exogenous regressors in the model</p>
 </dd></dl>
 
@@ -1222,7 +1222,7 @@ <h1 id="arch-univariate-archinmean-x">arch.univariate.ARCHInMean.x<a class="head
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1269,7 +1269,7 @@ <h1 id="arch-univariate-archinmean-x">arch.univariate.ARCHInMean.x<a class="head
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diff --git a/univariate/generated/generated/arch.univariate.ARCHInMean.y.html b/univariate/generated/generated/arch.univariate.ARCHInMean.y.html
index 3425c8aac5..8a3eec0e74 100644
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+++ b/univariate/generated/generated/arch.univariate.ARCHInMean.y.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCHInMean.y - arch 6.1.0</title>
+        <title>arch.univariate.ARCHInMean.y - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1148,10 +1148,10 @@
                   
 
 
-<h1 id="arch-univariate-archinmean-y">arch.univariate.ARCHInMean.y<a class="headerlink" href="#arch-univariate-archinmean-y" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-archinmean-y">arch.univariate.ARCHInMean.y<a class="headerlink" href="#arch-univariate-archinmean-y" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ARCHInMean.y">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHInMean.</span></span><span class="sig-name descname"><span class="pre">y</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.0.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.0.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.ARCHInMean.y" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHInMean.</span></span><span class="sig-name descname"><span class="pre">y</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.ARCHInMean.y" title="Link to this definition">¶</a></dt>
 <dd><p>Returns the dependent variable</p>
 </dd></dl>
 
@@ -1222,7 +1222,7 @@ <h1 id="arch-univariate-archinmean-y">arch.univariate.ARCHInMean.y<a class="head
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
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@@ -1269,7 +1269,7 @@ <h1 id="arch-univariate-archinmean-y">arch.univariate.ARCHInMean.y<a class="head
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diff --git a/univariate/generated/generated/arch.univariate.ARX.bounds.html b/univariate/generated/generated/arch.univariate.ARX.bounds.html
index a88290ba89..5219e6bfb0 100644
--- a/univariate/generated/generated/arch.univariate.ARX.bounds.html
+++ b/univariate/generated/generated/arch.univariate.ARX.bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARX.bounds - arch 6.1.0</title>
+        <title>arch.univariate.ARX.bounds - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1133,17 +1133,17 @@
                   
 
 
-<h1 id="arch-univariate-arx-bounds">arch.univariate.ARX.bounds<a class="headerlink" href="#arch-univariate-arx-bounds" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-arx-bounds">arch.univariate.ARX.bounds<a class="headerlink" href="#arch-univariate-arx-bounds" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ARX.bounds">
-<span class="sig-prename descclassname"><span class="pre">ARX.</span></span><span class="sig-name descname"><span class="pre">bounds</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.ARX.bounds" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ARX.</span></span><span class="sig-name descname"><span class="pre">bounds</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#arch.univariate.ARX.bounds" title="Link to this definition">¶</a></dt>
 <dd><p>Construct bounds for parameters to use in non-linear optimization</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ARX.bounds-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARX.bounds-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p><strong>bounds</strong> – Bounds for parameters to use in estimation.</p>
 </dd>
 <dt class="field-even" id="arch.univariate.ARX.bounds-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARX.bounds-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a> (2-tuple of <a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>)</p>
+<dd class="field-even"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span> <span class="pre">(2-tuple</span></code> of <code class="xref py py-class docutils literal notranslate"><span class="pre">float)</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1215,7 +1215,7 @@ <h1 id="arch-univariate-arx-bounds">arch.univariate.ARX.bounds<a class="headerli
   
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diff --git a/univariate/generated/generated/arch.univariate.ARX.compute_param_cov.html b/univariate/generated/generated/arch.univariate.ARX.compute_param_cov.html
index b7f3d4c35a..a23790558b 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -1187,26 +1173,23 @@
                   
 
 
-<h1 id="arch-univariate-arx-compute-param-cov">arch.univariate.ARX.compute_param_cov<a class="headerlink" href="#arch-univariate-arx-compute-param-cov" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-arx-compute-param-cov">arch.univariate.ARX.compute_param_cov<a class="headerlink" href="#arch-univariate-arx-compute-param-cov" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.ARX.compute_param_cov">
-<span class="sig-prename descclassname"><span class="pre">ARX.</span></span><span class="sig-name descname"><span class="pre">compute_param_cov</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.compute_param_cov.params" title="arch.univariate.ARX.compute_param_cov.params (Python parameter) — Model parameters"><span class="n"><span class="pre">params</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.compute_param_cov.backcast" title="arch.univariate.ARX.compute_param_cov.backcast (Python parameter) — Value to use for pre-sample observations"><span class="n"><span class="pre">backcast</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.compute_param_cov.robust" title="arch.univariate.ARX.compute_param_cov.robust (Python parameter) — Flag indicating whether to use robust standard errors (True) or classic MLE (False)"><span class="n"><span class="pre">robust</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.ARX.compute_param_cov" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ARX.compute_param_cov">
+<span class="sig-prename descclassname"><span class="pre">ARX.</span></span><span class="sig-name descname"><span class="pre">compute_param_cov</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.compute_param_cov.params" title="arch.univariate.ARX.compute_param_cov.params (Python parameter) — Model parameters"><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.compute_param_cov.backcast" title="arch.univariate.ARX.compute_param_cov.backcast (Python parameter) — Value to use for pre-sample observations"><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.compute_param_cov.robust" title="arch.univariate.ARX.compute_param_cov.robust (Python parameter) — Flag indicating whether to use robust standard errors (True) or classic MLE (False)"><span class="n"><span class="pre">robust</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.univariate.ARX.compute_param_cov" title="Link to this definition">¶</a></dt>
 <dd><p>Computes parameter covariances using numerical derivatives.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ARX.compute_param_cov-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARX.compute_param_cov-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ARX.compute_param_cov.params"><span class="sig-name descname">params</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.ARX.compute_param_cov.params" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
+<dt id="arch.univariate.ARX.compute_param_cov.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.ARX.compute_param_cov.params" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
 </dd>
-<dt id="arch.univariate.ARX.compute_param_cov.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><a class="headerlink" href="#arch.univariate.ARX.compute_param_cov.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use for pre-sample observations</p>
+<dt id="arch.univariate.ARX.compute_param_cov.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARX.compute_param_cov.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use for pre-sample observations</p>
 </dd>
-<dt id="arch.univariate.ARX.compute_param_cov.robust"><span class="sig-name descname">robust</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ARX.compute_param_cov.robust" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to use robust standard errors (True) or
+<dt id="arch.univariate.ARX.compute_param_cov.robust"><span class="n sig-name">robust</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.univariate.ARX.compute_param_cov.robust" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to use robust standard errors (True) or
 classic MLE (False)</p>
 </dd>
 </dl>
 </dd>
-<dt class="field-even" id="arch.univariate.ARX.compute_param_cov-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARX.compute_param_cov-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a></p>
-</dd>
 </dl>
 </dd></dl>
 
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diff --git a/univariate/generated/generated/arch.univariate.ARX.constraints.html b/univariate/generated/generated/arch.univariate.ARX.constraints.html
index 297831eac0..9b322aa5bb 100644
--- a/univariate/generated/generated/arch.univariate.ARX.constraints.html
+++ b/univariate/generated/generated/arch.univariate.ARX.constraints.html
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@@ -92,7 +92,7 @@
 
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@@ -102,7 +102,7 @@
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+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
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-    arch 6.1.0
+    arch 6.2.0
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@@ -551,13 +551,6 @@
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@@ -1133,19 +1119,16 @@
                   
 
 
-<h1 id="arch-univariate-arx-constraints">arch.univariate.ARX.constraints<a class="headerlink" href="#arch-univariate-arx-constraints" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-arx-constraints">arch.univariate.ARX.constraints<a class="headerlink" href="#arch-univariate-arx-constraints" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ARX.constraints">
-<span class="sig-prename descclassname"><span class="pre">ARX.</span></span><span class="sig-name descname"><span class="pre">constraints</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.ARX.constraints" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ARX.</span></span><span class="sig-name descname"><span class="pre">constraints</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#arch.univariate.ARX.constraints" title="Link to this definition">¶</a></dt>
 <dd><p>Construct linear constraint arrays  for use in non-linear optimization</p>
 <dl class="field-list simple">
-<dt class="field-odd" id="arch.univariate.ARX.constraints-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARX.constraints-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>]</p>
-</dd>
-<dt class="field-even" id="arch.univariate.ARX.constraints-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARX.constraints-returns" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><ul class="simple">
-<li><p><strong>a</strong> (<em>ndarray</em>) – Number of constraints by number of parameters loading array</p></li>
-<li><p><strong>b</strong> (<em>ndarray</em>) – Number of constraints array of lower bounds</p></li>
+<dt class="field-odd" id="arch.univariate.ARX.constraints-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARX.constraints-returns" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p><strong>a</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – Number of constraints by number of parameters loading array</p></li>
+<li><p><strong>b</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – Number of constraints array of lower bounds</p></li>
 </ul>
 </p>
 </dd>
@@ -1221,7 +1204,7 @@ <h1 id="arch-univariate-arx-constraints">arch.univariate.ARX.constraints<a class
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
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diff --git a/univariate/generated/generated/arch.univariate.ARX.distribution.html b/univariate/generated/generated/arch.univariate.ARX.distribution.html
index a12f9919d7..84e2c63092 100644
--- a/univariate/generated/generated/arch.univariate.ARX.distribution.html
+++ b/univariate/generated/generated/arch.univariate.ARX.distribution.html
@@ -15,7 +15,7 @@
     
   
       
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-            arch 6.1.0
+            arch 6.2.0
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1093,10 +1093,10 @@
                   
 
 
-<h1 id="arch-univariate-arx-distribution">arch.univariate.ARX.distribution<a class="headerlink" href="#arch-univariate-arx-distribution" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-arx-distribution">arch.univariate.ARX.distribution<a class="headerlink" href="#arch-univariate-arx-distribution" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ARX.distribution">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARX.</span></span><span class="sig-name descname"><span class="pre">distribution</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.univariate.distribution.Distribution.html#arch.univariate.distribution.Distribution" title="arch.univariate.distribution.Distribution (Python class) — Template for subclassing only"><span class="pre">Distribution</span></a></span><a class="headerlink" href="#arch.univariate.ARX.distribution" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARX.</span></span><span class="sig-name descname"><span class="pre">distribution</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.univariate.distribution.Distribution.html#arch.univariate.distribution.Distribution" title="arch.univariate.distribution.Distribution (Python class) — Template for subclassing only"><span class="pre">Distribution</span></a></span><a class="headerlink" href="#arch.univariate.ARX.distribution" title="Link to this definition">¶</a></dt>
 <dd><p>Set or gets the error distribution</p>
 <p>Distributions must be a subclass of Distribution</p>
 </dd></dl>
@@ -1168,7 +1168,7 @@ <h1 id="arch-univariate-arx-distribution">arch.univariate.ARX.distribution<a cla
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1215,7 +1215,7 @@ <h1 id="arch-univariate-arx-distribution">arch.univariate.ARX.distribution<a cla
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diff --git a/univariate/generated/generated/arch.univariate.ARX.fit.html b/univariate/generated/generated/arch.univariate.ARX.fit.html
index 499fb41df4..ac8cc04aef 100644
--- a/univariate/generated/generated/arch.univariate.ARX.fit.html
+++ b/univariate/generated/generated/arch.univariate.ARX.fit.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARX.fit - arch 6.1.0</title>
+        <title>arch.univariate.ARX.fit - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -1299,40 +1299,40 @@
                   
 
 
-<h1 id="arch-univariate-arx-fit">arch.univariate.ARX.fit<a class="headerlink" href="#arch-univariate-arx-fit" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-arx-fit">arch.univariate.ARX.fit<a class="headerlink" href="#arch-univariate-arx-fit" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ARX.fit">
-<span class="sig-prename descclassname"><span class="pre">ARX.</span></span><span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.fit.update_freq" title="arch.univariate.ARX.fit.update_freq (Python parameter) — Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."><span class="n"><span class="pre">update_freq</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.fit.disp" title="arch.univariate.ARX.fit.disp (Python parameter) — Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is &quot;off&quot; and True is &quot;final&quot;"><span class="n"><span class="pre">disp</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;final&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.fit.starting_values" title="arch.univariate.ARX.fit.starting_values (Python parameter) — Array of starting values to use.  If not provided, starting values are constructed by the model components."><span class="n"><span class="pre">starting_values</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.fit.cov_type" title="arch.univariate.ARX.fit.cov_type (Python parameter) — Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."><span class="n"><span class="pre">cov_type</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;robust&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.fit.show_warning" title="arch.univariate.ARX.fit.show_warning (Python parameter) — Flag indicating whether convergence warnings should be shown."><span class="n"><span class="pre">show_warning</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.fit.first_obs" title="arch.univariate.ARX.fit.first_obs (Python parameter) — First observation to use when estimating model"><span class="n"><span class="pre">first_obs</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.fit.last_obs" title="arch.univariate.ARX.fit.last_obs (Python parameter) — Last observation to use when estimating model"><span class="n"><span class="pre">last_obs</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.fit.tol" title="arch.univariate.ARX.fit.tol (Python parameter) — Tolerance for termination."><span class="n"><span class="pre">tol</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.fit.options" title="arch.univariate.ARX.fit.options (Python parameter) — Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."><span class="n"><span class="pre">options</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.fit.backcast" title="arch.univariate.ARX.fit.backcast (Python parameter) — Value to use as backcast. Should be measure \sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."><span class="n"><span class="pre">backcast</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.ARX.fit" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ARX.</span></span><span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.fit.update_freq" title="arch.univariate.ARX.fit.update_freq (Python parameter) — Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."><span class="n"><span class="pre">update_freq</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.fit.disp" title="arch.univariate.ARX.fit.disp (Python parameter) — Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is &quot;off&quot; and True is &quot;final&quot;"><span class="n"><span class="pre">disp</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'off'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'final'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;final&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.fit.starting_values" title="arch.univariate.ARX.fit.starting_values (Python parameter) — Array of starting values to use.  If not provided, starting values are constructed by the model components."><span class="n"><span class="pre">starting_values</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.fit.cov_type" title="arch.univariate.ARX.fit.cov_type (Python parameter) — Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."><span class="n"><span class="pre">cov_type</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'robust'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'classic'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;robust&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.fit.show_warning" title="arch.univariate.ARX.fit.show_warning (Python parameter) — Flag indicating whether convergence warnings should be shown."><span class="n"><span class="pre">show_warning</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.fit.first_obs" title="arch.univariate.ARX.fit.first_obs (Python parameter) — First observation to use when estimating model"><span class="n"><span class="pre">first_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.fit.last_obs" title="arch.univariate.ARX.fit.last_obs (Python parameter) — Last observation to use when estimating model"><span class="n"><span class="pre">last_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.fit.tol" title="arch.univariate.ARX.fit.tol (Python parameter) — Tolerance for termination."><span class="n"><span class="pre">tol</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.fit.options" title="arch.univariate.ARX.fit.options (Python parameter) — Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."><span class="n"><span class="pre">options</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.fit.backcast" title="arch.univariate.ARX.fit.backcast (Python parameter) — Value to use as backcast. Should be measure \sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelResult.html#arch.univariate.base.ARCHModelResult" title="arch.univariate.base.ARCHModelResult (Python class) — Results from estimation of an ARCHModel model"><span class="pre">ARCHModelResult</span></a></span></span></span><a class="headerlink" href="#arch.univariate.ARX.fit" title="Link to this definition">¶</a></dt>
 <dd><p>Estimate model parameters</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ARX.fit-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARX.fit-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ARX.fit.update_freq"><span class="sig-name descname">update_freq</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ARX.fit.update_freq" title="Permalink to this definition">¶</a></dt><dd><p>Frequency of iteration updates.  Output is generated every
+<dt id="arch.univariate.ARX.fit.update_freq"><span class="n sig-name">update_freq</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code><a class="headerlink" href="#arch.univariate.ARX.fit.update_freq" title="Permalink to this definition">¶</a></dt><dd><p>Frequency of iteration updates.  Output is generated every
 <cite>update_freq</cite> iterations. Set to 0 to disable iterative output.</p>
 </dd>
-<dt id="arch.univariate.ARX.fit.disp"><span class="sig-name descname">disp</span><span class="p"> : </span><span class="desctype"><span class="desctype">{bool, &quot;off&quot;, &quot;final&quot;}</span></span><a class="headerlink" href="#arch.univariate.ARX.fit.disp" title="Permalink to this definition">¶</a></dt><dd><p>Either ‘final’ to print optimization result or ‘off’ to display
+<dt id="arch.univariate.ARX.fit.disp"><span class="n sig-name">disp</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'off'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'final'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;final&#39;</span></code><a class="headerlink" href="#arch.univariate.ARX.fit.disp" title="Permalink to this definition">¶</a></dt><dd><p>Either ‘final’ to print optimization result or ‘off’ to display
 nothing. If using a boolean, False is “off” and True is “final”</p>
 </dd>
-<dt id="arch.univariate.ARX.fit.starting_values"><span class="sig-name descname">starting_values</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray, optional</span></span><a class="headerlink" href="#arch.univariate.ARX.fit.starting_values" title="Permalink to this definition">¶</a></dt><dd><p>Array of starting values to use.  If not provided, starting values
+<dt id="arch.univariate.ARX.fit.starting_values"><span class="n sig-name">starting_values</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARX.fit.starting_values" title="Permalink to this definition">¶</a></dt><dd><p>Array of starting values to use.  If not provided, starting values
 are constructed by the model components.</p>
 </dd>
-<dt id="arch.univariate.ARX.fit.cov_type"><span class="sig-name descname">cov_type</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ARX.fit.cov_type" title="Permalink to this definition">¶</a></dt><dd><p>Estimation method of parameter covariance.  Supported options are
+<dt id="arch.univariate.ARX.fit.cov_type"><span class="n sig-name">cov_type</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'robust'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'classic'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;robust&#39;</span></code><a class="headerlink" href="#arch.univariate.ARX.fit.cov_type" title="Permalink to this definition">¶</a></dt><dd><p>Estimation method of parameter covariance.  Supported options are
 ‘robust’, which does not assume the Information Matrix Equality
 holds and ‘classic’ which does.  In the ARCH literature, ‘robust’
 corresponds to Bollerslev-Wooldridge covariance estimator.</p>
 </dd>
-<dt id="arch.univariate.ARX.fit.show_warning"><span class="sig-name descname">show_warning</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ARX.fit.show_warning" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether convergence warnings should be shown.</p>
+<dt id="arch.univariate.ARX.fit.show_warning"><span class="n sig-name">show_warning</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.univariate.ARX.fit.show_warning" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether convergence warnings should be shown.</p>
 </dd>
-<dt id="arch.univariate.ARX.fit.first_obs"><span class="sig-name descname">first_obs</span><span class="p"> : </span><span class="desctype"><span class="desctype">{int, <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, datetime, Timestamp}</span></span><a class="headerlink" href="#arch.univariate.ARX.fit.first_obs" title="Permalink to this definition">¶</a></dt><dd><p>First observation to use when estimating model</p>
+<dt id="arch.univariate.ARX.fit.first_obs"><span class="n sig-name">first_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARX.fit.first_obs" title="Permalink to this definition">¶</a></dt><dd><p>First observation to use when estimating model</p>
 </dd>
-<dt id="arch.univariate.ARX.fit.last_obs"><span class="sig-name descname">last_obs</span><span class="p"> : </span><span class="desctype"><span class="desctype">{int, <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, datetime, Timestamp}</span></span><a class="headerlink" href="#arch.univariate.ARX.fit.last_obs" title="Permalink to this definition">¶</a></dt><dd><p>Last observation to use when estimating model</p>
+<dt id="arch.univariate.ARX.fit.last_obs"><span class="n sig-name">last_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARX.fit.last_obs" title="Permalink to this definition">¶</a></dt><dd><p>Last observation to use when estimating model</p>
 </dd>
-<dt id="arch.univariate.ARX.fit.tol"><span class="sig-name descname">tol</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ARX.fit.tol" title="Permalink to this definition">¶</a></dt><dd><p>Tolerance for termination.</p>
+<dt id="arch.univariate.ARX.fit.tol"><span class="n sig-name">tol</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARX.fit.tol" title="Permalink to this definition">¶</a></dt><dd><p>Tolerance for termination.</p>
 </dd>
-<dt id="arch.univariate.ARX.fit.options"><span class="sig-name descname">options</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ARX.fit.options" title="Permalink to this definition">¶</a></dt><dd><p>Options to pass to <cite>scipy.optimize.minimize</cite>.  Valid entries
+<dt id="arch.univariate.ARX.fit.options"><span class="n sig-name">options</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)">Any</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARX.fit.options" title="Permalink to this definition">¶</a></dt><dd><p>Options to pass to <cite>scipy.optimize.minimize</cite>.  Valid entries
 include ‘ftol’, ‘eps’, ‘disp’, and ‘maxiter’.</p>
 </dd>
-<dt id="arch.univariate.ARX.fit.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}, optional</span></span><a class="headerlink" href="#arch.univariate.ARX.fit.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use as backcast. Should be measure <span class="math notranslate nohighlight">\(\sigma^2_0\)</span>
+<dt id="arch.univariate.ARX.fit.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARX.fit.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use as backcast. Should be measure <span class="math notranslate nohighlight">\(\sigma^2_0\)</span>
 since model-specific non-linear transformations are applied to
 value before computing the variance recursions.</p>
 </dd>
@@ -1342,7 +1342,7 @@ <h1 id="arch-univariate-arx-fit">arch.univariate.ARX.fit<a class="headerlink" hr
 <dd class="field-even"><p><strong>results</strong> – Object containing model results</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.ARX.fit-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARX.fit-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference internal" href="../arch.univariate.base.ARCHModelResult.html#arch.univariate.base.ARCHModelResult" title="arch.univariate.base.ARCHModelResult (Python class) — Results from estimation of an ARCHModel model">ARCHModelResult</a></p>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">ARCHModelResult</span></code></p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -1418,7 +1418,7 @@ <h1 id="arch-univariate-arx-fit">arch.univariate.ARX.fit<a class="headerlink" hr
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
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@@ -1465,10 +1465,10 @@ <h1 id="arch-univariate-arx-fit">arch.univariate.ARX.fit<a class="headerlink" hr
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+        <script id="MathJax-script" src="../../../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
     
   </body>
 </html>
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diff --git a/univariate/generated/generated/arch.univariate.ARX.fix.html b/univariate/generated/generated/arch.univariate.ARX.fix.html
index 5e440799db..47d525dd79 100644
--- a/univariate/generated/generated/arch.univariate.ARX.fix.html
+++ b/univariate/generated/generated/arch.univariate.ARX.fix.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARX.fix - arch 6.1.0</title>
+        <title>arch.univariate.ARX.fix - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1201,21 +1201,21 @@
                   
 
 
-<h1 id="arch-univariate-arx-fix">arch.univariate.ARX.fix<a class="headerlink" href="#arch-univariate-arx-fix" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-arx-fix">arch.univariate.ARX.fix<a class="headerlink" href="#arch-univariate-arx-fix" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.ARX.fix">
-<span class="sig-prename descclassname"><span class="pre">ARX.</span></span><span class="sig-name descname"><span class="pre">fix</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.fix.params" title="arch.univariate.ARX.fix.params (Python parameter) — User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."><span class="n"><span class="pre">params</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.fix.first_obs" title="arch.univariate.ARX.fix.first_obs (Python parameter) — First observation to use when fixing model"><span class="n"><span class="pre">first_obs</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.fix.last_obs" title="arch.univariate.ARX.fix.last_obs (Python parameter) — Last observation to use when fixing model"><span class="n"><span class="pre">last_obs</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.ARX.fix" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ARX.fix">
+<span class="sig-prename descclassname"><span class="pre">ARX.</span></span><span class="sig-name descname"><span class="pre">fix</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.fix.params" title="arch.univariate.ARX.fix.params (Python parameter) — User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.fix.first_obs" title="arch.univariate.ARX.fix.first_obs (Python parameter) — First observation to use when fixing model"><span class="n"><span class="pre">first_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.fix.last_obs" title="arch.univariate.ARX.fix.last_obs (Python parameter) — Last observation to use when fixing model"><span class="n"><span class="pre">last_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelFixedResult.html#arch.univariate.base.ARCHModelFixedResult" title="arch.univariate.base.ARCHModelFixedResult (Python class) — Results for fixed parameters for an ARCHModel model"><span class="pre">ARCHModelFixedResult</span></a></span></span></span><a class="headerlink" href="#arch.univariate.ARX.fix" title="Link to this definition">¶</a></dt>
 <dd><p>Allows an ARCHModelFixedResult to be constructed from fixed parameters.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ARX.fix-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARX.fix-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ARX.fix.params"><span class="sig-name descname">params</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, Series}</span></span><a class="headerlink" href="#arch.univariate.ARX.fix.params" title="Permalink to this definition">¶</a></dt><dd><p>User specified parameters to use when generating the result. Must
+<dt id="arch.univariate.ARX.fix.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.ARX.fix.params" title="Permalink to this definition">¶</a></dt><dd><p>User specified parameters to use when generating the result. Must
 have the correct number of parameters for a given choice of mean
 model, volatility model and distribution.</p>
 </dd>
-<dt id="arch.univariate.ARX.fix.first_obs"><span class="sig-name descname">first_obs</span><span class="p"> : </span><span class="desctype"><span class="desctype">{int, <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, datetime, Timestamp}</span></span><a class="headerlink" href="#arch.univariate.ARX.fix.first_obs" title="Permalink to this definition">¶</a></dt><dd><p>First observation to use when fixing model</p>
+<dt id="arch.univariate.ARX.fix.first_obs"><span class="n sig-name">first_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARX.fix.first_obs" title="Permalink to this definition">¶</a></dt><dd><p>First observation to use when fixing model</p>
 </dd>
-<dt id="arch.univariate.ARX.fix.last_obs"><span class="sig-name descname">last_obs</span><span class="p"> : </span><span class="desctype"><span class="desctype">{int, <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, datetime, Timestamp}</span></span><a class="headerlink" href="#arch.univariate.ARX.fix.last_obs" title="Permalink to this definition">¶</a></dt><dd><p>Last observation to use when fixing model</p>
+<dt id="arch.univariate.ARX.fix.last_obs"><span class="n sig-name">last_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARX.fix.last_obs" title="Permalink to this definition">¶</a></dt><dd><p>Last observation to use when fixing model</p>
 </dd>
 </dl>
 </dd>
@@ -1223,7 +1223,7 @@ <h1 id="arch-univariate-arx-fix">arch.univariate.ARX.fix<a class="headerlink" hr
 <dd class="field-even"><p><strong>results</strong> – Object containing model results</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.ARX.fix-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARX.fix-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference internal" href="../arch.univariate.base.ARCHModelFixedResult.html#arch.univariate.base.ARCHModelFixedResult" title="arch.univariate.base.ARCHModelFixedResult (Python class) — Results for fixed parameters for an ARCHModel model">ARCHModelFixedResult</a></p>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">ARCHModelFixedResult</span></code></p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -1297,7 +1297,7 @@ <h1 id="arch-univariate-arx-fix">arch.univariate.ARX.fix<a class="headerlink" hr
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1344,7 +1344,7 @@ <h1 id="arch-univariate-arx-fix">arch.univariate.ARX.fix<a class="headerlink" hr
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diff --git a/univariate/generated/generated/arch.univariate.ARX.forecast.html b/univariate/generated/generated/arch.univariate.ARX.forecast.html
index 6a3317036c..bc18d6d1c5 100644
--- a/univariate/generated/generated/arch.univariate.ARX.forecast.html
+++ b/univariate/generated/generated/arch.univariate.ARX.forecast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARX.forecast - arch 6.1.0</title>
+        <title>arch.univariate.ARX.forecast - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1299,56 +1299,56 @@
                   
 
 
-<h1 id="arch-univariate-arx-forecast">arch.univariate.ARX.forecast<a class="headerlink" href="#arch-univariate-arx-forecast" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-arx-forecast">arch.univariate.ARX.forecast<a class="headerlink" href="#arch-univariate-arx-forecast" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ARX.forecast">
-<span class="sig-prename descclassname"><span class="pre">ARX.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.forecast.params" title="arch.univariate.ARX.forecast.params (Python parameter) — Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."><span class="n"><span class="pre">params</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.forecast.horizon" title="arch.univariate.ARX.forecast.horizon (Python parameter) — Number of steps to forecast"><span class="n"><span class="pre">horizon</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.forecast.start" title="arch.univariate.ARX.forecast.start (Python parameter) — An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."><span class="n"><span class="pre">start</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.forecast.align" title="arch.univariate.ARX.forecast.align (Python parameter) — Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."><span class="n"><span class="pre">align</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;origin&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.forecast.method" title="arch.univariate.ARX.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons &gt; 1."><span class="n"><span class="pre">method</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.forecast.simulations" title="arch.univariate.ARX.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.forecast.rng" title="arch.univariate.ARX.forecast.rng (Python parameter) — Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."><span class="n"><span class="pre">rng</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.forecast.random_state" title="arch.univariate.ARX.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.forecast.reindex" title="arch.univariate.ARX.forecast.reindex (Python parameter) — Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."><span class="n"><span class="pre">reindex</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.forecast.x" title="arch.univariate.ARX.forecast.x (Python parameter) — Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"><span class="n"><span class="pre">x</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.ARX.forecast" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ARX.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.forecast.params" title="arch.univariate.ARX.forecast.params (Python parameter) — Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.forecast.horizon" title="arch.univariate.ARX.forecast.horizon (Python parameter) — Number of steps to forecast"><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.forecast.start" title="arch.univariate.ARX.forecast.start (Python parameter) — An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.forecast.align" title="arch.univariate.ARX.forecast.align (Python parameter) — Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."><span class="n"><span class="pre">align</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'origin'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'target'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;origin&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.forecast.method" title="arch.univariate.ARX.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons &gt; 1."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.forecast.simulations" title="arch.univariate.ARX.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.forecast.rng" title="arch.univariate.ARX.forecast.rng (Python parameter) — Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.forecast.random_state" title="arch.univariate.ARX.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.forecast.reindex" title="arch.univariate.ARX.forecast.reindex (Python parameter) — Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."><span class="n"><span class="pre">reindex</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.forecast.x" title="arch.univariate.ARX.forecast.x (Python parameter) — Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.11)"><span class="pre">Hashable</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelForecast.html#arch.univariate.base.ARCHModelForecast" title="arch.univariate.base.ARCHModelForecast (Python class) — Container for forecasts from an ARCH Model"><span class="pre">ARCHModelForecast</span></a></span></span></span><a class="headerlink" href="#arch.univariate.ARX.forecast" title="Link to this definition">¶</a></dt>
 <dd><p>Construct forecasts from estimated model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ARX.forecast-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARX.forecast-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ARX.forecast.params"><span class="sig-name descname">params</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, Series}</span></span><a class="headerlink" href="#arch.univariate.ARX.forecast.params" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast. Must be identical in
+<dt id="arch.univariate.ARX.forecast.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.ARX.forecast.params" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast. Must be identical in
 shape to the parameters computed by fitting the model.</p>
 </dd>
-<dt id="arch.univariate.ARX.forecast.horizon"><span class="sig-name descname">horizon</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ARX.forecast.horizon" title="Permalink to this definition">¶</a></dt><dd><p>Number of steps to forecast</p>
+<dt id="arch.univariate.ARX.forecast.horizon"><span class="n sig-name">horizon</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code><a class="headerlink" href="#arch.univariate.ARX.forecast.horizon" title="Permalink to this definition">¶</a></dt><dd><p>Number of steps to forecast</p>
 </dd>
-<dt id="arch.univariate.ARX.forecast.start"><span class="sig-name descname">start</span><span class="p"> : </span><span class="desctype"><span class="desctype">{int, datetime, Timestamp, str}, optional</span></span><a class="headerlink" href="#arch.univariate.ARX.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>An integer, datetime or str indicating the first observation to
+<dt id="arch.univariate.ARX.forecast.start"><span class="n sig-name">start</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARX.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>An integer, datetime or str indicating the first observation to
 produce the forecast for.  Datetimes can only be used with pandas
 inputs that have a datetime index. Strings must be convertible
 to a date time, such as in ‘1945-01-01’.</p>
 </dd>
-<dt id="arch.univariate.ARX.forecast.align"><span class="sig-name descname">align</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ARX.forecast.align" title="Permalink to this definition">¶</a></dt><dd><p>Either ‘origin’ or ‘target’.  When set of ‘origin’, the t-th row
+<dt id="arch.univariate.ARX.forecast.align"><span class="n sig-name">align</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'origin'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'target'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;origin&#39;</span></code><a class="headerlink" href="#arch.univariate.ARX.forecast.align" title="Permalink to this definition">¶</a></dt><dd><p>Either ‘origin’ or ‘target’.  When set of ‘origin’, the t-th row
 of forecasts contains the forecasts for t+1, t+2, …, t+h. When
 set to ‘target’, the t-th row contains the 1-step ahead forecast
 from time t-1, the 2 step from time t-2, …, and the h-step from
 time t-h.  ‘target’ simplified computing forecast errors since the
 realization and h-step forecast are aligned.</p>
 </dd>
-<dt id="arch.univariate.ARX.forecast.method"><span class="sig-name descname">method</span><span class="p"> : </span><span class="desctype"><span class="desctype">{'analytic', 'simulation', 'bootstrap'}</span></span><a class="headerlink" href="#arch.univariate.ARX.forecast.method" title="Permalink to this definition">¶</a></dt><dd><p>Method to use when producing the forecast. The default is analytic.
+<dt id="arch.univariate.ARX.forecast.method"><span class="n sig-name">method</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'analytic'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'simulation'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'bootstrap'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code><a class="headerlink" href="#arch.univariate.ARX.forecast.method" title="Permalink to this definition">¶</a></dt><dd><p>Method to use when producing the forecast. The default is analytic.
 The method only affects the variance forecast generation.  Not all
 volatility models support all methods. In particular, volatility
 models that do not evolve in squares such as EGARCH or TARCH do not
 support the ‘analytic’ method for horizons &gt; 1.</p>
 </dd>
-<dt id="arch.univariate.ARX.forecast.simulations"><span class="sig-name descname">simulations</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.ARX.forecast.simulations" title="Permalink to this definition">¶</a></dt><dd><p>Number of simulations to run when computing the forecast using
+<dt id="arch.univariate.ARX.forecast.simulations"><span class="n sig-name">simulations</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.univariate.ARX.forecast.simulations" title="Permalink to this definition">¶</a></dt><dd><p>Number of simulations to run when computing the forecast using
 either simulation or bootstrap.</p>
 </dd>
-<dt id="arch.univariate.ARX.forecast.rng"><span class="sig-name descname">rng</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable, optional</span></span><a class="headerlink" href="#arch.univariate.ARX.forecast.rng" title="Permalink to this definition">¶</a></dt><dd><p>Custom random number generator to use in simulation-based forecasts.
+<dt id="arch.univariate.ARX.forecast.rng"><span class="n sig-name">rng</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)">Callable</a><span class="p">[</span><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">,</span><span class="w"> </span><span class="p">...</span><span class="p">]</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARX.forecast.rng" title="Permalink to this definition">¶</a></dt><dd><p>Custom random number generator to use in simulation-based forecasts.
 Must produce random samples using the syntax <cite>rng(size)</cite> where size
 the 2-element tuple (simulations, horizon).</p>
 </dd>
-<dt id="arch.univariate.ARX.forecast.random_state"><span class="sig-name descname">random_state</span><span class="p"> : </span><span class="desctype"><span class="desctype">RandomState, optional</span></span><a class="headerlink" href="#arch.univariate.ARX.forecast.random_state" title="Permalink to this definition">¶</a></dt><dd><p>NumPy RandomState instance to use when method is ‘bootstrap’</p>
+<dt id="arch.univariate.ARX.forecast.random_state"><span class="n sig-name">random_state</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)">RandomState</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARX.forecast.random_state" title="Permalink to this definition">¶</a></dt><dd><p>NumPy RandomState instance to use when method is ‘bootstrap’</p>
 </dd>
-<dt id="arch.univariate.ARX.forecast.reindex"><span class="sig-name descname">reindex</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ARX.forecast.reindex" title="Permalink to this definition">¶</a></dt><dd><p><p>Whether to reindex the forecasts to have the same dimension as the series
+<dt id="arch.univariate.ARX.forecast.reindex"><span class="n sig-name">reindex</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.univariate.ARX.forecast.reindex" title="Permalink to this definition">¶</a></dt><dd><p><p>Whether to reindex the forecasts to have the same dimension as the series
 being forecast. Prior to 4.18 this was the default. As of 4.19 this is
 now optional. If not provided, a warning is raised about the future
 change in the default which will occur after September 2021.</p>
-<div class="versionadded">
-<p><span class="versionmodified added">New in version 4.19.</span></p>
+<div class="admonition versionadded">
+<p class="admonition-title"><span class="versionmodified added">New in version 4.19.</span></p>
 </div>
 </p>
 </dd>
-<dt id="arch.univariate.ARX.forecast.x"><span class="sig-name descname">x</span><span class="p"> : </span><span class="desctype"><span class="desctype">{dict[label, array_like], array_like}</span></span><a class="headerlink" href="#arch.univariate.ARX.forecast.x" title="Permalink to this definition">¶</a></dt><dd><p><p>Values to use for exogenous regressors if any are included in the
+<dt id="arch.univariate.ARX.forecast.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.11)">Hashable</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARX.forecast.x" title="Permalink to this definition">¶</a></dt><dd><p><p>Values to use for exogenous regressors if any are included in the
 model. Three formats are accepted:</p>
 <ul class="simple">
 <li><p>2-d array-like: This format can be used when there is a single
@@ -1371,8 +1371,8 @@ <h1 id="arch-univariate-arx-forecast">arch.univariate.ARX.forecast<a class="head
 <p>Due to the complexity required to accommodate all scenarios, please
 see the example notebook that demonstrates the valid formats for
 x.</p>
-<div class="versionadded">
-<p><span class="versionmodified added">New in version 4.19.</span></p>
+<div class="admonition versionadded">
+<p class="admonition-title"><span class="versionmodified added">New in version 4.19.</span></p>
 </div>
 </p>
 </dd>
@@ -1383,7 +1383,7 @@ <h1 id="arch-univariate-arx-forecast">arch.univariate.ARX.forecast<a class="head
 <code class="docutils literal notranslate"><span class="pre">variance</span></code> and <code class="docutils literal notranslate"><span class="pre">residual_variance</span></code>.</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.ARX.forecast-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARX.forecast-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference internal" href="../arch.univariate.base.ARCHModelForecast.html#arch.univariate.base.ARCHModelForecast" title="arch.univariate.base.ARCHModelForecast (Python class) — Container for forecasts from an ARCH Model">arch.univariate.base.ARCHModelForecast</a></p>
+<dd class="field-odd"><p><a class="reference internal" href="../arch.univariate.base.ARCHModelForecast.html#arch.univariate.base.ARCHModelForecast" title="arch.univariate.base.ARCHModelForecast (Python class) — Container for forecasts from an ARCH Model"><code class="xref py py-class docutils literal notranslate"><span class="pre">arch.univariate.base.ARCHModelForecast</span></code></a></p>
 </dd>
 </dl>
 <p class="example rubric">Examples</p>
@@ -1483,7 +1483,7 @@ <h1 id="arch-univariate-arx-forecast">arch.univariate.ARX.forecast<a class="head
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1530,7 +1530,7 @@ <h1 id="arch-univariate-arx-forecast">arch.univariate.ARX.forecast<a class="head
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.ARX.name.html b/univariate/generated/generated/arch.univariate.ARX.name.html
index 5a7adbde1a..f4016da1c2 100644
--- a/univariate/generated/generated/arch.univariate.ARX.name.html
+++ b/univariate/generated/generated/arch.univariate.ARX.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARX.name - arch 6.1.0</title>
+        <title>arch.univariate.ARX.name - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1093,10 +1093,10 @@
                   
 
 
-<h1 id="arch-univariate-arx-name">arch.univariate.ARX.name<a class="headerlink" href="#arch-univariate-arx-name" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-arx-name">arch.univariate.ARX.name<a class="headerlink" href="#arch-univariate-arx-name" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ARX.name">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARX.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.univariate.ARX.name" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARX.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.univariate.ARX.name" title="Link to this definition">¶</a></dt>
 <dd><p>The name of the model.</p>
 </dd></dl>
 
@@ -1167,7 +1167,7 @@ <h1 id="arch-univariate-arx-name">arch.univariate.ARX.name<a class="headerlink"
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1214,7 +1214,7 @@ <h1 id="arch-univariate-arx-name">arch.univariate.ARX.name<a class="headerlink"
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.ARX.num_params.html b/univariate/generated/generated/arch.univariate.ARX.num_params.html
index 3e19874e51..d8cabe7278 100644
--- a/univariate/generated/generated/arch.univariate.ARX.num_params.html
+++ b/univariate/generated/generated/arch.univariate.ARX.num_params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARX.num_params - arch 6.1.0</title>
+        <title>arch.univariate.ARX.num_params - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1093,10 +1093,10 @@
                   
 
 
-<h1 id="arch-univariate-arx-num-params">arch.univariate.ARX.num_params<a class="headerlink" href="#arch-univariate-arx-num-params" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-arx-num-params">arch.univariate.ARX.num_params<a class="headerlink" href="#arch-univariate-arx-num-params" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ARX.num_params">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARX.</span></span><span class="sig-name descname"><span class="pre">num_params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.ARX.num_params" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARX.</span></span><span class="sig-name descname"><span class="pre">num_params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.ARX.num_params" title="Link to this definition">¶</a></dt>
 <dd><p>Returns the number of parameters</p>
 </dd></dl>
 
@@ -1167,7 +1167,7 @@ <h1 id="arch-univariate-arx-num-params">arch.univariate.ARX.num_params<a class="
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1214,7 +1214,7 @@ <h1 id="arch-univariate-arx-num-params">arch.univariate.ARX.num_params<a class="
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.ARX.parameter_names.html b/univariate/generated/generated/arch.univariate.ARX.parameter_names.html
index 03b16f5daf..60c48a9f9c 100644
--- a/univariate/generated/generated/arch.univariate.ARX.parameter_names.html
+++ b/univariate/generated/generated/arch.univariate.ARX.parameter_names.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARX.parameter_names - arch 6.1.0</title>
+        <title>arch.univariate.ARX.parameter_names - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
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@@ -1133,17 +1133,17 @@
                   
 
 
-<h1 id="arch-univariate-arx-parameter-names">arch.univariate.ARX.parameter_names<a class="headerlink" href="#arch-univariate-arx-parameter-names" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-arx-parameter-names">arch.univariate.ARX.parameter_names<a class="headerlink" href="#arch-univariate-arx-parameter-names" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ARX.parameter_names">
-<span class="sig-prename descclassname"><span class="pre">ARX.</span></span><span class="sig-name descname"><span class="pre">parameter_names</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.ARX.parameter_names" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ARX.</span></span><span class="sig-name descname"><span class="pre">parameter_names</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#arch.univariate.ARX.parameter_names" title="Link to this definition">¶</a></dt>
 <dd><p>List of parameters names</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ARX.parameter_names-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARX.parameter_names-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p><strong>names</strong> – List of variable names for the mean model</p>
 </dd>
 <dt class="field-even" id="arch.univariate.ARX.parameter_names-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARX.parameter_names-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>)</p>
+<dd class="field-even"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span> <span class="pre">(str)</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1215,7 +1215,7 @@ <h1 id="arch-univariate-arx-parameter-names">arch.univariate.ARX.parameter_names
   
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diff --git a/univariate/generated/generated/arch.univariate.ARX.resids.html b/univariate/generated/generated/arch.univariate.ARX.resids.html
index d8e875080b..6b2c77aa4d 100644
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+++ b/univariate/generated/generated/arch.univariate.ARX.resids.html
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+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -1201,19 +1201,19 @@
                   
 
 
-<h1 id="arch-univariate-arx-resids">arch.univariate.ARX.resids<a class="headerlink" href="#arch-univariate-arx-resids" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-arx-resids">arch.univariate.ARX.resids<a class="headerlink" href="#arch-univariate-arx-resids" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.ARX.resids">
-<span class="sig-prename descclassname"><span class="pre">ARX.</span></span><span class="sig-name descname"><span class="pre">resids</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.resids.params" title="arch.univariate.ARX.resids.params (Python parameter) — Model parameters"><span class="n"><span class="pre">params</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.resids.y" title="arch.univariate.ARX.resids.y (Python parameter) — Alternative values to use when computing model residuals"><span class="n"><span class="pre">y</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.resids.regressors" title="arch.univariate.ARX.resids.regressors (Python parameter) — Alternative regressor values to use when computing model residuals"><span class="n"><span class="pre">regressors</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.ARX.resids" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ARX.resids">
+<span class="sig-prename descclassname"><span class="pre">ARX.</span></span><span class="sig-name descname"><span class="pre">resids</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.resids.params" title="arch.univariate.ARX.resids.params (Python parameter) — Model parameters"><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.resids.y" title="arch.univariate.ARX.resids.y (Python parameter) — Alternative values to use when computing model residuals"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.resids.regressors" title="arch.univariate.ARX.resids.regressors (Python parameter) — Alternative regressor values to use when computing model residuals"><span class="n"><span class="pre">regressors</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></span><a class="headerlink" href="#arch.univariate.ARX.resids" title="Link to this definition">¶</a></dt>
 <dd><p>Compute model residuals</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ARX.resids-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARX.resids-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ARX.resids.params"><span class="sig-name descname">params</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.ARX.resids.params" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
+<dt id="arch.univariate.ARX.resids.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.ARX.resids.params" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
 </dd>
-<dt id="arch.univariate.ARX.resids.y"><span class="sig-name descname">y</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray, optional</span></span><a class="headerlink" href="#arch.univariate.ARX.resids.y" title="Permalink to this definition">¶</a></dt><dd><p>Alternative values to use when computing model residuals</p>
+<dt id="arch.univariate.ARX.resids.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARX.resids.y" title="Permalink to this definition">¶</a></dt><dd><p>Alternative values to use when computing model residuals</p>
 </dd>
-<dt id="arch.univariate.ARX.resids.regressors"><span class="sig-name descname">regressors</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray, optional</span></span><a class="headerlink" href="#arch.univariate.ARX.resids.regressors" title="Permalink to this definition">¶</a></dt><dd><p>Alternative regressor values to use when computing model residuals</p>
+<dt id="arch.univariate.ARX.resids.regressors"><span class="n sig-name">regressors</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARX.resids.regressors" title="Permalink to this definition">¶</a></dt><dd><p>Alternative regressor values to use when computing model residuals</p>
 </dd>
 </dl>
 </dd>
@@ -1221,7 +1221,7 @@ <h1 id="arch-univariate-arx-resids">arch.univariate.ARX.resids<a class="headerli
 <dd class="field-even"><p><strong>resids</strong> – Model residuals</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.ARX.resids-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARX.resids-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
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@@ -1293,7 +1293,7 @@ <h1 id="arch-univariate-arx-resids">arch.univariate.ARX.resids<a class="headerli
   
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diff --git a/univariate/generated/generated/arch.univariate.ARX.simulate.html b/univariate/generated/generated/arch.univariate.ARX.simulate.html
index 5096aa044b..0e4845c81a 100644
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+++ b/univariate/generated/generated/arch.univariate.ARX.simulate.html
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch 6.1.0
+    arch 6.2.0
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@@ -1243,33 +1243,33 @@
                   
 
 
-<h1 id="arch-univariate-arx-simulate">arch.univariate.ARX.simulate<a class="headerlink" href="#arch-univariate-arx-simulate" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-arx-simulate">arch.univariate.ARX.simulate<a class="headerlink" href="#arch-univariate-arx-simulate" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ARX.simulate">
-<span class="sig-prename descclassname"><span class="pre">ARX.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.simulate.params" title="arch.univariate.ARX.simulate.params (Python parameter) — Parameters to use when simulating the model.  Parameter order is [mean volatility distribution] where the parameters of the mean model are ordered [constant lag[0] lag[1] ... lag[p] ex[0] ... ex[k-1]] where lag[j] indicates the coefficient on the jth lag in the model and ex[j] is the coefficient on the jth exogenous variable."><span class="n"><span class="pre">params</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.simulate.nobs" title="arch.univariate.ARX.simulate.nobs (Python parameter) — Length of series to simulate"><span class="n"><span class="pre">nobs</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.simulate.burn" title="arch.univariate.ARX.simulate.burn (Python parameter) — Number of values to simulate to initialize the model and remove dependence on initial values."><span class="n"><span class="pre">burn</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.simulate.initial_value" title="arch.univariate.ARX.simulate.initial_value (Python parameter) — Either a scalar value or max(lags) array set of initial values to use when initializing the model.  If omitted, 0.0 is used."><span class="n"><span class="pre">initial_value</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.simulate.x" title="arch.univariate.ARX.simulate.x (Python parameter) — nobs + burn by k array of exogenous variables to include in the simulation."><span class="n"><span class="pre">x</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.simulate.initial_value_vol" title="arch.univariate.ARX.simulate.initial_value_vol (Python parameter) — An array or scalar to use when initializing the volatility process."><span class="n"><span class="pre">initial_value_vol</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.ARX.simulate" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ARX.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.simulate.params" title="arch.univariate.ARX.simulate.params (Python parameter) — Parameters to use when simulating the model.  Parameter order is [mean volatility distribution] where the parameters of the mean model are ordered [constant lag[0] lag[1] ... lag[p] ex[0] ... ex[k-1]] where lag[j] indicates the coefficient on the jth lag in the model and ex[j] is the coefficient on the jth exogenous variable."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.simulate.nobs" title="arch.univariate.ARX.simulate.nobs (Python parameter) — Length of series to simulate"><span class="n"><span class="pre">nobs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.simulate.burn" title="arch.univariate.ARX.simulate.burn (Python parameter) — Number of values to simulate to initialize the model and remove dependence on initial values."><span class="n"><span class="pre">burn</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.simulate.initial_value" title="arch.univariate.ARX.simulate.initial_value (Python parameter) — Either a scalar value or max(lags) array set of initial values to use when initializing the model.  If omitted, 0.0 is used."><span class="n"><span class="pre">initial_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.simulate.x" title="arch.univariate.ARX.simulate.x (Python parameter) — nobs + burn by k array of exogenous variables to include in the simulation."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.simulate.initial_value_vol" title="arch.univariate.ARX.simulate.initial_value_vol (Python parameter) — An array or scalar to use when initializing the volatility process."><span class="n"><span class="pre">initial_value_vol</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a></span></span></span><a class="headerlink" href="#arch.univariate.ARX.simulate" title="Link to this definition">¶</a></dt>
 <dd><p>Simulates data from a linear regression, AR or HAR models</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ARX.simulate-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARX.simulate-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ARX.simulate.params"><span class="sig-name descname">params</span><span class="p"> : </span><span class="desctype"><span class="desctype">array_like</span></span><a class="headerlink" href="#arch.univariate.ARX.simulate.params" title="Permalink to this definition">¶</a></dt><dd><p>Parameters to use when simulating the model.  Parameter order is
+<dt id="arch.univariate.ARX.simulate.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.univariate.ARX.simulate.params" title="Permalink to this definition">¶</a></dt><dd><p>Parameters to use when simulating the model.  Parameter order is
 [mean volatility distribution] where the parameters of the mean
 model are ordered [constant lag[0] lag[1] … lag[p] ex[0] …
 ex[k-1]] where lag[j] indicates the coefficient on the jth lag in
 the model and ex[j] is the coefficient on the jth exogenous
 variable.</p>
 </dd>
-<dt id="arch.univariate.ARX.simulate.nobs"><span class="sig-name descname">nobs</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.ARX.simulate.nobs" title="Permalink to this definition">¶</a></dt><dd><p>Length of series to simulate</p>
+<dt id="arch.univariate.ARX.simulate.nobs"><span class="n sig-name">nobs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.ARX.simulate.nobs" title="Permalink to this definition">¶</a></dt><dd><p>Length of series to simulate</p>
 </dd>
-<dt id="arch.univariate.ARX.simulate.burn"><span class="sig-name descname">burn</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ARX.simulate.burn" title="Permalink to this definition">¶</a></dt><dd><p>Number of values to simulate to initialize the model and remove
+<dt id="arch.univariate.ARX.simulate.burn"><span class="n sig-name">burn</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code><a class="headerlink" href="#arch.univariate.ARX.simulate.burn" title="Permalink to this definition">¶</a></dt><dd><p>Number of values to simulate to initialize the model and remove
 dependence on initial values.</p>
 </dd>
-<dt id="arch.univariate.ARX.simulate.initial_value"><span class="sig-name descname">initial_value</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, float}, optional</span></span><a class="headerlink" href="#arch.univariate.ARX.simulate.initial_value" title="Permalink to this definition">¶</a></dt><dd><p>Either a scalar value or <cite>max(lags)</cite> array set of initial values to
+<dt id="arch.univariate.ARX.simulate.initial_value"><span class="n sig-name">initial_value</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARX.simulate.initial_value" title="Permalink to this definition">¶</a></dt><dd><p>Either a scalar value or <cite>max(lags)</cite> array set of initial values to
 use when initializing the model.  If omitted, 0.0 is used.</p>
 </dd>
-<dt id="arch.univariate.ARX.simulate.x"><span class="sig-name descname">x</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, DataFrame}, optional</span></span><a class="headerlink" href="#arch.univariate.ARX.simulate.x" title="Permalink to this definition">¶</a></dt><dd><p>nobs + burn by k array of exogenous variables to include in the
+<dt id="arch.univariate.ARX.simulate.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARX.simulate.x" title="Permalink to this definition">¶</a></dt><dd><p>nobs + burn by k array of exogenous variables to include in the
 simulation.</p>
 </dd>
-<dt id="arch.univariate.ARX.simulate.initial_value_vol"><span class="sig-name descname">initial_value_vol</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, float}, optional</span></span><a class="headerlink" href="#arch.univariate.ARX.simulate.initial_value_vol" title="Permalink to this definition">¶</a></dt><dd><p>An array or scalar to use when initializing the volatility process.</p>
+<dt id="arch.univariate.ARX.simulate.initial_value_vol"><span class="n sig-name">initial_value_vol</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARX.simulate.initial_value_vol" title="Permalink to this definition">¶</a></dt><dd><p>An array or scalar to use when initializing the volatility process.</p>
 </dd>
 </dl>
 </dd>
@@ -1279,7 +1279,7 @@ <h1 id="arch-univariate-arx-simulate">arch.univariate.ARX.simulate<a class="head
 containing the errors used in the simulation</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.ARX.simulate-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARX.simulate-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>DataFrame</p>
+<dd class="field-odd"><p><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code></a></p>
 </dd>
 </dl>
 <p class="example rubric">Examples</p>
@@ -1372,7 +1372,7 @@ <h1 id="arch-univariate-arx-simulate">arch.univariate.ARX.simulate<a class="head
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1419,7 +1419,7 @@ <h1 id="arch-univariate-arx-simulate">arch.univariate.ARX.simulate<a class="head
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.ARX.starting_values.html b/univariate/generated/generated/arch.univariate.ARX.starting_values.html
index 92244d486c..c0990033bd 100644
--- a/univariate/generated/generated/arch.univariate.ARX.starting_values.html
+++ b/univariate/generated/generated/arch.univariate.ARX.starting_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARX.starting_values - arch 6.1.0</title>
+        <title>arch.univariate.ARX.starting_values - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
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+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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@@ -1133,10 +1133,10 @@
                   
 
 
-<h1 id="arch-univariate-arx-starting-values">arch.univariate.ARX.starting_values<a class="headerlink" href="#arch-univariate-arx-starting-values" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-arx-starting-values">arch.univariate.ARX.starting_values<a class="headerlink" href="#arch-univariate-arx-starting-values" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ARX.starting_values">
-<span class="sig-prename descclassname"><span class="pre">ARX.</span></span><span class="sig-name descname"><span class="pre">starting_values</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.ARX.starting_values" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ARX.</span></span><span class="sig-name descname"><span class="pre">starting_values</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.univariate.ARX.starting_values" title="Link to this definition">¶</a></dt>
 <dd><p>Returns starting values for the mean model, often the same as the
 values returned from fit</p>
 <dl class="field-list simple">
@@ -1144,7 +1144,7 @@ <h1 id="arch-univariate-arx-starting-values">arch.univariate.ARX.starting_values
 <dd class="field-odd"><p><strong>sv</strong> – Starting values</p>
 </dd>
 <dt class="field-even" id="arch.univariate.ARX.starting_values-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARX.starting_values-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p>ndarray</p>
+<dd class="field-even"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
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@@ -1216,7 +1216,7 @@ <h1 id="arch-univariate-arx-starting-values">arch.univariate.ARX.starting_values
   
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-    7.0.1.
+    7.2.6.
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diff --git a/univariate/generated/generated/arch.univariate.ARX.volatility.html b/univariate/generated/generated/arch.univariate.ARX.volatility.html
index 0626a8bcf2..cefa62fb44 100644
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+++ b/univariate/generated/generated/arch.univariate.ARX.volatility.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARX.volatility - arch 6.1.0</title>
+        <title>arch.univariate.ARX.volatility - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1093,10 +1093,10 @@
                   
 
 
-<h1 id="arch-univariate-arx-volatility">arch.univariate.ARX.volatility<a class="headerlink" href="#arch-univariate-arx-volatility" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-arx-volatility">arch.univariate.ARX.volatility<a class="headerlink" href="#arch-univariate-arx-volatility" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ARX.volatility">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARX.</span></span><span class="sig-name descname"><span class="pre">volatility</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.univariate.volatility.VolatilityProcess.html#arch.univariate.volatility.VolatilityProcess" title="arch.univariate.volatility.VolatilityProcess (Python class) — Abstract base class for ARCH models.  Allows the conditional mean model to be specified separately from the conditional variance, even though parameters are estimated jointly."><span class="pre">VolatilityProcess</span></a></span><a class="headerlink" href="#arch.univariate.ARX.volatility" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARX.</span></span><span class="sig-name descname"><span class="pre">volatility</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.univariate.volatility.VolatilityProcess.html#arch.univariate.volatility.VolatilityProcess" title="arch.univariate.volatility.VolatilityProcess (Python class) — Abstract base class for ARCH models.  Allows the conditional mean model to be specified separately from the conditional variance, even though parameters are estimated jointly."><span class="pre">VolatilityProcess</span></a></span><a class="headerlink" href="#arch.univariate.ARX.volatility" title="Link to this definition">¶</a></dt>
 <dd><p>Set or gets the volatility process</p>
 <p>Volatility processes must be a subclass of VolatilityProcess</p>
 </dd></dl>
@@ -1168,7 +1168,7 @@ <h1 id="arch-univariate-arx-volatility">arch.univariate.ARX.volatility<a class="
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
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diff --git a/univariate/generated/generated/arch.univariate.ARX.x.html b/univariate/generated/generated/arch.univariate.ARX.x.html
index 3326efcc05..e5f9507434 100644
--- a/univariate/generated/generated/arch.univariate.ARX.x.html
+++ b/univariate/generated/generated/arch.univariate.ARX.x.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARX.x - arch 6.1.0</title>
+        <title>arch.univariate.ARX.x - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1093,10 +1093,10 @@
                   
 
 
-<h1 id="arch-univariate-arx-x">arch.univariate.ARX.x<a class="headerlink" href="#arch-univariate-arx-x" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-arx-x">arch.univariate.ARX.x<a class="headerlink" href="#arch-univariate-arx-x" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ARX.x">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARX.</span></span><span class="sig-name descname"><span class="pre">x</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.0.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.ARX.x" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARX.</span></span><span class="sig-name descname"><span class="pre">x</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.ARX.x" title="Link to this definition">¶</a></dt>
 <dd><p>Gets the value of the exogenous regressors in the model</p>
 </dd></dl>
 
@@ -1167,7 +1167,7 @@ <h1 id="arch-univariate-arx-x">arch.univariate.ARX.x<a class="headerlink" href="
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1214,7 +1214,7 @@ <h1 id="arch-univariate-arx-x">arch.univariate.ARX.x<a class="headerlink" href="
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
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diff --git a/univariate/generated/generated/arch.univariate.ARX.y.html b/univariate/generated/generated/arch.univariate.ARX.y.html
index f7efa47cab..eb580607ec 100644
--- a/univariate/generated/generated/arch.univariate.ARX.y.html
+++ b/univariate/generated/generated/arch.univariate.ARX.y.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARX.y - arch 6.1.0</title>
+        <title>arch.univariate.ARX.y - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1093,10 +1093,10 @@
                   
 
 
-<h1 id="arch-univariate-arx-y">arch.univariate.ARX.y<a class="headerlink" href="#arch-univariate-arx-y" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-arx-y">arch.univariate.ARX.y<a class="headerlink" href="#arch-univariate-arx-y" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ARX.y">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARX.</span></span><span class="sig-name descname"><span class="pre">y</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.0.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.0.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.ARX.y" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARX.</span></span><span class="sig-name descname"><span class="pre">y</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.ARX.y" title="Link to this definition">¶</a></dt>
 <dd><p>Returns the dependent variable</p>
 </dd></dl>
 
@@ -1167,7 +1167,7 @@ <h1 id="arch-univariate-arx-y">arch.univariate.ARX.y<a class="headerlink" href="
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1214,7 +1214,7 @@ <h1 id="arch-univariate-arx-y">arch.univariate.ARX.y<a class="headerlink" href="
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+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.ConstantMean.bounds.html b/univariate/generated/generated/arch.univariate.ConstantMean.bounds.html
index 352f055cbe..871477556e 100644
--- a/univariate/generated/generated/arch.univariate.ConstantMean.bounds.html
+++ b/univariate/generated/generated/arch.univariate.ConstantMean.bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantMean.bounds - arch 6.1.0</title>
+        <title>arch.univariate.ConstantMean.bounds - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1133,17 +1133,17 @@
                   
 
 
-<h1 id="arch-univariate-constantmean-bounds">arch.univariate.ConstantMean.bounds<a class="headerlink" href="#arch-univariate-constantmean-bounds" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-constantmean-bounds">arch.univariate.ConstantMean.bounds<a class="headerlink" href="#arch-univariate-constantmean-bounds" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ConstantMean.bounds">
-<span class="sig-prename descclassname"><span class="pre">ConstantMean.</span></span><span class="sig-name descname"><span class="pre">bounds</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.ConstantMean.bounds" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ConstantMean.</span></span><span class="sig-name descname"><span class="pre">bounds</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#arch.univariate.ConstantMean.bounds" title="Link to this definition">¶</a></dt>
 <dd><p>Construct bounds for parameters to use in non-linear optimization</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ConstantMean.bounds-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantMean.bounds-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p><strong>bounds</strong> – Bounds for parameters to use in estimation.</p>
 </dd>
 <dt class="field-even" id="arch.univariate.ConstantMean.bounds-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantMean.bounds-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a> (2-tuple of <a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>)</p>
+<dd class="field-even"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span> <span class="pre">(2-tuple</span></code> of <code class="xref py py-class docutils literal notranslate"><span class="pre">float)</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1215,7 +1215,7 @@ <h1 id="arch-univariate-constantmean-bounds">arch.univariate.ConstantMean.bounds
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1262,7 +1262,7 @@ <h1 id="arch-univariate-constantmean-bounds">arch.univariate.ConstantMean.bounds
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diff --git a/univariate/generated/generated/arch.univariate.ConstantMean.compute_param_cov.html b/univariate/generated/generated/arch.univariate.ConstantMean.compute_param_cov.html
index adcdb30dd4..900555801d 100644
--- a/univariate/generated/generated/arch.univariate.ConstantMean.compute_param_cov.html
+++ b/univariate/generated/generated/arch.univariate.ConstantMean.compute_param_cov.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantMean.compute_param_cov - arch 6.1.0</title>
+        <title>arch.univariate.ConstantMean.compute_param_cov - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -553,13 +553,6 @@
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     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.univariate.ConstantMean.compute_param_cov-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
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       </ul>
@@ -1159,13 +1152,6 @@
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     </nav>
   
-</li>
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-          <li class="md-nav__item">
-  <a href="#arch.univariate.ConstantMean.compute_param_cov-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
       </ul>
@@ -1187,26 +1173,23 @@
                   
 
 
-<h1 id="arch-univariate-constantmean-compute-param-cov">arch.univariate.ConstantMean.compute_param_cov<a class="headerlink" href="#arch-univariate-constantmean-compute-param-cov" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-constantmean-compute-param-cov">arch.univariate.ConstantMean.compute_param_cov<a class="headerlink" href="#arch-univariate-constantmean-compute-param-cov" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.ConstantMean.compute_param_cov">
-<span class="sig-prename descclassname"><span class="pre">ConstantMean.</span></span><span class="sig-name descname"><span class="pre">compute_param_cov</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.compute_param_cov.params" title="arch.univariate.ConstantMean.compute_param_cov.params (Python parameter) — Model parameters"><span class="n"><span class="pre">params</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.compute_param_cov.backcast" title="arch.univariate.ConstantMean.compute_param_cov.backcast (Python parameter) — Value to use for pre-sample observations"><span class="n"><span class="pre">backcast</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.compute_param_cov.robust" title="arch.univariate.ConstantMean.compute_param_cov.robust (Python parameter) — Flag indicating whether to use robust standard errors (True) or classic MLE (False)"><span class="n"><span class="pre">robust</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.ConstantMean.compute_param_cov" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ConstantMean.compute_param_cov">
+<span class="sig-prename descclassname"><span class="pre">ConstantMean.</span></span><span class="sig-name descname"><span class="pre">compute_param_cov</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.compute_param_cov.params" title="arch.univariate.ConstantMean.compute_param_cov.params (Python parameter) — Model parameters"><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.compute_param_cov.backcast" title="arch.univariate.ConstantMean.compute_param_cov.backcast (Python parameter) — Value to use for pre-sample observations"><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.compute_param_cov.robust" title="arch.univariate.ConstantMean.compute_param_cov.robust (Python parameter) — Flag indicating whether to use robust standard errors (True) or classic MLE (False)"><span class="n"><span class="pre">robust</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.univariate.ConstantMean.compute_param_cov" title="Link to this definition">¶</a></dt>
 <dd><p>Computes parameter covariances using numerical derivatives.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ConstantMean.compute_param_cov-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantMean.compute_param_cov-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ConstantMean.compute_param_cov.params"><span class="sig-name descname">params</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.ConstantMean.compute_param_cov.params" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
+<dt id="arch.univariate.ConstantMean.compute_param_cov.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.ConstantMean.compute_param_cov.params" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
 </dd>
-<dt id="arch.univariate.ConstantMean.compute_param_cov.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><a class="headerlink" href="#arch.univariate.ConstantMean.compute_param_cov.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use for pre-sample observations</p>
+<dt id="arch.univariate.ConstantMean.compute_param_cov.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.compute_param_cov.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use for pre-sample observations</p>
 </dd>
-<dt id="arch.univariate.ConstantMean.compute_param_cov.robust"><span class="sig-name descname">robust</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ConstantMean.compute_param_cov.robust" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to use robust standard errors (True) or
+<dt id="arch.univariate.ConstantMean.compute_param_cov.robust"><span class="n sig-name">robust</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.compute_param_cov.robust" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to use robust standard errors (True) or
 classic MLE (False)</p>
 </dd>
 </dl>
 </dd>
-<dt class="field-even" id="arch.univariate.ConstantMean.compute_param_cov-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantMean.compute_param_cov-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a></p>
-</dd>
 </dl>
 </dd></dl>
 
@@ -1277,7 +1260,7 @@ <h1 id="arch-univariate-constantmean-compute-param-cov">arch.univariate.Constant
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1324,7 +1307,7 @@ <h1 id="arch-univariate-constantmean-compute-param-cov">arch.univariate.Constant
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diff --git a/univariate/generated/generated/arch.univariate.ConstantMean.constraints.html b/univariate/generated/generated/arch.univariate.ConstantMean.constraints.html
index 0c854f9d16..a86c08b5a5 100644
--- a/univariate/generated/generated/arch.univariate.ConstantMean.constraints.html
+++ b/univariate/generated/generated/arch.univariate.ConstantMean.constraints.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantMean.constraints - arch 6.1.0</title>
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@@ -92,7 +92,7 @@
 
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@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
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-            arch 6.1.0
+            arch 6.2.0
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         </div>
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch 6.1.0
+    arch 6.2.0
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@@ -536,13 +536,6 @@
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-          <li class="md-nav__item">
-  <a href="#arch.univariate.ConstantMean.constraints-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
-</li>
-        
           <li class="md-nav__item">
   <a href="#arch.univariate.ConstantMean.constraints-returns" class="md-nav__link">
     <span class="md-ellipsis">Returns</span>
@@ -1100,13 +1093,6 @@
     <nav class="md-nav" aria-label="ConstantMean.constraints">
       <ul class="md-nav__list">
         
-          <li class="md-nav__item">
-  <a href="#arch.univariate.ConstantMean.constraints-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
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-  
-</li>
-        
           <li class="md-nav__item">
   <a href="#arch.univariate.ConstantMean.constraints-returns" class="md-nav__link">
     <span class="md-ellipsis">Returns</span>
@@ -1133,19 +1119,16 @@
                   
 
 
-<h1 id="arch-univariate-constantmean-constraints">arch.univariate.ConstantMean.constraints<a class="headerlink" href="#arch-univariate-constantmean-constraints" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-constantmean-constraints">arch.univariate.ConstantMean.constraints<a class="headerlink" href="#arch-univariate-constantmean-constraints" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ConstantMean.constraints">
-<span class="sig-prename descclassname"><span class="pre">ConstantMean.</span></span><span class="sig-name descname"><span class="pre">constraints</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.ConstantMean.constraints" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ConstantMean.</span></span><span class="sig-name descname"><span class="pre">constraints</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#arch.univariate.ConstantMean.constraints" title="Link to this definition">¶</a></dt>
 <dd><p>Construct linear constraint arrays  for use in non-linear optimization</p>
 <dl class="field-list simple">
-<dt class="field-odd" id="arch.univariate.ConstantMean.constraints-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantMean.constraints-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>]</p>
-</dd>
-<dt class="field-even" id="arch.univariate.ConstantMean.constraints-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantMean.constraints-returns" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><ul class="simple">
-<li><p><strong>a</strong> (<em>ndarray</em>) – Number of constraints by number of parameters loading array</p></li>
-<li><p><strong>b</strong> (<em>ndarray</em>) – Number of constraints array of lower bounds</p></li>
+<dt class="field-odd" id="arch.univariate.ConstantMean.constraints-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantMean.constraints-returns" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p><strong>a</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – Number of constraints by number of parameters loading array</p></li>
+<li><p><strong>b</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – Number of constraints array of lower bounds</p></li>
 </ul>
 </p>
 </dd>
@@ -1221,7 +1204,7 @@ <h1 id="arch-univariate-constantmean-constraints">arch.univariate.ConstantMean.c
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1268,7 +1251,7 @@ <h1 id="arch-univariate-constantmean-constraints">arch.univariate.ConstantMean.c
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diff --git a/univariate/generated/generated/arch.univariate.ConstantMean.distribution.html b/univariate/generated/generated/arch.univariate.ConstantMean.distribution.html
index 7576972530..8338e94173 100644
--- a/univariate/generated/generated/arch.univariate.ConstantMean.distribution.html
+++ b/univariate/generated/generated/arch.univariate.ConstantMean.distribution.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantMean.distribution - arch 6.1.0</title>
+        <title>arch.univariate.ConstantMean.distribution - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1093,10 +1093,10 @@
                   
 
 
-<h1 id="arch-univariate-constantmean-distribution">arch.univariate.ConstantMean.distribution<a class="headerlink" href="#arch-univariate-constantmean-distribution" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-constantmean-distribution">arch.univariate.ConstantMean.distribution<a class="headerlink" href="#arch-univariate-constantmean-distribution" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ConstantMean.distribution">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ConstantMean.</span></span><span class="sig-name descname"><span class="pre">distribution</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.univariate.distribution.Distribution.html#arch.univariate.distribution.Distribution" title="arch.univariate.distribution.Distribution (Python class) — Template for subclassing only"><span class="pre">Distribution</span></a></span><a class="headerlink" href="#arch.univariate.ConstantMean.distribution" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ConstantMean.</span></span><span class="sig-name descname"><span class="pre">distribution</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.univariate.distribution.Distribution.html#arch.univariate.distribution.Distribution" title="arch.univariate.distribution.Distribution (Python class) — Template for subclassing only"><span class="pre">Distribution</span></a></span><a class="headerlink" href="#arch.univariate.ConstantMean.distribution" title="Link to this definition">¶</a></dt>
 <dd><p>Set or gets the error distribution</p>
 <p>Distributions must be a subclass of Distribution</p>
 </dd></dl>
@@ -1168,7 +1168,7 @@ <h1 id="arch-univariate-constantmean-distribution">arch.univariate.ConstantMean.
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
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@@ -1215,7 +1215,7 @@ <h1 id="arch-univariate-constantmean-distribution">arch.univariate.ConstantMean.
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.ConstantMean.fit.html b/univariate/generated/generated/arch.univariate.ConstantMean.fit.html
index e0ea6f5951..c06b7218ce 100644
--- a/univariate/generated/generated/arch.univariate.ConstantMean.fit.html
+++ b/univariate/generated/generated/arch.univariate.ConstantMean.fit.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantMean.fit - arch 6.1.0</title>
+        <title>arch.univariate.ConstantMean.fit - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch 6.1.0
+    arch 6.2.0
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@@ -1299,40 +1299,40 @@
                   
 
 
-<h1 id="arch-univariate-constantmean-fit">arch.univariate.ConstantMean.fit<a class="headerlink" href="#arch-univariate-constantmean-fit" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-constantmean-fit">arch.univariate.ConstantMean.fit<a class="headerlink" href="#arch-univariate-constantmean-fit" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ConstantMean.fit">
-<span class="sig-prename descclassname"><span class="pre">ConstantMean.</span></span><span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.fit.update_freq" title="arch.univariate.ConstantMean.fit.update_freq (Python parameter) — Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."><span class="n"><span class="pre">update_freq</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.fit.disp" title="arch.univariate.ConstantMean.fit.disp (Python parameter) — Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is &quot;off&quot; and True is &quot;final&quot;"><span class="n"><span class="pre">disp</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;final&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.fit.starting_values" title="arch.univariate.ConstantMean.fit.starting_values (Python parameter) — Array of starting values to use.  If not provided, starting values are constructed by the model components."><span class="n"><span class="pre">starting_values</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.fit.cov_type" title="arch.univariate.ConstantMean.fit.cov_type (Python parameter) — Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."><span class="n"><span class="pre">cov_type</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;robust&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.fit.show_warning" title="arch.univariate.ConstantMean.fit.show_warning (Python parameter) — Flag indicating whether convergence warnings should be shown."><span class="n"><span class="pre">show_warning</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.fit.first_obs" title="arch.univariate.ConstantMean.fit.first_obs (Python parameter) — First observation to use when estimating model"><span class="n"><span class="pre">first_obs</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.fit.last_obs" title="arch.univariate.ConstantMean.fit.last_obs (Python parameter) — Last observation to use when estimating model"><span class="n"><span class="pre">last_obs</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.fit.tol" title="arch.univariate.ConstantMean.fit.tol (Python parameter) — Tolerance for termination."><span class="n"><span class="pre">tol</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.fit.options" title="arch.univariate.ConstantMean.fit.options (Python parameter) — Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."><span class="n"><span class="pre">options</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.fit.backcast" title="arch.univariate.ConstantMean.fit.backcast (Python parameter) — Value to use as backcast. Should be measure \sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."><span class="n"><span class="pre">backcast</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.ConstantMean.fit" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ConstantMean.</span></span><span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.fit.update_freq" title="arch.univariate.ConstantMean.fit.update_freq (Python parameter) — Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."><span class="n"><span class="pre">update_freq</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.fit.disp" title="arch.univariate.ConstantMean.fit.disp (Python parameter) — Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is &quot;off&quot; and True is &quot;final&quot;"><span class="n"><span class="pre">disp</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'off'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'final'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;final&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.fit.starting_values" title="arch.univariate.ConstantMean.fit.starting_values (Python parameter) — Array of starting values to use.  If not provided, starting values are constructed by the model components."><span class="n"><span class="pre">starting_values</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.fit.cov_type" title="arch.univariate.ConstantMean.fit.cov_type (Python parameter) — Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."><span class="n"><span class="pre">cov_type</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'robust'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'classic'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;robust&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.fit.show_warning" title="arch.univariate.ConstantMean.fit.show_warning (Python parameter) — Flag indicating whether convergence warnings should be shown."><span class="n"><span class="pre">show_warning</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.fit.first_obs" title="arch.univariate.ConstantMean.fit.first_obs (Python parameter) — First observation to use when estimating model"><span class="n"><span class="pre">first_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.fit.last_obs" title="arch.univariate.ConstantMean.fit.last_obs (Python parameter) — Last observation to use when estimating model"><span class="n"><span class="pre">last_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.fit.tol" title="arch.univariate.ConstantMean.fit.tol (Python parameter) — Tolerance for termination."><span class="n"><span class="pre">tol</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.fit.options" title="arch.univariate.ConstantMean.fit.options (Python parameter) — Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."><span class="n"><span class="pre">options</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.fit.backcast" title="arch.univariate.ConstantMean.fit.backcast (Python parameter) — Value to use as backcast. Should be measure \sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelResult.html#arch.univariate.base.ARCHModelResult" title="arch.univariate.base.ARCHModelResult (Python class) — Results from estimation of an ARCHModel model"><span class="pre">ARCHModelResult</span></a></span></span></span><a class="headerlink" href="#arch.univariate.ConstantMean.fit" title="Link to this definition">¶</a></dt>
 <dd><p>Estimate model parameters</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ConstantMean.fit-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantMean.fit-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ConstantMean.fit.update_freq"><span class="sig-name descname">update_freq</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ConstantMean.fit.update_freq" title="Permalink to this definition">¶</a></dt><dd><p>Frequency of iteration updates.  Output is generated every
+<dt id="arch.univariate.ConstantMean.fit.update_freq"><span class="n sig-name">update_freq</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.fit.update_freq" title="Permalink to this definition">¶</a></dt><dd><p>Frequency of iteration updates.  Output is generated every
 <cite>update_freq</cite> iterations. Set to 0 to disable iterative output.</p>
 </dd>
-<dt id="arch.univariate.ConstantMean.fit.disp"><span class="sig-name descname">disp</span><span class="p"> : </span><span class="desctype"><span class="desctype">{bool, &quot;off&quot;, &quot;final&quot;}</span></span><a class="headerlink" href="#arch.univariate.ConstantMean.fit.disp" title="Permalink to this definition">¶</a></dt><dd><p>Either ‘final’ to print optimization result or ‘off’ to display
+<dt id="arch.univariate.ConstantMean.fit.disp"><span class="n sig-name">disp</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'off'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'final'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;final&#39;</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.fit.disp" title="Permalink to this definition">¶</a></dt><dd><p>Either ‘final’ to print optimization result or ‘off’ to display
 nothing. If using a boolean, False is “off” and True is “final”</p>
 </dd>
-<dt id="arch.univariate.ConstantMean.fit.starting_values"><span class="sig-name descname">starting_values</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray, optional</span></span><a class="headerlink" href="#arch.univariate.ConstantMean.fit.starting_values" title="Permalink to this definition">¶</a></dt><dd><p>Array of starting values to use.  If not provided, starting values
+<dt id="arch.univariate.ConstantMean.fit.starting_values"><span class="n sig-name">starting_values</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.fit.starting_values" title="Permalink to this definition">¶</a></dt><dd><p>Array of starting values to use.  If not provided, starting values
 are constructed by the model components.</p>
 </dd>
-<dt id="arch.univariate.ConstantMean.fit.cov_type"><span class="sig-name descname">cov_type</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ConstantMean.fit.cov_type" title="Permalink to this definition">¶</a></dt><dd><p>Estimation method of parameter covariance.  Supported options are
+<dt id="arch.univariate.ConstantMean.fit.cov_type"><span class="n sig-name">cov_type</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'robust'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'classic'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;robust&#39;</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.fit.cov_type" title="Permalink to this definition">¶</a></dt><dd><p>Estimation method of parameter covariance.  Supported options are
 ‘robust’, which does not assume the Information Matrix Equality
 holds and ‘classic’ which does.  In the ARCH literature, ‘robust’
 corresponds to Bollerslev-Wooldridge covariance estimator.</p>
 </dd>
-<dt id="arch.univariate.ConstantMean.fit.show_warning"><span class="sig-name descname">show_warning</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ConstantMean.fit.show_warning" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether convergence warnings should be shown.</p>
+<dt id="arch.univariate.ConstantMean.fit.show_warning"><span class="n sig-name">show_warning</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.fit.show_warning" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether convergence warnings should be shown.</p>
 </dd>
-<dt id="arch.univariate.ConstantMean.fit.first_obs"><span class="sig-name descname">first_obs</span><span class="p"> : </span><span class="desctype"><span class="desctype">{int, <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, datetime, Timestamp}</span></span><a class="headerlink" href="#arch.univariate.ConstantMean.fit.first_obs" title="Permalink to this definition">¶</a></dt><dd><p>First observation to use when estimating model</p>
+<dt id="arch.univariate.ConstantMean.fit.first_obs"><span class="n sig-name">first_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.fit.first_obs" title="Permalink to this definition">¶</a></dt><dd><p>First observation to use when estimating model</p>
 </dd>
-<dt id="arch.univariate.ConstantMean.fit.last_obs"><span class="sig-name descname">last_obs</span><span class="p"> : </span><span class="desctype"><span class="desctype">{int, <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, datetime, Timestamp}</span></span><a class="headerlink" href="#arch.univariate.ConstantMean.fit.last_obs" title="Permalink to this definition">¶</a></dt><dd><p>Last observation to use when estimating model</p>
+<dt id="arch.univariate.ConstantMean.fit.last_obs"><span class="n sig-name">last_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.fit.last_obs" title="Permalink to this definition">¶</a></dt><dd><p>Last observation to use when estimating model</p>
 </dd>
-<dt id="arch.univariate.ConstantMean.fit.tol"><span class="sig-name descname">tol</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ConstantMean.fit.tol" title="Permalink to this definition">¶</a></dt><dd><p>Tolerance for termination.</p>
+<dt id="arch.univariate.ConstantMean.fit.tol"><span class="n sig-name">tol</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.fit.tol" title="Permalink to this definition">¶</a></dt><dd><p>Tolerance for termination.</p>
 </dd>
-<dt id="arch.univariate.ConstantMean.fit.options"><span class="sig-name descname">options</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ConstantMean.fit.options" title="Permalink to this definition">¶</a></dt><dd><p>Options to pass to <cite>scipy.optimize.minimize</cite>.  Valid entries
+<dt id="arch.univariate.ConstantMean.fit.options"><span class="n sig-name">options</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)">Any</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.fit.options" title="Permalink to this definition">¶</a></dt><dd><p>Options to pass to <cite>scipy.optimize.minimize</cite>.  Valid entries
 include ‘ftol’, ‘eps’, ‘disp’, and ‘maxiter’.</p>
 </dd>
-<dt id="arch.univariate.ConstantMean.fit.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}, optional</span></span><a class="headerlink" href="#arch.univariate.ConstantMean.fit.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use as backcast. Should be measure <span class="math notranslate nohighlight">\(\sigma^2_0\)</span>
+<dt id="arch.univariate.ConstantMean.fit.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.fit.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use as backcast. Should be measure <span class="math notranslate nohighlight">\(\sigma^2_0\)</span>
 since model-specific non-linear transformations are applied to
 value before computing the variance recursions.</p>
 </dd>
@@ -1342,7 +1342,7 @@ <h1 id="arch-univariate-constantmean-fit">arch.univariate.ConstantMean.fit<a cla
 <dd class="field-even"><p><strong>results</strong> – Object containing model results</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.ConstantMean.fit-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantMean.fit-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference internal" href="../arch.univariate.base.ARCHModelResult.html#arch.univariate.base.ARCHModelResult" title="arch.univariate.base.ARCHModelResult (Python class) — Results from estimation of an ARCHModel model">ARCHModelResult</a></p>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">ARCHModelResult</span></code></p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -1418,7 +1418,7 @@ <h1 id="arch-univariate-constantmean-fit">arch.univariate.ConstantMean.fit<a cla
   
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diff --git a/univariate/generated/generated/arch.univariate.ConstantMean.fix.html b/univariate/generated/generated/arch.univariate.ConstantMean.fix.html
index 47f3134257..6c71464752 100644
--- a/univariate/generated/generated/arch.univariate.ConstantMean.fix.html
+++ b/univariate/generated/generated/arch.univariate.ConstantMean.fix.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantMean.fix - arch 6.1.0</title>
+        <title>arch.univariate.ConstantMean.fix - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1201,21 +1201,21 @@
                   
 
 
-<h1 id="arch-univariate-constantmean-fix">arch.univariate.ConstantMean.fix<a class="headerlink" href="#arch-univariate-constantmean-fix" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-constantmean-fix">arch.univariate.ConstantMean.fix<a class="headerlink" href="#arch-univariate-constantmean-fix" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.ConstantMean.fix">
-<span class="sig-prename descclassname"><span class="pre">ConstantMean.</span></span><span class="sig-name descname"><span class="pre">fix</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.fix.params" title="arch.univariate.ConstantMean.fix.params (Python parameter) — User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."><span class="n"><span class="pre">params</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.fix.first_obs" title="arch.univariate.ConstantMean.fix.first_obs (Python parameter) — First observation to use when fixing model"><span class="n"><span class="pre">first_obs</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.fix.last_obs" title="arch.univariate.ConstantMean.fix.last_obs (Python parameter) — Last observation to use when fixing model"><span class="n"><span class="pre">last_obs</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.ConstantMean.fix" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ConstantMean.fix">
+<span class="sig-prename descclassname"><span class="pre">ConstantMean.</span></span><span class="sig-name descname"><span class="pre">fix</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.fix.params" title="arch.univariate.ConstantMean.fix.params (Python parameter) — User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.fix.first_obs" title="arch.univariate.ConstantMean.fix.first_obs (Python parameter) — First observation to use when fixing model"><span class="n"><span class="pre">first_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.fix.last_obs" title="arch.univariate.ConstantMean.fix.last_obs (Python parameter) — Last observation to use when fixing model"><span class="n"><span class="pre">last_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelFixedResult.html#arch.univariate.base.ARCHModelFixedResult" title="arch.univariate.base.ARCHModelFixedResult (Python class) — Results for fixed parameters for an ARCHModel model"><span class="pre">ARCHModelFixedResult</span></a></span></span></span><a class="headerlink" href="#arch.univariate.ConstantMean.fix" title="Link to this definition">¶</a></dt>
 <dd><p>Allows an ARCHModelFixedResult to be constructed from fixed parameters.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ConstantMean.fix-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantMean.fix-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ConstantMean.fix.params"><span class="sig-name descname">params</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, Series}</span></span><a class="headerlink" href="#arch.univariate.ConstantMean.fix.params" title="Permalink to this definition">¶</a></dt><dd><p>User specified parameters to use when generating the result. Must
+<dt id="arch.univariate.ConstantMean.fix.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.ConstantMean.fix.params" title="Permalink to this definition">¶</a></dt><dd><p>User specified parameters to use when generating the result. Must
 have the correct number of parameters for a given choice of mean
 model, volatility model and distribution.</p>
 </dd>
-<dt id="arch.univariate.ConstantMean.fix.first_obs"><span class="sig-name descname">first_obs</span><span class="p"> : </span><span class="desctype"><span class="desctype">{int, <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, datetime, Timestamp}</span></span><a class="headerlink" href="#arch.univariate.ConstantMean.fix.first_obs" title="Permalink to this definition">¶</a></dt><dd><p>First observation to use when fixing model</p>
+<dt id="arch.univariate.ConstantMean.fix.first_obs"><span class="n sig-name">first_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.fix.first_obs" title="Permalink to this definition">¶</a></dt><dd><p>First observation to use when fixing model</p>
 </dd>
-<dt id="arch.univariate.ConstantMean.fix.last_obs"><span class="sig-name descname">last_obs</span><span class="p"> : </span><span class="desctype"><span class="desctype">{int, <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, datetime, Timestamp}</span></span><a class="headerlink" href="#arch.univariate.ConstantMean.fix.last_obs" title="Permalink to this definition">¶</a></dt><dd><p>Last observation to use when fixing model</p>
+<dt id="arch.univariate.ConstantMean.fix.last_obs"><span class="n sig-name">last_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.fix.last_obs" title="Permalink to this definition">¶</a></dt><dd><p>Last observation to use when fixing model</p>
 </dd>
 </dl>
 </dd>
@@ -1223,7 +1223,7 @@ <h1 id="arch-univariate-constantmean-fix">arch.univariate.ConstantMean.fix<a cla
 <dd class="field-even"><p><strong>results</strong> – Object containing model results</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.ConstantMean.fix-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantMean.fix-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference internal" href="../arch.univariate.base.ARCHModelFixedResult.html#arch.univariate.base.ARCHModelFixedResult" title="arch.univariate.base.ARCHModelFixedResult (Python class) — Results for fixed parameters for an ARCHModel model">ARCHModelFixedResult</a></p>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">ARCHModelFixedResult</span></code></p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -1297,7 +1297,7 @@ <h1 id="arch-univariate-constantmean-fix">arch.univariate.ConstantMean.fix<a cla
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1344,7 +1344,7 @@ <h1 id="arch-univariate-constantmean-fix">arch.univariate.ConstantMean.fix<a cla
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+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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diff --git a/univariate/generated/generated/arch.univariate.ConstantMean.forecast.html b/univariate/generated/generated/arch.univariate.ConstantMean.forecast.html
index 4796dd54f1..54107a628b 100644
--- a/univariate/generated/generated/arch.univariate.ConstantMean.forecast.html
+++ b/univariate/generated/generated/arch.univariate.ConstantMean.forecast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantMean.forecast - arch 6.1.0</title>
+        <title>arch.univariate.ConstantMean.forecast - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1299,56 +1299,56 @@
                   
 
 
-<h1 id="arch-univariate-constantmean-forecast">arch.univariate.ConstantMean.forecast<a class="headerlink" href="#arch-univariate-constantmean-forecast" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-constantmean-forecast">arch.univariate.ConstantMean.forecast<a class="headerlink" href="#arch-univariate-constantmean-forecast" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ConstantMean.forecast">
-<span class="sig-prename descclassname"><span class="pre">ConstantMean.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.forecast.params" title="arch.univariate.ConstantMean.forecast.params (Python parameter) — Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."><span class="n"><span class="pre">params</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.forecast.horizon" title="arch.univariate.ConstantMean.forecast.horizon (Python parameter) — Number of steps to forecast"><span class="n"><span class="pre">horizon</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.forecast.start" title="arch.univariate.ConstantMean.forecast.start (Python parameter) — An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."><span class="n"><span class="pre">start</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.forecast.align" title="arch.univariate.ConstantMean.forecast.align (Python parameter) — Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."><span class="n"><span class="pre">align</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;origin&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.forecast.method" title="arch.univariate.ConstantMean.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons &gt; 1."><span class="n"><span class="pre">method</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.forecast.simulations" title="arch.univariate.ConstantMean.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.forecast.rng" title="arch.univariate.ConstantMean.forecast.rng (Python parameter) — Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."><span class="n"><span class="pre">rng</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.forecast.random_state" title="arch.univariate.ConstantMean.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.forecast.reindex" title="arch.univariate.ConstantMean.forecast.reindex (Python parameter) — Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."><span class="n"><span class="pre">reindex</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.forecast.x" title="arch.univariate.ConstantMean.forecast.x (Python parameter) — Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"><span class="n"><span class="pre">x</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.ConstantMean.forecast" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ConstantMean.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.forecast.params" title="arch.univariate.ConstantMean.forecast.params (Python parameter) — Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.forecast.horizon" title="arch.univariate.ConstantMean.forecast.horizon (Python parameter) — Number of steps to forecast"><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.forecast.start" title="arch.univariate.ConstantMean.forecast.start (Python parameter) — An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.forecast.align" title="arch.univariate.ConstantMean.forecast.align (Python parameter) — Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."><span class="n"><span class="pre">align</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'origin'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'target'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;origin&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.forecast.method" title="arch.univariate.ConstantMean.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons &gt; 1."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.forecast.simulations" title="arch.univariate.ConstantMean.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.forecast.rng" title="arch.univariate.ConstantMean.forecast.rng (Python parameter) — Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.forecast.random_state" title="arch.univariate.ConstantMean.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.forecast.reindex" title="arch.univariate.ConstantMean.forecast.reindex (Python parameter) — Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."><span class="n"><span class="pre">reindex</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.forecast.x" title="arch.univariate.ConstantMean.forecast.x (Python parameter) — Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.11)"><span class="pre">Hashable</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelForecast.html#arch.univariate.base.ARCHModelForecast" title="arch.univariate.base.ARCHModelForecast (Python class) — Container for forecasts from an ARCH Model"><span class="pre">ARCHModelForecast</span></a></span></span></span><a class="headerlink" href="#arch.univariate.ConstantMean.forecast" title="Link to this definition">¶</a></dt>
 <dd><p>Construct forecasts from estimated model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ConstantMean.forecast-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantMean.forecast-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ConstantMean.forecast.params"><span class="sig-name descname">params</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, Series}</span></span><a class="headerlink" href="#arch.univariate.ConstantMean.forecast.params" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast. Must be identical in
+<dt id="arch.univariate.ConstantMean.forecast.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.ConstantMean.forecast.params" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast. Must be identical in
 shape to the parameters computed by fitting the model.</p>
 </dd>
-<dt id="arch.univariate.ConstantMean.forecast.horizon"><span class="sig-name descname">horizon</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ConstantMean.forecast.horizon" title="Permalink to this definition">¶</a></dt><dd><p>Number of steps to forecast</p>
+<dt id="arch.univariate.ConstantMean.forecast.horizon"><span class="n sig-name">horizon</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.forecast.horizon" title="Permalink to this definition">¶</a></dt><dd><p>Number of steps to forecast</p>
 </dd>
-<dt id="arch.univariate.ConstantMean.forecast.start"><span class="sig-name descname">start</span><span class="p"> : </span><span class="desctype"><span class="desctype">{int, datetime, Timestamp, str}, optional</span></span><a class="headerlink" href="#arch.univariate.ConstantMean.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>An integer, datetime or str indicating the first observation to
+<dt id="arch.univariate.ConstantMean.forecast.start"><span class="n sig-name">start</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>An integer, datetime or str indicating the first observation to
 produce the forecast for.  Datetimes can only be used with pandas
 inputs that have a datetime index. Strings must be convertible
 to a date time, such as in ‘1945-01-01’.</p>
 </dd>
-<dt id="arch.univariate.ConstantMean.forecast.align"><span class="sig-name descname">align</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ConstantMean.forecast.align" title="Permalink to this definition">¶</a></dt><dd><p>Either ‘origin’ or ‘target’.  When set of ‘origin’, the t-th row
+<dt id="arch.univariate.ConstantMean.forecast.align"><span class="n sig-name">align</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'origin'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'target'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;origin&#39;</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.forecast.align" title="Permalink to this definition">¶</a></dt><dd><p>Either ‘origin’ or ‘target’.  When set of ‘origin’, the t-th row
 of forecasts contains the forecasts for t+1, t+2, …, t+h. When
 set to ‘target’, the t-th row contains the 1-step ahead forecast
 from time t-1, the 2 step from time t-2, …, and the h-step from
 time t-h.  ‘target’ simplified computing forecast errors since the
 realization and h-step forecast are aligned.</p>
 </dd>
-<dt id="arch.univariate.ConstantMean.forecast.method"><span class="sig-name descname">method</span><span class="p"> : </span><span class="desctype"><span class="desctype">{'analytic', 'simulation', 'bootstrap'}</span></span><a class="headerlink" href="#arch.univariate.ConstantMean.forecast.method" title="Permalink to this definition">¶</a></dt><dd><p>Method to use when producing the forecast. The default is analytic.
+<dt id="arch.univariate.ConstantMean.forecast.method"><span class="n sig-name">method</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'analytic'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'simulation'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'bootstrap'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.forecast.method" title="Permalink to this definition">¶</a></dt><dd><p>Method to use when producing the forecast. The default is analytic.
 The method only affects the variance forecast generation.  Not all
 volatility models support all methods. In particular, volatility
 models that do not evolve in squares such as EGARCH or TARCH do not
 support the ‘analytic’ method for horizons &gt; 1.</p>
 </dd>
-<dt id="arch.univariate.ConstantMean.forecast.simulations"><span class="sig-name descname">simulations</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.ConstantMean.forecast.simulations" title="Permalink to this definition">¶</a></dt><dd><p>Number of simulations to run when computing the forecast using
+<dt id="arch.univariate.ConstantMean.forecast.simulations"><span class="n sig-name">simulations</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.forecast.simulations" title="Permalink to this definition">¶</a></dt><dd><p>Number of simulations to run when computing the forecast using
 either simulation or bootstrap.</p>
 </dd>
-<dt id="arch.univariate.ConstantMean.forecast.rng"><span class="sig-name descname">rng</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable, optional</span></span><a class="headerlink" href="#arch.univariate.ConstantMean.forecast.rng" title="Permalink to this definition">¶</a></dt><dd><p>Custom random number generator to use in simulation-based forecasts.
+<dt id="arch.univariate.ConstantMean.forecast.rng"><span class="n sig-name">rng</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)">Callable</a><span class="p">[</span><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">,</span><span class="w"> </span><span class="p">...</span><span class="p">]</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.forecast.rng" title="Permalink to this definition">¶</a></dt><dd><p>Custom random number generator to use in simulation-based forecasts.
 Must produce random samples using the syntax <cite>rng(size)</cite> where size
 the 2-element tuple (simulations, horizon).</p>
 </dd>
-<dt id="arch.univariate.ConstantMean.forecast.random_state"><span class="sig-name descname">random_state</span><span class="p"> : </span><span class="desctype"><span class="desctype">RandomState, optional</span></span><a class="headerlink" href="#arch.univariate.ConstantMean.forecast.random_state" title="Permalink to this definition">¶</a></dt><dd><p>NumPy RandomState instance to use when method is ‘bootstrap’</p>
+<dt id="arch.univariate.ConstantMean.forecast.random_state"><span class="n sig-name">random_state</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)">RandomState</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.forecast.random_state" title="Permalink to this definition">¶</a></dt><dd><p>NumPy RandomState instance to use when method is ‘bootstrap’</p>
 </dd>
-<dt id="arch.univariate.ConstantMean.forecast.reindex"><span class="sig-name descname">reindex</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ConstantMean.forecast.reindex" title="Permalink to this definition">¶</a></dt><dd><p><p>Whether to reindex the forecasts to have the same dimension as the series
+<dt id="arch.univariate.ConstantMean.forecast.reindex"><span class="n sig-name">reindex</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.forecast.reindex" title="Permalink to this definition">¶</a></dt><dd><p><p>Whether to reindex the forecasts to have the same dimension as the series
 being forecast. Prior to 4.18 this was the default. As of 4.19 this is
 now optional. If not provided, a warning is raised about the future
 change in the default which will occur after September 2021.</p>
-<div class="versionadded">
-<p><span class="versionmodified added">New in version 4.19.</span></p>
+<div class="admonition versionadded">
+<p class="admonition-title"><span class="versionmodified added">New in version 4.19.</span></p>
 </div>
 </p>
 </dd>
-<dt id="arch.univariate.ConstantMean.forecast.x"><span class="sig-name descname">x</span><span class="p"> : </span><span class="desctype"><span class="desctype">{dict[label, array_like], array_like}</span></span><a class="headerlink" href="#arch.univariate.ConstantMean.forecast.x" title="Permalink to this definition">¶</a></dt><dd><p><p>Values to use for exogenous regressors if any are included in the
+<dt id="arch.univariate.ConstantMean.forecast.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.11)">Hashable</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.forecast.x" title="Permalink to this definition">¶</a></dt><dd><p><p>Values to use for exogenous regressors if any are included in the
 model. Three formats are accepted:</p>
 <ul class="simple">
 <li><p>2-d array-like: This format can be used when there is a single
@@ -1371,8 +1371,8 @@ <h1 id="arch-univariate-constantmean-forecast">arch.univariate.ConstantMean.fore
 <p>Due to the complexity required to accommodate all scenarios, please
 see the example notebook that demonstrates the valid formats for
 x.</p>
-<div class="versionadded">
-<p><span class="versionmodified added">New in version 4.19.</span></p>
+<div class="admonition versionadded">
+<p class="admonition-title"><span class="versionmodified added">New in version 4.19.</span></p>
 </div>
 </p>
 </dd>
@@ -1383,7 +1383,7 @@ <h1 id="arch-univariate-constantmean-forecast">arch.univariate.ConstantMean.fore
 <code class="docutils literal notranslate"><span class="pre">variance</span></code> and <code class="docutils literal notranslate"><span class="pre">residual_variance</span></code>.</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.ConstantMean.forecast-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantMean.forecast-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference internal" href="../arch.univariate.base.ARCHModelForecast.html#arch.univariate.base.ARCHModelForecast" title="arch.univariate.base.ARCHModelForecast (Python class) — Container for forecasts from an ARCH Model">arch.univariate.base.ARCHModelForecast</a></p>
+<dd class="field-odd"><p><a class="reference internal" href="../arch.univariate.base.ARCHModelForecast.html#arch.univariate.base.ARCHModelForecast" title="arch.univariate.base.ARCHModelForecast (Python class) — Container for forecasts from an ARCH Model"><code class="xref py py-class docutils literal notranslate"><span class="pre">arch.univariate.base.ARCHModelForecast</span></code></a></p>
 </dd>
 </dl>
 <p class="example rubric">Examples</p>
@@ -1483,7 +1483,7 @@ <h1 id="arch-univariate-constantmean-forecast">arch.univariate.ConstantMean.fore
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1530,7 +1530,7 @@ <h1 id="arch-univariate-constantmean-forecast">arch.univariate.ConstantMean.fore
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.ConstantMean.name.html b/univariate/generated/generated/arch.univariate.ConstantMean.name.html
index e208ed9a65..9117467b5e 100644
--- a/univariate/generated/generated/arch.univariate.ConstantMean.name.html
+++ b/univariate/generated/generated/arch.univariate.ConstantMean.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantMean.name - arch 6.1.0</title>
+        <title>arch.univariate.ConstantMean.name - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1093,10 +1093,10 @@
                   
 
 
-<h1 id="arch-univariate-constantmean-name">arch.univariate.ConstantMean.name<a class="headerlink" href="#arch-univariate-constantmean-name" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-constantmean-name">arch.univariate.ConstantMean.name<a class="headerlink" href="#arch-univariate-constantmean-name" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ConstantMean.name">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ConstantMean.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.univariate.ConstantMean.name" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ConstantMean.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.univariate.ConstantMean.name" title="Link to this definition">¶</a></dt>
 <dd><p>The name of the model.</p>
 </dd></dl>
 
@@ -1167,7 +1167,7 @@ <h1 id="arch-univariate-constantmean-name">arch.univariate.ConstantMean.name<a c
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1214,7 +1214,7 @@ <h1 id="arch-univariate-constantmean-name">arch.univariate.ConstantMean.name<a c
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.ConstantMean.num_params.html b/univariate/generated/generated/arch.univariate.ConstantMean.num_params.html
index eaf2fc410e..2f20f53cfd 100644
--- a/univariate/generated/generated/arch.univariate.ConstantMean.num_params.html
+++ b/univariate/generated/generated/arch.univariate.ConstantMean.num_params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantMean.num_params - arch 6.1.0</title>
+        <title>arch.univariate.ConstantMean.num_params - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1093,10 +1093,10 @@
                   
 
 
-<h1 id="arch-univariate-constantmean-num-params">arch.univariate.ConstantMean.num_params<a class="headerlink" href="#arch-univariate-constantmean-num-params" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-constantmean-num-params">arch.univariate.ConstantMean.num_params<a class="headerlink" href="#arch-univariate-constantmean-num-params" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ConstantMean.num_params">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ConstantMean.</span></span><span class="sig-name descname"><span class="pre">num_params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.ConstantMean.num_params" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ConstantMean.</span></span><span class="sig-name descname"><span class="pre">num_params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.ConstantMean.num_params" title="Link to this definition">¶</a></dt>
 <dd><p>Returns the number of parameters</p>
 </dd></dl>
 
@@ -1167,7 +1167,7 @@ <h1 id="arch-univariate-constantmean-num-params">arch.univariate.ConstantMean.nu
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
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@@ -1214,7 +1214,7 @@ <h1 id="arch-univariate-constantmean-num-params">arch.univariate.ConstantMean.nu
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.ConstantMean.parameter_names.html b/univariate/generated/generated/arch.univariate.ConstantMean.parameter_names.html
index 965fe67dba..37de387786 100644
--- a/univariate/generated/generated/arch.univariate.ConstantMean.parameter_names.html
+++ b/univariate/generated/generated/arch.univariate.ConstantMean.parameter_names.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantMean.parameter_names - arch 6.1.0</title>
+        <title>arch.univariate.ConstantMean.parameter_names - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1133,17 +1133,17 @@
                   
 
 
-<h1 id="arch-univariate-constantmean-parameter-names">arch.univariate.ConstantMean.parameter_names<a class="headerlink" href="#arch-univariate-constantmean-parameter-names" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-constantmean-parameter-names">arch.univariate.ConstantMean.parameter_names<a class="headerlink" href="#arch-univariate-constantmean-parameter-names" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ConstantMean.parameter_names">
-<span class="sig-prename descclassname"><span class="pre">ConstantMean.</span></span><span class="sig-name descname"><span class="pre">parameter_names</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/mean.html#ConstantMean.parameter_names"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ConstantMean.parameter_names" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ConstantMean.</span></span><span class="sig-name descname"><span class="pre">parameter_names</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/mean.html#ConstantMean.parameter_names"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ConstantMean.parameter_names" title="Link to this definition">¶</a></dt>
 <dd><p>List of parameters names</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ConstantMean.parameter_names-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantMean.parameter_names-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p><strong>names</strong> – List of variable names for the mean model</p>
 </dd>
 <dt class="field-even" id="arch.univariate.ConstantMean.parameter_names-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantMean.parameter_names-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>)</p>
+<dd class="field-even"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span> <span class="pre">(str)</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1215,7 +1215,7 @@ <h1 id="arch-univariate-constantmean-parameter-names">arch.univariate.ConstantMe
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1262,7 +1262,7 @@ <h1 id="arch-univariate-constantmean-parameter-names">arch.univariate.ConstantMe
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.ConstantMean.resids.html b/univariate/generated/generated/arch.univariate.ConstantMean.resids.html
index f2f31eb61f..74bc3d18e0 100644
--- a/univariate/generated/generated/arch.univariate.ConstantMean.resids.html
+++ b/univariate/generated/generated/arch.univariate.ConstantMean.resids.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantMean.resids - arch 6.1.0</title>
+        <title>arch.univariate.ConstantMean.resids - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1201,19 +1201,19 @@
                   
 
 
-<h1 id="arch-univariate-constantmean-resids">arch.univariate.ConstantMean.resids<a class="headerlink" href="#arch-univariate-constantmean-resids" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-constantmean-resids">arch.univariate.ConstantMean.resids<a class="headerlink" href="#arch-univariate-constantmean-resids" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.ConstantMean.resids">
-<span class="sig-prename descclassname"><span class="pre">ConstantMean.</span></span><span class="sig-name descname"><span class="pre">resids</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.resids.params" title="arch.univariate.ConstantMean.resids.params (Python parameter) — Model parameters"><span class="n"><span class="pre">params</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.resids.y" title="arch.univariate.ConstantMean.resids.y (Python parameter) — Alternative values to use when computing model residuals"><span class="n"><span class="pre">y</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.resids.regressors" title="arch.univariate.ConstantMean.resids.regressors (Python parameter) — Alternative regressor values to use when computing model residuals"><span class="n"><span class="pre">regressors</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/mean.html#ConstantMean.resids"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ConstantMean.resids" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ConstantMean.resids">
+<span class="sig-prename descclassname"><span class="pre">ConstantMean.</span></span><span class="sig-name descname"><span class="pre">resids</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.resids.params" title="arch.univariate.ConstantMean.resids.params (Python parameter) — Model parameters"><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.resids.y" title="arch.univariate.ConstantMean.resids.y (Python parameter) — Alternative values to use when computing model residuals"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.resids.regressors" title="arch.univariate.ConstantMean.resids.regressors (Python parameter) — Alternative regressor values to use when computing model residuals"><span class="n"><span class="pre">regressors</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/mean.html#ConstantMean.resids"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ConstantMean.resids" title="Link to this definition">¶</a></dt>
 <dd><p>Compute model residuals</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ConstantMean.resids-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantMean.resids-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ConstantMean.resids.params"><span class="sig-name descname">params</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.ConstantMean.resids.params" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
+<dt id="arch.univariate.ConstantMean.resids.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.ConstantMean.resids.params" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
 </dd>
-<dt id="arch.univariate.ConstantMean.resids.y"><span class="sig-name descname">y</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray, optional</span></span><a class="headerlink" href="#arch.univariate.ConstantMean.resids.y" title="Permalink to this definition">¶</a></dt><dd><p>Alternative values to use when computing model residuals</p>
+<dt id="arch.univariate.ConstantMean.resids.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.resids.y" title="Permalink to this definition">¶</a></dt><dd><p>Alternative values to use when computing model residuals</p>
 </dd>
-<dt id="arch.univariate.ConstantMean.resids.regressors"><span class="sig-name descname">regressors</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray, optional</span></span><a class="headerlink" href="#arch.univariate.ConstantMean.resids.regressors" title="Permalink to this definition">¶</a></dt><dd><p>Alternative regressor values to use when computing model residuals</p>
+<dt id="arch.univariate.ConstantMean.resids.regressors"><span class="n sig-name">regressors</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.resids.regressors" title="Permalink to this definition">¶</a></dt><dd><p>Alternative regressor values to use when computing model residuals</p>
 </dd>
 </dl>
 </dd>
@@ -1221,7 +1221,7 @@ <h1 id="arch-univariate-constantmean-resids">arch.univariate.ConstantMean.resids
 <dd class="field-even"><p><strong>resids</strong> – Model residuals</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.ConstantMean.resids-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantMean.resids-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1293,7 +1293,7 @@ <h1 id="arch-univariate-constantmean-resids">arch.univariate.ConstantMean.resids
   
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diff --git a/univariate/generated/generated/arch.univariate.ConstantMean.simulate.html b/univariate/generated/generated/arch.univariate.ConstantMean.simulate.html
index 4f80d2d273..c40da2aed7 100644
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+++ b/univariate/generated/generated/arch.univariate.ConstantMean.simulate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantMean.simulate - arch 6.1.0</title>
+        <title>arch.univariate.ConstantMean.simulate - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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@@ -102,7 +102,7 @@
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@@ -1243,28 +1243,28 @@
                   
 
 
-<h1 id="arch-univariate-constantmean-simulate">arch.univariate.ConstantMean.simulate<a class="headerlink" href="#arch-univariate-constantmean-simulate" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-constantmean-simulate">arch.univariate.ConstantMean.simulate<a class="headerlink" href="#arch-univariate-constantmean-simulate" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ConstantMean.simulate">
-<span class="sig-prename descclassname"><span class="pre">ConstantMean.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.simulate.params" title="arch.univariate.ConstantMean.simulate.params (Python parameter) — Parameters to use when simulating the model.  Parameter order is [mean volatility distribution]. There is one parameter in the mean model, mu."><span class="n"><span class="pre">params</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.simulate.nobs" title="arch.univariate.ConstantMean.simulate.nobs (Python parameter) — Length of series to simulate"><span class="n"><span class="pre">nobs</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.simulate.burn" title="arch.univariate.ConstantMean.simulate.burn (Python parameter) — Number of values to simulate to initialize the model and remove dependence on initial values."><span class="n"><span class="pre">burn</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.simulate.initial_value" title="arch.univariate.ConstantMean.simulate.initial_value (Python parameter) — This value is not used."><span class="n"><span class="pre">initial_value</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.simulate.x" title="arch.univariate.ConstantMean.simulate.x (Python parameter) — This value is not used."><span class="n"><span class="pre">x</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.simulate.initial_value_vol" title="arch.univariate.ConstantMean.simulate.initial_value_vol (Python parameter) — An array or scalar to use when initializing the volatility process."><span class="n"><span class="pre">initial_value_vol</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/mean.html#ConstantMean.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ConstantMean.simulate" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ConstantMean.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.simulate.params" title="arch.univariate.ConstantMean.simulate.params (Python parameter) — Parameters to use when simulating the model.  Parameter order is [mean volatility distribution]. There is one parameter in the mean model, mu."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.simulate.nobs" title="arch.univariate.ConstantMean.simulate.nobs (Python parameter) — Length of series to simulate"><span class="n"><span class="pre">nobs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.simulate.burn" title="arch.univariate.ConstantMean.simulate.burn (Python parameter) — Number of values to simulate to initialize the model and remove dependence on initial values."><span class="n"><span class="pre">burn</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.simulate.initial_value" title="arch.univariate.ConstantMean.simulate.initial_value (Python parameter) — This value is not used."><span class="n"><span class="pre">initial_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.simulate.x" title="arch.univariate.ConstantMean.simulate.x (Python parameter) — This value is not used."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.simulate.initial_value_vol" title="arch.univariate.ConstantMean.simulate.initial_value_vol (Python parameter) — An array or scalar to use when initializing the volatility process."><span class="n"><span class="pre">initial_value_vol</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/mean.html#ConstantMean.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ConstantMean.simulate" title="Link to this definition">¶</a></dt>
 <dd><p>Simulated data from a constant mean model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ConstantMean.simulate-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantMean.simulate-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ConstantMean.simulate.params"><span class="sig-name descname">params</span><span class="p"> : </span><span class="desctype"><span class="desctype">array_like</span></span><a class="headerlink" href="#arch.univariate.ConstantMean.simulate.params" title="Permalink to this definition">¶</a></dt><dd><p>Parameters to use when simulating the model.  Parameter order is
+<dt id="arch.univariate.ConstantMean.simulate.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.univariate.ConstantMean.simulate.params" title="Permalink to this definition">¶</a></dt><dd><p>Parameters to use when simulating the model.  Parameter order is
 [mean volatility distribution]. There is one parameter in the mean
 model, mu.</p>
 </dd>
-<dt id="arch.univariate.ConstantMean.simulate.nobs"><span class="sig-name descname">nobs</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.ConstantMean.simulate.nobs" title="Permalink to this definition">¶</a></dt><dd><p>Length of series to simulate</p>
+<dt id="arch.univariate.ConstantMean.simulate.nobs"><span class="n sig-name">nobs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.ConstantMean.simulate.nobs" title="Permalink to this definition">¶</a></dt><dd><p>Length of series to simulate</p>
 </dd>
-<dt id="arch.univariate.ConstantMean.simulate.burn"><span class="sig-name descname">burn</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ConstantMean.simulate.burn" title="Permalink to this definition">¶</a></dt><dd><p>Number of values to simulate to initialize the model and remove
+<dt id="arch.univariate.ConstantMean.simulate.burn"><span class="n sig-name">burn</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.simulate.burn" title="Permalink to this definition">¶</a></dt><dd><p>Number of values to simulate to initialize the model and remove
 dependence on initial values.</p>
 </dd>
-<dt id="arch.univariate.ConstantMean.simulate.initial_value"><span class="sig-name descname">initial_value</span><span class="p"> : </span><span class="desctype"><span class="desctype">None</span></span><a class="headerlink" href="#arch.univariate.ConstantMean.simulate.initial_value" title="Permalink to this definition">¶</a></dt><dd><p>This value is not used.</p>
+<dt id="arch.univariate.ConstantMean.simulate.initial_value"><span class="n sig-name">initial_value</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.simulate.initial_value" title="Permalink to this definition">¶</a></dt><dd><p>This value is not used.</p>
 </dd>
-<dt id="arch.univariate.ConstantMean.simulate.x"><span class="sig-name descname">x</span><span class="p"> : </span><span class="desctype"><span class="desctype">None</span></span><a class="headerlink" href="#arch.univariate.ConstantMean.simulate.x" title="Permalink to this definition">¶</a></dt><dd><p>This value is not used.</p>
+<dt id="arch.univariate.ConstantMean.simulate.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.simulate.x" title="Permalink to this definition">¶</a></dt><dd><p>This value is not used.</p>
 </dd>
-<dt id="arch.univariate.ConstantMean.simulate.initial_value_vol"><span class="sig-name descname">initial_value_vol</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, float}, optional</span></span><a class="headerlink" href="#arch.univariate.ConstantMean.simulate.initial_value_vol" title="Permalink to this definition">¶</a></dt><dd><p>An array or scalar to use when initializing the volatility process.</p>
+<dt id="arch.univariate.ConstantMean.simulate.initial_value_vol"><span class="n sig-name">initial_value_vol</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.simulate.initial_value_vol" title="Permalink to this definition">¶</a></dt><dd><p>An array or scalar to use when initializing the volatility process.</p>
 </dd>
 </dl>
 </dd>
@@ -1274,7 +1274,7 @@ <h1 id="arch-univariate-constantmean-simulate">arch.univariate.ConstantMean.simu
 containing the errors used in the simulation</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.ConstantMean.simulate-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantMean.simulate-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>DataFrame</p>
+<dd class="field-odd"><p><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code></a></p>
 </dd>
 </dl>
 <p class="example rubric">Examples</p>
@@ -1358,7 +1358,7 @@ <h1 id="arch-univariate-constantmean-simulate">arch.univariate.ConstantMean.simu
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1405,7 +1405,7 @@ <h1 id="arch-univariate-constantmean-simulate">arch.univariate.ConstantMean.simu
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.ConstantMean.starting_values.html b/univariate/generated/generated/arch.univariate.ConstantMean.starting_values.html
index a8f971b458..8a09522447 100644
--- a/univariate/generated/generated/arch.univariate.ConstantMean.starting_values.html
+++ b/univariate/generated/generated/arch.univariate.ConstantMean.starting_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantMean.starting_values - arch 6.1.0</title>
+        <title>arch.univariate.ConstantMean.starting_values - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1133,10 +1133,10 @@
                   
 
 
-<h1 id="arch-univariate-constantmean-starting-values">arch.univariate.ConstantMean.starting_values<a class="headerlink" href="#arch-univariate-constantmean-starting-values" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-constantmean-starting-values">arch.univariate.ConstantMean.starting_values<a class="headerlink" href="#arch-univariate-constantmean-starting-values" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ConstantMean.starting_values">
-<span class="sig-prename descclassname"><span class="pre">ConstantMean.</span></span><span class="sig-name descname"><span class="pre">starting_values</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.ConstantMean.starting_values" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ConstantMean.</span></span><span class="sig-name descname"><span class="pre">starting_values</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.univariate.ConstantMean.starting_values" title="Link to this definition">¶</a></dt>
 <dd><p>Returns starting values for the mean model, often the same as the
 values returned from fit</p>
 <dl class="field-list simple">
@@ -1144,7 +1144,7 @@ <h1 id="arch-univariate-constantmean-starting-values">arch.univariate.ConstantMe
 <dd class="field-odd"><p><strong>sv</strong> – Starting values</p>
 </dd>
 <dt class="field-even" id="arch.univariate.ConstantMean.starting_values-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantMean.starting_values-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p>ndarray</p>
+<dd class="field-even"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1216,7 +1216,7 @@ <h1 id="arch-univariate-constantmean-starting-values">arch.univariate.ConstantMe
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1263,7 +1263,7 @@ <h1 id="arch-univariate-constantmean-starting-values">arch.univariate.ConstantMe
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.ConstantMean.volatility.html b/univariate/generated/generated/arch.univariate.ConstantMean.volatility.html
index 924d8129d2..b711993b24 100644
--- a/univariate/generated/generated/arch.univariate.ConstantMean.volatility.html
+++ b/univariate/generated/generated/arch.univariate.ConstantMean.volatility.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantMean.volatility - arch 6.1.0</title>
+        <title>arch.univariate.ConstantMean.volatility - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1093,10 +1093,10 @@
                   
 
 
-<h1 id="arch-univariate-constantmean-volatility">arch.univariate.ConstantMean.volatility<a class="headerlink" href="#arch-univariate-constantmean-volatility" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-constantmean-volatility">arch.univariate.ConstantMean.volatility<a class="headerlink" href="#arch-univariate-constantmean-volatility" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ConstantMean.volatility">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ConstantMean.</span></span><span class="sig-name descname"><span class="pre">volatility</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.univariate.volatility.VolatilityProcess.html#arch.univariate.volatility.VolatilityProcess" title="arch.univariate.volatility.VolatilityProcess (Python class) — Abstract base class for ARCH models.  Allows the conditional mean model to be specified separately from the conditional variance, even though parameters are estimated jointly."><span class="pre">VolatilityProcess</span></a></span><a class="headerlink" href="#arch.univariate.ConstantMean.volatility" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ConstantMean.</span></span><span class="sig-name descname"><span class="pre">volatility</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.univariate.volatility.VolatilityProcess.html#arch.univariate.volatility.VolatilityProcess" title="arch.univariate.volatility.VolatilityProcess (Python class) — Abstract base class for ARCH models.  Allows the conditional mean model to be specified separately from the conditional variance, even though parameters are estimated jointly."><span class="pre">VolatilityProcess</span></a></span><a class="headerlink" href="#arch.univariate.ConstantMean.volatility" title="Link to this definition">¶</a></dt>
 <dd><p>Set or gets the volatility process</p>
 <p>Volatility processes must be a subclass of VolatilityProcess</p>
 </dd></dl>
@@ -1168,7 +1168,7 @@ <h1 id="arch-univariate-constantmean-volatility">arch.univariate.ConstantMean.vo
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1215,7 +1215,7 @@ <h1 id="arch-univariate-constantmean-volatility">arch.univariate.ConstantMean.vo
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diff --git a/univariate/generated/generated/arch.univariate.ConstantMean.x.html b/univariate/generated/generated/arch.univariate.ConstantMean.x.html
index 8f46707b6a..5030d97b09 100644
--- a/univariate/generated/generated/arch.univariate.ConstantMean.x.html
+++ b/univariate/generated/generated/arch.univariate.ConstantMean.x.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantMean.x - arch 6.1.0</title>
+        <title>arch.univariate.ConstantMean.x - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1093,10 +1093,10 @@
                   
 
 
-<h1 id="arch-univariate-constantmean-x">arch.univariate.ConstantMean.x<a class="headerlink" href="#arch-univariate-constantmean-x" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-constantmean-x">arch.univariate.ConstantMean.x<a class="headerlink" href="#arch-univariate-constantmean-x" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ConstantMean.x">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ConstantMean.</span></span><span class="sig-name descname"><span class="pre">x</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.0.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.ConstantMean.x" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ConstantMean.</span></span><span class="sig-name descname"><span class="pre">x</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.ConstantMean.x" title="Link to this definition">¶</a></dt>
 <dd><p>Gets the value of the exogenous regressors in the model</p>
 </dd></dl>
 
@@ -1167,7 +1167,7 @@ <h1 id="arch-univariate-constantmean-x">arch.univariate.ConstantMean.x<a class="
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1214,7 +1214,7 @@ <h1 id="arch-univariate-constantmean-x">arch.univariate.ConstantMean.x<a class="
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
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diff --git a/univariate/generated/generated/arch.univariate.ConstantMean.y.html b/univariate/generated/generated/arch.univariate.ConstantMean.y.html
index eed3d48b1c..3d9aa48b9a 100644
--- a/univariate/generated/generated/arch.univariate.ConstantMean.y.html
+++ b/univariate/generated/generated/arch.univariate.ConstantMean.y.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantMean.y - arch 6.1.0</title>
+        <title>arch.univariate.ConstantMean.y - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
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-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1093,10 +1093,10 @@
                   
 
 
-<h1 id="arch-univariate-constantmean-y">arch.univariate.ConstantMean.y<a class="headerlink" href="#arch-univariate-constantmean-y" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-constantmean-y">arch.univariate.ConstantMean.y<a class="headerlink" href="#arch-univariate-constantmean-y" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ConstantMean.y">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ConstantMean.</span></span><span class="sig-name descname"><span class="pre">y</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.0.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.0.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.ConstantMean.y" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ConstantMean.</span></span><span class="sig-name descname"><span class="pre">y</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.ConstantMean.y" title="Link to this definition">¶</a></dt>
 <dd><p>Returns the dependent variable</p>
 </dd></dl>
 
@@ -1167,7 +1167,7 @@ <h1 id="arch-univariate-constantmean-y">arch.univariate.ConstantMean.y<a class="
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1214,7 +1214,7 @@ <h1 id="arch-univariate-constantmean-y">arch.univariate.ConstantMean.y<a class="
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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diff --git a/univariate/generated/generated/arch.univariate.ConstantVariance.backcast.html b/univariate/generated/generated/arch.univariate.ConstantVariance.backcast.html
index 853bb8fef3..e4db24c027 100644
--- a/univariate/generated/generated/arch.univariate.ConstantVariance.backcast.html
+++ b/univariate/generated/generated/arch.univariate.ConstantVariance.backcast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantVariance.backcast - arch 6.1.0</title>
+        <title>arch.univariate.ConstantVariance.backcast - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1248,15 +1248,15 @@
                   
 
 
-<h1 id="arch-univariate-constantvariance-backcast">arch.univariate.ConstantVariance.backcast<a class="headerlink" href="#arch-univariate-constantvariance-backcast" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-constantvariance-backcast">arch.univariate.ConstantVariance.backcast<a class="headerlink" href="#arch-univariate-constantvariance-backcast" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ConstantVariance.backcast">
-<span class="sig-prename descclassname"><span class="pre">ConstantVariance.</span></span><span class="sig-name descname"><span class="pre">backcast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.backcast.resids" title="arch.univariate.ConstantVariance.backcast.resids (Python parameter) — Vector of (approximate) residuals"><span class="n"><span class="pre">resids</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#ConstantVariance.backcast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ConstantVariance.backcast" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ConstantVariance.</span></span><span class="sig-name descname"><span class="pre">backcast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.backcast.resids" title="arch.univariate.ConstantVariance.backcast.resids (Python parameter) — Vector of (approximate) residuals"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#ConstantVariance.backcast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ConstantVariance.backcast" title="Link to this definition">¶</a></dt>
 <dd><p>Construct values for backcasting to start the recursion</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ConstantVariance.backcast-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantVariance.backcast-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ConstantVariance.backcast.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.backcast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of (approximate) residuals</p>
+<dt id="arch.univariate.ConstantVariance.backcast.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.backcast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of (approximate) residuals</p>
 </dd>
 </dl>
 </dd>
@@ -1264,7 +1264,7 @@ <h1 id="arch-univariate-constantvariance-backcast">arch.univariate.ConstantVaria
 <dd class="field-even"><p><strong>backcast</strong> – Value to use in backcasting in the volatility recursion</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.ConstantVariance.backcast-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantVariance.backcast-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1336,7 +1336,7 @@ <h1 id="arch-univariate-constantvariance-backcast">arch.univariate.ConstantVaria
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1383,7 +1383,7 @@ <h1 id="arch-univariate-constantvariance-backcast">arch.univariate.ConstantVaria
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
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diff --git a/univariate/generated/generated/arch.univariate.ConstantVariance.backcast_transform.html b/univariate/generated/generated/arch.univariate.ConstantVariance.backcast_transform.html
index 862aebe276..c0a6e5fd0c 100644
--- a/univariate/generated/generated/arch.univariate.ConstantVariance.backcast_transform.html
+++ b/univariate/generated/generated/arch.univariate.ConstantVariance.backcast_transform.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantVariance.backcast_transform - arch 6.1.0</title>
+        <title>arch.univariate.ConstantVariance.backcast_transform - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1248,15 +1248,15 @@
                   
 
 
-<h1 id="arch-univariate-constantvariance-backcast-transform">arch.univariate.ConstantVariance.backcast_transform<a class="headerlink" href="#arch-univariate-constantvariance-backcast-transform" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-constantvariance-backcast-transform">arch.univariate.ConstantVariance.backcast_transform<a class="headerlink" href="#arch-univariate-constantvariance-backcast-transform" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.ConstantVariance.backcast_transform">
-<span class="sig-prename descclassname"><span class="pre">ConstantVariance.</span></span><span class="sig-name descname"><span class="pre">backcast_transform</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.backcast_transform.backcast" title="arch.univariate.ConstantVariance.backcast_transform.backcast (Python parameter) — User-provided backcast that approximates sigma2[0]."><span class="n"><span class="pre">backcast</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#ConstantVariance.backcast_transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ConstantVariance.backcast_transform" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ConstantVariance.backcast_transform">
+<span class="sig-prename descclassname"><span class="pre">ConstantVariance.</span></span><span class="sig-name descname"><span class="pre">backcast_transform</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.backcast_transform.backcast" title="arch.univariate.ConstantVariance.backcast_transform.backcast (Python parameter) — User-provided backcast that approximates sigma2[0]."><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#ConstantVariance.backcast_transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ConstantVariance.backcast_transform" title="Link to this definition">¶</a></dt>
 <dd><p>Transformation to apply to user-provided backcast values</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ConstantVariance.backcast_transform-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantVariance.backcast_transform-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ConstantVariance.backcast_transform.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}</span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.backcast_transform.backcast" title="Permalink to this definition">¶</a></dt><dd><p>User-provided <code class="docutils literal notranslate"><span class="pre">backcast</span></code> that approximates sigma2[0].</p>
+<dt id="arch.univariate.ConstantVariance.backcast_transform.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.backcast_transform.backcast" title="Permalink to this definition">¶</a></dt><dd><p>User-provided <code class="docutils literal notranslate"><span class="pre">backcast</span></code> that approximates sigma2[0].</p>
 </dd>
 </dl>
 </dd>
@@ -1264,7 +1264,7 @@ <h1 id="arch-univariate-constantvariance-backcast-transform">arch.univariate.Con
 <dd class="field-even"><p><strong>backcast</strong> – Backcast transformed to the model-appropriate scale</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.ConstantVariance.backcast_transform-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantVariance.backcast_transform-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>{float, ndarray}</p>
+<dd class="field-odd"><p><code class="docutils literal notranslate"><span class="pre">{float,</span> <span class="pre">ndarray}</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1336,7 +1336,7 @@ <h1 id="arch-univariate-constantvariance-backcast-transform">arch.univariate.Con
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1383,7 +1383,7 @@ <h1 id="arch-univariate-constantvariance-backcast-transform">arch.univariate.Con
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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diff --git a/univariate/generated/generated/arch.univariate.ConstantVariance.bounds.html b/univariate/generated/generated/arch.univariate.ConstantVariance.bounds.html
index 81ef597a31..59dd65852e 100644
--- a/univariate/generated/generated/arch.univariate.ConstantVariance.bounds.html
+++ b/univariate/generated/generated/arch.univariate.ConstantVariance.bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantVariance.bounds - arch 6.1.0</title>
+        <title>arch.univariate.ConstantVariance.bounds - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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+<h1 id="arch-univariate-constantvariance-bounds">arch.univariate.ConstantVariance.bounds<a class="headerlink" href="#arch-univariate-constantvariance-bounds" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.ConstantVariance.bounds">
-<span class="sig-prename descclassname"><span class="pre">ConstantVariance.</span></span><span class="sig-name descname"><span class="pre">bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.bounds.resids" title="arch.univariate.ConstantVariance.bounds.resids (Python parameter) — Vector of (approximate) residuals"><span class="n"><span class="pre">resids</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#ConstantVariance.bounds"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ConstantVariance.bounds" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ConstantVariance.bounds">
+<span class="sig-prename descclassname"><span class="pre">ConstantVariance.</span></span><span class="sig-name descname"><span class="pre">bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.bounds.resids" title="arch.univariate.ConstantVariance.bounds.resids (Python parameter) — Vector of (approximate) residuals"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#ConstantVariance.bounds"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ConstantVariance.bounds" title="Link to this definition">¶</a></dt>
 <dd><p>Returns bounds for parameters</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ConstantVariance.bounds-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantVariance.bounds-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
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+<dt id="arch.univariate.ConstantVariance.bounds.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.bounds.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of (approximate) residuals</p>
 </dd>
 </dl>
 </dd>
@@ -1264,7 +1264,7 @@ <h1 id="arch-univariate-constantvariance-bounds">arch.univariate.ConstantVarianc
 <dd class="field-even"><p><strong>bounds</strong> – List of bounds where each element is (lower, upper).</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.ConstantVariance.bounds-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantVariance.bounds-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a>[<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a>[<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>,<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>]]</p>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">list[tuple[float,float]]</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1336,7 +1336,7 @@ <h1 id="arch-univariate-constantvariance-bounds">arch.univariate.ConstantVarianc
   
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-  
 </li>
         
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@@ -1290,30 +1276,27 @@
                   
 
 
-<h1 id="arch-univariate-constantvariance-compute-variance">arch.univariate.ConstantVariance.compute_variance<a class="headerlink" href="#arch-univariate-constantvariance-compute-variance" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-constantvariance-compute-variance">arch.univariate.ConstantVariance.compute_variance<a class="headerlink" href="#arch-univariate-constantvariance-compute-variance" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ConstantVariance.compute_variance">
-<span class="sig-prename descclassname"><span class="pre">ConstantVariance.</span></span><span class="sig-name descname"><span class="pre">compute_variance</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.compute_variance.parameters" title="arch.univariate.ConstantVariance.compute_variance.parameters (Python parameter) — Model parameters"><span class="n"><span class="pre">parameters</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.compute_variance.resids" title="arch.univariate.ConstantVariance.compute_variance.resids (Python parameter) — Vector of mean zero residuals"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.compute_variance.sigma2" title="arch.univariate.ConstantVariance.compute_variance.sigma2 (Python parameter) — Array with same size as resids to store the conditional variance"><span class="n"><span class="pre">sigma2</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.compute_variance.backcast" title="arch.univariate.ConstantVariance.compute_variance.backcast (Python parameter) — Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."><span class="n"><span class="pre">backcast</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.compute_variance.var_bounds" title="arch.univariate.ConstantVariance.compute_variance.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#ConstantVariance.compute_variance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ConstantVariance.compute_variance" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ConstantVariance.</span></span><span class="sig-name descname"><span class="pre">compute_variance</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.compute_variance.parameters" title="arch.univariate.ConstantVariance.compute_variance.parameters (Python parameter) — Model parameters"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.compute_variance.resids" title="arch.univariate.ConstantVariance.compute_variance.resids (Python parameter) — Vector of mean zero residuals"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.compute_variance.sigma2" title="arch.univariate.ConstantVariance.compute_variance.sigma2 (Python parameter) — Array with same size as resids to store the conditional variance"><span class="n"><span class="pre">sigma2</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.compute_variance.backcast" title="arch.univariate.ConstantVariance.compute_variance.backcast (Python parameter) — Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.compute_variance.var_bounds" title="arch.univariate.ConstantVariance.compute_variance.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#ConstantVariance.compute_variance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ConstantVariance.compute_variance" title="Link to this definition">¶</a></dt>
 <dd><p>Compute the variance for the ARCH model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ConstantVariance.compute_variance-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantVariance.compute_variance-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ConstantVariance.compute_variance.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.compute_variance.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
+<dt id="arch.univariate.ConstantVariance.compute_variance.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.compute_variance.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
 </dd>
-<dt id="arch.univariate.ConstantVariance.compute_variance.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.compute_variance.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of mean zero residuals</p>
+<dt id="arch.univariate.ConstantVariance.compute_variance.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.compute_variance.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of mean zero residuals</p>
 </dd>
-<dt id="arch.univariate.ConstantVariance.compute_variance.sigma2"><span class="sig-name descname">sigma2</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.compute_variance.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>Array with same size as resids to store the conditional variance</p>
+<dt id="arch.univariate.ConstantVariance.compute_variance.sigma2"><span class="n sig-name">sigma2</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.compute_variance.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>Array with same size as resids to store the conditional variance</p>
 </dd>
-<dt id="arch.univariate.ConstantVariance.compute_variance.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}</span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.compute_variance.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing ARCH recursion. Can be an ndarray
+<dt id="arch.univariate.ConstantVariance.compute_variance.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.compute_variance.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing ARCH recursion. Can be an ndarray
 when the model contains multiple components.</p>
 </dd>
-<dt id="arch.univariate.ConstantVariance.compute_variance.var_bounds"><span class="sig-name descname">var_bounds</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.compute_variance.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
+<dt id="arch.univariate.ConstantVariance.compute_variance.var_bounds"><span class="n sig-name">var_bounds</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.compute_variance.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
 </dd>
 </dl>
 </dd>
-<dt class="field-even" id="arch.univariate.ConstantVariance.compute_variance-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantVariance.compute_variance-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a></p>
-</dd>
 </dl>
 </dd></dl>
 
@@ -1384,7 +1367,7 @@ <h1 id="arch-univariate-constantvariance-compute-variance">arch.univariate.Const
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1431,7 +1414,7 @@ <h1 id="arch-univariate-constantvariance-compute-variance">arch.univariate.Const
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diff --git a/univariate/generated/generated/arch.univariate.ConstantVariance.constraints.html b/univariate/generated/generated/arch.univariate.ConstantVariance.constraints.html
index 03e067e4fe..07c26e50f6 100644
--- a/univariate/generated/generated/arch.univariate.ConstantVariance.constraints.html
+++ b/univariate/generated/generated/arch.univariate.ConstantVariance.constraints.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantVariance.constraints - arch 6.1.0</title>
+        <title>arch.univariate.ConstantVariance.constraints - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -566,13 +566,6 @@
     <nav class="md-nav" aria-label="ConstantVariance.constraints">
       <ul class="md-nav__list">
         
-          <li class="md-nav__item">
-  <a href="#arch.univariate.ConstantVariance.constraints-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
-</li>
-        
           <li class="md-nav__item">
   <a href="#arch.univariate.ConstantVariance.constraints-returns" class="md-nav__link">
     <span class="md-ellipsis">Returns</span>
@@ -1175,13 +1168,6 @@
     <nav class="md-nav" aria-label="ConstantVariance.constraints">
       <ul class="md-nav__list">
         
-          <li class="md-nav__item">
-  <a href="#arch.univariate.ConstantVariance.constraints-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
-</li>
-        
           <li class="md-nav__item">
   <a href="#arch.univariate.ConstantVariance.constraints-returns" class="md-nav__link">
     <span class="md-ellipsis">Returns</span>
@@ -1208,20 +1194,17 @@
                   
 
 
-<h1 id="arch-univariate-constantvariance-constraints">arch.univariate.ConstantVariance.constraints<a class="headerlink" href="#arch-univariate-constantvariance-constraints" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-constantvariance-constraints">arch.univariate.ConstantVariance.constraints<a class="headerlink" href="#arch-univariate-constantvariance-constraints" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ConstantVariance.constraints">
-<span class="sig-prename descclassname"><span class="pre">ConstantVariance.</span></span><span class="sig-name descname"><span class="pre">constraints</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#ConstantVariance.constraints"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ConstantVariance.constraints" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ConstantVariance.</span></span><span class="sig-name descname"><span class="pre">constraints</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#ConstantVariance.constraints"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ConstantVariance.constraints" title="Link to this definition">¶</a></dt>
 <dd><p>Construct parameter constraints arrays for parameter estimation</p>
 <dl class="field-list simple">
-<dt class="field-odd" id="arch.univariate.ConstantVariance.constraints-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantVariance.constraints-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>]</p>
-</dd>
-<dt class="field-even" id="arch.univariate.ConstantVariance.constraints-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantVariance.constraints-returns" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><ul class="simple">
-<li><p><strong>A</strong> (<em>ndarray</em>) – Parameters loadings in constraint. Shape is number of constraints
+<dt class="field-odd" id="arch.univariate.ConstantVariance.constraints-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantVariance.constraints-returns" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p><strong>A</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – Parameters loadings in constraint. Shape is number of constraints
 by number of parameters</p></li>
-<li><p><strong>b</strong> (<em>ndarray</em>) – Constraint values, one for each constraint</p></li>
+<li><p><strong>b</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – Constraint values, one for each constraint</p></li>
 </ul>
 </p>
 </dd>
@@ -1298,7 +1281,7 @@ <h1 id="arch-univariate-constantvariance-constraints">arch.univariate.ConstantVa
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1345,7 +1328,7 @@ <h1 id="arch-univariate-constantvariance-constraints">arch.univariate.ConstantVa
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.ConstantVariance.forecast.html b/univariate/generated/generated/arch.univariate.ConstantVariance.forecast.html
index c08a067356..7e0c7729cf 100644
--- a/univariate/generated/generated/arch.univariate.ConstantVariance.forecast.html
+++ b/univariate/generated/generated/arch.univariate.ConstantVariance.forecast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantVariance.forecast - arch 6.1.0</title>
+        <title>arch.univariate.ConstantVariance.forecast - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1388,38 +1388,38 @@
                   
 
 
-<h1 id="arch-univariate-constantvariance-forecast">arch.univariate.ConstantVariance.forecast<a class="headerlink" href="#arch-univariate-constantvariance-forecast" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-constantvariance-forecast">arch.univariate.ConstantVariance.forecast<a class="headerlink" href="#arch-univariate-constantvariance-forecast" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ConstantVariance.forecast">
-<span class="sig-prename descclassname"><span class="pre">ConstantVariance.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.forecast.parameters" title="arch.univariate.ConstantVariance.forecast.parameters (Python parameter) — Parameters required to forecast the volatility model"><span class="n"><span class="pre">parameters</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.forecast.resids" title="arch.univariate.ConstantVariance.forecast.resids (Python parameter) — Residuals to use in the recursion"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.forecast.backcast" title="arch.univariate.ConstantVariance.forecast.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.forecast.var_bounds" title="arch.univariate.ConstantVariance.forecast.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.forecast.start" title="arch.univariate.ConstantVariance.forecast.start (Python parameter) — Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."><span class="n"><span class="pre">start</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.forecast.horizon" title="arch.univariate.ConstantVariance.forecast.horizon (Python parameter) — Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."><span class="n"><span class="pre">horizon</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.forecast.method" title="arch.univariate.ConstantVariance.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic."><span class="n"><span class="pre">method</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.forecast.simulations" title="arch.univariate.ConstantVariance.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.forecast.rng" title="arch.univariate.ConstantVariance.forecast.rng (Python parameter) — Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."><span class="n"><span class="pre">rng</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.forecast.random_state" title="arch.univariate.ConstantVariance.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.ConstantVariance.forecast" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ConstantVariance.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.forecast.parameters" title="arch.univariate.ConstantVariance.forecast.parameters (Python parameter) — Parameters required to forecast the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.forecast.resids" title="arch.univariate.ConstantVariance.forecast.resids (Python parameter) — Residuals to use in the recursion"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.forecast.backcast" title="arch.univariate.ConstantVariance.forecast.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.forecast.var_bounds" title="arch.univariate.ConstantVariance.forecast.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.forecast.start" title="arch.univariate.ConstantVariance.forecast.start (Python parameter) — Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.forecast.horizon" title="arch.univariate.ConstantVariance.forecast.horizon (Python parameter) — Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.forecast.method" title="arch.univariate.ConstantVariance.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.forecast.simulations" title="arch.univariate.ConstantVariance.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.forecast.rng" title="arch.univariate.ConstantVariance.forecast.rng (Python parameter) — Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.forecast.random_state" title="arch.univariate.ConstantVariance.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><span class="pre">VarianceForecast</span></span></span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.forecast" title="Link to this definition">¶</a></dt>
 <dd><p>Forecast volatility from the model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ConstantVariance.forecast-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantVariance.forecast-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ConstantVariance.forecast.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, Series}</span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.forecast.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast the volatility model</p>
+<dt id="arch.univariate.ConstantVariance.forecast.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.forecast.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast the volatility model</p>
 </dd>
-<dt id="arch.univariate.ConstantVariance.forecast.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.forecast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Residuals to use in the recursion</p>
+<dt id="arch.univariate.ConstantVariance.forecast.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.forecast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Residuals to use in the recursion</p>
 </dd>
-<dt id="arch.univariate.ConstantVariance.forecast.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.forecast.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing the recursion</p>
+<dt id="arch.univariate.ConstantVariance.forecast.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.forecast.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing the recursion</p>
 </dd>
-<dt id="arch.univariate.ConstantVariance.forecast.var_bounds"><span class="sig-name descname">var_bounds</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray, 2-d</span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.forecast.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
+<dt id="arch.univariate.ConstantVariance.forecast.var_bounds"><span class="n sig-name">var_bounds</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.forecast.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
 </dd>
-<dt id="arch.univariate.ConstantVariance.forecast.start"><span class="sig-name descname">start</span><span class="p"> : </span><span class="desctype"><span class="desctype">{None, int}</span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>Index of the first observation to use as the starting point for
+<dt id="arch.univariate.ConstantVariance.forecast.start"><span class="n sig-name">start</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ConstantVariance.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>Index of the first observation to use as the starting point for
 the forecast.  Default is len(resids).</p>
 </dd>
-<dt id="arch.univariate.ConstantVariance.forecast.horizon"><span class="sig-name descname">horizon</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.forecast.horizon" title="Permalink to this definition">¶</a></dt><dd><p>Forecast horizon.  Must be 1 or larger.  Forecasts are produced
+<dt id="arch.univariate.ConstantVariance.forecast.horizon"><span class="n sig-name">horizon</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code><a class="headerlink" href="#arch.univariate.ConstantVariance.forecast.horizon" title="Permalink to this definition">¶</a></dt><dd><p>Forecast horizon.  Must be 1 or larger.  Forecasts are produced
 for horizons in [1, horizon].</p>
 </dd>
-<dt id="arch.univariate.ConstantVariance.forecast.method"><span class="sig-name descname">method</span><span class="p"> : </span><span class="desctype"><span class="desctype">{'analytic', 'simulation', 'bootstrap'}</span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.forecast.method" title="Permalink to this definition">¶</a></dt><dd><p>Method to use when producing the forecast. The default is analytic.</p>
+<dt id="arch.univariate.ConstantVariance.forecast.method"><span class="n sig-name">method</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'analytic'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'simulation'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'bootstrap'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code><a class="headerlink" href="#arch.univariate.ConstantVariance.forecast.method" title="Permalink to this definition">¶</a></dt><dd><p>Method to use when producing the forecast. The default is analytic.</p>
 </dd>
-<dt id="arch.univariate.ConstantVariance.forecast.simulations"><span class="sig-name descname">simulations</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.forecast.simulations" title="Permalink to this definition">¶</a></dt><dd><p>Number of simulations to run when computing the forecast using
+<dt id="arch.univariate.ConstantVariance.forecast.simulations"><span class="n sig-name">simulations</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.univariate.ConstantVariance.forecast.simulations" title="Permalink to this definition">¶</a></dt><dd><p>Number of simulations to run when computing the forecast using
 either simulation or bootstrap.</p>
 </dd>
-<dt id="arch.univariate.ConstantVariance.forecast.rng"><span class="sig-name descname">rng</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable</span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.forecast.rng" title="Permalink to this definition">¶</a></dt><dd><p>Callable random number generator required if method is
+<dt id="arch.univariate.ConstantVariance.forecast.rng"><span class="n sig-name">rng</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)">Callable</a><span class="p">[</span><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">,</span><span class="w"> </span><span class="p">...</span><span class="p">]</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ConstantVariance.forecast.rng" title="Permalink to this definition">¶</a></dt><dd><p>Callable random number generator required if method is
 ‘simulation’. Must take a single shape input and return random
 samples numbers with that shape.</p>
 </dd>
-<dt id="arch.univariate.ConstantVariance.forecast.random_state"><span class="sig-name descname">random_state</span><span class="p"> : </span><span class="desctype"><span class="desctype">RandomState, optional</span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.forecast.random_state" title="Permalink to this definition">¶</a></dt><dd><p>NumPy RandomState instance to use when method is ‘bootstrap’</p>
+<dt id="arch.univariate.ConstantVariance.forecast.random_state"><span class="n sig-name">random_state</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)">RandomState</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ConstantVariance.forecast.random_state" title="Permalink to this definition">¶</a></dt><dd><p>NumPy RandomState instance to use when method is ‘bootstrap’</p>
 </dd>
 </dl>
 </dd>
@@ -1428,7 +1428,7 @@ <h1 id="arch-univariate-constantvariance-forecast">arch.univariate.ConstantVaria
 or bootstrap, the simulated paths.</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.ConstantVariance.forecast-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantVariance.forecast-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>VarianceForecast</p>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">arch.univariate.volatility.VarianceForecast</span></code></p>
 </dd>
 <dt class="field-even" id="arch.univariate.ConstantVariance.forecast-raises">Raises<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantVariance.forecast-raises" title="Permalink to this headline">¶</a></dt>
 <dd class="field-even"><ul class="simple">
@@ -1515,7 +1515,7 @@ <h1 id="arch-univariate-constantvariance-forecast">arch.univariate.ConstantVaria
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1562,7 +1562,7 @@ <h1 id="arch-univariate-constantvariance-forecast">arch.univariate.ConstantVaria
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.ConstantVariance.name.html b/univariate/generated/generated/arch.univariate.ConstantVariance.name.html
index 45429766e2..b3985a237e 100644
--- a/univariate/generated/generated/arch.univariate.ConstantVariance.name.html
+++ b/univariate/generated/generated/arch.univariate.ConstantVariance.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantVariance.name - arch 6.1.0</title>
+        <title>arch.univariate.ConstantVariance.name - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1168,10 +1168,10 @@
                   
 
 
-<h1 id="arch-univariate-constantvariance-name">arch.univariate.ConstantVariance.name<a class="headerlink" href="#arch-univariate-constantvariance-name" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-constantvariance-name">arch.univariate.ConstantVariance.name<a class="headerlink" href="#arch-univariate-constantvariance-name" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ConstantVariance.name">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ConstantVariance.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.univariate.ConstantVariance.name" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ConstantVariance.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.univariate.ConstantVariance.name" title="Link to this definition">¶</a></dt>
 <dd><p>The name of the volatility process</p>
 </dd></dl>
 
@@ -1242,7 +1242,7 @@ <h1 id="arch-univariate-constantvariance-name">arch.univariate.ConstantVariance.
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1289,7 +1289,7 @@ <h1 id="arch-univariate-constantvariance-name">arch.univariate.ConstantVariance.
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.ConstantVariance.num_params.html b/univariate/generated/generated/arch.univariate.ConstantVariance.num_params.html
index a59a915a8f..0452939f37 100644
--- a/univariate/generated/generated/arch.univariate.ConstantVariance.num_params.html
+++ b/univariate/generated/generated/arch.univariate.ConstantVariance.num_params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantVariance.num_params - arch 6.1.0</title>
+        <title>arch.univariate.ConstantVariance.num_params - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1168,10 +1168,10 @@
                   
 
 
-<h1 id="arch-univariate-constantvariance-num-params">arch.univariate.ConstantVariance.num_params<a class="headerlink" href="#arch-univariate-constantvariance-num-params" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-constantvariance-num-params">arch.univariate.ConstantVariance.num_params<a class="headerlink" href="#arch-univariate-constantvariance-num-params" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ConstantVariance.num_params">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ConstantVariance.</span></span><span class="sig-name descname"><span class="pre">num_params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.ConstantVariance.num_params" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ConstantVariance.</span></span><span class="sig-name descname"><span class="pre">num_params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.ConstantVariance.num_params" title="Link to this definition">¶</a></dt>
 <dd><p>The number of parameters in the model</p>
 </dd></dl>
 
@@ -1242,7 +1242,7 @@ <h1 id="arch-univariate-constantvariance-num-params">arch.univariate.ConstantVar
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1289,7 +1289,7 @@ <h1 id="arch-univariate-constantvariance-num-params">arch.univariate.ConstantVar
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.ConstantVariance.parameter_names.html b/univariate/generated/generated/arch.univariate.ConstantVariance.parameter_names.html
index e1b6efb6a0..74bdb9066c 100644
--- a/univariate/generated/generated/arch.univariate.ConstantVariance.parameter_names.html
+++ b/univariate/generated/generated/arch.univariate.ConstantVariance.parameter_names.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantVariance.parameter_names - arch 6.1.0</title>
+        <title>arch.univariate.ConstantVariance.parameter_names - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1208,17 +1208,17 @@
                   
 
 
-<h1 id="arch-univariate-constantvariance-parameter-names">arch.univariate.ConstantVariance.parameter_names<a class="headerlink" href="#arch-univariate-constantvariance-parameter-names" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-constantvariance-parameter-names">arch.univariate.ConstantVariance.parameter_names<a class="headerlink" href="#arch-univariate-constantvariance-parameter-names" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ConstantVariance.parameter_names">
-<span class="sig-prename descclassname"><span class="pre">ConstantVariance.</span></span><span class="sig-name descname"><span class="pre">parameter_names</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#ConstantVariance.parameter_names"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ConstantVariance.parameter_names" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ConstantVariance.</span></span><span class="sig-name descname"><span class="pre">parameter_names</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#ConstantVariance.parameter_names"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ConstantVariance.parameter_names" title="Link to this definition">¶</a></dt>
 <dd><p>Names of model parameters</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ConstantVariance.parameter_names-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantVariance.parameter_names-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p><strong>names</strong> – Variables names</p>
 </dd>
 <dt class="field-even" id="arch.univariate.ConstantVariance.parameter_names-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantVariance.parameter_names-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>)</p>
+<dd class="field-even"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span> <span class="pre">(str)</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1290,7 +1290,7 @@ <h1 id="arch-univariate-constantvariance-parameter-names">arch.univariate.Consta
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
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@@ -1337,7 +1337,7 @@ <h1 id="arch-univariate-constantvariance-parameter-names">arch.univariate.Consta
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diff --git a/univariate/generated/generated/arch.univariate.ConstantVariance.simulate.html b/univariate/generated/generated/arch.univariate.ConstantVariance.simulate.html
index db78235fe2..0f31520c46 100644
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+++ b/univariate/generated/generated/arch.univariate.ConstantVariance.simulate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantVariance.simulate - arch 6.1.0</title>
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@@ -1304,36 +1290,33 @@
                   
 
 
-<h1 id="arch-univariate-constantvariance-simulate">arch.univariate.ConstantVariance.simulate<a class="headerlink" href="#arch-univariate-constantvariance-simulate" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-constantvariance-simulate">arch.univariate.ConstantVariance.simulate<a class="headerlink" href="#arch-univariate-constantvariance-simulate" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ConstantVariance.simulate">
-<span class="sig-prename descclassname"><span class="pre">ConstantVariance.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.simulate.parameters" title="arch.univariate.ConstantVariance.simulate.parameters (Python parameter) — Parameters required to simulate the volatility model"><span class="n"><span class="pre">parameters</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.simulate.nobs" title="arch.univariate.ConstantVariance.simulate.nobs (Python parameter) — Number of data points to simulate"><span class="n"><span class="pre">nobs</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.simulate.rng" title="arch.univariate.ConstantVariance.simulate.rng (Python parameter) — Callable function that takes a single integer input and returns a vector of random numbers"><span class="n"><span class="pre">rng</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.simulate.burn" title="arch.univariate.ConstantVariance.simulate.burn (Python parameter) — Number of additional observations to generate when initializing the simulation"><span class="n"><span class="pre">burn</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.simulate.initial_value" title="arch.univariate.ConstantVariance.simulate.initial_value (Python parameter) — Scalar or array of initial values to use when initializing the simulation"><span class="n"><span class="pre">initial_value</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#ConstantVariance.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ConstantVariance.simulate" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ConstantVariance.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.simulate.parameters" title="arch.univariate.ConstantVariance.simulate.parameters (Python parameter) — Parameters required to simulate the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.simulate.nobs" title="arch.univariate.ConstantVariance.simulate.nobs (Python parameter) — Number of data points to simulate"><span class="n"><span class="pre">nobs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.simulate.rng" title="arch.univariate.ConstantVariance.simulate.rng (Python parameter) — Callable function that takes a single integer input and returns a vector of random numbers"><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.simulate.burn" title="arch.univariate.ConstantVariance.simulate.burn (Python parameter) — Number of additional observations to generate when initializing the simulation"><span class="n"><span class="pre">burn</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.simulate.initial_value" title="arch.univariate.ConstantVariance.simulate.initial_value (Python parameter) — Scalar or array of initial values to use when initializing the simulation"><span class="n"><span class="pre">initial_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#ConstantVariance.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ConstantVariance.simulate" title="Link to this definition">¶</a></dt>
 <dd><p>Simulate data from the model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ConstantVariance.simulate-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantVariance.simulate-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ConstantVariance.simulate.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, Series}</span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to simulate the volatility model</p>
+<dt id="arch.univariate.ConstantVariance.simulate.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to simulate the volatility model</p>
 </dd>
-<dt id="arch.univariate.ConstantVariance.simulate.nobs"><span class="sig-name descname">nobs</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.simulate.nobs" title="Permalink to this definition">¶</a></dt><dd><p>Number of data points to simulate</p>
+<dt id="arch.univariate.ConstantVariance.simulate.nobs"><span class="n sig-name">nobs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.simulate.nobs" title="Permalink to this definition">¶</a></dt><dd><p>Number of data points to simulate</p>
 </dd>
-<dt id="arch.univariate.ConstantVariance.simulate.rng"><span class="sig-name descname">rng</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable</span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.simulate.rng" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that takes a single integer input and returns
+<dt id="arch.univariate.ConstantVariance.simulate.rng"><span class="n sig-name">rng</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)">Callable</a><span class="p">[</span><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">,</span><span class="w"> </span><span class="p">...</span><span class="p">]</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.simulate.rng" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that takes a single integer input and returns
 a vector of random numbers</p>
 </dd>
-<dt id="arch.univariate.ConstantVariance.simulate.burn"><span class="sig-name descname">burn</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.simulate.burn" title="Permalink to this definition">¶</a></dt><dd><p>Number of additional observations to generate when initializing
+<dt id="arch.univariate.ConstantVariance.simulate.burn"><span class="n sig-name">burn</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code><a class="headerlink" href="#arch.univariate.ConstantVariance.simulate.burn" title="Permalink to this definition">¶</a></dt><dd><p>Number of additional observations to generate when initializing
 the simulation</p>
 </dd>
-<dt id="arch.univariate.ConstantVariance.simulate.initial_value"><span class="sig-name descname">initial_value</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}, optional</span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.simulate.initial_value" title="Permalink to this definition">¶</a></dt><dd><p>Scalar or array of initial values to use when initializing the
+<dt id="arch.univariate.ConstantVariance.simulate.initial_value"><span class="n sig-name">initial_value</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ConstantVariance.simulate.initial_value" title="Permalink to this definition">¶</a></dt><dd><p>Scalar or array of initial values to use when initializing the
 simulation</p>
 </dd>
 </dl>
 </dd>
-<dt class="field-even" id="arch.univariate.ConstantVariance.simulate-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantVariance.simulate-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>]</p>
-</dd>
-<dt class="field-odd" id="arch.univariate.ConstantVariance.simulate-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantVariance.simulate-returns" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><ul class="simple">
-<li><p><strong>resids</strong> (<em>ndarray</em>) – The simulated residuals</p></li>
-<li><p><strong>variance</strong> (<em>ndarray</em>) – The simulated variance</p></li>
+<dt class="field-even" id="arch.univariate.ConstantVariance.simulate-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantVariance.simulate-returns" title="Permalink to this headline">¶</a></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><strong>resids</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – The simulated residuals</p></li>
+<li><p><strong>variance</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – The simulated variance</p></li>
 </ul>
 </p>
 </dd>
@@ -1407,7 +1390,7 @@ <h1 id="arch-univariate-constantvariance-simulate">arch.univariate.ConstantVaria
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1454,7 +1437,7 @@ <h1 id="arch-univariate-constantvariance-simulate">arch.univariate.ConstantVaria
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.ConstantVariance.start.html b/univariate/generated/generated/arch.univariate.ConstantVariance.start.html
index ec2bc8acdd..7cca8654e1 100644
--- a/univariate/generated/generated/arch.univariate.ConstantVariance.start.html
+++ b/univariate/generated/generated/arch.univariate.ConstantVariance.start.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantVariance.start - arch 6.1.0</title>
+        <title>arch.univariate.ConstantVariance.start - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1168,10 +1168,10 @@
                   
 
 
-<h1 id="arch-univariate-constantvariance-start">arch.univariate.ConstantVariance.start<a class="headerlink" href="#arch-univariate-constantvariance-start" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-constantvariance-start">arch.univariate.ConstantVariance.start<a class="headerlink" href="#arch-univariate-constantvariance-start" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ConstantVariance.start">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ConstantVariance.</span></span><span class="sig-name descname"><span class="pre">start</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.ConstantVariance.start" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ConstantVariance.</span></span><span class="sig-name descname"><span class="pre">start</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.ConstantVariance.start" title="Link to this definition">¶</a></dt>
 <dd><p>Index to use to start variance subarray selection</p>
 </dd></dl>
 
@@ -1242,7 +1242,7 @@ <h1 id="arch-univariate-constantvariance-start">arch.univariate.ConstantVariance
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1289,7 +1289,7 @@ <h1 id="arch-univariate-constantvariance-start">arch.univariate.ConstantVariance
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.ConstantVariance.starting_values.html b/univariate/generated/generated/arch.univariate.ConstantVariance.starting_values.html
index 86c6c653e7..5e83f4e096 100644
--- a/univariate/generated/generated/arch.univariate.ConstantVariance.starting_values.html
+++ b/univariate/generated/generated/arch.univariate.ConstantVariance.starting_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantVariance.starting_values - arch 6.1.0</title>
+        <title>arch.univariate.ConstantVariance.starting_values - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1248,15 +1248,15 @@
                   
 
 
-<h1 id="arch-univariate-constantvariance-starting-values">arch.univariate.ConstantVariance.starting_values<a class="headerlink" href="#arch-univariate-constantvariance-starting-values" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-constantvariance-starting-values">arch.univariate.ConstantVariance.starting_values<a class="headerlink" href="#arch-univariate-constantvariance-starting-values" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ConstantVariance.starting_values">
-<span class="sig-prename descclassname"><span class="pre">ConstantVariance.</span></span><span class="sig-name descname"><span class="pre">starting_values</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.starting_values.resids" title="arch.univariate.ConstantVariance.starting_values.resids (Python parameter) — Array of (approximate) residuals to use when computing starting values"><span class="n"><span class="pre">resids</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#ConstantVariance.starting_values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ConstantVariance.starting_values" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ConstantVariance.</span></span><span class="sig-name descname"><span class="pre">starting_values</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.starting_values.resids" title="arch.univariate.ConstantVariance.starting_values.resids (Python parameter) — Array of (approximate) residuals to use when computing starting values"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#ConstantVariance.starting_values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ConstantVariance.starting_values" title="Link to this definition">¶</a></dt>
 <dd><p>Returns starting values for the ARCH model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ConstantVariance.starting_values-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantVariance.starting_values-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ConstantVariance.starting_values.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.starting_values.resids" title="Permalink to this definition">¶</a></dt><dd><p>Array of (approximate) residuals to use when computing starting
+<dt id="arch.univariate.ConstantVariance.starting_values.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.starting_values.resids" title="Permalink to this definition">¶</a></dt><dd><p>Array of (approximate) residuals to use when computing starting
 values</p>
 </dd>
 </dl>
@@ -1265,7 +1265,7 @@ <h1 id="arch-univariate-constantvariance-starting-values">arch.univariate.Consta
 <dd class="field-even"><p><strong>sv</strong> – Array of starting values</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.ConstantVariance.starting_values-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantVariance.starting_values-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1337,7 +1337,7 @@ <h1 id="arch-univariate-constantvariance-starting-values">arch.univariate.Consta
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1384,7 +1384,7 @@ <h1 id="arch-univariate-constantvariance-starting-values">arch.univariate.Consta
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.ConstantVariance.stop.html b/univariate/generated/generated/arch.univariate.ConstantVariance.stop.html
index abe0259fb0..0a66e909be 100644
--- a/univariate/generated/generated/arch.univariate.ConstantVariance.stop.html
+++ b/univariate/generated/generated/arch.univariate.ConstantVariance.stop.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantVariance.stop - arch 6.1.0</title>
+        <title>arch.univariate.ConstantVariance.stop - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1168,10 +1168,10 @@
                   
 
 
-<h1 id="arch-univariate-constantvariance-stop">arch.univariate.ConstantVariance.stop<a class="headerlink" href="#arch-univariate-constantvariance-stop" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-constantvariance-stop">arch.univariate.ConstantVariance.stop<a class="headerlink" href="#arch-univariate-constantvariance-stop" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ConstantVariance.stop">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ConstantVariance.</span></span><span class="sig-name descname"><span class="pre">stop</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.ConstantVariance.stop" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ConstantVariance.</span></span><span class="sig-name descname"><span class="pre">stop</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.ConstantVariance.stop" title="Link to this definition">¶</a></dt>
 <dd><p>Index to use to stop variance subarray selection</p>
 </dd></dl>
 
@@ -1242,7 +1242,7 @@ <h1 id="arch-univariate-constantvariance-stop">arch.univariate.ConstantVariance.
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1289,7 +1289,7 @@ <h1 id="arch-univariate-constantvariance-stop">arch.univariate.ConstantVariance.
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diff --git a/univariate/generated/generated/arch.univariate.ConstantVariance.update.html b/univariate/generated/generated/arch.univariate.ConstantVariance.update.html
index a9ed314c65..cdff481c22 100644
--- a/univariate/generated/generated/arch.univariate.ConstantVariance.update.html
+++ b/univariate/generated/generated/arch.univariate.ConstantVariance.update.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantVariance.update - arch 6.1.0</title>
+        <title>arch.univariate.ConstantVariance.update - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1318,28 +1318,28 @@
                   
 
 
-<h1 id="arch-univariate-constantvariance-update">arch.univariate.ConstantVariance.update<a class="headerlink" href="#arch-univariate-constantvariance-update" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-constantvariance-update">arch.univariate.ConstantVariance.update<a class="headerlink" href="#arch-univariate-constantvariance-update" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ConstantVariance.update">
-<span class="sig-prename descclassname"><span class="pre">ConstantVariance.</span></span><span class="sig-name descname"><span class="pre">update</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.update.index" title="arch.univariate.ConstantVariance.update.index (Python parameter) — The numerical index of the variance to compute"><span class="n"><span class="pre">index</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.update.parameters" title="arch.univariate.ConstantVariance.update.parameters (Python parameter) — The variance model parameters"><span class="n"><span class="pre">parameters</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.update.resids" title="arch.univariate.ConstantVariance.update.resids (Python parameter) — The residual array. Only uses resids[:index] when computing sigma2[index]"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.update.sigma2" title="arch.univariate.ConstantVariance.update.sigma2 (Python parameter) — The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."><span class="n"><span class="pre">sigma2</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.update.backcast" title="arch.univariate.ConstantVariance.update.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.update.var_bounds" title="arch.univariate.ConstantVariance.update.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.ConstantVariance.update" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ConstantVariance.</span></span><span class="sig-name descname"><span class="pre">update</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.update.index" title="arch.univariate.ConstantVariance.update.index (Python parameter) — The numerical index of the variance to compute"><span class="n"><span class="pre">index</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.update.parameters" title="arch.univariate.ConstantVariance.update.parameters (Python parameter) — The variance model parameters"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.update.resids" title="arch.univariate.ConstantVariance.update.resids (Python parameter) — The residual array. Only uses resids[:index] when computing sigma2[index]"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.update.sigma2" title="arch.univariate.ConstantVariance.update.sigma2 (Python parameter) — The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."><span class="n"><span class="pre">sigma2</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.update.backcast" title="arch.univariate.ConstantVariance.update.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.update.var_bounds" title="arch.univariate.ConstantVariance.update.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.update" title="Link to this definition">¶</a></dt>
 <dd><p>Compute the variance for a single observation</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ConstantVariance.update-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantVariance.update-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ConstantVariance.update.index"><span class="sig-name descname">index</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.update.index" title="Permalink to this definition">¶</a></dt><dd><p>The numerical index of the variance to compute</p>
+<dt id="arch.univariate.ConstantVariance.update.index"><span class="n sig-name">index</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.update.index" title="Permalink to this definition">¶</a></dt><dd><p>The numerical index of the variance to compute</p>
 </dd>
-<dt id="arch.univariate.ConstantVariance.update.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.update.parameters" title="Permalink to this definition">¶</a></dt><dd><p>The variance model parameters</p>
+<dt id="arch.univariate.ConstantVariance.update.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.update.parameters" title="Permalink to this definition">¶</a></dt><dd><p>The variance model parameters</p>
 </dd>
-<dt id="arch.univariate.ConstantVariance.update.resids"><span class="n sig-name">resids</span><a class="headerlink" href="#arch.univariate.ConstantVariance.update.resids" title="Permalink to this definition">¶</a></dt><dd><p>The residual array. Only uses <code class="docutils literal notranslate"><span class="pre">resids[:index]</span></code> when computing
+<dt id="arch.univariate.ConstantVariance.update.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.update.resids" title="Permalink to this definition">¶</a></dt><dd><p>The residual array. Only uses <code class="docutils literal notranslate"><span class="pre">resids[:index]</span></code> when computing
 <code class="docutils literal notranslate"><span class="pre">sigma2[index]</span></code></p>
 </dd>
-<dt id="arch.univariate.ConstantVariance.update.sigma2"><span class="sig-name descname">sigma2</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.update.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>The array containing the variances. Only uses <code class="docutils literal notranslate"><span class="pre">sigma2[:index]</span></code>
+<dt id="arch.univariate.ConstantVariance.update.sigma2"><span class="n sig-name">sigma2</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.update.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>The array containing the variances. Only uses <code class="docutils literal notranslate"><span class="pre">sigma2[:index]</span></code>
 when computing <code class="docutils literal notranslate"><span class="pre">sigma2[index]</span></code>. The computed value is stored
 in <code class="docutils literal notranslate"><span class="pre">sigma2[index]</span></code>.</p>
 </dd>
-<dt id="arch.univariate.ConstantVariance.update.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}</span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.update.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing the recursion</p>
+<dt id="arch.univariate.ConstantVariance.update.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.update.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing the recursion</p>
 </dd>
-<dt id="arch.univariate.ConstantVariance.update.var_bounds"><span class="sig-name descname">var_bounds</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.update.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
+<dt id="arch.univariate.ConstantVariance.update.var_bounds"><span class="n sig-name">var_bounds</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.update.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
 </dd>
 </dl>
 </dd>
@@ -1347,7 +1347,7 @@ <h1 id="arch-univariate-constantvariance-update">arch.univariate.ConstantVarianc
 <dd class="field-even"><p>The variance computed for location <code class="docutils literal notranslate"><span class="pre">index</span></code></p>
 </dd>
 <dt class="field-odd" id="arch.univariate.ConstantVariance.update-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantVariance.update-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1419,7 +1419,7 @@ <h1 id="arch-univariate-constantvariance-update">arch.univariate.ConstantVarianc
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1466,7 +1466,7 @@ <h1 id="arch-univariate-constantvariance-update">arch.univariate.ConstantVarianc
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diff --git a/univariate/generated/generated/arch.univariate.ConstantVariance.updateable.html b/univariate/generated/generated/arch.univariate.ConstantVariance.updateable.html
index e27600f219..9aaa1d0e0b 100644
--- a/univariate/generated/generated/arch.univariate.ConstantVariance.updateable.html
+++ b/univariate/generated/generated/arch.univariate.ConstantVariance.updateable.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantVariance.updateable - arch 6.1.0</title>
+        <title>arch.univariate.ConstantVariance.updateable - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1168,10 +1168,10 @@
                   
 
 
-<h1 id="arch-univariate-constantvariance-updateable">arch.univariate.ConstantVariance.updateable<a class="headerlink" href="#arch-univariate-constantvariance-updateable" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-constantvariance-updateable">arch.univariate.ConstantVariance.updateable<a class="headerlink" href="#arch-univariate-constantvariance-updateable" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ConstantVariance.updateable">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ConstantVariance.</span></span><span class="sig-name descname"><span class="pre">updateable</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.univariate.ConstantVariance.updateable" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ConstantVariance.</span></span><span class="sig-name descname"><span class="pre">updateable</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.univariate.ConstantVariance.updateable" title="Link to this definition">¶</a></dt>
 <dd><p>Flag indicating that the volatility process supports update</p>
 </dd></dl>
 
@@ -1242,7 +1242,7 @@ <h1 id="arch-univariate-constantvariance-updateable">arch.univariate.ConstantVar
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1289,7 +1289,7 @@ <h1 id="arch-univariate-constantvariance-updateable">arch.univariate.ConstantVar
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.ConstantVariance.variance_bounds.html b/univariate/generated/generated/arch.univariate.ConstantVariance.variance_bounds.html
index db59c4e9ac..38856c8fad 100644
--- a/univariate/generated/generated/arch.univariate.ConstantVariance.variance_bounds.html
+++ b/univariate/generated/generated/arch.univariate.ConstantVariance.variance_bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantVariance.variance_bounds - arch 6.1.0</title>
+        <title>arch.univariate.ConstantVariance.variance_bounds - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1262,20 +1262,20 @@
                   
 
 
-<h1 id="arch-univariate-constantvariance-variance-bounds">arch.univariate.ConstantVariance.variance_bounds<a class="headerlink" href="#arch-univariate-constantvariance-variance-bounds" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-constantvariance-variance-bounds">arch.univariate.ConstantVariance.variance_bounds<a class="headerlink" href="#arch-univariate-constantvariance-variance-bounds" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.ConstantVariance.variance_bounds">
-<span class="sig-prename descclassname"><span class="pre">ConstantVariance.</span></span><span class="sig-name descname"><span class="pre">variance_bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.variance_bounds.resids" title="arch.univariate.ConstantVariance.variance_bounds.resids (Python parameter) — Approximate residuals to use to compute the lower and upper bounds on the conditional variance"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.variance_bounds.power" title="arch.univariate.ConstantVariance.variance_bounds.power (Python parameter) — Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."><span class="n"><span class="pre">power</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">2.0</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.ConstantVariance.variance_bounds" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ConstantVariance.variance_bounds">
+<span class="sig-prename descclassname"><span class="pre">ConstantVariance.</span></span><span class="sig-name descname"><span class="pre">variance_bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.variance_bounds.resids" title="arch.univariate.ConstantVariance.variance_bounds.resids (Python parameter) — Approximate residuals to use to compute the lower and upper bounds on the conditional variance"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.variance_bounds.power" title="arch.univariate.ConstantVariance.variance_bounds.power (Python parameter) — Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."><span class="n"><span class="pre">power</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">2.0</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.variance_bounds" title="Link to this definition">¶</a></dt>
 <dd><p>Construct loose bounds for conditional variances.</p>
 <p>These bounds are used in parameter estimation to ensure
 that the log-likelihood does not produce NaN values.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ConstantVariance.variance_bounds-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantVariance.variance_bounds-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ConstantVariance.variance_bounds.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.variance_bounds.resids" title="Permalink to this definition">¶</a></dt><dd><p>Approximate residuals to use to compute the lower and upper bounds
+<dt id="arch.univariate.ConstantVariance.variance_bounds.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.variance_bounds.resids" title="Permalink to this definition">¶</a></dt><dd><p>Approximate residuals to use to compute the lower and upper bounds
 on the conditional variance</p>
 </dd>
-<dt id="arch.univariate.ConstantVariance.variance_bounds.power"><span class="sig-name descname">power</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.variance_bounds.power" title="Permalink to this definition">¶</a></dt><dd><p>Power used in the model. 2.0, the default corresponds to standard
+<dt id="arch.univariate.ConstantVariance.variance_bounds.power"><span class="n sig-name">power</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">2.0</span></code><a class="headerlink" href="#arch.univariate.ConstantVariance.variance_bounds.power" title="Permalink to this definition">¶</a></dt><dd><p>Power used in the model. 2.0, the default corresponds to standard
 ARCH models that evolve in squares.</p>
 </dd>
 </dl>
@@ -1285,7 +1285,7 @@ <h1 id="arch-univariate-constantvariance-variance-bounds">arch.univariate.Consta
 number of elements as resids</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.ConstantVariance.variance_bounds-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantVariance.variance_bounds-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1357,7 +1357,7 @@ <h1 id="arch-univariate-constantvariance-variance-bounds">arch.univariate.Consta
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1404,7 +1404,7 @@ <h1 id="arch-univariate-constantvariance-variance-bounds">arch.univariate.Consta
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.ConstantVariance.volatility_updater.html b/univariate/generated/generated/arch.univariate.ConstantVariance.volatility_updater.html
index 7426d50cd5..017931e436 100644
--- a/univariate/generated/generated/arch.univariate.ConstantVariance.volatility_updater.html
+++ b/univariate/generated/generated/arch.univariate.ConstantVariance.volatility_updater.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantVariance.volatility_updater - arch 6.1.0</title>
+        <title>arch.univariate.ConstantVariance.volatility_updater - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1222,17 +1222,17 @@
                   
 
 
-<h1 id="arch-univariate-constantvariance-volatility-updater">arch.univariate.ConstantVariance.volatility_updater<a class="headerlink" href="#arch-univariate-constantvariance-volatility-updater" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-constantvariance-volatility-updater">arch.univariate.ConstantVariance.volatility_updater<a class="headerlink" href="#arch-univariate-constantvariance-volatility-updater" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ConstantVariance.volatility_updater">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ConstantVariance.</span></span><span class="sig-name descname"><span class="pre">volatility_updater</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.univariate.recursions_python.VolatilityUpdater.html#arch.univariate.recursions_python.VolatilityUpdater" title="arch.univariate.recursions_python.VolatilityUpdater (Python class) — Base class that all volatility updaters must inherit from."><span class="pre">VolatilityUpdater</span></a></span><a class="headerlink" href="#arch.univariate.ConstantVariance.volatility_updater" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ConstantVariance.</span></span><span class="sig-name descname"><span class="pre">volatility_updater</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><span class="pre">VolatilityUpdater</span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.volatility_updater" title="Link to this definition">¶</a></dt>
 <dd><p>Get the volatility updater associated with the volatility process</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ConstantVariance.volatility_updater-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantVariance.volatility_updater-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The updater class</p>
 </dd>
 <dt class="field-even" id="arch.univariate.ConstantVariance.volatility_updater-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantVariance.volatility_updater-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference internal" href="../arch.univariate.recursions_python.VolatilityUpdater.html#arch.univariate.recursions_python.VolatilityUpdater" title="arch.univariate.recursions_python.VolatilityUpdater (Python class) — Base class that all volatility updaters must inherit from.">VolatilityUpdater</a></p>
+<dd class="field-even"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">VolatilityUpdater</span></code></p>
 </dd>
 <dt class="field-odd" id="arch.univariate.ConstantVariance.volatility_updater-raises">Raises<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantVariance.volatility_updater-raises" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#NotImplementedError" title="(in Python v3.11)"><strong>NotImplementedError</strong></a> – If the process is not updateable</p>
@@ -1307,7 +1307,7 @@ <h1 id="arch-univariate-constantvariance-volatility-updater">arch.univariate.Con
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1354,7 +1354,7 @@ <h1 id="arch-univariate-constantvariance-volatility-updater">arch.univariate.Con
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.EGARCH.backcast.html b/univariate/generated/generated/arch.univariate.EGARCH.backcast.html
index 2c5329317b..1bbca8288a 100644
--- a/univariate/generated/generated/arch.univariate.EGARCH.backcast.html
+++ b/univariate/generated/generated/arch.univariate.EGARCH.backcast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EGARCH.backcast - arch 6.1.0</title>
+        <title>arch.univariate.EGARCH.backcast - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1248,15 +1248,15 @@
                   
 
 
-<h1 id="arch-univariate-egarch-backcast">arch.univariate.EGARCH.backcast<a class="headerlink" href="#arch-univariate-egarch-backcast" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-egarch-backcast">arch.univariate.EGARCH.backcast<a class="headerlink" href="#arch-univariate-egarch-backcast" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.EGARCH.backcast">
-<span class="sig-prename descclassname"><span class="pre">EGARCH.</span></span><span class="sig-name descname"><span class="pre">backcast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.backcast.resids" title="arch.univariate.EGARCH.backcast.resids (Python parameter) — Vector of (approximate) residuals"><span class="n"><span class="pre">resids</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#EGARCH.backcast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.EGARCH.backcast" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">EGARCH.</span></span><span class="sig-name descname"><span class="pre">backcast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.backcast.resids" title="arch.univariate.EGARCH.backcast.resids (Python parameter) — Vector of (approximate) residuals"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#EGARCH.backcast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.EGARCH.backcast" title="Link to this definition">¶</a></dt>
 <dd><p>Construct values for backcasting to start the recursion</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.EGARCH.backcast-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EGARCH.backcast-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.EGARCH.backcast.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.EGARCH.backcast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of (approximate) residuals</p>
+<dt id="arch.univariate.EGARCH.backcast.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.EGARCH.backcast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of (approximate) residuals</p>
 </dd>
 </dl>
 </dd>
@@ -1264,7 +1264,7 @@ <h1 id="arch-univariate-egarch-backcast">arch.univariate.EGARCH.backcast<a class
 <dd class="field-even"><p><strong>backcast</strong> – Value to use in backcasting in the volatility recursion</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.EGARCH.backcast-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EGARCH.backcast-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1336,7 +1336,7 @@ <h1 id="arch-univariate-egarch-backcast">arch.univariate.EGARCH.backcast<a class
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1383,7 +1383,7 @@ <h1 id="arch-univariate-egarch-backcast">arch.univariate.EGARCH.backcast<a class
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.EGARCH.backcast_transform.html b/univariate/generated/generated/arch.univariate.EGARCH.backcast_transform.html
index 9ea7359637..ea6ae53600 100644
--- a/univariate/generated/generated/arch.univariate.EGARCH.backcast_transform.html
+++ b/univariate/generated/generated/arch.univariate.EGARCH.backcast_transform.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EGARCH.backcast_transform - arch 6.1.0</title>
+        <title>arch.univariate.EGARCH.backcast_transform - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1248,15 +1248,15 @@
                   
 
 
-<h1 id="arch-univariate-egarch-backcast-transform">arch.univariate.EGARCH.backcast_transform<a class="headerlink" href="#arch-univariate-egarch-backcast-transform" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-egarch-backcast-transform">arch.univariate.EGARCH.backcast_transform<a class="headerlink" href="#arch-univariate-egarch-backcast-transform" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.EGARCH.backcast_transform">
-<span class="sig-prename descclassname"><span class="pre">EGARCH.</span></span><span class="sig-name descname"><span class="pre">backcast_transform</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.backcast_transform.backcast" title="arch.univariate.EGARCH.backcast_transform.backcast (Python parameter) — User-provided backcast that approximates sigma2[0]."><span class="n"><span class="pre">backcast</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#EGARCH.backcast_transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.EGARCH.backcast_transform" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.EGARCH.backcast_transform">
+<span class="sig-prename descclassname"><span class="pre">EGARCH.</span></span><span class="sig-name descname"><span class="pre">backcast_transform</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.backcast_transform.backcast" title="arch.univariate.EGARCH.backcast_transform.backcast (Python parameter) — User-provided backcast that approximates sigma2[0]."><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#EGARCH.backcast_transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.EGARCH.backcast_transform" title="Link to this definition">¶</a></dt>
 <dd><p>Transformation to apply to user-provided backcast values</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.EGARCH.backcast_transform-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EGARCH.backcast_transform-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.EGARCH.backcast_transform.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}</span></span><a class="headerlink" href="#arch.univariate.EGARCH.backcast_transform.backcast" title="Permalink to this definition">¶</a></dt><dd><p>User-provided <code class="docutils literal notranslate"><span class="pre">backcast</span></code> that approximates sigma2[0].</p>
+<dt id="arch.univariate.EGARCH.backcast_transform.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.EGARCH.backcast_transform.backcast" title="Permalink to this definition">¶</a></dt><dd><p>User-provided <code class="docutils literal notranslate"><span class="pre">backcast</span></code> that approximates sigma2[0].</p>
 </dd>
 </dl>
 </dd>
@@ -1264,7 +1264,7 @@ <h1 id="arch-univariate-egarch-backcast-transform">arch.univariate.EGARCH.backca
 <dd class="field-even"><p><strong>backcast</strong> – Backcast transformed to the model-appropriate scale</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.EGARCH.backcast_transform-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EGARCH.backcast_transform-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>{float, ndarray}</p>
+<dd class="field-odd"><p><code class="docutils literal notranslate"><span class="pre">{float,</span> <span class="pre">ndarray}</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1336,7 +1336,7 @@ <h1 id="arch-univariate-egarch-backcast-transform">arch.univariate.EGARCH.backca
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1383,7 +1383,7 @@ <h1 id="arch-univariate-egarch-backcast-transform">arch.univariate.EGARCH.backca
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diff --git a/univariate/generated/generated/arch.univariate.EGARCH.bounds.html b/univariate/generated/generated/arch.univariate.EGARCH.bounds.html
index 356df3be68..c4ed8b6e6e 100644
--- a/univariate/generated/generated/arch.univariate.EGARCH.bounds.html
+++ b/univariate/generated/generated/arch.univariate.EGARCH.bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EGARCH.bounds - arch 6.1.0</title>
+        <title>arch.univariate.EGARCH.bounds - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1248,15 +1248,15 @@
                   
 
 
-<h1 id="arch-univariate-egarch-bounds">arch.univariate.EGARCH.bounds<a class="headerlink" href="#arch-univariate-egarch-bounds" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-egarch-bounds">arch.univariate.EGARCH.bounds<a class="headerlink" href="#arch-univariate-egarch-bounds" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.EGARCH.bounds">
-<span class="sig-prename descclassname"><span class="pre">EGARCH.</span></span><span class="sig-name descname"><span class="pre">bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.bounds.resids" title="arch.univariate.EGARCH.bounds.resids (Python parameter) — Vector of (approximate) residuals"><span class="n"><span class="pre">resids</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#EGARCH.bounds"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.EGARCH.bounds" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">EGARCH.</span></span><span class="sig-name descname"><span class="pre">bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.bounds.resids" title="arch.univariate.EGARCH.bounds.resids (Python parameter) — Vector of (approximate) residuals"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#EGARCH.bounds"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.EGARCH.bounds" title="Link to this definition">¶</a></dt>
 <dd><p>Returns bounds for parameters</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.EGARCH.bounds-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EGARCH.bounds-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.EGARCH.bounds.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.EGARCH.bounds.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of (approximate) residuals</p>
+<dt id="arch.univariate.EGARCH.bounds.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.EGARCH.bounds.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of (approximate) residuals</p>
 </dd>
 </dl>
 </dd>
@@ -1264,7 +1264,7 @@ <h1 id="arch-univariate-egarch-bounds">arch.univariate.EGARCH.bounds<a class="he
 <dd class="field-even"><p><strong>bounds</strong> – List of bounds where each element is (lower, upper).</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.EGARCH.bounds-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EGARCH.bounds-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a>[<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a>[<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>,<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>]]</p>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">list[tuple[float,float]]</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1336,7 +1336,7 @@ <h1 id="arch-univariate-egarch-bounds">arch.univariate.EGARCH.bounds<a class="he
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
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@@ -1383,7 +1383,7 @@ <h1 id="arch-univariate-egarch-bounds">arch.univariate.EGARCH.bounds<a class="he
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diff --git a/univariate/generated/generated/arch.univariate.EGARCH.compute_variance.html b/univariate/generated/generated/arch.univariate.EGARCH.compute_variance.html
index 21d74525f3..bd587b9045 100644
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+++ b/univariate/generated/generated/arch.univariate.EGARCH.compute_variance.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EGARCH.compute_variance - arch 6.1.0</title>
+        <title>arch.univariate.EGARCH.compute_variance - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -1262,13 +1255,6 @@
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-</li>
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-  <a href="#arch.univariate.EGARCH.compute_variance-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
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-  
 </li>
         
       </ul>
@@ -1290,30 +1276,27 @@
                   
 
 
-<h1 id="arch-univariate-egarch-compute-variance">arch.univariate.EGARCH.compute_variance<a class="headerlink" href="#arch-univariate-egarch-compute-variance" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-egarch-compute-variance">arch.univariate.EGARCH.compute_variance<a class="headerlink" href="#arch-univariate-egarch-compute-variance" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.EGARCH.compute_variance">
-<span class="sig-prename descclassname"><span class="pre">EGARCH.</span></span><span class="sig-name descname"><span class="pre">compute_variance</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.compute_variance.parameters" title="arch.univariate.EGARCH.compute_variance.parameters (Python parameter) — Model parameters"><span class="n"><span class="pre">parameters</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.compute_variance.resids" title="arch.univariate.EGARCH.compute_variance.resids (Python parameter) — Vector of mean zero residuals"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.compute_variance.sigma2" title="arch.univariate.EGARCH.compute_variance.sigma2 (Python parameter) — Array with same size as resids to store the conditional variance"><span class="n"><span class="pre">sigma2</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.compute_variance.backcast" title="arch.univariate.EGARCH.compute_variance.backcast (Python parameter) — Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."><span class="n"><span class="pre">backcast</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.compute_variance.var_bounds" title="arch.univariate.EGARCH.compute_variance.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#EGARCH.compute_variance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.EGARCH.compute_variance" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">EGARCH.</span></span><span class="sig-name descname"><span class="pre">compute_variance</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.compute_variance.parameters" title="arch.univariate.EGARCH.compute_variance.parameters (Python parameter) — Model parameters"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.compute_variance.resids" title="arch.univariate.EGARCH.compute_variance.resids (Python parameter) — Vector of mean zero residuals"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.compute_variance.sigma2" title="arch.univariate.EGARCH.compute_variance.sigma2 (Python parameter) — Array with same size as resids to store the conditional variance"><span class="n"><span class="pre">sigma2</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.compute_variance.backcast" title="arch.univariate.EGARCH.compute_variance.backcast (Python parameter) — Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.compute_variance.var_bounds" title="arch.univariate.EGARCH.compute_variance.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#EGARCH.compute_variance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.EGARCH.compute_variance" title="Link to this definition">¶</a></dt>
 <dd><p>Compute the variance for the ARCH model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.EGARCH.compute_variance-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EGARCH.compute_variance-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.EGARCH.compute_variance.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.EGARCH.compute_variance.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
+<dt id="arch.univariate.EGARCH.compute_variance.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.EGARCH.compute_variance.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
 </dd>
-<dt id="arch.univariate.EGARCH.compute_variance.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.EGARCH.compute_variance.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of mean zero residuals</p>
+<dt id="arch.univariate.EGARCH.compute_variance.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.EGARCH.compute_variance.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of mean zero residuals</p>
 </dd>
-<dt id="arch.univariate.EGARCH.compute_variance.sigma2"><span class="sig-name descname">sigma2</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.EGARCH.compute_variance.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>Array with same size as resids to store the conditional variance</p>
+<dt id="arch.univariate.EGARCH.compute_variance.sigma2"><span class="n sig-name">sigma2</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.EGARCH.compute_variance.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>Array with same size as resids to store the conditional variance</p>
 </dd>
-<dt id="arch.univariate.EGARCH.compute_variance.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}</span></span><a class="headerlink" href="#arch.univariate.EGARCH.compute_variance.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing ARCH recursion. Can be an ndarray
+<dt id="arch.univariate.EGARCH.compute_variance.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.EGARCH.compute_variance.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing ARCH recursion. Can be an ndarray
 when the model contains multiple components.</p>
 </dd>
-<dt id="arch.univariate.EGARCH.compute_variance.var_bounds"><span class="sig-name descname">var_bounds</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.EGARCH.compute_variance.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
+<dt id="arch.univariate.EGARCH.compute_variance.var_bounds"><span class="n sig-name">var_bounds</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.EGARCH.compute_variance.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
 </dd>
 </dl>
 </dd>
-<dt class="field-even" id="arch.univariate.EGARCH.compute_variance-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EGARCH.compute_variance-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a></p>
-</dd>
 </dl>
 </dd></dl>
 
@@ -1384,7 +1367,7 @@ <h1 id="arch-univariate-egarch-compute-variance">arch.univariate.EGARCH.compute_
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1431,7 +1414,7 @@ <h1 id="arch-univariate-egarch-compute-variance">arch.univariate.EGARCH.compute_
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diff --git a/univariate/generated/generated/arch.univariate.EGARCH.constraints.html b/univariate/generated/generated/arch.univariate.EGARCH.constraints.html
index 978d85d9fd..e7c782d6fe 100644
--- a/univariate/generated/generated/arch.univariate.EGARCH.constraints.html
+++ b/univariate/generated/generated/arch.univariate.EGARCH.constraints.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EGARCH.constraints - arch 6.1.0</title>
+        <title>arch.univariate.EGARCH.constraints - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch 6.1.0
+    arch 6.2.0
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@@ -611,13 +611,6 @@
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       <ul class="md-nav__list">
         
-          <li class="md-nav__item">
-  <a href="#arch.univariate.EGARCH.constraints-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
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-  
-</li>
-        
           <li class="md-nav__item">
   <a href="#arch.univariate.EGARCH.constraints-returns" class="md-nav__link">
     <span class="md-ellipsis">Returns</span>
@@ -1175,13 +1168,6 @@
     <nav class="md-nav" aria-label="EGARCH.constraints">
       <ul class="md-nav__list">
         
-          <li class="md-nav__item">
-  <a href="#arch.univariate.EGARCH.constraints-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
-</li>
-        
           <li class="md-nav__item">
   <a href="#arch.univariate.EGARCH.constraints-returns" class="md-nav__link">
     <span class="md-ellipsis">Returns</span>
@@ -1208,20 +1194,17 @@
                   
 
 
-<h1 id="arch-univariate-egarch-constraints">arch.univariate.EGARCH.constraints<a class="headerlink" href="#arch-univariate-egarch-constraints" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-egarch-constraints">arch.univariate.EGARCH.constraints<a class="headerlink" href="#arch-univariate-egarch-constraints" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.EGARCH.constraints">
-<span class="sig-prename descclassname"><span class="pre">EGARCH.</span></span><span class="sig-name descname"><span class="pre">constraints</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#EGARCH.constraints"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.EGARCH.constraints" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">EGARCH.</span></span><span class="sig-name descname"><span class="pre">constraints</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#EGARCH.constraints"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.EGARCH.constraints" title="Link to this definition">¶</a></dt>
 <dd><p>Construct parameter constraints arrays for parameter estimation</p>
 <dl class="field-list simple">
-<dt class="field-odd" id="arch.univariate.EGARCH.constraints-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EGARCH.constraints-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>]</p>
-</dd>
-<dt class="field-even" id="arch.univariate.EGARCH.constraints-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EGARCH.constraints-returns" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><ul class="simple">
-<li><p><strong>A</strong> (<em>ndarray</em>) – Parameters loadings in constraint. Shape is number of constraints
+<dt class="field-odd" id="arch.univariate.EGARCH.constraints-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EGARCH.constraints-returns" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p><strong>A</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – Parameters loadings in constraint. Shape is number of constraints
 by number of parameters</p></li>
-<li><p><strong>b</strong> (<em>ndarray</em>) – Constraint values, one for each constraint</p></li>
+<li><p><strong>b</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – Constraint values, one for each constraint</p></li>
 </ul>
 </p>
 </dd>
@@ -1298,7 +1281,7 @@ <h1 id="arch-univariate-egarch-constraints">arch.univariate.EGARCH.constraints<a
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1345,7 +1328,7 @@ <h1 id="arch-univariate-egarch-constraints">arch.univariate.EGARCH.constraints<a
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diff --git a/univariate/generated/generated/arch.univariate.EGARCH.forecast.html b/univariate/generated/generated/arch.univariate.EGARCH.forecast.html
index 02a9890c75..50e3b3ef2c 100644
--- a/univariate/generated/generated/arch.univariate.EGARCH.forecast.html
+++ b/univariate/generated/generated/arch.univariate.EGARCH.forecast.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -47,8 +47,8 @@
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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+    arch 6.2.0
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@@ -1388,38 +1388,38 @@
                   
 
 
-<h1 id="arch-univariate-egarch-forecast">arch.univariate.EGARCH.forecast<a class="headerlink" href="#arch-univariate-egarch-forecast" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-egarch-forecast">arch.univariate.EGARCH.forecast<a class="headerlink" href="#arch-univariate-egarch-forecast" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.EGARCH.forecast">
-<span class="sig-prename descclassname"><span class="pre">EGARCH.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.forecast.parameters" title="arch.univariate.EGARCH.forecast.parameters (Python parameter) — Parameters required to forecast the volatility model"><span class="n"><span class="pre">parameters</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.forecast.resids" title="arch.univariate.EGARCH.forecast.resids (Python parameter) — Residuals to use in the recursion"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.forecast.backcast" title="arch.univariate.EGARCH.forecast.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.forecast.var_bounds" title="arch.univariate.EGARCH.forecast.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.forecast.start" title="arch.univariate.EGARCH.forecast.start (Python parameter) — Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."><span class="n"><span class="pre">start</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.forecast.horizon" title="arch.univariate.EGARCH.forecast.horizon (Python parameter) — Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."><span class="n"><span class="pre">horizon</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.forecast.method" title="arch.univariate.EGARCH.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic."><span class="n"><span class="pre">method</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.forecast.simulations" title="arch.univariate.EGARCH.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.forecast.rng" title="arch.univariate.EGARCH.forecast.rng (Python parameter) — Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."><span class="n"><span class="pre">rng</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.forecast.random_state" title="arch.univariate.EGARCH.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.EGARCH.forecast" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">EGARCH.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.forecast.parameters" title="arch.univariate.EGARCH.forecast.parameters (Python parameter) — Parameters required to forecast the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.forecast.resids" title="arch.univariate.EGARCH.forecast.resids (Python parameter) — Residuals to use in the recursion"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.forecast.backcast" title="arch.univariate.EGARCH.forecast.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.forecast.var_bounds" title="arch.univariate.EGARCH.forecast.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.forecast.start" title="arch.univariate.EGARCH.forecast.start (Python parameter) — Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.forecast.horizon" title="arch.univariate.EGARCH.forecast.horizon (Python parameter) — Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.forecast.method" title="arch.univariate.EGARCH.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.forecast.simulations" title="arch.univariate.EGARCH.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.forecast.rng" title="arch.univariate.EGARCH.forecast.rng (Python parameter) — Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.forecast.random_state" title="arch.univariate.EGARCH.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><span class="pre">VarianceForecast</span></span></span></span><a class="headerlink" href="#arch.univariate.EGARCH.forecast" title="Link to this definition">¶</a></dt>
 <dd><p>Forecast volatility from the model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.EGARCH.forecast-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EGARCH.forecast-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.EGARCH.forecast.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, Series}</span></span><a class="headerlink" href="#arch.univariate.EGARCH.forecast.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast the volatility model</p>
+<dt id="arch.univariate.EGARCH.forecast.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.EGARCH.forecast.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast the volatility model</p>
 </dd>
-<dt id="arch.univariate.EGARCH.forecast.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.EGARCH.forecast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Residuals to use in the recursion</p>
+<dt id="arch.univariate.EGARCH.forecast.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.EGARCH.forecast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Residuals to use in the recursion</p>
 </dd>
-<dt id="arch.univariate.EGARCH.forecast.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><a class="headerlink" href="#arch.univariate.EGARCH.forecast.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing the recursion</p>
+<dt id="arch.univariate.EGARCH.forecast.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><a class="headerlink" href="#arch.univariate.EGARCH.forecast.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing the recursion</p>
 </dd>
-<dt id="arch.univariate.EGARCH.forecast.var_bounds"><span class="sig-name descname">var_bounds</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray, 2-d</span></span><a class="headerlink" href="#arch.univariate.EGARCH.forecast.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
+<dt id="arch.univariate.EGARCH.forecast.var_bounds"><span class="n sig-name">var_bounds</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.EGARCH.forecast.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
 </dd>
-<dt id="arch.univariate.EGARCH.forecast.start"><span class="sig-name descname">start</span><span class="p"> : </span><span class="desctype"><span class="desctype">{None, int}</span></span><a class="headerlink" href="#arch.univariate.EGARCH.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>Index of the first observation to use as the starting point for
+<dt id="arch.univariate.EGARCH.forecast.start"><span class="n sig-name">start</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.EGARCH.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>Index of the first observation to use as the starting point for
 the forecast.  Default is len(resids).</p>
 </dd>
-<dt id="arch.univariate.EGARCH.forecast.horizon"><span class="sig-name descname">horizon</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.EGARCH.forecast.horizon" title="Permalink to this definition">¶</a></dt><dd><p>Forecast horizon.  Must be 1 or larger.  Forecasts are produced
+<dt id="arch.univariate.EGARCH.forecast.horizon"><span class="n sig-name">horizon</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code><a class="headerlink" href="#arch.univariate.EGARCH.forecast.horizon" title="Permalink to this definition">¶</a></dt><dd><p>Forecast horizon.  Must be 1 or larger.  Forecasts are produced
 for horizons in [1, horizon].</p>
 </dd>
-<dt id="arch.univariate.EGARCH.forecast.method"><span class="sig-name descname">method</span><span class="p"> : </span><span class="desctype"><span class="desctype">{'analytic', 'simulation', 'bootstrap'}</span></span><a class="headerlink" href="#arch.univariate.EGARCH.forecast.method" title="Permalink to this definition">¶</a></dt><dd><p>Method to use when producing the forecast. The default is analytic.</p>
+<dt id="arch.univariate.EGARCH.forecast.method"><span class="n sig-name">method</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'analytic'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'simulation'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'bootstrap'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code><a class="headerlink" href="#arch.univariate.EGARCH.forecast.method" title="Permalink to this definition">¶</a></dt><dd><p>Method to use when producing the forecast. The default is analytic.</p>
 </dd>
-<dt id="arch.univariate.EGARCH.forecast.simulations"><span class="sig-name descname">simulations</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.EGARCH.forecast.simulations" title="Permalink to this definition">¶</a></dt><dd><p>Number of simulations to run when computing the forecast using
+<dt id="arch.univariate.EGARCH.forecast.simulations"><span class="n sig-name">simulations</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.univariate.EGARCH.forecast.simulations" title="Permalink to this definition">¶</a></dt><dd><p>Number of simulations to run when computing the forecast using
 either simulation or bootstrap.</p>
 </dd>
-<dt id="arch.univariate.EGARCH.forecast.rng"><span class="sig-name descname">rng</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable</span></span><a class="headerlink" href="#arch.univariate.EGARCH.forecast.rng" title="Permalink to this definition">¶</a></dt><dd><p>Callable random number generator required if method is
+<dt id="arch.univariate.EGARCH.forecast.rng"><span class="n sig-name">rng</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)">Callable</a><span class="p">[</span><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">,</span><span class="w"> </span><span class="p">...</span><span class="p">]</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.EGARCH.forecast.rng" title="Permalink to this definition">¶</a></dt><dd><p>Callable random number generator required if method is
 ‘simulation’. Must take a single shape input and return random
 samples numbers with that shape.</p>
 </dd>
-<dt id="arch.univariate.EGARCH.forecast.random_state"><span class="sig-name descname">random_state</span><span class="p"> : </span><span class="desctype"><span class="desctype">RandomState, optional</span></span><a class="headerlink" href="#arch.univariate.EGARCH.forecast.random_state" title="Permalink to this definition">¶</a></dt><dd><p>NumPy RandomState instance to use when method is ‘bootstrap’</p>
+<dt id="arch.univariate.EGARCH.forecast.random_state"><span class="n sig-name">random_state</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)">RandomState</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.EGARCH.forecast.random_state" title="Permalink to this definition">¶</a></dt><dd><p>NumPy RandomState instance to use when method is ‘bootstrap’</p>
 </dd>
 </dl>
 </dd>
@@ -1428,7 +1428,7 @@ <h1 id="arch-univariate-egarch-forecast">arch.univariate.EGARCH.forecast<a class
 or bootstrap, the simulated paths.</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.EGARCH.forecast-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EGARCH.forecast-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>VarianceForecast</p>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">arch.univariate.volatility.VarianceForecast</span></code></p>
 </dd>
 <dt class="field-even" id="arch.univariate.EGARCH.forecast-raises">Raises<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EGARCH.forecast-raises" title="Permalink to this headline">¶</a></dt>
 <dd class="field-even"><ul class="simple">
@@ -1515,7 +1515,7 @@ <h1 id="arch-univariate-egarch-forecast">arch.univariate.EGARCH.forecast<a class
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1562,7 +1562,7 @@ <h1 id="arch-univariate-egarch-forecast">arch.univariate.EGARCH.forecast<a class
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.EGARCH.name.html b/univariate/generated/generated/arch.univariate.EGARCH.name.html
index 5899fdfad3..7517816efc 100644
--- a/univariate/generated/generated/arch.univariate.EGARCH.name.html
+++ b/univariate/generated/generated/arch.univariate.EGARCH.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EGARCH.name - arch 6.1.0</title>
+        <title>arch.univariate.EGARCH.name - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1168,10 +1168,10 @@
                   
 
 
-<h1 id="arch-univariate-egarch-name">arch.univariate.EGARCH.name<a class="headerlink" href="#arch-univariate-egarch-name" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-egarch-name">arch.univariate.EGARCH.name<a class="headerlink" href="#arch-univariate-egarch-name" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.EGARCH.name">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">EGARCH.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.univariate.EGARCH.name" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">EGARCH.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.univariate.EGARCH.name" title="Link to this definition">¶</a></dt>
 <dd><p>The name of the volatility process</p>
 </dd></dl>
 
@@ -1242,7 +1242,7 @@ <h1 id="arch-univariate-egarch-name">arch.univariate.EGARCH.name<a class="header
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1289,7 +1289,7 @@ <h1 id="arch-univariate-egarch-name">arch.univariate.EGARCH.name<a class="header
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.EGARCH.num_params.html b/univariate/generated/generated/arch.univariate.EGARCH.num_params.html
index f52a3701c2..678325dbbc 100644
--- a/univariate/generated/generated/arch.univariate.EGARCH.num_params.html
+++ b/univariate/generated/generated/arch.univariate.EGARCH.num_params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EGARCH.num_params - arch 6.1.0</title>
+        <title>arch.univariate.EGARCH.num_params - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1168,10 +1168,10 @@
                   
 
 
-<h1 id="arch-univariate-egarch-num-params">arch.univariate.EGARCH.num_params<a class="headerlink" href="#arch-univariate-egarch-num-params" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-egarch-num-params">arch.univariate.EGARCH.num_params<a class="headerlink" href="#arch-univariate-egarch-num-params" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.EGARCH.num_params">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">EGARCH.</span></span><span class="sig-name descname"><span class="pre">num_params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.EGARCH.num_params" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">EGARCH.</span></span><span class="sig-name descname"><span class="pre">num_params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.EGARCH.num_params" title="Link to this definition">¶</a></dt>
 <dd><p>The number of parameters in the model</p>
 </dd></dl>
 
@@ -1242,7 +1242,7 @@ <h1 id="arch-univariate-egarch-num-params">arch.univariate.EGARCH.num_params<a c
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1289,7 +1289,7 @@ <h1 id="arch-univariate-egarch-num-params">arch.univariate.EGARCH.num_params<a c
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.EGARCH.parameter_names.html b/univariate/generated/generated/arch.univariate.EGARCH.parameter_names.html
index b6ad1a5b4f..8a4674ef97 100644
--- a/univariate/generated/generated/arch.univariate.EGARCH.parameter_names.html
+++ b/univariate/generated/generated/arch.univariate.EGARCH.parameter_names.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EGARCH.parameter_names - arch 6.1.0</title>
+        <title>arch.univariate.EGARCH.parameter_names - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1208,17 +1208,17 @@
                   
 
 
-<h1 id="arch-univariate-egarch-parameter-names">arch.univariate.EGARCH.parameter_names<a class="headerlink" href="#arch-univariate-egarch-parameter-names" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-egarch-parameter-names">arch.univariate.EGARCH.parameter_names<a class="headerlink" href="#arch-univariate-egarch-parameter-names" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.EGARCH.parameter_names">
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+<span class="sig-prename descclassname"><span class="pre">EGARCH.</span></span><span class="sig-name descname"><span class="pre">parameter_names</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#EGARCH.parameter_names"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.EGARCH.parameter_names" title="Link to this definition">¶</a></dt>
 <dd><p>Names of model parameters</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.EGARCH.parameter_names-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EGARCH.parameter_names-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p><strong>names</strong> – Variables names</p>
 </dd>
 <dt class="field-even" id="arch.univariate.EGARCH.parameter_names-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EGARCH.parameter_names-return-type" title="Permalink to this headline">¶</a></dt>
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+<dd class="field-even"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span> <span class="pre">(str)</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1290,7 +1290,7 @@ <h1 id="arch-univariate-egarch-parameter-names">arch.univariate.EGARCH.parameter
   
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@@ -1304,36 +1290,33 @@
                   
 
 
-<h1 id="arch-univariate-egarch-simulate">arch.univariate.EGARCH.simulate<a class="headerlink" href="#arch-univariate-egarch-simulate" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-egarch-simulate">arch.univariate.EGARCH.simulate<a class="headerlink" href="#arch-univariate-egarch-simulate" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.EGARCH.simulate">
-<span class="sig-prename descclassname"><span class="pre">EGARCH.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.simulate.parameters" title="arch.univariate.EGARCH.simulate.parameters (Python parameter) — Parameters required to simulate the volatility model"><span class="n"><span class="pre">parameters</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.simulate.nobs" title="arch.univariate.EGARCH.simulate.nobs (Python parameter) — Number of data points to simulate"><span class="n"><span class="pre">nobs</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.simulate.rng" title="arch.univariate.EGARCH.simulate.rng (Python parameter) — Callable function that takes a single integer input and returns a vector of random numbers"><span class="n"><span class="pre">rng</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.simulate.burn" title="arch.univariate.EGARCH.simulate.burn (Python parameter) — Number of additional observations to generate when initializing the simulation"><span class="n"><span class="pre">burn</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.simulate.initial_value" title="arch.univariate.EGARCH.simulate.initial_value (Python parameter) — Scalar or array of initial values to use when initializing the simulation"><span class="n"><span class="pre">initial_value</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#EGARCH.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.EGARCH.simulate" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.EGARCH.simulate">
+<span class="sig-prename descclassname"><span class="pre">EGARCH.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.simulate.parameters" title="arch.univariate.EGARCH.simulate.parameters (Python parameter) — Parameters required to simulate the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.simulate.nobs" title="arch.univariate.EGARCH.simulate.nobs (Python parameter) — Number of data points to simulate"><span class="n"><span class="pre">nobs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.simulate.rng" title="arch.univariate.EGARCH.simulate.rng (Python parameter) — Callable function that takes a single integer input and returns a vector of random numbers"><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.simulate.burn" title="arch.univariate.EGARCH.simulate.burn (Python parameter) — Number of additional observations to generate when initializing the simulation"><span class="n"><span class="pre">burn</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.simulate.initial_value" title="arch.univariate.EGARCH.simulate.initial_value (Python parameter) — Scalar or array of initial values to use when initializing the simulation"><span class="n"><span class="pre">initial_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#EGARCH.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.EGARCH.simulate" title="Link to this definition">¶</a></dt>
 <dd><p>Simulate data from the model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.EGARCH.simulate-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EGARCH.simulate-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.EGARCH.simulate.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, Series}</span></span><a class="headerlink" href="#arch.univariate.EGARCH.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to simulate the volatility model</p>
+<dt id="arch.univariate.EGARCH.simulate.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.EGARCH.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to simulate the volatility model</p>
 </dd>
-<dt id="arch.univariate.EGARCH.simulate.nobs"><span class="sig-name descname">nobs</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.EGARCH.simulate.nobs" title="Permalink to this definition">¶</a></dt><dd><p>Number of data points to simulate</p>
+<dt id="arch.univariate.EGARCH.simulate.nobs"><span class="n sig-name">nobs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.EGARCH.simulate.nobs" title="Permalink to this definition">¶</a></dt><dd><p>Number of data points to simulate</p>
 </dd>
-<dt id="arch.univariate.EGARCH.simulate.rng"><span class="sig-name descname">rng</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable</span></span><a class="headerlink" href="#arch.univariate.EGARCH.simulate.rng" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that takes a single integer input and returns
+<dt id="arch.univariate.EGARCH.simulate.rng"><span class="n sig-name">rng</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)">Callable</a><span class="p">[</span><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">,</span><span class="w"> </span><span class="p">...</span><span class="p">]</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.univariate.EGARCH.simulate.rng" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that takes a single integer input and returns
 a vector of random numbers</p>
 </dd>
-<dt id="arch.univariate.EGARCH.simulate.burn"><span class="sig-name descname">burn</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.univariate.EGARCH.simulate.burn" title="Permalink to this definition">¶</a></dt><dd><p>Number of additional observations to generate when initializing
+<dt id="arch.univariate.EGARCH.simulate.burn"><span class="n sig-name">burn</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code><a class="headerlink" href="#arch.univariate.EGARCH.simulate.burn" title="Permalink to this definition">¶</a></dt><dd><p>Number of additional observations to generate when initializing
 the simulation</p>
 </dd>
-<dt id="arch.univariate.EGARCH.simulate.initial_value"><span class="sig-name descname">initial_value</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}, optional</span></span><a class="headerlink" href="#arch.univariate.EGARCH.simulate.initial_value" title="Permalink to this definition">¶</a></dt><dd><p>Scalar or array of initial values to use when initializing the
+<dt id="arch.univariate.EGARCH.simulate.initial_value"><span class="n sig-name">initial_value</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.EGARCH.simulate.initial_value" title="Permalink to this definition">¶</a></dt><dd><p>Scalar or array of initial values to use when initializing the
 simulation</p>
 </dd>
 </dl>
 </dd>
-<dt class="field-even" id="arch.univariate.EGARCH.simulate-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EGARCH.simulate-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>]</p>
-</dd>
-<dt class="field-odd" id="arch.univariate.EGARCH.simulate-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EGARCH.simulate-returns" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><ul class="simple">
-<li><p><strong>resids</strong> (<em>ndarray</em>) – The simulated residuals</p></li>
-<li><p><strong>variance</strong> (<em>ndarray</em>) – The simulated variance</p></li>
+<dt class="field-even" id="arch.univariate.EGARCH.simulate-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EGARCH.simulate-returns" title="Permalink to this headline">¶</a></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><strong>resids</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – The simulated residuals</p></li>
+<li><p><strong>variance</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – The simulated variance</p></li>
 </ul>
 </p>
 </dd>
@@ -1407,7 +1390,7 @@ <h1 id="arch-univariate-egarch-simulate">arch.univariate.EGARCH.simulate<a class
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1454,7 +1437,7 @@ <h1 id="arch-univariate-egarch-simulate">arch.univariate.EGARCH.simulate<a class
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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diff --git a/univariate/generated/generated/arch.univariate.EGARCH.start.html b/univariate/generated/generated/arch.univariate.EGARCH.start.html
index f80f02e9ac..cc8dd0e372 100644
--- a/univariate/generated/generated/arch.univariate.EGARCH.start.html
+++ b/univariate/generated/generated/arch.univariate.EGARCH.start.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EGARCH.start - arch 6.1.0</title>
+        <title>arch.univariate.EGARCH.start - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1168,10 +1168,10 @@
                   
 
 
-<h1 id="arch-univariate-egarch-start">arch.univariate.EGARCH.start<a class="headerlink" href="#arch-univariate-egarch-start" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-egarch-start">arch.univariate.EGARCH.start<a class="headerlink" href="#arch-univariate-egarch-start" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.EGARCH.start">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">EGARCH.</span></span><span class="sig-name descname"><span class="pre">start</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.EGARCH.start" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">EGARCH.</span></span><span class="sig-name descname"><span class="pre">start</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.EGARCH.start" title="Link to this definition">¶</a></dt>
 <dd><p>Index to use to start variance subarray selection</p>
 </dd></dl>
 
@@ -1242,7 +1242,7 @@ <h1 id="arch-univariate-egarch-start">arch.univariate.EGARCH.start<a class="head
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1289,7 +1289,7 @@ <h1 id="arch-univariate-egarch-start">arch.univariate.EGARCH.start<a class="head
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.EGARCH.starting_values.html b/univariate/generated/generated/arch.univariate.EGARCH.starting_values.html
index 1f4992f30b..e89cfecd54 100644
--- a/univariate/generated/generated/arch.univariate.EGARCH.starting_values.html
+++ b/univariate/generated/generated/arch.univariate.EGARCH.starting_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EGARCH.starting_values - arch 6.1.0</title>
+        <title>arch.univariate.EGARCH.starting_values - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1248,15 +1248,15 @@
                   
 
 
-<h1 id="arch-univariate-egarch-starting-values">arch.univariate.EGARCH.starting_values<a class="headerlink" href="#arch-univariate-egarch-starting-values" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-egarch-starting-values">arch.univariate.EGARCH.starting_values<a class="headerlink" href="#arch-univariate-egarch-starting-values" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.EGARCH.starting_values">
-<span class="sig-prename descclassname"><span class="pre">EGARCH.</span></span><span class="sig-name descname"><span class="pre">starting_values</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.starting_values.resids" title="arch.univariate.EGARCH.starting_values.resids (Python parameter) — Array of (approximate) residuals to use when computing starting values"><span class="n"><span class="pre">resids</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#EGARCH.starting_values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.EGARCH.starting_values" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">EGARCH.</span></span><span class="sig-name descname"><span class="pre">starting_values</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.starting_values.resids" title="arch.univariate.EGARCH.starting_values.resids (Python parameter) — Array of (approximate) residuals to use when computing starting values"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#EGARCH.starting_values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.EGARCH.starting_values" title="Link to this definition">¶</a></dt>
 <dd><p>Returns starting values for the ARCH model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.EGARCH.starting_values-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EGARCH.starting_values-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.EGARCH.starting_values.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.EGARCH.starting_values.resids" title="Permalink to this definition">¶</a></dt><dd><p>Array of (approximate) residuals to use when computing starting
+<dt id="arch.univariate.EGARCH.starting_values.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.EGARCH.starting_values.resids" title="Permalink to this definition">¶</a></dt><dd><p>Array of (approximate) residuals to use when computing starting
 values</p>
 </dd>
 </dl>
@@ -1265,7 +1265,7 @@ <h1 id="arch-univariate-egarch-starting-values">arch.univariate.EGARCH.starting_
 <dd class="field-even"><p><strong>sv</strong> – Array of starting values</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.EGARCH.starting_values-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EGARCH.starting_values-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1337,7 +1337,7 @@ <h1 id="arch-univariate-egarch-starting-values">arch.univariate.EGARCH.starting_
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1384,7 +1384,7 @@ <h1 id="arch-univariate-egarch-starting-values">arch.univariate.EGARCH.starting_
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.EGARCH.stop.html b/univariate/generated/generated/arch.univariate.EGARCH.stop.html
index fe4463ca83..09fcae9e51 100644
--- a/univariate/generated/generated/arch.univariate.EGARCH.stop.html
+++ b/univariate/generated/generated/arch.univariate.EGARCH.stop.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EGARCH.stop - arch 6.1.0</title>
+        <title>arch.univariate.EGARCH.stop - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1168,10 +1168,10 @@
                   
 
 
-<h1 id="arch-univariate-egarch-stop">arch.univariate.EGARCH.stop<a class="headerlink" href="#arch-univariate-egarch-stop" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-egarch-stop">arch.univariate.EGARCH.stop<a class="headerlink" href="#arch-univariate-egarch-stop" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.EGARCH.stop">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">EGARCH.</span></span><span class="sig-name descname"><span class="pre">stop</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.EGARCH.stop" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">EGARCH.</span></span><span class="sig-name descname"><span class="pre">stop</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.EGARCH.stop" title="Link to this definition">¶</a></dt>
 <dd><p>Index to use to stop variance subarray selection</p>
 </dd></dl>
 
@@ -1242,7 +1242,7 @@ <h1 id="arch-univariate-egarch-stop">arch.univariate.EGARCH.stop<a class="header
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1289,7 +1289,7 @@ <h1 id="arch-univariate-egarch-stop">arch.univariate.EGARCH.stop<a class="header
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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diff --git a/univariate/generated/generated/arch.univariate.EGARCH.update.html b/univariate/generated/generated/arch.univariate.EGARCH.update.html
index d5fef01c90..41a45111c5 100644
--- a/univariate/generated/generated/arch.univariate.EGARCH.update.html
+++ b/univariate/generated/generated/arch.univariate.EGARCH.update.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EGARCH.update - arch 6.1.0</title>
+        <title>arch.univariate.EGARCH.update - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1318,28 +1318,28 @@
                   
 
 
-<h1 id="arch-univariate-egarch-update">arch.univariate.EGARCH.update<a class="headerlink" href="#arch-univariate-egarch-update" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-egarch-update">arch.univariate.EGARCH.update<a class="headerlink" href="#arch-univariate-egarch-update" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.EGARCH.update">
-<span class="sig-prename descclassname"><span class="pre">EGARCH.</span></span><span class="sig-name descname"><span class="pre">update</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.update.index" title="arch.univariate.EGARCH.update.index (Python parameter) — The numerical index of the variance to compute"><span class="n"><span class="pre">index</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.update.parameters" title="arch.univariate.EGARCH.update.parameters (Python parameter) — The variance model parameters"><span class="n"><span class="pre">parameters</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.update.resids" title="arch.univariate.EGARCH.update.resids (Python parameter) — The residual array. Only uses resids[:index] when computing sigma2[index]"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.update.sigma2" title="arch.univariate.EGARCH.update.sigma2 (Python parameter) — The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."><span class="n"><span class="pre">sigma2</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.update.backcast" title="arch.univariate.EGARCH.update.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.update.var_bounds" title="arch.univariate.EGARCH.update.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.EGARCH.update" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">EGARCH.</span></span><span class="sig-name descname"><span class="pre">update</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.update.index" title="arch.univariate.EGARCH.update.index (Python parameter) — The numerical index of the variance to compute"><span class="n"><span class="pre">index</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.update.parameters" title="arch.univariate.EGARCH.update.parameters (Python parameter) — The variance model parameters"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.update.resids" title="arch.univariate.EGARCH.update.resids (Python parameter) — The residual array. Only uses resids[:index] when computing sigma2[index]"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.update.sigma2" title="arch.univariate.EGARCH.update.sigma2 (Python parameter) — The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."><span class="n"><span class="pre">sigma2</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.update.backcast" title="arch.univariate.EGARCH.update.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.update.var_bounds" title="arch.univariate.EGARCH.update.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span></span><a class="headerlink" href="#arch.univariate.EGARCH.update" title="Link to this definition">¶</a></dt>
 <dd><p>Compute the variance for a single observation</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.EGARCH.update-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EGARCH.update-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.EGARCH.update.index"><span class="sig-name descname">index</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.EGARCH.update.index" title="Permalink to this definition">¶</a></dt><dd><p>The numerical index of the variance to compute</p>
+<dt id="arch.univariate.EGARCH.update.index"><span class="n sig-name">index</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.EGARCH.update.index" title="Permalink to this definition">¶</a></dt><dd><p>The numerical index of the variance to compute</p>
 </dd>
-<dt id="arch.univariate.EGARCH.update.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.EGARCH.update.parameters" title="Permalink to this definition">¶</a></dt><dd><p>The variance model parameters</p>
+<dt id="arch.univariate.EGARCH.update.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.EGARCH.update.parameters" title="Permalink to this definition">¶</a></dt><dd><p>The variance model parameters</p>
 </dd>
-<dt id="arch.univariate.EGARCH.update.resids"><span class="n sig-name">resids</span><a class="headerlink" href="#arch.univariate.EGARCH.update.resids" title="Permalink to this definition">¶</a></dt><dd><p>The residual array. Only uses <code class="docutils literal notranslate"><span class="pre">resids[:index]</span></code> when computing
+<dt id="arch.univariate.EGARCH.update.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.EGARCH.update.resids" title="Permalink to this definition">¶</a></dt><dd><p>The residual array. Only uses <code class="docutils literal notranslate"><span class="pre">resids[:index]</span></code> when computing
 <code class="docutils literal notranslate"><span class="pre">sigma2[index]</span></code></p>
 </dd>
-<dt id="arch.univariate.EGARCH.update.sigma2"><span class="sig-name descname">sigma2</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.EGARCH.update.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>The array containing the variances. Only uses <code class="docutils literal notranslate"><span class="pre">sigma2[:index]</span></code>
+<dt id="arch.univariate.EGARCH.update.sigma2"><span class="n sig-name">sigma2</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.EGARCH.update.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>The array containing the variances. Only uses <code class="docutils literal notranslate"><span class="pre">sigma2[:index]</span></code>
 when computing <code class="docutils literal notranslate"><span class="pre">sigma2[index]</span></code>. The computed value is stored
 in <code class="docutils literal notranslate"><span class="pre">sigma2[index]</span></code>.</p>
 </dd>
-<dt id="arch.univariate.EGARCH.update.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}</span></span><a class="headerlink" href="#arch.univariate.EGARCH.update.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing the recursion</p>
+<dt id="arch.univariate.EGARCH.update.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.EGARCH.update.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing the recursion</p>
 </dd>
-<dt id="arch.univariate.EGARCH.update.var_bounds"><span class="sig-name descname">var_bounds</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.EGARCH.update.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
+<dt id="arch.univariate.EGARCH.update.var_bounds"><span class="n sig-name">var_bounds</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.EGARCH.update.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
 </dd>
 </dl>
 </dd>
@@ -1347,7 +1347,7 @@ <h1 id="arch-univariate-egarch-update">arch.univariate.EGARCH.update<a class="he
 <dd class="field-even"><p>The variance computed for location <code class="docutils literal notranslate"><span class="pre">index</span></code></p>
 </dd>
 <dt class="field-odd" id="arch.univariate.EGARCH.update-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EGARCH.update-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1419,7 +1419,7 @@ <h1 id="arch-univariate-egarch-update">arch.univariate.EGARCH.update<a class="he
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1466,7 +1466,7 @@ <h1 id="arch-univariate-egarch-update">arch.univariate.EGARCH.update<a class="he
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
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diff --git a/univariate/generated/generated/arch.univariate.EGARCH.updateable.html b/univariate/generated/generated/arch.univariate.EGARCH.updateable.html
index e3aaed5fc4..f375daa4ed 100644
--- a/univariate/generated/generated/arch.univariate.EGARCH.updateable.html
+++ b/univariate/generated/generated/arch.univariate.EGARCH.updateable.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EGARCH.updateable - arch 6.1.0</title>
+        <title>arch.univariate.EGARCH.updateable - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1168,10 +1168,10 @@
                   
 
 
-<h1 id="arch-univariate-egarch-updateable">arch.univariate.EGARCH.updateable<a class="headerlink" href="#arch-univariate-egarch-updateable" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-egarch-updateable">arch.univariate.EGARCH.updateable<a class="headerlink" href="#arch-univariate-egarch-updateable" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.EGARCH.updateable">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">EGARCH.</span></span><span class="sig-name descname"><span class="pre">updateable</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.univariate.EGARCH.updateable" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">EGARCH.</span></span><span class="sig-name descname"><span class="pre">updateable</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.univariate.EGARCH.updateable" title="Link to this definition">¶</a></dt>
 <dd><p>Flag indicating that the volatility process supports update</p>
 </dd></dl>
 
@@ -1242,7 +1242,7 @@ <h1 id="arch-univariate-egarch-updateable">arch.univariate.EGARCH.updateable<a c
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1289,7 +1289,7 @@ <h1 id="arch-univariate-egarch-updateable">arch.univariate.EGARCH.updateable<a c
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.EGARCH.variance_bounds.html b/univariate/generated/generated/arch.univariate.EGARCH.variance_bounds.html
index 91c298ec3d..80daa47a93 100644
--- a/univariate/generated/generated/arch.univariate.EGARCH.variance_bounds.html
+++ b/univariate/generated/generated/arch.univariate.EGARCH.variance_bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EGARCH.variance_bounds - arch 6.1.0</title>
+        <title>arch.univariate.EGARCH.variance_bounds - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1262,20 +1262,20 @@
                   
 
 
-<h1 id="arch-univariate-egarch-variance-bounds">arch.univariate.EGARCH.variance_bounds<a class="headerlink" href="#arch-univariate-egarch-variance-bounds" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-egarch-variance-bounds">arch.univariate.EGARCH.variance_bounds<a class="headerlink" href="#arch-univariate-egarch-variance-bounds" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.EGARCH.variance_bounds">
-<span class="sig-prename descclassname"><span class="pre">EGARCH.</span></span><span class="sig-name descname"><span class="pre">variance_bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.variance_bounds.resids" title="arch.univariate.EGARCH.variance_bounds.resids (Python parameter) — Approximate residuals to use to compute the lower and upper bounds on the conditional variance"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.variance_bounds.power" title="arch.univariate.EGARCH.variance_bounds.power (Python parameter) — Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."><span class="n"><span class="pre">power</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">2.0</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#EGARCH.variance_bounds"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.EGARCH.variance_bounds" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.EGARCH.variance_bounds">
+<span class="sig-prename descclassname"><span class="pre">EGARCH.</span></span><span class="sig-name descname"><span class="pre">variance_bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.variance_bounds.resids" title="arch.univariate.EGARCH.variance_bounds.resids (Python parameter) — Approximate residuals to use to compute the lower and upper bounds on the conditional variance"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.variance_bounds.power" title="arch.univariate.EGARCH.variance_bounds.power (Python parameter) — Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."><span class="n"><span class="pre">power</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">2.0</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#EGARCH.variance_bounds"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.EGARCH.variance_bounds" title="Link to this definition">¶</a></dt>
 <dd><p>Construct loose bounds for conditional variances.</p>
 <p>These bounds are used in parameter estimation to ensure
 that the log-likelihood does not produce NaN values.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.EGARCH.variance_bounds-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EGARCH.variance_bounds-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.EGARCH.variance_bounds.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.EGARCH.variance_bounds.resids" title="Permalink to this definition">¶</a></dt><dd><p>Approximate residuals to use to compute the lower and upper bounds
+<dt id="arch.univariate.EGARCH.variance_bounds.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.EGARCH.variance_bounds.resids" title="Permalink to this definition">¶</a></dt><dd><p>Approximate residuals to use to compute the lower and upper bounds
 on the conditional variance</p>
 </dd>
-<dt id="arch.univariate.EGARCH.variance_bounds.power"><span class="sig-name descname">power</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>, optional</span></span><a class="headerlink" href="#arch.univariate.EGARCH.variance_bounds.power" title="Permalink to this definition">¶</a></dt><dd><p>Power used in the model. 2.0, the default corresponds to standard
+<dt id="arch.univariate.EGARCH.variance_bounds.power"><span class="n sig-name">power</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">2.0</span></code><a class="headerlink" href="#arch.univariate.EGARCH.variance_bounds.power" title="Permalink to this definition">¶</a></dt><dd><p>Power used in the model. 2.0, the default corresponds to standard
 ARCH models that evolve in squares.</p>
 </dd>
 </dl>
@@ -1285,7 +1285,7 @@ <h1 id="arch-univariate-egarch-variance-bounds">arch.univariate.EGARCH.variance_
 number of elements as resids</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.EGARCH.variance_bounds-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EGARCH.variance_bounds-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1357,7 +1357,7 @@ <h1 id="arch-univariate-egarch-variance-bounds">arch.univariate.EGARCH.variance_
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1404,7 +1404,7 @@ <h1 id="arch-univariate-egarch-variance-bounds">arch.univariate.EGARCH.variance_
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.EGARCH.volatility_updater.html b/univariate/generated/generated/arch.univariate.EGARCH.volatility_updater.html
index 3253bab787..8b98fc9ad3 100644
--- a/univariate/generated/generated/arch.univariate.EGARCH.volatility_updater.html
+++ b/univariate/generated/generated/arch.univariate.EGARCH.volatility_updater.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EGARCH.volatility_updater - arch 6.1.0</title>
+        <title>arch.univariate.EGARCH.volatility_updater - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1222,17 +1222,17 @@
                   
 
 
-<h1 id="arch-univariate-egarch-volatility-updater">arch.univariate.EGARCH.volatility_updater<a class="headerlink" href="#arch-univariate-egarch-volatility-updater" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-egarch-volatility-updater">arch.univariate.EGARCH.volatility_updater<a class="headerlink" href="#arch-univariate-egarch-volatility-updater" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.EGARCH.volatility_updater">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">EGARCH.</span></span><span class="sig-name descname"><span class="pre">volatility_updater</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.univariate.recursions_python.VolatilityUpdater.html#arch.univariate.recursions_python.VolatilityUpdater" title="arch.univariate.recursions_python.VolatilityUpdater (Python class) — Base class that all volatility updaters must inherit from."><span class="pre">VolatilityUpdater</span></a></span><a class="headerlink" href="#arch.univariate.EGARCH.volatility_updater" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">EGARCH.</span></span><span class="sig-name descname"><span class="pre">volatility_updater</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><span class="pre">VolatilityUpdater</span></span><a class="headerlink" href="#arch.univariate.EGARCH.volatility_updater" title="Link to this definition">¶</a></dt>
 <dd><p>Get the volatility updater associated with the volatility process</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.EGARCH.volatility_updater-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EGARCH.volatility_updater-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The updater class</p>
 </dd>
 <dt class="field-even" id="arch.univariate.EGARCH.volatility_updater-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EGARCH.volatility_updater-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference internal" href="../arch.univariate.recursions_python.VolatilityUpdater.html#arch.univariate.recursions_python.VolatilityUpdater" title="arch.univariate.recursions_python.VolatilityUpdater (Python class) — Base class that all volatility updaters must inherit from.">VolatilityUpdater</a></p>
+<dd class="field-even"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">VolatilityUpdater</span></code></p>
 </dd>
 <dt class="field-odd" id="arch.univariate.EGARCH.volatility_updater-raises">Raises<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EGARCH.volatility_updater-raises" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#NotImplementedError" title="(in Python v3.11)"><strong>NotImplementedError</strong></a> – If the process is not updateable</p>
@@ -1307,7 +1307,7 @@ <h1 id="arch-univariate-egarch-volatility-updater">arch.univariate.EGARCH.volati
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
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@@ -1354,7 +1354,7 @@ <h1 id="arch-univariate-egarch-volatility-updater">arch.univariate.EGARCH.volati
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.EWMAVariance.backcast.html b/univariate/generated/generated/arch.univariate.EWMAVariance.backcast.html
index 9c9bf066ac..8592b581e1 100644
--- a/univariate/generated/generated/arch.univariate.EWMAVariance.backcast.html
+++ b/univariate/generated/generated/arch.univariate.EWMAVariance.backcast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EWMAVariance.backcast - arch 6.1.0</title>
+        <title>arch.univariate.EWMAVariance.backcast - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1303,15 +1303,15 @@
                   
 
 
-<h1 id="arch-univariate-ewmavariance-backcast">arch.univariate.EWMAVariance.backcast<a class="headerlink" href="#arch-univariate-ewmavariance-backcast" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-ewmavariance-backcast">arch.univariate.EWMAVariance.backcast<a class="headerlink" href="#arch-univariate-ewmavariance-backcast" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.EWMAVariance.backcast">
-<span class="sig-prename descclassname"><span class="pre">EWMAVariance.</span></span><span class="sig-name descname"><span class="pre">backcast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.backcast.resids" title="arch.univariate.EWMAVariance.backcast.resids (Python parameter) — Vector of (approximate) residuals"><span class="n"><span class="pre">resids</span></span></a></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.EWMAVariance.backcast" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">EWMAVariance.</span></span><span class="sig-name descname"><span class="pre">backcast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.backcast.resids" title="arch.univariate.EWMAVariance.backcast.resids (Python parameter) — Vector of (approximate) residuals"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.backcast" title="Link to this definition">¶</a></dt>
 <dd><p>Construct values for backcasting to start the recursion</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.EWMAVariance.backcast-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EWMAVariance.backcast-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.EWMAVariance.backcast.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.backcast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of (approximate) residuals</p>
+<dt id="arch.univariate.EWMAVariance.backcast.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.backcast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of (approximate) residuals</p>
 </dd>
 </dl>
 </dd>
@@ -1319,7 +1319,7 @@ <h1 id="arch-univariate-ewmavariance-backcast">arch.univariate.EWMAVariance.back
 <dd class="field-even"><p><strong>backcast</strong> – Value to use in backcasting in the volatility recursion</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.EWMAVariance.backcast-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EWMAVariance.backcast-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1391,7 +1391,7 @@ <h1 id="arch-univariate-ewmavariance-backcast">arch.univariate.EWMAVariance.back
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1438,7 +1438,7 @@ <h1 id="arch-univariate-ewmavariance-backcast">arch.univariate.EWMAVariance.back
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.EWMAVariance.backcast_transform.html b/univariate/generated/generated/arch.univariate.EWMAVariance.backcast_transform.html
index dc6d165844..dc5a578813 100644
--- a/univariate/generated/generated/arch.univariate.EWMAVariance.backcast_transform.html
+++ b/univariate/generated/generated/arch.univariate.EWMAVariance.backcast_transform.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EWMAVariance.backcast_transform - arch 6.1.0</title>
+        <title>arch.univariate.EWMAVariance.backcast_transform - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1303,15 +1303,15 @@
                   
 
 
-<h1 id="arch-univariate-ewmavariance-backcast-transform">arch.univariate.EWMAVariance.backcast_transform<a class="headerlink" href="#arch-univariate-ewmavariance-backcast-transform" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-ewmavariance-backcast-transform">arch.univariate.EWMAVariance.backcast_transform<a class="headerlink" href="#arch-univariate-ewmavariance-backcast-transform" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.EWMAVariance.backcast_transform">
-<span class="sig-prename descclassname"><span class="pre">EWMAVariance.</span></span><span class="sig-name descname"><span class="pre">backcast_transform</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.backcast_transform.backcast" title="arch.univariate.EWMAVariance.backcast_transform.backcast (Python parameter) — User-provided backcast that approximates sigma2[0]."><span class="n"><span class="pre">backcast</span></span></a></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.EWMAVariance.backcast_transform" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.EWMAVariance.backcast_transform">
+<span class="sig-prename descclassname"><span class="pre">EWMAVariance.</span></span><span class="sig-name descname"><span class="pre">backcast_transform</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.backcast_transform.backcast" title="arch.univariate.EWMAVariance.backcast_transform.backcast (Python parameter) — User-provided backcast that approximates sigma2[0]."><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.backcast_transform" title="Link to this definition">¶</a></dt>
 <dd><p>Transformation to apply to user-provided backcast values</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.EWMAVariance.backcast_transform-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EWMAVariance.backcast_transform-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.EWMAVariance.backcast_transform.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}</span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.backcast_transform.backcast" title="Permalink to this definition">¶</a></dt><dd><p>User-provided <code class="docutils literal notranslate"><span class="pre">backcast</span></code> that approximates sigma2[0].</p>
+<dt id="arch.univariate.EWMAVariance.backcast_transform.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.backcast_transform.backcast" title="Permalink to this definition">¶</a></dt><dd><p>User-provided <code class="docutils literal notranslate"><span class="pre">backcast</span></code> that approximates sigma2[0].</p>
 </dd>
 </dl>
 </dd>
@@ -1319,7 +1319,7 @@ <h1 id="arch-univariate-ewmavariance-backcast-transform">arch.univariate.EWMAVar
 <dd class="field-even"><p><strong>backcast</strong> – Backcast transformed to the model-appropriate scale</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.EWMAVariance.backcast_transform-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EWMAVariance.backcast_transform-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>{float, ndarray}</p>
+<dd class="field-odd"><p><code class="docutils literal notranslate"><span class="pre">{float,</span> <span class="pre">ndarray}</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1391,7 +1391,7 @@ <h1 id="arch-univariate-ewmavariance-backcast-transform">arch.univariate.EWMAVar
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1438,7 +1438,7 @@ <h1 id="arch-univariate-ewmavariance-backcast-transform">arch.univariate.EWMAVar
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.EWMAVariance.bounds.html b/univariate/generated/generated/arch.univariate.EWMAVariance.bounds.html
index 36cfc79191..a6e6ea2f5b 100644
--- a/univariate/generated/generated/arch.univariate.EWMAVariance.bounds.html
+++ b/univariate/generated/generated/arch.univariate.EWMAVariance.bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EWMAVariance.bounds - arch 6.1.0</title>
+        <title>arch.univariate.EWMAVariance.bounds - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1303,15 +1303,15 @@
                   
 
 
-<h1 id="arch-univariate-ewmavariance-bounds">arch.univariate.EWMAVariance.bounds<a class="headerlink" href="#arch-univariate-ewmavariance-bounds" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-ewmavariance-bounds">arch.univariate.EWMAVariance.bounds<a class="headerlink" href="#arch-univariate-ewmavariance-bounds" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.EWMAVariance.bounds">
-<span class="sig-prename descclassname"><span class="pre">EWMAVariance.</span></span><span class="sig-name descname"><span class="pre">bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.bounds.resids" title="arch.univariate.EWMAVariance.bounds.resids (Python parameter) — Vector of (approximate) residuals"><span class="n"><span class="pre">resids</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#EWMAVariance.bounds"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.EWMAVariance.bounds" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">EWMAVariance.</span></span><span class="sig-name descname"><span class="pre">bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.bounds.resids" title="arch.univariate.EWMAVariance.bounds.resids (Python parameter) — Vector of (approximate) residuals"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#EWMAVariance.bounds"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.EWMAVariance.bounds" title="Link to this definition">¶</a></dt>
 <dd><p>Returns bounds for parameters</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.EWMAVariance.bounds-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EWMAVariance.bounds-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.EWMAVariance.bounds.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.bounds.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of (approximate) residuals</p>
+<dt id="arch.univariate.EWMAVariance.bounds.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.bounds.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of (approximate) residuals</p>
 </dd>
 </dl>
 </dd>
@@ -1319,7 +1319,7 @@ <h1 id="arch-univariate-ewmavariance-bounds">arch.univariate.EWMAVariance.bounds
 <dd class="field-even"><p><strong>bounds</strong> – List of bounds where each element is (lower, upper).</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.EWMAVariance.bounds-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EWMAVariance.bounds-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a>[<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a>[<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>,<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>]]</p>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">list[tuple[float,float]]</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1391,7 +1391,7 @@ <h1 id="arch-univariate-ewmavariance-bounds">arch.univariate.EWMAVariance.bounds
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1438,7 +1438,7 @@ <h1 id="arch-univariate-ewmavariance-bounds">arch.univariate.EWMAVariance.bounds
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.EWMAVariance.compute_variance.html b/univariate/generated/generated/arch.univariate.EWMAVariance.compute_variance.html
index 68eabebaf0..461e1be41c 100644
--- a/univariate/generated/generated/arch.univariate.EWMAVariance.compute_variance.html
+++ b/univariate/generated/generated/arch.univariate.EWMAVariance.compute_variance.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EWMAVariance.compute_variance - arch 6.1.0</title>
+        <title>arch.univariate.EWMAVariance.compute_variance - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -749,13 +749,6 @@
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     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.univariate.EWMAVariance.compute_variance-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
       </ul>
@@ -1317,13 +1310,6 @@
       </ul>
     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.univariate.EWMAVariance.compute_variance-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
       </ul>
@@ -1345,30 +1331,27 @@
                   
 
 
-<h1 id="arch-univariate-ewmavariance-compute-variance">arch.univariate.EWMAVariance.compute_variance<a class="headerlink" href="#arch-univariate-ewmavariance-compute-variance" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-ewmavariance-compute-variance">arch.univariate.EWMAVariance.compute_variance<a class="headerlink" href="#arch-univariate-ewmavariance-compute-variance" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.EWMAVariance.compute_variance">
-<span class="sig-prename descclassname"><span class="pre">EWMAVariance.</span></span><span class="sig-name descname"><span class="pre">compute_variance</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.compute_variance.parameters" title="arch.univariate.EWMAVariance.compute_variance.parameters (Python parameter) — Model parameters"><span class="n"><span class="pre">parameters</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.compute_variance.resids" title="arch.univariate.EWMAVariance.compute_variance.resids (Python parameter) — Vector of mean zero residuals"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.compute_variance.sigma2" title="arch.univariate.EWMAVariance.compute_variance.sigma2 (Python parameter) — Array with same size as resids to store the conditional variance"><span class="n"><span class="pre">sigma2</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.compute_variance.backcast" title="arch.univariate.EWMAVariance.compute_variance.backcast (Python parameter) — Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."><span class="n"><span class="pre">backcast</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.compute_variance.var_bounds" title="arch.univariate.EWMAVariance.compute_variance.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#EWMAVariance.compute_variance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.EWMAVariance.compute_variance" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">EWMAVariance.</span></span><span class="sig-name descname"><span class="pre">compute_variance</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.compute_variance.parameters" title="arch.univariate.EWMAVariance.compute_variance.parameters (Python parameter) — Model parameters"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.compute_variance.resids" title="arch.univariate.EWMAVariance.compute_variance.resids (Python parameter) — Vector of mean zero residuals"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.compute_variance.sigma2" title="arch.univariate.EWMAVariance.compute_variance.sigma2 (Python parameter) — Array with same size as resids to store the conditional variance"><span class="n"><span class="pre">sigma2</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.compute_variance.backcast" title="arch.univariate.EWMAVariance.compute_variance.backcast (Python parameter) — Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.compute_variance.var_bounds" title="arch.univariate.EWMAVariance.compute_variance.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#EWMAVariance.compute_variance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.EWMAVariance.compute_variance" title="Link to this definition">¶</a></dt>
 <dd><p>Compute the variance for the ARCH model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.EWMAVariance.compute_variance-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EWMAVariance.compute_variance-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.EWMAVariance.compute_variance.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.compute_variance.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
+<dt id="arch.univariate.EWMAVariance.compute_variance.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.compute_variance.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
 </dd>
-<dt id="arch.univariate.EWMAVariance.compute_variance.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.compute_variance.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of mean zero residuals</p>
+<dt id="arch.univariate.EWMAVariance.compute_variance.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.compute_variance.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of mean zero residuals</p>
 </dd>
-<dt id="arch.univariate.EWMAVariance.compute_variance.sigma2"><span class="sig-name descname">sigma2</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.compute_variance.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>Array with same size as resids to store the conditional variance</p>
+<dt id="arch.univariate.EWMAVariance.compute_variance.sigma2"><span class="n sig-name">sigma2</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.compute_variance.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>Array with same size as resids to store the conditional variance</p>
 </dd>
-<dt id="arch.univariate.EWMAVariance.compute_variance.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}</span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.compute_variance.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing ARCH recursion. Can be an ndarray
+<dt id="arch.univariate.EWMAVariance.compute_variance.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.compute_variance.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing ARCH recursion. Can be an ndarray
 when the model contains multiple components.</p>
 </dd>
-<dt id="arch.univariate.EWMAVariance.compute_variance.var_bounds"><span class="sig-name descname">var_bounds</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.compute_variance.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
+<dt id="arch.univariate.EWMAVariance.compute_variance.var_bounds"><span class="n sig-name">var_bounds</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.compute_variance.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
 </dd>
 </dl>
 </dd>
-<dt class="field-even" id="arch.univariate.EWMAVariance.compute_variance-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EWMAVariance.compute_variance-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a></p>
-</dd>
 </dl>
 </dd></dl>
 
@@ -1439,7 +1422,7 @@ <h1 id="arch-univariate-ewmavariance-compute-variance">arch.univariate.EWMAVaria
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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diff --git a/univariate/generated/generated/arch.univariate.EWMAVariance.constraints.html b/univariate/generated/generated/arch.univariate.EWMAVariance.constraints.html
index 19dcb92d8a..597bc64dd1 100644
--- a/univariate/generated/generated/arch.univariate.EWMAVariance.constraints.html
+++ b/univariate/generated/generated/arch.univariate.EWMAVariance.constraints.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EWMAVariance.constraints - arch 6.1.0</title>
+        <title>arch.univariate.EWMAVariance.constraints - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -718,13 +718,6 @@
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       <ul class="md-nav__list">
         
-          <li class="md-nav__item">
-  <a href="#arch.univariate.EWMAVariance.constraints-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
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-  
-</li>
-        
           <li class="md-nav__item">
   <a href="#arch.univariate.EWMAVariance.constraints-returns" class="md-nav__link">
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@@ -1230,13 +1223,6 @@
     <nav class="md-nav" aria-label="EWMAVariance.constraints">
       <ul class="md-nav__list">
         
-          <li class="md-nav__item">
-  <a href="#arch.univariate.EWMAVariance.constraints-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
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-  
-</li>
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           <li class="md-nav__item">
   <a href="#arch.univariate.EWMAVariance.constraints-returns" class="md-nav__link">
     <span class="md-ellipsis">Returns</span>
@@ -1263,20 +1249,17 @@
                   
 
 
-<h1 id="arch-univariate-ewmavariance-constraints">arch.univariate.EWMAVariance.constraints<a class="headerlink" href="#arch-univariate-ewmavariance-constraints" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-ewmavariance-constraints">arch.univariate.EWMAVariance.constraints<a class="headerlink" href="#arch-univariate-ewmavariance-constraints" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.EWMAVariance.constraints">
-<span class="sig-prename descclassname"><span class="pre">EWMAVariance.</span></span><span class="sig-name descname"><span class="pre">constraints</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#EWMAVariance.constraints"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.EWMAVariance.constraints" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">EWMAVariance.</span></span><span class="sig-name descname"><span class="pre">constraints</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#EWMAVariance.constraints"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.EWMAVariance.constraints" title="Link to this definition">¶</a></dt>
 <dd><p>Construct parameter constraints arrays for parameter estimation</p>
 <dl class="field-list simple">
-<dt class="field-odd" id="arch.univariate.EWMAVariance.constraints-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EWMAVariance.constraints-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>]</p>
-</dd>
-<dt class="field-even" id="arch.univariate.EWMAVariance.constraints-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EWMAVariance.constraints-returns" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><ul class="simple">
-<li><p><strong>A</strong> (<em>ndarray</em>) – Parameters loadings in constraint. Shape is number of constraints
+<dt class="field-odd" id="arch.univariate.EWMAVariance.constraints-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EWMAVariance.constraints-returns" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p><strong>A</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – Parameters loadings in constraint. Shape is number of constraints
 by number of parameters</p></li>
-<li><p><strong>b</strong> (<em>ndarray</em>) – Constraint values, one for each constraint</p></li>
+<li><p><strong>b</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – Constraint values, one for each constraint</p></li>
 </ul>
 </p>
 </dd>
@@ -1353,7 +1336,7 @@ <h1 id="arch-univariate-ewmavariance-constraints">arch.univariate.EWMAVariance.c
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1400,7 +1383,7 @@ <h1 id="arch-univariate-ewmavariance-constraints">arch.univariate.EWMAVariance.c
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-        <title>arch.univariate.EWMAVariance.forecast - arch 6.1.0</title>
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-<h1 id="arch-univariate-ewmavariance-forecast">arch.univariate.EWMAVariance.forecast<a class="headerlink" href="#arch-univariate-ewmavariance-forecast" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-ewmavariance-forecast">arch.univariate.EWMAVariance.forecast<a class="headerlink" href="#arch-univariate-ewmavariance-forecast" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.EWMAVariance.forecast">
-<span class="sig-prename descclassname"><span class="pre">EWMAVariance.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.forecast.parameters" title="arch.univariate.EWMAVariance.forecast.parameters (Python parameter) — Parameters required to forecast the volatility model"><span class="n"><span class="pre">parameters</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.forecast.resids" title="arch.univariate.EWMAVariance.forecast.resids (Python parameter) — Residuals to use in the recursion"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.forecast.backcast" title="arch.univariate.EWMAVariance.forecast.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.forecast.var_bounds" title="arch.univariate.EWMAVariance.forecast.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.forecast.start" title="arch.univariate.EWMAVariance.forecast.start (Python parameter) — Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."><span class="n"><span class="pre">start</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.forecast.horizon" title="arch.univariate.EWMAVariance.forecast.horizon (Python parameter) — Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."><span class="n"><span class="pre">horizon</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.forecast.method" title="arch.univariate.EWMAVariance.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic."><span class="n"><span class="pre">method</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.forecast.simulations" title="arch.univariate.EWMAVariance.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.forecast.rng" title="arch.univariate.EWMAVariance.forecast.rng (Python parameter) — Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."><span class="n"><span class="pre">rng</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.forecast.random_state" title="arch.univariate.EWMAVariance.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.EWMAVariance.forecast" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">EWMAVariance.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.forecast.parameters" title="arch.univariate.EWMAVariance.forecast.parameters (Python parameter) — Parameters required to forecast the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.forecast.resids" title="arch.univariate.EWMAVariance.forecast.resids (Python parameter) — Residuals to use in the recursion"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.forecast.backcast" title="arch.univariate.EWMAVariance.forecast.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.forecast.var_bounds" title="arch.univariate.EWMAVariance.forecast.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.forecast.start" title="arch.univariate.EWMAVariance.forecast.start (Python parameter) — Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.forecast.horizon" title="arch.univariate.EWMAVariance.forecast.horizon (Python parameter) — Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.forecast.method" title="arch.univariate.EWMAVariance.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.forecast.simulations" title="arch.univariate.EWMAVariance.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.forecast.rng" title="arch.univariate.EWMAVariance.forecast.rng (Python parameter) — Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.forecast.random_state" title="arch.univariate.EWMAVariance.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><span class="pre">VarianceForecast</span></span></span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.forecast" title="Link to this definition">¶</a></dt>
 <dd><p>Forecast volatility from the model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.EWMAVariance.forecast-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EWMAVariance.forecast-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.EWMAVariance.forecast.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, Series}</span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.forecast.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast the volatility model</p>
+<dt id="arch.univariate.EWMAVariance.forecast.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.forecast.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast the volatility model</p>
 </dd>
-<dt id="arch.univariate.EWMAVariance.forecast.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.forecast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Residuals to use in the recursion</p>
+<dt id="arch.univariate.EWMAVariance.forecast.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.forecast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Residuals to use in the recursion</p>
 </dd>
-<dt id="arch.univariate.EWMAVariance.forecast.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.forecast.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing the recursion</p>
+<dt id="arch.univariate.EWMAVariance.forecast.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.forecast.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing the recursion</p>
 </dd>
-<dt id="arch.univariate.EWMAVariance.forecast.var_bounds"><span class="sig-name descname">var_bounds</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray, 2-d</span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.forecast.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
+<dt id="arch.univariate.EWMAVariance.forecast.var_bounds"><span class="n sig-name">var_bounds</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.forecast.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
 </dd>
-<dt id="arch.univariate.EWMAVariance.forecast.start"><span class="sig-name descname">start</span><span class="p"> : </span><span class="desctype"><span class="desctype">{None, int}</span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>Index of the first observation to use as the starting point for
+<dt id="arch.univariate.EWMAVariance.forecast.start"><span class="n sig-name">start</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.EWMAVariance.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>Index of the first observation to use as the starting point for
 the forecast.  Default is len(resids).</p>
 </dd>
-<dt id="arch.univariate.EWMAVariance.forecast.horizon"><span class="sig-name descname">horizon</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.forecast.horizon" title="Permalink to this definition">¶</a></dt><dd><p>Forecast horizon.  Must be 1 or larger.  Forecasts are produced
+<dt id="arch.univariate.EWMAVariance.forecast.horizon"><span class="n sig-name">horizon</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code><a class="headerlink" href="#arch.univariate.EWMAVariance.forecast.horizon" title="Permalink to this definition">¶</a></dt><dd><p>Forecast horizon.  Must be 1 or larger.  Forecasts are produced
 for horizons in [1, horizon].</p>
 </dd>
-<dt id="arch.univariate.EWMAVariance.forecast.method"><span class="sig-name descname">method</span><span class="p"> : </span><span class="desctype"><span class="desctype">{'analytic', 'simulation', 'bootstrap'}</span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.forecast.method" title="Permalink to this definition">¶</a></dt><dd><p>Method to use when producing the forecast. The default is analytic.</p>
+<dt id="arch.univariate.EWMAVariance.forecast.method"><span class="n sig-name">method</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'analytic'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'simulation'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'bootstrap'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code><a class="headerlink" href="#arch.univariate.EWMAVariance.forecast.method" title="Permalink to this definition">¶</a></dt><dd><p>Method to use when producing the forecast. The default is analytic.</p>
 </dd>
-<dt id="arch.univariate.EWMAVariance.forecast.simulations"><span class="sig-name descname">simulations</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.forecast.simulations" title="Permalink to this definition">¶</a></dt><dd><p>Number of simulations to run when computing the forecast using
+<dt id="arch.univariate.EWMAVariance.forecast.simulations"><span class="n sig-name">simulations</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.univariate.EWMAVariance.forecast.simulations" title="Permalink to this definition">¶</a></dt><dd><p>Number of simulations to run when computing the forecast using
 either simulation or bootstrap.</p>
 </dd>
-<dt id="arch.univariate.EWMAVariance.forecast.rng"><span class="sig-name descname">rng</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable</span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.forecast.rng" title="Permalink to this definition">¶</a></dt><dd><p>Callable random number generator required if method is
+<dt id="arch.univariate.EWMAVariance.forecast.rng"><span class="n sig-name">rng</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)">Callable</a><span class="p">[</span><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">,</span><span class="w"> </span><span class="p">...</span><span class="p">]</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.EWMAVariance.forecast.rng" title="Permalink to this definition">¶</a></dt><dd><p>Callable random number generator required if method is
 ‘simulation’. Must take a single shape input and return random
 samples numbers with that shape.</p>
 </dd>
-<dt id="arch.univariate.EWMAVariance.forecast.random_state"><span class="sig-name descname">random_state</span><span class="p"> : </span><span class="desctype"><span class="desctype">RandomState, optional</span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.forecast.random_state" title="Permalink to this definition">¶</a></dt><dd><p>NumPy RandomState instance to use when method is ‘bootstrap’</p>
+<dt id="arch.univariate.EWMAVariance.forecast.random_state"><span class="n sig-name">random_state</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)">RandomState</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.EWMAVariance.forecast.random_state" title="Permalink to this definition">¶</a></dt><dd><p>NumPy RandomState instance to use when method is ‘bootstrap’</p>
 </dd>
 </dl>
 </dd>
@@ -1483,7 +1483,7 @@ <h1 id="arch-univariate-ewmavariance-forecast">arch.univariate.EWMAVariance.fore
 or bootstrap, the simulated paths.</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.EWMAVariance.forecast-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EWMAVariance.forecast-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>VarianceForecast</p>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">arch.univariate.volatility.VarianceForecast</span></code></p>
 </dd>
 <dt class="field-even" id="arch.univariate.EWMAVariance.forecast-raises">Raises<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EWMAVariance.forecast-raises" title="Permalink to this headline">¶</a></dt>
 <dd class="field-even"><ul class="simple">
@@ -1570,7 +1570,7 @@ <h1 id="arch-univariate-ewmavariance-forecast">arch.univariate.EWMAVariance.fore
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1617,7 +1617,7 @@ <h1 id="arch-univariate-ewmavariance-forecast">arch.univariate.EWMAVariance.fore
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diff --git a/univariate/generated/generated/arch.univariate.EWMAVariance.name.html b/univariate/generated/generated/arch.univariate.EWMAVariance.name.html
index 91e7b7c7b7..d50f042fbc 100644
--- a/univariate/generated/generated/arch.univariate.EWMAVariance.name.html
+++ b/univariate/generated/generated/arch.univariate.EWMAVariance.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EWMAVariance.name - arch 6.1.0</title>
+        <title>arch.univariate.EWMAVariance.name - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1223,10 +1223,10 @@
                   
 
 
-<h1 id="arch-univariate-ewmavariance-name">arch.univariate.EWMAVariance.name<a class="headerlink" href="#arch-univariate-ewmavariance-name" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-ewmavariance-name">arch.univariate.EWMAVariance.name<a class="headerlink" href="#arch-univariate-ewmavariance-name" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.EWMAVariance.name">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">EWMAVariance.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.univariate.EWMAVariance.name" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">EWMAVariance.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.univariate.EWMAVariance.name" title="Link to this definition">¶</a></dt>
 <dd><p>The name of the volatility process</p>
 </dd></dl>
 
@@ -1297,7 +1297,7 @@ <h1 id="arch-univariate-ewmavariance-name">arch.univariate.EWMAVariance.name<a c
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1344,7 +1344,7 @@ <h1 id="arch-univariate-ewmavariance-name">arch.univariate.EWMAVariance.name<a c
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
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diff --git a/univariate/generated/generated/arch.univariate.EWMAVariance.num_params.html b/univariate/generated/generated/arch.univariate.EWMAVariance.num_params.html
index 94ea644929..2ce64a20bd 100644
--- a/univariate/generated/generated/arch.univariate.EWMAVariance.num_params.html
+++ b/univariate/generated/generated/arch.univariate.EWMAVariance.num_params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EWMAVariance.num_params - arch 6.1.0</title>
+        <title>arch.univariate.EWMAVariance.num_params - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1223,10 +1223,10 @@
                   
 
 
-<h1 id="arch-univariate-ewmavariance-num-params">arch.univariate.EWMAVariance.num_params<a class="headerlink" href="#arch-univariate-ewmavariance-num-params" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-ewmavariance-num-params">arch.univariate.EWMAVariance.num_params<a class="headerlink" href="#arch-univariate-ewmavariance-num-params" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.EWMAVariance.num_params">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">EWMAVariance.</span></span><span class="sig-name descname"><span class="pre">num_params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.EWMAVariance.num_params" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">EWMAVariance.</span></span><span class="sig-name descname"><span class="pre">num_params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.EWMAVariance.num_params" title="Link to this definition">¶</a></dt>
 <dd><p>The number of parameters in the model</p>
 </dd></dl>
 
@@ -1297,7 +1297,7 @@ <h1 id="arch-univariate-ewmavariance-num-params">arch.univariate.EWMAVariance.nu
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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diff --git a/univariate/generated/generated/arch.univariate.EWMAVariance.parameter_names.html b/univariate/generated/generated/arch.univariate.EWMAVariance.parameter_names.html
index 7bdaa30f1d..e5d784387e 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
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@@ -102,7 +102,7 @@
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@@ -1263,17 +1263,17 @@
                   
 
 
-<h1 id="arch-univariate-ewmavariance-parameter-names">arch.univariate.EWMAVariance.parameter_names<a class="headerlink" href="#arch-univariate-ewmavariance-parameter-names" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-ewmavariance-parameter-names">arch.univariate.EWMAVariance.parameter_names<a class="headerlink" href="#arch-univariate-ewmavariance-parameter-names" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.EWMAVariance.parameter_names">
-<span class="sig-prename descclassname"><span class="pre">EWMAVariance.</span></span><span class="sig-name descname"><span class="pre">parameter_names</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#EWMAVariance.parameter_names"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.EWMAVariance.parameter_names" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">EWMAVariance.</span></span><span class="sig-name descname"><span class="pre">parameter_names</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#EWMAVariance.parameter_names"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.EWMAVariance.parameter_names" title="Link to this definition">¶</a></dt>
 <dd><p>Names of model parameters</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.EWMAVariance.parameter_names-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EWMAVariance.parameter_names-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p><strong>names</strong> – Variables names</p>
 </dd>
 <dt class="field-even" id="arch.univariate.EWMAVariance.parameter_names-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EWMAVariance.parameter_names-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>)</p>
+<dd class="field-even"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span> <span class="pre">(str)</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1345,7 +1345,7 @@ <h1 id="arch-univariate-ewmavariance-parameter-names">arch.univariate.EWMAVarian
   
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diff --git a/univariate/generated/generated/arch.univariate.EWMAVariance.simulate.html b/univariate/generated/generated/arch.univariate.EWMAVariance.simulate.html
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-        <title>arch.univariate.EWMAVariance.simulate - arch 6.1.0</title>
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@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch 6.1.0
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-  </a>
-  
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           <li class="md-nav__item">
@@ -1359,36 +1345,33 @@
                   
 
 
-<h1 id="arch-univariate-ewmavariance-simulate">arch.univariate.EWMAVariance.simulate<a class="headerlink" href="#arch-univariate-ewmavariance-simulate" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-ewmavariance-simulate">arch.univariate.EWMAVariance.simulate<a class="headerlink" href="#arch-univariate-ewmavariance-simulate" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.EWMAVariance.simulate">
-<span class="sig-prename descclassname"><span class="pre">EWMAVariance.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.simulate.parameters" title="arch.univariate.EWMAVariance.simulate.parameters (Python parameter) — Parameters required to simulate the volatility model"><span class="n"><span class="pre">parameters</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.simulate.nobs" title="arch.univariate.EWMAVariance.simulate.nobs (Python parameter) — Number of data points to simulate"><span class="n"><span class="pre">nobs</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.simulate.rng" title="arch.univariate.EWMAVariance.simulate.rng (Python parameter) — Callable function that takes a single integer input and returns a vector of random numbers"><span class="n"><span class="pre">rng</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.simulate.burn" title="arch.univariate.EWMAVariance.simulate.burn (Python parameter) — Number of additional observations to generate when initializing the simulation"><span class="n"><span class="pre">burn</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.simulate.initial_value" title="arch.univariate.EWMAVariance.simulate.initial_value (Python parameter) — Scalar or array of initial values to use when initializing the simulation"><span class="n"><span class="pre">initial_value</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#EWMAVariance.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.EWMAVariance.simulate" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">EWMAVariance.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.simulate.parameters" title="arch.univariate.EWMAVariance.simulate.parameters (Python parameter) — Parameters required to simulate the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.simulate.nobs" title="arch.univariate.EWMAVariance.simulate.nobs (Python parameter) — Number of data points to simulate"><span class="n"><span class="pre">nobs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.simulate.rng" title="arch.univariate.EWMAVariance.simulate.rng (Python parameter) — Callable function that takes a single integer input and returns a vector of random numbers"><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.simulate.burn" title="arch.univariate.EWMAVariance.simulate.burn (Python parameter) — Number of additional observations to generate when initializing the simulation"><span class="n"><span class="pre">burn</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.simulate.initial_value" title="arch.univariate.EWMAVariance.simulate.initial_value (Python parameter) — Scalar or array of initial values to use when initializing the simulation"><span class="n"><span class="pre">initial_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#EWMAVariance.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.EWMAVariance.simulate" title="Link to this definition">¶</a></dt>
 <dd><p>Simulate data from the model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.EWMAVariance.simulate-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EWMAVariance.simulate-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.EWMAVariance.simulate.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, Series}</span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to simulate the volatility model</p>
+<dt id="arch.univariate.EWMAVariance.simulate.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to simulate the volatility model</p>
 </dd>
-<dt id="arch.univariate.EWMAVariance.simulate.nobs"><span class="sig-name descname">nobs</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.simulate.nobs" title="Permalink to this definition">¶</a></dt><dd><p>Number of data points to simulate</p>
+<dt id="arch.univariate.EWMAVariance.simulate.nobs"><span class="n sig-name">nobs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.simulate.nobs" title="Permalink to this definition">¶</a></dt><dd><p>Number of data points to simulate</p>
 </dd>
-<dt id="arch.univariate.EWMAVariance.simulate.rng"><span class="sig-name descname">rng</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable</span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.simulate.rng" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that takes a single integer input and returns
+<dt id="arch.univariate.EWMAVariance.simulate.rng"><span class="n sig-name">rng</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)">Callable</a><span class="p">[</span><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">,</span><span class="w"> </span><span class="p">...</span><span class="p">]</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.simulate.rng" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that takes a single integer input and returns
 a vector of random numbers</p>
 </dd>
-<dt id="arch.univariate.EWMAVariance.simulate.burn"><span class="sig-name descname">burn</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.simulate.burn" title="Permalink to this definition">¶</a></dt><dd><p>Number of additional observations to generate when initializing
+<dt id="arch.univariate.EWMAVariance.simulate.burn"><span class="n sig-name">burn</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code><a class="headerlink" href="#arch.univariate.EWMAVariance.simulate.burn" title="Permalink to this definition">¶</a></dt><dd><p>Number of additional observations to generate when initializing
 the simulation</p>
 </dd>
-<dt id="arch.univariate.EWMAVariance.simulate.initial_value"><span class="sig-name descname">initial_value</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}, optional</span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.simulate.initial_value" title="Permalink to this definition">¶</a></dt><dd><p>Scalar or array of initial values to use when initializing the
+<dt id="arch.univariate.EWMAVariance.simulate.initial_value"><span class="n sig-name">initial_value</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.EWMAVariance.simulate.initial_value" title="Permalink to this definition">¶</a></dt><dd><p>Scalar or array of initial values to use when initializing the
 simulation</p>
 </dd>
 </dl>
 </dd>
-<dt class="field-even" id="arch.univariate.EWMAVariance.simulate-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EWMAVariance.simulate-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>]</p>
-</dd>
-<dt class="field-odd" id="arch.univariate.EWMAVariance.simulate-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EWMAVariance.simulate-returns" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><ul class="simple">
-<li><p><strong>resids</strong> (<em>ndarray</em>) – The simulated residuals</p></li>
-<li><p><strong>variance</strong> (<em>ndarray</em>) – The simulated variance</p></li>
+<dt class="field-even" id="arch.univariate.EWMAVariance.simulate-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EWMAVariance.simulate-returns" title="Permalink to this headline">¶</a></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><strong>resids</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – The simulated residuals</p></li>
+<li><p><strong>variance</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – The simulated variance</p></li>
 </ul>
 </p>
 </dd>
@@ -1462,7 +1445,7 @@ <h1 id="arch-univariate-ewmavariance-simulate">arch.univariate.EWMAVariance.simu
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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diff --git a/univariate/generated/generated/arch.univariate.EWMAVariance.start.html b/univariate/generated/generated/arch.univariate.EWMAVariance.start.html
index 427af87496..62f112fdc4 100644
--- a/univariate/generated/generated/arch.univariate.EWMAVariance.start.html
+++ b/univariate/generated/generated/arch.univariate.EWMAVariance.start.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EWMAVariance.start - arch 6.1.0</title>
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@@ -47,8 +47,8 @@
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+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1223,10 +1223,10 @@
                   
 
 
-<h1 id="arch-univariate-ewmavariance-start">arch.univariate.EWMAVariance.start<a class="headerlink" href="#arch-univariate-ewmavariance-start" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-ewmavariance-start">arch.univariate.EWMAVariance.start<a class="headerlink" href="#arch-univariate-ewmavariance-start" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.EWMAVariance.start">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">EWMAVariance.</span></span><span class="sig-name descname"><span class="pre">start</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.EWMAVariance.start" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">EWMAVariance.</span></span><span class="sig-name descname"><span class="pre">start</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.EWMAVariance.start" title="Link to this definition">¶</a></dt>
 <dd><p>Index to use to start variance subarray selection</p>
 </dd></dl>
 
@@ -1297,7 +1297,7 @@ <h1 id="arch-univariate-ewmavariance-start">arch.univariate.EWMAVariance.start<a
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1344,7 +1344,7 @@ <h1 id="arch-univariate-ewmavariance-start">arch.univariate.EWMAVariance.start<a
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diff --git a/univariate/generated/generated/arch.univariate.EWMAVariance.starting_values.html b/univariate/generated/generated/arch.univariate.EWMAVariance.starting_values.html
index 5a265aee64..8d451ead27 100644
--- a/univariate/generated/generated/arch.univariate.EWMAVariance.starting_values.html
+++ b/univariate/generated/generated/arch.univariate.EWMAVariance.starting_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EWMAVariance.starting_values - arch 6.1.0</title>
+        <title>arch.univariate.EWMAVariance.starting_values - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
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@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1303,15 +1303,15 @@
                   
 
 
-<h1 id="arch-univariate-ewmavariance-starting-values">arch.univariate.EWMAVariance.starting_values<a class="headerlink" href="#arch-univariate-ewmavariance-starting-values" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-ewmavariance-starting-values">arch.univariate.EWMAVariance.starting_values<a class="headerlink" href="#arch-univariate-ewmavariance-starting-values" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.EWMAVariance.starting_values">
-<span class="sig-prename descclassname"><span class="pre">EWMAVariance.</span></span><span class="sig-name descname"><span class="pre">starting_values</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.starting_values.resids" title="arch.univariate.EWMAVariance.starting_values.resids (Python parameter) — Array of (approximate) residuals to use when computing starting values"><span class="n"><span class="pre">resids</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#EWMAVariance.starting_values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.EWMAVariance.starting_values" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">EWMAVariance.</span></span><span class="sig-name descname"><span class="pre">starting_values</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.starting_values.resids" title="arch.univariate.EWMAVariance.starting_values.resids (Python parameter) — Array of (approximate) residuals to use when computing starting values"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#EWMAVariance.starting_values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.EWMAVariance.starting_values" title="Link to this definition">¶</a></dt>
 <dd><p>Returns starting values for the ARCH model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.EWMAVariance.starting_values-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EWMAVariance.starting_values-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.EWMAVariance.starting_values.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.starting_values.resids" title="Permalink to this definition">¶</a></dt><dd><p>Array of (approximate) residuals to use when computing starting
+<dt id="arch.univariate.EWMAVariance.starting_values.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.starting_values.resids" title="Permalink to this definition">¶</a></dt><dd><p>Array of (approximate) residuals to use when computing starting
 values</p>
 </dd>
 </dl>
@@ -1320,7 +1320,7 @@ <h1 id="arch-univariate-ewmavariance-starting-values">arch.univariate.EWMAVarian
 <dd class="field-even"><p><strong>sv</strong> – Array of starting values</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.EWMAVariance.starting_values-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EWMAVariance.starting_values-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1392,7 +1392,7 @@ <h1 id="arch-univariate-ewmavariance-starting-values">arch.univariate.EWMAVarian
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1439,7 +1439,7 @@ <h1 id="arch-univariate-ewmavariance-starting-values">arch.univariate.EWMAVarian
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.EWMAVariance.stop.html b/univariate/generated/generated/arch.univariate.EWMAVariance.stop.html
index fbcd658714..9580feb50a 100644
--- a/univariate/generated/generated/arch.univariate.EWMAVariance.stop.html
+++ b/univariate/generated/generated/arch.univariate.EWMAVariance.stop.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EWMAVariance.stop - arch 6.1.0</title>
+        <title>arch.univariate.EWMAVariance.stop - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1223,10 +1223,10 @@
                   
 
 
-<h1 id="arch-univariate-ewmavariance-stop">arch.univariate.EWMAVariance.stop<a class="headerlink" href="#arch-univariate-ewmavariance-stop" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-ewmavariance-stop">arch.univariate.EWMAVariance.stop<a class="headerlink" href="#arch-univariate-ewmavariance-stop" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.EWMAVariance.stop">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">EWMAVariance.</span></span><span class="sig-name descname"><span class="pre">stop</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.EWMAVariance.stop" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">EWMAVariance.</span></span><span class="sig-name descname"><span class="pre">stop</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.EWMAVariance.stop" title="Link to this definition">¶</a></dt>
 <dd><p>Index to use to stop variance subarray selection</p>
 </dd></dl>
 
@@ -1297,7 +1297,7 @@ <h1 id="arch-univariate-ewmavariance-stop">arch.univariate.EWMAVariance.stop<a c
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1344,7 +1344,7 @@ <h1 id="arch-univariate-ewmavariance-stop">arch.univariate.EWMAVariance.stop<a c
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.EWMAVariance.update.html b/univariate/generated/generated/arch.univariate.EWMAVariance.update.html
index 8a52c27208..15eae9e713 100644
--- a/univariate/generated/generated/arch.univariate.EWMAVariance.update.html
+++ b/univariate/generated/generated/arch.univariate.EWMAVariance.update.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EWMAVariance.update - arch 6.1.0</title>
+        <title>arch.univariate.EWMAVariance.update - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1373,28 +1373,28 @@
                   
 
 
-<h1 id="arch-univariate-ewmavariance-update">arch.univariate.EWMAVariance.update<a class="headerlink" href="#arch-univariate-ewmavariance-update" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-ewmavariance-update">arch.univariate.EWMAVariance.update<a class="headerlink" href="#arch-univariate-ewmavariance-update" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.EWMAVariance.update">
-<span class="sig-prename descclassname"><span class="pre">EWMAVariance.</span></span><span class="sig-name descname"><span class="pre">update</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.update.index" title="arch.univariate.EWMAVariance.update.index (Python parameter) — The numerical index of the variance to compute"><span class="n"><span class="pre">index</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.update.parameters" title="arch.univariate.EWMAVariance.update.parameters (Python parameter) — The variance model parameters"><span class="n"><span class="pre">parameters</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.update.resids" title="arch.univariate.EWMAVariance.update.resids (Python parameter) — The residual array. Only uses resids[:index] when computing sigma2[index]"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.update.sigma2" title="arch.univariate.EWMAVariance.update.sigma2 (Python parameter) — The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."><span class="n"><span class="pre">sigma2</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.update.backcast" title="arch.univariate.EWMAVariance.update.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.update.var_bounds" title="arch.univariate.EWMAVariance.update.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.EWMAVariance.update" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">EWMAVariance.</span></span><span class="sig-name descname"><span class="pre">update</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.update.index" title="arch.univariate.EWMAVariance.update.index (Python parameter) — The numerical index of the variance to compute"><span class="n"><span class="pre">index</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.update.parameters" title="arch.univariate.EWMAVariance.update.parameters (Python parameter) — The variance model parameters"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.update.resids" title="arch.univariate.EWMAVariance.update.resids (Python parameter) — The residual array. Only uses resids[:index] when computing sigma2[index]"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.update.sigma2" title="arch.univariate.EWMAVariance.update.sigma2 (Python parameter) — The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."><span class="n"><span class="pre">sigma2</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.update.backcast" title="arch.univariate.EWMAVariance.update.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.update.var_bounds" title="arch.univariate.EWMAVariance.update.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.update" title="Link to this definition">¶</a></dt>
 <dd><p>Compute the variance for a single observation</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.EWMAVariance.update-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EWMAVariance.update-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.EWMAVariance.update.index"><span class="sig-name descname">index</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.update.index" title="Permalink to this definition">¶</a></dt><dd><p>The numerical index of the variance to compute</p>
+<dt id="arch.univariate.EWMAVariance.update.index"><span class="n sig-name">index</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.update.index" title="Permalink to this definition">¶</a></dt><dd><p>The numerical index of the variance to compute</p>
 </dd>
-<dt id="arch.univariate.EWMAVariance.update.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.update.parameters" title="Permalink to this definition">¶</a></dt><dd><p>The variance model parameters</p>
+<dt id="arch.univariate.EWMAVariance.update.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.update.parameters" title="Permalink to this definition">¶</a></dt><dd><p>The variance model parameters</p>
 </dd>
-<dt id="arch.univariate.EWMAVariance.update.resids"><span class="n sig-name">resids</span><a class="headerlink" href="#arch.univariate.EWMAVariance.update.resids" title="Permalink to this definition">¶</a></dt><dd><p>The residual array. Only uses <code class="docutils literal notranslate"><span class="pre">resids[:index]</span></code> when computing
+<dt id="arch.univariate.EWMAVariance.update.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.update.resids" title="Permalink to this definition">¶</a></dt><dd><p>The residual array. Only uses <code class="docutils literal notranslate"><span class="pre">resids[:index]</span></code> when computing
 <code class="docutils literal notranslate"><span class="pre">sigma2[index]</span></code></p>
 </dd>
-<dt id="arch.univariate.EWMAVariance.update.sigma2"><span class="sig-name descname">sigma2</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.update.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>The array containing the variances. Only uses <code class="docutils literal notranslate"><span class="pre">sigma2[:index]</span></code>
+<dt id="arch.univariate.EWMAVariance.update.sigma2"><span class="n sig-name">sigma2</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.update.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>The array containing the variances. Only uses <code class="docutils literal notranslate"><span class="pre">sigma2[:index]</span></code>
 when computing <code class="docutils literal notranslate"><span class="pre">sigma2[index]</span></code>. The computed value is stored
 in <code class="docutils literal notranslate"><span class="pre">sigma2[index]</span></code>.</p>
 </dd>
-<dt id="arch.univariate.EWMAVariance.update.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}</span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.update.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing the recursion</p>
+<dt id="arch.univariate.EWMAVariance.update.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.update.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing the recursion</p>
 </dd>
-<dt id="arch.univariate.EWMAVariance.update.var_bounds"><span class="sig-name descname">var_bounds</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.update.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
+<dt id="arch.univariate.EWMAVariance.update.var_bounds"><span class="n sig-name">var_bounds</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.update.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
 </dd>
 </dl>
 </dd>
@@ -1402,7 +1402,7 @@ <h1 id="arch-univariate-ewmavariance-update">arch.univariate.EWMAVariance.update
 <dd class="field-even"><p>The variance computed for location <code class="docutils literal notranslate"><span class="pre">index</span></code></p>
 </dd>
 <dt class="field-odd" id="arch.univariate.EWMAVariance.update-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EWMAVariance.update-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1474,7 +1474,7 @@ <h1 id="arch-univariate-ewmavariance-update">arch.univariate.EWMAVariance.update
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1521,7 +1521,7 @@ <h1 id="arch-univariate-ewmavariance-update">arch.univariate.EWMAVariance.update
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diff --git a/univariate/generated/generated/arch.univariate.EWMAVariance.updateable.html b/univariate/generated/generated/arch.univariate.EWMAVariance.updateable.html
index 01699d5056..d756540c87 100644
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+++ b/univariate/generated/generated/arch.univariate.EWMAVariance.updateable.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EWMAVariance.updateable - arch 6.1.0</title>
+        <title>arch.univariate.EWMAVariance.updateable - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1223,10 +1223,10 @@
                   
 
 
-<h1 id="arch-univariate-ewmavariance-updateable">arch.univariate.EWMAVariance.updateable<a class="headerlink" href="#arch-univariate-ewmavariance-updateable" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-ewmavariance-updateable">arch.univariate.EWMAVariance.updateable<a class="headerlink" href="#arch-univariate-ewmavariance-updateable" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.EWMAVariance.updateable">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">EWMAVariance.</span></span><span class="sig-name descname"><span class="pre">updateable</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.univariate.EWMAVariance.updateable" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">EWMAVariance.</span></span><span class="sig-name descname"><span class="pre">updateable</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.univariate.EWMAVariance.updateable" title="Link to this definition">¶</a></dt>
 <dd><p>Flag indicating that the volatility process supports update</p>
 </dd></dl>
 
@@ -1297,7 +1297,7 @@ <h1 id="arch-univariate-ewmavariance-updateable">arch.univariate.EWMAVariance.up
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1344,7 +1344,7 @@ <h1 id="arch-univariate-ewmavariance-updateable">arch.univariate.EWMAVariance.up
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diff --git a/univariate/generated/generated/arch.univariate.EWMAVariance.variance_bounds.html b/univariate/generated/generated/arch.univariate.EWMAVariance.variance_bounds.html
index 491e307f06..85854e2aef 100644
--- a/univariate/generated/generated/arch.univariate.EWMAVariance.variance_bounds.html
+++ b/univariate/generated/generated/arch.univariate.EWMAVariance.variance_bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EWMAVariance.variance_bounds - arch 6.1.0</title>
+        <title>arch.univariate.EWMAVariance.variance_bounds - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1317,20 +1317,20 @@
                   
 
 
-<h1 id="arch-univariate-ewmavariance-variance-bounds">arch.univariate.EWMAVariance.variance_bounds<a class="headerlink" href="#arch-univariate-ewmavariance-variance-bounds" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-ewmavariance-variance-bounds">arch.univariate.EWMAVariance.variance_bounds<a class="headerlink" href="#arch-univariate-ewmavariance-variance-bounds" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.EWMAVariance.variance_bounds">
-<span class="sig-prename descclassname"><span class="pre">EWMAVariance.</span></span><span class="sig-name descname"><span class="pre">variance_bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.variance_bounds.resids" title="arch.univariate.EWMAVariance.variance_bounds.resids (Python parameter) — Approximate residuals to use to compute the lower and upper bounds on the conditional variance"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.variance_bounds.power" title="arch.univariate.EWMAVariance.variance_bounds.power (Python parameter) — Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."><span class="n"><span class="pre">power</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">2.0</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.EWMAVariance.variance_bounds" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.EWMAVariance.variance_bounds">
+<span class="sig-prename descclassname"><span class="pre">EWMAVariance.</span></span><span class="sig-name descname"><span class="pre">variance_bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.variance_bounds.resids" title="arch.univariate.EWMAVariance.variance_bounds.resids (Python parameter) — Approximate residuals to use to compute the lower and upper bounds on the conditional variance"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.variance_bounds.power" title="arch.univariate.EWMAVariance.variance_bounds.power (Python parameter) — Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."><span class="n"><span class="pre">power</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">2.0</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.variance_bounds" title="Link to this definition">¶</a></dt>
 <dd><p>Construct loose bounds for conditional variances.</p>
 <p>These bounds are used in parameter estimation to ensure
 that the log-likelihood does not produce NaN values.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.EWMAVariance.variance_bounds-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EWMAVariance.variance_bounds-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.EWMAVariance.variance_bounds.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.variance_bounds.resids" title="Permalink to this definition">¶</a></dt><dd><p>Approximate residuals to use to compute the lower and upper bounds
+<dt id="arch.univariate.EWMAVariance.variance_bounds.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.variance_bounds.resids" title="Permalink to this definition">¶</a></dt><dd><p>Approximate residuals to use to compute the lower and upper bounds
 on the conditional variance</p>
 </dd>
-<dt id="arch.univariate.EWMAVariance.variance_bounds.power"><span class="sig-name descname">power</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>, optional</span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.variance_bounds.power" title="Permalink to this definition">¶</a></dt><dd><p>Power used in the model. 2.0, the default corresponds to standard
+<dt id="arch.univariate.EWMAVariance.variance_bounds.power"><span class="n sig-name">power</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">2.0</span></code><a class="headerlink" href="#arch.univariate.EWMAVariance.variance_bounds.power" title="Permalink to this definition">¶</a></dt><dd><p>Power used in the model. 2.0, the default corresponds to standard
 ARCH models that evolve in squares.</p>
 </dd>
 </dl>
@@ -1340,7 +1340,7 @@ <h1 id="arch-univariate-ewmavariance-variance-bounds">arch.univariate.EWMAVarian
 number of elements as resids</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.EWMAVariance.variance_bounds-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EWMAVariance.variance_bounds-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1412,7 +1412,7 @@ <h1 id="arch-univariate-ewmavariance-variance-bounds">arch.univariate.EWMAVarian
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1459,7 +1459,7 @@ <h1 id="arch-univariate-ewmavariance-variance-bounds">arch.univariate.EWMAVarian
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.EWMAVariance.volatility_updater.html b/univariate/generated/generated/arch.univariate.EWMAVariance.volatility_updater.html
index 82f527d25c..6ac91948d4 100644
--- a/univariate/generated/generated/arch.univariate.EWMAVariance.volatility_updater.html
+++ b/univariate/generated/generated/arch.univariate.EWMAVariance.volatility_updater.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EWMAVariance.volatility_updater - arch 6.1.0</title>
+        <title>arch.univariate.EWMAVariance.volatility_updater - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1277,17 +1277,17 @@
                   
 
 
-<h1 id="arch-univariate-ewmavariance-volatility-updater">arch.univariate.EWMAVariance.volatility_updater<a class="headerlink" href="#arch-univariate-ewmavariance-volatility-updater" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-ewmavariance-volatility-updater">arch.univariate.EWMAVariance.volatility_updater<a class="headerlink" href="#arch-univariate-ewmavariance-volatility-updater" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.EWMAVariance.volatility_updater">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">EWMAVariance.</span></span><span class="sig-name descname"><span class="pre">volatility_updater</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.univariate.recursions_python.VolatilityUpdater.html#arch.univariate.recursions_python.VolatilityUpdater" title="arch.univariate.recursions_python.VolatilityUpdater (Python class) — Base class that all volatility updaters must inherit from."><span class="pre">VolatilityUpdater</span></a></span><a class="headerlink" href="#arch.univariate.EWMAVariance.volatility_updater" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">EWMAVariance.</span></span><span class="sig-name descname"><span class="pre">volatility_updater</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><span class="pre">VolatilityUpdater</span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.volatility_updater" title="Link to this definition">¶</a></dt>
 <dd><p>Get the volatility updater associated with the volatility process</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.EWMAVariance.volatility_updater-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EWMAVariance.volatility_updater-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The updater class</p>
 </dd>
 <dt class="field-even" id="arch.univariate.EWMAVariance.volatility_updater-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EWMAVariance.volatility_updater-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference internal" href="../arch.univariate.recursions_python.VolatilityUpdater.html#arch.univariate.recursions_python.VolatilityUpdater" title="arch.univariate.recursions_python.VolatilityUpdater (Python class) — Base class that all volatility updaters must inherit from.">VolatilityUpdater</a></p>
+<dd class="field-even"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">VolatilityUpdater</span></code></p>
 </dd>
 <dt class="field-odd" id="arch.univariate.EWMAVariance.volatility_updater-raises">Raises<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EWMAVariance.volatility_updater-raises" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#NotImplementedError" title="(in Python v3.11)"><strong>NotImplementedError</strong></a> – If the process is not updateable</p>
@@ -1362,7 +1362,7 @@ <h1 id="arch-univariate-ewmavariance-volatility-updater">arch.univariate.EWMAVar
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1409,7 +1409,7 @@ <h1 id="arch-univariate-ewmavariance-volatility-updater">arch.univariate.EWMAVar
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.FIGARCH.backcast.html b/univariate/generated/generated/arch.univariate.FIGARCH.backcast.html
index 1dd580b9d1..57b0dd57c8 100644
--- a/univariate/generated/generated/arch.univariate.FIGARCH.backcast.html
+++ b/univariate/generated/generated/arch.univariate.FIGARCH.backcast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FIGARCH.backcast - arch 6.1.0</title>
+        <title>arch.univariate.FIGARCH.backcast - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1263,15 +1263,15 @@
                   
 
 
-<h1 id="arch-univariate-figarch-backcast">arch.univariate.FIGARCH.backcast<a class="headerlink" href="#arch-univariate-figarch-backcast" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-figarch-backcast">arch.univariate.FIGARCH.backcast<a class="headerlink" href="#arch-univariate-figarch-backcast" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.FIGARCH.backcast">
-<span class="sig-prename descclassname"><span class="pre">FIGARCH.</span></span><span class="sig-name descname"><span class="pre">backcast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.backcast.resids" title="arch.univariate.FIGARCH.backcast.resids (Python parameter) — Vector of (approximate) residuals"><span class="n"><span class="pre">resids</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#FIGARCH.backcast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.FIGARCH.backcast" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">FIGARCH.</span></span><span class="sig-name descname"><span class="pre">backcast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.backcast.resids" title="arch.univariate.FIGARCH.backcast.resids (Python parameter) — Vector of (approximate) residuals"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#FIGARCH.backcast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.FIGARCH.backcast" title="Link to this definition">¶</a></dt>
 <dd><p>Construct values for backcasting to start the recursion</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.FIGARCH.backcast-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FIGARCH.backcast-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.FIGARCH.backcast.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.FIGARCH.backcast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of (approximate) residuals</p>
+<dt id="arch.univariate.FIGARCH.backcast.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.FIGARCH.backcast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of (approximate) residuals</p>
 </dd>
 </dl>
 </dd>
@@ -1279,7 +1279,7 @@ <h1 id="arch-univariate-figarch-backcast">arch.univariate.FIGARCH.backcast<a cla
 <dd class="field-even"><p><strong>backcast</strong> – Value to use in backcasting in the volatility recursion</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.FIGARCH.backcast-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FIGARCH.backcast-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1351,7 +1351,7 @@ <h1 id="arch-univariate-figarch-backcast">arch.univariate.FIGARCH.backcast<a cla
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1398,7 +1398,7 @@ <h1 id="arch-univariate-figarch-backcast">arch.univariate.FIGARCH.backcast<a cla
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diff --git a/univariate/generated/generated/arch.univariate.FIGARCH.backcast_transform.html b/univariate/generated/generated/arch.univariate.FIGARCH.backcast_transform.html
index 1a594eea83..a825e029cf 100644
--- a/univariate/generated/generated/arch.univariate.FIGARCH.backcast_transform.html
+++ b/univariate/generated/generated/arch.univariate.FIGARCH.backcast_transform.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FIGARCH.backcast_transform - arch 6.1.0</title>
+        <title>arch.univariate.FIGARCH.backcast_transform - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1263,15 +1263,15 @@
                   
 
 
-<h1 id="arch-univariate-figarch-backcast-transform">arch.univariate.FIGARCH.backcast_transform<a class="headerlink" href="#arch-univariate-figarch-backcast-transform" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-figarch-backcast-transform">arch.univariate.FIGARCH.backcast_transform<a class="headerlink" href="#arch-univariate-figarch-backcast-transform" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.FIGARCH.backcast_transform">
-<span class="sig-prename descclassname"><span class="pre">FIGARCH.</span></span><span class="sig-name descname"><span class="pre">backcast_transform</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.backcast_transform.backcast" title="arch.univariate.FIGARCH.backcast_transform.backcast (Python parameter) — User-provided backcast that approximates sigma2[0]."><span class="n"><span class="pre">backcast</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#FIGARCH.backcast_transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.FIGARCH.backcast_transform" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.FIGARCH.backcast_transform">
+<span class="sig-prename descclassname"><span class="pre">FIGARCH.</span></span><span class="sig-name descname"><span class="pre">backcast_transform</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.backcast_transform.backcast" title="arch.univariate.FIGARCH.backcast_transform.backcast (Python parameter) — User-provided backcast that approximates sigma2[0]."><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#FIGARCH.backcast_transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.FIGARCH.backcast_transform" title="Link to this definition">¶</a></dt>
 <dd><p>Transformation to apply to user-provided backcast values</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.FIGARCH.backcast_transform-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FIGARCH.backcast_transform-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.FIGARCH.backcast_transform.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}</span></span><a class="headerlink" href="#arch.univariate.FIGARCH.backcast_transform.backcast" title="Permalink to this definition">¶</a></dt><dd><p>User-provided <code class="docutils literal notranslate"><span class="pre">backcast</span></code> that approximates sigma2[0].</p>
+<dt id="arch.univariate.FIGARCH.backcast_transform.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.FIGARCH.backcast_transform.backcast" title="Permalink to this definition">¶</a></dt><dd><p>User-provided <code class="docutils literal notranslate"><span class="pre">backcast</span></code> that approximates sigma2[0].</p>
 </dd>
 </dl>
 </dd>
@@ -1279,7 +1279,7 @@ <h1 id="arch-univariate-figarch-backcast-transform">arch.univariate.FIGARCH.back
 <dd class="field-even"><p><strong>backcast</strong> – Backcast transformed to the model-appropriate scale</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.FIGARCH.backcast_transform-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FIGARCH.backcast_transform-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>{float, ndarray}</p>
+<dd class="field-odd"><p><code class="docutils literal notranslate"><span class="pre">{float,</span> <span class="pre">ndarray}</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1351,7 +1351,7 @@ <h1 id="arch-univariate-figarch-backcast-transform">arch.univariate.FIGARCH.back
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1398,7 +1398,7 @@ <h1 id="arch-univariate-figarch-backcast-transform">arch.univariate.FIGARCH.back
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diff --git a/univariate/generated/generated/arch.univariate.FIGARCH.bounds.html b/univariate/generated/generated/arch.univariate.FIGARCH.bounds.html
index 8a6b6b7b38..db745a7eca 100644
--- a/univariate/generated/generated/arch.univariate.FIGARCH.bounds.html
+++ b/univariate/generated/generated/arch.univariate.FIGARCH.bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FIGARCH.bounds - arch 6.1.0</title>
+        <title>arch.univariate.FIGARCH.bounds - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+    arch 6.2.0
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@@ -1263,15 +1263,15 @@
                   
 
 
-<h1 id="arch-univariate-figarch-bounds">arch.univariate.FIGARCH.bounds<a class="headerlink" href="#arch-univariate-figarch-bounds" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-figarch-bounds">arch.univariate.FIGARCH.bounds<a class="headerlink" href="#arch-univariate-figarch-bounds" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.FIGARCH.bounds">
-<span class="sig-prename descclassname"><span class="pre">FIGARCH.</span></span><span class="sig-name descname"><span class="pre">bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.bounds.resids" title="arch.univariate.FIGARCH.bounds.resids (Python parameter) — Vector of (approximate) residuals"><span class="n"><span class="pre">resids</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#FIGARCH.bounds"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.FIGARCH.bounds" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">FIGARCH.</span></span><span class="sig-name descname"><span class="pre">bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.bounds.resids" title="arch.univariate.FIGARCH.bounds.resids (Python parameter) — Vector of (approximate) residuals"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#FIGARCH.bounds"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.FIGARCH.bounds" title="Link to this definition">¶</a></dt>
 <dd><p>Returns bounds for parameters</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.FIGARCH.bounds-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FIGARCH.bounds-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.FIGARCH.bounds.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.FIGARCH.bounds.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of (approximate) residuals</p>
+<dt id="arch.univariate.FIGARCH.bounds.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.FIGARCH.bounds.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of (approximate) residuals</p>
 </dd>
 </dl>
 </dd>
@@ -1279,7 +1279,7 @@ <h1 id="arch-univariate-figarch-bounds">arch.univariate.FIGARCH.bounds<a class="
 <dd class="field-even"><p><strong>bounds</strong> – List of bounds where each element is (lower, upper).</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.FIGARCH.bounds-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FIGARCH.bounds-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a>[<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a>[<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>,<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>]]</p>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">list[tuple[float,float]]</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1351,7 +1351,7 @@ <h1 id="arch-univariate-figarch-bounds">arch.univariate.FIGARCH.bounds<a class="
   
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-    7.0.1.
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diff --git a/univariate/generated/generated/arch.univariate.FIGARCH.compute_variance.html b/univariate/generated/generated/arch.univariate.FIGARCH.compute_variance.html
index 3eade22461..3a8bbdf3dd 100644
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+++ b/univariate/generated/generated/arch.univariate.FIGARCH.compute_variance.html
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@@ -102,7 +102,7 @@
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-  
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@@ -1305,30 +1291,27 @@
                   
 
 
-<h1 id="arch-univariate-figarch-compute-variance">arch.univariate.FIGARCH.compute_variance<a class="headerlink" href="#arch-univariate-figarch-compute-variance" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-figarch-compute-variance">arch.univariate.FIGARCH.compute_variance<a class="headerlink" href="#arch-univariate-figarch-compute-variance" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.FIGARCH.compute_variance">
-<span class="sig-prename descclassname"><span class="pre">FIGARCH.</span></span><span class="sig-name descname"><span class="pre">compute_variance</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.compute_variance.parameters" title="arch.univariate.FIGARCH.compute_variance.parameters (Python parameter) — Model parameters"><span class="n"><span class="pre">parameters</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.compute_variance.resids" title="arch.univariate.FIGARCH.compute_variance.resids (Python parameter) — Vector of mean zero residuals"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.compute_variance.sigma2" title="arch.univariate.FIGARCH.compute_variance.sigma2 (Python parameter) — Array with same size as resids to store the conditional variance"><span class="n"><span class="pre">sigma2</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.compute_variance.backcast" title="arch.univariate.FIGARCH.compute_variance.backcast (Python parameter) — Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."><span class="n"><span class="pre">backcast</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.compute_variance.var_bounds" title="arch.univariate.FIGARCH.compute_variance.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#FIGARCH.compute_variance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.FIGARCH.compute_variance" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">FIGARCH.</span></span><span class="sig-name descname"><span class="pre">compute_variance</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.compute_variance.parameters" title="arch.univariate.FIGARCH.compute_variance.parameters (Python parameter) — Model parameters"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.compute_variance.resids" title="arch.univariate.FIGARCH.compute_variance.resids (Python parameter) — Vector of mean zero residuals"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.compute_variance.sigma2" title="arch.univariate.FIGARCH.compute_variance.sigma2 (Python parameter) — Array with same size as resids to store the conditional variance"><span class="n"><span class="pre">sigma2</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.compute_variance.backcast" title="arch.univariate.FIGARCH.compute_variance.backcast (Python parameter) — Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.compute_variance.var_bounds" title="arch.univariate.FIGARCH.compute_variance.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#FIGARCH.compute_variance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.FIGARCH.compute_variance" title="Link to this definition">¶</a></dt>
 <dd><p>Compute the variance for the ARCH model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.FIGARCH.compute_variance-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FIGARCH.compute_variance-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.FIGARCH.compute_variance.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.FIGARCH.compute_variance.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
+<dt id="arch.univariate.FIGARCH.compute_variance.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.FIGARCH.compute_variance.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
 </dd>
-<dt id="arch.univariate.FIGARCH.compute_variance.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.FIGARCH.compute_variance.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of mean zero residuals</p>
+<dt id="arch.univariate.FIGARCH.compute_variance.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.FIGARCH.compute_variance.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of mean zero residuals</p>
 </dd>
-<dt id="arch.univariate.FIGARCH.compute_variance.sigma2"><span class="sig-name descname">sigma2</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.FIGARCH.compute_variance.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>Array with same size as resids to store the conditional variance</p>
+<dt id="arch.univariate.FIGARCH.compute_variance.sigma2"><span class="n sig-name">sigma2</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.FIGARCH.compute_variance.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>Array with same size as resids to store the conditional variance</p>
 </dd>
-<dt id="arch.univariate.FIGARCH.compute_variance.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}</span></span><a class="headerlink" href="#arch.univariate.FIGARCH.compute_variance.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing ARCH recursion. Can be an ndarray
+<dt id="arch.univariate.FIGARCH.compute_variance.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.FIGARCH.compute_variance.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing ARCH recursion. Can be an ndarray
 when the model contains multiple components.</p>
 </dd>
-<dt id="arch.univariate.FIGARCH.compute_variance.var_bounds"><span class="sig-name descname">var_bounds</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.FIGARCH.compute_variance.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
+<dt id="arch.univariate.FIGARCH.compute_variance.var_bounds"><span class="n sig-name">var_bounds</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.FIGARCH.compute_variance.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
 </dd>
 </dl>
 </dd>
-<dt class="field-even" id="arch.univariate.FIGARCH.compute_variance-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FIGARCH.compute_variance-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a></p>
-</dd>
 </dl>
 </dd></dl>
 
@@ -1399,7 +1382,7 @@ <h1 id="arch-univariate-figarch-compute-variance">arch.univariate.FIGARCH.comput
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1446,7 +1429,7 @@ <h1 id="arch-univariate-figarch-compute-variance">arch.univariate.FIGARCH.comput
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diff --git a/univariate/generated/generated/arch.univariate.FIGARCH.constraints.html b/univariate/generated/generated/arch.univariate.FIGARCH.constraints.html
index de42e1bcbd..a8c1fe84a0 100644
--- a/univariate/generated/generated/arch.univariate.FIGARCH.constraints.html
+++ b/univariate/generated/generated/arch.univariate.FIGARCH.constraints.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FIGARCH.constraints - arch 6.1.0</title>
+        <title>arch.univariate.FIGARCH.constraints - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -596,13 +596,6 @@
     <nav class="md-nav" aria-label="FIGARCH.constraints">
       <ul class="md-nav__list">
         
-          <li class="md-nav__item">
-  <a href="#arch.univariate.FIGARCH.constraints-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
-</li>
-        
           <li class="md-nav__item">
   <a href="#arch.univariate.FIGARCH.constraints-returns" class="md-nav__link">
     <span class="md-ellipsis">Returns</span>
@@ -1190,13 +1183,6 @@
     <nav class="md-nav" aria-label="FIGARCH.constraints">
       <ul class="md-nav__list">
         
-          <li class="md-nav__item">
-  <a href="#arch.univariate.FIGARCH.constraints-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
-</li>
-        
           <li class="md-nav__item">
   <a href="#arch.univariate.FIGARCH.constraints-returns" class="md-nav__link">
     <span class="md-ellipsis">Returns</span>
@@ -1223,20 +1209,17 @@
                   
 
 
-<h1 id="arch-univariate-figarch-constraints">arch.univariate.FIGARCH.constraints<a class="headerlink" href="#arch-univariate-figarch-constraints" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-figarch-constraints">arch.univariate.FIGARCH.constraints<a class="headerlink" href="#arch-univariate-figarch-constraints" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.FIGARCH.constraints">
-<span class="sig-prename descclassname"><span class="pre">FIGARCH.</span></span><span class="sig-name descname"><span class="pre">constraints</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#FIGARCH.constraints"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.FIGARCH.constraints" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">FIGARCH.</span></span><span class="sig-name descname"><span class="pre">constraints</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#FIGARCH.constraints"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.FIGARCH.constraints" title="Link to this definition">¶</a></dt>
 <dd><p>Construct parameter constraints arrays for parameter estimation</p>
 <dl class="field-list simple">
-<dt class="field-odd" id="arch.univariate.FIGARCH.constraints-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FIGARCH.constraints-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>]</p>
-</dd>
-<dt class="field-even" id="arch.univariate.FIGARCH.constraints-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FIGARCH.constraints-returns" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><ul class="simple">
-<li><p><strong>A</strong> (<em>ndarray</em>) – Parameters loadings in constraint. Shape is number of constraints
+<dt class="field-odd" id="arch.univariate.FIGARCH.constraints-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FIGARCH.constraints-returns" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p><strong>A</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – Parameters loadings in constraint. Shape is number of constraints
 by number of parameters</p></li>
-<li><p><strong>b</strong> (<em>ndarray</em>) – Constraint values, one for each constraint</p></li>
+<li><p><strong>b</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – Constraint values, one for each constraint</p></li>
 </ul>
 </p>
 </dd>
@@ -1313,7 +1296,7 @@ <h1 id="arch-univariate-figarch-constraints">arch.univariate.FIGARCH.constraints
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1360,7 +1343,7 @@ <h1 id="arch-univariate-figarch-constraints">arch.univariate.FIGARCH.constraints
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diff --git a/univariate/generated/generated/arch.univariate.FIGARCH.forecast.html b/univariate/generated/generated/arch.univariate.FIGARCH.forecast.html
index ca141b1490..5cd6c27ee5 100644
--- a/univariate/generated/generated/arch.univariate.FIGARCH.forecast.html
+++ b/univariate/generated/generated/arch.univariate.FIGARCH.forecast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FIGARCH.forecast - arch 6.1.0</title>
+        <title>arch.univariate.FIGARCH.forecast - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1403,38 +1403,38 @@
                   
 
 
-<h1 id="arch-univariate-figarch-forecast">arch.univariate.FIGARCH.forecast<a class="headerlink" href="#arch-univariate-figarch-forecast" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-figarch-forecast">arch.univariate.FIGARCH.forecast<a class="headerlink" href="#arch-univariate-figarch-forecast" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.FIGARCH.forecast">
-<span class="sig-prename descclassname"><span class="pre">FIGARCH.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.forecast.parameters" title="arch.univariate.FIGARCH.forecast.parameters (Python parameter) — Parameters required to forecast the volatility model"><span class="n"><span class="pre">parameters</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.forecast.resids" title="arch.univariate.FIGARCH.forecast.resids (Python parameter) — Residuals to use in the recursion"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.forecast.backcast" title="arch.univariate.FIGARCH.forecast.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.forecast.var_bounds" title="arch.univariate.FIGARCH.forecast.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.forecast.start" title="arch.univariate.FIGARCH.forecast.start (Python parameter) — Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."><span class="n"><span class="pre">start</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.forecast.horizon" title="arch.univariate.FIGARCH.forecast.horizon (Python parameter) — Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."><span class="n"><span class="pre">horizon</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.forecast.method" title="arch.univariate.FIGARCH.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic."><span class="n"><span class="pre">method</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.forecast.simulations" title="arch.univariate.FIGARCH.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.forecast.rng" title="arch.univariate.FIGARCH.forecast.rng (Python parameter) — Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."><span class="n"><span class="pre">rng</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.forecast.random_state" title="arch.univariate.FIGARCH.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.FIGARCH.forecast" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">FIGARCH.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.forecast.parameters" title="arch.univariate.FIGARCH.forecast.parameters (Python parameter) — Parameters required to forecast the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.forecast.resids" title="arch.univariate.FIGARCH.forecast.resids (Python parameter) — Residuals to use in the recursion"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.forecast.backcast" title="arch.univariate.FIGARCH.forecast.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.forecast.var_bounds" title="arch.univariate.FIGARCH.forecast.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.forecast.start" title="arch.univariate.FIGARCH.forecast.start (Python parameter) — Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.forecast.horizon" title="arch.univariate.FIGARCH.forecast.horizon (Python parameter) — Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.forecast.method" title="arch.univariate.FIGARCH.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.forecast.simulations" title="arch.univariate.FIGARCH.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.forecast.rng" title="arch.univariate.FIGARCH.forecast.rng (Python parameter) — Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.forecast.random_state" title="arch.univariate.FIGARCH.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><span class="pre">VarianceForecast</span></span></span></span><a class="headerlink" href="#arch.univariate.FIGARCH.forecast" title="Link to this definition">¶</a></dt>
 <dd><p>Forecast volatility from the model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.FIGARCH.forecast-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FIGARCH.forecast-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.FIGARCH.forecast.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, Series}</span></span><a class="headerlink" href="#arch.univariate.FIGARCH.forecast.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast the volatility model</p>
+<dt id="arch.univariate.FIGARCH.forecast.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.FIGARCH.forecast.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast the volatility model</p>
 </dd>
-<dt id="arch.univariate.FIGARCH.forecast.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.FIGARCH.forecast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Residuals to use in the recursion</p>
+<dt id="arch.univariate.FIGARCH.forecast.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.FIGARCH.forecast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Residuals to use in the recursion</p>
 </dd>
-<dt id="arch.univariate.FIGARCH.forecast.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><a class="headerlink" href="#arch.univariate.FIGARCH.forecast.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing the recursion</p>
+<dt id="arch.univariate.FIGARCH.forecast.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><a class="headerlink" href="#arch.univariate.FIGARCH.forecast.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing the recursion</p>
 </dd>
-<dt id="arch.univariate.FIGARCH.forecast.var_bounds"><span class="sig-name descname">var_bounds</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray, 2-d</span></span><a class="headerlink" href="#arch.univariate.FIGARCH.forecast.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
+<dt id="arch.univariate.FIGARCH.forecast.var_bounds"><span class="n sig-name">var_bounds</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.FIGARCH.forecast.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
 </dd>
-<dt id="arch.univariate.FIGARCH.forecast.start"><span class="sig-name descname">start</span><span class="p"> : </span><span class="desctype"><span class="desctype">{None, int}</span></span><a class="headerlink" href="#arch.univariate.FIGARCH.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>Index of the first observation to use as the starting point for
+<dt id="arch.univariate.FIGARCH.forecast.start"><span class="n sig-name">start</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.FIGARCH.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>Index of the first observation to use as the starting point for
 the forecast.  Default is len(resids).</p>
 </dd>
-<dt id="arch.univariate.FIGARCH.forecast.horizon"><span class="sig-name descname">horizon</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.FIGARCH.forecast.horizon" title="Permalink to this definition">¶</a></dt><dd><p>Forecast horizon.  Must be 1 or larger.  Forecasts are produced
+<dt id="arch.univariate.FIGARCH.forecast.horizon"><span class="n sig-name">horizon</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code><a class="headerlink" href="#arch.univariate.FIGARCH.forecast.horizon" title="Permalink to this definition">¶</a></dt><dd><p>Forecast horizon.  Must be 1 or larger.  Forecasts are produced
 for horizons in [1, horizon].</p>
 </dd>
-<dt id="arch.univariate.FIGARCH.forecast.method"><span class="sig-name descname">method</span><span class="p"> : </span><span class="desctype"><span class="desctype">{'analytic', 'simulation', 'bootstrap'}</span></span><a class="headerlink" href="#arch.univariate.FIGARCH.forecast.method" title="Permalink to this definition">¶</a></dt><dd><p>Method to use when producing the forecast. The default is analytic.</p>
+<dt id="arch.univariate.FIGARCH.forecast.method"><span class="n sig-name">method</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'analytic'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'simulation'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'bootstrap'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code><a class="headerlink" href="#arch.univariate.FIGARCH.forecast.method" title="Permalink to this definition">¶</a></dt><dd><p>Method to use when producing the forecast. The default is analytic.</p>
 </dd>
-<dt id="arch.univariate.FIGARCH.forecast.simulations"><span class="sig-name descname">simulations</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.FIGARCH.forecast.simulations" title="Permalink to this definition">¶</a></dt><dd><p>Number of simulations to run when computing the forecast using
+<dt id="arch.univariate.FIGARCH.forecast.simulations"><span class="n sig-name">simulations</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.univariate.FIGARCH.forecast.simulations" title="Permalink to this definition">¶</a></dt><dd><p>Number of simulations to run when computing the forecast using
 either simulation or bootstrap.</p>
 </dd>
-<dt id="arch.univariate.FIGARCH.forecast.rng"><span class="sig-name descname">rng</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable</span></span><a class="headerlink" href="#arch.univariate.FIGARCH.forecast.rng" title="Permalink to this definition">¶</a></dt><dd><p>Callable random number generator required if method is
+<dt id="arch.univariate.FIGARCH.forecast.rng"><span class="n sig-name">rng</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)">Callable</a><span class="p">[</span><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">,</span><span class="w"> </span><span class="p">...</span><span class="p">]</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.FIGARCH.forecast.rng" title="Permalink to this definition">¶</a></dt><dd><p>Callable random number generator required if method is
 ‘simulation’. Must take a single shape input and return random
 samples numbers with that shape.</p>
 </dd>
-<dt id="arch.univariate.FIGARCH.forecast.random_state"><span class="sig-name descname">random_state</span><span class="p"> : </span><span class="desctype"><span class="desctype">RandomState, optional</span></span><a class="headerlink" href="#arch.univariate.FIGARCH.forecast.random_state" title="Permalink to this definition">¶</a></dt><dd><p>NumPy RandomState instance to use when method is ‘bootstrap’</p>
+<dt id="arch.univariate.FIGARCH.forecast.random_state"><span class="n sig-name">random_state</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)">RandomState</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.FIGARCH.forecast.random_state" title="Permalink to this definition">¶</a></dt><dd><p>NumPy RandomState instance to use when method is ‘bootstrap’</p>
 </dd>
 </dl>
 </dd>
@@ -1443,7 +1443,7 @@ <h1 id="arch-univariate-figarch-forecast">arch.univariate.FIGARCH.forecast<a cla
 or bootstrap, the simulated paths.</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.FIGARCH.forecast-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FIGARCH.forecast-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>VarianceForecast</p>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">arch.univariate.volatility.VarianceForecast</span></code></p>
 </dd>
 <dt class="field-even" id="arch.univariate.FIGARCH.forecast-raises">Raises<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FIGARCH.forecast-raises" title="Permalink to this headline">¶</a></dt>
 <dd class="field-even"><ul class="simple">
@@ -1530,7 +1530,7 @@ <h1 id="arch-univariate-figarch-forecast">arch.univariate.FIGARCH.forecast<a cla
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1577,7 +1577,7 @@ <h1 id="arch-univariate-figarch-forecast">arch.univariate.FIGARCH.forecast<a cla
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.FIGARCH.name.html b/univariate/generated/generated/arch.univariate.FIGARCH.name.html
index 84fa8907ae..a60d030dea 100644
--- a/univariate/generated/generated/arch.univariate.FIGARCH.name.html
+++ b/univariate/generated/generated/arch.univariate.FIGARCH.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FIGARCH.name - arch 6.1.0</title>
+        <title>arch.univariate.FIGARCH.name - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1183,10 +1183,10 @@
                   
 
 
-<h1 id="arch-univariate-figarch-name">arch.univariate.FIGARCH.name<a class="headerlink" href="#arch-univariate-figarch-name" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-figarch-name">arch.univariate.FIGARCH.name<a class="headerlink" href="#arch-univariate-figarch-name" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.FIGARCH.name">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">FIGARCH.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.univariate.FIGARCH.name" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">FIGARCH.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.univariate.FIGARCH.name" title="Link to this definition">¶</a></dt>
 <dd><p>The name of the volatility process</p>
 </dd></dl>
 
@@ -1257,7 +1257,7 @@ <h1 id="arch-univariate-figarch-name">arch.univariate.FIGARCH.name<a class="head
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1304,7 +1304,7 @@ <h1 id="arch-univariate-figarch-name">arch.univariate.FIGARCH.name<a class="head
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.FIGARCH.num_params.html b/univariate/generated/generated/arch.univariate.FIGARCH.num_params.html
index f5edb5c154..b53481871d 100644
--- a/univariate/generated/generated/arch.univariate.FIGARCH.num_params.html
+++ b/univariate/generated/generated/arch.univariate.FIGARCH.num_params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FIGARCH.num_params - arch 6.1.0</title>
+        <title>arch.univariate.FIGARCH.num_params - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1183,10 +1183,10 @@
                   
 
 
-<h1 id="arch-univariate-figarch-num-params">arch.univariate.FIGARCH.num_params<a class="headerlink" href="#arch-univariate-figarch-num-params" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-figarch-num-params">arch.univariate.FIGARCH.num_params<a class="headerlink" href="#arch-univariate-figarch-num-params" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.FIGARCH.num_params">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">FIGARCH.</span></span><span class="sig-name descname"><span class="pre">num_params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.FIGARCH.num_params" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">FIGARCH.</span></span><span class="sig-name descname"><span class="pre">num_params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.FIGARCH.num_params" title="Link to this definition">¶</a></dt>
 <dd><p>The number of parameters in the model</p>
 </dd></dl>
 
@@ -1257,7 +1257,7 @@ <h1 id="arch-univariate-figarch-num-params">arch.univariate.FIGARCH.num_params<a
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1304,7 +1304,7 @@ <h1 id="arch-univariate-figarch-num-params">arch.univariate.FIGARCH.num_params<a
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.FIGARCH.parameter_names.html b/univariate/generated/generated/arch.univariate.FIGARCH.parameter_names.html
index b0f3209a53..e7a996850c 100644
--- a/univariate/generated/generated/arch.univariate.FIGARCH.parameter_names.html
+++ b/univariate/generated/generated/arch.univariate.FIGARCH.parameter_names.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FIGARCH.parameter_names - arch 6.1.0</title>
+        <title>arch.univariate.FIGARCH.parameter_names - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1223,17 +1223,17 @@
                   
 
 
-<h1 id="arch-univariate-figarch-parameter-names">arch.univariate.FIGARCH.parameter_names<a class="headerlink" href="#arch-univariate-figarch-parameter-names" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-figarch-parameter-names">arch.univariate.FIGARCH.parameter_names<a class="headerlink" href="#arch-univariate-figarch-parameter-names" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.FIGARCH.parameter_names">
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+<span class="sig-prename descclassname"><span class="pre">FIGARCH.</span></span><span class="sig-name descname"><span class="pre">parameter_names</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#FIGARCH.parameter_names"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.FIGARCH.parameter_names" title="Link to this definition">¶</a></dt>
 <dd><p>Names of model parameters</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.FIGARCH.parameter_names-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FIGARCH.parameter_names-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p><strong>names</strong> – Variables names</p>
 </dd>
 <dt class="field-even" id="arch.univariate.FIGARCH.parameter_names-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FIGARCH.parameter_names-return-type" title="Permalink to this headline">¶</a></dt>
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+<dd class="field-even"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span> <span class="pre">(str)</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1305,7 +1305,7 @@ <h1 id="arch-univariate-figarch-parameter-names">arch.univariate.FIGARCH.paramet
   
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diff --git a/univariate/generated/generated/arch.univariate.FIGARCH.simulate.html b/univariate/generated/generated/arch.univariate.FIGARCH.simulate.html
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@@ -1319,36 +1305,33 @@
                   
 
 
-<h1 id="arch-univariate-figarch-simulate">arch.univariate.FIGARCH.simulate<a class="headerlink" href="#arch-univariate-figarch-simulate" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-figarch-simulate">arch.univariate.FIGARCH.simulate<a class="headerlink" href="#arch-univariate-figarch-simulate" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.FIGARCH.simulate">
-<span class="sig-prename descclassname"><span class="pre">FIGARCH.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.simulate.parameters" title="arch.univariate.FIGARCH.simulate.parameters (Python parameter) — Parameters required to simulate the volatility model"><span class="n"><span class="pre">parameters</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.simulate.nobs" title="arch.univariate.FIGARCH.simulate.nobs (Python parameter) — Number of data points to simulate"><span class="n"><span class="pre">nobs</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.simulate.rng" title="arch.univariate.FIGARCH.simulate.rng (Python parameter) — Callable function that takes a single integer input and returns a vector of random numbers"><span class="n"><span class="pre">rng</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.simulate.burn" title="arch.univariate.FIGARCH.simulate.burn (Python parameter) — Number of additional observations to generate when initializing the simulation"><span class="n"><span class="pre">burn</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.simulate.initial_value" title="arch.univariate.FIGARCH.simulate.initial_value (Python parameter) — Scalar or array of initial values to use when initializing the simulation"><span class="n"><span class="pre">initial_value</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#FIGARCH.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.FIGARCH.simulate" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">FIGARCH.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.simulate.parameters" title="arch.univariate.FIGARCH.simulate.parameters (Python parameter) — Parameters required to simulate the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.simulate.nobs" title="arch.univariate.FIGARCH.simulate.nobs (Python parameter) — Number of data points to simulate"><span class="n"><span class="pre">nobs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.simulate.rng" title="arch.univariate.FIGARCH.simulate.rng (Python parameter) — Callable function that takes a single integer input and returns a vector of random numbers"><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.simulate.burn" title="arch.univariate.FIGARCH.simulate.burn (Python parameter) — Number of additional observations to generate when initializing the simulation"><span class="n"><span class="pre">burn</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.simulate.initial_value" title="arch.univariate.FIGARCH.simulate.initial_value (Python parameter) — Scalar or array of initial values to use when initializing the simulation"><span class="n"><span class="pre">initial_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#FIGARCH.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.FIGARCH.simulate" title="Link to this definition">¶</a></dt>
 <dd><p>Simulate data from the model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.FIGARCH.simulate-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FIGARCH.simulate-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.FIGARCH.simulate.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, Series}</span></span><a class="headerlink" href="#arch.univariate.FIGARCH.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to simulate the volatility model</p>
+<dt id="arch.univariate.FIGARCH.simulate.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.FIGARCH.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to simulate the volatility model</p>
 </dd>
-<dt id="arch.univariate.FIGARCH.simulate.nobs"><span class="sig-name descname">nobs</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.FIGARCH.simulate.nobs" title="Permalink to this definition">¶</a></dt><dd><p>Number of data points to simulate</p>
+<dt id="arch.univariate.FIGARCH.simulate.nobs"><span class="n sig-name">nobs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.FIGARCH.simulate.nobs" title="Permalink to this definition">¶</a></dt><dd><p>Number of data points to simulate</p>
 </dd>
-<dt id="arch.univariate.FIGARCH.simulate.rng"><span class="sig-name descname">rng</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable</span></span><a class="headerlink" href="#arch.univariate.FIGARCH.simulate.rng" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that takes a single integer input and returns
+<dt id="arch.univariate.FIGARCH.simulate.rng"><span class="n sig-name">rng</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)">Callable</a><span class="p">[</span><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">,</span><span class="w"> </span><span class="p">...</span><span class="p">]</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.univariate.FIGARCH.simulate.rng" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that takes a single integer input and returns
 a vector of random numbers</p>
 </dd>
-<dt id="arch.univariate.FIGARCH.simulate.burn"><span class="sig-name descname">burn</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.univariate.FIGARCH.simulate.burn" title="Permalink to this definition">¶</a></dt><dd><p>Number of additional observations to generate when initializing
+<dt id="arch.univariate.FIGARCH.simulate.burn"><span class="n sig-name">burn</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code><a class="headerlink" href="#arch.univariate.FIGARCH.simulate.burn" title="Permalink to this definition">¶</a></dt><dd><p>Number of additional observations to generate when initializing
 the simulation</p>
 </dd>
-<dt id="arch.univariate.FIGARCH.simulate.initial_value"><span class="sig-name descname">initial_value</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}, optional</span></span><a class="headerlink" href="#arch.univariate.FIGARCH.simulate.initial_value" title="Permalink to this definition">¶</a></dt><dd><p>Scalar or array of initial values to use when initializing the
+<dt id="arch.univariate.FIGARCH.simulate.initial_value"><span class="n sig-name">initial_value</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.FIGARCH.simulate.initial_value" title="Permalink to this definition">¶</a></dt><dd><p>Scalar or array of initial values to use when initializing the
 simulation</p>
 </dd>
 </dl>
 </dd>
-<dt class="field-even" id="arch.univariate.FIGARCH.simulate-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FIGARCH.simulate-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>]</p>
-</dd>
-<dt class="field-odd" id="arch.univariate.FIGARCH.simulate-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FIGARCH.simulate-returns" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><ul class="simple">
-<li><p><strong>resids</strong> (<em>ndarray</em>) – The simulated residuals</p></li>
-<li><p><strong>variance</strong> (<em>ndarray</em>) – The simulated variance</p></li>
+<dt class="field-even" id="arch.univariate.FIGARCH.simulate-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FIGARCH.simulate-returns" title="Permalink to this headline">¶</a></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><strong>resids</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – The simulated residuals</p></li>
+<li><p><strong>variance</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – The simulated variance</p></li>
 </ul>
 </p>
 </dd>
@@ -1422,7 +1405,7 @@ <h1 id="arch-univariate-figarch-simulate">arch.univariate.FIGARCH.simulate<a cla
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1469,7 +1452,7 @@ <h1 id="arch-univariate-figarch-simulate">arch.univariate.FIGARCH.simulate<a cla
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.FIGARCH.start.html b/univariate/generated/generated/arch.univariate.FIGARCH.start.html
index a6f02e98ba..d234dafd33 100644
--- a/univariate/generated/generated/arch.univariate.FIGARCH.start.html
+++ b/univariate/generated/generated/arch.univariate.FIGARCH.start.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FIGARCH.start - arch 6.1.0</title>
+        <title>arch.univariate.FIGARCH.start - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1183,10 +1183,10 @@
                   
 
 
-<h1 id="arch-univariate-figarch-start">arch.univariate.FIGARCH.start<a class="headerlink" href="#arch-univariate-figarch-start" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-figarch-start">arch.univariate.FIGARCH.start<a class="headerlink" href="#arch-univariate-figarch-start" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.FIGARCH.start">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">FIGARCH.</span></span><span class="sig-name descname"><span class="pre">start</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.FIGARCH.start" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">FIGARCH.</span></span><span class="sig-name descname"><span class="pre">start</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.FIGARCH.start" title="Link to this definition">¶</a></dt>
 <dd><p>Index to use to start variance subarray selection</p>
 </dd></dl>
 
@@ -1257,7 +1257,7 @@ <h1 id="arch-univariate-figarch-start">arch.univariate.FIGARCH.start<a class="he
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1304,7 +1304,7 @@ <h1 id="arch-univariate-figarch-start">arch.univariate.FIGARCH.start<a class="he
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.FIGARCH.starting_values.html b/univariate/generated/generated/arch.univariate.FIGARCH.starting_values.html
index 430aa1518f..9785f5a3dc 100644
--- a/univariate/generated/generated/arch.univariate.FIGARCH.starting_values.html
+++ b/univariate/generated/generated/arch.univariate.FIGARCH.starting_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FIGARCH.starting_values - arch 6.1.0</title>
+        <title>arch.univariate.FIGARCH.starting_values - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1263,15 +1263,15 @@
                   
 
 
-<h1 id="arch-univariate-figarch-starting-values">arch.univariate.FIGARCH.starting_values<a class="headerlink" href="#arch-univariate-figarch-starting-values" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-figarch-starting-values">arch.univariate.FIGARCH.starting_values<a class="headerlink" href="#arch-univariate-figarch-starting-values" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.FIGARCH.starting_values">
-<span class="sig-prename descclassname"><span class="pre">FIGARCH.</span></span><span class="sig-name descname"><span class="pre">starting_values</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.starting_values.resids" title="arch.univariate.FIGARCH.starting_values.resids (Python parameter) — Array of (approximate) residuals to use when computing starting values"><span class="n"><span class="pre">resids</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#FIGARCH.starting_values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.FIGARCH.starting_values" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">FIGARCH.</span></span><span class="sig-name descname"><span class="pre">starting_values</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.starting_values.resids" title="arch.univariate.FIGARCH.starting_values.resids (Python parameter) — Array of (approximate) residuals to use when computing starting values"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#FIGARCH.starting_values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.FIGARCH.starting_values" title="Link to this definition">¶</a></dt>
 <dd><p>Returns starting values for the ARCH model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.FIGARCH.starting_values-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FIGARCH.starting_values-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.FIGARCH.starting_values.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.FIGARCH.starting_values.resids" title="Permalink to this definition">¶</a></dt><dd><p>Array of (approximate) residuals to use when computing starting
+<dt id="arch.univariate.FIGARCH.starting_values.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.FIGARCH.starting_values.resids" title="Permalink to this definition">¶</a></dt><dd><p>Array of (approximate) residuals to use when computing starting
 values</p>
 </dd>
 </dl>
@@ -1280,7 +1280,7 @@ <h1 id="arch-univariate-figarch-starting-values">arch.univariate.FIGARCH.startin
 <dd class="field-even"><p><strong>sv</strong> – Array of starting values</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.FIGARCH.starting_values-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FIGARCH.starting_values-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1352,7 +1352,7 @@ <h1 id="arch-univariate-figarch-starting-values">arch.univariate.FIGARCH.startin
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1399,7 +1399,7 @@ <h1 id="arch-univariate-figarch-starting-values">arch.univariate.FIGARCH.startin
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.FIGARCH.stop.html b/univariate/generated/generated/arch.univariate.FIGARCH.stop.html
index bf7a77092c..1ebb48cb23 100644
--- a/univariate/generated/generated/arch.univariate.FIGARCH.stop.html
+++ b/univariate/generated/generated/arch.univariate.FIGARCH.stop.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FIGARCH.stop - arch 6.1.0</title>
+        <title>arch.univariate.FIGARCH.stop - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1183,10 +1183,10 @@
                   
 
 
-<h1 id="arch-univariate-figarch-stop">arch.univariate.FIGARCH.stop<a class="headerlink" href="#arch-univariate-figarch-stop" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-figarch-stop">arch.univariate.FIGARCH.stop<a class="headerlink" href="#arch-univariate-figarch-stop" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.FIGARCH.stop">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">FIGARCH.</span></span><span class="sig-name descname"><span class="pre">stop</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.FIGARCH.stop" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">FIGARCH.</span></span><span class="sig-name descname"><span class="pre">stop</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.FIGARCH.stop" title="Link to this definition">¶</a></dt>
 <dd><p>Index to use to stop variance subarray selection</p>
 </dd></dl>
 
@@ -1257,7 +1257,7 @@ <h1 id="arch-univariate-figarch-stop">arch.univariate.FIGARCH.stop<a class="head
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1304,7 +1304,7 @@ <h1 id="arch-univariate-figarch-stop">arch.univariate.FIGARCH.stop<a class="head
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.FIGARCH.truncation.html b/univariate/generated/generated/arch.univariate.FIGARCH.truncation.html
index 4ceb27ae83..bbb2f6b3cf 100644
--- a/univariate/generated/generated/arch.univariate.FIGARCH.truncation.html
+++ b/univariate/generated/generated/arch.univariate.FIGARCH.truncation.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FIGARCH.truncation - arch 6.1.0</title>
+        <title>arch.univariate.FIGARCH.truncation - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1183,10 +1183,10 @@
                   
 
 
-<h1 id="arch-univariate-figarch-truncation">arch.univariate.FIGARCH.truncation<a class="headerlink" href="#arch-univariate-figarch-truncation" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-figarch-truncation">arch.univariate.FIGARCH.truncation<a class="headerlink" href="#arch-univariate-figarch-truncation" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.FIGARCH.truncation">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">FIGARCH.</span></span><span class="sig-name descname"><span class="pre">truncation</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.FIGARCH.truncation" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">FIGARCH.</span></span><span class="sig-name descname"><span class="pre">truncation</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.FIGARCH.truncation" title="Link to this definition">¶</a></dt>
 <dd><p>Truncation lag for the ARCH-infinity approximation</p>
 </dd></dl>
 
@@ -1257,7 +1257,7 @@ <h1 id="arch-univariate-figarch-truncation">arch.univariate.FIGARCH.truncation<a
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1304,7 +1304,7 @@ <h1 id="arch-univariate-figarch-truncation">arch.univariate.FIGARCH.truncation<a
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.FIGARCH.update.html b/univariate/generated/generated/arch.univariate.FIGARCH.update.html
index b4f4ac8495..63cece2a3a 100644
--- a/univariate/generated/generated/arch.univariate.FIGARCH.update.html
+++ b/univariate/generated/generated/arch.univariate.FIGARCH.update.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FIGARCH.update - arch 6.1.0</title>
+        <title>arch.univariate.FIGARCH.update - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1333,28 +1333,28 @@
                   
 
 
-<h1 id="arch-univariate-figarch-update">arch.univariate.FIGARCH.update<a class="headerlink" href="#arch-univariate-figarch-update" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-figarch-update">arch.univariate.FIGARCH.update<a class="headerlink" href="#arch-univariate-figarch-update" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.FIGARCH.update">
-<span class="sig-prename descclassname"><span class="pre">FIGARCH.</span></span><span class="sig-name descname"><span class="pre">update</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.update.index" title="arch.univariate.FIGARCH.update.index (Python parameter) — The numerical index of the variance to compute"><span class="n"><span class="pre">index</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.update.parameters" title="arch.univariate.FIGARCH.update.parameters (Python parameter) — The variance model parameters"><span class="n"><span class="pre">parameters</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.update.resids" title="arch.univariate.FIGARCH.update.resids (Python parameter) — The residual array. Only uses resids[:index] when computing sigma2[index]"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.update.sigma2" title="arch.univariate.FIGARCH.update.sigma2 (Python parameter) — The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."><span class="n"><span class="pre">sigma2</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.update.backcast" title="arch.univariate.FIGARCH.update.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.update.var_bounds" title="arch.univariate.FIGARCH.update.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.FIGARCH.update" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">FIGARCH.</span></span><span class="sig-name descname"><span class="pre">update</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.update.index" title="arch.univariate.FIGARCH.update.index (Python parameter) — The numerical index of the variance to compute"><span class="n"><span class="pre">index</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.update.parameters" title="arch.univariate.FIGARCH.update.parameters (Python parameter) — The variance model parameters"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.update.resids" title="arch.univariate.FIGARCH.update.resids (Python parameter) — The residual array. Only uses resids[:index] when computing sigma2[index]"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.update.sigma2" title="arch.univariate.FIGARCH.update.sigma2 (Python parameter) — The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."><span class="n"><span class="pre">sigma2</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.update.backcast" title="arch.univariate.FIGARCH.update.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.update.var_bounds" title="arch.univariate.FIGARCH.update.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span></span><a class="headerlink" href="#arch.univariate.FIGARCH.update" title="Link to this definition">¶</a></dt>
 <dd><p>Compute the variance for a single observation</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.FIGARCH.update-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FIGARCH.update-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.FIGARCH.update.index"><span class="sig-name descname">index</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.FIGARCH.update.index" title="Permalink to this definition">¶</a></dt><dd><p>The numerical index of the variance to compute</p>
+<dt id="arch.univariate.FIGARCH.update.index"><span class="n sig-name">index</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.FIGARCH.update.index" title="Permalink to this definition">¶</a></dt><dd><p>The numerical index of the variance to compute</p>
 </dd>
-<dt id="arch.univariate.FIGARCH.update.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.FIGARCH.update.parameters" title="Permalink to this definition">¶</a></dt><dd><p>The variance model parameters</p>
+<dt id="arch.univariate.FIGARCH.update.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.FIGARCH.update.parameters" title="Permalink to this definition">¶</a></dt><dd><p>The variance model parameters</p>
 </dd>
-<dt id="arch.univariate.FIGARCH.update.resids"><span class="n sig-name">resids</span><a class="headerlink" href="#arch.univariate.FIGARCH.update.resids" title="Permalink to this definition">¶</a></dt><dd><p>The residual array. Only uses <code class="docutils literal notranslate"><span class="pre">resids[:index]</span></code> when computing
+<dt id="arch.univariate.FIGARCH.update.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.FIGARCH.update.resids" title="Permalink to this definition">¶</a></dt><dd><p>The residual array. Only uses <code class="docutils literal notranslate"><span class="pre">resids[:index]</span></code> when computing
 <code class="docutils literal notranslate"><span class="pre">sigma2[index]</span></code></p>
 </dd>
-<dt id="arch.univariate.FIGARCH.update.sigma2"><span class="sig-name descname">sigma2</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.FIGARCH.update.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>The array containing the variances. Only uses <code class="docutils literal notranslate"><span class="pre">sigma2[:index]</span></code>
+<dt id="arch.univariate.FIGARCH.update.sigma2"><span class="n sig-name">sigma2</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.FIGARCH.update.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>The array containing the variances. Only uses <code class="docutils literal notranslate"><span class="pre">sigma2[:index]</span></code>
 when computing <code class="docutils literal notranslate"><span class="pre">sigma2[index]</span></code>. The computed value is stored
 in <code class="docutils literal notranslate"><span class="pre">sigma2[index]</span></code>.</p>
 </dd>
-<dt id="arch.univariate.FIGARCH.update.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}</span></span><a class="headerlink" href="#arch.univariate.FIGARCH.update.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing the recursion</p>
+<dt id="arch.univariate.FIGARCH.update.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.FIGARCH.update.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing the recursion</p>
 </dd>
-<dt id="arch.univariate.FIGARCH.update.var_bounds"><span class="sig-name descname">var_bounds</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.FIGARCH.update.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
+<dt id="arch.univariate.FIGARCH.update.var_bounds"><span class="n sig-name">var_bounds</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.FIGARCH.update.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
 </dd>
 </dl>
 </dd>
@@ -1362,7 +1362,7 @@ <h1 id="arch-univariate-figarch-update">arch.univariate.FIGARCH.update<a class="
 <dd class="field-even"><p>The variance computed for location <code class="docutils literal notranslate"><span class="pre">index</span></code></p>
 </dd>
 <dt class="field-odd" id="arch.univariate.FIGARCH.update-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FIGARCH.update-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1434,7 +1434,7 @@ <h1 id="arch-univariate-figarch-update">arch.univariate.FIGARCH.update<a class="
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1481,7 +1481,7 @@ <h1 id="arch-univariate-figarch-update">arch.univariate.FIGARCH.update<a class="
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diff --git a/univariate/generated/generated/arch.univariate.FIGARCH.updateable.html b/univariate/generated/generated/arch.univariate.FIGARCH.updateable.html
index 402a918d64..c507fb0c0c 100644
--- a/univariate/generated/generated/arch.univariate.FIGARCH.updateable.html
+++ b/univariate/generated/generated/arch.univariate.FIGARCH.updateable.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FIGARCH.updateable - arch 6.1.0</title>
+        <title>arch.univariate.FIGARCH.updateable - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1183,10 +1183,10 @@
                   
 
 
-<h1 id="arch-univariate-figarch-updateable">arch.univariate.FIGARCH.updateable<a class="headerlink" href="#arch-univariate-figarch-updateable" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-figarch-updateable">arch.univariate.FIGARCH.updateable<a class="headerlink" href="#arch-univariate-figarch-updateable" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.FIGARCH.updateable">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">FIGARCH.</span></span><span class="sig-name descname"><span class="pre">updateable</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.univariate.FIGARCH.updateable" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">FIGARCH.</span></span><span class="sig-name descname"><span class="pre">updateable</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.univariate.FIGARCH.updateable" title="Link to this definition">¶</a></dt>
 <dd><p>Flag indicating that the volatility process supports update</p>
 </dd></dl>
 
@@ -1257,7 +1257,7 @@ <h1 id="arch-univariate-figarch-updateable">arch.univariate.FIGARCH.updateable<a
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1304,7 +1304,7 @@ <h1 id="arch-univariate-figarch-updateable">arch.univariate.FIGARCH.updateable<a
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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diff --git a/univariate/generated/generated/arch.univariate.FIGARCH.variance_bounds.html b/univariate/generated/generated/arch.univariate.FIGARCH.variance_bounds.html
index 905505a8be..91bff38a1e 100644
--- a/univariate/generated/generated/arch.univariate.FIGARCH.variance_bounds.html
+++ b/univariate/generated/generated/arch.univariate.FIGARCH.variance_bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FIGARCH.variance_bounds - arch 6.1.0</title>
+        <title>arch.univariate.FIGARCH.variance_bounds - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1277,20 +1277,20 @@
                   
 
 
-<h1 id="arch-univariate-figarch-variance-bounds">arch.univariate.FIGARCH.variance_bounds<a class="headerlink" href="#arch-univariate-figarch-variance-bounds" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-figarch-variance-bounds">arch.univariate.FIGARCH.variance_bounds<a class="headerlink" href="#arch-univariate-figarch-variance-bounds" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.FIGARCH.variance_bounds">
-<span class="sig-prename descclassname"><span class="pre">FIGARCH.</span></span><span class="sig-name descname"><span class="pre">variance_bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.variance_bounds.resids" title="arch.univariate.FIGARCH.variance_bounds.resids (Python parameter) — Approximate residuals to use to compute the lower and upper bounds on the conditional variance"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.variance_bounds.power" title="arch.univariate.FIGARCH.variance_bounds.power (Python parameter) — Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."><span class="n"><span class="pre">power</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">2.0</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#FIGARCH.variance_bounds"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.FIGARCH.variance_bounds" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.FIGARCH.variance_bounds">
+<span class="sig-prename descclassname"><span class="pre">FIGARCH.</span></span><span class="sig-name descname"><span class="pre">variance_bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.variance_bounds.resids" title="arch.univariate.FIGARCH.variance_bounds.resids (Python parameter) — Approximate residuals to use to compute the lower and upper bounds on the conditional variance"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.variance_bounds.power" title="arch.univariate.FIGARCH.variance_bounds.power (Python parameter) — Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."><span class="n"><span class="pre">power</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">2.0</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#FIGARCH.variance_bounds"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.FIGARCH.variance_bounds" title="Link to this definition">¶</a></dt>
 <dd><p>Construct loose bounds for conditional variances.</p>
 <p>These bounds are used in parameter estimation to ensure
 that the log-likelihood does not produce NaN values.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.FIGARCH.variance_bounds-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FIGARCH.variance_bounds-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.FIGARCH.variance_bounds.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.FIGARCH.variance_bounds.resids" title="Permalink to this definition">¶</a></dt><dd><p>Approximate residuals to use to compute the lower and upper bounds
+<dt id="arch.univariate.FIGARCH.variance_bounds.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.FIGARCH.variance_bounds.resids" title="Permalink to this definition">¶</a></dt><dd><p>Approximate residuals to use to compute the lower and upper bounds
 on the conditional variance</p>
 </dd>
-<dt id="arch.univariate.FIGARCH.variance_bounds.power"><span class="sig-name descname">power</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>, optional</span></span><a class="headerlink" href="#arch.univariate.FIGARCH.variance_bounds.power" title="Permalink to this definition">¶</a></dt><dd><p>Power used in the model. 2.0, the default corresponds to standard
+<dt id="arch.univariate.FIGARCH.variance_bounds.power"><span class="n sig-name">power</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">2.0</span></code><a class="headerlink" href="#arch.univariate.FIGARCH.variance_bounds.power" title="Permalink to this definition">¶</a></dt><dd><p>Power used in the model. 2.0, the default corresponds to standard
 ARCH models that evolve in squares.</p>
 </dd>
 </dl>
@@ -1300,7 +1300,7 @@ <h1 id="arch-univariate-figarch-variance-bounds">arch.univariate.FIGARCH.varianc
 number of elements as resids</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.FIGARCH.variance_bounds-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FIGARCH.variance_bounds-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1372,7 +1372,7 @@ <h1 id="arch-univariate-figarch-variance-bounds">arch.univariate.FIGARCH.varianc
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1419,7 +1419,7 @@ <h1 id="arch-univariate-figarch-variance-bounds">arch.univariate.FIGARCH.varianc
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.FIGARCH.volatility_updater.html b/univariate/generated/generated/arch.univariate.FIGARCH.volatility_updater.html
index db3d6bbbc5..6c53b81b2a 100644
--- a/univariate/generated/generated/arch.univariate.FIGARCH.volatility_updater.html
+++ b/univariate/generated/generated/arch.univariate.FIGARCH.volatility_updater.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FIGARCH.volatility_updater - arch 6.1.0</title>
+        <title>arch.univariate.FIGARCH.volatility_updater - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1237,17 +1237,17 @@
                   
 
 
-<h1 id="arch-univariate-figarch-volatility-updater">arch.univariate.FIGARCH.volatility_updater<a class="headerlink" href="#arch-univariate-figarch-volatility-updater" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-figarch-volatility-updater">arch.univariate.FIGARCH.volatility_updater<a class="headerlink" href="#arch-univariate-figarch-volatility-updater" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.FIGARCH.volatility_updater">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">FIGARCH.</span></span><span class="sig-name descname"><span class="pre">volatility_updater</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.univariate.recursions_python.VolatilityUpdater.html#arch.univariate.recursions_python.VolatilityUpdater" title="arch.univariate.recursions_python.VolatilityUpdater (Python class) — Base class that all volatility updaters must inherit from."><span class="pre">VolatilityUpdater</span></a></span><a class="headerlink" href="#arch.univariate.FIGARCH.volatility_updater" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">FIGARCH.</span></span><span class="sig-name descname"><span class="pre">volatility_updater</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><span class="pre">VolatilityUpdater</span></span><a class="headerlink" href="#arch.univariate.FIGARCH.volatility_updater" title="Link to this definition">¶</a></dt>
 <dd><p>Get the volatility updater associated with the volatility process</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.FIGARCH.volatility_updater-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FIGARCH.volatility_updater-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The updater class</p>
 </dd>
 <dt class="field-even" id="arch.univariate.FIGARCH.volatility_updater-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FIGARCH.volatility_updater-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference internal" href="../arch.univariate.recursions_python.VolatilityUpdater.html#arch.univariate.recursions_python.VolatilityUpdater" title="arch.univariate.recursions_python.VolatilityUpdater (Python class) — Base class that all volatility updaters must inherit from.">VolatilityUpdater</a></p>
+<dd class="field-even"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">VolatilityUpdater</span></code></p>
 </dd>
 <dt class="field-odd" id="arch.univariate.FIGARCH.volatility_updater-raises">Raises<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FIGARCH.volatility_updater-raises" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#NotImplementedError" title="(in Python v3.11)"><strong>NotImplementedError</strong></a> – If the process is not updateable</p>
@@ -1322,7 +1322,7 @@ <h1 id="arch-univariate-figarch-volatility-updater">arch.univariate.FIGARCH.vola
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1369,7 +1369,7 @@ <h1 id="arch-univariate-figarch-volatility-updater">arch.univariate.FIGARCH.vola
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.FixedVariance.backcast.html b/univariate/generated/generated/arch.univariate.FixedVariance.backcast.html
index 600912e513..ae3c857fb1 100644
--- a/univariate/generated/generated/arch.univariate.FixedVariance.backcast.html
+++ b/univariate/generated/generated/arch.univariate.FixedVariance.backcast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FixedVariance.backcast - arch 6.1.0</title>
+        <title>arch.univariate.FixedVariance.backcast - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1288,15 +1288,15 @@
                   
 
 
-<h1 id="arch-univariate-fixedvariance-backcast">arch.univariate.FixedVariance.backcast<a class="headerlink" href="#arch-univariate-fixedvariance-backcast" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-fixedvariance-backcast">arch.univariate.FixedVariance.backcast<a class="headerlink" href="#arch-univariate-fixedvariance-backcast" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.FixedVariance.backcast">
-<span class="sig-prename descclassname"><span class="pre">FixedVariance.</span></span><span class="sig-name descname"><span class="pre">backcast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.backcast.resids" title="arch.univariate.FixedVariance.backcast.resids (Python parameter) — Vector of (approximate) residuals"><span class="n"><span class="pre">resids</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#FixedVariance.backcast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.FixedVariance.backcast" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">FixedVariance.</span></span><span class="sig-name descname"><span class="pre">backcast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.backcast.resids" title="arch.univariate.FixedVariance.backcast.resids (Python parameter) — Vector of (approximate) residuals"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#FixedVariance.backcast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.FixedVariance.backcast" title="Link to this definition">¶</a></dt>
 <dd><p>Construct values for backcasting to start the recursion</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.FixedVariance.backcast-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FixedVariance.backcast-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.FixedVariance.backcast.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.FixedVariance.backcast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of (approximate) residuals</p>
+<dt id="arch.univariate.FixedVariance.backcast.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.FixedVariance.backcast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of (approximate) residuals</p>
 </dd>
 </dl>
 </dd>
@@ -1304,7 +1304,7 @@ <h1 id="arch-univariate-fixedvariance-backcast">arch.univariate.FixedVariance.ba
 <dd class="field-even"><p><strong>backcast</strong> – Value to use in backcasting in the volatility recursion</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.FixedVariance.backcast-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FixedVariance.backcast-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1376,7 +1376,7 @@ <h1 id="arch-univariate-fixedvariance-backcast">arch.univariate.FixedVariance.ba
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1423,7 +1423,7 @@ <h1 id="arch-univariate-fixedvariance-backcast">arch.univariate.FixedVariance.ba
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.FixedVariance.backcast_transform.html b/univariate/generated/generated/arch.univariate.FixedVariance.backcast_transform.html
index 3608b66238..e93d32819b 100644
--- a/univariate/generated/generated/arch.univariate.FixedVariance.backcast_transform.html
+++ b/univariate/generated/generated/arch.univariate.FixedVariance.backcast_transform.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FixedVariance.backcast_transform - arch 6.1.0</title>
+        <title>arch.univariate.FixedVariance.backcast_transform - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1288,15 +1288,15 @@
                   
 
 
-<h1 id="arch-univariate-fixedvariance-backcast-transform">arch.univariate.FixedVariance.backcast_transform<a class="headerlink" href="#arch-univariate-fixedvariance-backcast-transform" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-fixedvariance-backcast-transform">arch.univariate.FixedVariance.backcast_transform<a class="headerlink" href="#arch-univariate-fixedvariance-backcast-transform" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.FixedVariance.backcast_transform">
-<span class="sig-prename descclassname"><span class="pre">FixedVariance.</span></span><span class="sig-name descname"><span class="pre">backcast_transform</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.backcast_transform.backcast" title="arch.univariate.FixedVariance.backcast_transform.backcast (Python parameter) — User-provided backcast that approximates sigma2[0]."><span class="n"><span class="pre">backcast</span></span></a></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.FixedVariance.backcast_transform" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.FixedVariance.backcast_transform">
+<span class="sig-prename descclassname"><span class="pre">FixedVariance.</span></span><span class="sig-name descname"><span class="pre">backcast_transform</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.backcast_transform.backcast" title="arch.univariate.FixedVariance.backcast_transform.backcast (Python parameter) — User-provided backcast that approximates sigma2[0]."><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.univariate.FixedVariance.backcast_transform" title="Link to this definition">¶</a></dt>
 <dd><p>Transformation to apply to user-provided backcast values</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.FixedVariance.backcast_transform-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FixedVariance.backcast_transform-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.FixedVariance.backcast_transform.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}</span></span><a class="headerlink" href="#arch.univariate.FixedVariance.backcast_transform.backcast" title="Permalink to this definition">¶</a></dt><dd><p>User-provided <code class="docutils literal notranslate"><span class="pre">backcast</span></code> that approximates sigma2[0].</p>
+<dt id="arch.univariate.FixedVariance.backcast_transform.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.FixedVariance.backcast_transform.backcast" title="Permalink to this definition">¶</a></dt><dd><p>User-provided <code class="docutils literal notranslate"><span class="pre">backcast</span></code> that approximates sigma2[0].</p>
 </dd>
 </dl>
 </dd>
@@ -1304,7 +1304,7 @@ <h1 id="arch-univariate-fixedvariance-backcast-transform">arch.univariate.FixedV
 <dd class="field-even"><p><strong>backcast</strong> – Backcast transformed to the model-appropriate scale</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.FixedVariance.backcast_transform-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FixedVariance.backcast_transform-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>{float, ndarray}</p>
+<dd class="field-odd"><p><code class="docutils literal notranslate"><span class="pre">{float,</span> <span class="pre">ndarray}</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1376,7 +1376,7 @@ <h1 id="arch-univariate-fixedvariance-backcast-transform">arch.univariate.FixedV
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1423,7 +1423,7 @@ <h1 id="arch-univariate-fixedvariance-backcast-transform">arch.univariate.FixedV
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.FixedVariance.bounds.html b/univariate/generated/generated/arch.univariate.FixedVariance.bounds.html
index d917012e0b..070adce29c 100644
--- a/univariate/generated/generated/arch.univariate.FixedVariance.bounds.html
+++ b/univariate/generated/generated/arch.univariate.FixedVariance.bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FixedVariance.bounds - arch 6.1.0</title>
+        <title>arch.univariate.FixedVariance.bounds - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1288,15 +1288,15 @@
                   
 
 
-<h1 id="arch-univariate-fixedvariance-bounds">arch.univariate.FixedVariance.bounds<a class="headerlink" href="#arch-univariate-fixedvariance-bounds" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-fixedvariance-bounds">arch.univariate.FixedVariance.bounds<a class="headerlink" href="#arch-univariate-fixedvariance-bounds" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.FixedVariance.bounds">
-<span class="sig-prename descclassname"><span class="pre">FixedVariance.</span></span><span class="sig-name descname"><span class="pre">bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.bounds.resids" title="arch.univariate.FixedVariance.bounds.resids (Python parameter) — Vector of (approximate) residuals"><span class="n"><span class="pre">resids</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#FixedVariance.bounds"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.FixedVariance.bounds" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">FixedVariance.</span></span><span class="sig-name descname"><span class="pre">bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.bounds.resids" title="arch.univariate.FixedVariance.bounds.resids (Python parameter) — Vector of (approximate) residuals"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#FixedVariance.bounds"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.FixedVariance.bounds" title="Link to this definition">¶</a></dt>
 <dd><p>Returns bounds for parameters</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.FixedVariance.bounds-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FixedVariance.bounds-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.FixedVariance.bounds.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.FixedVariance.bounds.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of (approximate) residuals</p>
+<dt id="arch.univariate.FixedVariance.bounds.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.FixedVariance.bounds.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of (approximate) residuals</p>
 </dd>
 </dl>
 </dd>
@@ -1304,7 +1304,7 @@ <h1 id="arch-univariate-fixedvariance-bounds">arch.univariate.FixedVariance.boun
 <dd class="field-even"><p><strong>bounds</strong> – List of bounds where each element is (lower, upper).</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.FixedVariance.bounds-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FixedVariance.bounds-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a>[<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a>[<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>,<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>]]</p>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">list[tuple[float,float]]</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1376,7 +1376,7 @@ <h1 id="arch-univariate-fixedvariance-bounds">arch.univariate.FixedVariance.boun
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1423,7 +1423,7 @@ <h1 id="arch-univariate-fixedvariance-bounds">arch.univariate.FixedVariance.boun
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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diff --git a/univariate/generated/generated/arch.univariate.FixedVariance.compute_variance.html b/univariate/generated/generated/arch.univariate.FixedVariance.compute_variance.html
index a161c27afa..d1107f9a7d 100644
--- a/univariate/generated/generated/arch.univariate.FixedVariance.compute_variance.html
+++ b/univariate/generated/generated/arch.univariate.FixedVariance.compute_variance.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FixedVariance.compute_variance - arch 6.1.0</title>
+        <title>arch.univariate.FixedVariance.compute_variance - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch 6.1.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+    arch 6.2.0
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@@ -764,13 +764,6 @@
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-  <a href="#arch.univariate.FixedVariance.compute_variance-return-type" class="md-nav__link">
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-  </a>
-  
 </li>
         
       </ul>
@@ -1330,30 +1316,27 @@
                   
 
 
-<h1 id="arch-univariate-fixedvariance-compute-variance">arch.univariate.FixedVariance.compute_variance<a class="headerlink" href="#arch-univariate-fixedvariance-compute-variance" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-fixedvariance-compute-variance">arch.univariate.FixedVariance.compute_variance<a class="headerlink" href="#arch-univariate-fixedvariance-compute-variance" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.FixedVariance.compute_variance">
-<span class="sig-prename descclassname"><span class="pre">FixedVariance.</span></span><span class="sig-name descname"><span class="pre">compute_variance</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.compute_variance.parameters" title="arch.univariate.FixedVariance.compute_variance.parameters (Python parameter) — Model parameters"><span class="n"><span class="pre">parameters</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.compute_variance.resids" title="arch.univariate.FixedVariance.compute_variance.resids (Python parameter) — Vector of mean zero residuals"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.compute_variance.sigma2" title="arch.univariate.FixedVariance.compute_variance.sigma2 (Python parameter) — Array with same size as resids to store the conditional variance"><span class="n"><span class="pre">sigma2</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.compute_variance.backcast" title="arch.univariate.FixedVariance.compute_variance.backcast (Python parameter) — Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."><span class="n"><span class="pre">backcast</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.compute_variance.var_bounds" title="arch.univariate.FixedVariance.compute_variance.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#FixedVariance.compute_variance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.FixedVariance.compute_variance" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">FixedVariance.</span></span><span class="sig-name descname"><span class="pre">compute_variance</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.compute_variance.parameters" title="arch.univariate.FixedVariance.compute_variance.parameters (Python parameter) — Model parameters"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.compute_variance.resids" title="arch.univariate.FixedVariance.compute_variance.resids (Python parameter) — Vector of mean zero residuals"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.compute_variance.sigma2" title="arch.univariate.FixedVariance.compute_variance.sigma2 (Python parameter) — Array with same size as resids to store the conditional variance"><span class="n"><span class="pre">sigma2</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.compute_variance.backcast" title="arch.univariate.FixedVariance.compute_variance.backcast (Python parameter) — Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.compute_variance.var_bounds" title="arch.univariate.FixedVariance.compute_variance.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#FixedVariance.compute_variance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.FixedVariance.compute_variance" title="Link to this definition">¶</a></dt>
 <dd><p>Compute the variance for the ARCH model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.FixedVariance.compute_variance-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FixedVariance.compute_variance-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.FixedVariance.compute_variance.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.FixedVariance.compute_variance.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
+<dt id="arch.univariate.FixedVariance.compute_variance.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.FixedVariance.compute_variance.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
 </dd>
-<dt id="arch.univariate.FixedVariance.compute_variance.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.FixedVariance.compute_variance.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of mean zero residuals</p>
+<dt id="arch.univariate.FixedVariance.compute_variance.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.FixedVariance.compute_variance.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of mean zero residuals</p>
 </dd>
-<dt id="arch.univariate.FixedVariance.compute_variance.sigma2"><span class="sig-name descname">sigma2</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.FixedVariance.compute_variance.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>Array with same size as resids to store the conditional variance</p>
+<dt id="arch.univariate.FixedVariance.compute_variance.sigma2"><span class="n sig-name">sigma2</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.FixedVariance.compute_variance.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>Array with same size as resids to store the conditional variance</p>
 </dd>
-<dt id="arch.univariate.FixedVariance.compute_variance.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}</span></span><a class="headerlink" href="#arch.univariate.FixedVariance.compute_variance.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing ARCH recursion. Can be an ndarray
+<dt id="arch.univariate.FixedVariance.compute_variance.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.FixedVariance.compute_variance.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing ARCH recursion. Can be an ndarray
 when the model contains multiple components.</p>
 </dd>
-<dt id="arch.univariate.FixedVariance.compute_variance.var_bounds"><span class="sig-name descname">var_bounds</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.FixedVariance.compute_variance.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
+<dt id="arch.univariate.FixedVariance.compute_variance.var_bounds"><span class="n sig-name">var_bounds</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.FixedVariance.compute_variance.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
 </dd>
 </dl>
 </dd>
-<dt class="field-even" id="arch.univariate.FixedVariance.compute_variance-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FixedVariance.compute_variance-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a></p>
-</dd>
 </dl>
 </dd></dl>
 
@@ -1424,7 +1407,7 @@ <h1 id="arch-univariate-fixedvariance-compute-variance">arch.univariate.FixedVar
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
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diff --git a/univariate/generated/generated/arch.univariate.FixedVariance.constraints.html b/univariate/generated/generated/arch.univariate.FixedVariance.constraints.html
index e96cf10110..c6e7d649d5 100644
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+++ b/univariate/generated/generated/arch.univariate.FixedVariance.constraints.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FixedVariance.constraints - arch 6.1.0</title>
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@@ -47,8 +47,8 @@
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -733,13 +733,6 @@
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-          <li class="md-nav__item">
-  <a href="#arch.univariate.FixedVariance.constraints-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
-</li>
-        
           <li class="md-nav__item">
   <a href="#arch.univariate.FixedVariance.constraints-returns" class="md-nav__link">
     <span class="md-ellipsis">Returns</span>
@@ -1215,13 +1208,6 @@
     <nav class="md-nav" aria-label="FixedVariance.constraints">
       <ul class="md-nav__list">
         
-          <li class="md-nav__item">
-  <a href="#arch.univariate.FixedVariance.constraints-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
-</li>
-        
           <li class="md-nav__item">
   <a href="#arch.univariate.FixedVariance.constraints-returns" class="md-nav__link">
     <span class="md-ellipsis">Returns</span>
@@ -1248,20 +1234,17 @@
                   
 
 
-<h1 id="arch-univariate-fixedvariance-constraints">arch.univariate.FixedVariance.constraints<a class="headerlink" href="#arch-univariate-fixedvariance-constraints" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-fixedvariance-constraints">arch.univariate.FixedVariance.constraints<a class="headerlink" href="#arch-univariate-fixedvariance-constraints" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.FixedVariance.constraints">
-<span class="sig-prename descclassname"><span class="pre">FixedVariance.</span></span><span class="sig-name descname"><span class="pre">constraints</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#FixedVariance.constraints"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.FixedVariance.constraints" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">FixedVariance.</span></span><span class="sig-name descname"><span class="pre">constraints</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#FixedVariance.constraints"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.FixedVariance.constraints" title="Link to this definition">¶</a></dt>
 <dd><p>Construct parameter constraints arrays for parameter estimation</p>
 <dl class="field-list simple">
-<dt class="field-odd" id="arch.univariate.FixedVariance.constraints-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FixedVariance.constraints-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>]</p>
-</dd>
-<dt class="field-even" id="arch.univariate.FixedVariance.constraints-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FixedVariance.constraints-returns" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><ul class="simple">
-<li><p><strong>A</strong> (<em>ndarray</em>) – Parameters loadings in constraint. Shape is number of constraints
+<dt class="field-odd" id="arch.univariate.FixedVariance.constraints-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FixedVariance.constraints-returns" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p><strong>A</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – Parameters loadings in constraint. Shape is number of constraints
 by number of parameters</p></li>
-<li><p><strong>b</strong> (<em>ndarray</em>) – Constraint values, one for each constraint</p></li>
+<li><p><strong>b</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – Constraint values, one for each constraint</p></li>
 </ul>
 </p>
 </dd>
@@ -1338,7 +1321,7 @@ <h1 id="arch-univariate-fixedvariance-constraints">arch.univariate.FixedVariance
   
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+++ b/univariate/generated/generated/arch.univariate.FixedVariance.forecast.html
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-<h1 id="arch-univariate-fixedvariance-forecast">arch.univariate.FixedVariance.forecast<a class="headerlink" href="#arch-univariate-fixedvariance-forecast" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-fixedvariance-forecast">arch.univariate.FixedVariance.forecast<a class="headerlink" href="#arch-univariate-fixedvariance-forecast" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.FixedVariance.forecast">
-<span class="sig-prename descclassname"><span class="pre">FixedVariance.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.forecast.parameters" title="arch.univariate.FixedVariance.forecast.parameters (Python parameter) — Parameters required to forecast the volatility model"><span class="n"><span class="pre">parameters</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.forecast.resids" title="arch.univariate.FixedVariance.forecast.resids (Python parameter) — Residuals to use in the recursion"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.forecast.backcast" title="arch.univariate.FixedVariance.forecast.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.forecast.var_bounds" title="arch.univariate.FixedVariance.forecast.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.forecast.start" title="arch.univariate.FixedVariance.forecast.start (Python parameter) — Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."><span class="n"><span class="pre">start</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.forecast.horizon" title="arch.univariate.FixedVariance.forecast.horizon (Python parameter) — Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."><span class="n"><span class="pre">horizon</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.forecast.method" title="arch.univariate.FixedVariance.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic."><span class="n"><span class="pre">method</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.forecast.simulations" title="arch.univariate.FixedVariance.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.forecast.rng" title="arch.univariate.FixedVariance.forecast.rng (Python parameter) — Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."><span class="n"><span class="pre">rng</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.forecast.random_state" title="arch.univariate.FixedVariance.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.FixedVariance.forecast" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">FixedVariance.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.forecast.parameters" title="arch.univariate.FixedVariance.forecast.parameters (Python parameter) — Parameters required to forecast the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.forecast.resids" title="arch.univariate.FixedVariance.forecast.resids (Python parameter) — Residuals to use in the recursion"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.forecast.backcast" title="arch.univariate.FixedVariance.forecast.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.forecast.var_bounds" title="arch.univariate.FixedVariance.forecast.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.forecast.start" title="arch.univariate.FixedVariance.forecast.start (Python parameter) — Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.forecast.horizon" title="arch.univariate.FixedVariance.forecast.horizon (Python parameter) — Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.forecast.method" title="arch.univariate.FixedVariance.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.forecast.simulations" title="arch.univariate.FixedVariance.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.forecast.rng" title="arch.univariate.FixedVariance.forecast.rng (Python parameter) — Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.forecast.random_state" title="arch.univariate.FixedVariance.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><span class="pre">VarianceForecast</span></span></span></span><a class="headerlink" href="#arch.univariate.FixedVariance.forecast" title="Link to this definition">¶</a></dt>
 <dd><p>Forecast volatility from the model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.FixedVariance.forecast-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FixedVariance.forecast-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.FixedVariance.forecast.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, Series}</span></span><a class="headerlink" href="#arch.univariate.FixedVariance.forecast.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast the volatility model</p>
+<dt id="arch.univariate.FixedVariance.forecast.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.FixedVariance.forecast.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast the volatility model</p>
 </dd>
-<dt id="arch.univariate.FixedVariance.forecast.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.FixedVariance.forecast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Residuals to use in the recursion</p>
+<dt id="arch.univariate.FixedVariance.forecast.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.FixedVariance.forecast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Residuals to use in the recursion</p>
 </dd>
-<dt id="arch.univariate.FixedVariance.forecast.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><a class="headerlink" href="#arch.univariate.FixedVariance.forecast.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing the recursion</p>
+<dt id="arch.univariate.FixedVariance.forecast.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><a class="headerlink" href="#arch.univariate.FixedVariance.forecast.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing the recursion</p>
 </dd>
-<dt id="arch.univariate.FixedVariance.forecast.var_bounds"><span class="sig-name descname">var_bounds</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray, 2-d</span></span><a class="headerlink" href="#arch.univariate.FixedVariance.forecast.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
+<dt id="arch.univariate.FixedVariance.forecast.var_bounds"><span class="n sig-name">var_bounds</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.FixedVariance.forecast.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
 </dd>
-<dt id="arch.univariate.FixedVariance.forecast.start"><span class="sig-name descname">start</span><span class="p"> : </span><span class="desctype"><span class="desctype">{None, int}</span></span><a class="headerlink" href="#arch.univariate.FixedVariance.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>Index of the first observation to use as the starting point for
+<dt id="arch.univariate.FixedVariance.forecast.start"><span class="n sig-name">start</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.FixedVariance.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>Index of the first observation to use as the starting point for
 the forecast.  Default is len(resids).</p>
 </dd>
-<dt id="arch.univariate.FixedVariance.forecast.horizon"><span class="sig-name descname">horizon</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.FixedVariance.forecast.horizon" title="Permalink to this definition">¶</a></dt><dd><p>Forecast horizon.  Must be 1 or larger.  Forecasts are produced
+<dt id="arch.univariate.FixedVariance.forecast.horizon"><span class="n sig-name">horizon</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code><a class="headerlink" href="#arch.univariate.FixedVariance.forecast.horizon" title="Permalink to this definition">¶</a></dt><dd><p>Forecast horizon.  Must be 1 or larger.  Forecasts are produced
 for horizons in [1, horizon].</p>
 </dd>
-<dt id="arch.univariate.FixedVariance.forecast.method"><span class="sig-name descname">method</span><span class="p"> : </span><span class="desctype"><span class="desctype">{'analytic', 'simulation', 'bootstrap'}</span></span><a class="headerlink" href="#arch.univariate.FixedVariance.forecast.method" title="Permalink to this definition">¶</a></dt><dd><p>Method to use when producing the forecast. The default is analytic.</p>
+<dt id="arch.univariate.FixedVariance.forecast.method"><span class="n sig-name">method</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'analytic'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'simulation'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'bootstrap'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code><a class="headerlink" href="#arch.univariate.FixedVariance.forecast.method" title="Permalink to this definition">¶</a></dt><dd><p>Method to use when producing the forecast. The default is analytic.</p>
 </dd>
-<dt id="arch.univariate.FixedVariance.forecast.simulations"><span class="sig-name descname">simulations</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.FixedVariance.forecast.simulations" title="Permalink to this definition">¶</a></dt><dd><p>Number of simulations to run when computing the forecast using
+<dt id="arch.univariate.FixedVariance.forecast.simulations"><span class="n sig-name">simulations</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.univariate.FixedVariance.forecast.simulations" title="Permalink to this definition">¶</a></dt><dd><p>Number of simulations to run when computing the forecast using
 either simulation or bootstrap.</p>
 </dd>
-<dt id="arch.univariate.FixedVariance.forecast.rng"><span class="sig-name descname">rng</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable</span></span><a class="headerlink" href="#arch.univariate.FixedVariance.forecast.rng" title="Permalink to this definition">¶</a></dt><dd><p>Callable random number generator required if method is
+<dt id="arch.univariate.FixedVariance.forecast.rng"><span class="n sig-name">rng</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)">Callable</a><span class="p">[</span><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">,</span><span class="w"> </span><span class="p">...</span><span class="p">]</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.FixedVariance.forecast.rng" title="Permalink to this definition">¶</a></dt><dd><p>Callable random number generator required if method is
 ‘simulation’. Must take a single shape input and return random
 samples numbers with that shape.</p>
 </dd>
-<dt id="arch.univariate.FixedVariance.forecast.random_state"><span class="sig-name descname">random_state</span><span class="p"> : </span><span class="desctype"><span class="desctype">RandomState, optional</span></span><a class="headerlink" href="#arch.univariate.FixedVariance.forecast.random_state" title="Permalink to this definition">¶</a></dt><dd><p>NumPy RandomState instance to use when method is ‘bootstrap’</p>
+<dt id="arch.univariate.FixedVariance.forecast.random_state"><span class="n sig-name">random_state</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)">RandomState</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.FixedVariance.forecast.random_state" title="Permalink to this definition">¶</a></dt><dd><p>NumPy RandomState instance to use when method is ‘bootstrap’</p>
 </dd>
 </dl>
 </dd>
@@ -1468,7 +1468,7 @@ <h1 id="arch-univariate-fixedvariance-forecast">arch.univariate.FixedVariance.fo
 or bootstrap, the simulated paths.</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.FixedVariance.forecast-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FixedVariance.forecast-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>VarianceForecast</p>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">arch.univariate.volatility.VarianceForecast</span></code></p>
 </dd>
 <dt class="field-even" id="arch.univariate.FixedVariance.forecast-raises">Raises<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FixedVariance.forecast-raises" title="Permalink to this headline">¶</a></dt>
 <dd class="field-even"><ul class="simple">
@@ -1555,7 +1555,7 @@ <h1 id="arch-univariate-fixedvariance-forecast">arch.univariate.FixedVariance.fo
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1602,7 +1602,7 @@ <h1 id="arch-univariate-fixedvariance-forecast">arch.univariate.FixedVariance.fo
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diff --git a/univariate/generated/generated/arch.univariate.FixedVariance.name.html b/univariate/generated/generated/arch.univariate.FixedVariance.name.html
index a3d7d934ad..a161c904dd 100644
--- a/univariate/generated/generated/arch.univariate.FixedVariance.name.html
+++ b/univariate/generated/generated/arch.univariate.FixedVariance.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FixedVariance.name - arch 6.1.0</title>
+        <title>arch.univariate.FixedVariance.name - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1208,10 +1208,10 @@
                   
 
 
-<h1 id="arch-univariate-fixedvariance-name">arch.univariate.FixedVariance.name<a class="headerlink" href="#arch-univariate-fixedvariance-name" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-fixedvariance-name">arch.univariate.FixedVariance.name<a class="headerlink" href="#arch-univariate-fixedvariance-name" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.FixedVariance.name">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">FixedVariance.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.univariate.FixedVariance.name" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">FixedVariance.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.univariate.FixedVariance.name" title="Link to this definition">¶</a></dt>
 <dd><p>The name of the volatility process</p>
 </dd></dl>
 
@@ -1282,7 +1282,7 @@ <h1 id="arch-univariate-fixedvariance-name">arch.univariate.FixedVariance.name<a
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1329,7 +1329,7 @@ <h1 id="arch-univariate-fixedvariance-name">arch.univariate.FixedVariance.name<a
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
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diff --git a/univariate/generated/generated/arch.univariate.FixedVariance.num_params.html b/univariate/generated/generated/arch.univariate.FixedVariance.num_params.html
index 2fcd3afdff..5df844d413 100644
--- a/univariate/generated/generated/arch.univariate.FixedVariance.num_params.html
+++ b/univariate/generated/generated/arch.univariate.FixedVariance.num_params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FixedVariance.num_params - arch 6.1.0</title>
+        <title>arch.univariate.FixedVariance.num_params - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1208,10 +1208,10 @@
                   
 
 
-<h1 id="arch-univariate-fixedvariance-num-params">arch.univariate.FixedVariance.num_params<a class="headerlink" href="#arch-univariate-fixedvariance-num-params" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-fixedvariance-num-params">arch.univariate.FixedVariance.num_params<a class="headerlink" href="#arch-univariate-fixedvariance-num-params" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.FixedVariance.num_params">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">FixedVariance.</span></span><span class="sig-name descname"><span class="pre">num_params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.FixedVariance.num_params" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">FixedVariance.</span></span><span class="sig-name descname"><span class="pre">num_params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.FixedVariance.num_params" title="Link to this definition">¶</a></dt>
 <dd><p>The number of parameters in the model</p>
 </dd></dl>
 
@@ -1282,7 +1282,7 @@ <h1 id="arch-univariate-fixedvariance-num-params">arch.univariate.FixedVariance.
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
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@@ -1329,7 +1329,7 @@ <h1 id="arch-univariate-fixedvariance-num-params">arch.univariate.FixedVariance.
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diff --git a/univariate/generated/generated/arch.univariate.FixedVariance.parameter_names.html b/univariate/generated/generated/arch.univariate.FixedVariance.parameter_names.html
index 4b39eb040e..552d7250e5 100644
--- a/univariate/generated/generated/arch.univariate.FixedVariance.parameter_names.html
+++ b/univariate/generated/generated/arch.univariate.FixedVariance.parameter_names.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FixedVariance.parameter_names - arch 6.1.0</title>
+        <title>arch.univariate.FixedVariance.parameter_names - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1248,17 +1248,17 @@
                   
 
 
-<h1 id="arch-univariate-fixedvariance-parameter-names">arch.univariate.FixedVariance.parameter_names<a class="headerlink" href="#arch-univariate-fixedvariance-parameter-names" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-fixedvariance-parameter-names">arch.univariate.FixedVariance.parameter_names<a class="headerlink" href="#arch-univariate-fixedvariance-parameter-names" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.FixedVariance.parameter_names">
-<span class="sig-prename descclassname"><span class="pre">FixedVariance.</span></span><span class="sig-name descname"><span class="pre">parameter_names</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#FixedVariance.parameter_names"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.FixedVariance.parameter_names" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">FixedVariance.</span></span><span class="sig-name descname"><span class="pre">parameter_names</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#FixedVariance.parameter_names"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.FixedVariance.parameter_names" title="Link to this definition">¶</a></dt>
 <dd><p>Names of model parameters</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.FixedVariance.parameter_names-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FixedVariance.parameter_names-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p><strong>names</strong> – Variables names</p>
 </dd>
 <dt class="field-even" id="arch.univariate.FixedVariance.parameter_names-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FixedVariance.parameter_names-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>)</p>
+<dd class="field-even"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span> <span class="pre">(str)</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1330,7 +1330,7 @@ <h1 id="arch-univariate-fixedvariance-parameter-names">arch.univariate.FixedVari
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1377,7 +1377,7 @@ <h1 id="arch-univariate-fixedvariance-parameter-names">arch.univariate.FixedVari
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.FixedVariance.simulate.html b/univariate/generated/generated/arch.univariate.FixedVariance.simulate.html
index 61d6286c44..6aa0cb73b9 100644
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+++ b/univariate/generated/generated/arch.univariate.FixedVariance.simulate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FixedVariance.simulate - arch 6.1.0</title>
+        <title>arch.univariate.FixedVariance.simulate - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -824,13 +824,6 @@
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     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.univariate.FixedVariance.simulate-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
           <li class="md-nav__item">
@@ -1309,13 +1302,6 @@
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-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.univariate.FixedVariance.simulate-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
           <li class="md-nav__item">
@@ -1344,36 +1330,33 @@
                   
 
 
-<h1 id="arch-univariate-fixedvariance-simulate">arch.univariate.FixedVariance.simulate<a class="headerlink" href="#arch-univariate-fixedvariance-simulate" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-fixedvariance-simulate">arch.univariate.FixedVariance.simulate<a class="headerlink" href="#arch-univariate-fixedvariance-simulate" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.FixedVariance.simulate">
-<span class="sig-prename descclassname"><span class="pre">FixedVariance.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.simulate.parameters" title="arch.univariate.FixedVariance.simulate.parameters (Python parameter) — Parameters required to simulate the volatility model"><span class="n"><span class="pre">parameters</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.simulate.nobs" title="arch.univariate.FixedVariance.simulate.nobs (Python parameter) — Number of data points to simulate"><span class="n"><span class="pre">nobs</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.simulate.rng" title="arch.univariate.FixedVariance.simulate.rng (Python parameter) — Callable function that takes a single integer input and returns a vector of random numbers"><span class="n"><span class="pre">rng</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.simulate.burn" title="arch.univariate.FixedVariance.simulate.burn (Python parameter) — Number of additional observations to generate when initializing the simulation"><span class="n"><span class="pre">burn</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.simulate.initial_value" title="arch.univariate.FixedVariance.simulate.initial_value (Python parameter) — Scalar or array of initial values to use when initializing the simulation"><span class="n"><span class="pre">initial_value</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#FixedVariance.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.FixedVariance.simulate" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">FixedVariance.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.simulate.parameters" title="arch.univariate.FixedVariance.simulate.parameters (Python parameter) — Parameters required to simulate the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.simulate.nobs" title="arch.univariate.FixedVariance.simulate.nobs (Python parameter) — Number of data points to simulate"><span class="n"><span class="pre">nobs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.simulate.rng" title="arch.univariate.FixedVariance.simulate.rng (Python parameter) — Callable function that takes a single integer input and returns a vector of random numbers"><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.simulate.burn" title="arch.univariate.FixedVariance.simulate.burn (Python parameter) — Number of additional observations to generate when initializing the simulation"><span class="n"><span class="pre">burn</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.simulate.initial_value" title="arch.univariate.FixedVariance.simulate.initial_value (Python parameter) — Scalar or array of initial values to use when initializing the simulation"><span class="n"><span class="pre">initial_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#FixedVariance.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.FixedVariance.simulate" title="Link to this definition">¶</a></dt>
 <dd><p>Simulate data from the model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.FixedVariance.simulate-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FixedVariance.simulate-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.FixedVariance.simulate.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, Series}</span></span><a class="headerlink" href="#arch.univariate.FixedVariance.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to simulate the volatility model</p>
+<dt id="arch.univariate.FixedVariance.simulate.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.FixedVariance.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to simulate the volatility model</p>
 </dd>
-<dt id="arch.univariate.FixedVariance.simulate.nobs"><span class="sig-name descname">nobs</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.FixedVariance.simulate.nobs" title="Permalink to this definition">¶</a></dt><dd><p>Number of data points to simulate</p>
+<dt id="arch.univariate.FixedVariance.simulate.nobs"><span class="n sig-name">nobs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.FixedVariance.simulate.nobs" title="Permalink to this definition">¶</a></dt><dd><p>Number of data points to simulate</p>
 </dd>
-<dt id="arch.univariate.FixedVariance.simulate.rng"><span class="sig-name descname">rng</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable</span></span><a class="headerlink" href="#arch.univariate.FixedVariance.simulate.rng" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that takes a single integer input and returns
+<dt id="arch.univariate.FixedVariance.simulate.rng"><span class="n sig-name">rng</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)">Callable</a><span class="p">[</span><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">,</span><span class="w"> </span><span class="p">...</span><span class="p">]</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.univariate.FixedVariance.simulate.rng" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that takes a single integer input and returns
 a vector of random numbers</p>
 </dd>
-<dt id="arch.univariate.FixedVariance.simulate.burn"><span class="sig-name descname">burn</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.univariate.FixedVariance.simulate.burn" title="Permalink to this definition">¶</a></dt><dd><p>Number of additional observations to generate when initializing
+<dt id="arch.univariate.FixedVariance.simulate.burn"><span class="n sig-name">burn</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code><a class="headerlink" href="#arch.univariate.FixedVariance.simulate.burn" title="Permalink to this definition">¶</a></dt><dd><p>Number of additional observations to generate when initializing
 the simulation</p>
 </dd>
-<dt id="arch.univariate.FixedVariance.simulate.initial_value"><span class="sig-name descname">initial_value</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}, optional</span></span><a class="headerlink" href="#arch.univariate.FixedVariance.simulate.initial_value" title="Permalink to this definition">¶</a></dt><dd><p>Scalar or array of initial values to use when initializing the
+<dt id="arch.univariate.FixedVariance.simulate.initial_value"><span class="n sig-name">initial_value</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.FixedVariance.simulate.initial_value" title="Permalink to this definition">¶</a></dt><dd><p>Scalar or array of initial values to use when initializing the
 simulation</p>
 </dd>
 </dl>
 </dd>
-<dt class="field-even" id="arch.univariate.FixedVariance.simulate-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FixedVariance.simulate-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>]</p>
-</dd>
-<dt class="field-odd" id="arch.univariate.FixedVariance.simulate-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FixedVariance.simulate-returns" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><ul class="simple">
-<li><p><strong>resids</strong> (<em>ndarray</em>) – The simulated residuals</p></li>
-<li><p><strong>variance</strong> (<em>ndarray</em>) – The simulated variance</p></li>
+<dt class="field-even" id="arch.univariate.FixedVariance.simulate-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FixedVariance.simulate-returns" title="Permalink to this headline">¶</a></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><strong>resids</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – The simulated residuals</p></li>
+<li><p><strong>variance</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – The simulated variance</p></li>
 </ul>
 </p>
 </dd>
@@ -1447,7 +1430,7 @@ <h1 id="arch-univariate-fixedvariance-simulate">arch.univariate.FixedVariance.si
   
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     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
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diff --git a/univariate/generated/generated/arch.univariate.FixedVariance.start.html b/univariate/generated/generated/arch.univariate.FixedVariance.start.html
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+++ b/univariate/generated/generated/arch.univariate.FixedVariance.start.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FixedVariance.start - arch 6.1.0</title>
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@@ -47,8 +47,8 @@
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1208,10 +1208,10 @@
                   
 
 
-<h1 id="arch-univariate-fixedvariance-start">arch.univariate.FixedVariance.start<a class="headerlink" href="#arch-univariate-fixedvariance-start" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-fixedvariance-start">arch.univariate.FixedVariance.start<a class="headerlink" href="#arch-univariate-fixedvariance-start" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.FixedVariance.start">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">FixedVariance.</span></span><span class="sig-name descname"><span class="pre">start</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.FixedVariance.start" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">FixedVariance.</span></span><span class="sig-name descname"><span class="pre">start</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.FixedVariance.start" title="Link to this definition">¶</a></dt>
 <dd><p>Index to use to start variance subarray selection</p>
 </dd></dl>
 
@@ -1282,7 +1282,7 @@ <h1 id="arch-univariate-fixedvariance-start">arch.univariate.FixedVariance.start
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1329,7 +1329,7 @@ <h1 id="arch-univariate-fixedvariance-start">arch.univariate.FixedVariance.start
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diff --git a/univariate/generated/generated/arch.univariate.FixedVariance.starting_values.html b/univariate/generated/generated/arch.univariate.FixedVariance.starting_values.html
index b06819e26a..fdc053cb2a 100644
--- a/univariate/generated/generated/arch.univariate.FixedVariance.starting_values.html
+++ b/univariate/generated/generated/arch.univariate.FixedVariance.starting_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FixedVariance.starting_values - arch 6.1.0</title>
+        <title>arch.univariate.FixedVariance.starting_values - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1288,15 +1288,15 @@
                   
 
 
-<h1 id="arch-univariate-fixedvariance-starting-values">arch.univariate.FixedVariance.starting_values<a class="headerlink" href="#arch-univariate-fixedvariance-starting-values" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-fixedvariance-starting-values">arch.univariate.FixedVariance.starting_values<a class="headerlink" href="#arch-univariate-fixedvariance-starting-values" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.FixedVariance.starting_values">
-<span class="sig-prename descclassname"><span class="pre">FixedVariance.</span></span><span class="sig-name descname"><span class="pre">starting_values</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.starting_values.resids" title="arch.univariate.FixedVariance.starting_values.resids (Python parameter) — Array of (approximate) residuals to use when computing starting values"><span class="n"><span class="pre">resids</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#FixedVariance.starting_values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.FixedVariance.starting_values" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">FixedVariance.</span></span><span class="sig-name descname"><span class="pre">starting_values</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.starting_values.resids" title="arch.univariate.FixedVariance.starting_values.resids (Python parameter) — Array of (approximate) residuals to use when computing starting values"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#FixedVariance.starting_values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.FixedVariance.starting_values" title="Link to this definition">¶</a></dt>
 <dd><p>Returns starting values for the ARCH model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.FixedVariance.starting_values-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FixedVariance.starting_values-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.FixedVariance.starting_values.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.FixedVariance.starting_values.resids" title="Permalink to this definition">¶</a></dt><dd><p>Array of (approximate) residuals to use when computing starting
+<dt id="arch.univariate.FixedVariance.starting_values.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.FixedVariance.starting_values.resids" title="Permalink to this definition">¶</a></dt><dd><p>Array of (approximate) residuals to use when computing starting
 values</p>
 </dd>
 </dl>
@@ -1305,7 +1305,7 @@ <h1 id="arch-univariate-fixedvariance-starting-values">arch.univariate.FixedVari
 <dd class="field-even"><p><strong>sv</strong> – Array of starting values</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.FixedVariance.starting_values-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FixedVariance.starting_values-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1377,7 +1377,7 @@ <h1 id="arch-univariate-fixedvariance-starting-values">arch.univariate.FixedVari
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1424,7 +1424,7 @@ <h1 id="arch-univariate-fixedvariance-starting-values">arch.univariate.FixedVari
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.FixedVariance.stop.html b/univariate/generated/generated/arch.univariate.FixedVariance.stop.html
index cedb3cf242..7bb60b5d2b 100644
--- a/univariate/generated/generated/arch.univariate.FixedVariance.stop.html
+++ b/univariate/generated/generated/arch.univariate.FixedVariance.stop.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FixedVariance.stop - arch 6.1.0</title>
+        <title>arch.univariate.FixedVariance.stop - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1208,10 +1208,10 @@
                   
 
 
-<h1 id="arch-univariate-fixedvariance-stop">arch.univariate.FixedVariance.stop<a class="headerlink" href="#arch-univariate-fixedvariance-stop" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-fixedvariance-stop">arch.univariate.FixedVariance.stop<a class="headerlink" href="#arch-univariate-fixedvariance-stop" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.FixedVariance.stop">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">FixedVariance.</span></span><span class="sig-name descname"><span class="pre">stop</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.FixedVariance.stop" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">FixedVariance.</span></span><span class="sig-name descname"><span class="pre">stop</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.FixedVariance.stop" title="Link to this definition">¶</a></dt>
 <dd><p>Index to use to stop variance subarray selection</p>
 </dd></dl>
 
@@ -1282,7 +1282,7 @@ <h1 id="arch-univariate-fixedvariance-stop">arch.univariate.FixedVariance.stop<a
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1329,7 +1329,7 @@ <h1 id="arch-univariate-fixedvariance-stop">arch.univariate.FixedVariance.stop<a
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.FixedVariance.update.html b/univariate/generated/generated/arch.univariate.FixedVariance.update.html
index 5fc83a604f..77df0fb0ef 100644
--- a/univariate/generated/generated/arch.univariate.FixedVariance.update.html
+++ b/univariate/generated/generated/arch.univariate.FixedVariance.update.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FixedVariance.update - arch 6.1.0</title>
+        <title>arch.univariate.FixedVariance.update - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1358,28 +1358,28 @@
                   
 
 
-<h1 id="arch-univariate-fixedvariance-update">arch.univariate.FixedVariance.update<a class="headerlink" href="#arch-univariate-fixedvariance-update" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-fixedvariance-update">arch.univariate.FixedVariance.update<a class="headerlink" href="#arch-univariate-fixedvariance-update" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.FixedVariance.update">
-<span class="sig-prename descclassname"><span class="pre">FixedVariance.</span></span><span class="sig-name descname"><span class="pre">update</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.update.index" title="arch.univariate.FixedVariance.update.index (Python parameter) — The numerical index of the variance to compute"><span class="n"><span class="pre">index</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.update.parameters" title="arch.univariate.FixedVariance.update.parameters (Python parameter) — The variance model parameters"><span class="n"><span class="pre">parameters</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.update.resids" title="arch.univariate.FixedVariance.update.resids (Python parameter) — The residual array. Only uses resids[:index] when computing sigma2[index]"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.update.sigma2" title="arch.univariate.FixedVariance.update.sigma2 (Python parameter) — The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."><span class="n"><span class="pre">sigma2</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.update.backcast" title="arch.univariate.FixedVariance.update.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.update.var_bounds" title="arch.univariate.FixedVariance.update.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.FixedVariance.update" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">FixedVariance.</span></span><span class="sig-name descname"><span class="pre">update</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.update.index" title="arch.univariate.FixedVariance.update.index (Python parameter) — The numerical index of the variance to compute"><span class="n"><span class="pre">index</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.update.parameters" title="arch.univariate.FixedVariance.update.parameters (Python parameter) — The variance model parameters"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.update.resids" title="arch.univariate.FixedVariance.update.resids (Python parameter) — The residual array. Only uses resids[:index] when computing sigma2[index]"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.update.sigma2" title="arch.univariate.FixedVariance.update.sigma2 (Python parameter) — The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."><span class="n"><span class="pre">sigma2</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.update.backcast" title="arch.univariate.FixedVariance.update.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.update.var_bounds" title="arch.univariate.FixedVariance.update.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span></span><a class="headerlink" href="#arch.univariate.FixedVariance.update" title="Link to this definition">¶</a></dt>
 <dd><p>Compute the variance for a single observation</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.FixedVariance.update-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FixedVariance.update-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.FixedVariance.update.index"><span class="sig-name descname">index</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.FixedVariance.update.index" title="Permalink to this definition">¶</a></dt><dd><p>The numerical index of the variance to compute</p>
+<dt id="arch.univariate.FixedVariance.update.index"><span class="n sig-name">index</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.FixedVariance.update.index" title="Permalink to this definition">¶</a></dt><dd><p>The numerical index of the variance to compute</p>
 </dd>
-<dt id="arch.univariate.FixedVariance.update.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.FixedVariance.update.parameters" title="Permalink to this definition">¶</a></dt><dd><p>The variance model parameters</p>
+<dt id="arch.univariate.FixedVariance.update.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.FixedVariance.update.parameters" title="Permalink to this definition">¶</a></dt><dd><p>The variance model parameters</p>
 </dd>
-<dt id="arch.univariate.FixedVariance.update.resids"><span class="n sig-name">resids</span><a class="headerlink" href="#arch.univariate.FixedVariance.update.resids" title="Permalink to this definition">¶</a></dt><dd><p>The residual array. Only uses <code class="docutils literal notranslate"><span class="pre">resids[:index]</span></code> when computing
+<dt id="arch.univariate.FixedVariance.update.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.FixedVariance.update.resids" title="Permalink to this definition">¶</a></dt><dd><p>The residual array. Only uses <code class="docutils literal notranslate"><span class="pre">resids[:index]</span></code> when computing
 <code class="docutils literal notranslate"><span class="pre">sigma2[index]</span></code></p>
 </dd>
-<dt id="arch.univariate.FixedVariance.update.sigma2"><span class="sig-name descname">sigma2</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.FixedVariance.update.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>The array containing the variances. Only uses <code class="docutils literal notranslate"><span class="pre">sigma2[:index]</span></code>
+<dt id="arch.univariate.FixedVariance.update.sigma2"><span class="n sig-name">sigma2</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.FixedVariance.update.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>The array containing the variances. Only uses <code class="docutils literal notranslate"><span class="pre">sigma2[:index]</span></code>
 when computing <code class="docutils literal notranslate"><span class="pre">sigma2[index]</span></code>. The computed value is stored
 in <code class="docutils literal notranslate"><span class="pre">sigma2[index]</span></code>.</p>
 </dd>
-<dt id="arch.univariate.FixedVariance.update.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}</span></span><a class="headerlink" href="#arch.univariate.FixedVariance.update.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing the recursion</p>
+<dt id="arch.univariate.FixedVariance.update.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.FixedVariance.update.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing the recursion</p>
 </dd>
-<dt id="arch.univariate.FixedVariance.update.var_bounds"><span class="sig-name descname">var_bounds</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.FixedVariance.update.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
+<dt id="arch.univariate.FixedVariance.update.var_bounds"><span class="n sig-name">var_bounds</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.FixedVariance.update.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
 </dd>
 </dl>
 </dd>
@@ -1387,7 +1387,7 @@ <h1 id="arch-univariate-fixedvariance-update">arch.univariate.FixedVariance.upda
 <dd class="field-even"><p>The variance computed for location <code class="docutils literal notranslate"><span class="pre">index</span></code></p>
 </dd>
 <dt class="field-odd" id="arch.univariate.FixedVariance.update-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FixedVariance.update-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1459,7 +1459,7 @@ <h1 id="arch-univariate-fixedvariance-update">arch.univariate.FixedVariance.upda
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
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@@ -1506,7 +1506,7 @@ <h1 id="arch-univariate-fixedvariance-update">arch.univariate.FixedVariance.upda
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diff --git a/univariate/generated/generated/arch.univariate.FixedVariance.updateable.html b/univariate/generated/generated/arch.univariate.FixedVariance.updateable.html
index cbb7e2fe43..8fadaef416 100644
--- a/univariate/generated/generated/arch.univariate.FixedVariance.updateable.html
+++ b/univariate/generated/generated/arch.univariate.FixedVariance.updateable.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FixedVariance.updateable - arch 6.1.0</title>
+        <title>arch.univariate.FixedVariance.updateable - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1208,10 +1208,10 @@
                   
 
 
-<h1 id="arch-univariate-fixedvariance-updateable">arch.univariate.FixedVariance.updateable<a class="headerlink" href="#arch-univariate-fixedvariance-updateable" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-fixedvariance-updateable">arch.univariate.FixedVariance.updateable<a class="headerlink" href="#arch-univariate-fixedvariance-updateable" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.FixedVariance.updateable">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">FixedVariance.</span></span><span class="sig-name descname"><span class="pre">updateable</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.univariate.FixedVariance.updateable" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">FixedVariance.</span></span><span class="sig-name descname"><span class="pre">updateable</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.univariate.FixedVariance.updateable" title="Link to this definition">¶</a></dt>
 <dd><p>Flag indicating that the volatility process supports update</p>
 </dd></dl>
 
@@ -1282,7 +1282,7 @@ <h1 id="arch-univariate-fixedvariance-updateable">arch.univariate.FixedVariance.
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1329,7 +1329,7 @@ <h1 id="arch-univariate-fixedvariance-updateable">arch.univariate.FixedVariance.
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diff --git a/univariate/generated/generated/arch.univariate.FixedVariance.variance_bounds.html b/univariate/generated/generated/arch.univariate.FixedVariance.variance_bounds.html
index 35a9523bbb..18126251d6 100644
--- a/univariate/generated/generated/arch.univariate.FixedVariance.variance_bounds.html
+++ b/univariate/generated/generated/arch.univariate.FixedVariance.variance_bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FixedVariance.variance_bounds - arch 6.1.0</title>
+        <title>arch.univariate.FixedVariance.variance_bounds - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1302,20 +1302,20 @@
                   
 
 
-<h1 id="arch-univariate-fixedvariance-variance-bounds">arch.univariate.FixedVariance.variance_bounds<a class="headerlink" href="#arch-univariate-fixedvariance-variance-bounds" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-fixedvariance-variance-bounds">arch.univariate.FixedVariance.variance_bounds<a class="headerlink" href="#arch-univariate-fixedvariance-variance-bounds" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.FixedVariance.variance_bounds">
-<span class="sig-prename descclassname"><span class="pre">FixedVariance.</span></span><span class="sig-name descname"><span class="pre">variance_bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.variance_bounds.resids" title="arch.univariate.FixedVariance.variance_bounds.resids (Python parameter) — Approximate residuals to use to compute the lower and upper bounds on the conditional variance"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.variance_bounds.power" title="arch.univariate.FixedVariance.variance_bounds.power (Python parameter) — Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."><span class="n"><span class="pre">power</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">2.0</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.FixedVariance.variance_bounds" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.FixedVariance.variance_bounds">
+<span class="sig-prename descclassname"><span class="pre">FixedVariance.</span></span><span class="sig-name descname"><span class="pre">variance_bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.variance_bounds.resids" title="arch.univariate.FixedVariance.variance_bounds.resids (Python parameter) — Approximate residuals to use to compute the lower and upper bounds on the conditional variance"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.variance_bounds.power" title="arch.univariate.FixedVariance.variance_bounds.power (Python parameter) — Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."><span class="n"><span class="pre">power</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">2.0</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.univariate.FixedVariance.variance_bounds" title="Link to this definition">¶</a></dt>
 <dd><p>Construct loose bounds for conditional variances.</p>
 <p>These bounds are used in parameter estimation to ensure
 that the log-likelihood does not produce NaN values.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.FixedVariance.variance_bounds-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FixedVariance.variance_bounds-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.FixedVariance.variance_bounds.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.FixedVariance.variance_bounds.resids" title="Permalink to this definition">¶</a></dt><dd><p>Approximate residuals to use to compute the lower and upper bounds
+<dt id="arch.univariate.FixedVariance.variance_bounds.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.FixedVariance.variance_bounds.resids" title="Permalink to this definition">¶</a></dt><dd><p>Approximate residuals to use to compute the lower and upper bounds
 on the conditional variance</p>
 </dd>
-<dt id="arch.univariate.FixedVariance.variance_bounds.power"><span class="sig-name descname">power</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>, optional</span></span><a class="headerlink" href="#arch.univariate.FixedVariance.variance_bounds.power" title="Permalink to this definition">¶</a></dt><dd><p>Power used in the model. 2.0, the default corresponds to standard
+<dt id="arch.univariate.FixedVariance.variance_bounds.power"><span class="n sig-name">power</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">2.0</span></code><a class="headerlink" href="#arch.univariate.FixedVariance.variance_bounds.power" title="Permalink to this definition">¶</a></dt><dd><p>Power used in the model. 2.0, the default corresponds to standard
 ARCH models that evolve in squares.</p>
 </dd>
 </dl>
@@ -1325,7 +1325,7 @@ <h1 id="arch-univariate-fixedvariance-variance-bounds">arch.univariate.FixedVari
 number of elements as resids</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.FixedVariance.variance_bounds-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FixedVariance.variance_bounds-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1397,7 +1397,7 @@ <h1 id="arch-univariate-fixedvariance-variance-bounds">arch.univariate.FixedVari
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1444,7 +1444,7 @@ <h1 id="arch-univariate-fixedvariance-variance-bounds">arch.univariate.FixedVari
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.FixedVariance.volatility_updater.html b/univariate/generated/generated/arch.univariate.FixedVariance.volatility_updater.html
index fa32250fc3..042d9c5b9a 100644
--- a/univariate/generated/generated/arch.univariate.FixedVariance.volatility_updater.html
+++ b/univariate/generated/generated/arch.univariate.FixedVariance.volatility_updater.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FixedVariance.volatility_updater - arch 6.1.0</title>
+        <title>arch.univariate.FixedVariance.volatility_updater - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1262,17 +1262,17 @@
                   
 
 
-<h1 id="arch-univariate-fixedvariance-volatility-updater">arch.univariate.FixedVariance.volatility_updater<a class="headerlink" href="#arch-univariate-fixedvariance-volatility-updater" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-fixedvariance-volatility-updater">arch.univariate.FixedVariance.volatility_updater<a class="headerlink" href="#arch-univariate-fixedvariance-volatility-updater" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.FixedVariance.volatility_updater">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">FixedVariance.</span></span><span class="sig-name descname"><span class="pre">volatility_updater</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.univariate.recursions_python.VolatilityUpdater.html#arch.univariate.recursions_python.VolatilityUpdater" title="arch.univariate.recursions_python.VolatilityUpdater (Python class) — Base class that all volatility updaters must inherit from."><span class="pre">VolatilityUpdater</span></a></span><a class="headerlink" href="#arch.univariate.FixedVariance.volatility_updater" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">FixedVariance.</span></span><span class="sig-name descname"><span class="pre">volatility_updater</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><span class="pre">VolatilityUpdater</span></span><a class="headerlink" href="#arch.univariate.FixedVariance.volatility_updater" title="Link to this definition">¶</a></dt>
 <dd><p>Get the volatility updater associated with the volatility process</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.FixedVariance.volatility_updater-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FixedVariance.volatility_updater-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The updater class</p>
 </dd>
 <dt class="field-even" id="arch.univariate.FixedVariance.volatility_updater-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FixedVariance.volatility_updater-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference internal" href="../arch.univariate.recursions_python.VolatilityUpdater.html#arch.univariate.recursions_python.VolatilityUpdater" title="arch.univariate.recursions_python.VolatilityUpdater (Python class) — Base class that all volatility updaters must inherit from.">VolatilityUpdater</a></p>
+<dd class="field-even"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">VolatilityUpdater</span></code></p>
 </dd>
 <dt class="field-odd" id="arch.univariate.FixedVariance.volatility_updater-raises">Raises<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FixedVariance.volatility_updater-raises" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#NotImplementedError" title="(in Python v3.11)"><strong>NotImplementedError</strong></a> – If the process is not updateable</p>
@@ -1347,7 +1347,7 @@ <h1 id="arch-univariate-fixedvariance-volatility-updater">arch.univariate.FixedV
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1394,7 +1394,7 @@ <h1 id="arch-univariate-fixedvariance-volatility-updater">arch.univariate.FixedV
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.GARCH.backcast.html b/univariate/generated/generated/arch.univariate.GARCH.backcast.html
index a28127a670..94b2687f14 100644
--- a/univariate/generated/generated/arch.univariate.GARCH.backcast.html
+++ b/univariate/generated/generated/arch.univariate.GARCH.backcast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GARCH.backcast - arch 6.1.0</title>
+        <title>arch.univariate.GARCH.backcast - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1248,15 +1248,15 @@
                   
 
 
-<h1 id="arch-univariate-garch-backcast">arch.univariate.GARCH.backcast<a class="headerlink" href="#arch-univariate-garch-backcast" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-garch-backcast">arch.univariate.GARCH.backcast<a class="headerlink" href="#arch-univariate-garch-backcast" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.GARCH.backcast">
-<span class="sig-prename descclassname"><span class="pre">GARCH.</span></span><span class="sig-name descname"><span class="pre">backcast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.backcast.resids" title="arch.univariate.GARCH.backcast.resids (Python parameter) — Vector of (approximate) residuals"><span class="n"><span class="pre">resids</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#GARCH.backcast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.GARCH.backcast" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">GARCH.</span></span><span class="sig-name descname"><span class="pre">backcast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.backcast.resids" title="arch.univariate.GARCH.backcast.resids (Python parameter) — Vector of (approximate) residuals"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#GARCH.backcast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.GARCH.backcast" title="Link to this definition">¶</a></dt>
 <dd><p>Construct values for backcasting to start the recursion</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.GARCH.backcast-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GARCH.backcast-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.GARCH.backcast.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.GARCH.backcast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of (approximate) residuals</p>
+<dt id="arch.univariate.GARCH.backcast.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.GARCH.backcast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of (approximate) residuals</p>
 </dd>
 </dl>
 </dd>
@@ -1264,7 +1264,7 @@ <h1 id="arch-univariate-garch-backcast">arch.univariate.GARCH.backcast<a class="
 <dd class="field-even"><p><strong>backcast</strong> – Value to use in backcasting in the volatility recursion</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.GARCH.backcast-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GARCH.backcast-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1336,7 +1336,7 @@ <h1 id="arch-univariate-garch-backcast">arch.univariate.GARCH.backcast<a class="
   
     Created using
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-    7.0.1.
+    7.2.6.
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@@ -1383,7 +1383,7 @@ <h1 id="arch-univariate-garch-backcast">arch.univariate.GARCH.backcast<a class="
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diff --git a/univariate/generated/generated/arch.univariate.GARCH.backcast_transform.html b/univariate/generated/generated/arch.univariate.GARCH.backcast_transform.html
index 5bbbee1c06..5e3f3378a5 100644
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+++ b/univariate/generated/generated/arch.univariate.GARCH.backcast_transform.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -47,8 +47,8 @@
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
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+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1248,15 +1248,15 @@
                   
 
 
-<h1 id="arch-univariate-garch-backcast-transform">arch.univariate.GARCH.backcast_transform<a class="headerlink" href="#arch-univariate-garch-backcast-transform" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-garch-backcast-transform">arch.univariate.GARCH.backcast_transform<a class="headerlink" href="#arch-univariate-garch-backcast-transform" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.GARCH.backcast_transform">
-<span class="sig-prename descclassname"><span class="pre">GARCH.</span></span><span class="sig-name descname"><span class="pre">backcast_transform</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.backcast_transform.backcast" title="arch.univariate.GARCH.backcast_transform.backcast (Python parameter) — User-provided backcast that approximates sigma2[0]."><span class="n"><span class="pre">backcast</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#GARCH.backcast_transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.GARCH.backcast_transform" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.GARCH.backcast_transform">
+<span class="sig-prename descclassname"><span class="pre">GARCH.</span></span><span class="sig-name descname"><span class="pre">backcast_transform</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.backcast_transform.backcast" title="arch.univariate.GARCH.backcast_transform.backcast (Python parameter) — User-provided backcast that approximates sigma2[0]."><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#GARCH.backcast_transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.GARCH.backcast_transform" title="Link to this definition">¶</a></dt>
 <dd><p>Transformation to apply to user-provided backcast values</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.GARCH.backcast_transform-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GARCH.backcast_transform-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.GARCH.backcast_transform.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}</span></span><a class="headerlink" href="#arch.univariate.GARCH.backcast_transform.backcast" title="Permalink to this definition">¶</a></dt><dd><p>User-provided <code class="docutils literal notranslate"><span class="pre">backcast</span></code> that approximates sigma2[0].</p>
+<dt id="arch.univariate.GARCH.backcast_transform.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.GARCH.backcast_transform.backcast" title="Permalink to this definition">¶</a></dt><dd><p>User-provided <code class="docutils literal notranslate"><span class="pre">backcast</span></code> that approximates sigma2[0].</p>
 </dd>
 </dl>
 </dd>
@@ -1264,7 +1264,7 @@ <h1 id="arch-univariate-garch-backcast-transform">arch.univariate.GARCH.backcast
 <dd class="field-even"><p><strong>backcast</strong> – Backcast transformed to the model-appropriate scale</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.GARCH.backcast_transform-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GARCH.backcast_transform-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>{float, ndarray}</p>
+<dd class="field-odd"><p><code class="docutils literal notranslate"><span class="pre">{float,</span> <span class="pre">ndarray}</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1336,7 +1336,7 @@ <h1 id="arch-univariate-garch-backcast-transform">arch.univariate.GARCH.backcast
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1383,7 +1383,7 @@ <h1 id="arch-univariate-garch-backcast-transform">arch.univariate.GARCH.backcast
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diff --git a/univariate/generated/generated/arch.univariate.GARCH.bounds.html b/univariate/generated/generated/arch.univariate.GARCH.bounds.html
index 48f1300af1..5b3d95bde5 100644
--- a/univariate/generated/generated/arch.univariate.GARCH.bounds.html
+++ b/univariate/generated/generated/arch.univariate.GARCH.bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GARCH.bounds - arch 6.1.0</title>
+        <title>arch.univariate.GARCH.bounds - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
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-    arch 6.1.0
+    arch 6.2.0
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@@ -1248,15 +1248,15 @@
                   
 
 
-<h1 id="arch-univariate-garch-bounds">arch.univariate.GARCH.bounds<a class="headerlink" href="#arch-univariate-garch-bounds" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-garch-bounds">arch.univariate.GARCH.bounds<a class="headerlink" href="#arch-univariate-garch-bounds" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.GARCH.bounds">
-<span class="sig-prename descclassname"><span class="pre">GARCH.</span></span><span class="sig-name descname"><span class="pre">bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.bounds.resids" title="arch.univariate.GARCH.bounds.resids (Python parameter) — Vector of (approximate) residuals"><span class="n"><span class="pre">resids</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#GARCH.bounds"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.GARCH.bounds" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">GARCH.</span></span><span class="sig-name descname"><span class="pre">bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.bounds.resids" title="arch.univariate.GARCH.bounds.resids (Python parameter) — Vector of (approximate) residuals"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#GARCH.bounds"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.GARCH.bounds" title="Link to this definition">¶</a></dt>
 <dd><p>Returns bounds for parameters</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.GARCH.bounds-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GARCH.bounds-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.GARCH.bounds.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.GARCH.bounds.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of (approximate) residuals</p>
+<dt id="arch.univariate.GARCH.bounds.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.GARCH.bounds.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of (approximate) residuals</p>
 </dd>
 </dl>
 </dd>
@@ -1264,7 +1264,7 @@ <h1 id="arch-univariate-garch-bounds">arch.univariate.GARCH.bounds<a class="head
 <dd class="field-even"><p><strong>bounds</strong> – List of bounds where each element is (lower, upper).</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.GARCH.bounds-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GARCH.bounds-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a>[<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a>[<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>,<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>]]</p>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">list[tuple[float,float]]</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1336,7 +1336,7 @@ <h1 id="arch-univariate-garch-bounds">arch.univariate.GARCH.bounds<a class="head
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
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@@ -1383,7 +1383,7 @@ <h1 id="arch-univariate-garch-bounds">arch.univariate.GARCH.bounds<a class="head
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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diff --git a/univariate/generated/generated/arch.univariate.GARCH.compute_variance.html b/univariate/generated/generated/arch.univariate.GARCH.compute_variance.html
index 1040add792..297390ba93 100644
--- a/univariate/generated/generated/arch.univariate.GARCH.compute_variance.html
+++ b/univariate/generated/generated/arch.univariate.GARCH.compute_variance.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GARCH.compute_variance - arch 6.1.0</title>
+        <title>arch.univariate.GARCH.compute_variance - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+    arch 6.2.0
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     <div class="md-nav__source">
@@ -612,13 +612,6 @@
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-        
-          <li class="md-nav__item">
-  <a href="#arch.univariate.GARCH.compute_variance-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
       </ul>
@@ -1262,13 +1255,6 @@
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     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.univariate.GARCH.compute_variance-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
       </ul>
@@ -1290,30 +1276,27 @@
                   
 
 
-<h1 id="arch-univariate-garch-compute-variance">arch.univariate.GARCH.compute_variance<a class="headerlink" href="#arch-univariate-garch-compute-variance" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-garch-compute-variance">arch.univariate.GARCH.compute_variance<a class="headerlink" href="#arch-univariate-garch-compute-variance" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.GARCH.compute_variance">
-<span class="sig-prename descclassname"><span class="pre">GARCH.</span></span><span class="sig-name descname"><span class="pre">compute_variance</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.compute_variance.parameters" title="arch.univariate.GARCH.compute_variance.parameters (Python parameter) — Model parameters"><span class="n"><span class="pre">parameters</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.compute_variance.resids" title="arch.univariate.GARCH.compute_variance.resids (Python parameter) — Vector of mean zero residuals"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.compute_variance.sigma2" title="arch.univariate.GARCH.compute_variance.sigma2 (Python parameter) — Array with same size as resids to store the conditional variance"><span class="n"><span class="pre">sigma2</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.compute_variance.backcast" title="arch.univariate.GARCH.compute_variance.backcast (Python parameter) — Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."><span class="n"><span class="pre">backcast</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.compute_variance.var_bounds" title="arch.univariate.GARCH.compute_variance.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#GARCH.compute_variance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.GARCH.compute_variance" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">GARCH.</span></span><span class="sig-name descname"><span class="pre">compute_variance</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.compute_variance.parameters" title="arch.univariate.GARCH.compute_variance.parameters (Python parameter) — Model parameters"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.compute_variance.resids" title="arch.univariate.GARCH.compute_variance.resids (Python parameter) — Vector of mean zero residuals"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.compute_variance.sigma2" title="arch.univariate.GARCH.compute_variance.sigma2 (Python parameter) — Array with same size as resids to store the conditional variance"><span class="n"><span class="pre">sigma2</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.compute_variance.backcast" title="arch.univariate.GARCH.compute_variance.backcast (Python parameter) — Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.compute_variance.var_bounds" title="arch.univariate.GARCH.compute_variance.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#GARCH.compute_variance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.GARCH.compute_variance" title="Link to this definition">¶</a></dt>
 <dd><p>Compute the variance for the ARCH model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.GARCH.compute_variance-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GARCH.compute_variance-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.GARCH.compute_variance.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.GARCH.compute_variance.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
+<dt id="arch.univariate.GARCH.compute_variance.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.GARCH.compute_variance.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
 </dd>
-<dt id="arch.univariate.GARCH.compute_variance.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.GARCH.compute_variance.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of mean zero residuals</p>
+<dt id="arch.univariate.GARCH.compute_variance.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.GARCH.compute_variance.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of mean zero residuals</p>
 </dd>
-<dt id="arch.univariate.GARCH.compute_variance.sigma2"><span class="sig-name descname">sigma2</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.GARCH.compute_variance.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>Array with same size as resids to store the conditional variance</p>
+<dt id="arch.univariate.GARCH.compute_variance.sigma2"><span class="n sig-name">sigma2</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.GARCH.compute_variance.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>Array with same size as resids to store the conditional variance</p>
 </dd>
-<dt id="arch.univariate.GARCH.compute_variance.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}</span></span><a class="headerlink" href="#arch.univariate.GARCH.compute_variance.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing ARCH recursion. Can be an ndarray
+<dt id="arch.univariate.GARCH.compute_variance.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.GARCH.compute_variance.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing ARCH recursion. Can be an ndarray
 when the model contains multiple components.</p>
 </dd>
-<dt id="arch.univariate.GARCH.compute_variance.var_bounds"><span class="sig-name descname">var_bounds</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.GARCH.compute_variance.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
+<dt id="arch.univariate.GARCH.compute_variance.var_bounds"><span class="n sig-name">var_bounds</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.GARCH.compute_variance.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
 </dd>
 </dl>
 </dd>
-<dt class="field-even" id="arch.univariate.GARCH.compute_variance-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GARCH.compute_variance-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a></p>
-</dd>
 </dl>
 </dd></dl>
 
@@ -1384,7 +1367,7 @@ <h1 id="arch-univariate-garch-compute-variance">arch.univariate.GARCH.compute_va
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1431,7 +1414,7 @@ <h1 id="arch-univariate-garch-compute-variance">arch.univariate.GARCH.compute_va
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diff --git a/univariate/generated/generated/arch.univariate.GARCH.constraints.html b/univariate/generated/generated/arch.univariate.GARCH.constraints.html
index a5b911c9bc..900fe4bab3 100644
--- a/univariate/generated/generated/arch.univariate.GARCH.constraints.html
+++ b/univariate/generated/generated/arch.univariate.GARCH.constraints.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GARCH.constraints - arch 6.1.0</title>
+        <title>arch.univariate.GARCH.constraints - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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+            arch 6.2.0
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-  
-</li>
-        
           <li class="md-nav__item">
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@@ -1175,13 +1168,6 @@
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       <ul class="md-nav__list">
         
-          <li class="md-nav__item">
-  <a href="#arch.univariate.GARCH.constraints-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
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-  
-</li>
-        
           <li class="md-nav__item">
   <a href="#arch.univariate.GARCH.constraints-returns" class="md-nav__link">
     <span class="md-ellipsis">Returns</span>
@@ -1208,20 +1194,17 @@
                   
 
 
-<h1 id="arch-univariate-garch-constraints">arch.univariate.GARCH.constraints<a class="headerlink" href="#arch-univariate-garch-constraints" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-garch-constraints">arch.univariate.GARCH.constraints<a class="headerlink" href="#arch-univariate-garch-constraints" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.GARCH.constraints">
-<span class="sig-prename descclassname"><span class="pre">GARCH.</span></span><span class="sig-name descname"><span class="pre">constraints</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#GARCH.constraints"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.GARCH.constraints" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">GARCH.</span></span><span class="sig-name descname"><span class="pre">constraints</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#GARCH.constraints"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.GARCH.constraints" title="Link to this definition">¶</a></dt>
 <dd><p>Construct parameter constraints arrays for parameter estimation</p>
 <dl class="field-list simple">
-<dt class="field-odd" id="arch.univariate.GARCH.constraints-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GARCH.constraints-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>]</p>
-</dd>
-<dt class="field-even" id="arch.univariate.GARCH.constraints-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GARCH.constraints-returns" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><ul class="simple">
-<li><p><strong>A</strong> (<em>ndarray</em>) – Parameters loadings in constraint. Shape is number of constraints
+<dt class="field-odd" id="arch.univariate.GARCH.constraints-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GARCH.constraints-returns" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p><strong>A</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – Parameters loadings in constraint. Shape is number of constraints
 by number of parameters</p></li>
-<li><p><strong>b</strong> (<em>ndarray</em>) – Constraint values, one for each constraint</p></li>
+<li><p><strong>b</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – Constraint values, one for each constraint</p></li>
 </ul>
 </p>
 </dd>
@@ -1298,7 +1281,7 @@ <h1 id="arch-univariate-garch-constraints">arch.univariate.GARCH.constraints<a c
   
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@@ -1345,7 +1328,7 @@ <h1 id="arch-univariate-garch-constraints">arch.univariate.GARCH.constraints<a c
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diff --git a/univariate/generated/generated/arch.univariate.GARCH.forecast.html b/univariate/generated/generated/arch.univariate.GARCH.forecast.html
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@@ -47,8 +47,8 @@
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -1388,38 +1388,38 @@
                   
 
 
-<h1 id="arch-univariate-garch-forecast">arch.univariate.GARCH.forecast<a class="headerlink" href="#arch-univariate-garch-forecast" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-garch-forecast">arch.univariate.GARCH.forecast<a class="headerlink" href="#arch-univariate-garch-forecast" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.GARCH.forecast">
-<span class="sig-prename descclassname"><span class="pre">GARCH.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.forecast.parameters" title="arch.univariate.GARCH.forecast.parameters (Python parameter) — Parameters required to forecast the volatility model"><span class="n"><span class="pre">parameters</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.forecast.resids" title="arch.univariate.GARCH.forecast.resids (Python parameter) — Residuals to use in the recursion"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.forecast.backcast" title="arch.univariate.GARCH.forecast.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.forecast.var_bounds" title="arch.univariate.GARCH.forecast.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.forecast.start" title="arch.univariate.GARCH.forecast.start (Python parameter) — Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."><span class="n"><span class="pre">start</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.forecast.horizon" title="arch.univariate.GARCH.forecast.horizon (Python parameter) — Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."><span class="n"><span class="pre">horizon</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.forecast.method" title="arch.univariate.GARCH.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic."><span class="n"><span class="pre">method</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.forecast.simulations" title="arch.univariate.GARCH.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.forecast.rng" title="arch.univariate.GARCH.forecast.rng (Python parameter) — Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."><span class="n"><span class="pre">rng</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.forecast.random_state" title="arch.univariate.GARCH.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.GARCH.forecast" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">GARCH.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.forecast.parameters" title="arch.univariate.GARCH.forecast.parameters (Python parameter) — Parameters required to forecast the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.forecast.resids" title="arch.univariate.GARCH.forecast.resids (Python parameter) — Residuals to use in the recursion"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.forecast.backcast" title="arch.univariate.GARCH.forecast.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.forecast.var_bounds" title="arch.univariate.GARCH.forecast.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.forecast.start" title="arch.univariate.GARCH.forecast.start (Python parameter) — Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.forecast.horizon" title="arch.univariate.GARCH.forecast.horizon (Python parameter) — Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.forecast.method" title="arch.univariate.GARCH.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.forecast.simulations" title="arch.univariate.GARCH.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.forecast.rng" title="arch.univariate.GARCH.forecast.rng (Python parameter) — Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.forecast.random_state" title="arch.univariate.GARCH.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><span class="pre">VarianceForecast</span></span></span></span><a class="headerlink" href="#arch.univariate.GARCH.forecast" title="Link to this definition">¶</a></dt>
 <dd><p>Forecast volatility from the model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.GARCH.forecast-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GARCH.forecast-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.GARCH.forecast.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, Series}</span></span><a class="headerlink" href="#arch.univariate.GARCH.forecast.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast the volatility model</p>
+<dt id="arch.univariate.GARCH.forecast.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.GARCH.forecast.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast the volatility model</p>
 </dd>
-<dt id="arch.univariate.GARCH.forecast.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.GARCH.forecast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Residuals to use in the recursion</p>
+<dt id="arch.univariate.GARCH.forecast.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.GARCH.forecast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Residuals to use in the recursion</p>
 </dd>
-<dt id="arch.univariate.GARCH.forecast.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><a class="headerlink" href="#arch.univariate.GARCH.forecast.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing the recursion</p>
+<dt id="arch.univariate.GARCH.forecast.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><a class="headerlink" href="#arch.univariate.GARCH.forecast.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing the recursion</p>
 </dd>
-<dt id="arch.univariate.GARCH.forecast.var_bounds"><span class="sig-name descname">var_bounds</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray, 2-d</span></span><a class="headerlink" href="#arch.univariate.GARCH.forecast.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
+<dt id="arch.univariate.GARCH.forecast.var_bounds"><span class="n sig-name">var_bounds</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.GARCH.forecast.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
 </dd>
-<dt id="arch.univariate.GARCH.forecast.start"><span class="sig-name descname">start</span><span class="p"> : </span><span class="desctype"><span class="desctype">{None, int}</span></span><a class="headerlink" href="#arch.univariate.GARCH.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>Index of the first observation to use as the starting point for
+<dt id="arch.univariate.GARCH.forecast.start"><span class="n sig-name">start</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.GARCH.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>Index of the first observation to use as the starting point for
 the forecast.  Default is len(resids).</p>
 </dd>
-<dt id="arch.univariate.GARCH.forecast.horizon"><span class="sig-name descname">horizon</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.GARCH.forecast.horizon" title="Permalink to this definition">¶</a></dt><dd><p>Forecast horizon.  Must be 1 or larger.  Forecasts are produced
+<dt id="arch.univariate.GARCH.forecast.horizon"><span class="n sig-name">horizon</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code><a class="headerlink" href="#arch.univariate.GARCH.forecast.horizon" title="Permalink to this definition">¶</a></dt><dd><p>Forecast horizon.  Must be 1 or larger.  Forecasts are produced
 for horizons in [1, horizon].</p>
 </dd>
-<dt id="arch.univariate.GARCH.forecast.method"><span class="sig-name descname">method</span><span class="p"> : </span><span class="desctype"><span class="desctype">{'analytic', 'simulation', 'bootstrap'}</span></span><a class="headerlink" href="#arch.univariate.GARCH.forecast.method" title="Permalink to this definition">¶</a></dt><dd><p>Method to use when producing the forecast. The default is analytic.</p>
+<dt id="arch.univariate.GARCH.forecast.method"><span class="n sig-name">method</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'analytic'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'simulation'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'bootstrap'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code><a class="headerlink" href="#arch.univariate.GARCH.forecast.method" title="Permalink to this definition">¶</a></dt><dd><p>Method to use when producing the forecast. The default is analytic.</p>
 </dd>
-<dt id="arch.univariate.GARCH.forecast.simulations"><span class="sig-name descname">simulations</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.GARCH.forecast.simulations" title="Permalink to this definition">¶</a></dt><dd><p>Number of simulations to run when computing the forecast using
+<dt id="arch.univariate.GARCH.forecast.simulations"><span class="n sig-name">simulations</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.univariate.GARCH.forecast.simulations" title="Permalink to this definition">¶</a></dt><dd><p>Number of simulations to run when computing the forecast using
 either simulation or bootstrap.</p>
 </dd>
-<dt id="arch.univariate.GARCH.forecast.rng"><span class="sig-name descname">rng</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable</span></span><a class="headerlink" href="#arch.univariate.GARCH.forecast.rng" title="Permalink to this definition">¶</a></dt><dd><p>Callable random number generator required if method is
+<dt id="arch.univariate.GARCH.forecast.rng"><span class="n sig-name">rng</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)">Callable</a><span class="p">[</span><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">,</span><span class="w"> </span><span class="p">...</span><span class="p">]</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.GARCH.forecast.rng" title="Permalink to this definition">¶</a></dt><dd><p>Callable random number generator required if method is
 ‘simulation’. Must take a single shape input and return random
 samples numbers with that shape.</p>
 </dd>
-<dt id="arch.univariate.GARCH.forecast.random_state"><span class="sig-name descname">random_state</span><span class="p"> : </span><span class="desctype"><span class="desctype">RandomState, optional</span></span><a class="headerlink" href="#arch.univariate.GARCH.forecast.random_state" title="Permalink to this definition">¶</a></dt><dd><p>NumPy RandomState instance to use when method is ‘bootstrap’</p>
+<dt id="arch.univariate.GARCH.forecast.random_state"><span class="n sig-name">random_state</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)">RandomState</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.GARCH.forecast.random_state" title="Permalink to this definition">¶</a></dt><dd><p>NumPy RandomState instance to use when method is ‘bootstrap’</p>
 </dd>
 </dl>
 </dd>
@@ -1428,7 +1428,7 @@ <h1 id="arch-univariate-garch-forecast">arch.univariate.GARCH.forecast<a class="
 or bootstrap, the simulated paths.</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.GARCH.forecast-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GARCH.forecast-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>VarianceForecast</p>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">arch.univariate.volatility.VarianceForecast</span></code></p>
 </dd>
 <dt class="field-even" id="arch.univariate.GARCH.forecast-raises">Raises<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GARCH.forecast-raises" title="Permalink to this headline">¶</a></dt>
 <dd class="field-even"><ul class="simple">
@@ -1515,7 +1515,7 @@ <h1 id="arch-univariate-garch-forecast">arch.univariate.GARCH.forecast<a class="
   
     Created using
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+    7.2.6.
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@@ -1562,7 +1562,7 @@ <h1 id="arch-univariate-garch-forecast">arch.univariate.GARCH.forecast<a class="
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diff --git a/univariate/generated/generated/arch.univariate.GARCH.name.html b/univariate/generated/generated/arch.univariate.GARCH.name.html
index 44225ec1c3..b13c0b72c2 100644
--- a/univariate/generated/generated/arch.univariate.GARCH.name.html
+++ b/univariate/generated/generated/arch.univariate.GARCH.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GARCH.name - arch 6.1.0</title>
+        <title>arch.univariate.GARCH.name - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1168,10 +1168,10 @@
                   
 
 
-<h1 id="arch-univariate-garch-name">arch.univariate.GARCH.name<a class="headerlink" href="#arch-univariate-garch-name" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-garch-name">arch.univariate.GARCH.name<a class="headerlink" href="#arch-univariate-garch-name" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.GARCH.name">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">GARCH.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.univariate.GARCH.name" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">GARCH.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.univariate.GARCH.name" title="Link to this definition">¶</a></dt>
 <dd><p>The name of the volatility process</p>
 </dd></dl>
 
@@ -1242,7 +1242,7 @@ <h1 id="arch-univariate-garch-name">arch.univariate.GARCH.name<a class="headerli
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1289,7 +1289,7 @@ <h1 id="arch-univariate-garch-name">arch.univariate.GARCH.name<a class="headerli
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.GARCH.num_params.html b/univariate/generated/generated/arch.univariate.GARCH.num_params.html
index 8a1dd8e7f7..47a3c23482 100644
--- a/univariate/generated/generated/arch.univariate.GARCH.num_params.html
+++ b/univariate/generated/generated/arch.univariate.GARCH.num_params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GARCH.num_params - arch 6.1.0</title>
+        <title>arch.univariate.GARCH.num_params - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1168,10 +1168,10 @@
                   
 
 
-<h1 id="arch-univariate-garch-num-params">arch.univariate.GARCH.num_params<a class="headerlink" href="#arch-univariate-garch-num-params" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-garch-num-params">arch.univariate.GARCH.num_params<a class="headerlink" href="#arch-univariate-garch-num-params" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.GARCH.num_params">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">GARCH.</span></span><span class="sig-name descname"><span class="pre">num_params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.GARCH.num_params" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">GARCH.</span></span><span class="sig-name descname"><span class="pre">num_params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.GARCH.num_params" title="Link to this definition">¶</a></dt>
 <dd><p>The number of parameters in the model</p>
 </dd></dl>
 
@@ -1242,7 +1242,7 @@ <h1 id="arch-univariate-garch-num-params">arch.univariate.GARCH.num_params<a cla
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1289,7 +1289,7 @@ <h1 id="arch-univariate-garch-num-params">arch.univariate.GARCH.num_params<a cla
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.GARCH.parameter_names.html b/univariate/generated/generated/arch.univariate.GARCH.parameter_names.html
index 79904cfb6e..32e30919a7 100644
--- a/univariate/generated/generated/arch.univariate.GARCH.parameter_names.html
+++ b/univariate/generated/generated/arch.univariate.GARCH.parameter_names.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GARCH.parameter_names - arch 6.1.0</title>
+        <title>arch.univariate.GARCH.parameter_names - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1208,17 +1208,17 @@
                   
 
 
-<h1 id="arch-univariate-garch-parameter-names">arch.univariate.GARCH.parameter_names<a class="headerlink" href="#arch-univariate-garch-parameter-names" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-garch-parameter-names">arch.univariate.GARCH.parameter_names<a class="headerlink" href="#arch-univariate-garch-parameter-names" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.GARCH.parameter_names">
-<span class="sig-prename descclassname"><span class="pre">GARCH.</span></span><span class="sig-name descname"><span class="pre">parameter_names</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#GARCH.parameter_names"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.GARCH.parameter_names" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">GARCH.</span></span><span class="sig-name descname"><span class="pre">parameter_names</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#GARCH.parameter_names"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.GARCH.parameter_names" title="Link to this definition">¶</a></dt>
 <dd><p>Names of model parameters</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.GARCH.parameter_names-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GARCH.parameter_names-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p><strong>names</strong> – Variables names</p>
 </dd>
 <dt class="field-even" id="arch.univariate.GARCH.parameter_names-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GARCH.parameter_names-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>)</p>
+<dd class="field-even"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span> <span class="pre">(str)</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1290,7 +1290,7 @@ <h1 id="arch-univariate-garch-parameter-names">arch.univariate.GARCH.parameter_n
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1337,7 +1337,7 @@ <h1 id="arch-univariate-garch-parameter-names">arch.univariate.GARCH.parameter_n
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.GARCH.simulate.html b/univariate/generated/generated/arch.univariate.GARCH.simulate.html
index 5cc819fc67..2ae91ed40c 100644
--- a/univariate/generated/generated/arch.univariate.GARCH.simulate.html
+++ b/univariate/generated/generated/arch.univariate.GARCH.simulate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GARCH.simulate - arch 6.1.0</title>
+        <title>arch.univariate.GARCH.simulate - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -672,13 +672,6 @@
       </ul>
     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.univariate.GARCH.simulate-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
           <li class="md-nav__item">
@@ -1269,13 +1262,6 @@
       </ul>
     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.univariate.GARCH.simulate-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
           <li class="md-nav__item">
@@ -1304,36 +1290,33 @@
                   
 
 
-<h1 id="arch-univariate-garch-simulate">arch.univariate.GARCH.simulate<a class="headerlink" href="#arch-univariate-garch-simulate" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-garch-simulate">arch.univariate.GARCH.simulate<a class="headerlink" href="#arch-univariate-garch-simulate" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.GARCH.simulate">
-<span class="sig-prename descclassname"><span class="pre">GARCH.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.simulate.parameters" title="arch.univariate.GARCH.simulate.parameters (Python parameter) — Parameters required to simulate the volatility model"><span class="n"><span class="pre">parameters</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.simulate.nobs" title="arch.univariate.GARCH.simulate.nobs (Python parameter) — Number of data points to simulate"><span class="n"><span class="pre">nobs</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.simulate.rng" title="arch.univariate.GARCH.simulate.rng (Python parameter) — Callable function that takes a single integer input and returns a vector of random numbers"><span class="n"><span class="pre">rng</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.simulate.burn" title="arch.univariate.GARCH.simulate.burn (Python parameter) — Number of additional observations to generate when initializing the simulation"><span class="n"><span class="pre">burn</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.simulate.initial_value" title="arch.univariate.GARCH.simulate.initial_value (Python parameter) — Scalar or array of initial values to use when initializing the simulation"><span class="n"><span class="pre">initial_value</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#GARCH.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.GARCH.simulate" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.GARCH.simulate">
+<span class="sig-prename descclassname"><span class="pre">GARCH.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.simulate.parameters" title="arch.univariate.GARCH.simulate.parameters (Python parameter) — Parameters required to simulate the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.simulate.nobs" title="arch.univariate.GARCH.simulate.nobs (Python parameter) — Number of data points to simulate"><span class="n"><span class="pre">nobs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.simulate.rng" title="arch.univariate.GARCH.simulate.rng (Python parameter) — Callable function that takes a single integer input and returns a vector of random numbers"><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.simulate.burn" title="arch.univariate.GARCH.simulate.burn (Python parameter) — Number of additional observations to generate when initializing the simulation"><span class="n"><span class="pre">burn</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.simulate.initial_value" title="arch.univariate.GARCH.simulate.initial_value (Python parameter) — Scalar or array of initial values to use when initializing the simulation"><span class="n"><span class="pre">initial_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#GARCH.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.GARCH.simulate" title="Link to this definition">¶</a></dt>
 <dd><p>Simulate data from the model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.GARCH.simulate-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GARCH.simulate-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.GARCH.simulate.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, Series}</span></span><a class="headerlink" href="#arch.univariate.GARCH.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to simulate the volatility model</p>
+<dt id="arch.univariate.GARCH.simulate.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.GARCH.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to simulate the volatility model</p>
 </dd>
-<dt id="arch.univariate.GARCH.simulate.nobs"><span class="sig-name descname">nobs</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.GARCH.simulate.nobs" title="Permalink to this definition">¶</a></dt><dd><p>Number of data points to simulate</p>
+<dt id="arch.univariate.GARCH.simulate.nobs"><span class="n sig-name">nobs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.GARCH.simulate.nobs" title="Permalink to this definition">¶</a></dt><dd><p>Number of data points to simulate</p>
 </dd>
-<dt id="arch.univariate.GARCH.simulate.rng"><span class="sig-name descname">rng</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable</span></span><a class="headerlink" href="#arch.univariate.GARCH.simulate.rng" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that takes a single integer input and returns
+<dt id="arch.univariate.GARCH.simulate.rng"><span class="n sig-name">rng</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)">Callable</a><span class="p">[</span><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">,</span><span class="w"> </span><span class="p">...</span><span class="p">]</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.univariate.GARCH.simulate.rng" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that takes a single integer input and returns
 a vector of random numbers</p>
 </dd>
-<dt id="arch.univariate.GARCH.simulate.burn"><span class="sig-name descname">burn</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.univariate.GARCH.simulate.burn" title="Permalink to this definition">¶</a></dt><dd><p>Number of additional observations to generate when initializing
+<dt id="arch.univariate.GARCH.simulate.burn"><span class="n sig-name">burn</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code><a class="headerlink" href="#arch.univariate.GARCH.simulate.burn" title="Permalink to this definition">¶</a></dt><dd><p>Number of additional observations to generate when initializing
 the simulation</p>
 </dd>
-<dt id="arch.univariate.GARCH.simulate.initial_value"><span class="sig-name descname">initial_value</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}, optional</span></span><a class="headerlink" href="#arch.univariate.GARCH.simulate.initial_value" title="Permalink to this definition">¶</a></dt><dd><p>Scalar or array of initial values to use when initializing the
+<dt id="arch.univariate.GARCH.simulate.initial_value"><span class="n sig-name">initial_value</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.GARCH.simulate.initial_value" title="Permalink to this definition">¶</a></dt><dd><p>Scalar or array of initial values to use when initializing the
 simulation</p>
 </dd>
 </dl>
 </dd>
-<dt class="field-even" id="arch.univariate.GARCH.simulate-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GARCH.simulate-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>]</p>
-</dd>
-<dt class="field-odd" id="arch.univariate.GARCH.simulate-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GARCH.simulate-returns" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><ul class="simple">
-<li><p><strong>resids</strong> (<em>ndarray</em>) – The simulated residuals</p></li>
-<li><p><strong>variance</strong> (<em>ndarray</em>) – The simulated variance</p></li>
+<dt class="field-even" id="arch.univariate.GARCH.simulate-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GARCH.simulate-returns" title="Permalink to this headline">¶</a></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><strong>resids</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – The simulated residuals</p></li>
+<li><p><strong>variance</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – The simulated variance</p></li>
 </ul>
 </p>
 </dd>
@@ -1407,7 +1390,7 @@ <h1 id="arch-univariate-garch-simulate">arch.univariate.GARCH.simulate<a class="
   
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diff --git a/univariate/generated/generated/arch.univariate.GARCH.start.html b/univariate/generated/generated/arch.univariate.GARCH.start.html
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1168,10 +1168,10 @@
                   
 
 
-<h1 id="arch-univariate-garch-start">arch.univariate.GARCH.start<a class="headerlink" href="#arch-univariate-garch-start" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-garch-start">arch.univariate.GARCH.start<a class="headerlink" href="#arch-univariate-garch-start" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.GARCH.start">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">GARCH.</span></span><span class="sig-name descname"><span class="pre">start</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.GARCH.start" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">GARCH.</span></span><span class="sig-name descname"><span class="pre">start</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.GARCH.start" title="Link to this definition">¶</a></dt>
 <dd><p>Index to use to start variance subarray selection</p>
 </dd></dl>
 
@@ -1242,7 +1242,7 @@ <h1 id="arch-univariate-garch-start">arch.univariate.GARCH.start<a class="header
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1289,7 +1289,7 @@ <h1 id="arch-univariate-garch-start">arch.univariate.GARCH.start<a class="header
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.GARCH.starting_values.html b/univariate/generated/generated/arch.univariate.GARCH.starting_values.html
index 168cb7b3cc..8e65a11771 100644
--- a/univariate/generated/generated/arch.univariate.GARCH.starting_values.html
+++ b/univariate/generated/generated/arch.univariate.GARCH.starting_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GARCH.starting_values - arch 6.1.0</title>
+        <title>arch.univariate.GARCH.starting_values - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1248,15 +1248,15 @@
                   
 
 
-<h1 id="arch-univariate-garch-starting-values">arch.univariate.GARCH.starting_values<a class="headerlink" href="#arch-univariate-garch-starting-values" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-garch-starting-values">arch.univariate.GARCH.starting_values<a class="headerlink" href="#arch-univariate-garch-starting-values" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.GARCH.starting_values">
-<span class="sig-prename descclassname"><span class="pre">GARCH.</span></span><span class="sig-name descname"><span class="pre">starting_values</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.starting_values.resids" title="arch.univariate.GARCH.starting_values.resids (Python parameter) — Array of (approximate) residuals to use when computing starting values"><span class="n"><span class="pre">resids</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#GARCH.starting_values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.GARCH.starting_values" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">GARCH.</span></span><span class="sig-name descname"><span class="pre">starting_values</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.starting_values.resids" title="arch.univariate.GARCH.starting_values.resids (Python parameter) — Array of (approximate) residuals to use when computing starting values"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#GARCH.starting_values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.GARCH.starting_values" title="Link to this definition">¶</a></dt>
 <dd><p>Returns starting values for the ARCH model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.GARCH.starting_values-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GARCH.starting_values-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.GARCH.starting_values.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.GARCH.starting_values.resids" title="Permalink to this definition">¶</a></dt><dd><p>Array of (approximate) residuals to use when computing starting
+<dt id="arch.univariate.GARCH.starting_values.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.GARCH.starting_values.resids" title="Permalink to this definition">¶</a></dt><dd><p>Array of (approximate) residuals to use when computing starting
 values</p>
 </dd>
 </dl>
@@ -1265,7 +1265,7 @@ <h1 id="arch-univariate-garch-starting-values">arch.univariate.GARCH.starting_va
 <dd class="field-even"><p><strong>sv</strong> – Array of starting values</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.GARCH.starting_values-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GARCH.starting_values-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1337,7 +1337,7 @@ <h1 id="arch-univariate-garch-starting-values">arch.univariate.GARCH.starting_va
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1384,7 +1384,7 @@ <h1 id="arch-univariate-garch-starting-values">arch.univariate.GARCH.starting_va
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.GARCH.stop.html b/univariate/generated/generated/arch.univariate.GARCH.stop.html
index 80227f9770..fa07498b98 100644
--- a/univariate/generated/generated/arch.univariate.GARCH.stop.html
+++ b/univariate/generated/generated/arch.univariate.GARCH.stop.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GARCH.stop - arch 6.1.0</title>
+        <title>arch.univariate.GARCH.stop - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1168,10 +1168,10 @@
                   
 
 
-<h1 id="arch-univariate-garch-stop">arch.univariate.GARCH.stop<a class="headerlink" href="#arch-univariate-garch-stop" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-garch-stop">arch.univariate.GARCH.stop<a class="headerlink" href="#arch-univariate-garch-stop" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.GARCH.stop">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">GARCH.</span></span><span class="sig-name descname"><span class="pre">stop</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.GARCH.stop" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">GARCH.</span></span><span class="sig-name descname"><span class="pre">stop</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.GARCH.stop" title="Link to this definition">¶</a></dt>
 <dd><p>Index to use to stop variance subarray selection</p>
 </dd></dl>
 
@@ -1242,7 +1242,7 @@ <h1 id="arch-univariate-garch-stop">arch.univariate.GARCH.stop<a class="headerli
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1289,7 +1289,7 @@ <h1 id="arch-univariate-garch-stop">arch.univariate.GARCH.stop<a class="headerli
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.GARCH.update.html b/univariate/generated/generated/arch.univariate.GARCH.update.html
index 8a1b46f006..eb43698aaa 100644
--- a/univariate/generated/generated/arch.univariate.GARCH.update.html
+++ b/univariate/generated/generated/arch.univariate.GARCH.update.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GARCH.update - arch 6.1.0</title>
+        <title>arch.univariate.GARCH.update - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1318,28 +1318,28 @@
                   
 
 
-<h1 id="arch-univariate-garch-update">arch.univariate.GARCH.update<a class="headerlink" href="#arch-univariate-garch-update" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-garch-update">arch.univariate.GARCH.update<a class="headerlink" href="#arch-univariate-garch-update" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.GARCH.update">
-<span class="sig-prename descclassname"><span class="pre">GARCH.</span></span><span class="sig-name descname"><span class="pre">update</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.update.index" title="arch.univariate.GARCH.update.index (Python parameter) — The numerical index of the variance to compute"><span class="n"><span class="pre">index</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.update.parameters" title="arch.univariate.GARCH.update.parameters (Python parameter) — The variance model parameters"><span class="n"><span class="pre">parameters</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.update.resids" title="arch.univariate.GARCH.update.resids (Python parameter) — The residual array. Only uses resids[:index] when computing sigma2[index]"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.update.sigma2" title="arch.univariate.GARCH.update.sigma2 (Python parameter) — The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."><span class="n"><span class="pre">sigma2</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.update.backcast" title="arch.univariate.GARCH.update.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.update.var_bounds" title="arch.univariate.GARCH.update.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.GARCH.update" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">GARCH.</span></span><span class="sig-name descname"><span class="pre">update</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.update.index" title="arch.univariate.GARCH.update.index (Python parameter) — The numerical index of the variance to compute"><span class="n"><span class="pre">index</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.update.parameters" title="arch.univariate.GARCH.update.parameters (Python parameter) — The variance model parameters"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.update.resids" title="arch.univariate.GARCH.update.resids (Python parameter) — The residual array. Only uses resids[:index] when computing sigma2[index]"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.update.sigma2" title="arch.univariate.GARCH.update.sigma2 (Python parameter) — The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."><span class="n"><span class="pre">sigma2</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.update.backcast" title="arch.univariate.GARCH.update.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.update.var_bounds" title="arch.univariate.GARCH.update.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span></span><a class="headerlink" href="#arch.univariate.GARCH.update" title="Link to this definition">¶</a></dt>
 <dd><p>Compute the variance for a single observation</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.GARCH.update-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GARCH.update-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.GARCH.update.index"><span class="sig-name descname">index</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.GARCH.update.index" title="Permalink to this definition">¶</a></dt><dd><p>The numerical index of the variance to compute</p>
+<dt id="arch.univariate.GARCH.update.index"><span class="n sig-name">index</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.GARCH.update.index" title="Permalink to this definition">¶</a></dt><dd><p>The numerical index of the variance to compute</p>
 </dd>
-<dt id="arch.univariate.GARCH.update.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.GARCH.update.parameters" title="Permalink to this definition">¶</a></dt><dd><p>The variance model parameters</p>
+<dt id="arch.univariate.GARCH.update.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.GARCH.update.parameters" title="Permalink to this definition">¶</a></dt><dd><p>The variance model parameters</p>
 </dd>
-<dt id="arch.univariate.GARCH.update.resids"><span class="n sig-name">resids</span><a class="headerlink" href="#arch.univariate.GARCH.update.resids" title="Permalink to this definition">¶</a></dt><dd><p>The residual array. Only uses <code class="docutils literal notranslate"><span class="pre">resids[:index]</span></code> when computing
+<dt id="arch.univariate.GARCH.update.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.GARCH.update.resids" title="Permalink to this definition">¶</a></dt><dd><p>The residual array. Only uses <code class="docutils literal notranslate"><span class="pre">resids[:index]</span></code> when computing
 <code class="docutils literal notranslate"><span class="pre">sigma2[index]</span></code></p>
 </dd>
-<dt id="arch.univariate.GARCH.update.sigma2"><span class="sig-name descname">sigma2</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.GARCH.update.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>The array containing the variances. Only uses <code class="docutils literal notranslate"><span class="pre">sigma2[:index]</span></code>
+<dt id="arch.univariate.GARCH.update.sigma2"><span class="n sig-name">sigma2</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.GARCH.update.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>The array containing the variances. Only uses <code class="docutils literal notranslate"><span class="pre">sigma2[:index]</span></code>
 when computing <code class="docutils literal notranslate"><span class="pre">sigma2[index]</span></code>. The computed value is stored
 in <code class="docutils literal notranslate"><span class="pre">sigma2[index]</span></code>.</p>
 </dd>
-<dt id="arch.univariate.GARCH.update.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}</span></span><a class="headerlink" href="#arch.univariate.GARCH.update.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing the recursion</p>
+<dt id="arch.univariate.GARCH.update.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.GARCH.update.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing the recursion</p>
 </dd>
-<dt id="arch.univariate.GARCH.update.var_bounds"><span class="sig-name descname">var_bounds</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.GARCH.update.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
+<dt id="arch.univariate.GARCH.update.var_bounds"><span class="n sig-name">var_bounds</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.GARCH.update.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
 </dd>
 </dl>
 </dd>
@@ -1347,7 +1347,7 @@ <h1 id="arch-univariate-garch-update">arch.univariate.GARCH.update<a class="head
 <dd class="field-even"><p>The variance computed for location <code class="docutils literal notranslate"><span class="pre">index</span></code></p>
 </dd>
 <dt class="field-odd" id="arch.univariate.GARCH.update-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GARCH.update-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1419,7 +1419,7 @@ <h1 id="arch-univariate-garch-update">arch.univariate.GARCH.update<a class="head
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1466,7 +1466,7 @@ <h1 id="arch-univariate-garch-update">arch.univariate.GARCH.update<a class="head
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diff --git a/univariate/generated/generated/arch.univariate.GARCH.updateable.html b/univariate/generated/generated/arch.univariate.GARCH.updateable.html
index 9c584b702b..4f71c9a48a 100644
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+++ b/univariate/generated/generated/arch.univariate.GARCH.updateable.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GARCH.updateable - arch 6.1.0</title>
+        <title>arch.univariate.GARCH.updateable - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1168,10 +1168,10 @@
                   
 
 
-<h1 id="arch-univariate-garch-updateable">arch.univariate.GARCH.updateable<a class="headerlink" href="#arch-univariate-garch-updateable" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-garch-updateable">arch.univariate.GARCH.updateable<a class="headerlink" href="#arch-univariate-garch-updateable" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.GARCH.updateable">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">GARCH.</span></span><span class="sig-name descname"><span class="pre">updateable</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.univariate.GARCH.updateable" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">GARCH.</span></span><span class="sig-name descname"><span class="pre">updateable</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.univariate.GARCH.updateable" title="Link to this definition">¶</a></dt>
 <dd><p>Flag indicating that the volatility process supports update</p>
 </dd></dl>
 
@@ -1242,7 +1242,7 @@ <h1 id="arch-univariate-garch-updateable">arch.univariate.GARCH.updateable<a cla
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1289,7 +1289,7 @@ <h1 id="arch-univariate-garch-updateable">arch.univariate.GARCH.updateable<a cla
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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diff --git a/univariate/generated/generated/arch.univariate.GARCH.variance_bounds.html b/univariate/generated/generated/arch.univariate.GARCH.variance_bounds.html
index 835bce44d3..f406e718e6 100644
--- a/univariate/generated/generated/arch.univariate.GARCH.variance_bounds.html
+++ b/univariate/generated/generated/arch.univariate.GARCH.variance_bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GARCH.variance_bounds - arch 6.1.0</title>
+        <title>arch.univariate.GARCH.variance_bounds - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1262,20 +1262,20 @@
                   
 
 
-<h1 id="arch-univariate-garch-variance-bounds">arch.univariate.GARCH.variance_bounds<a class="headerlink" href="#arch-univariate-garch-variance-bounds" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-garch-variance-bounds">arch.univariate.GARCH.variance_bounds<a class="headerlink" href="#arch-univariate-garch-variance-bounds" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.GARCH.variance_bounds">
-<span class="sig-prename descclassname"><span class="pre">GARCH.</span></span><span class="sig-name descname"><span class="pre">variance_bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.variance_bounds.resids" title="arch.univariate.GARCH.variance_bounds.resids (Python parameter) — Approximate residuals to use to compute the lower and upper bounds on the conditional variance"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.variance_bounds.power" title="arch.univariate.GARCH.variance_bounds.power (Python parameter) — Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."><span class="n"><span class="pre">power</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">2.0</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#GARCH.variance_bounds"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.GARCH.variance_bounds" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.GARCH.variance_bounds">
+<span class="sig-prename descclassname"><span class="pre">GARCH.</span></span><span class="sig-name descname"><span class="pre">variance_bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.variance_bounds.resids" title="arch.univariate.GARCH.variance_bounds.resids (Python parameter) — Approximate residuals to use to compute the lower and upper bounds on the conditional variance"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.variance_bounds.power" title="arch.univariate.GARCH.variance_bounds.power (Python parameter) — Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."><span class="n"><span class="pre">power</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">2.0</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#GARCH.variance_bounds"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.GARCH.variance_bounds" title="Link to this definition">¶</a></dt>
 <dd><p>Construct loose bounds for conditional variances.</p>
 <p>These bounds are used in parameter estimation to ensure
 that the log-likelihood does not produce NaN values.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.GARCH.variance_bounds-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GARCH.variance_bounds-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.GARCH.variance_bounds.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.GARCH.variance_bounds.resids" title="Permalink to this definition">¶</a></dt><dd><p>Approximate residuals to use to compute the lower and upper bounds
+<dt id="arch.univariate.GARCH.variance_bounds.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.GARCH.variance_bounds.resids" title="Permalink to this definition">¶</a></dt><dd><p>Approximate residuals to use to compute the lower and upper bounds
 on the conditional variance</p>
 </dd>
-<dt id="arch.univariate.GARCH.variance_bounds.power"><span class="sig-name descname">power</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>, optional</span></span><a class="headerlink" href="#arch.univariate.GARCH.variance_bounds.power" title="Permalink to this definition">¶</a></dt><dd><p>Power used in the model. 2.0, the default corresponds to standard
+<dt id="arch.univariate.GARCH.variance_bounds.power"><span class="n sig-name">power</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">2.0</span></code><a class="headerlink" href="#arch.univariate.GARCH.variance_bounds.power" title="Permalink to this definition">¶</a></dt><dd><p>Power used in the model. 2.0, the default corresponds to standard
 ARCH models that evolve in squares.</p>
 </dd>
 </dl>
@@ -1285,7 +1285,7 @@ <h1 id="arch-univariate-garch-variance-bounds">arch.univariate.GARCH.variance_bo
 number of elements as resids</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.GARCH.variance_bounds-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GARCH.variance_bounds-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1357,7 +1357,7 @@ <h1 id="arch-univariate-garch-variance-bounds">arch.univariate.GARCH.variance_bo
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1404,7 +1404,7 @@ <h1 id="arch-univariate-garch-variance-bounds">arch.univariate.GARCH.variance_bo
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.GARCH.volatility_updater.html b/univariate/generated/generated/arch.univariate.GARCH.volatility_updater.html
index b23d5e4ea7..f91841f20a 100644
--- a/univariate/generated/generated/arch.univariate.GARCH.volatility_updater.html
+++ b/univariate/generated/generated/arch.univariate.GARCH.volatility_updater.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GARCH.volatility_updater - arch 6.1.0</title>
+        <title>arch.univariate.GARCH.volatility_updater - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1222,17 +1222,17 @@
                   
 
 
-<h1 id="arch-univariate-garch-volatility-updater">arch.univariate.GARCH.volatility_updater<a class="headerlink" href="#arch-univariate-garch-volatility-updater" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-garch-volatility-updater">arch.univariate.GARCH.volatility_updater<a class="headerlink" href="#arch-univariate-garch-volatility-updater" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.GARCH.volatility_updater">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">GARCH.</span></span><span class="sig-name descname"><span class="pre">volatility_updater</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.univariate.recursions_python.VolatilityUpdater.html#arch.univariate.recursions_python.VolatilityUpdater" title="arch.univariate.recursions_python.VolatilityUpdater (Python class) — Base class that all volatility updaters must inherit from."><span class="pre">VolatilityUpdater</span></a></span><a class="headerlink" href="#arch.univariate.GARCH.volatility_updater" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">GARCH.</span></span><span class="sig-name descname"><span class="pre">volatility_updater</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><span class="pre">VolatilityUpdater</span></span><a class="headerlink" href="#arch.univariate.GARCH.volatility_updater" title="Link to this definition">¶</a></dt>
 <dd><p>Get the volatility updater associated with the volatility process</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.GARCH.volatility_updater-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GARCH.volatility_updater-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The updater class</p>
 </dd>
 <dt class="field-even" id="arch.univariate.GARCH.volatility_updater-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GARCH.volatility_updater-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference internal" href="../arch.univariate.recursions_python.VolatilityUpdater.html#arch.univariate.recursions_python.VolatilityUpdater" title="arch.univariate.recursions_python.VolatilityUpdater (Python class) — Base class that all volatility updaters must inherit from.">VolatilityUpdater</a></p>
+<dd class="field-even"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">VolatilityUpdater</span></code></p>
 </dd>
 <dt class="field-odd" id="arch.univariate.GARCH.volatility_updater-raises">Raises<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GARCH.volatility_updater-raises" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#NotImplementedError" title="(in Python v3.11)"><strong>NotImplementedError</strong></a> – If the process is not updateable</p>
@@ -1307,7 +1307,7 @@ <h1 id="arch-univariate-garch-volatility-updater">arch.univariate.GARCH.volatili
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1354,7 +1354,7 @@ <h1 id="arch-univariate-garch-volatility-updater">arch.univariate.GARCH.volatili
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.GeneralizedError.bounds.html b/univariate/generated/generated/arch.univariate.GeneralizedError.bounds.html
index 9905a8a177..97ea40d405 100644
--- a/univariate/generated/generated/arch.univariate.GeneralizedError.bounds.html
+++ b/univariate/generated/generated/arch.univariate.GeneralizedError.bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GeneralizedError.bounds - arch 6.1.0</title>
+        <title>arch.univariate.GeneralizedError.bounds - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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+<h1 id="arch-univariate-generalizederror-bounds">arch.univariate.GeneralizedError.bounds<a class="headerlink" href="#arch-univariate-generalizederror-bounds" title="Link to this heading">¶</a></h1>
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+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.GeneralizedError.bounds">
+<span class="sig-prename descclassname"><span class="pre">GeneralizedError.</span></span><span class="sig-name descname"><span class="pre">bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.bounds.resids" title="arch.univariate.GeneralizedError.bounds.resids (Python parameter) — Residuals to use when computing the bounds"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#GeneralizedError.bounds"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.GeneralizedError.bounds" title="Link to this definition">¶</a></dt>
 <dd><p>Parameter bounds for use in optimization.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.GeneralizedError.bounds-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GeneralizedError.bounds-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
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+<dt id="arch.univariate.GeneralizedError.bounds.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.GeneralizedError.bounds.resids" title="Permalink to this definition">¶</a></dt><dd><p>Residuals to use when computing the bounds</p>
 </dd>
 </dl>
 </dd>
@@ -1114,7 +1114,7 @@ <h1 id="arch-univariate-generalizederror-bounds">arch.univariate.GeneralizedErro
 <dd class="field-even"><p><strong>bounds</strong> – List containing a single tuple with (lower, upper) bounds</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.GeneralizedError.bounds-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GeneralizedError.bounds-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1186,7 +1186,7 @@ <h1 id="arch-univariate-generalizederror-bounds">arch.univariate.GeneralizedErro
   
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-<h1 id="arch-univariate-generalizederror-cdf">arch.univariate.GeneralizedError.cdf<a class="headerlink" href="#arch-univariate-generalizederror-cdf" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-generalizederror-cdf">arch.univariate.GeneralizedError.cdf<a class="headerlink" href="#arch-univariate-generalizederror-cdf" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.GeneralizedError.cdf">
-<span class="sig-prename descclassname"><span class="pre">GeneralizedError.</span></span><span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.cdf.resids" title="arch.univariate.GeneralizedError.cdf.resids (Python parameter) — Values at which to evaluate the cdf"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.cdf.parameters" title="arch.univariate.GeneralizedError.cdf.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#GeneralizedError.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.GeneralizedError.cdf" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.GeneralizedError.cdf">
+<span class="sig-prename descclassname"><span class="pre">GeneralizedError.</span></span><span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.cdf.resids" title="arch.univariate.GeneralizedError.cdf.resids (Python parameter) — Values at which to evaluate the cdf"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.cdf.parameters" title="arch.univariate.GeneralizedError.cdf.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#GeneralizedError.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.GeneralizedError.cdf" title="Link to this definition">¶</a></dt>
 <dd><p>Cumulative distribution function</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.GeneralizedError.cdf-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GeneralizedError.cdf-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.GeneralizedError.cdf.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.GeneralizedError.cdf.resids" title="Permalink to this definition">¶</a></dt><dd><p>Values at which to evaluate the cdf</p>
+<dt id="arch.univariate.GeneralizedError.cdf.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.GeneralizedError.cdf.resids" title="Permalink to this definition">¶</a></dt><dd><p>Values at which to evaluate the cdf</p>
 </dd>
-<dt id="arch.univariate.GeneralizedError.cdf.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.GeneralizedError.cdf.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use <code class="docutils literal notranslate"><span class="pre">None</span></code> for parameterless
+<dt id="arch.univariate.GeneralizedError.cdf.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.GeneralizedError.cdf.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use <code class="docutils literal notranslate"><span class="pre">None</span></code> for parameterless
 distributions.</p>
 </dd>
 </dl>
@@ -1131,7 +1131,7 @@ <h1 id="arch-univariate-generalizederror-cdf">arch.univariate.GeneralizedError.c
 <dd class="field-even"><p><strong>f</strong> – CDF values</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.GeneralizedError.cdf-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GeneralizedError.cdf-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
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diff --git a/univariate/generated/generated/arch.univariate.GeneralizedError.constraints.html b/univariate/generated/generated/arch.univariate.GeneralizedError.constraints.html
index ce01752180..db91fa1275 100644
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+    arch 6.2.0
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     <div class="md-nav__source">
@@ -611,13 +611,6 @@
     <nav class="md-nav" aria-label="GeneralizedError.constraints">
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-          <li class="md-nav__item">
-  <a href="#arch.univariate.GeneralizedError.constraints-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
-</li>
-        
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   <a href="#arch.univariate.GeneralizedError.constraints-returns" class="md-nav__link">
     <span class="md-ellipsis">Returns</span>
@@ -1025,13 +1018,6 @@
     <nav class="md-nav" aria-label="GeneralizedError.constraints">
       <ul class="md-nav__list">
         
-          <li class="md-nav__item">
-  <a href="#arch.univariate.GeneralizedError.constraints-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
-</li>
-        
           <li class="md-nav__item">
   <a href="#arch.univariate.GeneralizedError.constraints-returns" class="md-nav__link">
     <span class="md-ellipsis">Returns</span>
@@ -1058,19 +1044,16 @@
                   
 
 
-<h1 id="arch-univariate-generalizederror-constraints">arch.univariate.GeneralizedError.constraints<a class="headerlink" href="#arch-univariate-generalizederror-constraints" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-generalizederror-constraints">arch.univariate.GeneralizedError.constraints<a class="headerlink" href="#arch-univariate-generalizederror-constraints" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.GeneralizedError.constraints">
-<span class="sig-prename descclassname"><span class="pre">GeneralizedError.</span></span><span class="sig-name descname"><span class="pre">constraints</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#GeneralizedError.constraints"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.GeneralizedError.constraints" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">GeneralizedError.</span></span><span class="sig-name descname"><span class="pre">constraints</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#GeneralizedError.constraints"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.GeneralizedError.constraints" title="Link to this definition">¶</a></dt>
 <dd><p>Construct arrays to use in constrained optimization.</p>
 <dl class="field-list simple">
-<dt class="field-odd" id="arch.univariate.GeneralizedError.constraints-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GeneralizedError.constraints-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>]</p>
-</dd>
-<dt class="field-even" id="arch.univariate.GeneralizedError.constraints-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GeneralizedError.constraints-returns" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><ul class="simple">
-<li><p><strong>A</strong> (<em>ndarray</em>) – Constraint loadings</p></li>
-<li><p><strong>b</strong> (<em>ndarray</em>) – Constraint values</p></li>
+<dt class="field-odd" id="arch.univariate.GeneralizedError.constraints-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GeneralizedError.constraints-returns" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p><strong>A</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – Constraint loadings</p></li>
+<li><p><strong>b</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – Constraint values</p></li>
 </ul>
 </p>
 </dd>
@@ -1146,7 +1129,7 @@ <h1 id="arch-univariate-generalizederror-constraints">arch.univariate.Generalize
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1193,7 +1176,7 @@ <h1 id="arch-univariate-generalizederror-constraints">arch.univariate.Generalize
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diff --git a/univariate/generated/generated/arch.univariate.GeneralizedError.generator.html b/univariate/generated/generated/arch.univariate.GeneralizedError.generator.html
index 11a110ad05..f9efc04f80 100644
--- a/univariate/generated/generated/arch.univariate.GeneralizedError.generator.html
+++ b/univariate/generated/generated/arch.univariate.GeneralizedError.generator.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GeneralizedError.generator - arch 6.1.0</title>
+        <title>arch.univariate.GeneralizedError.generator - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1018,10 +1018,10 @@
                   
 
 
-<h1 id="arch-univariate-generalizederror-generator">arch.univariate.GeneralizedError.generator<a class="headerlink" href="#arch-univariate-generalizederror-generator" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-generalizederror-generator">arch.univariate.GeneralizedError.generator<a class="headerlink" href="#arch-univariate-generalizederror-generator" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.GeneralizedError.generator">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">GeneralizedError.</span></span><span class="sig-name descname"><span class="pre">generator</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.24)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.24)"><span class="pre">Generator</span></a></span><a class="headerlink" href="#arch.univariate.GeneralizedError.generator" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">GeneralizedError.</span></span><span class="sig-name descname"><span class="pre">generator</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><span class="pre">Generator</span></a></span><a class="headerlink" href="#arch.univariate.GeneralizedError.generator" title="Link to this definition">¶</a></dt>
 <dd><p>The NumPy Generator or RandomState attached to the distribution</p>
 </dd></dl>
 
@@ -1092,7 +1092,7 @@ <h1 id="arch-univariate-generalizederror-generator">arch.univariate.GeneralizedE
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1139,7 +1139,7 @@ <h1 id="arch-univariate-generalizederror-generator">arch.univariate.GeneralizedE
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.GeneralizedError.loglikelihood.html b/univariate/generated/generated/arch.univariate.GeneralizedError.loglikelihood.html
index abf6c0b5d4..8970871397 100644
--- a/univariate/generated/generated/arch.univariate.GeneralizedError.loglikelihood.html
+++ b/univariate/generated/generated/arch.univariate.GeneralizedError.loglikelihood.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GeneralizedError.loglikelihood - arch 6.1.0</title>
+        <title>arch.univariate.GeneralizedError.loglikelihood - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1140,22 +1140,22 @@
                   
 
 
-<h1 id="arch-univariate-generalizederror-loglikelihood">arch.univariate.GeneralizedError.loglikelihood<a class="headerlink" href="#arch-univariate-generalizederror-loglikelihood" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-generalizederror-loglikelihood">arch.univariate.GeneralizedError.loglikelihood<a class="headerlink" href="#arch-univariate-generalizederror-loglikelihood" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.GeneralizedError.loglikelihood">
-<span class="sig-prename descclassname"><span class="pre">GeneralizedError.</span></span><span class="sig-name descname"><span class="pre">loglikelihood</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.loglikelihood.parameters" title="arch.univariate.GeneralizedError.loglikelihood.parameters (Python parameter) — Shape parameter of the GED distribution"><span class="n"><span class="pre">parameters</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.loglikelihood.resids" title="arch.univariate.GeneralizedError.loglikelihood.resids (Python parameter) — The residuals to use in the log-likelihood calculation"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.loglikelihood.sigma2" title="arch.univariate.GeneralizedError.loglikelihood.sigma2 (Python parameter) — Conditional variances of resids"><span class="n"><span class="pre">sigma2</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.loglikelihood.individual" title="arch.univariate.GeneralizedError.loglikelihood.individual (Python parameter) — Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"><span class="n"><span class="pre">individual</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#GeneralizedError.loglikelihood"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.GeneralizedError.loglikelihood" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">GeneralizedError.</span></span><span class="sig-name descname"><span class="pre">loglikelihood</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.loglikelihood.parameters" title="arch.univariate.GeneralizedError.loglikelihood.parameters (Python parameter) — Shape parameter of the GED distribution"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.loglikelihood.resids" title="arch.univariate.GeneralizedError.loglikelihood.resids (Python parameter) — The residuals to use in the log-likelihood calculation"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.loglikelihood.sigma2" title="arch.univariate.GeneralizedError.loglikelihood.sigma2 (Python parameter) — Conditional variances of resids"><span class="n"><span class="pre">sigma2</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.loglikelihood.individual" title="arch.univariate.GeneralizedError.loglikelihood.individual (Python parameter) — Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"><span class="n"><span class="pre">individual</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#GeneralizedError.loglikelihood"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.GeneralizedError.loglikelihood" title="Link to this definition">¶</a></dt>
 <dd><p>Computes the log-likelihood of assuming residuals are have a
 Generalized Error Distribution, conditional on the variance.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.GeneralizedError.loglikelihood-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GeneralizedError.loglikelihood-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.GeneralizedError.loglikelihood.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.GeneralizedError.loglikelihood.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Shape parameter of the GED distribution</p>
+<dt id="arch.univariate.GeneralizedError.loglikelihood.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.GeneralizedError.loglikelihood.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Shape parameter of the GED distribution</p>
 </dd>
-<dt id="arch.univariate.GeneralizedError.loglikelihood.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.GeneralizedError.loglikelihood.resids" title="Permalink to this definition">¶</a></dt><dd><p>The residuals to use in the log-likelihood calculation</p>
+<dt id="arch.univariate.GeneralizedError.loglikelihood.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.GeneralizedError.loglikelihood.resids" title="Permalink to this definition">¶</a></dt><dd><p>The residuals to use in the log-likelihood calculation</p>
 </dd>
-<dt id="arch.univariate.GeneralizedError.loglikelihood.sigma2"><span class="sig-name descname">sigma2</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.GeneralizedError.loglikelihood.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>Conditional variances of resids</p>
+<dt id="arch.univariate.GeneralizedError.loglikelihood.sigma2"><span class="n sig-name">sigma2</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.GeneralizedError.loglikelihood.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>Conditional variances of resids</p>
 </dd>
-<dt id="arch.univariate.GeneralizedError.loglikelihood.individual"><span class="sig-name descname">individual</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, optional</span></span><a class="headerlink" href="#arch.univariate.GeneralizedError.loglikelihood.individual" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to return the vector of individual log
+<dt id="arch.univariate.GeneralizedError.loglikelihood.individual"><span class="n sig-name">individual</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.univariate.GeneralizedError.loglikelihood.individual" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to return the vector of individual log
 likelihoods (True) or the sum (False)</p>
 </dd>
 </dl>
@@ -1164,7 +1164,7 @@ <h1 id="arch-univariate-generalizederror-loglikelihood">arch.univariate.Generali
 <dd class="field-even"><p><strong>ll</strong> – The log-likelihood</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.GeneralizedError.loglikelihood-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GeneralizedError.loglikelihood-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -1246,7 +1246,7 @@ <h1 id="arch-univariate-generalizederror-loglikelihood">arch.univariate.Generali
   
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-    7.0.1.
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+        <script id="MathJax-script" src="../../../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
     
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diff --git a/univariate/generated/generated/arch.univariate.GeneralizedError.moment.html b/univariate/generated/generated/arch.univariate.GeneralizedError.moment.html
index 895672ba26..4ed5557a95 100644
--- a/univariate/generated/generated/arch.univariate.GeneralizedError.moment.html
+++ b/univariate/generated/generated/arch.univariate.GeneralizedError.moment.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GeneralizedError.moment - arch 6.1.0</title>
+        <title>arch.univariate.GeneralizedError.moment - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1112,17 +1112,17 @@
                   
 
 
-<h1 id="arch-univariate-generalizederror-moment">arch.univariate.GeneralizedError.moment<a class="headerlink" href="#arch-univariate-generalizederror-moment" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-generalizederror-moment">arch.univariate.GeneralizedError.moment<a class="headerlink" href="#arch-univariate-generalizederror-moment" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.GeneralizedError.moment">
-<span class="sig-prename descclassname"><span class="pre">GeneralizedError.</span></span><span class="sig-name descname"><span class="pre">moment</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.moment.n" title="arch.univariate.GeneralizedError.moment.n (Python parameter) — Order of moment"><span class="n"><span class="pre">n</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.moment.parameters" title="arch.univariate.GeneralizedError.moment.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#GeneralizedError.moment"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.GeneralizedError.moment" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.GeneralizedError.moment">
+<span class="sig-prename descclassname"><span class="pre">GeneralizedError.</span></span><span class="sig-name descname"><span class="pre">moment</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.moment.n" title="arch.univariate.GeneralizedError.moment.n (Python parameter) — Order of moment"><span class="n"><span class="pre">n</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.moment.parameters" title="arch.univariate.GeneralizedError.moment.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#GeneralizedError.moment"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.GeneralizedError.moment" title="Link to this definition">¶</a></dt>
 <dd><p>Moment of order n</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.GeneralizedError.moment-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GeneralizedError.moment-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.GeneralizedError.moment.n"><span class="sig-name descname">n</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.GeneralizedError.moment.n" title="Permalink to this definition">¶</a></dt><dd><p>Order of moment</p>
+<dt id="arch.univariate.GeneralizedError.moment.n"><span class="n sig-name">n</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.GeneralizedError.moment.n" title="Permalink to this definition">¶</a></dt><dd><p>Order of moment</p>
 </dd>
-<dt id="arch.univariate.GeneralizedError.moment.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray, optional</span></span><a class="headerlink" href="#arch.univariate.GeneralizedError.moment.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use None for parameterless distributions.</p>
+<dt id="arch.univariate.GeneralizedError.moment.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.GeneralizedError.moment.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use None for parameterless distributions.</p>
 </dd>
 </dl>
 </dd>
@@ -1130,7 +1130,7 @@ <h1 id="arch-univariate-generalizederror-moment">arch.univariate.GeneralizedErro
 <dd class="field-even"><p>Calculated moment</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.GeneralizedError.moment-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GeneralizedError.moment-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1202,7 +1202,7 @@ <h1 id="arch-univariate-generalizederror-moment">arch.univariate.GeneralizedErro
   
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     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1249,7 +1249,7 @@ <h1 id="arch-univariate-generalizederror-moment">arch.univariate.GeneralizedErro
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diff --git a/univariate/generated/generated/arch.univariate.GeneralizedError.name.html b/univariate/generated/generated/arch.univariate.GeneralizedError.name.html
index 1d4afff61f..2be72139c5 100644
--- a/univariate/generated/generated/arch.univariate.GeneralizedError.name.html
+++ b/univariate/generated/generated/arch.univariate.GeneralizedError.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GeneralizedError.name - arch 6.1.0</title>
+        <title>arch.univariate.GeneralizedError.name - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
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           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1018,10 +1018,10 @@
                   
 
 
-<h1 id="arch-univariate-generalizederror-name">arch.univariate.GeneralizedError.name<a class="headerlink" href="#arch-univariate-generalizederror-name" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-generalizederror-name">arch.univariate.GeneralizedError.name<a class="headerlink" href="#arch-univariate-generalizederror-name" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.GeneralizedError.name">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">GeneralizedError.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.univariate.GeneralizedError.name" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">GeneralizedError.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.univariate.GeneralizedError.name" title="Link to this definition">¶</a></dt>
 <dd><p>The name of the distribution</p>
 </dd></dl>
 
@@ -1092,7 +1092,7 @@ <h1 id="arch-univariate-generalizederror-name">arch.univariate.GeneralizedError.
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1139,7 +1139,7 @@ <h1 id="arch-univariate-generalizederror-name">arch.univariate.GeneralizedError.
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diff --git a/univariate/generated/generated/arch.univariate.GeneralizedError.parameter_names.html b/univariate/generated/generated/arch.univariate.GeneralizedError.parameter_names.html
index 5675043166..7904c94c23 100644
--- a/univariate/generated/generated/arch.univariate.GeneralizedError.parameter_names.html
+++ b/univariate/generated/generated/arch.univariate.GeneralizedError.parameter_names.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GeneralizedError.parameter_names - arch 6.1.0</title>
+        <title>arch.univariate.GeneralizedError.parameter_names - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch 6.1.0
+    arch 6.2.0
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@@ -1058,17 +1058,17 @@
                   
 
 
-<h1 id="arch-univariate-generalizederror-parameter-names">arch.univariate.GeneralizedError.parameter_names<a class="headerlink" href="#arch-univariate-generalizederror-parameter-names" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-generalizederror-parameter-names">arch.univariate.GeneralizedError.parameter_names<a class="headerlink" href="#arch-univariate-generalizederror-parameter-names" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.GeneralizedError.parameter_names">
-<span class="sig-prename descclassname"><span class="pre">GeneralizedError.</span></span><span class="sig-name descname"><span class="pre">parameter_names</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#GeneralizedError.parameter_names"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.GeneralizedError.parameter_names" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">GeneralizedError.</span></span><span class="sig-name descname"><span class="pre">parameter_names</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#GeneralizedError.parameter_names"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.GeneralizedError.parameter_names" title="Link to this definition">¶</a></dt>
 <dd><p>Names of distribution shape parameters</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.GeneralizedError.parameter_names-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GeneralizedError.parameter_names-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p><strong>names</strong> – Parameter names</p>
 </dd>
 <dt class="field-even" id="arch.univariate.GeneralizedError.parameter_names-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GeneralizedError.parameter_names-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>)</p>
+<dd class="field-even"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span> <span class="pre">(str)</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1140,7 +1140,7 @@ <h1 id="arch-univariate-generalizederror-parameter-names">arch.univariate.Genera
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1187,7 +1187,7 @@ <h1 id="arch-univariate-generalizederror-parameter-names">arch.univariate.Genera
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diff --git a/univariate/generated/generated/arch.univariate.GeneralizedError.partial_moment.html b/univariate/generated/generated/arch.univariate.GeneralizedError.partial_moment.html
index 2a499684df..2b749dcd74 100644
--- a/univariate/generated/generated/arch.univariate.GeneralizedError.partial_moment.html
+++ b/univariate/generated/generated/arch.univariate.GeneralizedError.partial_moment.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GeneralizedError.partial_moment - arch 6.1.0</title>
+        <title>arch.univariate.GeneralizedError.partial_moment - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch 6.1.0
+    arch 6.2.0
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@@ -1126,19 +1126,19 @@
                   
 
 
-<h1 id="arch-univariate-generalizederror-partial-moment">arch.univariate.GeneralizedError.partial_moment<a class="headerlink" href="#arch-univariate-generalizederror-partial-moment" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-generalizederror-partial-moment">arch.univariate.GeneralizedError.partial_moment<a class="headerlink" href="#arch-univariate-generalizederror-partial-moment" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.GeneralizedError.partial_moment">
-<span class="sig-prename descclassname"><span class="pre">GeneralizedError.</span></span><span class="sig-name descname"><span class="pre">partial_moment</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.partial_moment.n" title="arch.univariate.GeneralizedError.partial_moment.n (Python parameter) — Order of partial moment"><span class="n"><span class="pre">n</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.partial_moment.z" title="arch.univariate.GeneralizedError.partial_moment.z (Python parameter) — Upper bound for partial moment integral"><span class="n"><span class="pre">z</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.partial_moment.parameters" title="arch.univariate.GeneralizedError.partial_moment.parameters (Python parameter) — Distribution parameters.  Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#GeneralizedError.partial_moment"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.GeneralizedError.partial_moment" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.GeneralizedError.partial_moment">
+<span class="sig-prename descclassname"><span class="pre">GeneralizedError.</span></span><span class="sig-name descname"><span class="pre">partial_moment</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.partial_moment.n" title="arch.univariate.GeneralizedError.partial_moment.n (Python parameter) — Order of partial moment"><span class="n"><span class="pre">n</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.partial_moment.z" title="arch.univariate.GeneralizedError.partial_moment.z (Python parameter) — Upper bound for partial moment integral"><span class="n"><span class="pre">z</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.partial_moment.parameters" title="arch.univariate.GeneralizedError.partial_moment.parameters (Python parameter) — Distribution parameters.  Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#GeneralizedError.partial_moment"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.GeneralizedError.partial_moment" title="Link to this definition">¶</a></dt>
 <dd><p>Order n lower partial moment from -inf to z</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.GeneralizedError.partial_moment-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GeneralizedError.partial_moment-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.GeneralizedError.partial_moment.n"><span class="sig-name descname">n</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.GeneralizedError.partial_moment.n" title="Permalink to this definition">¶</a></dt><dd><p>Order of partial moment</p>
+<dt id="arch.univariate.GeneralizedError.partial_moment.n"><span class="n sig-name">n</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.GeneralizedError.partial_moment.n" title="Permalink to this definition">¶</a></dt><dd><p>Order of partial moment</p>
 </dd>
-<dt id="arch.univariate.GeneralizedError.partial_moment.z"><span class="sig-name descname">z</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>, optional</span></span><a class="headerlink" href="#arch.univariate.GeneralizedError.partial_moment.z" title="Permalink to this definition">¶</a></dt><dd><p>Upper bound for partial moment integral</p>
+<dt id="arch.univariate.GeneralizedError.partial_moment.z"><span class="n sig-name">z</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.0</span></code><a class="headerlink" href="#arch.univariate.GeneralizedError.partial_moment.z" title="Permalink to this definition">¶</a></dt><dd><p>Upper bound for partial moment integral</p>
 </dd>
-<dt id="arch.univariate.GeneralizedError.partial_moment.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray, optional</span></span><a class="headerlink" href="#arch.univariate.GeneralizedError.partial_moment.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters.  Use None for parameterless distributions.</p>
+<dt id="arch.univariate.GeneralizedError.partial_moment.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.GeneralizedError.partial_moment.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters.  Use None for parameterless distributions.</p>
 </dd>
 </dl>
 </dd>
@@ -1146,7 +1146,7 @@ <h1 id="arch-univariate-generalizederror-partial-moment">arch.univariate.General
 <dd class="field-even"><p>Partial moment</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.GeneralizedError.partial_moment-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GeneralizedError.partial_moment-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 <p class="rubric">References</p>
@@ -1231,7 +1231,7 @@ <h1 id="arch-univariate-generalizederror-partial-moment">arch.univariate.General
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1278,10 +1278,10 @@ <h1 id="arch-univariate-generalizederror-partial-moment">arch.univariate.General
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+        <script id="MathJax-script" src="../../../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
     
   </body>
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diff --git a/univariate/generated/generated/arch.univariate.GeneralizedError.ppf.html b/univariate/generated/generated/arch.univariate.GeneralizedError.ppf.html
index 61d899f5a7..881c0edfa3 100644
--- a/univariate/generated/generated/arch.univariate.GeneralizedError.ppf.html
+++ b/univariate/generated/generated/arch.univariate.GeneralizedError.ppf.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GeneralizedError.ppf - arch 6.1.0</title>
+        <title>arch.univariate.GeneralizedError.ppf - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1112,17 +1112,17 @@
                   
 
 
-<h1 id="arch-univariate-generalizederror-ppf">arch.univariate.GeneralizedError.ppf<a class="headerlink" href="#arch-univariate-generalizederror-ppf" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-generalizederror-ppf">arch.univariate.GeneralizedError.ppf<a class="headerlink" href="#arch-univariate-generalizederror-ppf" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.GeneralizedError.ppf">
-<span class="sig-prename descclassname"><span class="pre">GeneralizedError.</span></span><span class="sig-name descname"><span class="pre">ppf</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.ppf.pits" title="arch.univariate.GeneralizedError.ppf.pits (Python parameter) — Probability-integral-transformed values in the interval (0, 1)."><span class="n"><span class="pre">pits</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.ppf.parameters" title="arch.univariate.GeneralizedError.ppf.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#GeneralizedError.ppf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.GeneralizedError.ppf" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.GeneralizedError.ppf">
+<span class="sig-prename descclassname"><span class="pre">GeneralizedError.</span></span><span class="sig-name descname"><span class="pre">ppf</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.ppf.pits" title="arch.univariate.GeneralizedError.ppf.pits (Python parameter) — Probability-integral-transformed values in the interval (0, 1)."><span class="n"><span class="pre">pits</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.ppf.parameters" title="arch.univariate.GeneralizedError.ppf.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#GeneralizedError.ppf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.GeneralizedError.ppf" title="Link to this definition">¶</a></dt>
 <dd><p>Inverse cumulative density function (ICDF)</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.GeneralizedError.ppf-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GeneralizedError.ppf-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.GeneralizedError.ppf.pits"><span class="sig-name descname">pits</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}</span></span><a class="headerlink" href="#arch.univariate.GeneralizedError.ppf.pits" title="Permalink to this definition">¶</a></dt><dd><p>Probability-integral-transformed values in the interval (0, 1).</p>
+<dt id="arch.univariate.GeneralizedError.ppf.pits"><span class="n sig-name">pits</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.GeneralizedError.ppf.pits" title="Permalink to this definition">¶</a></dt><dd><p>Probability-integral-transformed values in the interval (0, 1).</p>
 </dd>
-<dt id="arch.univariate.GeneralizedError.ppf.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray, optional</span></span><a class="headerlink" href="#arch.univariate.GeneralizedError.ppf.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use <code class="docutils literal notranslate"><span class="pre">None</span></code> for parameterless
+<dt id="arch.univariate.GeneralizedError.ppf.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.GeneralizedError.ppf.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use <code class="docutils literal notranslate"><span class="pre">None</span></code> for parameterless
 distributions.</p>
 </dd>
 </dl>
@@ -1131,7 +1131,7 @@ <h1 id="arch-univariate-generalizederror-ppf">arch.univariate.GeneralizedError.p
 <dd class="field-even"><p><strong>i</strong> – Inverse CDF values</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.GeneralizedError.ppf-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GeneralizedError.ppf-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>{float, ndarray}</p>
+<dd class="field-odd"><p><code class="docutils literal notranslate"><span class="pre">{float,</span> <span class="pre">ndarray}</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1203,7 +1203,7 @@ <h1 id="arch-univariate-generalizederror-ppf">arch.univariate.GeneralizedError.p
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1250,7 +1250,7 @@ <h1 id="arch-univariate-generalizederror-ppf">arch.univariate.GeneralizedError.p
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diff --git a/univariate/generated/generated/arch.univariate.GeneralizedError.random_state.html b/univariate/generated/generated/arch.univariate.GeneralizedError.random_state.html
index 0f8bd2696a..0b98c890dc 100644
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+++ b/univariate/generated/generated/arch.univariate.GeneralizedError.random_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GeneralizedError.random_state - arch 6.1.0</title>
+        <title>arch.univariate.GeneralizedError.random_state - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch 6.1.0
+    arch 6.2.0
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@@ -1018,13 +1018,13 @@
                   
 
 
-<h1 id="arch-univariate-generalizederror-random-state">arch.univariate.GeneralizedError.random_state<a class="headerlink" href="#arch-univariate-generalizederror-random-state" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-generalizederror-random-state">arch.univariate.GeneralizedError.random_state<a class="headerlink" href="#arch-univariate-generalizederror-random-state" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.GeneralizedError.random_state">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">GeneralizedError.</span></span><span class="sig-name descname"><span class="pre">random_state</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.24)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.24)"><span class="pre">Generator</span></a></span><a class="headerlink" href="#arch.univariate.GeneralizedError.random_state" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">GeneralizedError.</span></span><span class="sig-name descname"><span class="pre">random_state</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><span class="pre">Generator</span></a></span><a class="headerlink" href="#arch.univariate.GeneralizedError.random_state" title="Link to this definition">¶</a></dt>
 <dd><p>The NumPy RandomState attached to the distribution</p>
-<div class="deprecated">
-<p><span class="versionmodified deprecated">Deprecated since version 5.0: </span>random_state is deprecated. Use generator instead.</p>
+<div class="admonition deprecated">
+<p class="admonition-title"><span class="versionmodified deprecated">Deprecated since version 5.0: </span>random_state is deprecated. Use generator instead.</p>
 </div>
 </dd></dl>
 
@@ -1095,7 +1095,7 @@ <h1 id="arch-univariate-generalizederror-random-state">arch.univariate.Generaliz
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1142,7 +1142,7 @@ <h1 id="arch-univariate-generalizederror-random-state">arch.univariate.Generaliz
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diff --git a/univariate/generated/generated/arch.univariate.GeneralizedError.simulate.html b/univariate/generated/generated/arch.univariate.GeneralizedError.simulate.html
index 285e131388..d588763fc2 100644
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+++ b/univariate/generated/generated/arch.univariate.GeneralizedError.simulate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GeneralizedError.simulate - arch 6.1.0</title>
+        <title>arch.univariate.GeneralizedError.simulate - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1098,15 +1098,15 @@
                   
 
 
-<h1 id="arch-univariate-generalizederror-simulate">arch.univariate.GeneralizedError.simulate<a class="headerlink" href="#arch-univariate-generalizederror-simulate" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-generalizederror-simulate">arch.univariate.GeneralizedError.simulate<a class="headerlink" href="#arch-univariate-generalizederror-simulate" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.GeneralizedError.simulate">
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+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.GeneralizedError.simulate">
+<span class="sig-prename descclassname"><span class="pre">GeneralizedError.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.simulate.parameters" title="arch.univariate.GeneralizedError.simulate.parameters (Python parameter) — Distribution parameters"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#GeneralizedError.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.GeneralizedError.simulate" title="Link to this definition">¶</a></dt>
 <dd><p>Simulates i.i.d. draws from the distribution</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.GeneralizedError.simulate-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GeneralizedError.simulate-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.GeneralizedError.simulate.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.GeneralizedError.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters</p>
+<dt id="arch.univariate.GeneralizedError.simulate.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.GeneralizedError.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters</p>
 </dd>
 </dl>
 </dd>
@@ -1115,7 +1115,7 @@ <h1 id="arch-univariate-generalizederror-simulate">arch.univariate.GeneralizedEr
 draws from the distribution</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.GeneralizedError.simulate-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GeneralizedError.simulate-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>callable</p>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">callable</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1187,7 +1187,7 @@ <h1 id="arch-univariate-generalizederror-simulate">arch.univariate.GeneralizedEr
   
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-    7.0.1.
+    7.2.6.
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diff --git a/univariate/generated/generated/arch.univariate.GeneralizedError.starting_values.html b/univariate/generated/generated/arch.univariate.GeneralizedError.starting_values.html
index 032b942bcc..c86fa3e8a3 100644
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+++ b/univariate/generated/generated/arch.univariate.GeneralizedError.starting_values.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1098,15 +1098,15 @@
                   
 
 
-<h1 id="arch-univariate-generalizederror-starting-values">arch.univariate.GeneralizedError.starting_values<a class="headerlink" href="#arch-univariate-generalizederror-starting-values" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-generalizederror-starting-values">arch.univariate.GeneralizedError.starting_values<a class="headerlink" href="#arch-univariate-generalizederror-starting-values" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.GeneralizedError.starting_values">
-<span class="sig-prename descclassname"><span class="pre">GeneralizedError.</span></span><span class="sig-name descname"><span class="pre">starting_values</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.starting_values.std_resid" title="arch.univariate.GeneralizedError.starting_values.std_resid (Python parameter) — Estimated standardized residuals to use in computing starting values for the shape parameter"><span class="n"><span class="pre">std_resid</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#GeneralizedError.starting_values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.GeneralizedError.starting_values" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">GeneralizedError.</span></span><span class="sig-name descname"><span class="pre">starting_values</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.starting_values.std_resid" title="arch.univariate.GeneralizedError.starting_values.std_resid (Python parameter) — Estimated standardized residuals to use in computing starting values for the shape parameter"><span class="n"><span class="pre">std_resid</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#GeneralizedError.starting_values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.GeneralizedError.starting_values" title="Link to this definition">¶</a></dt>
 <dd><p>Construct starting values for use in optimization.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.GeneralizedError.starting_values-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GeneralizedError.starting_values-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.GeneralizedError.starting_values.std_resid"><span class="sig-name descname">std_resid</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.GeneralizedError.starting_values.std_resid" title="Permalink to this definition">¶</a></dt><dd><p>Estimated standardized residuals to use in computing starting
+<dt id="arch.univariate.GeneralizedError.starting_values.std_resid"><span class="n sig-name">std_resid</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.GeneralizedError.starting_values.std_resid" title="Permalink to this definition">¶</a></dt><dd><p>Estimated standardized residuals to use in computing starting
 values for the shape parameter</p>
 </dd>
 </dl>
@@ -1115,7 +1115,7 @@ <h1 id="arch-univariate-generalizederror-starting-values">arch.univariate.Genera
 <dd class="field-even"><p><strong>sv</strong> – Array containing starting valuer for shape parameter</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.GeneralizedError.starting_values-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GeneralizedError.starting_values-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -1189,7 +1189,7 @@ <h1 id="arch-univariate-generalizederror-starting-values">arch.univariate.Genera
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
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@@ -1236,7 +1236,7 @@ <h1 id="arch-univariate-generalizederror-starting-values">arch.univariate.Genera
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diff --git a/univariate/generated/generated/arch.univariate.HARCH.backcast.html b/univariate/generated/generated/arch.univariate.HARCH.backcast.html
index 1f292bf15c..176e599d39 100644
--- a/univariate/generated/generated/arch.univariate.HARCH.backcast.html
+++ b/univariate/generated/generated/arch.univariate.HARCH.backcast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.HARCH.backcast - arch 6.1.0</title>
+        <title>arch.univariate.HARCH.backcast - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
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-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1248,15 +1248,15 @@
                   
 
 
-<h1 id="arch-univariate-harch-backcast">arch.univariate.HARCH.backcast<a class="headerlink" href="#arch-univariate-harch-backcast" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-harch-backcast">arch.univariate.HARCH.backcast<a class="headerlink" href="#arch-univariate-harch-backcast" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.HARCH.backcast">
-<span class="sig-prename descclassname"><span class="pre">HARCH.</span></span><span class="sig-name descname"><span class="pre">backcast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.backcast.resids" title="arch.univariate.HARCH.backcast.resids (Python parameter) — Vector of (approximate) residuals"><span class="n"><span class="pre">resids</span></span></a></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.HARCH.backcast" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">HARCH.</span></span><span class="sig-name descname"><span class="pre">backcast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.backcast.resids" title="arch.univariate.HARCH.backcast.resids (Python parameter) — Vector of (approximate) residuals"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.univariate.HARCH.backcast" title="Link to this definition">¶</a></dt>
 <dd><p>Construct values for backcasting to start the recursion</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.HARCH.backcast-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARCH.backcast-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.HARCH.backcast.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.HARCH.backcast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of (approximate) residuals</p>
+<dt id="arch.univariate.HARCH.backcast.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.HARCH.backcast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of (approximate) residuals</p>
 </dd>
 </dl>
 </dd>
@@ -1264,7 +1264,7 @@ <h1 id="arch-univariate-harch-backcast">arch.univariate.HARCH.backcast<a class="
 <dd class="field-even"><p><strong>backcast</strong> – Value to use in backcasting in the volatility recursion</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.HARCH.backcast-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARCH.backcast-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1336,7 +1336,7 @@ <h1 id="arch-univariate-harch-backcast">arch.univariate.HARCH.backcast<a class="
   
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     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1383,7 +1383,7 @@ <h1 id="arch-univariate-harch-backcast">arch.univariate.HARCH.backcast<a class="
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diff --git a/univariate/generated/generated/arch.univariate.HARCH.backcast_transform.html b/univariate/generated/generated/arch.univariate.HARCH.backcast_transform.html
index a79e50aeac..817e0156b0 100644
--- a/univariate/generated/generated/arch.univariate.HARCH.backcast_transform.html
+++ b/univariate/generated/generated/arch.univariate.HARCH.backcast_transform.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1248,15 +1248,15 @@
                   
 
 
-<h1 id="arch-univariate-harch-backcast-transform">arch.univariate.HARCH.backcast_transform<a class="headerlink" href="#arch-univariate-harch-backcast-transform" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-harch-backcast-transform">arch.univariate.HARCH.backcast_transform<a class="headerlink" href="#arch-univariate-harch-backcast-transform" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.HARCH.backcast_transform">
-<span class="sig-prename descclassname"><span class="pre">HARCH.</span></span><span class="sig-name descname"><span class="pre">backcast_transform</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.backcast_transform.backcast" title="arch.univariate.HARCH.backcast_transform.backcast (Python parameter) — User-provided backcast that approximates sigma2[0]."><span class="n"><span class="pre">backcast</span></span></a></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.HARCH.backcast_transform" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.HARCH.backcast_transform">
+<span class="sig-prename descclassname"><span class="pre">HARCH.</span></span><span class="sig-name descname"><span class="pre">backcast_transform</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.backcast_transform.backcast" title="arch.univariate.HARCH.backcast_transform.backcast (Python parameter) — User-provided backcast that approximates sigma2[0]."><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.univariate.HARCH.backcast_transform" title="Link to this definition">¶</a></dt>
 <dd><p>Transformation to apply to user-provided backcast values</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.HARCH.backcast_transform-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARCH.backcast_transform-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.HARCH.backcast_transform.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}</span></span><a class="headerlink" href="#arch.univariate.HARCH.backcast_transform.backcast" title="Permalink to this definition">¶</a></dt><dd><p>User-provided <code class="docutils literal notranslate"><span class="pre">backcast</span></code> that approximates sigma2[0].</p>
+<dt id="arch.univariate.HARCH.backcast_transform.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.HARCH.backcast_transform.backcast" title="Permalink to this definition">¶</a></dt><dd><p>User-provided <code class="docutils literal notranslate"><span class="pre">backcast</span></code> that approximates sigma2[0].</p>
 </dd>
 </dl>
 </dd>
@@ -1264,7 +1264,7 @@ <h1 id="arch-univariate-harch-backcast-transform">arch.univariate.HARCH.backcast
 <dd class="field-even"><p><strong>backcast</strong> – Backcast transformed to the model-appropriate scale</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.HARCH.backcast_transform-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARCH.backcast_transform-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>{float, ndarray}</p>
+<dd class="field-odd"><p><code class="docutils literal notranslate"><span class="pre">{float,</span> <span class="pre">ndarray}</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1336,7 +1336,7 @@ <h1 id="arch-univariate-harch-backcast-transform">arch.univariate.HARCH.backcast
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1383,7 +1383,7 @@ <h1 id="arch-univariate-harch-backcast-transform">arch.univariate.HARCH.backcast
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diff --git a/univariate/generated/generated/arch.univariate.HARCH.bounds.html b/univariate/generated/generated/arch.univariate.HARCH.bounds.html
index ca735f727f..8f2ab46010 100644
--- a/univariate/generated/generated/arch.univariate.HARCH.bounds.html
+++ b/univariate/generated/generated/arch.univariate.HARCH.bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.HARCH.bounds - arch 6.1.0</title>
+        <title>arch.univariate.HARCH.bounds - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch 6.1.0
+    arch 6.2.0
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@@ -1248,15 +1248,15 @@
                   
 
 
-<h1 id="arch-univariate-harch-bounds">arch.univariate.HARCH.bounds<a class="headerlink" href="#arch-univariate-harch-bounds" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-harch-bounds">arch.univariate.HARCH.bounds<a class="headerlink" href="#arch-univariate-harch-bounds" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.HARCH.bounds">
-<span class="sig-prename descclassname"><span class="pre">HARCH.</span></span><span class="sig-name descname"><span class="pre">bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.bounds.resids" title="arch.univariate.HARCH.bounds.resids (Python parameter) — Vector of (approximate) residuals"><span class="n"><span class="pre">resids</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#HARCH.bounds"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.HARCH.bounds" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">HARCH.</span></span><span class="sig-name descname"><span class="pre">bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.bounds.resids" title="arch.univariate.HARCH.bounds.resids (Python parameter) — Vector of (approximate) residuals"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#HARCH.bounds"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.HARCH.bounds" title="Link to this definition">¶</a></dt>
 <dd><p>Returns bounds for parameters</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.HARCH.bounds-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARCH.bounds-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.HARCH.bounds.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.HARCH.bounds.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of (approximate) residuals</p>
+<dt id="arch.univariate.HARCH.bounds.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.HARCH.bounds.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of (approximate) residuals</p>
 </dd>
 </dl>
 </dd>
@@ -1264,7 +1264,7 @@ <h1 id="arch-univariate-harch-bounds">arch.univariate.HARCH.bounds<a class="head
 <dd class="field-even"><p><strong>bounds</strong> – List of bounds where each element is (lower, upper).</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.HARCH.bounds-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARCH.bounds-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a>[<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a>[<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>,<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>]]</p>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">list[tuple[float,float]]</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1336,7 +1336,7 @@ <h1 id="arch-univariate-harch-bounds">arch.univariate.HARCH.bounds<a class="head
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
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@@ -1383,7 +1383,7 @@ <h1 id="arch-univariate-harch-bounds">arch.univariate.HARCH.bounds<a class="head
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diff --git a/univariate/generated/generated/arch.univariate.HARCH.compute_variance.html b/univariate/generated/generated/arch.univariate.HARCH.compute_variance.html
index 79c45ba4ec..a11c440066 100644
--- a/univariate/generated/generated/arch.univariate.HARCH.compute_variance.html
+++ b/univariate/generated/generated/arch.univariate.HARCH.compute_variance.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.HARCH.compute_variance - arch 6.1.0</title>
+        <title>arch.univariate.HARCH.compute_variance - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+    arch 6.2.0
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@@ -657,13 +657,6 @@
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-        
-          <li class="md-nav__item">
-  <a href="#arch.univariate.HARCH.compute_variance-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
       </ul>
@@ -1262,13 +1255,6 @@
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     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.univariate.HARCH.compute_variance-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
       </ul>
@@ -1290,30 +1276,27 @@
                   
 
 
-<h1 id="arch-univariate-harch-compute-variance">arch.univariate.HARCH.compute_variance<a class="headerlink" href="#arch-univariate-harch-compute-variance" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-harch-compute-variance">arch.univariate.HARCH.compute_variance<a class="headerlink" href="#arch-univariate-harch-compute-variance" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.HARCH.compute_variance">
-<span class="sig-prename descclassname"><span class="pre">HARCH.</span></span><span class="sig-name descname"><span class="pre">compute_variance</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.compute_variance.parameters" title="arch.univariate.HARCH.compute_variance.parameters (Python parameter) — Model parameters"><span class="n"><span class="pre">parameters</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.compute_variance.resids" title="arch.univariate.HARCH.compute_variance.resids (Python parameter) — Vector of mean zero residuals"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.compute_variance.sigma2" title="arch.univariate.HARCH.compute_variance.sigma2 (Python parameter) — Array with same size as resids to store the conditional variance"><span class="n"><span class="pre">sigma2</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.compute_variance.backcast" title="arch.univariate.HARCH.compute_variance.backcast (Python parameter) — Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."><span class="n"><span class="pre">backcast</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.compute_variance.var_bounds" title="arch.univariate.HARCH.compute_variance.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#HARCH.compute_variance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.HARCH.compute_variance" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">HARCH.</span></span><span class="sig-name descname"><span class="pre">compute_variance</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.compute_variance.parameters" title="arch.univariate.HARCH.compute_variance.parameters (Python parameter) — Model parameters"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.compute_variance.resids" title="arch.univariate.HARCH.compute_variance.resids (Python parameter) — Vector of mean zero residuals"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.compute_variance.sigma2" title="arch.univariate.HARCH.compute_variance.sigma2 (Python parameter) — Array with same size as resids to store the conditional variance"><span class="n"><span class="pre">sigma2</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.compute_variance.backcast" title="arch.univariate.HARCH.compute_variance.backcast (Python parameter) — Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.compute_variance.var_bounds" title="arch.univariate.HARCH.compute_variance.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#HARCH.compute_variance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.HARCH.compute_variance" title="Link to this definition">¶</a></dt>
 <dd><p>Compute the variance for the ARCH model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.HARCH.compute_variance-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARCH.compute_variance-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.HARCH.compute_variance.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.HARCH.compute_variance.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
+<dt id="arch.univariate.HARCH.compute_variance.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.HARCH.compute_variance.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
 </dd>
-<dt id="arch.univariate.HARCH.compute_variance.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.HARCH.compute_variance.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of mean zero residuals</p>
+<dt id="arch.univariate.HARCH.compute_variance.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.HARCH.compute_variance.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of mean zero residuals</p>
 </dd>
-<dt id="arch.univariate.HARCH.compute_variance.sigma2"><span class="sig-name descname">sigma2</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.HARCH.compute_variance.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>Array with same size as resids to store the conditional variance</p>
+<dt id="arch.univariate.HARCH.compute_variance.sigma2"><span class="n sig-name">sigma2</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.HARCH.compute_variance.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>Array with same size as resids to store the conditional variance</p>
 </dd>
-<dt id="arch.univariate.HARCH.compute_variance.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}</span></span><a class="headerlink" href="#arch.univariate.HARCH.compute_variance.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing ARCH recursion. Can be an ndarray
+<dt id="arch.univariate.HARCH.compute_variance.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.HARCH.compute_variance.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing ARCH recursion. Can be an ndarray
 when the model contains multiple components.</p>
 </dd>
-<dt id="arch.univariate.HARCH.compute_variance.var_bounds"><span class="sig-name descname">var_bounds</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.HARCH.compute_variance.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
+<dt id="arch.univariate.HARCH.compute_variance.var_bounds"><span class="n sig-name">var_bounds</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.HARCH.compute_variance.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
 </dd>
 </dl>
 </dd>
-<dt class="field-even" id="arch.univariate.HARCH.compute_variance-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARCH.compute_variance-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a></p>
-</dd>
 </dl>
 </dd></dl>
 
@@ -1384,7 +1367,7 @@ <h1 id="arch-univariate-harch-compute-variance">arch.univariate.HARCH.compute_va
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1431,7 +1414,7 @@ <h1 id="arch-univariate-harch-compute-variance">arch.univariate.HARCH.compute_va
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diff --git a/univariate/generated/generated/arch.univariate.HARCH.constraints.html b/univariate/generated/generated/arch.univariate.HARCH.constraints.html
index aabb2c6cae..9f10370f54 100644
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+++ b/univariate/generated/generated/arch.univariate.HARCH.constraints.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.HARCH.constraints - arch 6.1.0</title>
+        <title>arch.univariate.HARCH.constraints - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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@@ -626,13 +626,6 @@
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           <li class="md-nav__item">
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@@ -1175,13 +1168,6 @@
     <nav class="md-nav" aria-label="HARCH.constraints">
       <ul class="md-nav__list">
         
-          <li class="md-nav__item">
-  <a href="#arch.univariate.HARCH.constraints-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
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   <a href="#arch.univariate.HARCH.constraints-returns" class="md-nav__link">
     <span class="md-ellipsis">Returns</span>
@@ -1208,20 +1194,17 @@
                   
 
 
-<h1 id="arch-univariate-harch-constraints">arch.univariate.HARCH.constraints<a class="headerlink" href="#arch-univariate-harch-constraints" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-harch-constraints">arch.univariate.HARCH.constraints<a class="headerlink" href="#arch-univariate-harch-constraints" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.HARCH.constraints">
-<span class="sig-prename descclassname"><span class="pre">HARCH.</span></span><span class="sig-name descname"><span class="pre">constraints</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#HARCH.constraints"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.HARCH.constraints" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">HARCH.</span></span><span class="sig-name descname"><span class="pre">constraints</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#HARCH.constraints"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.HARCH.constraints" title="Link to this definition">¶</a></dt>
 <dd><p>Construct parameter constraints arrays for parameter estimation</p>
 <dl class="field-list simple">
-<dt class="field-odd" id="arch.univariate.HARCH.constraints-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARCH.constraints-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>]</p>
-</dd>
-<dt class="field-even" id="arch.univariate.HARCH.constraints-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARCH.constraints-returns" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><ul class="simple">
-<li><p><strong>A</strong> (<em>ndarray</em>) – Parameters loadings in constraint. Shape is number of constraints
+<dt class="field-odd" id="arch.univariate.HARCH.constraints-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARCH.constraints-returns" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p><strong>A</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – Parameters loadings in constraint. Shape is number of constraints
 by number of parameters</p></li>
-<li><p><strong>b</strong> (<em>ndarray</em>) – Constraint values, one for each constraint</p></li>
+<li><p><strong>b</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – Constraint values, one for each constraint</p></li>
 </ul>
 </p>
 </dd>
@@ -1298,7 +1281,7 @@ <h1 id="arch-univariate-harch-constraints">arch.univariate.HARCH.constraints<a c
   
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@@ -1345,7 +1328,7 @@ <h1 id="arch-univariate-harch-constraints">arch.univariate.HARCH.constraints<a c
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diff --git a/univariate/generated/generated/arch.univariate.HARCH.forecast.html b/univariate/generated/generated/arch.univariate.HARCH.forecast.html
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch 6.1.0
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@@ -1388,38 +1388,38 @@
                   
 
 
-<h1 id="arch-univariate-harch-forecast">arch.univariate.HARCH.forecast<a class="headerlink" href="#arch-univariate-harch-forecast" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-harch-forecast">arch.univariate.HARCH.forecast<a class="headerlink" href="#arch-univariate-harch-forecast" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.HARCH.forecast">
-<span class="sig-prename descclassname"><span class="pre">HARCH.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.forecast.parameters" title="arch.univariate.HARCH.forecast.parameters (Python parameter) — Parameters required to forecast the volatility model"><span class="n"><span class="pre">parameters</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.forecast.resids" title="arch.univariate.HARCH.forecast.resids (Python parameter) — Residuals to use in the recursion"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.forecast.backcast" title="arch.univariate.HARCH.forecast.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.forecast.var_bounds" title="arch.univariate.HARCH.forecast.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.forecast.start" title="arch.univariate.HARCH.forecast.start (Python parameter) — Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."><span class="n"><span class="pre">start</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.forecast.horizon" title="arch.univariate.HARCH.forecast.horizon (Python parameter) — Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."><span class="n"><span class="pre">horizon</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.forecast.method" title="arch.univariate.HARCH.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic."><span class="n"><span class="pre">method</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.forecast.simulations" title="arch.univariate.HARCH.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.forecast.rng" title="arch.univariate.HARCH.forecast.rng (Python parameter) — Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."><span class="n"><span class="pre">rng</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.forecast.random_state" title="arch.univariate.HARCH.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.HARCH.forecast" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">HARCH.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.forecast.parameters" title="arch.univariate.HARCH.forecast.parameters (Python parameter) — Parameters required to forecast the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.forecast.resids" title="arch.univariate.HARCH.forecast.resids (Python parameter) — Residuals to use in the recursion"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.forecast.backcast" title="arch.univariate.HARCH.forecast.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.forecast.var_bounds" title="arch.univariate.HARCH.forecast.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.forecast.start" title="arch.univariate.HARCH.forecast.start (Python parameter) — Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.forecast.horizon" title="arch.univariate.HARCH.forecast.horizon (Python parameter) — Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.forecast.method" title="arch.univariate.HARCH.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.forecast.simulations" title="arch.univariate.HARCH.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.forecast.rng" title="arch.univariate.HARCH.forecast.rng (Python parameter) — Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.forecast.random_state" title="arch.univariate.HARCH.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><span class="pre">VarianceForecast</span></span></span></span><a class="headerlink" href="#arch.univariate.HARCH.forecast" title="Link to this definition">¶</a></dt>
 <dd><p>Forecast volatility from the model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.HARCH.forecast-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARCH.forecast-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.HARCH.forecast.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, Series}</span></span><a class="headerlink" href="#arch.univariate.HARCH.forecast.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast the volatility model</p>
+<dt id="arch.univariate.HARCH.forecast.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.HARCH.forecast.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast the volatility model</p>
 </dd>
-<dt id="arch.univariate.HARCH.forecast.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.HARCH.forecast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Residuals to use in the recursion</p>
+<dt id="arch.univariate.HARCH.forecast.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.HARCH.forecast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Residuals to use in the recursion</p>
 </dd>
-<dt id="arch.univariate.HARCH.forecast.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><a class="headerlink" href="#arch.univariate.HARCH.forecast.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing the recursion</p>
+<dt id="arch.univariate.HARCH.forecast.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><a class="headerlink" href="#arch.univariate.HARCH.forecast.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing the recursion</p>
 </dd>
-<dt id="arch.univariate.HARCH.forecast.var_bounds"><span class="sig-name descname">var_bounds</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray, 2-d</span></span><a class="headerlink" href="#arch.univariate.HARCH.forecast.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
+<dt id="arch.univariate.HARCH.forecast.var_bounds"><span class="n sig-name">var_bounds</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.HARCH.forecast.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
 </dd>
-<dt id="arch.univariate.HARCH.forecast.start"><span class="sig-name descname">start</span><span class="p"> : </span><span class="desctype"><span class="desctype">{None, int}</span></span><a class="headerlink" href="#arch.univariate.HARCH.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>Index of the first observation to use as the starting point for
+<dt id="arch.univariate.HARCH.forecast.start"><span class="n sig-name">start</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.HARCH.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>Index of the first observation to use as the starting point for
 the forecast.  Default is len(resids).</p>
 </dd>
-<dt id="arch.univariate.HARCH.forecast.horizon"><span class="sig-name descname">horizon</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.HARCH.forecast.horizon" title="Permalink to this definition">¶</a></dt><dd><p>Forecast horizon.  Must be 1 or larger.  Forecasts are produced
+<dt id="arch.univariate.HARCH.forecast.horizon"><span class="n sig-name">horizon</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code><a class="headerlink" href="#arch.univariate.HARCH.forecast.horizon" title="Permalink to this definition">¶</a></dt><dd><p>Forecast horizon.  Must be 1 or larger.  Forecasts are produced
 for horizons in [1, horizon].</p>
 </dd>
-<dt id="arch.univariate.HARCH.forecast.method"><span class="sig-name descname">method</span><span class="p"> : </span><span class="desctype"><span class="desctype">{'analytic', 'simulation', 'bootstrap'}</span></span><a class="headerlink" href="#arch.univariate.HARCH.forecast.method" title="Permalink to this definition">¶</a></dt><dd><p>Method to use when producing the forecast. The default is analytic.</p>
+<dt id="arch.univariate.HARCH.forecast.method"><span class="n sig-name">method</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'analytic'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'simulation'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'bootstrap'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code><a class="headerlink" href="#arch.univariate.HARCH.forecast.method" title="Permalink to this definition">¶</a></dt><dd><p>Method to use when producing the forecast. The default is analytic.</p>
 </dd>
-<dt id="arch.univariate.HARCH.forecast.simulations"><span class="sig-name descname">simulations</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.HARCH.forecast.simulations" title="Permalink to this definition">¶</a></dt><dd><p>Number of simulations to run when computing the forecast using
+<dt id="arch.univariate.HARCH.forecast.simulations"><span class="n sig-name">simulations</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.univariate.HARCH.forecast.simulations" title="Permalink to this definition">¶</a></dt><dd><p>Number of simulations to run when computing the forecast using
 either simulation or bootstrap.</p>
 </dd>
-<dt id="arch.univariate.HARCH.forecast.rng"><span class="sig-name descname">rng</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable</span></span><a class="headerlink" href="#arch.univariate.HARCH.forecast.rng" title="Permalink to this definition">¶</a></dt><dd><p>Callable random number generator required if method is
+<dt id="arch.univariate.HARCH.forecast.rng"><span class="n sig-name">rng</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)">Callable</a><span class="p">[</span><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">,</span><span class="w"> </span><span class="p">...</span><span class="p">]</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.HARCH.forecast.rng" title="Permalink to this definition">¶</a></dt><dd><p>Callable random number generator required if method is
 ‘simulation’. Must take a single shape input and return random
 samples numbers with that shape.</p>
 </dd>
-<dt id="arch.univariate.HARCH.forecast.random_state"><span class="sig-name descname">random_state</span><span class="p"> : </span><span class="desctype"><span class="desctype">RandomState, optional</span></span><a class="headerlink" href="#arch.univariate.HARCH.forecast.random_state" title="Permalink to this definition">¶</a></dt><dd><p>NumPy RandomState instance to use when method is ‘bootstrap’</p>
+<dt id="arch.univariate.HARCH.forecast.random_state"><span class="n sig-name">random_state</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)">RandomState</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.HARCH.forecast.random_state" title="Permalink to this definition">¶</a></dt><dd><p>NumPy RandomState instance to use when method is ‘bootstrap’</p>
 </dd>
 </dl>
 </dd>
@@ -1428,7 +1428,7 @@ <h1 id="arch-univariate-harch-forecast">arch.univariate.HARCH.forecast<a class="
 or bootstrap, the simulated paths.</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.HARCH.forecast-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARCH.forecast-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>VarianceForecast</p>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">arch.univariate.volatility.VarianceForecast</span></code></p>
 </dd>
 <dt class="field-even" id="arch.univariate.HARCH.forecast-raises">Raises<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARCH.forecast-raises" title="Permalink to this headline">¶</a></dt>
 <dd class="field-even"><ul class="simple">
@@ -1515,7 +1515,7 @@ <h1 id="arch-univariate-harch-forecast">arch.univariate.HARCH.forecast<a class="
   
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diff --git a/univariate/generated/generated/arch.univariate.HARCH.name.html b/univariate/generated/generated/arch.univariate.HARCH.name.html
index 9a81b9eaa4..5c58010fec 100644
--- a/univariate/generated/generated/arch.univariate.HARCH.name.html
+++ b/univariate/generated/generated/arch.univariate.HARCH.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.HARCH.name - arch 6.1.0</title>
+        <title>arch.univariate.HARCH.name - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1168,10 +1168,10 @@
                   
 
 
-<h1 id="arch-univariate-harch-name">arch.univariate.HARCH.name<a class="headerlink" href="#arch-univariate-harch-name" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-harch-name">arch.univariate.HARCH.name<a class="headerlink" href="#arch-univariate-harch-name" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.HARCH.name">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">HARCH.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.univariate.HARCH.name" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">HARCH.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.univariate.HARCH.name" title="Link to this definition">¶</a></dt>
 <dd><p>The name of the volatility process</p>
 </dd></dl>
 
@@ -1242,7 +1242,7 @@ <h1 id="arch-univariate-harch-name">arch.univariate.HARCH.name<a class="headerli
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1289,7 +1289,7 @@ <h1 id="arch-univariate-harch-name">arch.univariate.HARCH.name<a class="headerli
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.HARCH.num_params.html b/univariate/generated/generated/arch.univariate.HARCH.num_params.html
index 2898e0dec9..cd1a449739 100644
--- a/univariate/generated/generated/arch.univariate.HARCH.num_params.html
+++ b/univariate/generated/generated/arch.univariate.HARCH.num_params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.HARCH.num_params - arch 6.1.0</title>
+        <title>arch.univariate.HARCH.num_params - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1168,10 +1168,10 @@
                   
 
 
-<h1 id="arch-univariate-harch-num-params">arch.univariate.HARCH.num_params<a class="headerlink" href="#arch-univariate-harch-num-params" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-harch-num-params">arch.univariate.HARCH.num_params<a class="headerlink" href="#arch-univariate-harch-num-params" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.HARCH.num_params">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">HARCH.</span></span><span class="sig-name descname"><span class="pre">num_params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.HARCH.num_params" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">HARCH.</span></span><span class="sig-name descname"><span class="pre">num_params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.HARCH.num_params" title="Link to this definition">¶</a></dt>
 <dd><p>The number of parameters in the model</p>
 </dd></dl>
 
@@ -1242,7 +1242,7 @@ <h1 id="arch-univariate-harch-num-params">arch.univariate.HARCH.num_params<a cla
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1289,7 +1289,7 @@ <h1 id="arch-univariate-harch-num-params">arch.univariate.HARCH.num_params<a cla
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.HARCH.parameter_names.html b/univariate/generated/generated/arch.univariate.HARCH.parameter_names.html
index df42d8337a..04d57718db 100644
--- a/univariate/generated/generated/arch.univariate.HARCH.parameter_names.html
+++ b/univariate/generated/generated/arch.univariate.HARCH.parameter_names.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.HARCH.parameter_names - arch 6.1.0</title>
+        <title>arch.univariate.HARCH.parameter_names - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1208,17 +1208,17 @@
                   
 
 
-<h1 id="arch-univariate-harch-parameter-names">arch.univariate.HARCH.parameter_names<a class="headerlink" href="#arch-univariate-harch-parameter-names" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-harch-parameter-names">arch.univariate.HARCH.parameter_names<a class="headerlink" href="#arch-univariate-harch-parameter-names" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.HARCH.parameter_names">
-<span class="sig-prename descclassname"><span class="pre">HARCH.</span></span><span class="sig-name descname"><span class="pre">parameter_names</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#HARCH.parameter_names"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.HARCH.parameter_names" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">HARCH.</span></span><span class="sig-name descname"><span class="pre">parameter_names</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#HARCH.parameter_names"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.HARCH.parameter_names" title="Link to this definition">¶</a></dt>
 <dd><p>Names of model parameters</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.HARCH.parameter_names-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARCH.parameter_names-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p><strong>names</strong> – Variables names</p>
 </dd>
 <dt class="field-even" id="arch.univariate.HARCH.parameter_names-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARCH.parameter_names-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>)</p>
+<dd class="field-even"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span> <span class="pre">(str)</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1290,7 +1290,7 @@ <h1 id="arch-univariate-harch-parameter-names">arch.univariate.HARCH.parameter_n
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1337,7 +1337,7 @@ <h1 id="arch-univariate-harch-parameter-names">arch.univariate.HARCH.parameter_n
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.HARCH.simulate.html b/univariate/generated/generated/arch.univariate.HARCH.simulate.html
index 47f8b17d07..e5018dc6c3 100644
--- a/univariate/generated/generated/arch.univariate.HARCH.simulate.html
+++ b/univariate/generated/generated/arch.univariate.HARCH.simulate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.HARCH.simulate - arch 6.1.0</title>
+        <title>arch.univariate.HARCH.simulate - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -717,13 +717,6 @@
       </ul>
     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.univariate.HARCH.simulate-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
           <li class="md-nav__item">
@@ -1269,13 +1262,6 @@
       </ul>
     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.univariate.HARCH.simulate-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
           <li class="md-nav__item">
@@ -1304,36 +1290,33 @@
                   
 
 
-<h1 id="arch-univariate-harch-simulate">arch.univariate.HARCH.simulate<a class="headerlink" href="#arch-univariate-harch-simulate" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-harch-simulate">arch.univariate.HARCH.simulate<a class="headerlink" href="#arch-univariate-harch-simulate" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.HARCH.simulate">
-<span class="sig-prename descclassname"><span class="pre">HARCH.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.simulate.parameters" title="arch.univariate.HARCH.simulate.parameters (Python parameter) — Parameters required to simulate the volatility model"><span class="n"><span class="pre">parameters</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.simulate.nobs" title="arch.univariate.HARCH.simulate.nobs (Python parameter) — Number of data points to simulate"><span class="n"><span class="pre">nobs</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.simulate.rng" title="arch.univariate.HARCH.simulate.rng (Python parameter) — Callable function that takes a single integer input and returns a vector of random numbers"><span class="n"><span class="pre">rng</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.simulate.burn" title="arch.univariate.HARCH.simulate.burn (Python parameter) — Number of additional observations to generate when initializing the simulation"><span class="n"><span class="pre">burn</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.simulate.initial_value" title="arch.univariate.HARCH.simulate.initial_value (Python parameter) — Scalar or array of initial values to use when initializing the simulation"><span class="n"><span class="pre">initial_value</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#HARCH.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.HARCH.simulate" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.HARCH.simulate">
+<span class="sig-prename descclassname"><span class="pre">HARCH.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.simulate.parameters" title="arch.univariate.HARCH.simulate.parameters (Python parameter) — Parameters required to simulate the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.simulate.nobs" title="arch.univariate.HARCH.simulate.nobs (Python parameter) — Number of data points to simulate"><span class="n"><span class="pre">nobs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.simulate.rng" title="arch.univariate.HARCH.simulate.rng (Python parameter) — Callable function that takes a single integer input and returns a vector of random numbers"><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.simulate.burn" title="arch.univariate.HARCH.simulate.burn (Python parameter) — Number of additional observations to generate when initializing the simulation"><span class="n"><span class="pre">burn</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.simulate.initial_value" title="arch.univariate.HARCH.simulate.initial_value (Python parameter) — Scalar or array of initial values to use when initializing the simulation"><span class="n"><span class="pre">initial_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#HARCH.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.HARCH.simulate" title="Link to this definition">¶</a></dt>
 <dd><p>Simulate data from the model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.HARCH.simulate-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARCH.simulate-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.HARCH.simulate.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, Series}</span></span><a class="headerlink" href="#arch.univariate.HARCH.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to simulate the volatility model</p>
+<dt id="arch.univariate.HARCH.simulate.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.HARCH.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to simulate the volatility model</p>
 </dd>
-<dt id="arch.univariate.HARCH.simulate.nobs"><span class="sig-name descname">nobs</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.HARCH.simulate.nobs" title="Permalink to this definition">¶</a></dt><dd><p>Number of data points to simulate</p>
+<dt id="arch.univariate.HARCH.simulate.nobs"><span class="n sig-name">nobs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.HARCH.simulate.nobs" title="Permalink to this definition">¶</a></dt><dd><p>Number of data points to simulate</p>
 </dd>
-<dt id="arch.univariate.HARCH.simulate.rng"><span class="sig-name descname">rng</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable</span></span><a class="headerlink" href="#arch.univariate.HARCH.simulate.rng" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that takes a single integer input and returns
+<dt id="arch.univariate.HARCH.simulate.rng"><span class="n sig-name">rng</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)">Callable</a><span class="p">[</span><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">,</span><span class="w"> </span><span class="p">...</span><span class="p">]</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.univariate.HARCH.simulate.rng" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that takes a single integer input and returns
 a vector of random numbers</p>
 </dd>
-<dt id="arch.univariate.HARCH.simulate.burn"><span class="sig-name descname">burn</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.univariate.HARCH.simulate.burn" title="Permalink to this definition">¶</a></dt><dd><p>Number of additional observations to generate when initializing
+<dt id="arch.univariate.HARCH.simulate.burn"><span class="n sig-name">burn</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code><a class="headerlink" href="#arch.univariate.HARCH.simulate.burn" title="Permalink to this definition">¶</a></dt><dd><p>Number of additional observations to generate when initializing
 the simulation</p>
 </dd>
-<dt id="arch.univariate.HARCH.simulate.initial_value"><span class="sig-name descname">initial_value</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}, optional</span></span><a class="headerlink" href="#arch.univariate.HARCH.simulate.initial_value" title="Permalink to this definition">¶</a></dt><dd><p>Scalar or array of initial values to use when initializing the
+<dt id="arch.univariate.HARCH.simulate.initial_value"><span class="n sig-name">initial_value</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.HARCH.simulate.initial_value" title="Permalink to this definition">¶</a></dt><dd><p>Scalar or array of initial values to use when initializing the
 simulation</p>
 </dd>
 </dl>
 </dd>
-<dt class="field-even" id="arch.univariate.HARCH.simulate-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARCH.simulate-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>]</p>
-</dd>
-<dt class="field-odd" id="arch.univariate.HARCH.simulate-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARCH.simulate-returns" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><ul class="simple">
-<li><p><strong>resids</strong> (<em>ndarray</em>) – The simulated residuals</p></li>
-<li><p><strong>variance</strong> (<em>ndarray</em>) – The simulated variance</p></li>
+<dt class="field-even" id="arch.univariate.HARCH.simulate-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARCH.simulate-returns" title="Permalink to this headline">¶</a></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><strong>resids</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – The simulated residuals</p></li>
+<li><p><strong>variance</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – The simulated variance</p></li>
 </ul>
 </p>
 </dd>
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-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1168,10 +1168,10 @@
                   
 
 
-<h1 id="arch-univariate-harch-start">arch.univariate.HARCH.start<a class="headerlink" href="#arch-univariate-harch-start" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-harch-start">arch.univariate.HARCH.start<a class="headerlink" href="#arch-univariate-harch-start" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.HARCH.start">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">HARCH.</span></span><span class="sig-name descname"><span class="pre">start</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.HARCH.start" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">HARCH.</span></span><span class="sig-name descname"><span class="pre">start</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.HARCH.start" title="Link to this definition">¶</a></dt>
 <dd><p>Index to use to start variance subarray selection</p>
 </dd></dl>
 
@@ -1242,7 +1242,7 @@ <h1 id="arch-univariate-harch-start">arch.univariate.HARCH.start<a class="header
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1289,7 +1289,7 @@ <h1 id="arch-univariate-harch-start">arch.univariate.HARCH.start<a class="header
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.HARCH.starting_values.html b/univariate/generated/generated/arch.univariate.HARCH.starting_values.html
index f715e9efe0..8ec4b10613 100644
--- a/univariate/generated/generated/arch.univariate.HARCH.starting_values.html
+++ b/univariate/generated/generated/arch.univariate.HARCH.starting_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.HARCH.starting_values - arch 6.1.0</title>
+        <title>arch.univariate.HARCH.starting_values - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1248,15 +1248,15 @@
                   
 
 
-<h1 id="arch-univariate-harch-starting-values">arch.univariate.HARCH.starting_values<a class="headerlink" href="#arch-univariate-harch-starting-values" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-harch-starting-values">arch.univariate.HARCH.starting_values<a class="headerlink" href="#arch-univariate-harch-starting-values" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.HARCH.starting_values">
-<span class="sig-prename descclassname"><span class="pre">HARCH.</span></span><span class="sig-name descname"><span class="pre">starting_values</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.starting_values.resids" title="arch.univariate.HARCH.starting_values.resids (Python parameter) — Array of (approximate) residuals to use when computing starting values"><span class="n"><span class="pre">resids</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#HARCH.starting_values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.HARCH.starting_values" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">HARCH.</span></span><span class="sig-name descname"><span class="pre">starting_values</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.starting_values.resids" title="arch.univariate.HARCH.starting_values.resids (Python parameter) — Array of (approximate) residuals to use when computing starting values"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#HARCH.starting_values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.HARCH.starting_values" title="Link to this definition">¶</a></dt>
 <dd><p>Returns starting values for the ARCH model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.HARCH.starting_values-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARCH.starting_values-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.HARCH.starting_values.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.HARCH.starting_values.resids" title="Permalink to this definition">¶</a></dt><dd><p>Array of (approximate) residuals to use when computing starting
+<dt id="arch.univariate.HARCH.starting_values.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.HARCH.starting_values.resids" title="Permalink to this definition">¶</a></dt><dd><p>Array of (approximate) residuals to use when computing starting
 values</p>
 </dd>
 </dl>
@@ -1265,7 +1265,7 @@ <h1 id="arch-univariate-harch-starting-values">arch.univariate.HARCH.starting_va
 <dd class="field-even"><p><strong>sv</strong> – Array of starting values</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.HARCH.starting_values-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARCH.starting_values-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1337,7 +1337,7 @@ <h1 id="arch-univariate-harch-starting-values">arch.univariate.HARCH.starting_va
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1384,7 +1384,7 @@ <h1 id="arch-univariate-harch-starting-values">arch.univariate.HARCH.starting_va
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.HARCH.stop.html b/univariate/generated/generated/arch.univariate.HARCH.stop.html
index 4a5ab50996..e24dbbba35 100644
--- a/univariate/generated/generated/arch.univariate.HARCH.stop.html
+++ b/univariate/generated/generated/arch.univariate.HARCH.stop.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.HARCH.stop - arch 6.1.0</title>
+        <title>arch.univariate.HARCH.stop - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1168,10 +1168,10 @@
                   
 
 
-<h1 id="arch-univariate-harch-stop">arch.univariate.HARCH.stop<a class="headerlink" href="#arch-univariate-harch-stop" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-harch-stop">arch.univariate.HARCH.stop<a class="headerlink" href="#arch-univariate-harch-stop" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.HARCH.stop">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">HARCH.</span></span><span class="sig-name descname"><span class="pre">stop</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.HARCH.stop" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">HARCH.</span></span><span class="sig-name descname"><span class="pre">stop</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.HARCH.stop" title="Link to this definition">¶</a></dt>
 <dd><p>Index to use to stop variance subarray selection</p>
 </dd></dl>
 
@@ -1242,7 +1242,7 @@ <h1 id="arch-univariate-harch-stop">arch.univariate.HARCH.stop<a class="headerli
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1289,7 +1289,7 @@ <h1 id="arch-univariate-harch-stop">arch.univariate.HARCH.stop<a class="headerli
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.HARCH.update.html b/univariate/generated/generated/arch.univariate.HARCH.update.html
index c0319d4341..2dd8eee2d2 100644
--- a/univariate/generated/generated/arch.univariate.HARCH.update.html
+++ b/univariate/generated/generated/arch.univariate.HARCH.update.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.HARCH.update - arch 6.1.0</title>
+        <title>arch.univariate.HARCH.update - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1318,28 +1318,28 @@
                   
 
 
-<h1 id="arch-univariate-harch-update">arch.univariate.HARCH.update<a class="headerlink" href="#arch-univariate-harch-update" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-harch-update">arch.univariate.HARCH.update<a class="headerlink" href="#arch-univariate-harch-update" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.HARCH.update">
-<span class="sig-prename descclassname"><span class="pre">HARCH.</span></span><span class="sig-name descname"><span class="pre">update</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.update.index" title="arch.univariate.HARCH.update.index (Python parameter) — The numerical index of the variance to compute"><span class="n"><span class="pre">index</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.update.parameters" title="arch.univariate.HARCH.update.parameters (Python parameter) — The variance model parameters"><span class="n"><span class="pre">parameters</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.update.resids" title="arch.univariate.HARCH.update.resids (Python parameter) — The residual array. Only uses resids[:index] when computing sigma2[index]"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.update.sigma2" title="arch.univariate.HARCH.update.sigma2 (Python parameter) — The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."><span class="n"><span class="pre">sigma2</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.update.backcast" title="arch.univariate.HARCH.update.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.update.var_bounds" title="arch.univariate.HARCH.update.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.HARCH.update" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">HARCH.</span></span><span class="sig-name descname"><span class="pre">update</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.update.index" title="arch.univariate.HARCH.update.index (Python parameter) — The numerical index of the variance to compute"><span class="n"><span class="pre">index</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.update.parameters" title="arch.univariate.HARCH.update.parameters (Python parameter) — The variance model parameters"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.update.resids" title="arch.univariate.HARCH.update.resids (Python parameter) — The residual array. Only uses resids[:index] when computing sigma2[index]"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.update.sigma2" title="arch.univariate.HARCH.update.sigma2 (Python parameter) — The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."><span class="n"><span class="pre">sigma2</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.update.backcast" title="arch.univariate.HARCH.update.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.update.var_bounds" title="arch.univariate.HARCH.update.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span></span><a class="headerlink" href="#arch.univariate.HARCH.update" title="Link to this definition">¶</a></dt>
 <dd><p>Compute the variance for a single observation</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.HARCH.update-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARCH.update-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.HARCH.update.index"><span class="sig-name descname">index</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.HARCH.update.index" title="Permalink to this definition">¶</a></dt><dd><p>The numerical index of the variance to compute</p>
+<dt id="arch.univariate.HARCH.update.index"><span class="n sig-name">index</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.HARCH.update.index" title="Permalink to this definition">¶</a></dt><dd><p>The numerical index of the variance to compute</p>
 </dd>
-<dt id="arch.univariate.HARCH.update.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.HARCH.update.parameters" title="Permalink to this definition">¶</a></dt><dd><p>The variance model parameters</p>
+<dt id="arch.univariate.HARCH.update.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.HARCH.update.parameters" title="Permalink to this definition">¶</a></dt><dd><p>The variance model parameters</p>
 </dd>
-<dt id="arch.univariate.HARCH.update.resids"><span class="n sig-name">resids</span><a class="headerlink" href="#arch.univariate.HARCH.update.resids" title="Permalink to this definition">¶</a></dt><dd><p>The residual array. Only uses <code class="docutils literal notranslate"><span class="pre">resids[:index]</span></code> when computing
+<dt id="arch.univariate.HARCH.update.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.HARCH.update.resids" title="Permalink to this definition">¶</a></dt><dd><p>The residual array. Only uses <code class="docutils literal notranslate"><span class="pre">resids[:index]</span></code> when computing
 <code class="docutils literal notranslate"><span class="pre">sigma2[index]</span></code></p>
 </dd>
-<dt id="arch.univariate.HARCH.update.sigma2"><span class="sig-name descname">sigma2</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.HARCH.update.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>The array containing the variances. Only uses <code class="docutils literal notranslate"><span class="pre">sigma2[:index]</span></code>
+<dt id="arch.univariate.HARCH.update.sigma2"><span class="n sig-name">sigma2</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.HARCH.update.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>The array containing the variances. Only uses <code class="docutils literal notranslate"><span class="pre">sigma2[:index]</span></code>
 when computing <code class="docutils literal notranslate"><span class="pre">sigma2[index]</span></code>. The computed value is stored
 in <code class="docutils literal notranslate"><span class="pre">sigma2[index]</span></code>.</p>
 </dd>
-<dt id="arch.univariate.HARCH.update.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}</span></span><a class="headerlink" href="#arch.univariate.HARCH.update.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing the recursion</p>
+<dt id="arch.univariate.HARCH.update.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.HARCH.update.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing the recursion</p>
 </dd>
-<dt id="arch.univariate.HARCH.update.var_bounds"><span class="sig-name descname">var_bounds</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.HARCH.update.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
+<dt id="arch.univariate.HARCH.update.var_bounds"><span class="n sig-name">var_bounds</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.HARCH.update.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
 </dd>
 </dl>
 </dd>
@@ -1347,7 +1347,7 @@ <h1 id="arch-univariate-harch-update">arch.univariate.HARCH.update<a class="head
 <dd class="field-even"><p>The variance computed for location <code class="docutils literal notranslate"><span class="pre">index</span></code></p>
 </dd>
 <dt class="field-odd" id="arch.univariate.HARCH.update-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARCH.update-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1419,7 +1419,7 @@ <h1 id="arch-univariate-harch-update">arch.univariate.HARCH.update<a class="head
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
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@@ -1466,7 +1466,7 @@ <h1 id="arch-univariate-harch-update">arch.univariate.HARCH.update<a class="head
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diff --git a/univariate/generated/generated/arch.univariate.HARCH.updateable.html b/univariate/generated/generated/arch.univariate.HARCH.updateable.html
index eab2ce321a..86b4e2f645 100644
--- a/univariate/generated/generated/arch.univariate.HARCH.updateable.html
+++ b/univariate/generated/generated/arch.univariate.HARCH.updateable.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.HARCH.updateable - arch 6.1.0</title>
+        <title>arch.univariate.HARCH.updateable - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1168,10 +1168,10 @@
                   
 
 
-<h1 id="arch-univariate-harch-updateable">arch.univariate.HARCH.updateable<a class="headerlink" href="#arch-univariate-harch-updateable" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-harch-updateable">arch.univariate.HARCH.updateable<a class="headerlink" href="#arch-univariate-harch-updateable" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.HARCH.updateable">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">HARCH.</span></span><span class="sig-name descname"><span class="pre">updateable</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.univariate.HARCH.updateable" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">HARCH.</span></span><span class="sig-name descname"><span class="pre">updateable</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.univariate.HARCH.updateable" title="Link to this definition">¶</a></dt>
 <dd><p>Flag indicating that the volatility process supports update</p>
 </dd></dl>
 
@@ -1242,7 +1242,7 @@ <h1 id="arch-univariate-harch-updateable">arch.univariate.HARCH.updateable<a cla
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1289,7 +1289,7 @@ <h1 id="arch-univariate-harch-updateable">arch.univariate.HARCH.updateable<a cla
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diff --git a/univariate/generated/generated/arch.univariate.HARCH.variance_bounds.html b/univariate/generated/generated/arch.univariate.HARCH.variance_bounds.html
index 63244111cd..54a0112ae2 100644
--- a/univariate/generated/generated/arch.univariate.HARCH.variance_bounds.html
+++ b/univariate/generated/generated/arch.univariate.HARCH.variance_bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.HARCH.variance_bounds - arch 6.1.0</title>
+        <title>arch.univariate.HARCH.variance_bounds - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1262,20 +1262,20 @@
                   
 
 
-<h1 id="arch-univariate-harch-variance-bounds">arch.univariate.HARCH.variance_bounds<a class="headerlink" href="#arch-univariate-harch-variance-bounds" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-harch-variance-bounds">arch.univariate.HARCH.variance_bounds<a class="headerlink" href="#arch-univariate-harch-variance-bounds" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.HARCH.variance_bounds">
-<span class="sig-prename descclassname"><span class="pre">HARCH.</span></span><span class="sig-name descname"><span class="pre">variance_bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.variance_bounds.resids" title="arch.univariate.HARCH.variance_bounds.resids (Python parameter) — Approximate residuals to use to compute the lower and upper bounds on the conditional variance"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.variance_bounds.power" title="arch.univariate.HARCH.variance_bounds.power (Python parameter) — Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."><span class="n"><span class="pre">power</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">2.0</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.HARCH.variance_bounds" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.HARCH.variance_bounds">
+<span class="sig-prename descclassname"><span class="pre">HARCH.</span></span><span class="sig-name descname"><span class="pre">variance_bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.variance_bounds.resids" title="arch.univariate.HARCH.variance_bounds.resids (Python parameter) — Approximate residuals to use to compute the lower and upper bounds on the conditional variance"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.variance_bounds.power" title="arch.univariate.HARCH.variance_bounds.power (Python parameter) — Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."><span class="n"><span class="pre">power</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">2.0</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.univariate.HARCH.variance_bounds" title="Link to this definition">¶</a></dt>
 <dd><p>Construct loose bounds for conditional variances.</p>
 <p>These bounds are used in parameter estimation to ensure
 that the log-likelihood does not produce NaN values.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.HARCH.variance_bounds-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARCH.variance_bounds-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.HARCH.variance_bounds.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.HARCH.variance_bounds.resids" title="Permalink to this definition">¶</a></dt><dd><p>Approximate residuals to use to compute the lower and upper bounds
+<dt id="arch.univariate.HARCH.variance_bounds.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.HARCH.variance_bounds.resids" title="Permalink to this definition">¶</a></dt><dd><p>Approximate residuals to use to compute the lower and upper bounds
 on the conditional variance</p>
 </dd>
-<dt id="arch.univariate.HARCH.variance_bounds.power"><span class="sig-name descname">power</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>, optional</span></span><a class="headerlink" href="#arch.univariate.HARCH.variance_bounds.power" title="Permalink to this definition">¶</a></dt><dd><p>Power used in the model. 2.0, the default corresponds to standard
+<dt id="arch.univariate.HARCH.variance_bounds.power"><span class="n sig-name">power</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">2.0</span></code><a class="headerlink" href="#arch.univariate.HARCH.variance_bounds.power" title="Permalink to this definition">¶</a></dt><dd><p>Power used in the model. 2.0, the default corresponds to standard
 ARCH models that evolve in squares.</p>
 </dd>
 </dl>
@@ -1285,7 +1285,7 @@ <h1 id="arch-univariate-harch-variance-bounds">arch.univariate.HARCH.variance_bo
 number of elements as resids</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.HARCH.variance_bounds-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARCH.variance_bounds-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1357,7 +1357,7 @@ <h1 id="arch-univariate-harch-variance-bounds">arch.univariate.HARCH.variance_bo
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1404,7 +1404,7 @@ <h1 id="arch-univariate-harch-variance-bounds">arch.univariate.HARCH.variance_bo
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.HARCH.volatility_updater.html b/univariate/generated/generated/arch.univariate.HARCH.volatility_updater.html
index 9e685bc6fa..49fb95d9c0 100644
--- a/univariate/generated/generated/arch.univariate.HARCH.volatility_updater.html
+++ b/univariate/generated/generated/arch.univariate.HARCH.volatility_updater.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.HARCH.volatility_updater - arch 6.1.0</title>
+        <title>arch.univariate.HARCH.volatility_updater - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1222,17 +1222,17 @@
                   
 
 
-<h1 id="arch-univariate-harch-volatility-updater">arch.univariate.HARCH.volatility_updater<a class="headerlink" href="#arch-univariate-harch-volatility-updater" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-harch-volatility-updater">arch.univariate.HARCH.volatility_updater<a class="headerlink" href="#arch-univariate-harch-volatility-updater" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.HARCH.volatility_updater">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">HARCH.</span></span><span class="sig-name descname"><span class="pre">volatility_updater</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.univariate.recursions_python.VolatilityUpdater.html#arch.univariate.recursions_python.VolatilityUpdater" title="arch.univariate.recursions_python.VolatilityUpdater (Python class) — Base class that all volatility updaters must inherit from."><span class="pre">VolatilityUpdater</span></a></span><a class="headerlink" href="#arch.univariate.HARCH.volatility_updater" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">HARCH.</span></span><span class="sig-name descname"><span class="pre">volatility_updater</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><span class="pre">VolatilityUpdater</span></span><a class="headerlink" href="#arch.univariate.HARCH.volatility_updater" title="Link to this definition">¶</a></dt>
 <dd><p>Get the volatility updater associated with the volatility process</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.HARCH.volatility_updater-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARCH.volatility_updater-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The updater class</p>
 </dd>
 <dt class="field-even" id="arch.univariate.HARCH.volatility_updater-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARCH.volatility_updater-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference internal" href="../arch.univariate.recursions_python.VolatilityUpdater.html#arch.univariate.recursions_python.VolatilityUpdater" title="arch.univariate.recursions_python.VolatilityUpdater (Python class) — Base class that all volatility updaters must inherit from.">VolatilityUpdater</a></p>
+<dd class="field-even"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">VolatilityUpdater</span></code></p>
 </dd>
 <dt class="field-odd" id="arch.univariate.HARCH.volatility_updater-raises">Raises<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARCH.volatility_updater-raises" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#NotImplementedError" title="(in Python v3.11)"><strong>NotImplementedError</strong></a> – If the process is not updateable</p>
@@ -1307,7 +1307,7 @@ <h1 id="arch-univariate-harch-volatility-updater">arch.univariate.HARCH.volatili
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1354,7 +1354,7 @@ <h1 id="arch-univariate-harch-volatility-updater">arch.univariate.HARCH.volatili
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.HARX.bounds.html b/univariate/generated/generated/arch.univariate.HARX.bounds.html
index 266ab8800e..a83d833319 100644
--- a/univariate/generated/generated/arch.univariate.HARX.bounds.html
+++ b/univariate/generated/generated/arch.univariate.HARX.bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.HARX.bounds - arch 6.1.0</title>
+        <title>arch.univariate.HARX.bounds - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1133,17 +1133,17 @@
                   
 
 
-<h1 id="arch-univariate-harx-bounds">arch.univariate.HARX.bounds<a class="headerlink" href="#arch-univariate-harx-bounds" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-harx-bounds">arch.univariate.HARX.bounds<a class="headerlink" href="#arch-univariate-harx-bounds" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.HARX.bounds">
-<span class="sig-prename descclassname"><span class="pre">HARX.</span></span><span class="sig-name descname"><span class="pre">bounds</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.HARX.bounds" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">HARX.</span></span><span class="sig-name descname"><span class="pre">bounds</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#arch.univariate.HARX.bounds" title="Link to this definition">¶</a></dt>
 <dd><p>Construct bounds for parameters to use in non-linear optimization</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.HARX.bounds-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARX.bounds-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p><strong>bounds</strong> – Bounds for parameters to use in estimation.</p>
 </dd>
 <dt class="field-even" id="arch.univariate.HARX.bounds-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARX.bounds-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a> (2-tuple of <a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>)</p>
+<dd class="field-even"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span> <span class="pre">(2-tuple</span></code> of <code class="xref py py-class docutils literal notranslate"><span class="pre">float)</span></code></p>
 </dd>
 </dl>
 </dd></dl>
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diff --git a/univariate/generated/generated/arch.univariate.HARX.compute_param_cov.html b/univariate/generated/generated/arch.univariate.HARX.compute_param_cov.html
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@@ -1187,26 +1173,23 @@
                   
 
 
-<h1 id="arch-univariate-harx-compute-param-cov">arch.univariate.HARX.compute_param_cov<a class="headerlink" href="#arch-univariate-harx-compute-param-cov" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-harx-compute-param-cov">arch.univariate.HARX.compute_param_cov<a class="headerlink" href="#arch-univariate-harx-compute-param-cov" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.HARX.compute_param_cov">
-<span class="sig-prename descclassname"><span class="pre">HARX.</span></span><span class="sig-name descname"><span class="pre">compute_param_cov</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.compute_param_cov.params" title="arch.univariate.HARX.compute_param_cov.params (Python parameter) — Model parameters"><span class="n"><span class="pre">params</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.compute_param_cov.backcast" title="arch.univariate.HARX.compute_param_cov.backcast (Python parameter) — Value to use for pre-sample observations"><span class="n"><span class="pre">backcast</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.compute_param_cov.robust" title="arch.univariate.HARX.compute_param_cov.robust (Python parameter) — Flag indicating whether to use robust standard errors (True) or classic MLE (False)"><span class="n"><span class="pre">robust</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.HARX.compute_param_cov" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.HARX.compute_param_cov">
+<span class="sig-prename descclassname"><span class="pre">HARX.</span></span><span class="sig-name descname"><span class="pre">compute_param_cov</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.compute_param_cov.params" title="arch.univariate.HARX.compute_param_cov.params (Python parameter) — Model parameters"><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.compute_param_cov.backcast" title="arch.univariate.HARX.compute_param_cov.backcast (Python parameter) — Value to use for pre-sample observations"><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.compute_param_cov.robust" title="arch.univariate.HARX.compute_param_cov.robust (Python parameter) — Flag indicating whether to use robust standard errors (True) or classic MLE (False)"><span class="n"><span class="pre">robust</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.univariate.HARX.compute_param_cov" title="Link to this definition">¶</a></dt>
 <dd><p>Computes parameter covariances using numerical derivatives.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.HARX.compute_param_cov-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARX.compute_param_cov-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.HARX.compute_param_cov.params"><span class="sig-name descname">params</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.HARX.compute_param_cov.params" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
+<dt id="arch.univariate.HARX.compute_param_cov.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.HARX.compute_param_cov.params" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
 </dd>
-<dt id="arch.univariate.HARX.compute_param_cov.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><a class="headerlink" href="#arch.univariate.HARX.compute_param_cov.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use for pre-sample observations</p>
+<dt id="arch.univariate.HARX.compute_param_cov.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.HARX.compute_param_cov.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use for pre-sample observations</p>
 </dd>
-<dt id="arch.univariate.HARX.compute_param_cov.robust"><span class="sig-name descname">robust</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, optional</span></span><a class="headerlink" href="#arch.univariate.HARX.compute_param_cov.robust" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to use robust standard errors (True) or
+<dt id="arch.univariate.HARX.compute_param_cov.robust"><span class="n sig-name">robust</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.univariate.HARX.compute_param_cov.robust" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to use robust standard errors (True) or
 classic MLE (False)</p>
 </dd>
 </dl>
 </dd>
-<dt class="field-even" id="arch.univariate.HARX.compute_param_cov-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARX.compute_param_cov-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a></p>
-</dd>
 </dl>
 </dd></dl>
 
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diff --git a/univariate/generated/generated/arch.univariate.HARX.constraints.html b/univariate/generated/generated/arch.univariate.HARX.constraints.html
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+++ b/univariate/generated/generated/arch.univariate.HARX.constraints.html
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@@ -102,7 +102,7 @@
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-    arch 6.1.0
+    arch 6.2.0
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@@ -566,13 +566,6 @@
     <nav class="md-nav" aria-label="HARX.constraints">
       <ul class="md-nav__list">
         
-          <li class="md-nav__item">
-  <a href="#arch.univariate.HARX.constraints-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
-</li>
-        
           <li class="md-nav__item">
   <a href="#arch.univariate.HARX.constraints-returns" class="md-nav__link">
     <span class="md-ellipsis">Returns</span>
@@ -1100,13 +1093,6 @@
     <nav class="md-nav" aria-label="HARX.constraints">
       <ul class="md-nav__list">
         
-          <li class="md-nav__item">
-  <a href="#arch.univariate.HARX.constraints-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
-</li>
-        
           <li class="md-nav__item">
   <a href="#arch.univariate.HARX.constraints-returns" class="md-nav__link">
     <span class="md-ellipsis">Returns</span>
@@ -1133,19 +1119,16 @@
                   
 
 
-<h1 id="arch-univariate-harx-constraints">arch.univariate.HARX.constraints<a class="headerlink" href="#arch-univariate-harx-constraints" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-harx-constraints">arch.univariate.HARX.constraints<a class="headerlink" href="#arch-univariate-harx-constraints" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.HARX.constraints">
-<span class="sig-prename descclassname"><span class="pre">HARX.</span></span><span class="sig-name descname"><span class="pre">constraints</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.HARX.constraints" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">HARX.</span></span><span class="sig-name descname"><span class="pre">constraints</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#arch.univariate.HARX.constraints" title="Link to this definition">¶</a></dt>
 <dd><p>Construct linear constraint arrays  for use in non-linear optimization</p>
 <dl class="field-list simple">
-<dt class="field-odd" id="arch.univariate.HARX.constraints-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARX.constraints-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>]</p>
-</dd>
-<dt class="field-even" id="arch.univariate.HARX.constraints-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARX.constraints-returns" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><ul class="simple">
-<li><p><strong>a</strong> (<em>ndarray</em>) – Number of constraints by number of parameters loading array</p></li>
-<li><p><strong>b</strong> (<em>ndarray</em>) – Number of constraints array of lower bounds</p></li>
+<dt class="field-odd" id="arch.univariate.HARX.constraints-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARX.constraints-returns" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p><strong>a</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – Number of constraints by number of parameters loading array</p></li>
+<li><p><strong>b</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – Number of constraints array of lower bounds</p></li>
 </ul>
 </p>
 </dd>
@@ -1221,7 +1204,7 @@ <h1 id="arch-univariate-harx-constraints">arch.univariate.HARX.constraints<a cla
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1268,7 +1251,7 @@ <h1 id="arch-univariate-harx-constraints">arch.univariate.HARX.constraints<a cla
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diff --git a/univariate/generated/generated/arch.univariate.HARX.distribution.html b/univariate/generated/generated/arch.univariate.HARX.distribution.html
index 8656c5515c..ee36d7de3e 100644
--- a/univariate/generated/generated/arch.univariate.HARX.distribution.html
+++ b/univariate/generated/generated/arch.univariate.HARX.distribution.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.univariate.HARX.distribution - arch 6.2.0</title>
       
     
 
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+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1093,10 +1093,10 @@
                   
 
 
-<h1 id="arch-univariate-harx-distribution">arch.univariate.HARX.distribution<a class="headerlink" href="#arch-univariate-harx-distribution" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-harx-distribution">arch.univariate.HARX.distribution<a class="headerlink" href="#arch-univariate-harx-distribution" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.HARX.distribution">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">HARX.</span></span><span class="sig-name descname"><span class="pre">distribution</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.univariate.distribution.Distribution.html#arch.univariate.distribution.Distribution" title="arch.univariate.distribution.Distribution (Python class) — Template for subclassing only"><span class="pre">Distribution</span></a></span><a class="headerlink" href="#arch.univariate.HARX.distribution" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">HARX.</span></span><span class="sig-name descname"><span class="pre">distribution</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.univariate.distribution.Distribution.html#arch.univariate.distribution.Distribution" title="arch.univariate.distribution.Distribution (Python class) — Template for subclassing only"><span class="pre">Distribution</span></a></span><a class="headerlink" href="#arch.univariate.HARX.distribution" title="Link to this definition">¶</a></dt>
 <dd><p>Set or gets the error distribution</p>
 <p>Distributions must be a subclass of Distribution</p>
 </dd></dl>
@@ -1168,7 +1168,7 @@ <h1 id="arch-univariate-harx-distribution">arch.univariate.HARX.distribution<a c
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1215,7 +1215,7 @@ <h1 id="arch-univariate-harx-distribution">arch.univariate.HARX.distribution<a c
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diff --git a/univariate/generated/generated/arch.univariate.HARX.fit.html b/univariate/generated/generated/arch.univariate.HARX.fit.html
index 6162ce8fe5..741016f5d1 100644
--- a/univariate/generated/generated/arch.univariate.HARX.fit.html
+++ b/univariate/generated/generated/arch.univariate.HARX.fit.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.HARX.fit - arch 6.1.0</title>
+        <title>arch.univariate.HARX.fit - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1299,40 +1299,40 @@
                   
 
 
-<h1 id="arch-univariate-harx-fit">arch.univariate.HARX.fit<a class="headerlink" href="#arch-univariate-harx-fit" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-harx-fit">arch.univariate.HARX.fit<a class="headerlink" href="#arch-univariate-harx-fit" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.HARX.fit">
-<span class="sig-prename descclassname"><span class="pre">HARX.</span></span><span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.fit.update_freq" title="arch.univariate.HARX.fit.update_freq (Python parameter) — Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."><span class="n"><span class="pre">update_freq</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.fit.disp" title="arch.univariate.HARX.fit.disp (Python parameter) — Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is &quot;off&quot; and True is &quot;final&quot;"><span class="n"><span class="pre">disp</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;final&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.fit.starting_values" title="arch.univariate.HARX.fit.starting_values (Python parameter) — Array of starting values to use.  If not provided, starting values are constructed by the model components."><span class="n"><span class="pre">starting_values</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.fit.cov_type" title="arch.univariate.HARX.fit.cov_type (Python parameter) — Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."><span class="n"><span class="pre">cov_type</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;robust&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.fit.show_warning" title="arch.univariate.HARX.fit.show_warning (Python parameter) — Flag indicating whether convergence warnings should be shown."><span class="n"><span class="pre">show_warning</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.fit.first_obs" title="arch.univariate.HARX.fit.first_obs (Python parameter) — First observation to use when estimating model"><span class="n"><span class="pre">first_obs</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.fit.last_obs" title="arch.univariate.HARX.fit.last_obs (Python parameter) — Last observation to use when estimating model"><span class="n"><span class="pre">last_obs</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.fit.tol" title="arch.univariate.HARX.fit.tol (Python parameter) — Tolerance for termination."><span class="n"><span class="pre">tol</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.fit.options" title="arch.univariate.HARX.fit.options (Python parameter) — Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."><span class="n"><span class="pre">options</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.fit.backcast" title="arch.univariate.HARX.fit.backcast (Python parameter) — Value to use as backcast. Should be measure \sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."><span class="n"><span class="pre">backcast</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.HARX.fit" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">HARX.</span></span><span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.fit.update_freq" title="arch.univariate.HARX.fit.update_freq (Python parameter) — Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."><span class="n"><span class="pre">update_freq</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.fit.disp" title="arch.univariate.HARX.fit.disp (Python parameter) — Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is &quot;off&quot; and True is &quot;final&quot;"><span class="n"><span class="pre">disp</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'off'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'final'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;final&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.fit.starting_values" title="arch.univariate.HARX.fit.starting_values (Python parameter) — Array of starting values to use.  If not provided, starting values are constructed by the model components."><span class="n"><span class="pre">starting_values</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.fit.cov_type" title="arch.univariate.HARX.fit.cov_type (Python parameter) — Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."><span class="n"><span class="pre">cov_type</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'robust'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'classic'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;robust&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.fit.show_warning" title="arch.univariate.HARX.fit.show_warning (Python parameter) — Flag indicating whether convergence warnings should be shown."><span class="n"><span class="pre">show_warning</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.fit.first_obs" title="arch.univariate.HARX.fit.first_obs (Python parameter) — First observation to use when estimating model"><span class="n"><span class="pre">first_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.fit.last_obs" title="arch.univariate.HARX.fit.last_obs (Python parameter) — Last observation to use when estimating model"><span class="n"><span class="pre">last_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.fit.tol" title="arch.univariate.HARX.fit.tol (Python parameter) — Tolerance for termination."><span class="n"><span class="pre">tol</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.fit.options" title="arch.univariate.HARX.fit.options (Python parameter) — Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."><span class="n"><span class="pre">options</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.fit.backcast" title="arch.univariate.HARX.fit.backcast (Python parameter) — Value to use as backcast. Should be measure \sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelResult.html#arch.univariate.base.ARCHModelResult" title="arch.univariate.base.ARCHModelResult (Python class) — Results from estimation of an ARCHModel model"><span class="pre">ARCHModelResult</span></a></span></span></span><a class="headerlink" href="#arch.univariate.HARX.fit" title="Link to this definition">¶</a></dt>
 <dd><p>Estimate model parameters</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.HARX.fit-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARX.fit-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.HARX.fit.update_freq"><span class="sig-name descname">update_freq</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.univariate.HARX.fit.update_freq" title="Permalink to this definition">¶</a></dt><dd><p>Frequency of iteration updates.  Output is generated every
+<dt id="arch.univariate.HARX.fit.update_freq"><span class="n sig-name">update_freq</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code><a class="headerlink" href="#arch.univariate.HARX.fit.update_freq" title="Permalink to this definition">¶</a></dt><dd><p>Frequency of iteration updates.  Output is generated every
 <cite>update_freq</cite> iterations. Set to 0 to disable iterative output.</p>
 </dd>
-<dt id="arch.univariate.HARX.fit.disp"><span class="sig-name descname">disp</span><span class="p"> : </span><span class="desctype"><span class="desctype">{bool, &quot;off&quot;, &quot;final&quot;}</span></span><a class="headerlink" href="#arch.univariate.HARX.fit.disp" title="Permalink to this definition">¶</a></dt><dd><p>Either ‘final’ to print optimization result or ‘off’ to display
+<dt id="arch.univariate.HARX.fit.disp"><span class="n sig-name">disp</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'off'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'final'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;final&#39;</span></code><a class="headerlink" href="#arch.univariate.HARX.fit.disp" title="Permalink to this definition">¶</a></dt><dd><p>Either ‘final’ to print optimization result or ‘off’ to display
 nothing. If using a boolean, False is “off” and True is “final”</p>
 </dd>
-<dt id="arch.univariate.HARX.fit.starting_values"><span class="sig-name descname">starting_values</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray, optional</span></span><a class="headerlink" href="#arch.univariate.HARX.fit.starting_values" title="Permalink to this definition">¶</a></dt><dd><p>Array of starting values to use.  If not provided, starting values
+<dt id="arch.univariate.HARX.fit.starting_values"><span class="n sig-name">starting_values</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.HARX.fit.starting_values" title="Permalink to this definition">¶</a></dt><dd><p>Array of starting values to use.  If not provided, starting values
 are constructed by the model components.</p>
 </dd>
-<dt id="arch.univariate.HARX.fit.cov_type"><span class="sig-name descname">cov_type</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, optional</span></span><a class="headerlink" href="#arch.univariate.HARX.fit.cov_type" title="Permalink to this definition">¶</a></dt><dd><p>Estimation method of parameter covariance.  Supported options are
+<dt id="arch.univariate.HARX.fit.cov_type"><span class="n sig-name">cov_type</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'robust'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'classic'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;robust&#39;</span></code><a class="headerlink" href="#arch.univariate.HARX.fit.cov_type" title="Permalink to this definition">¶</a></dt><dd><p>Estimation method of parameter covariance.  Supported options are
 ‘robust’, which does not assume the Information Matrix Equality
 holds and ‘classic’ which does.  In the ARCH literature, ‘robust’
 corresponds to Bollerslev-Wooldridge covariance estimator.</p>
 </dd>
-<dt id="arch.univariate.HARX.fit.show_warning"><span class="sig-name descname">show_warning</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, optional</span></span><a class="headerlink" href="#arch.univariate.HARX.fit.show_warning" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether convergence warnings should be shown.</p>
+<dt id="arch.univariate.HARX.fit.show_warning"><span class="n sig-name">show_warning</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.univariate.HARX.fit.show_warning" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether convergence warnings should be shown.</p>
 </dd>
-<dt id="arch.univariate.HARX.fit.first_obs"><span class="sig-name descname">first_obs</span><span class="p"> : </span><span class="desctype"><span class="desctype">{int, <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, datetime, Timestamp}</span></span><a class="headerlink" href="#arch.univariate.HARX.fit.first_obs" title="Permalink to this definition">¶</a></dt><dd><p>First observation to use when estimating model</p>
+<dt id="arch.univariate.HARX.fit.first_obs"><span class="n sig-name">first_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.HARX.fit.first_obs" title="Permalink to this definition">¶</a></dt><dd><p>First observation to use when estimating model</p>
 </dd>
-<dt id="arch.univariate.HARX.fit.last_obs"><span class="sig-name descname">last_obs</span><span class="p"> : </span><span class="desctype"><span class="desctype">{int, <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, datetime, Timestamp}</span></span><a class="headerlink" href="#arch.univariate.HARX.fit.last_obs" title="Permalink to this definition">¶</a></dt><dd><p>Last observation to use when estimating model</p>
+<dt id="arch.univariate.HARX.fit.last_obs"><span class="n sig-name">last_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.HARX.fit.last_obs" title="Permalink to this definition">¶</a></dt><dd><p>Last observation to use when estimating model</p>
 </dd>
-<dt id="arch.univariate.HARX.fit.tol"><span class="sig-name descname">tol</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>, optional</span></span><a class="headerlink" href="#arch.univariate.HARX.fit.tol" title="Permalink to this definition">¶</a></dt><dd><p>Tolerance for termination.</p>
+<dt id="arch.univariate.HARX.fit.tol"><span class="n sig-name">tol</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.HARX.fit.tol" title="Permalink to this definition">¶</a></dt><dd><p>Tolerance for termination.</p>
 </dd>
-<dt id="arch.univariate.HARX.fit.options"><span class="sig-name descname">options</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a>, optional</span></span><a class="headerlink" href="#arch.univariate.HARX.fit.options" title="Permalink to this definition">¶</a></dt><dd><p>Options to pass to <cite>scipy.optimize.minimize</cite>.  Valid entries
+<dt id="arch.univariate.HARX.fit.options"><span class="n sig-name">options</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)">Any</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.HARX.fit.options" title="Permalink to this definition">¶</a></dt><dd><p>Options to pass to <cite>scipy.optimize.minimize</cite>.  Valid entries
 include ‘ftol’, ‘eps’, ‘disp’, and ‘maxiter’.</p>
 </dd>
-<dt id="arch.univariate.HARX.fit.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}, optional</span></span><a class="headerlink" href="#arch.univariate.HARX.fit.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use as backcast. Should be measure <span class="math notranslate nohighlight">\(\sigma^2_0\)</span>
+<dt id="arch.univariate.HARX.fit.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.HARX.fit.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use as backcast. Should be measure <span class="math notranslate nohighlight">\(\sigma^2_0\)</span>
 since model-specific non-linear transformations are applied to
 value before computing the variance recursions.</p>
 </dd>
@@ -1342,7 +1342,7 @@ <h1 id="arch-univariate-harx-fit">arch.univariate.HARX.fit<a class="headerlink"
 <dd class="field-even"><p><strong>results</strong> – Object containing model results</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.HARX.fit-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARX.fit-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference internal" href="../arch.univariate.base.ARCHModelResult.html#arch.univariate.base.ARCHModelResult" title="arch.univariate.base.ARCHModelResult (Python class) — Results from estimation of an ARCHModel model">ARCHModelResult</a></p>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">ARCHModelResult</span></code></p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -1418,7 +1418,7 @@ <h1 id="arch-univariate-harx-fit">arch.univariate.HARX.fit<a class="headerlink"
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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+        <script id="MathJax-script" src="../../../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
     
   </body>
 </html>
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diff --git a/univariate/generated/generated/arch.univariate.HARX.fix.html b/univariate/generated/generated/arch.univariate.HARX.fix.html
index c8bae7f44b..fa8b4aa074 100644
--- a/univariate/generated/generated/arch.univariate.HARX.fix.html
+++ b/univariate/generated/generated/arch.univariate.HARX.fix.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.HARX.fix - arch 6.1.0</title>
+        <title>arch.univariate.HARX.fix - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1201,21 +1201,21 @@
                   
 
 
-<h1 id="arch-univariate-harx-fix">arch.univariate.HARX.fix<a class="headerlink" href="#arch-univariate-harx-fix" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-harx-fix">arch.univariate.HARX.fix<a class="headerlink" href="#arch-univariate-harx-fix" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.HARX.fix">
-<span class="sig-prename descclassname"><span class="pre">HARX.</span></span><span class="sig-name descname"><span class="pre">fix</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.fix.params" title="arch.univariate.HARX.fix.params (Python parameter) — User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."><span class="n"><span class="pre">params</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.fix.first_obs" title="arch.univariate.HARX.fix.first_obs (Python parameter) — First observation to use when fixing model"><span class="n"><span class="pre">first_obs</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.fix.last_obs" title="arch.univariate.HARX.fix.last_obs (Python parameter) — Last observation to use when fixing model"><span class="n"><span class="pre">last_obs</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.HARX.fix" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.HARX.fix">
+<span class="sig-prename descclassname"><span class="pre">HARX.</span></span><span class="sig-name descname"><span class="pre">fix</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.fix.params" title="arch.univariate.HARX.fix.params (Python parameter) — User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.fix.first_obs" title="arch.univariate.HARX.fix.first_obs (Python parameter) — First observation to use when fixing model"><span class="n"><span class="pre">first_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.fix.last_obs" title="arch.univariate.HARX.fix.last_obs (Python parameter) — Last observation to use when fixing model"><span class="n"><span class="pre">last_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelFixedResult.html#arch.univariate.base.ARCHModelFixedResult" title="arch.univariate.base.ARCHModelFixedResult (Python class) — Results for fixed parameters for an ARCHModel model"><span class="pre">ARCHModelFixedResult</span></a></span></span></span><a class="headerlink" href="#arch.univariate.HARX.fix" title="Link to this definition">¶</a></dt>
 <dd><p>Allows an ARCHModelFixedResult to be constructed from fixed parameters.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.HARX.fix-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARX.fix-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.HARX.fix.params"><span class="sig-name descname">params</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, Series}</span></span><a class="headerlink" href="#arch.univariate.HARX.fix.params" title="Permalink to this definition">¶</a></dt><dd><p>User specified parameters to use when generating the result. Must
+<dt id="arch.univariate.HARX.fix.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.HARX.fix.params" title="Permalink to this definition">¶</a></dt><dd><p>User specified parameters to use when generating the result. Must
 have the correct number of parameters for a given choice of mean
 model, volatility model and distribution.</p>
 </dd>
-<dt id="arch.univariate.HARX.fix.first_obs"><span class="sig-name descname">first_obs</span><span class="p"> : </span><span class="desctype"><span class="desctype">{int, <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, datetime, Timestamp}</span></span><a class="headerlink" href="#arch.univariate.HARX.fix.first_obs" title="Permalink to this definition">¶</a></dt><dd><p>First observation to use when fixing model</p>
+<dt id="arch.univariate.HARX.fix.first_obs"><span class="n sig-name">first_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.HARX.fix.first_obs" title="Permalink to this definition">¶</a></dt><dd><p>First observation to use when fixing model</p>
 </dd>
-<dt id="arch.univariate.HARX.fix.last_obs"><span class="sig-name descname">last_obs</span><span class="p"> : </span><span class="desctype"><span class="desctype">{int, <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, datetime, Timestamp}</span></span><a class="headerlink" href="#arch.univariate.HARX.fix.last_obs" title="Permalink to this definition">¶</a></dt><dd><p>Last observation to use when fixing model</p>
+<dt id="arch.univariate.HARX.fix.last_obs"><span class="n sig-name">last_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.HARX.fix.last_obs" title="Permalink to this definition">¶</a></dt><dd><p>Last observation to use when fixing model</p>
 </dd>
 </dl>
 </dd>
@@ -1223,7 +1223,7 @@ <h1 id="arch-univariate-harx-fix">arch.univariate.HARX.fix<a class="headerlink"
 <dd class="field-even"><p><strong>results</strong> – Object containing model results</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.HARX.fix-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARX.fix-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference internal" href="../arch.univariate.base.ARCHModelFixedResult.html#arch.univariate.base.ARCHModelFixedResult" title="arch.univariate.base.ARCHModelFixedResult (Python class) — Results for fixed parameters for an ARCHModel model">ARCHModelFixedResult</a></p>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">ARCHModelFixedResult</span></code></p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -1297,7 +1297,7 @@ <h1 id="arch-univariate-harx-fix">arch.univariate.HARX.fix<a class="headerlink"
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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diff --git a/univariate/generated/generated/arch.univariate.HARX.forecast.html b/univariate/generated/generated/arch.univariate.HARX.forecast.html
index 33522231e5..a85df31260 100644
--- a/univariate/generated/generated/arch.univariate.HARX.forecast.html
+++ b/univariate/generated/generated/arch.univariate.HARX.forecast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.HARX.forecast - arch 6.1.0</title>
+        <title>arch.univariate.HARX.forecast - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1299,56 +1299,56 @@
                   
 
 
-<h1 id="arch-univariate-harx-forecast">arch.univariate.HARX.forecast<a class="headerlink" href="#arch-univariate-harx-forecast" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-harx-forecast">arch.univariate.HARX.forecast<a class="headerlink" href="#arch-univariate-harx-forecast" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.HARX.forecast">
-<span class="sig-prename descclassname"><span class="pre">HARX.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.forecast.params" title="arch.univariate.HARX.forecast.params (Python parameter) — Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."><span class="n"><span class="pre">params</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.forecast.horizon" title="arch.univariate.HARX.forecast.horizon (Python parameter) — Number of steps to forecast"><span class="n"><span class="pre">horizon</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.forecast.start" title="arch.univariate.HARX.forecast.start (Python parameter) — An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."><span class="n"><span class="pre">start</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.forecast.align" title="arch.univariate.HARX.forecast.align (Python parameter) — Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."><span class="n"><span class="pre">align</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;origin&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.forecast.method" title="arch.univariate.HARX.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons &gt; 1."><span class="n"><span class="pre">method</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.forecast.simulations" title="arch.univariate.HARX.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.forecast.rng" title="arch.univariate.HARX.forecast.rng (Python parameter) — Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."><span class="n"><span class="pre">rng</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.forecast.random_state" title="arch.univariate.HARX.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.forecast.reindex" title="arch.univariate.HARX.forecast.reindex (Python parameter) — Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."><span class="n"><span class="pre">reindex</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.forecast.x" title="arch.univariate.HARX.forecast.x (Python parameter) — Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"><span class="n"><span class="pre">x</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/mean.html#HARX.forecast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.HARX.forecast" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">HARX.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.forecast.params" title="arch.univariate.HARX.forecast.params (Python parameter) — Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.forecast.horizon" title="arch.univariate.HARX.forecast.horizon (Python parameter) — Number of steps to forecast"><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.forecast.start" title="arch.univariate.HARX.forecast.start (Python parameter) — An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.forecast.align" title="arch.univariate.HARX.forecast.align (Python parameter) — Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."><span class="n"><span class="pre">align</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'origin'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'target'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;origin&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.forecast.method" title="arch.univariate.HARX.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons &gt; 1."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.forecast.simulations" title="arch.univariate.HARX.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.forecast.rng" title="arch.univariate.HARX.forecast.rng (Python parameter) — Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.forecast.random_state" title="arch.univariate.HARX.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.forecast.reindex" title="arch.univariate.HARX.forecast.reindex (Python parameter) — Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."><span class="n"><span class="pre">reindex</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.forecast.x" title="arch.univariate.HARX.forecast.x (Python parameter) — Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.11)"><span class="pre">Hashable</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelForecast.html#arch.univariate.base.ARCHModelForecast" title="arch.univariate.base.ARCHModelForecast (Python class) — Container for forecasts from an ARCH Model"><span class="pre">ARCHModelForecast</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/mean.html#HARX.forecast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.HARX.forecast" title="Link to this definition">¶</a></dt>
 <dd><p>Construct forecasts from estimated model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.HARX.forecast-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARX.forecast-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.HARX.forecast.params"><span class="sig-name descname">params</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, Series}</span></span><a class="headerlink" href="#arch.univariate.HARX.forecast.params" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast. Must be identical in
+<dt id="arch.univariate.HARX.forecast.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.HARX.forecast.params" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast. Must be identical in
 shape to the parameters computed by fitting the model.</p>
 </dd>
-<dt id="arch.univariate.HARX.forecast.horizon"><span class="sig-name descname">horizon</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.univariate.HARX.forecast.horizon" title="Permalink to this definition">¶</a></dt><dd><p>Number of steps to forecast</p>
+<dt id="arch.univariate.HARX.forecast.horizon"><span class="n sig-name">horizon</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code><a class="headerlink" href="#arch.univariate.HARX.forecast.horizon" title="Permalink to this definition">¶</a></dt><dd><p>Number of steps to forecast</p>
 </dd>
-<dt id="arch.univariate.HARX.forecast.start"><span class="sig-name descname">start</span><span class="p"> : </span><span class="desctype"><span class="desctype">{int, datetime, Timestamp, str}, optional</span></span><a class="headerlink" href="#arch.univariate.HARX.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>An integer, datetime or str indicating the first observation to
+<dt id="arch.univariate.HARX.forecast.start"><span class="n sig-name">start</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.HARX.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>An integer, datetime or str indicating the first observation to
 produce the forecast for.  Datetimes can only be used with pandas
 inputs that have a datetime index. Strings must be convertible
 to a date time, such as in ‘1945-01-01’.</p>
 </dd>
-<dt id="arch.univariate.HARX.forecast.align"><span class="sig-name descname">align</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, optional</span></span><a class="headerlink" href="#arch.univariate.HARX.forecast.align" title="Permalink to this definition">¶</a></dt><dd><p>Either ‘origin’ or ‘target’.  When set of ‘origin’, the t-th row
+<dt id="arch.univariate.HARX.forecast.align"><span class="n sig-name">align</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'origin'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'target'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;origin&#39;</span></code><a class="headerlink" href="#arch.univariate.HARX.forecast.align" title="Permalink to this definition">¶</a></dt><dd><p>Either ‘origin’ or ‘target’.  When set of ‘origin’, the t-th row
 of forecasts contains the forecasts for t+1, t+2, …, t+h. When
 set to ‘target’, the t-th row contains the 1-step ahead forecast
 from time t-1, the 2 step from time t-2, …, and the h-step from
 time t-h.  ‘target’ simplified computing forecast errors since the
 realization and h-step forecast are aligned.</p>
 </dd>
-<dt id="arch.univariate.HARX.forecast.method"><span class="sig-name descname">method</span><span class="p"> : </span><span class="desctype"><span class="desctype">{'analytic', 'simulation', 'bootstrap'}</span></span><a class="headerlink" href="#arch.univariate.HARX.forecast.method" title="Permalink to this definition">¶</a></dt><dd><p>Method to use when producing the forecast. The default is analytic.
+<dt id="arch.univariate.HARX.forecast.method"><span class="n sig-name">method</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'analytic'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'simulation'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'bootstrap'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code><a class="headerlink" href="#arch.univariate.HARX.forecast.method" title="Permalink to this definition">¶</a></dt><dd><p>Method to use when producing the forecast. The default is analytic.
 The method only affects the variance forecast generation.  Not all
 volatility models support all methods. In particular, volatility
 models that do not evolve in squares such as EGARCH or TARCH do not
 support the ‘analytic’ method for horizons &gt; 1.</p>
 </dd>
-<dt id="arch.univariate.HARX.forecast.simulations"><span class="sig-name descname">simulations</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.HARX.forecast.simulations" title="Permalink to this definition">¶</a></dt><dd><p>Number of simulations to run when computing the forecast using
+<dt id="arch.univariate.HARX.forecast.simulations"><span class="n sig-name">simulations</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.univariate.HARX.forecast.simulations" title="Permalink to this definition">¶</a></dt><dd><p>Number of simulations to run when computing the forecast using
 either simulation or bootstrap.</p>
 </dd>
-<dt id="arch.univariate.HARX.forecast.rng"><span class="sig-name descname">rng</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable, optional</span></span><a class="headerlink" href="#arch.univariate.HARX.forecast.rng" title="Permalink to this definition">¶</a></dt><dd><p>Custom random number generator to use in simulation-based forecasts.
+<dt id="arch.univariate.HARX.forecast.rng"><span class="n sig-name">rng</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)">Callable</a><span class="p">[</span><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">,</span><span class="w"> </span><span class="p">...</span><span class="p">]</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.HARX.forecast.rng" title="Permalink to this definition">¶</a></dt><dd><p>Custom random number generator to use in simulation-based forecasts.
 Must produce random samples using the syntax <cite>rng(size)</cite> where size
 the 2-element tuple (simulations, horizon).</p>
 </dd>
-<dt id="arch.univariate.HARX.forecast.random_state"><span class="sig-name descname">random_state</span><span class="p"> : </span><span class="desctype"><span class="desctype">RandomState, optional</span></span><a class="headerlink" href="#arch.univariate.HARX.forecast.random_state" title="Permalink to this definition">¶</a></dt><dd><p>NumPy RandomState instance to use when method is ‘bootstrap’</p>
+<dt id="arch.univariate.HARX.forecast.random_state"><span class="n sig-name">random_state</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)">RandomState</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.HARX.forecast.random_state" title="Permalink to this definition">¶</a></dt><dd><p>NumPy RandomState instance to use when method is ‘bootstrap’</p>
 </dd>
-<dt id="arch.univariate.HARX.forecast.reindex"><span class="sig-name descname">reindex</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, optional</span></span><a class="headerlink" href="#arch.univariate.HARX.forecast.reindex" title="Permalink to this definition">¶</a></dt><dd><p><p>Whether to reindex the forecasts to have the same dimension as the series
+<dt id="arch.univariate.HARX.forecast.reindex"><span class="n sig-name">reindex</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.univariate.HARX.forecast.reindex" title="Permalink to this definition">¶</a></dt><dd><p><p>Whether to reindex the forecasts to have the same dimension as the series
 being forecast. Prior to 4.18 this was the default. As of 4.19 this is
 now optional. If not provided, a warning is raised about the future
 change in the default which will occur after September 2021.</p>
-<div class="versionadded">
-<p><span class="versionmodified added">New in version 4.19.</span></p>
+<div class="admonition versionadded">
+<p class="admonition-title"><span class="versionmodified added">New in version 4.19.</span></p>
 </div>
 </p>
 </dd>
-<dt id="arch.univariate.HARX.forecast.x"><span class="sig-name descname">x</span><span class="p"> : </span><span class="desctype"><span class="desctype">{dict[label, array_like], array_like}</span></span><a class="headerlink" href="#arch.univariate.HARX.forecast.x" title="Permalink to this definition">¶</a></dt><dd><p><p>Values to use for exogenous regressors if any are included in the
+<dt id="arch.univariate.HARX.forecast.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.11)">Hashable</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.HARX.forecast.x" title="Permalink to this definition">¶</a></dt><dd><p><p>Values to use for exogenous regressors if any are included in the
 model. Three formats are accepted:</p>
 <ul class="simple">
 <li><p>2-d array-like: This format can be used when there is a single
@@ -1371,8 +1371,8 @@ <h1 id="arch-univariate-harx-forecast">arch.univariate.HARX.forecast<a class="he
 <p>Due to the complexity required to accommodate all scenarios, please
 see the example notebook that demonstrates the valid formats for
 x.</p>
-<div class="versionadded">
-<p><span class="versionmodified added">New in version 4.19.</span></p>
+<div class="admonition versionadded">
+<p class="admonition-title"><span class="versionmodified added">New in version 4.19.</span></p>
 </div>
 </p>
 </dd>
@@ -1383,7 +1383,7 @@ <h1 id="arch-univariate-harx-forecast">arch.univariate.HARX.forecast<a class="he
 <code class="docutils literal notranslate"><span class="pre">variance</span></code> and <code class="docutils literal notranslate"><span class="pre">residual_variance</span></code>.</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.HARX.forecast-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARX.forecast-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference internal" href="../arch.univariate.base.ARCHModelForecast.html#arch.univariate.base.ARCHModelForecast" title="arch.univariate.base.ARCHModelForecast (Python class) — Container for forecasts from an ARCH Model">arch.univariate.base.ARCHModelForecast</a></p>
+<dd class="field-odd"><p><a class="reference internal" href="../arch.univariate.base.ARCHModelForecast.html#arch.univariate.base.ARCHModelForecast" title="arch.univariate.base.ARCHModelForecast (Python class) — Container for forecasts from an ARCH Model"><code class="xref py py-class docutils literal notranslate"><span class="pre">arch.univariate.base.ARCHModelForecast</span></code></a></p>
 </dd>
 </dl>
 <p class="example rubric">Examples</p>
@@ -1483,7 +1483,7 @@ <h1 id="arch-univariate-harx-forecast">arch.univariate.HARX.forecast<a class="he
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1530,7 +1530,7 @@ <h1 id="arch-univariate-harx-forecast">arch.univariate.HARX.forecast<a class="he
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.HARX.name.html b/univariate/generated/generated/arch.univariate.HARX.name.html
index 0fe56e8872..e116db3efa 100644
--- a/univariate/generated/generated/arch.univariate.HARX.name.html
+++ b/univariate/generated/generated/arch.univariate.HARX.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.HARX.name - arch 6.1.0</title>
+        <title>arch.univariate.HARX.name - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1093,10 +1093,10 @@
                   
 
 
-<h1 id="arch-univariate-harx-name">arch.univariate.HARX.name<a class="headerlink" href="#arch-univariate-harx-name" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-harx-name">arch.univariate.HARX.name<a class="headerlink" href="#arch-univariate-harx-name" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.HARX.name">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">HARX.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.univariate.HARX.name" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">HARX.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.univariate.HARX.name" title="Link to this definition">¶</a></dt>
 <dd><p>The name of the model.</p>
 </dd></dl>
 
@@ -1167,7 +1167,7 @@ <h1 id="arch-univariate-harx-name">arch.univariate.HARX.name<a class="headerlink
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1214,7 +1214,7 @@ <h1 id="arch-univariate-harx-name">arch.univariate.HARX.name<a class="headerlink
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.HARX.num_params.html b/univariate/generated/generated/arch.univariate.HARX.num_params.html
index ff8cbe3d0e..fc75554a70 100644
--- a/univariate/generated/generated/arch.univariate.HARX.num_params.html
+++ b/univariate/generated/generated/arch.univariate.HARX.num_params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.HARX.num_params - arch 6.1.0</title>
+        <title>arch.univariate.HARX.num_params - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1093,10 +1093,10 @@
                   
 
 
-<h1 id="arch-univariate-harx-num-params">arch.univariate.HARX.num_params<a class="headerlink" href="#arch-univariate-harx-num-params" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-harx-num-params">arch.univariate.HARX.num_params<a class="headerlink" href="#arch-univariate-harx-num-params" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.HARX.num_params">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">HARX.</span></span><span class="sig-name descname"><span class="pre">num_params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.HARX.num_params" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">HARX.</span></span><span class="sig-name descname"><span class="pre">num_params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.HARX.num_params" title="Link to this definition">¶</a></dt>
 <dd><p>Returns the number of parameters</p>
 </dd></dl>
 
@@ -1167,7 +1167,7 @@ <h1 id="arch-univariate-harx-num-params">arch.univariate.HARX.num_params<a class
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1214,7 +1214,7 @@ <h1 id="arch-univariate-harx-num-params">arch.univariate.HARX.num_params<a class
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.HARX.parameter_names.html b/univariate/generated/generated/arch.univariate.HARX.parameter_names.html
index 688a00bb3b..d6dc3d8fbc 100644
--- a/univariate/generated/generated/arch.univariate.HARX.parameter_names.html
+++ b/univariate/generated/generated/arch.univariate.HARX.parameter_names.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.HARX.parameter_names - arch 6.1.0</title>
+        <title>arch.univariate.HARX.parameter_names - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -1133,17 +1133,17 @@
                   
 
 
-<h1 id="arch-univariate-harx-parameter-names">arch.univariate.HARX.parameter_names<a class="headerlink" href="#arch-univariate-harx-parameter-names" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-harx-parameter-names">arch.univariate.HARX.parameter_names<a class="headerlink" href="#arch-univariate-harx-parameter-names" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.HARX.parameter_names">
-<span class="sig-prename descclassname"><span class="pre">HARX.</span></span><span class="sig-name descname"><span class="pre">parameter_names</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/mean.html#HARX.parameter_names"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.HARX.parameter_names" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">HARX.</span></span><span class="sig-name descname"><span class="pre">parameter_names</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/mean.html#HARX.parameter_names"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.HARX.parameter_names" title="Link to this definition">¶</a></dt>
 <dd><p>List of parameters names</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.HARX.parameter_names-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARX.parameter_names-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p><strong>names</strong> – List of variable names for the mean model</p>
 </dd>
 <dt class="field-even" id="arch.univariate.HARX.parameter_names-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARX.parameter_names-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>)</p>
+<dd class="field-even"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span> <span class="pre">(str)</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1215,7 +1215,7 @@ <h1 id="arch-univariate-harx-parameter-names">arch.univariate.HARX.parameter_nam
   
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diff --git a/univariate/generated/generated/arch.univariate.HARX.resids.html b/univariate/generated/generated/arch.univariate.HARX.resids.html
index d7c1c6b84a..dd8ae19eea 100644
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@@ -1201,19 +1201,19 @@
                   
 
 
-<h1 id="arch-univariate-harx-resids">arch.univariate.HARX.resids<a class="headerlink" href="#arch-univariate-harx-resids" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-harx-resids">arch.univariate.HARX.resids<a class="headerlink" href="#arch-univariate-harx-resids" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.HARX.resids">
-<span class="sig-prename descclassname"><span class="pre">HARX.</span></span><span class="sig-name descname"><span class="pre">resids</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.resids.params" title="arch.univariate.HARX.resids.params (Python parameter) — Model parameters"><span class="n"><span class="pre">params</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.resids.y" title="arch.univariate.HARX.resids.y (Python parameter) — Alternative values to use when computing model residuals"><span class="n"><span class="pre">y</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.resids.regressors" title="arch.univariate.HARX.resids.regressors (Python parameter) — Alternative regressor values to use when computing model residuals"><span class="n"><span class="pre">regressors</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/mean.html#HARX.resids"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.HARX.resids" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.HARX.resids">
+<span class="sig-prename descclassname"><span class="pre">HARX.</span></span><span class="sig-name descname"><span class="pre">resids</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.resids.params" title="arch.univariate.HARX.resids.params (Python parameter) — Model parameters"><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.resids.y" title="arch.univariate.HARX.resids.y (Python parameter) — Alternative values to use when computing model residuals"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.resids.regressors" title="arch.univariate.HARX.resids.regressors (Python parameter) — Alternative regressor values to use when computing model residuals"><span class="n"><span class="pre">regressors</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/mean.html#HARX.resids"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.HARX.resids" title="Link to this definition">¶</a></dt>
 <dd><p>Compute model residuals</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.HARX.resids-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARX.resids-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.HARX.resids.params"><span class="sig-name descname">params</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.HARX.resids.params" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
+<dt id="arch.univariate.HARX.resids.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.HARX.resids.params" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
 </dd>
-<dt id="arch.univariate.HARX.resids.y"><span class="sig-name descname">y</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray, optional</span></span><a class="headerlink" href="#arch.univariate.HARX.resids.y" title="Permalink to this definition">¶</a></dt><dd><p>Alternative values to use when computing model residuals</p>
+<dt id="arch.univariate.HARX.resids.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.HARX.resids.y" title="Permalink to this definition">¶</a></dt><dd><p>Alternative values to use when computing model residuals</p>
 </dd>
-<dt id="arch.univariate.HARX.resids.regressors"><span class="sig-name descname">regressors</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray, optional</span></span><a class="headerlink" href="#arch.univariate.HARX.resids.regressors" title="Permalink to this definition">¶</a></dt><dd><p>Alternative regressor values to use when computing model residuals</p>
+<dt id="arch.univariate.HARX.resids.regressors"><span class="n sig-name">regressors</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.HARX.resids.regressors" title="Permalink to this definition">¶</a></dt><dd><p>Alternative regressor values to use when computing model residuals</p>
 </dd>
 </dl>
 </dd>
@@ -1221,7 +1221,7 @@ <h1 id="arch-univariate-harx-resids">arch.univariate.HARX.resids<a class="header
 <dd class="field-even"><p><strong>resids</strong> – Model residuals</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.HARX.resids-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARX.resids-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1243,33 +1243,33 @@
                   
 
 
-<h1 id="arch-univariate-harx-simulate">arch.univariate.HARX.simulate<a class="headerlink" href="#arch-univariate-harx-simulate" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-harx-simulate">arch.univariate.HARX.simulate<a class="headerlink" href="#arch-univariate-harx-simulate" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.HARX.simulate">
-<span class="sig-prename descclassname"><span class="pre">HARX.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.simulate.params" title="arch.univariate.HARX.simulate.params (Python parameter) — Parameters to use when simulating the model.  Parameter order is [mean volatility distribution] where the parameters of the mean model are ordered [constant lag[0] lag[1] ... lag[p] ex[0] ... ex[k-1]] where lag[j] indicates the coefficient on the jth lag in the model and ex[j] is the coefficient on the jth exogenous variable."><span class="n"><span class="pre">params</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.simulate.nobs" title="arch.univariate.HARX.simulate.nobs (Python parameter) — Length of series to simulate"><span class="n"><span class="pre">nobs</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.simulate.burn" title="arch.univariate.HARX.simulate.burn (Python parameter) — Number of values to simulate to initialize the model and remove dependence on initial values."><span class="n"><span class="pre">burn</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.simulate.initial_value" title="arch.univariate.HARX.simulate.initial_value (Python parameter) — Either a scalar value or max(lags) array set of initial values to use when initializing the model.  If omitted, 0.0 is used."><span class="n"><span class="pre">initial_value</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.simulate.x" title="arch.univariate.HARX.simulate.x (Python parameter) — nobs + burn by k array of exogenous variables to include in the simulation."><span class="n"><span class="pre">x</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.simulate.initial_value_vol" title="arch.univariate.HARX.simulate.initial_value_vol (Python parameter) — An array or scalar to use when initializing the volatility process."><span class="n"><span class="pre">initial_value_vol</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/mean.html#HARX.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.HARX.simulate" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">HARX.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.simulate.params" title="arch.univariate.HARX.simulate.params (Python parameter) — Parameters to use when simulating the model.  Parameter order is [mean volatility distribution] where the parameters of the mean model are ordered [constant lag[0] lag[1] ... lag[p] ex[0] ... ex[k-1]] where lag[j] indicates the coefficient on the jth lag in the model and ex[j] is the coefficient on the jth exogenous variable."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.simulate.nobs" title="arch.univariate.HARX.simulate.nobs (Python parameter) — Length of series to simulate"><span class="n"><span class="pre">nobs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.simulate.burn" title="arch.univariate.HARX.simulate.burn (Python parameter) — Number of values to simulate to initialize the model and remove dependence on initial values."><span class="n"><span class="pre">burn</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.simulate.initial_value" title="arch.univariate.HARX.simulate.initial_value (Python parameter) — Either a scalar value or max(lags) array set of initial values to use when initializing the model.  If omitted, 0.0 is used."><span class="n"><span class="pre">initial_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.simulate.x" title="arch.univariate.HARX.simulate.x (Python parameter) — nobs + burn by k array of exogenous variables to include in the simulation."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.simulate.initial_value_vol" title="arch.univariate.HARX.simulate.initial_value_vol (Python parameter) — An array or scalar to use when initializing the volatility process."><span class="n"><span class="pre">initial_value_vol</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/mean.html#HARX.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.HARX.simulate" title="Link to this definition">¶</a></dt>
 <dd><p>Simulates data from a linear regression, AR or HAR models</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.HARX.simulate-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARX.simulate-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.HARX.simulate.params"><span class="sig-name descname">params</span><span class="p"> : </span><span class="desctype"><span class="desctype">array_like</span></span><a class="headerlink" href="#arch.univariate.HARX.simulate.params" title="Permalink to this definition">¶</a></dt><dd><p>Parameters to use when simulating the model.  Parameter order is
+<dt id="arch.univariate.HARX.simulate.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.univariate.HARX.simulate.params" title="Permalink to this definition">¶</a></dt><dd><p>Parameters to use when simulating the model.  Parameter order is
 [mean volatility distribution] where the parameters of the mean
 model are ordered [constant lag[0] lag[1] … lag[p] ex[0] …
 ex[k-1]] where lag[j] indicates the coefficient on the jth lag in
 the model and ex[j] is the coefficient on the jth exogenous
 variable.</p>
 </dd>
-<dt id="arch.univariate.HARX.simulate.nobs"><span class="sig-name descname">nobs</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.HARX.simulate.nobs" title="Permalink to this definition">¶</a></dt><dd><p>Length of series to simulate</p>
+<dt id="arch.univariate.HARX.simulate.nobs"><span class="n sig-name">nobs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.HARX.simulate.nobs" title="Permalink to this definition">¶</a></dt><dd><p>Length of series to simulate</p>
 </dd>
-<dt id="arch.univariate.HARX.simulate.burn"><span class="sig-name descname">burn</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.univariate.HARX.simulate.burn" title="Permalink to this definition">¶</a></dt><dd><p>Number of values to simulate to initialize the model and remove
+<dt id="arch.univariate.HARX.simulate.burn"><span class="n sig-name">burn</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code><a class="headerlink" href="#arch.univariate.HARX.simulate.burn" title="Permalink to this definition">¶</a></dt><dd><p>Number of values to simulate to initialize the model and remove
 dependence on initial values.</p>
 </dd>
-<dt id="arch.univariate.HARX.simulate.initial_value"><span class="sig-name descname">initial_value</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, float}, optional</span></span><a class="headerlink" href="#arch.univariate.HARX.simulate.initial_value" title="Permalink to this definition">¶</a></dt><dd><p>Either a scalar value or <cite>max(lags)</cite> array set of initial values to
+<dt id="arch.univariate.HARX.simulate.initial_value"><span class="n sig-name">initial_value</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.HARX.simulate.initial_value" title="Permalink to this definition">¶</a></dt><dd><p>Either a scalar value or <cite>max(lags)</cite> array set of initial values to
 use when initializing the model.  If omitted, 0.0 is used.</p>
 </dd>
-<dt id="arch.univariate.HARX.simulate.x"><span class="sig-name descname">x</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, DataFrame}, optional</span></span><a class="headerlink" href="#arch.univariate.HARX.simulate.x" title="Permalink to this definition">¶</a></dt><dd><p>nobs + burn by k array of exogenous variables to include in the
+<dt id="arch.univariate.HARX.simulate.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.HARX.simulate.x" title="Permalink to this definition">¶</a></dt><dd><p>nobs + burn by k array of exogenous variables to include in the
 simulation.</p>
 </dd>
-<dt id="arch.univariate.HARX.simulate.initial_value_vol"><span class="sig-name descname">initial_value_vol</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, float}, optional</span></span><a class="headerlink" href="#arch.univariate.HARX.simulate.initial_value_vol" title="Permalink to this definition">¶</a></dt><dd><p>An array or scalar to use when initializing the volatility process.</p>
+<dt id="arch.univariate.HARX.simulate.initial_value_vol"><span class="n sig-name">initial_value_vol</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.HARX.simulate.initial_value_vol" title="Permalink to this definition">¶</a></dt><dd><p>An array or scalar to use when initializing the volatility process.</p>
 </dd>
 </dl>
 </dd>
@@ -1279,7 +1279,7 @@ <h1 id="arch-univariate-harx-simulate">arch.univariate.HARX.simulate<a class="he
 containing the errors used in the simulation</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.HARX.simulate-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARX.simulate-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>DataFrame</p>
+<dd class="field-odd"><p><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code></a></p>
 </dd>
 </dl>
 <p class="example rubric">Examples</p>
@@ -1372,7 +1372,7 @@ <h1 id="arch-univariate-harx-simulate">arch.univariate.HARX.simulate<a class="he
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1419,7 +1419,7 @@ <h1 id="arch-univariate-harx-simulate">arch.univariate.HARX.simulate<a class="he
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diff --git a/univariate/generated/generated/arch.univariate.HARX.starting_values.html b/univariate/generated/generated/arch.univariate.HARX.starting_values.html
index 6ce88f9e75..751d3324ff 100644
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+++ b/univariate/generated/generated/arch.univariate.HARX.starting_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.HARX.starting_values - arch 6.1.0</title>
+        <title>arch.univariate.HARX.starting_values - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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@@ -1133,10 +1133,10 @@
                   
 
 
-<h1 id="arch-univariate-harx-starting-values">arch.univariate.HARX.starting_values<a class="headerlink" href="#arch-univariate-harx-starting-values" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-harx-starting-values">arch.univariate.HARX.starting_values<a class="headerlink" href="#arch-univariate-harx-starting-values" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.HARX.starting_values">
-<span class="sig-prename descclassname"><span class="pre">HARX.</span></span><span class="sig-name descname"><span class="pre">starting_values</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.HARX.starting_values" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">HARX.</span></span><span class="sig-name descname"><span class="pre">starting_values</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.univariate.HARX.starting_values" title="Link to this definition">¶</a></dt>
 <dd><p>Returns starting values for the mean model, often the same as the
 values returned from fit</p>
 <dl class="field-list simple">
@@ -1144,7 +1144,7 @@ <h1 id="arch-univariate-harx-starting-values">arch.univariate.HARX.starting_valu
 <dd class="field-odd"><p><strong>sv</strong> – Starting values</p>
 </dd>
 <dt class="field-even" id="arch.univariate.HARX.starting_values-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARX.starting_values-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p>ndarray</p>
+<dd class="field-even"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
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@@ -1216,7 +1216,7 @@ <h1 id="arch-univariate-harx-starting-values">arch.univariate.HARX.starting_valu
   
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diff --git a/univariate/generated/generated/arch.univariate.HARX.volatility.html b/univariate/generated/generated/arch.univariate.HARX.volatility.html
index c856d03c60..cd39113447 100644
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@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.univariate.HARX.volatility - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1093,10 +1093,10 @@
                   
 
 
-<h1 id="arch-univariate-harx-volatility">arch.univariate.HARX.volatility<a class="headerlink" href="#arch-univariate-harx-volatility" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-harx-volatility">arch.univariate.HARX.volatility<a class="headerlink" href="#arch-univariate-harx-volatility" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.HARX.volatility">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">HARX.</span></span><span class="sig-name descname"><span class="pre">volatility</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.univariate.volatility.VolatilityProcess.html#arch.univariate.volatility.VolatilityProcess" title="arch.univariate.volatility.VolatilityProcess (Python class) — Abstract base class for ARCH models.  Allows the conditional mean model to be specified separately from the conditional variance, even though parameters are estimated jointly."><span class="pre">VolatilityProcess</span></a></span><a class="headerlink" href="#arch.univariate.HARX.volatility" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">HARX.</span></span><span class="sig-name descname"><span class="pre">volatility</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.univariate.volatility.VolatilityProcess.html#arch.univariate.volatility.VolatilityProcess" title="arch.univariate.volatility.VolatilityProcess (Python class) — Abstract base class for ARCH models.  Allows the conditional mean model to be specified separately from the conditional variance, even though parameters are estimated jointly."><span class="pre">VolatilityProcess</span></a></span><a class="headerlink" href="#arch.univariate.HARX.volatility" title="Link to this definition">¶</a></dt>
 <dd><p>Set or gets the volatility process</p>
 <p>Volatility processes must be a subclass of VolatilityProcess</p>
 </dd></dl>
@@ -1168,7 +1168,7 @@ <h1 id="arch-univariate-harx-volatility">arch.univariate.HARX.volatility<a class
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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diff --git a/univariate/generated/generated/arch.univariate.HARX.x.html b/univariate/generated/generated/arch.univariate.HARX.x.html
index 3f730318c1..2f2577580b 100644
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+++ b/univariate/generated/generated/arch.univariate.HARX.x.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.HARX.x - arch 6.1.0</title>
+        <title>arch.univariate.HARX.x - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1093,10 +1093,10 @@
                   
 
 
-<h1 id="arch-univariate-harx-x">arch.univariate.HARX.x<a class="headerlink" href="#arch-univariate-harx-x" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-harx-x">arch.univariate.HARX.x<a class="headerlink" href="#arch-univariate-harx-x" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.HARX.x">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">HARX.</span></span><span class="sig-name descname"><span class="pre">x</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.0.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.HARX.x" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">HARX.</span></span><span class="sig-name descname"><span class="pre">x</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.HARX.x" title="Link to this definition">¶</a></dt>
 <dd><p>Gets the value of the exogenous regressors in the model</p>
 </dd></dl>
 
@@ -1167,7 +1167,7 @@ <h1 id="arch-univariate-harx-x">arch.univariate.HARX.x<a class="headerlink" href
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1214,7 +1214,7 @@ <h1 id="arch-univariate-harx-x">arch.univariate.HARX.x<a class="headerlink" href
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diff --git a/univariate/generated/generated/arch.univariate.HARX.y.html b/univariate/generated/generated/arch.univariate.HARX.y.html
index d5f37b5628..aa10da2b41 100644
--- a/univariate/generated/generated/arch.univariate.HARX.y.html
+++ b/univariate/generated/generated/arch.univariate.HARX.y.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.HARX.y - arch 6.1.0</title>
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@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1093,10 +1093,10 @@
                   
 
 
-<h1 id="arch-univariate-harx-y">arch.univariate.HARX.y<a class="headerlink" href="#arch-univariate-harx-y" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-harx-y">arch.univariate.HARX.y<a class="headerlink" href="#arch-univariate-harx-y" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.HARX.y">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">HARX.</span></span><span class="sig-name descname"><span class="pre">y</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.0.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.0.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.HARX.y" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">HARX.</span></span><span class="sig-name descname"><span class="pre">y</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.HARX.y" title="Link to this definition">¶</a></dt>
 <dd><p>Returns the dependent variable</p>
 </dd></dl>
 
@@ -1167,7 +1167,7 @@ <h1 id="arch-univariate-harx-y">arch.univariate.HARX.y<a class="headerlink" href
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1214,7 +1214,7 @@ <h1 id="arch-univariate-harx-y">arch.univariate.HARX.y<a class="headerlink" href
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diff --git a/univariate/generated/generated/arch.univariate.LS.bounds.html b/univariate/generated/generated/arch.univariate.LS.bounds.html
index ba99220496..6182ede962 100644
--- a/univariate/generated/generated/arch.univariate.LS.bounds.html
+++ b/univariate/generated/generated/arch.univariate.LS.bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.LS.bounds - arch 6.1.0</title>
+        <title>arch.univariate.LS.bounds - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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@@ -189,10 +189,10 @@
 
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     <div class="md-nav__source">
@@ -1133,17 +1133,17 @@
                   
 
 
-<h1 id="arch-univariate-ls-bounds">arch.univariate.LS.bounds<a class="headerlink" href="#arch-univariate-ls-bounds" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-ls-bounds">arch.univariate.LS.bounds<a class="headerlink" href="#arch-univariate-ls-bounds" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.LS.bounds">
-<span class="sig-prename descclassname"><span class="pre">LS.</span></span><span class="sig-name descname"><span class="pre">bounds</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.LS.bounds" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">LS.</span></span><span class="sig-name descname"><span class="pre">bounds</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#arch.univariate.LS.bounds" title="Link to this definition">¶</a></dt>
 <dd><p>Construct bounds for parameters to use in non-linear optimization</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.LS.bounds-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.LS.bounds-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p><strong>bounds</strong> – Bounds for parameters to use in estimation.</p>
 </dd>
 <dt class="field-even" id="arch.univariate.LS.bounds-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.LS.bounds-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a> (2-tuple of <a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>)</p>
+<dd class="field-even"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span> <span class="pre">(2-tuple</span></code> of <code class="xref py py-class docutils literal notranslate"><span class="pre">float)</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1215,7 +1215,7 @@ <h1 id="arch-univariate-ls-bounds">arch.univariate.LS.bounds<a class="headerlink
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
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@@ -1262,7 +1262,7 @@ <h1 id="arch-univariate-ls-bounds">arch.univariate.LS.bounds<a class="headerlink
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diff --git a/univariate/generated/generated/arch.univariate.LS.compute_param_cov.html b/univariate/generated/generated/arch.univariate.LS.compute_param_cov.html
index a32389abf8..49fd472888 100644
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+++ b/univariate/generated/generated/arch.univariate.LS.compute_param_cov.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.LS.compute_param_cov - arch 6.1.0</title>
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@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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@@ -189,10 +189,10 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -598,13 +598,6 @@
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-  
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@@ -1187,26 +1173,23 @@
                   
 
 
-<h1 id="arch-univariate-ls-compute-param-cov">arch.univariate.LS.compute_param_cov<a class="headerlink" href="#arch-univariate-ls-compute-param-cov" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-ls-compute-param-cov">arch.univariate.LS.compute_param_cov<a class="headerlink" href="#arch-univariate-ls-compute-param-cov" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.LS.compute_param_cov">
-<span class="sig-prename descclassname"><span class="pre">LS.</span></span><span class="sig-name descname"><span class="pre">compute_param_cov</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.compute_param_cov.params" title="arch.univariate.LS.compute_param_cov.params (Python parameter) — Model parameters"><span class="n"><span class="pre">params</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.compute_param_cov.backcast" title="arch.univariate.LS.compute_param_cov.backcast (Python parameter) — Value to use for pre-sample observations"><span class="n"><span class="pre">backcast</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.compute_param_cov.robust" title="arch.univariate.LS.compute_param_cov.robust (Python parameter) — Flag indicating whether to use robust standard errors (True) or classic MLE (False)"><span class="n"><span class="pre">robust</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.LS.compute_param_cov" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.LS.compute_param_cov">
+<span class="sig-prename descclassname"><span class="pre">LS.</span></span><span class="sig-name descname"><span class="pre">compute_param_cov</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.compute_param_cov.params" title="arch.univariate.LS.compute_param_cov.params (Python parameter) — Model parameters"><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.compute_param_cov.backcast" title="arch.univariate.LS.compute_param_cov.backcast (Python parameter) — Value to use for pre-sample observations"><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.compute_param_cov.robust" title="arch.univariate.LS.compute_param_cov.robust (Python parameter) — Flag indicating whether to use robust standard errors (True) or classic MLE (False)"><span class="n"><span class="pre">robust</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.univariate.LS.compute_param_cov" title="Link to this definition">¶</a></dt>
 <dd><p>Computes parameter covariances using numerical derivatives.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.LS.compute_param_cov-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.LS.compute_param_cov-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.LS.compute_param_cov.params"><span class="sig-name descname">params</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.LS.compute_param_cov.params" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
+<dt id="arch.univariate.LS.compute_param_cov.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.LS.compute_param_cov.params" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
 </dd>
-<dt id="arch.univariate.LS.compute_param_cov.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><a class="headerlink" href="#arch.univariate.LS.compute_param_cov.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use for pre-sample observations</p>
+<dt id="arch.univariate.LS.compute_param_cov.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.LS.compute_param_cov.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use for pre-sample observations</p>
 </dd>
-<dt id="arch.univariate.LS.compute_param_cov.robust"><span class="sig-name descname">robust</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, optional</span></span><a class="headerlink" href="#arch.univariate.LS.compute_param_cov.robust" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to use robust standard errors (True) or
+<dt id="arch.univariate.LS.compute_param_cov.robust"><span class="n sig-name">robust</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.univariate.LS.compute_param_cov.robust" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to use robust standard errors (True) or
 classic MLE (False)</p>
 </dd>
 </dl>
 </dd>
-<dt class="field-even" id="arch.univariate.LS.compute_param_cov-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.LS.compute_param_cov-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a></p>
-</dd>
 </dl>
 </dd></dl>
 
@@ -1277,7 +1260,7 @@ <h1 id="arch-univariate-ls-compute-param-cov">arch.univariate.LS.compute_param_c
   
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diff --git a/univariate/generated/generated/arch.univariate.LS.constraints.html b/univariate/generated/generated/arch.univariate.LS.constraints.html
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+++ b/univariate/generated/generated/arch.univariate.LS.constraints.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -102,7 +102,7 @@
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
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+    arch 6.2.0
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@@ -581,13 +581,6 @@
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-          <li class="md-nav__item">
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-    <span class="md-ellipsis">Return type</span>
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-  
-</li>
-        
           <li class="md-nav__item">
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@@ -1100,13 +1093,6 @@
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-          <li class="md-nav__item">
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   <a href="#arch.univariate.LS.constraints-returns" class="md-nav__link">
     <span class="md-ellipsis">Returns</span>
@@ -1133,19 +1119,16 @@
                   
 
 
-<h1 id="arch-univariate-ls-constraints">arch.univariate.LS.constraints<a class="headerlink" href="#arch-univariate-ls-constraints" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-ls-constraints">arch.univariate.LS.constraints<a class="headerlink" href="#arch-univariate-ls-constraints" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.LS.constraints">
-<span class="sig-prename descclassname"><span class="pre">LS.</span></span><span class="sig-name descname"><span class="pre">constraints</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.LS.constraints" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">LS.</span></span><span class="sig-name descname"><span class="pre">constraints</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#arch.univariate.LS.constraints" title="Link to this definition">¶</a></dt>
 <dd><p>Construct linear constraint arrays  for use in non-linear optimization</p>
 <dl class="field-list simple">
-<dt class="field-odd" id="arch.univariate.LS.constraints-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.LS.constraints-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>]</p>
-</dd>
-<dt class="field-even" id="arch.univariate.LS.constraints-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.LS.constraints-returns" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><ul class="simple">
-<li><p><strong>a</strong> (<em>ndarray</em>) – Number of constraints by number of parameters loading array</p></li>
-<li><p><strong>b</strong> (<em>ndarray</em>) – Number of constraints array of lower bounds</p></li>
+<dt class="field-odd" id="arch.univariate.LS.constraints-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.LS.constraints-returns" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p><strong>a</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – Number of constraints by number of parameters loading array</p></li>
+<li><p><strong>b</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – Number of constraints array of lower bounds</p></li>
 </ul>
 </p>
 </dd>
@@ -1221,7 +1204,7 @@ <h1 id="arch-univariate-ls-constraints">arch.univariate.LS.constraints<a class="
   
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     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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diff --git a/univariate/generated/generated/arch.univariate.LS.distribution.html b/univariate/generated/generated/arch.univariate.LS.distribution.html
index 9e494e0c28..03051eeaea 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
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           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1093,10 +1093,10 @@
                   
 
 
-<h1 id="arch-univariate-ls-distribution">arch.univariate.LS.distribution<a class="headerlink" href="#arch-univariate-ls-distribution" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-ls-distribution">arch.univariate.LS.distribution<a class="headerlink" href="#arch-univariate-ls-distribution" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.LS.distribution">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">LS.</span></span><span class="sig-name descname"><span class="pre">distribution</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.univariate.distribution.Distribution.html#arch.univariate.distribution.Distribution" title="arch.univariate.distribution.Distribution (Python class) — Template for subclassing only"><span class="pre">Distribution</span></a></span><a class="headerlink" href="#arch.univariate.LS.distribution" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">LS.</span></span><span class="sig-name descname"><span class="pre">distribution</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.univariate.distribution.Distribution.html#arch.univariate.distribution.Distribution" title="arch.univariate.distribution.Distribution (Python class) — Template for subclassing only"><span class="pre">Distribution</span></a></span><a class="headerlink" href="#arch.univariate.LS.distribution" title="Link to this definition">¶</a></dt>
 <dd><p>Set or gets the error distribution</p>
 <p>Distributions must be a subclass of Distribution</p>
 </dd></dl>
@@ -1168,7 +1168,7 @@ <h1 id="arch-univariate-ls-distribution">arch.univariate.LS.distribution<a class
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
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@@ -1215,7 +1215,7 @@ <h1 id="arch-univariate-ls-distribution">arch.univariate.LS.distribution<a class
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diff --git a/univariate/generated/generated/arch.univariate.LS.fit.html b/univariate/generated/generated/arch.univariate.LS.fit.html
index 3cc52b97b5..40e0477912 100644
--- a/univariate/generated/generated/arch.univariate.LS.fit.html
+++ b/univariate/generated/generated/arch.univariate.LS.fit.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.LS.fit - arch 6.1.0</title>
+        <title>arch.univariate.LS.fit - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
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+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    arch 6.1.0
+    arch 6.2.0
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@@ -1299,40 +1299,40 @@
                   
 
 
-<h1 id="arch-univariate-ls-fit">arch.univariate.LS.fit<a class="headerlink" href="#arch-univariate-ls-fit" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-ls-fit">arch.univariate.LS.fit<a class="headerlink" href="#arch-univariate-ls-fit" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.LS.fit">
-<span class="sig-prename descclassname"><span class="pre">LS.</span></span><span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.fit.update_freq" title="arch.univariate.LS.fit.update_freq (Python parameter) — Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."><span class="n"><span class="pre">update_freq</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.fit.disp" title="arch.univariate.LS.fit.disp (Python parameter) — Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is &quot;off&quot; and True is &quot;final&quot;"><span class="n"><span class="pre">disp</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;final&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.fit.starting_values" title="arch.univariate.LS.fit.starting_values (Python parameter) — Array of starting values to use.  If not provided, starting values are constructed by the model components."><span class="n"><span class="pre">starting_values</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.fit.cov_type" title="arch.univariate.LS.fit.cov_type (Python parameter) — Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."><span class="n"><span class="pre">cov_type</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;robust&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.fit.show_warning" title="arch.univariate.LS.fit.show_warning (Python parameter) — Flag indicating whether convergence warnings should be shown."><span class="n"><span class="pre">show_warning</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.fit.first_obs" title="arch.univariate.LS.fit.first_obs (Python parameter) — First observation to use when estimating model"><span class="n"><span class="pre">first_obs</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.fit.last_obs" title="arch.univariate.LS.fit.last_obs (Python parameter) — Last observation to use when estimating model"><span class="n"><span class="pre">last_obs</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.fit.tol" title="arch.univariate.LS.fit.tol (Python parameter) — Tolerance for termination."><span class="n"><span class="pre">tol</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.fit.options" title="arch.univariate.LS.fit.options (Python parameter) — Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."><span class="n"><span class="pre">options</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.fit.backcast" title="arch.univariate.LS.fit.backcast (Python parameter) — Value to use as backcast. Should be measure \sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."><span class="n"><span class="pre">backcast</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.LS.fit" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">LS.</span></span><span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.fit.update_freq" title="arch.univariate.LS.fit.update_freq (Python parameter) — Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."><span class="n"><span class="pre">update_freq</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.fit.disp" title="arch.univariate.LS.fit.disp (Python parameter) — Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is &quot;off&quot; and True is &quot;final&quot;"><span class="n"><span class="pre">disp</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'off'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'final'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;final&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.fit.starting_values" title="arch.univariate.LS.fit.starting_values (Python parameter) — Array of starting values to use.  If not provided, starting values are constructed by the model components."><span class="n"><span class="pre">starting_values</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.fit.cov_type" title="arch.univariate.LS.fit.cov_type (Python parameter) — Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."><span class="n"><span class="pre">cov_type</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'robust'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'classic'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;robust&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.fit.show_warning" title="arch.univariate.LS.fit.show_warning (Python parameter) — Flag indicating whether convergence warnings should be shown."><span class="n"><span class="pre">show_warning</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.fit.first_obs" title="arch.univariate.LS.fit.first_obs (Python parameter) — First observation to use when estimating model"><span class="n"><span class="pre">first_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.fit.last_obs" title="arch.univariate.LS.fit.last_obs (Python parameter) — Last observation to use when estimating model"><span class="n"><span class="pre">last_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.fit.tol" title="arch.univariate.LS.fit.tol (Python parameter) — Tolerance for termination."><span class="n"><span class="pre">tol</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.fit.options" title="arch.univariate.LS.fit.options (Python parameter) — Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."><span class="n"><span class="pre">options</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.fit.backcast" title="arch.univariate.LS.fit.backcast (Python parameter) — Value to use as backcast. Should be measure \sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelResult.html#arch.univariate.base.ARCHModelResult" title="arch.univariate.base.ARCHModelResult (Python class) — Results from estimation of an ARCHModel model"><span class="pre">ARCHModelResult</span></a></span></span></span><a class="headerlink" href="#arch.univariate.LS.fit" title="Link to this definition">¶</a></dt>
 <dd><p>Estimate model parameters</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.LS.fit-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.LS.fit-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.LS.fit.update_freq"><span class="sig-name descname">update_freq</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.univariate.LS.fit.update_freq" title="Permalink to this definition">¶</a></dt><dd><p>Frequency of iteration updates.  Output is generated every
+<dt id="arch.univariate.LS.fit.update_freq"><span class="n sig-name">update_freq</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code><a class="headerlink" href="#arch.univariate.LS.fit.update_freq" title="Permalink to this definition">¶</a></dt><dd><p>Frequency of iteration updates.  Output is generated every
 <cite>update_freq</cite> iterations. Set to 0 to disable iterative output.</p>
 </dd>
-<dt id="arch.univariate.LS.fit.disp"><span class="sig-name descname">disp</span><span class="p"> : </span><span class="desctype"><span class="desctype">{bool, &quot;off&quot;, &quot;final&quot;}</span></span><a class="headerlink" href="#arch.univariate.LS.fit.disp" title="Permalink to this definition">¶</a></dt><dd><p>Either ‘final’ to print optimization result or ‘off’ to display
+<dt id="arch.univariate.LS.fit.disp"><span class="n sig-name">disp</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'off'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'final'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;final&#39;</span></code><a class="headerlink" href="#arch.univariate.LS.fit.disp" title="Permalink to this definition">¶</a></dt><dd><p>Either ‘final’ to print optimization result or ‘off’ to display
 nothing. If using a boolean, False is “off” and True is “final”</p>
 </dd>
-<dt id="arch.univariate.LS.fit.starting_values"><span class="sig-name descname">starting_values</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray, optional</span></span><a class="headerlink" href="#arch.univariate.LS.fit.starting_values" title="Permalink to this definition">¶</a></dt><dd><p>Array of starting values to use.  If not provided, starting values
+<dt id="arch.univariate.LS.fit.starting_values"><span class="n sig-name">starting_values</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.LS.fit.starting_values" title="Permalink to this definition">¶</a></dt><dd><p>Array of starting values to use.  If not provided, starting values
 are constructed by the model components.</p>
 </dd>
-<dt id="arch.univariate.LS.fit.cov_type"><span class="sig-name descname">cov_type</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, optional</span></span><a class="headerlink" href="#arch.univariate.LS.fit.cov_type" title="Permalink to this definition">¶</a></dt><dd><p>Estimation method of parameter covariance.  Supported options are
+<dt id="arch.univariate.LS.fit.cov_type"><span class="n sig-name">cov_type</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'robust'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'classic'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;robust&#39;</span></code><a class="headerlink" href="#arch.univariate.LS.fit.cov_type" title="Permalink to this definition">¶</a></dt><dd><p>Estimation method of parameter covariance.  Supported options are
 ‘robust’, which does not assume the Information Matrix Equality
 holds and ‘classic’ which does.  In the ARCH literature, ‘robust’
 corresponds to Bollerslev-Wooldridge covariance estimator.</p>
 </dd>
-<dt id="arch.univariate.LS.fit.show_warning"><span class="sig-name descname">show_warning</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, optional</span></span><a class="headerlink" href="#arch.univariate.LS.fit.show_warning" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether convergence warnings should be shown.</p>
+<dt id="arch.univariate.LS.fit.show_warning"><span class="n sig-name">show_warning</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.univariate.LS.fit.show_warning" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether convergence warnings should be shown.</p>
 </dd>
-<dt id="arch.univariate.LS.fit.first_obs"><span class="sig-name descname">first_obs</span><span class="p"> : </span><span class="desctype"><span class="desctype">{int, <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, datetime, Timestamp}</span></span><a class="headerlink" href="#arch.univariate.LS.fit.first_obs" title="Permalink to this definition">¶</a></dt><dd><p>First observation to use when estimating model</p>
+<dt id="arch.univariate.LS.fit.first_obs"><span class="n sig-name">first_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.LS.fit.first_obs" title="Permalink to this definition">¶</a></dt><dd><p>First observation to use when estimating model</p>
 </dd>
-<dt id="arch.univariate.LS.fit.last_obs"><span class="sig-name descname">last_obs</span><span class="p"> : </span><span class="desctype"><span class="desctype">{int, <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, datetime, Timestamp}</span></span><a class="headerlink" href="#arch.univariate.LS.fit.last_obs" title="Permalink to this definition">¶</a></dt><dd><p>Last observation to use when estimating model</p>
+<dt id="arch.univariate.LS.fit.last_obs"><span class="n sig-name">last_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.LS.fit.last_obs" title="Permalink to this definition">¶</a></dt><dd><p>Last observation to use when estimating model</p>
 </dd>
-<dt id="arch.univariate.LS.fit.tol"><span class="sig-name descname">tol</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>, optional</span></span><a class="headerlink" href="#arch.univariate.LS.fit.tol" title="Permalink to this definition">¶</a></dt><dd><p>Tolerance for termination.</p>
+<dt id="arch.univariate.LS.fit.tol"><span class="n sig-name">tol</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.LS.fit.tol" title="Permalink to this definition">¶</a></dt><dd><p>Tolerance for termination.</p>
 </dd>
-<dt id="arch.univariate.LS.fit.options"><span class="sig-name descname">options</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a>, optional</span></span><a class="headerlink" href="#arch.univariate.LS.fit.options" title="Permalink to this definition">¶</a></dt><dd><p>Options to pass to <cite>scipy.optimize.minimize</cite>.  Valid entries
+<dt id="arch.univariate.LS.fit.options"><span class="n sig-name">options</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)">Any</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.LS.fit.options" title="Permalink to this definition">¶</a></dt><dd><p>Options to pass to <cite>scipy.optimize.minimize</cite>.  Valid entries
 include ‘ftol’, ‘eps’, ‘disp’, and ‘maxiter’.</p>
 </dd>
-<dt id="arch.univariate.LS.fit.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}, optional</span></span><a class="headerlink" href="#arch.univariate.LS.fit.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use as backcast. Should be measure <span class="math notranslate nohighlight">\(\sigma^2_0\)</span>
+<dt id="arch.univariate.LS.fit.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.LS.fit.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use as backcast. Should be measure <span class="math notranslate nohighlight">\(\sigma^2_0\)</span>
 since model-specific non-linear transformations are applied to
 value before computing the variance recursions.</p>
 </dd>
@@ -1342,7 +1342,7 @@ <h1 id="arch-univariate-ls-fit">arch.univariate.LS.fit<a class="headerlink" href
 <dd class="field-even"><p><strong>results</strong> – Object containing model results</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.LS.fit-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.LS.fit-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference internal" href="../arch.univariate.base.ARCHModelResult.html#arch.univariate.base.ARCHModelResult" title="arch.univariate.base.ARCHModelResult (Python class) — Results from estimation of an ARCHModel model">ARCHModelResult</a></p>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">ARCHModelResult</span></code></p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -1418,7 +1418,7 @@ <h1 id="arch-univariate-ls-fit">arch.univariate.LS.fit<a class="headerlink" href
   
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diff --git a/univariate/generated/generated/arch.univariate.LS.fix.html b/univariate/generated/generated/arch.univariate.LS.fix.html
index 852bbb93e1..3e6c88e035 100644
--- a/univariate/generated/generated/arch.univariate.LS.fix.html
+++ b/univariate/generated/generated/arch.univariate.LS.fix.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.LS.fix - arch 6.1.0</title>
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@@ -47,8 +47,8 @@
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     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch 6.1.0
+    arch 6.2.0
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@@ -1201,21 +1201,21 @@
                   
 
 
-<h1 id="arch-univariate-ls-fix">arch.univariate.LS.fix<a class="headerlink" href="#arch-univariate-ls-fix" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-ls-fix">arch.univariate.LS.fix<a class="headerlink" href="#arch-univariate-ls-fix" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.LS.fix">
-<span class="sig-prename descclassname"><span class="pre">LS.</span></span><span class="sig-name descname"><span class="pre">fix</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.fix.params" title="arch.univariate.LS.fix.params (Python parameter) — User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."><span class="n"><span class="pre">params</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.fix.first_obs" title="arch.univariate.LS.fix.first_obs (Python parameter) — First observation to use when fixing model"><span class="n"><span class="pre">first_obs</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.fix.last_obs" title="arch.univariate.LS.fix.last_obs (Python parameter) — Last observation to use when fixing model"><span class="n"><span class="pre">last_obs</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.LS.fix" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.LS.fix">
+<span class="sig-prename descclassname"><span class="pre">LS.</span></span><span class="sig-name descname"><span class="pre">fix</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.fix.params" title="arch.univariate.LS.fix.params (Python parameter) — User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.fix.first_obs" title="arch.univariate.LS.fix.first_obs (Python parameter) — First observation to use when fixing model"><span class="n"><span class="pre">first_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.fix.last_obs" title="arch.univariate.LS.fix.last_obs (Python parameter) — Last observation to use when fixing model"><span class="n"><span class="pre">last_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelFixedResult.html#arch.univariate.base.ARCHModelFixedResult" title="arch.univariate.base.ARCHModelFixedResult (Python class) — Results for fixed parameters for an ARCHModel model"><span class="pre">ARCHModelFixedResult</span></a></span></span></span><a class="headerlink" href="#arch.univariate.LS.fix" title="Link to this definition">¶</a></dt>
 <dd><p>Allows an ARCHModelFixedResult to be constructed from fixed parameters.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.LS.fix-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.LS.fix-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.LS.fix.params"><span class="sig-name descname">params</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, Series}</span></span><a class="headerlink" href="#arch.univariate.LS.fix.params" title="Permalink to this definition">¶</a></dt><dd><p>User specified parameters to use when generating the result. Must
+<dt id="arch.univariate.LS.fix.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.LS.fix.params" title="Permalink to this definition">¶</a></dt><dd><p>User specified parameters to use when generating the result. Must
 have the correct number of parameters for a given choice of mean
 model, volatility model and distribution.</p>
 </dd>
-<dt id="arch.univariate.LS.fix.first_obs"><span class="sig-name descname">first_obs</span><span class="p"> : </span><span class="desctype"><span class="desctype">{int, <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, datetime, Timestamp}</span></span><a class="headerlink" href="#arch.univariate.LS.fix.first_obs" title="Permalink to this definition">¶</a></dt><dd><p>First observation to use when fixing model</p>
+<dt id="arch.univariate.LS.fix.first_obs"><span class="n sig-name">first_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.LS.fix.first_obs" title="Permalink to this definition">¶</a></dt><dd><p>First observation to use when fixing model</p>
 </dd>
-<dt id="arch.univariate.LS.fix.last_obs"><span class="sig-name descname">last_obs</span><span class="p"> : </span><span class="desctype"><span class="desctype">{int, <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, datetime, Timestamp}</span></span><a class="headerlink" href="#arch.univariate.LS.fix.last_obs" title="Permalink to this definition">¶</a></dt><dd><p>Last observation to use when fixing model</p>
+<dt id="arch.univariate.LS.fix.last_obs"><span class="n sig-name">last_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.LS.fix.last_obs" title="Permalink to this definition">¶</a></dt><dd><p>Last observation to use when fixing model</p>
 </dd>
 </dl>
 </dd>
@@ -1223,7 +1223,7 @@ <h1 id="arch-univariate-ls-fix">arch.univariate.LS.fix<a class="headerlink" href
 <dd class="field-even"><p><strong>results</strong> – Object containing model results</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.LS.fix-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.LS.fix-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference internal" href="../arch.univariate.base.ARCHModelFixedResult.html#arch.univariate.base.ARCHModelFixedResult" title="arch.univariate.base.ARCHModelFixedResult (Python class) — Results for fixed parameters for an ARCHModel model">ARCHModelFixedResult</a></p>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">ARCHModelFixedResult</span></code></p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -1297,7 +1297,7 @@ <h1 id="arch-univariate-ls-fix">arch.univariate.LS.fix<a class="headerlink" href
   
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     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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diff --git a/univariate/generated/generated/arch.univariate.LS.forecast.html b/univariate/generated/generated/arch.univariate.LS.forecast.html
index dae9f157b8..fa96225e8b 100644
--- a/univariate/generated/generated/arch.univariate.LS.forecast.html
+++ b/univariate/generated/generated/arch.univariate.LS.forecast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.LS.forecast - arch 6.1.0</title>
+        <title>arch.univariate.LS.forecast - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1299,56 +1299,56 @@
                   
 
 
-<h1 id="arch-univariate-ls-forecast">arch.univariate.LS.forecast<a class="headerlink" href="#arch-univariate-ls-forecast" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-ls-forecast">arch.univariate.LS.forecast<a class="headerlink" href="#arch-univariate-ls-forecast" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.LS.forecast">
-<span class="sig-prename descclassname"><span class="pre">LS.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.forecast.params" title="arch.univariate.LS.forecast.params (Python parameter) — Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."><span class="n"><span class="pre">params</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.forecast.horizon" title="arch.univariate.LS.forecast.horizon (Python parameter) — Number of steps to forecast"><span class="n"><span class="pre">horizon</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.forecast.start" title="arch.univariate.LS.forecast.start (Python parameter) — An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."><span class="n"><span class="pre">start</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.forecast.align" title="arch.univariate.LS.forecast.align (Python parameter) — Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."><span class="n"><span class="pre">align</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;origin&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.forecast.method" title="arch.univariate.LS.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons &gt; 1."><span class="n"><span class="pre">method</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.forecast.simulations" title="arch.univariate.LS.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.forecast.rng" title="arch.univariate.LS.forecast.rng (Python parameter) — Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."><span class="n"><span class="pre">rng</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.forecast.random_state" title="arch.univariate.LS.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.forecast.reindex" title="arch.univariate.LS.forecast.reindex (Python parameter) — Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."><span class="n"><span class="pre">reindex</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.forecast.x" title="arch.univariate.LS.forecast.x (Python parameter) — Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"><span class="n"><span class="pre">x</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.LS.forecast" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">LS.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.forecast.params" title="arch.univariate.LS.forecast.params (Python parameter) — Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.forecast.horizon" title="arch.univariate.LS.forecast.horizon (Python parameter) — Number of steps to forecast"><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.forecast.start" title="arch.univariate.LS.forecast.start (Python parameter) — An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.forecast.align" title="arch.univariate.LS.forecast.align (Python parameter) — Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."><span class="n"><span class="pre">align</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'origin'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'target'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;origin&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.forecast.method" title="arch.univariate.LS.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons &gt; 1."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.forecast.simulations" title="arch.univariate.LS.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.forecast.rng" title="arch.univariate.LS.forecast.rng (Python parameter) — Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.forecast.random_state" title="arch.univariate.LS.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.forecast.reindex" title="arch.univariate.LS.forecast.reindex (Python parameter) — Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."><span class="n"><span class="pre">reindex</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.forecast.x" title="arch.univariate.LS.forecast.x (Python parameter) — Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.11)"><span class="pre">Hashable</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelForecast.html#arch.univariate.base.ARCHModelForecast" title="arch.univariate.base.ARCHModelForecast (Python class) — Container for forecasts from an ARCH Model"><span class="pre">ARCHModelForecast</span></a></span></span></span><a class="headerlink" href="#arch.univariate.LS.forecast" title="Link to this definition">¶</a></dt>
 <dd><p>Construct forecasts from estimated model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.LS.forecast-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.LS.forecast-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.LS.forecast.params"><span class="sig-name descname">params</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, Series}</span></span><a class="headerlink" href="#arch.univariate.LS.forecast.params" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast. Must be identical in
+<dt id="arch.univariate.LS.forecast.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.LS.forecast.params" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast. Must be identical in
 shape to the parameters computed by fitting the model.</p>
 </dd>
-<dt id="arch.univariate.LS.forecast.horizon"><span class="sig-name descname">horizon</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.univariate.LS.forecast.horizon" title="Permalink to this definition">¶</a></dt><dd><p>Number of steps to forecast</p>
+<dt id="arch.univariate.LS.forecast.horizon"><span class="n sig-name">horizon</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code><a class="headerlink" href="#arch.univariate.LS.forecast.horizon" title="Permalink to this definition">¶</a></dt><dd><p>Number of steps to forecast</p>
 </dd>
-<dt id="arch.univariate.LS.forecast.start"><span class="sig-name descname">start</span><span class="p"> : </span><span class="desctype"><span class="desctype">{int, datetime, Timestamp, str}, optional</span></span><a class="headerlink" href="#arch.univariate.LS.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>An integer, datetime or str indicating the first observation to
+<dt id="arch.univariate.LS.forecast.start"><span class="n sig-name">start</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.LS.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>An integer, datetime or str indicating the first observation to
 produce the forecast for.  Datetimes can only be used with pandas
 inputs that have a datetime index. Strings must be convertible
 to a date time, such as in ‘1945-01-01’.</p>
 </dd>
-<dt id="arch.univariate.LS.forecast.align"><span class="sig-name descname">align</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, optional</span></span><a class="headerlink" href="#arch.univariate.LS.forecast.align" title="Permalink to this definition">¶</a></dt><dd><p>Either ‘origin’ or ‘target’.  When set of ‘origin’, the t-th row
+<dt id="arch.univariate.LS.forecast.align"><span class="n sig-name">align</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'origin'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'target'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;origin&#39;</span></code><a class="headerlink" href="#arch.univariate.LS.forecast.align" title="Permalink to this definition">¶</a></dt><dd><p>Either ‘origin’ or ‘target’.  When set of ‘origin’, the t-th row
 of forecasts contains the forecasts for t+1, t+2, …, t+h. When
 set to ‘target’, the t-th row contains the 1-step ahead forecast
 from time t-1, the 2 step from time t-2, …, and the h-step from
 time t-h.  ‘target’ simplified computing forecast errors since the
 realization and h-step forecast are aligned.</p>
 </dd>
-<dt id="arch.univariate.LS.forecast.method"><span class="sig-name descname">method</span><span class="p"> : </span><span class="desctype"><span class="desctype">{'analytic', 'simulation', 'bootstrap'}</span></span><a class="headerlink" href="#arch.univariate.LS.forecast.method" title="Permalink to this definition">¶</a></dt><dd><p>Method to use when producing the forecast. The default is analytic.
+<dt id="arch.univariate.LS.forecast.method"><span class="n sig-name">method</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'analytic'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'simulation'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'bootstrap'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code><a class="headerlink" href="#arch.univariate.LS.forecast.method" title="Permalink to this definition">¶</a></dt><dd><p>Method to use when producing the forecast. The default is analytic.
 The method only affects the variance forecast generation.  Not all
 volatility models support all methods. In particular, volatility
 models that do not evolve in squares such as EGARCH or TARCH do not
 support the ‘analytic’ method for horizons &gt; 1.</p>
 </dd>
-<dt id="arch.univariate.LS.forecast.simulations"><span class="sig-name descname">simulations</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.LS.forecast.simulations" title="Permalink to this definition">¶</a></dt><dd><p>Number of simulations to run when computing the forecast using
+<dt id="arch.univariate.LS.forecast.simulations"><span class="n sig-name">simulations</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.univariate.LS.forecast.simulations" title="Permalink to this definition">¶</a></dt><dd><p>Number of simulations to run when computing the forecast using
 either simulation or bootstrap.</p>
 </dd>
-<dt id="arch.univariate.LS.forecast.rng"><span class="sig-name descname">rng</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable, optional</span></span><a class="headerlink" href="#arch.univariate.LS.forecast.rng" title="Permalink to this definition">¶</a></dt><dd><p>Custom random number generator to use in simulation-based forecasts.
+<dt id="arch.univariate.LS.forecast.rng"><span class="n sig-name">rng</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)">Callable</a><span class="p">[</span><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">,</span><span class="w"> </span><span class="p">...</span><span class="p">]</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.LS.forecast.rng" title="Permalink to this definition">¶</a></dt><dd><p>Custom random number generator to use in simulation-based forecasts.
 Must produce random samples using the syntax <cite>rng(size)</cite> where size
 the 2-element tuple (simulations, horizon).</p>
 </dd>
-<dt id="arch.univariate.LS.forecast.random_state"><span class="sig-name descname">random_state</span><span class="p"> : </span><span class="desctype"><span class="desctype">RandomState, optional</span></span><a class="headerlink" href="#arch.univariate.LS.forecast.random_state" title="Permalink to this definition">¶</a></dt><dd><p>NumPy RandomState instance to use when method is ‘bootstrap’</p>
+<dt id="arch.univariate.LS.forecast.random_state"><span class="n sig-name">random_state</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)">RandomState</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.LS.forecast.random_state" title="Permalink to this definition">¶</a></dt><dd><p>NumPy RandomState instance to use when method is ‘bootstrap’</p>
 </dd>
-<dt id="arch.univariate.LS.forecast.reindex"><span class="sig-name descname">reindex</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, optional</span></span><a class="headerlink" href="#arch.univariate.LS.forecast.reindex" title="Permalink to this definition">¶</a></dt><dd><p><p>Whether to reindex the forecasts to have the same dimension as the series
+<dt id="arch.univariate.LS.forecast.reindex"><span class="n sig-name">reindex</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.univariate.LS.forecast.reindex" title="Permalink to this definition">¶</a></dt><dd><p><p>Whether to reindex the forecasts to have the same dimension as the series
 being forecast. Prior to 4.18 this was the default. As of 4.19 this is
 now optional. If not provided, a warning is raised about the future
 change in the default which will occur after September 2021.</p>
-<div class="versionadded">
-<p><span class="versionmodified added">New in version 4.19.</span></p>
+<div class="admonition versionadded">
+<p class="admonition-title"><span class="versionmodified added">New in version 4.19.</span></p>
 </div>
 </p>
 </dd>
-<dt id="arch.univariate.LS.forecast.x"><span class="sig-name descname">x</span><span class="p"> : </span><span class="desctype"><span class="desctype">{dict[label, array_like], array_like}</span></span><a class="headerlink" href="#arch.univariate.LS.forecast.x" title="Permalink to this definition">¶</a></dt><dd><p><p>Values to use for exogenous regressors if any are included in the
+<dt id="arch.univariate.LS.forecast.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.11)">Hashable</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.LS.forecast.x" title="Permalink to this definition">¶</a></dt><dd><p><p>Values to use for exogenous regressors if any are included in the
 model. Three formats are accepted:</p>
 <ul class="simple">
 <li><p>2-d array-like: This format can be used when there is a single
@@ -1371,8 +1371,8 @@ <h1 id="arch-univariate-ls-forecast">arch.univariate.LS.forecast<a class="header
 <p>Due to the complexity required to accommodate all scenarios, please
 see the example notebook that demonstrates the valid formats for
 x.</p>
-<div class="versionadded">
-<p><span class="versionmodified added">New in version 4.19.</span></p>
+<div class="admonition versionadded">
+<p class="admonition-title"><span class="versionmodified added">New in version 4.19.</span></p>
 </div>
 </p>
 </dd>
@@ -1383,7 +1383,7 @@ <h1 id="arch-univariate-ls-forecast">arch.univariate.LS.forecast<a class="header
 <code class="docutils literal notranslate"><span class="pre">variance</span></code> and <code class="docutils literal notranslate"><span class="pre">residual_variance</span></code>.</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.LS.forecast-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.LS.forecast-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference internal" href="../arch.univariate.base.ARCHModelForecast.html#arch.univariate.base.ARCHModelForecast" title="arch.univariate.base.ARCHModelForecast (Python class) — Container for forecasts from an ARCH Model">arch.univariate.base.ARCHModelForecast</a></p>
+<dd class="field-odd"><p><a class="reference internal" href="../arch.univariate.base.ARCHModelForecast.html#arch.univariate.base.ARCHModelForecast" title="arch.univariate.base.ARCHModelForecast (Python class) — Container for forecasts from an ARCH Model"><code class="xref py py-class docutils literal notranslate"><span class="pre">arch.univariate.base.ARCHModelForecast</span></code></a></p>
 </dd>
 </dl>
 <p class="example rubric">Examples</p>
@@ -1483,7 +1483,7 @@ <h1 id="arch-univariate-ls-forecast">arch.univariate.LS.forecast<a class="header
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1530,7 +1530,7 @@ <h1 id="arch-univariate-ls-forecast">arch.univariate.LS.forecast<a class="header
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.LS.name.html b/univariate/generated/generated/arch.univariate.LS.name.html
index 4629bf9aa5..45bddc2801 100644
--- a/univariate/generated/generated/arch.univariate.LS.name.html
+++ b/univariate/generated/generated/arch.univariate.LS.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.LS.name - arch 6.1.0</title>
+        <title>arch.univariate.LS.name - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1093,10 +1093,10 @@
                   
 
 
-<h1 id="arch-univariate-ls-name">arch.univariate.LS.name<a class="headerlink" href="#arch-univariate-ls-name" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-ls-name">arch.univariate.LS.name<a class="headerlink" href="#arch-univariate-ls-name" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.LS.name">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">LS.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.univariate.LS.name" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">LS.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.univariate.LS.name" title="Link to this definition">¶</a></dt>
 <dd><p>The name of the model.</p>
 </dd></dl>
 
@@ -1167,7 +1167,7 @@ <h1 id="arch-univariate-ls-name">arch.univariate.LS.name<a class="headerlink" hr
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1214,7 +1214,7 @@ <h1 id="arch-univariate-ls-name">arch.univariate.LS.name<a class="headerlink" hr
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.LS.num_params.html b/univariate/generated/generated/arch.univariate.LS.num_params.html
index 9d194c79c0..04ec283096 100644
--- a/univariate/generated/generated/arch.univariate.LS.num_params.html
+++ b/univariate/generated/generated/arch.univariate.LS.num_params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.LS.num_params - arch 6.1.0</title>
+        <title>arch.univariate.LS.num_params - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1093,10 +1093,10 @@
                   
 
 
-<h1 id="arch-univariate-ls-num-params">arch.univariate.LS.num_params<a class="headerlink" href="#arch-univariate-ls-num-params" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-ls-num-params">arch.univariate.LS.num_params<a class="headerlink" href="#arch-univariate-ls-num-params" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.LS.num_params">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">LS.</span></span><span class="sig-name descname"><span class="pre">num_params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.LS.num_params" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">LS.</span></span><span class="sig-name descname"><span class="pre">num_params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.LS.num_params" title="Link to this definition">¶</a></dt>
 <dd><p>Returns the number of parameters</p>
 </dd></dl>
 
@@ -1167,7 +1167,7 @@ <h1 id="arch-univariate-ls-num-params">arch.univariate.LS.num_params<a class="he
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1214,7 +1214,7 @@ <h1 id="arch-univariate-ls-num-params">arch.univariate.LS.num_params<a class="he
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.LS.parameter_names.html b/univariate/generated/generated/arch.univariate.LS.parameter_names.html
index 8eef2cc711..7da9086113 100644
--- a/univariate/generated/generated/arch.univariate.LS.parameter_names.html
+++ b/univariate/generated/generated/arch.univariate.LS.parameter_names.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.LS.parameter_names - arch 6.1.0</title>
+        <title>arch.univariate.LS.parameter_names - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1133,17 +1133,17 @@
                   
 
 
-<h1 id="arch-univariate-ls-parameter-names">arch.univariate.LS.parameter_names<a class="headerlink" href="#arch-univariate-ls-parameter-names" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-ls-parameter-names">arch.univariate.LS.parameter_names<a class="headerlink" href="#arch-univariate-ls-parameter-names" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.LS.parameter_names">
-<span class="sig-prename descclassname"><span class="pre">LS.</span></span><span class="sig-name descname"><span class="pre">parameter_names</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.LS.parameter_names" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">LS.</span></span><span class="sig-name descname"><span class="pre">parameter_names</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#arch.univariate.LS.parameter_names" title="Link to this definition">¶</a></dt>
 <dd><p>List of parameters names</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.LS.parameter_names-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.LS.parameter_names-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p><strong>names</strong> – List of variable names for the mean model</p>
 </dd>
 <dt class="field-even" id="arch.univariate.LS.parameter_names-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.LS.parameter_names-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>)</p>
+<dd class="field-even"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span> <span class="pre">(str)</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1215,7 +1215,7 @@ <h1 id="arch-univariate-ls-parameter-names">arch.univariate.LS.parameter_names<a
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1262,7 +1262,7 @@ <h1 id="arch-univariate-ls-parameter-names">arch.univariate.LS.parameter_names<a
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.LS.resids.html b/univariate/generated/generated/arch.univariate.LS.resids.html
index 98a3b46036..8ed4e4c972 100644
--- a/univariate/generated/generated/arch.univariate.LS.resids.html
+++ b/univariate/generated/generated/arch.univariate.LS.resids.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.LS.resids - arch 6.1.0</title>
+        <title>arch.univariate.LS.resids - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1201,19 +1201,19 @@
                   
 
 
-<h1 id="arch-univariate-ls-resids">arch.univariate.LS.resids<a class="headerlink" href="#arch-univariate-ls-resids" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-ls-resids">arch.univariate.LS.resids<a class="headerlink" href="#arch-univariate-ls-resids" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.LS.resids">
-<span class="sig-prename descclassname"><span class="pre">LS.</span></span><span class="sig-name descname"><span class="pre">resids</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.resids.params" title="arch.univariate.LS.resids.params (Python parameter) — Model parameters"><span class="n"><span class="pre">params</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.resids.y" title="arch.univariate.LS.resids.y (Python parameter) — Alternative values to use when computing model residuals"><span class="n"><span class="pre">y</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.resids.regressors" title="arch.univariate.LS.resids.regressors (Python parameter) — Alternative regressor values to use when computing model residuals"><span class="n"><span class="pre">regressors</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.LS.resids" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.LS.resids">
+<span class="sig-prename descclassname"><span class="pre">LS.</span></span><span class="sig-name descname"><span class="pre">resids</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.resids.params" title="arch.univariate.LS.resids.params (Python parameter) — Model parameters"><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.resids.y" title="arch.univariate.LS.resids.y (Python parameter) — Alternative values to use when computing model residuals"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.resids.regressors" title="arch.univariate.LS.resids.regressors (Python parameter) — Alternative regressor values to use when computing model residuals"><span class="n"><span class="pre">regressors</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></span><a class="headerlink" href="#arch.univariate.LS.resids" title="Link to this definition">¶</a></dt>
 <dd><p>Compute model residuals</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.LS.resids-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.LS.resids-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.LS.resids.params"><span class="sig-name descname">params</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.LS.resids.params" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
+<dt id="arch.univariate.LS.resids.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.LS.resids.params" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
 </dd>
-<dt id="arch.univariate.LS.resids.y"><span class="sig-name descname">y</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray, optional</span></span><a class="headerlink" href="#arch.univariate.LS.resids.y" title="Permalink to this definition">¶</a></dt><dd><p>Alternative values to use when computing model residuals</p>
+<dt id="arch.univariate.LS.resids.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.LS.resids.y" title="Permalink to this definition">¶</a></dt><dd><p>Alternative values to use when computing model residuals</p>
 </dd>
-<dt id="arch.univariate.LS.resids.regressors"><span class="sig-name descname">regressors</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray, optional</span></span><a class="headerlink" href="#arch.univariate.LS.resids.regressors" title="Permalink to this definition">¶</a></dt><dd><p>Alternative regressor values to use when computing model residuals</p>
+<dt id="arch.univariate.LS.resids.regressors"><span class="n sig-name">regressors</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.LS.resids.regressors" title="Permalink to this definition">¶</a></dt><dd><p>Alternative regressor values to use when computing model residuals</p>
 </dd>
 </dl>
 </dd>
@@ -1221,7 +1221,7 @@ <h1 id="arch-univariate-ls-resids">arch.univariate.LS.resids<a class="headerlink
 <dd class="field-even"><p><strong>resids</strong> – Model residuals</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.LS.resids-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.LS.resids-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1293,7 +1293,7 @@ <h1 id="arch-univariate-ls-resids">arch.univariate.LS.resids<a class="headerlink
   
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diff --git a/univariate/generated/generated/arch.univariate.LS.simulate.html b/univariate/generated/generated/arch.univariate.LS.simulate.html
index aea36bf66f..61a5494c73 100644
--- a/univariate/generated/generated/arch.univariate.LS.simulate.html
+++ b/univariate/generated/generated/arch.univariate.LS.simulate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.LS.simulate - arch 6.1.0</title>
+        <title>arch.univariate.LS.simulate - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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@@ -1243,33 +1243,33 @@
                   
 
 
-<h1 id="arch-univariate-ls-simulate">arch.univariate.LS.simulate<a class="headerlink" href="#arch-univariate-ls-simulate" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-ls-simulate">arch.univariate.LS.simulate<a class="headerlink" href="#arch-univariate-ls-simulate" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.LS.simulate">
-<span class="sig-prename descclassname"><span class="pre">LS.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.simulate.params" title="arch.univariate.LS.simulate.params (Python parameter) — Parameters to use when simulating the model.  Parameter order is [mean volatility distribution] where the parameters of the mean model are ordered [constant lag[0] lag[1] ... lag[p] ex[0] ... ex[k-1]] where lag[j] indicates the coefficient on the jth lag in the model and ex[j] is the coefficient on the jth exogenous variable."><span class="n"><span class="pre">params</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.simulate.nobs" title="arch.univariate.LS.simulate.nobs (Python parameter) — Length of series to simulate"><span class="n"><span class="pre">nobs</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.simulate.burn" title="arch.univariate.LS.simulate.burn (Python parameter) — Number of values to simulate to initialize the model and remove dependence on initial values."><span class="n"><span class="pre">burn</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.simulate.initial_value" title="arch.univariate.LS.simulate.initial_value (Python parameter) — Either a scalar value or max(lags) array set of initial values to use when initializing the model.  If omitted, 0.0 is used."><span class="n"><span class="pre">initial_value</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.simulate.x" title="arch.univariate.LS.simulate.x (Python parameter) — nobs + burn by k array of exogenous variables to include in the simulation."><span class="n"><span class="pre">x</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.simulate.initial_value_vol" title="arch.univariate.LS.simulate.initial_value_vol (Python parameter) — An array or scalar to use when initializing the volatility process."><span class="n"><span class="pre">initial_value_vol</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.LS.simulate" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">LS.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.simulate.params" title="arch.univariate.LS.simulate.params (Python parameter) — Parameters to use when simulating the model.  Parameter order is [mean volatility distribution] where the parameters of the mean model are ordered [constant lag[0] lag[1] ... lag[p] ex[0] ... ex[k-1]] where lag[j] indicates the coefficient on the jth lag in the model and ex[j] is the coefficient on the jth exogenous variable."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.simulate.nobs" title="arch.univariate.LS.simulate.nobs (Python parameter) — Length of series to simulate"><span class="n"><span class="pre">nobs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.simulate.burn" title="arch.univariate.LS.simulate.burn (Python parameter) — Number of values to simulate to initialize the model and remove dependence on initial values."><span class="n"><span class="pre">burn</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.simulate.initial_value" title="arch.univariate.LS.simulate.initial_value (Python parameter) — Either a scalar value or max(lags) array set of initial values to use when initializing the model.  If omitted, 0.0 is used."><span class="n"><span class="pre">initial_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.simulate.x" title="arch.univariate.LS.simulate.x (Python parameter) — nobs + burn by k array of exogenous variables to include in the simulation."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.simulate.initial_value_vol" title="arch.univariate.LS.simulate.initial_value_vol (Python parameter) — An array or scalar to use when initializing the volatility process."><span class="n"><span class="pre">initial_value_vol</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a></span></span></span><a class="headerlink" href="#arch.univariate.LS.simulate" title="Link to this definition">¶</a></dt>
 <dd><p>Simulates data from a linear regression, AR or HAR models</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.LS.simulate-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.LS.simulate-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.LS.simulate.params"><span class="sig-name descname">params</span><span class="p"> : </span><span class="desctype"><span class="desctype">array_like</span></span><a class="headerlink" href="#arch.univariate.LS.simulate.params" title="Permalink to this definition">¶</a></dt><dd><p>Parameters to use when simulating the model.  Parameter order is
+<dt id="arch.univariate.LS.simulate.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.univariate.LS.simulate.params" title="Permalink to this definition">¶</a></dt><dd><p>Parameters to use when simulating the model.  Parameter order is
 [mean volatility distribution] where the parameters of the mean
 model are ordered [constant lag[0] lag[1] … lag[p] ex[0] …
 ex[k-1]] where lag[j] indicates the coefficient on the jth lag in
 the model and ex[j] is the coefficient on the jth exogenous
 variable.</p>
 </dd>
-<dt id="arch.univariate.LS.simulate.nobs"><span class="sig-name descname">nobs</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.LS.simulate.nobs" title="Permalink to this definition">¶</a></dt><dd><p>Length of series to simulate</p>
+<dt id="arch.univariate.LS.simulate.nobs"><span class="n sig-name">nobs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.LS.simulate.nobs" title="Permalink to this definition">¶</a></dt><dd><p>Length of series to simulate</p>
 </dd>
-<dt id="arch.univariate.LS.simulate.burn"><span class="sig-name descname">burn</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.univariate.LS.simulate.burn" title="Permalink to this definition">¶</a></dt><dd><p>Number of values to simulate to initialize the model and remove
+<dt id="arch.univariate.LS.simulate.burn"><span class="n sig-name">burn</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code><a class="headerlink" href="#arch.univariate.LS.simulate.burn" title="Permalink to this definition">¶</a></dt><dd><p>Number of values to simulate to initialize the model and remove
 dependence on initial values.</p>
 </dd>
-<dt id="arch.univariate.LS.simulate.initial_value"><span class="sig-name descname">initial_value</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, float}, optional</span></span><a class="headerlink" href="#arch.univariate.LS.simulate.initial_value" title="Permalink to this definition">¶</a></dt><dd><p>Either a scalar value or <cite>max(lags)</cite> array set of initial values to
+<dt id="arch.univariate.LS.simulate.initial_value"><span class="n sig-name">initial_value</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.LS.simulate.initial_value" title="Permalink to this definition">¶</a></dt><dd><p>Either a scalar value or <cite>max(lags)</cite> array set of initial values to
 use when initializing the model.  If omitted, 0.0 is used.</p>
 </dd>
-<dt id="arch.univariate.LS.simulate.x"><span class="sig-name descname">x</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, DataFrame}, optional</span></span><a class="headerlink" href="#arch.univariate.LS.simulate.x" title="Permalink to this definition">¶</a></dt><dd><p>nobs + burn by k array of exogenous variables to include in the
+<dt id="arch.univariate.LS.simulate.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.LS.simulate.x" title="Permalink to this definition">¶</a></dt><dd><p>nobs + burn by k array of exogenous variables to include in the
 simulation.</p>
 </dd>
-<dt id="arch.univariate.LS.simulate.initial_value_vol"><span class="sig-name descname">initial_value_vol</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, float}, optional</span></span><a class="headerlink" href="#arch.univariate.LS.simulate.initial_value_vol" title="Permalink to this definition">¶</a></dt><dd><p>An array or scalar to use when initializing the volatility process.</p>
+<dt id="arch.univariate.LS.simulate.initial_value_vol"><span class="n sig-name">initial_value_vol</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.LS.simulate.initial_value_vol" title="Permalink to this definition">¶</a></dt><dd><p>An array or scalar to use when initializing the volatility process.</p>
 </dd>
 </dl>
 </dd>
@@ -1279,7 +1279,7 @@ <h1 id="arch-univariate-ls-simulate">arch.univariate.LS.simulate<a class="header
 containing the errors used in the simulation</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.LS.simulate-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.LS.simulate-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>DataFrame</p>
+<dd class="field-odd"><p><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code></a></p>
 </dd>
 </dl>
 <p class="example rubric">Examples</p>
@@ -1372,7 +1372,7 @@ <h1 id="arch-univariate-ls-simulate">arch.univariate.LS.simulate<a class="header
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.LS.starting_values.html b/univariate/generated/generated/arch.univariate.LS.starting_values.html
index 9bdde4ded0..5358e7e531 100644
--- a/univariate/generated/generated/arch.univariate.LS.starting_values.html
+++ b/univariate/generated/generated/arch.univariate.LS.starting_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.LS.starting_values - arch 6.1.0</title>
+        <title>arch.univariate.LS.starting_values - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1133,10 +1133,10 @@
                   
 
 
-<h1 id="arch-univariate-ls-starting-values">arch.univariate.LS.starting_values<a class="headerlink" href="#arch-univariate-ls-starting-values" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-ls-starting-values">arch.univariate.LS.starting_values<a class="headerlink" href="#arch-univariate-ls-starting-values" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.LS.starting_values">
-<span class="sig-prename descclassname"><span class="pre">LS.</span></span><span class="sig-name descname"><span class="pre">starting_values</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.LS.starting_values" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">LS.</span></span><span class="sig-name descname"><span class="pre">starting_values</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.univariate.LS.starting_values" title="Link to this definition">¶</a></dt>
 <dd><p>Returns starting values for the mean model, often the same as the
 values returned from fit</p>
 <dl class="field-list simple">
@@ -1144,7 +1144,7 @@ <h1 id="arch-univariate-ls-starting-values">arch.univariate.LS.starting_values<a
 <dd class="field-odd"><p><strong>sv</strong> – Starting values</p>
 </dd>
 <dt class="field-even" id="arch.univariate.LS.starting_values-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.LS.starting_values-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p>ndarray</p>
+<dd class="field-even"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1216,7 +1216,7 @@ <h1 id="arch-univariate-ls-starting-values">arch.univariate.LS.starting_values<a
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1263,7 +1263,7 @@ <h1 id="arch-univariate-ls-starting-values">arch.univariate.LS.starting_values<a
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.LS.volatility.html b/univariate/generated/generated/arch.univariate.LS.volatility.html
index 3f3fbad549..317e627eed 100644
--- a/univariate/generated/generated/arch.univariate.LS.volatility.html
+++ b/univariate/generated/generated/arch.univariate.LS.volatility.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.LS.volatility - arch 6.1.0</title>
+        <title>arch.univariate.LS.volatility - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1093,10 +1093,10 @@
                   
 
 
-<h1 id="arch-univariate-ls-volatility">arch.univariate.LS.volatility<a class="headerlink" href="#arch-univariate-ls-volatility" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-ls-volatility">arch.univariate.LS.volatility<a class="headerlink" href="#arch-univariate-ls-volatility" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.LS.volatility">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">LS.</span></span><span class="sig-name descname"><span class="pre">volatility</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.univariate.volatility.VolatilityProcess.html#arch.univariate.volatility.VolatilityProcess" title="arch.univariate.volatility.VolatilityProcess (Python class) — Abstract base class for ARCH models.  Allows the conditional mean model to be specified separately from the conditional variance, even though parameters are estimated jointly."><span class="pre">VolatilityProcess</span></a></span><a class="headerlink" href="#arch.univariate.LS.volatility" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">LS.</span></span><span class="sig-name descname"><span class="pre">volatility</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.univariate.volatility.VolatilityProcess.html#arch.univariate.volatility.VolatilityProcess" title="arch.univariate.volatility.VolatilityProcess (Python class) — Abstract base class for ARCH models.  Allows the conditional mean model to be specified separately from the conditional variance, even though parameters are estimated jointly."><span class="pre">VolatilityProcess</span></a></span><a class="headerlink" href="#arch.univariate.LS.volatility" title="Link to this definition">¶</a></dt>
 <dd><p>Set or gets the volatility process</p>
 <p>Volatility processes must be a subclass of VolatilityProcess</p>
 </dd></dl>
@@ -1168,7 +1168,7 @@ <h1 id="arch-univariate-ls-volatility">arch.univariate.LS.volatility<a class="he
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1215,7 +1215,7 @@ <h1 id="arch-univariate-ls-volatility">arch.univariate.LS.volatility<a class="he
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.LS.x.html b/univariate/generated/generated/arch.univariate.LS.x.html
index e9db9ca6d7..77e30e5eb1 100644
--- a/univariate/generated/generated/arch.univariate.LS.x.html
+++ b/univariate/generated/generated/arch.univariate.LS.x.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.LS.x - arch 6.1.0</title>
+        <title>arch.univariate.LS.x - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1093,10 +1093,10 @@
                   
 
 
-<h1 id="arch-univariate-ls-x">arch.univariate.LS.x<a class="headerlink" href="#arch-univariate-ls-x" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-ls-x">arch.univariate.LS.x<a class="headerlink" href="#arch-univariate-ls-x" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.LS.x">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">LS.</span></span><span class="sig-name descname"><span class="pre">x</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.0.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.LS.x" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">LS.</span></span><span class="sig-name descname"><span class="pre">x</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.LS.x" title="Link to this definition">¶</a></dt>
 <dd><p>Gets the value of the exogenous regressors in the model</p>
 </dd></dl>
 
@@ -1167,7 +1167,7 @@ <h1 id="arch-univariate-ls-x">arch.univariate.LS.x<a class="headerlink" href="#a
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1214,7 +1214,7 @@ <h1 id="arch-univariate-ls-x">arch.univariate.LS.x<a class="headerlink" href="#a
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.LS.y.html b/univariate/generated/generated/arch.univariate.LS.y.html
index 5c2b594e66..f084caa069 100644
--- a/univariate/generated/generated/arch.univariate.LS.y.html
+++ b/univariate/generated/generated/arch.univariate.LS.y.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.LS.y - arch 6.1.0</title>
+        <title>arch.univariate.LS.y - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1093,10 +1093,10 @@
                   
 
 
-<h1 id="arch-univariate-ls-y">arch.univariate.LS.y<a class="headerlink" href="#arch-univariate-ls-y" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-ls-y">arch.univariate.LS.y<a class="headerlink" href="#arch-univariate-ls-y" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.LS.y">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">LS.</span></span><span class="sig-name descname"><span class="pre">y</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.0.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.0.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.LS.y" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">LS.</span></span><span class="sig-name descname"><span class="pre">y</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.LS.y" title="Link to this definition">¶</a></dt>
 <dd><p>Returns the dependent variable</p>
 </dd></dl>
 
@@ -1167,7 +1167,7 @@ <h1 id="arch-univariate-ls-y">arch.univariate.LS.y<a class="headerlink" href="#a
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1214,7 +1214,7 @@ <h1 id="arch-univariate-ls-y">arch.univariate.LS.y<a class="headerlink" href="#a
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.MIDASHyperbolic.backcast.html b/univariate/generated/generated/arch.univariate.MIDASHyperbolic.backcast.html
index 823d9d5aaa..5d0eac276a 100644
--- a/univariate/generated/generated/arch.univariate.MIDASHyperbolic.backcast.html
+++ b/univariate/generated/generated/arch.univariate.MIDASHyperbolic.backcast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.MIDASHyperbolic.backcast - arch 6.1.0</title>
+        <title>arch.univariate.MIDASHyperbolic.backcast - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1248,15 +1248,15 @@
                   
 
 
-<h1 id="arch-univariate-midashyperbolic-backcast">arch.univariate.MIDASHyperbolic.backcast<a class="headerlink" href="#arch-univariate-midashyperbolic-backcast" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-midashyperbolic-backcast">arch.univariate.MIDASHyperbolic.backcast<a class="headerlink" href="#arch-univariate-midashyperbolic-backcast" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.MIDASHyperbolic.backcast">
-<span class="sig-prename descclassname"><span class="pre">MIDASHyperbolic.</span></span><span class="sig-name descname"><span class="pre">backcast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.backcast.resids" title="arch.univariate.MIDASHyperbolic.backcast.resids (Python parameter) — Vector of (approximate) residuals"><span class="n"><span class="pre">resids</span></span></a></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.backcast" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">MIDASHyperbolic.</span></span><span class="sig-name descname"><span class="pre">backcast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.backcast.resids" title="arch.univariate.MIDASHyperbolic.backcast.resids (Python parameter) — Vector of (approximate) residuals"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.backcast" title="Link to this definition">¶</a></dt>
 <dd><p>Construct values for backcasting to start the recursion</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.MIDASHyperbolic.backcast-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.backcast-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.MIDASHyperbolic.backcast.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.backcast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of (approximate) residuals</p>
+<dt id="arch.univariate.MIDASHyperbolic.backcast.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.backcast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of (approximate) residuals</p>
 </dd>
 </dl>
 </dd>
@@ -1264,7 +1264,7 @@ <h1 id="arch-univariate-midashyperbolic-backcast">arch.univariate.MIDASHyperboli
 <dd class="field-even"><p><strong>backcast</strong> – Value to use in backcasting in the volatility recursion</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.MIDASHyperbolic.backcast-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.backcast-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1336,7 +1336,7 @@ <h1 id="arch-univariate-midashyperbolic-backcast">arch.univariate.MIDASHyperboli
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1383,7 +1383,7 @@ <h1 id="arch-univariate-midashyperbolic-backcast">arch.univariate.MIDASHyperboli
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.MIDASHyperbolic.backcast_transform.html b/univariate/generated/generated/arch.univariate.MIDASHyperbolic.backcast_transform.html
index e8e52afba6..9cc377d529 100644
--- a/univariate/generated/generated/arch.univariate.MIDASHyperbolic.backcast_transform.html
+++ b/univariate/generated/generated/arch.univariate.MIDASHyperbolic.backcast_transform.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.MIDASHyperbolic.backcast_transform - arch 6.1.0</title>
+        <title>arch.univariate.MIDASHyperbolic.backcast_transform - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1248,15 +1248,15 @@
                   
 
 
-<h1 id="arch-univariate-midashyperbolic-backcast-transform">arch.univariate.MIDASHyperbolic.backcast_transform<a class="headerlink" href="#arch-univariate-midashyperbolic-backcast-transform" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-midashyperbolic-backcast-transform">arch.univariate.MIDASHyperbolic.backcast_transform<a class="headerlink" href="#arch-univariate-midashyperbolic-backcast-transform" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.MIDASHyperbolic.backcast_transform">
-<span class="sig-prename descclassname"><span class="pre">MIDASHyperbolic.</span></span><span class="sig-name descname"><span class="pre">backcast_transform</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.backcast_transform.backcast" title="arch.univariate.MIDASHyperbolic.backcast_transform.backcast (Python parameter) — User-provided backcast that approximates sigma2[0]."><span class="n"><span class="pre">backcast</span></span></a></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.backcast_transform" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.MIDASHyperbolic.backcast_transform">
+<span class="sig-prename descclassname"><span class="pre">MIDASHyperbolic.</span></span><span class="sig-name descname"><span class="pre">backcast_transform</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.backcast_transform.backcast" title="arch.univariate.MIDASHyperbolic.backcast_transform.backcast (Python parameter) — User-provided backcast that approximates sigma2[0]."><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.backcast_transform" title="Link to this definition">¶</a></dt>
 <dd><p>Transformation to apply to user-provided backcast values</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.MIDASHyperbolic.backcast_transform-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.backcast_transform-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.MIDASHyperbolic.backcast_transform.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}</span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.backcast_transform.backcast" title="Permalink to this definition">¶</a></dt><dd><p>User-provided <code class="docutils literal notranslate"><span class="pre">backcast</span></code> that approximates sigma2[0].</p>
+<dt id="arch.univariate.MIDASHyperbolic.backcast_transform.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.backcast_transform.backcast" title="Permalink to this definition">¶</a></dt><dd><p>User-provided <code class="docutils literal notranslate"><span class="pre">backcast</span></code> that approximates sigma2[0].</p>
 </dd>
 </dl>
 </dd>
@@ -1264,7 +1264,7 @@ <h1 id="arch-univariate-midashyperbolic-backcast-transform">arch.univariate.MIDA
 <dd class="field-even"><p><strong>backcast</strong> – Backcast transformed to the model-appropriate scale</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.MIDASHyperbolic.backcast_transform-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.backcast_transform-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>{float, ndarray}</p>
+<dd class="field-odd"><p><code class="docutils literal notranslate"><span class="pre">{float,</span> <span class="pre">ndarray}</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1336,7 +1336,7 @@ <h1 id="arch-univariate-midashyperbolic-backcast-transform">arch.univariate.MIDA
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1383,7 +1383,7 @@ <h1 id="arch-univariate-midashyperbolic-backcast-transform">arch.univariate.MIDA
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.MIDASHyperbolic.bounds.html b/univariate/generated/generated/arch.univariate.MIDASHyperbolic.bounds.html
index ecf9017e90..e8462dfe4e 100644
--- a/univariate/generated/generated/arch.univariate.MIDASHyperbolic.bounds.html
+++ b/univariate/generated/generated/arch.univariate.MIDASHyperbolic.bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.MIDASHyperbolic.bounds - arch 6.1.0</title>
+        <title>arch.univariate.MIDASHyperbolic.bounds - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1248,15 +1248,15 @@
                   
 
 
-<h1 id="arch-univariate-midashyperbolic-bounds">arch.univariate.MIDASHyperbolic.bounds<a class="headerlink" href="#arch-univariate-midashyperbolic-bounds" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-midashyperbolic-bounds">arch.univariate.MIDASHyperbolic.bounds<a class="headerlink" href="#arch-univariate-midashyperbolic-bounds" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.MIDASHyperbolic.bounds">
-<span class="sig-prename descclassname"><span class="pre">MIDASHyperbolic.</span></span><span class="sig-name descname"><span class="pre">bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.bounds.resids" title="arch.univariate.MIDASHyperbolic.bounds.resids (Python parameter) — Vector of (approximate) residuals"><span class="n"><span class="pre">resids</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#MIDASHyperbolic.bounds"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.bounds" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">MIDASHyperbolic.</span></span><span class="sig-name descname"><span class="pre">bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.bounds.resids" title="arch.univariate.MIDASHyperbolic.bounds.resids (Python parameter) — Vector of (approximate) residuals"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#MIDASHyperbolic.bounds"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.bounds" title="Link to this definition">¶</a></dt>
 <dd><p>Returns bounds for parameters</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.MIDASHyperbolic.bounds-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.bounds-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.MIDASHyperbolic.bounds.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.bounds.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of (approximate) residuals</p>
+<dt id="arch.univariate.MIDASHyperbolic.bounds.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.bounds.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of (approximate) residuals</p>
 </dd>
 </dl>
 </dd>
@@ -1264,7 +1264,7 @@ <h1 id="arch-univariate-midashyperbolic-bounds">arch.univariate.MIDASHyperbolic.
 <dd class="field-even"><p><strong>bounds</strong> – List of bounds where each element is (lower, upper).</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.MIDASHyperbolic.bounds-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.bounds-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a>[<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a>[<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>,<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>]]</p>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">list[tuple[float,float]]</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1336,7 +1336,7 @@ <h1 id="arch-univariate-midashyperbolic-bounds">arch.univariate.MIDASHyperbolic.
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1383,7 +1383,7 @@ <h1 id="arch-univariate-midashyperbolic-bounds">arch.univariate.MIDASHyperbolic.
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.MIDASHyperbolic.compute_variance.html b/univariate/generated/generated/arch.univariate.MIDASHyperbolic.compute_variance.html
index b5a98e9121..618edff648 100644
--- a/univariate/generated/generated/arch.univariate.MIDASHyperbolic.compute_variance.html
+++ b/univariate/generated/generated/arch.univariate.MIDASHyperbolic.compute_variance.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.MIDASHyperbolic.compute_variance - arch 6.1.0</title>
+        <title>arch.univariate.MIDASHyperbolic.compute_variance - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -672,13 +672,6 @@
       </ul>
     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.univariate.MIDASHyperbolic.compute_variance-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
       </ul>
@@ -1262,13 +1255,6 @@
       </ul>
     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.univariate.MIDASHyperbolic.compute_variance-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
       </ul>
@@ -1290,30 +1276,27 @@
                   
 
 
-<h1 id="arch-univariate-midashyperbolic-compute-variance">arch.univariate.MIDASHyperbolic.compute_variance<a class="headerlink" href="#arch-univariate-midashyperbolic-compute-variance" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-midashyperbolic-compute-variance">arch.univariate.MIDASHyperbolic.compute_variance<a class="headerlink" href="#arch-univariate-midashyperbolic-compute-variance" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.MIDASHyperbolic.compute_variance">
-<span class="sig-prename descclassname"><span class="pre">MIDASHyperbolic.</span></span><span class="sig-name descname"><span class="pre">compute_variance</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.compute_variance.parameters" title="arch.univariate.MIDASHyperbolic.compute_variance.parameters (Python parameter) — Model parameters"><span class="n"><span class="pre">parameters</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.compute_variance.resids" title="arch.univariate.MIDASHyperbolic.compute_variance.resids (Python parameter) — Vector of mean zero residuals"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.compute_variance.sigma2" title="arch.univariate.MIDASHyperbolic.compute_variance.sigma2 (Python parameter) — Array with same size as resids to store the conditional variance"><span class="n"><span class="pre">sigma2</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.compute_variance.backcast" title="arch.univariate.MIDASHyperbolic.compute_variance.backcast (Python parameter) — Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."><span class="n"><span class="pre">backcast</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.compute_variance.var_bounds" title="arch.univariate.MIDASHyperbolic.compute_variance.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#MIDASHyperbolic.compute_variance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.compute_variance" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">MIDASHyperbolic.</span></span><span class="sig-name descname"><span class="pre">compute_variance</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.compute_variance.parameters" title="arch.univariate.MIDASHyperbolic.compute_variance.parameters (Python parameter) — Model parameters"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.compute_variance.resids" title="arch.univariate.MIDASHyperbolic.compute_variance.resids (Python parameter) — Vector of mean zero residuals"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.compute_variance.sigma2" title="arch.univariate.MIDASHyperbolic.compute_variance.sigma2 (Python parameter) — Array with same size as resids to store the conditional variance"><span class="n"><span class="pre">sigma2</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.compute_variance.backcast" title="arch.univariate.MIDASHyperbolic.compute_variance.backcast (Python parameter) — Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.compute_variance.var_bounds" title="arch.univariate.MIDASHyperbolic.compute_variance.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#MIDASHyperbolic.compute_variance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.compute_variance" title="Link to this definition">¶</a></dt>
 <dd><p>Compute the variance for the ARCH model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.MIDASHyperbolic.compute_variance-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.compute_variance-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.MIDASHyperbolic.compute_variance.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.compute_variance.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
+<dt id="arch.univariate.MIDASHyperbolic.compute_variance.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.compute_variance.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
 </dd>
-<dt id="arch.univariate.MIDASHyperbolic.compute_variance.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.compute_variance.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of mean zero residuals</p>
+<dt id="arch.univariate.MIDASHyperbolic.compute_variance.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.compute_variance.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of mean zero residuals</p>
 </dd>
-<dt id="arch.univariate.MIDASHyperbolic.compute_variance.sigma2"><span class="sig-name descname">sigma2</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.compute_variance.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>Array with same size as resids to store the conditional variance</p>
+<dt id="arch.univariate.MIDASHyperbolic.compute_variance.sigma2"><span class="n sig-name">sigma2</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.compute_variance.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>Array with same size as resids to store the conditional variance</p>
 </dd>
-<dt id="arch.univariate.MIDASHyperbolic.compute_variance.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}</span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.compute_variance.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing ARCH recursion. Can be an ndarray
+<dt id="arch.univariate.MIDASHyperbolic.compute_variance.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.compute_variance.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing ARCH recursion. Can be an ndarray
 when the model contains multiple components.</p>
 </dd>
-<dt id="arch.univariate.MIDASHyperbolic.compute_variance.var_bounds"><span class="sig-name descname">var_bounds</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.compute_variance.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
+<dt id="arch.univariate.MIDASHyperbolic.compute_variance.var_bounds"><span class="n sig-name">var_bounds</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.compute_variance.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
 </dd>
 </dl>
 </dd>
-<dt class="field-even" id="arch.univariate.MIDASHyperbolic.compute_variance-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.compute_variance-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a></p>
-</dd>
 </dl>
 </dd></dl>
 
@@ -1384,7 +1367,7 @@ <h1 id="arch-univariate-midashyperbolic-compute-variance">arch.univariate.MIDASH
   
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diff --git a/univariate/generated/generated/arch.univariate.MIDASHyperbolic.constraints.html b/univariate/generated/generated/arch.univariate.MIDASHyperbolic.constraints.html
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@@ -15,7 +15,7 @@
     
   
       
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@@ -102,7 +102,7 @@
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+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-  
-</li>
-        
-      </ul>
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-  
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@@ -1165,19 +1152,6 @@
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-  
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@@ -1194,16 +1168,11 @@
                   
 
 
-<h1 id="arch-univariate-midashyperbolic-constraints">arch.univariate.MIDASHyperbolic.constraints<a class="headerlink" href="#arch-univariate-midashyperbolic-constraints" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-midashyperbolic-constraints">arch.univariate.MIDASHyperbolic.constraints<a class="headerlink" href="#arch-univariate-midashyperbolic-constraints" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.MIDASHyperbolic.constraints">
-<span class="sig-prename descclassname"><span class="pre">MIDASHyperbolic.</span></span><span class="sig-name descname"><span class="pre">constraints</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#MIDASHyperbolic.constraints"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.constraints" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">MIDASHyperbolic.</span></span><span class="sig-name descname"><span class="pre">constraints</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#MIDASHyperbolic.constraints"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.constraints" title="Link to this definition">¶</a></dt>
 <dd><p>Constraints</p>
-<dl class="field-list simple">
-<dt class="field-odd" id="arch.univariate.MIDASHyperbolic.constraints-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.constraints-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>]</p>
-</dd>
-</dl>
 <p class="rubric">Notes</p>
 <p>Parameters are (omega, alpha, gamma, theta)</p>
 <p>A.dot(parameters) - b &gt;= 0</p>
@@ -1283,7 +1252,7 @@ <h1 id="arch-univariate-midashyperbolic-constraints">arch.univariate.MIDASHyperb
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
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@@ -1330,7 +1299,7 @@ <h1 id="arch-univariate-midashyperbolic-constraints">arch.univariate.MIDASHyperb
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-<h1 id="arch-univariate-midashyperbolic-forecast">arch.univariate.MIDASHyperbolic.forecast<a class="headerlink" href="#arch-univariate-midashyperbolic-forecast" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-midashyperbolic-forecast">arch.univariate.MIDASHyperbolic.forecast<a class="headerlink" href="#arch-univariate-midashyperbolic-forecast" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.MIDASHyperbolic.forecast">
-<span class="sig-prename descclassname"><span class="pre">MIDASHyperbolic.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.forecast.parameters" title="arch.univariate.MIDASHyperbolic.forecast.parameters (Python parameter) — Parameters required to forecast the volatility model"><span class="n"><span class="pre">parameters</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.forecast.resids" title="arch.univariate.MIDASHyperbolic.forecast.resids (Python parameter) — Residuals to use in the recursion"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.forecast.backcast" title="arch.univariate.MIDASHyperbolic.forecast.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.forecast.var_bounds" title="arch.univariate.MIDASHyperbolic.forecast.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.forecast.start" title="arch.univariate.MIDASHyperbolic.forecast.start (Python parameter) — Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."><span class="n"><span class="pre">start</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.forecast.horizon" title="arch.univariate.MIDASHyperbolic.forecast.horizon (Python parameter) — Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."><span class="n"><span class="pre">horizon</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.forecast.method" title="arch.univariate.MIDASHyperbolic.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic."><span class="n"><span class="pre">method</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.forecast.simulations" title="arch.univariate.MIDASHyperbolic.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.forecast.rng" title="arch.univariate.MIDASHyperbolic.forecast.rng (Python parameter) — Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."><span class="n"><span class="pre">rng</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.forecast.random_state" title="arch.univariate.MIDASHyperbolic.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.forecast" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">MIDASHyperbolic.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.forecast.parameters" title="arch.univariate.MIDASHyperbolic.forecast.parameters (Python parameter) — Parameters required to forecast the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.forecast.resids" title="arch.univariate.MIDASHyperbolic.forecast.resids (Python parameter) — Residuals to use in the recursion"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.forecast.backcast" title="arch.univariate.MIDASHyperbolic.forecast.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.forecast.var_bounds" title="arch.univariate.MIDASHyperbolic.forecast.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.forecast.start" title="arch.univariate.MIDASHyperbolic.forecast.start (Python parameter) — Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.forecast.horizon" title="arch.univariate.MIDASHyperbolic.forecast.horizon (Python parameter) — Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.forecast.method" title="arch.univariate.MIDASHyperbolic.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.forecast.simulations" title="arch.univariate.MIDASHyperbolic.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.forecast.rng" title="arch.univariate.MIDASHyperbolic.forecast.rng (Python parameter) — Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.forecast.random_state" title="arch.univariate.MIDASHyperbolic.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><span class="pre">VarianceForecast</span></span></span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.forecast" title="Link to this definition">¶</a></dt>
 <dd><p>Forecast volatility from the model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.MIDASHyperbolic.forecast-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.forecast-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.MIDASHyperbolic.forecast.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, Series}</span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.forecast.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast the volatility model</p>
+<dt id="arch.univariate.MIDASHyperbolic.forecast.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.forecast.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast the volatility model</p>
 </dd>
-<dt id="arch.univariate.MIDASHyperbolic.forecast.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.forecast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Residuals to use in the recursion</p>
+<dt id="arch.univariate.MIDASHyperbolic.forecast.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.forecast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Residuals to use in the recursion</p>
 </dd>
-<dt id="arch.univariate.MIDASHyperbolic.forecast.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.forecast.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing the recursion</p>
+<dt id="arch.univariate.MIDASHyperbolic.forecast.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.forecast.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing the recursion</p>
 </dd>
-<dt id="arch.univariate.MIDASHyperbolic.forecast.var_bounds"><span class="sig-name descname">var_bounds</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray, 2-d</span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.forecast.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
+<dt id="arch.univariate.MIDASHyperbolic.forecast.var_bounds"><span class="n sig-name">var_bounds</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.forecast.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
 </dd>
-<dt id="arch.univariate.MIDASHyperbolic.forecast.start"><span class="sig-name descname">start</span><span class="p"> : </span><span class="desctype"><span class="desctype">{None, int}</span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>Index of the first observation to use as the starting point for
+<dt id="arch.univariate.MIDASHyperbolic.forecast.start"><span class="n sig-name">start</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>Index of the first observation to use as the starting point for
 the forecast.  Default is len(resids).</p>
 </dd>
-<dt id="arch.univariate.MIDASHyperbolic.forecast.horizon"><span class="sig-name descname">horizon</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.forecast.horizon" title="Permalink to this definition">¶</a></dt><dd><p>Forecast horizon.  Must be 1 or larger.  Forecasts are produced
+<dt id="arch.univariate.MIDASHyperbolic.forecast.horizon"><span class="n sig-name">horizon</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.forecast.horizon" title="Permalink to this definition">¶</a></dt><dd><p>Forecast horizon.  Must be 1 or larger.  Forecasts are produced
 for horizons in [1, horizon].</p>
 </dd>
-<dt id="arch.univariate.MIDASHyperbolic.forecast.method"><span class="sig-name descname">method</span><span class="p"> : </span><span class="desctype"><span class="desctype">{'analytic', 'simulation', 'bootstrap'}</span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.forecast.method" title="Permalink to this definition">¶</a></dt><dd><p>Method to use when producing the forecast. The default is analytic.</p>
+<dt id="arch.univariate.MIDASHyperbolic.forecast.method"><span class="n sig-name">method</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'analytic'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'simulation'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'bootstrap'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.forecast.method" title="Permalink to this definition">¶</a></dt><dd><p>Method to use when producing the forecast. The default is analytic.</p>
 </dd>
-<dt id="arch.univariate.MIDASHyperbolic.forecast.simulations"><span class="sig-name descname">simulations</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.forecast.simulations" title="Permalink to this definition">¶</a></dt><dd><p>Number of simulations to run when computing the forecast using
+<dt id="arch.univariate.MIDASHyperbolic.forecast.simulations"><span class="n sig-name">simulations</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.forecast.simulations" title="Permalink to this definition">¶</a></dt><dd><p>Number of simulations to run when computing the forecast using
 either simulation or bootstrap.</p>
 </dd>
-<dt id="arch.univariate.MIDASHyperbolic.forecast.rng"><span class="sig-name descname">rng</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable</span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.forecast.rng" title="Permalink to this definition">¶</a></dt><dd><p>Callable random number generator required if method is
+<dt id="arch.univariate.MIDASHyperbolic.forecast.rng"><span class="n sig-name">rng</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)">Callable</a><span class="p">[</span><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">,</span><span class="w"> </span><span class="p">...</span><span class="p">]</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.forecast.rng" title="Permalink to this definition">¶</a></dt><dd><p>Callable random number generator required if method is
 ‘simulation’. Must take a single shape input and return random
 samples numbers with that shape.</p>
 </dd>
-<dt id="arch.univariate.MIDASHyperbolic.forecast.random_state"><span class="sig-name descname">random_state</span><span class="p"> : </span><span class="desctype"><span class="desctype">RandomState, optional</span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.forecast.random_state" title="Permalink to this definition">¶</a></dt><dd><p>NumPy RandomState instance to use when method is ‘bootstrap’</p>
+<dt id="arch.univariate.MIDASHyperbolic.forecast.random_state"><span class="n sig-name">random_state</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)">RandomState</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.forecast.random_state" title="Permalink to this definition">¶</a></dt><dd><p>NumPy RandomState instance to use when method is ‘bootstrap’</p>
 </dd>
 </dl>
 </dd>
@@ -1428,7 +1428,7 @@ <h1 id="arch-univariate-midashyperbolic-forecast">arch.univariate.MIDASHyperboli
 or bootstrap, the simulated paths.</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.MIDASHyperbolic.forecast-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.forecast-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>VarianceForecast</p>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">arch.univariate.volatility.VarianceForecast</span></code></p>
 </dd>
 <dt class="field-even" id="arch.univariate.MIDASHyperbolic.forecast-raises">Raises<span class="colon">:</span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.forecast-raises" title="Permalink to this headline">¶</a></dt>
 <dd class="field-even"><ul class="simple">
@@ -1515,7 +1515,7 @@ <h1 id="arch-univariate-midashyperbolic-forecast">arch.univariate.MIDASHyperboli
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1562,7 +1562,7 @@ <h1 id="arch-univariate-midashyperbolic-forecast">arch.univariate.MIDASHyperboli
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.MIDASHyperbolic.name.html b/univariate/generated/generated/arch.univariate.MIDASHyperbolic.name.html
index 643f1fc7fa..c01f49567c 100644
--- a/univariate/generated/generated/arch.univariate.MIDASHyperbolic.name.html
+++ b/univariate/generated/generated/arch.univariate.MIDASHyperbolic.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.MIDASHyperbolic.name - arch 6.1.0</title>
+        <title>arch.univariate.MIDASHyperbolic.name - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1168,10 +1168,10 @@
                   
 
 
-<h1 id="arch-univariate-midashyperbolic-name">arch.univariate.MIDASHyperbolic.name<a class="headerlink" href="#arch-univariate-midashyperbolic-name" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-midashyperbolic-name">arch.univariate.MIDASHyperbolic.name<a class="headerlink" href="#arch-univariate-midashyperbolic-name" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.MIDASHyperbolic.name">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">MIDASHyperbolic.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.name" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">MIDASHyperbolic.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.name" title="Link to this definition">¶</a></dt>
 <dd><p>The name of the volatility process</p>
 </dd></dl>
 
@@ -1242,7 +1242,7 @@ <h1 id="arch-univariate-midashyperbolic-name">arch.univariate.MIDASHyperbolic.na
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1289,7 +1289,7 @@ <h1 id="arch-univariate-midashyperbolic-name">arch.univariate.MIDASHyperbolic.na
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
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diff --git a/univariate/generated/generated/arch.univariate.MIDASHyperbolic.num_params.html b/univariate/generated/generated/arch.univariate.MIDASHyperbolic.num_params.html
index 59030bd218..4c19577920 100644
--- a/univariate/generated/generated/arch.univariate.MIDASHyperbolic.num_params.html
+++ b/univariate/generated/generated/arch.univariate.MIDASHyperbolic.num_params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.MIDASHyperbolic.num_params - arch 6.1.0</title>
+        <title>arch.univariate.MIDASHyperbolic.num_params - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1168,10 +1168,10 @@
                   
 
 
-<h1 id="arch-univariate-midashyperbolic-num-params">arch.univariate.MIDASHyperbolic.num_params<a class="headerlink" href="#arch-univariate-midashyperbolic-num-params" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-midashyperbolic-num-params">arch.univariate.MIDASHyperbolic.num_params<a class="headerlink" href="#arch-univariate-midashyperbolic-num-params" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.MIDASHyperbolic.num_params">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">MIDASHyperbolic.</span></span><span class="sig-name descname"><span class="pre">num_params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.num_params" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">MIDASHyperbolic.</span></span><span class="sig-name descname"><span class="pre">num_params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.num_params" title="Link to this definition">¶</a></dt>
 <dd><p>The number of parameters in the model</p>
 </dd></dl>
 
@@ -1242,7 +1242,7 @@ <h1 id="arch-univariate-midashyperbolic-num-params">arch.univariate.MIDASHyperbo
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1289,7 +1289,7 @@ <h1 id="arch-univariate-midashyperbolic-num-params">arch.univariate.MIDASHyperbo
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
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diff --git a/univariate/generated/generated/arch.univariate.MIDASHyperbolic.parameter_names.html b/univariate/generated/generated/arch.univariate.MIDASHyperbolic.parameter_names.html
index 247afb4da3..7742cec487 100644
--- a/univariate/generated/generated/arch.univariate.MIDASHyperbolic.parameter_names.html
+++ b/univariate/generated/generated/arch.univariate.MIDASHyperbolic.parameter_names.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.MIDASHyperbolic.parameter_names - arch 6.1.0</title>
+        <title>arch.univariate.MIDASHyperbolic.parameter_names - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1208,17 +1208,17 @@
                   
 
 
-<h1 id="arch-univariate-midashyperbolic-parameter-names">arch.univariate.MIDASHyperbolic.parameter_names<a class="headerlink" href="#arch-univariate-midashyperbolic-parameter-names" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-midashyperbolic-parameter-names">arch.univariate.MIDASHyperbolic.parameter_names<a class="headerlink" href="#arch-univariate-midashyperbolic-parameter-names" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.MIDASHyperbolic.parameter_names">
-<span class="sig-prename descclassname"><span class="pre">MIDASHyperbolic.</span></span><span class="sig-name descname"><span class="pre">parameter_names</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#MIDASHyperbolic.parameter_names"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.parameter_names" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">MIDASHyperbolic.</span></span><span class="sig-name descname"><span class="pre">parameter_names</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#MIDASHyperbolic.parameter_names"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.parameter_names" title="Link to this definition">¶</a></dt>
 <dd><p>Names of model parameters</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.MIDASHyperbolic.parameter_names-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.parameter_names-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p><strong>names</strong> – Variables names</p>
 </dd>
 <dt class="field-even" id="arch.univariate.MIDASHyperbolic.parameter_names-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.parameter_names-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>)</p>
+<dd class="field-even"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span> <span class="pre">(str)</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1290,7 +1290,7 @@ <h1 id="arch-univariate-midashyperbolic-parameter-names">arch.univariate.MIDASHy
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1337,7 +1337,7 @@ <h1 id="arch-univariate-midashyperbolic-parameter-names">arch.univariate.MIDASHy
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diff --git a/univariate/generated/generated/arch.univariate.MIDASHyperbolic.simulate.html b/univariate/generated/generated/arch.univariate.MIDASHyperbolic.simulate.html
index 1dfbd5c1e6..898b206cc8 100644
--- a/univariate/generated/generated/arch.univariate.MIDASHyperbolic.simulate.html
+++ b/univariate/generated/generated/arch.univariate.MIDASHyperbolic.simulate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.MIDASHyperbolic.simulate - arch 6.1.0</title>
+        <title>arch.univariate.MIDASHyperbolic.simulate - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -732,13 +732,6 @@
       </ul>
     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.univariate.MIDASHyperbolic.simulate-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
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           <li class="md-nav__item">
@@ -1269,13 +1262,6 @@
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     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.univariate.MIDASHyperbolic.simulate-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
           <li class="md-nav__item">
@@ -1304,36 +1290,33 @@
                   
 
 
-<h1 id="arch-univariate-midashyperbolic-simulate">arch.univariate.MIDASHyperbolic.simulate<a class="headerlink" href="#arch-univariate-midashyperbolic-simulate" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-midashyperbolic-simulate">arch.univariate.MIDASHyperbolic.simulate<a class="headerlink" href="#arch-univariate-midashyperbolic-simulate" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.MIDASHyperbolic.simulate">
-<span class="sig-prename descclassname"><span class="pre">MIDASHyperbolic.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.simulate.parameters" title="arch.univariate.MIDASHyperbolic.simulate.parameters (Python parameter) — Parameters required to simulate the volatility model"><span class="n"><span class="pre">parameters</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.simulate.nobs" title="arch.univariate.MIDASHyperbolic.simulate.nobs (Python parameter) — Number of data points to simulate"><span class="n"><span class="pre">nobs</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.simulate.rng" title="arch.univariate.MIDASHyperbolic.simulate.rng (Python parameter) — Callable function that takes a single integer input and returns a vector of random numbers"><span class="n"><span class="pre">rng</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.simulate.burn" title="arch.univariate.MIDASHyperbolic.simulate.burn (Python parameter) — Number of additional observations to generate when initializing the simulation"><span class="n"><span class="pre">burn</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.simulate.initial_value" title="arch.univariate.MIDASHyperbolic.simulate.initial_value (Python parameter) — Scalar or array of initial values to use when initializing the simulation"><span class="n"><span class="pre">initial_value</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#MIDASHyperbolic.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.simulate" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">MIDASHyperbolic.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.simulate.parameters" title="arch.univariate.MIDASHyperbolic.simulate.parameters (Python parameter) — Parameters required to simulate the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.simulate.nobs" title="arch.univariate.MIDASHyperbolic.simulate.nobs (Python parameter) — Number of data points to simulate"><span class="n"><span class="pre">nobs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.simulate.rng" title="arch.univariate.MIDASHyperbolic.simulate.rng (Python parameter) — Callable function that takes a single integer input and returns a vector of random numbers"><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.simulate.burn" title="arch.univariate.MIDASHyperbolic.simulate.burn (Python parameter) — Number of additional observations to generate when initializing the simulation"><span class="n"><span class="pre">burn</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.simulate.initial_value" title="arch.univariate.MIDASHyperbolic.simulate.initial_value (Python parameter) — Scalar or array of initial values to use when initializing the simulation"><span class="n"><span class="pre">initial_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#MIDASHyperbolic.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.simulate" title="Link to this definition">¶</a></dt>
 <dd><p>Simulate data from the model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.MIDASHyperbolic.simulate-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.simulate-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.MIDASHyperbolic.simulate.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, Series}</span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to simulate the volatility model</p>
+<dt id="arch.univariate.MIDASHyperbolic.simulate.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to simulate the volatility model</p>
 </dd>
-<dt id="arch.univariate.MIDASHyperbolic.simulate.nobs"><span class="sig-name descname">nobs</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.simulate.nobs" title="Permalink to this definition">¶</a></dt><dd><p>Number of data points to simulate</p>
+<dt id="arch.univariate.MIDASHyperbolic.simulate.nobs"><span class="n sig-name">nobs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.simulate.nobs" title="Permalink to this definition">¶</a></dt><dd><p>Number of data points to simulate</p>
 </dd>
-<dt id="arch.univariate.MIDASHyperbolic.simulate.rng"><span class="sig-name descname">rng</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable</span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.simulate.rng" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that takes a single integer input and returns
+<dt id="arch.univariate.MIDASHyperbolic.simulate.rng"><span class="n sig-name">rng</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)">Callable</a><span class="p">[</span><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">,</span><span class="w"> </span><span class="p">...</span><span class="p">]</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.simulate.rng" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that takes a single integer input and returns
 a vector of random numbers</p>
 </dd>
-<dt id="arch.univariate.MIDASHyperbolic.simulate.burn"><span class="sig-name descname">burn</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.simulate.burn" title="Permalink to this definition">¶</a></dt><dd><p>Number of additional observations to generate when initializing
+<dt id="arch.univariate.MIDASHyperbolic.simulate.burn"><span class="n sig-name">burn</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.simulate.burn" title="Permalink to this definition">¶</a></dt><dd><p>Number of additional observations to generate when initializing
 the simulation</p>
 </dd>
-<dt id="arch.univariate.MIDASHyperbolic.simulate.initial_value"><span class="sig-name descname">initial_value</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}, optional</span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.simulate.initial_value" title="Permalink to this definition">¶</a></dt><dd><p>Scalar or array of initial values to use when initializing the
+<dt id="arch.univariate.MIDASHyperbolic.simulate.initial_value"><span class="n sig-name">initial_value</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.simulate.initial_value" title="Permalink to this definition">¶</a></dt><dd><p>Scalar or array of initial values to use when initializing the
 simulation</p>
 </dd>
 </dl>
 </dd>
-<dt class="field-even" id="arch.univariate.MIDASHyperbolic.simulate-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.simulate-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>]</p>
-</dd>
-<dt class="field-odd" id="arch.univariate.MIDASHyperbolic.simulate-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.simulate-returns" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><ul class="simple">
-<li><p><strong>resids</strong> (<em>ndarray</em>) – The simulated residuals</p></li>
-<li><p><strong>variance</strong> (<em>ndarray</em>) – The simulated variance</p></li>
+<dt class="field-even" id="arch.univariate.MIDASHyperbolic.simulate-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.simulate-returns" title="Permalink to this headline">¶</a></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><strong>resids</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – The simulated residuals</p></li>
+<li><p><strong>variance</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – The simulated variance</p></li>
 </ul>
 </p>
 </dd>
@@ -1407,7 +1390,7 @@ <h1 id="arch-univariate-midashyperbolic-simulate">arch.univariate.MIDASHyperboli
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
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diff --git a/univariate/generated/generated/arch.univariate.MIDASHyperbolic.start.html b/univariate/generated/generated/arch.univariate.MIDASHyperbolic.start.html
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+++ b/univariate/generated/generated/arch.univariate.MIDASHyperbolic.start.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.MIDASHyperbolic.start - arch 6.1.0</title>
+        <title>arch.univariate.MIDASHyperbolic.start - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1168,10 +1168,10 @@
                   
 
 
-<h1 id="arch-univariate-midashyperbolic-start">arch.univariate.MIDASHyperbolic.start<a class="headerlink" href="#arch-univariate-midashyperbolic-start" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-midashyperbolic-start">arch.univariate.MIDASHyperbolic.start<a class="headerlink" href="#arch-univariate-midashyperbolic-start" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.MIDASHyperbolic.start">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">MIDASHyperbolic.</span></span><span class="sig-name descname"><span class="pre">start</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.start" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">MIDASHyperbolic.</span></span><span class="sig-name descname"><span class="pre">start</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.start" title="Link to this definition">¶</a></dt>
 <dd><p>Index to use to start variance subarray selection</p>
 </dd></dl>
 
@@ -1242,7 +1242,7 @@ <h1 id="arch-univariate-midashyperbolic-start">arch.univariate.MIDASHyperbolic.s
   
     Created using
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-    7.0.1.
+    7.2.6.
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@@ -1289,7 +1289,7 @@ <h1 id="arch-univariate-midashyperbolic-start">arch.univariate.MIDASHyperbolic.s
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+++ b/univariate/generated/generated/arch.univariate.MIDASHyperbolic.starting_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.MIDASHyperbolic.starting_values - arch 6.1.0</title>
+        <title>arch.univariate.MIDASHyperbolic.starting_values - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
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           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1248,15 +1248,15 @@
                   
 
 
-<h1 id="arch-univariate-midashyperbolic-starting-values">arch.univariate.MIDASHyperbolic.starting_values<a class="headerlink" href="#arch-univariate-midashyperbolic-starting-values" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-midashyperbolic-starting-values">arch.univariate.MIDASHyperbolic.starting_values<a class="headerlink" href="#arch-univariate-midashyperbolic-starting-values" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.MIDASHyperbolic.starting_values">
-<span class="sig-prename descclassname"><span class="pre">MIDASHyperbolic.</span></span><span class="sig-name descname"><span class="pre">starting_values</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.starting_values.resids" title="arch.univariate.MIDASHyperbolic.starting_values.resids (Python parameter) — Array of (approximate) residuals to use when computing starting values"><span class="n"><span class="pre">resids</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#MIDASHyperbolic.starting_values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.starting_values" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">MIDASHyperbolic.</span></span><span class="sig-name descname"><span class="pre">starting_values</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.starting_values.resids" title="arch.univariate.MIDASHyperbolic.starting_values.resids (Python parameter) — Array of (approximate) residuals to use when computing starting values"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#MIDASHyperbolic.starting_values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.starting_values" title="Link to this definition">¶</a></dt>
 <dd><p>Returns starting values for the ARCH model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.MIDASHyperbolic.starting_values-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.starting_values-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.MIDASHyperbolic.starting_values.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.starting_values.resids" title="Permalink to this definition">¶</a></dt><dd><p>Array of (approximate) residuals to use when computing starting
+<dt id="arch.univariate.MIDASHyperbolic.starting_values.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.starting_values.resids" title="Permalink to this definition">¶</a></dt><dd><p>Array of (approximate) residuals to use when computing starting
 values</p>
 </dd>
 </dl>
@@ -1265,7 +1265,7 @@ <h1 id="arch-univariate-midashyperbolic-starting-values">arch.univariate.MIDASHy
 <dd class="field-even"><p><strong>sv</strong> – Array of starting values</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.MIDASHyperbolic.starting_values-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.starting_values-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1337,7 +1337,7 @@ <h1 id="arch-univariate-midashyperbolic-starting-values">arch.univariate.MIDASHy
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1384,7 +1384,7 @@ <h1 id="arch-univariate-midashyperbolic-starting-values">arch.univariate.MIDASHy
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.MIDASHyperbolic.stop.html b/univariate/generated/generated/arch.univariate.MIDASHyperbolic.stop.html
index 20ed3df5ad..3c656e23a2 100644
--- a/univariate/generated/generated/arch.univariate.MIDASHyperbolic.stop.html
+++ b/univariate/generated/generated/arch.univariate.MIDASHyperbolic.stop.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.MIDASHyperbolic.stop - arch 6.1.0</title>
+        <title>arch.univariate.MIDASHyperbolic.stop - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1168,10 +1168,10 @@
                   
 
 
-<h1 id="arch-univariate-midashyperbolic-stop">arch.univariate.MIDASHyperbolic.stop<a class="headerlink" href="#arch-univariate-midashyperbolic-stop" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-midashyperbolic-stop">arch.univariate.MIDASHyperbolic.stop<a class="headerlink" href="#arch-univariate-midashyperbolic-stop" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.MIDASHyperbolic.stop">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">MIDASHyperbolic.</span></span><span class="sig-name descname"><span class="pre">stop</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.stop" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">MIDASHyperbolic.</span></span><span class="sig-name descname"><span class="pre">stop</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.stop" title="Link to this definition">¶</a></dt>
 <dd><p>Index to use to stop variance subarray selection</p>
 </dd></dl>
 
@@ -1242,7 +1242,7 @@ <h1 id="arch-univariate-midashyperbolic-stop">arch.univariate.MIDASHyperbolic.st
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1289,7 +1289,7 @@ <h1 id="arch-univariate-midashyperbolic-stop">arch.univariate.MIDASHyperbolic.st
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.MIDASHyperbolic.update.html b/univariate/generated/generated/arch.univariate.MIDASHyperbolic.update.html
index 47cc26c08d..59087b6897 100644
--- a/univariate/generated/generated/arch.univariate.MIDASHyperbolic.update.html
+++ b/univariate/generated/generated/arch.univariate.MIDASHyperbolic.update.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.MIDASHyperbolic.update - arch 6.1.0</title>
+        <title>arch.univariate.MIDASHyperbolic.update - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1318,28 +1318,28 @@
                   
 
 
-<h1 id="arch-univariate-midashyperbolic-update">arch.univariate.MIDASHyperbolic.update<a class="headerlink" href="#arch-univariate-midashyperbolic-update" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-midashyperbolic-update">arch.univariate.MIDASHyperbolic.update<a class="headerlink" href="#arch-univariate-midashyperbolic-update" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.MIDASHyperbolic.update">
-<span class="sig-prename descclassname"><span class="pre">MIDASHyperbolic.</span></span><span class="sig-name descname"><span class="pre">update</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.update.index" title="arch.univariate.MIDASHyperbolic.update.index (Python parameter) — The numerical index of the variance to compute"><span class="n"><span class="pre">index</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.update.parameters" title="arch.univariate.MIDASHyperbolic.update.parameters (Python parameter) — The variance model parameters"><span class="n"><span class="pre">parameters</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.update.resids" title="arch.univariate.MIDASHyperbolic.update.resids (Python parameter) — The residual array. Only uses resids[:index] when computing sigma2[index]"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.update.sigma2" title="arch.univariate.MIDASHyperbolic.update.sigma2 (Python parameter) — The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."><span class="n"><span class="pre">sigma2</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.update.backcast" title="arch.univariate.MIDASHyperbolic.update.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.update.var_bounds" title="arch.univariate.MIDASHyperbolic.update.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.update" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">MIDASHyperbolic.</span></span><span class="sig-name descname"><span class="pre">update</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.update.index" title="arch.univariate.MIDASHyperbolic.update.index (Python parameter) — The numerical index of the variance to compute"><span class="n"><span class="pre">index</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.update.parameters" title="arch.univariate.MIDASHyperbolic.update.parameters (Python parameter) — The variance model parameters"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.update.resids" title="arch.univariate.MIDASHyperbolic.update.resids (Python parameter) — The residual array. Only uses resids[:index] when computing sigma2[index]"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.update.sigma2" title="arch.univariate.MIDASHyperbolic.update.sigma2 (Python parameter) — The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."><span class="n"><span class="pre">sigma2</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.update.backcast" title="arch.univariate.MIDASHyperbolic.update.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.update.var_bounds" title="arch.univariate.MIDASHyperbolic.update.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.update" title="Link to this definition">¶</a></dt>
 <dd><p>Compute the variance for a single observation</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.MIDASHyperbolic.update-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.update-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.MIDASHyperbolic.update.index"><span class="sig-name descname">index</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.update.index" title="Permalink to this definition">¶</a></dt><dd><p>The numerical index of the variance to compute</p>
+<dt id="arch.univariate.MIDASHyperbolic.update.index"><span class="n sig-name">index</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.update.index" title="Permalink to this definition">¶</a></dt><dd><p>The numerical index of the variance to compute</p>
 </dd>
-<dt id="arch.univariate.MIDASHyperbolic.update.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.update.parameters" title="Permalink to this definition">¶</a></dt><dd><p>The variance model parameters</p>
+<dt id="arch.univariate.MIDASHyperbolic.update.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.update.parameters" title="Permalink to this definition">¶</a></dt><dd><p>The variance model parameters</p>
 </dd>
-<dt id="arch.univariate.MIDASHyperbolic.update.resids"><span class="n sig-name">resids</span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.update.resids" title="Permalink to this definition">¶</a></dt><dd><p>The residual array. Only uses <code class="docutils literal notranslate"><span class="pre">resids[:index]</span></code> when computing
+<dt id="arch.univariate.MIDASHyperbolic.update.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.update.resids" title="Permalink to this definition">¶</a></dt><dd><p>The residual array. Only uses <code class="docutils literal notranslate"><span class="pre">resids[:index]</span></code> when computing
 <code class="docutils literal notranslate"><span class="pre">sigma2[index]</span></code></p>
 </dd>
-<dt id="arch.univariate.MIDASHyperbolic.update.sigma2"><span class="sig-name descname">sigma2</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.update.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>The array containing the variances. Only uses <code class="docutils literal notranslate"><span class="pre">sigma2[:index]</span></code>
+<dt id="arch.univariate.MIDASHyperbolic.update.sigma2"><span class="n sig-name">sigma2</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.update.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>The array containing the variances. Only uses <code class="docutils literal notranslate"><span class="pre">sigma2[:index]</span></code>
 when computing <code class="docutils literal notranslate"><span class="pre">sigma2[index]</span></code>. The computed value is stored
 in <code class="docutils literal notranslate"><span class="pre">sigma2[index]</span></code>.</p>
 </dd>
-<dt id="arch.univariate.MIDASHyperbolic.update.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}</span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.update.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing the recursion</p>
+<dt id="arch.univariate.MIDASHyperbolic.update.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.update.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing the recursion</p>
 </dd>
-<dt id="arch.univariate.MIDASHyperbolic.update.var_bounds"><span class="sig-name descname">var_bounds</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.update.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
+<dt id="arch.univariate.MIDASHyperbolic.update.var_bounds"><span class="n sig-name">var_bounds</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.update.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
 </dd>
 </dl>
 </dd>
@@ -1347,7 +1347,7 @@ <h1 id="arch-univariate-midashyperbolic-update">arch.univariate.MIDASHyperbolic.
 <dd class="field-even"><p>The variance computed for location <code class="docutils literal notranslate"><span class="pre">index</span></code></p>
 </dd>
 <dt class="field-odd" id="arch.univariate.MIDASHyperbolic.update-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.update-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1419,7 +1419,7 @@ <h1 id="arch-univariate-midashyperbolic-update">arch.univariate.MIDASHyperbolic.
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1466,7 +1466,7 @@ <h1 id="arch-univariate-midashyperbolic-update">arch.univariate.MIDASHyperbolic.
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diff --git a/univariate/generated/generated/arch.univariate.MIDASHyperbolic.updateable.html b/univariate/generated/generated/arch.univariate.MIDASHyperbolic.updateable.html
index 3cfa11f20d..a9fa0c1a59 100644
--- a/univariate/generated/generated/arch.univariate.MIDASHyperbolic.updateable.html
+++ b/univariate/generated/generated/arch.univariate.MIDASHyperbolic.updateable.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.MIDASHyperbolic.updateable - arch 6.1.0</title>
+        <title>arch.univariate.MIDASHyperbolic.updateable - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1168,10 +1168,10 @@
                   
 
 
-<h1 id="arch-univariate-midashyperbolic-updateable">arch.univariate.MIDASHyperbolic.updateable<a class="headerlink" href="#arch-univariate-midashyperbolic-updateable" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-midashyperbolic-updateable">arch.univariate.MIDASHyperbolic.updateable<a class="headerlink" href="#arch-univariate-midashyperbolic-updateable" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.MIDASHyperbolic.updateable">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">MIDASHyperbolic.</span></span><span class="sig-name descname"><span class="pre">updateable</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.updateable" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">MIDASHyperbolic.</span></span><span class="sig-name descname"><span class="pre">updateable</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.updateable" title="Link to this definition">¶</a></dt>
 <dd><p>Flag indicating that the volatility process supports update</p>
 </dd></dl>
 
@@ -1242,7 +1242,7 @@ <h1 id="arch-univariate-midashyperbolic-updateable">arch.univariate.MIDASHyperbo
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1289,7 +1289,7 @@ <h1 id="arch-univariate-midashyperbolic-updateable">arch.univariate.MIDASHyperbo
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diff --git a/univariate/generated/generated/arch.univariate.MIDASHyperbolic.variance_bounds.html b/univariate/generated/generated/arch.univariate.MIDASHyperbolic.variance_bounds.html
index aa99fa0ef5..bb1774a37f 100644
--- a/univariate/generated/generated/arch.univariate.MIDASHyperbolic.variance_bounds.html
+++ b/univariate/generated/generated/arch.univariate.MIDASHyperbolic.variance_bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.MIDASHyperbolic.variance_bounds - arch 6.1.0</title>
+        <title>arch.univariate.MIDASHyperbolic.variance_bounds - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1262,20 +1262,20 @@
                   
 
 
-<h1 id="arch-univariate-midashyperbolic-variance-bounds">arch.univariate.MIDASHyperbolic.variance_bounds<a class="headerlink" href="#arch-univariate-midashyperbolic-variance-bounds" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-midashyperbolic-variance-bounds">arch.univariate.MIDASHyperbolic.variance_bounds<a class="headerlink" href="#arch-univariate-midashyperbolic-variance-bounds" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.MIDASHyperbolic.variance_bounds">
-<span class="sig-prename descclassname"><span class="pre">MIDASHyperbolic.</span></span><span class="sig-name descname"><span class="pre">variance_bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.variance_bounds.resids" title="arch.univariate.MIDASHyperbolic.variance_bounds.resids (Python parameter) — Approximate residuals to use to compute the lower and upper bounds on the conditional variance"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.variance_bounds.power" title="arch.univariate.MIDASHyperbolic.variance_bounds.power (Python parameter) — Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."><span class="n"><span class="pre">power</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">2.0</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.variance_bounds" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.MIDASHyperbolic.variance_bounds">
+<span class="sig-prename descclassname"><span class="pre">MIDASHyperbolic.</span></span><span class="sig-name descname"><span class="pre">variance_bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.variance_bounds.resids" title="arch.univariate.MIDASHyperbolic.variance_bounds.resids (Python parameter) — Approximate residuals to use to compute the lower and upper bounds on the conditional variance"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.variance_bounds.power" title="arch.univariate.MIDASHyperbolic.variance_bounds.power (Python parameter) — Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."><span class="n"><span class="pre">power</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">2.0</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.variance_bounds" title="Link to this definition">¶</a></dt>
 <dd><p>Construct loose bounds for conditional variances.</p>
 <p>These bounds are used in parameter estimation to ensure
 that the log-likelihood does not produce NaN values.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.MIDASHyperbolic.variance_bounds-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.variance_bounds-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.MIDASHyperbolic.variance_bounds.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.variance_bounds.resids" title="Permalink to this definition">¶</a></dt><dd><p>Approximate residuals to use to compute the lower and upper bounds
+<dt id="arch.univariate.MIDASHyperbolic.variance_bounds.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.variance_bounds.resids" title="Permalink to this definition">¶</a></dt><dd><p>Approximate residuals to use to compute the lower and upper bounds
 on the conditional variance</p>
 </dd>
-<dt id="arch.univariate.MIDASHyperbolic.variance_bounds.power"><span class="sig-name descname">power</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>, optional</span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.variance_bounds.power" title="Permalink to this definition">¶</a></dt><dd><p>Power used in the model. 2.0, the default corresponds to standard
+<dt id="arch.univariate.MIDASHyperbolic.variance_bounds.power"><span class="n sig-name">power</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">2.0</span></code><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.variance_bounds.power" title="Permalink to this definition">¶</a></dt><dd><p>Power used in the model. 2.0, the default corresponds to standard
 ARCH models that evolve in squares.</p>
 </dd>
 </dl>
@@ -1285,7 +1285,7 @@ <h1 id="arch-univariate-midashyperbolic-variance-bounds">arch.univariate.MIDASHy
 number of elements as resids</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.MIDASHyperbolic.variance_bounds-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.variance_bounds-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1357,7 +1357,7 @@ <h1 id="arch-univariate-midashyperbolic-variance-bounds">arch.univariate.MIDASHy
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1404,7 +1404,7 @@ <h1 id="arch-univariate-midashyperbolic-variance-bounds">arch.univariate.MIDASHy
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.MIDASHyperbolic.volatility_updater.html b/univariate/generated/generated/arch.univariate.MIDASHyperbolic.volatility_updater.html
index 2f5a41c0ad..85a7979061 100644
--- a/univariate/generated/generated/arch.univariate.MIDASHyperbolic.volatility_updater.html
+++ b/univariate/generated/generated/arch.univariate.MIDASHyperbolic.volatility_updater.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.MIDASHyperbolic.volatility_updater - arch 6.1.0</title>
+        <title>arch.univariate.MIDASHyperbolic.volatility_updater - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1222,17 +1222,17 @@
                   
 
 
-<h1 id="arch-univariate-midashyperbolic-volatility-updater">arch.univariate.MIDASHyperbolic.volatility_updater<a class="headerlink" href="#arch-univariate-midashyperbolic-volatility-updater" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-midashyperbolic-volatility-updater">arch.univariate.MIDASHyperbolic.volatility_updater<a class="headerlink" href="#arch-univariate-midashyperbolic-volatility-updater" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.MIDASHyperbolic.volatility_updater">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">MIDASHyperbolic.</span></span><span class="sig-name descname"><span class="pre">volatility_updater</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.univariate.recursions_python.VolatilityUpdater.html#arch.univariate.recursions_python.VolatilityUpdater" title="arch.univariate.recursions_python.VolatilityUpdater (Python class) — Base class that all volatility updaters must inherit from."><span class="pre">VolatilityUpdater</span></a></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.volatility_updater" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">MIDASHyperbolic.</span></span><span class="sig-name descname"><span class="pre">volatility_updater</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><span class="pre">VolatilityUpdater</span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.volatility_updater" title="Link to this definition">¶</a></dt>
 <dd><p>Get the volatility updater associated with the volatility process</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.MIDASHyperbolic.volatility_updater-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.volatility_updater-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The updater class</p>
 </dd>
 <dt class="field-even" id="arch.univariate.MIDASHyperbolic.volatility_updater-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.volatility_updater-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference internal" href="../arch.univariate.recursions_python.VolatilityUpdater.html#arch.univariate.recursions_python.VolatilityUpdater" title="arch.univariate.recursions_python.VolatilityUpdater (Python class) — Base class that all volatility updaters must inherit from.">VolatilityUpdater</a></p>
+<dd class="field-even"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">VolatilityUpdater</span></code></p>
 </dd>
 <dt class="field-odd" id="arch.univariate.MIDASHyperbolic.volatility_updater-raises">Raises<span class="colon">:</span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.volatility_updater-raises" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#NotImplementedError" title="(in Python v3.11)"><strong>NotImplementedError</strong></a> – If the process is not updateable</p>
@@ -1307,7 +1307,7 @@ <h1 id="arch-univariate-midashyperbolic-volatility-updater">arch.univariate.MIDA
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1354,7 +1354,7 @@ <h1 id="arch-univariate-midashyperbolic-volatility-updater">arch.univariate.MIDA
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.Normal.bounds.html b/univariate/generated/generated/arch.univariate.Normal.bounds.html
index ae3391d943..78fdead86e 100644
--- a/univariate/generated/generated/arch.univariate.Normal.bounds.html
+++ b/univariate/generated/generated/arch.univariate.Normal.bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.Normal.bounds - arch 6.1.0</title>
+        <title>arch.univariate.Normal.bounds - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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@@ -1098,15 +1098,15 @@
                   
 
 
-<h1 id="arch-univariate-normal-bounds">arch.univariate.Normal.bounds<a class="headerlink" href="#arch-univariate-normal-bounds" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-normal-bounds">arch.univariate.Normal.bounds<a class="headerlink" href="#arch-univariate-normal-bounds" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.Normal.bounds">
-<span class="sig-prename descclassname"><span class="pre">Normal.</span></span><span class="sig-name descname"><span class="pre">bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.bounds.resids" title="arch.univariate.Normal.bounds.resids (Python parameter) — Residuals to use when computing the bounds"><span class="n"><span class="pre">resids</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Normal.bounds"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.Normal.bounds" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Normal.</span></span><span class="sig-name descname"><span class="pre">bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.bounds.resids" title="arch.univariate.Normal.bounds.resids (Python parameter) — Residuals to use when computing the bounds"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Normal.bounds"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.Normal.bounds" title="Link to this definition">¶</a></dt>
 <dd><p>Parameter bounds for use in optimization.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.Normal.bounds-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.Normal.bounds-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.Normal.bounds.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.Normal.bounds.resids" title="Permalink to this definition">¶</a></dt><dd><p>Residuals to use when computing the bounds</p>
+<dt id="arch.univariate.Normal.bounds.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.Normal.bounds.resids" title="Permalink to this definition">¶</a></dt><dd><p>Residuals to use when computing the bounds</p>
 </dd>
 </dl>
 </dd>
@@ -1114,7 +1114,7 @@ <h1 id="arch-univariate-normal-bounds">arch.univariate.Normal.bounds<a class="he
 <dd class="field-even"><p><strong>bounds</strong> – List containing a single tuple with (lower, upper) bounds</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.Normal.bounds-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.Normal.bounds-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1186,7 +1186,7 @@ <h1 id="arch-univariate-normal-bounds">arch.univariate.Normal.bounds<a class="he
   
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-    7.0.1.
+    7.2.6.
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diff --git a/univariate/generated/generated/arch.univariate.Normal.cdf.html b/univariate/generated/generated/arch.univariate.Normal.cdf.html
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@@ -15,7 +15,7 @@
     
   
       
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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@@ -189,10 +189,10 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -1112,17 +1112,17 @@
                   
 
 
-<h1 id="arch-univariate-normal-cdf">arch.univariate.Normal.cdf<a class="headerlink" href="#arch-univariate-normal-cdf" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-normal-cdf">arch.univariate.Normal.cdf<a class="headerlink" href="#arch-univariate-normal-cdf" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.Normal.cdf">
-<span class="sig-prename descclassname"><span class="pre">Normal.</span></span><span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.cdf.resids" title="arch.univariate.Normal.cdf.resids (Python parameter) — Values at which to evaluate the cdf"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.cdf.parameters" title="arch.univariate.Normal.cdf.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Normal.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.Normal.cdf" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.Normal.cdf">
+<span class="sig-prename descclassname"><span class="pre">Normal.</span></span><span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.cdf.resids" title="arch.univariate.Normal.cdf.resids (Python parameter) — Values at which to evaluate the cdf"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.cdf.parameters" title="arch.univariate.Normal.cdf.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Normal.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.Normal.cdf" title="Link to this definition">¶</a></dt>
 <dd><p>Cumulative distribution function</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.Normal.cdf-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.Normal.cdf-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.Normal.cdf.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.Normal.cdf.resids" title="Permalink to this definition">¶</a></dt><dd><p>Values at which to evaluate the cdf</p>
+<dt id="arch.univariate.Normal.cdf.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.Normal.cdf.resids" title="Permalink to this definition">¶</a></dt><dd><p>Values at which to evaluate the cdf</p>
 </dd>
-<dt id="arch.univariate.Normal.cdf.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.Normal.cdf.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use <code class="docutils literal notranslate"><span class="pre">None</span></code> for parameterless
+<dt id="arch.univariate.Normal.cdf.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.Normal.cdf.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use <code class="docutils literal notranslate"><span class="pre">None</span></code> for parameterless
 distributions.</p>
 </dd>
 </dl>
@@ -1131,7 +1131,7 @@ <h1 id="arch-univariate-normal-cdf">arch.univariate.Normal.cdf<a class="headerli
 <dd class="field-even"><p><strong>f</strong> – CDF values</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.Normal.cdf-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.Normal.cdf-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
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-    7.0.1.
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+++ b/univariate/generated/generated/arch.univariate.Normal.constraints.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.univariate.Normal.constraints - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch 6.1.0
+    arch 6.2.0
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@@ -566,13 +566,6 @@
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       <ul class="md-nav__list">
         
-          <li class="md-nav__item">
-  <a href="#arch.univariate.Normal.constraints-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
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-  
-</li>
-        
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   <a href="#arch.univariate.Normal.constraints-returns" class="md-nav__link">
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@@ -1025,13 +1018,6 @@
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       <ul class="md-nav__list">
         
-          <li class="md-nav__item">
-  <a href="#arch.univariate.Normal.constraints-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
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-  
-</li>
-        
           <li class="md-nav__item">
   <a href="#arch.univariate.Normal.constraints-returns" class="md-nav__link">
     <span class="md-ellipsis">Returns</span>
@@ -1058,19 +1044,16 @@
                   
 
 
-<h1 id="arch-univariate-normal-constraints">arch.univariate.Normal.constraints<a class="headerlink" href="#arch-univariate-normal-constraints" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-normal-constraints">arch.univariate.Normal.constraints<a class="headerlink" href="#arch-univariate-normal-constraints" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.Normal.constraints">
-<span class="sig-prename descclassname"><span class="pre">Normal.</span></span><span class="sig-name descname"><span class="pre">constraints</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Normal.constraints"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.Normal.constraints" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Normal.</span></span><span class="sig-name descname"><span class="pre">constraints</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Normal.constraints"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.Normal.constraints" title="Link to this definition">¶</a></dt>
 <dd><p>Construct arrays to use in constrained optimization.</p>
 <dl class="field-list simple">
-<dt class="field-odd" id="arch.univariate.Normal.constraints-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.Normal.constraints-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>]</p>
-</dd>
-<dt class="field-even" id="arch.univariate.Normal.constraints-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.Normal.constraints-returns" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><ul class="simple">
-<li><p><strong>A</strong> (<em>ndarray</em>) – Constraint loadings</p></li>
-<li><p><strong>b</strong> (<em>ndarray</em>) – Constraint values</p></li>
+<dt class="field-odd" id="arch.univariate.Normal.constraints-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.Normal.constraints-returns" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p><strong>A</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – Constraint loadings</p></li>
+<li><p><strong>b</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – Constraint values</p></li>
 </ul>
 </p>
 </dd>
@@ -1146,7 +1129,7 @@ <h1 id="arch-univariate-normal-constraints">arch.univariate.Normal.constraints<a
   
     Created using
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-    7.0.1.
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     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
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diff --git a/univariate/generated/generated/arch.univariate.Normal.generator.html b/univariate/generated/generated/arch.univariate.Normal.generator.html
index b1d940e427..b29c2708c7 100644
--- a/univariate/generated/generated/arch.univariate.Normal.generator.html
+++ b/univariate/generated/generated/arch.univariate.Normal.generator.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1018,10 +1018,10 @@
                   
 
 
-<h1 id="arch-univariate-normal-generator">arch.univariate.Normal.generator<a class="headerlink" href="#arch-univariate-normal-generator" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-normal-generator">arch.univariate.Normal.generator<a class="headerlink" href="#arch-univariate-normal-generator" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.Normal.generator">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Normal.</span></span><span class="sig-name descname"><span class="pre">generator</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.24)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.24)"><span class="pre">Generator</span></a></span><a class="headerlink" href="#arch.univariate.Normal.generator" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Normal.</span></span><span class="sig-name descname"><span class="pre">generator</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><span class="pre">Generator</span></a></span><a class="headerlink" href="#arch.univariate.Normal.generator" title="Link to this definition">¶</a></dt>
 <dd><p>The NumPy Generator or RandomState attached to the distribution</p>
 </dd></dl>
 
@@ -1092,7 +1092,7 @@ <h1 id="arch-univariate-normal-generator">arch.univariate.Normal.generator<a cla
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1139,7 +1139,7 @@ <h1 id="arch-univariate-normal-generator">arch.univariate.Normal.generator<a cla
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diff --git a/univariate/generated/generated/arch.univariate.Normal.loglikelihood.html b/univariate/generated/generated/arch.univariate.Normal.loglikelihood.html
index 3ed5035220..631afb1547 100644
--- a/univariate/generated/generated/arch.univariate.Normal.loglikelihood.html
+++ b/univariate/generated/generated/arch.univariate.Normal.loglikelihood.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.Normal.loglikelihood - arch 6.1.0</title>
+        <title>arch.univariate.Normal.loglikelihood - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1140,23 +1140,23 @@
                   
 
 
-<h1 id="arch-univariate-normal-loglikelihood">arch.univariate.Normal.loglikelihood<a class="headerlink" href="#arch-univariate-normal-loglikelihood" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-normal-loglikelihood">arch.univariate.Normal.loglikelihood<a class="headerlink" href="#arch-univariate-normal-loglikelihood" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.Normal.loglikelihood">
-<span class="sig-prename descclassname"><span class="pre">Normal.</span></span><span class="sig-name descname"><span class="pre">loglikelihood</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.loglikelihood.parameters" title="arch.univariate.Normal.loglikelihood.parameters (Python parameter) — The normal likelihood has no shape parameters. Empty since the standard normal has no shape parameters."><span class="n"><span class="pre">parameters</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.loglikelihood.resids" title="arch.univariate.Normal.loglikelihood.resids (Python parameter) — The residuals to use in the log-likelihood calculation"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.loglikelihood.sigma2" title="arch.univariate.Normal.loglikelihood.sigma2 (Python parameter) — Conditional variances of resids"><span class="n"><span class="pre">sigma2</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.loglikelihood.individual" title="arch.univariate.Normal.loglikelihood.individual (Python parameter) — Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"><span class="n"><span class="pre">individual</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Normal.loglikelihood"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.Normal.loglikelihood" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.Normal.loglikelihood">
+<span class="sig-prename descclassname"><span class="pre">Normal.</span></span><span class="sig-name descname"><span class="pre">loglikelihood</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.loglikelihood.parameters" title="arch.univariate.Normal.loglikelihood.parameters (Python parameter) — The normal likelihood has no shape parameters. Empty since the standard normal has no shape parameters."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.loglikelihood.resids" title="arch.univariate.Normal.loglikelihood.resids (Python parameter) — The residuals to use in the log-likelihood calculation"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.loglikelihood.sigma2" title="arch.univariate.Normal.loglikelihood.sigma2 (Python parameter) — Conditional variances of resids"><span class="n"><span class="pre">sigma2</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.loglikelihood.individual" title="arch.univariate.Normal.loglikelihood.individual (Python parameter) — Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"><span class="n"><span class="pre">individual</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Normal.loglikelihood"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.Normal.loglikelihood" title="Link to this definition">¶</a></dt>
 <dd><p>Computes the log-likelihood of assuming residuals are normally
 distributed, conditional on the variance</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.Normal.loglikelihood-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.Normal.loglikelihood-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.Normal.loglikelihood.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.Normal.loglikelihood.parameters" title="Permalink to this definition">¶</a></dt><dd><p>The normal likelihood has no shape parameters. Empty since the
+<dt id="arch.univariate.Normal.loglikelihood.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.Normal.loglikelihood.parameters" title="Permalink to this definition">¶</a></dt><dd><p>The normal likelihood has no shape parameters. Empty since the
 standard normal has no shape parameters.</p>
 </dd>
-<dt id="arch.univariate.Normal.loglikelihood.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.Normal.loglikelihood.resids" title="Permalink to this definition">¶</a></dt><dd><p>The residuals to use in the log-likelihood calculation</p>
+<dt id="arch.univariate.Normal.loglikelihood.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.Normal.loglikelihood.resids" title="Permalink to this definition">¶</a></dt><dd><p>The residuals to use in the log-likelihood calculation</p>
 </dd>
-<dt id="arch.univariate.Normal.loglikelihood.sigma2"><span class="sig-name descname">sigma2</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.Normal.loglikelihood.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>Conditional variances of resids</p>
+<dt id="arch.univariate.Normal.loglikelihood.sigma2"><span class="n sig-name">sigma2</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.Normal.loglikelihood.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>Conditional variances of resids</p>
 </dd>
-<dt id="arch.univariate.Normal.loglikelihood.individual"><span class="sig-name descname">individual</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, optional</span></span><a class="headerlink" href="#arch.univariate.Normal.loglikelihood.individual" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to return the vector of individual log
+<dt id="arch.univariate.Normal.loglikelihood.individual"><span class="n sig-name">individual</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.univariate.Normal.loglikelihood.individual" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to return the vector of individual log
 likelihoods (True) or the sum (False)</p>
 </dd>
 </dl>
@@ -1165,7 +1165,7 @@ <h1 id="arch-univariate-normal-loglikelihood">arch.univariate.Normal.loglikeliho
 <dd class="field-even"><p><strong>ll</strong> – The log-likelihood</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.Normal.loglikelihood-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.Normal.loglikelihood-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -1242,7 +1242,7 @@ <h1 id="arch-univariate-normal-loglikelihood">arch.univariate.Normal.loglikeliho
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1289,10 +1289,10 @@ <h1 id="arch-univariate-normal-loglikelihood">arch.univariate.Normal.loglikeliho
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+        <script id="MathJax-script" src="../../../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
     
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 </html>
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diff --git a/univariate/generated/generated/arch.univariate.Normal.moment.html b/univariate/generated/generated/arch.univariate.Normal.moment.html
index 7729469dcf..7e017e7d99 100644
--- a/univariate/generated/generated/arch.univariate.Normal.moment.html
+++ b/univariate/generated/generated/arch.univariate.Normal.moment.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.Normal.moment - arch 6.1.0</title>
+        <title>arch.univariate.Normal.moment - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1112,17 +1112,17 @@
                   
 
 
-<h1 id="arch-univariate-normal-moment">arch.univariate.Normal.moment<a class="headerlink" href="#arch-univariate-normal-moment" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-normal-moment">arch.univariate.Normal.moment<a class="headerlink" href="#arch-univariate-normal-moment" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.Normal.moment">
-<span class="sig-prename descclassname"><span class="pre">Normal.</span></span><span class="sig-name descname"><span class="pre">moment</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.moment.n" title="arch.univariate.Normal.moment.n (Python parameter) — Order of moment"><span class="n"><span class="pre">n</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.moment.parameters" title="arch.univariate.Normal.moment.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Normal.moment"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.Normal.moment" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.Normal.moment">
+<span class="sig-prename descclassname"><span class="pre">Normal.</span></span><span class="sig-name descname"><span class="pre">moment</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.moment.n" title="arch.univariate.Normal.moment.n (Python parameter) — Order of moment"><span class="n"><span class="pre">n</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.moment.parameters" title="arch.univariate.Normal.moment.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Normal.moment"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.Normal.moment" title="Link to this definition">¶</a></dt>
 <dd><p>Moment of order n</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.Normal.moment-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.Normal.moment-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.Normal.moment.n"><span class="sig-name descname">n</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.Normal.moment.n" title="Permalink to this definition">¶</a></dt><dd><p>Order of moment</p>
+<dt id="arch.univariate.Normal.moment.n"><span class="n sig-name">n</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.Normal.moment.n" title="Permalink to this definition">¶</a></dt><dd><p>Order of moment</p>
 </dd>
-<dt id="arch.univariate.Normal.moment.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray, optional</span></span><a class="headerlink" href="#arch.univariate.Normal.moment.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use None for parameterless distributions.</p>
+<dt id="arch.univariate.Normal.moment.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.Normal.moment.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use None for parameterless distributions.</p>
 </dd>
 </dl>
 </dd>
@@ -1130,7 +1130,7 @@ <h1 id="arch-univariate-normal-moment">arch.univariate.Normal.moment<a class="he
 <dd class="field-even"><p>Calculated moment</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.Normal.moment-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.Normal.moment-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1202,7 +1202,7 @@ <h1 id="arch-univariate-normal-moment">arch.univariate.Normal.moment<a class="he
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1249,7 +1249,7 @@ <h1 id="arch-univariate-normal-moment">arch.univariate.Normal.moment<a class="he
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.Normal.name.html b/univariate/generated/generated/arch.univariate.Normal.name.html
index cc873fa220..0c17037913 100644
--- a/univariate/generated/generated/arch.univariate.Normal.name.html
+++ b/univariate/generated/generated/arch.univariate.Normal.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.Normal.name - arch 6.1.0</title>
+        <title>arch.univariate.Normal.name - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1018,10 +1018,10 @@
                   
 
 
-<h1 id="arch-univariate-normal-name">arch.univariate.Normal.name<a class="headerlink" href="#arch-univariate-normal-name" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-normal-name">arch.univariate.Normal.name<a class="headerlink" href="#arch-univariate-normal-name" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.Normal.name">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Normal.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.univariate.Normal.name" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Normal.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.univariate.Normal.name" title="Link to this definition">¶</a></dt>
 <dd><p>The name of the distribution</p>
 </dd></dl>
 
@@ -1092,7 +1092,7 @@ <h1 id="arch-univariate-normal-name">arch.univariate.Normal.name<a class="header
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1139,7 +1139,7 @@ <h1 id="arch-univariate-normal-name">arch.univariate.Normal.name<a class="header
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.Normal.parameter_names.html b/univariate/generated/generated/arch.univariate.Normal.parameter_names.html
index 16e1a4c7b5..bf701077ec 100644
--- a/univariate/generated/generated/arch.univariate.Normal.parameter_names.html
+++ b/univariate/generated/generated/arch.univariate.Normal.parameter_names.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.Normal.parameter_names - arch 6.1.0</title>
+        <title>arch.univariate.Normal.parameter_names - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1058,17 +1058,17 @@
                   
 
 
-<h1 id="arch-univariate-normal-parameter-names">arch.univariate.Normal.parameter_names<a class="headerlink" href="#arch-univariate-normal-parameter-names" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-normal-parameter-names">arch.univariate.Normal.parameter_names<a class="headerlink" href="#arch-univariate-normal-parameter-names" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.Normal.parameter_names">
-<span class="sig-prename descclassname"><span class="pre">Normal.</span></span><span class="sig-name descname"><span class="pre">parameter_names</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Normal.parameter_names"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.Normal.parameter_names" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Normal.</span></span><span class="sig-name descname"><span class="pre">parameter_names</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Normal.parameter_names"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.Normal.parameter_names" title="Link to this definition">¶</a></dt>
 <dd><p>Names of distribution shape parameters</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.Normal.parameter_names-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.Normal.parameter_names-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p><strong>names</strong> – Parameter names</p>
 </dd>
 <dt class="field-even" id="arch.univariate.Normal.parameter_names-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.Normal.parameter_names-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>)</p>
+<dd class="field-even"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span> <span class="pre">(str)</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1140,7 +1140,7 @@ <h1 id="arch-univariate-normal-parameter-names">arch.univariate.Normal.parameter
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1187,7 +1187,7 @@ <h1 id="arch-univariate-normal-parameter-names">arch.univariate.Normal.parameter
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.Normal.partial_moment.html b/univariate/generated/generated/arch.univariate.Normal.partial_moment.html
index 5e98e5072d..64bc06ddb2 100644
--- a/univariate/generated/generated/arch.univariate.Normal.partial_moment.html
+++ b/univariate/generated/generated/arch.univariate.Normal.partial_moment.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.Normal.partial_moment - arch 6.1.0</title>
+        <title>arch.univariate.Normal.partial_moment - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1126,19 +1126,19 @@
                   
 
 
-<h1 id="arch-univariate-normal-partial-moment">arch.univariate.Normal.partial_moment<a class="headerlink" href="#arch-univariate-normal-partial-moment" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-normal-partial-moment">arch.univariate.Normal.partial_moment<a class="headerlink" href="#arch-univariate-normal-partial-moment" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.Normal.partial_moment">
-<span class="sig-prename descclassname"><span class="pre">Normal.</span></span><span class="sig-name descname"><span class="pre">partial_moment</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.partial_moment.n" title="arch.univariate.Normal.partial_moment.n (Python parameter) — Order of partial moment"><span class="n"><span class="pre">n</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.partial_moment.z" title="arch.univariate.Normal.partial_moment.z (Python parameter) — Upper bound for partial moment integral"><span class="n"><span class="pre">z</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.partial_moment.parameters" title="arch.univariate.Normal.partial_moment.parameters (Python parameter) — Distribution parameters.  Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Normal.partial_moment"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.Normal.partial_moment" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.Normal.partial_moment">
+<span class="sig-prename descclassname"><span class="pre">Normal.</span></span><span class="sig-name descname"><span class="pre">partial_moment</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.partial_moment.n" title="arch.univariate.Normal.partial_moment.n (Python parameter) — Order of partial moment"><span class="n"><span class="pre">n</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.partial_moment.z" title="arch.univariate.Normal.partial_moment.z (Python parameter) — Upper bound for partial moment integral"><span class="n"><span class="pre">z</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.partial_moment.parameters" title="arch.univariate.Normal.partial_moment.parameters (Python parameter) — Distribution parameters.  Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Normal.partial_moment"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.Normal.partial_moment" title="Link to this definition">¶</a></dt>
 <dd><p>Order n lower partial moment from -inf to z</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.Normal.partial_moment-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.Normal.partial_moment-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.Normal.partial_moment.n"><span class="sig-name descname">n</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.Normal.partial_moment.n" title="Permalink to this definition">¶</a></dt><dd><p>Order of partial moment</p>
+<dt id="arch.univariate.Normal.partial_moment.n"><span class="n sig-name">n</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.Normal.partial_moment.n" title="Permalink to this definition">¶</a></dt><dd><p>Order of partial moment</p>
 </dd>
-<dt id="arch.univariate.Normal.partial_moment.z"><span class="sig-name descname">z</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>, optional</span></span><a class="headerlink" href="#arch.univariate.Normal.partial_moment.z" title="Permalink to this definition">¶</a></dt><dd><p>Upper bound for partial moment integral</p>
+<dt id="arch.univariate.Normal.partial_moment.z"><span class="n sig-name">z</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.0</span></code><a class="headerlink" href="#arch.univariate.Normal.partial_moment.z" title="Permalink to this definition">¶</a></dt><dd><p>Upper bound for partial moment integral</p>
 </dd>
-<dt id="arch.univariate.Normal.partial_moment.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray, optional</span></span><a class="headerlink" href="#arch.univariate.Normal.partial_moment.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters.  Use None for parameterless distributions.</p>
+<dt id="arch.univariate.Normal.partial_moment.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.Normal.partial_moment.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters.  Use None for parameterless distributions.</p>
 </dd>
 </dl>
 </dd>
@@ -1146,7 +1146,7 @@ <h1 id="arch-univariate-normal-partial-moment">arch.univariate.Normal.partial_mo
 <dd class="field-even"><p>Partial moment</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.Normal.partial_moment-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.Normal.partial_moment-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 <p class="rubric">References</p>
@@ -1231,7 +1231,7 @@ <h1 id="arch-univariate-normal-partial-moment">arch.univariate.Normal.partial_mo
   
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+        <script id="MathJax-script" src="../../../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
     
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diff --git a/univariate/generated/generated/arch.univariate.Normal.ppf.html b/univariate/generated/generated/arch.univariate.Normal.ppf.html
index 472f826d83..61ac50fe99 100644
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+++ b/univariate/generated/generated/arch.univariate.Normal.ppf.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.Normal.ppf - arch 6.1.0</title>
+        <title>arch.univariate.Normal.ppf - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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@@ -189,10 +189,10 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch 6.1.0
+    arch 6.2.0
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@@ -1112,17 +1112,17 @@
                   
 
 
-<h1 id="arch-univariate-normal-ppf">arch.univariate.Normal.ppf<a class="headerlink" href="#arch-univariate-normal-ppf" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-normal-ppf">arch.univariate.Normal.ppf<a class="headerlink" href="#arch-univariate-normal-ppf" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.Normal.ppf">
-<span class="sig-prename descclassname"><span class="pre">Normal.</span></span><span class="sig-name descname"><span class="pre">ppf</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.ppf.pits" title="arch.univariate.Normal.ppf.pits (Python parameter) — Probability-integral-transformed values in the interval (0, 1)."><span class="n"><span class="pre">pits</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.ppf.parameters" title="arch.univariate.Normal.ppf.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Normal.ppf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.Normal.ppf" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.Normal.ppf">
+<span class="sig-prename descclassname"><span class="pre">Normal.</span></span><span class="sig-name descname"><span class="pre">ppf</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.ppf.pits" title="arch.univariate.Normal.ppf.pits (Python parameter) — Probability-integral-transformed values in the interval (0, 1)."><span class="n"><span class="pre">pits</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.ppf.parameters" title="arch.univariate.Normal.ppf.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Normal.ppf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.Normal.ppf" title="Link to this definition">¶</a></dt>
 <dd><p>Inverse cumulative density function (ICDF)</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.Normal.ppf-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.Normal.ppf-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.Normal.ppf.pits"><span class="sig-name descname">pits</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}</span></span><a class="headerlink" href="#arch.univariate.Normal.ppf.pits" title="Permalink to this definition">¶</a></dt><dd><p>Probability-integral-transformed values in the interval (0, 1).</p>
+<dt id="arch.univariate.Normal.ppf.pits"><span class="n sig-name">pits</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.Normal.ppf.pits" title="Permalink to this definition">¶</a></dt><dd><p>Probability-integral-transformed values in the interval (0, 1).</p>
 </dd>
-<dt id="arch.univariate.Normal.ppf.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray, optional</span></span><a class="headerlink" href="#arch.univariate.Normal.ppf.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use <code class="docutils literal notranslate"><span class="pre">None</span></code> for parameterless
+<dt id="arch.univariate.Normal.ppf.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.Normal.ppf.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use <code class="docutils literal notranslate"><span class="pre">None</span></code> for parameterless
 distributions.</p>
 </dd>
 </dl>
@@ -1131,7 +1131,7 @@ <h1 id="arch-univariate-normal-ppf">arch.univariate.Normal.ppf<a class="headerli
 <dd class="field-even"><p><strong>i</strong> – Inverse CDF values</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.Normal.ppf-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.Normal.ppf-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>{float, ndarray}</p>
+<dd class="field-odd"><p><code class="docutils literal notranslate"><span class="pre">{float,</span> <span class="pre">ndarray}</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1203,7 +1203,7 @@ <h1 id="arch-univariate-normal-ppf">arch.univariate.Normal.ppf<a class="headerli
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1250,7 +1250,7 @@ <h1 id="arch-univariate-normal-ppf">arch.univariate.Normal.ppf<a class="headerli
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diff --git a/univariate/generated/generated/arch.univariate.Normal.random_state.html b/univariate/generated/generated/arch.univariate.Normal.random_state.html
index e7dc1584dc..1311e0771d 100644
--- a/univariate/generated/generated/arch.univariate.Normal.random_state.html
+++ b/univariate/generated/generated/arch.univariate.Normal.random_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.Normal.random_state - arch 6.1.0</title>
+        <title>arch.univariate.Normal.random_state - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1018,13 +1018,13 @@
                   
 
 
-<h1 id="arch-univariate-normal-random-state">arch.univariate.Normal.random_state<a class="headerlink" href="#arch-univariate-normal-random-state" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-normal-random-state">arch.univariate.Normal.random_state<a class="headerlink" href="#arch-univariate-normal-random-state" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.Normal.random_state">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Normal.</span></span><span class="sig-name descname"><span class="pre">random_state</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.24)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.24)"><span class="pre">Generator</span></a></span><a class="headerlink" href="#arch.univariate.Normal.random_state" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Normal.</span></span><span class="sig-name descname"><span class="pre">random_state</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><span class="pre">Generator</span></a></span><a class="headerlink" href="#arch.univariate.Normal.random_state" title="Link to this definition">¶</a></dt>
 <dd><p>The NumPy RandomState attached to the distribution</p>
-<div class="deprecated">
-<p><span class="versionmodified deprecated">Deprecated since version 5.0: </span>random_state is deprecated. Use generator instead.</p>
+<div class="admonition deprecated">
+<p class="admonition-title"><span class="versionmodified deprecated">Deprecated since version 5.0: </span>random_state is deprecated. Use generator instead.</p>
 </div>
 </dd></dl>
 
@@ -1095,7 +1095,7 @@ <h1 id="arch-univariate-normal-random-state">arch.univariate.Normal.random_state
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1142,7 +1142,7 @@ <h1 id="arch-univariate-normal-random-state">arch.univariate.Normal.random_state
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diff --git a/univariate/generated/generated/arch.univariate.Normal.simulate.html b/univariate/generated/generated/arch.univariate.Normal.simulate.html
index f1a137640a..866e451dbf 100644
--- a/univariate/generated/generated/arch.univariate.Normal.simulate.html
+++ b/univariate/generated/generated/arch.univariate.Normal.simulate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.Normal.simulate - arch 6.1.0</title>
+        <title>arch.univariate.Normal.simulate - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1098,15 +1098,15 @@
                   
 
 
-<h1 id="arch-univariate-normal-simulate">arch.univariate.Normal.simulate<a class="headerlink" href="#arch-univariate-normal-simulate" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-normal-simulate">arch.univariate.Normal.simulate<a class="headerlink" href="#arch-univariate-normal-simulate" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.Normal.simulate">
-<span class="sig-prename descclassname"><span class="pre">Normal.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.simulate.parameters" title="arch.univariate.Normal.simulate.parameters (Python parameter) — Distribution parameters"><span class="n"><span class="pre">parameters</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Normal.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.Normal.simulate" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.Normal.simulate">
+<span class="sig-prename descclassname"><span class="pre">Normal.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.simulate.parameters" title="arch.univariate.Normal.simulate.parameters (Python parameter) — Distribution parameters"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Normal.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.Normal.simulate" title="Link to this definition">¶</a></dt>
 <dd><p>Simulates i.i.d. draws from the distribution</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.Normal.simulate-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.Normal.simulate-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.Normal.simulate.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.Normal.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters</p>
+<dt id="arch.univariate.Normal.simulate.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.Normal.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters</p>
 </dd>
 </dl>
 </dd>
@@ -1115,7 +1115,7 @@ <h1 id="arch-univariate-normal-simulate">arch.univariate.Normal.simulate<a class
 draws from the distribution</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.Normal.simulate-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.Normal.simulate-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>callable</p>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">callable</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1187,7 +1187,7 @@ <h1 id="arch-univariate-normal-simulate">arch.univariate.Normal.simulate<a class
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1234,7 +1234,7 @@ <h1 id="arch-univariate-normal-simulate">arch.univariate.Normal.simulate<a class
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diff --git a/univariate/generated/generated/arch.univariate.Normal.starting_values.html b/univariate/generated/generated/arch.univariate.Normal.starting_values.html
index 24d4beb370..b3986b77a5 100644
--- a/univariate/generated/generated/arch.univariate.Normal.starting_values.html
+++ b/univariate/generated/generated/arch.univariate.Normal.starting_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.Normal.starting_values - arch 6.1.0</title>
+        <title>arch.univariate.Normal.starting_values - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1098,15 +1098,15 @@
                   
 
 
-<h1 id="arch-univariate-normal-starting-values">arch.univariate.Normal.starting_values<a class="headerlink" href="#arch-univariate-normal-starting-values" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-normal-starting-values">arch.univariate.Normal.starting_values<a class="headerlink" href="#arch-univariate-normal-starting-values" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.Normal.starting_values">
-<span class="sig-prename descclassname"><span class="pre">Normal.</span></span><span class="sig-name descname"><span class="pre">starting_values</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.starting_values.std_resid" title="arch.univariate.Normal.starting_values.std_resid (Python parameter) — Estimated standardized residuals to use in computing starting values for the shape parameter"><span class="n"><span class="pre">std_resid</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Normal.starting_values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.Normal.starting_values" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Normal.</span></span><span class="sig-name descname"><span class="pre">starting_values</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.starting_values.std_resid" title="arch.univariate.Normal.starting_values.std_resid (Python parameter) — Estimated standardized residuals to use in computing starting values for the shape parameter"><span class="n"><span class="pre">std_resid</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Normal.starting_values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.Normal.starting_values" title="Link to this definition">¶</a></dt>
 <dd><p>Construct starting values for use in optimization.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.Normal.starting_values-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.Normal.starting_values-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.Normal.starting_values.std_resid"><span class="sig-name descname">std_resid</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.Normal.starting_values.std_resid" title="Permalink to this definition">¶</a></dt><dd><p>Estimated standardized residuals to use in computing starting
+<dt id="arch.univariate.Normal.starting_values.std_resid"><span class="n sig-name">std_resid</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.Normal.starting_values.std_resid" title="Permalink to this definition">¶</a></dt><dd><p>Estimated standardized residuals to use in computing starting
 values for the shape parameter</p>
 </dd>
 </dl>
@@ -1115,7 +1115,7 @@ <h1 id="arch-univariate-normal-starting-values">arch.univariate.Normal.starting_
 <dd class="field-even"><p><strong>sv</strong> – The estimated shape parameters for the distribution</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.Normal.starting_values-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.Normal.starting_values-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -1189,7 +1189,7 @@ <h1 id="arch-univariate-normal-starting-values">arch.univariate.Normal.starting_
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1236,7 +1236,7 @@ <h1 id="arch-univariate-normal-starting-values">arch.univariate.Normal.starting_
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.RiskMetrics2006.backcast.html b/univariate/generated/generated/arch.univariate.RiskMetrics2006.backcast.html
index 35223a5dd9..00de4ac831 100644
--- a/univariate/generated/generated/arch.univariate.RiskMetrics2006.backcast.html
+++ b/univariate/generated/generated/arch.univariate.RiskMetrics2006.backcast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.RiskMetrics2006.backcast - arch 6.1.0</title>
+        <title>arch.univariate.RiskMetrics2006.backcast - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1303,15 +1303,15 @@
                   
 
 
-<h1 id="arch-univariate-riskmetrics2006-backcast">arch.univariate.RiskMetrics2006.backcast<a class="headerlink" href="#arch-univariate-riskmetrics2006-backcast" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-riskmetrics2006-backcast">arch.univariate.RiskMetrics2006.backcast<a class="headerlink" href="#arch-univariate-riskmetrics2006-backcast" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.RiskMetrics2006.backcast">
-<span class="sig-prename descclassname"><span class="pre">RiskMetrics2006.</span></span><span class="sig-name descname"><span class="pre">backcast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.backcast.resids" title="arch.univariate.RiskMetrics2006.backcast.resids (Python parameter) — Vector of (approximate) residuals"><span class="n"><span class="pre">resids</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#RiskMetrics2006.backcast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.RiskMetrics2006.backcast" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">RiskMetrics2006.</span></span><span class="sig-name descname"><span class="pre">backcast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.backcast.resids" title="arch.univariate.RiskMetrics2006.backcast.resids (Python parameter) — Vector of (approximate) residuals"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#RiskMetrics2006.backcast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.RiskMetrics2006.backcast" title="Link to this definition">¶</a></dt>
 <dd><p>Construct values for backcasting to start the recursion</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.RiskMetrics2006.backcast-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.backcast-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.RiskMetrics2006.backcast.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.backcast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of (approximate) residuals</p>
+<dt id="arch.univariate.RiskMetrics2006.backcast.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.backcast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of (approximate) residuals</p>
 </dd>
 </dl>
 </dd>
@@ -1319,7 +1319,7 @@ <h1 id="arch-univariate-riskmetrics2006-backcast">arch.univariate.RiskMetrics200
 <dd class="field-even"><p><strong>backcast</strong> – Backcast values for each EWMA component</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.RiskMetrics2006.backcast-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.backcast-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1391,7 +1391,7 @@ <h1 id="arch-univariate-riskmetrics2006-backcast">arch.univariate.RiskMetrics200
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1438,7 +1438,7 @@ <h1 id="arch-univariate-riskmetrics2006-backcast">arch.univariate.RiskMetrics200
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diff --git a/univariate/generated/generated/arch.univariate.RiskMetrics2006.backcast_transform.html b/univariate/generated/generated/arch.univariate.RiskMetrics2006.backcast_transform.html
index cbf1021158..bc18f191d3 100644
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+++ b/univariate/generated/generated/arch.univariate.RiskMetrics2006.backcast_transform.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+    arch 6.2.0
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@@ -1303,15 +1303,15 @@
                   
 
 
-<h1 id="arch-univariate-riskmetrics2006-backcast-transform">arch.univariate.RiskMetrics2006.backcast_transform<a class="headerlink" href="#arch-univariate-riskmetrics2006-backcast-transform" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-riskmetrics2006-backcast-transform">arch.univariate.RiskMetrics2006.backcast_transform<a class="headerlink" href="#arch-univariate-riskmetrics2006-backcast-transform" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.RiskMetrics2006.backcast_transform">
-<span class="sig-prename descclassname"><span class="pre">RiskMetrics2006.</span></span><span class="sig-name descname"><span class="pre">backcast_transform</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.backcast_transform.backcast" title="arch.univariate.RiskMetrics2006.backcast_transform.backcast (Python parameter) — User-provided backcast that approximates sigma2[0]."><span class="n"><span class="pre">backcast</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#RiskMetrics2006.backcast_transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.RiskMetrics2006.backcast_transform" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.RiskMetrics2006.backcast_transform">
+<span class="sig-prename descclassname"><span class="pre">RiskMetrics2006.</span></span><span class="sig-name descname"><span class="pre">backcast_transform</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.backcast_transform.backcast" title="arch.univariate.RiskMetrics2006.backcast_transform.backcast (Python parameter) — User-provided backcast that approximates sigma2[0]."><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#RiskMetrics2006.backcast_transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.RiskMetrics2006.backcast_transform" title="Link to this definition">¶</a></dt>
 <dd><p>Transformation to apply to user-provided backcast values</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.RiskMetrics2006.backcast_transform-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.backcast_transform-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.RiskMetrics2006.backcast_transform.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}</span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.backcast_transform.backcast" title="Permalink to this definition">¶</a></dt><dd><p>User-provided <code class="docutils literal notranslate"><span class="pre">backcast</span></code> that approximates sigma2[0].</p>
+<dt id="arch.univariate.RiskMetrics2006.backcast_transform.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.backcast_transform.backcast" title="Permalink to this definition">¶</a></dt><dd><p>User-provided <code class="docutils literal notranslate"><span class="pre">backcast</span></code> that approximates sigma2[0].</p>
 </dd>
 </dl>
 </dd>
@@ -1319,7 +1319,7 @@ <h1 id="arch-univariate-riskmetrics2006-backcast-transform">arch.univariate.Risk
 <dd class="field-even"><p><strong>backcast</strong> – Backcast transformed to the model-appropriate scale</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.RiskMetrics2006.backcast_transform-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.backcast_transform-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>{float, ndarray}</p>
+<dd class="field-odd"><p><code class="docutils literal notranslate"><span class="pre">{float,</span> <span class="pre">ndarray}</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1391,7 +1391,7 @@ <h1 id="arch-univariate-riskmetrics2006-backcast-transform">arch.univariate.Risk
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1438,7 +1438,7 @@ <h1 id="arch-univariate-riskmetrics2006-backcast-transform">arch.univariate.Risk
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diff --git a/univariate/generated/generated/arch.univariate.RiskMetrics2006.bounds.html b/univariate/generated/generated/arch.univariate.RiskMetrics2006.bounds.html
index ff8fd1fc3d..589e6166aa 100644
--- a/univariate/generated/generated/arch.univariate.RiskMetrics2006.bounds.html
+++ b/univariate/generated/generated/arch.univariate.RiskMetrics2006.bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.RiskMetrics2006.bounds - arch 6.1.0</title>
+        <title>arch.univariate.RiskMetrics2006.bounds - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -1303,15 +1303,15 @@
                   
 
 
-<h1 id="arch-univariate-riskmetrics2006-bounds">arch.univariate.RiskMetrics2006.bounds<a class="headerlink" href="#arch-univariate-riskmetrics2006-bounds" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-riskmetrics2006-bounds">arch.univariate.RiskMetrics2006.bounds<a class="headerlink" href="#arch-univariate-riskmetrics2006-bounds" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.RiskMetrics2006.bounds">
-<span class="sig-prename descclassname"><span class="pre">RiskMetrics2006.</span></span><span class="sig-name descname"><span class="pre">bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.bounds.resids" title="arch.univariate.RiskMetrics2006.bounds.resids (Python parameter) — Vector of (approximate) residuals"><span class="n"><span class="pre">resids</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#RiskMetrics2006.bounds"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.RiskMetrics2006.bounds" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">RiskMetrics2006.</span></span><span class="sig-name descname"><span class="pre">bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.bounds.resids" title="arch.univariate.RiskMetrics2006.bounds.resids (Python parameter) — Vector of (approximate) residuals"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#RiskMetrics2006.bounds"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.RiskMetrics2006.bounds" title="Link to this definition">¶</a></dt>
 <dd><p>Returns bounds for parameters</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.RiskMetrics2006.bounds-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.bounds-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.RiskMetrics2006.bounds.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.bounds.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of (approximate) residuals</p>
+<dt id="arch.univariate.RiskMetrics2006.bounds.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.bounds.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of (approximate) residuals</p>
 </dd>
 </dl>
 </dd>
@@ -1319,7 +1319,7 @@ <h1 id="arch-univariate-riskmetrics2006-bounds">arch.univariate.RiskMetrics2006.
 <dd class="field-even"><p><strong>bounds</strong> – List of bounds where each element is (lower, upper).</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.RiskMetrics2006.bounds-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.bounds-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a>[<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a>[<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>,<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>]]</p>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">list[tuple[float,float]]</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1391,7 +1391,7 @@ <h1 id="arch-univariate-riskmetrics2006-bounds">arch.univariate.RiskMetrics2006.
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
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diff --git a/univariate/generated/generated/arch.univariate.RiskMetrics2006.compute_variance.html b/univariate/generated/generated/arch.univariate.RiskMetrics2006.compute_variance.html
index ecbd0f6547..be5ea67213 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -1345,30 +1331,27 @@
                   
 
 
-<h1 id="arch-univariate-riskmetrics2006-compute-variance">arch.univariate.RiskMetrics2006.compute_variance<a class="headerlink" href="#arch-univariate-riskmetrics2006-compute-variance" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-riskmetrics2006-compute-variance">arch.univariate.RiskMetrics2006.compute_variance<a class="headerlink" href="#arch-univariate-riskmetrics2006-compute-variance" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.RiskMetrics2006.compute_variance">
-<span class="sig-prename descclassname"><span class="pre">RiskMetrics2006.</span></span><span class="sig-name descname"><span class="pre">compute_variance</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.compute_variance.parameters" title="arch.univariate.RiskMetrics2006.compute_variance.parameters (Python parameter) — Model parameters"><span class="n"><span class="pre">parameters</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.compute_variance.resids" title="arch.univariate.RiskMetrics2006.compute_variance.resids (Python parameter) — Vector of mean zero residuals"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.compute_variance.sigma2" title="arch.univariate.RiskMetrics2006.compute_variance.sigma2 (Python parameter) — Array with same size as resids to store the conditional variance"><span class="n"><span class="pre">sigma2</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.compute_variance.backcast" title="arch.univariate.RiskMetrics2006.compute_variance.backcast (Python parameter) — Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."><span class="n"><span class="pre">backcast</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.compute_variance.var_bounds" title="arch.univariate.RiskMetrics2006.compute_variance.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#RiskMetrics2006.compute_variance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.RiskMetrics2006.compute_variance" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">RiskMetrics2006.</span></span><span class="sig-name descname"><span class="pre">compute_variance</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.compute_variance.parameters" title="arch.univariate.RiskMetrics2006.compute_variance.parameters (Python parameter) — Model parameters"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.compute_variance.resids" title="arch.univariate.RiskMetrics2006.compute_variance.resids (Python parameter) — Vector of mean zero residuals"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.compute_variance.sigma2" title="arch.univariate.RiskMetrics2006.compute_variance.sigma2 (Python parameter) — Array with same size as resids to store the conditional variance"><span class="n"><span class="pre">sigma2</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.compute_variance.backcast" title="arch.univariate.RiskMetrics2006.compute_variance.backcast (Python parameter) — Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.compute_variance.var_bounds" title="arch.univariate.RiskMetrics2006.compute_variance.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#RiskMetrics2006.compute_variance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.RiskMetrics2006.compute_variance" title="Link to this definition">¶</a></dt>
 <dd><p>Compute the variance for the ARCH model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.RiskMetrics2006.compute_variance-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.compute_variance-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.RiskMetrics2006.compute_variance.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.compute_variance.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
+<dt id="arch.univariate.RiskMetrics2006.compute_variance.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.compute_variance.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
 </dd>
-<dt id="arch.univariate.RiskMetrics2006.compute_variance.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.compute_variance.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of mean zero residuals</p>
+<dt id="arch.univariate.RiskMetrics2006.compute_variance.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.compute_variance.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of mean zero residuals</p>
 </dd>
-<dt id="arch.univariate.RiskMetrics2006.compute_variance.sigma2"><span class="sig-name descname">sigma2</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.compute_variance.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>Array with same size as resids to store the conditional variance</p>
+<dt id="arch.univariate.RiskMetrics2006.compute_variance.sigma2"><span class="n sig-name">sigma2</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.compute_variance.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>Array with same size as resids to store the conditional variance</p>
 </dd>
-<dt id="arch.univariate.RiskMetrics2006.compute_variance.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}</span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.compute_variance.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing ARCH recursion. Can be an ndarray
+<dt id="arch.univariate.RiskMetrics2006.compute_variance.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.compute_variance.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing ARCH recursion. Can be an ndarray
 when the model contains multiple components.</p>
 </dd>
-<dt id="arch.univariate.RiskMetrics2006.compute_variance.var_bounds"><span class="sig-name descname">var_bounds</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.compute_variance.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
+<dt id="arch.univariate.RiskMetrics2006.compute_variance.var_bounds"><span class="n sig-name">var_bounds</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.compute_variance.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
 </dd>
 </dl>
 </dd>
-<dt class="field-even" id="arch.univariate.RiskMetrics2006.compute_variance-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.compute_variance-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a></p>
-</dd>
 </dl>
 </dd></dl>
 
@@ -1439,7 +1422,7 @@ <h1 id="arch-univariate-riskmetrics2006-compute-variance">arch.univariate.RiskMe
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
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diff --git a/univariate/generated/generated/arch.univariate.RiskMetrics2006.constraints.html b/univariate/generated/generated/arch.univariate.RiskMetrics2006.constraints.html
index 56d1c52a34..90712f5bbb 100644
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+++ b/univariate/generated/generated/arch.univariate.RiskMetrics2006.constraints.html
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-        <title>arch.univariate.RiskMetrics2006.constraints - arch 6.1.0</title>
+        <title>arch.univariate.RiskMetrics2006.constraints - arch 6.2.0</title>
       
     
 
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+    arch 6.2.0
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@@ -733,13 +733,6 @@
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       <ul class="md-nav__list">
         
-          <li class="md-nav__item">
-  <a href="#arch.univariate.RiskMetrics2006.constraints-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
-</li>
-        
           <li class="md-nav__item">
   <a href="#arch.univariate.RiskMetrics2006.constraints-returns" class="md-nav__link">
     <span class="md-ellipsis">Returns</span>
@@ -1230,13 +1223,6 @@
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       <ul class="md-nav__list">
         
-          <li class="md-nav__item">
-  <a href="#arch.univariate.RiskMetrics2006.constraints-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
-</li>
-        
           <li class="md-nav__item">
   <a href="#arch.univariate.RiskMetrics2006.constraints-returns" class="md-nav__link">
     <span class="md-ellipsis">Returns</span>
@@ -1263,20 +1249,17 @@
                   
 
 
-<h1 id="arch-univariate-riskmetrics2006-constraints">arch.univariate.RiskMetrics2006.constraints<a class="headerlink" href="#arch-univariate-riskmetrics2006-constraints" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-riskmetrics2006-constraints">arch.univariate.RiskMetrics2006.constraints<a class="headerlink" href="#arch-univariate-riskmetrics2006-constraints" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.RiskMetrics2006.constraints">
-<span class="sig-prename descclassname"><span class="pre">RiskMetrics2006.</span></span><span class="sig-name descname"><span class="pre">constraints</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#RiskMetrics2006.constraints"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.RiskMetrics2006.constraints" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">RiskMetrics2006.</span></span><span class="sig-name descname"><span class="pre">constraints</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#RiskMetrics2006.constraints"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.RiskMetrics2006.constraints" title="Link to this definition">¶</a></dt>
 <dd><p>Construct parameter constraints arrays for parameter estimation</p>
 <dl class="field-list simple">
-<dt class="field-odd" id="arch.univariate.RiskMetrics2006.constraints-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.constraints-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>]</p>
-</dd>
-<dt class="field-even" id="arch.univariate.RiskMetrics2006.constraints-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.constraints-returns" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><ul class="simple">
-<li><p><strong>A</strong> (<em>ndarray</em>) – Parameters loadings in constraint. Shape is number of constraints
+<dt class="field-odd" id="arch.univariate.RiskMetrics2006.constraints-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.constraints-returns" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p><strong>A</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – Parameters loadings in constraint. Shape is number of constraints
 by number of parameters</p></li>
-<li><p><strong>b</strong> (<em>ndarray</em>) – Constraint values, one for each constraint</p></li>
+<li><p><strong>b</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – Constraint values, one for each constraint</p></li>
 </ul>
 </p>
 </dd>
@@ -1353,7 +1336,7 @@ <h1 id="arch-univariate-riskmetrics2006-constraints">arch.univariate.RiskMetrics
   
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index 15a9cef824..9fb5b5e1e2 100644
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-<h1 id="arch-univariate-riskmetrics2006-forecast">arch.univariate.RiskMetrics2006.forecast<a class="headerlink" href="#arch-univariate-riskmetrics2006-forecast" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-riskmetrics2006-forecast">arch.univariate.RiskMetrics2006.forecast<a class="headerlink" href="#arch-univariate-riskmetrics2006-forecast" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.RiskMetrics2006.forecast">
-<span class="sig-prename descclassname"><span class="pre">RiskMetrics2006.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.forecast.parameters" title="arch.univariate.RiskMetrics2006.forecast.parameters (Python parameter) — Parameters required to forecast the volatility model"><span class="n"><span class="pre">parameters</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.forecast.resids" title="arch.univariate.RiskMetrics2006.forecast.resids (Python parameter) — Residuals to use in the recursion"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.forecast.backcast" title="arch.univariate.RiskMetrics2006.forecast.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.forecast.var_bounds" title="arch.univariate.RiskMetrics2006.forecast.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.forecast.start" title="arch.univariate.RiskMetrics2006.forecast.start (Python parameter) — Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."><span class="n"><span class="pre">start</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.forecast.horizon" title="arch.univariate.RiskMetrics2006.forecast.horizon (Python parameter) — Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."><span class="n"><span class="pre">horizon</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.forecast.method" title="arch.univariate.RiskMetrics2006.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic."><span class="n"><span class="pre">method</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.forecast.simulations" title="arch.univariate.RiskMetrics2006.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.forecast.rng" title="arch.univariate.RiskMetrics2006.forecast.rng (Python parameter) — Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."><span class="n"><span class="pre">rng</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.forecast.random_state" title="arch.univariate.RiskMetrics2006.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.forecast" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">RiskMetrics2006.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.forecast.parameters" title="arch.univariate.RiskMetrics2006.forecast.parameters (Python parameter) — Parameters required to forecast the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.forecast.resids" title="arch.univariate.RiskMetrics2006.forecast.resids (Python parameter) — Residuals to use in the recursion"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.forecast.backcast" title="arch.univariate.RiskMetrics2006.forecast.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.forecast.var_bounds" title="arch.univariate.RiskMetrics2006.forecast.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.forecast.start" title="arch.univariate.RiskMetrics2006.forecast.start (Python parameter) — Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.forecast.horizon" title="arch.univariate.RiskMetrics2006.forecast.horizon (Python parameter) — Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.forecast.method" title="arch.univariate.RiskMetrics2006.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.forecast.simulations" title="arch.univariate.RiskMetrics2006.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.forecast.rng" title="arch.univariate.RiskMetrics2006.forecast.rng (Python parameter) — Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.forecast.random_state" title="arch.univariate.RiskMetrics2006.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><span class="pre">VarianceForecast</span></span></span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.forecast" title="Link to this definition">¶</a></dt>
 <dd><p>Forecast volatility from the model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.RiskMetrics2006.forecast-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.forecast-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.RiskMetrics2006.forecast.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, Series}</span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.forecast.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast the volatility model</p>
+<dt id="arch.univariate.RiskMetrics2006.forecast.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.forecast.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast the volatility model</p>
 </dd>
-<dt id="arch.univariate.RiskMetrics2006.forecast.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.forecast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Residuals to use in the recursion</p>
+<dt id="arch.univariate.RiskMetrics2006.forecast.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.forecast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Residuals to use in the recursion</p>
 </dd>
-<dt id="arch.univariate.RiskMetrics2006.forecast.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.forecast.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing the recursion</p>
+<dt id="arch.univariate.RiskMetrics2006.forecast.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.forecast.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing the recursion</p>
 </dd>
-<dt id="arch.univariate.RiskMetrics2006.forecast.var_bounds"><span class="sig-name descname">var_bounds</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray, 2-d</span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.forecast.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
+<dt id="arch.univariate.RiskMetrics2006.forecast.var_bounds"><span class="n sig-name">var_bounds</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.forecast.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
 </dd>
-<dt id="arch.univariate.RiskMetrics2006.forecast.start"><span class="sig-name descname">start</span><span class="p"> : </span><span class="desctype"><span class="desctype">{None, int}</span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>Index of the first observation to use as the starting point for
+<dt id="arch.univariate.RiskMetrics2006.forecast.start"><span class="n sig-name">start</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.RiskMetrics2006.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>Index of the first observation to use as the starting point for
 the forecast.  Default is len(resids).</p>
 </dd>
-<dt id="arch.univariate.RiskMetrics2006.forecast.horizon"><span class="sig-name descname">horizon</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.forecast.horizon" title="Permalink to this definition">¶</a></dt><dd><p>Forecast horizon.  Must be 1 or larger.  Forecasts are produced
+<dt id="arch.univariate.RiskMetrics2006.forecast.horizon"><span class="n sig-name">horizon</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code><a class="headerlink" href="#arch.univariate.RiskMetrics2006.forecast.horizon" title="Permalink to this definition">¶</a></dt><dd><p>Forecast horizon.  Must be 1 or larger.  Forecasts are produced
 for horizons in [1, horizon].</p>
 </dd>
-<dt id="arch.univariate.RiskMetrics2006.forecast.method"><span class="sig-name descname">method</span><span class="p"> : </span><span class="desctype"><span class="desctype">{'analytic', 'simulation', 'bootstrap'}</span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.forecast.method" title="Permalink to this definition">¶</a></dt><dd><p>Method to use when producing the forecast. The default is analytic.</p>
+<dt id="arch.univariate.RiskMetrics2006.forecast.method"><span class="n sig-name">method</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'analytic'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'simulation'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'bootstrap'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code><a class="headerlink" href="#arch.univariate.RiskMetrics2006.forecast.method" title="Permalink to this definition">¶</a></dt><dd><p>Method to use when producing the forecast. The default is analytic.</p>
 </dd>
-<dt id="arch.univariate.RiskMetrics2006.forecast.simulations"><span class="sig-name descname">simulations</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.forecast.simulations" title="Permalink to this definition">¶</a></dt><dd><p>Number of simulations to run when computing the forecast using
+<dt id="arch.univariate.RiskMetrics2006.forecast.simulations"><span class="n sig-name">simulations</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.univariate.RiskMetrics2006.forecast.simulations" title="Permalink to this definition">¶</a></dt><dd><p>Number of simulations to run when computing the forecast using
 either simulation or bootstrap.</p>
 </dd>
-<dt id="arch.univariate.RiskMetrics2006.forecast.rng"><span class="sig-name descname">rng</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable</span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.forecast.rng" title="Permalink to this definition">¶</a></dt><dd><p>Callable random number generator required if method is
+<dt id="arch.univariate.RiskMetrics2006.forecast.rng"><span class="n sig-name">rng</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)">Callable</a><span class="p">[</span><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">,</span><span class="w"> </span><span class="p">...</span><span class="p">]</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.RiskMetrics2006.forecast.rng" title="Permalink to this definition">¶</a></dt><dd><p>Callable random number generator required if method is
 ‘simulation’. Must take a single shape input and return random
 samples numbers with that shape.</p>
 </dd>
-<dt id="arch.univariate.RiskMetrics2006.forecast.random_state"><span class="sig-name descname">random_state</span><span class="p"> : </span><span class="desctype"><span class="desctype">RandomState, optional</span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.forecast.random_state" title="Permalink to this definition">¶</a></dt><dd><p>NumPy RandomState instance to use when method is ‘bootstrap’</p>
+<dt id="arch.univariate.RiskMetrics2006.forecast.random_state"><span class="n sig-name">random_state</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)">RandomState</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.RiskMetrics2006.forecast.random_state" title="Permalink to this definition">¶</a></dt><dd><p>NumPy RandomState instance to use when method is ‘bootstrap’</p>
 </dd>
 </dl>
 </dd>
@@ -1483,7 +1483,7 @@ <h1 id="arch-univariate-riskmetrics2006-forecast">arch.univariate.RiskMetrics200
 or bootstrap, the simulated paths.</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.RiskMetrics2006.forecast-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.forecast-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>VarianceForecast</p>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">arch.univariate.volatility.VarianceForecast</span></code></p>
 </dd>
 <dt class="field-even" id="arch.univariate.RiskMetrics2006.forecast-raises">Raises<span class="colon">:</span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.forecast-raises" title="Permalink to this headline">¶</a></dt>
 <dd class="field-even"><ul class="simple">
@@ -1570,7 +1570,7 @@ <h1 id="arch-univariate-riskmetrics2006-forecast">arch.univariate.RiskMetrics200
   
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diff --git a/univariate/generated/generated/arch.univariate.RiskMetrics2006.name.html b/univariate/generated/generated/arch.univariate.RiskMetrics2006.name.html
index da10e40bc5..37e64d8348 100644
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+++ b/univariate/generated/generated/arch.univariate.RiskMetrics2006.name.html
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+    arch 6.2.0
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@@ -1223,10 +1223,10 @@
                   
 
 
-<h1 id="arch-univariate-riskmetrics2006-name">arch.univariate.RiskMetrics2006.name<a class="headerlink" href="#arch-univariate-riskmetrics2006-name" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-riskmetrics2006-name">arch.univariate.RiskMetrics2006.name<a class="headerlink" href="#arch-univariate-riskmetrics2006-name" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.RiskMetrics2006.name">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">RiskMetrics2006.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.name" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">RiskMetrics2006.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.name" title="Link to this definition">¶</a></dt>
 <dd><p>The name of the volatility process</p>
 </dd></dl>
 
@@ -1297,7 +1297,7 @@ <h1 id="arch-univariate-riskmetrics2006-name">arch.univariate.RiskMetrics2006.na
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1344,7 +1344,7 @@ <h1 id="arch-univariate-riskmetrics2006-name">arch.univariate.RiskMetrics2006.na
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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diff --git a/univariate/generated/generated/arch.univariate.RiskMetrics2006.num_params.html b/univariate/generated/generated/arch.univariate.RiskMetrics2006.num_params.html
index 052f92c10f..26a8538412 100644
--- a/univariate/generated/generated/arch.univariate.RiskMetrics2006.num_params.html
+++ b/univariate/generated/generated/arch.univariate.RiskMetrics2006.num_params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.RiskMetrics2006.num_params - arch 6.1.0</title>
+        <title>arch.univariate.RiskMetrics2006.num_params - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1223,10 +1223,10 @@
                   
 
 
-<h1 id="arch-univariate-riskmetrics2006-num-params">arch.univariate.RiskMetrics2006.num_params<a class="headerlink" href="#arch-univariate-riskmetrics2006-num-params" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-riskmetrics2006-num-params">arch.univariate.RiskMetrics2006.num_params<a class="headerlink" href="#arch-univariate-riskmetrics2006-num-params" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.RiskMetrics2006.num_params">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">RiskMetrics2006.</span></span><span class="sig-name descname"><span class="pre">num_params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.num_params" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">RiskMetrics2006.</span></span><span class="sig-name descname"><span class="pre">num_params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.num_params" title="Link to this definition">¶</a></dt>
 <dd><p>The number of parameters in the model</p>
 </dd></dl>
 
@@ -1297,7 +1297,7 @@ <h1 id="arch-univariate-riskmetrics2006-num-params">arch.univariate.RiskMetrics2
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
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@@ -1344,7 +1344,7 @@ <h1 id="arch-univariate-riskmetrics2006-num-params">arch.univariate.RiskMetrics2
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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diff --git a/univariate/generated/generated/arch.univariate.RiskMetrics2006.parameter_names.html b/univariate/generated/generated/arch.univariate.RiskMetrics2006.parameter_names.html
index 3d606d9c35..73d797e756 100644
--- a/univariate/generated/generated/arch.univariate.RiskMetrics2006.parameter_names.html
+++ b/univariate/generated/generated/arch.univariate.RiskMetrics2006.parameter_names.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.RiskMetrics2006.parameter_names - arch 6.1.0</title>
+        <title>arch.univariate.RiskMetrics2006.parameter_names - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1263,17 +1263,17 @@
                   
 
 
-<h1 id="arch-univariate-riskmetrics2006-parameter-names">arch.univariate.RiskMetrics2006.parameter_names<a class="headerlink" href="#arch-univariate-riskmetrics2006-parameter-names" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-riskmetrics2006-parameter-names">arch.univariate.RiskMetrics2006.parameter_names<a class="headerlink" href="#arch-univariate-riskmetrics2006-parameter-names" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.RiskMetrics2006.parameter_names">
-<span class="sig-prename descclassname"><span class="pre">RiskMetrics2006.</span></span><span class="sig-name descname"><span class="pre">parameter_names</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#RiskMetrics2006.parameter_names"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.RiskMetrics2006.parameter_names" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">RiskMetrics2006.</span></span><span class="sig-name descname"><span class="pre">parameter_names</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#RiskMetrics2006.parameter_names"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.RiskMetrics2006.parameter_names" title="Link to this definition">¶</a></dt>
 <dd><p>Names of model parameters</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.RiskMetrics2006.parameter_names-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.parameter_names-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p><strong>names</strong> – Variables names</p>
 </dd>
 <dt class="field-even" id="arch.univariate.RiskMetrics2006.parameter_names-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.parameter_names-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>)</p>
+<dd class="field-even"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span> <span class="pre">(str)</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1345,7 +1345,7 @@ <h1 id="arch-univariate-riskmetrics2006-parameter-names">arch.univariate.RiskMet
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1392,7 +1392,7 @@ <h1 id="arch-univariate-riskmetrics2006-parameter-names">arch.univariate.RiskMet
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
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diff --git a/univariate/generated/generated/arch.univariate.RiskMetrics2006.simulate.html b/univariate/generated/generated/arch.univariate.RiskMetrics2006.simulate.html
index 86aef7d26c..c737885f3e 100644
--- a/univariate/generated/generated/arch.univariate.RiskMetrics2006.simulate.html
+++ b/univariate/generated/generated/arch.univariate.RiskMetrics2006.simulate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.RiskMetrics2006.simulate - arch 6.1.0</title>
+        <title>arch.univariate.RiskMetrics2006.simulate - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -824,13 +824,6 @@
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-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.univariate.RiskMetrics2006.simulate-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
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-  
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           <li class="md-nav__item">
@@ -1324,13 +1317,6 @@
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-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.univariate.RiskMetrics2006.simulate-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
           <li class="md-nav__item">
@@ -1359,36 +1345,33 @@
                   
 
 
-<h1 id="arch-univariate-riskmetrics2006-simulate">arch.univariate.RiskMetrics2006.simulate<a class="headerlink" href="#arch-univariate-riskmetrics2006-simulate" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-riskmetrics2006-simulate">arch.univariate.RiskMetrics2006.simulate<a class="headerlink" href="#arch-univariate-riskmetrics2006-simulate" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.RiskMetrics2006.simulate">
-<span class="sig-prename descclassname"><span class="pre">RiskMetrics2006.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.simulate.parameters" title="arch.univariate.RiskMetrics2006.simulate.parameters (Python parameter) — Parameters required to simulate the volatility model"><span class="n"><span class="pre">parameters</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.simulate.nobs" title="arch.univariate.RiskMetrics2006.simulate.nobs (Python parameter) — Number of data points to simulate"><span class="n"><span class="pre">nobs</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.simulate.rng" title="arch.univariate.RiskMetrics2006.simulate.rng (Python parameter) — Callable function that takes a single integer input and returns a vector of random numbers"><span class="n"><span class="pre">rng</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.simulate.burn" title="arch.univariate.RiskMetrics2006.simulate.burn (Python parameter) — Number of additional observations to generate when initializing the simulation"><span class="n"><span class="pre">burn</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.simulate.initial_value" title="arch.univariate.RiskMetrics2006.simulate.initial_value (Python parameter) — Scalar or array of initial values to use when initializing the simulation"><span class="n"><span class="pre">initial_value</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#RiskMetrics2006.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.RiskMetrics2006.simulate" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">RiskMetrics2006.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.simulate.parameters" title="arch.univariate.RiskMetrics2006.simulate.parameters (Python parameter) — Parameters required to simulate the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.simulate.nobs" title="arch.univariate.RiskMetrics2006.simulate.nobs (Python parameter) — Number of data points to simulate"><span class="n"><span class="pre">nobs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.simulate.rng" title="arch.univariate.RiskMetrics2006.simulate.rng (Python parameter) — Callable function that takes a single integer input and returns a vector of random numbers"><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.simulate.burn" title="arch.univariate.RiskMetrics2006.simulate.burn (Python parameter) — Number of additional observations to generate when initializing the simulation"><span class="n"><span class="pre">burn</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.simulate.initial_value" title="arch.univariate.RiskMetrics2006.simulate.initial_value (Python parameter) — Scalar or array of initial values to use when initializing the simulation"><span class="n"><span class="pre">initial_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#RiskMetrics2006.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.RiskMetrics2006.simulate" title="Link to this definition">¶</a></dt>
 <dd><p>Simulate data from the model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.RiskMetrics2006.simulate-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.simulate-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.RiskMetrics2006.simulate.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, Series}</span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to simulate the volatility model</p>
+<dt id="arch.univariate.RiskMetrics2006.simulate.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to simulate the volatility model</p>
 </dd>
-<dt id="arch.univariate.RiskMetrics2006.simulate.nobs"><span class="sig-name descname">nobs</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.simulate.nobs" title="Permalink to this definition">¶</a></dt><dd><p>Number of data points to simulate</p>
+<dt id="arch.univariate.RiskMetrics2006.simulate.nobs"><span class="n sig-name">nobs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.simulate.nobs" title="Permalink to this definition">¶</a></dt><dd><p>Number of data points to simulate</p>
 </dd>
-<dt id="arch.univariate.RiskMetrics2006.simulate.rng"><span class="sig-name descname">rng</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable</span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.simulate.rng" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that takes a single integer input and returns
+<dt id="arch.univariate.RiskMetrics2006.simulate.rng"><span class="n sig-name">rng</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)">Callable</a><span class="p">[</span><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">,</span><span class="w"> </span><span class="p">...</span><span class="p">]</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.simulate.rng" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that takes a single integer input and returns
 a vector of random numbers</p>
 </dd>
-<dt id="arch.univariate.RiskMetrics2006.simulate.burn"><span class="sig-name descname">burn</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.simulate.burn" title="Permalink to this definition">¶</a></dt><dd><p>Number of additional observations to generate when initializing
+<dt id="arch.univariate.RiskMetrics2006.simulate.burn"><span class="n sig-name">burn</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code><a class="headerlink" href="#arch.univariate.RiskMetrics2006.simulate.burn" title="Permalink to this definition">¶</a></dt><dd><p>Number of additional observations to generate when initializing
 the simulation</p>
 </dd>
-<dt id="arch.univariate.RiskMetrics2006.simulate.initial_value"><span class="sig-name descname">initial_value</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}, optional</span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.simulate.initial_value" title="Permalink to this definition">¶</a></dt><dd><p>Scalar or array of initial values to use when initializing the
+<dt id="arch.univariate.RiskMetrics2006.simulate.initial_value"><span class="n sig-name">initial_value</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.RiskMetrics2006.simulate.initial_value" title="Permalink to this definition">¶</a></dt><dd><p>Scalar or array of initial values to use when initializing the
 simulation</p>
 </dd>
 </dl>
 </dd>
-<dt class="field-even" id="arch.univariate.RiskMetrics2006.simulate-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.simulate-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>]</p>
-</dd>
-<dt class="field-odd" id="arch.univariate.RiskMetrics2006.simulate-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.simulate-returns" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><ul class="simple">
-<li><p><strong>resids</strong> (<em>ndarray</em>) – The simulated residuals</p></li>
-<li><p><strong>variance</strong> (<em>ndarray</em>) – The simulated variance</p></li>
+<dt class="field-even" id="arch.univariate.RiskMetrics2006.simulate-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.simulate-returns" title="Permalink to this headline">¶</a></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><strong>resids</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – The simulated residuals</p></li>
+<li><p><strong>variance</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – The simulated variance</p></li>
 </ul>
 </p>
 </dd>
@@ -1462,7 +1445,7 @@ <h1 id="arch-univariate-riskmetrics2006-simulate">arch.univariate.RiskMetrics200
   
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index fec7e475d4..29db483446 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.RiskMetrics2006.start - arch 6.1.0</title>
+        <title>arch.univariate.RiskMetrics2006.start - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1223,10 +1223,10 @@
                   
 
 
-<h1 id="arch-univariate-riskmetrics2006-start">arch.univariate.RiskMetrics2006.start<a class="headerlink" href="#arch-univariate-riskmetrics2006-start" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-riskmetrics2006-start">arch.univariate.RiskMetrics2006.start<a class="headerlink" href="#arch-univariate-riskmetrics2006-start" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.RiskMetrics2006.start">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">RiskMetrics2006.</span></span><span class="sig-name descname"><span class="pre">start</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.start" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">RiskMetrics2006.</span></span><span class="sig-name descname"><span class="pre">start</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.start" title="Link to this definition">¶</a></dt>
 <dd><p>Index to use to start variance subarray selection</p>
 </dd></dl>
 
@@ -1297,7 +1297,7 @@ <h1 id="arch-univariate-riskmetrics2006-start">arch.univariate.RiskMetrics2006.s
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1344,7 +1344,7 @@ <h1 id="arch-univariate-riskmetrics2006-start">arch.univariate.RiskMetrics2006.s
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.RiskMetrics2006.starting_values.html b/univariate/generated/generated/arch.univariate.RiskMetrics2006.starting_values.html
index 6ed8c72d0f..a419dad4f5 100644
--- a/univariate/generated/generated/arch.univariate.RiskMetrics2006.starting_values.html
+++ b/univariate/generated/generated/arch.univariate.RiskMetrics2006.starting_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.RiskMetrics2006.starting_values - arch 6.1.0</title>
+        <title>arch.univariate.RiskMetrics2006.starting_values - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1303,15 +1303,15 @@
                   
 
 
-<h1 id="arch-univariate-riskmetrics2006-starting-values">arch.univariate.RiskMetrics2006.starting_values<a class="headerlink" href="#arch-univariate-riskmetrics2006-starting-values" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-riskmetrics2006-starting-values">arch.univariate.RiskMetrics2006.starting_values<a class="headerlink" href="#arch-univariate-riskmetrics2006-starting-values" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.RiskMetrics2006.starting_values">
-<span class="sig-prename descclassname"><span class="pre">RiskMetrics2006.</span></span><span class="sig-name descname"><span class="pre">starting_values</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.starting_values.resids" title="arch.univariate.RiskMetrics2006.starting_values.resids (Python parameter) — Array of (approximate) residuals to use when computing starting values"><span class="n"><span class="pre">resids</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#RiskMetrics2006.starting_values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.RiskMetrics2006.starting_values" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">RiskMetrics2006.</span></span><span class="sig-name descname"><span class="pre">starting_values</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.starting_values.resids" title="arch.univariate.RiskMetrics2006.starting_values.resids (Python parameter) — Array of (approximate) residuals to use when computing starting values"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#RiskMetrics2006.starting_values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.RiskMetrics2006.starting_values" title="Link to this definition">¶</a></dt>
 <dd><p>Returns starting values for the ARCH model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.RiskMetrics2006.starting_values-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.starting_values-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.RiskMetrics2006.starting_values.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.starting_values.resids" title="Permalink to this definition">¶</a></dt><dd><p>Array of (approximate) residuals to use when computing starting
+<dt id="arch.univariate.RiskMetrics2006.starting_values.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.starting_values.resids" title="Permalink to this definition">¶</a></dt><dd><p>Array of (approximate) residuals to use when computing starting
 values</p>
 </dd>
 </dl>
@@ -1320,7 +1320,7 @@ <h1 id="arch-univariate-riskmetrics2006-starting-values">arch.univariate.RiskMet
 <dd class="field-even"><p><strong>sv</strong> – Array of starting values</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.RiskMetrics2006.starting_values-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.starting_values-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1392,7 +1392,7 @@ <h1 id="arch-univariate-riskmetrics2006-starting-values">arch.univariate.RiskMet
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1439,7 +1439,7 @@ <h1 id="arch-univariate-riskmetrics2006-starting-values">arch.univariate.RiskMet
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.RiskMetrics2006.stop.html b/univariate/generated/generated/arch.univariate.RiskMetrics2006.stop.html
index d97a7b0713..dc5298973d 100644
--- a/univariate/generated/generated/arch.univariate.RiskMetrics2006.stop.html
+++ b/univariate/generated/generated/arch.univariate.RiskMetrics2006.stop.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.RiskMetrics2006.stop - arch 6.1.0</title>
+        <title>arch.univariate.RiskMetrics2006.stop - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1223,10 +1223,10 @@
                   
 
 
-<h1 id="arch-univariate-riskmetrics2006-stop">arch.univariate.RiskMetrics2006.stop<a class="headerlink" href="#arch-univariate-riskmetrics2006-stop" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-riskmetrics2006-stop">arch.univariate.RiskMetrics2006.stop<a class="headerlink" href="#arch-univariate-riskmetrics2006-stop" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.RiskMetrics2006.stop">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">RiskMetrics2006.</span></span><span class="sig-name descname"><span class="pre">stop</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.stop" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">RiskMetrics2006.</span></span><span class="sig-name descname"><span class="pre">stop</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.stop" title="Link to this definition">¶</a></dt>
 <dd><p>Index to use to stop variance subarray selection</p>
 </dd></dl>
 
@@ -1297,7 +1297,7 @@ <h1 id="arch-univariate-riskmetrics2006-stop">arch.univariate.RiskMetrics2006.st
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1344,7 +1344,7 @@ <h1 id="arch-univariate-riskmetrics2006-stop">arch.univariate.RiskMetrics2006.st
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.RiskMetrics2006.update.html b/univariate/generated/generated/arch.univariate.RiskMetrics2006.update.html
index d56bd347a7..2815d499ee 100644
--- a/univariate/generated/generated/arch.univariate.RiskMetrics2006.update.html
+++ b/univariate/generated/generated/arch.univariate.RiskMetrics2006.update.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.RiskMetrics2006.update - arch 6.1.0</title>
+        <title>arch.univariate.RiskMetrics2006.update - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1373,28 +1373,28 @@
                   
 
 
-<h1 id="arch-univariate-riskmetrics2006-update">arch.univariate.RiskMetrics2006.update<a class="headerlink" href="#arch-univariate-riskmetrics2006-update" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-riskmetrics2006-update">arch.univariate.RiskMetrics2006.update<a class="headerlink" href="#arch-univariate-riskmetrics2006-update" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.RiskMetrics2006.update">
-<span class="sig-prename descclassname"><span class="pre">RiskMetrics2006.</span></span><span class="sig-name descname"><span class="pre">update</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.update.index" title="arch.univariate.RiskMetrics2006.update.index (Python parameter) — The numerical index of the variance to compute"><span class="n"><span class="pre">index</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.update.parameters" title="arch.univariate.RiskMetrics2006.update.parameters (Python parameter) — The variance model parameters"><span class="n"><span class="pre">parameters</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.update.resids" title="arch.univariate.RiskMetrics2006.update.resids (Python parameter) — The residual array. Only uses resids[:index] when computing sigma2[index]"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.update.sigma2" title="arch.univariate.RiskMetrics2006.update.sigma2 (Python parameter) — The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."><span class="n"><span class="pre">sigma2</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.update.backcast" title="arch.univariate.RiskMetrics2006.update.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.update.var_bounds" title="arch.univariate.RiskMetrics2006.update.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.update" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">RiskMetrics2006.</span></span><span class="sig-name descname"><span class="pre">update</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.update.index" title="arch.univariate.RiskMetrics2006.update.index (Python parameter) — The numerical index of the variance to compute"><span class="n"><span class="pre">index</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.update.parameters" title="arch.univariate.RiskMetrics2006.update.parameters (Python parameter) — The variance model parameters"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.update.resids" title="arch.univariate.RiskMetrics2006.update.resids (Python parameter) — The residual array. Only uses resids[:index] when computing sigma2[index]"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.update.sigma2" title="arch.univariate.RiskMetrics2006.update.sigma2 (Python parameter) — The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."><span class="n"><span class="pre">sigma2</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.update.backcast" title="arch.univariate.RiskMetrics2006.update.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.update.var_bounds" title="arch.univariate.RiskMetrics2006.update.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.update" title="Link to this definition">¶</a></dt>
 <dd><p>Compute the variance for a single observation</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.RiskMetrics2006.update-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.update-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.RiskMetrics2006.update.index"><span class="sig-name descname">index</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.update.index" title="Permalink to this definition">¶</a></dt><dd><p>The numerical index of the variance to compute</p>
+<dt id="arch.univariate.RiskMetrics2006.update.index"><span class="n sig-name">index</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.update.index" title="Permalink to this definition">¶</a></dt><dd><p>The numerical index of the variance to compute</p>
 </dd>
-<dt id="arch.univariate.RiskMetrics2006.update.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.update.parameters" title="Permalink to this definition">¶</a></dt><dd><p>The variance model parameters</p>
+<dt id="arch.univariate.RiskMetrics2006.update.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.update.parameters" title="Permalink to this definition">¶</a></dt><dd><p>The variance model parameters</p>
 </dd>
-<dt id="arch.univariate.RiskMetrics2006.update.resids"><span class="n sig-name">resids</span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.update.resids" title="Permalink to this definition">¶</a></dt><dd><p>The residual array. Only uses <code class="docutils literal notranslate"><span class="pre">resids[:index]</span></code> when computing
+<dt id="arch.univariate.RiskMetrics2006.update.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.update.resids" title="Permalink to this definition">¶</a></dt><dd><p>The residual array. Only uses <code class="docutils literal notranslate"><span class="pre">resids[:index]</span></code> when computing
 <code class="docutils literal notranslate"><span class="pre">sigma2[index]</span></code></p>
 </dd>
-<dt id="arch.univariate.RiskMetrics2006.update.sigma2"><span class="sig-name descname">sigma2</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.update.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>The array containing the variances. Only uses <code class="docutils literal notranslate"><span class="pre">sigma2[:index]</span></code>
+<dt id="arch.univariate.RiskMetrics2006.update.sigma2"><span class="n sig-name">sigma2</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.update.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>The array containing the variances. Only uses <code class="docutils literal notranslate"><span class="pre">sigma2[:index]</span></code>
 when computing <code class="docutils literal notranslate"><span class="pre">sigma2[index]</span></code>. The computed value is stored
 in <code class="docutils literal notranslate"><span class="pre">sigma2[index]</span></code>.</p>
 </dd>
-<dt id="arch.univariate.RiskMetrics2006.update.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}</span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.update.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing the recursion</p>
+<dt id="arch.univariate.RiskMetrics2006.update.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.update.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing the recursion</p>
 </dd>
-<dt id="arch.univariate.RiskMetrics2006.update.var_bounds"><span class="sig-name descname">var_bounds</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.update.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
+<dt id="arch.univariate.RiskMetrics2006.update.var_bounds"><span class="n sig-name">var_bounds</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.update.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
 </dd>
 </dl>
 </dd>
@@ -1402,7 +1402,7 @@ <h1 id="arch-univariate-riskmetrics2006-update">arch.univariate.RiskMetrics2006.
 <dd class="field-even"><p>The variance computed for location <code class="docutils literal notranslate"><span class="pre">index</span></code></p>
 </dd>
 <dt class="field-odd" id="arch.univariate.RiskMetrics2006.update-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.update-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1474,7 +1474,7 @@ <h1 id="arch-univariate-riskmetrics2006-update">arch.univariate.RiskMetrics2006.
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1521,7 +1521,7 @@ <h1 id="arch-univariate-riskmetrics2006-update">arch.univariate.RiskMetrics2006.
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diff --git a/univariate/generated/generated/arch.univariate.RiskMetrics2006.updateable.html b/univariate/generated/generated/arch.univariate.RiskMetrics2006.updateable.html
index 37e8fd32a7..43f5d72198 100644
--- a/univariate/generated/generated/arch.univariate.RiskMetrics2006.updateable.html
+++ b/univariate/generated/generated/arch.univariate.RiskMetrics2006.updateable.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.RiskMetrics2006.updateable - arch 6.1.0</title>
+        <title>arch.univariate.RiskMetrics2006.updateable - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1223,10 +1223,10 @@
                   
 
 
-<h1 id="arch-univariate-riskmetrics2006-updateable">arch.univariate.RiskMetrics2006.updateable<a class="headerlink" href="#arch-univariate-riskmetrics2006-updateable" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-riskmetrics2006-updateable">arch.univariate.RiskMetrics2006.updateable<a class="headerlink" href="#arch-univariate-riskmetrics2006-updateable" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.RiskMetrics2006.updateable">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">RiskMetrics2006.</span></span><span class="sig-name descname"><span class="pre">updateable</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.updateable" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">RiskMetrics2006.</span></span><span class="sig-name descname"><span class="pre">updateable</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.updateable" title="Link to this definition">¶</a></dt>
 <dd><p>Flag indicating that the volatility process supports update</p>
 </dd></dl>
 
@@ -1297,7 +1297,7 @@ <h1 id="arch-univariate-riskmetrics2006-updateable">arch.univariate.RiskMetrics2
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1344,7 +1344,7 @@ <h1 id="arch-univariate-riskmetrics2006-updateable">arch.univariate.RiskMetrics2
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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diff --git a/univariate/generated/generated/arch.univariate.RiskMetrics2006.variance_bounds.html b/univariate/generated/generated/arch.univariate.RiskMetrics2006.variance_bounds.html
index 75abc4902d..1be6f6a1cf 100644
--- a/univariate/generated/generated/arch.univariate.RiskMetrics2006.variance_bounds.html
+++ b/univariate/generated/generated/arch.univariate.RiskMetrics2006.variance_bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.RiskMetrics2006.variance_bounds - arch 6.1.0</title>
+        <title>arch.univariate.RiskMetrics2006.variance_bounds - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1317,20 +1317,20 @@
                   
 
 
-<h1 id="arch-univariate-riskmetrics2006-variance-bounds">arch.univariate.RiskMetrics2006.variance_bounds<a class="headerlink" href="#arch-univariate-riskmetrics2006-variance-bounds" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-riskmetrics2006-variance-bounds">arch.univariate.RiskMetrics2006.variance_bounds<a class="headerlink" href="#arch-univariate-riskmetrics2006-variance-bounds" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.RiskMetrics2006.variance_bounds">
-<span class="sig-prename descclassname"><span class="pre">RiskMetrics2006.</span></span><span class="sig-name descname"><span class="pre">variance_bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.variance_bounds.resids" title="arch.univariate.RiskMetrics2006.variance_bounds.resids (Python parameter) — Approximate residuals to use to compute the lower and upper bounds on the conditional variance"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.variance_bounds.power" title="arch.univariate.RiskMetrics2006.variance_bounds.power (Python parameter) — Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."><span class="n"><span class="pre">power</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">2.0</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#RiskMetrics2006.variance_bounds"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.RiskMetrics2006.variance_bounds" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.RiskMetrics2006.variance_bounds">
+<span class="sig-prename descclassname"><span class="pre">RiskMetrics2006.</span></span><span class="sig-name descname"><span class="pre">variance_bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.variance_bounds.resids" title="arch.univariate.RiskMetrics2006.variance_bounds.resids (Python parameter) — Approximate residuals to use to compute the lower and upper bounds on the conditional variance"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.variance_bounds.power" title="arch.univariate.RiskMetrics2006.variance_bounds.power (Python parameter) — Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."><span class="n"><span class="pre">power</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">2.0</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#RiskMetrics2006.variance_bounds"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.RiskMetrics2006.variance_bounds" title="Link to this definition">¶</a></dt>
 <dd><p>Construct loose bounds for conditional variances.</p>
 <p>These bounds are used in parameter estimation to ensure
 that the log-likelihood does not produce NaN values.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.RiskMetrics2006.variance_bounds-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.variance_bounds-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.RiskMetrics2006.variance_bounds.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.variance_bounds.resids" title="Permalink to this definition">¶</a></dt><dd><p>Approximate residuals to use to compute the lower and upper bounds
+<dt id="arch.univariate.RiskMetrics2006.variance_bounds.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.variance_bounds.resids" title="Permalink to this definition">¶</a></dt><dd><p>Approximate residuals to use to compute the lower and upper bounds
 on the conditional variance</p>
 </dd>
-<dt id="arch.univariate.RiskMetrics2006.variance_bounds.power"><span class="sig-name descname">power</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>, optional</span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.variance_bounds.power" title="Permalink to this definition">¶</a></dt><dd><p>Power used in the model. 2.0, the default corresponds to standard
+<dt id="arch.univariate.RiskMetrics2006.variance_bounds.power"><span class="n sig-name">power</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">2.0</span></code><a class="headerlink" href="#arch.univariate.RiskMetrics2006.variance_bounds.power" title="Permalink to this definition">¶</a></dt><dd><p>Power used in the model. 2.0, the default corresponds to standard
 ARCH models that evolve in squares.</p>
 </dd>
 </dl>
@@ -1340,7 +1340,7 @@ <h1 id="arch-univariate-riskmetrics2006-variance-bounds">arch.univariate.RiskMet
 number of elements as resids</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.RiskMetrics2006.variance_bounds-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.variance_bounds-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1412,7 +1412,7 @@ <h1 id="arch-univariate-riskmetrics2006-variance-bounds">arch.univariate.RiskMet
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1459,7 +1459,7 @@ <h1 id="arch-univariate-riskmetrics2006-variance-bounds">arch.univariate.RiskMet
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.RiskMetrics2006.volatility_updater.html b/univariate/generated/generated/arch.univariate.RiskMetrics2006.volatility_updater.html
index 943c0d0def..b662c02677 100644
--- a/univariate/generated/generated/arch.univariate.RiskMetrics2006.volatility_updater.html
+++ b/univariate/generated/generated/arch.univariate.RiskMetrics2006.volatility_updater.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.RiskMetrics2006.volatility_updater - arch 6.1.0</title>
+        <title>arch.univariate.RiskMetrics2006.volatility_updater - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1277,17 +1277,17 @@
                   
 
 
-<h1 id="arch-univariate-riskmetrics2006-volatility-updater">arch.univariate.RiskMetrics2006.volatility_updater<a class="headerlink" href="#arch-univariate-riskmetrics2006-volatility-updater" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-riskmetrics2006-volatility-updater">arch.univariate.RiskMetrics2006.volatility_updater<a class="headerlink" href="#arch-univariate-riskmetrics2006-volatility-updater" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.RiskMetrics2006.volatility_updater">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">RiskMetrics2006.</span></span><span class="sig-name descname"><span class="pre">volatility_updater</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.univariate.recursions_python.VolatilityUpdater.html#arch.univariate.recursions_python.VolatilityUpdater" title="arch.univariate.recursions_python.VolatilityUpdater (Python class) — Base class that all volatility updaters must inherit from."><span class="pre">VolatilityUpdater</span></a></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.volatility_updater" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">RiskMetrics2006.</span></span><span class="sig-name descname"><span class="pre">volatility_updater</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><span class="pre">VolatilityUpdater</span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.volatility_updater" title="Link to this definition">¶</a></dt>
 <dd><p>Get the volatility updater associated with the volatility process</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.RiskMetrics2006.volatility_updater-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.volatility_updater-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The updater class</p>
 </dd>
 <dt class="field-even" id="arch.univariate.RiskMetrics2006.volatility_updater-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.volatility_updater-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference internal" href="../arch.univariate.recursions_python.VolatilityUpdater.html#arch.univariate.recursions_python.VolatilityUpdater" title="arch.univariate.recursions_python.VolatilityUpdater (Python class) — Base class that all volatility updaters must inherit from.">VolatilityUpdater</a></p>
+<dd class="field-even"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">VolatilityUpdater</span></code></p>
 </dd>
 <dt class="field-odd" id="arch.univariate.RiskMetrics2006.volatility_updater-raises">Raises<span class="colon">:</span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.volatility_updater-raises" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#NotImplementedError" title="(in Python v3.11)"><strong>NotImplementedError</strong></a> – If the process is not updateable</p>
@@ -1362,7 +1362,7 @@ <h1 id="arch-univariate-riskmetrics2006-volatility-updater">arch.univariate.Risk
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1409,7 +1409,7 @@ <h1 id="arch-univariate-riskmetrics2006-volatility-updater">arch.univariate.Risk
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.SkewStudent.bounds.html b/univariate/generated/generated/arch.univariate.SkewStudent.bounds.html
index 4fb703536a..8142695923 100644
--- a/univariate/generated/generated/arch.univariate.SkewStudent.bounds.html
+++ b/univariate/generated/generated/arch.univariate.SkewStudent.bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.SkewStudent.bounds - arch 6.1.0</title>
+        <title>arch.univariate.SkewStudent.bounds - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
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-<h1 id="arch-univariate-skewstudent-bounds">arch.univariate.SkewStudent.bounds<a class="headerlink" href="#arch-univariate-skewstudent-bounds" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-skewstudent-bounds">arch.univariate.SkewStudent.bounds<a class="headerlink" href="#arch-univariate-skewstudent-bounds" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.SkewStudent.bounds">
-<span class="sig-prename descclassname"><span class="pre">SkewStudent.</span></span><span class="sig-name descname"><span class="pre">bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.bounds.resids" title="arch.univariate.SkewStudent.bounds.resids (Python parameter) — Residuals to use when computing the bounds"><span class="n"><span class="pre">resids</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#SkewStudent.bounds"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.SkewStudent.bounds" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">SkewStudent.</span></span><span class="sig-name descname"><span class="pre">bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.bounds.resids" title="arch.univariate.SkewStudent.bounds.resids (Python parameter) — Residuals to use when computing the bounds"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#SkewStudent.bounds"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.SkewStudent.bounds" title="Link to this definition">¶</a></dt>
 <dd><p>Parameter bounds for use in optimization.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.SkewStudent.bounds-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.SkewStudent.bounds-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.SkewStudent.bounds.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.SkewStudent.bounds.resids" title="Permalink to this definition">¶</a></dt><dd><p>Residuals to use when computing the bounds</p>
+<dt id="arch.univariate.SkewStudent.bounds.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.SkewStudent.bounds.resids" title="Permalink to this definition">¶</a></dt><dd><p>Residuals to use when computing the bounds</p>
 </dd>
 </dl>
 </dd>
@@ -1114,7 +1114,7 @@ <h1 id="arch-univariate-skewstudent-bounds">arch.univariate.SkewStudent.bounds<a
 <dd class="field-even"><p><strong>bounds</strong> – List containing a single tuple with (lower, upper) bounds</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.SkewStudent.bounds-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.SkewStudent.bounds-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1186,7 +1186,7 @@ <h1 id="arch-univariate-skewstudent-bounds">arch.univariate.SkewStudent.bounds<a
   
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diff --git a/univariate/generated/generated/arch.univariate.SkewStudent.cdf.html b/univariate/generated/generated/arch.univariate.SkewStudent.cdf.html
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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@@ -1112,17 +1112,17 @@
                   
 
 
-<h1 id="arch-univariate-skewstudent-cdf">arch.univariate.SkewStudent.cdf<a class="headerlink" href="#arch-univariate-skewstudent-cdf" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-skewstudent-cdf">arch.univariate.SkewStudent.cdf<a class="headerlink" href="#arch-univariate-skewstudent-cdf" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.SkewStudent.cdf">
-<span class="sig-prename descclassname"><span class="pre">SkewStudent.</span></span><span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.cdf.resids" title="arch.univariate.SkewStudent.cdf.resids (Python parameter) — Values at which to evaluate the cdf"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.cdf.parameters" title="arch.univariate.SkewStudent.cdf.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#SkewStudent.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.SkewStudent.cdf" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.SkewStudent.cdf">
+<span class="sig-prename descclassname"><span class="pre">SkewStudent.</span></span><span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.cdf.resids" title="arch.univariate.SkewStudent.cdf.resids (Python parameter) — Values at which to evaluate the cdf"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.cdf.parameters" title="arch.univariate.SkewStudent.cdf.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#SkewStudent.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.SkewStudent.cdf" title="Link to this definition">¶</a></dt>
 <dd><p>Cumulative distribution function</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.SkewStudent.cdf-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.SkewStudent.cdf-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.SkewStudent.cdf.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.SkewStudent.cdf.resids" title="Permalink to this definition">¶</a></dt><dd><p>Values at which to evaluate the cdf</p>
+<dt id="arch.univariate.SkewStudent.cdf.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.SkewStudent.cdf.resids" title="Permalink to this definition">¶</a></dt><dd><p>Values at which to evaluate the cdf</p>
 </dd>
-<dt id="arch.univariate.SkewStudent.cdf.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.SkewStudent.cdf.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use <code class="docutils literal notranslate"><span class="pre">None</span></code> for parameterless
+<dt id="arch.univariate.SkewStudent.cdf.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.SkewStudent.cdf.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use <code class="docutils literal notranslate"><span class="pre">None</span></code> for parameterless
 distributions.</p>
 </dd>
 </dl>
@@ -1131,7 +1131,7 @@ <h1 id="arch-univariate-skewstudent-cdf">arch.univariate.SkewStudent.cdf<a class
 <dd class="field-even"><p><strong>f</strong> – CDF values</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.SkewStudent.cdf-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.SkewStudent.cdf-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
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diff --git a/univariate/generated/generated/arch.univariate.SkewStudent.constraints.html b/univariate/generated/generated/arch.univariate.SkewStudent.constraints.html
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--- a/univariate/generated/generated/arch.univariate.SkewStudent.constraints.html
+++ b/univariate/generated/generated/arch.univariate.SkewStudent.constraints.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.SkewStudent.constraints - arch 6.1.0</title>
+        <title>arch.univariate.SkewStudent.constraints - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+    arch 6.2.0
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@@ -596,13 +596,6 @@
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-          <li class="md-nav__item">
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-    <span class="md-ellipsis">Return type</span>
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-  
-</li>
-        
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@@ -1025,13 +1018,6 @@
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       <ul class="md-nav__list">
         
-          <li class="md-nav__item">
-  <a href="#arch.univariate.SkewStudent.constraints-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
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-  
-</li>
-        
           <li class="md-nav__item">
   <a href="#arch.univariate.SkewStudent.constraints-returns" class="md-nav__link">
     <span class="md-ellipsis">Returns</span>
@@ -1058,19 +1044,16 @@
                   
 
 
-<h1 id="arch-univariate-skewstudent-constraints">arch.univariate.SkewStudent.constraints<a class="headerlink" href="#arch-univariate-skewstudent-constraints" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-skewstudent-constraints">arch.univariate.SkewStudent.constraints<a class="headerlink" href="#arch-univariate-skewstudent-constraints" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.SkewStudent.constraints">
-<span class="sig-prename descclassname"><span class="pre">SkewStudent.</span></span><span class="sig-name descname"><span class="pre">constraints</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#SkewStudent.constraints"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.SkewStudent.constraints" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">SkewStudent.</span></span><span class="sig-name descname"><span class="pre">constraints</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#SkewStudent.constraints"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.SkewStudent.constraints" title="Link to this definition">¶</a></dt>
 <dd><p>Construct arrays to use in constrained optimization.</p>
 <dl class="field-list simple">
-<dt class="field-odd" id="arch.univariate.SkewStudent.constraints-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.SkewStudent.constraints-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>]</p>
-</dd>
-<dt class="field-even" id="arch.univariate.SkewStudent.constraints-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.SkewStudent.constraints-returns" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><ul class="simple">
-<li><p><strong>A</strong> (<em>ndarray</em>) – Constraint loadings</p></li>
-<li><p><strong>b</strong> (<em>ndarray</em>) – Constraint values</p></li>
+<dt class="field-odd" id="arch.univariate.SkewStudent.constraints-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.SkewStudent.constraints-returns" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p><strong>A</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – Constraint loadings</p></li>
+<li><p><strong>b</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – Constraint values</p></li>
 </ul>
 </p>
 </dd>
@@ -1146,7 +1129,7 @@ <h1 id="arch-univariate-skewstudent-constraints">arch.univariate.SkewStudent.con
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1193,7 +1176,7 @@ <h1 id="arch-univariate-skewstudent-constraints">arch.univariate.SkewStudent.con
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diff --git a/univariate/generated/generated/arch.univariate.SkewStudent.generator.html b/univariate/generated/generated/arch.univariate.SkewStudent.generator.html
index 83fab52e14..c32e5cca4e 100644
--- a/univariate/generated/generated/arch.univariate.SkewStudent.generator.html
+++ b/univariate/generated/generated/arch.univariate.SkewStudent.generator.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.SkewStudent.generator - arch 6.1.0</title>
+        <title>arch.univariate.SkewStudent.generator - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1018,10 +1018,10 @@
                   
 
 
-<h1 id="arch-univariate-skewstudent-generator">arch.univariate.SkewStudent.generator<a class="headerlink" href="#arch-univariate-skewstudent-generator" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-skewstudent-generator">arch.univariate.SkewStudent.generator<a class="headerlink" href="#arch-univariate-skewstudent-generator" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.SkewStudent.generator">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">SkewStudent.</span></span><span class="sig-name descname"><span class="pre">generator</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.24)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.24)"><span class="pre">Generator</span></a></span><a class="headerlink" href="#arch.univariate.SkewStudent.generator" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">SkewStudent.</span></span><span class="sig-name descname"><span class="pre">generator</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><span class="pre">Generator</span></a></span><a class="headerlink" href="#arch.univariate.SkewStudent.generator" title="Link to this definition">¶</a></dt>
 <dd><p>The NumPy Generator or RandomState attached to the distribution</p>
 </dd></dl>
 
@@ -1092,7 +1092,7 @@ <h1 id="arch-univariate-skewstudent-generator">arch.univariate.SkewStudent.gener
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1139,7 +1139,7 @@ <h1 id="arch-univariate-skewstudent-generator">arch.univariate.SkewStudent.gener
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diff --git a/univariate/generated/generated/arch.univariate.SkewStudent.loglikelihood.html b/univariate/generated/generated/arch.univariate.SkewStudent.loglikelihood.html
index 7e028fb011..31afe46626 100644
--- a/univariate/generated/generated/arch.univariate.SkewStudent.loglikelihood.html
+++ b/univariate/generated/generated/arch.univariate.SkewStudent.loglikelihood.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.SkewStudent.loglikelihood - arch 6.1.0</title>
+        <title>arch.univariate.SkewStudent.loglikelihood - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
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-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1140,23 +1140,23 @@
                   
 
 
-<h1 id="arch-univariate-skewstudent-loglikelihood">arch.univariate.SkewStudent.loglikelihood<a class="headerlink" href="#arch-univariate-skewstudent-loglikelihood" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-skewstudent-loglikelihood">arch.univariate.SkewStudent.loglikelihood<a class="headerlink" href="#arch-univariate-skewstudent-loglikelihood" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.SkewStudent.loglikelihood">
-<span class="sig-prename descclassname"><span class="pre">SkewStudent.</span></span><span class="sig-name descname"><span class="pre">loglikelihood</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.loglikelihood.parameters" title="arch.univariate.SkewStudent.loglikelihood.parameters (Python parameter) — Shape parameter of the skew-t distribution"><span class="n"><span class="pre">parameters</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.loglikelihood.resids" title="arch.univariate.SkewStudent.loglikelihood.resids (Python parameter) — The residuals to use in the log-likelihood calculation"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.loglikelihood.sigma2" title="arch.univariate.SkewStudent.loglikelihood.sigma2 (Python parameter) — Conditional variances of resids"><span class="n"><span class="pre">sigma2</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.loglikelihood.individual" title="arch.univariate.SkewStudent.loglikelihood.individual (Python parameter) — Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"><span class="n"><span class="pre">individual</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#SkewStudent.loglikelihood"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.SkewStudent.loglikelihood" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">SkewStudent.</span></span><span class="sig-name descname"><span class="pre">loglikelihood</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.loglikelihood.parameters" title="arch.univariate.SkewStudent.loglikelihood.parameters (Python parameter) — Shape parameter of the skew-t distribution"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.loglikelihood.resids" title="arch.univariate.SkewStudent.loglikelihood.resids (Python parameter) — The residuals to use in the log-likelihood calculation"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.loglikelihood.sigma2" title="arch.univariate.SkewStudent.loglikelihood.sigma2 (Python parameter) — Conditional variances of resids"><span class="n"><span class="pre">sigma2</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.loglikelihood.individual" title="arch.univariate.SkewStudent.loglikelihood.individual (Python parameter) — Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"><span class="n"><span class="pre">individual</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#SkewStudent.loglikelihood"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.SkewStudent.loglikelihood" title="Link to this definition">¶</a></dt>
 <dd><p>Computes the log-likelihood of assuming residuals are have a
 standardized (to have unit variance) Skew Student’s t distribution,
 conditional on the variance.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.SkewStudent.loglikelihood-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.SkewStudent.loglikelihood-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.SkewStudent.loglikelihood.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.SkewStudent.loglikelihood.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Shape parameter of the skew-t distribution</p>
+<dt id="arch.univariate.SkewStudent.loglikelihood.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.SkewStudent.loglikelihood.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Shape parameter of the skew-t distribution</p>
 </dd>
-<dt id="arch.univariate.SkewStudent.loglikelihood.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.SkewStudent.loglikelihood.resids" title="Permalink to this definition">¶</a></dt><dd><p>The residuals to use in the log-likelihood calculation</p>
+<dt id="arch.univariate.SkewStudent.loglikelihood.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.SkewStudent.loglikelihood.resids" title="Permalink to this definition">¶</a></dt><dd><p>The residuals to use in the log-likelihood calculation</p>
 </dd>
-<dt id="arch.univariate.SkewStudent.loglikelihood.sigma2"><span class="sig-name descname">sigma2</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.SkewStudent.loglikelihood.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>Conditional variances of resids</p>
+<dt id="arch.univariate.SkewStudent.loglikelihood.sigma2"><span class="n sig-name">sigma2</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.SkewStudent.loglikelihood.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>Conditional variances of resids</p>
 </dd>
-<dt id="arch.univariate.SkewStudent.loglikelihood.individual"><span class="sig-name descname">individual</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, optional</span></span><a class="headerlink" href="#arch.univariate.SkewStudent.loglikelihood.individual" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to return the vector of individual log
+<dt id="arch.univariate.SkewStudent.loglikelihood.individual"><span class="n sig-name">individual</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.univariate.SkewStudent.loglikelihood.individual" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to return the vector of individual log
 likelihoods (True) or the sum (False)</p>
 </dd>
 </dl>
@@ -1165,7 +1165,7 @@ <h1 id="arch-univariate-skewstudent-loglikelihood">arch.univariate.SkewStudent.l
 <dd class="field-even"><p><strong>ll</strong> – The log-likelihood</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.SkewStudent.loglikelihood-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.SkewStudent.loglikelihood-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -1253,7 +1253,7 @@ <h1 id="arch-univariate-skewstudent-loglikelihood">arch.univariate.SkewStudent.l
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1300,10 +1300,10 @@ <h1 id="arch-univariate-skewstudent-loglikelihood">arch.univariate.SkewStudent.l
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diff --git a/univariate/generated/generated/arch.univariate.SkewStudent.moment.html b/univariate/generated/generated/arch.univariate.SkewStudent.moment.html
index 5d2f929d27..e515d23ac3 100644
--- a/univariate/generated/generated/arch.univariate.SkewStudent.moment.html
+++ b/univariate/generated/generated/arch.univariate.SkewStudent.moment.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.SkewStudent.moment - arch 6.1.0</title>
+        <title>arch.univariate.SkewStudent.moment - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1112,17 +1112,17 @@
                   
 
 
-<h1 id="arch-univariate-skewstudent-moment">arch.univariate.SkewStudent.moment<a class="headerlink" href="#arch-univariate-skewstudent-moment" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-skewstudent-moment">arch.univariate.SkewStudent.moment<a class="headerlink" href="#arch-univariate-skewstudent-moment" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.SkewStudent.moment">
-<span class="sig-prename descclassname"><span class="pre">SkewStudent.</span></span><span class="sig-name descname"><span class="pre">moment</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.moment.n" title="arch.univariate.SkewStudent.moment.n (Python parameter) — Order of moment"><span class="n"><span class="pre">n</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.moment.parameters" title="arch.univariate.SkewStudent.moment.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#SkewStudent.moment"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.SkewStudent.moment" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.SkewStudent.moment">
+<span class="sig-prename descclassname"><span class="pre">SkewStudent.</span></span><span class="sig-name descname"><span class="pre">moment</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.moment.n" title="arch.univariate.SkewStudent.moment.n (Python parameter) — Order of moment"><span class="n"><span class="pre">n</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.moment.parameters" title="arch.univariate.SkewStudent.moment.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#SkewStudent.moment"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.SkewStudent.moment" title="Link to this definition">¶</a></dt>
 <dd><p>Moment of order n</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.SkewStudent.moment-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.SkewStudent.moment-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.SkewStudent.moment.n"><span class="sig-name descname">n</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.SkewStudent.moment.n" title="Permalink to this definition">¶</a></dt><dd><p>Order of moment</p>
+<dt id="arch.univariate.SkewStudent.moment.n"><span class="n sig-name">n</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.SkewStudent.moment.n" title="Permalink to this definition">¶</a></dt><dd><p>Order of moment</p>
 </dd>
-<dt id="arch.univariate.SkewStudent.moment.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray, optional</span></span><a class="headerlink" href="#arch.univariate.SkewStudent.moment.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use None for parameterless distributions.</p>
+<dt id="arch.univariate.SkewStudent.moment.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.SkewStudent.moment.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use None for parameterless distributions.</p>
 </dd>
 </dl>
 </dd>
@@ -1130,7 +1130,7 @@ <h1 id="arch-univariate-skewstudent-moment">arch.univariate.SkewStudent.moment<a
 <dd class="field-even"><p>Calculated moment</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.SkewStudent.moment-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.SkewStudent.moment-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1202,7 +1202,7 @@ <h1 id="arch-univariate-skewstudent-moment">arch.univariate.SkewStudent.moment<a
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1249,7 +1249,7 @@ <h1 id="arch-univariate-skewstudent-moment">arch.univariate.SkewStudent.moment<a
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diff --git a/univariate/generated/generated/arch.univariate.SkewStudent.name.html b/univariate/generated/generated/arch.univariate.SkewStudent.name.html
index f6fc12fa3b..41ec067123 100644
--- a/univariate/generated/generated/arch.univariate.SkewStudent.name.html
+++ b/univariate/generated/generated/arch.univariate.SkewStudent.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.SkewStudent.name - arch 6.1.0</title>
+        <title>arch.univariate.SkewStudent.name - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1018,10 +1018,10 @@
                   
 
 
-<h1 id="arch-univariate-skewstudent-name">arch.univariate.SkewStudent.name<a class="headerlink" href="#arch-univariate-skewstudent-name" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-skewstudent-name">arch.univariate.SkewStudent.name<a class="headerlink" href="#arch-univariate-skewstudent-name" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.SkewStudent.name">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">SkewStudent.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.univariate.SkewStudent.name" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">SkewStudent.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.univariate.SkewStudent.name" title="Link to this definition">¶</a></dt>
 <dd><p>The name of the distribution</p>
 </dd></dl>
 
@@ -1092,7 +1092,7 @@ <h1 id="arch-univariate-skewstudent-name">arch.univariate.SkewStudent.name<a cla
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1139,7 +1139,7 @@ <h1 id="arch-univariate-skewstudent-name">arch.univariate.SkewStudent.name<a cla
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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diff --git a/univariate/generated/generated/arch.univariate.SkewStudent.parameter_names.html b/univariate/generated/generated/arch.univariate.SkewStudent.parameter_names.html
index 11d94bf370..836b5b487e 100644
--- a/univariate/generated/generated/arch.univariate.SkewStudent.parameter_names.html
+++ b/univariate/generated/generated/arch.univariate.SkewStudent.parameter_names.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.SkewStudent.parameter_names - arch 6.1.0</title>
+        <title>arch.univariate.SkewStudent.parameter_names - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1058,17 +1058,17 @@
                   
 
 
-<h1 id="arch-univariate-skewstudent-parameter-names">arch.univariate.SkewStudent.parameter_names<a class="headerlink" href="#arch-univariate-skewstudent-parameter-names" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-skewstudent-parameter-names">arch.univariate.SkewStudent.parameter_names<a class="headerlink" href="#arch-univariate-skewstudent-parameter-names" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.SkewStudent.parameter_names">
-<span class="sig-prename descclassname"><span class="pre">SkewStudent.</span></span><span class="sig-name descname"><span class="pre">parameter_names</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#SkewStudent.parameter_names"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.SkewStudent.parameter_names" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">SkewStudent.</span></span><span class="sig-name descname"><span class="pre">parameter_names</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#SkewStudent.parameter_names"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.SkewStudent.parameter_names" title="Link to this definition">¶</a></dt>
 <dd><p>Names of distribution shape parameters</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.SkewStudent.parameter_names-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.SkewStudent.parameter_names-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p><strong>names</strong> – Parameter names</p>
 </dd>
 <dt class="field-even" id="arch.univariate.SkewStudent.parameter_names-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.SkewStudent.parameter_names-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>)</p>
+<dd class="field-even"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span> <span class="pre">(str)</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1140,7 +1140,7 @@ <h1 id="arch-univariate-skewstudent-parameter-names">arch.univariate.SkewStudent
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1187,7 +1187,7 @@ <h1 id="arch-univariate-skewstudent-parameter-names">arch.univariate.SkewStudent
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diff --git a/univariate/generated/generated/arch.univariate.SkewStudent.partial_moment.html b/univariate/generated/generated/arch.univariate.SkewStudent.partial_moment.html
index 14079f4ac9..e1d30ee98f 100644
--- a/univariate/generated/generated/arch.univariate.SkewStudent.partial_moment.html
+++ b/univariate/generated/generated/arch.univariate.SkewStudent.partial_moment.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.SkewStudent.partial_moment - arch 6.1.0</title>
+        <title>arch.univariate.SkewStudent.partial_moment - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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@@ -1126,19 +1126,19 @@
                   
 
 
-<h1 id="arch-univariate-skewstudent-partial-moment">arch.univariate.SkewStudent.partial_moment<a class="headerlink" href="#arch-univariate-skewstudent-partial-moment" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-skewstudent-partial-moment">arch.univariate.SkewStudent.partial_moment<a class="headerlink" href="#arch-univariate-skewstudent-partial-moment" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.SkewStudent.partial_moment">
-<span class="sig-prename descclassname"><span class="pre">SkewStudent.</span></span><span class="sig-name descname"><span class="pre">partial_moment</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.partial_moment.n" title="arch.univariate.SkewStudent.partial_moment.n (Python parameter) — Order of partial moment"><span class="n"><span class="pre">n</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.partial_moment.z" title="arch.univariate.SkewStudent.partial_moment.z (Python parameter) — Upper bound for partial moment integral"><span class="n"><span class="pre">z</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.partial_moment.parameters" title="arch.univariate.SkewStudent.partial_moment.parameters (Python parameter) — Distribution parameters.  Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#SkewStudent.partial_moment"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.SkewStudent.partial_moment" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.SkewStudent.partial_moment">
+<span class="sig-prename descclassname"><span class="pre">SkewStudent.</span></span><span class="sig-name descname"><span class="pre">partial_moment</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.partial_moment.n" title="arch.univariate.SkewStudent.partial_moment.n (Python parameter) — Order of partial moment"><span class="n"><span class="pre">n</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.partial_moment.z" title="arch.univariate.SkewStudent.partial_moment.z (Python parameter) — Upper bound for partial moment integral"><span class="n"><span class="pre">z</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.partial_moment.parameters" title="arch.univariate.SkewStudent.partial_moment.parameters (Python parameter) — Distribution parameters.  Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#SkewStudent.partial_moment"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.SkewStudent.partial_moment" title="Link to this definition">¶</a></dt>
 <dd><p>Order n lower partial moment from -inf to z</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.SkewStudent.partial_moment-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.SkewStudent.partial_moment-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.SkewStudent.partial_moment.n"><span class="sig-name descname">n</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.SkewStudent.partial_moment.n" title="Permalink to this definition">¶</a></dt><dd><p>Order of partial moment</p>
+<dt id="arch.univariate.SkewStudent.partial_moment.n"><span class="n sig-name">n</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.SkewStudent.partial_moment.n" title="Permalink to this definition">¶</a></dt><dd><p>Order of partial moment</p>
 </dd>
-<dt id="arch.univariate.SkewStudent.partial_moment.z"><span class="sig-name descname">z</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>, optional</span></span><a class="headerlink" href="#arch.univariate.SkewStudent.partial_moment.z" title="Permalink to this definition">¶</a></dt><dd><p>Upper bound for partial moment integral</p>
+<dt id="arch.univariate.SkewStudent.partial_moment.z"><span class="n sig-name">z</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.0</span></code><a class="headerlink" href="#arch.univariate.SkewStudent.partial_moment.z" title="Permalink to this definition">¶</a></dt><dd><p>Upper bound for partial moment integral</p>
 </dd>
-<dt id="arch.univariate.SkewStudent.partial_moment.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray, optional</span></span><a class="headerlink" href="#arch.univariate.SkewStudent.partial_moment.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters.  Use None for parameterless distributions.</p>
+<dt id="arch.univariate.SkewStudent.partial_moment.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.SkewStudent.partial_moment.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters.  Use None for parameterless distributions.</p>
 </dd>
 </dl>
 </dd>
@@ -1146,7 +1146,7 @@ <h1 id="arch-univariate-skewstudent-partial-moment">arch.univariate.SkewStudent.
 <dd class="field-even"><p>Partial moment</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.SkewStudent.partial_moment-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.SkewStudent.partial_moment-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 <p class="rubric">References</p>
@@ -1231,7 +1231,7 @@ <h1 id="arch-univariate-skewstudent-partial-moment">arch.univariate.SkewStudent.
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1278,10 +1278,10 @@ <h1 id="arch-univariate-skewstudent-partial-moment">arch.univariate.SkewStudent.
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+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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+        <script id="MathJax-script" src="../../../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
     
   </body>
 </html>
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diff --git a/univariate/generated/generated/arch.univariate.SkewStudent.ppf.html b/univariate/generated/generated/arch.univariate.SkewStudent.ppf.html
index f26667eec7..1f2e7b536c 100644
--- a/univariate/generated/generated/arch.univariate.SkewStudent.ppf.html
+++ b/univariate/generated/generated/arch.univariate.SkewStudent.ppf.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.SkewStudent.ppf - arch 6.1.0</title>
+        <title>arch.univariate.SkewStudent.ppf - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
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-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1112,17 +1112,17 @@
                   
 
 
-<h1 id="arch-univariate-skewstudent-ppf">arch.univariate.SkewStudent.ppf<a class="headerlink" href="#arch-univariate-skewstudent-ppf" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-skewstudent-ppf">arch.univariate.SkewStudent.ppf<a class="headerlink" href="#arch-univariate-skewstudent-ppf" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.SkewStudent.ppf">
-<span class="sig-prename descclassname"><span class="pre">SkewStudent.</span></span><span class="sig-name descname"><span class="pre">ppf</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.ppf.pits" title="arch.univariate.SkewStudent.ppf.pits (Python parameter) — Probability-integral-transformed values in the interval (0, 1)."><span class="n"><span class="pre">pits</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.ppf.parameters" title="arch.univariate.SkewStudent.ppf.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#SkewStudent.ppf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.SkewStudent.ppf" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.SkewStudent.ppf">
+<span class="sig-prename descclassname"><span class="pre">SkewStudent.</span></span><span class="sig-name descname"><span class="pre">ppf</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.ppf.pits" title="arch.univariate.SkewStudent.ppf.pits (Python parameter) — Probability-integral-transformed values in the interval (0, 1)."><span class="n"><span class="pre">pits</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.ppf.parameters" title="arch.univariate.SkewStudent.ppf.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#SkewStudent.ppf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.SkewStudent.ppf" title="Link to this definition">¶</a></dt>
 <dd><p>Inverse cumulative density function (ICDF)</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.SkewStudent.ppf-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.SkewStudent.ppf-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.SkewStudent.ppf.pits"><span class="sig-name descname">pits</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}</span></span><a class="headerlink" href="#arch.univariate.SkewStudent.ppf.pits" title="Permalink to this definition">¶</a></dt><dd><p>Probability-integral-transformed values in the interval (0, 1).</p>
+<dt id="arch.univariate.SkewStudent.ppf.pits"><span class="n sig-name">pits</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.SkewStudent.ppf.pits" title="Permalink to this definition">¶</a></dt><dd><p>Probability-integral-transformed values in the interval (0, 1).</p>
 </dd>
-<dt id="arch.univariate.SkewStudent.ppf.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray, optional</span></span><a class="headerlink" href="#arch.univariate.SkewStudent.ppf.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use <code class="docutils literal notranslate"><span class="pre">None</span></code> for parameterless
+<dt id="arch.univariate.SkewStudent.ppf.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.SkewStudent.ppf.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use <code class="docutils literal notranslate"><span class="pre">None</span></code> for parameterless
 distributions.</p>
 </dd>
 </dl>
@@ -1131,7 +1131,7 @@ <h1 id="arch-univariate-skewstudent-ppf">arch.univariate.SkewStudent.ppf<a class
 <dd class="field-even"><p><strong>i</strong> – Inverse CDF values</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.SkewStudent.ppf-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.SkewStudent.ppf-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>{float, ndarray}</p>
+<dd class="field-odd"><p><code class="docutils literal notranslate"><span class="pre">{float,</span> <span class="pre">ndarray}</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1203,7 +1203,7 @@ <h1 id="arch-univariate-skewstudent-ppf">arch.univariate.SkewStudent.ppf<a class
   
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-    7.0.1.
+    7.2.6.
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diff --git a/univariate/generated/generated/arch.univariate.SkewStudent.random_state.html b/univariate/generated/generated/arch.univariate.SkewStudent.random_state.html
index ab39c24bc9..7b413f92c0 100644
--- a/univariate/generated/generated/arch.univariate.SkewStudent.random_state.html
+++ b/univariate/generated/generated/arch.univariate.SkewStudent.random_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.SkewStudent.random_state - arch 6.1.0</title>
+        <title>arch.univariate.SkewStudent.random_state - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1018,13 +1018,13 @@
                   
 
 
-<h1 id="arch-univariate-skewstudent-random-state">arch.univariate.SkewStudent.random_state<a class="headerlink" href="#arch-univariate-skewstudent-random-state" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-skewstudent-random-state">arch.univariate.SkewStudent.random_state<a class="headerlink" href="#arch-univariate-skewstudent-random-state" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.SkewStudent.random_state">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">SkewStudent.</span></span><span class="sig-name descname"><span class="pre">random_state</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.24)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.24)"><span class="pre">Generator</span></a></span><a class="headerlink" href="#arch.univariate.SkewStudent.random_state" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">SkewStudent.</span></span><span class="sig-name descname"><span class="pre">random_state</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><span class="pre">Generator</span></a></span><a class="headerlink" href="#arch.univariate.SkewStudent.random_state" title="Link to this definition">¶</a></dt>
 <dd><p>The NumPy RandomState attached to the distribution</p>
-<div class="deprecated">
-<p><span class="versionmodified deprecated">Deprecated since version 5.0: </span>random_state is deprecated. Use generator instead.</p>
+<div class="admonition deprecated">
+<p class="admonition-title"><span class="versionmodified deprecated">Deprecated since version 5.0: </span>random_state is deprecated. Use generator instead.</p>
 </div>
 </dd></dl>
 
@@ -1095,7 +1095,7 @@ <h1 id="arch-univariate-skewstudent-random-state">arch.univariate.SkewStudent.ra
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1142,7 +1142,7 @@ <h1 id="arch-univariate-skewstudent-random-state">arch.univariate.SkewStudent.ra
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diff --git a/univariate/generated/generated/arch.univariate.SkewStudent.simulate.html b/univariate/generated/generated/arch.univariate.SkewStudent.simulate.html
index 9b127d17df..2b68873aec 100644
--- a/univariate/generated/generated/arch.univariate.SkewStudent.simulate.html
+++ b/univariate/generated/generated/arch.univariate.SkewStudent.simulate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.SkewStudent.simulate - arch 6.1.0</title>
+        <title>arch.univariate.SkewStudent.simulate - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1098,15 +1098,15 @@
                   
 
 
-<h1 id="arch-univariate-skewstudent-simulate">arch.univariate.SkewStudent.simulate<a class="headerlink" href="#arch-univariate-skewstudent-simulate" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-skewstudent-simulate">arch.univariate.SkewStudent.simulate<a class="headerlink" href="#arch-univariate-skewstudent-simulate" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.SkewStudent.simulate">
-<span class="sig-prename descclassname"><span class="pre">SkewStudent.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.simulate.parameters" title="arch.univariate.SkewStudent.simulate.parameters (Python parameter) — Distribution parameters"><span class="n"><span class="pre">parameters</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#SkewStudent.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.SkewStudent.simulate" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.SkewStudent.simulate">
+<span class="sig-prename descclassname"><span class="pre">SkewStudent.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.simulate.parameters" title="arch.univariate.SkewStudent.simulate.parameters (Python parameter) — Distribution parameters"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#SkewStudent.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.SkewStudent.simulate" title="Link to this definition">¶</a></dt>
 <dd><p>Simulates i.i.d. draws from the distribution</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.SkewStudent.simulate-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.SkewStudent.simulate-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.SkewStudent.simulate.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.SkewStudent.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters</p>
+<dt id="arch.univariate.SkewStudent.simulate.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.SkewStudent.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters</p>
 </dd>
 </dl>
 </dd>
@@ -1115,7 +1115,7 @@ <h1 id="arch-univariate-skewstudent-simulate">arch.univariate.SkewStudent.simula
 draws from the distribution</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.SkewStudent.simulate-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.SkewStudent.simulate-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>callable</p>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">callable</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1187,7 +1187,7 @@ <h1 id="arch-univariate-skewstudent-simulate">arch.univariate.SkewStudent.simula
   
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     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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diff --git a/univariate/generated/generated/arch.univariate.SkewStudent.starting_values.html b/univariate/generated/generated/arch.univariate.SkewStudent.starting_values.html
index 7448d04a02..2729c810cd 100644
--- a/univariate/generated/generated/arch.univariate.SkewStudent.starting_values.html
+++ b/univariate/generated/generated/arch.univariate.SkewStudent.starting_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.SkewStudent.starting_values - arch 6.1.0</title>
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@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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@@ -1098,15 +1098,15 @@
                   
 
 
-<h1 id="arch-univariate-skewstudent-starting-values">arch.univariate.SkewStudent.starting_values<a class="headerlink" href="#arch-univariate-skewstudent-starting-values" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-skewstudent-starting-values">arch.univariate.SkewStudent.starting_values<a class="headerlink" href="#arch-univariate-skewstudent-starting-values" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.SkewStudent.starting_values">
-<span class="sig-prename descclassname"><span class="pre">SkewStudent.</span></span><span class="sig-name descname"><span class="pre">starting_values</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.starting_values.std_resid" title="arch.univariate.SkewStudent.starting_values.std_resid (Python parameter) — Estimated standardized residuals to use in computing starting values for the shape parameter"><span class="n"><span class="pre">std_resid</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#SkewStudent.starting_values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.SkewStudent.starting_values" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">SkewStudent.</span></span><span class="sig-name descname"><span class="pre">starting_values</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.starting_values.std_resid" title="arch.univariate.SkewStudent.starting_values.std_resid (Python parameter) — Estimated standardized residuals to use in computing starting values for the shape parameter"><span class="n"><span class="pre">std_resid</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#SkewStudent.starting_values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.SkewStudent.starting_values" title="Link to this definition">¶</a></dt>
 <dd><p>Construct starting values for use in optimization.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.SkewStudent.starting_values-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.SkewStudent.starting_values-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.SkewStudent.starting_values.std_resid"><span class="sig-name descname">std_resid</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.SkewStudent.starting_values.std_resid" title="Permalink to this definition">¶</a></dt><dd><p>Estimated standardized residuals to use in computing starting
+<dt id="arch.univariate.SkewStudent.starting_values.std_resid"><span class="n sig-name">std_resid</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.SkewStudent.starting_values.std_resid" title="Permalink to this definition">¶</a></dt><dd><p>Estimated standardized residuals to use in computing starting
 values for the shape parameter</p>
 </dd>
 </dl>
@@ -1115,7 +1115,7 @@ <h1 id="arch-univariate-skewstudent-starting-values">arch.univariate.SkewStudent
 <dd class="field-even"><p><strong>sv</strong> – Array containing starting valuer for shape parameter</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.SkewStudent.starting_values-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.SkewStudent.starting_values-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -1190,7 +1190,7 @@ <h1 id="arch-univariate-skewstudent-starting-values">arch.univariate.SkewStudent
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1237,7 +1237,7 @@ <h1 id="arch-univariate-skewstudent-starting-values">arch.univariate.SkewStudent
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diff --git a/univariate/generated/generated/arch.univariate.StudentsT.bounds.html b/univariate/generated/generated/arch.univariate.StudentsT.bounds.html
index 05de8bda07..f69988069b 100644
--- a/univariate/generated/generated/arch.univariate.StudentsT.bounds.html
+++ b/univariate/generated/generated/arch.univariate.StudentsT.bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.StudentsT.bounds - arch 6.1.0</title>
+        <title>arch.univariate.StudentsT.bounds - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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@@ -1098,15 +1098,15 @@
                   
 
 
-<h1 id="arch-univariate-studentst-bounds">arch.univariate.StudentsT.bounds<a class="headerlink" href="#arch-univariate-studentst-bounds" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-studentst-bounds">arch.univariate.StudentsT.bounds<a class="headerlink" href="#arch-univariate-studentst-bounds" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.StudentsT.bounds">
-<span class="sig-prename descclassname"><span class="pre">StudentsT.</span></span><span class="sig-name descname"><span class="pre">bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.bounds.resids" title="arch.univariate.StudentsT.bounds.resids (Python parameter) — Residuals to use when computing the bounds"><span class="n"><span class="pre">resids</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#StudentsT.bounds"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.StudentsT.bounds" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">StudentsT.</span></span><span class="sig-name descname"><span class="pre">bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.bounds.resids" title="arch.univariate.StudentsT.bounds.resids (Python parameter) — Residuals to use when computing the bounds"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#StudentsT.bounds"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.StudentsT.bounds" title="Link to this definition">¶</a></dt>
 <dd><p>Parameter bounds for use in optimization.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.StudentsT.bounds-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.StudentsT.bounds-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.StudentsT.bounds.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.StudentsT.bounds.resids" title="Permalink to this definition">¶</a></dt><dd><p>Residuals to use when computing the bounds</p>
+<dt id="arch.univariate.StudentsT.bounds.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.StudentsT.bounds.resids" title="Permalink to this definition">¶</a></dt><dd><p>Residuals to use when computing the bounds</p>
 </dd>
 </dl>
 </dd>
@@ -1114,7 +1114,7 @@ <h1 id="arch-univariate-studentst-bounds">arch.univariate.StudentsT.bounds<a cla
 <dd class="field-even"><p><strong>bounds</strong> – List containing a single tuple with (lower, upper) bounds</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.StudentsT.bounds-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.StudentsT.bounds-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1186,7 +1186,7 @@ <h1 id="arch-univariate-studentst-bounds">arch.univariate.StudentsT.bounds<a cla
   
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-    7.0.1.
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diff --git a/univariate/generated/generated/arch.univariate.StudentsT.cdf.html b/univariate/generated/generated/arch.univariate.StudentsT.cdf.html
index 853335d01b..55afa08d18 100644
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+++ b/univariate/generated/generated/arch.univariate.StudentsT.cdf.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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@@ -1112,17 +1112,17 @@
                   
 
 
-<h1 id="arch-univariate-studentst-cdf">arch.univariate.StudentsT.cdf<a class="headerlink" href="#arch-univariate-studentst-cdf" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-studentst-cdf">arch.univariate.StudentsT.cdf<a class="headerlink" href="#arch-univariate-studentst-cdf" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.StudentsT.cdf">
-<span class="sig-prename descclassname"><span class="pre">StudentsT.</span></span><span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.cdf.resids" title="arch.univariate.StudentsT.cdf.resids (Python parameter) — Values at which to evaluate the cdf"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.cdf.parameters" title="arch.univariate.StudentsT.cdf.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#StudentsT.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.StudentsT.cdf" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.StudentsT.cdf">
+<span class="sig-prename descclassname"><span class="pre">StudentsT.</span></span><span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.cdf.resids" title="arch.univariate.StudentsT.cdf.resids (Python parameter) — Values at which to evaluate the cdf"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.cdf.parameters" title="arch.univariate.StudentsT.cdf.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#StudentsT.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.StudentsT.cdf" title="Link to this definition">¶</a></dt>
 <dd><p>Cumulative distribution function</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.StudentsT.cdf-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.StudentsT.cdf-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.StudentsT.cdf.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.StudentsT.cdf.resids" title="Permalink to this definition">¶</a></dt><dd><p>Values at which to evaluate the cdf</p>
+<dt id="arch.univariate.StudentsT.cdf.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.StudentsT.cdf.resids" title="Permalink to this definition">¶</a></dt><dd><p>Values at which to evaluate the cdf</p>
 </dd>
-<dt id="arch.univariate.StudentsT.cdf.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.StudentsT.cdf.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use <code class="docutils literal notranslate"><span class="pre">None</span></code> for parameterless
+<dt id="arch.univariate.StudentsT.cdf.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.StudentsT.cdf.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use <code class="docutils literal notranslate"><span class="pre">None</span></code> for parameterless
 distributions.</p>
 </dd>
 </dl>
@@ -1131,7 +1131,7 @@ <h1 id="arch-univariate-studentst-cdf">arch.univariate.StudentsT.cdf<a class="he
 <dd class="field-even"><p><strong>f</strong> – CDF values</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.StudentsT.cdf-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.StudentsT.cdf-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
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 </dd></dl>
@@ -1203,7 +1203,7 @@ <h1 id="arch-univariate-studentst-cdf">arch.univariate.StudentsT.cdf<a class="he
   
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diff --git a/univariate/generated/generated/arch.univariate.StudentsT.constraints.html b/univariate/generated/generated/arch.univariate.StudentsT.constraints.html
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+++ b/univariate/generated/generated/arch.univariate.StudentsT.constraints.html
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+        <title>arch.univariate.StudentsT.constraints - arch 6.2.0</title>
       
     
 
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+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -581,13 +581,6 @@
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       <ul class="md-nav__list">
         
-          <li class="md-nav__item">
-  <a href="#arch.univariate.StudentsT.constraints-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
-</li>
-        
           <li class="md-nav__item">
   <a href="#arch.univariate.StudentsT.constraints-returns" class="md-nav__link">
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@@ -1025,13 +1018,6 @@
     <nav class="md-nav" aria-label="StudentsT.constraints">
       <ul class="md-nav__list">
         
-          <li class="md-nav__item">
-  <a href="#arch.univariate.StudentsT.constraints-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
-</li>
-        
           <li class="md-nav__item">
   <a href="#arch.univariate.StudentsT.constraints-returns" class="md-nav__link">
     <span class="md-ellipsis">Returns</span>
@@ -1058,19 +1044,16 @@
                   
 
 
-<h1 id="arch-univariate-studentst-constraints">arch.univariate.StudentsT.constraints<a class="headerlink" href="#arch-univariate-studentst-constraints" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-studentst-constraints">arch.univariate.StudentsT.constraints<a class="headerlink" href="#arch-univariate-studentst-constraints" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.StudentsT.constraints">
-<span class="sig-prename descclassname"><span class="pre">StudentsT.</span></span><span class="sig-name descname"><span class="pre">constraints</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#StudentsT.constraints"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.StudentsT.constraints" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">StudentsT.</span></span><span class="sig-name descname"><span class="pre">constraints</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#StudentsT.constraints"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.StudentsT.constraints" title="Link to this definition">¶</a></dt>
 <dd><p>Construct arrays to use in constrained optimization.</p>
 <dl class="field-list simple">
-<dt class="field-odd" id="arch.univariate.StudentsT.constraints-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.StudentsT.constraints-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>]</p>
-</dd>
-<dt class="field-even" id="arch.univariate.StudentsT.constraints-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.StudentsT.constraints-returns" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><ul class="simple">
-<li><p><strong>A</strong> (<em>ndarray</em>) – Constraint loadings</p></li>
-<li><p><strong>b</strong> (<em>ndarray</em>) – Constraint values</p></li>
+<dt class="field-odd" id="arch.univariate.StudentsT.constraints-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.StudentsT.constraints-returns" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p><strong>A</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – Constraint loadings</p></li>
+<li><p><strong>b</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – Constraint values</p></li>
 </ul>
 </p>
 </dd>
@@ -1146,7 +1129,7 @@ <h1 id="arch-univariate-studentst-constraints">arch.univariate.StudentsT.constra
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1193,7 +1176,7 @@ <h1 id="arch-univariate-studentst-constraints">arch.univariate.StudentsT.constra
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diff --git a/univariate/generated/generated/arch.univariate.StudentsT.generator.html b/univariate/generated/generated/arch.univariate.StudentsT.generator.html
index 89287dab7a..c6601c86bb 100644
--- a/univariate/generated/generated/arch.univariate.StudentsT.generator.html
+++ b/univariate/generated/generated/arch.univariate.StudentsT.generator.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
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       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1018,10 +1018,10 @@
                   
 
 
-<h1 id="arch-univariate-studentst-generator">arch.univariate.StudentsT.generator<a class="headerlink" href="#arch-univariate-studentst-generator" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-studentst-generator">arch.univariate.StudentsT.generator<a class="headerlink" href="#arch-univariate-studentst-generator" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.StudentsT.generator">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">StudentsT.</span></span><span class="sig-name descname"><span class="pre">generator</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.24)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.24)"><span class="pre">Generator</span></a></span><a class="headerlink" href="#arch.univariate.StudentsT.generator" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">StudentsT.</span></span><span class="sig-name descname"><span class="pre">generator</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><span class="pre">Generator</span></a></span><a class="headerlink" href="#arch.univariate.StudentsT.generator" title="Link to this definition">¶</a></dt>
 <dd><p>The NumPy Generator or RandomState attached to the distribution</p>
 </dd></dl>
 
@@ -1092,7 +1092,7 @@ <h1 id="arch-univariate-studentst-generator">arch.univariate.StudentsT.generator
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1139,7 +1139,7 @@ <h1 id="arch-univariate-studentst-generator">arch.univariate.StudentsT.generator
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.StudentsT.loglikelihood.html b/univariate/generated/generated/arch.univariate.StudentsT.loglikelihood.html
index 5370ee6b65..9724dd1f0a 100644
--- a/univariate/generated/generated/arch.univariate.StudentsT.loglikelihood.html
+++ b/univariate/generated/generated/arch.univariate.StudentsT.loglikelihood.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.StudentsT.loglikelihood - arch 6.1.0</title>
+        <title>arch.univariate.StudentsT.loglikelihood - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1140,23 +1140,23 @@
                   
 
 
-<h1 id="arch-univariate-studentst-loglikelihood">arch.univariate.StudentsT.loglikelihood<a class="headerlink" href="#arch-univariate-studentst-loglikelihood" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-studentst-loglikelihood">arch.univariate.StudentsT.loglikelihood<a class="headerlink" href="#arch-univariate-studentst-loglikelihood" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.StudentsT.loglikelihood">
-<span class="sig-prename descclassname"><span class="pre">StudentsT.</span></span><span class="sig-name descname"><span class="pre">loglikelihood</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.loglikelihood.parameters" title="arch.univariate.StudentsT.loglikelihood.parameters (Python parameter) — Shape parameter of the t distribution"><span class="n"><span class="pre">parameters</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.loglikelihood.resids" title="arch.univariate.StudentsT.loglikelihood.resids (Python parameter) — The residuals to use in the log-likelihood calculation"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.loglikelihood.sigma2" title="arch.univariate.StudentsT.loglikelihood.sigma2 (Python parameter) — Conditional variances of resids"><span class="n"><span class="pre">sigma2</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.loglikelihood.individual" title="arch.univariate.StudentsT.loglikelihood.individual (Python parameter) — Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"><span class="n"><span class="pre">individual</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#StudentsT.loglikelihood"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.StudentsT.loglikelihood" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">StudentsT.</span></span><span class="sig-name descname"><span class="pre">loglikelihood</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.loglikelihood.parameters" title="arch.univariate.StudentsT.loglikelihood.parameters (Python parameter) — Shape parameter of the t distribution"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.loglikelihood.resids" title="arch.univariate.StudentsT.loglikelihood.resids (Python parameter) — The residuals to use in the log-likelihood calculation"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.loglikelihood.sigma2" title="arch.univariate.StudentsT.loglikelihood.sigma2 (Python parameter) — Conditional variances of resids"><span class="n"><span class="pre">sigma2</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.loglikelihood.individual" title="arch.univariate.StudentsT.loglikelihood.individual (Python parameter) — Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"><span class="n"><span class="pre">individual</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#StudentsT.loglikelihood"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.StudentsT.loglikelihood" title="Link to this definition">¶</a></dt>
 <dd><p>Computes the log-likelihood of assuming residuals are have a
 standardized (to have unit variance) Student’s t distribution,
 conditional on the variance.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.StudentsT.loglikelihood-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.StudentsT.loglikelihood-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.StudentsT.loglikelihood.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.StudentsT.loglikelihood.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Shape parameter of the t distribution</p>
+<dt id="arch.univariate.StudentsT.loglikelihood.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.StudentsT.loglikelihood.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Shape parameter of the t distribution</p>
 </dd>
-<dt id="arch.univariate.StudentsT.loglikelihood.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.StudentsT.loglikelihood.resids" title="Permalink to this definition">¶</a></dt><dd><p>The residuals to use in the log-likelihood calculation</p>
+<dt id="arch.univariate.StudentsT.loglikelihood.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.StudentsT.loglikelihood.resids" title="Permalink to this definition">¶</a></dt><dd><p>The residuals to use in the log-likelihood calculation</p>
 </dd>
-<dt id="arch.univariate.StudentsT.loglikelihood.sigma2"><span class="sig-name descname">sigma2</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.StudentsT.loglikelihood.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>Conditional variances of resids</p>
+<dt id="arch.univariate.StudentsT.loglikelihood.sigma2"><span class="n sig-name">sigma2</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.StudentsT.loglikelihood.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>Conditional variances of resids</p>
 </dd>
-<dt id="arch.univariate.StudentsT.loglikelihood.individual"><span class="sig-name descname">individual</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, optional</span></span><a class="headerlink" href="#arch.univariate.StudentsT.loglikelihood.individual" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to return the vector of individual log
+<dt id="arch.univariate.StudentsT.loglikelihood.individual"><span class="n sig-name">individual</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.univariate.StudentsT.loglikelihood.individual" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to return the vector of individual log
 likelihoods (True) or the sum (False)</p>
 </dd>
 </dl>
@@ -1165,7 +1165,7 @@ <h1 id="arch-univariate-studentst-loglikelihood">arch.univariate.StudentsT.logli
 <dd class="field-even"><p><strong>ll</strong> – The log-likelihood</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.StudentsT.loglikelihood-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.StudentsT.loglikelihood-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -1245,7 +1245,7 @@ <h1 id="arch-univariate-studentst-loglikelihood">arch.univariate.StudentsT.logli
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1292,10 +1292,10 @@ <h1 id="arch-univariate-studentst-loglikelihood">arch.univariate.StudentsT.logli
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+        <script id="MathJax-script" src="../../../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
     
   </body>
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diff --git a/univariate/generated/generated/arch.univariate.StudentsT.moment.html b/univariate/generated/generated/arch.univariate.StudentsT.moment.html
index e2f17b45c0..1cc755cff0 100644
--- a/univariate/generated/generated/arch.univariate.StudentsT.moment.html
+++ b/univariate/generated/generated/arch.univariate.StudentsT.moment.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.StudentsT.moment - arch 6.1.0</title>
+        <title>arch.univariate.StudentsT.moment - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1112,17 +1112,17 @@
                   
 
 
-<h1 id="arch-univariate-studentst-moment">arch.univariate.StudentsT.moment<a class="headerlink" href="#arch-univariate-studentst-moment" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-studentst-moment">arch.univariate.StudentsT.moment<a class="headerlink" href="#arch-univariate-studentst-moment" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.StudentsT.moment">
-<span class="sig-prename descclassname"><span class="pre">StudentsT.</span></span><span class="sig-name descname"><span class="pre">moment</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.moment.n" title="arch.univariate.StudentsT.moment.n (Python parameter) — Order of moment"><span class="n"><span class="pre">n</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.moment.parameters" title="arch.univariate.StudentsT.moment.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#StudentsT.moment"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.StudentsT.moment" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.StudentsT.moment">
+<span class="sig-prename descclassname"><span class="pre">StudentsT.</span></span><span class="sig-name descname"><span class="pre">moment</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.moment.n" title="arch.univariate.StudentsT.moment.n (Python parameter) — Order of moment"><span class="n"><span class="pre">n</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.moment.parameters" title="arch.univariate.StudentsT.moment.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#StudentsT.moment"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.StudentsT.moment" title="Link to this definition">¶</a></dt>
 <dd><p>Moment of order n</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.StudentsT.moment-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.StudentsT.moment-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.StudentsT.moment.n"><span class="sig-name descname">n</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.StudentsT.moment.n" title="Permalink to this definition">¶</a></dt><dd><p>Order of moment</p>
+<dt id="arch.univariate.StudentsT.moment.n"><span class="n sig-name">n</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.StudentsT.moment.n" title="Permalink to this definition">¶</a></dt><dd><p>Order of moment</p>
 </dd>
-<dt id="arch.univariate.StudentsT.moment.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray, optional</span></span><a class="headerlink" href="#arch.univariate.StudentsT.moment.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use None for parameterless distributions.</p>
+<dt id="arch.univariate.StudentsT.moment.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.StudentsT.moment.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use None for parameterless distributions.</p>
 </dd>
 </dl>
 </dd>
@@ -1130,7 +1130,7 @@ <h1 id="arch-univariate-studentst-moment">arch.univariate.StudentsT.moment<a cla
 <dd class="field-even"><p>Calculated moment</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.StudentsT.moment-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.StudentsT.moment-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1202,7 +1202,7 @@ <h1 id="arch-univariate-studentst-moment">arch.univariate.StudentsT.moment<a cla
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1249,7 +1249,7 @@ <h1 id="arch-univariate-studentst-moment">arch.univariate.StudentsT.moment<a cla
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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diff --git a/univariate/generated/generated/arch.univariate.StudentsT.name.html b/univariate/generated/generated/arch.univariate.StudentsT.name.html
index 6255ded3ab..fe27a34cb5 100644
--- a/univariate/generated/generated/arch.univariate.StudentsT.name.html
+++ b/univariate/generated/generated/arch.univariate.StudentsT.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.StudentsT.name - arch 6.1.0</title>
+        <title>arch.univariate.StudentsT.name - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1018,10 +1018,10 @@
                   
 
 
-<h1 id="arch-univariate-studentst-name">arch.univariate.StudentsT.name<a class="headerlink" href="#arch-univariate-studentst-name" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-studentst-name">arch.univariate.StudentsT.name<a class="headerlink" href="#arch-univariate-studentst-name" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.StudentsT.name">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">StudentsT.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.univariate.StudentsT.name" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">StudentsT.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.univariate.StudentsT.name" title="Link to this definition">¶</a></dt>
 <dd><p>The name of the distribution</p>
 </dd></dl>
 
@@ -1092,7 +1092,7 @@ <h1 id="arch-univariate-studentst-name">arch.univariate.StudentsT.name<a class="
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1139,7 +1139,7 @@ <h1 id="arch-univariate-studentst-name">arch.univariate.StudentsT.name<a class="
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
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diff --git a/univariate/generated/generated/arch.univariate.StudentsT.parameter_names.html b/univariate/generated/generated/arch.univariate.StudentsT.parameter_names.html
index 3185ca8624..bc415b1556 100644
--- a/univariate/generated/generated/arch.univariate.StudentsT.parameter_names.html
+++ b/univariate/generated/generated/arch.univariate.StudentsT.parameter_names.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.StudentsT.parameter_names - arch 6.1.0</title>
+        <title>arch.univariate.StudentsT.parameter_names - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1058,17 +1058,17 @@
                   
 
 
-<h1 id="arch-univariate-studentst-parameter-names">arch.univariate.StudentsT.parameter_names<a class="headerlink" href="#arch-univariate-studentst-parameter-names" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-studentst-parameter-names">arch.univariate.StudentsT.parameter_names<a class="headerlink" href="#arch-univariate-studentst-parameter-names" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.StudentsT.parameter_names">
-<span class="sig-prename descclassname"><span class="pre">StudentsT.</span></span><span class="sig-name descname"><span class="pre">parameter_names</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#StudentsT.parameter_names"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.StudentsT.parameter_names" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">StudentsT.</span></span><span class="sig-name descname"><span class="pre">parameter_names</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#StudentsT.parameter_names"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.StudentsT.parameter_names" title="Link to this definition">¶</a></dt>
 <dd><p>Names of distribution shape parameters</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.StudentsT.parameter_names-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.StudentsT.parameter_names-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p><strong>names</strong> – Parameter names</p>
 </dd>
 <dt class="field-even" id="arch.univariate.StudentsT.parameter_names-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.StudentsT.parameter_names-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>)</p>
+<dd class="field-even"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span> <span class="pre">(str)</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1140,7 +1140,7 @@ <h1 id="arch-univariate-studentst-parameter-names">arch.univariate.StudentsT.par
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1187,7 +1187,7 @@ <h1 id="arch-univariate-studentst-parameter-names">arch.univariate.StudentsT.par
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.StudentsT.partial_moment.html b/univariate/generated/generated/arch.univariate.StudentsT.partial_moment.html
index b137500a28..fce07a45fc 100644
--- a/univariate/generated/generated/arch.univariate.StudentsT.partial_moment.html
+++ b/univariate/generated/generated/arch.univariate.StudentsT.partial_moment.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.StudentsT.partial_moment - arch 6.1.0</title>
+        <title>arch.univariate.StudentsT.partial_moment - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1126,19 +1126,19 @@
                   
 
 
-<h1 id="arch-univariate-studentst-partial-moment">arch.univariate.StudentsT.partial_moment<a class="headerlink" href="#arch-univariate-studentst-partial-moment" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-studentst-partial-moment">arch.univariate.StudentsT.partial_moment<a class="headerlink" href="#arch-univariate-studentst-partial-moment" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.StudentsT.partial_moment">
-<span class="sig-prename descclassname"><span class="pre">StudentsT.</span></span><span class="sig-name descname"><span class="pre">partial_moment</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.partial_moment.n" title="arch.univariate.StudentsT.partial_moment.n (Python parameter) — Order of partial moment"><span class="n"><span class="pre">n</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.partial_moment.z" title="arch.univariate.StudentsT.partial_moment.z (Python parameter) — Upper bound for partial moment integral"><span class="n"><span class="pre">z</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.partial_moment.parameters" title="arch.univariate.StudentsT.partial_moment.parameters (Python parameter) — Distribution parameters.  Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#StudentsT.partial_moment"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.StudentsT.partial_moment" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.StudentsT.partial_moment">
+<span class="sig-prename descclassname"><span class="pre">StudentsT.</span></span><span class="sig-name descname"><span class="pre">partial_moment</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.partial_moment.n" title="arch.univariate.StudentsT.partial_moment.n (Python parameter) — Order of partial moment"><span class="n"><span class="pre">n</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.partial_moment.z" title="arch.univariate.StudentsT.partial_moment.z (Python parameter) — Upper bound for partial moment integral"><span class="n"><span class="pre">z</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.partial_moment.parameters" title="arch.univariate.StudentsT.partial_moment.parameters (Python parameter) — Distribution parameters.  Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#StudentsT.partial_moment"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.StudentsT.partial_moment" title="Link to this definition">¶</a></dt>
 <dd><p>Order n lower partial moment from -inf to z</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.StudentsT.partial_moment-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.StudentsT.partial_moment-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.StudentsT.partial_moment.n"><span class="sig-name descname">n</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.StudentsT.partial_moment.n" title="Permalink to this definition">¶</a></dt><dd><p>Order of partial moment</p>
+<dt id="arch.univariate.StudentsT.partial_moment.n"><span class="n sig-name">n</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.StudentsT.partial_moment.n" title="Permalink to this definition">¶</a></dt><dd><p>Order of partial moment</p>
 </dd>
-<dt id="arch.univariate.StudentsT.partial_moment.z"><span class="sig-name descname">z</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>, optional</span></span><a class="headerlink" href="#arch.univariate.StudentsT.partial_moment.z" title="Permalink to this definition">¶</a></dt><dd><p>Upper bound for partial moment integral</p>
+<dt id="arch.univariate.StudentsT.partial_moment.z"><span class="n sig-name">z</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.0</span></code><a class="headerlink" href="#arch.univariate.StudentsT.partial_moment.z" title="Permalink to this definition">¶</a></dt><dd><p>Upper bound for partial moment integral</p>
 </dd>
-<dt id="arch.univariate.StudentsT.partial_moment.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray, optional</span></span><a class="headerlink" href="#arch.univariate.StudentsT.partial_moment.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters.  Use None for parameterless distributions.</p>
+<dt id="arch.univariate.StudentsT.partial_moment.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.StudentsT.partial_moment.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters.  Use None for parameterless distributions.</p>
 </dd>
 </dl>
 </dd>
@@ -1146,7 +1146,7 @@ <h1 id="arch-univariate-studentst-partial-moment">arch.univariate.StudentsT.part
 <dd class="field-even"><p>Partial moment</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.StudentsT.partial_moment-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.StudentsT.partial_moment-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 <p class="rubric">References</p>
@@ -1231,7 +1231,7 @@ <h1 id="arch-univariate-studentst-partial-moment">arch.univariate.StudentsT.part
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1278,10 +1278,10 @@ <h1 id="arch-univariate-studentst-partial-moment">arch.univariate.StudentsT.part
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+        <script id="MathJax-script" src="../../../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
     
   </body>
 </html>
\ No newline at end of file
diff --git a/univariate/generated/generated/arch.univariate.StudentsT.ppf.html b/univariate/generated/generated/arch.univariate.StudentsT.ppf.html
index c9f5652af5..4cfb983245 100644
--- a/univariate/generated/generated/arch.univariate.StudentsT.ppf.html
+++ b/univariate/generated/generated/arch.univariate.StudentsT.ppf.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.StudentsT.ppf - arch 6.1.0</title>
+        <title>arch.univariate.StudentsT.ppf - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1112,17 +1112,17 @@
                   
 
 
-<h1 id="arch-univariate-studentst-ppf">arch.univariate.StudentsT.ppf<a class="headerlink" href="#arch-univariate-studentst-ppf" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-studentst-ppf">arch.univariate.StudentsT.ppf<a class="headerlink" href="#arch-univariate-studentst-ppf" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.StudentsT.ppf">
-<span class="sig-prename descclassname"><span class="pre">StudentsT.</span></span><span class="sig-name descname"><span class="pre">ppf</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.ppf.pits" title="arch.univariate.StudentsT.ppf.pits (Python parameter) — Probability-integral-transformed values in the interval (0, 1)."><span class="n"><span class="pre">pits</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.ppf.parameters" title="arch.univariate.StudentsT.ppf.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#StudentsT.ppf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.StudentsT.ppf" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.StudentsT.ppf">
+<span class="sig-prename descclassname"><span class="pre">StudentsT.</span></span><span class="sig-name descname"><span class="pre">ppf</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.ppf.pits" title="arch.univariate.StudentsT.ppf.pits (Python parameter) — Probability-integral-transformed values in the interval (0, 1)."><span class="n"><span class="pre">pits</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.ppf.parameters" title="arch.univariate.StudentsT.ppf.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#StudentsT.ppf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.StudentsT.ppf" title="Link to this definition">¶</a></dt>
 <dd><p>Inverse cumulative density function (ICDF)</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.StudentsT.ppf-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.StudentsT.ppf-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.StudentsT.ppf.pits"><span class="sig-name descname">pits</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}</span></span><a class="headerlink" href="#arch.univariate.StudentsT.ppf.pits" title="Permalink to this definition">¶</a></dt><dd><p>Probability-integral-transformed values in the interval (0, 1).</p>
+<dt id="arch.univariate.StudentsT.ppf.pits"><span class="n sig-name">pits</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.StudentsT.ppf.pits" title="Permalink to this definition">¶</a></dt><dd><p>Probability-integral-transformed values in the interval (0, 1).</p>
 </dd>
-<dt id="arch.univariate.StudentsT.ppf.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray, optional</span></span><a class="headerlink" href="#arch.univariate.StudentsT.ppf.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use <code class="docutils literal notranslate"><span class="pre">None</span></code> for parameterless
+<dt id="arch.univariate.StudentsT.ppf.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.StudentsT.ppf.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use <code class="docutils literal notranslate"><span class="pre">None</span></code> for parameterless
 distributions.</p>
 </dd>
 </dl>
@@ -1131,7 +1131,7 @@ <h1 id="arch-univariate-studentst-ppf">arch.univariate.StudentsT.ppf<a class="he
 <dd class="field-even"><p><strong>i</strong> – Inverse CDF values</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.StudentsT.ppf-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.StudentsT.ppf-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>{float, ndarray}</p>
+<dd class="field-odd"><p><code class="docutils literal notranslate"><span class="pre">{float,</span> <span class="pre">ndarray}</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1203,7 +1203,7 @@ <h1 id="arch-univariate-studentst-ppf">arch.univariate.StudentsT.ppf<a class="he
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1250,7 +1250,7 @@ <h1 id="arch-univariate-studentst-ppf">arch.univariate.StudentsT.ppf<a class="he
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
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diff --git a/univariate/generated/generated/arch.univariate.StudentsT.random_state.html b/univariate/generated/generated/arch.univariate.StudentsT.random_state.html
index ffa599f5b6..889994eae0 100644
--- a/univariate/generated/generated/arch.univariate.StudentsT.random_state.html
+++ b/univariate/generated/generated/arch.univariate.StudentsT.random_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.StudentsT.random_state - arch 6.1.0</title>
+        <title>arch.univariate.StudentsT.random_state - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1018,13 +1018,13 @@
                   
 
 
-<h1 id="arch-univariate-studentst-random-state">arch.univariate.StudentsT.random_state<a class="headerlink" href="#arch-univariate-studentst-random-state" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-studentst-random-state">arch.univariate.StudentsT.random_state<a class="headerlink" href="#arch-univariate-studentst-random-state" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.StudentsT.random_state">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">StudentsT.</span></span><span class="sig-name descname"><span class="pre">random_state</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.24)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.24)"><span class="pre">Generator</span></a></span><a class="headerlink" href="#arch.univariate.StudentsT.random_state" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">StudentsT.</span></span><span class="sig-name descname"><span class="pre">random_state</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><span class="pre">Generator</span></a></span><a class="headerlink" href="#arch.univariate.StudentsT.random_state" title="Link to this definition">¶</a></dt>
 <dd><p>The NumPy RandomState attached to the distribution</p>
-<div class="deprecated">
-<p><span class="versionmodified deprecated">Deprecated since version 5.0: </span>random_state is deprecated. Use generator instead.</p>
+<div class="admonition deprecated">
+<p class="admonition-title"><span class="versionmodified deprecated">Deprecated since version 5.0: </span>random_state is deprecated. Use generator instead.</p>
 </div>
 </dd></dl>
 
@@ -1095,7 +1095,7 @@ <h1 id="arch-univariate-studentst-random-state">arch.univariate.StudentsT.random
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1142,7 +1142,7 @@ <h1 id="arch-univariate-studentst-random-state">arch.univariate.StudentsT.random
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diff --git a/univariate/generated/generated/arch.univariate.StudentsT.simulate.html b/univariate/generated/generated/arch.univariate.StudentsT.simulate.html
index 3354659799..9a24dc34f5 100644
--- a/univariate/generated/generated/arch.univariate.StudentsT.simulate.html
+++ b/univariate/generated/generated/arch.univariate.StudentsT.simulate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.StudentsT.simulate - arch 6.1.0</title>
+        <title>arch.univariate.StudentsT.simulate - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1098,15 +1098,15 @@
                   
 
 
-<h1 id="arch-univariate-studentst-simulate">arch.univariate.StudentsT.simulate<a class="headerlink" href="#arch-univariate-studentst-simulate" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-studentst-simulate">arch.univariate.StudentsT.simulate<a class="headerlink" href="#arch-univariate-studentst-simulate" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.StudentsT.simulate">
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+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.StudentsT.simulate">
+<span class="sig-prename descclassname"><span class="pre">StudentsT.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.simulate.parameters" title="arch.univariate.StudentsT.simulate.parameters (Python parameter) — Distribution parameters"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#StudentsT.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.StudentsT.simulate" title="Link to this definition">¶</a></dt>
 <dd><p>Simulates i.i.d. draws from the distribution</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.StudentsT.simulate-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.StudentsT.simulate-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.StudentsT.simulate.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.StudentsT.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters</p>
+<dt id="arch.univariate.StudentsT.simulate.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.StudentsT.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters</p>
 </dd>
 </dl>
 </dd>
@@ -1115,7 +1115,7 @@ <h1 id="arch-univariate-studentst-simulate">arch.univariate.StudentsT.simulate<a
 draws from the distribution</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.StudentsT.simulate-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.StudentsT.simulate-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>callable</p>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">callable</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1187,7 +1187,7 @@ <h1 id="arch-univariate-studentst-simulate">arch.univariate.StudentsT.simulate<a
   
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     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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diff --git a/univariate/generated/generated/arch.univariate.StudentsT.starting_values.html b/univariate/generated/generated/arch.univariate.StudentsT.starting_values.html
index 21a21d3341..75ba61790d 100644
--- a/univariate/generated/generated/arch.univariate.StudentsT.starting_values.html
+++ b/univariate/generated/generated/arch.univariate.StudentsT.starting_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.StudentsT.starting_values - arch 6.1.0</title>
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@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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@@ -102,7 +102,7 @@
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+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1098,15 +1098,15 @@
                   
 
 
-<h1 id="arch-univariate-studentst-starting-values">arch.univariate.StudentsT.starting_values<a class="headerlink" href="#arch-univariate-studentst-starting-values" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-studentst-starting-values">arch.univariate.StudentsT.starting_values<a class="headerlink" href="#arch-univariate-studentst-starting-values" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.StudentsT.starting_values">
-<span class="sig-prename descclassname"><span class="pre">StudentsT.</span></span><span class="sig-name descname"><span class="pre">starting_values</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.starting_values.std_resid" title="arch.univariate.StudentsT.starting_values.std_resid (Python parameter) — Estimated standardized residuals to use in computing starting values for the shape parameter"><span class="n"><span class="pre">std_resid</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#StudentsT.starting_values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.StudentsT.starting_values" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">StudentsT.</span></span><span class="sig-name descname"><span class="pre">starting_values</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.starting_values.std_resid" title="arch.univariate.StudentsT.starting_values.std_resid (Python parameter) — Estimated standardized residuals to use in computing starting values for the shape parameter"><span class="n"><span class="pre">std_resid</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#StudentsT.starting_values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.StudentsT.starting_values" title="Link to this definition">¶</a></dt>
 <dd><p>Construct starting values for use in optimization.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.StudentsT.starting_values-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.StudentsT.starting_values-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.StudentsT.starting_values.std_resid"><span class="sig-name descname">std_resid</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.StudentsT.starting_values.std_resid" title="Permalink to this definition">¶</a></dt><dd><p>Estimated standardized residuals to use in computing starting
+<dt id="arch.univariate.StudentsT.starting_values.std_resid"><span class="n sig-name">std_resid</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.StudentsT.starting_values.std_resid" title="Permalink to this definition">¶</a></dt><dd><p>Estimated standardized residuals to use in computing starting
 values for the shape parameter</p>
 </dd>
 </dl>
@@ -1115,7 +1115,7 @@ <h1 id="arch-univariate-studentst-starting-values">arch.univariate.StudentsT.sta
 <dd class="field-even"><p><strong>sv</strong> – Array containing starting valuer for shape parameter</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.StudentsT.starting_values-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.StudentsT.starting_values-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -1190,7 +1190,7 @@ <h1 id="arch-univariate-studentst-starting-values">arch.univariate.StudentsT.sta
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
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@@ -1237,7 +1237,7 @@ <h1 id="arch-univariate-studentst-starting-values">arch.univariate.StudentsT.sta
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diff --git a/univariate/generated/generated/arch.univariate.ZeroMean.bounds.html b/univariate/generated/generated/arch.univariate.ZeroMean.bounds.html
index 702045ee86..4dfab15b51 100644
--- a/univariate/generated/generated/arch.univariate.ZeroMean.bounds.html
+++ b/univariate/generated/generated/arch.univariate.ZeroMean.bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ZeroMean.bounds - arch 6.1.0</title>
+        <title>arch.univariate.ZeroMean.bounds - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1133,17 +1133,17 @@
                   
 
 
-<h1 id="arch-univariate-zeromean-bounds">arch.univariate.ZeroMean.bounds<a class="headerlink" href="#arch-univariate-zeromean-bounds" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-zeromean-bounds">arch.univariate.ZeroMean.bounds<a class="headerlink" href="#arch-univariate-zeromean-bounds" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ZeroMean.bounds">
-<span class="sig-prename descclassname"><span class="pre">ZeroMean.</span></span><span class="sig-name descname"><span class="pre">bounds</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.ZeroMean.bounds" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ZeroMean.</span></span><span class="sig-name descname"><span class="pre">bounds</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#arch.univariate.ZeroMean.bounds" title="Link to this definition">¶</a></dt>
 <dd><p>Construct bounds for parameters to use in non-linear optimization</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ZeroMean.bounds-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ZeroMean.bounds-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p><strong>bounds</strong> – Bounds for parameters to use in estimation.</p>
 </dd>
 <dt class="field-even" id="arch.univariate.ZeroMean.bounds-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ZeroMean.bounds-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a> (2-tuple of <a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>)</p>
+<dd class="field-even"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span> <span class="pre">(2-tuple</span></code> of <code class="xref py py-class docutils literal notranslate"><span class="pre">float)</span></code></p>
 </dd>
 </dl>
 </dd></dl>
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diff --git a/univariate/generated/generated/arch.univariate.ZeroMean.compute_param_cov.html b/univariate/generated/generated/arch.univariate.ZeroMean.compute_param_cov.html
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@@ -1187,26 +1173,23 @@
                   
 
 
-<h1 id="arch-univariate-zeromean-compute-param-cov">arch.univariate.ZeroMean.compute_param_cov<a class="headerlink" href="#arch-univariate-zeromean-compute-param-cov" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-zeromean-compute-param-cov">arch.univariate.ZeroMean.compute_param_cov<a class="headerlink" href="#arch-univariate-zeromean-compute-param-cov" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.ZeroMean.compute_param_cov">
-<span class="sig-prename descclassname"><span class="pre">ZeroMean.</span></span><span class="sig-name descname"><span class="pre">compute_param_cov</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.compute_param_cov.params" title="arch.univariate.ZeroMean.compute_param_cov.params (Python parameter) — Model parameters"><span class="n"><span class="pre">params</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.compute_param_cov.backcast" title="arch.univariate.ZeroMean.compute_param_cov.backcast (Python parameter) — Value to use for pre-sample observations"><span class="n"><span class="pre">backcast</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.compute_param_cov.robust" title="arch.univariate.ZeroMean.compute_param_cov.robust (Python parameter) — Flag indicating whether to use robust standard errors (True) or classic MLE (False)"><span class="n"><span class="pre">robust</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.ZeroMean.compute_param_cov" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ZeroMean.compute_param_cov">
+<span class="sig-prename descclassname"><span class="pre">ZeroMean.</span></span><span class="sig-name descname"><span class="pre">compute_param_cov</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.compute_param_cov.params" title="arch.univariate.ZeroMean.compute_param_cov.params (Python parameter) — Model parameters"><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.compute_param_cov.backcast" title="arch.univariate.ZeroMean.compute_param_cov.backcast (Python parameter) — Value to use for pre-sample observations"><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.compute_param_cov.robust" title="arch.univariate.ZeroMean.compute_param_cov.robust (Python parameter) — Flag indicating whether to use robust standard errors (True) or classic MLE (False)"><span class="n"><span class="pre">robust</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.univariate.ZeroMean.compute_param_cov" title="Link to this definition">¶</a></dt>
 <dd><p>Computes parameter covariances using numerical derivatives.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ZeroMean.compute_param_cov-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ZeroMean.compute_param_cov-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ZeroMean.compute_param_cov.params"><span class="sig-name descname">params</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.ZeroMean.compute_param_cov.params" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
+<dt id="arch.univariate.ZeroMean.compute_param_cov.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.ZeroMean.compute_param_cov.params" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
 </dd>
-<dt id="arch.univariate.ZeroMean.compute_param_cov.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><a class="headerlink" href="#arch.univariate.ZeroMean.compute_param_cov.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use for pre-sample observations</p>
+<dt id="arch.univariate.ZeroMean.compute_param_cov.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.compute_param_cov.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use for pre-sample observations</p>
 </dd>
-<dt id="arch.univariate.ZeroMean.compute_param_cov.robust"><span class="sig-name descname">robust</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ZeroMean.compute_param_cov.robust" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to use robust standard errors (True) or
+<dt id="arch.univariate.ZeroMean.compute_param_cov.robust"><span class="n sig-name">robust</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.compute_param_cov.robust" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to use robust standard errors (True) or
 classic MLE (False)</p>
 </dd>
 </dl>
 </dd>
-<dt class="field-even" id="arch.univariate.ZeroMean.compute_param_cov-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ZeroMean.compute_param_cov-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a></p>
-</dd>
 </dl>
 </dd></dl>
 
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+++ b/univariate/generated/generated/arch.univariate.ZeroMean.constraints.html
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+            arch 6.2.0
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-    arch 6.1.0
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     <nav class="md-nav" aria-label="ZeroMean.constraints">
       <ul class="md-nav__list">
         
-          <li class="md-nav__item">
-  <a href="#arch.univariate.ZeroMean.constraints-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
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-  
-</li>
-        
           <li class="md-nav__item">
   <a href="#arch.univariate.ZeroMean.constraints-returns" class="md-nav__link">
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@@ -1100,13 +1093,6 @@
     <nav class="md-nav" aria-label="ZeroMean.constraints">
       <ul class="md-nav__list">
         
-          <li class="md-nav__item">
-  <a href="#arch.univariate.ZeroMean.constraints-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
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-</li>
-        
           <li class="md-nav__item">
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@@ -1133,19 +1119,16 @@
                   
 
 
-<h1 id="arch-univariate-zeromean-constraints">arch.univariate.ZeroMean.constraints<a class="headerlink" href="#arch-univariate-zeromean-constraints" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-zeromean-constraints">arch.univariate.ZeroMean.constraints<a class="headerlink" href="#arch-univariate-zeromean-constraints" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ZeroMean.constraints">
-<span class="sig-prename descclassname"><span class="pre">ZeroMean.</span></span><span class="sig-name descname"><span class="pre">constraints</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.ZeroMean.constraints" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ZeroMean.</span></span><span class="sig-name descname"><span class="pre">constraints</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#arch.univariate.ZeroMean.constraints" title="Link to this definition">¶</a></dt>
 <dd><p>Construct linear constraint arrays  for use in non-linear optimization</p>
 <dl class="field-list simple">
-<dt class="field-odd" id="arch.univariate.ZeroMean.constraints-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ZeroMean.constraints-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>]</p>
-</dd>
-<dt class="field-even" id="arch.univariate.ZeroMean.constraints-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ZeroMean.constraints-returns" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><ul class="simple">
-<li><p><strong>a</strong> (<em>ndarray</em>) – Number of constraints by number of parameters loading array</p></li>
-<li><p><strong>b</strong> (<em>ndarray</em>) – Number of constraints array of lower bounds</p></li>
+<dt class="field-odd" id="arch.univariate.ZeroMean.constraints-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ZeroMean.constraints-returns" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p><strong>a</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – Number of constraints by number of parameters loading array</p></li>
+<li><p><strong>b</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – Number of constraints array of lower bounds</p></li>
 </ul>
 </p>
 </dd>
@@ -1221,7 +1204,7 @@ <h1 id="arch-univariate-zeromean-constraints">arch.univariate.ZeroMean.constrain
   
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     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1268,7 +1251,7 @@ <h1 id="arch-univariate-zeromean-constraints">arch.univariate.ZeroMean.constrain
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diff --git a/univariate/generated/generated/arch.univariate.ZeroMean.distribution.html b/univariate/generated/generated/arch.univariate.ZeroMean.distribution.html
index a2f3b2487f..9c120ed98e 100644
--- a/univariate/generated/generated/arch.univariate.ZeroMean.distribution.html
+++ b/univariate/generated/generated/arch.univariate.ZeroMean.distribution.html
@@ -15,7 +15,7 @@
     
   
       
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+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-            arch 6.1.0
+            arch 6.2.0
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1093,10 +1093,10 @@
                   
 
 
-<h1 id="arch-univariate-zeromean-distribution">arch.univariate.ZeroMean.distribution<a class="headerlink" href="#arch-univariate-zeromean-distribution" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-zeromean-distribution">arch.univariate.ZeroMean.distribution<a class="headerlink" href="#arch-univariate-zeromean-distribution" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ZeroMean.distribution">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ZeroMean.</span></span><span class="sig-name descname"><span class="pre">distribution</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.univariate.distribution.Distribution.html#arch.univariate.distribution.Distribution" title="arch.univariate.distribution.Distribution (Python class) — Template for subclassing only"><span class="pre">Distribution</span></a></span><a class="headerlink" href="#arch.univariate.ZeroMean.distribution" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ZeroMean.</span></span><span class="sig-name descname"><span class="pre">distribution</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.univariate.distribution.Distribution.html#arch.univariate.distribution.Distribution" title="arch.univariate.distribution.Distribution (Python class) — Template for subclassing only"><span class="pre">Distribution</span></a></span><a class="headerlink" href="#arch.univariate.ZeroMean.distribution" title="Link to this definition">¶</a></dt>
 <dd><p>Set or gets the error distribution</p>
 <p>Distributions must be a subclass of Distribution</p>
 </dd></dl>
@@ -1168,7 +1168,7 @@ <h1 id="arch-univariate-zeromean-distribution">arch.univariate.ZeroMean.distribu
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1215,7 +1215,7 @@ <h1 id="arch-univariate-zeromean-distribution">arch.univariate.ZeroMean.distribu
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diff --git a/univariate/generated/generated/arch.univariate.ZeroMean.fit.html b/univariate/generated/generated/arch.univariate.ZeroMean.fit.html
index 682cf988a7..2ef8c4b24f 100644
--- a/univariate/generated/generated/arch.univariate.ZeroMean.fit.html
+++ b/univariate/generated/generated/arch.univariate.ZeroMean.fit.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ZeroMean.fit - arch 6.1.0</title>
+        <title>arch.univariate.ZeroMean.fit - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch 6.1.0
+    arch 6.2.0
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@@ -1299,40 +1299,40 @@
                   
 
 
-<h1 id="arch-univariate-zeromean-fit">arch.univariate.ZeroMean.fit<a class="headerlink" href="#arch-univariate-zeromean-fit" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-zeromean-fit">arch.univariate.ZeroMean.fit<a class="headerlink" href="#arch-univariate-zeromean-fit" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ZeroMean.fit">
-<span class="sig-prename descclassname"><span class="pre">ZeroMean.</span></span><span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.fit.update_freq" title="arch.univariate.ZeroMean.fit.update_freq (Python parameter) — Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."><span class="n"><span class="pre">update_freq</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.fit.disp" title="arch.univariate.ZeroMean.fit.disp (Python parameter) — Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is &quot;off&quot; and True is &quot;final&quot;"><span class="n"><span class="pre">disp</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;final&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.fit.starting_values" title="arch.univariate.ZeroMean.fit.starting_values (Python parameter) — Array of starting values to use.  If not provided, starting values are constructed by the model components."><span class="n"><span class="pre">starting_values</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.fit.cov_type" title="arch.univariate.ZeroMean.fit.cov_type (Python parameter) — Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."><span class="n"><span class="pre">cov_type</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;robust&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.fit.show_warning" title="arch.univariate.ZeroMean.fit.show_warning (Python parameter) — Flag indicating whether convergence warnings should be shown."><span class="n"><span class="pre">show_warning</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.fit.first_obs" title="arch.univariate.ZeroMean.fit.first_obs (Python parameter) — First observation to use when estimating model"><span class="n"><span class="pre">first_obs</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.fit.last_obs" title="arch.univariate.ZeroMean.fit.last_obs (Python parameter) — Last observation to use when estimating model"><span class="n"><span class="pre">last_obs</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.fit.tol" title="arch.univariate.ZeroMean.fit.tol (Python parameter) — Tolerance for termination."><span class="n"><span class="pre">tol</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.fit.options" title="arch.univariate.ZeroMean.fit.options (Python parameter) — Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."><span class="n"><span class="pre">options</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.fit.backcast" title="arch.univariate.ZeroMean.fit.backcast (Python parameter) — Value to use as backcast. Should be measure \sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."><span class="n"><span class="pre">backcast</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.ZeroMean.fit" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ZeroMean.</span></span><span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.fit.update_freq" title="arch.univariate.ZeroMean.fit.update_freq (Python parameter) — Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."><span class="n"><span class="pre">update_freq</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.fit.disp" title="arch.univariate.ZeroMean.fit.disp (Python parameter) — Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is &quot;off&quot; and True is &quot;final&quot;"><span class="n"><span class="pre">disp</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'off'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'final'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;final&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.fit.starting_values" title="arch.univariate.ZeroMean.fit.starting_values (Python parameter) — Array of starting values to use.  If not provided, starting values are constructed by the model components."><span class="n"><span class="pre">starting_values</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.fit.cov_type" title="arch.univariate.ZeroMean.fit.cov_type (Python parameter) — Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."><span class="n"><span class="pre">cov_type</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'robust'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'classic'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;robust&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.fit.show_warning" title="arch.univariate.ZeroMean.fit.show_warning (Python parameter) — Flag indicating whether convergence warnings should be shown."><span class="n"><span class="pre">show_warning</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.fit.first_obs" title="arch.univariate.ZeroMean.fit.first_obs (Python parameter) — First observation to use when estimating model"><span class="n"><span class="pre">first_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.fit.last_obs" title="arch.univariate.ZeroMean.fit.last_obs (Python parameter) — Last observation to use when estimating model"><span class="n"><span class="pre">last_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.fit.tol" title="arch.univariate.ZeroMean.fit.tol (Python parameter) — Tolerance for termination."><span class="n"><span class="pre">tol</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.fit.options" title="arch.univariate.ZeroMean.fit.options (Python parameter) — Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."><span class="n"><span class="pre">options</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.fit.backcast" title="arch.univariate.ZeroMean.fit.backcast (Python parameter) — Value to use as backcast. Should be measure \sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelResult.html#arch.univariate.base.ARCHModelResult" title="arch.univariate.base.ARCHModelResult (Python class) — Results from estimation of an ARCHModel model"><span class="pre">ARCHModelResult</span></a></span></span></span><a class="headerlink" href="#arch.univariate.ZeroMean.fit" title="Link to this definition">¶</a></dt>
 <dd><p>Estimate model parameters</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ZeroMean.fit-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ZeroMean.fit-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ZeroMean.fit.update_freq"><span class="sig-name descname">update_freq</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ZeroMean.fit.update_freq" title="Permalink to this definition">¶</a></dt><dd><p>Frequency of iteration updates.  Output is generated every
+<dt id="arch.univariate.ZeroMean.fit.update_freq"><span class="n sig-name">update_freq</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.fit.update_freq" title="Permalink to this definition">¶</a></dt><dd><p>Frequency of iteration updates.  Output is generated every
 <cite>update_freq</cite> iterations. Set to 0 to disable iterative output.</p>
 </dd>
-<dt id="arch.univariate.ZeroMean.fit.disp"><span class="sig-name descname">disp</span><span class="p"> : </span><span class="desctype"><span class="desctype">{bool, &quot;off&quot;, &quot;final&quot;}</span></span><a class="headerlink" href="#arch.univariate.ZeroMean.fit.disp" title="Permalink to this definition">¶</a></dt><dd><p>Either ‘final’ to print optimization result or ‘off’ to display
+<dt id="arch.univariate.ZeroMean.fit.disp"><span class="n sig-name">disp</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'off'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'final'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;final&#39;</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.fit.disp" title="Permalink to this definition">¶</a></dt><dd><p>Either ‘final’ to print optimization result or ‘off’ to display
 nothing. If using a boolean, False is “off” and True is “final”</p>
 </dd>
-<dt id="arch.univariate.ZeroMean.fit.starting_values"><span class="sig-name descname">starting_values</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray, optional</span></span><a class="headerlink" href="#arch.univariate.ZeroMean.fit.starting_values" title="Permalink to this definition">¶</a></dt><dd><p>Array of starting values to use.  If not provided, starting values
+<dt id="arch.univariate.ZeroMean.fit.starting_values"><span class="n sig-name">starting_values</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.fit.starting_values" title="Permalink to this definition">¶</a></dt><dd><p>Array of starting values to use.  If not provided, starting values
 are constructed by the model components.</p>
 </dd>
-<dt id="arch.univariate.ZeroMean.fit.cov_type"><span class="sig-name descname">cov_type</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ZeroMean.fit.cov_type" title="Permalink to this definition">¶</a></dt><dd><p>Estimation method of parameter covariance.  Supported options are
+<dt id="arch.univariate.ZeroMean.fit.cov_type"><span class="n sig-name">cov_type</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'robust'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'classic'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;robust&#39;</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.fit.cov_type" title="Permalink to this definition">¶</a></dt><dd><p>Estimation method of parameter covariance.  Supported options are
 ‘robust’, which does not assume the Information Matrix Equality
 holds and ‘classic’ which does.  In the ARCH literature, ‘robust’
 corresponds to Bollerslev-Wooldridge covariance estimator.</p>
 </dd>
-<dt id="arch.univariate.ZeroMean.fit.show_warning"><span class="sig-name descname">show_warning</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ZeroMean.fit.show_warning" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether convergence warnings should be shown.</p>
+<dt id="arch.univariate.ZeroMean.fit.show_warning"><span class="n sig-name">show_warning</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.fit.show_warning" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether convergence warnings should be shown.</p>
 </dd>
-<dt id="arch.univariate.ZeroMean.fit.first_obs"><span class="sig-name descname">first_obs</span><span class="p"> : </span><span class="desctype"><span class="desctype">{int, <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, datetime, Timestamp}</span></span><a class="headerlink" href="#arch.univariate.ZeroMean.fit.first_obs" title="Permalink to this definition">¶</a></dt><dd><p>First observation to use when estimating model</p>
+<dt id="arch.univariate.ZeroMean.fit.first_obs"><span class="n sig-name">first_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.fit.first_obs" title="Permalink to this definition">¶</a></dt><dd><p>First observation to use when estimating model</p>
 </dd>
-<dt id="arch.univariate.ZeroMean.fit.last_obs"><span class="sig-name descname">last_obs</span><span class="p"> : </span><span class="desctype"><span class="desctype">{int, <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, datetime, Timestamp}</span></span><a class="headerlink" href="#arch.univariate.ZeroMean.fit.last_obs" title="Permalink to this definition">¶</a></dt><dd><p>Last observation to use when estimating model</p>
+<dt id="arch.univariate.ZeroMean.fit.last_obs"><span class="n sig-name">last_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.fit.last_obs" title="Permalink to this definition">¶</a></dt><dd><p>Last observation to use when estimating model</p>
 </dd>
-<dt id="arch.univariate.ZeroMean.fit.tol"><span class="sig-name descname">tol</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ZeroMean.fit.tol" title="Permalink to this definition">¶</a></dt><dd><p>Tolerance for termination.</p>
+<dt id="arch.univariate.ZeroMean.fit.tol"><span class="n sig-name">tol</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.fit.tol" title="Permalink to this definition">¶</a></dt><dd><p>Tolerance for termination.</p>
 </dd>
-<dt id="arch.univariate.ZeroMean.fit.options"><span class="sig-name descname">options</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ZeroMean.fit.options" title="Permalink to this definition">¶</a></dt><dd><p>Options to pass to <cite>scipy.optimize.minimize</cite>.  Valid entries
+<dt id="arch.univariate.ZeroMean.fit.options"><span class="n sig-name">options</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)">Any</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.fit.options" title="Permalink to this definition">¶</a></dt><dd><p>Options to pass to <cite>scipy.optimize.minimize</cite>.  Valid entries
 include ‘ftol’, ‘eps’, ‘disp’, and ‘maxiter’.</p>
 </dd>
-<dt id="arch.univariate.ZeroMean.fit.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}, optional</span></span><a class="headerlink" href="#arch.univariate.ZeroMean.fit.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use as backcast. Should be measure <span class="math notranslate nohighlight">\(\sigma^2_0\)</span>
+<dt id="arch.univariate.ZeroMean.fit.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.fit.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use as backcast. Should be measure <span class="math notranslate nohighlight">\(\sigma^2_0\)</span>
 since model-specific non-linear transformations are applied to
 value before computing the variance recursions.</p>
 </dd>
@@ -1342,7 +1342,7 @@ <h1 id="arch-univariate-zeromean-fit">arch.univariate.ZeroMean.fit<a class="head
 <dd class="field-even"><p><strong>results</strong> – Object containing model results</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.ZeroMean.fit-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ZeroMean.fit-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference internal" href="../arch.univariate.base.ARCHModelResult.html#arch.univariate.base.ARCHModelResult" title="arch.univariate.base.ARCHModelResult (Python class) — Results from estimation of an ARCHModel model">ARCHModelResult</a></p>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">ARCHModelResult</span></code></p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -1418,7 +1418,7 @@ <h1 id="arch-univariate-zeromean-fit">arch.univariate.ZeroMean.fit<a class="head
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
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@@ -1465,10 +1465,10 @@ <h1 id="arch-univariate-zeromean-fit">arch.univariate.ZeroMean.fit<a class="head
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+        <script id="MathJax-script" src="../../../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
     
   </body>
 </html>
\ No newline at end of file
diff --git a/univariate/generated/generated/arch.univariate.ZeroMean.fix.html b/univariate/generated/generated/arch.univariate.ZeroMean.fix.html
index d41b7eab3f..f28d3a21cf 100644
--- a/univariate/generated/generated/arch.univariate.ZeroMean.fix.html
+++ b/univariate/generated/generated/arch.univariate.ZeroMean.fix.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ZeroMean.fix - arch 6.1.0</title>
+        <title>arch.univariate.ZeroMean.fix - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1201,21 +1201,21 @@
                   
 
 
-<h1 id="arch-univariate-zeromean-fix">arch.univariate.ZeroMean.fix<a class="headerlink" href="#arch-univariate-zeromean-fix" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-zeromean-fix">arch.univariate.ZeroMean.fix<a class="headerlink" href="#arch-univariate-zeromean-fix" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.ZeroMean.fix">
-<span class="sig-prename descclassname"><span class="pre">ZeroMean.</span></span><span class="sig-name descname"><span class="pre">fix</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.fix.params" title="arch.univariate.ZeroMean.fix.params (Python parameter) — User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."><span class="n"><span class="pre">params</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.fix.first_obs" title="arch.univariate.ZeroMean.fix.first_obs (Python parameter) — First observation to use when fixing model"><span class="n"><span class="pre">first_obs</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.fix.last_obs" title="arch.univariate.ZeroMean.fix.last_obs (Python parameter) — Last observation to use when fixing model"><span class="n"><span class="pre">last_obs</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.ZeroMean.fix" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ZeroMean.fix">
+<span class="sig-prename descclassname"><span class="pre">ZeroMean.</span></span><span class="sig-name descname"><span class="pre">fix</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.fix.params" title="arch.univariate.ZeroMean.fix.params (Python parameter) — User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.fix.first_obs" title="arch.univariate.ZeroMean.fix.first_obs (Python parameter) — First observation to use when fixing model"><span class="n"><span class="pre">first_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.fix.last_obs" title="arch.univariate.ZeroMean.fix.last_obs (Python parameter) — Last observation to use when fixing model"><span class="n"><span class="pre">last_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelFixedResult.html#arch.univariate.base.ARCHModelFixedResult" title="arch.univariate.base.ARCHModelFixedResult (Python class) — Results for fixed parameters for an ARCHModel model"><span class="pre">ARCHModelFixedResult</span></a></span></span></span><a class="headerlink" href="#arch.univariate.ZeroMean.fix" title="Link to this definition">¶</a></dt>
 <dd><p>Allows an ARCHModelFixedResult to be constructed from fixed parameters.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ZeroMean.fix-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ZeroMean.fix-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ZeroMean.fix.params"><span class="sig-name descname">params</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, Series}</span></span><a class="headerlink" href="#arch.univariate.ZeroMean.fix.params" title="Permalink to this definition">¶</a></dt><dd><p>User specified parameters to use when generating the result. Must
+<dt id="arch.univariate.ZeroMean.fix.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.ZeroMean.fix.params" title="Permalink to this definition">¶</a></dt><dd><p>User specified parameters to use when generating the result. Must
 have the correct number of parameters for a given choice of mean
 model, volatility model and distribution.</p>
 </dd>
-<dt id="arch.univariate.ZeroMean.fix.first_obs"><span class="sig-name descname">first_obs</span><span class="p"> : </span><span class="desctype"><span class="desctype">{int, <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, datetime, Timestamp}</span></span><a class="headerlink" href="#arch.univariate.ZeroMean.fix.first_obs" title="Permalink to this definition">¶</a></dt><dd><p>First observation to use when fixing model</p>
+<dt id="arch.univariate.ZeroMean.fix.first_obs"><span class="n sig-name">first_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.fix.first_obs" title="Permalink to this definition">¶</a></dt><dd><p>First observation to use when fixing model</p>
 </dd>
-<dt id="arch.univariate.ZeroMean.fix.last_obs"><span class="sig-name descname">last_obs</span><span class="p"> : </span><span class="desctype"><span class="desctype">{int, <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, datetime, Timestamp}</span></span><a class="headerlink" href="#arch.univariate.ZeroMean.fix.last_obs" title="Permalink to this definition">¶</a></dt><dd><p>Last observation to use when fixing model</p>
+<dt id="arch.univariate.ZeroMean.fix.last_obs"><span class="n sig-name">last_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.fix.last_obs" title="Permalink to this definition">¶</a></dt><dd><p>Last observation to use when fixing model</p>
 </dd>
 </dl>
 </dd>
@@ -1223,7 +1223,7 @@ <h1 id="arch-univariate-zeromean-fix">arch.univariate.ZeroMean.fix<a class="head
 <dd class="field-even"><p><strong>results</strong> – Object containing model results</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.ZeroMean.fix-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ZeroMean.fix-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference internal" href="../arch.univariate.base.ARCHModelFixedResult.html#arch.univariate.base.ARCHModelFixedResult" title="arch.univariate.base.ARCHModelFixedResult (Python class) — Results for fixed parameters for an ARCHModel model">ARCHModelFixedResult</a></p>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">ARCHModelFixedResult</span></code></p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -1297,7 +1297,7 @@ <h1 id="arch-univariate-zeromean-fix">arch.univariate.ZeroMean.fix<a class="head
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1344,7 +1344,7 @@ <h1 id="arch-univariate-zeromean-fix">arch.univariate.ZeroMean.fix<a class="head
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diff --git a/univariate/generated/generated/arch.univariate.ZeroMean.forecast.html b/univariate/generated/generated/arch.univariate.ZeroMean.forecast.html
index 677c6858b4..cd335f9437 100644
--- a/univariate/generated/generated/arch.univariate.ZeroMean.forecast.html
+++ b/univariate/generated/generated/arch.univariate.ZeroMean.forecast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ZeroMean.forecast - arch 6.1.0</title>
+        <title>arch.univariate.ZeroMean.forecast - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1299,56 +1299,56 @@
                   
 
 
-<h1 id="arch-univariate-zeromean-forecast">arch.univariate.ZeroMean.forecast<a class="headerlink" href="#arch-univariate-zeromean-forecast" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-zeromean-forecast">arch.univariate.ZeroMean.forecast<a class="headerlink" href="#arch-univariate-zeromean-forecast" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ZeroMean.forecast">
-<span class="sig-prename descclassname"><span class="pre">ZeroMean.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.forecast.params" title="arch.univariate.ZeroMean.forecast.params (Python parameter) — Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."><span class="n"><span class="pre">params</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.forecast.horizon" title="arch.univariate.ZeroMean.forecast.horizon (Python parameter) — Number of steps to forecast"><span class="n"><span class="pre">horizon</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.forecast.start" title="arch.univariate.ZeroMean.forecast.start (Python parameter) — An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."><span class="n"><span class="pre">start</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.forecast.align" title="arch.univariate.ZeroMean.forecast.align (Python parameter) — Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."><span class="n"><span class="pre">align</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;origin&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.forecast.method" title="arch.univariate.ZeroMean.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons &gt; 1."><span class="n"><span class="pre">method</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.forecast.simulations" title="arch.univariate.ZeroMean.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.forecast.rng" title="arch.univariate.ZeroMean.forecast.rng (Python parameter) — Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."><span class="n"><span class="pre">rng</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.forecast.random_state" title="arch.univariate.ZeroMean.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.forecast.reindex" title="arch.univariate.ZeroMean.forecast.reindex (Python parameter) — Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."><span class="n"><span class="pre">reindex</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.forecast.x" title="arch.univariate.ZeroMean.forecast.x (Python parameter) — Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"><span class="n"><span class="pre">x</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.ZeroMean.forecast" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ZeroMean.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.forecast.params" title="arch.univariate.ZeroMean.forecast.params (Python parameter) — Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.forecast.horizon" title="arch.univariate.ZeroMean.forecast.horizon (Python parameter) — Number of steps to forecast"><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.forecast.start" title="arch.univariate.ZeroMean.forecast.start (Python parameter) — An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.forecast.align" title="arch.univariate.ZeroMean.forecast.align (Python parameter) — Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."><span class="n"><span class="pre">align</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'origin'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'target'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;origin&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.forecast.method" title="arch.univariate.ZeroMean.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons &gt; 1."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.forecast.simulations" title="arch.univariate.ZeroMean.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.forecast.rng" title="arch.univariate.ZeroMean.forecast.rng (Python parameter) — Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.forecast.random_state" title="arch.univariate.ZeroMean.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.forecast.reindex" title="arch.univariate.ZeroMean.forecast.reindex (Python parameter) — Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."><span class="n"><span class="pre">reindex</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.forecast.x" title="arch.univariate.ZeroMean.forecast.x (Python parameter) — Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.11)"><span class="pre">Hashable</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelForecast.html#arch.univariate.base.ARCHModelForecast" title="arch.univariate.base.ARCHModelForecast (Python class) — Container for forecasts from an ARCH Model"><span class="pre">ARCHModelForecast</span></a></span></span></span><a class="headerlink" href="#arch.univariate.ZeroMean.forecast" title="Link to this definition">¶</a></dt>
 <dd><p>Construct forecasts from estimated model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ZeroMean.forecast-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ZeroMean.forecast-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ZeroMean.forecast.params"><span class="sig-name descname">params</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, Series}</span></span><a class="headerlink" href="#arch.univariate.ZeroMean.forecast.params" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast. Must be identical in
+<dt id="arch.univariate.ZeroMean.forecast.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.ZeroMean.forecast.params" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast. Must be identical in
 shape to the parameters computed by fitting the model.</p>
 </dd>
-<dt id="arch.univariate.ZeroMean.forecast.horizon"><span class="sig-name descname">horizon</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ZeroMean.forecast.horizon" title="Permalink to this definition">¶</a></dt><dd><p>Number of steps to forecast</p>
+<dt id="arch.univariate.ZeroMean.forecast.horizon"><span class="n sig-name">horizon</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.forecast.horizon" title="Permalink to this definition">¶</a></dt><dd><p>Number of steps to forecast</p>
 </dd>
-<dt id="arch.univariate.ZeroMean.forecast.start"><span class="sig-name descname">start</span><span class="p"> : </span><span class="desctype"><span class="desctype">{int, datetime, Timestamp, str}, optional</span></span><a class="headerlink" href="#arch.univariate.ZeroMean.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>An integer, datetime or str indicating the first observation to
+<dt id="arch.univariate.ZeroMean.forecast.start"><span class="n sig-name">start</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>An integer, datetime or str indicating the first observation to
 produce the forecast for.  Datetimes can only be used with pandas
 inputs that have a datetime index. Strings must be convertible
 to a date time, such as in ‘1945-01-01’.</p>
 </dd>
-<dt id="arch.univariate.ZeroMean.forecast.align"><span class="sig-name descname">align</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ZeroMean.forecast.align" title="Permalink to this definition">¶</a></dt><dd><p>Either ‘origin’ or ‘target’.  When set of ‘origin’, the t-th row
+<dt id="arch.univariate.ZeroMean.forecast.align"><span class="n sig-name">align</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'origin'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'target'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;origin&#39;</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.forecast.align" title="Permalink to this definition">¶</a></dt><dd><p>Either ‘origin’ or ‘target’.  When set of ‘origin’, the t-th row
 of forecasts contains the forecasts for t+1, t+2, …, t+h. When
 set to ‘target’, the t-th row contains the 1-step ahead forecast
 from time t-1, the 2 step from time t-2, …, and the h-step from
 time t-h.  ‘target’ simplified computing forecast errors since the
 realization and h-step forecast are aligned.</p>
 </dd>
-<dt id="arch.univariate.ZeroMean.forecast.method"><span class="sig-name descname">method</span><span class="p"> : </span><span class="desctype"><span class="desctype">{'analytic', 'simulation', 'bootstrap'}</span></span><a class="headerlink" href="#arch.univariate.ZeroMean.forecast.method" title="Permalink to this definition">¶</a></dt><dd><p>Method to use when producing the forecast. The default is analytic.
+<dt id="arch.univariate.ZeroMean.forecast.method"><span class="n sig-name">method</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'analytic'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'simulation'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'bootstrap'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.forecast.method" title="Permalink to this definition">¶</a></dt><dd><p>Method to use when producing the forecast. The default is analytic.
 The method only affects the variance forecast generation.  Not all
 volatility models support all methods. In particular, volatility
 models that do not evolve in squares such as EGARCH or TARCH do not
 support the ‘analytic’ method for horizons &gt; 1.</p>
 </dd>
-<dt id="arch.univariate.ZeroMean.forecast.simulations"><span class="sig-name descname">simulations</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.ZeroMean.forecast.simulations" title="Permalink to this definition">¶</a></dt><dd><p>Number of simulations to run when computing the forecast using
+<dt id="arch.univariate.ZeroMean.forecast.simulations"><span class="n sig-name">simulations</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.forecast.simulations" title="Permalink to this definition">¶</a></dt><dd><p>Number of simulations to run when computing the forecast using
 either simulation or bootstrap.</p>
 </dd>
-<dt id="arch.univariate.ZeroMean.forecast.rng"><span class="sig-name descname">rng</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable, optional</span></span><a class="headerlink" href="#arch.univariate.ZeroMean.forecast.rng" title="Permalink to this definition">¶</a></dt><dd><p>Custom random number generator to use in simulation-based forecasts.
+<dt id="arch.univariate.ZeroMean.forecast.rng"><span class="n sig-name">rng</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)">Callable</a><span class="p">[</span><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">,</span><span class="w"> </span><span class="p">...</span><span class="p">]</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.forecast.rng" title="Permalink to this definition">¶</a></dt><dd><p>Custom random number generator to use in simulation-based forecasts.
 Must produce random samples using the syntax <cite>rng(size)</cite> where size
 the 2-element tuple (simulations, horizon).</p>
 </dd>
-<dt id="arch.univariate.ZeroMean.forecast.random_state"><span class="sig-name descname">random_state</span><span class="p"> : </span><span class="desctype"><span class="desctype">RandomState, optional</span></span><a class="headerlink" href="#arch.univariate.ZeroMean.forecast.random_state" title="Permalink to this definition">¶</a></dt><dd><p>NumPy RandomState instance to use when method is ‘bootstrap’</p>
+<dt id="arch.univariate.ZeroMean.forecast.random_state"><span class="n sig-name">random_state</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)">RandomState</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.forecast.random_state" title="Permalink to this definition">¶</a></dt><dd><p>NumPy RandomState instance to use when method is ‘bootstrap’</p>
 </dd>
-<dt id="arch.univariate.ZeroMean.forecast.reindex"><span class="sig-name descname">reindex</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ZeroMean.forecast.reindex" title="Permalink to this definition">¶</a></dt><dd><p><p>Whether to reindex the forecasts to have the same dimension as the series
+<dt id="arch.univariate.ZeroMean.forecast.reindex"><span class="n sig-name">reindex</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.forecast.reindex" title="Permalink to this definition">¶</a></dt><dd><p><p>Whether to reindex the forecasts to have the same dimension as the series
 being forecast. Prior to 4.18 this was the default. As of 4.19 this is
 now optional. If not provided, a warning is raised about the future
 change in the default which will occur after September 2021.</p>
-<div class="versionadded">
-<p><span class="versionmodified added">New in version 4.19.</span></p>
+<div class="admonition versionadded">
+<p class="admonition-title"><span class="versionmodified added">New in version 4.19.</span></p>
 </div>
 </p>
 </dd>
-<dt id="arch.univariate.ZeroMean.forecast.x"><span class="sig-name descname">x</span><span class="p"> : </span><span class="desctype"><span class="desctype">{dict[label, array_like], array_like}</span></span><a class="headerlink" href="#arch.univariate.ZeroMean.forecast.x" title="Permalink to this definition">¶</a></dt><dd><p><p>Values to use for exogenous regressors if any are included in the
+<dt id="arch.univariate.ZeroMean.forecast.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.11)">Hashable</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.forecast.x" title="Permalink to this definition">¶</a></dt><dd><p><p>Values to use for exogenous regressors if any are included in the
 model. Three formats are accepted:</p>
 <ul class="simple">
 <li><p>2-d array-like: This format can be used when there is a single
@@ -1371,8 +1371,8 @@ <h1 id="arch-univariate-zeromean-forecast">arch.univariate.ZeroMean.forecast<a c
 <p>Due to the complexity required to accommodate all scenarios, please
 see the example notebook that demonstrates the valid formats for
 x.</p>
-<div class="versionadded">
-<p><span class="versionmodified added">New in version 4.19.</span></p>
+<div class="admonition versionadded">
+<p class="admonition-title"><span class="versionmodified added">New in version 4.19.</span></p>
 </div>
 </p>
 </dd>
@@ -1383,7 +1383,7 @@ <h1 id="arch-univariate-zeromean-forecast">arch.univariate.ZeroMean.forecast<a c
 <code class="docutils literal notranslate"><span class="pre">variance</span></code> and <code class="docutils literal notranslate"><span class="pre">residual_variance</span></code>.</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.ZeroMean.forecast-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ZeroMean.forecast-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference internal" href="../arch.univariate.base.ARCHModelForecast.html#arch.univariate.base.ARCHModelForecast" title="arch.univariate.base.ARCHModelForecast (Python class) — Container for forecasts from an ARCH Model">arch.univariate.base.ARCHModelForecast</a></p>
+<dd class="field-odd"><p><a class="reference internal" href="../arch.univariate.base.ARCHModelForecast.html#arch.univariate.base.ARCHModelForecast" title="arch.univariate.base.ARCHModelForecast (Python class) — Container for forecasts from an ARCH Model"><code class="xref py py-class docutils literal notranslate"><span class="pre">arch.univariate.base.ARCHModelForecast</span></code></a></p>
 </dd>
 </dl>
 <p class="example rubric">Examples</p>
@@ -1483,7 +1483,7 @@ <h1 id="arch-univariate-zeromean-forecast">arch.univariate.ZeroMean.forecast<a c
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1530,7 +1530,7 @@ <h1 id="arch-univariate-zeromean-forecast">arch.univariate.ZeroMean.forecast<a c
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.ZeroMean.name.html b/univariate/generated/generated/arch.univariate.ZeroMean.name.html
index 590bc41940..20340770ee 100644
--- a/univariate/generated/generated/arch.univariate.ZeroMean.name.html
+++ b/univariate/generated/generated/arch.univariate.ZeroMean.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ZeroMean.name - arch 6.1.0</title>
+        <title>arch.univariate.ZeroMean.name - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1093,10 +1093,10 @@
                   
 
 
-<h1 id="arch-univariate-zeromean-name">arch.univariate.ZeroMean.name<a class="headerlink" href="#arch-univariate-zeromean-name" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-zeromean-name">arch.univariate.ZeroMean.name<a class="headerlink" href="#arch-univariate-zeromean-name" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ZeroMean.name">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ZeroMean.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.univariate.ZeroMean.name" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ZeroMean.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.univariate.ZeroMean.name" title="Link to this definition">¶</a></dt>
 <dd><p>The name of the model.</p>
 </dd></dl>
 
@@ -1167,7 +1167,7 @@ <h1 id="arch-univariate-zeromean-name">arch.univariate.ZeroMean.name<a class="he
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1214,7 +1214,7 @@ <h1 id="arch-univariate-zeromean-name">arch.univariate.ZeroMean.name<a class="he
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.ZeroMean.num_params.html b/univariate/generated/generated/arch.univariate.ZeroMean.num_params.html
index a71b2408b3..750a132856 100644
--- a/univariate/generated/generated/arch.univariate.ZeroMean.num_params.html
+++ b/univariate/generated/generated/arch.univariate.ZeroMean.num_params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ZeroMean.num_params - arch 6.1.0</title>
+        <title>arch.univariate.ZeroMean.num_params - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1093,10 +1093,10 @@
                   
 
 
-<h1 id="arch-univariate-zeromean-num-params">arch.univariate.ZeroMean.num_params<a class="headerlink" href="#arch-univariate-zeromean-num-params" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-zeromean-num-params">arch.univariate.ZeroMean.num_params<a class="headerlink" href="#arch-univariate-zeromean-num-params" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ZeroMean.num_params">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ZeroMean.</span></span><span class="sig-name descname"><span class="pre">num_params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.ZeroMean.num_params" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ZeroMean.</span></span><span class="sig-name descname"><span class="pre">num_params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.ZeroMean.num_params" title="Link to this definition">¶</a></dt>
 <dd><p>Returns the number of parameters</p>
 </dd></dl>
 
@@ -1167,7 +1167,7 @@ <h1 id="arch-univariate-zeromean-num-params">arch.univariate.ZeroMean.num_params
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1214,7 +1214,7 @@ <h1 id="arch-univariate-zeromean-num-params">arch.univariate.ZeroMean.num_params
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.ZeroMean.parameter_names.html b/univariate/generated/generated/arch.univariate.ZeroMean.parameter_names.html
index 5d0c31a419..c1b5960598 100644
--- a/univariate/generated/generated/arch.univariate.ZeroMean.parameter_names.html
+++ b/univariate/generated/generated/arch.univariate.ZeroMean.parameter_names.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ZeroMean.parameter_names - arch 6.1.0</title>
+        <title>arch.univariate.ZeroMean.parameter_names - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1133,17 +1133,17 @@
                   
 
 
-<h1 id="arch-univariate-zeromean-parameter-names">arch.univariate.ZeroMean.parameter_names<a class="headerlink" href="#arch-univariate-zeromean-parameter-names" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-zeromean-parameter-names">arch.univariate.ZeroMean.parameter_names<a class="headerlink" href="#arch-univariate-zeromean-parameter-names" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ZeroMean.parameter_names">
-<span class="sig-prename descclassname"><span class="pre">ZeroMean.</span></span><span class="sig-name descname"><span class="pre">parameter_names</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/mean.html#ZeroMean.parameter_names"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ZeroMean.parameter_names" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ZeroMean.</span></span><span class="sig-name descname"><span class="pre">parameter_names</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/mean.html#ZeroMean.parameter_names"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ZeroMean.parameter_names" title="Link to this definition">¶</a></dt>
 <dd><p>List of parameters names</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ZeroMean.parameter_names-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ZeroMean.parameter_names-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p><strong>names</strong> – List of variable names for the mean model</p>
 </dd>
 <dt class="field-even" id="arch.univariate.ZeroMean.parameter_names-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ZeroMean.parameter_names-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>)</p>
+<dd class="field-even"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span> <span class="pre">(str)</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1215,7 +1215,7 @@ <h1 id="arch-univariate-zeromean-parameter-names">arch.univariate.ZeroMean.param
   
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-    7.0.1.
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diff --git a/univariate/generated/generated/arch.univariate.ZeroMean.resids.html b/univariate/generated/generated/arch.univariate.ZeroMean.resids.html
index c4bab70546..848eab4e44 100644
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+++ b/univariate/generated/generated/arch.univariate.ZeroMean.resids.html
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+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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+            arch 6.2.0
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@@ -1201,19 +1201,19 @@
                   
 
 
-<h1 id="arch-univariate-zeromean-resids">arch.univariate.ZeroMean.resids<a class="headerlink" href="#arch-univariate-zeromean-resids" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-zeromean-resids">arch.univariate.ZeroMean.resids<a class="headerlink" href="#arch-univariate-zeromean-resids" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.ZeroMean.resids">
-<span class="sig-prename descclassname"><span class="pre">ZeroMean.</span></span><span class="sig-name descname"><span class="pre">resids</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.resids.params" title="arch.univariate.ZeroMean.resids.params (Python parameter) — Model parameters"><span class="n"><span class="pre">params</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.resids.y" title="arch.univariate.ZeroMean.resids.y (Python parameter) — Alternative values to use when computing model residuals"><span class="n"><span class="pre">y</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.resids.regressors" title="arch.univariate.ZeroMean.resids.regressors (Python parameter) — Alternative regressor values to use when computing model residuals"><span class="n"><span class="pre">regressors</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/mean.html#ZeroMean.resids"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ZeroMean.resids" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ZeroMean.resids">
+<span class="sig-prename descclassname"><span class="pre">ZeroMean.</span></span><span class="sig-name descname"><span class="pre">resids</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.resids.params" title="arch.univariate.ZeroMean.resids.params (Python parameter) — Model parameters"><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.resids.y" title="arch.univariate.ZeroMean.resids.y (Python parameter) — Alternative values to use when computing model residuals"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.resids.regressors" title="arch.univariate.ZeroMean.resids.regressors (Python parameter) — Alternative regressor values to use when computing model residuals"><span class="n"><span class="pre">regressors</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/mean.html#ZeroMean.resids"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ZeroMean.resids" title="Link to this definition">¶</a></dt>
 <dd><p>Compute model residuals</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ZeroMean.resids-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ZeroMean.resids-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ZeroMean.resids.params"><span class="sig-name descname">params</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.ZeroMean.resids.params" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
+<dt id="arch.univariate.ZeroMean.resids.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.ZeroMean.resids.params" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
 </dd>
-<dt id="arch.univariate.ZeroMean.resids.y"><span class="sig-name descname">y</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray, optional</span></span><a class="headerlink" href="#arch.univariate.ZeroMean.resids.y" title="Permalink to this definition">¶</a></dt><dd><p>Alternative values to use when computing model residuals</p>
+<dt id="arch.univariate.ZeroMean.resids.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.resids.y" title="Permalink to this definition">¶</a></dt><dd><p>Alternative values to use when computing model residuals</p>
 </dd>
-<dt id="arch.univariate.ZeroMean.resids.regressors"><span class="sig-name descname">regressors</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray, optional</span></span><a class="headerlink" href="#arch.univariate.ZeroMean.resids.regressors" title="Permalink to this definition">¶</a></dt><dd><p>Alternative regressor values to use when computing model residuals</p>
+<dt id="arch.univariate.ZeroMean.resids.regressors"><span class="n sig-name">regressors</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.resids.regressors" title="Permalink to this definition">¶</a></dt><dd><p>Alternative regressor values to use when computing model residuals</p>
 </dd>
 </dl>
 </dd>
@@ -1221,7 +1221,7 @@ <h1 id="arch-univariate-zeromean-resids">arch.univariate.ZeroMean.resids<a class
 <dd class="field-even"><p><strong>resids</strong> – Model residuals</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.ZeroMean.resids-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ZeroMean.resids-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
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-    7.0.1.
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diff --git a/univariate/generated/generated/arch.univariate.ZeroMean.simulate.html b/univariate/generated/generated/arch.univariate.ZeroMean.simulate.html
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+++ b/univariate/generated/generated/arch.univariate.ZeroMean.simulate.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1243,27 +1243,27 @@
                   
 
 
-<h1 id="arch-univariate-zeromean-simulate">arch.univariate.ZeroMean.simulate<a class="headerlink" href="#arch-univariate-zeromean-simulate" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-zeromean-simulate">arch.univariate.ZeroMean.simulate<a class="headerlink" href="#arch-univariate-zeromean-simulate" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ZeroMean.simulate">
-<span class="sig-prename descclassname"><span class="pre">ZeroMean.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.simulate.params" title="arch.univariate.ZeroMean.simulate.params (Python parameter) — Parameters to use when simulating the model.  Parameter order is [volatility distribution]. There are no mean parameters."><span class="n"><span class="pre">params</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.simulate.nobs" title="arch.univariate.ZeroMean.simulate.nobs (Python parameter) — Length of series to simulate"><span class="n"><span class="pre">nobs</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.simulate.burn" title="arch.univariate.ZeroMean.simulate.burn (Python parameter) — Number of values to simulate to initialize the model and remove dependence on initial values."><span class="n"><span class="pre">burn</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.simulate.initial_value" title="arch.univariate.ZeroMean.simulate.initial_value (Python parameter) — This value is not used."><span class="n"><span class="pre">initial_value</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.simulate.x" title="arch.univariate.ZeroMean.simulate.x (Python parameter) — This value is not used."><span class="n"><span class="pre">x</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.simulate.initial_value_vol" title="arch.univariate.ZeroMean.simulate.initial_value_vol (Python parameter) — An array or scalar to use when initializing the volatility process."><span class="n"><span class="pre">initial_value_vol</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/mean.html#ZeroMean.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ZeroMean.simulate" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ZeroMean.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.simulate.params" title="arch.univariate.ZeroMean.simulate.params (Python parameter) — Parameters to use when simulating the model.  Parameter order is [volatility distribution]. There are no mean parameters."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.simulate.nobs" title="arch.univariate.ZeroMean.simulate.nobs (Python parameter) — Length of series to simulate"><span class="n"><span class="pre">nobs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.simulate.burn" title="arch.univariate.ZeroMean.simulate.burn (Python parameter) — Number of values to simulate to initialize the model and remove dependence on initial values."><span class="n"><span class="pre">burn</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.simulate.initial_value" title="arch.univariate.ZeroMean.simulate.initial_value (Python parameter) — This value is not used."><span class="n"><span class="pre">initial_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.simulate.x" title="arch.univariate.ZeroMean.simulate.x (Python parameter) — This value is not used."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.simulate.initial_value_vol" title="arch.univariate.ZeroMean.simulate.initial_value_vol (Python parameter) — An array or scalar to use when initializing the volatility process."><span class="n"><span class="pre">initial_value_vol</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/mean.html#ZeroMean.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ZeroMean.simulate" title="Link to this definition">¶</a></dt>
 <dd><p>Simulated data from a zero mean model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ZeroMean.simulate-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ZeroMean.simulate-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ZeroMean.simulate.params"><span class="sig-name descname">params</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, DataFrame}</span></span><a class="headerlink" href="#arch.univariate.ZeroMean.simulate.params" title="Permalink to this definition">¶</a></dt><dd><p>Parameters to use when simulating the model.  Parameter order is
+<dt id="arch.univariate.ZeroMean.simulate.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.univariate.ZeroMean.simulate.params" title="Permalink to this definition">¶</a></dt><dd><p>Parameters to use when simulating the model.  Parameter order is
 [volatility distribution]. There are no mean parameters.</p>
 </dd>
-<dt id="arch.univariate.ZeroMean.simulate.nobs"><span class="sig-name descname">nobs</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.ZeroMean.simulate.nobs" title="Permalink to this definition">¶</a></dt><dd><p>Length of series to simulate</p>
+<dt id="arch.univariate.ZeroMean.simulate.nobs"><span class="n sig-name">nobs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.ZeroMean.simulate.nobs" title="Permalink to this definition">¶</a></dt><dd><p>Length of series to simulate</p>
 </dd>
-<dt id="arch.univariate.ZeroMean.simulate.burn"><span class="sig-name descname">burn</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.univariate.ZeroMean.simulate.burn" title="Permalink to this definition">¶</a></dt><dd><p>Number of values to simulate to initialize the model and remove
+<dt id="arch.univariate.ZeroMean.simulate.burn"><span class="n sig-name">burn</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.simulate.burn" title="Permalink to this definition">¶</a></dt><dd><p>Number of values to simulate to initialize the model and remove
 dependence on initial values.</p>
 </dd>
-<dt id="arch.univariate.ZeroMean.simulate.initial_value"><span class="sig-name descname">initial_value</span><span class="p"> : </span><span class="desctype"><span class="desctype">None</span></span><a class="headerlink" href="#arch.univariate.ZeroMean.simulate.initial_value" title="Permalink to this definition">¶</a></dt><dd><p>This value is not used.</p>
+<dt id="arch.univariate.ZeroMean.simulate.initial_value"><span class="n sig-name">initial_value</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.simulate.initial_value" title="Permalink to this definition">¶</a></dt><dd><p>This value is not used.</p>
 </dd>
-<dt id="arch.univariate.ZeroMean.simulate.x"><span class="sig-name descname">x</span><span class="p"> : </span><span class="desctype"><span class="desctype">None</span></span><a class="headerlink" href="#arch.univariate.ZeroMean.simulate.x" title="Permalink to this definition">¶</a></dt><dd><p>This value is not used.</p>
+<dt id="arch.univariate.ZeroMean.simulate.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.simulate.x" title="Permalink to this definition">¶</a></dt><dd><p>This value is not used.</p>
 </dd>
-<dt id="arch.univariate.ZeroMean.simulate.initial_value_vol"><span class="sig-name descname">initial_value_vol</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, float}, optional</span></span><a class="headerlink" href="#arch.univariate.ZeroMean.simulate.initial_value_vol" title="Permalink to this definition">¶</a></dt><dd><p>An array or scalar to use when initializing the volatility process.</p>
+<dt id="arch.univariate.ZeroMean.simulate.initial_value_vol"><span class="n sig-name">initial_value_vol</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.simulate.initial_value_vol" title="Permalink to this definition">¶</a></dt><dd><p>An array or scalar to use when initializing the volatility process.</p>
 </dd>
 </dl>
 </dd>
@@ -1273,7 +1273,7 @@ <h1 id="arch-univariate-zeromean-simulate">arch.univariate.ZeroMean.simulate<a c
 containing the errors used in the simulation</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.ZeroMean.simulate-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ZeroMean.simulate-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>DataFrame</p>
+<dd class="field-odd"><p><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code></a></p>
 </dd>
 </dl>
 <p class="example rubric">Examples</p>
@@ -1360,7 +1360,7 @@ <h1 id="arch-univariate-zeromean-simulate">arch.univariate.ZeroMean.simulate<a c
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1407,7 +1407,7 @@ <h1 id="arch-univariate-zeromean-simulate">arch.univariate.ZeroMean.simulate<a c
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.ZeroMean.starting_values.html b/univariate/generated/generated/arch.univariate.ZeroMean.starting_values.html
index 691f515461..d4efdc0ab5 100644
--- a/univariate/generated/generated/arch.univariate.ZeroMean.starting_values.html
+++ b/univariate/generated/generated/arch.univariate.ZeroMean.starting_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ZeroMean.starting_values - arch 6.1.0</title>
+        <title>arch.univariate.ZeroMean.starting_values - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1133,10 +1133,10 @@
                   
 
 
-<h1 id="arch-univariate-zeromean-starting-values">arch.univariate.ZeroMean.starting_values<a class="headerlink" href="#arch-univariate-zeromean-starting-values" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-zeromean-starting-values">arch.univariate.ZeroMean.starting_values<a class="headerlink" href="#arch-univariate-zeromean-starting-values" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ZeroMean.starting_values">
-<span class="sig-prename descclassname"><span class="pre">ZeroMean.</span></span><span class="sig-name descname"><span class="pre">starting_values</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.ZeroMean.starting_values" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ZeroMean.</span></span><span class="sig-name descname"><span class="pre">starting_values</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.univariate.ZeroMean.starting_values" title="Link to this definition">¶</a></dt>
 <dd><p>Returns starting values for the mean model, often the same as the
 values returned from fit</p>
 <dl class="field-list simple">
@@ -1144,7 +1144,7 @@ <h1 id="arch-univariate-zeromean-starting-values">arch.univariate.ZeroMean.start
 <dd class="field-odd"><p><strong>sv</strong> – Starting values</p>
 </dd>
 <dt class="field-even" id="arch.univariate.ZeroMean.starting_values-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ZeroMean.starting_values-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p>ndarray</p>
+<dd class="field-even"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1216,7 +1216,7 @@ <h1 id="arch-univariate-zeromean-starting-values">arch.univariate.ZeroMean.start
   
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     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
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diff --git a/univariate/generated/generated/arch.univariate.ZeroMean.volatility.html b/univariate/generated/generated/arch.univariate.ZeroMean.volatility.html
index 95bc13f451..de730100b5 100644
--- a/univariate/generated/generated/arch.univariate.ZeroMean.volatility.html
+++ b/univariate/generated/generated/arch.univariate.ZeroMean.volatility.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ZeroMean.volatility - arch 6.1.0</title>
+        <title>arch.univariate.ZeroMean.volatility - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1093,10 +1093,10 @@
                   
 
 
-<h1 id="arch-univariate-zeromean-volatility">arch.univariate.ZeroMean.volatility<a class="headerlink" href="#arch-univariate-zeromean-volatility" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-zeromean-volatility">arch.univariate.ZeroMean.volatility<a class="headerlink" href="#arch-univariate-zeromean-volatility" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ZeroMean.volatility">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ZeroMean.</span></span><span class="sig-name descname"><span class="pre">volatility</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.univariate.volatility.VolatilityProcess.html#arch.univariate.volatility.VolatilityProcess" title="arch.univariate.volatility.VolatilityProcess (Python class) — Abstract base class for ARCH models.  Allows the conditional mean model to be specified separately from the conditional variance, even though parameters are estimated jointly."><span class="pre">VolatilityProcess</span></a></span><a class="headerlink" href="#arch.univariate.ZeroMean.volatility" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ZeroMean.</span></span><span class="sig-name descname"><span class="pre">volatility</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.univariate.volatility.VolatilityProcess.html#arch.univariate.volatility.VolatilityProcess" title="arch.univariate.volatility.VolatilityProcess (Python class) — Abstract base class for ARCH models.  Allows the conditional mean model to be specified separately from the conditional variance, even though parameters are estimated jointly."><span class="pre">VolatilityProcess</span></a></span><a class="headerlink" href="#arch.univariate.ZeroMean.volatility" title="Link to this definition">¶</a></dt>
 <dd><p>Set or gets the volatility process</p>
 <p>Volatility processes must be a subclass of VolatilityProcess</p>
 </dd></dl>
@@ -1168,7 +1168,7 @@ <h1 id="arch-univariate-zeromean-volatility">arch.univariate.ZeroMean.volatility
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1215,7 +1215,7 @@ <h1 id="arch-univariate-zeromean-volatility">arch.univariate.ZeroMean.volatility
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diff --git a/univariate/generated/generated/arch.univariate.ZeroMean.x.html b/univariate/generated/generated/arch.univariate.ZeroMean.x.html
index 017ba99d68..20b197c938 100644
--- a/univariate/generated/generated/arch.univariate.ZeroMean.x.html
+++ b/univariate/generated/generated/arch.univariate.ZeroMean.x.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ZeroMean.x - arch 6.1.0</title>
+        <title>arch.univariate.ZeroMean.x - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1093,10 +1093,10 @@
                   
 
 
-<h1 id="arch-univariate-zeromean-x">arch.univariate.ZeroMean.x<a class="headerlink" href="#arch-univariate-zeromean-x" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-zeromean-x">arch.univariate.ZeroMean.x<a class="headerlink" href="#arch-univariate-zeromean-x" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ZeroMean.x">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ZeroMean.</span></span><span class="sig-name descname"><span class="pre">x</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.0.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.ZeroMean.x" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ZeroMean.</span></span><span class="sig-name descname"><span class="pre">x</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.ZeroMean.x" title="Link to this definition">¶</a></dt>
 <dd><p>Gets the value of the exogenous regressors in the model</p>
 </dd></dl>
 
@@ -1167,7 +1167,7 @@ <h1 id="arch-univariate-zeromean-x">arch.univariate.ZeroMean.x<a class="headerli
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1214,7 +1214,7 @@ <h1 id="arch-univariate-zeromean-x">arch.univariate.ZeroMean.x<a class="headerli
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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diff --git a/univariate/generated/generated/arch.univariate.ZeroMean.y.html b/univariate/generated/generated/arch.univariate.ZeroMean.y.html
index 36acb308e4..0e875a07fb 100644
--- a/univariate/generated/generated/arch.univariate.ZeroMean.y.html
+++ b/univariate/generated/generated/arch.univariate.ZeroMean.y.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ZeroMean.y - arch 6.1.0</title>
+        <title>arch.univariate.ZeroMean.y - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1093,10 +1093,10 @@
                   
 
 
-<h1 id="arch-univariate-zeromean-y">arch.univariate.ZeroMean.y<a class="headerlink" href="#arch-univariate-zeromean-y" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-zeromean-y">arch.univariate.ZeroMean.y<a class="headerlink" href="#arch-univariate-zeromean-y" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ZeroMean.y">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ZeroMean.</span></span><span class="sig-name descname"><span class="pre">y</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.0.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.0.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.ZeroMean.y" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ZeroMean.</span></span><span class="sig-name descname"><span class="pre">y</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.ZeroMean.y" title="Link to this definition">¶</a></dt>
 <dd><p>Returns the dependent variable</p>
 </dd></dl>
 
@@ -1167,7 +1167,7 @@ <h1 id="arch-univariate-zeromean-y">arch.univariate.ZeroMean.y<a class="headerli
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
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@@ -1214,7 +1214,7 @@ <h1 id="arch-univariate-zeromean-y">arch.univariate.ZeroMean.y<a class="headerli
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diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModel.bounds.html b/univariate/generated/generated/arch.univariate.base.ARCHModel.bounds.html
index aac759cbba..796a07f5b6 100644
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+++ b/univariate/generated/generated/arch.univariate.base.ARCHModel.bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModel.bounds - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModel.bounds - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1158,17 +1158,17 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodel-bounds">arch.univariate.base.ARCHModel.bounds<a class="headerlink" href="#arch-univariate-base-archmodel-bounds" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodel-bounds">arch.univariate.base.ARCHModel.bounds<a class="headerlink" href="#arch-univariate-base-archmodel-bounds" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModel.bounds">
-<span class="sig-prename descclassname"><span class="pre">ARCHModel.</span></span><span class="sig-name descname"><span class="pre">bounds</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/base.html#ARCHModel.bounds"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModel.bounds" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ARCHModel.</span></span><span class="sig-name descname"><span class="pre">bounds</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/base.html#ARCHModel.bounds"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModel.bounds" title="Link to this definition">¶</a></dt>
 <dd><p>Construct bounds for parameters to use in non-linear optimization</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.base.ARCHModel.bounds-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModel.bounds-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p><strong>bounds</strong> – Bounds for parameters to use in estimation.</p>
 </dd>
 <dt class="field-even" id="arch.univariate.base.ARCHModel.bounds-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModel.bounds-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a> (2-tuple of <a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>)</p>
+<dd class="field-even"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span> <span class="pre">(2-tuple</span></code> of <code class="xref py py-class docutils literal notranslate"><span class="pre">float)</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1240,7 +1240,7 @@ <h1 id="arch-univariate-base-archmodel-bounds">arch.univariate.base.ARCHModel.bo
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1287,7 +1287,7 @@ <h1 id="arch-univariate-base-archmodel-bounds">arch.univariate.base.ARCHModel.bo
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diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModel.compute_param_cov.html b/univariate/generated/generated/arch.univariate.base.ARCHModel.compute_param_cov.html
index 52a6360e0e..a79b6f0dfc 100644
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+++ b/univariate/generated/generated/arch.univariate.base.ARCHModel.compute_param_cov.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModel.compute_param_cov - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModel.compute_param_cov - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -660,13 +660,6 @@
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     </nav>
   
-</li>
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-          <li class="md-nav__item">
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@@ -1184,13 +1177,6 @@
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-  </a>
-  
 </li>
         
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@@ -1212,26 +1198,23 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodel-compute-param-cov">arch.univariate.base.ARCHModel.compute_param_cov<a class="headerlink" href="#arch-univariate-base-archmodel-compute-param-cov" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodel-compute-param-cov">arch.univariate.base.ARCHModel.compute_param_cov<a class="headerlink" href="#arch-univariate-base-archmodel-compute-param-cov" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModel.compute_param_cov">
-<span class="sig-prename descclassname"><span class="pre">ARCHModel.</span></span><span class="sig-name descname"><span class="pre">compute_param_cov</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.compute_param_cov.params" title="arch.univariate.base.ARCHModel.compute_param_cov.params (Python parameter) — Model parameters"><span class="n"><span class="pre">params</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.compute_param_cov.backcast" title="arch.univariate.base.ARCHModel.compute_param_cov.backcast (Python parameter) — Value to use for pre-sample observations"><span class="n"><span class="pre">backcast</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.compute_param_cov.robust" title="arch.univariate.base.ARCHModel.compute_param_cov.robust (Python parameter) — Flag indicating whether to use robust standard errors (True) or classic MLE (False)"><span class="n"><span class="pre">robust</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/base.html#ARCHModel.compute_param_cov"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModel.compute_param_cov" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.base.ARCHModel.compute_param_cov">
+<span class="sig-prename descclassname"><span class="pre">ARCHModel.</span></span><span class="sig-name descname"><span class="pre">compute_param_cov</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.compute_param_cov.params" title="arch.univariate.base.ARCHModel.compute_param_cov.params (Python parameter) — Model parameters"><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.compute_param_cov.backcast" title="arch.univariate.base.ARCHModel.compute_param_cov.backcast (Python parameter) — Value to use for pre-sample observations"><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.compute_param_cov.robust" title="arch.univariate.base.ARCHModel.compute_param_cov.robust (Python parameter) — Flag indicating whether to use robust standard errors (True) or classic MLE (False)"><span class="n"><span class="pre">robust</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/base.html#ARCHModel.compute_param_cov"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModel.compute_param_cov" title="Link to this definition">¶</a></dt>
 <dd><p>Computes parameter covariances using numerical derivatives.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.base.ARCHModel.compute_param_cov-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModel.compute_param_cov-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.base.ARCHModel.compute_param_cov.params"><span class="sig-name descname">params</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModel.compute_param_cov.params" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
+<dt id="arch.univariate.base.ARCHModel.compute_param_cov.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModel.compute_param_cov.params" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModel.compute_param_cov.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModel.compute_param_cov.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use for pre-sample observations</p>
+<dt id="arch.univariate.base.ARCHModel.compute_param_cov.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModel.compute_param_cov.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use for pre-sample observations</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModel.compute_param_cov.robust"><span class="sig-name descname">robust</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, optional</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModel.compute_param_cov.robust" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to use robust standard errors (True) or
+<dt id="arch.univariate.base.ARCHModel.compute_param_cov.robust"><span class="n sig-name">robust</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModel.compute_param_cov.robust" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to use robust standard errors (True) or
 classic MLE (False)</p>
 </dd>
 </dl>
 </dd>
-<dt class="field-even" id="arch.univariate.base.ARCHModel.compute_param_cov-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModel.compute_param_cov-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a></p>
-</dd>
 </dl>
 </dd></dl>
 
@@ -1302,7 +1285,7 @@ <h1 id="arch-univariate-base-archmodel-compute-param-cov">arch.univariate.base.A
   
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diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModel.constraints.html b/univariate/generated/generated/arch.univariate.base.ARCHModel.constraints.html
index e8506d5305..8f4c0de5ae 100644
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+++ b/univariate/generated/generated/arch.univariate.base.ARCHModel.constraints.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModel.constraints - arch 6.1.0</title>
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@@ -47,8 +47,8 @@
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@@ -102,7 +102,7 @@
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+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-        
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@@ -1125,13 +1118,6 @@
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       <ul class="md-nav__list">
         
-          <li class="md-nav__item">
-  <a href="#arch.univariate.base.ARCHModel.constraints-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
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-  
-</li>
-        
           <li class="md-nav__item">
   <a href="#arch.univariate.base.ARCHModel.constraints-returns" class="md-nav__link">
     <span class="md-ellipsis">Returns</span>
@@ -1158,19 +1144,16 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodel-constraints">arch.univariate.base.ARCHModel.constraints<a class="headerlink" href="#arch-univariate-base-archmodel-constraints" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodel-constraints">arch.univariate.base.ARCHModel.constraints<a class="headerlink" href="#arch-univariate-base-archmodel-constraints" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModel.constraints">
-<span class="sig-prename descclassname"><span class="pre">ARCHModel.</span></span><span class="sig-name descname"><span class="pre">constraints</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/base.html#ARCHModel.constraints"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModel.constraints" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ARCHModel.</span></span><span class="sig-name descname"><span class="pre">constraints</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/base.html#ARCHModel.constraints"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModel.constraints" title="Link to this definition">¶</a></dt>
 <dd><p>Construct linear constraint arrays  for use in non-linear optimization</p>
 <dl class="field-list simple">
-<dt class="field-odd" id="arch.univariate.base.ARCHModel.constraints-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModel.constraints-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>]</p>
-</dd>
-<dt class="field-even" id="arch.univariate.base.ARCHModel.constraints-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModel.constraints-returns" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><ul class="simple">
-<li><p><strong>a</strong> (<em>ndarray</em>) – Number of constraints by number of parameters loading array</p></li>
-<li><p><strong>b</strong> (<em>ndarray</em>) – Number of constraints array of lower bounds</p></li>
+<dt class="field-odd" id="arch.univariate.base.ARCHModel.constraints-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModel.constraints-returns" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p><strong>a</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – Number of constraints by number of parameters loading array</p></li>
+<li><p><strong>b</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – Number of constraints array of lower bounds</p></li>
 </ul>
 </p>
 </dd>
@@ -1246,7 +1229,7 @@ <h1 id="arch-univariate-base-archmodel-constraints">arch.univariate.base.ARCHMod
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1293,7 +1276,7 @@ <h1 id="arch-univariate-base-archmodel-constraints">arch.univariate.base.ARCHMod
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diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModel.distribution.html b/univariate/generated/generated/arch.univariate.base.ARCHModel.distribution.html
index 978a03baad..1675ca6d23 100644
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@@ -47,8 +47,8 @@
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+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1118,10 +1118,10 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodel-distribution">arch.univariate.base.ARCHModel.distribution<a class="headerlink" href="#arch-univariate-base-archmodel-distribution" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodel-distribution">arch.univariate.base.ARCHModel.distribution<a class="headerlink" href="#arch-univariate-base-archmodel-distribution" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModel.distribution">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModel.</span></span><span class="sig-name descname"><span class="pre">distribution</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.univariate.distribution.Distribution.html#arch.univariate.distribution.Distribution" title="arch.univariate.distribution.Distribution (Python class) — Template for subclassing only"><span class="pre">Distribution</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModel.distribution" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModel.</span></span><span class="sig-name descname"><span class="pre">distribution</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.univariate.distribution.Distribution.html#arch.univariate.distribution.Distribution" title="arch.univariate.distribution.Distribution (Python class) — Template for subclassing only"><span class="pre">Distribution</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModel.distribution" title="Link to this definition">¶</a></dt>
 <dd><p>Set or gets the error distribution</p>
 <p>Distributions must be a subclass of Distribution</p>
 </dd></dl>
@@ -1193,7 +1193,7 @@ <h1 id="arch-univariate-base-archmodel-distribution">arch.univariate.base.ARCHMo
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
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@@ -1240,7 +1240,7 @@ <h1 id="arch-univariate-base-archmodel-distribution">arch.univariate.base.ARCHMo
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModel.fit.html b/univariate/generated/generated/arch.univariate.base.ARCHModel.fit.html
index 4394fe356c..86dab1523c 100644
--- a/univariate/generated/generated/arch.univariate.base.ARCHModel.fit.html
+++ b/univariate/generated/generated/arch.univariate.base.ARCHModel.fit.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModel.fit - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModel.fit - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1324,40 +1324,40 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodel-fit">arch.univariate.base.ARCHModel.fit<a class="headerlink" href="#arch-univariate-base-archmodel-fit" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodel-fit">arch.univariate.base.ARCHModel.fit<a class="headerlink" href="#arch-univariate-base-archmodel-fit" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.base.ARCHModel.fit">
-<span class="sig-prename descclassname"><span class="pre">ARCHModel.</span></span><span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.fit.update_freq" title="arch.univariate.base.ARCHModel.fit.update_freq (Python parameter) — Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."><span class="n"><span class="pre">update_freq</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.fit.disp" title="arch.univariate.base.ARCHModel.fit.disp (Python parameter) — Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is &quot;off&quot; and True is &quot;final&quot;"><span class="n"><span class="pre">disp</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;final&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.fit.starting_values" title="arch.univariate.base.ARCHModel.fit.starting_values (Python parameter) — Array of starting values to use.  If not provided, starting values are constructed by the model components."><span class="n"><span class="pre">starting_values</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.fit.cov_type" title="arch.univariate.base.ARCHModel.fit.cov_type (Python parameter) — Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."><span class="n"><span class="pre">cov_type</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;robust&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.fit.show_warning" title="arch.univariate.base.ARCHModel.fit.show_warning (Python parameter) — Flag indicating whether convergence warnings should be shown."><span class="n"><span class="pre">show_warning</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.fit.first_obs" title="arch.univariate.base.ARCHModel.fit.first_obs (Python parameter) — First observation to use when estimating model"><span class="n"><span class="pre">first_obs</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.fit.last_obs" title="arch.univariate.base.ARCHModel.fit.last_obs (Python parameter) — Last observation to use when estimating model"><span class="n"><span class="pre">last_obs</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.fit.tol" title="arch.univariate.base.ARCHModel.fit.tol (Python parameter) — Tolerance for termination."><span class="n"><span class="pre">tol</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.fit.options" title="arch.univariate.base.ARCHModel.fit.options (Python parameter) — Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."><span class="n"><span class="pre">options</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.fit.backcast" title="arch.univariate.base.ARCHModel.fit.backcast (Python parameter) — Value to use as backcast. Should be measure \sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."><span class="n"><span class="pre">backcast</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/base.html#ARCHModel.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModel.fit" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ARCHModel.</span></span><span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.fit.update_freq" title="arch.univariate.base.ARCHModel.fit.update_freq (Python parameter) — Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."><span class="n"><span class="pre">update_freq</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.fit.disp" title="arch.univariate.base.ARCHModel.fit.disp (Python parameter) — Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is &quot;off&quot; and True is &quot;final&quot;"><span class="n"><span class="pre">disp</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'off'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'final'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;final&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.fit.starting_values" title="arch.univariate.base.ARCHModel.fit.starting_values (Python parameter) — Array of starting values to use.  If not provided, starting values are constructed by the model components."><span class="n"><span class="pre">starting_values</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.fit.cov_type" title="arch.univariate.base.ARCHModel.fit.cov_type (Python parameter) — Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."><span class="n"><span class="pre">cov_type</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'robust'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'classic'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;robust&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.fit.show_warning" title="arch.univariate.base.ARCHModel.fit.show_warning (Python parameter) — Flag indicating whether convergence warnings should be shown."><span class="n"><span class="pre">show_warning</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.fit.first_obs" title="arch.univariate.base.ARCHModel.fit.first_obs (Python parameter) — First observation to use when estimating model"><span class="n"><span class="pre">first_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.fit.last_obs" title="arch.univariate.base.ARCHModel.fit.last_obs (Python parameter) — Last observation to use when estimating model"><span class="n"><span class="pre">last_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.fit.tol" title="arch.univariate.base.ARCHModel.fit.tol (Python parameter) — Tolerance for termination."><span class="n"><span class="pre">tol</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.fit.options" title="arch.univariate.base.ARCHModel.fit.options (Python parameter) — Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."><span class="n"><span class="pre">options</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.fit.backcast" title="arch.univariate.base.ARCHModel.fit.backcast (Python parameter) — Value to use as backcast. Should be measure \sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelResult.html#arch.univariate.base.ARCHModelResult" title="arch.univariate.base.ARCHModelResult (Python class) — Results from estimation of an ARCHModel model"><span class="pre">ARCHModelResult</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/base.html#ARCHModel.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModel.fit" title="Link to this definition">¶</a></dt>
 <dd><p>Estimate model parameters</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.base.ARCHModel.fit-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModel.fit-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.base.ARCHModel.fit.update_freq"><span class="sig-name descname">update_freq</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModel.fit.update_freq" title="Permalink to this definition">¶</a></dt><dd><p>Frequency of iteration updates.  Output is generated every
+<dt id="arch.univariate.base.ARCHModel.fit.update_freq"><span class="n sig-name">update_freq</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModel.fit.update_freq" title="Permalink to this definition">¶</a></dt><dd><p>Frequency of iteration updates.  Output is generated every
 <cite>update_freq</cite> iterations. Set to 0 to disable iterative output.</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModel.fit.disp"><span class="sig-name descname">disp</span><span class="p"> : </span><span class="desctype"><span class="desctype">{bool, &quot;off&quot;, &quot;final&quot;}</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModel.fit.disp" title="Permalink to this definition">¶</a></dt><dd><p>Either ‘final’ to print optimization result or ‘off’ to display
+<dt id="arch.univariate.base.ARCHModel.fit.disp"><span class="n sig-name">disp</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'off'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'final'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;final&#39;</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModel.fit.disp" title="Permalink to this definition">¶</a></dt><dd><p>Either ‘final’ to print optimization result or ‘off’ to display
 nothing. If using a boolean, False is “off” and True is “final”</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModel.fit.starting_values"><span class="sig-name descname">starting_values</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray, optional</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModel.fit.starting_values" title="Permalink to this definition">¶</a></dt><dd><p>Array of starting values to use.  If not provided, starting values
+<dt id="arch.univariate.base.ARCHModel.fit.starting_values"><span class="n sig-name">starting_values</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModel.fit.starting_values" title="Permalink to this definition">¶</a></dt><dd><p>Array of starting values to use.  If not provided, starting values
 are constructed by the model components.</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModel.fit.cov_type"><span class="sig-name descname">cov_type</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, optional</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModel.fit.cov_type" title="Permalink to this definition">¶</a></dt><dd><p>Estimation method of parameter covariance.  Supported options are
+<dt id="arch.univariate.base.ARCHModel.fit.cov_type"><span class="n sig-name">cov_type</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'robust'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'classic'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;robust&#39;</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModel.fit.cov_type" title="Permalink to this definition">¶</a></dt><dd><p>Estimation method of parameter covariance.  Supported options are
 ‘robust’, which does not assume the Information Matrix Equality
 holds and ‘classic’ which does.  In the ARCH literature, ‘robust’
 corresponds to Bollerslev-Wooldridge covariance estimator.</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModel.fit.show_warning"><span class="sig-name descname">show_warning</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, optional</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModel.fit.show_warning" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether convergence warnings should be shown.</p>
+<dt id="arch.univariate.base.ARCHModel.fit.show_warning"><span class="n sig-name">show_warning</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModel.fit.show_warning" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether convergence warnings should be shown.</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModel.fit.first_obs"><span class="sig-name descname">first_obs</span><span class="p"> : </span><span class="desctype"><span class="desctype">{int, <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, datetime, Timestamp}</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModel.fit.first_obs" title="Permalink to this definition">¶</a></dt><dd><p>First observation to use when estimating model</p>
+<dt id="arch.univariate.base.ARCHModel.fit.first_obs"><span class="n sig-name">first_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModel.fit.first_obs" title="Permalink to this definition">¶</a></dt><dd><p>First observation to use when estimating model</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModel.fit.last_obs"><span class="sig-name descname">last_obs</span><span class="p"> : </span><span class="desctype"><span class="desctype">{int, <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, datetime, Timestamp}</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModel.fit.last_obs" title="Permalink to this definition">¶</a></dt><dd><p>Last observation to use when estimating model</p>
+<dt id="arch.univariate.base.ARCHModel.fit.last_obs"><span class="n sig-name">last_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModel.fit.last_obs" title="Permalink to this definition">¶</a></dt><dd><p>Last observation to use when estimating model</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModel.fit.tol"><span class="sig-name descname">tol</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>, optional</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModel.fit.tol" title="Permalink to this definition">¶</a></dt><dd><p>Tolerance for termination.</p>
+<dt id="arch.univariate.base.ARCHModel.fit.tol"><span class="n sig-name">tol</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModel.fit.tol" title="Permalink to this definition">¶</a></dt><dd><p>Tolerance for termination.</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModel.fit.options"><span class="sig-name descname">options</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a>, optional</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModel.fit.options" title="Permalink to this definition">¶</a></dt><dd><p>Options to pass to <cite>scipy.optimize.minimize</cite>.  Valid entries
+<dt id="arch.univariate.base.ARCHModel.fit.options"><span class="n sig-name">options</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.11)">Any</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModel.fit.options" title="Permalink to this definition">¶</a></dt><dd><p>Options to pass to <cite>scipy.optimize.minimize</cite>.  Valid entries
 include ‘ftol’, ‘eps’, ‘disp’, and ‘maxiter’.</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModel.fit.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}, optional</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModel.fit.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use as backcast. Should be measure <span class="math notranslate nohighlight">\(\sigma^2_0\)</span>
+<dt id="arch.univariate.base.ARCHModel.fit.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModel.fit.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use as backcast. Should be measure <span class="math notranslate nohighlight">\(\sigma^2_0\)</span>
 since model-specific non-linear transformations are applied to
 value before computing the variance recursions.</p>
 </dd>
@@ -1367,7 +1367,7 @@ <h1 id="arch-univariate-base-archmodel-fit">arch.univariate.base.ARCHModel.fit<a
 <dd class="field-even"><p><strong>results</strong> – Object containing model results</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.base.ARCHModel.fit-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModel.fit-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference internal" href="../arch.univariate.base.ARCHModelResult.html#arch.univariate.base.ARCHModelResult" title="arch.univariate.base.ARCHModelResult (Python class) — Results from estimation of an ARCHModel model">ARCHModelResult</a></p>
+<dd class="field-odd"><p><a class="reference internal" href="../arch.univariate.base.ARCHModelResult.html#arch.univariate.base.ARCHModelResult" title="arch.univariate.base.ARCHModelResult (Python class) — Results from estimation of an ARCHModel model"><code class="xref py py-class docutils literal notranslate"><span class="pre">ARCHModelResult</span></code></a></p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -1443,7 +1443,7 @@ <h1 id="arch-univariate-base-archmodel-fit">arch.univariate.base.ARCHModel.fit<a
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1490,10 +1490,10 @@ <h1 id="arch-univariate-base-archmodel-fit">arch.univariate.base.ARCHModel.fit<a
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diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModel.fix.html b/univariate/generated/generated/arch.univariate.base.ARCHModel.fix.html
index 33b56412b0..298677dd77 100644
--- a/univariate/generated/generated/arch.univariate.base.ARCHModel.fix.html
+++ b/univariate/generated/generated/arch.univariate.base.ARCHModel.fix.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModel.fix - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModel.fix - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
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@@ -92,7 +92,7 @@
 
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@@ -102,7 +102,7 @@
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-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1226,21 +1226,21 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodel-fix">arch.univariate.base.ARCHModel.fix<a class="headerlink" href="#arch-univariate-base-archmodel-fix" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodel-fix">arch.univariate.base.ARCHModel.fix<a class="headerlink" href="#arch-univariate-base-archmodel-fix" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModel.fix">
-<span class="sig-prename descclassname"><span class="pre">ARCHModel.</span></span><span class="sig-name descname"><span class="pre">fix</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.fix.params" title="arch.univariate.base.ARCHModel.fix.params (Python parameter) — User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."><span class="n"><span class="pre">params</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.fix.first_obs" title="arch.univariate.base.ARCHModel.fix.first_obs (Python parameter) — First observation to use when fixing model"><span class="n"><span class="pre">first_obs</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.fix.last_obs" title="arch.univariate.base.ARCHModel.fix.last_obs (Python parameter) — Last observation to use when fixing model"><span class="n"><span class="pre">last_obs</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/base.html#ARCHModel.fix"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModel.fix" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.base.ARCHModel.fix">
+<span class="sig-prename descclassname"><span class="pre">ARCHModel.</span></span><span class="sig-name descname"><span class="pre">fix</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.fix.params" title="arch.univariate.base.ARCHModel.fix.params (Python parameter) — User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.fix.first_obs" title="arch.univariate.base.ARCHModel.fix.first_obs (Python parameter) — First observation to use when fixing model"><span class="n"><span class="pre">first_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.fix.last_obs" title="arch.univariate.base.ARCHModel.fix.last_obs (Python parameter) — Last observation to use when fixing model"><span class="n"><span class="pre">last_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelFixedResult.html#arch.univariate.base.ARCHModelFixedResult" title="arch.univariate.base.ARCHModelFixedResult (Python class) — Results for fixed parameters for an ARCHModel model"><span class="pre">ARCHModelFixedResult</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/base.html#ARCHModel.fix"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModel.fix" title="Link to this definition">¶</a></dt>
 <dd><p>Allows an ARCHModelFixedResult to be constructed from fixed parameters.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.base.ARCHModel.fix-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModel.fix-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.base.ARCHModel.fix.params"><span class="sig-name descname">params</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, Series}</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModel.fix.params" title="Permalink to this definition">¶</a></dt><dd><p>User specified parameters to use when generating the result. Must
+<dt id="arch.univariate.base.ARCHModel.fix.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModel.fix.params" title="Permalink to this definition">¶</a></dt><dd><p>User specified parameters to use when generating the result. Must
 have the correct number of parameters for a given choice of mean
 model, volatility model and distribution.</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModel.fix.first_obs"><span class="sig-name descname">first_obs</span><span class="p"> : </span><span class="desctype"><span class="desctype">{int, <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, datetime, Timestamp}</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModel.fix.first_obs" title="Permalink to this definition">¶</a></dt><dd><p>First observation to use when fixing model</p>
+<dt id="arch.univariate.base.ARCHModel.fix.first_obs"><span class="n sig-name">first_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModel.fix.first_obs" title="Permalink to this definition">¶</a></dt><dd><p>First observation to use when fixing model</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModel.fix.last_obs"><span class="sig-name descname">last_obs</span><span class="p"> : </span><span class="desctype"><span class="desctype">{int, <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, datetime, Timestamp}</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModel.fix.last_obs" title="Permalink to this definition">¶</a></dt><dd><p>Last observation to use when fixing model</p>
+<dt id="arch.univariate.base.ARCHModel.fix.last_obs"><span class="n sig-name">last_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModel.fix.last_obs" title="Permalink to this definition">¶</a></dt><dd><p>Last observation to use when fixing model</p>
 </dd>
 </dl>
 </dd>
@@ -1248,7 +1248,7 @@ <h1 id="arch-univariate-base-archmodel-fix">arch.univariate.base.ARCHModel.fix<a
 <dd class="field-even"><p><strong>results</strong> – Object containing model results</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.base.ARCHModel.fix-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModel.fix-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference internal" href="../arch.univariate.base.ARCHModelFixedResult.html#arch.univariate.base.ARCHModelFixedResult" title="arch.univariate.base.ARCHModelFixedResult (Python class) — Results for fixed parameters for an ARCHModel model">ARCHModelFixedResult</a></p>
+<dd class="field-odd"><p><a class="reference internal" href="../arch.univariate.base.ARCHModelFixedResult.html#arch.univariate.base.ARCHModelFixedResult" title="arch.univariate.base.ARCHModelFixedResult (Python class) — Results for fixed parameters for an ARCHModel model"><code class="xref py py-class docutils literal notranslate"><span class="pre">ARCHModelFixedResult</span></code></a></p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -1322,7 +1322,7 @@ <h1 id="arch-univariate-base-archmodel-fix">arch.univariate.base.ARCHModel.fix<a
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1369,7 +1369,7 @@ <h1 id="arch-univariate-base-archmodel-fix">arch.univariate.base.ARCHModel.fix<a
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diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModel.forecast.html b/univariate/generated/generated/arch.univariate.base.ARCHModel.forecast.html
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-<h1 id="arch-univariate-base-archmodel-forecast">arch.univariate.base.ARCHModel.forecast<a class="headerlink" href="#arch-univariate-base-archmodel-forecast" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodel-forecast">arch.univariate.base.ARCHModel.forecast<a class="headerlink" href="#arch-univariate-base-archmodel-forecast" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.base.ARCHModel.forecast">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModel.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.forecast.params" title="arch.univariate.base.ARCHModel.forecast.params (Python parameter) — Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."><span class="n"><span class="pre">params</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.forecast.horizon" title="arch.univariate.base.ARCHModel.forecast.horizon (Python parameter) — Number of steps to forecast"><span class="n"><span class="pre">horizon</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.forecast.start" title="arch.univariate.base.ARCHModel.forecast.start (Python parameter) — An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."><span class="n"><span class="pre">start</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.forecast.align" title="arch.univariate.base.ARCHModel.forecast.align (Python parameter) — Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."><span class="n"><span class="pre">align</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;origin&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.forecast.method" title="arch.univariate.base.ARCHModel.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons &gt; 1."><span class="n"><span class="pre">method</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.forecast.simulations" title="arch.univariate.base.ARCHModel.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.forecast.rng" title="arch.univariate.base.ARCHModel.forecast.rng (Python parameter) — Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."><span class="n"><span class="pre">rng</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.forecast.random_state" title="arch.univariate.base.ARCHModel.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.forecast.reindex" title="arch.univariate.base.ARCHModel.forecast.reindex (Python parameter) — Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."><span class="n"><span class="pre">reindex</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.forecast.x" title="arch.univariate.base.ARCHModel.forecast.x (Python parameter) — Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"><span class="n"><span class="pre">x</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/base.html#ARCHModel.forecast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModel.forecast" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModel.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.forecast.params" title="arch.univariate.base.ARCHModel.forecast.params (Python parameter) — Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.forecast.horizon" title="arch.univariate.base.ARCHModel.forecast.horizon (Python parameter) — Number of steps to forecast"><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.forecast.start" title="arch.univariate.base.ARCHModel.forecast.start (Python parameter) — An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.forecast.align" title="arch.univariate.base.ARCHModel.forecast.align (Python parameter) — Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."><span class="n"><span class="pre">align</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'origin'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'target'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;origin&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.forecast.method" title="arch.univariate.base.ARCHModel.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons &gt; 1."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.forecast.simulations" title="arch.univariate.base.ARCHModel.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.forecast.rng" title="arch.univariate.base.ARCHModel.forecast.rng (Python parameter) — Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.forecast.random_state" title="arch.univariate.base.ARCHModel.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.forecast.reindex" title="arch.univariate.base.ARCHModel.forecast.reindex (Python parameter) — Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."><span class="n"><span class="pre">reindex</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.forecast.x" title="arch.univariate.base.ARCHModel.forecast.x (Python parameter) — Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.11)"><span class="pre">Hashable</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelForecast.html#arch.univariate.base.ARCHModelForecast" title="arch.univariate.base.ARCHModelForecast (Python class) — Container for forecasts from an ARCH Model"><span class="pre">ARCHModelForecast</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/base.html#ARCHModel.forecast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModel.forecast" title="Link to this definition">¶</a></dt>
 <dd><p>Construct forecasts from estimated model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.base.ARCHModel.forecast-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModel.forecast-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.base.ARCHModel.forecast.params"><span class="sig-name descname">params</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, Series}</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModel.forecast.params" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast. Must be identical in
+<dt id="arch.univariate.base.ARCHModel.forecast.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModel.forecast.params" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast. Must be identical in
 shape to the parameters computed by fitting the model.</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModel.forecast.horizon"><span class="sig-name descname">horizon</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModel.forecast.horizon" title="Permalink to this definition">¶</a></dt><dd><p>Number of steps to forecast</p>
+<dt id="arch.univariate.base.ARCHModel.forecast.horizon"><span class="n sig-name">horizon</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModel.forecast.horizon" title="Permalink to this definition">¶</a></dt><dd><p>Number of steps to forecast</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModel.forecast.start"><span class="sig-name descname">start</span><span class="p"> : </span><span class="desctype"><span class="desctype">{int, datetime, Timestamp, str}, optional</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModel.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>An integer, datetime or str indicating the first observation to
+<dt id="arch.univariate.base.ARCHModel.forecast.start"><span class="n sig-name">start</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModel.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>An integer, datetime or str indicating the first observation to
 produce the forecast for.  Datetimes can only be used with pandas
 inputs that have a datetime index. Strings must be convertible
 to a date time, such as in ‘1945-01-01’.</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModel.forecast.align"><span class="sig-name descname">align</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, optional</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModel.forecast.align" title="Permalink to this definition">¶</a></dt><dd><p>Either ‘origin’ or ‘target’.  When set of ‘origin’, the t-th row
+<dt id="arch.univariate.base.ARCHModel.forecast.align"><span class="n sig-name">align</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'origin'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'target'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;origin&#39;</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModel.forecast.align" title="Permalink to this definition">¶</a></dt><dd><p>Either ‘origin’ or ‘target’.  When set of ‘origin’, the t-th row
 of forecasts contains the forecasts for t+1, t+2, …, t+h. When
 set to ‘target’, the t-th row contains the 1-step ahead forecast
 from time t-1, the 2 step from time t-2, …, and the h-step from
 time t-h.  ‘target’ simplified computing forecast errors since the
 realization and h-step forecast are aligned.</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModel.forecast.method"><span class="sig-name descname">method</span><span class="p"> : </span><span class="desctype"><span class="desctype">{'analytic', 'simulation', 'bootstrap'}</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModel.forecast.method" title="Permalink to this definition">¶</a></dt><dd><p>Method to use when producing the forecast. The default is analytic.
+<dt id="arch.univariate.base.ARCHModel.forecast.method"><span class="n sig-name">method</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'analytic'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'simulation'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'bootstrap'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModel.forecast.method" title="Permalink to this definition">¶</a></dt><dd><p>Method to use when producing the forecast. The default is analytic.
 The method only affects the variance forecast generation.  Not all
 volatility models support all methods. In particular, volatility
 models that do not evolve in squares such as EGARCH or TARCH do not
 support the ‘analytic’ method for horizons &gt; 1.</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModel.forecast.simulations"><span class="sig-name descname">simulations</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModel.forecast.simulations" title="Permalink to this definition">¶</a></dt><dd><p>Number of simulations to run when computing the forecast using
+<dt id="arch.univariate.base.ARCHModel.forecast.simulations"><span class="n sig-name">simulations</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModel.forecast.simulations" title="Permalink to this definition">¶</a></dt><dd><p>Number of simulations to run when computing the forecast using
 either simulation or bootstrap.</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModel.forecast.rng"><span class="sig-name descname">rng</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable, optional</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModel.forecast.rng" title="Permalink to this definition">¶</a></dt><dd><p>Custom random number generator to use in simulation-based forecasts.
+<dt id="arch.univariate.base.ARCHModel.forecast.rng"><span class="n sig-name">rng</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)">Callable</a><span class="p">[</span><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">,</span><span class="w"> </span><span class="p">...</span><span class="p">]</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModel.forecast.rng" title="Permalink to this definition">¶</a></dt><dd><p>Custom random number generator to use in simulation-based forecasts.
 Must produce random samples using the syntax <cite>rng(size)</cite> where size
 the 2-element tuple (simulations, horizon).</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModel.forecast.random_state"><span class="sig-name descname">random_state</span><span class="p"> : </span><span class="desctype"><span class="desctype">RandomState, optional</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModel.forecast.random_state" title="Permalink to this definition">¶</a></dt><dd><p>NumPy RandomState instance to use when method is ‘bootstrap’</p>
+<dt id="arch.univariate.base.ARCHModel.forecast.random_state"><span class="n sig-name">random_state</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)">RandomState</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModel.forecast.random_state" title="Permalink to this definition">¶</a></dt><dd><p>NumPy RandomState instance to use when method is ‘bootstrap’</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModel.forecast.reindex"><span class="sig-name descname">reindex</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, optional</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModel.forecast.reindex" title="Permalink to this definition">¶</a></dt><dd><p><p>Whether to reindex the forecasts to have the same dimension as the series
+<dt id="arch.univariate.base.ARCHModel.forecast.reindex"><span class="n sig-name">reindex</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModel.forecast.reindex" title="Permalink to this definition">¶</a></dt><dd><p><p>Whether to reindex the forecasts to have the same dimension as the series
 being forecast. Prior to 4.18 this was the default. As of 4.19 this is
 now optional. If not provided, a warning is raised about the future
 change in the default which will occur after September 2021.</p>
-<div class="versionadded">
-<p><span class="versionmodified added">New in version 4.19.</span></p>
+<div class="admonition versionadded">
+<p class="admonition-title"><span class="versionmodified added">New in version 4.19.</span></p>
 </div>
 </p>
 </dd>
-<dt id="arch.univariate.base.ARCHModel.forecast.x"><span class="sig-name descname">x</span><span class="p"> : </span><span class="desctype"><span class="desctype">{dict[label, array_like], array_like}</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModel.forecast.x" title="Permalink to this definition">¶</a></dt><dd><p><p>Values to use for exogenous regressors if any are included in the
+<dt id="arch.univariate.base.ARCHModel.forecast.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.11)">Hashable</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModel.forecast.x" title="Permalink to this definition">¶</a></dt><dd><p><p>Values to use for exogenous regressors if any are included in the
 model. Three formats are accepted:</p>
 <ul class="simple">
 <li><p>2-d array-like: This format can be used when there is a single
@@ -1396,8 +1396,8 @@ <h1 id="arch-univariate-base-archmodel-forecast">arch.univariate.base.ARCHModel.
 <p>Due to the complexity required to accommodate all scenarios, please
 see the example notebook that demonstrates the valid formats for
 x.</p>
-<div class="versionadded">
-<p><span class="versionmodified added">New in version 4.19.</span></p>
+<div class="admonition versionadded">
+<p class="admonition-title"><span class="versionmodified added">New in version 4.19.</span></p>
 </div>
 </p>
 </dd>
@@ -1408,7 +1408,7 @@ <h1 id="arch-univariate-base-archmodel-forecast">arch.univariate.base.ARCHModel.
 <code class="docutils literal notranslate"><span class="pre">variance</span></code> and <code class="docutils literal notranslate"><span class="pre">residual_variance</span></code>.</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.base.ARCHModel.forecast-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModel.forecast-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference internal" href="../arch.univariate.base.ARCHModelForecast.html#arch.univariate.base.ARCHModelForecast" title="arch.univariate.base.ARCHModelForecast (Python class) — Container for forecasts from an ARCH Model">arch.univariate.base.ARCHModelForecast</a></p>
+<dd class="field-odd"><p><a class="reference internal" href="../arch.univariate.base.ARCHModelForecast.html#arch.univariate.base.ARCHModelForecast" title="arch.univariate.base.ARCHModelForecast (Python class) — Container for forecasts from an ARCH Model"><code class="xref py py-class docutils literal notranslate"><span class="pre">arch.univariate.base.ARCHModelForecast</span></code></a></p>
 </dd>
 </dl>
 <p class="example rubric">Examples</p>
@@ -1508,7 +1508,7 @@ <h1 id="arch-univariate-base-archmodel-forecast">arch.univariate.base.ARCHModel.
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1555,7 +1555,7 @@ <h1 id="arch-univariate-base-archmodel-forecast">arch.univariate.base.ARCHModel.
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModel.name.html b/univariate/generated/generated/arch.univariate.base.ARCHModel.name.html
index e0399b46d9..e416442c16 100644
--- a/univariate/generated/generated/arch.univariate.base.ARCHModel.name.html
+++ b/univariate/generated/generated/arch.univariate.base.ARCHModel.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModel.name - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModel.name - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1118,10 +1118,10 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodel-name">arch.univariate.base.ARCHModel.name<a class="headerlink" href="#arch-univariate-base-archmodel-name" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodel-name">arch.univariate.base.ARCHModel.name<a class="headerlink" href="#arch-univariate-base-archmodel-name" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModel.name">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModel.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModel.name" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModel.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModel.name" title="Link to this definition">¶</a></dt>
 <dd><p>The name of the model.</p>
 </dd></dl>
 
@@ -1192,7 +1192,7 @@ <h1 id="arch-univariate-base-archmodel-name">arch.univariate.base.ARCHModel.name
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1239,7 +1239,7 @@ <h1 id="arch-univariate-base-archmodel-name">arch.univariate.base.ARCHModel.name
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModel.num_params.html b/univariate/generated/generated/arch.univariate.base.ARCHModel.num_params.html
index 69d158db59..d565aca79e 100644
--- a/univariate/generated/generated/arch.univariate.base.ARCHModel.num_params.html
+++ b/univariate/generated/generated/arch.univariate.base.ARCHModel.num_params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModel.num_params - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModel.num_params - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1118,10 +1118,10 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodel-num-params">arch.univariate.base.ARCHModel.num_params<a class="headerlink" href="#arch-univariate-base-archmodel-num-params" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodel-num-params">arch.univariate.base.ARCHModel.num_params<a class="headerlink" href="#arch-univariate-base-archmodel-num-params" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModel.num_params">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModel.</span></span><span class="sig-name descname"><span class="pre">num_params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModel.num_params" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModel.</span></span><span class="sig-name descname"><span class="pre">num_params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModel.num_params" title="Link to this definition">¶</a></dt>
 <dd><p>Number of parameters in the model</p>
 </dd></dl>
 
@@ -1192,7 +1192,7 @@ <h1 id="arch-univariate-base-archmodel-num-params">arch.univariate.base.ARCHMode
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1239,7 +1239,7 @@ <h1 id="arch-univariate-base-archmodel-num-params">arch.univariate.base.ARCHMode
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModel.parameter_names.html b/univariate/generated/generated/arch.univariate.base.ARCHModel.parameter_names.html
index c1177ca7b1..50c87e6980 100644
--- a/univariate/generated/generated/arch.univariate.base.ARCHModel.parameter_names.html
+++ b/univariate/generated/generated/arch.univariate.base.ARCHModel.parameter_names.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModel.parameter_names - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModel.parameter_names - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1158,17 +1158,17 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodel-parameter-names">arch.univariate.base.ARCHModel.parameter_names<a class="headerlink" href="#arch-univariate-base-archmodel-parameter-names" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodel-parameter-names">arch.univariate.base.ARCHModel.parameter_names<a class="headerlink" href="#arch-univariate-base-archmodel-parameter-names" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModel.parameter_names">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModel.</span></span><span class="sig-name descname"><span class="pre">parameter_names</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/base.html#ARCHModel.parameter_names"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModel.parameter_names" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModel.</span></span><span class="sig-name descname"><span class="pre">parameter_names</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/base.html#ARCHModel.parameter_names"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModel.parameter_names" title="Link to this definition">¶</a></dt>
 <dd><p>List of parameters names</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.base.ARCHModel.parameter_names-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModel.parameter_names-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p><strong>names</strong> – List of variable names for the mean model</p>
 </dd>
 <dt class="field-even" id="arch.univariate.base.ARCHModel.parameter_names-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModel.parameter_names-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>)</p>
+<dd class="field-even"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span> <span class="pre">(str)</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1240,7 +1240,7 @@ <h1 id="arch-univariate-base-archmodel-parameter-names">arch.univariate.base.ARC
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1287,7 +1287,7 @@ <h1 id="arch-univariate-base-archmodel-parameter-names">arch.univariate.base.ARC
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diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModel.resids.html b/univariate/generated/generated/arch.univariate.base.ARCHModel.resids.html
index edd0e2814a..9b37bbc768 100644
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+++ b/univariate/generated/generated/arch.univariate.base.ARCHModel.resids.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModel.resids - arch 6.1.0</title>
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@@ -47,8 +47,8 @@
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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+            arch 6.2.0
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@@ -1226,19 +1226,19 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodel-resids">arch.univariate.base.ARCHModel.resids<a class="headerlink" href="#arch-univariate-base-archmodel-resids" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodel-resids">arch.univariate.base.ARCHModel.resids<a class="headerlink" href="#arch-univariate-base-archmodel-resids" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModel.resids">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModel.</span></span><span class="sig-name descname"><span class="pre">resids</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.resids.params" title="arch.univariate.base.ARCHModel.resids.params (Python parameter) — Model parameters"><span class="n"><span class="pre">params</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.resids.y" title="arch.univariate.base.ARCHModel.resids.y (Python parameter) — Alternative values to use when computing model residuals"><span class="n"><span class="pre">y</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.resids.regressors" title="arch.univariate.base.ARCHModel.resids.regressors (Python parameter) — Alternative regressor values to use when computing model residuals"><span class="n"><span class="pre">regressors</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/base.html#ARCHModel.resids"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModel.resids" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.base.ARCHModel.resids">
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModel.</span></span><span class="sig-name descname"><span class="pre">resids</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.resids.params" title="arch.univariate.base.ARCHModel.resids.params (Python parameter) — Model parameters"><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.resids.y" title="arch.univariate.base.ARCHModel.resids.y (Python parameter) — Alternative values to use when computing model residuals"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.resids.regressors" title="arch.univariate.base.ARCHModel.resids.regressors (Python parameter) — Alternative regressor values to use when computing model residuals"><span class="n"><span class="pre">regressors</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/base.html#ARCHModel.resids"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModel.resids" title="Link to this definition">¶</a></dt>
 <dd><p>Compute model residuals</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.base.ARCHModel.resids-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModel.resids-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.base.ARCHModel.resids.params"><span class="sig-name descname">params</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModel.resids.params" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
+<dt id="arch.univariate.base.ARCHModel.resids.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModel.resids.params" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModel.resids.y"><span class="sig-name descname">y</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray, optional</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModel.resids.y" title="Permalink to this definition">¶</a></dt><dd><p>Alternative values to use when computing model residuals</p>
+<dt id="arch.univariate.base.ARCHModel.resids.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModel.resids.y" title="Permalink to this definition">¶</a></dt><dd><p>Alternative values to use when computing model residuals</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModel.resids.regressors"><span class="sig-name descname">regressors</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray, optional</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModel.resids.regressors" title="Permalink to this definition">¶</a></dt><dd><p>Alternative regressor values to use when computing model residuals</p>
+<dt id="arch.univariate.base.ARCHModel.resids.regressors"><span class="n sig-name">regressors</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModel.resids.regressors" title="Permalink to this definition">¶</a></dt><dd><p>Alternative regressor values to use when computing model residuals</p>
 </dd>
 </dl>
 </dd>
@@ -1246,7 +1246,7 @@ <h1 id="arch-univariate-base-archmodel-resids">arch.univariate.base.ARCHModel.re
 <dd class="field-even"><p><strong>resids</strong> – Model residuals</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.base.ARCHModel.resids-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModel.resids-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
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diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModel.simulate.html b/univariate/generated/generated/arch.univariate.base.ARCHModel.simulate.html
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+++ b/univariate/generated/generated/arch.univariate.base.ARCHModel.simulate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModel.simulate - arch 6.1.0</title>
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@@ -47,8 +47,8 @@
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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@@ -189,10 +189,10 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch 6.1.0
+    arch 6.2.0
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@@ -727,22 +727,9 @@
       
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-          <li class="md-nav__item">
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-        
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-  
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@@ -1112,22 +1099,9 @@
       
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+    <span aria-label="Python method" class="objinfo-icon objinfo-icon__procedure" title="Python method">M</span><span title="arch.univariate.base.ARCHModel.simulate (Python method)" class="md-ellipsis">ARCHModel.<wbr>simulate</span>
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-    <nav class="md-nav" aria-label="ARCHModel.simulate">
-      <ul class="md-nav__list">
-        
-          <li class="md-nav__item">
-  <a href="#arch.univariate.base.ARCHModel.simulate-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
-</li>
-        
-      </ul>
-    </nav>
-  
 </li>
       
     </ul>
@@ -1144,16 +1118,11 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodel-simulate">arch.univariate.base.ARCHModel.simulate<a class="headerlink" href="#arch-univariate-base-archmodel-simulate" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodel-simulate">arch.univariate.base.ARCHModel.simulate<a class="headerlink" href="#arch-univariate-base-archmodel-simulate" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.base.ARCHModel.simulate">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModel.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">nobs</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">burn</span></span><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">initial_value</span></span><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">initial_value_vol</span></span><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/base.html#ARCHModel.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModel.simulate" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="field-list simple">
-<dt class="field-odd" id="arch.univariate.base.ARCHModel.simulate-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModel.simulate-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.0.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataFrame</span></code></a></p>
-</dd>
-</dl>
-</dd></dl>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModel.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">params</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">nobs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">burn</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">initial_value</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">initial_value_vol</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/base.html#ARCHModel.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModel.simulate" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
 
 
 
@@ -1222,7 +1191,7 @@ <h1 id="arch-univariate-base-archmodel-simulate">arch.univariate.base.ARCHModel.
   
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+++ b/univariate/generated/generated/arch.univariate.base.ARCHModel.starting_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModel.starting_values - arch 6.1.0</title>
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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     <div class="md-nav__source">
@@ -1158,10 +1158,10 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodel-starting-values">arch.univariate.base.ARCHModel.starting_values<a class="headerlink" href="#arch-univariate-base-archmodel-starting-values" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodel-starting-values">arch.univariate.base.ARCHModel.starting_values<a class="headerlink" href="#arch-univariate-base-archmodel-starting-values" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModel.starting_values">
-<span class="sig-prename descclassname"><span class="pre">ARCHModel.</span></span><span class="sig-name descname"><span class="pre">starting_values</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/base.html#ARCHModel.starting_values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModel.starting_values" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ARCHModel.</span></span><span class="sig-name descname"><span class="pre">starting_values</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/base.html#ARCHModel.starting_values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModel.starting_values" title="Link to this definition">¶</a></dt>
 <dd><p>Returns starting values for the mean model, often the same as the
 values returned from fit</p>
 <dl class="field-list simple">
@@ -1169,7 +1169,7 @@ <h1 id="arch-univariate-base-archmodel-starting-values">arch.univariate.base.ARC
 <dd class="field-odd"><p><strong>sv</strong> – Starting values</p>
 </dd>
 <dt class="field-even" id="arch.univariate.base.ARCHModel.starting_values-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModel.starting_values-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p>ndarray</p>
+<dd class="field-even"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
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     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
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diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModel.volatility.html b/univariate/generated/generated/arch.univariate.base.ARCHModel.volatility.html
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModel.volatility - arch 6.1.0</title>
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@@ -47,8 +47,8 @@
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1118,10 +1118,10 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodel-volatility">arch.univariate.base.ARCHModel.volatility<a class="headerlink" href="#arch-univariate-base-archmodel-volatility" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodel-volatility">arch.univariate.base.ARCHModel.volatility<a class="headerlink" href="#arch-univariate-base-archmodel-volatility" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModel.volatility">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModel.</span></span><span class="sig-name descname"><span class="pre">volatility</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.univariate.volatility.VolatilityProcess.html#arch.univariate.volatility.VolatilityProcess" title="arch.univariate.volatility.VolatilityProcess (Python class) — Abstract base class for ARCH models.  Allows the conditional mean model to be specified separately from the conditional variance, even though parameters are estimated jointly."><span class="pre">VolatilityProcess</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModel.volatility" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModel.</span></span><span class="sig-name descname"><span class="pre">volatility</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.univariate.volatility.VolatilityProcess.html#arch.univariate.volatility.VolatilityProcess" title="arch.univariate.volatility.VolatilityProcess (Python class) — Abstract base class for ARCH models.  Allows the conditional mean model to be specified separately from the conditional variance, even though parameters are estimated jointly."><span class="pre">VolatilityProcess</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModel.volatility" title="Link to this definition">¶</a></dt>
 <dd><p>Set or gets the volatility process</p>
 <p>Volatility processes must be a subclass of VolatilityProcess</p>
 </dd></dl>
@@ -1193,7 +1193,7 @@ <h1 id="arch-univariate-base-archmodel-volatility">arch.univariate.base.ARCHMode
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1240,7 +1240,7 @@ <h1 id="arch-univariate-base-archmodel-volatility">arch.univariate.base.ARCHMode
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModel.y.html b/univariate/generated/generated/arch.univariate.base.ARCHModel.y.html
index 54b898eb94..45177665c4 100644
--- a/univariate/generated/generated/arch.univariate.base.ARCHModel.y.html
+++ b/univariate/generated/generated/arch.univariate.base.ARCHModel.y.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModel.y - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModel.y - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1118,10 +1118,10 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodel-y">arch.univariate.base.ARCHModel.y<a class="headerlink" href="#arch-univariate-base-archmodel-y" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodel-y">arch.univariate.base.ARCHModel.y<a class="headerlink" href="#arch-univariate-base-archmodel-y" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModel.y">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModel.</span></span><span class="sig-name descname"><span class="pre">y</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.0.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.0.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModel.y" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModel.</span></span><span class="sig-name descname"><span class="pre">y</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModel.y" title="Link to this definition">¶</a></dt>
 <dd><p>Returns the dependent variable</p>
 </dd></dl>
 
@@ -1192,7 +1192,7 @@ <h1 id="arch-univariate-base-archmodel-y">arch.univariate.base.ARCHModel.y<a cla
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1239,7 +1239,7 @@ <h1 id="arch-univariate-base-archmodel-y">arch.univariate.base.ARCHModel.y<a cla
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.aic.html b/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.aic.html
index 08979c641b..db2689db0b 100644
--- a/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.aic.html
+++ b/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.aic.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelFixedResult.aic - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModelFixedResult.aic - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1003,10 +1003,10 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodelfixedresult-aic">arch.univariate.base.ARCHModelFixedResult.aic<a class="headerlink" href="#arch-univariate-base-archmodelfixedresult-aic" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodelfixedresult-aic">arch.univariate.base.ARCHModelFixedResult.aic<a class="headerlink" href="#arch-univariate-base-archmodelfixedresult-aic" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelFixedResult.aic">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelFixedResult.</span></span><span class="sig-name descname"><span class="pre">aic</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.aic" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelFixedResult.</span></span><span class="sig-name descname"><span class="pre">aic</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.aic" title="Link to this definition">¶</a></dt>
 <dd><p>Akaike Information Criteria</p>
 <p>-2 * loglikelihood + 2 * num_params</p>
 </dd></dl>
@@ -1078,7 +1078,7 @@ <h1 id="arch-univariate-base-archmodelfixedresult-aic">arch.univariate.base.ARCH
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1125,7 +1125,7 @@ <h1 id="arch-univariate-base-archmodelfixedresult-aic">arch.univariate.base.ARCH
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.arch_lm_test.html b/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.arch_lm_test.html
index 07a3648e1d..1f6810e634 100644
--- a/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.arch_lm_test.html
+++ b/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.arch_lm_test.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelFixedResult.arch_lm_test - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModelFixedResult.arch_lm_test - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1097,17 +1097,17 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodelfixedresult-arch-lm-test">arch.univariate.base.ARCHModelFixedResult.arch_lm_test<a class="headerlink" href="#arch-univariate-base-archmodelfixedresult-arch-lm-test" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodelfixedresult-arch-lm-test">arch.univariate.base.ARCHModelFixedResult.arch_lm_test<a class="headerlink" href="#arch-univariate-base-archmodelfixedresult-arch-lm-test" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelFixedResult.arch_lm_test">
-<span class="sig-prename descclassname"><span class="pre">ARCHModelFixedResult.</span></span><span class="sig-name descname"><span class="pre">arch_lm_test</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.arch_lm_test.lags" title="arch.univariate.base.ARCHModelFixedResult.arch_lm_test.lags (Python parameter) — Number of lags to include in the model.  If not specified,"><span class="n"><span class="pre">lags</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.arch_lm_test.standardized" title="arch.univariate.base.ARCHModelFixedResult.arch_lm_test.standardized (Python parameter) — Flag indicating to test the model residuals divided by their conditional standard deviations.  If False, directly tests the estimated residuals."><span class="n"><span class="pre">standardized</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/base.html#ARCHModelFixedResult.arch_lm_test"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.arch_lm_test" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.base.ARCHModelFixedResult.arch_lm_test">
+<span class="sig-prename descclassname"><span class="pre">ARCHModelFixedResult.</span></span><span class="sig-name descname"><span class="pre">arch_lm_test</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.arch_lm_test.lags" title="arch.univariate.base.ARCHModelFixedResult.arch_lm_test.lags (Python parameter) — Number of lags to include in the model.  If not specified,"><span class="n"><span class="pre">lags</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.arch_lm_test.standardized" title="arch.univariate.base.ARCHModelFixedResult.arch_lm_test.standardized (Python parameter) — Flag indicating to test the model residuals divided by their conditional standard deviations.  If False, directly tests the estimated residuals."><span class="n"><span class="pre">standardized</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../../utility.html#arch.utility.testing.WaldTestStatistic" title="arch.utility.testing.WaldTestStatistic (Python class) — Test statistic holder for Wald-type tests"><span class="pre">WaldTestStatistic</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/base.html#ARCHModelFixedResult.arch_lm_test"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.arch_lm_test" title="Link to this definition">¶</a></dt>
 <dd><p>ARCH LM test for conditional heteroskedasticity</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.base.ARCHModelFixedResult.arch_lm_test-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.arch_lm_test-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.base.ARCHModelFixedResult.arch_lm_test.lags"><span class="sig-name descname">lags</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.arch_lm_test.lags" title="Permalink to this definition">¶</a></dt><dd><p>Number of lags to include in the model.  If not specified,</p>
+<dt id="arch.univariate.base.ARCHModelFixedResult.arch_lm_test.lags"><span class="n sig-name">lags</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.arch_lm_test.lags" title="Permalink to this definition">¶</a></dt><dd><p>Number of lags to include in the model.  If not specified,</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelFixedResult.arch_lm_test.standardized"><span class="sig-name descname">standardized</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, optional</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.arch_lm_test.standardized" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating to test the model residuals divided by their
+<dt id="arch.univariate.base.ARCHModelFixedResult.arch_lm_test.standardized"><span class="n sig-name">standardized</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.arch_lm_test.standardized" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating to test the model residuals divided by their
 conditional standard deviations.  If False, directly tests the
 estimated residuals.</p>
 </dd>
@@ -1117,7 +1117,7 @@ <h1 id="arch-univariate-base-archmodelfixedresult-arch-lm-test">arch.univariate.
 <dd class="field-even"><p><strong>result</strong> – Result of ARCH-LM test</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.base.ARCHModelFixedResult.arch_lm_test-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.arch_lm_test-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference internal" href="../../utility.html#arch.utility.testing.WaldTestStatistic" title="arch.utility.testing.WaldTestStatistic (Python class) — Test statistic holder for Wald-type tests">WaldTestStatistic</a></p>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">WaldTestStatistic</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1189,7 +1189,7 @@ <h1 id="arch-univariate-base-archmodelfixedresult-arch-lm-test">arch.univariate.
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
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@@ -1236,7 +1236,7 @@ <h1 id="arch-univariate-base-archmodelfixedresult-arch-lm-test">arch.univariate.
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.bic.html b/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.bic.html
index 02de2ce3b5..566f3dd213 100644
--- a/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.bic.html
+++ b/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.bic.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelFixedResult.bic - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModelFixedResult.bic - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1003,10 +1003,10 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodelfixedresult-bic">arch.univariate.base.ARCHModelFixedResult.bic<a class="headerlink" href="#arch-univariate-base-archmodelfixedresult-bic" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodelfixedresult-bic">arch.univariate.base.ARCHModelFixedResult.bic<a class="headerlink" href="#arch-univariate-base-archmodelfixedresult-bic" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelFixedResult.bic">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelFixedResult.</span></span><span class="sig-name descname"><span class="pre">bic</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.bic" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelFixedResult.</span></span><span class="sig-name descname"><span class="pre">bic</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.bic" title="Link to this definition">¶</a></dt>
 <dd><p>Schwarz/Bayesian Information Criteria</p>
 <p>-2 * loglikelihood + log(nobs) * num_params</p>
 </dd></dl>
@@ -1078,7 +1078,7 @@ <h1 id="arch-univariate-base-archmodelfixedresult-bic">arch.univariate.base.ARCH
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1125,7 +1125,7 @@ <h1 id="arch-univariate-base-archmodelfixedresult-bic">arch.univariate.base.ARCH
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.conditional_volatility.html b/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.conditional_volatility.html
index 6885551de0..b5cf886194 100644
--- a/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.conditional_volatility.html
+++ b/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.conditional_volatility.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelFixedResult.conditional_volatility - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModelFixedResult.conditional_volatility - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1043,10 +1043,10 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodelfixedresult-conditional-volatility">arch.univariate.base.ARCHModelFixedResult.conditional_volatility<a class="headerlink" href="#arch-univariate-base-archmodelfixedresult-conditional-volatility" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodelfixedresult-conditional-volatility">arch.univariate.base.ARCHModelFixedResult.conditional_volatility<a class="headerlink" href="#arch-univariate-base-archmodelfixedresult-conditional-volatility" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.base.ARCHModelFixedResult.conditional_volatility">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelFixedResult.</span></span><span class="sig-name descname"><span class="pre">conditional_volatility</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.0.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">ndarray</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.conditional_volatility" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelFixedResult.</span></span><span class="sig-name descname"><span class="pre">conditional_volatility</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.conditional_volatility" title="Link to this definition">¶</a></dt>
 <dd><p>Estimated conditional volatility</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.base.ARCHModelFixedResult.conditional_volatility-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.conditional_volatility-returns" title="Permalink to this headline">¶</a></dt>
@@ -1056,7 +1056,7 @@ <h1 id="arch-univariate-base-archmodelfixedresult-conditional-volatility">arch.u
 of t-th error, which is computed using time-(t-1) information.</p>
 </dd>
 <dt class="field-even" id="arch.univariate.base.ARCHModelFixedResult.conditional_volatility-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.conditional_volatility-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p>{ndarray, Series}</p>
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 </dd>
 </dl>
 </dd></dl>
@@ -1128,7 +1128,7 @@ <h1 id="arch-univariate-base-archmodelfixedresult-conditional-volatility">arch.u
   
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diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.forecast.html b/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.forecast.html
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--- a/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.forecast.html
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelFixedResult.forecast - arch 6.1.0</title>
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@@ -102,7 +102,7 @@
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@@ -647,7 +647,7 @@
         
           <li class="md-nav__item">
   <a href="#arch.univariate.base.ARCHModelFixedResult.forecast.reindex" class="md-nav__link">
-    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="arch.univariate.base.ARCHModelFixedResult.forecast.reindex (Python parameter) — Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021." class="md-ellipsis">reindex</span>
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@@ -1159,7 +1159,7 @@
         
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@@ -1209,57 +1209,55 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodelfixedresult-forecast">arch.univariate.base.ARCHModelFixedResult.forecast<a class="headerlink" href="#arch-univariate-base-archmodelfixedresult-forecast" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodelfixedresult-forecast">arch.univariate.base.ARCHModelFixedResult.forecast<a class="headerlink" href="#arch-univariate-base-archmodelfixedresult-forecast" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.base.ARCHModelFixedResult.forecast">
-<span class="sig-prename descclassname"><span class="pre">ARCHModelFixedResult.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.forecast.params" title="arch.univariate.base.ARCHModelFixedResult.forecast.params (Python parameter) — Alternative parameters to use.  If not provided, the parameters estimated when fitting the model are used.  Must be identical in shape to the parameters computed by fitting the model."><span class="n"><span class="pre">params</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.forecast.horizon" title="arch.univariate.base.ARCHModelFixedResult.forecast.horizon (Python parameter) — Number of steps to forecast"><span class="n"><span class="pre">horizon</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.forecast.start" title="arch.univariate.base.ARCHModelFixedResult.forecast.start (Python parameter) — An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."><span class="n"><span class="pre">start</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.forecast.align" title="arch.univariate.base.ARCHModelFixedResult.forecast.align (Python parameter) — Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."><span class="n"><span class="pre">align</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;origin&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.forecast.method" title="arch.univariate.base.ARCHModelFixedResult.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons &gt; 1."><span class="n"><span class="pre">method</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.forecast.simulations" title="arch.univariate.base.ARCHModelFixedResult.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.forecast.rng" title="arch.univariate.base.ARCHModelFixedResult.forecast.rng (Python parameter) — Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."><span class="n"><span class="pre">rng</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.forecast.random_state" title="arch.univariate.base.ARCHModelFixedResult.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.forecast.reindex" title="arch.univariate.base.ARCHModelFixedResult.forecast.reindex (Python parameter) — Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."><span class="n"><span class="pre">reindex</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.forecast.x" title="arch.univariate.base.ARCHModelFixedResult.forecast.x (Python parameter) — Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"><span class="n"><span class="pre">x</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/base.html#ARCHModelFixedResult.forecast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.forecast" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ARCHModelFixedResult.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.forecast.params" title="arch.univariate.base.ARCHModelFixedResult.forecast.params (Python parameter) — Alternative parameters to use.  If not provided, the parameters estimated when fitting the model are used.  Must be identical in shape to the parameters computed by fitting the model."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.forecast.horizon" title="arch.univariate.base.ARCHModelFixedResult.forecast.horizon (Python parameter) — Number of steps to forecast"><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.forecast.start" title="arch.univariate.base.ARCHModelFixedResult.forecast.start (Python parameter) — An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.forecast.align" title="arch.univariate.base.ARCHModelFixedResult.forecast.align (Python parameter) — Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."><span class="n"><span class="pre">align</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'origin'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'target'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;origin&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.forecast.method" title="arch.univariate.base.ARCHModelFixedResult.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons &gt; 1."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.forecast.simulations" title="arch.univariate.base.ARCHModelFixedResult.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.forecast.rng" title="arch.univariate.base.ARCHModelFixedResult.forecast.rng (Python parameter) — Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.forecast.random_state" title="arch.univariate.base.ARCHModelFixedResult.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.forecast.reindex" title="arch.univariate.base.ARCHModelFixedResult.forecast.reindex (Python parameter) — Whether to reindex the forecasts to have the same dimension as the series being forecast."><span class="n"><span class="pre">reindex</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.forecast.x" title="arch.univariate.base.ARCHModelFixedResult.forecast.x (Python parameter) — Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.11)"><span class="pre">Hashable</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelForecast.html#arch.univariate.base.ARCHModelForecast" title="arch.univariate.base.ARCHModelForecast (Python class) — Container for forecasts from an ARCH Model"><span class="pre">ARCHModelForecast</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/base.html#ARCHModelFixedResult.forecast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.forecast" title="Link to this definition">¶</a></dt>
 <dd><p>Construct forecasts from estimated model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.base.ARCHModelFixedResult.forecast-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.forecast-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.base.ARCHModelFixedResult.forecast.params"><span class="sig-name descname">params</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray, optional</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.forecast.params" title="Permalink to this definition">¶</a></dt><dd><p>Alternative parameters to use.  If not provided, the parameters
+<dt id="arch.univariate.base.ARCHModelFixedResult.forecast.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.forecast.params" title="Permalink to this definition">¶</a></dt><dd><p>Alternative parameters to use.  If not provided, the parameters
 estimated when fitting the model are used.  Must be identical in
 shape to the parameters computed by fitting the model.</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelFixedResult.forecast.horizon"><span class="sig-name descname">horizon</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.forecast.horizon" title="Permalink to this definition">¶</a></dt><dd><p>Number of steps to forecast</p>
+<dt id="arch.univariate.base.ARCHModelFixedResult.forecast.horizon"><span class="n sig-name">horizon</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.forecast.horizon" title="Permalink to this definition">¶</a></dt><dd><p>Number of steps to forecast</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelFixedResult.forecast.start"><span class="sig-name descname">start</span><span class="p"> : </span><span class="desctype"><span class="desctype">{int, datetime, Timestamp, str}, optional</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>An integer, datetime or str indicating the first observation to
+<dt id="arch.univariate.base.ARCHModelFixedResult.forecast.start"><span class="n sig-name">start</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>An integer, datetime or str indicating the first observation to
 produce the forecast for.  Datetimes can only be used with pandas
 inputs that have a datetime index. Strings must be convertible
 to a date time, such as in ‘1945-01-01’.</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelFixedResult.forecast.align"><span class="sig-name descname">align</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, optional</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.forecast.align" title="Permalink to this definition">¶</a></dt><dd><p>Either ‘origin’ or ‘target’.  When set of ‘origin’, the t-th row
+<dt id="arch.univariate.base.ARCHModelFixedResult.forecast.align"><span class="n sig-name">align</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'origin'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'target'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;origin&#39;</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.forecast.align" title="Permalink to this definition">¶</a></dt><dd><p>Either ‘origin’ or ‘target’.  When set of ‘origin’, the t-th row
 of forecasts contains the forecasts for t+1, t+2, …, t+h. When
 set to ‘target’, the t-th row contains the 1-step ahead forecast
 from time t-1, the 2 step from time t-2, …, and the h-step from
 time t-h.  ‘target’ simplified computing forecast errors since the
 realization and h-step forecast are aligned.</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelFixedResult.forecast.method"><span class="sig-name descname">method</span><span class="p"> : </span><span class="desctype"><span class="desctype">{'analytic', 'simulation', 'bootstrap'}, optional</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.forecast.method" title="Permalink to this definition">¶</a></dt><dd><p>Method to use when producing the forecast. The default is analytic.
+<dt id="arch.univariate.base.ARCHModelFixedResult.forecast.method"><span class="n sig-name">method</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'analytic'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'simulation'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'bootstrap'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.forecast.method" title="Permalink to this definition">¶</a></dt><dd><p>Method to use when producing the forecast. The default is analytic.
 The method only affects the variance forecast generation.  Not all
 volatility models support all methods. In particular, volatility
 models that do not evolve in squares such as EGARCH or TARCH do not
 support the ‘analytic’ method for horizons &gt; 1.</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelFixedResult.forecast.simulations"><span class="sig-name descname">simulations</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.forecast.simulations" title="Permalink to this definition">¶</a></dt><dd><p>Number of simulations to run when computing the forecast using
+<dt id="arch.univariate.base.ARCHModelFixedResult.forecast.simulations"><span class="n sig-name">simulations</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.forecast.simulations" title="Permalink to this definition">¶</a></dt><dd><p>Number of simulations to run when computing the forecast using
 either simulation or bootstrap.</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelFixedResult.forecast.rng"><span class="sig-name descname">rng</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable, optional</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.forecast.rng" title="Permalink to this definition">¶</a></dt><dd><p>Custom random number generator to use in simulation-based forecasts.
+<dt id="arch.univariate.base.ARCHModelFixedResult.forecast.rng"><span class="n sig-name">rng</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)">Callable</a><span class="p">[</span><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">,</span><span class="w"> </span><span class="p">...</span><span class="p">]</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.forecast.rng" title="Permalink to this definition">¶</a></dt><dd><p>Custom random number generator to use in simulation-based forecasts.
 Must produce random samples using the syntax <cite>rng(size)</cite> where size
 the 2-element tuple (simulations, horizon).</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelFixedResult.forecast.random_state"><span class="sig-name descname">random_state</span><span class="p"> : </span><span class="desctype"><span class="desctype">RandomState, optional</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.forecast.random_state" title="Permalink to this definition">¶</a></dt><dd><p>NumPy RandomState instance to use when method is ‘bootstrap’</p>
+<dt id="arch.univariate.base.ARCHModelFixedResult.forecast.random_state"><span class="n sig-name">random_state</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)">RandomState</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.forecast.random_state" title="Permalink to this definition">¶</a></dt><dd><p>NumPy RandomState instance to use when method is ‘bootstrap’</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelFixedResult.forecast.reindex"><span class="sig-name descname">reindex</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, optional</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.forecast.reindex" title="Permalink to this definition">¶</a></dt><dd><p><p>Whether to reindex the forecasts to have the same dimension as the series
-being forecast. Prior to 4.18 this was the default. As of 4.19 this is
-now optional. If not provided, a warning is raised about the future
-change in the default which will occur after September 2021.</p>
-<div class="versionadded">
-<p><span class="versionmodified added">New in version 4.19.</span></p>
+<dt id="arch.univariate.base.ARCHModelFixedResult.forecast.reindex"><span class="n sig-name">reindex</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.forecast.reindex" title="Permalink to this definition">¶</a></dt><dd><p><p>Whether to reindex the forecasts to have the same dimension as the series
+being forecast.</p>
+<div class="admonition versionchanged">
+<p class="admonition-title"><span class="versionmodified changed">Changed in version 6.2: </span>The default has been changed to False.</p>
 </div>
 </p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelFixedResult.forecast.x"><span class="sig-name descname">x</span><span class="p"> : </span><span class="desctype"><span class="desctype">{dict[label, array_like], array_like}</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.forecast.x" title="Permalink to this definition">¶</a></dt><dd><p><p>Values to use for exogenous regressors if any are included in the
+<dt id="arch.univariate.base.ARCHModelFixedResult.forecast.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.11)">Hashable</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.forecast.x" title="Permalink to this definition">¶</a></dt><dd><p><p>Values to use for exogenous regressors if any are included in the
 model. Three formats are accepted:</p>
 <ul class="simple">
 <li><p>2-d array-like: This format can be used when there is a single
@@ -1282,8 +1280,8 @@ <h1 id="arch-univariate-base-archmodelfixedresult-forecast">arch.univariate.base
 <p>Due to the complexity required to accommodate all scenarios, please
 see the example notebook that demonstrates the valid formats for
 x.</p>
-<div class="versionadded">
-<p><span class="versionmodified added">New in version 4.19.</span></p>
+<div class="admonition versionadded">
+<p class="admonition-title"><span class="versionmodified added">New in version 4.19.</span></p>
 </div>
 </p>
 </dd>
@@ -1294,7 +1292,7 @@ <h1 id="arch-univariate-base-archmodelfixedresult-forecast">arch.univariate.base
 <code class="docutils literal notranslate"><span class="pre">variance</span></code> and <code class="docutils literal notranslate"><span class="pre">residual_variance</span></code>.</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.base.ARCHModelFixedResult.forecast-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.forecast-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference internal" href="../arch.univariate.base.ARCHModelForecast.html#arch.univariate.base.ARCHModelForecast" title="arch.univariate.base.ARCHModelForecast (Python class) — Container for forecasts from an ARCH Model">arch.univariate.base.ARCHModelForecast</a></p>
+<dd class="field-odd"><p><a class="reference internal" href="../arch.univariate.base.ARCHModelForecast.html#arch.univariate.base.ARCHModelForecast" title="arch.univariate.base.ARCHModelForecast (Python class) — Container for forecasts from an ARCH Model"><code class="xref py py-class docutils literal notranslate"><span class="pre">arch.univariate.base.ARCHModelForecast</span></code></a></p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -1383,7 +1381,7 @@ <h1 id="arch-univariate-base-archmodelfixedresult-forecast">arch.univariate.base
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
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@@ -1430,7 +1428,7 @@ <h1 id="arch-univariate-base-archmodelfixedresult-forecast">arch.univariate.base
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.html b/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.html
index 39ae3f96fe..937e44cc4a 100644
--- a/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.html
+++ b/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelFixedResult.hedgehog_plot - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModelFixedResult.hedgehog_plot - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1167,37 +1167,37 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodelfixedresult-hedgehog-plot">arch.univariate.base.ARCHModelFixedResult.hedgehog_plot<a class="headerlink" href="#arch-univariate-base-archmodelfixedresult-hedgehog-plot" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodelfixedresult-hedgehog-plot">arch.univariate.base.ARCHModelFixedResult.hedgehog_plot<a class="headerlink" href="#arch-univariate-base-archmodelfixedresult-hedgehog-plot" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.base.ARCHModelFixedResult.hedgehog_plot">
-<span class="sig-prename descclassname"><span class="pre">ARCHModelFixedResult.</span></span><span class="sig-name descname"><span class="pre">hedgehog_plot</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.params" title="arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.params (Python parameter) — Alternative parameters to use.  If not provided, the parameters computed by fitting the model are used.  Must be 1-d and identical in shape to the parameters computed by fitting the model."><span class="n"><span class="pre">params</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.horizon" title="arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.horizon (Python parameter) — Number of steps to forecast"><span class="n"><span class="pre">horizon</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">10</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.step" title="arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.step (Python parameter) — Non-negative number of forecasts to skip between spines"><span class="n"><span class="pre">step</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">10</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.start" title="arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.start (Python parameter) — An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index.  Strings must be convertible to a date time, such as in '1945-01-01'.  If not provided, the start is set to the earliest forecastable date."><span class="n"><span class="pre">start</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.plot_type" title="arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.plot_type (Python parameter) — Quantity to plot, the forecast volatility or the forecast mean"><span class="n"><span class="pre">plot_type</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;volatility&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.method" title="arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.method (Python parameter) — Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons &gt; 1."><span class="n"><span class="pre">method</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.simulations" title="arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/base.html#ARCHModelFixedResult.hedgehog_plot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.hedgehog_plot" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ARCHModelFixedResult.</span></span><span class="sig-name descname"><span class="pre">hedgehog_plot</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.params" title="arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.params (Python parameter) — Alternative parameters to use.  If not provided, the parameters computed by fitting the model are used.  Must be 1-d and identical in shape to the parameters computed by fitting the model."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.horizon" title="arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.horizon (Python parameter) — Number of steps to forecast"><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">10</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.step" title="arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.step (Python parameter) — Non-negative number of forecasts to skip between spines"><span class="n"><span class="pre">step</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">10</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.start" title="arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.start (Python parameter) — An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index.  Strings must be convertible to a date time, such as in '1945-01-01'.  If not provided, the start is set to the earliest forecastable date."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.plot_type" title="arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.plot_type (Python parameter) — Quantity to plot, the forecast volatility or the forecast mean"><span class="n"><span class="pre">plot_type</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'volatility'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'mean'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;volatility&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.method" title="arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.method (Python parameter) — Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons &gt; 1."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.simulations" title="arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="(in Matplotlib v3.8.0)"><span class="pre">Figure</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/base.html#ARCHModelFixedResult.hedgehog_plot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.hedgehog_plot" title="Link to this definition">¶</a></dt>
 <dd><p>Plot forecasts from estimated model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.base.ARCHModelFixedResult.hedgehog_plot-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.hedgehog_plot-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.params"><span class="sig-name descname">params</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, Series}</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.params" title="Permalink to this definition">¶</a></dt><dd><p>Alternative parameters to use.  If not provided, the parameters
+<dt id="arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.params" title="Permalink to this definition">¶</a></dt><dd><p>Alternative parameters to use.  If not provided, the parameters
 computed by fitting the model are used.  Must be 1-d and identical
 in shape to the parameters computed by fitting the model.</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.horizon"><span class="sig-name descname">horizon</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.horizon" title="Permalink to this definition">¶</a></dt><dd><p>Number of steps to forecast</p>
+<dt id="arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.horizon"><span class="n sig-name">horizon</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">10</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.horizon" title="Permalink to this definition">¶</a></dt><dd><p>Number of steps to forecast</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.step"><span class="sig-name descname">step</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.step" title="Permalink to this definition">¶</a></dt><dd><p>Non-negative number of forecasts to skip between spines</p>
+<dt id="arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.step"><span class="n sig-name">step</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">10</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.step" title="Permalink to this definition">¶</a></dt><dd><p>Non-negative number of forecasts to skip between spines</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.start"><span class="sig-name descname">start</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, datetime or <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, optional</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.start" title="Permalink to this definition">¶</a></dt><dd><p>An integer, datetime or str indicating the first observation to
+<dt id="arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.start"><span class="n sig-name">start</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.start" title="Permalink to this definition">¶</a></dt><dd><p>An integer, datetime or str indicating the first observation to
 produce the forecast for.  Datetimes can only be used with pandas
 inputs that have a datetime index.  Strings must be convertible
 to a date time, such as in ‘1945-01-01’.  If not provided, the start
 is set to the earliest forecastable date.</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.plot_type"><span class="sig-name descname">plot_type</span><span class="p"> : </span><span class="desctype"><span class="desctype">{'volatility', 'mean'}</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.plot_type" title="Permalink to this definition">¶</a></dt><dd><p>Quantity to plot, the forecast volatility or the forecast mean</p>
+<dt id="arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.plot_type"><span class="n sig-name">plot_type</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'volatility'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'mean'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;volatility&#39;</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.plot_type" title="Permalink to this definition">¶</a></dt><dd><p>Quantity to plot, the forecast volatility or the forecast mean</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.method"><span class="sig-name descname">method</span><span class="p"> : </span><span class="desctype"><span class="desctype">{'analytic', 'simulation', 'bootstrap'}</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.method" title="Permalink to this definition">¶</a></dt><dd><p>Method to use when producing the forecast. The default is analytic.
+<dt id="arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.method"><span class="n sig-name">method</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'analytic'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'simulation'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'bootstrap'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.method" title="Permalink to this definition">¶</a></dt><dd><p>Method to use when producing the forecast. The default is analytic.
 The method only affects the variance forecast generation.  Not all
 volatility models support all methods. In particular, volatility
 models that do not evolve in squares such as EGARCH or TARCH do not
 support the ‘analytic’ method for horizons &gt; 1.</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.simulations"><span class="sig-name descname">simulations</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.simulations" title="Permalink to this definition">¶</a></dt><dd><p>Number of simulations to run when computing the forecast using
+<dt id="arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.simulations"><span class="n sig-name">simulations</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.simulations" title="Permalink to this definition">¶</a></dt><dd><p>Number of simulations to run when computing the forecast using
 either simulation or bootstrap.</p>
 </dd>
 </dl>
@@ -1206,7 +1206,7 @@ <h1 id="arch-univariate-base-archmodelfixedresult-hedgehog-plot">arch.univariate
 <dd class="field-even"><p><strong>fig</strong> – Handle to the figure</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.base.ARCHModelFixedResult.hedgehog_plot-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.hedgehog_plot-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>figure</p>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">figure</span></code></p>
 </dd>
 </dl>
 <p class="example rubric">Examples</p>
@@ -1289,7 +1289,7 @@ <h1 id="arch-univariate-base-archmodelfixedresult-hedgehog-plot">arch.univariate
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1336,7 +1336,7 @@ <h1 id="arch-univariate-base-archmodelfixedresult-hedgehog-plot">arch.univariate
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.loglikelihood.html b/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.loglikelihood.html
index 31feb38baa..7b66d9064b 100644
--- a/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.loglikelihood.html
+++ b/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.loglikelihood.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelFixedResult.loglikelihood - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModelFixedResult.loglikelihood - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1003,10 +1003,10 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodelfixedresult-loglikelihood">arch.univariate.base.ARCHModelFixedResult.loglikelihood<a class="headerlink" href="#arch-univariate-base-archmodelfixedresult-loglikelihood" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodelfixedresult-loglikelihood">arch.univariate.base.ARCHModelFixedResult.loglikelihood<a class="headerlink" href="#arch-univariate-base-archmodelfixedresult-loglikelihood" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelFixedResult.loglikelihood">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelFixedResult.</span></span><span class="sig-name descname"><span class="pre">loglikelihood</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.loglikelihood" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelFixedResult.</span></span><span class="sig-name descname"><span class="pre">loglikelihood</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.loglikelihood" title="Link to this definition">¶</a></dt>
 <dd><p>Model loglikelihood</p>
 </dd></dl>
 
@@ -1077,7 +1077,7 @@ <h1 id="arch-univariate-base-archmodelfixedresult-loglikelihood">arch.univariate
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1124,7 +1124,7 @@ <h1 id="arch-univariate-base-archmodelfixedresult-loglikelihood">arch.univariate
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.model.html b/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.model.html
index 0d895682d5..3bc9b79425 100644
--- a/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.model.html
+++ b/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.model.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelFixedResult.model - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModelFixedResult.model - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1003,10 +1003,10 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodelfixedresult-model">arch.univariate.base.ARCHModelFixedResult.model<a class="headerlink" href="#arch-univariate-base-archmodelfixedresult-model" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodelfixedresult-model">arch.univariate.base.ARCHModelFixedResult.model<a class="headerlink" href="#arch-univariate-base-archmodelfixedresult-model" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelFixedResult.model">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelFixedResult.</span></span><span class="sig-name descname"><span class="pre">model</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModel.html#arch.univariate.base.ARCHModel" title="arch.univariate.base.ARCHModel (Python class) — Abstract base class for mean models in ARCH processes.  Specifies the conditional mean process."><span class="pre">ARCHModel</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.model" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelFixedResult.</span></span><span class="sig-name descname"><span class="pre">model</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModel.html#arch.univariate.base.ARCHModel" title="arch.univariate.base.ARCHModel (Python class) — Abstract base class for mean models in ARCH processes.  Specifies the conditional mean process."><span class="pre">ARCHModel</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.model" title="Link to this definition">¶</a></dt>
 <dd><p>Model instance used to produce the fit</p>
 </dd></dl>
 
@@ -1077,7 +1077,7 @@ <h1 id="arch-univariate-base-archmodelfixedresult-model">arch.univariate.base.AR
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1124,7 +1124,7 @@ <h1 id="arch-univariate-base-archmodelfixedresult-model">arch.univariate.base.AR
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.nobs.html b/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.nobs.html
index a44fc6b277..67e3e0b32d 100644
--- a/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.nobs.html
+++ b/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.nobs.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelFixedResult.nobs - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModelFixedResult.nobs - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1003,10 +1003,10 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodelfixedresult-nobs">arch.univariate.base.ARCHModelFixedResult.nobs<a class="headerlink" href="#arch-univariate-base-archmodelfixedresult-nobs" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodelfixedresult-nobs">arch.univariate.base.ARCHModelFixedResult.nobs<a class="headerlink" href="#arch-univariate-base-archmodelfixedresult-nobs" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelFixedResult.nobs">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelFixedResult.</span></span><span class="sig-name descname"><span class="pre">nobs</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.nobs" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelFixedResult.</span></span><span class="sig-name descname"><span class="pre">nobs</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.nobs" title="Link to this definition">¶</a></dt>
 <dd><p>Number of data points used to estimate model</p>
 </dd></dl>
 
@@ -1077,7 +1077,7 @@ <h1 id="arch-univariate-base-archmodelfixedresult-nobs">arch.univariate.base.ARC
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1124,7 +1124,7 @@ <h1 id="arch-univariate-base-archmodelfixedresult-nobs">arch.univariate.base.ARC
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.num_params.html b/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.num_params.html
index 565bf3a07e..366cb7e09c 100644
--- a/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.num_params.html
+++ b/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.num_params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelFixedResult.num_params - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModelFixedResult.num_params - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1003,10 +1003,10 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodelfixedresult-num-params">arch.univariate.base.ARCHModelFixedResult.num_params<a class="headerlink" href="#arch-univariate-base-archmodelfixedresult-num-params" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodelfixedresult-num-params">arch.univariate.base.ARCHModelFixedResult.num_params<a class="headerlink" href="#arch-univariate-base-archmodelfixedresult-num-params" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelFixedResult.num_params">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelFixedResult.</span></span><span class="sig-name descname"><span class="pre">num_params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.num_params" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelFixedResult.</span></span><span class="sig-name descname"><span class="pre">num_params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.num_params" title="Link to this definition">¶</a></dt>
 <dd><p>Number of parameters in model</p>
 </dd></dl>
 
@@ -1077,7 +1077,7 @@ <h1 id="arch-univariate-base-archmodelfixedresult-num-params">arch.univariate.ba
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1124,7 +1124,7 @@ <h1 id="arch-univariate-base-archmodelfixedresult-num-params">arch.univariate.ba
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.params.html b/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.params.html
index 3970ccf773..81f234fd2f 100644
--- a/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.params.html
+++ b/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelFixedResult.params - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModelFixedResult.params - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1003,10 +1003,10 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodelfixedresult-params">arch.univariate.base.ARCHModelFixedResult.params<a class="headerlink" href="#arch-univariate-base-archmodelfixedresult-params" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodelfixedresult-params">arch.univariate.base.ARCHModelFixedResult.params<a class="headerlink" href="#arch-univariate-base-archmodelfixedresult-params" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelFixedResult.params">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelFixedResult.</span></span><span class="sig-name descname"><span class="pre">params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.0.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.params" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelFixedResult.</span></span><span class="sig-name descname"><span class="pre">params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.params" title="Link to this definition">¶</a></dt>
 <dd><p>Model Parameters</p>
 </dd></dl>
 
@@ -1077,7 +1077,7 @@ <h1 id="arch-univariate-base-archmodelfixedresult-params">arch.univariate.base.A
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1124,7 +1124,7 @@ <h1 id="arch-univariate-base-archmodelfixedresult-params">arch.univariate.base.A
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.plot.html b/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.plot.html
index cc81c28011..def1c0b52e 100644
--- a/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.plot.html
+++ b/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.plot.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelFixedResult.plot - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModelFixedResult.plot - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1097,20 +1097,20 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodelfixedresult-plot">arch.univariate.base.ARCHModelFixedResult.plot<a class="headerlink" href="#arch-univariate-base-archmodelfixedresult-plot" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodelfixedresult-plot">arch.univariate.base.ARCHModelFixedResult.plot<a class="headerlink" href="#arch-univariate-base-archmodelfixedresult-plot" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelFixedResult.plot">
-<span class="sig-prename descclassname"><span class="pre">ARCHModelFixedResult.</span></span><span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.plot.annualize" title="arch.univariate.base.ARCHModelFixedResult.plot.annualize (Python parameter) — String containing frequency of data that indicates plot should contain annualized volatility.  Supported values are 'D' (daily), 'W' (weekly) and 'M' (monthly), which scale variance by 252, 52, and 12, respectively."><span class="n"><span class="pre">annualize</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.plot.scale" title="arch.univariate.base.ARCHModelFixedResult.plot.scale (Python parameter) — Value to use when scaling returns to annualize.  If scale is provided, annualize is ignored and the value in scale is used."><span class="n"><span class="pre">scale</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/base.html#ARCHModelFixedResult.plot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.plot" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.base.ARCHModelFixedResult.plot">
+<span class="sig-prename descclassname"><span class="pre">ARCHModelFixedResult.</span></span><span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.plot.annualize" title="arch.univariate.base.ARCHModelFixedResult.plot.annualize (Python parameter) — String containing frequency of data that indicates plot should contain annualized volatility.  Supported values are 'D' (daily), 'W' (weekly) and 'M' (monthly), which scale variance by 252, 52, and 12, respectively."><span class="n"><span class="pre">annualize</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.plot.scale" title="arch.univariate.base.ARCHModelFixedResult.plot.scale (Python parameter) — Value to use when scaling returns to annualize.  If scale is provided, annualize is ignored and the value in scale is used."><span class="n"><span class="pre">scale</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="(in Matplotlib v3.8.0)"><span class="pre">Figure</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/base.html#ARCHModelFixedResult.plot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.plot" title="Link to this definition">¶</a></dt>
 <dd><p>Plot standardized residuals and conditional volatility</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.base.ARCHModelFixedResult.plot-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.plot-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.base.ARCHModelFixedResult.plot.annualize"><span class="sig-name descname">annualize</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, optional</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.plot.annualize" title="Permalink to this definition">¶</a></dt><dd><p>String containing frequency of data that indicates plot should
+<dt id="arch.univariate.base.ARCHModelFixedResult.plot.annualize"><span class="n sig-name">annualize</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.plot.annualize" title="Permalink to this definition">¶</a></dt><dd><p>String containing frequency of data that indicates plot should
 contain annualized volatility.  Supported values are ‘D’ (daily),
 ‘W’ (weekly) and ‘M’ (monthly), which scale variance by 252, 52,
 and 12, respectively.</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelFixedResult.plot.scale"><span class="sig-name descname">scale</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>, optional</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.plot.scale" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when scaling returns to annualize.  If scale is
+<dt id="arch.univariate.base.ARCHModelFixedResult.plot.scale"><span class="n sig-name">scale</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.plot.scale" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when scaling returns to annualize.  If scale is
 provided, annualize is ignored and the value in scale is used.</p>
 </dd>
 </dl>
@@ -1119,7 +1119,7 @@ <h1 id="arch-univariate-base-archmodelfixedresult-plot">arch.univariate.base.ARC
 <dd class="field-even"><p><strong>fig</strong> – Handle to the figure</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.base.ARCHModelFixedResult.plot-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.plot-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>figure</p>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">figure</span></code></p>
 </dd>
 </dl>
 <p class="example rubric">Examples</p>
@@ -1209,7 +1209,7 @@ <h1 id="arch-univariate-base-archmodelfixedresult-plot">arch.univariate.base.ARC
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1256,7 +1256,7 @@ <h1 id="arch-univariate-base-archmodelfixedresult-plot">arch.univariate.base.ARC
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.resid.html b/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.resid.html
index 99863177cb..ca70dd8c37 100644
--- a/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.resid.html
+++ b/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.resid.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelFixedResult.resid - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModelFixedResult.resid - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1003,10 +1003,10 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodelfixedresult-resid">arch.univariate.base.ARCHModelFixedResult.resid<a class="headerlink" href="#arch-univariate-base-archmodelfixedresult-resid" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodelfixedresult-resid">arch.univariate.base.ARCHModelFixedResult.resid<a class="headerlink" href="#arch-univariate-base-archmodelfixedresult-resid" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelFixedResult.resid">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelFixedResult.</span></span><span class="sig-name descname"><span class="pre">resid</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.0.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.resid" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelFixedResult.</span></span><span class="sig-name descname"><span class="pre">resid</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.resid" title="Link to this definition">¶</a></dt>
 <dd><p>Model residuals</p>
 </dd></dl>
 
@@ -1077,7 +1077,7 @@ <h1 id="arch-univariate-base-archmodelfixedresult-resid">arch.univariate.base.AR
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1124,7 +1124,7 @@ <h1 id="arch-univariate-base-archmodelfixedresult-resid">arch.univariate.base.AR
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.std_resid.html b/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.std_resid.html
index 3d75d959b1..987d80eba7 100644
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+++ b/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.std_resid.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.univariate.base.ARCHModelFixedResult.std_resid - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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@@ -102,7 +102,7 @@
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-            arch 6.1.0
+            arch 6.2.0
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@@ -1003,10 +1003,10 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodelfixedresult-std-resid">arch.univariate.base.ARCHModelFixedResult.std_resid<a class="headerlink" href="#arch-univariate-base-archmodelfixedresult-std-resid" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodelfixedresult-std-resid">arch.univariate.base.ARCHModelFixedResult.std_resid<a class="headerlink" href="#arch-univariate-base-archmodelfixedresult-std-resid" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelFixedResult.std_resid">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelFixedResult.</span></span><span class="sig-name descname"><span class="pre">std_resid</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.0.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.std_resid" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelFixedResult.</span></span><span class="sig-name descname"><span class="pre">std_resid</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.std_resid" title="Link to this definition">¶</a></dt>
 <dd><p>Residuals standardized by conditional volatility</p>
 </dd></dl>
 
@@ -1077,7 +1077,7 @@ <h1 id="arch-univariate-base-archmodelfixedresult-std-resid">arch.univariate.bas
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1124,7 +1124,7 @@ <h1 id="arch-univariate-base-archmodelfixedresult-std-resid">arch.univariate.bas
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diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.summary.html b/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.summary.html
index 314fdbdd15..d743666c50 100644
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+++ b/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.summary.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelFixedResult.summary - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModelFixedResult.summary - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1043,10 +1043,10 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodelfixedresult-summary">arch.univariate.base.ARCHModelFixedResult.summary<a class="headerlink" href="#arch-univariate-base-archmodelfixedresult-summary" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodelfixedresult-summary">arch.univariate.base.ARCHModelFixedResult.summary<a class="headerlink" href="#arch-univariate-base-archmodelfixedresult-summary" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelFixedResult.summary">
-<span class="sig-prename descclassname"><span class="pre">ARCHModelFixedResult.</span></span><span class="sig-name descname"><span class="pre">summary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/base.html#ARCHModelFixedResult.summary"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.summary" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ARCHModelFixedResult.</span></span><span class="sig-name descname"><span class="pre">summary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.iolib.summary.Summary.html#statsmodels.iolib.summary.Summary" title="(in statsmodels 0.15.0 (+59) v0.15.0 (+59))"><span class="pre">Summary</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/base.html#ARCHModelFixedResult.summary"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.summary" title="Link to this definition">¶</a></dt>
 <dd><p>Constructs a summary of the results from a fit model.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.base.ARCHModelFixedResult.summary-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.summary-returns" title="Permalink to this headline">¶</a></dt>
@@ -1054,7 +1054,7 @@ <h1 id="arch-univariate-base-archmodelfixedresult-summary">arch.univariate.base.
 latex</p>
 </dd>
 <dt class="field-even" id="arch.univariate.base.ARCHModelFixedResult.summary-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.summary-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p>Summary instance</p>
+<dd class="field-even"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">Summary</span> <span class="pre">instance</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1126,7 +1126,7 @@ <h1 id="arch-univariate-base-archmodelfixedresult-summary">arch.univariate.base.
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
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diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModelForecast.mean.html b/univariate/generated/generated/arch.univariate.base.ARCHModelForecast.mean.html
index e8cd5e03d1..e0ebe5bc87 100644
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+++ b/univariate/generated/generated/arch.univariate.base.ARCHModelForecast.mean.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelForecast.mean - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModelForecast.mean - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -878,10 +878,10 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodelforecast-mean">arch.univariate.base.ARCHModelForecast.mean<a class="headerlink" href="#arch-univariate-base-archmodelforecast-mean" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodelforecast-mean">arch.univariate.base.ARCHModelForecast.mean<a class="headerlink" href="#arch-univariate-base-archmodelforecast-mean" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelForecast.mean">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelForecast.</span></span><span class="sig-name descname"><span class="pre">mean</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.0.1)"><span class="pre">DataFrame</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelForecast.mean" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelForecast.</span></span><span class="sig-name descname"><span class="pre">mean</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelForecast.mean" title="Link to this definition">¶</a></dt>
 <dd><p>Forecast values for the conditional mean of the process</p>
 </dd></dl>
 
@@ -952,7 +952,7 @@ <h1 id="arch-univariate-base-archmodelforecast-mean">arch.univariate.base.ARCHMo
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
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@@ -999,7 +999,7 @@ <h1 id="arch-univariate-base-archmodelforecast-mean">arch.univariate.base.ARCHMo
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
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diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModelForecast.residual_variance.html b/univariate/generated/generated/arch.univariate.base.ARCHModelForecast.residual_variance.html
index 906aa741f9..ad4c1cd7c8 100644
--- a/univariate/generated/generated/arch.univariate.base.ARCHModelForecast.residual_variance.html
+++ b/univariate/generated/generated/arch.univariate.base.ARCHModelForecast.residual_variance.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelForecast.residual_variance - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModelForecast.residual_variance - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -878,10 +878,10 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodelforecast-residual-variance">arch.univariate.base.ARCHModelForecast.residual_variance<a class="headerlink" href="#arch-univariate-base-archmodelforecast-residual-variance" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodelforecast-residual-variance">arch.univariate.base.ARCHModelForecast.residual_variance<a class="headerlink" href="#arch-univariate-base-archmodelforecast-residual-variance" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelForecast.residual_variance">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelForecast.</span></span><span class="sig-name descname"><span class="pre">residual_variance</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.0.1)"><span class="pre">DataFrame</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelForecast.residual_variance" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelForecast.</span></span><span class="sig-name descname"><span class="pre">residual_variance</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelForecast.residual_variance" title="Link to this definition">¶</a></dt>
 <dd><p>Forecast values for the conditional variance of the residuals</p>
 </dd></dl>
 
@@ -952,7 +952,7 @@ <h1 id="arch-univariate-base-archmodelforecast-residual-variance">arch.univariat
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -999,7 +999,7 @@ <h1 id="arch-univariate-base-archmodelforecast-residual-variance">arch.univariat
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diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModelForecast.simulations.html b/univariate/generated/generated/arch.univariate.base.ARCHModelForecast.simulations.html
index d93184518e..cd163319b1 100644
--- a/univariate/generated/generated/arch.univariate.base.ARCHModelForecast.simulations.html
+++ b/univariate/generated/generated/arch.univariate.base.ARCHModelForecast.simulations.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelForecast.simulations - arch 6.1.0</title>
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@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -918,17 +918,17 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodelforecast-simulations">arch.univariate.base.ARCHModelForecast.simulations<a class="headerlink" href="#arch-univariate-base-archmodelforecast-simulations" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodelforecast-simulations">arch.univariate.base.ARCHModelForecast.simulations<a class="headerlink" href="#arch-univariate-base-archmodelforecast-simulations" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelForecast.simulations">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelForecast.</span></span><span class="sig-name descname"><span class="pre">simulations</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelForecastSimulation.html#arch.univariate.base.ARCHModelForecastSimulation" title="arch.univariate.base.ARCHModelForecastSimulation (Python class) — Container for a simulation or bootstrap-based forecasts from an ARCH Model"><span class="pre">ARCHModelForecastSimulation</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelForecast.simulations" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelForecast.</span></span><span class="sig-name descname"><span class="pre">simulations</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelForecastSimulation.html#arch.univariate.base.ARCHModelForecastSimulation" title="arch.univariate.base.ARCHModelForecastSimulation (Python class) — Container for a simulation or bootstrap-based forecasts from an ARCH Model"><span class="pre">ARCHModelForecastSimulation</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelForecast.simulations" title="Link to this definition">¶</a></dt>
 <dd><p>Detailed simulation results if using a simulation-based method</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.base.ARCHModelForecast.simulations-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModelForecast.simulations-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>Container for simulation results</p>
 </dd>
 <dt class="field-even" id="arch.univariate.base.ARCHModelForecast.simulations-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModelForecast.simulations-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference internal" href="../arch.univariate.base.ARCHModelForecastSimulation.html#arch.univariate.base.ARCHModelForecastSimulation" title="arch.univariate.base.ARCHModelForecastSimulation (Python class) — Container for a simulation or bootstrap-based forecasts from an ARCH Model">ARCHModelForecastSimulation</a></p>
+<dd class="field-even"><p><a class="reference internal" href="../arch.univariate.base.ARCHModelForecastSimulation.html#arch.univariate.base.ARCHModelForecastSimulation" title="arch.univariate.base.ARCHModelForecastSimulation (Python class) — Container for a simulation or bootstrap-based forecasts from an ARCH Model"><code class="xref py py-class docutils literal notranslate"><span class="pre">ARCHModelForecastSimulation</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1000,7 +1000,7 @@ <h1 id="arch-univariate-base-archmodelforecast-simulations">arch.univariate.base
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1047,7 +1047,7 @@ <h1 id="arch-univariate-base-archmodelforecast-simulations">arch.univariate.base
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diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModelForecast.variance.html b/univariate/generated/generated/arch.univariate.base.ARCHModelForecast.variance.html
index da8caa71fd..c4281c85f9 100644
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+++ b/univariate/generated/generated/arch.univariate.base.ARCHModelForecast.variance.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelForecast.variance - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModelForecast.variance - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -878,10 +878,10 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodelforecast-variance">arch.univariate.base.ARCHModelForecast.variance<a class="headerlink" href="#arch-univariate-base-archmodelforecast-variance" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodelforecast-variance">arch.univariate.base.ARCHModelForecast.variance<a class="headerlink" href="#arch-univariate-base-archmodelforecast-variance" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelForecast.variance">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelForecast.</span></span><span class="sig-name descname"><span class="pre">variance</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.0.1)"><span class="pre">DataFrame</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelForecast.variance" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelForecast.</span></span><span class="sig-name descname"><span class="pre">variance</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelForecast.variance" title="Link to this definition">¶</a></dt>
 <dd><p>Forecast values for the conditional variance of the process</p>
 </dd></dl>
 
@@ -952,7 +952,7 @@ <h1 id="arch-univariate-base-archmodelforecast-variance">arch.univariate.base.AR
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -999,7 +999,7 @@ <h1 id="arch-univariate-base-archmodelforecast-variance">arch.univariate.base.AR
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
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diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModelForecastSimulation.index.html b/univariate/generated/generated/arch.univariate.base.ARCHModelForecastSimulation.index.html
index dc67b08b37..6d46f57a9c 100644
--- a/univariate/generated/generated/arch.univariate.base.ARCHModelForecastSimulation.index.html
+++ b/univariate/generated/generated/arch.univariate.base.ARCHModelForecastSimulation.index.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelForecastSimulation.index - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModelForecastSimulation.index - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -893,10 +893,10 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodelforecastsimulation-index">arch.univariate.base.ARCHModelForecastSimulation.index<a class="headerlink" href="#arch-univariate-base-archmodelforecastsimulation-index" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodelforecastsimulation-index">arch.univariate.base.ARCHModelForecastSimulation.index<a class="headerlink" href="#arch-univariate-base-archmodelforecastsimulation-index" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelForecastSimulation.index">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelForecastSimulation.</span></span><span class="sig-name descname"><span class="pre">index</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Index.html#pandas.Index" title="(in pandas v2.0.1)"><span class="pre">Index</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelForecastSimulation.index" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelForecastSimulation.</span></span><span class="sig-name descname"><span class="pre">index</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Index.html#pandas.Index" title="(in pandas v2.1.1)"><span class="pre">Index</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelForecastSimulation.index" title="Link to this definition">¶</a></dt>
 <dd><p>The index aligned to dimension 0 of the simulation paths</p>
 </dd></dl>
 
@@ -967,7 +967,7 @@ <h1 id="arch-univariate-base-archmodelforecastsimulation-index">arch.univariate.
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
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@@ -1014,7 +1014,7 @@ <h1 id="arch-univariate-base-archmodelforecastsimulation-index">arch.univariate.
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diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModelForecastSimulation.residual_variances.html b/univariate/generated/generated/arch.univariate.base.ARCHModelForecastSimulation.residual_variances.html
index 36d21d2f9d..33ebdaba94 100644
--- a/univariate/generated/generated/arch.univariate.base.ARCHModelForecastSimulation.residual_variances.html
+++ b/univariate/generated/generated/arch.univariate.base.ARCHModelForecastSimulation.residual_variances.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelForecastSimulation.residual_variances - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModelForecastSimulation.residual_variances - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -893,10 +893,10 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodelforecastsimulation-residual-variances">arch.univariate.base.ARCHModelForecastSimulation.residual_variances<a class="headerlink" href="#arch-univariate-base-archmodelforecastsimulation-residual-variances" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodelforecastsimulation-residual-variances">arch.univariate.base.ARCHModelForecastSimulation.residual_variances<a class="headerlink" href="#arch-univariate-base-archmodelforecastsimulation-residual-variances" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.base.ARCHModelForecastSimulation.residual_variances">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelForecastSimulation.</span></span><span class="sig-name descname"><span class="pre">residual_variances</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelForecastSimulation.residual_variances" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelForecastSimulation.</span></span><span class="sig-name descname"><span class="pre">residual_variances</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelForecastSimulation.residual_variances" title="Link to this definition">¶</a></dt>
 <dd><p>Simulated variance of the residuals</p>
 </dd></dl>
 
@@ -967,7 +967,7 @@ <h1 id="arch-univariate-base-archmodelforecastsimulation-residual-variances">arc
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1014,7 +1014,7 @@ <h1 id="arch-univariate-base-archmodelforecastsimulation-residual-variances">arc
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diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModelForecastSimulation.residuals.html b/univariate/generated/generated/arch.univariate.base.ARCHModelForecastSimulation.residuals.html
index 20b9bf299d..338a5a13ca 100644
--- a/univariate/generated/generated/arch.univariate.base.ARCHModelForecastSimulation.residuals.html
+++ b/univariate/generated/generated/arch.univariate.base.ARCHModelForecastSimulation.residuals.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelForecastSimulation.residuals - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModelForecastSimulation.residuals - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -893,10 +893,10 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodelforecastsimulation-residuals">arch.univariate.base.ARCHModelForecastSimulation.residuals<a class="headerlink" href="#arch-univariate-base-archmodelforecastsimulation-residuals" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodelforecastsimulation-residuals">arch.univariate.base.ARCHModelForecastSimulation.residuals<a class="headerlink" href="#arch-univariate-base-archmodelforecastsimulation-residuals" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelForecastSimulation.residuals">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelForecastSimulation.</span></span><span class="sig-name descname"><span class="pre">residuals</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelForecastSimulation.residuals" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelForecastSimulation.</span></span><span class="sig-name descname"><span class="pre">residuals</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelForecastSimulation.residuals" title="Link to this definition">¶</a></dt>
 <dd><p>Simulated residuals used to produce the values</p>
 </dd></dl>
 
@@ -967,7 +967,7 @@ <h1 id="arch-univariate-base-archmodelforecastsimulation-residuals">arch.univari
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1014,7 +1014,7 @@ <h1 id="arch-univariate-base-archmodelforecastsimulation-residuals">arch.univari
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diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModelForecastSimulation.values.html b/univariate/generated/generated/arch.univariate.base.ARCHModelForecastSimulation.values.html
index 1e25572fc3..e3b9f1beb4 100644
--- a/univariate/generated/generated/arch.univariate.base.ARCHModelForecastSimulation.values.html
+++ b/univariate/generated/generated/arch.univariate.base.ARCHModelForecastSimulation.values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelForecastSimulation.values - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModelForecastSimulation.values - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -893,10 +893,10 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodelforecastsimulation-values">arch.univariate.base.ARCHModelForecastSimulation.values<a class="headerlink" href="#arch-univariate-base-archmodelforecastsimulation-values" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodelforecastsimulation-values">arch.univariate.base.ARCHModelForecastSimulation.values<a class="headerlink" href="#arch-univariate-base-archmodelforecastsimulation-values" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelForecastSimulation.values">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelForecastSimulation.</span></span><span class="sig-name descname"><span class="pre">values</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelForecastSimulation.values" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelForecastSimulation.</span></span><span class="sig-name descname"><span class="pre">values</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelForecastSimulation.values" title="Link to this definition">¶</a></dt>
 <dd><p>The values of the process</p>
 </dd></dl>
 
@@ -967,7 +967,7 @@ <h1 id="arch-univariate-base-archmodelforecastsimulation-values">arch.univariate
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
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@@ -1014,7 +1014,7 @@ <h1 id="arch-univariate-base-archmodelforecastsimulation-values">arch.univariate
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModelForecastSimulation.variances.html b/univariate/generated/generated/arch.univariate.base.ARCHModelForecastSimulation.variances.html
index 8e6d11e21d..73708d74d9 100644
--- a/univariate/generated/generated/arch.univariate.base.ARCHModelForecastSimulation.variances.html
+++ b/univariate/generated/generated/arch.univariate.base.ARCHModelForecastSimulation.variances.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelForecastSimulation.variances - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModelForecastSimulation.variances - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -893,10 +893,10 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodelforecastsimulation-variances">arch.univariate.base.ARCHModelForecastSimulation.variances<a class="headerlink" href="#arch-univariate-base-archmodelforecastsimulation-variances" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodelforecastsimulation-variances">arch.univariate.base.ARCHModelForecastSimulation.variances<a class="headerlink" href="#arch-univariate-base-archmodelforecastsimulation-variances" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelForecastSimulation.variances">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelForecastSimulation.</span></span><span class="sig-name descname"><span class="pre">variances</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelForecastSimulation.variances" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelForecastSimulation.</span></span><span class="sig-name descname"><span class="pre">variances</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelForecastSimulation.variances" title="Link to this definition">¶</a></dt>
 <dd><p>Simulated variances of the values</p>
 </dd></dl>
 
@@ -967,7 +967,7 @@ <h1 id="arch-univariate-base-archmodelforecastsimulation-variances">arch.univari
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1014,7 +1014,7 @@ <h1 id="arch-univariate-base-archmodelforecastsimulation-variances">arch.univari
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diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModelResult.aic.html b/univariate/generated/generated/arch.univariate.base.ARCHModelResult.aic.html
index 6660e28964..e7faab5ab1 100644
--- a/univariate/generated/generated/arch.univariate.base.ARCHModelResult.aic.html
+++ b/univariate/generated/generated/arch.univariate.base.ARCHModelResult.aic.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.aic - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModelResult.aic - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1183,10 +1183,10 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodelresult-aic">arch.univariate.base.ARCHModelResult.aic<a class="headerlink" href="#arch-univariate-base-archmodelresult-aic" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodelresult-aic">arch.univariate.base.ARCHModelResult.aic<a class="headerlink" href="#arch-univariate-base-archmodelresult-aic" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelResult.aic">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">aic</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.aic" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">aic</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.aic" title="Link to this definition">¶</a></dt>
 <dd><p>Akaike Information Criteria</p>
 <p>-2 * loglikelihood + 2 * num_params</p>
 </dd></dl>
@@ -1258,7 +1258,7 @@ <h1 id="arch-univariate-base-archmodelresult-aic">arch.univariate.base.ARCHModel
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1305,7 +1305,7 @@ <h1 id="arch-univariate-base-archmodelresult-aic">arch.univariate.base.ARCHModel
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModelResult.arch_lm_test.html b/univariate/generated/generated/arch.univariate.base.ARCHModelResult.arch_lm_test.html
index 3344383f16..28a83817bb 100644
--- a/univariate/generated/generated/arch.univariate.base.ARCHModelResult.arch_lm_test.html
+++ b/univariate/generated/generated/arch.univariate.base.ARCHModelResult.arch_lm_test.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.arch_lm_test - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModelResult.arch_lm_test - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1277,17 +1277,17 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodelresult-arch-lm-test">arch.univariate.base.ARCHModelResult.arch_lm_test<a class="headerlink" href="#arch-univariate-base-archmodelresult-arch-lm-test" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodelresult-arch-lm-test">arch.univariate.base.ARCHModelResult.arch_lm_test<a class="headerlink" href="#arch-univariate-base-archmodelresult-arch-lm-test" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelResult.arch_lm_test">
-<span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">arch_lm_test</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.arch_lm_test.lags" title="arch.univariate.base.ARCHModelResult.arch_lm_test.lags (Python parameter) — Number of lags to include in the model.  If not specified,"><span class="n"><span class="pre">lags</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.arch_lm_test.standardized" title="arch.univariate.base.ARCHModelResult.arch_lm_test.standardized (Python parameter) — Flag indicating to test the model residuals divided by their conditional standard deviations.  If False, directly tests the estimated residuals."><span class="n"><span class="pre">standardized</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.arch_lm_test" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.base.ARCHModelResult.arch_lm_test">
+<span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">arch_lm_test</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.arch_lm_test.lags" title="arch.univariate.base.ARCHModelResult.arch_lm_test.lags (Python parameter) — Number of lags to include in the model.  If not specified,"><span class="n"><span class="pre">lags</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.arch_lm_test.standardized" title="arch.univariate.base.ARCHModelResult.arch_lm_test.standardized (Python parameter) — Flag indicating to test the model residuals divided by their conditional standard deviations.  If False, directly tests the estimated residuals."><span class="n"><span class="pre">standardized</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../../utility.html#arch.utility.testing.WaldTestStatistic" title="arch.utility.testing.WaldTestStatistic (Python class) — Test statistic holder for Wald-type tests"><span class="pre">WaldTestStatistic</span></a></span></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.arch_lm_test" title="Link to this definition">¶</a></dt>
 <dd><p>ARCH LM test for conditional heteroskedasticity</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.base.ARCHModelResult.arch_lm_test-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.arch_lm_test-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.base.ARCHModelResult.arch_lm_test.lags"><span class="sig-name descname">lags</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.arch_lm_test.lags" title="Permalink to this definition">¶</a></dt><dd><p>Number of lags to include in the model.  If not specified,</p>
+<dt id="arch.univariate.base.ARCHModelResult.arch_lm_test.lags"><span class="n sig-name">lags</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.arch_lm_test.lags" title="Permalink to this definition">¶</a></dt><dd><p>Number of lags to include in the model.  If not specified,</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelResult.arch_lm_test.standardized"><span class="sig-name descname">standardized</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, optional</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.arch_lm_test.standardized" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating to test the model residuals divided by their
+<dt id="arch.univariate.base.ARCHModelResult.arch_lm_test.standardized"><span class="n sig-name">standardized</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.arch_lm_test.standardized" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating to test the model residuals divided by their
 conditional standard deviations.  If False, directly tests the
 estimated residuals.</p>
 </dd>
@@ -1297,7 +1297,7 @@ <h1 id="arch-univariate-base-archmodelresult-arch-lm-test">arch.univariate.base.
 <dd class="field-even"><p><strong>result</strong> – Result of ARCH-LM test</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.base.ARCHModelResult.arch_lm_test-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.arch_lm_test-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference internal" href="../../utility.html#arch.utility.testing.WaldTestStatistic" title="arch.utility.testing.WaldTestStatistic (Python class) — Test statistic holder for Wald-type tests">WaldTestStatistic</a></p>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">WaldTestStatistic</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1369,7 +1369,7 @@ <h1 id="arch-univariate-base-archmodelresult-arch-lm-test">arch.univariate.base.
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
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@@ -1416,7 +1416,7 @@ <h1 id="arch-univariate-base-archmodelresult-arch-lm-test">arch.univariate.base.
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModelResult.bic.html b/univariate/generated/generated/arch.univariate.base.ARCHModelResult.bic.html
index a649a5be65..aa0aad0e88 100644
--- a/univariate/generated/generated/arch.univariate.base.ARCHModelResult.bic.html
+++ b/univariate/generated/generated/arch.univariate.base.ARCHModelResult.bic.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.bic - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModelResult.bic - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1183,10 +1183,10 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodelresult-bic">arch.univariate.base.ARCHModelResult.bic<a class="headerlink" href="#arch-univariate-base-archmodelresult-bic" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodelresult-bic">arch.univariate.base.ARCHModelResult.bic<a class="headerlink" href="#arch-univariate-base-archmodelresult-bic" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelResult.bic">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">bic</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.bic" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">bic</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.bic" title="Link to this definition">¶</a></dt>
 <dd><p>Schwarz/Bayesian Information Criteria</p>
 <p>-2 * loglikelihood + log(nobs) * num_params</p>
 </dd></dl>
@@ -1258,7 +1258,7 @@ <h1 id="arch-univariate-base-archmodelresult-bic">arch.univariate.base.ARCHModel
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1305,7 +1305,7 @@ <h1 id="arch-univariate-base-archmodelresult-bic">arch.univariate.base.ARCHModel
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModelResult.conditional_volatility.html b/univariate/generated/generated/arch.univariate.base.ARCHModelResult.conditional_volatility.html
index 0bac051bcf..6bd4d4d99c 100644
--- a/univariate/generated/generated/arch.univariate.base.ARCHModelResult.conditional_volatility.html
+++ b/univariate/generated/generated/arch.univariate.base.ARCHModelResult.conditional_volatility.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.conditional_volatility - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModelResult.conditional_volatility - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1223,10 +1223,10 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodelresult-conditional-volatility">arch.univariate.base.ARCHModelResult.conditional_volatility<a class="headerlink" href="#arch-univariate-base-archmodelresult-conditional-volatility" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodelresult-conditional-volatility">arch.univariate.base.ARCHModelResult.conditional_volatility<a class="headerlink" href="#arch-univariate-base-archmodelresult-conditional-volatility" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelResult.conditional_volatility">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">conditional_volatility</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.0.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">ndarray</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.conditional_volatility" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">conditional_volatility</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.conditional_volatility" title="Link to this definition">¶</a></dt>
 <dd><p>Estimated conditional volatility</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.base.ARCHModelResult.conditional_volatility-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.conditional_volatility-returns" title="Permalink to this headline">¶</a></dt>
@@ -1236,7 +1236,7 @@ <h1 id="arch-univariate-base-archmodelresult-conditional-volatility">arch.univar
 of t-th error, which is computed using time-(t-1) information.</p>
 </dd>
 <dt class="field-even" id="arch.univariate.base.ARCHModelResult.conditional_volatility-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.conditional_volatility-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p>{ndarray, Series}</p>
+<dd class="field-even"><p><code class="docutils literal notranslate"><span class="pre">{ndarray,</span> <span class="pre">Series}</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1308,7 +1308,7 @@ <h1 id="arch-univariate-base-archmodelresult-conditional-volatility">arch.univar
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1355,7 +1355,7 @@ <h1 id="arch-univariate-base-archmodelresult-conditional-volatility">arch.univar
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModelResult.conf_int.html b/univariate/generated/generated/arch.univariate.base.ARCHModelResult.conf_int.html
index f8c4ba19b9..af1c6a75a1 100644
--- a/univariate/generated/generated/arch.univariate.base.ARCHModelResult.conf_int.html
+++ b/univariate/generated/generated/arch.univariate.base.ARCHModelResult.conf_int.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.conf_int - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModelResult.conf_int - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1263,15 +1263,15 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodelresult-conf-int">arch.univariate.base.ARCHModelResult.conf_int<a class="headerlink" href="#arch-univariate-base-archmodelresult-conf-int" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodelresult-conf-int">arch.univariate.base.ARCHModelResult.conf_int<a class="headerlink" href="#arch-univariate-base-archmodelresult-conf-int" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelResult.conf_int">
-<span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">conf_int</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.conf_int.alpha" title="arch.univariate.base.ARCHModelResult.conf_int.alpha (Python parameter) — Size (prob.) to use when constructing the confidence interval."><span class="n"><span class="pre">alpha</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.05</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/base.html#ARCHModelResult.conf_int"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.conf_int" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">conf_int</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.conf_int.alpha" title="arch.univariate.base.ARCHModelResult.conf_int.alpha (Python parameter) — Size (prob.) to use when constructing the confidence interval."><span class="n"><span class="pre">alpha</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.05</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/base.html#ARCHModelResult.conf_int"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.conf_int" title="Link to this definition">¶</a></dt>
 <dd><p>Parameter confidence intervals</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.base.ARCHModelResult.conf_int-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.conf_int-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.base.ARCHModelResult.conf_int.alpha"><span class="sig-name descname">alpha</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>, optional</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.conf_int.alpha" title="Permalink to this definition">¶</a></dt><dd><p>Size (prob.) to use when constructing the confidence interval.</p>
+<dt id="arch.univariate.base.ARCHModelResult.conf_int.alpha"><span class="n sig-name">alpha</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.05</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.conf_int.alpha" title="Permalink to this definition">¶</a></dt><dd><p>Size (prob.) to use when constructing the confidence interval.</p>
 </dd>
 </dl>
 </dd>
@@ -1280,7 +1280,7 @@ <h1 id="arch-univariate-base-archmodelresult-conf-int">arch.univariate.base.ARCH
 ith parameter</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.base.ARCHModelResult.conf_int-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.conf_int-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>DataFrame</p>
+<dd class="field-odd"><p><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1352,7 +1352,7 @@ <h1 id="arch-univariate-base-archmodelresult-conf-int">arch.univariate.base.ARCH
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1399,7 +1399,7 @@ <h1 id="arch-univariate-base-archmodelresult-conf-int">arch.univariate.base.ARCH
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModelResult.convergence_flag.html b/univariate/generated/generated/arch.univariate.base.ARCHModelResult.convergence_flag.html
index 980c90af79..959328a89a 100644
--- a/univariate/generated/generated/arch.univariate.base.ARCHModelResult.convergence_flag.html
+++ b/univariate/generated/generated/arch.univariate.base.ARCHModelResult.convergence_flag.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.convergence_flag - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModelResult.convergence_flag - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1183,10 +1183,10 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodelresult-convergence-flag">arch.univariate.base.ARCHModelResult.convergence_flag<a class="headerlink" href="#arch-univariate-base-archmodelresult-convergence-flag" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodelresult-convergence-flag">arch.univariate.base.ARCHModelResult.convergence_flag<a class="headerlink" href="#arch-univariate-base-archmodelresult-convergence-flag" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelResult.convergence_flag">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">convergence_flag</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.convergence_flag" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">convergence_flag</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.convergence_flag" title="Link to this definition">¶</a></dt>
 <dd><p>scipy.optimize.minimize result flag</p>
 </dd></dl>
 
@@ -1257,7 +1257,7 @@ <h1 id="arch-univariate-base-archmodelresult-convergence-flag">arch.univariate.b
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1304,7 +1304,7 @@ <h1 id="arch-univariate-base-archmodelresult-convergence-flag">arch.univariate.b
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModelResult.fit_start.html b/univariate/generated/generated/arch.univariate.base.ARCHModelResult.fit_start.html
index 05697fbb5b..578e9d10bb 100644
--- a/univariate/generated/generated/arch.univariate.base.ARCHModelResult.fit_start.html
+++ b/univariate/generated/generated/arch.univariate.base.ARCHModelResult.fit_start.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.fit_start - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModelResult.fit_start - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1183,10 +1183,10 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodelresult-fit-start">arch.univariate.base.ARCHModelResult.fit_start<a class="headerlink" href="#arch-univariate-base-archmodelresult-fit-start" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodelresult-fit-start">arch.univariate.base.ARCHModelResult.fit_start<a class="headerlink" href="#arch-univariate-base-archmodelresult-fit-start" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelResult.fit_start">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">fit_start</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.fit_start" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">fit_start</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.fit_start" title="Link to this definition">¶</a></dt>
 <dd><p>Start of sample used to estimate parameters</p>
 </dd></dl>
 
@@ -1257,7 +1257,7 @@ <h1 id="arch-univariate-base-archmodelresult-fit-start">arch.univariate.base.ARC
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1304,7 +1304,7 @@ <h1 id="arch-univariate-base-archmodelresult-fit-start">arch.univariate.base.ARC
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModelResult.fit_stop.html b/univariate/generated/generated/arch.univariate.base.ARCHModelResult.fit_stop.html
index ca0f689bc7..8e3d3c7f63 100644
--- a/univariate/generated/generated/arch.univariate.base.ARCHModelResult.fit_stop.html
+++ b/univariate/generated/generated/arch.univariate.base.ARCHModelResult.fit_stop.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.fit_stop - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModelResult.fit_stop - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1183,10 +1183,10 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodelresult-fit-stop">arch.univariate.base.ARCHModelResult.fit_stop<a class="headerlink" href="#arch-univariate-base-archmodelresult-fit-stop" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodelresult-fit-stop">arch.univariate.base.ARCHModelResult.fit_stop<a class="headerlink" href="#arch-univariate-base-archmodelresult-fit-stop" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelResult.fit_stop">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">fit_stop</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.fit_stop" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">fit_stop</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.fit_stop" title="Link to this definition">¶</a></dt>
 <dd><p>End of sample used to estimate parameters</p>
 </dd></dl>
 
@@ -1257,7 +1257,7 @@ <h1 id="arch-univariate-base-archmodelresult-fit-stop">arch.univariate.base.ARCH
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1304,7 +1304,7 @@ <h1 id="arch-univariate-base-archmodelresult-fit-stop">arch.univariate.base.ARCH
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModelResult.forecast.html b/univariate/generated/generated/arch.univariate.base.ARCHModelResult.forecast.html
index ba0cbc9195..8dbc1860f9 100644
--- a/univariate/generated/generated/arch.univariate.base.ARCHModelResult.forecast.html
+++ b/univariate/generated/generated/arch.univariate.base.ARCHModelResult.forecast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.forecast - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModelResult.forecast - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -647,7 +647,7 @@
         
           <li class="md-nav__item">
   <a href="#arch.univariate.base.ARCHModelResult.forecast.reindex" class="md-nav__link">
-    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="arch.univariate.base.ARCHModelResult.forecast.reindex (Python parameter) — Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021." class="md-ellipsis">reindex</span>
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="arch.univariate.base.ARCHModelResult.forecast.reindex (Python parameter) — Whether to reindex the forecasts to have the same dimension as the series being forecast." class="md-ellipsis">reindex</span>
   </a>
   
 </li>
@@ -1339,7 +1339,7 @@
         
           <li class="md-nav__item">
   <a href="#arch.univariate.base.ARCHModelResult.forecast.reindex" class="md-nav__link">
-    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="arch.univariate.base.ARCHModelResult.forecast.reindex (Python parameter) — Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021." class="md-ellipsis">reindex</span>
+    <span aria-label="Python parameter" class="objinfo-icon objinfo-icon__sub-data" title="Python parameter">p</span><span title="arch.univariate.base.ARCHModelResult.forecast.reindex (Python parameter) — Whether to reindex the forecasts to have the same dimension as the series being forecast." class="md-ellipsis">reindex</span>
   </a>
   
 </li>
@@ -1389,57 +1389,55 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodelresult-forecast">arch.univariate.base.ARCHModelResult.forecast<a class="headerlink" href="#arch-univariate-base-archmodelresult-forecast" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodelresult-forecast">arch.univariate.base.ARCHModelResult.forecast<a class="headerlink" href="#arch-univariate-base-archmodelresult-forecast" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.base.ARCHModelResult.forecast">
-<span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.forecast.params" title="arch.univariate.base.ARCHModelResult.forecast.params (Python parameter) — Alternative parameters to use.  If not provided, the parameters estimated when fitting the model are used.  Must be identical in shape to the parameters computed by fitting the model."><span class="n"><span class="pre">params</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.forecast.horizon" title="arch.univariate.base.ARCHModelResult.forecast.horizon (Python parameter) — Number of steps to forecast"><span class="n"><span class="pre">horizon</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.forecast.start" title="arch.univariate.base.ARCHModelResult.forecast.start (Python parameter) — An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."><span class="n"><span class="pre">start</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.forecast.align" title="arch.univariate.base.ARCHModelResult.forecast.align (Python parameter) — Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."><span class="n"><span class="pre">align</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;origin&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.forecast.method" title="arch.univariate.base.ARCHModelResult.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons &gt; 1."><span class="n"><span class="pre">method</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.forecast.simulations" title="arch.univariate.base.ARCHModelResult.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.forecast.rng" title="arch.univariate.base.ARCHModelResult.forecast.rng (Python parameter) — Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."><span class="n"><span class="pre">rng</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.forecast.random_state" title="arch.univariate.base.ARCHModelResult.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.forecast.reindex" title="arch.univariate.base.ARCHModelResult.forecast.reindex (Python parameter) — Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."><span class="n"><span class="pre">reindex</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.forecast.x" title="arch.univariate.base.ARCHModelResult.forecast.x (Python parameter) — Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"><span class="n"><span class="pre">x</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.forecast" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.forecast.params" title="arch.univariate.base.ARCHModelResult.forecast.params (Python parameter) — Alternative parameters to use.  If not provided, the parameters estimated when fitting the model are used.  Must be identical in shape to the parameters computed by fitting the model."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.forecast.horizon" title="arch.univariate.base.ARCHModelResult.forecast.horizon (Python parameter) — Number of steps to forecast"><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.forecast.start" title="arch.univariate.base.ARCHModelResult.forecast.start (Python parameter) — An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.forecast.align" title="arch.univariate.base.ARCHModelResult.forecast.align (Python parameter) — Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."><span class="n"><span class="pre">align</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'origin'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'target'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;origin&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.forecast.method" title="arch.univariate.base.ARCHModelResult.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons &gt; 1."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.forecast.simulations" title="arch.univariate.base.ARCHModelResult.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.forecast.rng" title="arch.univariate.base.ARCHModelResult.forecast.rng (Python parameter) — Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.forecast.random_state" title="arch.univariate.base.ARCHModelResult.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.forecast.reindex" title="arch.univariate.base.ARCHModelResult.forecast.reindex (Python parameter) — Whether to reindex the forecasts to have the same dimension as the series being forecast."><span class="n"><span class="pre">reindex</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.forecast.x" title="arch.univariate.base.ARCHModelResult.forecast.x (Python parameter) — Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.11)"><span class="pre">Hashable</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelForecast.html#arch.univariate.base.ARCHModelForecast" title="arch.univariate.base.ARCHModelForecast (Python class) — Container for forecasts from an ARCH Model"><span class="pre">ARCHModelForecast</span></a></span></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.forecast" title="Link to this definition">¶</a></dt>
 <dd><p>Construct forecasts from estimated model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.base.ARCHModelResult.forecast-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.forecast-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.base.ARCHModelResult.forecast.params"><span class="sig-name descname">params</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray, optional</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.forecast.params" title="Permalink to this definition">¶</a></dt><dd><p>Alternative parameters to use.  If not provided, the parameters
+<dt id="arch.univariate.base.ARCHModelResult.forecast.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.forecast.params" title="Permalink to this definition">¶</a></dt><dd><p>Alternative parameters to use.  If not provided, the parameters
 estimated when fitting the model are used.  Must be identical in
 shape to the parameters computed by fitting the model.</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelResult.forecast.horizon"><span class="sig-name descname">horizon</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.forecast.horizon" title="Permalink to this definition">¶</a></dt><dd><p>Number of steps to forecast</p>
+<dt id="arch.univariate.base.ARCHModelResult.forecast.horizon"><span class="n sig-name">horizon</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.forecast.horizon" title="Permalink to this definition">¶</a></dt><dd><p>Number of steps to forecast</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelResult.forecast.start"><span class="sig-name descname">start</span><span class="p"> : </span><span class="desctype"><span class="desctype">{int, datetime, Timestamp, str}, optional</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>An integer, datetime or str indicating the first observation to
+<dt id="arch.univariate.base.ARCHModelResult.forecast.start"><span class="n sig-name">start</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>An integer, datetime or str indicating the first observation to
 produce the forecast for.  Datetimes can only be used with pandas
 inputs that have a datetime index. Strings must be convertible
 to a date time, such as in ‘1945-01-01’.</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelResult.forecast.align"><span class="sig-name descname">align</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, optional</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.forecast.align" title="Permalink to this definition">¶</a></dt><dd><p>Either ‘origin’ or ‘target’.  When set of ‘origin’, the t-th row
+<dt id="arch.univariate.base.ARCHModelResult.forecast.align"><span class="n sig-name">align</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'origin'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'target'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;origin&#39;</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.forecast.align" title="Permalink to this definition">¶</a></dt><dd><p>Either ‘origin’ or ‘target’.  When set of ‘origin’, the t-th row
 of forecasts contains the forecasts for t+1, t+2, …, t+h. When
 set to ‘target’, the t-th row contains the 1-step ahead forecast
 from time t-1, the 2 step from time t-2, …, and the h-step from
 time t-h.  ‘target’ simplified computing forecast errors since the
 realization and h-step forecast are aligned.</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelResult.forecast.method"><span class="sig-name descname">method</span><span class="p"> : </span><span class="desctype"><span class="desctype">{'analytic', 'simulation', 'bootstrap'}, optional</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.forecast.method" title="Permalink to this definition">¶</a></dt><dd><p>Method to use when producing the forecast. The default is analytic.
+<dt id="arch.univariate.base.ARCHModelResult.forecast.method"><span class="n sig-name">method</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'analytic'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'simulation'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'bootstrap'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.forecast.method" title="Permalink to this definition">¶</a></dt><dd><p>Method to use when producing the forecast. The default is analytic.
 The method only affects the variance forecast generation.  Not all
 volatility models support all methods. In particular, volatility
 models that do not evolve in squares such as EGARCH or TARCH do not
 support the ‘analytic’ method for horizons &gt; 1.</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelResult.forecast.simulations"><span class="sig-name descname">simulations</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.forecast.simulations" title="Permalink to this definition">¶</a></dt><dd><p>Number of simulations to run when computing the forecast using
+<dt id="arch.univariate.base.ARCHModelResult.forecast.simulations"><span class="n sig-name">simulations</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.forecast.simulations" title="Permalink to this definition">¶</a></dt><dd><p>Number of simulations to run when computing the forecast using
 either simulation or bootstrap.</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelResult.forecast.rng"><span class="sig-name descname">rng</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable, optional</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.forecast.rng" title="Permalink to this definition">¶</a></dt><dd><p>Custom random number generator to use in simulation-based forecasts.
+<dt id="arch.univariate.base.ARCHModelResult.forecast.rng"><span class="n sig-name">rng</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)">Callable</a><span class="p">[</span><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">,</span><span class="w"> </span><span class="p">...</span><span class="p">]</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.forecast.rng" title="Permalink to this definition">¶</a></dt><dd><p>Custom random number generator to use in simulation-based forecasts.
 Must produce random samples using the syntax <cite>rng(size)</cite> where size
 the 2-element tuple (simulations, horizon).</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelResult.forecast.random_state"><span class="sig-name descname">random_state</span><span class="p"> : </span><span class="desctype"><span class="desctype">RandomState, optional</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.forecast.random_state" title="Permalink to this definition">¶</a></dt><dd><p>NumPy RandomState instance to use when method is ‘bootstrap’</p>
+<dt id="arch.univariate.base.ARCHModelResult.forecast.random_state"><span class="n sig-name">random_state</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)">RandomState</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.forecast.random_state" title="Permalink to this definition">¶</a></dt><dd><p>NumPy RandomState instance to use when method is ‘bootstrap’</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelResult.forecast.reindex"><span class="sig-name descname">reindex</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, optional</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.forecast.reindex" title="Permalink to this definition">¶</a></dt><dd><p><p>Whether to reindex the forecasts to have the same dimension as the series
-being forecast. Prior to 4.18 this was the default. As of 4.19 this is
-now optional. If not provided, a warning is raised about the future
-change in the default which will occur after September 2021.</p>
-<div class="versionadded">
-<p><span class="versionmodified added">New in version 4.19.</span></p>
+<dt id="arch.univariate.base.ARCHModelResult.forecast.reindex"><span class="n sig-name">reindex</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.forecast.reindex" title="Permalink to this definition">¶</a></dt><dd><p><p>Whether to reindex the forecasts to have the same dimension as the series
+being forecast.</p>
+<div class="admonition versionchanged">
+<p class="admonition-title"><span class="versionmodified changed">Changed in version 6.2: </span>The default has been changed to False.</p>
 </div>
 </p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelResult.forecast.x"><span class="sig-name descname">x</span><span class="p"> : </span><span class="desctype"><span class="desctype">{dict[label, array_like], array_like}</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.forecast.x" title="Permalink to this definition">¶</a></dt><dd><p><p>Values to use for exogenous regressors if any are included in the
+<dt id="arch.univariate.base.ARCHModelResult.forecast.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.11)">Hashable</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.forecast.x" title="Permalink to this definition">¶</a></dt><dd><p><p>Values to use for exogenous regressors if any are included in the
 model. Three formats are accepted:</p>
 <ul class="simple">
 <li><p>2-d array-like: This format can be used when there is a single
@@ -1462,8 +1460,8 @@ <h1 id="arch-univariate-base-archmodelresult-forecast">arch.univariate.base.ARCH
 <p>Due to the complexity required to accommodate all scenarios, please
 see the example notebook that demonstrates the valid formats for
 x.</p>
-<div class="versionadded">
-<p><span class="versionmodified added">New in version 4.19.</span></p>
+<div class="admonition versionadded">
+<p class="admonition-title"><span class="versionmodified added">New in version 4.19.</span></p>
 </div>
 </p>
 </dd>
@@ -1474,7 +1472,7 @@ <h1 id="arch-univariate-base-archmodelresult-forecast">arch.univariate.base.ARCH
 <code class="docutils literal notranslate"><span class="pre">variance</span></code> and <code class="docutils literal notranslate"><span class="pre">residual_variance</span></code>.</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.base.ARCHModelResult.forecast-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.forecast-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference internal" href="../arch.univariate.base.ARCHModelForecast.html#arch.univariate.base.ARCHModelForecast" title="arch.univariate.base.ARCHModelForecast (Python class) — Container for forecasts from an ARCH Model">arch.univariate.base.ARCHModelForecast</a></p>
+<dd class="field-odd"><p><a class="reference internal" href="../arch.univariate.base.ARCHModelForecast.html#arch.univariate.base.ARCHModelForecast" title="arch.univariate.base.ARCHModelForecast (Python class) — Container for forecasts from an ARCH Model"><code class="xref py py-class docutils literal notranslate"><span class="pre">arch.univariate.base.ARCHModelForecast</span></code></a></p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -1563,7 +1561,7 @@ <h1 id="arch-univariate-base-archmodelresult-forecast">arch.univariate.base.ARCH
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1610,7 +1608,7 @@ <h1 id="arch-univariate-base-archmodelresult-forecast">arch.univariate.base.ARCH
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModelResult.hedgehog_plot.html b/univariate/generated/generated/arch.univariate.base.ARCHModelResult.hedgehog_plot.html
index a7a39fa314..3ab1fbf25e 100644
--- a/univariate/generated/generated/arch.univariate.base.ARCHModelResult.hedgehog_plot.html
+++ b/univariate/generated/generated/arch.univariate.base.ARCHModelResult.hedgehog_plot.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.hedgehog_plot - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModelResult.hedgehog_plot - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1347,37 +1347,37 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodelresult-hedgehog-plot">arch.univariate.base.ARCHModelResult.hedgehog_plot<a class="headerlink" href="#arch-univariate-base-archmodelresult-hedgehog-plot" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodelresult-hedgehog-plot">arch.univariate.base.ARCHModelResult.hedgehog_plot<a class="headerlink" href="#arch-univariate-base-archmodelresult-hedgehog-plot" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.base.ARCHModelResult.hedgehog_plot">
-<span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">hedgehog_plot</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.hedgehog_plot.params" title="arch.univariate.base.ARCHModelResult.hedgehog_plot.params (Python parameter) — Alternative parameters to use.  If not provided, the parameters computed by fitting the model are used.  Must be 1-d and identical in shape to the parameters computed by fitting the model."><span class="n"><span class="pre">params</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.hedgehog_plot.horizon" title="arch.univariate.base.ARCHModelResult.hedgehog_plot.horizon (Python parameter) — Number of steps to forecast"><span class="n"><span class="pre">horizon</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">10</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.hedgehog_plot.step" title="arch.univariate.base.ARCHModelResult.hedgehog_plot.step (Python parameter) — Non-negative number of forecasts to skip between spines"><span class="n"><span class="pre">step</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">10</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.hedgehog_plot.start" title="arch.univariate.base.ARCHModelResult.hedgehog_plot.start (Python parameter) — An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index.  Strings must be convertible to a date time, such as in '1945-01-01'.  If not provided, the start is set to the earliest forecastable date."><span class="n"><span class="pre">start</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.hedgehog_plot.plot_type" title="arch.univariate.base.ARCHModelResult.hedgehog_plot.plot_type (Python parameter) — Quantity to plot, the forecast volatility or the forecast mean"><span class="n"><span class="pre">plot_type</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;volatility&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.hedgehog_plot.method" title="arch.univariate.base.ARCHModelResult.hedgehog_plot.method (Python parameter) — Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons &gt; 1."><span class="n"><span class="pre">method</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.hedgehog_plot.simulations" title="arch.univariate.base.ARCHModelResult.hedgehog_plot.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.hedgehog_plot" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">hedgehog_plot</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.hedgehog_plot.params" title="arch.univariate.base.ARCHModelResult.hedgehog_plot.params (Python parameter) — Alternative parameters to use.  If not provided, the parameters computed by fitting the model are used.  Must be 1-d and identical in shape to the parameters computed by fitting the model."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.hedgehog_plot.horizon" title="arch.univariate.base.ARCHModelResult.hedgehog_plot.horizon (Python parameter) — Number of steps to forecast"><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">10</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.hedgehog_plot.step" title="arch.univariate.base.ARCHModelResult.hedgehog_plot.step (Python parameter) — Non-negative number of forecasts to skip between spines"><span class="n"><span class="pre">step</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">10</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.hedgehog_plot.start" title="arch.univariate.base.ARCHModelResult.hedgehog_plot.start (Python parameter) — An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index.  Strings must be convertible to a date time, such as in '1945-01-01'.  If not provided, the start is set to the earliest forecastable date."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.hedgehog_plot.plot_type" title="arch.univariate.base.ARCHModelResult.hedgehog_plot.plot_type (Python parameter) — Quantity to plot, the forecast volatility or the forecast mean"><span class="n"><span class="pre">plot_type</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'volatility'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'mean'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;volatility&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.hedgehog_plot.method" title="arch.univariate.base.ARCHModelResult.hedgehog_plot.method (Python parameter) — Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons &gt; 1."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.hedgehog_plot.simulations" title="arch.univariate.base.ARCHModelResult.hedgehog_plot.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="(in Matplotlib v3.8.0)"><span class="pre">Figure</span></a></span></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.hedgehog_plot" title="Link to this definition">¶</a></dt>
 <dd><p>Plot forecasts from estimated model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.base.ARCHModelResult.hedgehog_plot-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.hedgehog_plot-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.base.ARCHModelResult.hedgehog_plot.params"><span class="sig-name descname">params</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, Series}</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.hedgehog_plot.params" title="Permalink to this definition">¶</a></dt><dd><p>Alternative parameters to use.  If not provided, the parameters
+<dt id="arch.univariate.base.ARCHModelResult.hedgehog_plot.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.hedgehog_plot.params" title="Permalink to this definition">¶</a></dt><dd><p>Alternative parameters to use.  If not provided, the parameters
 computed by fitting the model are used.  Must be 1-d and identical
 in shape to the parameters computed by fitting the model.</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelResult.hedgehog_plot.horizon"><span class="sig-name descname">horizon</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.hedgehog_plot.horizon" title="Permalink to this definition">¶</a></dt><dd><p>Number of steps to forecast</p>
+<dt id="arch.univariate.base.ARCHModelResult.hedgehog_plot.horizon"><span class="n sig-name">horizon</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">10</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.hedgehog_plot.horizon" title="Permalink to this definition">¶</a></dt><dd><p>Number of steps to forecast</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelResult.hedgehog_plot.step"><span class="sig-name descname">step</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.hedgehog_plot.step" title="Permalink to this definition">¶</a></dt><dd><p>Non-negative number of forecasts to skip between spines</p>
+<dt id="arch.univariate.base.ARCHModelResult.hedgehog_plot.step"><span class="n sig-name">step</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">10</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.hedgehog_plot.step" title="Permalink to this definition">¶</a></dt><dd><p>Non-negative number of forecasts to skip between spines</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelResult.hedgehog_plot.start"><span class="sig-name descname">start</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, datetime or <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, optional</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.hedgehog_plot.start" title="Permalink to this definition">¶</a></dt><dd><p>An integer, datetime or str indicating the first observation to
+<dt id="arch.univariate.base.ARCHModelResult.hedgehog_plot.start"><span class="n sig-name">start</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.11)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.hedgehog_plot.start" title="Permalink to this definition">¶</a></dt><dd><p>An integer, datetime or str indicating the first observation to
 produce the forecast for.  Datetimes can only be used with pandas
 inputs that have a datetime index.  Strings must be convertible
 to a date time, such as in ‘1945-01-01’.  If not provided, the start
 is set to the earliest forecastable date.</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelResult.hedgehog_plot.plot_type"><span class="sig-name descname">plot_type</span><span class="p"> : </span><span class="desctype"><span class="desctype">{'volatility', 'mean'}</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.hedgehog_plot.plot_type" title="Permalink to this definition">¶</a></dt><dd><p>Quantity to plot, the forecast volatility or the forecast mean</p>
+<dt id="arch.univariate.base.ARCHModelResult.hedgehog_plot.plot_type"><span class="n sig-name">plot_type</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'volatility'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'mean'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;volatility&#39;</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.hedgehog_plot.plot_type" title="Permalink to this definition">¶</a></dt><dd><p>Quantity to plot, the forecast volatility or the forecast mean</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelResult.hedgehog_plot.method"><span class="sig-name descname">method</span><span class="p"> : </span><span class="desctype"><span class="desctype">{'analytic', 'simulation', 'bootstrap'}</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.hedgehog_plot.method" title="Permalink to this definition">¶</a></dt><dd><p>Method to use when producing the forecast. The default is analytic.
+<dt id="arch.univariate.base.ARCHModelResult.hedgehog_plot.method"><span class="n sig-name">method</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'analytic'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'simulation'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'bootstrap'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.hedgehog_plot.method" title="Permalink to this definition">¶</a></dt><dd><p>Method to use when producing the forecast. The default is analytic.
 The method only affects the variance forecast generation.  Not all
 volatility models support all methods. In particular, volatility
 models that do not evolve in squares such as EGARCH or TARCH do not
 support the ‘analytic’ method for horizons &gt; 1.</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelResult.hedgehog_plot.simulations"><span class="sig-name descname">simulations</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.hedgehog_plot.simulations" title="Permalink to this definition">¶</a></dt><dd><p>Number of simulations to run when computing the forecast using
+<dt id="arch.univariate.base.ARCHModelResult.hedgehog_plot.simulations"><span class="n sig-name">simulations</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.hedgehog_plot.simulations" title="Permalink to this definition">¶</a></dt><dd><p>Number of simulations to run when computing the forecast using
 either simulation or bootstrap.</p>
 </dd>
 </dl>
@@ -1386,7 +1386,7 @@ <h1 id="arch-univariate-base-archmodelresult-hedgehog-plot">arch.univariate.base
 <dd class="field-even"><p><strong>fig</strong> – Handle to the figure</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.base.ARCHModelResult.hedgehog_plot-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.hedgehog_plot-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>figure</p>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">figure</span></code></p>
 </dd>
 </dl>
 <p class="example rubric">Examples</p>
@@ -1469,7 +1469,7 @@ <h1 id="arch-univariate-base-archmodelresult-hedgehog-plot">arch.univariate.base
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1516,7 +1516,7 @@ <h1 id="arch-univariate-base-archmodelresult-hedgehog-plot">arch.univariate.base
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModelResult.loglikelihood.html b/univariate/generated/generated/arch.univariate.base.ARCHModelResult.loglikelihood.html
index dfdb4839f8..976e648ccd 100644
--- a/univariate/generated/generated/arch.univariate.base.ARCHModelResult.loglikelihood.html
+++ b/univariate/generated/generated/arch.univariate.base.ARCHModelResult.loglikelihood.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.loglikelihood - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModelResult.loglikelihood - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1183,10 +1183,10 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodelresult-loglikelihood">arch.univariate.base.ARCHModelResult.loglikelihood<a class="headerlink" href="#arch-univariate-base-archmodelresult-loglikelihood" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodelresult-loglikelihood">arch.univariate.base.ARCHModelResult.loglikelihood<a class="headerlink" href="#arch-univariate-base-archmodelresult-loglikelihood" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelResult.loglikelihood">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">loglikelihood</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.loglikelihood" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">loglikelihood</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.loglikelihood" title="Link to this definition">¶</a></dt>
 <dd><p>Model loglikelihood</p>
 </dd></dl>
 
@@ -1257,7 +1257,7 @@ <h1 id="arch-univariate-base-archmodelresult-loglikelihood">arch.univariate.base
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1304,7 +1304,7 @@ <h1 id="arch-univariate-base-archmodelresult-loglikelihood">arch.univariate.base
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModelResult.model.html b/univariate/generated/generated/arch.univariate.base.ARCHModelResult.model.html
index dc73fd6cf6..ce093e299b 100644
--- a/univariate/generated/generated/arch.univariate.base.ARCHModelResult.model.html
+++ b/univariate/generated/generated/arch.univariate.base.ARCHModelResult.model.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.model - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModelResult.model - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1183,10 +1183,10 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodelresult-model">arch.univariate.base.ARCHModelResult.model<a class="headerlink" href="#arch-univariate-base-archmodelresult-model" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodelresult-model">arch.univariate.base.ARCHModelResult.model<a class="headerlink" href="#arch-univariate-base-archmodelresult-model" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelResult.model">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">model</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModel.html#arch.univariate.base.ARCHModel" title="arch.univariate.base.ARCHModel (Python class) — Abstract base class for mean models in ARCH processes.  Specifies the conditional mean process."><span class="pre">ARCHModel</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.model" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">model</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModel.html#arch.univariate.base.ARCHModel" title="arch.univariate.base.ARCHModel (Python class) — Abstract base class for mean models in ARCH processes.  Specifies the conditional mean process."><span class="pre">ARCHModel</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.model" title="Link to this definition">¶</a></dt>
 <dd><p>Model instance used to produce the fit</p>
 </dd></dl>
 
@@ -1257,7 +1257,7 @@ <h1 id="arch-univariate-base-archmodelresult-model">arch.univariate.base.ARCHMod
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1304,7 +1304,7 @@ <h1 id="arch-univariate-base-archmodelresult-model">arch.univariate.base.ARCHMod
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModelResult.nobs.html b/univariate/generated/generated/arch.univariate.base.ARCHModelResult.nobs.html
index 7dcf74822a..53020aecf7 100644
--- a/univariate/generated/generated/arch.univariate.base.ARCHModelResult.nobs.html
+++ b/univariate/generated/generated/arch.univariate.base.ARCHModelResult.nobs.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.nobs - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModelResult.nobs - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1183,10 +1183,10 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodelresult-nobs">arch.univariate.base.ARCHModelResult.nobs<a class="headerlink" href="#arch-univariate-base-archmodelresult-nobs" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodelresult-nobs">arch.univariate.base.ARCHModelResult.nobs<a class="headerlink" href="#arch-univariate-base-archmodelresult-nobs" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelResult.nobs">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">nobs</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.nobs" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">nobs</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.nobs" title="Link to this definition">¶</a></dt>
 <dd><p>Number of data points used to estimate model</p>
 </dd></dl>
 
@@ -1257,7 +1257,7 @@ <h1 id="arch-univariate-base-archmodelresult-nobs">arch.univariate.base.ARCHMode
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1304,7 +1304,7 @@ <h1 id="arch-univariate-base-archmodelresult-nobs">arch.univariate.base.ARCHMode
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModelResult.num_params.html b/univariate/generated/generated/arch.univariate.base.ARCHModelResult.num_params.html
index 9923c0b31e..1e80d473db 100644
--- a/univariate/generated/generated/arch.univariate.base.ARCHModelResult.num_params.html
+++ b/univariate/generated/generated/arch.univariate.base.ARCHModelResult.num_params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.num_params - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModelResult.num_params - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1183,10 +1183,10 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodelresult-num-params">arch.univariate.base.ARCHModelResult.num_params<a class="headerlink" href="#arch-univariate-base-archmodelresult-num-params" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodelresult-num-params">arch.univariate.base.ARCHModelResult.num_params<a class="headerlink" href="#arch-univariate-base-archmodelresult-num-params" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelResult.num_params">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">num_params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.num_params" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">num_params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.num_params" title="Link to this definition">¶</a></dt>
 <dd><p>Number of parameters in model</p>
 </dd></dl>
 
@@ -1257,7 +1257,7 @@ <h1 id="arch-univariate-base-archmodelresult-num-params">arch.univariate.base.AR
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1304,7 +1304,7 @@ <h1 id="arch-univariate-base-archmodelresult-num-params">arch.univariate.base.AR
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModelResult.optimization_result.html b/univariate/generated/generated/arch.univariate.base.ARCHModelResult.optimization_result.html
index 6171d8ebf7..16d6173cca 100644
--- a/univariate/generated/generated/arch.univariate.base.ARCHModelResult.optimization_result.html
+++ b/univariate/generated/generated/arch.univariate.base.ARCHModelResult.optimization_result.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.optimization_result - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModelResult.optimization_result - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1223,17 +1223,17 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodelresult-optimization-result">arch.univariate.base.ARCHModelResult.optimization_result<a class="headerlink" href="#arch-univariate-base-archmodelresult-optimization-result" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodelresult-optimization-result">arch.univariate.base.ARCHModelResult.optimization_result<a class="headerlink" href="#arch-univariate-base-archmodelresult-optimization-result" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelResult.optimization_result">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">optimization_result</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.OptimizeResult.html#scipy.optimize.OptimizeResult" title="(in SciPy v1.10.1)"><span class="pre">OptimizeResult</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.optimization_result" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">optimization_result</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.OptimizeResult.html#scipy.optimize.OptimizeResult" title="(in SciPy v1.11.2)"><span class="pre">OptimizeResult</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.optimization_result" title="Link to this definition">¶</a></dt>
 <dd><p>Information about the convergence of the loglikelihood optimization</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.base.ARCHModelResult.optimization_result-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.optimization_result-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p><strong>optim_result</strong> – Result from numerical optimization of the log-likelihood.</p>
 </dd>
 <dt class="field-even" id="arch.univariate.base.ARCHModelResult.optimization_result-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.optimization_result-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p>OptimizeResult</p>
+<dd class="field-even"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">OptimizeResult</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1305,7 +1305,7 @@ <h1 id="arch-univariate-base-archmodelresult-optimization-result">arch.univariat
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1352,7 +1352,7 @@ <h1 id="arch-univariate-base-archmodelresult-optimization-result">arch.univariat
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModelResult.param_cov.html b/univariate/generated/generated/arch.univariate.base.ARCHModelResult.param_cov.html
index d2eadeffd4..ad7bdbab4a 100644
--- a/univariate/generated/generated/arch.univariate.base.ARCHModelResult.param_cov.html
+++ b/univariate/generated/generated/arch.univariate.base.ARCHModelResult.param_cov.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.param_cov - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModelResult.param_cov - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1183,10 +1183,10 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodelresult-param-cov">arch.univariate.base.ARCHModelResult.param_cov<a class="headerlink" href="#arch-univariate-base-archmodelresult-param-cov" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodelresult-param-cov">arch.univariate.base.ARCHModelResult.param_cov<a class="headerlink" href="#arch-univariate-base-archmodelresult-param-cov" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelResult.param_cov">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">param_cov</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.0.1)"><span class="pre">DataFrame</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.param_cov" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">param_cov</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.param_cov" title="Link to this definition">¶</a></dt>
 <dd><p>Parameter covariance</p>
 </dd></dl>
 
@@ -1257,7 +1257,7 @@ <h1 id="arch-univariate-base-archmodelresult-param-cov">arch.univariate.base.ARC
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1304,7 +1304,7 @@ <h1 id="arch-univariate-base-archmodelresult-param-cov">arch.univariate.base.ARC
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModelResult.params.html b/univariate/generated/generated/arch.univariate.base.ARCHModelResult.params.html
index 42c0c35c21..fbce67cffe 100644
--- a/univariate/generated/generated/arch.univariate.base.ARCHModelResult.params.html
+++ b/univariate/generated/generated/arch.univariate.base.ARCHModelResult.params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.params - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModelResult.params - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1183,10 +1183,10 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodelresult-params">arch.univariate.base.ARCHModelResult.params<a class="headerlink" href="#arch-univariate-base-archmodelresult-params" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodelresult-params">arch.univariate.base.ARCHModelResult.params<a class="headerlink" href="#arch-univariate-base-archmodelresult-params" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelResult.params">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.0.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.params" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.params" title="Link to this definition">¶</a></dt>
 <dd><p>Model Parameters</p>
 </dd></dl>
 
@@ -1257,7 +1257,7 @@ <h1 id="arch-univariate-base-archmodelresult-params">arch.univariate.base.ARCHMo
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1304,7 +1304,7 @@ <h1 id="arch-univariate-base-archmodelresult-params">arch.univariate.base.ARCHMo
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModelResult.plot.html b/univariate/generated/generated/arch.univariate.base.ARCHModelResult.plot.html
index 5c60bbfc18..b5da419783 100644
--- a/univariate/generated/generated/arch.univariate.base.ARCHModelResult.plot.html
+++ b/univariate/generated/generated/arch.univariate.base.ARCHModelResult.plot.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.plot - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModelResult.plot - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1277,20 +1277,20 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodelresult-plot">arch.univariate.base.ARCHModelResult.plot<a class="headerlink" href="#arch-univariate-base-archmodelresult-plot" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodelresult-plot">arch.univariate.base.ARCHModelResult.plot<a class="headerlink" href="#arch-univariate-base-archmodelresult-plot" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelResult.plot">
-<span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.plot.annualize" title="arch.univariate.base.ARCHModelResult.plot.annualize (Python parameter) — String containing frequency of data that indicates plot should contain annualized volatility.  Supported values are 'D' (daily), 'W' (weekly) and 'M' (monthly), which scale variance by 252, 52, and 12, respectively."><span class="n"><span class="pre">annualize</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.plot.scale" title="arch.univariate.base.ARCHModelResult.plot.scale (Python parameter) — Value to use when scaling returns to annualize.  If scale is provided, annualize is ignored and the value in scale is used."><span class="n"><span class="pre">scale</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.plot" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.base.ARCHModelResult.plot">
+<span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.plot.annualize" title="arch.univariate.base.ARCHModelResult.plot.annualize (Python parameter) — String containing frequency of data that indicates plot should contain annualized volatility.  Supported values are 'D' (daily), 'W' (weekly) and 'M' (monthly), which scale variance by 252, 52, and 12, respectively."><span class="n"><span class="pre">annualize</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.plot.scale" title="arch.univariate.base.ARCHModelResult.plot.scale (Python parameter) — Value to use when scaling returns to annualize.  If scale is provided, annualize is ignored and the value in scale is used."><span class="n"><span class="pre">scale</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="(in Matplotlib v3.8.0)"><span class="pre">Figure</span></a></span></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.plot" title="Link to this definition">¶</a></dt>
 <dd><p>Plot standardized residuals and conditional volatility</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.base.ARCHModelResult.plot-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.plot-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.base.ARCHModelResult.plot.annualize"><span class="sig-name descname">annualize</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, optional</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.plot.annualize" title="Permalink to this definition">¶</a></dt><dd><p>String containing frequency of data that indicates plot should
+<dt id="arch.univariate.base.ARCHModelResult.plot.annualize"><span class="n sig-name">annualize</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.plot.annualize" title="Permalink to this definition">¶</a></dt><dd><p>String containing frequency of data that indicates plot should
 contain annualized volatility.  Supported values are ‘D’ (daily),
 ‘W’ (weekly) and ‘M’ (monthly), which scale variance by 252, 52,
 and 12, respectively.</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelResult.plot.scale"><span class="sig-name descname">scale</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>, optional</span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.plot.scale" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when scaling returns to annualize.  If scale is
+<dt id="arch.univariate.base.ARCHModelResult.plot.scale"><span class="n sig-name">scale</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.plot.scale" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when scaling returns to annualize.  If scale is
 provided, annualize is ignored and the value in scale is used.</p>
 </dd>
 </dl>
@@ -1299,7 +1299,7 @@ <h1 id="arch-univariate-base-archmodelresult-plot">arch.univariate.base.ARCHMode
 <dd class="field-even"><p><strong>fig</strong> – Handle to the figure</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.base.ARCHModelResult.plot-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.plot-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>figure</p>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">figure</span></code></p>
 </dd>
 </dl>
 <p class="example rubric">Examples</p>
@@ -1389,7 +1389,7 @@ <h1 id="arch-univariate-base-archmodelresult-plot">arch.univariate.base.ARCHMode
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1436,7 +1436,7 @@ <h1 id="arch-univariate-base-archmodelresult-plot">arch.univariate.base.ARCHMode
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModelResult.pvalues.html b/univariate/generated/generated/arch.univariate.base.ARCHModelResult.pvalues.html
index 96fcbcab06..10c7025f12 100644
--- a/univariate/generated/generated/arch.univariate.base.ARCHModelResult.pvalues.html
+++ b/univariate/generated/generated/arch.univariate.base.ARCHModelResult.pvalues.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.pvalues - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModelResult.pvalues - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1183,10 +1183,10 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodelresult-pvalues">arch.univariate.base.ARCHModelResult.pvalues<a class="headerlink" href="#arch-univariate-base-archmodelresult-pvalues" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodelresult-pvalues">arch.univariate.base.ARCHModelResult.pvalues<a class="headerlink" href="#arch-univariate-base-archmodelresult-pvalues" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelResult.pvalues">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">pvalues</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.0.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.pvalues" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">pvalues</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.pvalues" title="Link to this definition">¶</a></dt>
 <dd><p>Array of p-values for the t-statistics</p>
 </dd></dl>
 
@@ -1257,7 +1257,7 @@ <h1 id="arch-univariate-base-archmodelresult-pvalues">arch.univariate.base.ARCHM
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1304,7 +1304,7 @@ <h1 id="arch-univariate-base-archmodelresult-pvalues">arch.univariate.base.ARCHM
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModelResult.resid.html b/univariate/generated/generated/arch.univariate.base.ARCHModelResult.resid.html
index 7f3573625c..10b34ea6fa 100644
--- a/univariate/generated/generated/arch.univariate.base.ARCHModelResult.resid.html
+++ b/univariate/generated/generated/arch.univariate.base.ARCHModelResult.resid.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.resid - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModelResult.resid - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
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@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1183,10 +1183,10 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodelresult-resid">arch.univariate.base.ARCHModelResult.resid<a class="headerlink" href="#arch-univariate-base-archmodelresult-resid" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodelresult-resid">arch.univariate.base.ARCHModelResult.resid<a class="headerlink" href="#arch-univariate-base-archmodelresult-resid" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelResult.resid">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">resid</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.0.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.resid" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">resid</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.resid" title="Link to this definition">¶</a></dt>
 <dd><p>Model residuals</p>
 </dd></dl>
 
@@ -1257,7 +1257,7 @@ <h1 id="arch-univariate-base-archmodelresult-resid">arch.univariate.base.ARCHMod
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1304,7 +1304,7 @@ <h1 id="arch-univariate-base-archmodelresult-resid">arch.univariate.base.ARCHMod
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModelResult.rsquared.html b/univariate/generated/generated/arch.univariate.base.ARCHModelResult.rsquared.html
index 7eb12ba37b..a082c1f952 100644
--- a/univariate/generated/generated/arch.univariate.base.ARCHModelResult.rsquared.html
+++ b/univariate/generated/generated/arch.univariate.base.ARCHModelResult.rsquared.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.rsquared - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModelResult.rsquared - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1183,10 +1183,10 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodelresult-rsquared">arch.univariate.base.ARCHModelResult.rsquared<a class="headerlink" href="#arch-univariate-base-archmodelresult-rsquared" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodelresult-rsquared">arch.univariate.base.ARCHModelResult.rsquared<a class="headerlink" href="#arch-univariate-base-archmodelresult-rsquared" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelResult.rsquared">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">rsquared</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.rsquared" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">rsquared</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.rsquared" title="Link to this definition">¶</a></dt>
 <dd><p>R-squared</p>
 </dd></dl>
 
@@ -1257,7 +1257,7 @@ <h1 id="arch-univariate-base-archmodelresult-rsquared">arch.univariate.base.ARCH
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1304,7 +1304,7 @@ <h1 id="arch-univariate-base-archmodelresult-rsquared">arch.univariate.base.ARCH
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModelResult.rsquared_adj.html b/univariate/generated/generated/arch.univariate.base.ARCHModelResult.rsquared_adj.html
index 5ef224a344..96542d5d1e 100644
--- a/univariate/generated/generated/arch.univariate.base.ARCHModelResult.rsquared_adj.html
+++ b/univariate/generated/generated/arch.univariate.base.ARCHModelResult.rsquared_adj.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.rsquared_adj - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModelResult.rsquared_adj - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1183,10 +1183,10 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodelresult-rsquared-adj">arch.univariate.base.ARCHModelResult.rsquared_adj<a class="headerlink" href="#arch-univariate-base-archmodelresult-rsquared-adj" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodelresult-rsquared-adj">arch.univariate.base.ARCHModelResult.rsquared_adj<a class="headerlink" href="#arch-univariate-base-archmodelresult-rsquared-adj" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelResult.rsquared_adj">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">rsquared_adj</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.rsquared_adj" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">rsquared_adj</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.rsquared_adj" title="Link to this definition">¶</a></dt>
 <dd><p>Degree of freedom adjusted R-squared</p>
 </dd></dl>
 
@@ -1257,7 +1257,7 @@ <h1 id="arch-univariate-base-archmodelresult-rsquared-adj">arch.univariate.base.
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1304,7 +1304,7 @@ <h1 id="arch-univariate-base-archmodelresult-rsquared-adj">arch.univariate.base.
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModelResult.scale.html b/univariate/generated/generated/arch.univariate.base.ARCHModelResult.scale.html
index 144972dd3f..9f251106c0 100644
--- a/univariate/generated/generated/arch.univariate.base.ARCHModelResult.scale.html
+++ b/univariate/generated/generated/arch.univariate.base.ARCHModelResult.scale.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.scale - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModelResult.scale - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1183,10 +1183,10 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodelresult-scale">arch.univariate.base.ARCHModelResult.scale<a class="headerlink" href="#arch-univariate-base-archmodelresult-scale" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodelresult-scale">arch.univariate.base.ARCHModelResult.scale<a class="headerlink" href="#arch-univariate-base-archmodelresult-scale" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelResult.scale">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">scale</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.scale" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">scale</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.scale" title="Link to this definition">¶</a></dt>
 <dd><p>The scale applied to the original data before estimating the model.</p>
 <p>If scale=1.0, the the data have not been rescaled.  Otherwise, the
 model parameters have been estimated on scale * y.</p>
@@ -1259,7 +1259,7 @@ <h1 id="arch-univariate-base-archmodelresult-scale">arch.univariate.base.ARCHMod
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1306,7 +1306,7 @@ <h1 id="arch-univariate-base-archmodelresult-scale">arch.univariate.base.ARCHMod
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModelResult.std_err.html b/univariate/generated/generated/arch.univariate.base.ARCHModelResult.std_err.html
index e22174aa6b..d811089d24 100644
--- a/univariate/generated/generated/arch.univariate.base.ARCHModelResult.std_err.html
+++ b/univariate/generated/generated/arch.univariate.base.ARCHModelResult.std_err.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.std_err - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModelResult.std_err - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1183,10 +1183,10 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodelresult-std-err">arch.univariate.base.ARCHModelResult.std_err<a class="headerlink" href="#arch-univariate-base-archmodelresult-std-err" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodelresult-std-err">arch.univariate.base.ARCHModelResult.std_err<a class="headerlink" href="#arch-univariate-base-archmodelresult-std-err" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelResult.std_err">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">std_err</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.0.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.std_err" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">std_err</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.std_err" title="Link to this definition">¶</a></dt>
 <dd><p>Array of parameter standard errors</p>
 </dd></dl>
 
@@ -1257,7 +1257,7 @@ <h1 id="arch-univariate-base-archmodelresult-std-err">arch.univariate.base.ARCHM
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1304,7 +1304,7 @@ <h1 id="arch-univariate-base-archmodelresult-std-err">arch.univariate.base.ARCHM
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModelResult.std_resid.html b/univariate/generated/generated/arch.univariate.base.ARCHModelResult.std_resid.html
index 65590faf53..0edad4c600 100644
--- a/univariate/generated/generated/arch.univariate.base.ARCHModelResult.std_resid.html
+++ b/univariate/generated/generated/arch.univariate.base.ARCHModelResult.std_resid.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.std_resid - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModelResult.std_resid - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1183,10 +1183,10 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodelresult-std-resid">arch.univariate.base.ARCHModelResult.std_resid<a class="headerlink" href="#arch-univariate-base-archmodelresult-std-resid" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodelresult-std-resid">arch.univariate.base.ARCHModelResult.std_resid<a class="headerlink" href="#arch-univariate-base-archmodelresult-std-resid" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelResult.std_resid">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">std_resid</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.0.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.std_resid" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">std_resid</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.std_resid" title="Link to this definition">¶</a></dt>
 <dd><p>Residuals standardized by conditional volatility</p>
 </dd></dl>
 
@@ -1257,7 +1257,7 @@ <h1 id="arch-univariate-base-archmodelresult-std-resid">arch.univariate.base.ARC
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1304,7 +1304,7 @@ <h1 id="arch-univariate-base-archmodelresult-std-resid">arch.univariate.base.ARC
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModelResult.summary.html b/univariate/generated/generated/arch.univariate.base.ARCHModelResult.summary.html
index 53876703fa..905166f075 100644
--- a/univariate/generated/generated/arch.univariate.base.ARCHModelResult.summary.html
+++ b/univariate/generated/generated/arch.univariate.base.ARCHModelResult.summary.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.summary - arch 6.1.0</title>
+        <title>arch.univariate.base.ARCHModelResult.summary - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1223,10 +1223,10 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodelresult-summary">arch.univariate.base.ARCHModelResult.summary<a class="headerlink" href="#arch-univariate-base-archmodelresult-summary" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodelresult-summary">arch.univariate.base.ARCHModelResult.summary<a class="headerlink" href="#arch-univariate-base-archmodelresult-summary" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelResult.summary">
-<span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">summary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/base.html#ARCHModelResult.summary"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.summary" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">summary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.iolib.summary.Summary.html#statsmodels.iolib.summary.Summary" title="(in statsmodels 0.15.0 (+59) v0.15.0 (+59))"><span class="pre">Summary</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/base.html#ARCHModelResult.summary"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.summary" title="Link to this definition">¶</a></dt>
 <dd><p>Constructs a summary of the results from a fit model.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.base.ARCHModelResult.summary-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.summary-returns" title="Permalink to this headline">¶</a></dt>
@@ -1234,7 +1234,7 @@ <h1 id="arch-univariate-base-archmodelresult-summary">arch.univariate.base.ARCHM
 latex</p>
 </dd>
 <dt class="field-even" id="arch.univariate.base.ARCHModelResult.summary-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.summary-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p>Summary instance</p>
+<dd class="field-even"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">Summary</span> <span class="pre">instance</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1306,7 +1306,7 @@ <h1 id="arch-univariate-base-archmodelresult-summary">arch.univariate.base.ARCHM
   
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-    7.0.1.
+    7.2.6.
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diff --git a/univariate/generated/generated/arch.univariate.base.ARCHModelResult.tvalues.html b/univariate/generated/generated/arch.univariate.base.ARCHModelResult.tvalues.html
index ec6340045b..648cdda869 100644
--- a/univariate/generated/generated/arch.univariate.base.ARCHModelResult.tvalues.html
+++ b/univariate/generated/generated/arch.univariate.base.ARCHModelResult.tvalues.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.tvalues - arch 6.1.0</title>
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@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1183,10 +1183,10 @@
                   
 
 
-<h1 id="arch-univariate-base-archmodelresult-tvalues">arch.univariate.base.ARCHModelResult.tvalues<a class="headerlink" href="#arch-univariate-base-archmodelresult-tvalues" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-base-archmodelresult-tvalues">arch.univariate.base.ARCHModelResult.tvalues<a class="headerlink" href="#arch-univariate-base-archmodelresult-tvalues" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelResult.tvalues">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">tvalues</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.0.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.tvalues" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">tvalues</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.tvalues" title="Link to this definition">¶</a></dt>
 <dd><p>Array of t-statistics testing the null that the coefficient are 0</p>
 </dd></dl>
 
@@ -1257,7 +1257,7 @@ <h1 id="arch-univariate-base-archmodelresult-tvalues">arch.univariate.base.ARCHM
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1304,7 +1304,7 @@ <h1 id="arch-univariate-base-archmodelresult-tvalues">arch.univariate.base.ARCHM
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diff --git a/univariate/generated/generated/arch.univariate.distribution.Distribution.bounds.html b/univariate/generated/generated/arch.univariate.distribution.Distribution.bounds.html
index f54484e078..a86138db12 100644
--- a/univariate/generated/generated/arch.univariate.distribution.Distribution.bounds.html
+++ b/univariate/generated/generated/arch.univariate.distribution.Distribution.bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.distribution.Distribution.bounds - arch 6.1.0</title>
+        <title>arch.univariate.distribution.Distribution.bounds - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1138,15 +1138,15 @@
                   
 
 
-<h1 id="arch-univariate-distribution-distribution-bounds">arch.univariate.distribution.Distribution.bounds<a class="headerlink" href="#arch-univariate-distribution-distribution-bounds" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-distribution-distribution-bounds">arch.univariate.distribution.Distribution.bounds<a class="headerlink" href="#arch-univariate-distribution-distribution-bounds" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.distribution.Distribution.bounds">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Distribution.</span></span><span class="sig-name descname"><span class="pre">bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.bounds.resids" title="arch.univariate.distribution.Distribution.bounds.resids (Python parameter) — Residuals to use when computing the bounds"><span class="n"><span class="pre">resids</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Distribution.bounds"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.distribution.Distribution.bounds" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.distribution.Distribution.bounds">
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Distribution.</span></span><span class="sig-name descname"><span class="pre">bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.bounds.resids" title="arch.univariate.distribution.Distribution.bounds.resids (Python parameter) — Residuals to use when computing the bounds"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Distribution.bounds"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.distribution.Distribution.bounds" title="Link to this definition">¶</a></dt>
 <dd><p>Parameter bounds for use in optimization.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.distribution.Distribution.bounds-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.distribution.Distribution.bounds-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.distribution.Distribution.bounds.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.distribution.Distribution.bounds.resids" title="Permalink to this definition">¶</a></dt><dd><p>Residuals to use when computing the bounds</p>
+<dt id="arch.univariate.distribution.Distribution.bounds.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.distribution.Distribution.bounds.resids" title="Permalink to this definition">¶</a></dt><dd><p>Residuals to use when computing the bounds</p>
 </dd>
 </dl>
 </dd>
@@ -1154,7 +1154,7 @@ <h1 id="arch-univariate-distribution-distribution-bounds">arch.univariate.distri
 <dd class="field-even"><p><strong>bounds</strong> – List containing a single tuple with (lower, upper) bounds</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.distribution.Distribution.bounds-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.distribution.Distribution.bounds-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1226,7 +1226,7 @@ <h1 id="arch-univariate-distribution-distribution-bounds">arch.univariate.distri
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1273,7 +1273,7 @@ <h1 id="arch-univariate-distribution-distribution-bounds">arch.univariate.distri
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diff --git a/univariate/generated/generated/arch.univariate.distribution.Distribution.cdf.html b/univariate/generated/generated/arch.univariate.distribution.Distribution.cdf.html
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@@ -1152,17 +1152,17 @@
                   
 
 
-<h1 id="arch-univariate-distribution-distribution-cdf">arch.univariate.distribution.Distribution.cdf<a class="headerlink" href="#arch-univariate-distribution-distribution-cdf" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-distribution-distribution-cdf">arch.univariate.distribution.Distribution.cdf<a class="headerlink" href="#arch-univariate-distribution-distribution-cdf" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.distribution.Distribution.cdf">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Distribution.</span></span><span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.cdf.resids" title="arch.univariate.distribution.Distribution.cdf.resids (Python parameter) — Values at which to evaluate the cdf"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.cdf.parameters" title="arch.univariate.distribution.Distribution.cdf.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Distribution.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.distribution.Distribution.cdf" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.distribution.Distribution.cdf">
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Distribution.</span></span><span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.cdf.resids" title="arch.univariate.distribution.Distribution.cdf.resids (Python parameter) — Values at which to evaluate the cdf"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.cdf.parameters" title="arch.univariate.distribution.Distribution.cdf.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Distribution.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.distribution.Distribution.cdf" title="Link to this definition">¶</a></dt>
 <dd><p>Cumulative distribution function</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.distribution.Distribution.cdf-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.distribution.Distribution.cdf-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.distribution.Distribution.cdf.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.distribution.Distribution.cdf.resids" title="Permalink to this definition">¶</a></dt><dd><p>Values at which to evaluate the cdf</p>
+<dt id="arch.univariate.distribution.Distribution.cdf.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.distribution.Distribution.cdf.resids" title="Permalink to this definition">¶</a></dt><dd><p>Values at which to evaluate the cdf</p>
 </dd>
-<dt id="arch.univariate.distribution.Distribution.cdf.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.distribution.Distribution.cdf.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use <code class="docutils literal notranslate"><span class="pre">None</span></code> for parameterless
+<dt id="arch.univariate.distribution.Distribution.cdf.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.distribution.Distribution.cdf.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use <code class="docutils literal notranslate"><span class="pre">None</span></code> for parameterless
 distributions.</p>
 </dd>
 </dl>
@@ -1171,7 +1171,7 @@ <h1 id="arch-univariate-distribution-distribution-cdf">arch.univariate.distribut
 <dd class="field-even"><p><strong>f</strong> – CDF values</p>
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 <dt class="field-odd" id="arch.univariate.distribution.Distribution.cdf-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.distribution.Distribution.cdf-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
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diff --git a/univariate/generated/generated/arch.univariate.distribution.Distribution.constraints.html b/univariate/generated/generated/arch.univariate.distribution.Distribution.constraints.html
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
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@@ -92,7 +92,7 @@
 
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-          <li class="md-nav__item">
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@@ -1098,19 +1084,16 @@
                   
 
 
-<h1 id="arch-univariate-distribution-distribution-constraints">arch.univariate.distribution.Distribution.constraints<a class="headerlink" href="#arch-univariate-distribution-distribution-constraints" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-distribution-distribution-constraints">arch.univariate.distribution.Distribution.constraints<a class="headerlink" href="#arch-univariate-distribution-distribution-constraints" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.distribution.Distribution.constraints">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Distribution.</span></span><span class="sig-name descname"><span class="pre">constraints</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Distribution.constraints"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.distribution.Distribution.constraints" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Distribution.</span></span><span class="sig-name descname"><span class="pre">constraints</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Distribution.constraints"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.distribution.Distribution.constraints" title="Link to this definition">¶</a></dt>
 <dd><p>Construct arrays to use in constrained optimization.</p>
 <dl class="field-list simple">
-<dt class="field-odd" id="arch.univariate.distribution.Distribution.constraints-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.distribution.Distribution.constraints-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>]</p>
-</dd>
-<dt class="field-even" id="arch.univariate.distribution.Distribution.constraints-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.distribution.Distribution.constraints-returns" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><ul class="simple">
-<li><p><strong>A</strong> (<em>ndarray</em>) – Constraint loadings</p></li>
-<li><p><strong>b</strong> (<em>ndarray</em>) – Constraint values</p></li>
+<dt class="field-odd" id="arch.univariate.distribution.Distribution.constraints-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.distribution.Distribution.constraints-returns" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p><strong>A</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – Constraint loadings</p></li>
+<li><p><strong>b</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – Constraint values</p></li>
 </ul>
 </p>
 </dd>
@@ -1186,7 +1169,7 @@ <h1 id="arch-univariate-distribution-distribution-constraints">arch.univariate.d
   
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diff --git a/univariate/generated/generated/arch.univariate.distribution.Distribution.generator.html b/univariate/generated/generated/arch.univariate.distribution.Distribution.generator.html
index 6ddc46c719..774283aef5 100644
--- a/univariate/generated/generated/arch.univariate.distribution.Distribution.generator.html
+++ b/univariate/generated/generated/arch.univariate.distribution.Distribution.generator.html
@@ -15,7 +15,7 @@
     
   
       
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+            arch 6.2.0
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+    arch 6.2.0
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@@ -1058,10 +1058,10 @@
                   
 
 
-<h1 id="arch-univariate-distribution-distribution-generator">arch.univariate.distribution.Distribution.generator<a class="headerlink" href="#arch-univariate-distribution-distribution-generator" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-distribution-distribution-generator">arch.univariate.distribution.Distribution.generator<a class="headerlink" href="#arch-univariate-distribution-distribution-generator" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.distribution.Distribution.generator">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Distribution.</span></span><span class="sig-name descname"><span class="pre">generator</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.24)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.24)"><span class="pre">Generator</span></a></span><a class="headerlink" href="#arch.univariate.distribution.Distribution.generator" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Distribution.</span></span><span class="sig-name descname"><span class="pre">generator</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><span class="pre">Generator</span></a></span><a class="headerlink" href="#arch.univariate.distribution.Distribution.generator" title="Link to this definition">¶</a></dt>
 <dd><p>The NumPy Generator or RandomState attached to the distribution</p>
 </dd></dl>
 
@@ -1132,7 +1132,7 @@ <h1 id="arch-univariate-distribution-distribution-generator">arch.univariate.dis
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
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@@ -1179,7 +1179,7 @@ <h1 id="arch-univariate-distribution-distribution-generator">arch.univariate.dis
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diff --git a/univariate/generated/generated/arch.univariate.distribution.Distribution.loglikelihood.html b/univariate/generated/generated/arch.univariate.distribution.Distribution.loglikelihood.html
index 9fe653b255..47b1cfc36d 100644
--- a/univariate/generated/generated/arch.univariate.distribution.Distribution.loglikelihood.html
+++ b/univariate/generated/generated/arch.univariate.distribution.Distribution.loglikelihood.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.distribution.Distribution.loglikelihood - arch 6.1.0</title>
+        <title>arch.univariate.distribution.Distribution.loglikelihood - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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     <div class="md-nav__source">
@@ -712,13 +712,6 @@
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@@ -1138,13 +1131,6 @@
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-  
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@@ -1166,28 +1152,25 @@
                   
 
 
-<h1 id="arch-univariate-distribution-distribution-loglikelihood">arch.univariate.distribution.Distribution.loglikelihood<a class="headerlink" href="#arch-univariate-distribution-distribution-loglikelihood" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-distribution-distribution-loglikelihood">arch.univariate.distribution.Distribution.loglikelihood<a class="headerlink" href="#arch-univariate-distribution-distribution-loglikelihood" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.distribution.Distribution.loglikelihood">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Distribution.</span></span><span class="sig-name descname"><span class="pre">loglikelihood</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.loglikelihood.parameters" title="arch.univariate.distribution.Distribution.loglikelihood.parameters (Python parameter) — Distribution shape parameters"><span class="n"><span class="pre">parameters</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.loglikelihood.resids" title="arch.univariate.distribution.Distribution.loglikelihood.resids (Python parameter) — nobs array of model residuals"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.loglikelihood.sigma2" title="arch.univariate.distribution.Distribution.loglikelihood.sigma2 (Python parameter) — nobs array of conditional variances"><span class="n"><span class="pre">sigma2</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.loglikelihood.individual" title="arch.univariate.distribution.Distribution.loglikelihood.individual (Python parameter) — Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"><span class="n"><span class="pre">individual</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Distribution.loglikelihood"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.distribution.Distribution.loglikelihood" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Distribution.</span></span><span class="sig-name descname"><span class="pre">loglikelihood</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.loglikelihood.parameters" title="arch.univariate.distribution.Distribution.loglikelihood.parameters (Python parameter) — Distribution shape parameters"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.loglikelihood.resids" title="arch.univariate.distribution.Distribution.loglikelihood.resids (Python parameter) — nobs array of model residuals"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.loglikelihood.sigma2" title="arch.univariate.distribution.Distribution.loglikelihood.sigma2 (Python parameter) — nobs array of conditional variances"><span class="n"><span class="pre">sigma2</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.loglikelihood.individual" title="arch.univariate.distribution.Distribution.loglikelihood.individual (Python parameter) — Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"><span class="n"><span class="pre">individual</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Distribution.loglikelihood"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.distribution.Distribution.loglikelihood" title="Link to this definition">¶</a></dt>
 <dd><p>Loglikelihood evaluation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.distribution.Distribution.loglikelihood-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.distribution.Distribution.loglikelihood-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.distribution.Distribution.loglikelihood.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.distribution.Distribution.loglikelihood.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution shape parameters</p>
+<dt id="arch.univariate.distribution.Distribution.loglikelihood.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.distribution.Distribution.loglikelihood.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution shape parameters</p>
 </dd>
-<dt id="arch.univariate.distribution.Distribution.loglikelihood.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.distribution.Distribution.loglikelihood.resids" title="Permalink to this definition">¶</a></dt><dd><p>nobs array of model residuals</p>
+<dt id="arch.univariate.distribution.Distribution.loglikelihood.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.distribution.Distribution.loglikelihood.resids" title="Permalink to this definition">¶</a></dt><dd><p>nobs array of model residuals</p>
 </dd>
-<dt id="arch.univariate.distribution.Distribution.loglikelihood.sigma2"><span class="sig-name descname">sigma2</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.distribution.Distribution.loglikelihood.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>nobs array of conditional variances</p>
+<dt id="arch.univariate.distribution.Distribution.loglikelihood.sigma2"><span class="n sig-name">sigma2</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.distribution.Distribution.loglikelihood.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>nobs array of conditional variances</p>
 </dd>
-<dt id="arch.univariate.distribution.Distribution.loglikelihood.individual"><span class="sig-name descname">individual</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a>, optional</span></span><a class="headerlink" href="#arch.univariate.distribution.Distribution.loglikelihood.individual" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to return the vector of individual log
+<dt id="arch.univariate.distribution.Distribution.loglikelihood.individual"><span class="n sig-name">individual</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.univariate.distribution.Distribution.loglikelihood.individual" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to return the vector of individual log
 likelihoods (True) or the sum (False)</p>
 </dd>
 </dl>
 </dd>
-<dt class="field-even" id="arch.univariate.distribution.Distribution.loglikelihood-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.distribution.Distribution.loglikelihood-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a> | <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a></p>
-</dd>
 </dl>
 <p class="rubric">Notes</p>
 <p>Returns the loglikelihood where resids are the “data”,
@@ -1261,7 +1244,7 @@ <h1 id="arch-univariate-distribution-distribution-loglikelihood">arch.univariate
   
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diff --git a/univariate/generated/generated/arch.univariate.distribution.Distribution.moment.html b/univariate/generated/generated/arch.univariate.distribution.Distribution.moment.html
index 5f4cacf565..ce260c4de2 100644
--- a/univariate/generated/generated/arch.univariate.distribution.Distribution.moment.html
+++ b/univariate/generated/generated/arch.univariate.distribution.Distribution.moment.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -47,8 +47,8 @@
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@@ -102,7 +102,7 @@
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@@ -1152,17 +1152,17 @@
                   
 
 
-<h1 id="arch-univariate-distribution-distribution-moment">arch.univariate.distribution.Distribution.moment<a class="headerlink" href="#arch-univariate-distribution-distribution-moment" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-distribution-distribution-moment">arch.univariate.distribution.Distribution.moment<a class="headerlink" href="#arch-univariate-distribution-distribution-moment" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.distribution.Distribution.moment">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Distribution.</span></span><span class="sig-name descname"><span class="pre">moment</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.moment.n" title="arch.univariate.distribution.Distribution.moment.n (Python parameter) — Order of moment"><span class="n"><span class="pre">n</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.moment.parameters" title="arch.univariate.distribution.Distribution.moment.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Distribution.moment"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.distribution.Distribution.moment" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.distribution.Distribution.moment">
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Distribution.</span></span><span class="sig-name descname"><span class="pre">moment</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.moment.n" title="arch.univariate.distribution.Distribution.moment.n (Python parameter) — Order of moment"><span class="n"><span class="pre">n</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.moment.parameters" title="arch.univariate.distribution.Distribution.moment.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Distribution.moment"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.distribution.Distribution.moment" title="Link to this definition">¶</a></dt>
 <dd><p>Moment of order n</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.distribution.Distribution.moment-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.distribution.Distribution.moment-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.distribution.Distribution.moment.n"><span class="sig-name descname">n</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.distribution.Distribution.moment.n" title="Permalink to this definition">¶</a></dt><dd><p>Order of moment</p>
+<dt id="arch.univariate.distribution.Distribution.moment.n"><span class="n sig-name">n</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.distribution.Distribution.moment.n" title="Permalink to this definition">¶</a></dt><dd><p>Order of moment</p>
 </dd>
-<dt id="arch.univariate.distribution.Distribution.moment.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray, optional</span></span><a class="headerlink" href="#arch.univariate.distribution.Distribution.moment.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use None for parameterless distributions.</p>
+<dt id="arch.univariate.distribution.Distribution.moment.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.distribution.Distribution.moment.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use None for parameterless distributions.</p>
 </dd>
 </dl>
 </dd>
@@ -1170,7 +1170,7 @@ <h1 id="arch-univariate-distribution-distribution-moment">arch.univariate.distri
 <dd class="field-even"><p>Calculated moment</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.distribution.Distribution.moment-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.distribution.Distribution.moment-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1242,7 +1242,7 @@ <h1 id="arch-univariate-distribution-distribution-moment">arch.univariate.distri
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1289,7 +1289,7 @@ <h1 id="arch-univariate-distribution-distribution-moment">arch.univariate.distri
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.distribution.Distribution.name.html b/univariate/generated/generated/arch.univariate.distribution.Distribution.name.html
index b357527693..5a1abfdc23 100644
--- a/univariate/generated/generated/arch.univariate.distribution.Distribution.name.html
+++ b/univariate/generated/generated/arch.univariate.distribution.Distribution.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.distribution.Distribution.name - arch 6.1.0</title>
+        <title>arch.univariate.distribution.Distribution.name - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1058,10 +1058,10 @@
                   
 
 
-<h1 id="arch-univariate-distribution-distribution-name">arch.univariate.distribution.Distribution.name<a class="headerlink" href="#arch-univariate-distribution-distribution-name" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-distribution-distribution-name">arch.univariate.distribution.Distribution.name<a class="headerlink" href="#arch-univariate-distribution-distribution-name" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.distribution.Distribution.name">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Distribution.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.univariate.distribution.Distribution.name" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Distribution.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.univariate.distribution.Distribution.name" title="Link to this definition">¶</a></dt>
 <dd><p>The name of the distribution</p>
 </dd></dl>
 
@@ -1132,7 +1132,7 @@ <h1 id="arch-univariate-distribution-distribution-name">arch.univariate.distribu
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1179,7 +1179,7 @@ <h1 id="arch-univariate-distribution-distribution-name">arch.univariate.distribu
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.distribution.Distribution.parameter_names.html b/univariate/generated/generated/arch.univariate.distribution.Distribution.parameter_names.html
index 4d8e105af9..9e123a3d49 100644
--- a/univariate/generated/generated/arch.univariate.distribution.Distribution.parameter_names.html
+++ b/univariate/generated/generated/arch.univariate.distribution.Distribution.parameter_names.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.distribution.Distribution.parameter_names - arch 6.1.0</title>
+        <title>arch.univariate.distribution.Distribution.parameter_names - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1098,17 +1098,17 @@
                   
 
 
-<h1 id="arch-univariate-distribution-distribution-parameter-names">arch.univariate.distribution.Distribution.parameter_names<a class="headerlink" href="#arch-univariate-distribution-distribution-parameter-names" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-distribution-distribution-parameter-names">arch.univariate.distribution.Distribution.parameter_names<a class="headerlink" href="#arch-univariate-distribution-distribution-parameter-names" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.distribution.Distribution.parameter_names">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Distribution.</span></span><span class="sig-name descname"><span class="pre">parameter_names</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Distribution.parameter_names"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.distribution.Distribution.parameter_names" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Distribution.</span></span><span class="sig-name descname"><span class="pre">parameter_names</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Distribution.parameter_names"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.distribution.Distribution.parameter_names" title="Link to this definition">¶</a></dt>
 <dd><p>Names of distribution shape parameters</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.distribution.Distribution.parameter_names-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.distribution.Distribution.parameter_names-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p><strong>names</strong> – Parameter names</p>
 </dd>
 <dt class="field-even" id="arch.univariate.distribution.Distribution.parameter_names-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.distribution.Distribution.parameter_names-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>)</p>
+<dd class="field-even"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span> <span class="pre">(str)</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1180,7 +1180,7 @@ <h1 id="arch-univariate-distribution-distribution-parameter-names">arch.univaria
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1227,7 +1227,7 @@ <h1 id="arch-univariate-distribution-distribution-parameter-names">arch.univaria
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
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diff --git a/univariate/generated/generated/arch.univariate.distribution.Distribution.partial_moment.html b/univariate/generated/generated/arch.univariate.distribution.Distribution.partial_moment.html
index 7c9f75182c..0aa2580651 100644
--- a/univariate/generated/generated/arch.univariate.distribution.Distribution.partial_moment.html
+++ b/univariate/generated/generated/arch.univariate.distribution.Distribution.partial_moment.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.distribution.Distribution.partial_moment - arch 6.1.0</title>
+        <title>arch.univariate.distribution.Distribution.partial_moment - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1166,19 +1166,19 @@
                   
 
 
-<h1 id="arch-univariate-distribution-distribution-partial-moment">arch.univariate.distribution.Distribution.partial_moment<a class="headerlink" href="#arch-univariate-distribution-distribution-partial-moment" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-distribution-distribution-partial-moment">arch.univariate.distribution.Distribution.partial_moment<a class="headerlink" href="#arch-univariate-distribution-distribution-partial-moment" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.distribution.Distribution.partial_moment">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Distribution.</span></span><span class="sig-name descname"><span class="pre">partial_moment</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.partial_moment.n" title="arch.univariate.distribution.Distribution.partial_moment.n (Python parameter) — Order of partial moment"><span class="n"><span class="pre">n</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.partial_moment.z" title="arch.univariate.distribution.Distribution.partial_moment.z (Python parameter) — Upper bound for partial moment integral"><span class="n"><span class="pre">z</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.partial_moment.parameters" title="arch.univariate.distribution.Distribution.partial_moment.parameters (Python parameter) — Distribution parameters.  Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Distribution.partial_moment"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.distribution.Distribution.partial_moment" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.distribution.Distribution.partial_moment">
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Distribution.</span></span><span class="sig-name descname"><span class="pre">partial_moment</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.partial_moment.n" title="arch.univariate.distribution.Distribution.partial_moment.n (Python parameter) — Order of partial moment"><span class="n"><span class="pre">n</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.partial_moment.z" title="arch.univariate.distribution.Distribution.partial_moment.z (Python parameter) — Upper bound for partial moment integral"><span class="n"><span class="pre">z</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.partial_moment.parameters" title="arch.univariate.distribution.Distribution.partial_moment.parameters (Python parameter) — Distribution parameters.  Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Distribution.partial_moment"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.distribution.Distribution.partial_moment" title="Link to this definition">¶</a></dt>
 <dd><p>Order n lower partial moment from -inf to z</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.distribution.Distribution.partial_moment-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.distribution.Distribution.partial_moment-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.distribution.Distribution.partial_moment.n"><span class="sig-name descname">n</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.distribution.Distribution.partial_moment.n" title="Permalink to this definition">¶</a></dt><dd><p>Order of partial moment</p>
+<dt id="arch.univariate.distribution.Distribution.partial_moment.n"><span class="n sig-name">n</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.distribution.Distribution.partial_moment.n" title="Permalink to this definition">¶</a></dt><dd><p>Order of partial moment</p>
 </dd>
-<dt id="arch.univariate.distribution.Distribution.partial_moment.z"><span class="sig-name descname">z</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>, optional</span></span><a class="headerlink" href="#arch.univariate.distribution.Distribution.partial_moment.z" title="Permalink to this definition">¶</a></dt><dd><p>Upper bound for partial moment integral</p>
+<dt id="arch.univariate.distribution.Distribution.partial_moment.z"><span class="n sig-name">z</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.0</span></code><a class="headerlink" href="#arch.univariate.distribution.Distribution.partial_moment.z" title="Permalink to this definition">¶</a></dt><dd><p>Upper bound for partial moment integral</p>
 </dd>
-<dt id="arch.univariate.distribution.Distribution.partial_moment.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray, optional</span></span><a class="headerlink" href="#arch.univariate.distribution.Distribution.partial_moment.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters.  Use None for parameterless distributions.</p>
+<dt id="arch.univariate.distribution.Distribution.partial_moment.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.distribution.Distribution.partial_moment.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters.  Use None for parameterless distributions.</p>
 </dd>
 </dl>
 </dd>
@@ -1186,7 +1186,7 @@ <h1 id="arch-univariate-distribution-distribution-partial-moment">arch.univariat
 <dd class="field-even"><p>Partial moment</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.distribution.Distribution.partial_moment-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.distribution.Distribution.partial_moment-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 <p class="rubric">References</p>
@@ -1271,7 +1271,7 @@ <h1 id="arch-univariate-distribution-distribution-partial-moment">arch.univariat
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
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+        <script id="MathJax-script" src="../../../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
     
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diff --git a/univariate/generated/generated/arch.univariate.distribution.Distribution.ppf.html b/univariate/generated/generated/arch.univariate.distribution.Distribution.ppf.html
index cbbd17b7a5..7770736f68 100644
--- a/univariate/generated/generated/arch.univariate.distribution.Distribution.ppf.html
+++ b/univariate/generated/generated/arch.univariate.distribution.Distribution.ppf.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.distribution.Distribution.ppf - arch 6.1.0</title>
+        <title>arch.univariate.distribution.Distribution.ppf - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1152,17 +1152,17 @@
                   
 
 
-<h1 id="arch-univariate-distribution-distribution-ppf">arch.univariate.distribution.Distribution.ppf<a class="headerlink" href="#arch-univariate-distribution-distribution-ppf" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-distribution-distribution-ppf">arch.univariate.distribution.Distribution.ppf<a class="headerlink" href="#arch-univariate-distribution-distribution-ppf" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.distribution.Distribution.ppf">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Distribution.</span></span><span class="sig-name descname"><span class="pre">ppf</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.ppf.pits" title="arch.univariate.distribution.Distribution.ppf.pits (Python parameter) — Probability-integral-transformed values in the interval (0, 1)."><span class="n"><span class="pre">pits</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.ppf.parameters" title="arch.univariate.distribution.Distribution.ppf.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Distribution.ppf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.distribution.Distribution.ppf" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.distribution.Distribution.ppf">
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Distribution.</span></span><span class="sig-name descname"><span class="pre">ppf</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.ppf.pits" title="arch.univariate.distribution.Distribution.ppf.pits (Python parameter) — Probability-integral-transformed values in the interval (0, 1)."><span class="n"><span class="pre">pits</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.ppf.parameters" title="arch.univariate.distribution.Distribution.ppf.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Distribution.ppf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.distribution.Distribution.ppf" title="Link to this definition">¶</a></dt>
 <dd><p>Inverse cumulative density function (ICDF)</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.distribution.Distribution.ppf-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.distribution.Distribution.ppf-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.distribution.Distribution.ppf.pits"><span class="sig-name descname">pits</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}</span></span><a class="headerlink" href="#arch.univariate.distribution.Distribution.ppf.pits" title="Permalink to this definition">¶</a></dt><dd><p>Probability-integral-transformed values in the interval (0, 1).</p>
+<dt id="arch.univariate.distribution.Distribution.ppf.pits"><span class="n sig-name">pits</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.distribution.Distribution.ppf.pits" title="Permalink to this definition">¶</a></dt><dd><p>Probability-integral-transformed values in the interval (0, 1).</p>
 </dd>
-<dt id="arch.univariate.distribution.Distribution.ppf.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray, optional</span></span><a class="headerlink" href="#arch.univariate.distribution.Distribution.ppf.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use <code class="docutils literal notranslate"><span class="pre">None</span></code> for parameterless
+<dt id="arch.univariate.distribution.Distribution.ppf.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.distribution.Distribution.ppf.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use <code class="docutils literal notranslate"><span class="pre">None</span></code> for parameterless
 distributions.</p>
 </dd>
 </dl>
@@ -1171,7 +1171,7 @@ <h1 id="arch-univariate-distribution-distribution-ppf">arch.univariate.distribut
 <dd class="field-even"><p><strong>i</strong> – Inverse CDF values</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.distribution.Distribution.ppf-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.distribution.Distribution.ppf-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>{float, ndarray}</p>
+<dd class="field-odd"><p><code class="docutils literal notranslate"><span class="pre">{float,</span> <span class="pre">ndarray}</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1243,7 +1243,7 @@ <h1 id="arch-univariate-distribution-distribution-ppf">arch.univariate.distribut
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1290,7 +1290,7 @@ <h1 id="arch-univariate-distribution-distribution-ppf">arch.univariate.distribut
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diff --git a/univariate/generated/generated/arch.univariate.distribution.Distribution.random_state.html b/univariate/generated/generated/arch.univariate.distribution.Distribution.random_state.html
index 3d41fb676b..3feddfddee 100644
--- a/univariate/generated/generated/arch.univariate.distribution.Distribution.random_state.html
+++ b/univariate/generated/generated/arch.univariate.distribution.Distribution.random_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.distribution.Distribution.random_state - arch 6.1.0</title>
+        <title>arch.univariate.distribution.Distribution.random_state - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1058,13 +1058,13 @@
                   
 
 
-<h1 id="arch-univariate-distribution-distribution-random-state">arch.univariate.distribution.Distribution.random_state<a class="headerlink" href="#arch-univariate-distribution-distribution-random-state" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-distribution-distribution-random-state">arch.univariate.distribution.Distribution.random_state<a class="headerlink" href="#arch-univariate-distribution-distribution-random-state" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.distribution.Distribution.random_state">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Distribution.</span></span><span class="sig-name descname"><span class="pre">random_state</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.24)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.24)"><span class="pre">Generator</span></a></span><a class="headerlink" href="#arch.univariate.distribution.Distribution.random_state" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Distribution.</span></span><span class="sig-name descname"><span class="pre">random_state</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><span class="pre">Generator</span></a></span><a class="headerlink" href="#arch.univariate.distribution.Distribution.random_state" title="Link to this definition">¶</a></dt>
 <dd><p>The NumPy RandomState attached to the distribution</p>
-<div class="deprecated">
-<p><span class="versionmodified deprecated">Deprecated since version 5.0: </span>random_state is deprecated. Use generator instead.</p>
+<div class="admonition deprecated">
+<p class="admonition-title"><span class="versionmodified deprecated">Deprecated since version 5.0: </span>random_state is deprecated. Use generator instead.</p>
 </div>
 </dd></dl>
 
@@ -1135,7 +1135,7 @@ <h1 id="arch-univariate-distribution-distribution-random-state">arch.univariate.
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1182,7 +1182,7 @@ <h1 id="arch-univariate-distribution-distribution-random-state">arch.univariate.
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.distribution.Distribution.simulate.html b/univariate/generated/generated/arch.univariate.distribution.Distribution.simulate.html
index ca5af7cbf3..60a5a605d2 100644
--- a/univariate/generated/generated/arch.univariate.distribution.Distribution.simulate.html
+++ b/univariate/generated/generated/arch.univariate.distribution.Distribution.simulate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.distribution.Distribution.simulate - arch 6.1.0</title>
+        <title>arch.univariate.distribution.Distribution.simulate - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1138,15 +1138,15 @@
                   
 
 
-<h1 id="arch-univariate-distribution-distribution-simulate">arch.univariate.distribution.Distribution.simulate<a class="headerlink" href="#arch-univariate-distribution-distribution-simulate" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-distribution-distribution-simulate">arch.univariate.distribution.Distribution.simulate<a class="headerlink" href="#arch-univariate-distribution-distribution-simulate" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.distribution.Distribution.simulate">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Distribution.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.simulate.parameters" title="arch.univariate.distribution.Distribution.simulate.parameters (Python parameter) — Distribution parameters"><span class="n"><span class="pre">parameters</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Distribution.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.distribution.Distribution.simulate" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.distribution.Distribution.simulate">
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Distribution.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.simulate.parameters" title="arch.univariate.distribution.Distribution.simulate.parameters (Python parameter) — Distribution parameters"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Distribution.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.distribution.Distribution.simulate" title="Link to this definition">¶</a></dt>
 <dd><p>Simulates i.i.d. draws from the distribution</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.distribution.Distribution.simulate-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.distribution.Distribution.simulate-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.distribution.Distribution.simulate.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.distribution.Distribution.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters</p>
+<dt id="arch.univariate.distribution.Distribution.simulate.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.distribution.Distribution.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters</p>
 </dd>
 </dl>
 </dd>
@@ -1155,7 +1155,7 @@ <h1 id="arch-univariate-distribution-distribution-simulate">arch.univariate.dist
 draws from the distribution</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.distribution.Distribution.simulate-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.distribution.Distribution.simulate-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>callable</p>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">callable</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1227,7 +1227,7 @@ <h1 id="arch-univariate-distribution-distribution-simulate">arch.univariate.dist
   
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-    7.0.1.
+    7.2.6.
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diff --git a/univariate/generated/generated/arch.univariate.distribution.Distribution.starting_values.html b/univariate/generated/generated/arch.univariate.distribution.Distribution.starting_values.html
index a7d3adfe76..82aa606010 100644
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+++ b/univariate/generated/generated/arch.univariate.distribution.Distribution.starting_values.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -47,8 +47,8 @@
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+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1138,15 +1138,15 @@
                   
 
 
-<h1 id="arch-univariate-distribution-distribution-starting-values">arch.univariate.distribution.Distribution.starting_values<a class="headerlink" href="#arch-univariate-distribution-distribution-starting-values" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-distribution-distribution-starting-values">arch.univariate.distribution.Distribution.starting_values<a class="headerlink" href="#arch-univariate-distribution-distribution-starting-values" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.distribution.Distribution.starting_values">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Distribution.</span></span><span class="sig-name descname"><span class="pre">starting_values</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.starting_values.std_resid" title="arch.univariate.distribution.Distribution.starting_values.std_resid (Python parameter) — Estimated standardized residuals to use in computing starting values for the shape parameter"><span class="n"><span class="pre">std_resid</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Distribution.starting_values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.distribution.Distribution.starting_values" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Distribution.</span></span><span class="sig-name descname"><span class="pre">starting_values</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.starting_values.std_resid" title="arch.univariate.distribution.Distribution.starting_values.std_resid (Python parameter) — Estimated standardized residuals to use in computing starting values for the shape parameter"><span class="n"><span class="pre">std_resid</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Distribution.starting_values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.distribution.Distribution.starting_values" title="Link to this definition">¶</a></dt>
 <dd><p>Construct starting values for use in optimization.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.distribution.Distribution.starting_values-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.distribution.Distribution.starting_values-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.distribution.Distribution.starting_values.std_resid"><span class="sig-name descname">std_resid</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.distribution.Distribution.starting_values.std_resid" title="Permalink to this definition">¶</a></dt><dd><p>Estimated standardized residuals to use in computing starting
+<dt id="arch.univariate.distribution.Distribution.starting_values.std_resid"><span class="n sig-name">std_resid</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.distribution.Distribution.starting_values.std_resid" title="Permalink to this definition">¶</a></dt><dd><p>Estimated standardized residuals to use in computing starting
 values for the shape parameter</p>
 </dd>
 </dl>
@@ -1155,7 +1155,7 @@ <h1 id="arch-univariate-distribution-distribution-starting-values">arch.univaria
 <dd class="field-even"><p><strong>sv</strong> – The estimated shape parameters for the distribution</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.distribution.Distribution.starting_values-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.distribution.Distribution.starting_values-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -1229,7 +1229,7 @@ <h1 id="arch-univariate-distribution-distribution-starting-values">arch.univaria
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1276,7 +1276,7 @@ <h1 id="arch-univariate-distribution-distribution-starting-values">arch.univaria
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diff --git a/univariate/generated/generated/arch.univariate.recursions_python.VolatilityUpdater.initialize_update.html b/univariate/generated/generated/arch.univariate.recursions_python.VolatilityUpdater.initialize_update.html
index b7504148af..6460f39b74 100644
--- a/univariate/generated/generated/arch.univariate.recursions_python.VolatilityUpdater.initialize_update.html
+++ b/univariate/generated/generated/arch.univariate.recursions_python.VolatilityUpdater.initialize_update.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.recursions_python.VolatilityUpdater.initialize_update - arch 6.1.0</title>
+        <title>arch.univariate.recursions_python.VolatilityUpdater.initialize_update - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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+    arch 6.2.0
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     <div class="md-nav__source">
@@ -735,13 +735,6 @@
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-  <a href="#arch.univariate.recursions_python.VolatilityUpdater.initialize_update-return-type" class="md-nav__link">
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-  
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@@ -1064,13 +1057,6 @@
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-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
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       </ul>
@@ -1092,25 +1078,22 @@
                   
 
 
-<h1 id="arch-univariate-recursions-python-volatilityupdater-initialize-update">arch.univariate.recursions_python.VolatilityUpdater.initialize_update<a class="headerlink" href="#arch-univariate-recursions-python-volatilityupdater-initialize-update" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-recursions-python-volatilityupdater-initialize-update">arch.univariate.recursions_python.VolatilityUpdater.initialize_update<a class="headerlink" href="#arch-univariate-recursions-python-volatilityupdater-initialize-update" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.recursions_python.VolatilityUpdater.initialize_update">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VolatilityUpdater.</span></span><span class="sig-name descname"><span class="pre">initialize_update</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.recursions_python.VolatilityUpdater.initialize_update.parameters" title="arch.univariate.recursions_python.VolatilityUpdater.initialize_update.parameters (Python parameter) — The model parameters."><span class="n"><span class="pre">parameters</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.recursions_python.VolatilityUpdater.initialize_update.backcast" title="arch.univariate.recursions_python.VolatilityUpdater.initialize_update.backcast (Python parameter) — The backcast value(s)."><span class="n"><span class="pre">backcast</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.recursions_python.VolatilityUpdater.initialize_update.nobs" title="arch.univariate.recursions_python.VolatilityUpdater.initialize_update.nobs (Python parameter) — The number of observations in the sample."><span class="n"><span class="pre">nobs</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/recursions_python.html#VolatilityUpdater.initialize_update"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.recursions_python.VolatilityUpdater.initialize_update" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VolatilityUpdater.</span></span><span class="sig-name descname"><span class="pre">initialize_update</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.recursions_python.VolatilityUpdater.initialize_update.parameters" title="arch.univariate.recursions_python.VolatilityUpdater.initialize_update.parameters (Python parameter) — The model parameters."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.recursions_python.VolatilityUpdater.initialize_update.backcast" title="arch.univariate.recursions_python.VolatilityUpdater.initialize_update.backcast (Python parameter) — The backcast value(s)."><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.recursions_python.VolatilityUpdater.initialize_update.nobs" title="arch.univariate.recursions_python.VolatilityUpdater.initialize_update.nobs (Python parameter) — The number of observations in the sample."><span class="n"><span class="pre">nobs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/recursions_python.html#VolatilityUpdater.initialize_update"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.recursions_python.VolatilityUpdater.initialize_update" title="Link to this definition">¶</a></dt>
 <dd><p>Initialize the recursion prior to calling update</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.recursions_python.VolatilityUpdater.initialize_update-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.recursions_python.VolatilityUpdater.initialize_update-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.recursions_python.VolatilityUpdater.initialize_update.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.recursions_python.VolatilityUpdater.initialize_update.parameters" title="Permalink to this definition">¶</a></dt><dd><p>The model parameters.</p>
+<dt id="arch.univariate.recursions_python.VolatilityUpdater.initialize_update.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.recursions_python.VolatilityUpdater.initialize_update.parameters" title="Permalink to this definition">¶</a></dt><dd><p>The model parameters.</p>
 </dd>
-<dt id="arch.univariate.recursions_python.VolatilityUpdater.initialize_update.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}</span></span><a class="headerlink" href="#arch.univariate.recursions_python.VolatilityUpdater.initialize_update.backcast" title="Permalink to this definition">¶</a></dt><dd><p>The backcast value(s).</p>
+<dt id="arch.univariate.recursions_python.VolatilityUpdater.initialize_update.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.recursions_python.VolatilityUpdater.initialize_update.backcast" title="Permalink to this definition">¶</a></dt><dd><p>The backcast value(s).</p>
 </dd>
-<dt id="arch.univariate.recursions_python.VolatilityUpdater.initialize_update.nobs"><span class="sig-name descname">nobs</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.recursions_python.VolatilityUpdater.initialize_update.nobs" title="Permalink to this definition">¶</a></dt><dd><p>The number of observations in the sample.</p>
+<dt id="arch.univariate.recursions_python.VolatilityUpdater.initialize_update.nobs"><span class="n sig-name">nobs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.recursions_python.VolatilityUpdater.initialize_update.nobs" title="Permalink to this definition">¶</a></dt><dd><p>The number of observations in the sample.</p>
 </dd>
 </dl>
 </dd>
-<dt class="field-even" id="arch.univariate.recursions_python.VolatilityUpdater.initialize_update-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.recursions_python.VolatilityUpdater.initialize_update-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code></a></p>
-</dd>
 </dl>
 <p class="rubric">Notes</p>
 <p>This function is called once per likelihood evaluation and can be used
@@ -1185,7 +1168,7 @@ <h1 id="arch-univariate-recursions-python-volatilityupdater-initialize-update">a
   
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diff --git a/univariate/generated/generated/arch.univariate.recursions_python.VolatilityUpdater.update.html b/univariate/generated/generated/arch.univariate.recursions_python.VolatilityUpdater.update.html
index fffb6469af..5b4ed28104 100644
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+++ b/univariate/generated/generated/arch.univariate.recursions_python.VolatilityUpdater.update.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.recursions_python.VolatilityUpdater.update - arch 6.1.0</title>
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-<h1 id="arch-univariate-recursions-python-volatilityupdater-update">arch.univariate.recursions_python.VolatilityUpdater.update<a class="headerlink" href="#arch-univariate-recursions-python-volatilityupdater-update" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-recursions-python-volatilityupdater-update">arch.univariate.recursions_python.VolatilityUpdater.update<a class="headerlink" href="#arch-univariate-recursions-python-volatilityupdater-update" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.recursions_python.VolatilityUpdater.update">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VolatilityUpdater.</span></span><span class="sig-name descname"><span class="pre">update</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.recursions_python.VolatilityUpdater.update.t" title="arch.univariate.recursions_python.VolatilityUpdater.update.t (Python parameter) — The index of the value of sigma2 to update. Assumes but does not check that update has been called recursively for 0,1,...,t-1."><span class="n"><span class="pre">t</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.recursions_python.VolatilityUpdater.update.parameters" title="arch.univariate.recursions_python.VolatilityUpdater.update.parameters (Python parameter) — Model parameters"><span class="n"><span class="pre">parameters</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.recursions_python.VolatilityUpdater.update.resids" title="arch.univariate.recursions_python.VolatilityUpdater.update.resids (Python parameter) — Residuals to use in the recursion"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.recursions_python.VolatilityUpdater.update.sigma2" title="arch.univariate.recursions_python.VolatilityUpdater.update.sigma2 (Python parameter) — Conditional variances with same shape as resids"><span class="n"><span class="pre">sigma2</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.recursions_python.VolatilityUpdater.update.var_bounds" title="arch.univariate.recursions_python.VolatilityUpdater.update.var_bounds (Python parameter) — nobs by 2-element array of upper and lower bounds for conditional variances for each time period"><span class="n"><span class="pre">var_bounds</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/recursions_python.html#VolatilityUpdater.update"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.recursions_python.VolatilityUpdater.update" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VolatilityUpdater.</span></span><span class="sig-name descname"><span class="pre">update</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.recursions_python.VolatilityUpdater.update.t" title="arch.univariate.recursions_python.VolatilityUpdater.update.t (Python parameter) — The index of the value of sigma2 to update. Assumes but does not check that update has been called recursively for 0,1,...,t-1."><span class="n"><span class="pre">t</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.recursions_python.VolatilityUpdater.update.parameters" title="arch.univariate.recursions_python.VolatilityUpdater.update.parameters (Python parameter) — Model parameters"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.recursions_python.VolatilityUpdater.update.resids" title="arch.univariate.recursions_python.VolatilityUpdater.update.resids (Python parameter) — Residuals to use in the recursion"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.recursions_python.VolatilityUpdater.update.sigma2" title="arch.univariate.recursions_python.VolatilityUpdater.update.sigma2 (Python parameter) — Conditional variances with same shape as resids"><span class="n"><span class="pre">sigma2</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.recursions_python.VolatilityUpdater.update.var_bounds" title="arch.univariate.recursions_python.VolatilityUpdater.update.var_bounds (Python parameter) — nobs by 2-element array of upper and lower bounds for conditional variances for each time period"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/recursions_python.html#VolatilityUpdater.update"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.recursions_python.VolatilityUpdater.update" title="Link to this definition">¶</a></dt>
 <dd><p>Update the current variance at location t</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.recursions_python.VolatilityUpdater.update-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.recursions_python.VolatilityUpdater.update-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.recursions_python.VolatilityUpdater.update.t"><span class="sig-name descname">t</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.recursions_python.VolatilityUpdater.update.t" title="Permalink to this definition">¶</a></dt><dd><p>The index of the value of sigma2 to update. Assumes but does not check
+<dt id="arch.univariate.recursions_python.VolatilityUpdater.update.t"><span class="n sig-name">t</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.recursions_python.VolatilityUpdater.update.t" title="Permalink to this definition">¶</a></dt><dd><p>The index of the value of sigma2 to update. Assumes but does not check
 that update has been called recursively for 0,1,…,t-1.</p>
 </dd>
-<dt id="arch.univariate.recursions_python.VolatilityUpdater.update.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.recursions_python.VolatilityUpdater.update.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
+<dt id="arch.univariate.recursions_python.VolatilityUpdater.update.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.recursions_python.VolatilityUpdater.update.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
 </dd>
-<dt id="arch.univariate.recursions_python.VolatilityUpdater.update.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.recursions_python.VolatilityUpdater.update.resids" title="Permalink to this definition">¶</a></dt><dd><p>Residuals to use in the recursion</p>
+<dt id="arch.univariate.recursions_python.VolatilityUpdater.update.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.recursions_python.VolatilityUpdater.update.resids" title="Permalink to this definition">¶</a></dt><dd><p>Residuals to use in the recursion</p>
 </dd>
-<dt id="arch.univariate.recursions_python.VolatilityUpdater.update.sigma2"><span class="sig-name descname">sigma2</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.recursions_python.VolatilityUpdater.update.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>Conditional variances with same shape as resids</p>
+<dt id="arch.univariate.recursions_python.VolatilityUpdater.update.sigma2"><span class="n sig-name">sigma2</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.recursions_python.VolatilityUpdater.update.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>Conditional variances with same shape as resids</p>
 </dd>
-<dt id="arch.univariate.recursions_python.VolatilityUpdater.update.var_bounds"><span class="sig-name descname">var_bounds</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.recursions_python.VolatilityUpdater.update.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>nobs by 2-element array of upper and lower bounds for conditional
+<dt id="arch.univariate.recursions_python.VolatilityUpdater.update.var_bounds"><span class="n sig-name">var_bounds</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.recursions_python.VolatilityUpdater.update.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>nobs by 2-element array of upper and lower bounds for conditional
 variances for each time period</p>
 </dd>
 </dl>
 </dd>
-<dt class="field-even" id="arch.univariate.recursions_python.VolatilityUpdater.update-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.recursions_python.VolatilityUpdater.update-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code></a></p>
-</dd>
 </dl>
 <p class="rubric">Notes</p>
 <p>The update to sigma2 occurs inplace.</p>
@@ -1217,7 +1200,7 @@ <h1 id="arch-univariate-recursions-python-volatilityupdater-update">arch.univari
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
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@@ -1264,7 +1247,7 @@ <h1 id="arch-univariate-recursions-python-volatilityupdater-update">arch.univari
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.backcast.html b/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.backcast.html
index 7f50d9372f..86b3e30cbd 100644
--- a/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.backcast.html
+++ b/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.backcast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.volatility.VolatilityProcess.backcast - arch 6.1.0</title>
+        <title>arch.univariate.volatility.VolatilityProcess.backcast - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1303,15 +1303,15 @@
                   
 
 
-<h1 id="arch-univariate-volatility-volatilityprocess-backcast">arch.univariate.volatility.VolatilityProcess.backcast<a class="headerlink" href="#arch-univariate-volatility-volatilityprocess-backcast" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-volatility-volatilityprocess-backcast">arch.univariate.volatility.VolatilityProcess.backcast<a class="headerlink" href="#arch-univariate-volatility-volatilityprocess-backcast" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.volatility.VolatilityProcess.backcast">
-<span class="sig-prename descclassname"><span class="pre">VolatilityProcess.</span></span><span class="sig-name descname"><span class="pre">backcast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.backcast.resids" title="arch.univariate.volatility.VolatilityProcess.backcast.resids (Python parameter) — Vector of (approximate) residuals"><span class="n"><span class="pre">resids</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#VolatilityProcess.backcast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.backcast" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">VolatilityProcess.</span></span><span class="sig-name descname"><span class="pre">backcast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.backcast.resids" title="arch.univariate.volatility.VolatilityProcess.backcast.resids (Python parameter) — Vector of (approximate) residuals"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#VolatilityProcess.backcast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.backcast" title="Link to this definition">¶</a></dt>
 <dd><p>Construct values for backcasting to start the recursion</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.volatility.VolatilityProcess.backcast-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.backcast-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.volatility.VolatilityProcess.backcast.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.backcast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of (approximate) residuals</p>
+<dt id="arch.univariate.volatility.VolatilityProcess.backcast.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.backcast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of (approximate) residuals</p>
 </dd>
 </dl>
 </dd>
@@ -1319,7 +1319,7 @@ <h1 id="arch-univariate-volatility-volatilityprocess-backcast">arch.univariate.v
 <dd class="field-even"><p><strong>backcast</strong> – Value to use in backcasting in the volatility recursion</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.volatility.VolatilityProcess.backcast-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.backcast-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1391,7 +1391,7 @@ <h1 id="arch-univariate-volatility-volatilityprocess-backcast">arch.univariate.v
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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diff --git a/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.backcast_transform.html b/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.backcast_transform.html
index 185c8afe82..f9d74e380e 100644
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+++ b/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.backcast_transform.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.volatility.VolatilityProcess.backcast_transform - arch 6.1.0</title>
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@@ -47,8 +47,8 @@
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+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -1303,15 +1303,15 @@
                   
 
 
-<h1 id="arch-univariate-volatility-volatilityprocess-backcast-transform">arch.univariate.volatility.VolatilityProcess.backcast_transform<a class="headerlink" href="#arch-univariate-volatility-volatilityprocess-backcast-transform" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-volatility-volatilityprocess-backcast-transform">arch.univariate.volatility.VolatilityProcess.backcast_transform<a class="headerlink" href="#arch-univariate-volatility-volatilityprocess-backcast-transform" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.volatility.VolatilityProcess.backcast_transform">
-<span class="sig-prename descclassname"><span class="pre">VolatilityProcess.</span></span><span class="sig-name descname"><span class="pre">backcast_transform</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.backcast_transform.backcast" title="arch.univariate.volatility.VolatilityProcess.backcast_transform.backcast (Python parameter) — User-provided backcast that approximates sigma2[0]."><span class="n"><span class="pre">backcast</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#VolatilityProcess.backcast_transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.backcast_transform" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.volatility.VolatilityProcess.backcast_transform">
+<span class="sig-prename descclassname"><span class="pre">VolatilityProcess.</span></span><span class="sig-name descname"><span class="pre">backcast_transform</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.backcast_transform.backcast" title="arch.univariate.volatility.VolatilityProcess.backcast_transform.backcast (Python parameter) — User-provided backcast that approximates sigma2[0]."><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#VolatilityProcess.backcast_transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.backcast_transform" title="Link to this definition">¶</a></dt>
 <dd><p>Transformation to apply to user-provided backcast values</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.volatility.VolatilityProcess.backcast_transform-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.backcast_transform-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.volatility.VolatilityProcess.backcast_transform.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}</span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.backcast_transform.backcast" title="Permalink to this definition">¶</a></dt><dd><p>User-provided <code class="docutils literal notranslate"><span class="pre">backcast</span></code> that approximates sigma2[0].</p>
+<dt id="arch.univariate.volatility.VolatilityProcess.backcast_transform.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.backcast_transform.backcast" title="Permalink to this definition">¶</a></dt><dd><p>User-provided <code class="docutils literal notranslate"><span class="pre">backcast</span></code> that approximates sigma2[0].</p>
 </dd>
 </dl>
 </dd>
@@ -1319,7 +1319,7 @@ <h1 id="arch-univariate-volatility-volatilityprocess-backcast-transform">arch.un
 <dd class="field-even"><p><strong>backcast</strong> – Backcast transformed to the model-appropriate scale</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.volatility.VolatilityProcess.backcast_transform-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.backcast_transform-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>{float, ndarray}</p>
+<dd class="field-odd"><p><code class="docutils literal notranslate"><span class="pre">{float,</span> <span class="pre">ndarray}</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1391,7 +1391,7 @@ <h1 id="arch-univariate-volatility-volatilityprocess-backcast-transform">arch.un
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1438,7 +1438,7 @@ <h1 id="arch-univariate-volatility-volatilityprocess-backcast-transform">arch.un
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.bounds.html b/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.bounds.html
index 153873c47f..4ff4189f77 100644
--- a/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.bounds.html
+++ b/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.volatility.VolatilityProcess.bounds - arch 6.1.0</title>
+        <title>arch.univariate.volatility.VolatilityProcess.bounds - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1303,15 +1303,15 @@
                   
 
 
-<h1 id="arch-univariate-volatility-volatilityprocess-bounds">arch.univariate.volatility.VolatilityProcess.bounds<a class="headerlink" href="#arch-univariate-volatility-volatilityprocess-bounds" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-volatility-volatilityprocess-bounds">arch.univariate.volatility.VolatilityProcess.bounds<a class="headerlink" href="#arch-univariate-volatility-volatilityprocess-bounds" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.volatility.VolatilityProcess.bounds">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VolatilityProcess.</span></span><span class="sig-name descname"><span class="pre">bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.bounds.resids" title="arch.univariate.volatility.VolatilityProcess.bounds.resids (Python parameter) — Vector of (approximate) residuals"><span class="n"><span class="pre">resids</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#VolatilityProcess.bounds"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.bounds" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.volatility.VolatilityProcess.bounds">
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VolatilityProcess.</span></span><span class="sig-name descname"><span class="pre">bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.bounds.resids" title="arch.univariate.volatility.VolatilityProcess.bounds.resids (Python parameter) — Vector of (approximate) residuals"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#VolatilityProcess.bounds"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.bounds" title="Link to this definition">¶</a></dt>
 <dd><p>Returns bounds for parameters</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.volatility.VolatilityProcess.bounds-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.bounds-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.volatility.VolatilityProcess.bounds.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.bounds.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of (approximate) residuals</p>
+<dt id="arch.univariate.volatility.VolatilityProcess.bounds.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.bounds.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of (approximate) residuals</p>
 </dd>
 </dl>
 </dd>
@@ -1319,7 +1319,7 @@ <h1 id="arch-univariate-volatility-volatilityprocess-bounds">arch.univariate.vol
 <dd class="field-even"><p><strong>bounds</strong> – List of bounds where each element is (lower, upper).</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.volatility.VolatilityProcess.bounds-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.bounds-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a>[<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a>[<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>,<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>]]</p>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">list[tuple[float,float]]</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1391,7 +1391,7 @@ <h1 id="arch-univariate-volatility-volatilityprocess-bounds">arch.univariate.vol
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1438,7 +1438,7 @@ <h1 id="arch-univariate-volatility-volatilityprocess-bounds">arch.univariate.vol
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diff --git a/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.compute_variance.html b/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.compute_variance.html
index 22f7d90819..de9bc75f8f 100644
--- a/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.compute_variance.html
+++ b/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.compute_variance.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.volatility.VolatilityProcess.compute_variance - arch 6.1.0</title>
+        <title>arch.univariate.volatility.VolatilityProcess.compute_variance - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+    arch 6.2.0
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     <div class="md-nav__source">
@@ -779,13 +779,6 @@
       </ul>
     </nav>
   
-</li>
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-          <li class="md-nav__item">
-  <a href="#arch.univariate.volatility.VolatilityProcess.compute_variance-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
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-  
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@@ -1317,13 +1310,6 @@
       </ul>
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-</li>
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-          <li class="md-nav__item">
-  <a href="#arch.univariate.volatility.VolatilityProcess.compute_variance-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
       </ul>
@@ -1345,30 +1331,27 @@
                   
 
 
-<h1 id="arch-univariate-volatility-volatilityprocess-compute-variance">arch.univariate.volatility.VolatilityProcess.compute_variance<a class="headerlink" href="#arch-univariate-volatility-volatilityprocess-compute-variance" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-volatility-volatilityprocess-compute-variance">arch.univariate.volatility.VolatilityProcess.compute_variance<a class="headerlink" href="#arch-univariate-volatility-volatilityprocess-compute-variance" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.volatility.VolatilityProcess.compute_variance">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VolatilityProcess.</span></span><span class="sig-name descname"><span class="pre">compute_variance</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.compute_variance.parameters" title="arch.univariate.volatility.VolatilityProcess.compute_variance.parameters (Python parameter) — Model parameters"><span class="n"><span class="pre">parameters</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.compute_variance.resids" title="arch.univariate.volatility.VolatilityProcess.compute_variance.resids (Python parameter) — Vector of mean zero residuals"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.compute_variance.sigma2" title="arch.univariate.volatility.VolatilityProcess.compute_variance.sigma2 (Python parameter) — Array with same size as resids to store the conditional variance"><span class="n"><span class="pre">sigma2</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.compute_variance.backcast" title="arch.univariate.volatility.VolatilityProcess.compute_variance.backcast (Python parameter) — Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."><span class="n"><span class="pre">backcast</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.compute_variance.var_bounds" title="arch.univariate.volatility.VolatilityProcess.compute_variance.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#VolatilityProcess.compute_variance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.compute_variance" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VolatilityProcess.</span></span><span class="sig-name descname"><span class="pre">compute_variance</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.compute_variance.parameters" title="arch.univariate.volatility.VolatilityProcess.compute_variance.parameters (Python parameter) — Model parameters"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.compute_variance.resids" title="arch.univariate.volatility.VolatilityProcess.compute_variance.resids (Python parameter) — Vector of mean zero residuals"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.compute_variance.sigma2" title="arch.univariate.volatility.VolatilityProcess.compute_variance.sigma2 (Python parameter) — Array with same size as resids to store the conditional variance"><span class="n"><span class="pre">sigma2</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.compute_variance.backcast" title="arch.univariate.volatility.VolatilityProcess.compute_variance.backcast (Python parameter) — Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.compute_variance.var_bounds" title="arch.univariate.volatility.VolatilityProcess.compute_variance.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#VolatilityProcess.compute_variance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.compute_variance" title="Link to this definition">¶</a></dt>
 <dd><p>Compute the variance for the ARCH model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.volatility.VolatilityProcess.compute_variance-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.compute_variance-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.volatility.VolatilityProcess.compute_variance.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.compute_variance.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
+<dt id="arch.univariate.volatility.VolatilityProcess.compute_variance.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.compute_variance.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
 </dd>
-<dt id="arch.univariate.volatility.VolatilityProcess.compute_variance.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.compute_variance.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of mean zero residuals</p>
+<dt id="arch.univariate.volatility.VolatilityProcess.compute_variance.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.compute_variance.resids" title="Permalink to this definition">¶</a></dt><dd><p>Vector of mean zero residuals</p>
 </dd>
-<dt id="arch.univariate.volatility.VolatilityProcess.compute_variance.sigma2"><span class="sig-name descname">sigma2</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.compute_variance.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>Array with same size as resids to store the conditional variance</p>
+<dt id="arch.univariate.volatility.VolatilityProcess.compute_variance.sigma2"><span class="n sig-name">sigma2</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.compute_variance.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>Array with same size as resids to store the conditional variance</p>
 </dd>
-<dt id="arch.univariate.volatility.VolatilityProcess.compute_variance.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}</span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.compute_variance.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing ARCH recursion. Can be an ndarray
+<dt id="arch.univariate.volatility.VolatilityProcess.compute_variance.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.compute_variance.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing ARCH recursion. Can be an ndarray
 when the model contains multiple components.</p>
 </dd>
-<dt id="arch.univariate.volatility.VolatilityProcess.compute_variance.var_bounds"><span class="sig-name descname">var_bounds</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.compute_variance.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
+<dt id="arch.univariate.volatility.VolatilityProcess.compute_variance.var_bounds"><span class="n sig-name">var_bounds</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.compute_variance.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
 </dd>
 </dl>
 </dd>
-<dt class="field-even" id="arch.univariate.volatility.VolatilityProcess.compute_variance-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.compute_variance-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a></p>
-</dd>
 </dl>
 </dd></dl>
 
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diff --git a/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.constraints.html b/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.constraints.html
index 37680d340d..b48696b43e 100644
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+++ b/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.constraints.html
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@@ -102,7 +102,7 @@
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+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+    arch 6.2.0
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@@ -748,13 +748,6 @@
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-          <li class="md-nav__item">
-  <a href="#arch.univariate.volatility.VolatilityProcess.constraints-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
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-        
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       <ul class="md-nav__list">
         
-          <li class="md-nav__item">
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-    <span class="md-ellipsis">Return type</span>
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-        
           <li class="md-nav__item">
   <a href="#arch.univariate.volatility.VolatilityProcess.constraints-returns" class="md-nav__link">
     <span class="md-ellipsis">Returns</span>
@@ -1263,20 +1249,17 @@
                   
 
 
-<h1 id="arch-univariate-volatility-volatilityprocess-constraints">arch.univariate.volatility.VolatilityProcess.constraints<a class="headerlink" href="#arch-univariate-volatility-volatilityprocess-constraints" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-volatility-volatilityprocess-constraints">arch.univariate.volatility.VolatilityProcess.constraints<a class="headerlink" href="#arch-univariate-volatility-volatilityprocess-constraints" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.volatility.VolatilityProcess.constraints">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VolatilityProcess.</span></span><span class="sig-name descname"><span class="pre">constraints</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#VolatilityProcess.constraints"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.constraints" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VolatilityProcess.</span></span><span class="sig-name descname"><span class="pre">constraints</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#VolatilityProcess.constraints"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.constraints" title="Link to this definition">¶</a></dt>
 <dd><p>Construct parameter constraints arrays for parameter estimation</p>
 <dl class="field-list simple">
-<dt class="field-odd" id="arch.univariate.volatility.VolatilityProcess.constraints-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.constraints-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>]</p>
-</dd>
-<dt class="field-even" id="arch.univariate.volatility.VolatilityProcess.constraints-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.constraints-returns" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><ul class="simple">
-<li><p><strong>A</strong> (<em>ndarray</em>) – Parameters loadings in constraint. Shape is number of constraints
+<dt class="field-odd" id="arch.univariate.volatility.VolatilityProcess.constraints-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.constraints-returns" title="Permalink to this headline">¶</a></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p><strong>A</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – Parameters loadings in constraint. Shape is number of constraints
 by number of parameters</p></li>
-<li><p><strong>b</strong> (<em>ndarray</em>) – Constraint values, one for each constraint</p></li>
+<li><p><strong>b</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – Constraint values, one for each constraint</p></li>
 </ul>
 </p>
 </dd>
@@ -1353,7 +1336,7 @@ <h1 id="arch-univariate-volatility-volatilityprocess-constraints">arch.univariat
   
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.volatility.VolatilityProcess.forecast - arch 6.1.0</title>
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-<h1 id="arch-univariate-volatility-volatilityprocess-forecast">arch.univariate.volatility.VolatilityProcess.forecast<a class="headerlink" href="#arch-univariate-volatility-volatilityprocess-forecast" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-volatility-volatilityprocess-forecast">arch.univariate.volatility.VolatilityProcess.forecast<a class="headerlink" href="#arch-univariate-volatility-volatilityprocess-forecast" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.volatility.VolatilityProcess.forecast">
-<span class="sig-prename descclassname"><span class="pre">VolatilityProcess.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.forecast.parameters" title="arch.univariate.volatility.VolatilityProcess.forecast.parameters (Python parameter) — Parameters required to forecast the volatility model"><span class="n"><span class="pre">parameters</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.forecast.resids" title="arch.univariate.volatility.VolatilityProcess.forecast.resids (Python parameter) — Residuals to use in the recursion"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.forecast.backcast" title="arch.univariate.volatility.VolatilityProcess.forecast.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.forecast.var_bounds" title="arch.univariate.volatility.VolatilityProcess.forecast.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.forecast.start" title="arch.univariate.volatility.VolatilityProcess.forecast.start (Python parameter) — Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."><span class="n"><span class="pre">start</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.forecast.horizon" title="arch.univariate.volatility.VolatilityProcess.forecast.horizon (Python parameter) — Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."><span class="n"><span class="pre">horizon</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.forecast.method" title="arch.univariate.volatility.VolatilityProcess.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic."><span class="n"><span class="pre">method</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.forecast.simulations" title="arch.univariate.volatility.VolatilityProcess.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.forecast.rng" title="arch.univariate.volatility.VolatilityProcess.forecast.rng (Python parameter) — Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."><span class="n"><span class="pre">rng</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.forecast.random_state" title="arch.univariate.volatility.VolatilityProcess.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#VolatilityProcess.forecast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.forecast" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">VolatilityProcess.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.forecast.parameters" title="arch.univariate.volatility.VolatilityProcess.forecast.parameters (Python parameter) — Parameters required to forecast the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.forecast.resids" title="arch.univariate.volatility.VolatilityProcess.forecast.resids (Python parameter) — Residuals to use in the recursion"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.forecast.backcast" title="arch.univariate.volatility.VolatilityProcess.forecast.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.forecast.var_bounds" title="arch.univariate.volatility.VolatilityProcess.forecast.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.forecast.start" title="arch.univariate.volatility.VolatilityProcess.forecast.start (Python parameter) — Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.forecast.horizon" title="arch.univariate.volatility.VolatilityProcess.forecast.horizon (Python parameter) — Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.forecast.method" title="arch.univariate.volatility.VolatilityProcess.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.forecast.simulations" title="arch.univariate.volatility.VolatilityProcess.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.forecast.rng" title="arch.univariate.volatility.VolatilityProcess.forecast.rng (Python parameter) — Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.forecast.random_state" title="arch.univariate.volatility.VolatilityProcess.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><span class="pre">VarianceForecast</span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#VolatilityProcess.forecast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.forecast" title="Link to this definition">¶</a></dt>
 <dd><p>Forecast volatility from the model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.volatility.VolatilityProcess.forecast-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.forecast-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.volatility.VolatilityProcess.forecast.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, Series}</span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.forecast.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast the volatility model</p>
+<dt id="arch.univariate.volatility.VolatilityProcess.forecast.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.forecast.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast the volatility model</p>
 </dd>
-<dt id="arch.univariate.volatility.VolatilityProcess.forecast.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.forecast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Residuals to use in the recursion</p>
+<dt id="arch.univariate.volatility.VolatilityProcess.forecast.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.forecast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Residuals to use in the recursion</p>
 </dd>
-<dt id="arch.univariate.volatility.VolatilityProcess.forecast.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.forecast.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing the recursion</p>
+<dt id="arch.univariate.volatility.VolatilityProcess.forecast.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.forecast.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing the recursion</p>
 </dd>
-<dt id="arch.univariate.volatility.VolatilityProcess.forecast.var_bounds"><span class="sig-name descname">var_bounds</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray, 2-d</span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.forecast.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
+<dt id="arch.univariate.volatility.VolatilityProcess.forecast.var_bounds"><span class="n sig-name">var_bounds</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.forecast.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
 </dd>
-<dt id="arch.univariate.volatility.VolatilityProcess.forecast.start"><span class="sig-name descname">start</span><span class="p"> : </span><span class="desctype"><span class="desctype">{None, int}</span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>Index of the first observation to use as the starting point for
+<dt id="arch.univariate.volatility.VolatilityProcess.forecast.start"><span class="n sig-name">start</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>Index of the first observation to use as the starting point for
 the forecast.  Default is len(resids).</p>
 </dd>
-<dt id="arch.univariate.volatility.VolatilityProcess.forecast.horizon"><span class="sig-name descname">horizon</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.forecast.horizon" title="Permalink to this definition">¶</a></dt><dd><p>Forecast horizon.  Must be 1 or larger.  Forecasts are produced
+<dt id="arch.univariate.volatility.VolatilityProcess.forecast.horizon"><span class="n sig-name">horizon</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.forecast.horizon" title="Permalink to this definition">¶</a></dt><dd><p>Forecast horizon.  Must be 1 or larger.  Forecasts are produced
 for horizons in [1, horizon].</p>
 </dd>
-<dt id="arch.univariate.volatility.VolatilityProcess.forecast.method"><span class="sig-name descname">method</span><span class="p"> : </span><span class="desctype"><span class="desctype">{'analytic', 'simulation', 'bootstrap'}</span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.forecast.method" title="Permalink to this definition">¶</a></dt><dd><p>Method to use when producing the forecast. The default is analytic.</p>
+<dt id="arch.univariate.volatility.VolatilityProcess.forecast.method"><span class="n sig-name">method</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'analytic'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'simulation'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'bootstrap'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.forecast.method" title="Permalink to this definition">¶</a></dt><dd><p>Method to use when producing the forecast. The default is analytic.</p>
 </dd>
-<dt id="arch.univariate.volatility.VolatilityProcess.forecast.simulations"><span class="sig-name descname">simulations</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.forecast.simulations" title="Permalink to this definition">¶</a></dt><dd><p>Number of simulations to run when computing the forecast using
+<dt id="arch.univariate.volatility.VolatilityProcess.forecast.simulations"><span class="n sig-name">simulations</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.forecast.simulations" title="Permalink to this definition">¶</a></dt><dd><p>Number of simulations to run when computing the forecast using
 either simulation or bootstrap.</p>
 </dd>
-<dt id="arch.univariate.volatility.VolatilityProcess.forecast.rng"><span class="sig-name descname">rng</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable</span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.forecast.rng" title="Permalink to this definition">¶</a></dt><dd><p>Callable random number generator required if method is
+<dt id="arch.univariate.volatility.VolatilityProcess.forecast.rng"><span class="n sig-name">rng</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)">Callable</a><span class="p">[</span><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">,</span><span class="w"> </span><span class="p">...</span><span class="p">]</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.forecast.rng" title="Permalink to this definition">¶</a></dt><dd><p>Callable random number generator required if method is
 ‘simulation’. Must take a single shape input and return random
 samples numbers with that shape.</p>
 </dd>
-<dt id="arch.univariate.volatility.VolatilityProcess.forecast.random_state"><span class="sig-name descname">random_state</span><span class="p"> : </span><span class="desctype"><span class="desctype">RandomState, optional</span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.forecast.random_state" title="Permalink to this definition">¶</a></dt><dd><p>NumPy RandomState instance to use when method is ‘bootstrap’</p>
+<dt id="arch.univariate.volatility.VolatilityProcess.forecast.random_state"><span class="n sig-name">random_state</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)">RandomState</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.forecast.random_state" title="Permalink to this definition">¶</a></dt><dd><p>NumPy RandomState instance to use when method is ‘bootstrap’</p>
 </dd>
 </dl>
 </dd>
@@ -1483,7 +1483,7 @@ <h1 id="arch-univariate-volatility-volatilityprocess-forecast">arch.univariate.v
 or bootstrap, the simulated paths.</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.volatility.VolatilityProcess.forecast-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.forecast-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>VarianceForecast</p>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">arch.univariate.volatility.VarianceForecast</span></code></p>
 </dd>
 <dt class="field-even" id="arch.univariate.volatility.VolatilityProcess.forecast-raises">Raises<span class="colon">:</span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.forecast-raises" title="Permalink to this headline">¶</a></dt>
 <dd class="field-even"><ul class="simple">
@@ -1570,7 +1570,7 @@ <h1 id="arch-univariate-volatility-volatilityprocess-forecast">arch.univariate.v
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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diff --git a/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.name.html b/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.name.html
index eabccbe7b2..382027b32f 100644
--- a/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.name.html
+++ b/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.volatility.VolatilityProcess.name - arch 6.1.0</title>
+        <title>arch.univariate.volatility.VolatilityProcess.name - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1223,10 +1223,10 @@
                   
 
 
-<h1 id="arch-univariate-volatility-volatilityprocess-name">arch.univariate.volatility.VolatilityProcess.name<a class="headerlink" href="#arch-univariate-volatility-volatilityprocess-name" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-volatility-volatilityprocess-name">arch.univariate.volatility.VolatilityProcess.name<a class="headerlink" href="#arch-univariate-volatility-volatilityprocess-name" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.volatility.VolatilityProcess.name">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VolatilityProcess.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.name" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VolatilityProcess.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.name" title="Link to this definition">¶</a></dt>
 <dd><p>The name of the volatility process</p>
 </dd></dl>
 
@@ -1297,7 +1297,7 @@ <h1 id="arch-univariate-volatility-volatilityprocess-name">arch.univariate.volat
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1344,7 +1344,7 @@ <h1 id="arch-univariate-volatility-volatilityprocess-name">arch.univariate.volat
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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diff --git a/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.num_params.html b/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.num_params.html
index bfd66de1a3..c465fce42e 100644
--- a/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.num_params.html
+++ b/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.num_params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.volatility.VolatilityProcess.num_params - arch 6.1.0</title>
+        <title>arch.univariate.volatility.VolatilityProcess.num_params - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1223,10 +1223,10 @@
                   
 
 
-<h1 id="arch-univariate-volatility-volatilityprocess-num-params">arch.univariate.volatility.VolatilityProcess.num_params<a class="headerlink" href="#arch-univariate-volatility-volatilityprocess-num-params" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-volatility-volatilityprocess-num-params">arch.univariate.volatility.VolatilityProcess.num_params<a class="headerlink" href="#arch-univariate-volatility-volatilityprocess-num-params" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.volatility.VolatilityProcess.num_params">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VolatilityProcess.</span></span><span class="sig-name descname"><span class="pre">num_params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.num_params" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VolatilityProcess.</span></span><span class="sig-name descname"><span class="pre">num_params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.num_params" title="Link to this definition">¶</a></dt>
 <dd><p>The number of parameters in the model</p>
 </dd></dl>
 
@@ -1297,7 +1297,7 @@ <h1 id="arch-univariate-volatility-volatilityprocess-num-params">arch.univariate
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1344,7 +1344,7 @@ <h1 id="arch-univariate-volatility-volatilityprocess-num-params">arch.univariate
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diff --git a/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.parameter_names.html b/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.parameter_names.html
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+++ b/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.parameter_names.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.volatility.VolatilityProcess.parameter_names - arch 6.1.0</title>
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@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1263,17 +1263,17 @@
                   
 
 
-<h1 id="arch-univariate-volatility-volatilityprocess-parameter-names">arch.univariate.volatility.VolatilityProcess.parameter_names<a class="headerlink" href="#arch-univariate-volatility-volatilityprocess-parameter-names" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-volatility-volatilityprocess-parameter-names">arch.univariate.volatility.VolatilityProcess.parameter_names<a class="headerlink" href="#arch-univariate-volatility-volatilityprocess-parameter-names" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.volatility.VolatilityProcess.parameter_names">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VolatilityProcess.</span></span><span class="sig-name descname"><span class="pre">parameter_names</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#VolatilityProcess.parameter_names"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.parameter_names" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VolatilityProcess.</span></span><span class="sig-name descname"><span class="pre">parameter_names</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#VolatilityProcess.parameter_names"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.parameter_names" title="Link to this definition">¶</a></dt>
 <dd><p>Names of model parameters</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.volatility.VolatilityProcess.parameter_names-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.parameter_names-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p><strong>names</strong> – Variables names</p>
 </dd>
 <dt class="field-even" id="arch.univariate.volatility.VolatilityProcess.parameter_names-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.parameter_names-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>)</p>
+<dd class="field-even"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span> <span class="pre">(str)</span></code></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1345,7 +1345,7 @@ <h1 id="arch-univariate-volatility-volatilityprocess-parameter-names">arch.univa
   
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     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1392,7 +1392,7 @@ <h1 id="arch-univariate-volatility-volatilityprocess-parameter-names">arch.univa
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diff --git a/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.simulate.html b/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.simulate.html
index dbf653f14f..ce5b12420c 100644
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+++ b/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.simulate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.volatility.VolatilityProcess.simulate - arch 6.1.0</title>
+        <title>arch.univariate.volatility.VolatilityProcess.simulate - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -839,13 +839,6 @@
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     </nav>
   
-</li>
-        
-          <li class="md-nav__item">
-  <a href="#arch.univariate.volatility.VolatilityProcess.simulate-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
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@@ -1324,13 +1317,6 @@
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-</li>
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-          <li class="md-nav__item">
-  <a href="#arch.univariate.volatility.VolatilityProcess.simulate-return-type" class="md-nav__link">
-    <span class="md-ellipsis">Return type</span>
-  </a>
-  
 </li>
         
           <li class="md-nav__item">
@@ -1359,36 +1345,33 @@
                   
 
 
-<h1 id="arch-univariate-volatility-volatilityprocess-simulate">arch.univariate.volatility.VolatilityProcess.simulate<a class="headerlink" href="#arch-univariate-volatility-volatilityprocess-simulate" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-volatility-volatilityprocess-simulate">arch.univariate.volatility.VolatilityProcess.simulate<a class="headerlink" href="#arch-univariate-volatility-volatilityprocess-simulate" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.volatility.VolatilityProcess.simulate">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VolatilityProcess.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.simulate.parameters" title="arch.univariate.volatility.VolatilityProcess.simulate.parameters (Python parameter) — Parameters required to simulate the volatility model"><span class="n"><span class="pre">parameters</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.simulate.nobs" title="arch.univariate.volatility.VolatilityProcess.simulate.nobs (Python parameter) — Number of data points to simulate"><span class="n"><span class="pre">nobs</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.simulate.rng" title="arch.univariate.volatility.VolatilityProcess.simulate.rng (Python parameter) — Callable function that takes a single integer input and returns a vector of random numbers"><span class="n"><span class="pre">rng</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.simulate.burn" title="arch.univariate.volatility.VolatilityProcess.simulate.burn (Python parameter) — Number of additional observations to generate when initializing the simulation"><span class="n"><span class="pre">burn</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.simulate.initial_value" title="arch.univariate.volatility.VolatilityProcess.simulate.initial_value (Python parameter) — Scalar or array of initial values to use when initializing the simulation"><span class="n"><span class="pre">initial_value</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#VolatilityProcess.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.simulate" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VolatilityProcess.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.simulate.parameters" title="arch.univariate.volatility.VolatilityProcess.simulate.parameters (Python parameter) — Parameters required to simulate the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.simulate.nobs" title="arch.univariate.volatility.VolatilityProcess.simulate.nobs (Python parameter) — Number of data points to simulate"><span class="n"><span class="pre">nobs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.simulate.rng" title="arch.univariate.volatility.VolatilityProcess.simulate.rng (Python parameter) — Callable function that takes a single integer input and returns a vector of random numbers"><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.simulate.burn" title="arch.univariate.volatility.VolatilityProcess.simulate.burn (Python parameter) — Number of additional observations to generate when initializing the simulation"><span class="n"><span class="pre">burn</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.simulate.initial_value" title="arch.univariate.volatility.VolatilityProcess.simulate.initial_value (Python parameter) — Scalar or array of initial values to use when initializing the simulation"><span class="n"><span class="pre">initial_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#VolatilityProcess.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.simulate" title="Link to this definition">¶</a></dt>
 <dd><p>Simulate data from the model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.volatility.VolatilityProcess.simulate-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.simulate-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.volatility.VolatilityProcess.simulate.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, Series}</span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to simulate the volatility model</p>
+<dt id="arch.univariate.volatility.VolatilityProcess.simulate.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.11)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to simulate the volatility model</p>
 </dd>
-<dt id="arch.univariate.volatility.VolatilityProcess.simulate.nobs"><span class="sig-name descname">nobs</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.simulate.nobs" title="Permalink to this definition">¶</a></dt><dd><p>Number of data points to simulate</p>
+<dt id="arch.univariate.volatility.VolatilityProcess.simulate.nobs"><span class="n sig-name">nobs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.simulate.nobs" title="Permalink to this definition">¶</a></dt><dd><p>Number of data points to simulate</p>
 </dd>
-<dt id="arch.univariate.volatility.VolatilityProcess.simulate.rng"><span class="sig-name descname">rng</span><span class="p"> : </span><span class="desctype"><span class="desctype">callable</span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.simulate.rng" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that takes a single integer input and returns
+<dt id="arch.univariate.volatility.VolatilityProcess.simulate.rng"><span class="n sig-name">rng</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.11)">Callable</a><span class="p">[</span><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)">tuple</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">,</span><span class="w"> </span><span class="p">...</span><span class="p">]</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.simulate.rng" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that takes a single integer input and returns
 a vector of random numbers</p>
 </dd>
-<dt id="arch.univariate.volatility.VolatilityProcess.simulate.burn"><span class="sig-name descname">burn</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.simulate.burn" title="Permalink to this definition">¶</a></dt><dd><p>Number of additional observations to generate when initializing
+<dt id="arch.univariate.volatility.VolatilityProcess.simulate.burn"><span class="n sig-name">burn</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.simulate.burn" title="Permalink to this definition">¶</a></dt><dd><p>Number of additional observations to generate when initializing
 the simulation</p>
 </dd>
-<dt id="arch.univariate.volatility.VolatilityProcess.simulate.initial_value"><span class="sig-name descname">initial_value</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}, optional</span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.simulate.initial_value" title="Permalink to this definition">¶</a></dt><dd><p>Scalar or array of initial values to use when initializing the
+<dt id="arch.univariate.volatility.VolatilityProcess.simulate.initial_value"><span class="n sig-name">initial_value</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.simulate.initial_value" title="Permalink to this definition">¶</a></dt><dd><p>Scalar or array of initial values to use when initializing the
 simulation</p>
 </dd>
 </dl>
 </dd>
-<dt class="field-even" id="arch.univariate.volatility.VolatilityProcess.simulate-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.simulate-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a>[<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a>]</p>
-</dd>
-<dt class="field-odd" id="arch.univariate.volatility.VolatilityProcess.simulate-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.simulate-returns" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><ul class="simple">
-<li><p><strong>resids</strong> (<em>ndarray</em>) – The simulated residuals</p></li>
-<li><p><strong>variance</strong> (<em>ndarray</em>) – The simulated variance</p></li>
+<dt class="field-even" id="arch.univariate.volatility.VolatilityProcess.simulate-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.simulate-returns" title="Permalink to this headline">¶</a></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><strong>resids</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – The simulated residuals</p></li>
+<li><p><strong>variance</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>) – The simulated variance</p></li>
 </ul>
 </p>
 </dd>
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@@ -15,7 +15,7 @@
     
   
       
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch 6.1.0
+    arch 6.2.0
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@@ -1223,10 +1223,10 @@
                   
 
 
-<h1 id="arch-univariate-volatility-volatilityprocess-start">arch.univariate.volatility.VolatilityProcess.start<a class="headerlink" href="#arch-univariate-volatility-volatilityprocess-start" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-volatility-volatilityprocess-start">arch.univariate.volatility.VolatilityProcess.start<a class="headerlink" href="#arch-univariate-volatility-volatilityprocess-start" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.volatility.VolatilityProcess.start">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VolatilityProcess.</span></span><span class="sig-name descname"><span class="pre">start</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.start" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VolatilityProcess.</span></span><span class="sig-name descname"><span class="pre">start</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.start" title="Link to this definition">¶</a></dt>
 <dd><p>Index to use to start variance subarray selection</p>
 </dd></dl>
 
@@ -1297,7 +1297,7 @@ <h1 id="arch-univariate-volatility-volatilityprocess-start">arch.univariate.vola
   
     Created using
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diff --git a/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.starting_values.html b/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.starting_values.html
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@@ -47,8 +47,8 @@
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+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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+            arch 6.2.0
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+    arch 6.2.0
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@@ -1303,15 +1303,15 @@
                   
 
 
-<h1 id="arch-univariate-volatility-volatilityprocess-starting-values">arch.univariate.volatility.VolatilityProcess.starting_values<a class="headerlink" href="#arch-univariate-volatility-volatilityprocess-starting-values" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-volatility-volatilityprocess-starting-values">arch.univariate.volatility.VolatilityProcess.starting_values<a class="headerlink" href="#arch-univariate-volatility-volatilityprocess-starting-values" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.volatility.VolatilityProcess.starting_values">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VolatilityProcess.</span></span><span class="sig-name descname"><span class="pre">starting_values</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.starting_values.resids" title="arch.univariate.volatility.VolatilityProcess.starting_values.resids (Python parameter) — Array of (approximate) residuals to use when computing starting values"><span class="n"><span class="pre">resids</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#VolatilityProcess.starting_values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.starting_values" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.volatility.VolatilityProcess.starting_values">
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VolatilityProcess.</span></span><span class="sig-name descname"><span class="pre">starting_values</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.starting_values.resids" title="arch.univariate.volatility.VolatilityProcess.starting_values.resids (Python parameter) — Array of (approximate) residuals to use when computing starting values"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#VolatilityProcess.starting_values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.starting_values" title="Link to this definition">¶</a></dt>
 <dd><p>Returns starting values for the ARCH model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.volatility.VolatilityProcess.starting_values-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.starting_values-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.volatility.VolatilityProcess.starting_values.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.starting_values.resids" title="Permalink to this definition">¶</a></dt><dd><p>Array of (approximate) residuals to use when computing starting
+<dt id="arch.univariate.volatility.VolatilityProcess.starting_values.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.starting_values.resids" title="Permalink to this definition">¶</a></dt><dd><p>Array of (approximate) residuals to use when computing starting
 values</p>
 </dd>
 </dl>
@@ -1320,7 +1320,7 @@ <h1 id="arch-univariate-volatility-volatilityprocess-starting-values">arch.univa
 <dd class="field-even"><p><strong>sv</strong> – Array of starting values</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.volatility.VolatilityProcess.starting_values-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.starting_values-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1392,7 +1392,7 @@ <h1 id="arch-univariate-volatility-volatilityprocess-starting-values">arch.univa
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
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diff --git a/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.stop.html b/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.stop.html
index 96528c00b8..9ce697da20 100644
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+++ b/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.stop.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.volatility.VolatilityProcess.stop - arch 6.1.0</title>
+        <title>arch.univariate.volatility.VolatilityProcess.stop - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1223,10 +1223,10 @@
                   
 
 
-<h1 id="arch-univariate-volatility-volatilityprocess-stop">arch.univariate.volatility.VolatilityProcess.stop<a class="headerlink" href="#arch-univariate-volatility-volatilityprocess-stop" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-volatility-volatilityprocess-stop">arch.univariate.volatility.VolatilityProcess.stop<a class="headerlink" href="#arch-univariate-volatility-volatilityprocess-stop" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.volatility.VolatilityProcess.stop">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VolatilityProcess.</span></span><span class="sig-name descname"><span class="pre">stop</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.stop" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VolatilityProcess.</span></span><span class="sig-name descname"><span class="pre">stop</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.stop" title="Link to this definition">¶</a></dt>
 <dd><p>Index to use to stop variance subarray selection</p>
 </dd></dl>
 
@@ -1297,7 +1297,7 @@ <h1 id="arch-univariate-volatility-volatilityprocess-stop">arch.univariate.volat
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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diff --git a/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.update.html b/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.update.html
index 38f9042b7b..cc579da977 100644
--- a/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.update.html
+++ b/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.update.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.volatility.VolatilityProcess.update - arch 6.1.0</title>
+        <title>arch.univariate.volatility.VolatilityProcess.update - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1373,28 +1373,28 @@
                   
 
 
-<h1 id="arch-univariate-volatility-volatilityprocess-update">arch.univariate.volatility.VolatilityProcess.update<a class="headerlink" href="#arch-univariate-volatility-volatilityprocess-update" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-volatility-volatilityprocess-update">arch.univariate.volatility.VolatilityProcess.update<a class="headerlink" href="#arch-univariate-volatility-volatilityprocess-update" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.volatility.VolatilityProcess.update">
-<span class="sig-prename descclassname"><span class="pre">VolatilityProcess.</span></span><span class="sig-name descname"><span class="pre">update</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.update.index" title="arch.univariate.volatility.VolatilityProcess.update.index (Python parameter) — The numerical index of the variance to compute"><span class="n"><span class="pre">index</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.update.parameters" title="arch.univariate.volatility.VolatilityProcess.update.parameters (Python parameter) — The variance model parameters"><span class="n"><span class="pre">parameters</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.update.resids" title="arch.univariate.volatility.VolatilityProcess.update.resids (Python parameter) — The residual array. Only uses resids[:index] when computing sigma2[index]"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.update.sigma2" title="arch.univariate.volatility.VolatilityProcess.update.sigma2 (Python parameter) — The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."><span class="n"><span class="pre">sigma2</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.update.backcast" title="arch.univariate.volatility.VolatilityProcess.update.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.update.var_bounds" title="arch.univariate.volatility.VolatilityProcess.update.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#VolatilityProcess.update"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.update" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">VolatilityProcess.</span></span><span class="sig-name descname"><span class="pre">update</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.update.index" title="arch.univariate.volatility.VolatilityProcess.update.index (Python parameter) — The numerical index of the variance to compute"><span class="n"><span class="pre">index</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.update.parameters" title="arch.univariate.volatility.VolatilityProcess.update.parameters (Python parameter) — The variance model parameters"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.update.resids" title="arch.univariate.volatility.VolatilityProcess.update.resids (Python parameter) — The residual array. Only uses resids[:index] when computing sigma2[index]"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.update.sigma2" title="arch.univariate.volatility.VolatilityProcess.update.sigma2 (Python parameter) — The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."><span class="n"><span class="pre">sigma2</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.update.backcast" title="arch.univariate.volatility.VolatilityProcess.update.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.update.var_bounds" title="arch.univariate.volatility.VolatilityProcess.update.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#VolatilityProcess.update"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.update" title="Link to this definition">¶</a></dt>
 <dd><p>Compute the variance for a single observation</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.volatility.VolatilityProcess.update-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.update-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.volatility.VolatilityProcess.update.index"><span class="sig-name descname">index</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.update.index" title="Permalink to this definition">¶</a></dt><dd><p>The numerical index of the variance to compute</p>
+<dt id="arch.univariate.volatility.VolatilityProcess.update.index"><span class="n sig-name">index</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.update.index" title="Permalink to this definition">¶</a></dt><dd><p>The numerical index of the variance to compute</p>
 </dd>
-<dt id="arch.univariate.volatility.VolatilityProcess.update.parameters"><span class="sig-name descname">parameters</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.update.parameters" title="Permalink to this definition">¶</a></dt><dd><p>The variance model parameters</p>
+<dt id="arch.univariate.volatility.VolatilityProcess.update.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.update.parameters" title="Permalink to this definition">¶</a></dt><dd><p>The variance model parameters</p>
 </dd>
-<dt id="arch.univariate.volatility.VolatilityProcess.update.resids"><span class="n sig-name">resids</span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.update.resids" title="Permalink to this definition">¶</a></dt><dd><p>The residual array. Only uses <code class="docutils literal notranslate"><span class="pre">resids[:index]</span></code> when computing
+<dt id="arch.univariate.volatility.VolatilityProcess.update.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.update.resids" title="Permalink to this definition">¶</a></dt><dd><p>The residual array. Only uses <code class="docutils literal notranslate"><span class="pre">resids[:index]</span></code> when computing
 <code class="docutils literal notranslate"><span class="pre">sigma2[index]</span></code></p>
 </dd>
-<dt id="arch.univariate.volatility.VolatilityProcess.update.sigma2"><span class="sig-name descname">sigma2</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.update.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>The array containing the variances. Only uses <code class="docutils literal notranslate"><span class="pre">sigma2[:index]</span></code>
+<dt id="arch.univariate.volatility.VolatilityProcess.update.sigma2"><span class="n sig-name">sigma2</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.update.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>The array containing the variances. Only uses <code class="docutils literal notranslate"><span class="pre">sigma2[:index]</span></code>
 when computing <code class="docutils literal notranslate"><span class="pre">sigma2[index]</span></code>. The computed value is stored
 in <code class="docutils literal notranslate"><span class="pre">sigma2[index]</span></code>.</p>
 </dd>
-<dt id="arch.univariate.volatility.VolatilityProcess.update.backcast"><span class="sig-name descname">backcast</span><span class="p"> : </span><span class="desctype"><span class="desctype">{float, ndarray}</span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.update.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing the recursion</p>
+<dt id="arch.univariate.volatility.VolatilityProcess.update.backcast"><span class="n sig-name">backcast</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.update.backcast" title="Permalink to this definition">¶</a></dt><dd><p>Value to use when initializing the recursion</p>
 </dd>
-<dt id="arch.univariate.volatility.VolatilityProcess.update.var_bounds"><span class="sig-name descname">var_bounds</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.update.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
+<dt id="arch.univariate.volatility.VolatilityProcess.update.var_bounds"><span class="n sig-name">var_bounds</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.update.var_bounds" title="Permalink to this definition">¶</a></dt><dd><p>Array containing columns of lower and upper bounds</p>
 </dd>
 </dl>
 </dd>
@@ -1402,7 +1402,7 @@ <h1 id="arch-univariate-volatility-volatilityprocess-update">arch.univariate.vol
 <dd class="field-even"><p>The variance computed for location <code class="docutils literal notranslate"><span class="pre">index</span></code></p>
 </dd>
 <dt class="field-odd" id="arch.univariate.volatility.VolatilityProcess.update-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.update-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1474,7 +1474,7 @@ <h1 id="arch-univariate-volatility-volatilityprocess-update">arch.univariate.vol
   
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diff --git a/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.updateable.html b/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.updateable.html
index 891ceb277b..fbfc81397b 100644
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+++ b/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.updateable.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.volatility.VolatilityProcess.updateable - arch 6.1.0</title>
+        <title>arch.univariate.volatility.VolatilityProcess.updateable - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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     <div class="md-nav__source">
@@ -1223,10 +1223,10 @@
                   
 
 
-<h1 id="arch-univariate-volatility-volatilityprocess-updateable">arch.univariate.volatility.VolatilityProcess.updateable<a class="headerlink" href="#arch-univariate-volatility-volatilityprocess-updateable" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-volatility-volatilityprocess-updateable">arch.univariate.volatility.VolatilityProcess.updateable<a class="headerlink" href="#arch-univariate-volatility-volatilityprocess-updateable" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.volatility.VolatilityProcess.updateable">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VolatilityProcess.</span></span><span class="sig-name descname"><span class="pre">updateable</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.updateable" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VolatilityProcess.</span></span><span class="sig-name descname"><span class="pre">updateable</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.updateable" title="Link to this definition">¶</a></dt>
 <dd><p>Flag indicating that the volatility process supports update</p>
 </dd></dl>
 
@@ -1297,7 +1297,7 @@ <h1 id="arch-univariate-volatility-volatilityprocess-updateable">arch.univariate
   
     Created using
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-    7.0.1.
+    7.2.6.
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diff --git a/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.variance_bounds.html b/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.variance_bounds.html
index 78c36b5ba1..b329501979 100644
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+++ b/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.variance_bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.volatility.VolatilityProcess.variance_bounds - arch 6.1.0</title>
+        <title>arch.univariate.volatility.VolatilityProcess.variance_bounds - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
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+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
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-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1317,20 +1317,20 @@
                   
 
 
-<h1 id="arch-univariate-volatility-volatilityprocess-variance-bounds">arch.univariate.volatility.VolatilityProcess.variance_bounds<a class="headerlink" href="#arch-univariate-volatility-volatilityprocess-variance-bounds" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-volatility-volatilityprocess-variance-bounds">arch.univariate.volatility.VolatilityProcess.variance_bounds<a class="headerlink" href="#arch-univariate-volatility-volatilityprocess-variance-bounds" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
-<dt class="sig sig-object highlight py" id="arch.univariate.volatility.VolatilityProcess.variance_bounds">
-<span class="sig-prename descclassname"><span class="pre">VolatilityProcess.</span></span><span class="sig-name descname"><span class="pre">variance_bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.variance_bounds.resids" title="arch.univariate.volatility.VolatilityProcess.variance_bounds.resids (Python parameter) — Approximate residuals to use to compute the lower and upper bounds on the conditional variance"><span class="n"><span class="pre">resids</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.variance_bounds.power" title="arch.univariate.volatility.VolatilityProcess.variance_bounds.power (Python parameter) — Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."><span class="n"><span class="pre">power</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">2.0</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#VolatilityProcess.variance_bounds"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.variance_bounds" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.volatility.VolatilityProcess.variance_bounds">
+<span class="sig-prename descclassname"><span class="pre">VolatilityProcess.</span></span><span class="sig-name descname"><span class="pre">variance_bounds</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.variance_bounds.resids" title="arch.univariate.volatility.VolatilityProcess.variance_bounds.resids (Python parameter) — Approximate residuals to use to compute the lower and upper bounds on the conditional variance"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.variance_bounds.power" title="arch.univariate.volatility.VolatilityProcess.variance_bounds.power (Python parameter) — Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."><span class="n"><span class="pre">power</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">2.0</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#VolatilityProcess.variance_bounds"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.variance_bounds" title="Link to this definition">¶</a></dt>
 <dd><p>Construct loose bounds for conditional variances.</p>
 <p>These bounds are used in parameter estimation to ensure
 that the log-likelihood does not produce NaN values.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.volatility.VolatilityProcess.variance_bounds-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.variance_bounds-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.volatility.VolatilityProcess.variance_bounds.resids"><span class="sig-name descname">resids</span><span class="p"> : </span><span class="desctype"><span class="desctype">ndarray</span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.variance_bounds.resids" title="Permalink to this definition">¶</a></dt><dd><p>Approximate residuals to use to compute the lower and upper bounds
+<dt id="arch.univariate.volatility.VolatilityProcess.variance_bounds.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.variance_bounds.resids" title="Permalink to this definition">¶</a></dt><dd><p>Approximate residuals to use to compute the lower and upper bounds
 on the conditional variance</p>
 </dd>
-<dt id="arch.univariate.volatility.VolatilityProcess.variance_bounds.power"><span class="sig-name descname">power</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>, optional</span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.variance_bounds.power" title="Permalink to this definition">¶</a></dt><dd><p>Power used in the model. 2.0, the default corresponds to standard
+<dt id="arch.univariate.volatility.VolatilityProcess.variance_bounds.power"><span class="n sig-name">power</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">2.0</span></code><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.variance_bounds.power" title="Permalink to this definition">¶</a></dt><dd><p>Power used in the model. 2.0, the default corresponds to standard
 ARCH models that evolve in squares.</p>
 </dd>
 </dl>
@@ -1340,7 +1340,7 @@ <h1 id="arch-univariate-volatility-volatilityprocess-variance-bounds">arch.univa
 number of elements as resids</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.volatility.VolatilityProcess.variance_bounds-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.variance_bounds-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p>ndarray</p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1412,7 +1412,7 @@ <h1 id="arch-univariate-volatility-volatilityprocess-variance-bounds">arch.univa
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.volatility_updater.html b/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.volatility_updater.html
index 4d978925cb..32c7ce0efa 100644
--- a/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.volatility_updater.html
+++ b/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.volatility_updater.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.volatility.VolatilityProcess.volatility_updater - arch 6.1.0</title>
+        <title>arch.univariate.volatility.VolatilityProcess.volatility_updater - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../../../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1277,17 +1277,17 @@
                   
 
 
-<h1 id="arch-univariate-volatility-volatilityprocess-volatility-updater">arch.univariate.volatility.VolatilityProcess.volatility_updater<a class="headerlink" href="#arch-univariate-volatility-volatilityprocess-volatility-updater" title="Permalink to this heading">¶</a></h1>
+<h1 id="arch-univariate-volatility-volatilityprocess-volatility-updater">arch.univariate.volatility.VolatilityProcess.volatility_updater<a class="headerlink" href="#arch-univariate-volatility-volatilityprocess-volatility-updater" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.volatility.VolatilityProcess.volatility_updater">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VolatilityProcess.</span></span><span class="sig-name descname"><span class="pre">volatility_updater</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference internal" href="../arch.univariate.recursions_python.VolatilityUpdater.html#arch.univariate.recursions_python.VolatilityUpdater" title="arch.univariate.recursions_python.VolatilityUpdater (Python class) — Base class that all volatility updaters must inherit from."><span class="pre">VolatilityUpdater</span></a></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.volatility_updater" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VolatilityProcess.</span></span><span class="sig-name descname"><span class="pre">volatility_updater</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><span class="pre">VolatilityUpdater</span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.volatility_updater" title="Link to this definition">¶</a></dt>
 <dd><p>Get the volatility updater associated with the volatility process</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.volatility.VolatilityProcess.volatility_updater-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.volatility_updater-returns" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p>The updater class</p>
 </dd>
 <dt class="field-even" id="arch.univariate.volatility.VolatilityProcess.volatility_updater-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.volatility_updater-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference internal" href="../arch.univariate.recursions_python.VolatilityUpdater.html#arch.univariate.recursions_python.VolatilityUpdater" title="arch.univariate.recursions_python.VolatilityUpdater (Python class) — Base class that all volatility updaters must inherit from.">VolatilityUpdater</a></p>
+<dd class="field-even"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">VolatilityUpdater</span></code></p>
 </dd>
 <dt class="field-odd" id="arch.univariate.volatility.VolatilityProcess.volatility_updater-raises">Raises<span class="colon">:</span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.volatility_updater-raises" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#NotImplementedError" title="(in Python v3.11)"><strong>NotImplementedError</strong></a> – If the process is not updateable</p>
@@ -1362,7 +1362,7 @@ <h1 id="arch-univariate-volatility-volatilityprocess-volatility-updater">arch.un
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1409,7 +1409,7 @@ <h1 id="arch-univariate-volatility-volatilityprocess-volatility-updater">arch.un
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-        <script src="../../../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/univariate/introduction.html b/univariate/introduction.html
index b9fedb24cc..c89d7e9a2f 100644
--- a/univariate/introduction.html
+++ b/univariate/introduction.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Introduction to ARCH Models - arch 6.1.0</title>
+        <title>Introduction to ARCH Models - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
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-    arch 6.1.0
+    arch 6.2.0
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@@ -918,7 +918,7 @@
                   
 
 
-<h1 id="introduction-to-arch-models">Introduction to ARCH Models<a class="headerlink" href="#introduction-to-arch-models" title="Permalink to this heading">¶</a></h1>
+<h1 id="introduction-to-arch-models">Introduction to ARCH Models<a class="headerlink" href="#introduction-to-arch-models" title="Link to this heading">¶</a></h1>
 <p>ARCH models are a popular class of volatility models that use observed values
 of returns or residuals as volatility shocks.  A basic GARCH model is specified
 as</p>
@@ -1012,39 +1012,39 @@ <h1 id="introduction-to-arch-models">Introduction to ARCH Models<a class="header
 <span class="n">Covariance</span> <span class="n">estimator</span><span class="p">:</span> <span class="n">robust</span>
 </code></pre></div>
 </div>
-<h2 id="model-constructor">Model Constructor<a class="headerlink" href="#model-constructor" title="Permalink to this heading">¶</a></h2>
+<h2 id="model-constructor">Model Constructor<a class="headerlink" href="#model-constructor" title="Link to this heading">¶</a></h2>
 <p>While models can be carefully specified using the individual components, most common specifications can be specified
 using a simple model constructor.</p>
 <dl class="py function objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.arch_model">
-<span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">arch_model</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.arch_model.y" title="arch.univariate.arch_model.y (Python parameter) — The dependent variable"><span class="n"><span class="pre">y</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.arch_model.x" title="arch.univariate.arch_model.x (Python parameter) — Exogenous regressors.  Ignored if model does not permit exogenous regressors."><span class="n"><span class="pre">x</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.arch_model.mean" title="arch.univariate.arch_model.mean (Python parameter) — Name of the mean model.  Currently supported options are: 'Constant', 'Zero', 'LS', 'AR', 'ARX', 'HAR' and  'HARX'"><span class="n"><span class="pre">mean</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;Constant&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.arch_model.lags" title="arch.univariate.arch_model.lags (Python parameter) — Either a scalar integer value indicating lag length or a list of integers specifying lag locations."><span class="n"><span class="pre">lags</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.arch_model.vol" title="arch.univariate.arch_model.vol (Python parameter) — Name of the volatility model.  Currently supported options are: 'GARCH' (default), 'ARCH', 'EGARCH', 'FIGARCH', 'APARCH' and 'HARCH'"><span class="n"><span class="pre">vol</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;GARCH&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.arch_model.p" title="arch.univariate.arch_model.p (Python parameter) — Lag order of the symmetric innovation"><span class="n"><span class="pre">p</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.arch_model.o" title="arch.univariate.arch_model.o (Python parameter) — Lag order of the asymmetric innovation"><span class="n"><span class="pre">o</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.arch_model.q" title="arch.univariate.arch_model.q (Python parameter) — Lag order of lagged volatility or equivalent"><span class="n"><span class="pre">q</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.arch_model.power" title="arch.univariate.arch_model.power (Python parameter) — Power to use with GARCH and related models"><span class="n"><span class="pre">power</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">2.0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.arch_model.dist" title="arch.univariate.arch_model.dist (Python parameter) — Name of the error distribution.  Currently supported options are:"><span class="n"><span class="pre">dist</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;normal&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.arch_model.hold_back" title="arch.univariate.arch_model.hold_back (Python parameter) — Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."><span class="n"><span class="pre">hold_back</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.arch_model.rescale" title="arch.univariate.arch_model.rescale (Python parameter) — Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."><span class="n"><span class="pre">rescale</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/arch/univariate/mean.html#arch_model"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.arch_model" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">arch_model</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.arch_model.y" title="arch.univariate.arch_model.y (Python parameter) — The dependent variable"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.arch_model.x" title="arch.univariate.arch_model.x (Python parameter) — Exogenous regressors.  Ignored if model does not permit exogenous regressors."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.arch_model.mean" title="arch.univariate.arch_model.mean (Python parameter) — Name of the mean model.  Currently supported options are: 'Constant', 'Zero', 'LS', 'AR', 'ARX', 'HAR' and  'HARX'"><span class="n"><span class="pre">mean</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'Constant'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'Zero'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'LS'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'AR'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ARX'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'HAR'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'HARX'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'constant'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'zero'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;Constant&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.arch_model.lags" title="arch.univariate.arch_model.lags (Python parameter) — Either a scalar integer value indicating lag length or a list of integers specifying lag locations."><span class="n"><span class="pre">lags</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.arch_model.vol" title="arch.univariate.arch_model.vol (Python parameter) — Name of the volatility model.  Currently supported options are: 'GARCH' (default), 'ARCH', 'EGARCH', 'FIGARCH', 'APARCH' and 'HARCH'"><span class="n"><span class="pre">vol</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'GARCH'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ARCH'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'EGARCH'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'FIGARCH'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'APARCH'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'HARCH'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;GARCH&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.arch_model.p" title="arch.univariate.arch_model.p (Python parameter) — Lag order of the symmetric innovation"><span class="n"><span class="pre">p</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.arch_model.o" title="arch.univariate.arch_model.o (Python parameter) — Lag order of the asymmetric innovation"><span class="n"><span class="pre">o</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.arch_model.q" title="arch.univariate.arch_model.q (Python parameter) — Lag order of lagged volatility or equivalent"><span class="n"><span class="pre">q</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.arch_model.power" title="arch.univariate.arch_model.power (Python parameter) — Power to use with GARCH and related models"><span class="n"><span class="pre">power</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">2.0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.arch_model.dist" title="arch.univariate.arch_model.dist (Python parameter) — Name of the error distribution.  Currently supported options are:"><span class="n"><span class="pre">dist</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'normal'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'gaussian'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'t'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'studentst'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'skewstudent'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'skewt'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ged'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'generalized</span> <span class="pre">error'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;normal&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.arch_model.hold_back" title="arch.univariate.arch_model.hold_back (Python parameter) — Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."><span class="n"><span class="pre">hold_back</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.arch_model.rescale" title="arch.univariate.arch_model.rescale (Python parameter) — Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."><span class="n"><span class="pre">rescale</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="generated/arch.univariate.HARX.html#arch.univariate.HARX" title="arch.univariate.mean.HARX (Python class)"><span class="pre">HARX</span></a></span></span></span><a class="reference internal" href="../_modules/arch/univariate/mean.html#arch_model"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.arch_model" title="Link to this definition">¶</a></dt>
 <dd><p>Initialization of common ARCH model specifications</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.arch_model-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.arch_model-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.arch_model.y"><span class="sig-name descname">y</span><span class="p"> : </span><span class="desctype"><span class="desctype">{ndarray, Series, None}</span></span><a class="headerlink" href="#arch.univariate.arch_model.y" title="Permalink to this definition">¶</a></dt><dd><p>The dependent variable</p>
+<dt id="arch.univariate.arch_model.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><a class="headerlink" href="#arch.univariate.arch_model.y" title="Permalink to this definition">¶</a></dt><dd><p>The dependent variable</p>
 </dd>
-<dt id="arch.univariate.arch_model.x"><span class="sig-name descname">x</span><span class="p"> : </span><span class="desctype"><span class="desctype">{np.array, DataFrame}, optional</span></span><a class="headerlink" href="#arch.univariate.arch_model.x" title="Permalink to this definition">¶</a></dt><dd><p>Exogenous regressors.  Ignored if model does not permit exogenous
+<dt id="arch.univariate.arch_model.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.arch_model.x" title="Permalink to this definition">¶</a></dt><dd><p>Exogenous regressors.  Ignored if model does not permit exogenous
 regressors.</p>
 </dd>
-<dt id="arch.univariate.arch_model.mean"><span class="sig-name descname">mean</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, optional</span></span><a class="headerlink" href="#arch.univariate.arch_model.mean" title="Permalink to this definition">¶</a></dt><dd><p>Name of the mean model.  Currently supported options are: ‘Constant’,
+<dt id="arch.univariate.arch_model.mean"><span class="n sig-name">mean</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'Constant'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'Zero'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'LS'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'AR'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'ARX'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'HAR'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'HARX'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'constant'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'zero'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;Constant&#39;</span></code><a class="headerlink" href="#arch.univariate.arch_model.mean" title="Permalink to this definition">¶</a></dt><dd><p>Name of the mean model.  Currently supported options are: ‘Constant’,
 ‘Zero’, ‘LS’, ‘AR’, ‘ARX’, ‘HAR’ and  ‘HARX’</p>
 </dd>
-<dt id="arch.univariate.arch_model.lags"><span class="sig-name descname">lags</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a> or <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>), optional</span></span><a class="headerlink" href="#arch.univariate.arch_model.lags" title="Permalink to this definition">¶</a></dt><dd><p>Either a scalar integer value indicating lag length or a list of
+<dt id="arch.univariate.arch_model.lags"><span class="n sig-name">lags</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code><a class="headerlink" href="#arch.univariate.arch_model.lags" title="Permalink to this definition">¶</a></dt><dd><p>Either a scalar integer value indicating lag length or a list of
 integers specifying lag locations.</p>
 </dd>
-<dt id="arch.univariate.arch_model.vol"><span class="sig-name descname">vol</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, optional</span></span><a class="headerlink" href="#arch.univariate.arch_model.vol" title="Permalink to this definition">¶</a></dt><dd><p>Name of the volatility model.  Currently supported options are:
+<dt id="arch.univariate.arch_model.vol"><span class="n sig-name">vol</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'GARCH'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'ARCH'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'EGARCH'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'FIGARCH'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'APARCH'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'HARCH'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;GARCH&#39;</span></code><a class="headerlink" href="#arch.univariate.arch_model.vol" title="Permalink to this definition">¶</a></dt><dd><p>Name of the volatility model.  Currently supported options are:
 ‘GARCH’ (default), ‘ARCH’, ‘EGARCH’, ‘FIGARCH’, ‘APARCH’ and ‘HARCH’</p>
 </dd>
-<dt id="arch.univariate.arch_model.p"><span class="sig-name descname">p</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.univariate.arch_model.p" title="Permalink to this definition">¶</a></dt><dd><p>Lag order of the symmetric innovation</p>
+<dt id="arch.univariate.arch_model.p"><span class="n sig-name">p</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)">list</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="p">]</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code><a class="headerlink" href="#arch.univariate.arch_model.p" title="Permalink to this definition">¶</a></dt><dd><p>Lag order of the symmetric innovation</p>
 </dd>
-<dt id="arch.univariate.arch_model.o"><span class="sig-name descname">o</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.univariate.arch_model.o" title="Permalink to this definition">¶</a></dt><dd><p>Lag order of the asymmetric innovation</p>
+<dt id="arch.univariate.arch_model.o"><span class="n sig-name">o</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code><a class="headerlink" href="#arch.univariate.arch_model.o" title="Permalink to this definition">¶</a></dt><dd><p>Lag order of the asymmetric innovation</p>
 </dd>
-<dt id="arch.univariate.arch_model.q"><span class="sig-name descname">q</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.univariate.arch_model.q" title="Permalink to this definition">¶</a></dt><dd><p>Lag order of lagged volatility or equivalent</p>
+<dt id="arch.univariate.arch_model.q"><span class="n sig-name">q</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code><a class="headerlink" href="#arch.univariate.arch_model.q" title="Permalink to this definition">¶</a></dt><dd><p>Lag order of lagged volatility or equivalent</p>
 </dd>
-<dt id="arch.univariate.arch_model.power"><span class="sig-name descname">power</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a>, optional</span></span><a class="headerlink" href="#arch.univariate.arch_model.power" title="Permalink to this definition">¶</a></dt><dd><p>Power to use with GARCH and related models</p>
+<dt id="arch.univariate.arch_model.power"><span class="n sig-name">power</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">2.0</span></code><a class="headerlink" href="#arch.univariate.arch_model.power" title="Permalink to this definition">¶</a></dt><dd><p>Power to use with GARCH and related models</p>
 </dd>
-<dt id="arch.univariate.arch_model.dist"><span class="sig-name descname">dist</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a>, optional</span></span><a class="headerlink" href="#arch.univariate.arch_model.dist" title="Permalink to this definition">¶</a></dt><dd><p><p>Name of the error distribution.  Currently supported options are:</p>
+<dt id="arch.univariate.arch_model.dist"><span class="n sig-name">dist</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'normal'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'gaussian'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'t'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'studentst'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'skewstudent'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'skewt'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'ged'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'generalized error'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;normal&#39;</span></code><a class="headerlink" href="#arch.univariate.arch_model.dist" title="Permalink to this definition">¶</a></dt><dd><p><p>Name of the error distribution.  Currently supported options are:</p>
 <blockquote>
 <div><ul class="simple">
 <li><p>Normal: ‘normal’, ‘gaussian’ (default)</p></li>
@@ -1055,11 +1055,11 @@ <h2 id="model-constructor">Model Constructor<a class="headerlink" href="#model-c
 </div></blockquote>
 </p>
 </dd>
-<dt id="arch.univariate.arch_model.hold_back"><span class="sig-name descname">hold_back</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.univariate.arch_model.hold_back" title="Permalink to this definition">¶</a></dt><dd><p>Number of observations at the start of the sample to exclude when
+<dt id="arch.univariate.arch_model.hold_back"><span class="n sig-name">hold_back</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.arch_model.hold_back" title="Permalink to this definition">¶</a></dt><dd><p>Number of observations at the start of the sample to exclude when
 estimating model parameters.  Used when comparing models with different
 lag lengths to estimate on the common sample.</p>
 </dd>
-<dt id="arch.univariate.arch_model.rescale"><span class="sig-name descname">rescale</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a></span></span><a class="headerlink" href="#arch.univariate.arch_model.rescale" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to automatically rescale data if the scale
+<dt id="arch.univariate.arch_model.rescale"><span class="n sig-name">rescale</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.11)">bool</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.arch_model.rescale" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to automatically rescale data if the scale
 of the data is likely to produce convergence issues when estimating
 model parameters. If False, the model is estimated on the data without
 transformation.  If True, than y is rescaled and the new scale is
@@ -1071,7 +1071,7 @@ <h2 id="model-constructor">Model Constructor<a class="headerlink" href="#model-c
 <dd class="field-even"><p><strong>model</strong> – Configured ARCH model</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.arch_model-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.arch_model-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference internal" href="generated/arch.univariate.base.ARCHModel.html#arch.univariate.base.ARCHModel" title="arch.univariate.base.ARCHModel (Python class) — Abstract base class for mean models in ARCH processes.  Specifies the conditional mean process.">ARCHModel</a></p>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">ARCHModel</span></code></p>
 </dd>
 </dl>
 <p class="example rubric">Examples</p>
@@ -1169,7 +1169,7 @@ <h2 id="model-constructor">Model Constructor<a class="headerlink" href="#model-c
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
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@@ -1216,10 +1216,10 @@ <h2 id="model-constructor">Model Constructor<a class="headerlink" href="#model-c
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-        <script src="../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+        <script id="MathJax-script" src="../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
     
   </body>
 </html>
\ No newline at end of file
diff --git a/univariate/mean.html b/univariate/mean.html
index ea54cbd8fb..b748dd1def 100644
--- a/univariate/mean.html
+++ b/univariate/mean.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Mean Models - arch 6.1.0</title>
+        <title>Mean Models - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -964,7 +964,7 @@
                   
 
 
-<h1 id="mean-models"><span id="id1"></span>Mean Models<a class="headerlink" href="#mean-models" title="Permalink to this heading">¶</a></h1>
+<h1 id="mean-models"><span id="id1"></span>Mean Models<a class="headerlink" href="#mean-models" title="Link to this heading">¶</a></h1>
 <p>All ARCH models start by specifying a mean model.</p>
 <table class="autosummary longtable docutils data align-default">
 <tbody>
@@ -985,7 +985,7 @@ <h1 id="mean-models"><span id="id1"></span>Mean Models<a class="headerlink" href
 </tr>
 </tbody>
 </table>
-<h2 id="g-arch-in-mean-models">(G)ARCH-in-mean Models<a class="headerlink" href="#g-arch-in-mean-models" title="Permalink to this heading">¶</a></h2>
+<h2 id="g-arch-in-mean-models">(G)ARCH-in-mean Models<a class="headerlink" href="#g-arch-in-mean-models" title="Link to this heading">¶</a></h2>
 <p>(G)ARCH-in-mean models allow the conditional variance (or a transformation of it)
 to enter the conditional mean.</p>
 <table class="autosummary longtable docutils data align-default">
@@ -995,7 +995,7 @@ <h2 id="g-arch-in-mean-models">(G)ARCH-in-mean Models<a class="headerlink" href=
 </tr>
 </tbody>
 </table>
-<h3 id="special-requirements">Special Requirements<a class="headerlink" href="#special-requirements" title="Permalink to this heading">¶</a></h3>
+<h3 id="special-requirements">Special Requirements<a class="headerlink" href="#special-requirements" title="Link to this heading">¶</a></h3>
 <p>Not all volatility processes support application to AIM modeling.
 Specifically, the property <code class="docutils literal notranslate"><span class="pre">updateable</span></code> must be <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p>
 <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><code><span class="gp">In [1]: </span><span class="kn">from</span> <span class="nn">arch.univariate</span> <span class="kn">import</span> <span class="n">GARCH</span><span class="p">,</span> <span class="n">EGARCH</span>
@@ -1007,7 +1007,7 @@ <h3 id="special-requirements">Special Requirements<a class="headerlink" href="#s
 <span class="gh">Out[3]: </span><span class="go">True</span>
 </code></pre></div>
 </div>
-<h2 id="writing-new-mean-models">Writing New Mean Models<a class="headerlink" href="#writing-new-mean-models" title="Permalink to this heading">¶</a></h2>
+<h2 id="writing-new-mean-models">Writing New Mean Models<a class="headerlink" href="#writing-new-mean-models" title="Link to this heading">¶</a></h2>
 <p>All mean models must inherit from :class:ARCHModel and provide all public
 methods. There are two optional private methods that should be provided if
 applicable.</p>
@@ -1085,7 +1085,7 @@ <h2 id="writing-new-mean-models">Writing New Mean Models<a class="headerlink" hr
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1132,7 +1132,7 @@ <h2 id="writing-new-mean-models">Writing New Mean Models<a class="headerlink" hr
     <script id="__config" type="application/json">{"base": "..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/results.html b/univariate/results.html
index da07c1e6ce..7ae63647ab 100644
--- a/univariate/results.html
+++ b/univariate/results.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Model Results - arch 6.1.0</title>
+        <title>Model Results - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -705,7 +705,7 @@
                   
 
 
-<h1 id="model-results">Model Results<a class="headerlink" href="#model-results" title="Permalink to this heading">¶</a></h1>
+<h1 id="model-results">Model Results<a class="headerlink" href="#model-results" title="Link to this heading">¶</a></h1>
 <p>All model return the same object, a results class (<code class="xref py py-class docutils literal notranslate"><span class="pre">ARCHModelResult</span></code>).
 When using the <code class="docutils literal notranslate"><span class="pre">fix</span></code> method, a (<code class="xref py py-class docutils literal notranslate"><span class="pre">ARCHModelFixedResult</span></code>) is produced
 that lacks some properties of a (<code class="xref py py-class docutils literal notranslate"><span class="pre">ARCHModelResult</span></code>) that are not
@@ -787,7 +787,7 @@ <h1 id="model-results">Model Results<a class="headerlink" href="#model-results"
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -834,7 +834,7 @@ <h1 id="model-results">Model Results<a class="headerlink" href="#model-results"
     <script id="__config" type="application/json">{"base": "..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/univariate.html b/univariate/univariate.html
index f7ad0976bb..76b9ff7a30 100644
--- a/univariate/univariate.html
+++ b/univariate/univariate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Univariate Volatility Models - arch 6.1.0</title>
+        <title>Univariate Volatility Models - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -734,7 +734,7 @@
                   
 
 
-<h1 id="univariate-volatility-models">Univariate Volatility Models<a class="headerlink" href="#univariate-volatility-models" title="Permalink to this heading">¶</a></h1>
+<h1 id="univariate-volatility-models">Univariate Volatility Models<a class="headerlink" href="#univariate-volatility-models" title="Link to this heading">¶</a></h1>
 <p><code class="docutils literal notranslate"><span class="pre">arch.univaraite</span></code> provides both high-level (<a class="reference internal" href="introduction.html#arch.univariate.arch_model" title="arch.univariate.arch_model (Python function) — Initialization of common ARCH model specifications"><code class="xref py py-func docutils literal notranslate"><span class="pre">arch_model()</span></code></a>) and low-level
 methods (see <a class="reference internal" href="mean.html#mean-models"><span class="std std-ref">Mean Models</span></a>) to specify models. All models can be used to produce forecasts
 either analytically (when tractable) or using simulation-based methods (Monte Carlo or
@@ -824,7 +824,7 @@ <h1 id="univariate-volatility-models">Univariate Volatility Models<a class="head
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -871,7 +871,7 @@ <h1 id="univariate-volatility-models">Univariate Volatility Models<a class="head
     <script id="__config" type="application/json">{"base": "..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/univariate_forecasting_with_exogenous_variables.html b/univariate/univariate_forecasting_with_exogenous_variables.html
index 590f61653e..cdaea64a96 100644
--- a/univariate/univariate_forecasting_with_exogenous_variables.html
+++ b/univariate/univariate_forecasting_with_exogenous_variables.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Forecasting with Exogenous Regressors - arch 6.1.0</title>
+        <title>Forecasting with Exogenous Regressors - arch 6.2.0</title>
       
     
 
@@ -47,9 +47,9 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css" />
-        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css?v=2aa19091" />
+        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -93,7 +93,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -103,7 +103,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -190,10 +190,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -851,7 +851,7 @@
                   
 
 
-<h1 id="Forecasting-with-Exogenous-Regressors">Forecasting with Exogenous Regressors<a class="headerlink" href="#Forecasting-with-Exogenous-Regressors" title="Permalink to this heading">¶</a></h1>
+<h1 id="Forecasting-with-Exogenous-Regressors">Forecasting with Exogenous Regressors<a class="headerlink" href="#Forecasting-with-Exogenous-Regressors" title="Link to this heading">¶</a></h1>
 <p>This notebook provides examples of the accepted data structures for passing the expected value of exogenous variables when these are included in the mean. For example, consider an AR(1) with 2 exogenous variables. The mean dynamics are</p>
 <div class="math notranslate nohighlight">
 \[Y_t = \phi_0 + \phi_1 Y_{t-1} + \beta_0 X_{0,t} + \beta_1 X_{1,t} + \epsilon_t.\]</div>
@@ -878,7 +878,6 @@ <h1 id="Forecasting-with-Exogenous-Regressors">Forecasting with Exogenous Regres
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 <span class="kn">import</span> <span class="nn">seaborn</span>
-<span class="kn">from</span> <span class="nn">arch.__future__</span> <span class="kn">import</span> <span class="n">reindexing</span>
 
 <span class="n">seaborn</span><span class="o">.</span><span class="n">set_style</span><span class="p">(</span><span class="s2">&quot;darkgrid&quot;</span><span class="p">)</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">rc</span><span class="p">(</span><span class="s2">&quot;figure&quot;</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
@@ -888,7 +887,7 @@ <h1 id="Forecasting-with-Exogenous-Regressors">Forecasting with Exogenous Regres
 </code></pre></div>
 </div>
 </div>
-<h2 id="Simulating-data">Simulating data<a class="headerlink" href="#Simulating-data" title="Permalink to this heading">¶</a></h2>
+<h2 id="Simulating-data">Simulating data<a class="headerlink" href="#Simulating-data" title="Link to this heading">¶</a></h2>
 <p>Two <span class="math notranslate nohighlight">\(X\)</span> variables are simulated and are assumed to follow independent AR(1) processes. The data is then assumed to follow an ARX(1) with 2 exogenous regressors and GARCH(1,1) errors.</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[2]:
@@ -918,7 +917,7 @@ <h2 id="Simulating-data">Simulating data<a class="headerlink" href="#Simulating-
 </code></pre></div>
 </div>
 </div>
-<h2 id="Plotting-the-data">Plotting the data<a class="headerlink" href="#Plotting-the-data" title="Permalink to this heading">¶</a></h2>
+<h2 id="Plotting-the-data">Plotting the data<a class="headerlink" href="#Plotting-the-data" title="Link to this heading">¶</a></h2>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[3]:
 </code></pre></div>
@@ -936,7 +935,7 @@ <h2 id="Plotting-the-data">Plotting the data<a class="headerlink" href="#Plottin
 <img alt="../_images/univariate_univariate_forecasting_with_exogenous_variables_5_0.png" src="../_images/univariate_univariate_forecasting_with_exogenous_variables_5_0.png" />
 </div>
 </div>
-<h2 id="Forecasting-the-X-values">Forecasting the X values<a class="headerlink" href="#Forecasting-the-X-values" title="Permalink to this heading">¶</a></h2>
+<h2 id="Forecasting-the-X-values">Forecasting the X values<a class="headerlink" href="#Forecasting-the-X-values" title="Link to this heading">¶</a></h2>
 <p>The forecasts of <span class="math notranslate nohighlight">\(Y\)</span> depend on forecasts of <span class="math notranslate nohighlight">\(X_0\)</span> and <span class="math notranslate nohighlight">\(X_1\)</span>. Both of these follow simple AR(1), and so we can construct the forecasts for all time horizons. Note that the value in position <code class="docutils literal notranslate"><span class="pre">[i,j]</span></code> is the time-<code class="docutils literal notranslate"><span class="pre">i</span></code> forecast for horizon <code class="docutils literal notranslate"><span class="pre">j+1</span></code>.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[4]:
@@ -966,11 +965,11 @@ <h2 id="Forecasting-the-X-values">Forecasting the X values<a class="headerlink"
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-array([5.32610653, 5.16305327, 5.08152663, 5.04076332, 5.02038166,
-       5.01019083, 5.00509541, 5.00254771, 5.00127385, 5.00063693])
+array([5.65805541, 5.3290277 , 5.16451385, 5.08225693, 5.04112846,
+       5.02056423, 5.01028212, 5.00514106, 5.00257053, 5.00128526])
 </pre></div></div>
 </div>
-<h2 id="Fitting-the-model">Fitting the model<a class="headerlink" href="#Fitting-the-model" title="Permalink to this heading">¶</a></h2>
+<h2 id="Fitting-the-model">Fitting the model<a class="headerlink" href="#Fitting-the-model" title="Link to this heading">¶</a></h2>
 <p>Next, the most is fit. The parameters are accurately estimated.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[5]:
@@ -990,37 +989,37 @@ <h2 id="Fitting-the-model">Fitting the model<a class="headerlink" href="#Fitting
 <div class="highlight"><pre>
                           AR-X - GARCH Model Results
 ==============================================================================
-Dep. Variable:                   data   R-squared:                       0.993
-Mean Model:                      AR-X   Adj. R-squared:                  0.993
-Vol Model:                      GARCH   Log-Likelihood:               -1302.15
-Distribution:                  Normal   AIC:                           2618.31
-Method:            Maximum Likelihood   BIC:                           2652.65
+Dep. Variable:                   data   R-squared:                       0.992
+Mean Model:                      AR-X   Adj. R-squared:                  0.992
+Vol Model:                      GARCH   Log-Likelihood:               -1391.04
+Distribution:                  Normal   AIC:                           2796.08
+Method:            Maximum Likelihood   BIC:                           2830.43
                                         No. Observations:                  999
-Date:                Mon, May 17 2021   Df Residuals:                      995
-Time:                        16:04:11   Df Model:                            4
+Date:                Tue, May 30 2023   Df Residuals:                      995
+Time:                        10:55:41   Df Model:                            4
                                Mean Model
 ========================================================================
                  coef    std err          t      P&gt;|t|  95.0% Conf. Int.
 ------------------------------------------------------------------------
-Const          2.9480      0.149     19.818  2.088e-87 [  2.656,  3.240]
-data[1]        0.6971  3.421e-03    203.759      0.000 [  0.690,  0.704]
-x0             2.0186  2.063e-02     97.827      0.000 [  1.978,  2.059]
-x1            -2.0115  2.483e-02    -81.001      0.000 [ -2.060, -1.963]
-                              Volatility Model
-===========================================================================
-                 coef    std err          t      P&gt;|t|     95.0% Conf. Int.
----------------------------------------------------------------------------
-omega          0.1144  5.879e-02      1.946  5.162e-02 [-8.041e-04,  0.230]
-alpha[1]       0.0633  2.982e-02      2.123  3.374e-02  [4.866e-03,  0.122]
-beta[1]        0.7940  9.035e-02      8.788  1.522e-18    [  0.617,  0.971]
-===========================================================================
+Const          2.9447      0.151     19.443  3.308e-84 [  2.648,  3.242]
+data[1]        0.6947  3.807e-03    182.482      0.000 [  0.687,  0.702]
+x0             1.9813  2.175e-02     91.078      0.000 [  1.939,  2.024]
+x1            -1.9620  2.564e-02    -76.519      0.000 [ -2.012, -1.912]
+                             Volatility Model
+==========================================================================
+                 coef    std err          t      P&gt;|t|    95.0% Conf. Int.
+--------------------------------------------------------------------------
+omega          0.0946  3.606e-02      2.622  8.733e-03 [2.389e-02,  0.165]
+alpha[1]       0.0974  2.496e-02      3.903  9.516e-05 [4.849e-02,  0.146]
+beta[1]        0.8083  5.059e-02     15.977  1.839e-57   [  0.709,  0.907]
+==========================================================================
 
 Covariance estimator: robust
 </pre></div></div>
 </div>
-<h2 id="Using-a-dict">Using a <code class="docutils literal notranslate"><span class="pre">dict</span></code><a class="headerlink" href="#Using-a-dict" title="Permalink to this heading">¶</a></h2>
+<h2 id="Using-a-dict">Using a <code class="docutils literal notranslate"><span class="pre">dict</span></code><a class="headerlink" href="#Using-a-dict" title="Link to this heading">¶</a></h2>
 <p>The first approach uses a dict to pass the two variables. The key consideration here is the the keys of the dictionary must <strong>exactly</strong> match the variable names (<code class="docutils literal notranslate"><span class="pre">x0</span></code> and <code class="docutils literal notranslate"><span class="pre">x1</span></code> here). The dictionary here contains only the final row of the forecast values since <code class="docutils literal notranslate"><span class="pre">forecast</span></code> will only make forecasts beginning from the final in-sample observation by default.</p>
-<h3 id="Using-DataFrame">Using <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code><a class="headerlink" href="#Using-DataFrame" title="Permalink to this heading">¶</a></h3>
+<h3 id="Using-DataFrame">Using <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code><a class="headerlink" href="#Using-DataFrame" title="Link to this heading">¶</a></h3>
 <p>While these examples make use of NumPy arrays, these can be <code class="docutils literal notranslate"><span class="pre">DataFrames</span></code>. This allows the index to be used to track the forecast origination point, which can be a helpful device.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[6]:
@@ -1038,7 +1037,7 @@ <h3 id="Using-DataFrame">Using <code class="docutils literal notranslate"><span
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-&lt;AxesSubplot:&gt;
+&lt;Axes: &gt;
 </pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
@@ -1048,7 +1047,7 @@ <h3 id="Using-DataFrame">Using <code class="docutils literal notranslate"><span
 <img alt="../_images/univariate_univariate_forecasting_with_exogenous_variables_11_1.png" src="../_images/univariate_univariate_forecasting_with_exogenous_variables_11_1.png" />
 </div>
 </div>
-<h2 id="Using-an-array">Using an <code class="docutils literal notranslate"><span class="pre">array</span></code><a class="headerlink" href="#Using-an-array" title="Permalink to this heading">¶</a></h2>
+<h2 id="Using-an-array">Using an <code class="docutils literal notranslate"><span class="pre">array</span></code><a class="headerlink" href="#Using-an-array" title="Link to this heading">¶</a></h2>
 <p>An array can alternatively be used. This frees the restriction on matching the variable names although the order must match instead. The forecast values are 2 (variables) by 1 (forecast) by 10 (horizon).</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[7]:
@@ -1071,7 +1070,7 @@ <h2 id="Using-an-array">Using an <code class="docutils literal notranslate"><spa
 999   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0
 </pre></div></div>
 </div>
-<h2 id="Producing-multiple-forecasts">Producing multiple forecasts<a class="headerlink" href="#Producing-multiple-forecasts" title="Permalink to this heading">¶</a></h2>
+<h2 id="Producing-multiple-forecasts">Producing multiple forecasts<a class="headerlink" href="#Producing-multiple-forecasts" title="Link to this heading">¶</a></h2>
 <p><code class="docutils literal notranslate"><span class="pre">forecast</span></code> can produce multiple forecasts using the same fit model. Here the model is fit to the first 500 observations and then forecasting for the remaining values are produced. It must be the case that the <code class="docutils literal notranslate"><span class="pre">x</span></code> values passed for <code class="docutils literal notranslate"><span class="pre">forecast</span></code> have the same number of rows as the table of forecasts produced.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[8]:
@@ -1122,133 +1121,133 @@ <h2 id="Producing-multiple-forecasts">Producing multiple forecasts<a class="head
   <tbody>
     <tr>
       <th>990</th>
-      <td>0.197255</td>
-      <td>0.766334</td>
-      <td>1.577107</td>
-      <td>2.505601</td>
-      <td>3.459461</td>
-      <td>4.377620</td>
-      <td>5.224185</td>
-      <td>5.981486</td>
-      <td>6.644090</td>
-      <td>7.214210</td>
+      <td>17.638678</td>
+      <td>19.057943</td>
+      <td>19.305712</td>
+      <td>18.876070</td>
+      <td>18.090794</td>
+      <td>17.153279</td>
+      <td>16.187233</td>
+      <td>15.263806</td>
+      <td>14.420245</td>
+      <td>13.672511</td>
     </tr>
     <tr>
       <th>991</th>
-      <td>-1.890108</td>
-      <td>-1.296009</td>
-      <td>-0.321045</td>
-      <td>0.826688</td>
-      <td>2.010886</td>
-      <td>3.148451</td>
-      <td>4.193448</td>
-      <td>5.124665</td>
-      <td>5.936575</td>
-      <td>6.633031</td>
+      <td>20.307394</td>
+      <td>20.095492</td>
+      <td>19.176459</td>
+      <td>18.008276</td>
+      <td>16.816890</td>
+      <td>15.707369</td>
+      <td>14.722133</td>
+      <td>13.871672</td>
+      <td>13.150830</td>
+      <td>12.547444</td>
     </tr>
     <tr>
       <th>992</th>
-      <td>-8.507089</td>
-      <td>-7.755860</td>
-      <td>-6.044790</td>
-      <td>-4.011246</td>
-      <td>-1.975703</td>
-      <td>-0.089727</td>
-      <td>1.584868</td>
-      <td>3.033252</td>
-      <td>4.264283</td>
-      <td>5.297785</td>
+      <td>18.286380</td>
+      <td>16.896235</td>
+      <td>15.586099</td>
+      <td>14.454254</td>
+      <td>13.517502</td>
+      <td>12.759810</td>
+      <td>12.154606</td>
+      <td>11.674450</td>
+      <td>11.294780</td>
+      <td>10.994971</td>
     </tr>
     <tr>
       <th>993</th>
-      <td>-12.095946</td>
-      <td>-10.869621</td>
-      <td>-8.890602</td>
-      <td>-6.627904</td>
-      <td>-4.352285</td>
-      <td>-2.211499</td>
-      <td>-0.277740</td>
-      <td>1.422307</td>
-      <td>2.888448</td>
-      <td>4.135017</td>
+      <td>24.177049</td>
+      <td>22.846561</td>
+      <td>20.903832</td>
+      <td>18.944579</td>
+      <td>17.198290</td>
+      <td>15.728217</td>
+      <td>14.527010</td>
+      <td>13.561360</td>
+      <td>12.791967</td>
+      <td>12.181810</td>
     </tr>
     <tr>
       <th>994</th>
-      <td>-10.415586</td>
-      <td>-8.143577</td>
-      <td>-5.654791</td>
-      <td>-3.258179</td>
-      <td>-1.089347</td>
-      <td>0.805444</td>
-      <td>2.424787</td>
-      <td>3.788488</td>
-      <td>4.925033</td>
-      <td>5.865051</td>
+      <td>21.476395</td>
+      <td>19.945167</td>
+      <td>18.325031</td>
+      <td>16.830889</td>
+      <td>15.536242</td>
+      <td>14.450638</td>
+      <td>13.557059</td>
+      <td>12.829543</td>
+      <td>12.241130</td>
+      <td>11.767149</td>
     </tr>
     <tr>
       <th>995</th>
-      <td>-1.921109</td>
-      <td>1.075917</td>
-      <td>3.299730</td>
-      <td>4.950490</td>
-      <td>6.178127</td>
-      <td>7.093677</td>
-      <td>7.778853</td>
-      <td>8.293623</td>
-      <td>8.681971</td>
-      <td>8.976192</td>
+      <td>13.887372</td>
+      <td>11.827018</td>
+      <td>10.504285</td>
+      <td>9.701208</td>
+      <td>9.250338</td>
+      <td>9.029847</td>
+      <td>8.954356</td>
+      <td>8.965773</td>
+      <td>9.025626</td>
+      <td>9.109140</td>
     </tr>
     <tr>
       <th>996</th>
-      <td>-0.935776</td>
-      <td>1.954178</td>
-      <td>4.178160</td>
-      <td>5.832262</td>
-      <td>7.036522</td>
-      <td>7.901080</td>
-      <td>8.515904</td>
-      <td>8.950270</td>
-      <td>9.255725</td>
-      <td>9.469814</td>
+      <td>12.091224</td>
+      <td>10.738291</td>
+      <td>9.893515</td>
+      <td>9.402421</td>
+      <td>9.147641</td>
+      <td>9.044580</td>
+      <td>9.034509</td>
+      <td>9.077894</td>
+      <td>9.148903</td>
+      <td>9.231197</td>
     </tr>
     <tr>
       <th>997</th>
-      <td>2.416878</td>
-      <td>4.689798</td>
-      <td>6.409599</td>
-      <td>7.650164</td>
-      <td>8.515074</td>
-      <td>9.101508</td>
-      <td>9.488851</td>
-      <td>9.737583</td>
-      <td>9.891902</td>
-      <td>9.983206</td>
+      <td>14.752402</td>
+      <td>14.165799</td>
+      <td>13.584948</td>
+      <td>13.038435</td>
+      <td>12.542245</td>
+      <td>12.103274</td>
+      <td>11.722401</td>
+      <td>11.396828</td>
+      <td>11.121754</td>
+      <td>10.891488</td>
     </tr>
     <tr>
       <th>998</th>
-      <td>4.633381</td>
-      <td>6.555164</td>
-      <td>8.072909</td>
-      <td>9.153335</td>
-      <td>9.864477</td>
-      <td>10.296675</td>
-      <td>10.532205</td>
-      <td>10.636352</td>
-      <td>10.657221</td>
-      <td>10.628546</td>
+      <td>15.530046</td>
+      <td>14.262848</td>
+      <td>13.244627</td>
+      <td>12.449407</td>
+      <td>11.837882</td>
+      <td>11.371370</td>
+      <td>11.016716</td>
+      <td>10.747264</td>
+      <td>10.542302</td>
+      <td>10.386034</td>
     </tr>
     <tr>
       <th>999</th>
-      <td>10.573900</td>
-      <td>12.317818</td>
-      <td>13.245648</td>
-      <td>13.612879</td>
-      <td>13.620680</td>
-      <td>13.415346</td>
-      <td>13.097526</td>
-      <td>12.733225</td>
-      <td>12.363590</td>
-      <td>12.012647</td>
+      <td>11.481761</td>
+      <td>10.462828</td>
+      <td>10.056773</td>
+      <td>9.917344</td>
+      <td>9.885242</td>
+      <td>9.890079</td>
+      <td>9.902827</td>
+      <td>9.913039</td>
+      <td>9.918147</td>
+      <td>9.918574</td>
     </tr>
   </tbody>
 </table>
@@ -1296,20 +1295,10 @@ <h2 id="Producing-multiple-forecasts">Producing multiple forecasts<a class="head
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-h.01    0.0
-h.02    0.0
-h.03    0.0
-h.04    0.0
-h.05    0.0
-h.06    0.0
-h.07    0.0
-h.08    0.0
-h.09    0.0
-h.10    0.0
-dtype: float64
+0.0
 </pre></div></div>
 </div>
-<h2 id="x-input-array-sizes"><code class="docutils literal notranslate"><span class="pre">x</span></code> input array sizes<a class="headerlink" href="#x-input-array-sizes" title="Permalink to this heading">¶</a></h2>
+<h2 id="x-input-array-sizes"><code class="docutils literal notranslate"><span class="pre">x</span></code> input array sizes<a class="headerlink" href="#x-input-array-sizes" title="Link to this heading">¶</a></h2>
 <p>While the natural shape of the <code class="docutils literal notranslate"><span class="pre">x</span></code> data is the number of forecasts, it is also possible to pass an <code class="docutils literal notranslate"><span class="pre">x</span></code> that has the same shape as the <code class="docutils literal notranslate"><span class="pre">y</span></code> used to construct the model. The may simplify tracking the origin points of the forecast. Values are are not needed are ignored. In this example, the out-of-sample values are 2 by 1000 (original number of observations) by 10. Only the final 500 are used.</p>
 <div class="admonition warning">
 <div class="admonition-title"><h3></div><p>WARNING</p>
@@ -1340,20 +1329,10 @@ <h2 id="x-input-array-sizes"><code class="docutils literal notranslate"><span cl
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-h.01    0.0
-h.02    0.0
-h.03    0.0
-h.04    0.0
-h.05    0.0
-h.06    0.0
-h.07    0.0
-h.08    0.0
-h.09    0.0
-h.10    0.0
-dtype: float64
+0.0
 </pre></div></div>
 </div>
-<h2 id="Special-Cases-with-a-single-x-variable">Special Cases with a single <code class="docutils literal notranslate"><span class="pre">x</span></code> variable<a class="headerlink" href="#Special-Cases-with-a-single-x-variable" title="Permalink to this heading">¶</a></h2>
+<h2 id="Special-Cases-with-a-single-x-variable">Special Cases with a single <code class="docutils literal notranslate"><span class="pre">x</span></code> variable<a class="headerlink" href="#Special-Cases-with-a-single-x-variable" title="Link to this heading">¶</a></h2>
 <p>When a model consists of a single exogenous regressor, then <code class="docutils literal notranslate"><span class="pre">x</span></code> can be a 1-d or 2-d array (or <code class="docutils literal notranslate"><span class="pre">Series</span></code> or <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>).</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[12]:
@@ -1372,29 +1351,29 @@ <h2 id="Special-Cases-with-a-single-x-variable">Special Cases with a single <cod
 <div class="highlight"><pre>
                           AR-X - GARCH Model Results
 ==============================================================================
-Dep. Variable:                   data   R-squared:                       0.949
-Mean Model:                      AR-X   Adj. R-squared:                  0.949
-Vol Model:                      GARCH   Log-Likelihood:               -2310.31
-Distribution:                  Normal   AIC:                           4632.63
-Method:            Maximum Likelihood   BIC:                           4662.07
+Dep. Variable:                   data   R-squared:                       0.955
+Mean Model:                      AR-X   Adj. R-squared:                  0.955
+Vol Model:                      GARCH   Log-Likelihood:               -2280.96
+Distribution:                  Normal   AIC:                           4573.92
+Method:            Maximum Likelihood   BIC:                           4603.36
                                         No. Observations:                  999
-Date:                Mon, May 17 2021   Df Residuals:                      996
-Time:                        16:04:11   Df Model:                            3
+Date:                Tue, May 30 2023   Df Residuals:                      996
+Time:                        10:55:42   Df Model:                            3
                                Mean Model
 ========================================================================
                  coef    std err          t      P&gt;|t|  95.0% Conf. Int.
 ------------------------------------------------------------------------
-Const         -6.3468      0.283    -22.464 9.346e-112 [ -6.901, -5.793]
-data[1]        0.7555  9.631e-03     78.446      0.000 [  0.737,  0.774]
-x0             1.7840  6.195e-02     28.799 2.192e-182 [  1.663,  1.905]
-                             Volatility Model
-==========================================================================
-                 coef    std err          t      P&gt;|t|    95.0% Conf. Int.
---------------------------------------------------------------------------
-omega          3.0493      0.753      4.052  5.085e-05   [  1.574,  4.524]
-alpha[1]       0.1626  3.741e-02      4.346  1.385e-05 [8.926e-02,  0.236]
-beta[1]        0.3401      0.129      2.631  8.523e-03 [8.670e-02,  0.593]
-==========================================================================
+Const         -6.4609      0.226    -28.561 2.064e-179 [ -6.904, -6.018]
+data[1]        0.7695  8.995e-03     85.552      0.000 [  0.752,  0.787]
+x0             1.7394  5.329e-02     32.643 1.021e-233 [  1.635,  1.844]
+                               Volatility Model
+=============================================================================
+                 coef    std err          t      P&gt;|t|       95.0% Conf. Int.
+-----------------------------------------------------------------------------
+omega          0.1073  7.460e-02      1.438      0.150   [-3.895e-02,  0.253]
+alpha[1]   8.6689e-03  1.513e-02      0.573      0.567 [-2.099e-02,3.832e-02]
+beta[1]        0.9717  2.616e-02     37.149 4.549e-302      [  0.920,  1.023]
+=============================================================================
 
 Covariance estimator: robust
 </pre></div></div>
@@ -1424,9 +1403,9 @@ <h2 id="Special-Cases-with-a-single-x-variable">Special Cases with a single <cod
 999   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0
 </pre></div></div>
 </div>
-<h2 id="Simulation">Simulation<a class="headerlink" href="#Simulation" title="Permalink to this heading">¶</a></h2>
+<h2 id="Simulation">Simulation<a class="headerlink" href="#Simulation" title="Link to this heading">¶</a></h2>
 <p><code class="docutils literal notranslate"><span class="pre">forecast</span></code> supports simulating paths. When forecasting a model with exogenous variables, the same value is used to in all mean paths. If you wish to also simulate the paths of the <code class="docutils literal notranslate"><span class="pre">x</span></code> variables, these need to generated and then passed inside a loop.</p>
-<h3 id="Static-out-of-sample-x">Static out-of-sample <code class="docutils literal notranslate"><span class="pre">x</span></code><a class="headerlink" href="#Static-out-of-sample-x" title="Permalink to this heading">¶</a></h3>
+<h3 id="Static-out-of-sample-x">Static out-of-sample <code class="docutils literal notranslate"><span class="pre">x</span></code><a class="headerlink" href="#Static-out-of-sample-x" title="Link to this heading">¶</a></h3>
 <p>This first example shows that variance of the paths when the same <code class="docutils literal notranslate"><span class="pre">x</span></code> values are used in the forecast. There is a sense the out-of-sample <code class="docutils literal notranslate"><span class="pre">x</span></code> are treated as deterministic.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[14]:
@@ -1444,11 +1423,11 @@ <h3 id="Static-out-of-sample-x">Static out-of-sample <code class="docutils liter
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-array([[0.94510757, 0.97700028, 1.13035701, 1.10824845, 1.26825095,
-        1.26590793, 1.23342443, 1.18098812, 1.28265677, 1.2315994 ]])
+array([[1.04095499, 1.27798546, 1.30149937, 1.53498654, 1.60825322,
+        1.53831759, 1.55948399, 1.49811376, 1.74794163, 1.54496175]])
 </pre></div></div>
 </div>
-<h3 id="Simulating-the-out-of-sample-x">Simulating the out-of-sample <code class="docutils literal notranslate"><span class="pre">x</span></code><a class="headerlink" href="#Simulating-the-out-of-sample-x" title="Permalink to this heading">¶</a></h3>
+<h3 id="Simulating-the-out-of-sample-x">Simulating the out-of-sample <code class="docutils literal notranslate"><span class="pre">x</span></code><a class="headerlink" href="#Simulating-the-out-of-sample-x" title="Link to this heading">¶</a></h3>
 <p>This example simulates distinct paths for the two exogenous variables and then simulates a single path. This is then repeated 100 times. We see that variance is much higher when we account for variation in the x data.</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[15]:
@@ -1493,8 +1472,8 @@ <h3 id="Simulating-the-out-of-sample-x">Simulating the out-of-sample <code class
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-array([[3.21343234, 4.99297815, 6.29477322, 7.49884207, 8.47501636,
-        8.64628226, 8.72478144, 8.78606273, 9.03824963, 9.30368669]])
+array([[2.95980851, 4.9043695 , 6.49587772, 7.50716786, 8.27211937,
+        8.71428305, 8.71321666, 8.67711577, 8.80764753, 9.17554847]])
 </pre></div></div>
 </div>
 
@@ -1564,7 +1543,7 @@ <h3 id="Simulating-the-out-of-sample-x">Simulating the out-of-sample <code class
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1611,10 +1590,10 @@ <h3 id="Simulating-the-out-of-sample-x">Simulating the out-of-sample <code class
     <script id="__config" type="application/json">{"base": "..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
         <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+        <script id="MathJax-script" src="../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
     
   </body>
 </html>
\ No newline at end of file
diff --git a/univariate/univariate_forecasting_with_exogenous_variables.ipynb b/univariate/univariate_forecasting_with_exogenous_variables.ipynb
index b11a76f61d..5c98e7a4a6 100644
--- a/univariate/univariate_forecasting_with_exogenous_variables.ipynb
+++ b/univariate/univariate_forecasting_with_exogenous_variables.ipynb
@@ -53,7 +53,6 @@
     "import numpy as np\n",
     "import pandas as pd\n",
     "import seaborn\n",
-    "from arch.__future__ import reindexing\n",
     "\n",
     "seaborn.set_style(\"darkgrid\")\n",
     "plt.rc(\"figure\", figsize=(16, 6))\n",
@@ -138,9 +137,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 1152x432 with 1 Axes>"
+       "<Figure size 1600x600 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -182,8 +181,8 @@
     {
      "data": {
       "text/plain": [
-       "array([5.32610653, 5.16305327, 5.08152663, 5.04076332, 5.02038166,\n",
-       "       5.01019083, 5.00509541, 5.00254771, 5.00127385, 5.00063693])"
+       "array([5.65805541, 5.3290277 , 5.16451385, 5.08225693, 5.04112846,\n",
+       "       5.02056423, 5.01028212, 5.00514106, 5.00257053, 5.00128526])"
       ]
      },
      "execution_count": 4,
@@ -241,30 +240,30 @@
      "text": [
       "                          AR-X - GARCH Model Results                          \n",
       "==============================================================================\n",
-      "Dep. Variable:                   data   R-squared:                       0.993\n",
-      "Mean Model:                      AR-X   Adj. R-squared:                  0.993\n",
-      "Vol Model:                      GARCH   Log-Likelihood:               -1302.15\n",
-      "Distribution:                  Normal   AIC:                           2618.31\n",
-      "Method:            Maximum Likelihood   BIC:                           2652.65\n",
+      "Dep. Variable:                   data   R-squared:                       0.992\n",
+      "Mean Model:                      AR-X   Adj. R-squared:                  0.992\n",
+      "Vol Model:                      GARCH   Log-Likelihood:               -1391.04\n",
+      "Distribution:                  Normal   AIC:                           2796.08\n",
+      "Method:            Maximum Likelihood   BIC:                           2830.43\n",
       "                                        No. Observations:                  999\n",
-      "Date:                Mon, May 17 2021   Df Residuals:                      995\n",
-      "Time:                        16:04:11   Df Model:                            4\n",
+      "Date:                Tue, May 30 2023   Df Residuals:                      995\n",
+      "Time:                        10:55:41   Df Model:                            4\n",
       "                               Mean Model                               \n",
       "========================================================================\n",
       "                 coef    std err          t      P>|t|  95.0% Conf. Int.\n",
       "------------------------------------------------------------------------\n",
-      "Const          2.9480      0.149     19.818  2.088e-87 [  2.656,  3.240]\n",
-      "data[1]        0.6971  3.421e-03    203.759      0.000 [  0.690,  0.704]\n",
-      "x0             2.0186  2.063e-02     97.827      0.000 [  1.978,  2.059]\n",
-      "x1            -2.0115  2.483e-02    -81.001      0.000 [ -2.060, -1.963]\n",
-      "                              Volatility Model                             \n",
-      "===========================================================================\n",
-      "                 coef    std err          t      P>|t|     95.0% Conf. Int.\n",
-      "---------------------------------------------------------------------------\n",
-      "omega          0.1144  5.879e-02      1.946  5.162e-02 [-8.041e-04,  0.230]\n",
-      "alpha[1]       0.0633  2.982e-02      2.123  3.374e-02  [4.866e-03,  0.122]\n",
-      "beta[1]        0.7940  9.035e-02      8.788  1.522e-18    [  0.617,  0.971]\n",
-      "===========================================================================\n",
+      "Const          2.9447      0.151     19.443  3.308e-84 [  2.648,  3.242]\n",
+      "data[1]        0.6947  3.807e-03    182.482      0.000 [  0.687,  0.702]\n",
+      "x0             1.9813  2.175e-02     91.078      0.000 [  1.939,  2.024]\n",
+      "x1            -1.9620  2.564e-02    -76.519      0.000 [ -2.012, -1.912]\n",
+      "                             Volatility Model                             \n",
+      "==========================================================================\n",
+      "                 coef    std err          t      P>|t|    95.0% Conf. Int.\n",
+      "--------------------------------------------------------------------------\n",
+      "omega          0.0946  3.606e-02      2.622  8.733e-03 [2.389e-02,  0.165]\n",
+      "alpha[1]       0.0974  2.496e-02      3.903  9.516e-05 [4.849e-02,  0.146]\n",
+      "beta[1]        0.8083  5.059e-02     15.977  1.839e-57   [  0.709,  0.907]\n",
+      "==========================================================================\n",
       "\n",
       "Covariance estimator: robust\n"
      ]
@@ -310,7 +309,7 @@
     {
      "data": {
       "text/plain": [
-       "<AxesSubplot:>"
+       "<Axes: >"
       ]
      },
      "execution_count": 6,
@@ -319,9 +318,9 @@
     },
     {
      "data": {
-      "image/png": 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       "text/plain": [
-       "<Figure size 1152x432 with 1 Axes>"
+       "<Figure size 1600x600 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -431,162 +430,162 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>990</th>\n",
-       "      <td>0.197255</td>\n",
-       "      <td>0.766334</td>\n",
-       "      <td>1.577107</td>\n",
-       "      <td>2.505601</td>\n",
-       "      <td>3.459461</td>\n",
-       "      <td>4.377620</td>\n",
-       "      <td>5.224185</td>\n",
-       "      <td>5.981486</td>\n",
-       "      <td>6.644090</td>\n",
-       "      <td>7.214210</td>\n",
+       "      <td>17.638678</td>\n",
+       "      <td>19.057943</td>\n",
+       "      <td>19.305712</td>\n",
+       "      <td>18.876070</td>\n",
+       "      <td>18.090794</td>\n",
+       "      <td>17.153279</td>\n",
+       "      <td>16.187233</td>\n",
+       "      <td>15.263806</td>\n",
+       "      <td>14.420245</td>\n",
+       "      <td>13.672511</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>991</th>\n",
-       "      <td>-1.890108</td>\n",
-       "      <td>-1.296009</td>\n",
-       "      <td>-0.321045</td>\n",
-       "      <td>0.826688</td>\n",
-       "      <td>2.010886</td>\n",
-       "      <td>3.148451</td>\n",
-       "      <td>4.193448</td>\n",
-       "      <td>5.124665</td>\n",
-       "      <td>5.936575</td>\n",
-       "      <td>6.633031</td>\n",
+       "      <td>20.307394</td>\n",
+       "      <td>20.095492</td>\n",
+       "      <td>19.176459</td>\n",
+       "      <td>18.008276</td>\n",
+       "      <td>16.816890</td>\n",
+       "      <td>15.707369</td>\n",
+       "      <td>14.722133</td>\n",
+       "      <td>13.871672</td>\n",
+       "      <td>13.150830</td>\n",
+       "      <td>12.547444</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>992</th>\n",
-       "      <td>-8.507089</td>\n",
-       "      <td>-7.755860</td>\n",
-       "      <td>-6.044790</td>\n",
-       "      <td>-4.011246</td>\n",
-       "      <td>-1.975703</td>\n",
-       "      <td>-0.089727</td>\n",
-       "      <td>1.584868</td>\n",
-       "      <td>3.033252</td>\n",
-       "      <td>4.264283</td>\n",
-       "      <td>5.297785</td>\n",
+       "      <td>18.286380</td>\n",
+       "      <td>16.896235</td>\n",
+       "      <td>15.586099</td>\n",
+       "      <td>14.454254</td>\n",
+       "      <td>13.517502</td>\n",
+       "      <td>12.759810</td>\n",
+       "      <td>12.154606</td>\n",
+       "      <td>11.674450</td>\n",
+       "      <td>11.294780</td>\n",
+       "      <td>10.994971</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>993</th>\n",
-       "      <td>-12.095946</td>\n",
-       "      <td>-10.869621</td>\n",
-       "      <td>-8.890602</td>\n",
-       "      <td>-6.627904</td>\n",
-       "      <td>-4.352285</td>\n",
-       "      <td>-2.211499</td>\n",
-       "      <td>-0.277740</td>\n",
-       "      <td>1.422307</td>\n",
-       "      <td>2.888448</td>\n",
-       "      <td>4.135017</td>\n",
+       "      <td>24.177049</td>\n",
+       "      <td>22.846561</td>\n",
+       "      <td>20.903832</td>\n",
+       "      <td>18.944579</td>\n",
+       "      <td>17.198290</td>\n",
+       "      <td>15.728217</td>\n",
+       "      <td>14.527010</td>\n",
+       "      <td>13.561360</td>\n",
+       "      <td>12.791967</td>\n",
+       "      <td>12.181810</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>994</th>\n",
-       "      <td>-10.415586</td>\n",
-       "      <td>-8.143577</td>\n",
-       "      <td>-5.654791</td>\n",
-       "      <td>-3.258179</td>\n",
-       "      <td>-1.089347</td>\n",
-       "      <td>0.805444</td>\n",
-       "      <td>2.424787</td>\n",
-       "      <td>3.788488</td>\n",
-       "      <td>4.925033</td>\n",
-       "      <td>5.865051</td>\n",
+       "      <td>21.476395</td>\n",
+       "      <td>19.945167</td>\n",
+       "      <td>18.325031</td>\n",
+       "      <td>16.830889</td>\n",
+       "      <td>15.536242</td>\n",
+       "      <td>14.450638</td>\n",
+       "      <td>13.557059</td>\n",
+       "      <td>12.829543</td>\n",
+       "      <td>12.241130</td>\n",
+       "      <td>11.767149</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>995</th>\n",
-       "      <td>-1.921109</td>\n",
-       "      <td>1.075917</td>\n",
-       "      <td>3.299730</td>\n",
-       "      <td>4.950490</td>\n",
-       "      <td>6.178127</td>\n",
-       "      <td>7.093677</td>\n",
-       "      <td>7.778853</td>\n",
-       "      <td>8.293623</td>\n",
-       "      <td>8.681971</td>\n",
-       "      <td>8.976192</td>\n",
+       "      <td>13.887372</td>\n",
+       "      <td>11.827018</td>\n",
+       "      <td>10.504285</td>\n",
+       "      <td>9.701208</td>\n",
+       "      <td>9.250338</td>\n",
+       "      <td>9.029847</td>\n",
+       "      <td>8.954356</td>\n",
+       "      <td>8.965773</td>\n",
+       "      <td>9.025626</td>\n",
+       "      <td>9.109140</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>996</th>\n",
-       "      <td>-0.935776</td>\n",
-       "      <td>1.954178</td>\n",
-       "      <td>4.178160</td>\n",
-       "      <td>5.832262</td>\n",
-       "      <td>7.036522</td>\n",
-       "      <td>7.901080</td>\n",
-       "      <td>8.515904</td>\n",
-       "      <td>8.950270</td>\n",
-       "      <td>9.255725</td>\n",
-       "      <td>9.469814</td>\n",
+       "      <td>12.091224</td>\n",
+       "      <td>10.738291</td>\n",
+       "      <td>9.893515</td>\n",
+       "      <td>9.402421</td>\n",
+       "      <td>9.147641</td>\n",
+       "      <td>9.044580</td>\n",
+       "      <td>9.034509</td>\n",
+       "      <td>9.077894</td>\n",
+       "      <td>9.148903</td>\n",
+       "      <td>9.231197</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>997</th>\n",
-       "      <td>2.416878</td>\n",
-       "      <td>4.689798</td>\n",
-       "      <td>6.409599</td>\n",
-       "      <td>7.650164</td>\n",
-       "      <td>8.515074</td>\n",
-       "      <td>9.101508</td>\n",
-       "      <td>9.488851</td>\n",
-       "      <td>9.737583</td>\n",
-       "      <td>9.891902</td>\n",
-       "      <td>9.983206</td>\n",
+       "      <td>14.752402</td>\n",
+       "      <td>14.165799</td>\n",
+       "      <td>13.584948</td>\n",
+       "      <td>13.038435</td>\n",
+       "      <td>12.542245</td>\n",
+       "      <td>12.103274</td>\n",
+       "      <td>11.722401</td>\n",
+       "      <td>11.396828</td>\n",
+       "      <td>11.121754</td>\n",
+       "      <td>10.891488</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>998</th>\n",
-       "      <td>4.633381</td>\n",
-       "      <td>6.555164</td>\n",
-       "      <td>8.072909</td>\n",
-       "      <td>9.153335</td>\n",
-       "      <td>9.864477</td>\n",
-       "      <td>10.296675</td>\n",
-       "      <td>10.532205</td>\n",
-       "      <td>10.636352</td>\n",
-       "      <td>10.657221</td>\n",
-       "      <td>10.628546</td>\n",
+       "      <td>15.530046</td>\n",
+       "      <td>14.262848</td>\n",
+       "      <td>13.244627</td>\n",
+       "      <td>12.449407</td>\n",
+       "      <td>11.837882</td>\n",
+       "      <td>11.371370</td>\n",
+       "      <td>11.016716</td>\n",
+       "      <td>10.747264</td>\n",
+       "      <td>10.542302</td>\n",
+       "      <td>10.386034</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>999</th>\n",
-       "      <td>10.573900</td>\n",
-       "      <td>12.317818</td>\n",
-       "      <td>13.245648</td>\n",
-       "      <td>13.612879</td>\n",
-       "      <td>13.620680</td>\n",
-       "      <td>13.415346</td>\n",
-       "      <td>13.097526</td>\n",
-       "      <td>12.733225</td>\n",
-       "      <td>12.363590</td>\n",
-       "      <td>12.012647</td>\n",
+       "      <td>11.481761</td>\n",
+       "      <td>10.462828</td>\n",
+       "      <td>10.056773</td>\n",
+       "      <td>9.917344</td>\n",
+       "      <td>9.885242</td>\n",
+       "      <td>9.890079</td>\n",
+       "      <td>9.902827</td>\n",
+       "      <td>9.913039</td>\n",
+       "      <td>9.918147</td>\n",
+       "      <td>9.918574</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "          h.01       h.02       h.03       h.04       h.05       h.06  \\\n",
-       "990   0.197255   0.766334   1.577107   2.505601   3.459461   4.377620   \n",
-       "991  -1.890108  -1.296009  -0.321045   0.826688   2.010886   3.148451   \n",
-       "992  -8.507089  -7.755860  -6.044790  -4.011246  -1.975703  -0.089727   \n",
-       "993 -12.095946 -10.869621  -8.890602  -6.627904  -4.352285  -2.211499   \n",
-       "994 -10.415586  -8.143577  -5.654791  -3.258179  -1.089347   0.805444   \n",
-       "995  -1.921109   1.075917   3.299730   4.950490   6.178127   7.093677   \n",
-       "996  -0.935776   1.954178   4.178160   5.832262   7.036522   7.901080   \n",
-       "997   2.416878   4.689798   6.409599   7.650164   8.515074   9.101508   \n",
-       "998   4.633381   6.555164   8.072909   9.153335   9.864477  10.296675   \n",
-       "999  10.573900  12.317818  13.245648  13.612879  13.620680  13.415346   \n",
+       "          h.01       h.02       h.03       h.04       h.05       h.06   \n",
+       "990  17.638678  19.057943  19.305712  18.876070  18.090794  17.153279  \\\n",
+       "991  20.307394  20.095492  19.176459  18.008276  16.816890  15.707369   \n",
+       "992  18.286380  16.896235  15.586099  14.454254  13.517502  12.759810   \n",
+       "993  24.177049  22.846561  20.903832  18.944579  17.198290  15.728217   \n",
+       "994  21.476395  19.945167  18.325031  16.830889  15.536242  14.450638   \n",
+       "995  13.887372  11.827018  10.504285   9.701208   9.250338   9.029847   \n",
+       "996  12.091224  10.738291   9.893515   9.402421   9.147641   9.044580   \n",
+       "997  14.752402  14.165799  13.584948  13.038435  12.542245  12.103274   \n",
+       "998  15.530046  14.262848  13.244627  12.449407  11.837882  11.371370   \n",
+       "999  11.481761  10.462828  10.056773   9.917344   9.885242   9.890079   \n",
        "\n",
        "          h.07       h.08       h.09       h.10  \n",
-       "990   5.224185   5.981486   6.644090   7.214210  \n",
-       "991   4.193448   5.124665   5.936575   6.633031  \n",
-       "992   1.584868   3.033252   4.264283   5.297785  \n",
-       "993  -0.277740   1.422307   2.888448   4.135017  \n",
-       "994   2.424787   3.788488   4.925033   5.865051  \n",
-       "995   7.778853   8.293623   8.681971   8.976192  \n",
-       "996   8.515904   8.950270   9.255725   9.469814  \n",
-       "997   9.488851   9.737583   9.891902   9.983206  \n",
-       "998  10.532205  10.636352  10.657221  10.628546  \n",
-       "999  13.097526  12.733225  12.363590  12.012647  "
+       "990  16.187233  15.263806  14.420245  13.672511  \n",
+       "991  14.722133  13.871672  13.150830  12.547444  \n",
+       "992  12.154606  11.674450  11.294780  10.994971  \n",
+       "993  14.527010  13.561360  12.791967  12.181810  \n",
+       "994  13.557059  12.829543  12.241130  11.767149  \n",
+       "995   8.954356   8.965773   9.025626   9.109140  \n",
+       "996   9.034509   9.077894   9.148903   9.231197  \n",
+       "997  11.722401  11.396828  11.121754  10.891488  \n",
+       "998  11.016716  10.747264  10.542302  10.386034  \n",
+       "999   9.902827   9.913039   9.918147   9.918574  "
       ]
      },
      "execution_count": 8,
@@ -623,9 +622,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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TUbmtBYc3N6G5ygNBALImJGP6WQXIn+LigkJEREREY1h0mZzjHTYMdK82HB1au3pfowNt0KehozmAYJUHQd/Re19NVqnHkOGoocRRoTU60LL3dWCOK4ju27cPt956K4weA79ff/11TJkyBddddx0AoLi4GOvWrcMrr7zCIDpG6ZqOur3tOLS5CbUVbuiageQsG2acU4CiGemwn8DEeiIiIiKink5mteFI72s/oTW2F1ZBR5MfQa8KJXCM3tc+hgzH9MJGh1u7DLOVva9djiuIrl+/HkuWLMFNN92EWbNmRZ4/99xzsWzZsph9BUFAIBAY1EbSyGYYBtxHvDi0uRmVW5sR8Kiw2GVMmJ+JktkZcOXa+WkREREREcVNTO9r6on0vuox81uP1gvb0ehDs09FwKvB0I/e+3r0hZukHgs3yZBMidf7elxB9PLLL+/z+XHjxsU8bmpqwmuvvYbvf//7A28ZjXi+jiAOb2nG4U3NaGvwQZQE5E12oXhWOnInpXClMyIiIiIa1URJhNUpnnjva0DvY66r2ivQBjwKOhp9CPjUSMnCPtshC70WbkorcKDs1NzB+DbjYtBWzfV6vfj+97+PrKysfoNrNEkS4HLZB+v0Q0KSxBHfxuGmBjUc2tqMvV/UobqiFYYBZJUkYellEzF+TiasjuP/T0rDi9czJRJez5RoeE1TIuH1fHI0VUfAq8LvURDwKPB71PCtEn6ue1tnSwCCgVH9cx6UINrR0YHvfe97qK6uxpNPPgmbzXbM12iaAbfbOxinHzIul33Et3E4GLqBpspOHNrchOrtrVACGuwpZpSdmoviWelIzgz9e/sVBX63EufWUn94PVMi4fVMiYbXNCUSXs8DI9oE2Gxm2DKOvbbKSP85Z2Ym9bttwEG0paUF3/nOd9DU1IS///3vKCoqGughaYTobPHj0KZmHN7SDE9rALJZRMHUVJTMykBmSRIEllwhIiIiIqKTMKAgGgwGcd1116G1tRVPPPEEQ2gCCPpUVO1oxeFNTWiq7AQEIHt8MqaekYeC8lSWXCEiIiIiogEbUBB99NFHsWPHDvzlL3+BzWZDY2MjAMBkMsHlcg1G+2gY6JqB+v1tOLSpGTUVrdBVA0kZVkxfUYDimWmwpxz/ymJERERERETHMqAg+sYbb0BVVXzrW9+KeX7OnDl46qmnBnJoGgbuOi8ObWpC5dZm+DtVmO0yxs/NRMmsdKTmOxJuiWgiIiIiIhoZTjiI7t69O3L/+eefH9TG0NDzdyqhkiubm+CuC5VcyZ2UgpJZGciZlAJJZskVIiIiIiIaWoNWvoVGLk3RUVvhxqHNTajb1wZDB9LyHZh9QRGKpqXBwpIrREREREQ0jBhEE5RhGGiu7MShzc2o2t4Cxa/BlmzC5CU5KJmVgeSsY5fYISIiIiIiGgoMogmmszWAw5ubcHhzMzpbApBMIgrKU1EyOx2Z45IhsuQKERERERHFGYNoAlD8Gqp2tODw5mY0HuoAAGSNS0L5aXnIL0+FycKSK0RERERENHIwiI5Sum6gYX87Dm1qQs2uVmiqgaR0K6admY/iWelwuFhyhYiIiIiIRiYG0VGmrd6LQ5ubcXhLM/wdCsw2CSVzMlAyKwNpBSy5QkREREREIx+D6Cjg71RQua0Zhzc3o7XWC0HsKrmSjtzJLpZcISIiIiKiUYVBdITS1FDJlcObm3FkbxsM3UBqnh2zzitC0fQ0WJ0suUJERERERKMTg+gIYhgGWqo9OLS5CVXbWhD0abAlmTBpcTZKZqUjJdse7yYSERERERENGIPoCOBxB3B4c2jobUezH5JJRP4UF0pmZyBrPEuuEBERERFRYmEQjRMloKF6RysOb25Cw8FQyZXMkiSULctBQXkaTFaWXCEiIiIiosTEIDqMdN1Aw4F2HN7cjOqdrdAUHc40C6aekY+SWelwpLLkChERERERJT4G0WHQ3uDDoc1NOLylGb52BSarhOJZ6SiZlY70QidLrhARERER0ZjCIDpEAh4FldtacGhzE1prvBBEIKc0BbPOzUDeZBckE0uuEBERERHR2MQgOog0VceRPW04vLkJR/a0QdcMuHLsmHluIYpnpLPkChERERERERhEB8wwDLTWeHBoczMqtzYj6NNgdZpQekoWSmZlwJXDkitERERERETRGERPkrctVHLl0OZmdDT5IckC8qakomRWOrInpECUOO+TiIiIiIioLwyiJ0AJaKjZ1YpDm5rRcLAdMICMYicmLylBwbRUmK38cRIRERERER0Lk9MxGLqBhkMdOLy5CdU7WqEGdThSLZh6Wh6KZ6XDmWaNdxOJiIiIxgRD0wBVhaEqgKJG3VdgqOHHitL9vKrGbtM0wDBCXwjfGgjf6t2PYcDo2s8wQifX9e770dsMI/SSyPboY4Yf6933jehzdrVB79mmHu3Q9d7n7Xqs65FzxrY5fIzI9t7nDd3Ve52z1/d3jO09z+mRRaiaDggiIIYX6BRFQBAiX0LM8yIghO9DgCB27Rd+Xgi/VhQi9wVRACD0Om5oPzFUlUKIPk7X60OPu1/fY5/oY3S1r7/vIbrdfbRV6Pk9iLH7CFHnij5v9LmEvr6H8PctZWVBTM8YwP+o+GIQ7UdHsx97PqrHns/r4G0LwmSRUDg9DSWzM5BRxJIrRERENHoZhgFoWjikKT0CXCi8HTPkRQfByDYFhhp73NBzKqD03Fft8brwPlo4PCoKoMW+LhLIEkV08IgOQRDCgaUr7HQ/Dm0TYkNP12NEB5eeX4h6rdAj5MSes9cXuvaP3i6i7zYJMDSE/j27gm5X0A6HXkPvCuh67D7h52ICtaHHBmrdgBF1P2afrlCtGzBiAnZUmI/+QGGUE1ypSH/lzXg346QxiPZB1wy8+6edUPwasktTMOPsAuRNSYXMkitERER0EgxNg+H3wfB6o748MHyh59pNgK/N03cQ7DMk9t8DaCih8NfX67rDoTq037AsAyYTBNkEyDIEkwzIJghy6DbmOYsFcDohynL4eVPUPiZAkkK3JhMEqeu4ctQ5wscxhV8vh/btvi+H9pFkCJLUR8hCVCCMDWNC1+PIth5hres5sWvfPkJfH0Ez0Ts0XC473G5vvJtxVJGe5n5CrxEJut3hNiZMRwJudKiOCr0xPclRYTrqOaPXcREO5Xq44/noYVrMzY3fD3AQMIj2QZQELLtqMnIKkhE0tHg3h4iIiIaZoWnhkOjpDo6+vkNkZJ+ux77ox6H78PuPer7O/jaIYneAM8mRYBcdxrqCmyDLgN0CoedzUa+LDXnhcBYT8rrCX/gc0UGwV8jrEQTDjyFJCR+0aPQTBAGQpP63D2NbxioG0X6k5TtgT7EgOMI/zSEiIqLQMMCYYNhfiIwOkz5v30HS6wECgeM7sSBAsNkh2GwQ7PbwlwNCZhbErvt2O2CzQbA7ws/ZAVvoVgzfT8lIQYdXBaJ79eRwDx4RUQJiECUiIqJhFwqO3tihqr6eobC/3sjejxE8weBoj/qy2SFm50Q9dkCw28L7OcLP2brvh18j2O2A1dq96MoAmFx2iPzwm4jGEAZRIiIiOiZDVaNC4tFCYX9DVqNDpe/4g6Mo9hECbRBTcrsDZR/BMqZ3MtJb6QgFRw4bJSKKOwZRIiKiMcjweqG3tkBvbg7dtjRDb2mB0drc/VxrKwyPB4bPCwSDx3dgUeyzZ1HMzTtm72KfwdFiYXAkIkpADKJEREQJwvD5okJlV7DsHTb11hbA5+t9AEGAkOKCmJYOMS0Nprx8CA5nvyGxr95ImBkciYjo2E4oiAaDQaxatQp33nknFi9eDACoqanB3XffjY0bNyI3Nxd33HEHli9fPiSNJSIiGmuMgD8UHo8WLFuaYbS0hHou+xAKl2kQ09JhmjoNQmpaJGyGbkP3hRRXaOVTIiKiIXbcf20CgQBuvfVW7N27N/KcYRi44YYbMGHCBDz77LN477338IMf/ACvvvoqCgsLh6TBREREo50RCISDZHeI1FubowJnd+g0PJ4+jyEkJ3f3XE4pjwqX3QFTSEuD6EpluCQiohHnuP4y7du3D7feemuo6GqUzz77DAcPHsQTTzwBp9OJ0tJSrF27Fs8++yxuueWWIWkwERHRSGQEg9BbW8PBsnePpd7SEnne6Oy7aqSQlBwJkfKksqgey+5bITUNYmpaqK4jERHRKHVcQXT9+vVYsmQJbrrpJsyaNSvy/JYtW1BeXg6n0xl5bu7cuVi/fv2gN5SIiGi4GYoSCo4xw2F7DIkNP2d0tPd5DMHpDPVOpqZBmjgJptS07mCZmgYhPR1iajrE1FQIZvMwf4dERETxcVxB9PLLL+/z+cbGRmRlZcU8l56ejrq6uoG3jIiIaAgYqgrd3QqjV7CMXjU2NFTWaO8nXDocoZ7J9HRI48bDNHd+KFympoeDZWib6EqFYLEM83dIREQnQjd0qLoKzdCgGSpUXYVqhL40XQvd19XYbZHHoe3R2zRD67V/9HF67d9zW4/z93eeYmcJHpz//+L94ztpA5o04vP5YOoxNMhsNkNRlGO+VpIEuFz2gZx+yEmSOOLbSHS8eD1TIul5PRuqCq21FVpzM9SmJmjNzT3uh27V5mbora19HlOw2yFnZEBKT4d5UinktIWQ0tMhhZ/r2ialp0O0WofrW6Uxgr+jabQyDANBPYigFkBACyKgBdDe2YgAgqHApEeHNyXyuCusRQe7no+Vrv372d7/sZRjHjv6sW7ow/bzkkUZsiCHbkVTj8dy1GMJsijDIpkgi7Ye20JfE1JKR/XvjQEFUYvFgs4e81yCwSCsx/EHWtMMuN19r+43Urhc9hHfRqLjxeuZRjPd64FeVQm18jC0qkrIjXXw1zV0D49tcwM91jEAANhsod7JtHSIeQUwT5vZPc+yx6qxQj9/uwwAavgLAODXAT//L9Hg4u9oGgjDMKAZGoJ6AAEtEA6GQQT17vsBPYBgeFtkn3CA7Lofer57v8jroo+n9dhXP84awydJEqRI+JKE7jAmiVLosSD12mYSLLBKDshy722h18a+Tgrf794We57Y18k99o06R/T+Uc/J4f1EQRr08lYj/fdGZmZSv9sGFESzs7NRUVER81xTUxMyMzMHclgiIhqDDFWFXlsDraoSalVlJHjqVZXQm5u6dxQEyLm5QGoapPx8mKZPhxCebxmzamxqWqiuJRHRMNEMDYquRIXBQCS8BaJCXHegC8SEwq77SkwADMS+to/9g1oAOgbWq2cRLTBLZpijbrues4gWJJmSYRbNMItmWCRL6L4U3kc0wxTezyyZ4XI6EfBpfYSx6AAZ6vWLCYk9w+YQBDcaOQYURGfOnIk//elP8Hq9sIf/2G/YsCFmQSMiIqIuhmHAaGmGVlkJraoSWlWoh1OrPAyttgbQtMi+QooLUmERTAsXQSosglRUFLrNK0BqduqI/xSYiOIvqAXh07zwqB74VC98mj+qt6+PQNdXr2E4FCoxPYvdIVKJOoZqqMdu1FGYRFM47HWFwajQJ1qQYnb1EQa79w+Fwu77pl7H6LofFTpFM0yiaVADH3v46XgMKIguWLAAeXl5uOOOO3DTTTfh/fffx5YtW3D//fcPVvuIiGgUMrxeaNVVoYAZHTirKmPrYpotkAoKIY0vhfm0M8KBsxhSYRHE5JT4fQNEFDdBLQiv6oFX88KnhkKkV/WGnut5q3nD9/vefjLBUIQY6unrEd5M4fsO2YFUc2p3oAv3DHaFQktUiDRH7RN9PHPPHsXw/qIgDsFPlGhkGlAQlSQJv//973HXXXdh1apVKCoqwiOPPIKCgoLBah8REY1QhqpCr6/rDpuV3WFTb2zo3lEQIGbnQCosguWc87rDZlExxKxsCCLfeBGNdtHhsTsIDm14tEo2OGQHbLIdDtkOm2xHrj0XNske9XzsrU2yhnsDe/YQdodISRzQ22MiOk6CYfS1usPQUxRtxHfZc1gBJRJez3QyDMOA4W6NDZqVh6FVVUGrqQLU7jeMQlJy9/DZwuLuwFlQAMEyuKvM8nqmRBOPa7rv8NjzdujDo0N2HDU82iU7HKbYfWyyDZIgDfFPiE4Wf0dTlyFbrIiIiBKD4fdDq67sETjDQ2k7O7p3NJkg5RdAKi6GeempMYFTSEnhohJEQ2ykhMf+eh7tsh122cHwSETHxCBKRDRGGJoWGkpb1TNwHobeUB+zr5iVBamwGJazzo4Mo5UKiyBm50CQ+EaS6GQFtADcwVa0Bd1wB93h+21QJT9aO9sYHono2DQFguqFIZkB2Rbv1pw0BlEiogSju9095myGb2uqgWB3vTfB4YBUVAzTrDlRq9IWQyoohGAbvX/YiIaLYRjwaV64g+5QsAy4Y0JmWzhodt93w6/5+j3e8YTHSGhkeCQa+TQFguKBoHohKN7QfcUb9eWBoHgA1Re1LXyr9tw/6n64dqvmzEfLVZ/H+Zs8eQyiRESjkBEIhFal7SNwGu3t3TvKcmgobWERzKcsjlmVVkhN41BaoiiGYaBT7YA70HeIbAs/jn5O0YN9HsskmuEyu5BidsFldqHQUYgUc2rMcy5zKlLCj/MzMtHRHhjm75iIAABaMBz0fFHBsZ8QqHiAyHZfJEwKfYVJXTnuJhiCCMPkgGGyw5Dt4fsOGNZU6EkFoedN4efD29X0siH8oQw9BlEiohHK0HXoDfXdYTN6Vdr6OiBqrTkxIxNSUREsp58ZM29TzMmFIPNXPY1NmqGhI9jeb+9kW6/HbmiG1uexrJItEiLTLemYkFQaCZQp4VDpsnSHTJtkP6EPeiSRPZhER2UYgB7sIxxGh8DYHkccrXdRjeqZPKHAKHUHxq5gaLJDt6bBSCoEejwfCo627mDZtV22xzyGZAHG2IfDfHdCRBRnekd77xIolZXQqquAYHcPiWCzQyoqgmnadEjnXRBZnVYsLIRod8TxOyAaHoquoC0qOMYMgw30fq5DaYeBvosDOOWkUIi0uJBrz0OZqzyqhzIlFCyjQqZFsgzzd0s0ShkGoPoh+FshBPvpXew1VNUDxPQu9hy+Gg6T+vHXhTVEuUcYDAU+3Z4Bw1QceYyYUBnVExkJjz0Co2gec4FxqDCIEhENAyMYhFZT3T1fs7J7wSCjzd29oyRBzM0Lzd2cvwByUTHEwiLIhcUQ0tM5lJYSSp8L9wT6n1/pUTv7PI4AAcnmlEhwLEka1+8w2K7nZNaKJOqfrkEItkMItEMMtEEItEMIuCEG2yH420LPBdshBML3w/tE9tWDyDjOUxmiGYapd4+hbs/q1fMIuY/A2PXaXj2M5iH9EdHA8bcwEdEgMQwDekNDd9iM6uHU644Auh7ZV0hLD83bXHZazLxNKS+fQ2lpVBrMhXskQYoJjZNSJh81WCaZk7k4D1E0wwA0f1SIbIMYDpOx4bINYsANIdgO0R8VLoMdRz+8IMGwpEC3pMCwJMOwuKAlFcAIP7Ykp8OrmfoJj7FzHSGZhumHQiMN3+0QEZ0kIxCAursCyrYtULdvhbJta2zvptUKqbAIclk5pLPP7V6ZtqAIotMZt3YTHQ/DMOBRO9EaaB3wwj1m0RwTHAsdRT3mV0YFS4sLTjmJvf9Eht4dFsM9kUKw7ejhsitQBtoiK6v2e3jZDt2aEgqU5pRwkEyOCpddQTMl6vnQvjDZjzo81eSyw+/2DvZPhBIMgygR0XHSW5qhbN8KddvW0O3uCkAJLXAgFhTCvGgJ5PKp3TU3M7P4ZppGLMMw0K60o953BHW+OtR7j6Cu676vDvW+I/Conj5fa5PskRCZYcnAhKRSuCypUaHSFenBdJldsEo2/l+gsUnt2SvZNYz1WOGyDUKwA0I/c5yBrl7J5KiwmALNmdcjOLr6DpfmZPZEUtwxiBIR9cHQdWgHD4SD5xYo27dBr6kObTSZIE+eAttXvgp52gyYps+AmJoW3wYT9WAYBtqCbtT5jqDeV4c6X13ovrf7sU+L7bGwy3bk2PKQbcvBzLRZyLLlIM2S1itYmrlwD40Vhh4KhNEBMdAzOPYIl9HzJbWjl+QxZFtMkNSdedDSyyJh0bC6oJuT+wyXhsnBRXNoVGMQJSICYHi9UHZuh7p9W2io7c7tMDpDC6MIqWkwTZsO+aJVME2bAXlyGQQzF0Gg+DIMA63BVtR3Bc0+ejT9mj/mNU45Cdm2HOQ58jE7Yx5ybLnIseUg25aDHHsuh8RSYjIMCMEOiL4mCP5WiH531EI7PcNlW8wwVyHQfoxeSTEUGKOGseqOnO77PYe2xoTLZC6oQ72ougFPQEVHQEVn+LYjoKHT33W/+/mcZCuuX1IS7yafNAZRIhpzQosK1Yd6OreF5nZq+/eGFhMSBEjjxsNyxgrI02fANG0GxPwCvjmnYacbOloDLVE9mkdQ762LeRzsMQcs2ZSMLFsOCh1FmJ+xADn23FDItIVunaakOH03REMg6IHoa4To7ePL1wTR2wDRG7o9Ws+kIVtjh7E6c6ClTYpZiEe3RPdKuiL3DZOTvZIUQ9UNdEaHSH9soOwIqH2Gys6Ahs6ACk+w71rG0ZwWCUkWGVNz9GPuO5IxiBJRwjNUFeq+Pd1zO7dthd7YENpos8E0ZSrM3/gWTNNnQi6fBjGJb9Zp6OmGjuZAc9RQ2SORr3pfPep9db0W/0kxu5BtzUFJ0jgszFocCZhdtw4T68nSKKf6eoTKcKCMCZahbYLaezEcAwIMWzp0ewZ0exaUlHHQ7Znhrwzo1vQe4TIZ4FBziqJqOjrDgTEmLPqjg6PW5/OdAQ1e5ehBUgDgtMhIskihW6uMQpct/Fzoy2kNbU+yyHCGv7q22c0SJDExPvxgECWihKN3tHcPsd2+DcquHYA/NERRzMqGPGMWTNOmwzR9JqQJpSyXQkNCMzQ0+5v67dFs8NdD0ZWY16SaU5Fly8GEpFIsyT41Mmw2OzyE1ibb4/TdEA2AFogJkD17MYXosNlP2RDdmgrdFgqUSvYs6PasqICZGdlm2NIA1ogd0xRNjw2LffQ+dodHrVfY9KtH72UUBfQIiBKKUm2hoGiNDY2hoCnFPLabJYjsRQfAIEpEo5xhGNCrKqFEgudWaIcOhjZKEuTSibBecFFobue0GZCys+PbYEoYmq6iMdCIem9dHz2adWjw1UMzYj8ZTzWnIceei0kpk3FqzmndPZr2XGRZs2GTbXH6bohOkKaEeymjAmWP4bCR7YG2Pg+hW1LCITIDaub0SC+mYcvoETLTOZdyDAmqelQP47GHtHb4taj9VASOESQlAZGeyK5wmOGww9nVQ9n11StUSkiyyrCbJE7XGSQMokQ0qoRqd+6Csm1rr9qdgjMJ8rTpsKw4J7Sa7ZSpEGx8Y08nR9VVNPobukNmeAht1+MGfwP0HkEz3ZKBbFsOpqRMxem5Z4WHzYZ6NLNtObBwCCCNZLoGwdfcZ69lr7mX/ta+D2Fyhnom7ZnQ0iZDKVgS6r20ZcT2YtrSAdk6zN8gDQdF09HYEUBNi7ePeZJ9D2mNLMhzPEFSFMLhsDs4ZiXZe4XG3kNdQ182k8ggOUIwiBLRiKa3NEeFzi2h2p2qCqC7dqdp+kzI06dDKh4HQRTj3GIaLRRdQUN4LmZ0j2bXCrRN/kbo6H5DJEBAujUDObZcTEudHgmXObbccI9mFsua0Mhj6BB8Lce1qI/ga+5zhVhDtkVCpOYaByVvYVSwzIgZHgsTP/xLNH5Fg9unoNWnoNWrhO57Q4/d3ujng2j1KegMHH2OpNwVJK3dQ1uzkyzdwTGqJ9IZNU+ya5tVZpBMFAyiRDRiGJoG7dDByBBbZdtW6LU1oY0mE+SyctguvRzy9JkwTZvO2p10VEEtiAZ/fY8ezdBCQHW+UNA0ot50ixCRYc0M1dBMn91rIaAsWzZMIgvA0whgGBAC7j5CZfS8y/BCP74mCEbvYGBIlkiA1JKKoGTPjQyN7b4Nh0szF8FKFIZhwKto/QfKqPtubyhY+pS+eyglUUCqzYRUuwkumwlTkpMi93PTHJB1PaYnsitUWhgkKYxBlIjiRvd6oO7c0b2a7Y5tMDweAOHandNnQL74y6HbSazdSbGCWiASKuvD9TPrvEdQ7w/dtgSaY4OmICHTmokcWy7mpM8LLwIUmp+ZY8tFpjULMhc5oXgxjFAtyz56LYVIwOyae9kMocdCVwBgiKbuEOnMDc+77A6WRtc2WwYMcxLLjiQAwzDQGdDCvZLB2HDZI2i2eINo8/c/9NUsCXDZTEi1m5FqN6E41RYJltGBM9VuRqrNBKel/7mSLpcdbnfvVY2JovEvLhENi+Oq3XnW2aG5ndNnQszL5yemBCA0hLay8xD2t+/D/o692N++D5WeQ2jyN8XsJwkSsqzZyLbnYH7mwu5hs7ZcZNtzkGnJhMSgSfFiGBC8jZA6qiC1V0Jqr4TYXgmpvQpSexVEbwMy+qh1aQhSKEiGV4XV0st6LOTT3YtpWFwMl6Ocbhho96nh3slgj6GvvUOm26dA1XsPpwYAm0lEqs0El92MdIcZEzIdoUBpM8Fl7xkuTVyEh4Yd/yIT0ZBg7U46Ga2BlnDg3If97XtxoGMfDnceiqw+axbNGJc0HovyFiNDCoXOrrCZbs2AJEhx/g5oLBOCHRDbu4JmVThohsNmRxUE1Rezv2bPgp5cCCVnLkzphfBJqT3mXmbBsLoAgXPfRytVN9Dmi+2VPFqvZZtfQT+5Eg6zhDS7CS6bGbnJVpRnJ/UZKFNtoftWE38f0sjGIEpEg0Jvb4O6Y3vftTuzc0K1O6fPgGnaDNbuJKi6iirP4R6hcz9aAs2RfTKsmRifVIqFmYsxIbkUE5InosBeAEmUOeyL4kMLQuyoiQqXlVHBs7LXSrK6yQk9uRCaaxyCRcuhJRdCTy6GllwILakwZmEfl8sOH6/pEU/R9H7mVQZ79Vy6fQra/Gq/x0qxypHwWJxqw6z85EgPZs+eS5fNBLPMDyQosfCdIBGdsGPW7pw4KVS7c3q4dmcWa3eOZW3BNuxv34v9HftwIBw8D3cehBKe42YSTSh2lmB+xkKMTy7FhKRSTEguRYrZFd+G09hj6KE5mFHhMrpnU/TUQTC659cZoglaUj705CIEJpwfCppJRdBSiqAlF3Go7CjgV7RIgOxzFdiuYBl+zhPse0VYUQBSrKHgmGY3YWKmIxIyXTZzd09l+DbFZoIs8tqgsY1BlIiO6di1O2eEandOnwlTWTlrd45RmqGh2lMVCp3t+3CgIxQ6m/yNkX1SzWmYkFyKuSWXRQJnoaOYiwTRsBEC7T3mZ4bvd4Tmago95mlqjmzoyUVQ8k6BlhwKmHpyYejWkQOIHP440hiGgXa/iobOABo6gqjvDKChI/TVEgmaoZDp72fhnp4rwpYnW3sPf7WbkGoL9V4m22SI/NCB6ITwLz8R9cLanXQsnUpHZFjtgfAiQgc7DiCoBwGEFg4qdpZgVtocTEieiAlJpRifXIo0C0vu0BDTApA6aiC2He6xMFA4dAbaYnbXzcmhgJk6EcHiM8PDZ0OBU0vKB2R+sDaS6IaBVq8SDpkB1HcEI/cbOgNo7AyiviPQa2VYAUC6w4wMhxmuAa4IS0SDY1CCaFtbG+69916sWbMGFosFF110EW655RZIEj8lJBrpjlq702yGPHkKa3eOYbqho8ZTHend3N8ems/Z4K+P7JNidmFCUikuKl6F8eFeziJHCcwSy+3QEDB0iJ66XosBhYLmYYieeghRZXsM0QwtuQB6ciEC2bOhJRWGwmZKMbSkwtBiQDQiqLqBZk8wEirrO0I9mj2DZs9VYiVRQJbTjCynBZMynVg6Pg3ZSRZkOS3ISrIgyxkKoLLED02JRpJBCaL33HMPGhsb8fjjj6OlpQW33XYbXC4XrrnmmsE4PBENItbupP54FA8OduyPlEjZ37EPBzv2w6+FF50SJBQ6ijAtdQbGJ0/AhKSJmJBcinRLBnsOaFAJfnev4bPdCwNVQwj3vAOAAQG6MwdaUhGUgqV9DJ/N5qqzI0BQ1dHoCQfL6KDZ2f242RPstWKsRRZDITPJgln5Kch0WpCdZO4OmUkWpNlNHBZLNAoNShD98MMP8Ytf/AKTJk0CAFxwwQX47LPPGESJRgDD70fwi0/RuG0zOjdsZO1Ogm7oqPMdifRudg2vPeKrjezjlJMwIbkU5xV+KRI4S5zjYJYscWw5JQzVB6m9OhQ2w3MzpfbD4aBZBTHYHrO7bkmBllwMLX0KguPODt3vGkKblA/wuowrn6JFwmRXD2Z9R1cvZihotvqUXq+zm6RQz2WSGePTUyO9l1lRvZkpVpl/l4gS1KAEUZfLhZdffhlLlixBe3s7PvroI5x11lmDcWgiOgl6ZyeCaz9GcM37CH62FggEINhskKdMhfkbV4d6O1m7c0zwqb5wL2d36DzYsR9eNVQmQoCAfEchJqWU4dzCCyKhM9OaxTd/dPJ0LTx8tjK2lmbXXE1vfczuhmQJlTRJLoKaOw9aUlH4cTH05EIYluQ4fSNjm2EY6AxoqO8MoDGy4E/U4j/h4NkR6F2iJMUqRwJleY4zZphs1/NOC5cqIRrLBMMw+imbe/zWrFmD22+/HW1tbdB1Haeccgr++te/Qj5KnUBF0UZ8DTjWqaPRRG9tRfDjDxFY8wGU9V8AqgoxPQPmZafBvOw0ZC5fgjZP70+kKTEYhoF6f11otdqoRYRqvNUwwvPlHLID48OLBoVWrJ2IcUnjYZWscW79iePv5zgzDAj+1qj5mYfDdTWrILUdhthZC0Hv/n1jCCJ0R27sQkBdQ2eTi6DbM8f88NnhvqYNw4Dbp0SCZVfQrO8aKhsOmj6l96qyaXZTrzmYWUmWyHOZTjOsJq4TMpbxdzR1yczsv9NjUILo448/jvfeew833ngjOjs7ce+99+L000/HXXfd1e9rdF2Hpg341ENKkkRoWt/LehONBErdEXjefRed77wD/8aNgK5Dzi+Ac8VZcJx5FqwzZkRWtOX1nDj8qh/72/ZhT+se7HXvxR73Huxt3YMOpSOyT4GzEJNSJ2GSa1LkNteRlzC9nLyeh4GuAi37IbQeguA+DLgPQwh/wX0YQrAzZnfDlgbDVQK4imC4imG4ioGu25QCgItXHdVgXtOabqCpM4C6dj/q2vyo7wigrs0fehz1XLDHyrKSKIQCZbIFOcnW0FeKNeZ+ptMCszy2PzSgY+PvaOpiOsqHUgMOopWVlVi5ciXee+895OTkAAA+++wzfPvb38aaNWuQkZHR5+vYI0p0crSqSgTWvI/ghx9A3bUDACCNGw/zstNhWX46pNKJfYYNXs+jj2EYaAo0YX/73kiJlP3t+1DtqYKO0B94q2TD+KQJMSVSxieNh112xLn1Q4vX8+AS/K2Qm3ZCbt4FuWknpKadkFv3xtTUNGRraMhsShG0pNieTT25CIbZGcfvYPQ73mta0XQ0hVeW7Vrsp7FHKZOmzgB6ftZvkoTYHsyoxX6ywz2aaXYzJDExPqyi+OLvaOpytB7RAQ/O3759OxwORySEAsC0adOgaRpqa2v7DaJEdHwMw4C2f184fL4P7cB+AIBcNgX2794A8/LTIRcVx7mVNFBBLYjDnQe7S6R07MWB9v1oV7prHubYcjEhuRSn5Z4ZGV6ba8+DOMaHNNIJ0DVIbQdDobNpJ6Tm8K2nrnsXWwbUjHL4Cr4FNWMKtJRx0JKLYNgygATpUR+p/IqGxs7ei/00Rq0w2+IJomcPglUWw4v+WDCvMCVmsZ9sZ2gxIJfNlDAjIogoMQw4iGZlZaG9vR1HjhxBbm4uAGD//tAb5YKCgoEenmhMMnQd6q4dCH74PgJrPoBeUw0IAuQZs+D4wQ9hXnYapOycYx+IRqSWQHNo4aD2fZFFhCo9ldANDQBgES0YlzQBp+Ysj9TlHJ9UCqeJPU50/IRAG+TmXaHeza7ezuaKSC+nIcrQXBOg5C+CL70cakY51PQpMBxZcW554jIMA60+BVWtPlS2+lDl9qGq1Ycqtx8NnQG0envP40+yyMgKlyuZlOXsozfTAqdFYsgkolFnwEF01qxZmDJlCn784x/jjjvugN/vx3//93/joosuQloaC98THS9DVaFs3Yzgh+8j+NGH0BsbAEmCae582K/8BsxLl0FMS493M+kEKLqCys7DkSG1B8ILCLUGWyP7ZFmzMT65FEuyT8WE5IkYn1SKfEcBJIELfdBxMnRIbYdCgTM8tFZu2gmpsyayi25NhZpeDt+0b0LNKIeWPgVq2kSWPRkibT4FVe5w2AwHzspw+PQEtch+kgDkpVhRmGrD3JJUuMwSMqODptMCu5m/C4goMQ3KYkX19fV44IEH8Nlnn8FkMmHlypW47bbbYLX2vxIj54gSAUYwCGXDOgQ+fB/Bj9fAaHMDFgvMCxfBvOx0mBcvgZg0OGULeD0PLU1XUdG2Cztat+FAx37sb9+Hw50HoRqhsgYm0YwS5zhMiFqxdnxSKZLNLEtxMsbq9SwEOyFFhc1IL2e4HI8hiNBcEyK9m1r4VnfkcFjtIOsMqKGA2eJDpbs7cFa1+tDm7y5nIgpATrIVRS4bClNDX13385ItkKXQ0Pqxek1TYuL1TF2GfNXck8EgSmOV4fUi+PmnoRqfaz+B4fVAcDhgXrw0FD4XLoJgsw36eXk9Dy7DMHCo8wA2Nq3HxuYN2NKyMVKbM92SERlOGwqeE1HoKIQksmbeYEn469nQIbZXQQ7P4QyFzgpI7Ycju+iWFKjpU8I9nOGhtWkTAXnwf3+MVT5FixlGG93D2dJjGG12kiUmZBa6bChKtSE/xXpcq8wm/DVNYwqvZ+oypIsVEdGx6R3tCH7ycSh8fv4ZEAxASHHBfPqZsJx2Bkxz5kEws7TBSFfnOxIKnk3rsal5A1qDLQCAfHsBzsw9G7Mz5mFm2iykWjgtgU6A4g0Pqd0VCp7NuyA17YKohMqjGBCgucZByZwO/5SvRno7dWceezkHgV/RUN3mDwXM1tjezcbOYMy+GQ4zClNtOHV8enfvZqoNBSlW1s0kIjpBDKJEQ0RvaUbw4zUIfPAelI3rAU2DmJkF65cugnn56TBNnwlB5n/Bkawt6Mam5g3hXs/1qPWG5tylmtMwJ2Me5qTPw+yMucix5ca5pTQqGAbEjpruXs7mnZCadkFqOwQhvA6qbk6Clj4FgbIvQ00PBU41vQww2ePc+NFN0XTUuP0xIbOrd7O+IxCzCm2qzYTCVBsWFqeiKNyz2dXDyfmaRESDh++CiQaRVncEwTUfILDmfahbt4TeeOYXwPbVK2FedjrkKeUQRJbaGKl8qhdbW7ZgY3Oo13N/x14AgEN2YEbabKwquRSz0+ehxDmOK1TS0ak+yM27I6FTCs/lFAPd5Xi05GKoGeUITF4VCp0ZU6AnFbKX8ySpuoEjbVFhMzykttLtQ127H3pU2kyxyihMtWF2QUrMcNqiVBucFr41IiIaDvxtSzRAauXh0Eq3a96HWrELACCNL4X96mtgXnY6pPETGFpGKEVXUOHeiQ1N67CpeQN2urdDMzSYRBOmpk7Htyd9F3PS52FyShnnd1LfDAOi50hoWG0kcO6E5D4AwdBDu8h2qBlTECi9sHsRofQyGGaW4zlRmm6gviOAylYvKlv9UeVPfKhp80OLSpsOs4SiVBum5STh3ClZMb2bLpspjt8FEREBDKJEJ8wwDGj79iLw4XsIfvg+tEMHAQBy+TTYr/s+LMtOg1RYFOdWUl90Q8eBjn2RBYa2tmyGX/NBgIBJKZNx6bgrMCd9HqalzYBV6n/VbxqjVD/k1r29yqSIAXdkFy2pMNTLOeGC7rmcKcWAwJEQx0s3DDR0BCIhMzpwVrf5oGjdYdNmElHgsmFipgNnTMxAUbhXszDVhlSbiR8CEhGNYAyiRMfB0HWoO7YjuOZ9BD58H/qRWkAUYZo5G9aLVsG87DRIWdnxbib1YBgGar012NQcCp6bmjegLegGABQ6inBO/rmYkzEfs9JnI8nEMioUZhgQvQ2xgbN5F6TWfRCMUA1IQ7ZCTStDYMJ54cBZHurltPA6Oh6GYaDZE4wMo+3ZuxlQ9ci+FllEgcuK4jQbTp2QFunVLEq1IcNhZtgkIhqlGESJ+mGoKpTNGxFc8wGCaz6A3twEyDJM8xbC/s2rYV6yDGJqarybST20BFqwKby40Mbm9aj31QEIlVRZmLkotMBQ+lxk2rLi3FIaEbQgpNZ93TU5w4sIib7m7l2ceaFeznHnhEqlZJRDSy4GRC5cczSGYcDtUyKlT3oGTq+iRfaVRQEFLisKXTYsKHZFhtEWpdqQlWSByLBJRJRwGESJohiBAILrvwjN+fxkDYz2dsBqhXnhYpiXnwbzoqUQnZzXNZJ4FA+2tGzCxub12NS0Hgc7DwAAnHISZqXPwVfHfQ1zMuai0FHMnpMxTvA2hRcP2tW9iFDrPgh6qB6kIVmgpk1GoOSs7rqc6WUwrPzA6Wja/Uqk7EllS9SKtG4fOgPdYVMSgLwUKwpTbZiVnxwZQluUakNOkhWSyP+fRERjCYMojXm61wPls7UIfPgBlE8/geHzQnA6YV58KszLT4d5wSkQrJwvOFIEtSB2urdHVrataNsF3dBgFs2YnjoTZ+afjbnp81GaMgmSwB6rMUlTILn3x/RwSk27IHkbundxZENNL0ew+IzuBYRc4wEuStWnzoAa1avpi7nf5lcj+wkAcpMtKEq1Y9qU5JgVafOSLZAlzpUlIqIQ/sWlMUlvb0Pwk49CPZ/rPgeCQQipabCsOAfmZafBNGceBBNXVRwJNEPDvrY9kaG221u2IqAHIELEZNcUXDH+65iTMQ9TXdNglizxbi4NM8HfGjOsVmraCbllDwQ9CAAwRDPUtIlQipbDF+nlnALDlhbnlo9MqqbjQLMXFQ2d2BP+OtzqQ4tXidkvO8mCwlQbzpyUGamxWZRqQ36KFWaZYZOIiI6NQZTGDL2pCYGPQvM9lU0bAE2DmJUN60WrYFl2GuTpMyFI7EGLN8MwUOWpDC0w1LQBm1s2oEPpAACUOMfh/KILMTt9HmamzYbTxGHSY4quQm7YAlPNp5CaNiLtyFZInrruzbZMqBnl8M38dqQup+YqBSR+qNQXv6JhX5MHFfWd2N0Q+trX5ImsSms3SZiY6cCp49NDYTPcu1ngssJq4u9KIiIaGAZRSmhabQ2Caz5AYM0HULdvBQwDUmERbFd8A+blp0GePIXzBkeAJn9jeI7nBmxsXo9Gf2gIZZY1G0uyl0UWGEq3ZsS5pTSsdA1y0w6YataGvmq/gKh0AgCMjMkI5i+GLyOql9OeGecGj1ydATUSNnc3dKKivhOHWrzoKruZYpUxOcuJK+bkY3KWE5OznChMtXGRICIiGjIMopRw1EMHEfzwPQQ+/ADa3t0AAGniJNi//V2Yl58GqWQ8w2ecdSod2Ny8ERvCCwxVeg4DAJJNKZiVPgdfS78KczPmI8+ez3+rscTQITVXwFyzFqaaT2Gq/QxioA0AoKaWIjB5FYL5i6HkL0JKbiE63N44N3hkavEGI2GzK3hWu/2R7ZlOMyZnOXHGxAxMznKiLNuJ7CQL/68REdGwYhClUc8wDGh7KhD48H0E13wA7fAhAIA8bTrsN/wAluWnQ8rLj28jx7iAFsD21q3YGC6rsrdtN3TosEpWzEibhXMLv4Q56XMxIXkiRIHzy8YMw4DUug+mmrUw13wCU81nEP0tAAAtuRiBCedBCQdP3ZET58aOPIZhoL4jEAmdXfM6GzqDkX0KXFZMznLiwmk5kZ7OdIc5jq0mIiIKYRClUcnQNKg7tkXCp153BJAkmGbNgfXLl8F86nJIGRymFy+armJP+25sbFqPDc3rsKN1OxQ9CEmQMMU1FV8v/RZmZ8xFuWsaTCLn740ZhgGx7VA4dH4amusZXslWc+YjWHJmuMdzMfQkfngUTTcMVLX6YobW7m7ojKxYKwpASZodcwtdKMsOBc5JmU4kWflnnoiIRib+haJRw1BVKJs2hIbdfvQhjJYWwGSCaf5C2L91DcxLT4WY4op3M8ckwzBwuPMQNjavw8am9djSsgke1QMAGJ9UiouKLsGcjHmYkTYLdtkR59bScBLbq2Gq+SQ83HYtpM4jAADNng0lfzG8BYsRzF8MPbkY4NBQAKGVaw+2eGOG1u5p8MCrhGpymiQBpRkOnDYxA2XhXs6JmQ4uIERERKMKgyiNaEbAj+AXnyO45n0EP/4IRmcHYLPBfMpiWJafAdMpiyA6uHJqPNT76rCxaT02Na/HpuaNaA40AQBybXlYnnsG5qbPx6z0OUi1sEzGWCJ2HoksLmSu+RRSeyUAQLelI5i3CN65i6HkLwnV7GTwhF/RsL/Jg4qons79TR4EwyvX2kwiJmU6ccHUbEwO93SOT7fDxHqcREQ0yjGI0oijezqhfLoWgQ/fQ/DzTwGfD0JSMsxLl8G8/HSY5y+AYLHGu5ljTluwDZubN2Bj8wZsbFqHGm81AMBldmF2+jzMyZiHOenzkGvPi3NLaTgJ3kaYaz6NhE/ZfQAAoFtSoOSdAu/Ma6DkL4aWNnnMB8/OgIo9jaGwuachNKfzULMX4cyJ5PDKtZfNzo/0dBam2iCJY/vnRkREiYlBlEYE3e1G8JM1CHz4PpT1XwCKAiEtDdazz4X5tDNgmjUHgszLdTj5VB+2t27BhvACQ/vb98KAAZtkx8y0WbiweBXmpM/DuKTxXGBoDBH8rTDVfBpZ2VZuCa1MrZucUPJPgX/q16HkL4aaPgUQx+5Q0dY+Vq6tilq5NsNhRlm2E8tLQ8Nry7KdyOHKtURENIbwnT3Fje7pROCdtxB87x0oWzYBmgYxJxfWVZfCsux0yFOnQZDG7hvZ4abqKircO7GxORQ8d7Zuh2qokAUZ5anTcNXE72BOxnyUpUyBLPJXx1ghBNphqv28u8ezaScEGDBkG5S8BeicvCoUPDOnA2PwuoheuTY6eEavXJuXYkVZlhMXTM2JDK/N4Mq1REQ0xo29dw0Ud+qe3fC/9Dz8b78B+HyQiktg+9o3YVl2OqRJk9kjMEx0Q8fBjgPYGK7luaVlM3yaFwIETEieiC+P+yrmpM/FtNSZsMm2eDeXhkvQA9ORLyKLC8mN2yAYOgzJAiVnHrwLb0MwfzHUrJmANLbClG4YqHb7UVHfgd0NHuxuCN26fQqA0Mq1xal2zCl0hepzZjkxKcuBZCtXhiYiIuqJQZSGheH3I/Du2/C/9DzUXTsAiwWWs86G9aIvQy6bwvA5TI54a7GhaV14gaENcAfdAIACeyHOyj8Hc9LnYlb6XKSYU+LbUBo+qg+mIxsiK9vKDVsg6CoM0QQlew68c38ApWAxlOw5gDx25maruoFDzV5UhMPm7voO7Gn0wBMMrVwriwImZDiwfEJ6pJdzYqYDNq5cS0REdFwYRGlIqQcPwP/yCwi88RqMzk5IJePguPlWWM45D2JSUrybl/AMw8Dutl14u+YNfNH0KWo8NQCANEs65mUsiCwylG3LiXNLadhoAZjqNkaG2prqNkHQgzAECWrWTPhmXYdgwWIoOfMB09joCQ+oOvY1hcJmaPVaD/Y1dkZWrrXKIiZmOnFeeXZkEaHxGVy5loiIaCAYRGnQGcEggmveh+/F56Fu2QTIMiynnQHrxV+GPGMWez+HQZ3vCN6peRNv17yBKk8lTKIZi3IX4ZLiSzEnfT6KnSX8dxgrNAVyw5bIUFvTkXUQtAAMCFAzp8M342oo+Yuh5C2EYU78UkidARV7G8PlUsJDbA82eyIr1yZZZEzOcuDSWfkoC/d0FnHlWiIiokHHIEqDRqutgf/lF+B/7RUY7laIefmwX/d9WM+7AGIqa0kOtU6lE2vq3sdbNf/G1pbNAIAZabNw2fgrsTzndBRkZsPt9sa3kTT0dA1y47ZwHc+1MNV+AUEN/bur6VPgm/p1KAVLQsHTkthDsN1eJbSAUHgRoT2Nnahs9UW2p9lNKMt2YtmENEzOTsLkLAfykq38kIaIiGgYMIjSgBiqiuDaj+F/6XkoX3wGSBLMS06F9aJVMM1bAEHk0LWhpOoq1jV+jrdr38Da+o8Q1IMocBTh6knXYkXeSuTYc+PdRBpqhg6paVd3j2ftZxCDHQAANXUi/FMuRTB/MZS8RTBsifmBkGEYaOgMxtTn3N3QifqOQGSfvGQLJmcn4bzyLJRlhUJnhtMSx1YTERGNbYMSRBVFwcMPP4wXX3wRALBy5UrcddddMJvH1oqKY4nWUA//qy8h8MpL0JsaIWZmwf7t78JywYWQMrPi3byEFj3v8/0j78AddCPF7MJ5hRdiRf5KlKVw8aeEZhiQWvZ093jWfAox4AYAqCklCJReCKVgMYJ5i2A4EvP/YlNnAJ9Wt2PDwWbsDpdLaQ2vXCsAKE6zYVZ+cmjl2mwnJmU6kWLjyrVEREQjyaAE0Yceegjvvvsufv/730MQBNx222343e9+h1tuuWUwDk8jhKHrUNZ9Dv+LzyG49mPAMGBauAiOW38E8ymLIcjsYB9Kfc77zFqCs/PPxYLMU1jbM1EZBqS2gzBVrw2vbPspRF8TAEBLKkBg3DlQChZByV8M3ZkX58YOjXa/gg1VbVhf6ca6SjcOtoSGGsuigPHpdpw6IQ2Ts7pWrnXCbubKtURERCPdgN+5tre346mnnsKf/vQnzJ07FwDw/e9/H6+//vqAG0cjg97aAv9rr8D/8gvQj9RCSE2D7cpvwvqliyDl5ce7eQmta97n2zVvYEvLJgDA9NSZuHTcFTgt9ww4TVx5OBGJ7ZUwh4OnqWYtJE89AEBz5CBYuAxK/mIECxZDTy6Kc0uHhk/RsLmmO3hW1HfCQGj12lkFKbhgajZOK89BjlWCWebwfyIiotFowEF0w4YNsFqtWLx4ceS5VatWYdWqVQM9NMWRYRhQN2+C78XnEFzzPqCqMM2eC8f3boR52WkQTBzmNlSONu/zrLxzkGtPzF6vsUzsqI0aarsWUkc1AEC3ZSCYvxje/MVQChZDSxkHJOCwa0XTsf1IRzh4tmLbkQ6ougFZFDA9NwnXLirGvCIXpuUmRUqmuFx2Lr5FREQ0ig04iFZWViI/Px+vvvoq/vjHP8Lr9WLlypW45ZZbOEd0FNI72hF443X4X3oe2uFDEJxJsF7yFVgvWgW5uCTezUtYhmFgT1sF3q59A+/Vvg130I1kUwrOLfwSzs5fibKUcs77TCCCp6F7caGatZDbDgEAdIsLSv4ieGd9D0rBEmipExMyeGq6gT2NnVhf6cYXlW5srm6DX9UhACjLduLKufmYV+TCrPwU2EwcZktERJSIBhxEPR4Pqqur8fjjj+Oee+6Bx+PBPffcA1VVcdddd/X7OkkS4HLZB3r6ISVJ4ohv42AwDAOBbdvQ9swz6Hzj3zACAVhmzET6fffDec45EK3WeDcxYdV6avHvQ6/jtYOv4VD7QZhEE5bnL8f54y7A4twlMEmD1/M8Vq7nEcnbDOHwxxAOfwzx8EcQmvYAAAxLMoyixdAWXAu9+FQgqxyiIMIKIJH+1xmGgf2NHnx2sBlr97fgi0MtaAsvLjQh04FL5xbglPHpWDgu7bgXFeL1TImG1zQlEl7PdDwEwzCMgRzgz3/+M371q1/h7bffRlFRaL7SG2+8gdtvvx2bN2+G2E/5DkXRRvywqkQf+qV7PQi8/Wao93PvHgg2Oyxnrwz1fk6cFO/mJaz+5n2uyF85pPM+E/16HkkEvxum2s/Dw20/gdxcAQAwZDuUvAUI5i+BUrAYasY0QEzMHr8j7X6sC8/xXF/pRpMnCADITbZgfpEL84pcmF/oOukSKryeKdHwmqZEwuuZumRm9v++dsA9ollZWZBlORJCAWDcuHEIBAJoaWlBRkbGQE9Bg0zdtwf+F59H4K03YPi8kEonwnHbHbCsOAei3RHv5iUkVVexvulzvF3zBj7pmvdpL8TVE6/FWfmc95kIJPcBWPa8CPOhtyE3bocAA4ZkgZI7H56FP0KwYDHUzBnAIPZyjyQt3mBkcaH1VW5Uu/0AgDS7CfMKw8GzyIX8FCuHmRMREdHAg+isWbOgqip2796NyZMnAwD2798Ph8MBl8s10MPTIDECfgTeewf+l56HumM7YLbAcsZZsF68CnL5NL4xHAKc95n4xM5aWPa+Asvel2Bq3AoDAtTc+fDOvwVKwWIo2bMB6eR6/Ea6zoCKjdVtkR7PfU0eAIDDLGFuoQuXzc7H/CIXJqTbeZ0TERFRLwMOoiUlJTjzzDPx4x//GD/72c/g9/vx8MMP47LLLoPMupJxp1Yehv+l5xH492swOtohFRXDcdMtsKw8D2JySrybl5DqfXWRep+VnsMwiSYsylqCFfkrsSBzEUxiYvaIjRWCvxWW/a/BsudFmGo/hwADStZMdC75CQKlF0B35sa7iUPCr2jYWtse6fHcWdcB3QAssoiZecm4cWkJ5he5MDk7CbLI4ElERERHN+A5ogDQ2dmJ+++/H2+99RZkWcbFF1+M2267DaajlPjgHNGhYygKgh99AP9Lz0PZuAGQZZiXnQ7rxatgmjWHvRNDwKN4IvM+N7dsBNA17/McLM89A0mm5Di3cPRezyOC4oXl4Fuw7H0R5soPIOgqVNcEBCZdjMDEi6C5xse7hYNO1XTsrO/EuspWrK90Y2ttO4KaAUkUMDUnCfPDQ22n5ybHpZYnr2dKNLymKZHweqYuR5sjOihB9GQwiA4+rbYG/ldegv+1l2G0tkDMzYX1wlWwnncBxLT0eDcv4fQ373NF/soROe9ztF3PcacFYa78EJa9L8Jy8C0Iqg+aMxeB0gsRmHQJ1IypCVVaRTcM7Gv0RHo8N1a1watoAIBJmQ7ML0rF/CIXZhUkw2GO/2gXXs+UaHhNUyLh9UxdhnSxIoovQ1UR/GxtqPfz808BQYB58VJYL/4yTPMXQuhn1WI6OYZhYG/7brxV8++oeZ/JOLfgAqzIX4kprqnscR7NDB2m2s9g2fMSLPtfhRhog25xwT/5ywhMuhhK7gJASIz/U4ZhoMrtj/R4rq9qgztcUqUo1YZzy7Mwv8iFuQUuuOwcTk5ERESDi0F0lNIaGxB49WX4X30RekMDxPQM2K76DqwXXAQpOzvezUs49b46vFvzFt6ufQOHOw9x3mciMQzIjdtg2fMiLPtehuSpgyHbERh/DgITL0aw8FRAMse7lYOiviMQWtm2yo11h1vR0BkqqZLlNGPJ+DTMD69um52UmAssERER0cjBIDqKGLoOZf0X8L/0PIKffARoGkzzF8Jx860wLz4VAheHGlTR8z63tGyCAQPTU2fih9NuHzHzPunkSa37Q8Nu97wIue0gDNGEYNHp8Cy5G4GSFYBp9BfidvsUbKhyR+p5Vrb6AAApVrm7lmdRKgpdLKlCREREw4vJZRTQW1vh//er8L/8AvSaaggpLti+eiWsF14CKb8g3s1LKJquYl3TF+F5n2sQ1IPItxfgqonfwVn55yDPnh/vJtIAdJdbeRGmxm0wIEDJX4SOOdcjMP5cGNbUeDdxQDxBFZur28PBsxV7Gz0wANhNEuYUpmDVjFzML3KhNNMBkcGTiIiI4ohBdIQyDAPq1s2h0isfvAcoCuSZs+G45nswLzsdgjkxhgqOBN3zPt/A+7VvozXYynmfCUTwt8Ky77VQ+EywcitBVce2I+2RHs8ddR3QdANmScCMvGR8b0kx5helojzbCVlKjLmtRERElBgYREcYvaMDgTdfh/+l56EdOgjB6YT1olWwXngJ5HGJVyIinjjvM4EFPbAceguWvS/FlFvxLvjhqC63ouoGdtd3RILnltp2BFQdogCU5yThm/MLMK/QhRl5ybCapHg3l4iIiKhfDKIjhLJrJ/wvPYfAO28BgQDkKVPhvONuWM5cAcFqjXfzEkZk3mftG9jSHJr3OS11Bm6ZdjtO47zP0a2fciu+Gd8ZteVWDMPA/mZvqKRKpRsbq93oDIRKqpRmOLBqRi7mFbkwpyAFTgt/nRMREdHowXcucWR4vQi8+xb8Lz0PdXcFYLPBcva5sF20CvLksng3L2Fw3mcC0zWYjnweWvF2/2tR5Va+gsCki0ZluZVqty8SPNdXudHiDZVUKXBZsWJyJuaFV7ZNs3N4PhEREY1eDKJxoO7fB//LLyDw5uswPB5I4yfA8cPbYVmxEqLTGe/mJYTQvM89eKvm3zHzPlcWnI+z88/lvM/RLKbcykuQPPWjutxKU2cA66rCwbPSjdr2AAAgw2HGwuLU8Mq2LuQmc2QEERERJQ4G0WFiBAIIfPBeqPdz2xbAbIbl9DNhvejLkKdNZygaJA2+erxT+ybernkThzsPwiSacErmEqzIPwcLsxZz3uco1n+5lYsRKDlr1JRbafcr2FDVFqrnWenGwRYvACDZKmNuoQtfm1eIBUUuFKfZ+HuBiIiIEhaD6BDTqirhf/kF+F9/BUZ7O8SCQjhuvBmWc8+HmOKKd/MSwtHmfS7POQPJZs77HK1C5VZehmXvS6O23IpP0bC5pjt4VtR3wgBglUXMLkjBl6ZlY36RCxMznZBEBk8iIiIaGxhEh4Chqgh+9CH8Lz0PZcM6QJJgPnU5rBd/GaY589jLMQg0XcX6pnV4q+bfWFv/EQJ6AHn2fHxz4rdxVt45yHewvupoddRyKxO/BN2RE+8mHpWi6dh+pCMcPFux7UgHVN2ALAqYnpeMaxcVY36RC1Nzk2BiSRUiIiIaoxhEB5FWdwT+V16E/9WXYbQ0Q8zOgf3a62A970KIGRnxbt6o1zXv8+2aN/Be7dtoDbYg2ZSMcwrOw4r8lSh3TWPIH626yq3seRHmqg9HXbkVv6Jh7cEWvFHRiE8PtsCv6hAAlGU7ceXcfMwvcmFmfgpsLKlCREREBIBBdMAMTYPy+afwvfgclM/WAgBMi5bAdtEqmBYugiDxjedA9Zz3KQsyFmUt5bzP0a6/ciszr0Fg4sUjvtyKqhtYV9mKNysa8cHeJniCGtLsJlwwNRsLi1MxpzAFyVZem0RERER9YRA9SXpTE/yvvwz/yy9Cr6+DkJYO2zevhvWCiyDl5Ma7eaOeV/VgTd0HeKvm35F5n1NTp+M/pv4nTss9k/M+Rytdg6n2M1j2vtRdbsWaOmrKreiGgW217XizohHv7G5Eq0+B0yLhzEkZOLssC/MKXZznSURERHQcGERPgKHrUDauh/+l5xH86ENA02CatwCO7/8HzEuXQZD54xyIrnmfXfU+Oe8zQRgG5MatsOx5qZ9yK8sAaeT2HBqGgb2NHrxZ0Yi3dzfgSHsAFlnEqePTcU5ZJhaPS4NZHrnhmYiIiGgkYnI6DnqbG/5/vwb/S89Dr66CkJIC26VXwHrhxZAKi+LdvFGtr3mfSaYknF1wHs7mvM9RTWrdF6r1ufelUVlupdrtw5sVDXizohEHm72QBOCUkjRct6QEy0vT4TDz1ycRERHRyeI7qX4YhgHfpo3oePwpBD54FwgGIc+YCfvV18Ky/HQIFku8mziqaYaGNUfex1MH/oF97XshCzJOyVqCFfkrsTBzEcySOd5NpJMgdtTCsu9lWPa8CFPT9nC5lcXhcivnwbC64t3Eo2rqDOCt3Y14q6IRO+o6AACz85Nxx1mlOHNiJlz2kdtzS0RERDSaMIj2wVBVtN18PZq3boFgd8B6wUWwXngJ5Aml8W7aqKfoCt6peRNP7f8Hqr1VKHaW4Oapt+H03LM473OU6i638gLMtZ8DwKgqt9LuV/D+3ia8UdGIjVVu6AYwOcuJHywbhxWTM5GTbI13E4mIiIgSDoNoXyQJpllzkLrqEuiLToNgH9lDCEeDgBbAv6texdMHHkeDvx6lyZPw09n3Y2nOcogjeHEa6kfQA8vBN2HZ+1JMuRXPgtsQmHjhqCi3smZ/M96saMTagy1QdQNFqTZ8e2ERzinLQkk6/88TERERDSUG0T4IggDHtdcjxWWH2+2Nd3NGNa/qwcuVL+JfB55Ca7AF01Jn4JZpt2NB5imc+znaaEGYKz8IrXh78E0Iqn90lVvRdHx2uBVv7GrAmv3N8Ck6Mp1mXDY7DyunZKEsy8lrkoiIiGiYMIjSkGgPtuOFw//C84eeQYfSgbnp8/H10nsxI20W3+yPJpFyKy/Csv/1qHIrlyIw6WIoufNHfLmVTdVteKuiEe/uaUSbX0WyVcbKKVk4pywLs/JTWG6FiIiIKA4YRGlQtQRa8OzBp/DS4Rfg07xYnLUUXyv9Fqa4yuPdNDpekXIrL8Ky92VI3tFXbqWioRNv7gqVW2noDMIqi1hemo5zyrJwSkkqTNLIDc9EREREYwGDKA2Kel8d/nngSbxe9TJUXcXy3DNw5YRvYkIyF3gaLbrLrbwIue1QqNxK8RnwTLwIgZIVgMkW7yYe1aEWL94Kl1upbPVBFgUsKknFzcuzcOqEdNhMUrybSERERERhDKI0IDWeajy1/x94q+bfMGDg7PxzccWEb6DAURjvptFx6L/cyo0IjD93xJdbqe8I4K2KBrxV0YiKhk4IAOYWpuAb8wpw+sQMpNhGbs8tERER0VjGIEon5WDHfjyx7+/44Mi7kEUZFxRdjK+OvxLZtpFdqoMAwdcCy/7XYNn7Ymy5laU/RaD0ghFfbsXtU/Denka8UdGIzdVtMACU5yThltPGY8XkTGQ6WeOXiIiIaKQb1CB61113obKyEv/4xz8G87A0glS4d+KJ/Y/hk/qPYJPsuHT8Fbh03OVIs6THu2l0NIoXwvbXkLz5me5yK6ml8Cy4Df6JF0F3jYt3C4/KG9Tw4f4mvLmrEZ8dboWmGxiXZsd3FxfjnLIsFKaO7GHDRERERBRr0ILop59+imeffRYLFiwYrEPSCLKlZRMe3/coNjStQ5IpCd8s/TZWlVyGZHNyvJtGRyH4WmDb+lfYtj0KMdAGwZkH38xr4J94CbSM8hFdbiWo6vj0UAverGjEmv3NCKg6cpIs+NrcfJxTloWJmQ6uwExEREQ0Sg1KEPV6vbj77rsxZ86cwTgcjRCGYeCLxs/w5P6/Y1vrFqSaU/HdyTfgwuJLYJcd8W4eHYXYUQPb5j/BtvNJCKofgXHnQFryfbQmzxzR5VY03cCGKjfeqmjEe3ub0BFQ4bKZ8KWp2TinLAsz8pMhMnwSERERjXqDEkRXr16NBQsWIDMzExs3bhyMQ1Ic6YaOj+vX4Il9j2Fv+25kWbNxU/kPcV7hl2CROP9uJJNa9sK+6few7HkBABCYdAm8s6+HljYJLpcdcHvj3MLeDMPAjroOvFnRiLd3N6LZE4TdJOH0iek4uywLC4pckFluhYiIiCihDDiIbtq0CW+88QZeffVV/O1vfxuMNlGcaLqK9468gyf3/wOHOw8i316A/5x+J87KPwcmkauPjmRy/SbYNzwCy8E3YchW+KZ9E76Z34WeXBDvpvXrQLMHb+4KlVupafPDLAlYMj4d55RlYsm4NFhZboWIiIgoYQ0oiAaDQdx111248847kZKSckKvlSQh1EMzgkmSOOLbOBiCWhCvHHwZj+18FNWd1ShNKcXPFz+Is4pWQBIZBkYsw4Bw8AOIa38N8fBHMKwp0JbeBn3ed2FyZKDnRwcj4XqubvXitW11eHXrEVTUd0AUgEXj03HTGaU4uzwbSVZ+4EHHZyRcz0SDidc0JRJez3Q8BhREf/e736G4uBjnnnvuCb9W0wy4R+AwwWgul33Et3Eg/Jofr1a+hGcOPokmfyMmp0zBvXMfxKKspRAFER3tgXg3kfqiazAf+DfsG38PuXErNEc2OhffDf/Ur8EwOwEFfQ7Bjdf13OIN4p3dTXizogFba9sBANNzk/GfZ0zAmZMyke4wAwA0vwK3Xxn29tHolOi/n2ns4TVNiYTXM3XJzEzqd9uAgugrr7yCxsZGzJ49GwCgKAo0TcPs2bOxadOmgRyahlCn0omXDz+PZw89DXfQjZlps3H79LswN2M+VyEdybQArLufg23jHyC3HYSaMg4dpz8E/+QvAyNs7m5nQMUH+0LlVtZVtkIzgNIMB25YWoKzyzKRn8JyK0RERERj2YCC6D/+8Q+oqhp5/Oijj2L79u14+OGHB9wwGnxtQTeeO/QMXjj0LDxqJxZkLsLXJ1yFaWkz4t00Ogoh2Anrjidg2/JnSJ56KBnT0HbOHxEcfy4wgoZOB1QdnxxoxpsVjfj4QDOCmoG8FCu+uaAQZ5dloTSDKy0TERERUciAgmh+fn7M4+TkZFitVhQXFw+oUTS4mvyN+NfBp/BK5YsIaAGcmrMcV064CpNSJse7aXQUgq8Ztq1/i9QADeYvRscZ/w9K4bIRU/9T1Q2sq2zFmxWN+GBvEzxBDWl2Ey6ZkYtzyrIwLTeJvexERERE1MuglG+hkanOewRPHXgcb1S/Cs3QcUbuWbhywjdRkjQu3k2jo+irBqh3zo1Qc0ZGnV7DMLC1th1vVjTi3T2NaPEqcFoknDkpA2eXZWFuoQuyyPBJRERERP0b1CB6yy23DObh6CRVdh7Ck/v/gXdq34IkiDgn/zxcPuHryLPnH/vFFDdSyx7YN/2hRw3QG6ClTYxzy0Lhc1+TB2/sasTbuxtwpD0Aiyzi1PFpOKcsC4vHpcEss9YnERERER0f9ogmkH3te/DEvr9jTd37MItmXFL8FVw2/kpkWjPj3TQ6CrluI+wbfxdbA3TW96Anxf+Dg2q3D29VNOKNigYcbPZCEoCFJam4bkkJlpemw2HmrxAiIiIiOnF8F5kAdrRuwxP7HsNnjWvhkB24csI38OWSr8JlSY1306g/hgFT1RrYN/4O5pq10C0p8My7Gb4Z34FhS4tr05o6A3h7TxPe3NWAHXUdAIDZ+cn40ZmlOHNSBlLt5ri2j4iIiIhGPwbRUcowDGxq3oDH9z+Kzc0bkWxKwdWTrsUlxV+B09R/vR6Ks0gN0N/B1Litdw3QOGn3K3h/bxPeqGjExio3dAOYnOXED5aNw4rJmchJtsatbURERESUeBhERxnDMPBpwyd4Yv9j2OXegXRLBq4vuwkXFF0Em2yPd/OoPyOwBqhf0bBmfzPeqmjEJwdboOoGilJt+PbCIpxTloWSdF5PRERERDQ0GERHCc3QsObI+3hi/99xoGMfcmy5+I+p/4mVBefBHKcgQ8fWqwZo5vS41gBVNR2fHQ6VW/lwXxN8io5MpxmXzc7DOWVZmJLtZLkVIiIiIhpyDKIjnKqreKf2TTy5/x+o9lSiyFGMO2bcjTPyVkAW+c83UvWuAboEHWeuhlJwalxqgO440o431hzEv7cdQZtfRbJVxsopWTinLAuz8lMgsdwKERERUQzDMNDZ2QafrxO6rsW7OSOOKEqw2ZxwOlNOqiODSWaECmoB/Lv6VTx94AnU++pQmjwRP5l9H5bmLIckDH9PGh2fXjVAx6+Ed/YNcakBahgGPjvcise+qMKGqjbYTBKWTQiVWzmlJBUmieVWiIiIiPrT2toIQRCQlpYNSZI5aiyKYRjQNBUdHW60tjYiLS3rhI/BIDrC+FQvXq58Ef86+BRaAs0od03DzVNvw8LMRbz4R7DeNUBXwTv7+rjUANV0A+/tbcJjX1Rhd0Mnspxm3HLaeHxz6XiovuCwt4eIiIhoNAoG/cjOLoAg8MP7ngRBgCyb4HKlo76++qSOwSA6QnQo7Xjh0LN4/tAzaFfaMSd9Hu6a9VPMSpvDADqCxdYAtcE37Sr4Zn03LjVAg6qO13bW4x/rqlDl9qMo1Ya7z56ElVOyYJZFOC0y3AyiRERERMfJYAg9htDPxzip1zKIxllroAXPHvwnXqp8Dl7Vi0VZS/H10qswxTU13k2j/kRqgD4Cc82n4Rqg/wHfjG/HpQaoJ6ji+S1H8OSGGjR5gpiS7cQvvjQFy0szOPeTiIiIiEYkBtE4afQ14J8Hn8BrlS8jqAdxWu4ZuHLCVZiQXBrvplF/+qoBuuS/4S+/Mi41QFu9QTy9qRb/2lSLjoCK+UUu/PTcyVhQ5GIvOhERERGNaAyiw6zGU42nDvwDb1X/GwYMrMhfiSvGfwOFzqJ4N43602cN0F/CP3lVXGqAHmn344n11XhxWx2Cqo7TJmbgqvkFmJqbPOxtISIiIqKRx+12Y/XqX+CLLz6H3W7HZZddga9+9WsAgL17d2P16l9i797dKCgoxHXX3YSFCxdFXnv55atQXV0Zc7z/+78nMHHi5EFtI4PoMDnYcQBP7v873q99B5Io4/zCC/HVCV9Dji033k2jfoRqgD4O2+b/heSth5I5I641QPc3efCPdVV4o6IRAHDelCx8Y34hxqXbh70tRERERDRy3XnnbfD7/Vi9+hF4vV7cd99PIAgiVqxYiZtuug5Lly7D7bffhR07tuGuu/4TjzzyZ5SVlSMYDOLIkRr84Q9/RV5e95onKSmuQW8jg+gQ2+3ehcf3P4ZP6tfAKtnwlXGX49JxlyPdmhHvplE/+qwBetavoRQsjUsN0G217Xj0iyqs2d8Mqyzisll5uHJuPnKSrcPeFiIiIiIa2SoqdmHr1s148slnUVRUAgC44YYf4Le/XQ1N02C323HHHXdDlmWUlIzDzp3b8dRTj+Oeex5AZeVhAMCUKVMhy0MbFRlEh8jWls14Yt9jWNf0OZxyEr5Z+m1cUnIpUswp8W4a9UPsqIFt0x9h2/VUdw3QOTdCzZ497G3pqgH66OdV2FjdhhSrjGsXFeGyWflw2U3D3h4iIiIiGh1qa2uQlJQcCaEAUFo6Cc3NTdi4cR3KyspjQmZp6SQ88cRjAIBDhw4gLy9/yEMowCA6qAzDwPqmz/H4vsewrXULUs2puHby9biwaBUcJke8m0f9kFr2wL7x97DsfRHAyKwBevH0XNjNwz8cmIiIiIi6vbajHi9vrxvWc144LQfnT80+7v3T0tLg9Xrg9Xpgt4cySF3dEQBAefk0rF37Ucz+dXVH0NbmBgAcOnQQkiTh1lt/gL17d6OoqBjXX/8DTJ06bXC+mSgMooNAN3R8Uv8Rntj3GPa0VyDTmoXvl9+C8wq/BKvE4ZMjVd81QL8HPSlv2NvSXw3Qc8uzYJJYv4qIiIiIjk95+TRkZWXj4YcfxG233QGPx4O//e3PAIB58xbg0Uf/guee+ycuuujLqKjYhddeexmKogAIBdGOjnZcd91NyMzMwiuvvICbb74O//jHM8jNHdz3yIJhGCdXgXSAFEWD2+2Nx6mPm8tlP2obNV3F+0fexZP7/45DnQeRZ8/HFRO+gbPzz4VJ5PDJEckwYKr6EPaNv4vUAPVNvzpuNUA7Aype2BpbA/RbCwqHpAbosa5notGE1zMlGl7TlEgS5XquqzuMnJzieDfjpOzeXYGf/ORO1NZWw2534Prrb8Ivf/kA/v73p7F37x6sXv0QvF4v8vIKcO655+OZZ57Ea6+9C1VVEQj44XCEShMahoFvfesKnHbambj66mv7PNfRfk6ZmUn9tpE9oichqAXxVs2/8fSBx1HrrUGJcxzumvlTnJZ7BiSRP9IRSddg2f86bBt/B1PT9kgNUF/51wDz8A+bbvEG8c+NNfjX5iOsAUpEREREg2ry5DI8/fTzaGlpRlJSMqqrqyCKIrKzczB+fClWrFiJlpYWpKen44UXnkVOTqi3U5ZlyLIzchxBEFBUVILGxoZBbyNT0wnwa368XvUy/nngSTT6GzA5pQw/m/NzLM4+FaLA4ZMjkhaAteJZ2Db9AXLbIaiu8XGtAVrbFqoB+tL2qBqgCwoxNaf/T4uIiIiIiI5Xe3s77rjjh7j//oeQlpYOAPj44zWYNKkMu3dX4Pnnn8F99z2EjIyMyLY5c+YBAK699ptYvvwMfP3r3wIA6LqO/fv34qKLVg16OxlEj4NH8eClyufw7MGn4Q66MSNtFm6b/mPMy1jA3qsRqs8aoCv/hOC4lXGrAfr3dVV4c1cDBEHAeeVZ+Ma8QpSwBigRERERDaLk5GT4/X488shqfPvb38Pu3RV47LG/4Kc/vR9FRSX4/PNP8cwzT2Hp0mV4/fVXsHPnNtxxx38BABYvPhVPPfUPTJhQivz8Ajz99BNob2/H+edfNOjt5BzRo7EF8X9bHsMLh55Fp9qBBZmn4GsTrsL0tJnxbhn1o1cN0IKl8M65MW41QLfWtuOxqBqgq2bm4oo58akBmijzNYgAXs+UeHhNUyJJlOt5NM8RraqqxC9/+QB27tyOzMwsXH31tTj77HMBAJ99thaPPPJr1NXVYuLEybj55ltRVlYOANA0DY8++he89trLcLvdmDp1Gv7jP/4TEyaU9nuuk50jyiDaj7/v/Rv+efAJ+FQflmYvx9dLr8KklLJ4N4v6IbZXw7b5T7DtegpQAwiOXwnvnBviVgP000OteOyL7hqgX52dj0tn58Fli98iVonyR4EI4PVMiYfXNCWSRLmeR3MQHU5crGgQabqKT+o/wmkFp+MrhVdiXNL4eDeJ+iE174Z90x8iNUD9k74M3+zr4lYD9N09jXjsiyrsafSwBigRERERUT8YRPsgiTL+tPT/EubTnEQk122AfePvu2uATv8WfDO/G5caoIGoGqDVbj+KU224+5xJOHcKa4ASEREREfWFQZRGjz5qgHrm3wLf9KvjWgP0iQ01aA7XAP3FheVYPiF90GuAEhERERElkkEJopWVlXjggQewYcMG2Gw2nHfeebjllltgsQx/eQxKQL1qgOagc8lP4Cu/csTUAP3ZuZMxnzVAiYiIiIiOy4CDaDAYxHXXXYfS0lI8/fTTaG5uxp133gkAuOOOOwbcQBrD+qwB+jD8ky+JWw3Qx9dX4+VwDdDTJ2bgm6wBSkRERER0wgYcRLdu3YrKykr861//gsPhwIQJE3DzzTfjwQcfZBClkzLSaoDua/Lg719U4a2KqBqg8wtRksYaoEREREREJ2PAQXT8+PH485//DIeje4ikIAgIBoMDPTSNMYKvGbYtf4Vt+2ORGqAdZ/06rjVAH/28Eh8daIHNJOKrc/Jx5dwCZCdxyDkRERER0UAMOIimpaVh8eLFkce6ruPxxx/H3LlzB3poGiPE9mrYN/8R1l1PR9UAvRFq9qxhb4thGFgbrgG6KVwD9LuLiuNeA5SIiIiIKJEM+qq5P//5z7Fr1y48++yzR91PkgS4XCN7aKMkiSO+jaNaZwOkD+6FsPVpQBBgTLsM2qIfQMyYBOcwN0XVdLyxox5/+ugAKuo6kJNsxZ3nluGr8wpgNyfG4tK8nimR8HqmRMNrmhJJolzP9fUCJJbiOyZBOLlcN2jvsA3DwP3334+nnnoKv/nNbzBx4sSj7q9pxoiv0ck6okNE12Dd+QQcn/0CguIN1QCd9b3uGqDD+DMPqDpe21GHf6yv7rMGaNAbRNCbGMPMeT1TIuH1TImG1zQlkkS5ng3DgKbp8W7GSXG73Vi9+hf44ovPYbfbcdllV+CrX/0aAGDv3t1YvfqX2Lt3NwoKCnHddTdh4cJFkddu374Vv/71wzhwYD+Kiopx8823Yvbs/ke7Gkb/uS4zs/9FPQcliOq6jrvuuguvvPIKVq9ejbPOOmswDksJSG7YCueHP4apYQuC+UvQufwBaKkThr0dnQEVz285gic3hmqAluck4aELx2N5aTpElmAhIiIiolHszjtvg9/vx+rVj8Dr9eK++34CQRCxYsVK3HTTdVi6dBluv/0u7NixDXfd9Z945JE/o6ysHPX1dbjllu/jkku+gp/+9H68+upL+PGPb8NTTz2P1NTUQW3joATRBx98EK+88gp++9vf4vTTTx+MQ1KCEQJtcHz+S1i3/x26LQPtKx5BYOJFw74IUYs3iKc31uBfm2vRGdCwoMiFe8+bjHmFrAFKRERERKNfRcUubN26GU8++SyKikoAADfc8AP89reroWka7HY77rjjbsiyjJKScdi5czueeupx3HPPA3juuX+itHQibrjhBwCA733vRnzxxafYs6ciptd0MAw4iG7evBmPPfYYbr31VkybNg2NjY2RbZmZmQM9PI12hgHLnhfg/OReCP5m+KZdBe/C/4RhSR7WZvRVA/SqBYUoZw1QIiIiIkogtbU1SEpKjoRQACgtnYTm5iZs3LgOZWXlkGU5ZtsTTzwGANiwYT3OPntlZJsgCPjb354YknYOOIi++eabAIBf/epX+NWvfhWzbceOHTHfJI0tUus+OD+8E+aatVCyZqLzgsegZs0Y1jbsa/LgsS+q8Ha4Buj55dn4+vwC1gAlIiIiohNmqXg2VOlhGPmnXI5A2VeOe/+0tDR4vR54vR7Y7aESm3V1RwAA5eXTsHbtRzH719UdQVubGwBQU1MNm82On/70LmzYsA4FBQW48cZbMG3a9MH5ZqIMOCX+6Ec/wo9+9KPBaAslCsUH+4b/gX3TH2GY7OhY/nP4y68ERGnYmrClpg2PflGFj1kDlIiIiIjGkPLyacjKysbDDz+I2267Ax6PB3/7258BAPPmLcCjj/4Fzz33T1x00ZdRUbELr732MhRFAQD4fF788Y+P4Kqrvo0rr/wG3nzzddxyy4144ol/ISsre1Dbye5KGlTmQ+/AueZuSB1V8E/+CjoX/xcMe8awnDtSA/TzSmyqaQ/VAF1cjEtnsQYoEREREQ1coOwrJ9Q7GQ9msxn33fcQfvKTO7Fy5emw2x24/vqbsHPndjgcDtx550+wevVD+M1vfoW8vAJceunleOaZJwEAkiRh0aLFkRV2J00qw7p1n+PNN1/HN75x9aC2k0GUBoXYUQPnR/8Ny8E3oaZOgvvif0HJH9wJzf1RdQPv7WnEo19UYW+jB1lOM354+gRcPD0HNtPw9cISEREREY0EkyeX4emnn0dLSzOSkpJRXV0FURSRnZ2D8eNLsWLFSrS0tCA9PR0vvPAscnJCZRTT0zNj5pYCQGFhEerr6wa9jQyiNDBaELYt/wvHul8DADoX3QnfzGsAyTzkp+6qAfr3ddWoafOjJM2G/z5nElaGa4ASEREREY017e3tuOOOH+L++x9CWlo6AODjj9dg0qQy7N5dgeeffwb33fcQMjIyItvmzJkHAJg2bTr27KmIHMswDBw+fBhTpkwd9HYyiNJJM9V8CueHd0Fu3YPAuHPQufQe6MkFQ37ezoCK57YcwZMbqtHiVVCek4Sbl7MGKBERERFRcnIy/H4/HnlkNb797e9h9+4KPPbYX/DTn96PoqISfP75p3jmmaewdOkyvP76K9i5cxvuuOO/AACXXXYFrr/+O3jmmSexePGpePnlF9DQUI9zzjlv0NspGIZhDPpRj4OiaHC7vfE49XFzuewjvo3xIHib4Fx7H6y7n4WWVIjOU3+G4LgVQ37eZk+oBuizW0I1QBcWu3DVgkLWAD1OvJ4pkfB6pkTDa5oSSaJcz3V1h5GTUxzvZpyUqqpK/PKXD2Dnzu3IzMzC1Vdfi7PPPhcA8Nlna/HII79GXV0tJk6cjJtvvhVlZeWR165d+zH+8If/QU1NNcaPL8XNN9+K6dNn9nuuo/2cMjP7L5XIIHoUifKfaNDoGqw7n4TjswchKF54Z18H79wfACbbkJ62ps2Hx9dV45Ud9QiqOs6YlIFvzmcN0BPF65kSCa9nSjS8pimRJMr1PJqD6HA62SDKobl0XOTGbXB+8GOYGjYjmL8YncsfgJZaOqTn3NfowWPrWAOUiIiIiCjRMIjSUQmBdjg+fwjW7X+HYU1H+1n/g8CkS4AhHArbVw3Qr80tQBZrgBIRERERJQQGUeqbYcCy9yU4PvkZRG8j/NO/Cc/C22FYUobodAbWHmzFY1/E1gC9bFYeUlgDlIiIiIgooTCIUi9S634419wFc/XHULJmov38R6FmzRiSc6m6gXd3N+KxdaEaoNlJFtx6+gRcxBqgREREREQJi0GUuik+2Df8FvZNf4Ah29Cx/AH4y78GiEMTCDdUufHzt/ficKsP49Ls+MnKSTinjDVAiYiIiIgSHYMoAQDMh96Fc81/Qeqogn/yl9G5+L9g2DOH5FxtPgW/XXMQL22vQ36KFb+4sBynsQYoEREREdGYwSA6xokdNXB+/BNYDrwBNXUi3Bc/AyV/8ZCcyzAMvL27Eb96fz/afAq+Ob8Q1y4qgpVDcImIiIiIxhQG0bFKU2Db8hc41v0/AAY6F/0YvpnXApJ5SE53pN2PB9/Zi7UHWzEl24n/+fJ0TM5yDsm5iIiIiIhoZGMQHYNMtZ/B+eFdkFt2IzDuHHQuvQd6csGQnEvVDTyzqQZ/+PgQBAH44ekTcNmsPEgih+ESEREREY1VDKJjiOBrhnPtfbBW/AtaUgHazvs/BMetGLLz7a7vxP1v78Gu+k4sHZ+GH51Zipxk65Cdj4iIiIiIALfbjdWrf4Evvvgcdrsdl112Bb761a8BAPbu3Y3Vq3+JvXt3o6CgENdddxMWLlwEAPjKV76EurojvY533nlfwp13/mRQ28ggOhYYOqw7noTjs59DULzwzvk+PPN+AJjsQ3I6v6Lhz2sP48kN1UixmfDABVNw1qQMCFyMiIiIiIhoyN15523w+/1YvfoReL1e3HffTyAIIlasWImbbroOS5cuw+2334UdO7bhrrv+E4888meUlZXjf//379B1LXKc9eu/wIMP3otVqy4b9DYyiCY4uXE7nB/cAVPDZgTzF6Fz2QPQ0iYO2fk+O9SCn7+zD7Vtflw0PQc/WDYOyVbTkJ2PiIiIiIi6VVTswtatm/Hkk8+iqKgEAHDDDT/Ab3+7GpqmwW6344477oYsyygpGYedO7fjqacexz33PIDU1NTIcfx+P/70p9/hqqu+g7KyKYPeTgbRBCUE2mH//JewbX8MhjUN7Wf9DwKTLgGGqFey1RvE6g8O4N+7GlCUasMfL5uBuYWuITkXERERERH1rba2BklJyZEQCgClpZPQ3NyEjRvXoaysHLIsx2x74onHeh3nX/96CoIg4IorvjEk7WQQTTSGAcu+l+H4+B6I3kb4p30TnlNuh2FJGaLTGXh9ZwNWf7AfnqCG75xShKsXFsEii0NyPiIiIiKieHmr+t/4d/Wrw3rOcwsuwNkF5x73/mlpafB6PfB6PbDbHQAQmfdZXj4Na9d+FLN/Xd0RtLW5Y54LBoN4+unHcc0118NisQzsG+gH00ICkdwHkPLylUh+60bojhy4L30VncvvH7IQWtXqw43PbsNP39iN4jQ7Hv/GHFy3pIQhlIiIiIgoTsrLpyErKxsPP/wgvF4PGhsb8Le//RkAMG/eAuzZsxvPPfdPqKqK7du34bXXXoaiKDHHeP/9d2AYwLnnXjBk7WSPaCJQfbBveAT2jX+AIVvRsex++Kd+HRCloTmdpuPx9dX4y2eVkEUBPzqzFKtm5kLkYkRERERElMDOLjj3hHon48FsNuO++x7CT35yJ1auPB12uwPXX38Tdu7cDofDgTvv/AlWr34Iv/nNr5CXV4BLL70czzzzZMwx3n33bZx22hmwWoeu4gWD6ChnPvQunB/dDam9Ev5Jq9C5+L9gOLKG7Hw7jrTj/rf3Ym+jB6dPzMBtp09AVtLQdNcTEREREdGJmzy5DE8//TxaWpqRlJSM6uoqiKKI7OwcjB9fihUrVqKlpQXp6el44YVnkZOTF3ltMBjExo3rcP/9vxzSNnIM5SgldtQi+d/XIOW1q2BIZrgv+ic6VvzPkIVQT1DFw+/tw9VPbkabT8EvLyzHQxeWM4QSEREREY0g7e3tuOGGa9Da2oK0tHSYTCZ8/PEaTJpUht27K/Bf/3U7RFFERkaovOLHH6/BnDnzIq8/cGAfgsEgZsyYNaTtZI/oaKMpsG39Kxxf/D8AOjpPuQO+Wd8FJPOQnfKj/c34xbv70NARwFdm5eGGpSVwWnjpEBERERGNNMnJyfD7/XjkkdX49re/h927K/DYY3/BT396P4qKSvD555/imWeewtKly/D6669g585tuOOO/4q8/sCB/cjJyYXNZhvSdjJNjCJy7RdI+vDHkFt2I1CyAp2n/gx6cuGQna+pM4Bfvb8f7+xpwvh0O/5yxSzMyEsesvMREREREdHA3XPPA/jlLx/AVVddjszMLNx++11YunQ5AODee3+BRx75Nf78599h4sTJ+PWvf4+srOzIa1tampGcPDSLnUYTDMMwBnqQYDCIe++9F2+88QbMZjO+9a1v4dprrz3qaxRFg9vtHeiph5TLZR8RbRR8zXCuvR/WimegOfPRuexeBMedPWTn0w0DL22rw/+sOYCgquM7pxTjG/MLYJI4kns0GynXM9Fg4PVMiYbXNCWSRLme6+oOIyenON7NGPGO9nPKzEzq93WD0iP60EMPYdOmTfi///s/1NXV4fbbb0deXh7OP//8wTj82GXosO58Eo5Pfw5B8cA750Z45t0MmOxDdspDzV488PYebKppx9zCFPz4rIkoThu68xERERER0dgz4CDq9XrxzDPP4I9//COmTZuGadOm4ZprrsHjjz/OIDoAcuN2OD/8MUz1mxDMOwWdyx+AljZpyM4XVHU8tq4K//d5JWwmCXefPQlfmpYNgSVZiIiIiIhokA04iFZUVCAYDGLu3LmR5+bOnYvf//73UFUVssxpqCdCCHbA/vnDsG37PxjWNLSf9WsEJn0ZGMJAuLm6DQ+8vRcHW7w4e3Imfnj6BKQ7hm7xIyIiIiIiGtsGnBIbGxuRkpICi6W7jEdGRgYURUFLSwuysoaupmVCMQxY9r0Cx8f3QPQ2wD/tG/AsvB2G1TVkp+zwq/jdxwfx3JYjyEmy4NeXTMOS8WlDdj4iIiIiIiJgEIKoz+eD2Rzbe9b1OBgM9vs6SRLgco3suYeSJA5PG5v3QXrzPyEe/BB6zixoX30Cct4cDNVaVYZh4M2d9bj3tV1o6gzg6sXFuPmMiXCwJEtCG7brmWgY8HqmRMNrmhJJolzP9fUCJC7WeUyCcHK5bsDJw2Kx9AqcXY+PVntG04wRv5rWkK/4pfpg3/AI7Bv/AEO2oGPZffBP/QYgSsAQnbe+I4CH3t2HNfubMSnTgV9eWI7ynCQoviDcvv4/OKDRL1FWsCMCeD1T4uE1TYkkUa5nwzCgaXq8mzHiGUb/uW5IV83Nzs5Ge3s7gsFgpCe0sbERZrMZKSlDX39mtDIffg/ONXdDaj8M/6RL0Ln4bhiOoRvGrOkGnttSi99/fAiqbuAHy8bhirkFkEUuRkRERERERMNrwEF0ypQpMJlM2LRpExYuXAgA2LBhA6ZOncqFivogdtTC+clPYdn/OlTXBLgv+ieUgiVDes59jR488PYebDvSgYXFLtxx1kQUuPrvrSYiIiIiIhpKA06KNpsNF198Me655x48+OCDaGxsxN/+9jfce++9g9G+xKEpsG39Gxxf/AowNHgW/gje2d8FJMuxX3uS/IqGv31eib+vq0aSRcbPzpuMlWVZLMlCRERERERxNShdlj/+8Y/x05/+FFdddRUcDgduvPFGnHfeeYNx6IQgH1mHpA9/DLm5AoGSs9B56s+gJxcN6TnXVbbi52/vRZXbj/OnZuM/lo2Hy24a0nMSEREREVH8ud1urF79C3zxxeew2+247LIr8NWvfg0AsHfvbqxe/Uvs3bsbBQWFuO66m7Bw4aLIaz/88H387//+HvX1dSgtnYibb74NZWXlg95GwTAMY9CPehwURRvxk5gHOtFa8LXAsfZ+2Cr+Cc2Zj85Tf4bguLOHtCao26fgfz48gFd21KPAZcWPz5qIBcWpQ3Y+Gj0SZeEAIoDXMyUeXtOUSBLleq6rO4ycnOJ4N+Ok3HDDNfD7/bj99jvh9Xpx330/weWXfx0rVqzEFVeswtKly/D1r38LO3Zsw+rVD+GRR/6MsrJyHDiwH9dc803ceuuPMGvWHPzzn0/g/fffxT//+SLs9r5Xxj3az2lIFyuiPhg6rDufguPTn0NQOuGdcwM88/4DMA3dMtaGYeCtikb86v39aPcruGpBIa45pQhWkzRk5yQiIiIiopGlomIXtm7djCeffBZFRSUAgBtu+AF++9vV0DQNdrsdd9xxN2RZRknJOOzcuR1PPfU47rnnAaxb9xmKiopx/vkXAgCuu+77eP75f+HgwQOYOnXaoLaTQXSQSY07kPThj2Gq34hg3kJ0LnsAWvrkIT1nbZsfD76zF58eakV5ThIe+cp0TMpyDuk5iYiIiIho5KmtrUFSUnIkhAJAaekkNDc3YePGdSgrK49ZVLa0dBKeeOIxAEBycgoqKw9hy5ZNmD59Jl577RU4HA4UFhYOejsZRAeJEOyA/Ytfwbb1bzCsqWg/89cITP7ykA7DVXUDT2+swZ8+OQRBAG49fQIunZUHiSVZiIiIiIgGnf+N1+B/7ZVhPaf1/C/BuvL8494/LS0NXq8HXq8HdrsDAFBXdwQAUF4+DWvXfhSzf13dEbS1uQEAZ555Nj755CPceOO1kCQJgiDgwQf/H5KTB78spzjoRxxrDAOWva8g9cnTYNvyV/jLv4aWKz9EoOwrQxpCK+o7cPUTm/CbDw9gXpELz3xrHi6fk88QSkREREQ0hpWXT0NWVjYefvhBeL0eNDY24G9/+zMAYN68BdizZzeee+6fUFUV27dvw2uvvQxFUQAAHR3taGlpxg9+cCv+/OfHcOmlV+Dee+9GTU31oLeTPaIDILkPwLnmbpirPoSSMQ3tK/8Xas6cIT2nT9Hwp08O46mN1XDZTPj5BVNw5qQMlmQhIiIiIhpi1pXnn1DvZDyYzWbcd99D+MlP7sTKlafDbnfg+utvws6d2+FwOHDnnT/B6tUP4Te/+RXy8gpw6aWX45lnngQA/OEPv0VxcQku+//t3Xtc1HW+x/EXw02uIoi64AVBhdxEEc6mectK03ZrlbPeDh2rNR+mrrle6uStzuZWHo3ogmakpVmkmLa7pnRcZc8WW94CU0MQNRGkMRAJEZDLzPmjXVZWQNG50PB+Ph7zh/P7/ub3HvwwMx++v/n+Jk4BIDw8guPHv2bLlveZP/+/LJpTjejNqK3C88tEPDPWYHZx59Kw5VTdPhUM1l0Y6IszJaz4cy6FZVcY168Lc4b3xLedLskiIiIiIiL/FB4ewebN2ykpuYCPjy8FBfkYDAY6d+5CaGgvRo0aQ0lJCQEBAXz00Yd06RIEQHZ2Fg8+GHvNY+XlnbF4Rp2a20KueX/B/4N78Dr0ClfC7ufif/wfVZGPWrUJLamoZunO4zyx7RiuzgbenBTJktF91ISKiIiIiEgDZWVlzJr1GBcvluDvH4Crqyvp6Z/Sp08EOTnZLF36FAaDgY4dfzirMj39UwYOjAEgICCQU6dyGzzemTNnCA7uavGcakRvkKG8EN9PZuD38X9iNjhT+uBmLo1OxOTV2WrHNJvN7DhmZOI7h9h7opjpg7uTPDWagV39rHZMERERERH58fL19aWqqorExATOnSsgLW0PGzeu45FHptG9ewj7939BSsoHFBaeY926tWRlHWXChMkAjBsXy+7dqezc+SfOnSvgvfc2kJFxkNjYiRbP6WQ2m80Wf9QbUFNT1+ovdOvn50nphe/xOPoOngficTLVUhEzl4qoGeDsbtVj51+s5IU9uRw6W0r/IF8Wj+5NaICXVY8pjs1RLi4tAqpncTyqaXEkjlLPRmMeXbr0sHeMm5Kff5ZVq14gK+sYgYGdePTR6YwePRaAffs+JzHxFYzGQnr3Dmfu3AVERPSt3zc19WPef/9dzp//lpCQnsye/VsGDGh6HZzmfk6BgT5N7qdGtBkdLh2BnfNxuZDNlR53Uz5sOab21i3G2joTmw4VsH7fWVwMTswZ3pPxkT/BoMWI5BY5ypuCCKiexfGopsWROEo9/5gbUVu62UZUixU1weuzZ3A58jZ13kF8P3Yd1T3vs+rlWACOfVvG87tzOVl8mbt7d2Th3WEEelt35lVERERERMTW1Ig2xlSLS9HX1A2aQ0m/34CbdU+JvVxdyxvpZ0jJLCTQ242XftmXEb06WvWYIiIiIiIi9qJGtDEGF76P3YafnydY+bSCv568wMq9uRSVVzNhQBAzh4bg7a7/FhERERERcVzqeOykuPwKq9JOkZZbTFhHT1Y80Jd+Qb72jiUiIiIiImJ1akRtzGQ284cj3/L6Z99QXWti1tAQHorpiquzrqQjIiIiIiJtgxpRG/rmQgUv/PkEh8+VEdOtPYtG9aF7Bw97xxIREREREbEpNaI2UF1rYsOBs7yzPx9PN2eW3deHB37aGSddkkVERERERNogNaJWllnwPS/8+QRnSiq5LyKQ+SPD8Pd0s3csERERERERu1EjaiWXqmp5/bPTfHTEyE983Xkl9naG9PS3dywRERERERG7UyNqYWazmbTcYlalneJiRTVx0V2ZMaQHHq7O9o4mIiIiIiJtQGlpKQkJ/8OBA/vx9PRk4sQpTJoUB0Bubg4JCavIzc2ha9duPP74HO64Y3D9vnv2/C8bNqzj/Hkj/foNYP78p+jatZvFM2qpVgsyllWx4A9f8/SO43T0cmNDXBS/vStUTaiIiIiIiNjM4sULyc/PJyEhkSVL/pstW5JJSfmAixcvMmfO4wQFBfPWW+/yq19NZsmSJ8nOzgLg4MH9PPfcMn7xi1+yfv0mQkJCeOKJx6moqLB4Rs2IWkCdyczWw4W8kX6GOrOZuSNCmTwwGBeDFiMSERERERHbyc4+zpEjh0lO/pDu3UMAmDXrCV5/PYG6ujo8PT15+ulluLi4EBLSk6ysY3zwwXv87ncvsHXrZkaOvJfJkx8CYM6c+ezf/wW7d6cybty/WzSnZkRvUW5ROdM+OEz8X04RGezLlkeieSimq5pQERERERGxucLCc/j4+NY3oQC9evXhwoViMjIOEhHRFxcXlwbbvv76aP2+t98eWb/NycmJsLDe9dstSTOiN6mqpo51+87y3qECfN1dWH5/BPdFBOqSLCIiIiIiDupMZjHfZBTb9Jg9B3YkJKrjDY/39/enouIyFRWX8fT0AsBo/BaAvn1v5/PPP2sw3mj8lu+/L63ft6jo/DXb/fz8bv4JNEEzojfhQN5Fprz7JRsP5DP2tk6kPBrDmNs6qQkVERERERG76tv3djp16sxLL62gouIyRUXf8fbbSQDExPyMEydy2LZtC7W1tRw7dpSdO/9ETU0NAPfcM5o//nE7mZlfUltby44dfyAn53j9dkvSjGgLlFbW8MpfT7Pz6/N09WvHmgn9+LfuHewdS0REREREbCAkqmWzk/bg5ubG73+/kmefXcyYMSPx9PRi5sw5ZGUdw8vLi8WLnyUhYSWvvhpPUFBXJkyYTEpKMgAPPDCOvLxvmDdvNiaTiZiYnzF27C8oL79k8ZxqRG+A2Wzmk+zvePkvp7l0pZZHftaNaYO6006r4YqIiIiISCsTHh7B5s3bKSm5gI+PLwUF+RgMBjp37kJoaC9GjRpDSUkJAQEBfPTRh3TpEgSAwWDgiScWMGPGb6ioqKBDhw4sW/Z0/XZLuuVTcy9cuMCCBQsYNGgQgwcPZtGiRZSVlVkiW6tw7vtKnth+jGd25RDcvh2bHopi9rCeakJFRERERKTVKSsrY9asx7h4sQR//wBcXV1JT/+UPn0iyMnJZunSpzAYDHTs2BEnJyfS0z9l4MAYAFJSktm4cT3u7u506NCBqqoqvvzyYP12S7rlRnTBggWcP3+ed955h6SkJE6cOMGSJUsskc2uak1m1qV/w6QNX3LkXBkLR4axfsoAegd62zuaiIiIiIhIo3x9famqqiIxMYFz5wpIS9vDxo3reOSRaXTvHsL+/V+QkvIBhYXnWLduLVlZR5kwYTIAQUHBJCe/y4ED+zh79gzPPLOIoKBgBg8eYvGcTmaz2XyzOxuNRkaMGEFqaiqhoaEAZGRkEBcXR0ZGBh4eHk3uW1NTR2mp5S+Magl1JjMztnzFV4VlDAv156l7etHFt529Y4ncEj8/z1b7OyfSUqpncTSqaXEkjlLPRmMeXbr0sHeMm5Kff5ZVq14gK+sYgYGdePTR6YwePRaAffs+JzHxFYzGQnr3Dmfu3AVERPSt3/f99zeydetmKisruOOOO5k370k6dPBv8ljN/ZwCA32a3O+WviPq7e1NUlISISEh9fc5OTlhMpmorq5uthFtzZyc4Kc/8WHasFAGBftoNVwREREREfnR6NatO6+9trbRbYMG3cmgQXc2uW9c3MPExT1srWj1bmlGtDHz5s3j5MmT7Nixo9lxJpOJujqLHtrinJ0N1NWZ7B1DxCJUz+JIVM/iaFTT4kgcpZ5zcrIJCgqxd4xWr7DwDOHhEY1uc21mXZ3rzoheuXIFo9HY6LaAgAC8vf/5nckNGzbwySefsH79+us9LHV15lY/Ze8opxWIgOpZHIvqWRyNalociaPUs9lsdoiG2trM5qb7uls6Nffo0aPExcU1uu3FF18kNjYWgLfffpuVK1eybNky7ryz6aleERERERERaduu24jGxMSQk5PT7JjXXnuN1atXs3Tp0iabVhERERERERG4xcWKADZu3MiaNWt47rnnmDRpkiUyiYiIiIiI2J3ZbNbCpc24leWGbqkRLSws5KWXXmLKlCncfffdFBUV1W/z9/fH2bnpL6eKiIiIiIi0Vs7OLtTUVOPm5m7vKK1WTU01zs4311LeUiOalpZGdXU1ycnJJCcnN9i2e/duevT4cV53R0RERERE2jZvbz9KS4vw8wvE1dVNM6NXMZvN1NRUU1pahI9Ph5t6DItfvuVG1dTUtfrVtBxlxS8RUD2LY1E9i6NRTYsjcaR6rqy8THl5KXV1tfaO0uo4O7vg7e2Hh4dXk2NuadVcERERERGRtsjDw6vZRktunsHeAURERERERKRtUSMqIiIiIiIiNqVGVERERERERGxKjaiIiIiIiIjYlBpRERERERERsSm7Xb5FRERERERE2ibNiIqIiIiIiIhNqREVERERERERm1IjKiIiIiIiIjalRlRERERERERsSo2oiIiIiIiI2JQaUREREREREbGpNtOIFhQUEB4eTl5e3g2Nz87OZtKkSfTv35/Y2FiOHDnS6Lg1a9awcOFCS0YVuS5L1nNtbS0rVqxgyJAhREVFMXfuXIqLi60VXaRRlqxpk8lEZGQk4eHhDW5lZWXWii/SgCXr+V/r+B+3xMREa8UXacDSn6E3btzIvffeS3R0NHPnzuXChQvWiC0/Am2mEW2JiooKHnvsMfr378/27duJjo5mxowZlJeXNxj38ccf641AWr3r1fPrr7/O3r17efXVV0lJSaG0tJQnn3zSzqlFmna9ms7Pz6e6upq0tDTS09Prbz4+PnZOLnKt69Xz1TWcnp7O/Pnzad++PbGxsXZOLnKt69VzSkoKL7/8MrNnzyYlJQVnZ2emT5+OyWSyc3KxBzWijdi1axeurq48/fTThIWFsXjxYnx8fEhNTQV+mEF69tlnWbx4Md26dbNzWpHmXa+eTSYTS5YsISYmht69ezN16lQOHjxo59QiTbteTZ88eZKgoCCCg4MJDAysvzk5Odk5uci1rlfPV9ewyWTizTffZMmSJQQFBdk5uci1rlfPmzZtYurUqYwfP56wsDCef/55CgoK+Nvf/mbn5GIPba4RTUtLY9SoUURGRjJjxgwuXrx4zZivvvqKgQMHYjD88ONxcnJi4MCBZGZmAj/8tef06dOkpKQQFRVl0/wiV7NEPS9YsIC77roLgOLiYrZu3cqgQYNs9hxErmaJmj516hQ9e/a0aW6Rxliinq+2evVqevXqxYMPPmj17CL/yhL1nJ+fz4ABA+rHe3h40KNHDw4fPmyLpyCtTJtrRLdv3058fDybNm0iKyuLpKSka8YUFRXRqVOnBvcFBARw/vx5AHx9fdm0aRMRERE2ySzSFEvU8z8kJCQwZMgQMjIyWLRokVVzizTFEjV98uRJLl++TFxcHEOHDmX69OmcPn3aJvlFrmbJ1+jvvvuO7du3M3v2bM3ui11Yop4DAgIwGo3120wmE0ajsdGmVhyfi70D2NrChQuJjIwEYOzYsWRnZ18zprKyEjc3twb3ubm5UV1dbZOMIjfKkvU8fvx47r33XpKSkvj1r3/Nzp078fb2tl54kUZYoqZPnTpFRUUFzzzzDF5eXiQlJTF16lRSU1P1PVGxKUu+Rn/44Yd069aN4cOHWy+wSDMsUc8///nPeeutt4iOjiY0NJS1a9dSUlJCTU2N9Z+AtDptbkb06u90+vj4cOXKlWvGuLu7X/MGUF1dTbt27ayeT6QlLFnPISEh9OvXj5UrV1JZWcnu3butE1qkGZao6eTkZLZt28bgwYOJjIwkPj6e2tpa9u7da93wIv/Ckq/RqampjBs3TrOhYjeWqOeZM2cSHR3N+PHjiYqKIi8vj+HDh+sP321Um2tEnZ2dG/zbbDZfM6Zz584UFRU1uK+4uJjAwECrZhNpqVutZ5PJxJ49expcrsXDw4Pg4GCdJiN2YYnXaHd3dzw9Peu3ubu707Vr12tOdRSxNkt95jAajZw4cYJRo0ZZJ6jIDbBEPXt4eBAfH8+hQ4f4/PPPiY+Pp6ioiODgYOsFl1arzTWiN6J///5kZmbW/4KZzWYyMzMbfLla5MeiuXo2GAwsX76cHTt21I+/dOkSeXl5hIWF2SuySLOaq+na2lqGDRvGzp0768dfvnyZvLw8QkND7RVZpEk38pnj8OHDdOzYUTUsrd716nnVqlVs3boVLy8v2rdvj9Fo5Pjx49xxxx12TC32okb074qKiqiqqgJgzJgxVFRUsHz5ck6ePMmLL75IeXk5999/v51TityYltRzXFwca9eu5dNPP+XEiRMsXLiQkJAQfQ9JWpUbrWkXFxeGDh1KQkICBw8erK/pwMBARo4caednIfKDln7myM3NpVevXvaKK9KsltRz586dWb16NZmZmWRnZzNnzhzuueceevfubc+nIHaiRvTvhg4dyq5duwDw9vbmzTffJDMzk/Hjx5ORkUFSUpLOX5cfjZbU87Rp05g6dSrLli1j4sSJuLi48MYbb9QvvS7SGrSkppctW8aIESOYN28eEydOBGDdunW4uLS59fmklWrpZ47i4mLat29vr7gizWpJPcfFxTFmzBhmzpzJww8/zG233caKFSvsGV/syMnc2AneIiIiIiIiIlaiKQ8RERERERGxKTWiIiIiIiIiYlNqREVERERERMSm1IiKiIiIiIiITakRFREREREREZtSIyoiIiIiIiI2pUZUREREREREbEqNqIiIiIiIiNiUGlERERERERGxqf8HPG6GqajOPt4AAAAASUVORK5CYII=\n",
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",
       "text/plain": [
-       "<Figure size 1152x432 with 1 Axes>"
+       "<Figure size 1600x600 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -666,17 +665,7 @@
     {
      "data": {
       "text/plain": [
-       "h.01    0.0\n",
-       "h.02    0.0\n",
-       "h.03    0.0\n",
-       "h.04    0.0\n",
-       "h.05    0.0\n",
-       "h.06    0.0\n",
-       "h.07    0.0\n",
-       "h.08    0.0\n",
-       "h.09    0.0\n",
-       "h.10    0.0\n",
-       "dtype: float64"
+       "0.0"
       ]
      },
      "execution_count": 10,
@@ -728,17 +717,7 @@
     {
      "data": {
       "text/plain": [
-       "h.01    0.0\n",
-       "h.02    0.0\n",
-       "h.03    0.0\n",
-       "h.04    0.0\n",
-       "h.05    0.0\n",
-       "h.06    0.0\n",
-       "h.07    0.0\n",
-       "h.08    0.0\n",
-       "h.09    0.0\n",
-       "h.10    0.0\n",
-       "dtype: float64"
+       "0.0"
       ]
      },
      "execution_count": 11,
@@ -785,29 +764,29 @@
      "text": [
       "                          AR-X - GARCH Model Results                          \n",
       "==============================================================================\n",
-      "Dep. Variable:                   data   R-squared:                       0.949\n",
-      "Mean Model:                      AR-X   Adj. R-squared:                  0.949\n",
-      "Vol Model:                      GARCH   Log-Likelihood:               -2310.31\n",
-      "Distribution:                  Normal   AIC:                           4632.63\n",
-      "Method:            Maximum Likelihood   BIC:                           4662.07\n",
+      "Dep. Variable:                   data   R-squared:                       0.955\n",
+      "Mean Model:                      AR-X   Adj. R-squared:                  0.955\n",
+      "Vol Model:                      GARCH   Log-Likelihood:               -2280.96\n",
+      "Distribution:                  Normal   AIC:                           4573.92\n",
+      "Method:            Maximum Likelihood   BIC:                           4603.36\n",
       "                                        No. Observations:                  999\n",
-      "Date:                Mon, May 17 2021   Df Residuals:                      996\n",
-      "Time:                        16:04:11   Df Model:                            3\n",
+      "Date:                Tue, May 30 2023   Df Residuals:                      996\n",
+      "Time:                        10:55:42   Df Model:                            3\n",
       "                               Mean Model                               \n",
       "========================================================================\n",
       "                 coef    std err          t      P>|t|  95.0% Conf. Int.\n",
       "------------------------------------------------------------------------\n",
-      "Const         -6.3468      0.283    -22.464 9.346e-112 [ -6.901, -5.793]\n",
-      "data[1]        0.7555  9.631e-03     78.446      0.000 [  0.737,  0.774]\n",
-      "x0             1.7840  6.195e-02     28.799 2.192e-182 [  1.663,  1.905]\n",
-      "                             Volatility Model                             \n",
-      "==========================================================================\n",
-      "                 coef    std err          t      P>|t|    95.0% Conf. Int.\n",
-      "--------------------------------------------------------------------------\n",
-      "omega          3.0493      0.753      4.052  5.085e-05   [  1.574,  4.524]\n",
-      "alpha[1]       0.1626  3.741e-02      4.346  1.385e-05 [8.926e-02,  0.236]\n",
-      "beta[1]        0.3401      0.129      2.631  8.523e-03 [8.670e-02,  0.593]\n",
-      "==========================================================================\n",
+      "Const         -6.4609      0.226    -28.561 2.064e-179 [ -6.904, -6.018]\n",
+      "data[1]        0.7695  8.995e-03     85.552      0.000 [  0.752,  0.787]\n",
+      "x0             1.7394  5.329e-02     32.643 1.021e-233 [  1.635,  1.844]\n",
+      "                               Volatility Model                              \n",
+      "=============================================================================\n",
+      "                 coef    std err          t      P>|t|       95.0% Conf. Int.\n",
+      "-----------------------------------------------------------------------------\n",
+      "omega          0.1073  7.460e-02      1.438      0.150   [-3.895e-02,  0.253]\n",
+      "alpha[1]   8.6689e-03  1.513e-02      0.573      0.567 [-2.099e-02,3.832e-02]\n",
+      "beta[1]        0.9717  2.616e-02     37.149 4.549e-302      [  0.920,  1.023]\n",
+      "=============================================================================\n",
       "\n",
       "Covariance estimator: robust\n"
      ]
@@ -887,8 +866,8 @@
     {
      "data": {
       "text/plain": [
-       "array([[0.94510757, 0.97700028, 1.13035701, 1.10824845, 1.26825095,\n",
-       "        1.26590793, 1.23342443, 1.18098812, 1.28265677, 1.2315994 ]])"
+       "array([[1.04095499, 1.27798546, 1.30149937, 1.53498654, 1.60825322,\n",
+       "        1.53831759, 1.55948399, 1.49811376, 1.74794163, 1.54496175]])"
       ]
      },
      "execution_count": 14,
@@ -970,8 +949,8 @@
     {
      "data": {
       "text/plain": [
-       "array([[3.21343234, 4.99297815, 6.29477322, 7.49884207, 8.47501636,\n",
-       "        8.64628226, 8.72478144, 8.78606273, 9.03824963, 9.30368669]])"
+       "array([[2.95980851, 4.9043695 , 6.49587772, 7.50716786, 8.27211937,\n",
+       "        8.71428305, 8.71321666, 8.67711577, 8.80764753, 9.17554847]])"
       ]
      },
      "execution_count": 16,
@@ -987,7 +966,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1001,7 +980,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.8"
+   "version": "3.10.11"
   }
  },
  "nbformat": 4,
diff --git a/univariate/univariate_using_fixed_variance.html b/univariate/univariate_using_fixed_variance.html
index 8c2ab9e2a1..9865133658 100644
--- a/univariate/univariate_using_fixed_variance.html
+++ b/univariate/univariate_using_fixed_variance.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Using the Fixed Variance process - arch 6.1.0</title>
+        <title>Using the Fixed Variance process - arch 6.2.0</title>
       
     
 
@@ -47,9 +47,9 @@
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-        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -93,7 +93,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -103,7 +103,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -190,10 +190,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -755,7 +755,7 @@
                   
 
 
-<h1 id="Using-the-Fixed-Variance-process">Using the Fixed Variance process<a class="headerlink" href="#Using-the-Fixed-Variance-process" title="Permalink to this heading">¶</a></h1>
+<h1 id="Using-the-Fixed-Variance-process">Using the Fixed Variance process<a class="headerlink" href="#Using-the-Fixed-Variance-process" title="Link to this heading">¶</a></h1>
 <p>The <code class="docutils literal notranslate"><span class="pre">FixedVariance</span></code> volatility process can be used to implement zig-zag model estimation where two steps are repeated until convergence. This can be used to estimate models which may not be easy to estimate as a single process due to numerical issues or a high-dimensional parameter space.</p>
 <p><em>This setup code is required to run in an IPython notebook</em></p>
 <div class="nbinput nblast docutils container">
@@ -774,7 +774,7 @@ <h1 id="Using-the-Fixed-Variance-process">Using the Fixed Variance process<a cla
 </code></pre></div>
 </div>
 </div>
-<h2 id="Setup">Setup<a class="headerlink" href="#Setup" title="Permalink to this heading">¶</a></h2>
+<h2 id="Setup">Setup<a class="headerlink" href="#Setup" title="Link to this heading">¶</a></h2>
 <p>Imports used in this example.</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[2]:
@@ -786,7 +786,7 @@ <h2 id="Setup">Setup<a class="headerlink" href="#Setup" title="Permalink to this
 </code></pre></div>
 </div>
 </div>
-<h3 id="Data">Data<a class="headerlink" href="#Data" title="Permalink to this heading">¶</a></h3>
+<h3 id="Data">Data<a class="headerlink" href="#Data" title="Link to this heading">¶</a></h3>
 <p>The VIX index will be used to illustrate the use of the <code class="docutils literal notranslate"><span class="pre">FixedVariance</span></code> process. The data is from FRED and is provided by the <code class="docutils literal notranslate"><span class="pre">arch</span></code> package.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[3]:
@@ -808,7 +808,7 @@ <h3 id="Data">Data<a class="headerlink" href="#Data" title="Permalink to this he
 <img alt="../_images/univariate_univariate_using_fixed_variance_6_0.png" src="../_images/univariate_univariate_using_fixed_variance_6_0.png" />
 </div>
 </div>
-<h3 id="Initial-Mean-Model-Estimation">Initial Mean Model Estimation<a class="headerlink" href="#Initial-Mean-Model-Estimation" title="Permalink to this heading">¶</a></h3>
+<h3 id="Initial-Mean-Model-Estimation">Initial Mean Model Estimation<a class="headerlink" href="#Initial-Mean-Model-Estimation" title="Link to this heading">¶</a></h3>
 <p>The first step is to estimate the mean to filter the residuals using a constant variance.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[4]:
@@ -836,8 +836,8 @@ <h3 id="Initial-Mean-Model-Estimation">Initial Mean Model Estimation<a class="he
 Distribution:                  Normal   AIC:                           4545.90
 Method:            Maximum Likelihood   BIC:                           4571.50
                                         No. Observations:                 1237
-Date:                Fri, May 26 2023   Df Residuals:                     1233
-Time:                        17:06:08   Df Model:                            4
+Date:                Tue, Sep 26 2023   Df Residuals:                     1233
+Time:                        14:48:05   Df Model:                            4
                                    Mean Model
 ================================================================================
                       coef    std err          t      P&gt;|t|     95.0% Conf. Int.
@@ -856,7 +856,7 @@ <h3 id="Initial-Mean-Model-Estimation">Initial Mean Model Estimation<a class="he
 Covariance estimator: White&#39;s Heteroskedasticity Consistent Estimator
 </pre></div></div>
 </div>
-<h3 id="Initial-Volatility-Model-Estimation">Initial Volatility Model Estimation<a class="headerlink" href="#Initial-Volatility-Model-Estimation" title="Permalink to this heading">¶</a></h3>
+<h3 id="Initial-Volatility-Model-Estimation">Initial Volatility Model Estimation<a class="headerlink" href="#Initial-Volatility-Model-Estimation" title="Link to this heading">¶</a></h3>
 <p>Using the previously estimated residuals, a volatility model can be estimated using a <code class="docutils literal notranslate"><span class="pre">ZeroMean</span></code>. In this example, a GJR-GARCH process is used for the variance.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[5]:
@@ -881,8 +881,8 @@ <h3 id="Initial-Volatility-Model-Estimation">Initial Volatility Model Estimation
 Distribution:                  Normal   AIC:                           3881.86
 Method:            Maximum Likelihood   BIC:                           3902.35
                                         No. Observations:                 1237
-Date:                Fri, May 26 2023   Df Residuals:                     1237
-Time:                        17:06:08   Df Model:                            0
+Date:                Tue, Sep 26 2023   Df Residuals:                     1237
+Time:                        14:48:05   Df Model:                            0
                               Volatility Model
 ===========================================================================
                  coef    std err          t      P&gt;|t|     95.0% Conf. Int.
@@ -911,7 +911,7 @@ <h3 id="Initial-Volatility-Model-Estimation">Initial Volatility Model Estimation
 <img alt="../_images/univariate_univariate_using_fixed_variance_11_0.png" src="../_images/univariate_univariate_using_fixed_variance_11_0.png" />
 </div>
 </div>
-<h3 id="Re-estimating-the-mean-with-a-FixedVariance">Re-estimating the mean with a <code class="docutils literal notranslate"><span class="pre">FixedVariance</span></code><a class="headerlink" href="#Re-estimating-the-mean-with-a-FixedVariance" title="Permalink to this heading">¶</a></h3>
+<h3 id="Re-estimating-the-mean-with-a-FixedVariance">Re-estimating the mean with a <code class="docutils literal notranslate"><span class="pre">FixedVariance</span></code><a class="headerlink" href="#Re-estimating-the-mean-with-a-FixedVariance" title="Link to this heading">¶</a></h3>
 <p>The <code class="docutils literal notranslate"><span class="pre">FixedVariance</span></code> requires that the variance is provided when initializing the object. The variance provided should have the same shape as the original data. Since the variance estimated from the GJR-GARCH model is missing the first 22 observations due to the HAR lags, we simply fill these with 1. These values will not be used to estimate the model, and so the value is not important.</p>
 <p>The summary shows that there is a single parameter, <code class="docutils literal notranslate"><span class="pre">scale</span></code>, which is close to 1. The mean parameters have changed which reflects the GLS-like weighting that this re-estimation imposes.</p>
 <div class="nbinput docutils container">
@@ -933,16 +933,16 @@ <h3 id="Re-estimating-the-mean-with-a-FixedVariance">Re-estimating the mean with
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-Iteration:      1,   Func. Count:      7,   Neg. LLF: 255806977215.44
-Iteration:      2,   Func. Count:     19,   Neg. LLF: 930291.4462204215
-Iteration:      3,   Func. Count:     28,   Neg. LLF: 3486.7135083370713
-Iteration:      4,   Func. Count:     36,   Neg. LLF: 2885.692473212838
-Iteration:      5,   Func. Count:     44,   Neg. LLF: 65536430.69379793
-Iteration:      6,   Func. Count:     53,   Neg. LLF: 1935.9527543235638
-Iteration:      7,   Func. Count:     59,   Neg. LLF: 1935.9470521680555
-Iteration:      8,   Func. Count:     65,   Neg. LLF: 1935.9470515573437
+Iteration:      1,   Func. Count:      7,   Neg. LLF: 255813245019.088
+Iteration:      2,   Func. Count:     19,   Neg. LLF: 930291.9264107295
+Iteration:      3,   Func. Count:     28,   Neg. LLF: 3486.6798195388146
+Iteration:      4,   Func. Count:     36,   Neg. LLF: 2885.7035662797234
+Iteration:      5,   Func. Count:     44,   Neg. LLF: 65535916.590182975
+Iteration:      6,   Func. Count:     53,   Neg. LLF: 1935.9527546214572
+Iteration:      7,   Func. Count:     59,   Neg. LLF: 1935.9470520474433
+Iteration:      8,   Func. Count:     65,   Neg. LLF: 1935.9470514368204
 Optimization terminated successfully    (Exit mode 0)
-            Current function value: 1935.9470515573437
+            Current function value: 1935.9470514368204
             Iterations: 8
             Function evaluations: 65
             Gradient evaluations: 8
@@ -954,8 +954,8 @@ <h3 id="Re-estimating-the-mean-with-a-FixedVariance">Re-estimating the mean with
 Distribution:                  Normal   AIC:                           3881.89
 Method:            Maximum Likelihood   BIC:                           3907.50
                                         No. Observations:                 1237
-Date:                Fri, May 26 2023   Df Residuals:                     1233
-Time:                        17:06:09   Df Model:                            4
+Date:                Tue, Sep 26 2023   Df Residuals:                     1233
+Time:                        14:48:06   Df Model:                            4
                                     Mean Model
 ==================================================================================
                       coef    std err          t      P&gt;|t|       95.0% Conf. Int.
@@ -974,7 +974,7 @@ <h3 id="Re-estimating-the-mean-with-a-FixedVariance">Re-estimating the mean with
 Covariance estimator: robust
 </pre></div></div>
 </div>
-<h3 id="Zig-Zag-estimation">Zig-Zag estimation<a class="headerlink" href="#Zig-Zag-estimation" title="Permalink to this heading">¶</a></h3>
+<h3 id="Zig-Zag-estimation">Zig-Zag estimation<a class="headerlink" href="#Zig-Zag-estimation" title="Link to this heading">¶</a></h3>
 <p>A small repetitions of the previous two steps can be used to implement a so-called zig-zag estimation strategy.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[8]:
@@ -1010,22 +1010,22 @@ <h3 id="Zig-Zag-estimation">Zig-Zag estimation<a class="headerlink" href="#Zig-Z
 Distribution:                           Normal   AIC:                           3879.48
 Method:                     Maximum Likelihood   BIC:                           3899.96
                                                  No. Observations:                 1237
-Date:                         Fri, May 26 2023   Df Residuals:                     1233
-Time:                                 17:06:10   Df Model:                            4
+Date:                         Tue, Sep 26 2023   Df Residuals:                     1233
+Time:                                 14:48:06   Df Model:                            4
                                     Mean Model
 =================================================================================
                       coef    std err          t      P&gt;|t|      95.0% Conf. Int.
 ---------------------------------------------------------------------------------
 Const               0.5602      0.152      3.681  2.323e-04     [  0.262,  0.858]
-VIX Index[0:1]      0.9381  3.616e-02     25.940 2.388e-148     [  0.867,  1.009]
+VIX Index[0:1]      0.9381  3.616e-02     25.939 2.389e-148     [  0.867,  1.009]
 VIX Index[0:5]     -0.0262  3.774e-02     -0.693      0.488   [ -0.100,4.781e-02]
-VIX Index[0:22]     0.0499  2.099e-02      2.380  1.733e-02 [8.810e-03,9.109e-02]
+VIX Index[0:22]     0.0499  2.099e-02      2.380  1.733e-02 [8.809e-03,9.109e-02]
 =================================================================================
 
 Covariance estimator: robust
 </pre></div></div>
 </div>
-<h3 id="Direct-Estimation">Direct Estimation<a class="headerlink" href="#Direct-Estimation" title="Permalink to this heading">¶</a></h3>
+<h3 id="Direct-Estimation">Direct Estimation<a class="headerlink" href="#Direct-Estimation" title="Link to this heading">¶</a></h3>
 <p>This model can be directly estimated. The results are provided for comparison to the previous <code class="docutils literal notranslate"><span class="pre">FixedVariance</span></code> estimates of the mean parameters.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[9]:
@@ -1050,8 +1050,8 @@ <h3 id="Direct-Estimation">Direct Estimation<a class="headerlink" href="#Direct-
 Distribution:                  Normal   AIC:                           3881.23
 Method:            Maximum Likelihood   BIC:                           3922.19
                                         No. Observations:                 1237
-Date:                Fri, May 26 2023   Df Residuals:                     1233
-Time:                        17:06:10   Df Model:                            4
+Date:                Tue, Sep 26 2023   Df Residuals:                     1233
+Time:                        14:48:06   Df Model:                            4
                                    Mean Model
 ================================================================================
                       coef    std err          t      P&gt;|t|     95.0% Conf. Int.
@@ -1140,7 +1140,7 @@ <h3 id="Direct-Estimation">Direct Estimation<a class="headerlink" href="#Direct-
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1187,10 +1187,10 @@ <h3 id="Direct-Estimation">Direct Estimation<a class="headerlink" href="#Direct-
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+        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
 </html>
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diff --git a/univariate/univariate_using_fixed_variance.ipynb b/univariate/univariate_using_fixed_variance.ipynb
index 4787b3c1a2..de250d35d7 100644
--- a/univariate/univariate_using_fixed_variance.ipynb
+++ b/univariate/univariate_using_fixed_variance.ipynb
@@ -23,10 +23,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-05-26T17:06:06.042986Z",
-     "iopub.status.busy": "2023-05-26T17:06:06.042381Z",
-     "iopub.status.idle": "2023-05-26T17:06:07.408643Z",
-     "shell.execute_reply": "2023-05-26T17:06:07.407788Z"
+     "iopub.execute_input": "2023-09-26T14:48:03.977999Z",
+     "iopub.status.busy": "2023-09-26T14:48:03.977573Z",
+     "iopub.status.idle": "2023-09-26T14:48:05.058914Z",
+     "shell.execute_reply": "2023-09-26T14:48:05.058289Z"
     }
    },
    "outputs": [],
@@ -56,10 +56,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-05-26T17:06:07.415095Z",
-     "iopub.status.busy": "2023-05-26T17:06:07.413543Z",
-     "iopub.status.idle": "2023-05-26T17:06:07.419388Z",
-     "shell.execute_reply": "2023-05-26T17:06:07.418624Z"
+     "iopub.execute_input": "2023-09-26T14:48:05.063791Z",
+     "iopub.status.busy": "2023-09-26T14:48:05.062484Z",
+     "iopub.status.idle": "2023-09-26T14:48:05.067043Z",
+     "shell.execute_reply": "2023-09-26T14:48:05.066511Z"
     }
    },
    "outputs": [],
@@ -82,16 +82,16 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-05-26T17:06:07.424848Z",
-     "iopub.status.busy": "2023-05-26T17:06:07.423479Z",
-     "iopub.status.idle": "2023-05-26T17:06:08.436935Z",
-     "shell.execute_reply": "2023-05-26T17:06:08.435738Z"
+     "iopub.execute_input": "2023-09-26T14:48:05.071124Z",
+     "iopub.status.busy": "2023-09-26T14:48:05.070048Z",
+     "iopub.status.idle": "2023-09-26T14:48:05.550556Z",
+     "shell.execute_reply": "2023-09-26T14:48:05.549801Z"
     }
    },
    "outputs": [
     {
      "data": {
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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 1600x600 with 1 Axes>"
       ]
@@ -122,10 +122,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-05-26T17:06:08.489299Z",
-     "iopub.status.busy": "2023-05-26T17:06:08.488908Z",
-     "iopub.status.idle": "2023-05-26T17:06:08.510418Z",
-     "shell.execute_reply": "2023-05-26T17:06:08.509558Z"
+     "iopub.execute_input": "2023-09-26T14:48:05.587172Z",
+     "iopub.status.busy": "2023-09-26T14:48:05.586433Z",
+     "iopub.status.idle": "2023-09-26T14:48:05.602355Z",
+     "shell.execute_reply": "2023-09-26T14:48:05.601797Z"
     }
    },
    "outputs": [
@@ -141,8 +141,8 @@
       "Distribution:                  Normal   AIC:                           4545.90\n",
       "Method:            Maximum Likelihood   BIC:                           4571.50\n",
       "                                        No. Observations:                 1237\n",
-      "Date:                Fri, May 26 2023   Df Residuals:                     1233\n",
-      "Time:                        17:06:08   Df Model:                            4\n",
+      "Date:                Tue, Sep 26 2023   Df Residuals:                     1233\n",
+      "Time:                        14:48:05   Df Model:                            4\n",
       "                                   Mean Model                                   \n",
       "================================================================================\n",
       "                      coef    std err          t      P>|t|     95.0% Conf. Int.\n",
@@ -184,10 +184,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-05-26T17:06:08.516336Z",
-     "iopub.status.busy": "2023-05-26T17:06:08.514910Z",
-     "iopub.status.idle": "2023-05-26T17:06:08.574928Z",
-     "shell.execute_reply": "2023-05-26T17:06:08.574037Z"
+     "iopub.execute_input": "2023-09-26T14:48:05.607450Z",
+     "iopub.status.busy": "2023-09-26T14:48:05.605852Z",
+     "iopub.status.idle": "2023-09-26T14:48:05.677839Z",
+     "shell.execute_reply": "2023-09-26T14:48:05.677284Z"
     }
    },
    "outputs": [
@@ -203,8 +203,8 @@
       "Distribution:                  Normal   AIC:                           3881.86\n",
       "Method:            Maximum Likelihood   BIC:                           3902.35\n",
       "                                        No. Observations:                 1237\n",
-      "Date:                Fri, May 26 2023   Df Residuals:                     1237\n",
-      "Time:                        17:06:08   Df Model:                            0\n",
+      "Date:                Tue, Sep 26 2023   Df Residuals:                     1237\n",
+      "Time:                        14:48:05   Df Model:                            0\n",
       "                              Volatility Model                             \n",
       "===========================================================================\n",
       "                 coef    std err          t      P>|t|     95.0% Conf. Int.\n",
@@ -230,16 +230,16 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-05-26T17:06:08.580825Z",
-     "iopub.status.busy": "2023-05-26T17:06:08.579318Z",
-     "iopub.status.idle": "2023-05-26T17:06:09.969815Z",
-     "shell.execute_reply": "2023-05-26T17:06:09.968623Z"
+     "iopub.execute_input": "2023-09-26T14:48:05.683171Z",
+     "iopub.status.busy": "2023-09-26T14:48:05.681634Z",
+     "iopub.status.idle": "2023-09-26T14:48:06.642207Z",
+     "shell.execute_reply": "2023-09-26T14:48:06.641602Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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9hAULFuDzzz/H559/DiAWBuK8887DddddZ1mfPINpc4PhiSeewBtvvIHi4mL85je/wf/7f/8P06ZNQ2lpKRwOB1555RX8/ve/T9peZa677jrstttueO6557BlyxZUV1fj6aefhsPhwNe//nX87ne/M4UwIAiCIAiCyBQymBIEQRAEkXdUVlbirrvuwkcffQRN09I2mLJYn9/85jfxhz/8wfR9d3d3topqUF5eDgDo6Oiw/E1nZ6fy89deew0A8JOf/ATXXnut6Xu7Sr1k5WJu0umUyw6fffYZ/vKXv8DlcuH++++3NGoxxenVV19tSpaVjKEuvxVf+9rX8Pe//x2XXXYZnnzySZxxxhk49NBDje/Lysrg9Xrx9NNP42tf+1pa5x4/fjxuu+023Hrrrdi+fTtWrVqFL774Ah9//DGeeOIJNDc34/777095nsG0OWb4T6Zo9vl8ys9Ze/3d736HH//4x6bvM2mvDocDP/jBD/CDH/wAXV1dWLVqFZYtW4a3334bixYtwiWXXII333xTaYwnCIIgCIJIF0r6RBAEQRBEXlJZWWmoCnm3ejsZxBsbGwHEYjuqYJnPs8lee+0FIJbR24pt27YpP29oaAAwNOVl5fL7/cZ17JYrFdXV1fj1r3+NaDSKW265JWnsSZaAa+vWrWldYyjLn4rjjz8eJ554InRdxz333CN8l+n98DgcDuy77744//zz8cADD+Chhx4CALzzzju2jPqDaXNMwZrM2GyVeI3Vw5FHHqn8frDv1/jx43HqqafilltuwbvvvosZM2agp6cHCxYsGNR5CYIgCIIgGGQwJQiCIAgi5+jq6oKmaUl/s2PHDsOYw8cT5V2Vmdu2TElJCQCgvb3d9F0wGMRzzz2XdplTwTKbL1u2zIhvybN69WrLWJLsnlTl7erqMhSzmTBr1iwj9qfqvnVdz+h5dHR04Morr0R/fz8uvvhiXHjhhUl//61vfQsOhwMLFy7E9u3bbV9nqMpvF6b4XbZsGRYvXmx8fsYZZwCIhSOwaofpwuKoAkBra2vK3w+mze25555wOBwIBoPYtGmT6fuVK1daGoOTtdeamhp88sknKctul4qKCiMhl51nQhAEQRAEYQcymBIEQRAEkXO8/fbb+M53voOnn34aLS0twne6ruPzzz/HL37xC+i6junTp+O4444zvh83bhwqKysBxDKNq2BKzeeffx7r1q0zPu/s7MR1110nJOHJFkcddRQOP/xwAMANN9yA+vp647sdO3bgpptussymzsr76KOPora21vi8vr4eV1xxxaAMcg6HA5dffjmAmMHxrbfeMr4LBoO4/fbbhbLaIRAI4KqrrkJjYyNOOukk3HzzzSmPmT17Nn7wgx8gHA7j0ksvxSeffGJyB29tbcWcOXPw2GOPDWn50+Hggw/GSSedBAB48MEHjc/PO+887Lfffti5cycuvfRSpcFy27ZtuP/++/Hxxx8bnz355JN4/PHHDRU0w+/344EHHgAQU1fPnDkzZdkG0+aqqqoMheidd94pKFo3bdqE3/3udynb63333Ye2tjbj8y1btuCqq66C05neEmT79u245ZZbsGLFCtNGypdffmkYqg855JC0zksQBEEQBGEFxTAlCIIgCCLncDgc2LFjB/7617/ir3/9KyZNmoTJkycjEomgpaUFvb29AIBJkybhwQcfNBSj7Njvfve7eO6553DNNddgn332wdixYwEAl112GY4//nicd955ePnll1FTU4PzzjsPe+65J8rKyrBt2zY4HA784Q9/wK233pr1+7rnnnvw4x//GNXV1Tj99NOx7777Qtd1bNu2DTNmzMAFF1yAZ5991nTcz3/+c7zzzjtobGzEmWeeiZkzZ8LpdGL79u2oqKjA7373O9xxxx0Zl+sHP/gBli5divnz5+P666/Hvffei0mTJqG2thZ+vx833HAD7r77btvnW7t2rWGIbm1tVcaxZPDJlG677Tb4/X689dZbuPLKKzFmzBhDPdrW1mYY3773ve8NafnT5eqrr8Ynn3yCFStWYPHixfj617+OoqIiPPbYY/jFL36BVatW4bvf/S6mTZuGyZMnIxQKobGx0UgMdddddxnnampqwjPPPGPcw5QpUxAOh1FfXw+fzwe324077rhDaPPJyLTNAcCNN96ICy+8ECtWrMAJJ5yAWbNmIRAIYOfOnTjuuONw2GGHCQZqxi9/+UssXrwYGzduxCmnnIJZs2YhFAqhtrYW06ZNw9VXX4377rvP9vMNh8N49dVX8eqrr6K0tBR77LEHioqK0NraarSJU045Bd/+9rdtn5MgCIIgCCIZpDAlCIIgCCLnOP/88zFnzhz84he/MNRq1dXV2LFjBzweD772ta/hpptuwrvvvosDDzzQdPyNN96IK6+8EjNnzkRdXR2WLVuGZcuWGclvysrKMGfOHPzwhz/EpEmT0NjYiPb2dpx66qmYO3cuvv71rw/Jfe2+++547bXXjOvu2LED/f39+PGPf4xXXnkFY8aMUR43ZcoUvPTSSzjrrLNQVVWFuro69PX14ZxzzsG8efOwzz77DKpcDocD99xzD/70pz/hwAMPRHd3N3bt2oXDDjsMTz/9NE477bSMz71p0yasWrXK8j+eoqIi3HvvvXjyySfx7W9/G+Xl5aiurkZ1dTU8Hg9OPfVU3HnnnbjxxhuHrfx2OOSQQ3DiiScCgKECBYBp06bhpZdewp133onjjjsOwWAQGzduRF1dHSZOnIj/+7//w3/+8x985zvfMY754Q9/iF/96leG0XX79u2ora3FxIkTce655+LVV19NyzCYaZsDgEMPPRTPP/88TjzxRBQXF6O2thYejwc33HADHn30Ubjdau3F7Nmz8eKLL+KUU05BSUkJamtrEYlEcNFFF2HevHmYNGmS7fIDwMyZM3HnnXfizDPPxLRp09DS0oLNmzcjHA7jG9/4Bv72t7/hwQcfTFu5ShAEQRAEYYVDT5b6kiAIgiAIgiAIgiAIgiAIYhRB27AEQRAEQRAEQRAEQRAEQRBxyGBKEARBEARBEARBEARBEAQRhwymBEEQBEEQBEEQBEEQBEEQcchgShAEQRAEQRAEQRAEQRAEEYcMpgRBEARBEARBEARBEARBEHHIYEoQBEEQBEEQBEEQBEEQBBGHDKYEQRAEQRAEQRAEQRAEQRBxyGBKEARBEARBEARBEARBEAQRxz3SBcg1Ojv7oOsjXQoCABwOYMKESqqTUQjV/eiE6n30QnU/OqF6H71Q3Y8+qM5HL1T3oxOq99ELq/tCgAymEroOeqFzDKqT0QvV/eiE6n30QnU/OqF6H71Q3Y8+qM5HL1T3oxOqdyKfIZd8giAIgiAIgiAIgiAIgiCIOGQwJQiCIAiCIAiCIAiCIAiCiEMGU4IgCIIgCIIgCIIgCIIgiDhkMCUIgiAIgiAIgiAIgiAIgohDBlOCIAiCIAiCIAiCIAiCIIg4ZDAlCIIgCIIgCIIocAZCEXgD4ZEuBkEQBEHkBe6RLgBBEARBEARBEAQxdOi6jhMfWAQA+Py6Y1HicY1wiQiCIAgityGFKUEQBEEQBEEQRAGjc/9u9gZHrBwEQRAEkS+QwZQgCIIgCIIgCKKA0fXUvyEIgiAIIgEZTAmCIAiCIAiCIAoYnSymBEEQBJEWZDAlCIIgCIIgCIIoYMhcShAEQRDpQQZTgiAIgiAIgiCIAkYjiylBEARBpAUZTAmCIAiCIAiCIAoYcsknCIIgiPQggylBEARBEARBEEQBQ+ZSgiAIgkgPMpgSBEEQBEEQRJo8vrgO176yHuGoNtJFIYiUkMCUKFSCEQ1vb2pFx0BopItCEESBQQZTgiAIgiAIgkiTeeuasaSuGzs6fCNdFIJIiUYWU6JA+ai6Hbe/sxWPL6ob6aIQBFFgkMGUIAiCIAiCINIkGs+iE9FIYUoQBDFSdPTHlKXeQHiES0IQRKFBBlOCIAiCIAiCSBMm2ItQ+nEiDyCFKVGoBCJRAAB1xQRBZBsymBIEQRAEQRBEmjADFBlMiXyA7KVEoRIIx1T+1MQJgsg2ZDAlCIIgCIIgiAyJksGUyAPIYEoUKoFI3GBKjZwgiCxDBlOCIAiCIAiCSBONXPKJPEIn/R1RoATCMZd8spcSBJFtyGBKEARBEARBEGnCXPJJYUrkA3wzJeMpUUgE4wpTitNLEES2IYMpQRAEQRAEQaQJJX0i8gm+lVKTJQoJwyV/hMtBEEThQQZTgiAIgiAIgkgTUpgS+YQQ35GaLFFAkEs+QRBDhXskLnryySejsbFR+d3RRx+NZ5991va53nzzTTzzzDPYvn07PB4PvvrVr+K6667DQQcdlK3iEgRBEARBEIQAW5uTwpTIB3hj0mhwXe7yhVBR5EaRm/RBhU6AXPIJghgiRsRgCgCVlZW45JJLTJ9Pnz7d9jkefvhh3H///Zg+fTouuOACDAwMYMGCBbjgggvw1FNP4YgjjshmkQmCIAiCIAgCQEKxRwpTIh/gjUmF3mIbevz43v+WY8bYEsz72dEjXRxiiDEUpiNcDoIgCo8RM5hWVVXh2muvzfj4nTt34sEHH8TMmTPxyiuvoLKyEgDwox/9COeddx5uu+02zJ8/H04n7SoSBEEQBEEQ2UUzYphqI1sQgkgTvcCVeJ/v6AIANPQERrgkxHBgxDAt8HZN5D8r63vw6KI6/O6UfbD3xPKRLg5hg7y1Jr722muIRCK46qqrDGMpABxwwAE488wzUVNTg5UrV45gCQmCIAiCIIhChRSmRD6hUQhTokChGKZEvnDly+uwuqEX17+xcaSLQthkxAymoVAIr732Gh555BE899xzWLt2bVrHL1u2DABw7LHHmr477rjjhN8QBEEQBEEQRDZJKExplU7kPjpnJqUmSxQSQRbDdITLQRB26egPjXQRCJuMmEt+e3s7br75ZuGzQw45BPfddx/22GOPlMfv3LkTZWVlmDRpkum7PffcEwBQV1eXncISBEEQBEEQBAclfSLyCUF9R1I8ooBgLvnUrgkiM9Y3efHQF7X49Yl7Y/bkipEuTk4xIgbTc889F0cccQT2228/lJWVYefOnXjyySfxxhtv4Cc/+QnefPNNVFQkr6j+/n6MHz9e+R07tq+vL+2yORxpH0IMEawuqE5GH1T3oxOq99EL1f3oJJ/rnY+VF9X0vLyHkSSf674Q0DH8z34465y/BLWxkWco617T9YTCVKf6ziWon09OLj2XS19YAwC4eu46fHTNNwZ9vly6t8EyIgbTa665Rvj7gAMOwN///ncAwBtvvIG5c+fipz/96UgUDRMmVKb+ETGsUJ2MXqjuRydU76MXqvvRST7WOx+3tLi0CBMn5t895AL5WPf5ildPrGCrxpSNWJsdjjovKy82/k3vZu4wFHXvD0WNf7s9LqrvHIT6eTMOhyMn22pvIJKT5RpJRswlX8X555+PN954A6tWrUppMK2oqLBUkPb39wOAkAzKLp2dfaTmzxEcjlgHS3Uy+qC6H51QvY9eqO5HJ/lc75FoIlqety+Ajo70vZpGM/lc9/lKV7fP+HdPzwA6yod3GTicdT4wEDD+Te/myDOUdd/jCxv/DoUiVN85BPXz1ui6nrNtNRvlYnVfCOSUwXTcuHEAAJ/Pl+KXwMyZM7F69Wq0t7eb4piy2KUslmk66DqFP8k1qE5GL1T3o4Mvd3RhUW0Xfn3SXgCo3kczVPejk3ysd85eioim5135c4V8rPt8RdPEf4/Ucx+eOk+oaal95Q5DUff+cEJhqlF/kpNQP68mV59JrpZrpHCOdAF41q1bBwCYPn16yt8eddRRAIAvv/zS9N0XX3wBADj66KOzWDqCIAhiKHh00U68vKYJaxp7R7ooBEEQttC4FQUlfSLyAR0692+CKAwC4cROABl6iHyBmmr+MOwG05qaGvj9fuXn9957LwDgrLPOMj7v6+tDTU0N2trahN+fe+65cLvdePjhhwXX/M2bN2P+/PnYe++9ccQRRwzRXRAEQRDZgk12QxGaPhAEkX9EyWBK5AF8M9XIskQUCIFIQmGqkxmKIIgsM+wu+W+//TaefPJJHHXUUdhtt91QWlqKnTt34rPPPkM4HMYVV1xhqEcB4IMPPsDNN9+M733ve7j77ruNz2fNmoVrrrkG999/P7773e/i9NNPx8DAABYsWAAA+POf/wynM6cEtARBEISCSNxPUKcFHEEQeQJvfCKFKZEXjKJmSvOJ0UMwQgpTgiCGjmE3mB5zzDGoqanB5s2bsWLFCgQCAYwbNw7HH388fvSjH+G4446zfa6rrroK06dPx9NPP40XXngBHo8HRx55JH75y1/ioIMOGsK7IAiCILJFOBqb4WopfkcQBJEr8Ao9UpgS+QDfZklhShQKvEs+tWuCILLNsBtMjz766LRii5577rk499xzLb8/++yzcfbZZ2ejaARBEMQIEI4bG2iiSxBEvqALClPa7iFyH36EpeGWKBR4l3yCIIhsQz7rBEEQxIgSiTKX/BEuCEEQhE1IYUrkG7ybeqGPtw6HY6SLQAwTosJ0BAtCEERBQgZTgiAIYkQxXPILfQVHEETBwPdWFMOUyAcEheloCmhKFDRC0ieaRxIEkWXIYEoQBEGMKGGNFKYEQeQXOilMiTyDb6aF3mTJcDZ64BWmVOsEQWQbMpgSBEEQI4au64bClNY3BEHkC5oQw5Q6LyIP4F3yR7AYBJFNAhHOYEoNmyCILEMGU6IgaOsL4osdnbSjTBB5Bq/M0mgJRxBEnkAKUyLf4JspzZeJQiEQTrjkU2gngiCyDRlMiYLgO48txa/nbcRH1R0jXRSCINKAV2bRPJcgiHyBFKZEviHEMC3wJktJn0YPQVKYZow3EMbFz63CnBUNI10UgshZyGBKFBTLdnWPdBEIgkgD5o4PkDKAIIj8gZI+EfkGryot9CbL3yupaQsbwSWfPJXSYl2TF5tb+/H2ptaRLgoxwjhpj8kSMpgSBEEQIwZL+AQU/gKOIEYzmq4XlOGCvxcymBL5gPj6jZ42O3rudHQiuuSPYEHyEH88YRY9NoJU+daQwZQoKApoLUYQowJeYVpIxhSCIBIEIxq+/8Ry3PTW5pEuStbgF+YUw5TIB/g44aNpuKXXs7DhFaZEejBjM3l4ES6yl1riHukCEARBEKOXcJQUpgRR6DR7A6jvCaBzIDzSRcka/AIzEqXOi8h9eJvIaDIxxTZjyRpQqFDSp8xhClOafxMxhSk1BBWkMCUIgiBGjAgpTAmi8Im/2qFo4Zhp+O4qSn0XkQcIzbTA2yzvXkrGoMImQEmfMiYYiRmbaf5NuMgl3xIymBIEQRAjBsUwJYjCh7kCRzS9YBRAQtKnAjIEE4ULnxCn0MdbSvo0egiEeYMp1XU6+A2X/BEuCDHiOMkqaAk9GmLUENV0/OyFNbi5gGKoEUS+wydLKRRDCkEQIvxiLFwg7ut8f0UKUyIf4N/D0dRiR9O9jkYCkYRLPtV1ehhJn2gMG/U4SWFqCRlMiVFDdXs/1jV58WF1+0gXhSCIOGLSpxEsCEEQQwa/GAsXiBqT768ohimRF/AG01E04NJmbGETjJCnUqYESGFKxCGDqTVkMCVGDTQYEETuISZ9opeUIAoRXVCYFobBVEj6RBMMIg/QMDo3KEfTvY5G+KRPo2kjIBv4I6QwJWI4yV5qCRlMiYLCbndPAwNB5AZC0qcRLAdBEEMHP+SGCkSNKSR9IoMpkQfwbVYbRSMubcYWNrzClGo6PYKkMCXikMLUGjKYEqOSAlmvEUTeIyZ9oheTIAoR3jhTKApTPoEOKUyJfIAfYkfTcEuvZ2ETIJf8jGExTGn+TZDC1BoymBKjElKYEkRuwMcwpYkuQRQmmqAwLQyDqUYKUyLP0EepSz7JDgsXTddFhekwN+x8NzSyhFn5fRdENiCFqTVkMCUKC5s9Pi1uCCI34NVmtJFBEIWJmPSpMN5znWKYEnkG30z1UWQiGU3hB0YbvLEUGN6NgO0dAzjtP4vx3IqG4btolkkoTEe4IMSI4ySJqSVkMCVGJTQuEERuwBsaaMJGEIVJYSZ9SvybNmGJfECIYVrgTVYbRfc6muETPgHDu77b0OSFNxDB8l3dw3jV7MKeHwkWhp9ce+ZkL7WGDKZEYWHzZafFDUHkBkLSpxybPBAEkR14t8VCcckXFaaFcU9EYSOMsQU+3PL3SnOLwiIc1XD3h9uwcHuHQmE6fHXNEhjmc/cfoKRPRBxyybeGDKbEqITmTgSRG/BJn+i1JIjCRFCYRgrjTefvgjZhiXyAb6X5HnsxFaM1wdVoYE1jL15d24zHF+9CICwZTIexHGyjLJ/fJeaST5sKBClMrSGDKVFY2I1hSgMDQeQElPSJIAofPoZgOJ/lOByCS75OC04i9xFUlyNYjuGAN2Lls0GLMMOMfKGIZiQtYgxnXYci+W8wZc+P5t+FQ6bt0UEKU0vIYEqMSmhhQxC5ASV9IojCh3+1QwWY9AkglSmR++gW/y5ERtO9jjbYvDGq64bClKnjhnMaGY73+VFNx5sbWrCzyzd8F88COvf88tnoSyR48PNanP6fxWjxBtI+1sUZTKk9iJDBlBg18IubAlmvEUTeQ0mfCKLwEV3yC09hCoh9GUHkInwTLfQNSlKYFi4sDram64ZCstTjAjC8BtNIvByrG73483vV+MGTK4bv4lkgGEn4ftArMnRsbPbikS93muLtDgWLa7vQG4hga9tA2sfyAlPaABZxj3QBCCKb6En2kfmXv9AnigSRL5DClCAKn0JM+iQbYchgSuQ6YiKkESzIMCAah0euHET2YXGwNS2hkCzxuDAQig6vS36eq2/4+K+0qTB0/OT5NQAAt9OBn399zyG9VmAQYSJ4hWlU0xHfgyBAClNiFMH3HbRzQhC5AcUwJYjCR1CYFuiLTgZTItfh38OCb65kMC1YQoZLfsJAVOqJmTSGs6rDeb75x8d/pVdk6KnpSF/1mS6BMItJm36NOrmsTzSfESGDKTFq4BM9UTdAELmBaDClN5MgChEh6VPBuORTDFMiv9CT/FVokEt+4cLHDmUGooRL/vDVdTjPFaZ+UpgOK8PxhFmdZjIfcZJLviVkMCVGDaQwzQ8C4Sj++M5WvLexZaSLQgwDEY13yR/BghAEMWSISZ8KxWAq/k2KDCLXEY2II1iQYYDvZWhuUViEObfjoKEwjRtMh7MceT6W8QrTQu8PcoHh6IdYnWZSn5y9VBCZEWQwJUYR0VE0UcxnXljViPkbW3HFsytHuijEMCAmfaIXkyAKEcElP89VOQxZyUQbsUQ+UeitlX8/tYK/29GF4ZKv6YZLflncYDqc3XC+b/75w5xLPs2/h5yhfsIRTTfmV5msp/gjaD4jQgZTYtSgk3tOXtDeHxrpIhDDiJj0aQQLMkK8sKoR17++Me+VCgSRDH7MLZS2TgpTIt/g38NCN5BQ0qfChY0hmg4E44q6kngM0+Gs7Hzv83mXfHpHhp6h7nMDYV4xnIHBlDxxLSGDKTFq4N99MpjmLo7UPyEKiNEew3TOigYsrOlEdfvQB4MfanRdx0AoMtLFIHIQfvzN98zCDPkuaIFB5Dr6KDIijqZ7HW2EOBUd63fd8QCMw6owzfN43MGwmPSp0DdRCh3BYJpB0+RrP983A7KNe7gv2NrainfeeQefffYZduzYgY6ODowZMwZf/epX8fOf/xxf+cpXbJ1n6dKluPjiiy2/v+uuu3Duuedmq9hEnpCsrx9NsZvyGQdZTEcVgsF0BMsxUjCXqEgBqO4eW1SHJ5fuwuMXHIZDdqsa6eIQOYRegApTeXEZyWSFQhDDiD6KhAPkVVa4sDEkoulgU0hX3GA6rDFM83wxyStMgdizoyXY8JHt1sPXZyYxSPk+kzaARYbdYPrss8/i8ccfxx577IFjjz0W48ePR11dHT788EN8+OGH+Mc//oFvf/vbts939NFH4+ijjzZ9fsABB2Sz2EQBIChMqSMYUlr7gqgqcRtB2AnCCjHpk/hedvtC+GRbB07ffzIqiod9uBoWmMG0ELqkza39iOrA1rZ+MpiOIja39mFzSx++d+g0OCx2vPjmne9x3xjyO0sLDCLX0UdRLE9yyS9c+DiNbD3ndiWcZnVdtxyLsluO/B7L+KRPQOydcZLFdMgY6n5ITOI1SJd86jQFhn0Feuihh+LZZ581GTlXrFiBn/zkJ/jjH/+IU089FUVFRbbOd/TRR+Paa68diqISBQZvJKV+YOjoGAjhnP8uwyG7VeGx8+0pxnmGY5JD5A6iS7743a/nbcTGlj4s29WDu886cJhLNvTwAdoLwdjCJljBPHdTI9Ljbx9ux8aWPsyaUI7DZ4wxfR8IR7Fwe4fxd6RQXPJNCtPCuC+icBFDU41cOYYD3jhMSZ8KC7bppmm6Me9wc2sHTQdcaS4lev1hlBe5BMNrKvI9gaFJYaqTxnQoGerWIihMM5iGa6QwtWTYY5iefvrpSkXokUceiWOOOQa9vb3YunXrcBeLGAXwfQftnAwdTb0BRDQdDT3+jI6noXp0ISZ9Et/LjS19AICPqjswnDy7vB5XvLQWTb2BIb0OH2+oEPokNsGSVQtEYdMXjMWtre1Ux+G984NtWLCpzfibFKYEMTLwLbTQ4xWSwrRwYfPGqJ7od92chTTd6m7xBnDqfxbjR8+uyqgc+Qo/BwUKfxNlpEm3z41quqmOkuHnY9JmojCVrk0kyKmkT263W/i/HXbu3ImnnnoKjz76KF5//XW0trYOVfGIPEdUmFJHMFSwIOiZdrYkMB1d8KqsXBmf//1ZLVY19OKHT68c0usEOCVmIcRY00hhOiph73BDj3qD4d3NbcLf+b7IZMhvLClMiVyHn/sWemsV7rUAxlciAZ84kPW7bs6XPN36/nxHFwCgttOX1nGFqTDNnPpuPx78vBZdvtCgzlOopPt0f/r8ahz/7y/RF7CXTDUw6BimiX/nedPOOjkTFK6pqQmLFi3CpEmTsN9++9k+bv78+Zg/f77xt9vtxoUXXogbb7wRLlf68RPJWJM7sLpIVSdCB++w/r3onkN1PVQw9VBU0zN6xk7uIKqjwiei2MhQ1ftItAVfOIqopqXlopUOQSHeUP6394TCVEv7Xuz290Tuweq9oTdgq/5CUd1U3/lY7/LiMqpnNuaNVvK57gsBHcP/7IezznXp39TORpZs1j2/6aYymKZ7Hf7QdI4LKxL95VM7C0reQIN9T15Y1Yi5a5owrtSDC4+aAYD6eR4HzM8h2XPZ3NoPAFjZ0IOT9p2Y8vy8d5eewZpCsJNkYT5TSHWeEwbTcDiMG2+8EaFQCNdff70tQ+f48ePx29/+FieddBKmT58Ov9+P1atX4x//+AeeeuopOBwO3HTTTWmXZcKEykxugRhCUtUJrxwtKfZg4kT178srvMa/q6pKLX+Xyyyu6cQLy3bhD2cdiIkVxSNdHCXFLTG3TE1HRs+4rCwRv3j8+AqKaVrg6Fz9FhV7AKjf+eF8X8eUetDrDwMAwh4Ppo4vG5LrtIcTfVd5Rcmw3aM3EEZLbwD7Tcnu9Zxxw7LD7cr4XmgMzj/YkrGlP2Sr3h0up+l3+VjvFU19wt9lw/gOFxL5WPf5Sll5Yt5YVlo0Yu11OOq8OD6fAICqMYk5f68/jKoSN80tR4is1L0zsYntdMdsBhXcmmj8hAqUpJF0trKyxPh3Ou+ESoWXT2OALokBxo0vR2WJx+LXqWG6UofHXRBjfLbxFInPxQGHrfYyxqbNwl3cbfy7pCz9/t3FtYeKyvy0kwwVI24w1TQNN910E5YvX47zzjsP55xzjq3j9t13X+y7777G32VlZTj11FPxla98BWeffTaeffZZXHbZZZgwYUJa5ens7KNYNzmCwxHrYFPVCa9QCwTC6OjoU/6u15uIqdnVPYCOiswHhZHih48vAQD4A2HcddYBI1waNR3dMYNpOKpZ1kUyAv6EK0dbRx9cNKktaPzBhKuJPxAzUqre+UzaUqaEuF3ajTs7UaoNTUzOlvbEPfX0+obtHq99ZT0W7+zGK5ceiZlZNAYHQ7Hn1NMXSPte5P5+IBSBrgMVxSM+TSFSwN6Xuo4BtLd7UxoiBvwho33YHedzEX5OAQDdPcP3DhcC+Vz3+UpfXyJsRv9AcNjb63DWuY+bS3Z3+9BR7sGKXT248uV1+N6hU/H70+17MxKDJ5t1PxBI1G1/3P07ws0lOzr60jKY+gaCwrF2CSriS+bTGNDdFxT+7ujoR7Ak8zlXb3/sfH39gYIY47NNICjaKHTottpLf7+9OXVHTyKkRJ/NY3jCnEt/V/cAOioHZydhdV8IjOhKRNM03HLLLZg/fz7OPvts/OlPfxr0OSdNmoRTTjkFc+fOxdq1a3HyySendbyuU3DwXCNVnei8Sy+sfxuVYiXmcz039PhztvwsfmFU1zMsY2KxHY5oxu4xMXLouo4vdnThgCkVWVc2865VLAam6p0fzvYe4mJwtniDQ3ZtX4hL+qRl+r6kD0vI1uINYs9x2TOYsj42GNEyvhddj2VRP+HfiwAAi351HDxDFBKByA6s3n3hKLp8YYznvARUhKLmtp6Pcy/ZGzOsuC+Z7e0DqCh2YWpVSfIfjiLyse7zFTkR0kg99+G4Nh8XnM35H/1yJwBg3roW3HKafYPp2sZe7DamBJNy1LMrn8hG3fOxQ9kc0sX51Ue19K7Bb/Glc1xIEa89n/oyOaHQYOehgSTrP+rnY2hSrHOrZ8KH/HEk+R0Pv6bQ0nwHANElP2JjPjOaGLFViKZpuPnmmzFv3jyceeaZuPvuu+F0Zqc448aNAwD4/Zll6R4MH25tx10fbEOkQJIa5AN28yzwv8v37G+5XH42gYhkGDGaFydREo3cYFVDL37z+kb87aPtWT93WIhhmvXTp01E0wU3q2avOpFNNuCTPg3nOx0cZGI2K1iQ+UB4cOMfn+mzc4CSB+Q6fF9vlfiJp3CSPkkxTFO8Tz3+MC6eswpXv7J+KItFEJbwLbQQEg0mg7+9wdzrzk4ffv7iWvx+/uYslIrIBryhkuVNEJI+pZlex4HMPNlUa5R8SjDmj8hJnwZ3vsTccnDnKVR03X7iJ34+Ybd98gbwTJI+8c05kkfteDgYEYMpM5a+/vrr+Pa3v42///3vGSVosmLt2rUAgOnTp2ftnHZ5bFEdXlvXjI0t+SPJz3f4iVCy11vLMcPMYMjl4rPJi47BT8gLZWGd7zT1xowgnQPhrJ87olCYjiSyYqDFG7T45eDhJzfDuTfAJrXZft4JhengQhjkQDMg0oCfmDP1cjLyPbMwQ26nEUUCEJ62viDCUR3t/an7lHBUG9LNGmJ0otucLxcC/Ps5mDGlOR7GoJ0273IGfgwJRc1Jn9Kt70wjf4UUa5R8Gt7kkALaIHsFNqfNZVHPSBJbF9v7LS8msds+eRGGrGRNF6pDkWE3mDI3/Ndffx3f+ta3cM899yQ1lnZ1daGmpgZdXV3C5xs2bFD+/umnn8bSpUsxc+ZMHHLIIVktux36Q7EYKqpOlBhZ+BrJZOcll8jljizIddhdvvQNbHwnXygLaxWRqIYb3tiIOSsaRrooKemPu3kMhUGTr+NceC1lg+lQGi14JeXIKEyze17WPgIKN7V0UGWeJXIXXmHaaENhWijzI7k/TPUOM3c5O+/6VS+vw9mPL8PK+p6My0cQMqIRMQcG3CFEdMnP/F79bP6Tw/Pu0QY/hkQULvnp1jdvkLJ7rK7ryjVKLq/PZPySN9Bgiz5Um/F26Q9GsKU1hwVruv1+l59XOW0aTP2CCCP9OuDLlqwdj0Yx07DHMH3ooYcwb948lJWVYebMmXj44YdNvzn11FNxwAGxhDZz5szBgw8+iGuuuQbXXnut8ZvrrrsObrcbBx98MKZMmQK/34+1a9di06ZNqKqqSmmIHSpYY6X13vDBv9PJ+hR+spMLSrbBkMvl5w2mZzyyBHN/ciRmTrAfJ5F3cSlkl/wPqzvw6fZOfLq9Ez8+csZIFycp/fFg+kMxEYzk2HsZlCYCLX1DqTDlXPKH6d51XR+ySS2ryuBgDabR0dEHFAKaLjo/NvTaUZgWxgRJbpqpwtD4mPrGRpNe2+QFAMxb14wjdh+bSfEIwoRdj6xCgO9lBnOv7L2lsSh34MeQsKEwTWjA0p3aODmLaSSqo8id2kJl1R6s5lW6rqdMiDjcBCRvoMFuooy0wfSP72zFwppOPHvh4dh/Su4lG9Khm+JIW8F7rNh1yfcLa4q0i2crdOGCja2484Nq3H3WgTh+7/QSq+czw24wbWxsBAD4fD488sgjyt9Mnz7dMJhaccEFF+CLL77A8uXL0dPTA6fTid122w2XXHIJLr30UkydOjXrZU+FruvGTmS+KxjzCbsdM//u5/u8J5d3MGWF3psbWnDdCXvZPj4qKEwLY2GtwheKpP5RjmAYTIdEYcq75Gf99GljdskPQNN1YUKdLYTd4GG6+XA0YeDK9qSWLSDkRALpEhbUIznQKAhL5PqxE8M0VCB1Ki8uU/WPPlKqETlEoTdDIfzAYBSmYWYEGnSRiCwRUiR9cnM+s4Nxybc7z7XygFOtz55f2YAnluzCw+cdin0nVaRXuCEk2wrTwBDFx7cL8wir6fDlpMEUsN8X8e3LbpsMDHJNwR9hVYdL67oRjurY0Owlg+lQcvfdd+Puu++2/ftrr71WUJYyLr/8clx++eXZLNqgCUcTyUJUC9FVDT1o8Qbx7QOnDHPJChvBxSjJ7wT3nDyf+eTyelN2t0y3qNlWmN66YDO2dwzg6R9/FcVuyradCQPBIXTJ17KzqMkWTGFaUezCQDCKUFRHly+MieXJM39nghBvaJjuPTiEiaZYvzpYhSlvhBuse/4jX+6EDuCqY2cO6jyEGnkiby+GaWFshJlimKZSmMYNpjrsq41yoEskCgi76qZCQCWSyOSW/UMYkojIjJQK0zRr2glRYQpPemXgUc2rFtV2oTcQwYbmvpwymMqb24NXmI6saIytGXM1WWg6SZ8y8b7jvdYymd4LLvkW12SxnHmD7rx1zZi7pgn3nXMQplaVpH/hPICsB1lEjEdn/v6Kl9bh9ne2YnMux9fIQ+wOjILBdKgKM0zkssF3sMYSUWE6uPts7PXjvS3tqOnwoa7LN6hzZZ0cc81JBovNnHUDm64L58yFZs0UpuVFbkyqiBlJW4Yojik/ZgzytbFNcAgVvWyClc0YpoPZNOn1h/G/JbvwxJJd8Aayn7CMMBsJu3xhwzBoRaEYTOW7SLVIHBAy2Nq7Rg50iUQBkS3VZT4g3mvm5/FRIpucgx9DQsoYpumdT1CY2jzYahxTGbd8oZFxVV++qxvvbW5TfqfruslgOmiFKVNjj9AQbxhMfblpMNVgvw3wc1+7bZIPsZBZDFP19Xk64kkr+fb/1oYWbGsfwKqG3rSvmS+QwTRN/ru4Dm9valV+xy9+k01E6rtTKzAI+9jt4IXd5jyf+OTyTrdJYZpmUSOCsWRwo+6n2zqNfw+FS/VogbnkZ/u1kY0t6aoChgJmMC12O42d0hZv9uKY/nvhDtz78XYA8m7wcClMhy7RVDRLCtOw4G6XeRkzmXAS6cH30ZXFMaelxhRxTAvVJT+VwtQfSt9dLoeHeiIP0S3+XYjw786gkj6FKdRaLqHrutIl3+lMJMdJt6Y0wVBkb/4StujDVXMN/wgZ3X8xdz1ufXsLdinsDrxXLGMw70lU040510i9K+z5DrXCNKLFXNIjGWz+2n00fDu02278g1xT2HHJ74g/W35+zRI8F8pmuAoymKbBppY+PLqoDre/s1X5vd2Gmg034/puP/724TY09Q5dBud8QVi0JHnu/O9yxeDoC0Xx8upGtKaZWCaX15tyDMh0jWCCS/4gb3R7x4DxbzKYZE5/cGiSHsju1rlQRUyBWeRyYlpVMYBEXKTBEghH8eyKBry0ugltfcFB7wZnguCSn+Vr8gbTTO9nV7cfjdy4NphNk8EaByjJR2pYnTsdwIyxsQ2Gpt7k41lU03NmDB4McvNIqTANpf++58ImElE4ZMuImA8I4QcGcZ6RMnYRauRxmf3tciRS46SrnubfBbvjvrzWYaiWLSxnwUit3ToGzGMyP/9k4coy7RK6fKERmc/KDJdL/hvrm/HT59fguRUN6R2o25/7iDFM7Z1e9HTORGGaXGTgD0cT60GuUF1xRW+hbIarIINpGnT7k7v0+W26Ww3GAq/rOryBMK5+ZR1eWduMX83bkPG5CgW7i+Jcc/0FgH9+WoN7Pq7BL+auw5bWPtvJUnJZIZtVl/xBKkz5c0VybHHA611zfeFiKEyzbTCVOspceA5sElw0BApT/u5CUU3MaDlM7zT/fma7PvnTWS0mkuENhPHjZ1bilvmbjc8GozBNNflLxqfbOnDc/Z/jnc1qjxIihrFYdTpQXuQCIKqYrcikfeQa6SpM+UR/djcrMu0Sn1/ZgGtfXT/o8ZgoLHJhjB0u+M0G9q5mcvtGsrbR8+hyGtmLjf3tdDiMuNDp1rOwVrDrkm/xO6VLfnj4kyHx45NTkWWdzT/dTgc8rtj3mfQPS+u68c2Hl+B3b24yPhupzYWES/7QhmCq745t6rekKXbSYb8fycgln0/6lEEV8Ieo2kJHf8IQzdbn/nDUaEukMCUApN6xspvxeDALwGeWN+DUhxajOb6Ar+3MsbiMI4DdIPapOoKR4JNtHQBiqqqLnluNq+aus3VcrpRfhdUCbWOzF395r9rYibKCHyQGG8NUG4TBZDjJ9Uzg/UOU9EB2Z8mFZs0m38UuB6ZWZldhyj8/XRcnN8PVPkNDmGiKP18gnP7EaUf7gClr62BUnoNxyb/hzU2I6sAf3lZ7lAwlHQOhvIm5yp6x2+kwkm7YqbNCUO/Kt5BqU84nzBHtXSPTp/TPT3dgyc5uLNjYkuEZiEJEnAePWDGGBVXSp0xgY1I+91m6ruPvH23H8yvTVMSNEMnG63BE/I6tE1xOhxGLNN25TSZrhXSSPvmGKA9AMvhrqSKSsflnqcdlhCzLZEr41LJ6AMDSuh7u2umfJxuwNUXXECtMmb3HTjuTYynbVT/z6yP7MUwHN78XQ1OYj2/nlMrsvePX9IWwEW4FGUzTIFXbExWm1sqpwQy6axp7yUFLQuiMkvxOVJjmxlOUDYIbmu0lBMvlWEpWMUx/8vwavLGhBX/7cHvS4zPZ6bU+V+LfuazKzeX6BBIK02wvGOTz5cJ7GeQUptOYwjTNXWQreCOJJgXcH67mGRBc8jM/j2rSx9dnwIbKUKZekZhtMJsJ/LH5stgdCEVwxiNLcMpDi0e6KLbgFabuuErFTp3J40Q+It9laoWp9RzR8hqD7BPlDQhidDN6kz5lfq9soyOX55CpWN/ch7lrmvDPT3eMdFFS0jEQwhmPLMHfP1KvFWTPszCnMM00V0EqQ5GyHBb9vWzcikQ1w1U5kzlujz+MJ5bsSnvjnr8P1XNhm9olnoT+VMuShWGkXfJ7A5EhNd6lkwhOl/5tN1SIsOFv83kKwr2Mkj4l3zgQFKbx966bU/MOVuSUy5DBNA3E3Upzo0gWw5QfaAcjWR7quBz5iKgwtX5ZxdhNQ1igNMg0Pl8uqyVlhalc0rru5KroSJbeFUDaNc7hxUEuK0wjUc2o02w3O3lwzVYVDWZxZLjku5yYmuUYplFpcycwhPFEreDfz0W1Xfjze1sxwLkK28EbCOOc/y4zklcxBIVpBpPVBkVigsHEMM3m5stw0diTaGvpTni9gTDe2tBibHAMB+wZu51OuOMZN+zUWSEoEeR+JtW47MsghulgoVyHBE8OT4OyTrbm/CxZW8zYkZ8PcDjHhMGysdmLbn8Yy3d1K7+XN9tY3bqc5s/skk2FqXw471mQyTzkz+9V4+Evd+KKl9amdRx/LaXCNGJWmGZrmjTSBlMAKb0ZB0MgDYOp/BO7Gzn8utDOdSKanlHcUyuUBlPOBpWIF5swmAYLYCPcCjKYpkXyxitkQJW+jmRp4UYGUzNiMifr3/EGiVzZKc50NyZHiq/ErDAVC5tqB9iOkWNRbRc+2NqesizZUnanw65uP+79eDta0jCyDcYoNNSwAN9A9idB5qRPgz//uiYvTnloMd7ckJkrquGS73ZiQlkRgNgzyMYmheyaYzeMCwBUt/Xj8hfXYHVD76DKwBtMv9jRhTc3tOJ/i3eldY6XVzehyRvES6ubhM/5Z5RJ7MR6xWbKoBSmir7ks5pOPLV0V86qq/juMd02d9Nbm3HHe9X4y/vVWS6VNaJLvkP4LBmFoEQwz/OSt3lfBiE4BttMHWQxJTj4FprL88hsoEkblJmSzjidq+RTqTuNjNvqUssu+YyYwjT273Tnkvyyxb7C1J5LvuBZoMUyyftt5qsAYjFCARih+OzCz51UowArQ7Hbacw7sjUvGqkNav66QxnHlNWpnfsU+gxdtx0qhD+3nfYs50DJpK/iD1GJONo5hWmvP4K3N7ViFzdvL+QYpu6RLkA+ITQkTYfHJX6fbFDlG16mC0Bd19E1xIGM8xFB7p5UYcp1PkNYnnRIpyW8t7nN+HcuK0wHqxxKFcM0qun43ZubEIpqOGbPsagq8Viei39Ow/XMrnp5Ldr6Q1jX5MUzF37V8neZGnP/tXAH3E4Hrv5/swZVTrv084lKsvwMh0Jh+v6WNvQFI1hW142zD56a9vG8Sz5zMQZideRyDs74EJXGECHpU4qb//W8DWjrD+Hyl9Zi+W+Pz7gMqoQ89T1mZWcyvAGzWkWXJoJ2E9gJ5VC45A8m8Zv8/g+EIrhl/mYEIxr+394TsPfE8ozPPVQ4uKWNap6RjOW7egAAH1V3ZLlU1rBn7HI6jPfDlsE0hzeJ7JKuwnQglIHBNP1iCeUicykhMJpc8vl/D+Je5YS++bhwzqe6ZsIgK+OLVTgXl9MhjJ/pkJnCVP07eS7nk9ykr3llHba3D+D1nx+NiuLUrSnTPpzfwFOVlLnkF4rCVNd1oe6GMo5pIoZp6t/KGzfys9F1HQ6HA7qu4/o3NqG8yIU7vr2/0P7ttEmTwZS7jjcQhi8UNRLZWpHSJZ97puubvVjf7BW+LwTPIStIYZoGegoDh9wp8mQj87c3EMkbt8LhhH/WyR6PsKuTZ8+xvT+IW9/eYvydy25BqZRlaSlMFROjbl8IgYgGTQf6UrgZyZscw0FbfAduc2t/0t9l4i7sDYTx3IoGPLWsftiyH/OuXJk8w2SuYHL9ZqNdb2qJxQG2s9GpWkSwyXiRK+FiDGRHBawJda5JSZ+SH9uRpcmfakKTbrWq3jv5HFlzyR+UwlTj/q3jk20dxnuTsy6KXPeYD+O9oDCN+0TaqTMrlVA+kcyTSIUviReSFZkYOgRXzLSPJgoZu/HzhoNmbwDXvbreUNBlG9XaIJN7lhWC+UiqkHK5BDN0WfWnVoZUl4NP+pTeNTMRMFi75IvH+6X2s6W1H72BCOoUG8QqMnUSSJX00h/fPC9xOw1lbrYM6yOxHyrfYzoeubcu2IyLn1tlu+7TiWEqCUxNfRD7vrE3gM9qOvHO5jaEIlransnynJs/5PtPrMBZjy9De39ylTJ/FXUM0+THF4LnkBVkME0DvhmoBpyAtAvJIxhMM2xQnUMYjyOf0W1OBMTJU3691G1S0plcmLNtbe1HvcLAEUqhGkwl0ks1SAjxUlIYZaI5XOfCfdrsE/iJ+3C5PvAu+enG2fyouh0nPbgIr6xpUn4v94WDbdfhqIatbf3xcyU/mabruOzFtbju1fXCJJEZFIvdTsMABGQnzqysYkgno2WmyQxkVIbMdN8NlbFRnlyla9DXdB2Niv4kzJ23oz+I+z/doex3VMgLhnc2JVT62ZrYBcLRtMJvpIKv5XwwmPIK03Rc8gsi6VOaCtNMEjJk0gL4d2+0eeTnk5JuJBBVlyNWDADAHe9uxeKd3bjmlfVDcn67a4NUDDaRSi4geNjl+LjSlcIlX15jMJxOcNne07vHTLzR7CZ94j0LInGXfADo9tvzGM1UNZsqaZCfU5g6BqEwVZVuJHJGyPOOdGwm721px+bWfqxrshfyij07ewbTxG90mPtd1lb5U/HtRP7OukzWClPW1lJtTulCGczf8y75KlTzuj/P35j0mHyBDKZpkEqtJiR9kl3yub8zlSxT/FI1/AuerI+2GzckF+kPpe/eOpT0BSL46QurcflLa00TSLl9ywNnqphqqTYXeKVdKqOMlsEkaLjIRGE62ODx/1q4A9/73zJ4A/ZDe/DGsXQn2h/HXYO3tKnVttl2y63pGLCdjbTZG8DaJi8W7+wW2lGAM5i6sqz2408RiurCdVOVd7DhABiqdybdd0Plki+XP12X/Pb+kHKyxZ57Q48ff3x3K+asbMCclQ22zsnXWYs3YLisA9kLTv+jZ1birMeXoaZjICvn459iPhhMmYpXjGFqI+lTIRhMpb+T1Zeu65m55GfQBMTNtNFjMf3la+vx0+fX2Hq2Hf1B7OjMzjubT4gx/0e2f2npSy8mY7pk4/Yimi4Y6PKhT1YhG2NyGZasJ22XfE5hmm7di8/H3thkVQ65/xEN7gnPKrsh9rKhMFXdEpujlXgGpzBVGkxHoI2ZDKYD6YcwtHv7TDVsxzAs2yhMScEVpwhFtbRd8nkbFH8Mf55UBk89Ra6eVJ5uqnd2xc7kRtp8gQymacB3oqqGJLjkm45N/D6giCFnh0xe/tGAXeVorilM0xmYfDlmMO3yhRCO6ugYCJnUr3KMRFmZl2rsF2OYmjtffuMgEE5hMB2BpE92EZW09iZo/tDgDKbPrWhAQ08Ar65ttn2MEMNUT6/dbojHt1GFVgDM9zDY93Jj3B1fdW4AWFnfg3P/twxLd3aLsSK56zIDcUWxGw5HwgiUDUUvf39yZvpUEyJXlqRi2TCYqlzy5eedrsK0uVet0mRt53v/W46ldT0AgBabyQ/4+5q/sVUwcKXauLRrn66PZ7VfuL3T3gEpyOVNHhWs3tNVmGZDsT3SJAu9JBOKivHVkocP4hUp6T8n/t3L5YSC2SQY0bCothsbW/psKbfOeHQpzn9qJVqH2GiXawiL95ErRuz6Q1yAbMz5k8UFzCfsJsfNBQyFqUVBrcaOWAzTGOneouj9Y+8Yq/Ili2EaiWqGB2r3MBpMVYY9tn4qGaTCVMVIvCdmg6k9kVkmZU3HJV989rqp32PXl8PRDTrpU/wQ3mCf0mDKCwPldhyKCpu+KlTq71xbe2cKGUzTIJxilzFgM+lTprEHC1lhGghHsamlL6PdLbsTwFxTmKbTDmTjCiMS1bCuyWtpkBoqeLfeHZ2JODwRTTeFo5BVhKkMEamUl6LCNHnnHU3S+WcTbyCMm97alJbRhJ/02TWM8BOvbl8IP39hjaW7ezLSUbnzLvmA/XenYyCEprhhyzoWVeqd1nTY2JwwmKomGFe+vA71PQFc8+p6oR3y5WATgvKiWLaddIxAqeDrWTY6pjSYDqHCNBsu+SaFaZrjXKOVwVTxXFRxmALhKBbVdgltmz92sbTL/d6WNvzlvWrLdyHd7OJZqh4xQeQQGbt6spg8krXbmMLUfgzTQlCYyk0zWR/hkzdIkm3ucufJZNgSFHEFYJi2A9+fprO5VW3h/VCoiPH0hq9tBMJR0xg31FfnWwG7dLq3LIsV8mETSwVf6ly/B7bWjWq6cm5iNXY4HQ7O8JfePfK/TxXnkRG2mDvIwzbfhviy2zaYZuglEE2xxgioYphmdCXFtUdgeJfH3y6bLvnpjpFRLeEdZudd4vtZHYqkT/H/8+NWKKpLoprU15EVpuw6/OfNKcJHCQZT6Zp28iiovcTyf64HkME0LezGAwFUyoPEv1Op4qwoZIPpda+uxyVzVuOtDa1pHyvs3SRTbQjuCSM/YUgn6YhstGLc9+kO/OyFNbjv0x3ZKpYt+M2BnVzgcpXxQTaKpTJE8J1rKoVpOi75Q9lnL6vrwUfVHXhuRb3tYyIZqF/5PmZlfS/WNnnx2jr7atF0rweY26nct721oQULt5uzcm9oSmRPtLqebOgf7AKOV5imU998OQbi7xrLXuphiWyy7JKfriE6WwY51Ttq3z1Yx8LtnWhWKDzl553uxmCTpcI0+aYJ4/Z3tuKXr23AvxYm+kLVfe01oQxALJP8GxtaMM/i/Un3cWfLoC0qTLNySoGnlu7CaQ8vxmtr099oUcHqx+10wu2yv7kwXDGYhxLWX7G6T/YeycqMZHMQ/vml2+sEwlFc9fJa4+9CMEzboT/AG0ztP7WhmArWdflw8XOr8Mk287iYujxDOzflFcvDZS99cuku/L9/f4mv/fNz/P2j7dz1h/heeUOF4lp2ru8L547BtK7LZztRkEyqkHK5QiAsqthU43+ypE+ZGv74+cudH2wT1jVW2FaYcvfDz4u6/fbW9JkrTJOHfOJjmGYa+9WKEVGYSu3Crs0kXTEN7yVs51hZrGWlMBUNpum75MsCInYMrzC1mmfLZVFds2Mg9UaCan1RKJu2ZDBNg4jQeM3f+4SMx7LBNPF3xi75BZz0aXVjzLgyb336xp98dcm3MoKqsMoGPzeuLpzLqQzf29yGi59blbJjHAxWClO1wXQoFab2XfKHcpLIymF3xxgQBxG7Awrvks8GwUyyfadnMLVeMLR4A7jjvWpc/8Ym03HrmxMGU6sFbDYVpgOhCGp5tXOKd1wI/cD9m4UgGAqFKd8e5XpLNfEa0himNm9tdWMvrn9DHcBdft7pxjC16q9UcW67fGHT5PjjuHHiZa4vVNXZuDKPeC4L9910H3e26kdU7Gff2PXQFzsBAHd9uD35DyWW7OzCH9/datlu3c703hWrxB35BGvyHhv3LSdk6A9FTJ8xhNOk+ZjmrW9BG+d2Z7WoLzT4OVJ6RuLsP5+/vF+Nza39uPFN87goc89H23Hvx7F38Z3NrTj5wUVYOoQx34RESEN2FZH/xPscAHh5daJ/HuppuJj0yfy9nS5IfkdHIpkNEJtHX/rCGlz6wpqMPMqEpEY5sP6xQo7rqRr/LRWmnEUj7aRP0u8/2Nqe8hgrw6281rAymNqOYWrrV2bEpJfm7/kYpuwa6Q4Xuq4r16dDtd4KRzW8vLoJDT3mxJ+ZxjDl1192jNP+NGORC7YHTYcmjTns67C0JhRD+NhRmIp9FTuEn4unGhv5q5gMpvF5xZgSt+Xxqncilzdo0oEMphLNvdYWdDnjrkxACuzMw/8+X1zye/zhYc84monrQb4mfeq3cLNXoUqyYqUsvPXtLdjc2i/s5GcbXiXNG6lUiVTkQSy1wjS5ITGdGKbCuSwah67r6FC44HQMhGwnR2KDRI/NrJfA4JM+scHRypiejLQMpklcSXu5dhnVdKG/WC8oTMV6ikQ1/HvhDny+QwxhMJj+ZktrvzDYp1KR81/zhn5ZYcpUc9FBGHieXlaPHzy5XDD2mxSmQ+ySzxZZqg07uxtIVtnpe3xhLNrRJXyW7jhn6ZJv8dyt3IP456QapyuLxcme26I/suOSz58/GzFmn1iyC3e8V608/0jzxJJdWLCxFUskYw6rH5fLmVbSp0JQmLI7KHLHptLJ6kt27b3ipXU4+cFFyncv3YXSuiYv7v5wG7yBMHqkjXUrt9FCIx2XfCFG7BC8Yr2K+ZqKHl8YL69pwkurm9AfjOAPb2/FQCiK297ekv1CxRFud4QNZ0MpXAiEo1jVkMh4rbqSHU8zU+bpEXqdevxheAMReAMRk+rVDqlycOQKshu1arPdarPN6XAYSsl0b1Fui8Wu1OYRq35GPhdfX/y8yG5onHTDAzFSe8XGysUrTNN9J2+Lrzdlhsoo/9TSetzz8XY8vrjO9B27XzYP8YWjtvJ/8O+GnWL7wvy7lPr3grpb101tkz3zkKQwTWVzkjElfYqfV4yha79e5DUzi386rarE8hhlDNMCmOsBZDA10dBjLcMPp4gHwg+scmchKEwzdskfvqRPqxt6cdp/FuMP72wdtmsCmbmd8i5G+aUwTcdgaq77v324LekxmRjS7MJL/2s7fYahy44cP1Udi8o/8/l4Q0mqOIl23JCeWLoLZzy6VHArD4SjOO/JFbj4udXJCyuVWWXYtj4m/cQcfsFgGjtmIBi11Z55Y2Q6E+YB2SXfoqh13T5865EleHLpLkSiGjZxkyh50rtgUyueXdGA97bEdvE9caPkYIbVTXF3fONcKZ5JVGhniX8PWCpMMy/dg5/XYmeXH08s2WW6jlGeVArTQRjkmr0BnPTgItzz0XalIdOuC6uVgvrOD6rx5/erhc/sxDD9ZFsH5qyIZby3VpimZzB1cx2MamOgQjKYOi1mQXaeNv8sB2vQjmo6Hv5yp/BZLgXL74zXvZXiyi0kSOMNUup7yGWD6T8/rcENb2xM2Yewe7PTR6iSJUQ0PWWIDDtP6WcvrMGra5vx38W7TG1mtChM+4P2XfL555uNp9MfjAhtxW5fbRWWZyjDKORS8p+hvPzt0tpF1Q/ZMer4Q2ojxHDDG+HTiUHP4N+JXFj/WCGvc1XGFqtNIJfTkVAIpnmL8mVKPKnNI1aGW/lc1gpTmy75tn5lhl97qTYHWFlK3E7juaXbNNgcXsZuG0unLYYiGl6JhxJSrbVYH1pZ7EZJfBPTzjNON7lSMluPCtnTUe6L2OX5sToc1UQvRBudtaEYjt+7KoZpqvPIalgew2A6JpnBVGEDyOH+Jh3IYCqRbJ6VaoeOl2nLLwTfYJK55H9U3Y6nl6njIA6nwvTJpbGF/bub24btmkByOfz7W9rwkzmrTQtrQTma5NyywVTTdaWsf7hIxyVfNTik6j+Hso/ijf59wYjRNlUKU9nomWrwFzMFxmIm/uOTGkTiA01nGkmf+Dp/cVWjUgG6oj6mQuCVsi19QfQFI2jsDdgaQNlAl84jz0hhyvUxbHDUYVYvMfzhKB74bAdW1vdIAcQzT/pkZWxdVteDLl8YX+7owraOASlTs3rgZRS5xAE+E9hEm+1+pjIKyxkpGex+y4viClOFEYjR4g3gD29vwZbWPtN3KnijSZ/0Tg9l0qenl9UjENHw8pom5YTGyjVYxir79IZm8/2zd/ODre34+0fbTW1A03X88Z2tuH/hDhz3ry/QEs9WXSotVCJRTbnYtcr26U6iMC12O1HsFs9vZdxw2jB68P2Pc5AG07pu82ZtLimBWL8rtx8jhqnLAbci3q/VLeSyS/7zKxvx6fZOZbvmYffG4hynozBlqPr+SJJFSzJqO32mfirXDNMNPX4s3N6Zde+ldBSmg4kRK1Pf7cdJDy7CL1/bYHyWSVfAt50Sj2uQpbJG8MgasqvYYyjnqB9L8WOVLvk23q1ciWHKiyZU8+xUpJtAZqQwKUwVZbXaBHI5Ej6KsttzKkwKU3fmClPZiOa3cInutunFmXkM0+SGQL/hkp+5wtQKO+/Jwu2dOOmBRbZjPX9Y3W6EMVC14Sg3FxlfXgTAnt0k7eRKg3HJVyhMWRvg1yHhqC6sne28sv74mqssLvZgcwfe+znV2Jgs6RMLCzm1stjyeNX5KYZpgZJst0CIL5Ei6ZPcPviGl0wiftNbm/Hg57WCOysQe5Gs3H2HYvDL9g7kX96vxq/nbUg5OCRzPfj9gi3Y2NKHf35aI3yeKrA7Q3bJv/P9anzvf8vx5vqWFKUfGtJRmMrGFXsMXSclG/1r4wHSs5P0idtl03Rc/8ZGvLiqEW9vbMVAKCoo11Kp2Pj3ble3Hy+tajT9Zle87Px1e7l3zY76LhOXg1ShB1T4FS75gLWa+A9vb8Ezyxtw01ubhYlaOkkxkrnk8zXJFH8RTTf6L2bkk++PDegMNjkdTLfjC8fKyRSEqW6Rvw/2PKKabiyQKoqZwtQ66dN7W9rxzuY2vKhoVyr4yYT8XFN144MxmPLPX6UwtWswVcXc0nRdaYRlmyq3zN+MuWua8HG1qERo8QaNZ83KVOR2YlKFOBkLa7ry2VsZTF2CwlQsV7HbaRjnVb/nsbNI4Z/lYBfSqg204ZhoNvT4ceXLa/FlbZflb4IRzTD2y4ZONhdycQrTSApvHGBoDXm6ruOBz3bg7U3pJZGMRDVhjEi9uIjdm8dGsitLg6mijtPZTOPLWFbkMr2LuWYw/d7/luP6NzZicZbjdPJzpFQKTaFNDnKu+3p8/siHqrDbV/Pzhta+RFggd5biIavQbM6Xh4PhVDom7jV138Tjl5O1jdAz8/q5DYFI+mWws4mVC8gGrnSSPjmd4JIXpXdduV6LbBhMrcZneZNrwEJhGo7qSs+DbCF6sVnbLEo8rowVplbYaWPXv7ERvnDUVqxnXdeFebbq3eXnIhPK7BtMhfi+aW6i2Pm9HEvZSmHKz61CksLUznUCxtpFXAPx8/tU8wnBE9EiL0GVIoYpi2uq9phJVfL8gAymEskaZap4EnyjlDtM/vd2OkjZ3bDbH7Y0fw3FhDibA2owouGN9S34YkcXdlnEwGO4bMwTV9T34O4PtxlxJ2VDqBXyLs+bG2KLqccUsVCywUAogmeX1xuK2EhUw0Of12JlfQ8AoD+NgTKTQXUoJ0VyWAmmzlS7+w4i6RN3bKM3YBr80kn6BMBQsTH84aiRIIN/v3sEg6mdeHyDm8DadfPiN2X4e7cyvn+6PRYjtMcfFibZ7NpLd3bjxjc3JZ1UmJK8WJSb9VnhqGa85ywjuWy4khU02VCYMoNEZbG4u2qF6JKvCecAOIWpYQxJ3MPWtn78et4GrGmMqZOt3MNNngbcNU0xTFPc+2DW0PxO9WAMpt0K9yZN05XGoGBEVIbKxtZaRRbaGeNKDeMTI6LpyvfLKmNnKoWpvBCyMm7YUZgGkqio00VemAP2+4VAOIq1jb2mz32haMr+668fbMPK+l78ilPIyfD1Lk+IBYWpwjXd6h4y6TPtsqi2G88sbzC55coEwlG8v6XNMLZ9saML936S2JBN9fjZ90xh2heMWG6eWcUdVIWdScdg2uxNvAcOB9DrF/vroXzOjPpuP5bVpWcAXSuJAgZLX5BX0SS/Z8FVdQgeT7JN4fb+IP69cAcaevxC3fIeLkOZP4A/a4F4StpCda+ZJFIZKYVp72AVpmlm3M42dmOa20r6ZOWS73Bwhr/07lF+JKp8GoFwFD97YY0RP9NqY0Z+vnwbkp+DVZijvkAE5z+1Ao98uTMrMUxVbZ0ZwEo9zoTCNEtCm4GQvVBhdlnf3CfESlUaTOP14XY5UFkSWwfUdvlSjqHpJuBN1yVfHs+tFKZhSdSSbqgAti5n4cTYedNxyee/lZ8xG1dlwQuQMKKqxt6hSF46EpDBVCJZo0wWD0TTdWHxJJ9HVpimavzy98kMGpkmkUpGNidr/GIr1c67nYGhPxjFq2ubjeQYQknTUJgm/j00k4d7Pq7Bvz+rxdWvrAMQU6M9tazeyBhqZeRSDQSZBHkfyimRrDBlmZ2VMUylmC3pKEz5jl7XdZNhKrXBVPxbVmnzBvzBGEwzGRDk0AN2UCV9AtQKU/78kyuKhMkde27XvLoen2zrwD84I4FMMsMeX262gRHWEopDlmBHHqRLJKNVkQ2Faar3lLWVxO6q/T6WGZNZXFGPy2GUSZX5+8JnV+GLHV34Ip7oSBVf+rOaTnzz4SVYvDOh3OMXLeskg0GmLvkRTU+ZkT7EGcuVBtNQ1Fafr5rc94eiyklYIBIV2qVsCK1TGEx3H1dmus9IVFO+X3Zc8uVyFbudpmQOVi756SpMBxvYXtXH2+0Xbp6/GT9/ca3wWSAcxekPL8Y5/12W9Fg7ier4Razcx7P3TFCY2lBsDGWcxsZee6F27vl4O36/YAtueiumcGnyyuF+UvUhsf974ve9s8uP855aofytlcJUtcAQ+9jkz6meCyvU7QujR4p3PhwK03OfWI6rX1mPrYrkH1ZYJVvLlP60XPKt5+o8ty7YjB89szLF+czHJ9v4f3NDC55d0YA5KxqE92QHnzwzog1ZDPrR4pIvw2qQf90ycskfoYfmHWQM00w26LPF44vrcNKDXxpx5pMxmKRPsRimcYVpmmWU1/SqOce7m9uwrsmLxxbFDKZWoQHk58v3/XLdWcXYfH19M3Z0+vC/Jbsy3iwXbRbm75nNosTtMq6RLVt650Ao6SZsujChEQvZpDSYxj9zOx3wxD3DHvmyDr9OUY60XfIHozDVksQwFQymmvB3OuViBk1DYcq1v1TzCf4y8pyFzdnKlQZTj/CbxPn0gtmYI4OpRHKFqbVqQlbcyW1SDjKfKnObKXZEkoRPySZzuq7j4S9q8VG1OjCz5fWz2MD5BVmqBWA6A8PWttjk3G4Qe35A1Gws6AbLB1ti8V8bemKLsM9qYmo/1qlZGUxVHaOqvYwt9SS9/lC6W7H2Pr4sVgZDYaqKYSpl+0tWxwOhiPDbTm4yEYnqpo2DVIYiuW57kxlMubbZ47efQELX1Qq4VKgG6VBES/o+8wMfb0zuC4jPYVe3H2c/vtT42+FwCAOZrGZr9qoT7gAKhalFogrDJT+qGRNaNnDL92Ryf1IoTE2TihTvqU92R8lAYcpU3xVFCZcTwyU/fk+qBTbfLlm5P6vpRLc/jOV1Pdx1rMuUqrxWisfzn1qB7/53WdLNg0gKhWlUt7fxpophapXpNRjR0MWNW1aqdJ7dx5eaDCkRS5f8IH4zbwN+9ZoY6sWVwmAqG26tNnDsDEWBNFydUqHq4+0Gy2eGe566bj+CEQ1t/aGkdVviTh0vkTeUy308r+pwK1zTrcb8wRjy6rv9uP71jabQRQy7HhnMy2TZrh4A5g2BVEZdlnCSVy3X9wSUY69VmZQxv9JYwLVwfXeXL2Qa44ZDYcpgczIr+HZolWwtU3gDY6p6E8L+WDwffziK97a0Y1v7ALa1D1ieS/WKJtsUZoavJm9AMpiK12jrG5qcBXyS1JFyyWdj+Ui45IseMqmPyx2Fqf32rSJd995ssq7Ji3BUTxkTGgC6TC75qb3WGE4+hmmat5gsUbPVb6yST8nt2irpE2C9Ycn3DRknfeLHkRQKU8PQnMV3MpthV5hxlxnmVEb/hMHUacxFAGBJCu+HVPlpZHyh9H6vSYmp5SNYe+HHorCm2dp45jEMph7Ry05UxNrvd5ft6sG8dc2JMsXfu7Iis0t+QmGq9kAqBMhgKpFUYZpkEivvQsqvhNxZqSbPWhLDX6YK05oOH55YWo+/f7Td8jcqstlpCuqUFAO91UQzmWHMvkt+4t/RFEZWXddNWazThd8FDUY0Q2nGOj4rl3ylwlQqy1/PPACzxpcOqnyDgQ1eB0ypBJAwfljFMOUHApWrC6NVcpnv4FRkPf5wBgpT8VmaFaYJow0/aPK/S9Zmt7b141uPLMHcNU1Jy6FCdNPQENV0nPX4Upz9+DLLwdFKYSobNZfs7DJCDQCxCRdfB6b+yuK9iUQ1U5xY/nHwz4zVVTiqGxPJ0vjAzVTGrJzyIFpkZHXkriOVKZXhwCcZO1NNClQxTAfi5SsvThiRZCOQavHc4w8jEtXwq9c24IqX1kLTE8b9VIvzYsW9q1ApIbt9Iezq9qPLF7bMMi+XweqdSaU01HVdqTC1SgQVCGvChodXaqMqhemUyhIjcRBD7j8YtZ0+fL6jC1/WdgnjY3KXfJfJJd9KbWMv6ZOoCEiGruvY1t5v2Y4zUZgmm0TzSStUoRSM30lJtnRdx8fV7UJCRF4FY5oQc6oOVbxfK6PvYJI+3fTWJiys6cSlL6xRfp9OQkUeWe0jG/llZIUpQ/WOyWM4Q5m8Ig2DKb8p0eINmvqBoVTyAuJcMZURtI9Tv2bbViYmfUp+crFPVj8fvn9KN6ZoMoUpaxutfUGhnuUNpPohSkjKN6fBVkFfIJLRPFnlZj3UsPtOO16hHMN0hAymQtKnNBSmhrtvmkahbMLeMTuq6U7ZJV+pMLV2yWevavou+dKaXfGMPFwH9/ePtlvmUEjmki+XXRUXHhD7nEyrS1DSK05ixDDNUGE6nMnDmOGcKUxVcyNxLmK/z1bZdpbs7MI/PqkxrUmB9BWmfHcXM1iK37M/hTwTETE3gC2X/Hh7LJe87PzSPMZqXql6Z/jY3GzOVuJ2moRP+02uMH4jxEEdof5yKCCDqUSyuSU/sZIbgWzQk7+X/1ZNMvjfmFzy4xP5MYpgu8kmxMxA0eULp1Tj8WSzjfOL7GQKK8BafZhsMS8kfZJ2z19b12y4ifEx78QgzOYyPfBZLU55aHFaLmbJWFnfY3RarJ4HLCYPy+q6cdXLa40JNK/YYxy313gjbpoVQ9lPsba076RyADGjiT8cVbbFsORSm8wOIQ9O7f2Jv7t8YUNpXRRfkbyzuQ13f7jNcnIkv3c9Unw3Oy75yYwWf/twO7p84YxizMqDdLc/jC5fzChsleTLb7FTLRtM5UmYwyH2E3ZjVvYLMT3NsUGFRFnxMke0xEDPdjrDUR0PfFaLUx9ahM2tfSYjVbHLvLttigOdyiWfxTCNxy5K1f4FhWk0mcJUNJhuVLiV6YjFx/2ytgurG71o7EnE2+XDV6jcvMoN95nkBXYpDIF8HFBZOcnDj11Wiy059qFMf1Dtem9lMA1GNMGQKbfp2i6zQcDjdkLu1iKa2iWf3xDgxxU3t6hRuuTLBlOLhsL3U7qu493Nbbjo2VVCIiHBJT9Fg3t+ZSN+9Mwq3PVBtfL7dGOYbmrpw8kPLsILFgnH+LJZ1RFgDo+xobkPv3trM+78YJvxGd+fyO2HPT8Xt0iJ2oiXNxiFaapY6Pz8Kp0Ju9xvpuonWX8lG/lVm6FW5xps0id+UyLAJedipJpzJaPHH8Zr65rR5Qth4fZO5bxVUI2m2GTgN03shIJIh/Rc8rm+3+L58C7yyebYqqP5jX+rtUJrX1AoR2Pc0M2SDe6UNpT6g5HsCBmEuW/mpwlGNFz03Cqc/9TKtMORMCPYcK6lDYWplMsgFSaF6Qipcnv99ts3460NLTjxgUVYWtctxe0dboNp7Hp2Et2yOQObz6iM61Zzcqcz8e79/MW1SYUMH2/rwMLtiezs8mVU4wavWpy7psnwGpSRi8f3ybKR1SqGKT/fyzQ8R8oYpvG5aYnHaQhZ0ulj0rEppEN9tx81HaIwga2BmQhDqTDl46mnWB+rjgMS9f7AZ7V4cVUjfvzMStPv+c1tO++SJhkQzS75uqkcoagmbNakk/SJrSlYu5ND6FnNKVQfN3IbsKzfKXI5BfvD12aOw4+PmM79zv78JZ8gg6lEso3PZPJoeVDlv+4LRPDb1zcK3ysz4iY5PxtEJleKGYSB5PFs/NyLotopsSKbA6qgTlGUVYhvaaE+TLaYF42fiX9/saMLd32wDRc+twpvbWjBmkYv97vkOyCbWvsQ1XRsak3tQpKKYrdTGFhZPVtNHm54cxNW1PfiD29vAaBWHjkdjpRZHDOZXLd4A7jrg20m9zAZNuiP4cIChCKa0hhj5VKrotUrtlHeUNzlC6EzbvSeVlVifP7q2mZhccMjX5Yl5fjPF7V4dnl9Epd8ewrTwYRhEzJYSrtyVgoMuzFMZaWUrouTbNsG0/h5S9yJAVJwybfIYCoHB49oOja39SOqA5tb+20pTOU+KJUohT0bq7ipMiqjhFJhGp+4sudntYnSwrVdHYk+eyBFMhLDYJpyMynR2FhZeFVSVNOxtrEX5z25Aku4uKmArHZX130qA4ZVrK0ei88DkaigFuEVMj2+sPJ6J86eZFLSRiwUpjx8X5rKJb9ImkhbLfZ5o8e1r67HbW9vwZa2fry7uc34XM54m4xHvtwJIOEGLqNWmGqWhpI/vbsVvnAU91nEH+Yn21aLsroun2AwjWi6EZ6DD7XQlSzpE+8GZyOGKdvsGozBlD9jdVs/Hv5yp9Cn8QvUdK4jPycrBZFRjnhB5M0KVXgFS5d8VdKnNGKY8mV2OYAfHLabeP5BxLh/bkUD7vpgG7758BJc/8ZG/Olds7Gfv69U4yGf6ful1U2Ys6Ih47LJ9Akuy+n3/TK8wTKpwVRxuJPrg+Q5EWtT/cGo0Ccy/t9eEwCICtc1Db046cFF+FuanmIqkoW9SYcvdnSisTeA1r5gWglMgUQfMrwu+bH/p7MZAZjVWSOlmFIpTCNRDb+ZtwHPLq83/T4QjuKO96rhC0fx38V1abv3ZhNjQzqF4S/IbfhMriiOH2suq6XC1CmuIK08Kxt6/Pjdm5twy/zNxrOUDXCqtmF3LOFDTui6nlSRmGzDWfXvdIgkMVzxyTRLPZkpTFONj5mg6TrOfWI5Lnh6pXITjPdak4nGx9JYDFP7izMxzFjsHNVxT7LeQMQ050k3FJOccNqkMI3/LShMNV0YW+0ZTOMK0/iaoqk3gIe/qDWt96zaseoKfDx41g49blHB+/vT9hXc9EM2NszzETKYSiSLGRZW7EJ4A2Fc+fJavLBSVHnwL8gTS3eZzpW2wjSurJuiMJgmcz/jB3s5Q3gystnGUxmf+Pu2mnAnW8xrFjGZeGPCXzm1DJA66RNLkmJnR1RZJu6cY0rc+JwzmBou+fF6kxU+DLazo1p8uRxIqTDl76pzIGQr4Pot87fgtXXNuPT5NVha140/vL3FFBMNMO9kAXFloQ2X/GSTZGbUV8Vn5RWm8ntgNXCrrrWxpQ9PLq3Hvz+rFRZFvAGTv+dkCh2rumO8vq7ZMsajPHEXFaDq+wnYNZhKMY81Kc6q3Kas7pC1/4pit2GIEmN/KnZ5uXspZQbTqGaozgaCEdMkgxmxBFcOGzGleNjzYDFMU7nO8fNw1m7Zwq88icJ0i0WcPj4OrKbphrEwlfGm3GYIAb6psbrk+zhNBz6s7kBtlw8fb+sQjuWNLlZuxr2KxTuPldHNrsKUT1ohq6cA4MIjZ2D/qVWmpE/+sJZyUsqXjT9clWzMpDC1dMlP/HspF4eWb4fpuOSnWvSo+vnq9gGc+p/FyoVfquvZMZje8OYm4Xn0ByJGX8L3h91Jwuqwtuh2qmOYqsIixM6TnUnGj59dhSeW7MLj8UQcgOi9kY7CweySn9wQxN5Z2Qivmt/ZSfrU1hfEg5/XCm71bf2hpBnoWZlvPGUfvHX5MbjxlH3E8w9iMtcouYV/IvUrQPKEJjK9ksr8n5/usF2Wfy/cgVvmb1bORwBxDExlZBZj7Kp/W89tpqabZIfvwWR1Dz9uy+ETHAC+MWs8gFgCMcYji3YCiG0O3/Hu1kEZOvkjB2Ov5DeO0lURDUciMhn2riZbZ6kwi2GybwDoD0awsr4nab2qYph+UN2Oz3d04d+f1Zp+v5aL7+wPi7Hxk6lkA+EoXljVaLlBmgmsj7PynGKwa3pcDoyL50dQGUyt2o/L4bAlYngn3nZDUd0IlSI/e9V8M1WIFv7Yuz/chm8/uhQtfcGk62mrcDlWG2zpGKCShZ7jx7ZizsU6Vd+i6Ykko3YUpun2Dfw8iQ/JxvpgQ2FqsfYARG8XO6gMy0XcJqg8x01X+cn/JKrpZjFI/E9+rA5L3iJW06WF2zvwo2dWoqZjwJT0CQCeWFpvmntY1Ymq7vmNPV5hyj/fIik3gJisavj7+qGCDKYSyZM+mRfzTy9rwMr6Xry1UVSN8Ofh3YoZAyqFKW+QlYrB3K7YrhtPm+L8DL5DUylMo5qOui5f2glW0oF3c0uV8dDKpUtlME0EqU58xhe7xCMa83iSxYuNlTP2kntTDPBW8OVt6w8J7qOGwjQ+UFslb2IdkFJh6nQIHXoqvvXIElwyZzW2tPZhIBTBY4t2KlWkG5pjk6yBUBTXvLIe72xuw+OL60y/C3CDl4szKKkM4r5wVFjkJOs/WRudMbbE9J03kIhhesDUSuE7qwWN6n3mlYD8ZoPKvRxIvvji25iKOz/YhieXmTdM5OvFjM2Jv60UoL6w2vAl9yfyZFeuG7shBNjzqSh2GXHZUmVwDkc14/MybjeY3W+fymCqUphKp062mRWLOSwaO1PGMFUYfgcMAzEfwzQRlzES1bC9PbXCtGMglAi9wRlPVH0NU7Om6nN5xSOrSz5TfFTXjQQw8gSJ73etrtLjDyd1++yyMFTIYS4YgbAmjD+8QaNWYTA9eFrsnZYNpp0DQURTGNf4/ra6fQB/fCemzjcb65ymjSYr9z65d03Emk38Xgy3kLyMqUZUVT+/qr4XUU1XJtNJpWjl252VUbuuyyeMv95gxBjz+PsRFKbSddmCs7zIZUthyp7jYIwmqnd7M+cNwive7Mbw1HXd5JKfSkGTWFyJbUplHFXVLyA+h1fXNePpZfV4drmovLz6lfWWZWBl3mtCGSbF54c3n7av8vzpkix2PoO/11QGhb5gZm74gXAUz65owAdb2/Hn99QhLfjN7WwkfeL71mTGfVUSJX5cNClMuWfEEoIyplYVY5+J5fHv1N4vb21sRY2FR40dxBBWmeENhPFlbcKLIV2X/BFRmMb/P9gYpkNh6739na248uV1WB5PPqeCX4uw5ycn++Th53g7u3y2VV8vr27CfZ/U4Nz/LbdTdFvYjWHKEj6NLysyxmmVwcXqfXQ6HEkTrgGJ8DoMI1SadErVeG5XURnVdby6thmdAyE8tsi8fuKximFqtcGWjgFSCMMgHcfuxYHYmMyeW6rTX/biWpz4wJfo9YdtPQ/WVv/27hb8V7GWBBJzPm8gLCZQ5c8jxTBNZjB1Ox0pBUWq49i/A+Go0MZk0YuYRCl1fYiJqXUskOxFRtInfgNe04S451b3e+/HNdjWPoCPqzuMed44yaYgj8uWClOLW2HiLfZM5PlOkcsJpyNhpA4pDNCFABlMJZItWlUxTK0CnvMTAdWErD8YwSfbOnDTW5vwzLL6WDyjJK6urBNRKUzbkihHBYOp1/y7Bz6rxfefXIE3N7RYlh8AVuzqwcur009sA4gDvepF5Qdyq02hZOonq3ikyYLRp3LJD9kc4K1QDYKTKoqE6zFjlJXBlHU+qoHTgdQKU9VkdPmuHjz0+U48vngXfvzMKtP3qtavMrQHjGDhvBumOUEQELtfPs5eMsMXS6gza3yZ6btQRDMMpqfNnoSbT9vXMCpbGUxVr7OdrJR2XfJLPam7UDkWD0OeuPPXsdq55eMc8uWV26kcOD+q6aY4lnZ2h3mFKdvMSOXSFo7qpnhDvPtPLBam+EwNhSnXAtNRmIajuvE9i2GaaqDWhEVzXP2qUJh6OCPQjk6f5WSdV5g2cf9OZZxm10oluBNCNsTLyxvsNE033lX5mnYmLavjbp83vbVZ+b2V673KrRSIuf7zY18qhSlTJ7qlzDHtAyHTOyi/d3J7X7AptiCSx9Fit9MUysTq2cgLL6Ymt1KYDjYbqCqGKXOHUs0hUtXpgKAwVdedposxqtY19RrvPH+fQuJGqa813plidyLpEz9hluMVGwbTzJ+Xagjh64tvk3av4w+bQ8qkMgDKqnaGyuuHPSd5jsOrSrzxcUdOAJQMZsyeUFZkfHbuodNw3zkHxc4/GIOpxWKeZyCceK9TLaAz3YDmN6O2KTasghExznuqOlfFr5ZpH7AOQ5HqvHxZ5DbEP6NGSWE6Y2wpplbF5vi9gYjRvkzutFkKZ5GpwfKj6g7R4y7N87BnPpyhNNm9CmIJLTbHOe+pFfj3QrXaebAxTFMp9nr8YXy5I+aBlixxI9+nsfaVrP740DvBiIbazoQBPplogXnQDISiSdeW6cD6uL4UyfhYfzO+zGPMu5K55MveIk6nw3INydjY0ieE4mIbWaaY+Yrx1SqckQx/qCq5JY/VesQ6SaD9d18l8mKw+X+JJ2YsdSoEESrWNXkR1WPJgII21hChiIbWviAe/rQGj3xZp/S0cTlibfT7T6zA/z2RMNTz81vWDkpsKEz58EB2iEgJ0WRPiG6/OH/i1+R2qoNvwoGwZhLYsUfOzwVi6mcxVmp/MIL/Lq4z5oWfbOswPIdb+gLG+Dq1ShQcyX3Yx9s6lKHFrGq+Mb6xxytMSzmxELNFsHWcqDAlg2nBkqyzUGVSsz5P4t+qCdlAKIob39yEj6o78MDntbjj3a1JJ3GJGKZFkOF3wmX4yZnK8DVnZUzJ8NDnO4XP5bu7au463PPxdqxp6LW8lmUZpAyB3b6QcH/8v60ea1KXfIs6Sxb3lO/kVMcbO6IZTvBVbmNsQRPVxYzhVgbTtv4QtrX3qw2mDodpl0dG9Vh0PWb8Bux3ZPKkBBCDhRsG06jaJR8AFm5PhCSw2pToD0ZQHV8MHbHHWNP3UT3RDiZVFOHcQ6dhv0mxzHxBiwWEqm6t1FZskR/RdMnIb/2cStzJFaaAWUViXE/oTzTh2akW6poUC4lHNpjKBpKorpsMfXyyEKtJPVNBVxQlXPI3tvQbi3RVG9KRaB9lRYkBlI/dm0kM02QGU169xWKYpmreqlis7J3kQ00YbsZRzdIdHwCauf61uTcdg6m9GKb8q8XaJN9ONF03DAvyNe2o7D6sjrnbyu78DCslhJURJBTVhQVGXyqDadzYJitMw1HdpJjebYw4IbQyCJqSi7ldKJb6TavnLs+3WRvlm66Y9Ck9I8acFQ14LO5qC6gViOyZWyXTS4Ydl3wA2NWdqIs/vVtt/JZ/t5O55Cc2GVzGBpawALF41weTvV1VYy7BYJp+ghSVC2qqBTJ7/2SDqY8zIq6s70GXL5RISif9lu8L2XzNSo1qLp9mGGeZCyuDxRcfTOgDOwpTfyjxfF9c1Zh0rtY5kJnCtKVPDFEgjw3y+Jc66VPyBZ2u6+jgPLfktuoNhPHlji5TOcKGwZTfnBTrkp8PyyEP9hhXiopit+HhwPpzuYxWsf7tkI31K6/QA9LfLGLPZziX0uy+hXlXXG1Y2+nDsxbxdE0G0zQe4Lb2fnzrkSV4ba212GTh9g5jTLEK2xKKaEKYJpVCV57DBaU55Ja2hAI/mWhhQnlijbmm0Xq9l46xPVUM07WNvdja1m8oTCeUJxSmqneZfSaHxHI5Ur8b72wS2y7rl+X7USpM03DJZ1glKGTzPqt5lWXM67g44M31Lbh1wWbcMn+zZV3IhkAeNtawNYzKY1OGb2Mel8OWwjQYFcUZKiGT0+FAszeAbn9Y6J/8ku0ASJH0SVCYpmMwFW07sgjAlAySu++o9A52DISwvskrjIOpVP3sHIKhMSq65Ec0HQ99XotHF9UZHigvrEz0WUxsVOJ2CrlFAHMf9s9Pd+Dm+ZtM5bBaB7KNHNbveNwOTKxI9BPM29UQMFEM09FBst3DiKIRWLmQCwpTRYOROw02qWbIGa3lQNg8yVzy+ReFn3QC4sux10RR0WfVyNttTKBlgtKO+nceW4rr30gkwQoJuxHqDljl9smevJXC1Er5JP9OdatskZGpwlS1aKgqiSejieoIRhIGpDGlbtNvGT99fo2la4Y8IMiDpqoGNV23TChkhcooGOQGWyMzsq52yQfE52jVtjY0e6HpwG5VxZgxxuySzzOmJDYgGItvro3puo5t7f0IRjTlteSFMZt0sfqQ202yxZedQbnFG1AqQvjJlzmGqbnOgxEtZaxRdqwqUYGc/KO9L/W7LLjkx+v5no+347pXN0DXrZN5sTZbVpQwXvKqbVMynvjkuL0/hDlL6xAIR02G9WQTdPa8it1OeJxm12kGX1uiS76oMOUNILybMduVnVRh3rjijaS82nQgRR/C4g2lWoDw37NnyU9Y/WHN2AwYCEYwf2MLfvbCGnQMhDLOZrqppQ/f/e8yfLi13dLolmxTiZ/weTl3fxafj3cd8hgKU/M71SR5R0yTdtBVi45QRDO9d2kpTKW/2QaV6JLPb/zFN1yiGmo7zWFuePoCEdy/cAceX7zL2FxLlohNVcZUG16CwTSJEUtWQ7L5RIRTMXVbJH169MudRkJDK5d8k8LUYiHc7Qvh2eX19mLnqd5trsL6hJAqqSfsLoe6DfF10tDjF4xo/PeVxeIYyUKkLNzegStfXofLX1xrGEFl4yo/50n3Pe2Lj1UOJOYXjCJuoycT+HlnMvi5bMdACLfMVyvUAWBrmzmOuh3VVjP3/kc03TCsMPqlPiidpE+qDVFvICKcg2/zkaiGbz68BL+atwFL67qFpsjaGt+2kylMeU8EAJgen/dMrYz9n40j2YwDJ7uHpktfIILVceGERxGzeG1jL777+FIhA7lMOJLoW4aN+KWEGKaaLiToUsH6UebVkE6Zv9zRhS5fGJ/v6LL8zUfViedkZTCV56RMIJAsvIB8LiGPQJKxg5+vWI35qxp6cPKDi/Dm+hbl9zLJYph29Afx8xfX4sJnVxmb+BPKioy2xa+fNT22ecrCV5VzfanTETP6JfPIj0Q1vL+13fg9kNicYo+H9ZvKGKY2leb8hpesVmSwOWSPz7wBBCR3yV+8swt/fr8a721pxwdb2433UfVbhnyNIKcwBRLPw3qVIbYhj8tpy4AclIz9Ku8Ll9MhxCtl8OdPJH1K4pLP4qm7HCZPpWTIXnOyCEBez1t5+j3wWS3OeGQJLn1hDX76/OrE+VP0GWx8kb0A+TbQF4jg7bixv8sXxromL9Y3J8bT6riYY3JlMeRlqardtvQFjbHgj+9uxe/nb7bcTGOeELzClPdoYcZ2NrcOR8TnWSiMmMF03bp1uOyyy3DkkUfisMMOw3nnnYe33347rXOEQiE8+OCDOP3003HIIYfguOOOw2233YbOzs7UB1tgO4apYTBNfR7VhFWeiEZ14FNO2cNP0Nguf7HbaRiKeNqSKEyFpE/SopN3B9pNWoCKk8DEOVRqw1TwL+vqhl6EozpqOhKqFuHlspjoJlMt8G68mh7LKLp8V3dS9y958PggPogaZRqkS75KYVoVr7uorhsx1pyOxOcqghHNUm0iK0xNE3/Fo9T09NUASoUpN9gaMR7jhmCZMdJCzmrCuboxFj/1sBljDFWfCpfTYRjv2OKbNzYuq+vBj55ZhX9+WqO8lqzInjUhtlnAFiVyW0umMLUzGER1ccEHAG9taMF2zlU/EpUMpopFZDKDCm8wVamCeIMlg99osboPQ3FZ7Bb6uq1t/cps9wzDYMq5TrM2o0z6xLWx38/bgGteWY8e6f1N9qwHjEWNS5mciv3NfxIVDJC6cB5BYepMGNSZwvQgKYYuICr4eQOfbZf8lF4L/CI/Vpe8sYE30vrCUfzp3Wqsa/LiySW7LJOIAcBkhfGX8Yu569DUG8DN8zdbGt28SfpIeaPEH49ryozL+04qN75nLnh8t8Zi4LZIhgV5vFIlVvOFogqFqVOITwskCYAv/V2scMmvaefe4fjnv1+wBec9tcI0pvDw7z6rS6sFEqB2CU6ZCIurr2QKUxmmAkxsIEXEBGmGO62O/y5JxGcWXPKTzH+KjU0usfxPxRPx2Qn9o7pzl+HCKYaGkfs9lSHb7XIqVcpswdYxEML3/rccP3xmlXC8YTCVxjj2zrN7qev2G5/JCtOwQmEqYzXv4mOJyyEkmEKLH5O7fCEjrE0qVOMIv0HY7A3g+ZUNJrd9qziMuq5js8INsNuG6lR+/+UEpskUpuGoZqpz2btDRp4j8MbTD6rbjeN3dfmFtsg23vjfm8M8JN5z2XjAjN7MLZ/dpzzOJlMIpoI/1Oo0uq7jd29uwjWvrDPNobzBMHSI6xH+eV7/xiY0eYO4/o2YgilZyKvB2EtfWtWIP7y9xbaCKeGSn/gsqulCeBfVhoVfUpHf9NZm3PHuVlvXZPVn1bd7A2Es494XK08p2eiWUJgmPuOP7faF8LakAuZJ9sz4+YrVuuuOd6sxEIriz+8n4gnXdAzgno+2K/sXfj0lv4t8PPO2+Cb++HIPF94lcV9//2g7vvnwEqyP51qoEgymsb4pmfl7SV03evxhjC/zYP8psTmcoTCNPxPWbw7GJd+OMn9iXPwU1dWGZKt1XySqGS7SDKvEz8nyothVmNZ2+gyPIL4dxxSm9lzy+U01lcrY6XCgfUAV+o1XmMYKVuKxDrnF+nI+AaXM1tZ+U6gJWWEqr9/l+RNfN3w74VXqvGdhqn6OPUc5hBV/2MaWPuO6vlAEr69rBhATGAGJ+p1cUWQS8qn6n3B8vd4fjGDBxla8v7VdmW8HiIWGimi6cQ2PyykoTBnGnIMUptljyZIl+NGPfoSVK1fijDPOwAUXXICOjg78+te/xhNPPGHrHJqm4aqrrsIDDzyAcePG4ZJLLsHhhx+OuXPn4vzzz0dXl/WOXtLzJqlbVcweqwDToku++aSqTuNDbqeRn3CxzndieRFcik7AdgxTbkcBgGC0NMUL5JWaXFnTNZh+sLVdSFjB4rmELBY0VotAq8yogPisQxENl720Fr+Yu960e291DACTKsJQw2Wc9Ml8HFOSRqIa5/rrTqlStDK4eKS6kBfVKmOhlkQVaIVqd4wfbHlVESvDabMn4dDdqvDY+V/BwdOqhGOtrs/CPRw2fUzS3UEPZ7lj7VFQMcfrfUenTxkXUl4MMRckVi55kpjMddSuu2dDr+iWc4eUuIJ/doDa9SeZQYWPDaVSSsViiIrn5A18fD/x7PJ6XPPKOvjD0YTClHPJZ/z1g22452Nz9m4gMaHg49ywOuoPRZMaTAFgTaMXl724Rvgs2cDr5wy0rOnwv+8YCOGHT6+0PB+blCcMxLzB1GGUn+3iHiK1aUBs17zaNNX7xhQNKUMIKFRRfDvhN8D4JGCBSDSpi+9kRVzsLfEEOnbiYKo2ptg9yRN5byCMXd0xI8OYEreg1GWTLd61evpYprSSFKZjZIWpuWz9IXPoh2K3EzPGlAqfWbUrq2RFvGvnivoe43v2frGQBs9wiXv4vtjtdBjuUwDwwqpG3P3htqSGdZWXSqq+hzeg83WXShPPniW7f3mxwPpDeRFfUeQy5ieRqI5nltXjgc92mNqeVdInppKxY9y1CjcDKIxn3LN7Y30zTn94CTY0e4X30u10KON1snFuZdyo0eMPC6F+mDFFNoKy+HM7uFik7HlWlMgG0+SeBYD1vMsvqYR4WJlYIjdN1/HNh5fgjEeW2FKyqhb9rByBcBRnP74M//x0h22VWUNPIsYav0nTqVgoy8jvvxxeyspg6gvFyvmreRuE7/l+QTU/lxfv/BxgO/fuOhwOwaCjUpg+vbxeOJeVihBIjJdT431yi6EwlQymWQpnYbWQb+kL4uNtHVha12NSfvHx61Wbk3Y8dIwYpukWnuPeT2rwzuY2Q+GeClYKQZWp64ZHCmBuR7quJ5ThXFxzOQahFUykYvVeL9zeaRkTO6rp+McnNZi/scU0xrL2yB/Lzx+vfHkdNrXExnBVPoBkijc7yQJVa5YLn12Fl9c04a/vV5u+Y+03llBHbA/8Gou5/o7nFKb8u//q2mbhWH6jirVF2VjEvyvMHf/0/ScbXgEJhWmsjEUu86Yfw0pRKffPqr5Tnj+Xup1GH60a81SJoVm55PmO1VwgWfJFYw0nK0x5D5pwFOc9tQI/eHIFQpJ4J6rbC1EQlFzLVbYPK4WpX6kwtQ5hxT5zOx0mTyVN17Gz04cLn1uF7zy21Pi8xxfG7e9sFc5himEqJ4OU3mctHmbPilSqdPYc+XpMJhLzhaKGt+/x+0wUvptcWWx6B5KFcrOTwKqpNyCUrcjlFEJ3JD5PbFozSGE6CCKRCG677TY4HA7MmTMHf/7zn3HTTTfhjTfewMyZM3HfffehsbEx5XnmzZuHL774AmeeeSZefPFFXH/99XjggQdw++23o76+Hvfff39G5bOvMI39X1aYergGw9y31C755gbMxzrhG1wnF9fFrTDQdg6ELCdQfIcWiGhCR8AvqGRjG//yCy9uGm2/xxc2GSLZ4MdPDEQ1QOwCUS3mVs12/VSdB3sU/DvO727WW8SOAVJ3YIYLyRC45Ed1MRu3ygWVNyRYdZxyWzApaRTHaLpoOLOz+6NaXAkKU95gGj/3IbtV4X8/PAyHzxhjZL9OlMF8jVBEw8b4BO/w6WOSGpF545rKJZ/dkzcQVroeybtoTE3IFjuysTuZItduQpH6bmvjfew8YsIR1QCXTGHK79zL7ooASyollpU3HPMTk39/VouldT1YsLE1EcO02GUahFWZuxP3E7tWGafUZOWPxTCVVGeSWtrldJiMbckGXjaRKytyGwY3/h3/7+I6U2Z2lQHScMnnFkdsp7qmYwCBiIZSjxN7TyxHMmQFVDKMhVyKPklUxMaOEtw7OYMpv9Ap9biSvueqRIIXPbcaOzljz4yxJUlimJo/L46rFuT+0xuIGGqFPceXCYnrVO88c1Ft7pUVpmKZVWWzUpiaXfLVY6f8apcYBtPYF5ta+oRxXB7n+fPymx3Fbie2dyTenWeWN+DVtc2Wsc4A9YIoVdctGEy59pDKBTURwzR2TXlxxvpaWfVXXuQ2xgJ/OIoHPq/FM8sbsC7uOcAw3LaE5xMx+pNkyRoZqltnRkp5k5Pva/7y/jb0+MP4+0fbhXHNY6kwjf2mmjOS8ZtfVkmfWLtQqa0qikSFM99u5AUoM5z5w1Fsae3DH9/ZIjx3w3jlMYfN4ecbO7v8uOrldcZ32zsG8NPnV+OJJbssN4VVBmTWTy7a2S2cyw7MgHPQ1Eq8edkxRjZ41XglI/enssFUXoSzsW7xzi50DISwqLZb+D5V0ieTwpTrZ3nlUDiqCfXHxih+XrB8V49RPl3XkxqrWT2ykCPNFjFM+fOvaegV+upUiPH01J3IRs7VkxdMDIQiRl9W4lGH4JBRfceeZ6bhAfj6SxZuhIddix8TopoYoqrXH0FU03HV3HW44Y2NQjJJk2eCDaM164OtNrs/jcf2V238v7u5DS+uasSf3q02CUbY8wtIiZ0Y/EbNQdL8Gxi8wlTeIAIS9SzPC3VdFAPI8wX+GkzkMr7MY7St51Y0WKri+XKweZ+8RGZzJE3XsaQu1g+cut9Ew/DG+nDWLDw2XPLlqYrs7Sd7c8plBWJeDWPjIhq5Deu6jvoe9XwgoummvtlK1CG45MshG9gazs0MprGb+tO71UZ98QbSbn9Y+DsS1ZQKU9l4HIxEhTAP/Yp3welQj5VCDFPDmyJ2/oFQFO9vkWIpGwZTpykpcjgqbnAzPpZCh0SiZoWpHPZQfp+jmp5UVcxPMb97yFTccPI+wveBiGbyjElmMO0PRY1xS56/jy8rgqw3ChgCFvELbyBiuQ7kafIGhXGryOXA4dPHmH7nUXh8ksJ0ECxZsgS7du3CmWeeiQMOOMD4vLKyEldeeSXC4TDmzZuX8jxz584FAPzmN78RVJ4XXHABdt99d7z11lsIBJIbKVQkW7QKjYApTCW9BnOv/HR7J854dCk2tvQp3emsdo9U1+qIuyxNKC8y7VIBscWD1YAid2j8RJNfUMmuYHwb51/cVPEvt3cMGAusHsVCmp2Wl2zzhhy2SHtpdSN++PQqPL14p6kM5nOqd2iTxZtJ9g7zA7zKhSQZH2xtx0Of11oYTBOu98zduKLYrTSY/uTo3Y2WZTdTZSiq2YiDKA6idhanqniYhnuEoDBNGP2KuRmFrMZTdaCbW/sQjGgYW+rBnuNLTYNdmces+APUCUTY+Xv8EeVyQDauMKNeRgpTm4PBu5tb8c9Pa4yBdqK0O+ePaMJ10lWYRrVEOIQui3dFXqjxkwA2MeHbQ48/zBn21e00FSVul9GO+bjA8iJKNpb94Zv7mc6VbFHGFKaCSz738xUKN1Exq3JqhWlN3DCw14RyFLkzT7ohs1dcAZLSJZ9rEiqXfKsMu6mS66jiYgPAh9UJl/IplcWWfbBq04CpFmRDRl8wYTCdNb5MaFNup6jgBIDpcTVos2SckxWmSoVGKGra7ODjLjGsnru8wGB9DWuHS+tEI4zcPkXVlfgceJWaHTLJdM4v2lgoBMA6jBCDlZrdD+svS6RFvaz6K+c2//ixV15AJxSmieezrqHXeF+ThY9IBluIyW1O1T49LqepP+1SuIaz+9jUkjD68gazZAZTlZKw2O00eU+wmLf9ktqDP284quPRRXVYsKkN721JvJdG8kWFArXE4zIUHze9tQmruDh3n9d0YkNzHx7+cidOfmiRyfgNqFVSLC54X5L48FZsiqvWD5xaCZfTgbHxJFW2DKbx8jEjq1x38sYMM2bJ3kfG9wojJ4+sdmJ96I7OASEpXigqxklm76k5Pm84/rmu9HphsAXtNGmjSO6jWHm6fCFc+fJaXPfaejT0+HHeUytSKn75M1kNOcy4DSQMXK19QXz38WW44uW1AFhbNid5k6egqg1nQ2HKfZXOwpo3uqkMl+pEXubraLouhATrDYSxvWMAK3b14NPtncJ1yqV33MoNOnE9PaXClI1r+0+OJS/l2+jyXYnxxUphKiSCsmhYE8uLjI0XRlKFKXdfbX0h3PzWJvxm3gZhbcH3d/L6iBn9dV3H8ysb8NvXNwptzivNI/i1K5vDTCgvEsaNHR0DyjGQN0KyblX2+mTjXm2nD95ABCVuJw6aWmnM+1lfwp6JEXpH5VlnhGARw6jJRkJ5vhIrq2hwdzkcxnOU12FzVjZazncjUd0037FqX0LSJ+l+2NhWHK8v/ql9GjcihiQDHt/vhqO6crOtXNoQjLnkmxWmsoeHWmEqXg8QvdZ+v2CLsNHJ5zKQ1yuyQZJRK202RXXdGOMPiW82yAZwVSI4FpdfBYsLu9+kctx6+n74/mHTcNOp+xjCqK6BEM5+fBm+4GIdq7xqZ8f7CV8oatSNHFLL7XSYxC2sa5CN9p/XdOKSOasT96VYY3pcDkQ13fBgcyAmaDlyj7H40xmz8eSPDjN+y96DZ5c3GO8KKUwHwbJlywAAxx13nOk79tny5cuTniMYDGLt2rWYNWsWpk+fLnzncDjwjW98Az6fDxs2bLA4gzVa3DX2+ZUNwq6truuC4YoNuvJulrwLOWdFg9Io1S99Jk94hRimRiBsjykuB2ug8o44Q1588C8+v+CRJ/f84Mi7oCVT2+3s9OHCZ1biZy+sQTiqJVXERbWEa7jKJX99U2yBsjNupGDKWN7QxJ5EJu+jVWZUVjb2LYvzYZdb5m/GU8vq8Yki0zSflIF1hhVFLqX7+dSqEsNoY5nUS2p7ISnJEbsffkITltwjksV5Zcj1yD+PWAzThBsmq0texXXQtEox2Y5iIrKGxS+dXgWHQ3SnKHY7hcRY/G5usSJOW8Jgam9RV+aJh0qIHycPVMwo+tTSXfjTu1tNz1M8l1npAwDrm/vw/MpGPLk0FvNPTtAxEBSTTPDPnF0v2fsEJBaNVgtQv9SO+f6MTST42Eg9/rCQ9IkfhO0aTz0ucyyhgWDUZGiWDeQqBadq44mRUJg6jXImXIpDqFOo9wSVe/y3fKgMBovRywxH48o8ghtfmceVUWxnAHjmwsONeHWpNjv494a1Vd6wbmUwVRmCeFQu+YC4sRAIa7bfJyAxLskT6pjCNFYX8saIKoEIc8mXXaSmVYoGU1XIgYFQxNTXsPMdNj2xiWM1oZPrw0j6FP/9srjB9Gt7joudR+oL+L6YnwMEIpptVR4jk0znshcLU7FYJaqU0XQx4RNrp2zhIi8K+aRPPBs54wvAxzBNPK+VnPHZZ2MTTwW7X9l4xsdcZYwr9QhtJhzVlCplZjTks7s39JgVpvJCZCAUVSrwy4tckLo6rG304rynVuCGNzaaFnT8fJJt+vDPx59EYQokNml3SIvCrW1i+/tYMV+xUsz4w9Gkrpgq4y2QMMIdODW26GMJ3+SFcn8wgtfWNWNtYy9+Mmc1PqvpNMaqGfH3N6zp+GRbB+79eDsimnnhzt4X3mDAKyX5d17pki/NuVhb/bMUSicU0YTj5TktG/rYu8cLGFQqPRZPcFr8XWu2cMln1+zyhY0Y6f/+rBa1nT4hriTjtbVN+DK+GOdPZdWr8O8se7bPLq9HbyBiXJt3yd/Y3IclO7uU51SJLFQu+ekYTHkvINW4pDKistOLiZLE9Yc3EBHOx/qEIpfD5AWTyvusLxgx+hirTSA2B2HzQX4DlFe1y/fIjuPnhEEjDqLkveN2CrHCAXHzVYYfq9Y3e/FhdQc+39ElzBd5g6kcq7vY7UQgHMVtb2/BPz/dYUp4Ja85+LUra+cTyoqM3AKAtShIcMm3iGEaiGhY1+TFnfH34uDdquB2OQ3DG+vL5BimqrUue+6y8VOe/6k8SOVNNbfLYRjhZQEVC4mkIqJpJq8Pq3WcEMNUdsmXFKa8oZnVAz9H7vGFTcmOVPOYUmksCkZ0pcGUb6exGKYKDw9F6D55rOM3Loz1Sok51F0kqgvvC5vf1XYOSL/TjLH7hLi7e5M3YMwfIgpvvYimG0IAFezR82Ej/u8ru+GAeBzdNY29pvat8lI7Zs+xAGLvKCtDRbFbUI46nQ4hrBWPHGv9oS92Cn/zz+eR8w7FvJ8dZXg7MMFIkdtptJVvHzhFCLnH3p3lu3rw789qAZDCdFDs3LkTALDnnnuavps0aRLKyspQV1eX9By7du2CpmmYOXOm8nv2ObtWOmi6jqeX1eOfn+7AeU+tMD7v8YeFl541Ankiwy+02e9UA6tslDlMkjfzgzjvki+/CGxiZWVUk40sgsKUW2DLRkH+vvjBOtluwaKdXYjqMQXGgo2tKeN/+kNRrKzvERQh7PzMwOELRREIJ9Qa48vEnT1d1/HU0l1IxnRFxnWVooJdQ+4MrSZGwYhmuFH2+MK44OlEe1HtZPEJu9gzLS92K4NTT60sNtyCrYzh8lGhqGQwjf+f/0x25VDFk5GRlYn8pL+IU8xEOJUjb9SsKHYLkx9VB7q2MRG/FBAVh+NKPcL5+O+SKUztdtSGS75F0qdIPGnE44vrMH9jqxArUp6cqgJh8zwVj+snP3d+xxBItJ/fz9+Msx5fhvVNXvhSKK8Mg6mF67RZYZpoV1EtFqqhgbu3+h5/wiW/yC0YWn5w2G5Jy8Io4rLWM3SYVQZyXZV4nMr2raLFGzDuudTjEvpITdexVnIJZvDPIxIPicB27vldWz6GKRCLvcm/syUedTwfOxwwpdKYRCXLXAuIBjy24cT3x1ZhADpTZB3n3d94eBVDe38w5cYUvwnAFv7yIX2BiGE8mFxRLLzLqmQLVurXqlK3Sckg41MoTHeLjwV/O/tAYyyxVHJIn7MwA5quYyAUMTKUHrvXeOXv+fvwSXOHdFWUUU0fdEZp9o6kIxSPcu5/LHM3ew9lA31ZkdvYXFBdl6Fy21rFG0zjzyaqxbIBJ4tfzsPU8H3S4pMZePiN37GlHmEOEI5q6Pab35PG3gB2dA4IYx7r/zU9EY9PXkD7QhFsaDb3OyUec2gTlrV5RX2vac5U4nYZRreAYSThPRHUrnYMeWOOUd0uGnNVxoFkBtNkm8gq421E07ElnvDpwHjCPGYw7ZLihf75vWrc9cE2/PzFtdjY0offvr4RUT3WD0+NL94iUR3/+aIWL61uwsZmr1HnLMFkIgZ9ot7Ys9V1XXDlZON+VNPxx3e24F8LdxhzLvl8G5pFQ0Yoqptc8nVdN9oc24xifSlrLy6nQzkvLZVc8tv7QwhHNVPYEPa3aNBQ19faxl7c9eF2I46roAhU9ClRrq6AxGbVyvpe4XfFnHfRvz+rxbWvblC2GaVLvqK9qX739qZW3PzWJtPchZ+jqZMMqQURmm5O/CgaTMPCPXRz8wo5lEkqgykvSPGFo0pPNbPBNFGWOj4ZUnx9x8ZL9vyEpDjxY+W1YLHbif2nVAif2XXJ5/EJm/iJz3+/YIswNgUjGq58eZ2ghOeRXfJV9Te+3INrj9/L+DsU0ZSednz/xvpVeXwLhDX87IU1xni9d3wtwsQN7L7YPRgxTJPkbpAVpqoY0jIml3ynwwjPct+nNcJGHHsXvv+VaabzRDTdJIqwWscJLvnS7RgK0/gaim+fbA4tJAz0h0ybjNVtZoNpWZFsMI0KRnhmHObP7XQ6jBCGQhml6wHmsY6/LfYcKopcZpd8TRPOd/XcdRgIRUwK00BEQ03ciHr83hPgiJfV2PRSbM5rutpgyp4pm9vL6mdmrFYlQ2T1xbdnlmyWXy8Wu53C+tgJmFzygVjfweblVvD2osNnjMGMsaXGnPkv728DAEtjLJCIYQrE+m7AOuRVPjLsBtP+/ljDqKw0x1UBgIqKCvT1We+uADC+r6ioUH7PPmfXSoeopmNZ3BVCR0xB6nDAFE8kqutwOMwGE1lhurPLJ+yAsAmGvIgYJxkCQ1HduDbrHCdVFJmMa/zEiv2e/491Sswlo7UvYHzX3CcaTPnj5EkEI6Jpyus4HMC6psQCobq9P+WE4vX1zbjy5XW4493EjnhsINeN+KO+cNRw7fe4HMIuncMBrGzoMQZCK/abbFaryZ0ku1ZU19HaJy4E+4MR5f3e/NYm/N8Ty7G2sRf/XVInJNFSMZZTSfbG76myWJ30aWpVcUJhqpgosDLwxNy9eIVp7Df8Z/KEtj8k3puK1Y1evLelzdSmiuMKA7fhAp0IZVDscQrnPWS3xC7Urm4/7nh3K1q8ibbIkqAcNK0SDodoFB1b5hF2cD2uxLl5tRL7LFUsSJmy4oRLvsOhdsn3haPGJJV/V+SJebKM44xnljcY78avToxNCmPnFxfCHQNBvL+1Ha19Qfzl/eqUiTpYO1UlwAHMmydyW/CFomjk+rld3X5jAlJZ4gY/1h6713hccvTuKe40NvlUbQjIikE5O2ypx2maeIWj5r6nur0f3/vfcvxr4Q4AcQUXd5iu61jTKC70GPzzDkV1NHsD0OPnGFfmMa4hv59VJR5hMlbqcZlCLNjF4YAQQsCqb3U4RINqbJc7eXtgqBTH/B3FFNwe0294AxIzrvNKbxnmVsjOycOMI95gwq2rrFic1LIwB/z7O7lS/VxdTkfKzYkBRQzT8fF6nVBehCuOjW3aRjVd/bxNroaJkAGrGnoR1XRMH1OCPcfHwgaEo6JRk80RHI7U6nA78OXMJF1Kjz8Mh8O+wpRdU1aYsv5vdYP4XnlcjpQJDIHEpJr1t4COVZz7qS8+Jr25oQXXvboB17663lQ3KgZCUTgc5sUjm7O0cOO6Bl0wfoWjuqUS+/OaLsFI6Q/HrhOKaEYtyO66A6GoybgGxDwiVGGVGPKcyeV0mAyQgUjUNBaXuF3KNmxlMJU3YSO6+R2w2mjxh6NJsyOXuJ2mc9V1+RCIaCgvcmHm+DI4HIk5b9eAOHdVqV2BWFgQw11WSyxg/eGosbHHzrlkZzdeX98szF3Pe2oFOn0hfFDdLqjewvG59or6bizY1IbnVjQY8a6ZUTM2Vuim2IVhTXTJj2iaYKRgBtOeQOwe2ThX4nZiSpV5M6i0yBnvn2JzHh0xA5hs0GZjoRCPnvsJ/zw3CApv3eSSb6qrbp9gGOkLRhDVNJNBoNTjNG2QyHMnhyNZDFPxc03RBm9/Zys+rO7Aa+uahc97A6LB1OGIzZm2tfcjqutKVasOs/u4xhm3gZhSjzc4sjGw1GNWz8tzZ/k/vr9hG5zyb0KGwTTWblnf2hsIC0Zlthk6KT7P+LymE9Xt/ZJLfuxYOb5viceFU/abJN431GOeDt0y9BPr92LtWPwNv9m/q9uPjS19qCpx41cn7CWfBt74OMT6fnktVl7kQlWJG1OrinH4jCrj3lSiIMFg6nTEzyvWk+zhWeqJ9ZVsfsnuizXTIq6PkZ8P6/fkfrU4hSFKdQy/nu3yhXHNK4lxjr0zqjmOKoZpn8UalTdK8u+XNxA23lX2PPj5sCv+LPnPWrxBPPh5rfF3fyiCum7zmlc1bxcUpvH3RgzjoVvEME3UAXsfZC8+/r7YOFBZYhYiRaSN6hX1vfjfkl2CcASIKSnDUT02Vk0oNdzmm+PrVTaX40+v6boh8uLnvuydZ2+yS6ofNqeU49fffdYBeOS8Q3HDyXvjl9w7xOIRa3rCY6LI7RTmdC4LhWmpx6Vci/H4uXHdGS+jvLHn4/oB+T/ecMvmYxk4R+Us1qufUYqn2AONa2wTJ8YaaHeduDgoLvFg4sRKOKWOcpykiJFdoUo9LvQFI6ZdvCnjxEyGDpfTuHZP3OA6a9oYTJkkGppnTanE4p3d6Ivqxu8ZK3Z2GUbMfadWoqUviJ6QhokTK9EfjIiu9rp4PD+JCDkSL8FfP9iOYw+Yir0mVWBZbRd+P289Lj1uFi44anesbUpMzKJOJ+BJ3rw2xY1k/ORMAxAtKkqoKUJRoCjWYY0vL0IpN+hocKAjmHz3oqzIhf12G4NPtsWCq9/6nQPwlwWblQrQ4vISXPvKWiznEhoAgLOkSHg2r69uxIebW41J99tbO7ClJbVxfo9pCRVxUI+1sYljSjGmUszaXOpxYa8Z4zCuvBjo8JmMS0CsXZaXiW2tuLwYY8YmjMMOpwMTJ1ZiG+fe0SOfy+MxtRsVty7Yggv/394AgO54dZUVuTBxYiVK4oN+WXkxonFTzKTxFcJ5j509GW9wsbXe2tiKaRPKccu3D0BfIGxMCI/ebwrGlHlQxE2IJ48pFWKmlRa7jXOPjauenJ7EZ8Ul6RmvpoyPPTMdsefl52Ll+MNRFBV7gOLEOVe29OOo2VPgcjqgS5Po6RPKsUJSYqhgA/d+08cCAIJRHZ7ihNFKczqxujXRd3iDETiLzUYtHtZO+0Lqd6I/HqtrfHkRugZCpoXM1p4gOrjNHT5W34wpVSjm1PN77TYW48eW4ellYgZgmWlTqlDkdgGQYllK/d8EKXP59CljUFHiFvrJ4rJiU1t9ZnWzcB/jq0oxeVLCOD92fAU2KHbBAcDh5vonlxO98UvNnFiOSdw5xkju31PHl2PyxIRxsKLEg2njSoUNI7tMnFiJCaGEeyK7v/5gBHe9vRnfOXQavrF3zC1I58al0vJilFWas98CsUkLP9FVxbQtK3IZz3byhHJMrCg2GdDlOgKAiRXFwrjB8+8fH4Hb39yAHx+zJ/7z6Xbhu8lVJej2hxFxOg2jwbSJldjVl7jm5ImxZ+7kJl2z9xivvNbEiZXYY0I56pLEjYLHBYe0oOfrdVxV7Pk53S5lHyiv88ewsd3hwKb22Lt5wv6TMXFcufF5CdeXR3WHcV53c/qbtzKVY8sMZUswicHKCs3iPo3zF7tNBruqceUYiPcbe02tAta3xIz1brfgMgrE27KuY+aEsqRJaCrL427V8TnLjvZ+Qc0ciMQ+/2DbegAx9YWdMSqi6agcWwZNmpOVlsf6jcYdiXFddzqxhdvg5F0+v7bXeCzZ0YUDplVhc7MXzQNhUQ0Tf44tnMJ2xlTRQygY1U2hCIDY2FVean98Ki3xoLxY7AfhStSjqzg2rxlTUaR8RhOrSgELhT1PUbF5HuC1iNFYXF4CZ7K5ncMhnOvl5fV4eGENAOCQGWMwZXLsHZwxOfabzv4QJkxIXb97TChHVfwddBW5DcVoUVkxQvF3dcqYUiPkx53vb8M39p4gnOOtLe3YIhmyne7YXHv5l3XGZ2wBO2tyJTa19sPpdqOootRY/F114t54+NOa2Pyf96apLEXl2ES/vPuEcqxt9CLkiF2jNd7XlxW7MXNSBRbGk/4wpk8Zg4lxI+uMcaWoaR/AAJzm5HXxsbCESybJ95tjx5Ubxswebj5QMaYcHq7u3B5zn/DJzh7h74jDic6o2fBZWVaEqEMcW0oqxLFywoQKdCmaUa8/jPlbxXsfMy42DhnX5cawfml9E+HeZW8wiokTK3HD3LWYu7IB+0+txP9+cpTpmsUlHowdL4psyitK4BlI9Hkhh1MwsIWcsb6kstSDUklV6ChKPnfu2yoa/ksrSzFO2lhl6uTJ8fWf7oy1k127xDVIR7x/nDW5Ak3eICKajkufX4PZUxPXLykvwcSJlfBJ88+JY8vwtQOmYmyZBz0sHjU3l1rf0Is9xpdhTJkHH21uNYw73z9iBjwuJ1bs7MK2tn64uHWQ7pC0Voo59xUn7I1LvjET98c3sxneQMR432va+wWPLQDYfXyZMU5XxPvK4rIidCo87Xbj1sOVJbE1gEvarO2V2t/Yqthzmhh/T7X4M2fvT3m8nl3cmoLBhoEJVWI7r+DaxphSj9IrYtJYcY5bUVYsbJ419gaM67F5y+6TFe2ryGNS+IfgULbFXm7c8BTF7scbCOOUez8zPh9bGXseUW4bvTL+jEp6E8/8iaW7hFAstT1qr6MqKU68p7QIEU6bF4mPDz5HYpzuC0aVXjc6Zwdh/c9UyQZSFi8/ADBzwLSJFaYNjoqqUsi1skGh7GRhBg6aPgaTJ1VhjwnlaOsPoT8+n+uNr93Li93oD0ag6cCYseWoj6+XnvzJUfjRf5fGrjm2DFUlHpS3xN3Zi8Q2Na7S7GUAAHtOHYOvx8euN9c2GZ8fNGtizACrJ7yUJ0+ogJtr8xXlxagaa14blBe7UZZiDekqSnzP3sH9dhsDrG0WfmfV71VIdT9xYiVKW9MLP5XLDLvBlKk/rVSk/f39GDNmjPI7BlOnWilI2edWCtRkDPhCGOB3Lzti5dxcLw5gff1BdHT0oc8n7nqpmqPL6TBcIIosLPweXews+n0h49qt8aysnmgUPd1i4xtXFHtRdrb1Gb9n3LVgk/Hv6XGlTl1HPzo6+kxJJ2raB/CrOStxazzZCt8hN3clfusPR/GDRxbjrcuOxnmPLgYA/OfjbZg9rkSQ1Hf3BdHcmXyRuFlhYGjo9uO21xLZXH2hCOpaYhOAqmI3oly5guEomjuTv4xlHhfGx5+Ry+nAOQdMwiOf1ih3sxpavSZjKft8ZkWiZn/10hrhe38gjE0K9zuZ0EDAaAub46o3l6YhKLkCTqksQmdnP4qTKFE6OvowILW95vY+jOPWiuGIhteX1eFXryVi+bZJoQia4+0BsHbB46/JrgPEDDMdHX3QWQKCHh98wdi7ExwICO3x/+1ehVP2m4iPqhOTyNU7u9DR0WcYmiZXFCHsC6DDFxAMAuUuBwa4BYNT141zR0Ox6/X2J67X12+d7M3pMBtC9Pg5guEoOjr60BafwE2qKMKubj96+gKoaUy0i3ve24o1O7tw11kHwCcZoKvSjGVZGn/vvf4wur0J409PfxBvr200/vaHomjvUrd11qbqWrzomFCKVq/aiNQS3w0eV+pWqg5fX1kvKDd4Iv4gItzGhiMUQtBn3vEvcjkEZYS3ZwAuh8LdM95XHDClAnuMK8XXposDsK/Pb4oZ1tE9YOrjFm5pFf52RKPo6Ur0O7uaerAh/q7NnlwhxBXs4drJp1vbMLU89o5PqygSrhOUJr8uLYq+3oSxxeMEKjOMYdrR0YdAvBwDwYhx3X8t3IE5yxswZ+kurLj+eACx95nR3etHU6vZMO9AbEe4llMDqeINlngSBtPAQBAVRebytyrCloxRxN0DYoopdziMO8+YDSD2jorHxTqm1i4f+uNtLOIPwsep2Ly9AxhfXoQAZ7TTAyHlO9vR0YdLj5qB2vZ+wbDP097twwBXd6fsN1GoV3+8/foCYVO7AswGAo3FiItEURd3aZ5a5sZAvP78oQjqm3uM3w8EE+dtTTEWMhyw1o62tvUhGFfQ2XVT5+no9mHzzg5Ll8uyIidkZ4b29j60xFXnVVyis1eW7gQA7DGuFN5ABMfsOda417k/OQJH/eNzy3JE4v1tKBLrbxduim2kTSjzoNMXRn/8ufFtSFU/Kmobe9Aq9ZOd3T60t3vxn08SRvye/qBpg4MZi+/45n7QdB2rGnpxy3wvtrV4BYWwzx8r3xkPLTY+6+oS67emvV+5mHRCRyiUuu7KPK6Yx0skKiRQZGVnz6OjJ/aeOzVd+YxKbXZL3v6A6fgWi5jITW1edCn6BoYvlOjHNF3Hja8m5nP7TigzvvPEVWpdAyF0dvZB182hoXgmlLoRivcdLd0+4/l2dA+gM16e3auKsZQ7pk6KsdfVG8BCLpkdAAz4w2ho7sHrqxshMzGuqPcOBLGtPrZBXl7kgitedu9AED5uzLxrwSbD6wsAxsQVRI3tsXlWdXwdMa7UjSqFG6+vz4eOYKxPnFJehJr2AWys6zJ5snT3+mJzFW7+6+f6zZqGbiNMTE1rop3vau5BZ19ifhDi6oqxdFvs+bB+t6VrAEu2itmogVgfr0ueIS3t4rkaW3rRrogfft/7W039UHtHHxBIjAe8t4uXa/MAUN+W+Hd1ax92NnZjWW3MALulpQ87m3oAxOrqoqNm4JEv69DW48MfuLUFEHuOPdzz6Oz1CaFtGthc1+lAJCzO9Zra+9DRYb3GrJHWBA0tvYhKai3mNeSOq+36/LF137pa0ZjcEJ+7nbbPBBwwsRz/XbILwYiGBm6cb+8aQEdHCeqkjZpQIHbO1y49Epe9uBY1HT54vX50dPRhTWMvfv7CWkytLMb8K47B5c+uNI773Ymz4HA4cGlDDwCgub0fHeNKjHLybGswr5uKdQ1+r3njzOsPG+/7WysbTN9PLufmX/Hn0tntM4mPAMDB9aXlHpcwf2dsktbukfg8SwvHju3ui/V9wbih3BG/JuvneVjYl2J5acapJSeWqw2mbmlOFAlFUCSpMdn1fPHj9VAsSRUv8Nne2GM6d6fX3H8DQDOnAH19dSOu+vruZs+H+Djcx5W5s5v1L4lxTZ5HrozHLObtG4B57tfZ40MX94519MbK2sL1zVYhBXr6EvfF1oTBAbF+2zr6MSU+f+2Jryv0YBg+aQBu6+hHq+QpXK3Y1GR97d7jStHR0YdJ8TnX1oYeHDi+BOviG5Albif8YQe0qI5bXllr9FeTi5zGPK6p1YtQeRE64nabaPxZM3SLje+gL3HfX51chq/PHIdjZo5DZ2c/yuJzdzY/HejzCzEyAoEQOhTzzSKXA3oK93g2n3Ag0Rb3VHh5Wc3HdEl53tbuRU+v9eZ5vjHsLvksvqgqTml7ezt8Pp8yvinP7rvvDqfTaRmjlH1uFeM0GVFNF9wLdD32nyyZ/t+SXegeCCMUEV9KVVy187h4f0UWC+sqafcyFNWg62IMsfFl5himLK5Ye1/QKKuux+I18TGHvrZnTKnT6o39jgWT521yr69vga7H4kPxHWCPJP/vHAgJyh5fWMNqaVfTF4qkTCjEdhavP2lvQ/YOiEHE/aGooT4ZU+oRyhuOainjpJYXuYxMy2UeF3Qd2H1cqfK3VhnjewMR4bnKqBI8ATDFYCwvSmQaZxlr+azejCmVxdB1c3gHHl0HHNIV+oNizL7WviBuf3uL8Bs5FEQgohn3dvFzqy2vx66p6wmXhBK3E7qeCDMRjibenSKXU2iPbqcTP/vaHsL5trb1Q9N0w3i/18Ry4/cuLgjLGCmGqZs7N3PpDXL3kSzO7jhFluyyuHKyoz+IcCSR3Ia5WYcVWSk/2NqOcEQzheSYZJFAR0V5kcuIU+uTsiq39weNpDJArJ6sXKWYu/Mf39mK9za3WbqWMoO47D4+Nn58Q4/fMD59Y9Y4saweUeU0psSjTHTEn9vldMABh1CXDNY3nH/4dPzlOwcI8X0BwON0KLNt6nosA+N3H1+Gbe0DpslfqcclvBdrG72IajomVxRhd2mHPyhl4Hx5dWwnd8bYUqHtyu9nVYlbSNRW4nZivMIlf5zCzZ1nckURdB2oiu/6DoSiCEd16DqkhIOx/wR376iuVMlPKC8yuUSpKOMW60Uup+n5A1AqSccr3h8gFqeYf2ZyrCT2fLyBhHdFicclWAfd8bGNH3tcDofymroOHDytCtefvI/VLaI/mBg77vzO/vjrmQeI9Rq/XizuIEz/yTFljdhmXJKZqmIPlylaF2Im+sMaguFYmx2wUH3LyEH5eS5+bhVufGMTOvpDlkZiINZeVcnnAhENP3threm3jLIi87VjyZBi/cZuVSWGG9r78fh0ZxwwGe9ccQz+/O39jecGODAlST9oxKrVY+8dW4AcMzPW5/hCUWiaLrg5hrn+PVnEle6BsGneEY5q6PVHhDlcdVs/vIGISYXidMTmYhPKi43+okYyukW0WL12cwtMuUys6cj14HE5bYVEYEnvXA6zS74/HDWeAzO4FLudyjacrD3xRKV3QNN0yw1UX0hTKpxviL+LAa58vT6xLg6cUml8N9aIYRpKzLOTKManVhYnQlpxZQuGNcPYPXuyaMCSF+JrGnuVCZTe2tBq8uRxOmC041BEM8bVCeVFiTi8UtKnlfW9mL8xtolX7HYa840efxi6DkOFNGNMKaoUm0/FLpfxLKaNEZNt8IQiOsIRDcvjicAACNmiuwZi1wtHdWzkxsiBYBTNvQmDILvWq2ub8avXNuDVNU34sjZmCGHJPLyBCDYrDAtFbnN4Cfl59wejwkafUQ7FXGZNgxe/n7/ZaA/1XB/X7BXXN7zrvz+sYd66FmF+xNYsbqfDMGA39QaMMZ4RW+8l6i8WlihxXfYOlBaZYw97uXWB6j85Kd5AKCp8H4kmYpCz+JbBcCyOurzeZGPx+PIiXHHsTOP3fF+3sSVmhJRDMrG5eGWxB/vE+xU25i2pjc0xW/qCaOoNGGNvzA3XAV1PuFgPhBL3G5SMZ6o8C7E1rbmv8wbCxnm+lBJCAbG8HOx7PqmrKjQZH6KtqiQ2B5EVq/KzLIr3lSzZa3+8Xli3kEj6pJnqlPW3fDxSl9MhbMTvPcEcAk4+hh0nz3HZdZjy2O10mFz5VbHqvYEINE2PuZPHx0pN04VESoGIhu/9dzn6pX6OjR18n87WU8k2sNhzPWiqKHaQhQ6t3hB2dibqoD8YSXluvszsXsKc5x8Pv/bjk7bKY3soqpnWcVabxwCw76TYepTliWnqDeDkBxfj1/M2GuVgc8gP40KgsaWe2Ho1vjYKhjX0ByO4dUFsHe50OJLOkxn8+rnY7cK//+8Q/PiIGdB1s42pSJpTOODA7mNLcep+E4XflXlcpvWqDEvqxVSsbJ4tY9XnyfkqWrxBU4LffGbYDaZHHRVzlfjiiy9M37HP2G+sKCkpwaGHHora2lo0Noq7wrquY9GiRSgrK8PBBx+cdvmiujorOgvIvAdnbJu3vtmUiESOZTW1shiXfX1P4285AzhD7hTXNHrRH4wZHdkgpkrOwWKT1nX7jc5c13Vc+OwqALGg9V/+8jgjjmdbfxDBiIam+KRp5nizdFu+JzleSux6nLtwIIxVcRUXS+7jC6U2ZjJO2nci/vP9Q5Xf+UJRY3I0tkRMPBOK6kLmUxXlxW7sP7kCVSVuI/7HHpzhpLzIhQPiAdHlTIUMfhKomrwyw8WvTthLSG4k12mZIotwWZHLFPeNGcHlrIoy8rprIBQxLQZU7vw8fFuXYx9ZEZAy87K4KDUdPjTFA92rNgZUE866Lj+WxFW9/ESDxXoBYoYn3jjHq7SNGKZJskHyyEnDgNjONBBbwF/+0jqjvTEjvmqgBYCt7QOmAeiYPcfiOwdOtrw+T1WJ25iM+kIRYaNmc2s/QlHdMJrzyePkAZOPf/z7BVssYwez91g2mDLD3kAoaqgK95skLjwril1CW/JwWUYTnzmEjR9WT8mUy+x9kI0CDodDmW0TiKkvG3sD+MPbW0x1XVYkbkCsjG9MHDZ9jCl+j9zPs79lw6o8AeSNZKzsqqRPUxUx6hhTKovx/MVHAIhl9GSwyayqBfP3GtE0pXJ0alWxrTiS/PMudjsN40UqrAxhcpuUkyCw984bjBgLW/kYD2eQ5JkkxfH6zkFTEsckUeEHIlFj8bj3xHJT/8PaQ1QxidR13VQHrK/h40dVliQm5pGoZtp0YzG1VDFMVXFvVZmzGU3eID7e1oF7Pt6OtUnCP0Q1XRn7rHMgZErUNINTPFUoDO0RLbFZNL7MY4xJG7iEV25XImsq438/PAy3nb6fsi/k672h229skH4tbjDV9NjGHl+1qgy6Krp8IdO8Q7XhxeKWHTytUljSjy31GP3HjHg/IC+qIpquzMyt4ivTxcVGkcs6gy3D43Jgj3hcXJdTsUAMi0Z5wPwbhlUMUxn5nfMGzHMJhi8cVfY9e8bnxiFuw12Og3ogt7hmGyH8+LAjicfQtKoSw82cn5OGuI1zeTNcnv+okmuEohpeWBVbR+w9MTF/m1BeZCQYCXEbB3wSylBUN80BxpV6cMp+E/GbE/cyxtbOuLGVqZBmjC0xrRUAcQPDyE6sUNaFoxoe+LwWL65KrH/458jKunxXt7BJ3heMoJ3zBGOu/nd9sA1f7OjCXR9uN5IVsffRG4hgS5v5uZW4nab5rCouuioBk4qb52/Ge1va8Yd3tgIQDV9yv8X+ZtnfX1zVKFyb1bvH5TRi8DX1Bkzx+FiyS4Y3IG6sGMkkbRiHZfikT4A4BnT5Qjj78YQWmm1sbGnrx2n/WYyXVpnVzkBCWMNv0jGeWLILG1v6TKIIfu5sxEtn7s3cHIV5f40v8+CZCw83Pi835qiJ8ssxjDsUBlOrOYXXH1vTVrf1G3GwZ3Hr0N24OjIMTxHNFGsSMBtMVcixd5lin/2e9R1sQzqxoaejqTeAHz2zEm9uaIHO2Qb4zQ6P0yH0M5Mt5kjyes7tdCj7ACARjsLtdJjiy6sSFvtCEczf2Irzn1qJS19YE9skDERM716XLyx4+QGJuSDfp7P75OfI1x0/S1lW2WAqz/3mrGwQPJ764+3IjsGUtTn+PmQjI2+vYOeuKFYYTCOa7TUukNh8Y/2wbIiX1xoAMDM+bpdw7fbTbQm1uDz0q0QnseOtRQ+yIMLjcgrxVGOxRx2466wDhTlmiccl5NNRwRJu8vO5YrcTM8aqQwfIyOv+zS19tpMv5wPDbjD9+te/jt133x3z58/H5s2bjc/7+vrwyCOPwOPx4JxzzjE+b2trQ01NjcmF/7zzzgMA3HfffeADeb/44ouor6/HWWedhZISe5XMo+m6YHw576kVWNvYawyivIHx85pO5WSJcfK+E/HCJUcIu/yRqKY0hKmSbjy5dJexACtxO2PqBFmNGB/wunxh/PT5NQBiO7Ps5b76/81CkduJieVFqCx2Q9NjAfjZ7idv4ANinYochFtlJOQVpuGojoXbY53wN2bGlKz+cDRl0icgtoCYqEhmxfCFooZ7w5hSj7DYiGp6ShVreZELY0o9WHD5Mbj/ezEDOj+p9ricygkBD38N1bNgnLLfROw3KWH0kycMTofD1MHGjKjia8iMEuUKxQ+PbNQaCEZNnVOqjMjpxMJjAxwzELNBgZX/meWJeJZFLnPXokp2cf7TK4wkD/xixeFwGLtVY0vdpqRP8nX4+0iW9En1nvED0Ppmr7FAnhSPp/Xq2mZTvD4g5p4jT0jKPC788Yz9BaPOHhaK5srihME0qquzrp42OxGsny3SZaOKSsmo2pRh9Scb91gin13dfkT12AApTFpdDrhdTlN20xJpcBxbKiZD4tW/ViQLQi5PDPhFK5BIFCYc43EJbZ5lqj50tyrTM1EpNAFgxv9n777D26rOP4B/r7bkbcd2HNuxnWFnTzLIIISwIZAEwg6rlEAgUEopq+2vhUIDhbYQRikUUmhpIWWWUfYuECBsQkISskP2sOOl9ftDOlfnXt0rS7Zkyfb38zx9SiRZutJd57znPe8p0N43xKI+Qo5Lu1Cby2ZBkUEWZKwsu4Mq89Rj0SZlGoiSCEaHsHxc+wJBw4VXeuc4o1YHNeLRBEytmgXpYjFaqASIHigcqLs2iYbbjoZI3SuPw6pJQIksfhU0/FsAmDuqD359dJ36b6PrjNDY6lc7MkYDJUadzme//AEPfbjBsFC9CJwHgkG1pnKuM7Iy/M4DrVENUpHx36gLmBZlOQwDpvpOn1EWymurduIpXU0pPaP3Xm+wiuthUhaC0f3mQKtfvR4WZTk0+7nQY4/K6BNKc5w4YXhv1JVG17qyWhT1ej53ycfqbza+KpLVfvbfl2s6oUbZRUb2NnmjrqPeQNB0AaPhZbmaRr6czZzttBleW33+oCa7NJbRFXmafxu14fTcdqs6qOu0WdSgnRgcl69bzdJsDyP6mUtm9PcxUbLI6J7d1Oo3vHYWZkU+S1zz9dluZdL1Q/y2+5q8aoDAaKVhoTTXJMPUF8kwjTXgYGbl9gZs2NOEbKcVF0+uVh/vleWQAqMBNRBVKD0eWsFe+9v1L87CoplDMGdkHwwItwe/2LIfrb6Amhlenu9ucyaA+K3WGrQ5fYEgHv1EG1STd+E1/1mB/32/O2pl+/V7GjWvO9DiN8w6ynfb1fbl3iaveq+VA94uW3QCwK4D+gwu48C7w2rBIbr6soLImpWn5G/Z16z289btasSb4dqvC6ZUo9Bjx7Z6bT1F0VZyWBU1SLqtvkU9LsX9wB8IahacXLPzgGZ7xUCLx2GNmnFQ30bwQQS2RFNB7l/c/voaTQBQn21slvmWF74/mLWbXl+1M+qc0wRMdbM45K/01Behe8rRg0s0140sNcNUm4EoM7q+mgZMm7146outOPOR5fAFgqjMd6mBb0DbbpLPv7YyTM3OfX07UVzvxb1WtGvFb+K0RX6jm19Zhe92HMCNL61Cqz8yiCoPcjtsFhw2MHQfvXhytekCoEYZpvIgpdymFMegzWqJahPoA/FAaOBG9FFWbGvAJxv3GgaxAeCtNdpyD+LeIV/TW9WAaWifT+lXiHnjKjF9oDZrETDIMDUI9vXv5cEvjhsMIDLQ0FbwDgC++aEeDS0+TVBUnxAgMsSDwaBaMiHbaY3Kdly5vSHu+7bNoqixEZHpr4+LGM0OFfteHGOtvgC2SAFu/X1WH1wWzAKpQHQ7zWFVNAFOeZvkto3bbmlzkEcM6uivLvefNirm3wn6+8HXP9RHLVjYlXV6wNRms+G3v/0tgsEgzjzzTPzyl7/EokWLcOKJJ2LdunX46U9/ioqKCvX1f/jDH3DsscfilVde0bzP7NmzMWXKFDz33HM47bTTcNttt+Gyyy7Db37zG1RUVOAnP/lJu7YvENROyf9+VyOueuab0Ipf0I5+fbW1Hj/oLl5yg7BXliMqOOoLBA2n7ecZjI69unIHmlq1GQT6LBn54rx65wH4A0F8HC4YProiD7NHlAEIBaBEQGrRq6vx949DtWOqdBmmjQaruxoFPPQN24aW0O8zsTo/9D4xAqbyCZ3lCGWNGgV4gNAJvDc8HSXfbY/qbBjVYpSJ39olXdzkAJbNoqhTEQ+YrGT8ztrdeOD99fjlC99G3WyEIb1z0DvXpbnQGQXn9PvPY7D6psgG1neC9P+eVFOA/zu6Vu2UNbT6ogKmYgqOnthOMaqoXz3UiNifaifNLgKm0fvO6IJvlFkjb24/3baKG2O+x6EJjMgBIad0YxJijWgZdYCNOi4WRfvapZ9tiXpNkzcQNd1AbJuclXj1DONpwzlShikQvcIsABxeGx0w1TdECwyCQUYBIkEfMNW/X3meS3MjFyPQ+gaOPis0z2XXNOTjCdyZnfdA9HTWVl/AcOVpuf3kcVihKIoaNJUHuvSNG7NArj7DtLYkWxOEzXXZNN/NZrWgV1b0790713zATn8dULMdWrTZDjK5/JAvEDTM8irJccYMIgryceewRWcxmDELAuuzE/WdYRGIku+Xbt2UfEF//soZG/rGcqxsWpFNYFWMr8VWXcD0QKsPN768Cve8u85wYCyyQndkoC7HZVOvy4Eg8NcP1mu/S3g/6gfjBvbKMgxG6dsLxVkOw2vp9waBJRFInlxTGJWVCyBqgaybjhuEGQND1xc5iCkT2Wh2ayhwK3f6JtUUtjm9vL9uQBYIDQbov6fDatG0ZbY3tGrqwLWVGSLu8x+s26OeQ6JN5ZWCXfqffLhuIEV/LTUq3xPKuo20O+Qgm150hqnFcBBzlPQ6l82CE4aV4sRhvXHK6HL0DS8II1a6ljPV1MFLkwxTo3alETlotvNAK1aEF4o0Ot8bvX7DgVa53I0YzJEzey+cVKXp2OW6bepvsbfZh417mgwzQIWyXJd6vsvB9BZfQO0IxlOCQB6Ylc0ZUaYGOIHQfVIN2PiCaluz0GOHPRxUafUFojqE+VKwaVBpNoqyHGj0+vHppn3YHF6PoCLPZZjRrf++QHQZJSDSBjNT3+LD5U9+FfW6NTt17fZWn+G5VVeSpc5u+XZ7A1p8AWQ5rBhSGhkgcdqjsy63GExDN1qx/rwp1fjZYf0Ntz1UHiKoKTvS6PVje0MrWnwBnPOPT9XHB5Vk42Sp5Jkgrh02qwVF4WtoIBgZCBD3gz++uVazGOkWXX9OBFjcdmvUFHuj/s3KbQ249j/fYNX2BnW/lYfbE03SDEC57JhVCU35N1KmG6DM02WY6lktiMqml/sNov9097vrsGFPk6YNJNpKo8q1gzxq/yhWwNSgH5ZnMgi7v8mrWSz04OpCzb1Hvg/I/RQxy0D+7m6DdqrM6GcS57QICu9XM0xDz8szXb6VrkfyuSQHuG0WBb84shZ/mj0M506oNJxtBEQHTNftbtS0w+T2rgishabka+9J+uMw9PqA5p4U+r2M75n6fSfa+vL3a/EH8MP+Zvz25e8ARPbDDcfUafqUoZXktddTfTviuKGleOSsMeripeI42t/SdvCy2RfAS99u19yf9FmMIpja5A2og0E5TlvUoMK7BuUfzPTvlaUeB31yIwMuMo/DGtWnFXcC9bj1+TWZqfp2oFkmaayAqf440pdG0WSHSn0Bt93aZsBUHAP686ZXlkPNMDYbnAWi2+5f/1AfM4Gpq+n0gCkATJw4EY8++ijGjBmDF154Af/85z9RVFSEP/7xjzj//PPjeg+LxYJ7770XCxcuxO7du7FkyRIsX74cJ598Mh577DEUFha2a9v8gWBUY1DcNEtznOoIBhA6Odbr6qPY5APU4CZYVegxDJgaZQJs2d+ilgJwm4xE6IMSTV6/ukr3uMp8zXOiLtaXUjHyGl3A9IDXh29+MJ/uJ+i/NxCaHiOy8hpb/Zp6bjJ5SrxoAJs1ABpbfWqgKJRhqn1+k8nCBILRlAdthqkSqdFjcjH55od63Pe/9fjviu34X7jmj96M8MibPLomB6LEBVAfmNRP07/uiIGYGg426C+a+u+iKAqOH9pbDZgeaPHDp3t/s3qtoiEmbp6tBqNAFgW47cSh6r/FdGHRSRNBfKN9ZzQl3yhAIOunu/HabSYZpvIImprFGNr+Zq8/Zg1To1FvowBTnssec3qg+Cx9doa4V8nbW5jlwOljyqP+PscZGiwQ5/ZeXc3IHKcNYyry1KntoiGsn25r9J2MarUK+hFwff3Kiny35jg2u0FGZ5jadPsm9N9/PX2U6bbos6tlRhmmGw2uO3JmmjgmxbEmGsPl+a6o47RFPY61AXj97+O2WzXBT31pFJtFMWwkD9B1zOWGjn7gR1+TrK0M09B9KrojmuO0xTUl3x2VYdrBKfm6a1PfArcmaF+UFSn7EPp881qO+kaXvD/0+9AWIzgsgrP5HofhZ8kZpl9s2Y9DF/9PfU5ftxvQBkxFwzPXZUN5nkudjvylLqAvvkuTrqE8oDi+gGmpLmNYzsQBtNfUw+uK8di5Y/GbY+o0x6PY7o26BQ8O6V+E6iIPKvJdGFORZ3jciCyoArcdiqJo6mqP1nWsjfQ3GbDL0dXn/pNBBoMcFNNfG/XEIOh/vt6mDqqIIL3XH1CDXX10U3KHl+Vorv/686DSYCqaPxDJMD2obz7O19XmlvUrjB4ENBo4HNEn8ltmOWzole3EL46qRV1JNq44tB8ePXsMjqgLtTHkgRLRwTFrH8Zbw1Tc0z9Ytxsn3v8hfvPfVQCiAzZAqI1pNFiT7bBGDcSKqfOH1xZrSlMBoUEjce/5YN0ezHnwo5id2lKphqkslIEUOs9ynDYsmjk45nc1G0Q+ZXQ5SqVVixVE2iCaDFOPXe2ItvoDUQFBOVBkURRM6Rfqi7yzdlekFqWn7VrTZXnmA25GM16M6MtriVqk4jyob/YZBmCyHDa1vrpQV5KtGXgKTcnXHndf665/uxu9UQHlU0f3wdVHDUKfPBeeu3BCVJseCAUn9FNg1+46gI837tVk6xdlOdSkEFlkSn4o+6qPbvDygol9UVXgjlnDEIjUhfU4rJqaqkDouHvqi6046cGPcN1zK9DiC+Csvy/Hq6t24rbXQwvMue0WNTP8QKsfzd5QAEXziyjGg1UWJXoGYJbTvM0NhAJt+gC7vI/kv3vzu52GbQj9IE+kbFRiAVPRp9XfV/Y3+zBImnkwqUYbMJXvXeLavHV/qL6qRdGWEZDbqaLP8ac5w3B4bTFeu+RgvLVwMt67fAqOGlQs/Y02w/RAqx8+f0BtX4nPlGeoAJHBAKtFWy7KbrUgx2XD5H6hAcQig8FzIHpNCrfdCvn0dGsCpoHwe0fXMDXKHG31BTT73RsImmaY6omgnT7D9N/SLBbxm7nsVs36BgOLs6L6T3K/4KbjBuHXR9fBYbOo9yLRdmqrZN/kmtB185kvf1Cvm1aLEtWWE7+VGMAQg7/6DNP3DRZ0BozXCqkr0WY8G51tLlt0hukFE0P3OPGbNLT61bITQPRMI7MMU7MBUCB6FpJ+EFY+3eR+uMtuVUsWmGkymJIvnD62AtceMVAtJWZE3/de8UODJpmpq0tLwBQARowYgQceeACffPIJPv/8cyxduhTHHnts1OsWLVqElStXYs6cOVHPORwOXHrppXjllVfw1Vdf4d1338Vvf/tb9OoVnTYer/pmv+F0PACoKHCbTu0S5Maw3Ih95KzROG5ICW44ps4wiCefBGMr89S6mm+GMxpjnUDy5zR5/fhk497Q+/TVdmiMps/pp5seaPFH1QsyIqb39ZFuXqPK89Sba2hKfnSnM8dpQ5EU8BEdXrPASbM3MnKW77ZFXSyNbtTytHifwXQjuW6bPCVfP50oEdPVgKmUYSrtUzF6qL+gZDmsmpGwkeW56nfUX0zNMhKy1SkzPvh1B69Y8EpPBIBEw8eoxl6uy45pA4rUfbxfl2EqvqvR9CCjIGSseOkpo/pE1WEry3PDqoQy/uQLv136b/H4tvpmXLL0C0y98z08EWO6qtHUY6Ptz3fbDaefyJoMAqZi5NmhC/DGqlssRvD1U6km1RTAZrWojcK90sCNflv1zDJMsxzWqCkz+uy7inxtprTZaKc+WNW3wGM4JX9En+ii4ep7SL+L/nP001lbfYGoaeg1hR4cKu0ncf2RrxNWRbtgiLBbva5Evn9lvtuwsTBzaCnsVgU/mdYPuS675rvbLMYLE03rrz1+SqTVJvVBk8j0sOgMU/HfAd2UfKPRYptFiSuzV+7EOK0W0wxTowXpjBgNAmZrpm9rfx+jBYYE/XVPDpiKATkhVnBYZEeanQvinuMPBPG4boVso9kG4pxu9mkzGRRFwakGAyKA+ZT8gcVZhtcEfRBleJ9czXcc2SdXvY+P7JOr3vudNgucNgv6FWUhz23X/E5i3+jvPaJj8e/zxuGuk4cbHjeR39Chfl8hVskJodBjR4lBtqt8bPzssP44dnh00EPOuJdrwxrNIDAqeyKmiHv9QewOv5ccOOmd40SvbG3NX33nyTzDNPR+sRZ2syjR9xaHyZR8+RpZqOtw260WDCzOjtSak6fkq+VxzGqYxjcQIu7p//h4s2bw1Gh6aaPBlHyLEjoGI9M7Q+8nZ2UaERm9/9bN4BjfN1/zb5ElaNROFFNuXTYLPA4rZtQWq/U3BTmobVQSa2JVQVRA1usPRr6PN1IPucAjLfrkD0ZNs9Tfj6eqAdPd6u/islvaLLlU5DFeWBGA4SrcRvSBbTHdfVp4UL7ZF4gKBAKhbNss3bkwqDRbc847bZao41uf+f79rgNRs3BGV+Sp50BpjtNwwcTdjV41WUQEDb/f1Yh3pBlehw4ogqIohseo6BeIoIl+oGRCVQEeP+8gnDCsNOpvZXKN4J0N2gyzd9fuxs2vfIcN4TrMSz7coD73XXiGQu9cl3qM3/PuOky98z11fQnBHwga7ueyXJdmoO38iX3Vdo1Zf2nXgdaodqQ80Cq3i77cuj8qOSg00Kn9PUWN/0bpGqw///X9sFyptre+L1Df5FWv4YNKsjGppkBzb5L3p7jnfhMO9FfmuzXXOk37Mfw5k2sK8buZg5HrssNlt8Jhs2j+RrynfC/b3+JTSy6Ic1s/y2RVuI5vqDxedGJAZPuN74v6wav5k6o0fUR9+w4IHb/6WQJG4Ql9uSpfIKBmU8uM+gpGAcNWf0AT6NMvuiv0N5gp47RZcMOxdTh7XKWmpJi4F7X4AvD5A22W05szsgw2i4IV2xowd8nHAIzv/aIPKwKm2eFZZtY4EgcAoKYwegBNLnfnsFkMZ+x4HNrMzoVTa9QZCuJ8XvLhBk08RT9wblS+AIg+pmT648hm0U7J19cfVbfXbjWdfSkGRo3iAPLnzBlRZpqEBWjbmG67BY1ev2EN7q4qbQHTTBWrxkXffLfpCtRA6CCX72NyAGhQaQ5+fcwg9M41noojN+L8gaCaNSVqg5oV9QeAh84Yrf7311tDRb+dNguG9dYGKmbURgeAKnQBtQOt/jZHIcTrLAo0o7ujKvLUwGCLL2CYFdK3wK0ZgRIZabFqGYosl3y3HWMq285qkevMGtV7cdmt6qivzRJZ0betjEIj1x0xENcdMVC9iMgXKPnmJBqf+k6rx6GttWIxudhZFeOsTUBbY0ifYWqUISI/LhpMosNktypqY0B0BnPCNzpxQ2rRTQOMd0q+2ah4lsOKqwymrf/tvHF4ZN4YFGc7TTNMxePbG1qxTFox1oxRg8Fou/LdNhzSv0gduDDS6PWrncul5x6ENy6dpDYmnLrAodHxnRNeHV3sP7HSaG04+0ycW+I7imtTiS5oZPRbm9VNzXfbo7ZFH0Quz9Oeo/rBGqPPG9EnFwsPqdEG4mJM3RBsMV5/zOASXHfEQHV6d6s/EJXZ0LfAjUMHRKZ/i30pByRLw4uFRE0fDDdk5GPCrLj5BQdX4e2Fk3HmQRXh9488Z7UocNi0tab65LmQ79FmxMtBLH1MVj89TD6LRadXrqHm8wcNM4OsFiVmY0vQX2fMMkz1ASGzwIdREEI+hvSdYqMAq3D9EbWoKfTgxmMHhbZB+sxjBmsXEdJnEchEHMNsm8XxUN/iU2viCXsMBkb1GdUOa2QFc7PfXFyPRUN0+sBeOG5oKaYP7GWYYZolnWuH1xaHz6nI52Y5bfjj7KE4ZVQf/OKoWvX7G03lF8wWDxSfL7I2jAa5xDVJBPHkwV6jhaX0FEXB7bOG4vcnDFEfCwKa7DV5APP2WZEZDXLjXi4HYjSDwCjTuMAtMkwj06nlrL1+4Qxw+fcVKycL+vIcoc8PqMHcWKVPjAZpHDZLVEZdRb5L81uK7dYTAwlNXj8CwSA+2xSprW+WqWI2JV9/XovsMX1CgHxMiGM8lGGqbSOKUij6BUREIEUfBBbEdUd/LugHy0R7zWiARGQOjanMU69r8na77RbNFFo5UFlV4MaCKdX47XGDot53aFmOmgG7r9mnZnAVeSKLPnn9gahBU/3g0/iqAjisCrbsa1YDcC67tc0MU0VR1O+tZ1Z/W0+/WFoQoczZU8dEprGv0i3odNuJQzGhuiAqw3RwaY6mbS2XudIbFB7UWburMWqRGv05YZRxvX5Po5r9OSWcabZm5wE1YPrT6f3x62Pqov5O+HB96JjoHw5g6Bd78oRXva8wOL+B6PPabbfihmMHwapoFx2UyTVlxfWqLNep9i/ElF6jrFajtlJlgVuzuJlc+sOsv7Rud2NUAF8esJL315db66PaU0azBjxS/wIIXX/FtTmSbKI9zuTrjv677W/2qsHXeeMqoCiK5vquX5ASgBrUry3JNu1HmPWP9Nsgzl2rRdHM7BEBS4cot6H7HdWBGbvVNIAIwDTDVA7Q/mn2MPTOdWFkea5a/1TeF+KaYrNGT8k30uoPamIT+5p86r1b3v9ypq1g1D7Y1+xTg9QATPtf+W57VCKMy27FMYNLsfCQGk3wTrO4aYtxyT45WF6W68ShA9pOfhO/lbjOiPPNaEHQQo89akp7TVH0NUCfXFZmUF7Lbbdq7uVyO1W0FT/brJ2tGyvDVE58M0raEORzS9QvteoSRNTnbcbvryd+kyaTKfnxku+FIov88837zF7e5TBgqmPUURIq8l1qIxuIDkbddfIITUfULCtU7nQUuO149OwxmpuAPxAa8QYiN175YNcfzP17ZakjIO+sDTUoRvbJjbqB5LrsUSvd6UcrDrT6DG/oRudPWa4LJ40MNbwcVgVjKvIMazK6bBZce/gAzJ9UhSun9zesRxmrlqEIDOS77Zg1vAzXHzFQfe5gXSYBoO3ImQUMRYDTbrWoZQm+jVE/S1Zd6MZfTx+Fx889CLNHlGmCxmYBUxFI1o/wuO3aDFNtxl3kt3Q7rIb7AIh0ABpaoxd9MrrQy49HMkwjI+li/4hAmjhG6pt9ePnb7bjn3XWa72c02m20P80WuzCbFlWS60Jt+Mal7bhF/rtU6kjNGt7bsAHvsVtx/NBS3HBsnUnANHr788JTUMfqylrImryR3zvPbdM0PLQNtNgZpqJoutWiYHBpNv40ZxjeuWyy+tnivURDWJOpaFEMGzzzJ1UbbnOe2x61LUYZppqAafi/fxTO0rn+yIHQG9knN7wyZeTv2qrRBsTOMFWUUNB4SO/QMdDii14ZPstpRaHHgYsmV2FGbS8MLQsNEsnHgQjI6L+3OFW0AVPz0VO5YSw3aMT7ioWfjh1SgofPHB31N6VSwFTfSVTPsRbtAgRAaOGq+mafZuaDLxiMmrIoPi+eDFP597FbFfPAiq7jaDaN3igAKl+/sh1WTbAj1gBgdZEHj593EI4OB0cn1xRi9ojeuOm4QVHHqj6Acs/c4bji0H6672AcgBL7bUe4Np7MqG6g/n6aI3VkzH5zkfEvAlJH1BXj10fXGS4YAGjL+BRl2WFRFE3DP9thRXG2E1fNGIDqQo/6/fVT3OV7oFlwWr8vjTrhopMvfkM5MKwfuDEzqDRHkwUeDAY116xiKSg0KRwc0ZMzTI0WqdFnJAKRoEejNxLskgMn4h4oH0P6rLq2Mkzla4d+QLoszxW1j+3W6FIUYyvzNYFysw63WxqMvv65b/Hjxz5XA6Zm2b5mC6HoO4Ti/q/PXJTfV+yzxlZ/1Pki2jdyhun+Zq9a872myHgavLi+6Ot361eaFu24WO3ECVJpFrmNEMo0k+5L0j522604b0JfzXXlH/PG4IKJfXHO+ErkeyILU4mMrQKPQ7N6tz6Arx+AdNutOEh3fLpslqjzUs7GEsym5cezwjRgvKL2hKoClOe51X32qlRPEwCm9g+dg/qAbm1JlqaEVzAYNN0f4tq9dmcjPtBNhdWfE0bXQTG1vzjboWZfP/vVNmxvaIXTZsHs4b01ge/jhmgH0sTA4yHh7F45w1SehWE2s0J/frjtVhw5qARvXTYFp42OrpkKRAdDAGBAr+yo6+SCKdX4jS7YaxQwrSpwG86gE9/BiAjqeexWPHr2GNx/6kj0kj5f/rNdB1rV64egn44PRI6DN1fvwvb6Fk1WqhowbTS/bkQHTH3q/VC8t1ltRf0g3sDiLNNgsX7hLM37GARMAe3MHnEam9WAF4k7TptFO8tN93qzskjytVgMzCuKgsumhfrk8jktZ5jqp2Ab8QeCmmvo7W+sUdddkPfF4bXFUeebUab7Jxv2aq5rcltOM4Br0LbQl1YTrBZFveY0tPoMp+TL/VW33YrZI3prnjdaD0L8bpulBfUA4/bM+KqCqPUBqgs9moFaAJpa1oDxddjj0LbhtOsCRH4jefBPf90WfSSxKHc85Han+Bx5l8p9E33iy03HDYLDquB3x2vL1ohjLLLoU/sipnK/RfRr9XWzuzIGTHX0BbNlfQvc+Mm0fjhzbAX+dc5YjJMaQX8/a0xoqol0nJlF9OWLzJjKvKgVhQPBIEb0yTXN8tLXHAx9Vuj5d9aEakDpG2jCvHGVak0lIwda/OrNTO5EDzLItKsqdCPHZcPS8w7Cg6ePRlFWaKqS/mb+4kUTMWdkH1xwcBWG98nVrXguMky1v5U8LVgEivJcdlgtCmaNKMPhtcWozHfhF0fWRm1XcbYTD581GjOHluKyQ/pFPQ9EMvDs1shqePrsYrMb1YBeWRjRJzeqvhCgm5IvNZzFPtcHNLN0NUwtJhe70OrfJsEKZ6QGa9SiT8XRHRWHVVEzTJp1U/JDdamU8PaHM0zDF/K9TV5c//y3kfcO1wIzarwZjZAZZRLESz7+5RthRb4bj597EJ798QRcf2Stmtkgc9gs+L+j63DM4NIEMkxDr4uVJSlPKdEHTDRBcKvxVEIRJPvNMXV4/sIJePfyKXj4rFBGrdHCVoLcAHfbLThyULEmo3RidYFp7bp8ty06YKq7nvTJdWkaSM7wbz9/UhVeungijhkcnWEhGldidB4wrh+sJ2+Lvj6jIGfz6Kfki8bejyZWYdHMIer7yV+xPN84YCrIQUGjmoVtEftWBDrGVOSp545cv0cOdOsHD0RjX9x/5Kkxl/z7S1z9n280r/f5g2oDUbst2kxBs0aYHLBUFMU0w1TOtNEvwiUzajjLQYpQBq70XuHXBw0nmGnZrBZcd0QtjhxUEvWc/rwrznJG1c1sK8PUiFE7QH8eyh00s06WPsNU/g0Np+Qb/MZy41s/OCLum/r93CuODNOozzH4DqKmZHX4+iIPWBhN5YuXfGwU62rUGu0WuUOtD5g6bRYcNahEcw1UEMlqfOqLH9QsOnlKvhhkjtUBlGfgqKUN/JHOqXzt+N3xg/HOZZNx7vhKOG0W/HzGgKhjLDQLKfLYJVOqcdVhAzSBWqNF/ADttPtXV4WCXEfUFeNPc4ZhpEk92WynzbDrc+0R2kGvZp8fwWBQrdn3p9nDcNqYcpw+NlJqokJavEaf4Siuw+K+s7fJh7fX7ML+Zh9qCj3qFHA90Q7Rl4GSg59ApISQWc1ip82iLooFaO9DeS6b5reTzwejwGNtSTbmT66G225Ffvj7BKVtLPTY1YGDhhZf1EIeRu3zKf203z80rTfyXY4bUmKY5aqvvRlru42IOs5yRvEJw0NBCHH+yvU+J1YXRLJ0dde73jkuNYAMhAZTDOvXWxUcOjD0fTfsbcJyaWovEGfANJzdVpHnwghdEG9iVUFUMspVMwbg0qk1mvqKNouiztSTB0rke1++QTvJokAdqBc8jvDMIZsl5vVU7jO4bBacO74yKvh/SP8iHDtE24by2K1R60lU5rtx+bRQ/+XscRWa58zaMqIdVuCxY2BxNkbpMrX1/YOPw+XbgND1bbzuvAO07c3nv9mmOfbEea9/X7nmu/6+0uoLqH2tSPaqScBUdwwaZZheeHAVxlTk4ajB0e0Do/eR/zsSMPVGMkz1bYrwfUIEr102i2bgUB8cVRRFHTyXOW0WXHFoP1w8uVqz2LKYjdbiC6hrXIj9aIsxmK1n1pKSA6YlOU51MEMwarv9oFvgyCldT226IKHcL33i/HGGx5AgrjkNLT7DDFO5Pq3LHjoeb5k5GBdM7IuXLp5ouMCbqNMsEgjEuW40+2hCVX5UwLRfURYO6V+EP58yAkAoNqA/x/sYJF5lOWyaa5d8TZIDjldO7x/1t8Kg0hycNqYcV07vH0dLOCTXFd3u1JYgUwxfqw76LJyMw+uKNftdfF9RMqC9XXWfQcC0O2HAVMesfikQajAWeBz4yaH90L9XlqZjJlbNlA9csw5mdhsdrUAwCIfNohmZkG/yRoE88by6GEGMzLgFU6phtyqYF55eetLISIZkKMM0vMqs1MgyrH8aXr21utCDOimgKo+0KIgeqZZvWCLYYFUiQaqaIg9umTkkqlEod+p/N3Mw/n3+uKjGCBCagjC4NAe/OrrO8HkgMtXOZlFQnu82HBE062iXx8hCM80wdUQa3jKPw6YLmErvZZc7cjbTi1iWtIqlT1qAYPFJwwzrxxV4HOp7i984UlvLqu4f0XETGRMf6aa8i9VmY5VTkOmz2kSnLdYNRTC6SQg1RR41QGC00Jr8mxoGTE1qmAKxVwTcFyNgKgeM7SYZpiLIoSgKSkwWtACiAzVFmgBDqBbaE+ePUx+LVVcv3yDDNMuhnVqSp1tkS/wGimJcqxOIBDHkaXyxpl3L2y/84shaHDmoGEsvOljzGnlhr6gMU5PP0GaYinPdeF/Gm2FqRhw/J43sg1HluZrOvny+yx1p/a4WMxde/nYHGlp8UbWEvtyindrjCwTUBqJcRsBmUTTHs1m22kF98zGppgBnhAMiWQ6r4fVOzs40qv8rtDUl36Ioummxof0mgqBGAx3x0F+3XXaLphwGEF0/VYiVrWY000Rfb6osL3JvMc0wDQTR7PWrpWHkIKPR58vXL9Gu12SY6n5n8Zz+cTnDNN6Aqfwd9Ft2xKDoFdpjTRtri3we5+uOO6M20Uvf7sAJ93+IF77ZFjXlNNsZ6rjMkWZ6ZDttmoEd0QGUO2TiniFnwuuvJzkum3p9EL+jWQ1TMSX9kqk1eOPSSehXlAWLos3VsFstmoUfD68rhjNcezOyDcb7S38f8Nit+O1xg9TFMYxYLUrU4FlVgTuqpqOoSyraA6MqcnHl9P7IddnxyyNrccKwUrXeY6PJlHwgFFQDQpmNa8OZJeOr8k0HJ0QbQ96n8w6qQGmOE/8+7yC1/dJWhunRg0o0gQFNFplbm2Eq7+MWg2xlmc1qiRqMEIkBQHhRI12wyChYIi80FbpG6/alI7o+P2A+QyrehTTEtoljOM9lU4PX8nVhSr9CvHnpZNwxZ5jpe4l9LPoChw7oZbg/+vfKQlmuC3aroi5OqC9hI5M7+OL7ioXb+uSHamrKgyEiA1aW5bDhnPGVmt95dEWe+h37aAKm2uxjvYp8d1S2otyXi5UJdu74SvW/B5dmI8dli+qDGGWDK4qCR+aFMkKFXtkOzBreG0/9aBwumVqjeb38u5flOvHsj8drnje75+lnc4kZjD+a2BdLzzvIcNvGVuap50xDSyS73CGV79KTS0nI1zax2WLmQla4BMrl0/pBAaIW0NPfy2uLs6LOnR9PqsJ9p46Mmdwgv4/833lSKSQR9LXr3kfM9FIDpnarZgDGaOq3UbkaRVFwxtiK6O9oE4PHkfNVnZJvMZ+S//BZo9VZX7HIGcYehxW/OLIWo6RBCDFYp5+ZI5OvcfI+9zisKMl2wGZRkOWwmpa0Uj9LzIZs8RvWMDWaQn5YbTHmT642PaZb/UF8umkf/rk8VBJDBEyN+nUTqgqgr+hTHZ6SP7JPLs4eV4mrDovuj/Y2GLjKd2sDpvI1Qi7vV2uSCAKEroVXTu+Pw+uKDRd6NSIH0MV9SB6Elbs58rEjfk9x/sh9anEfFuuUtLdpJ5/rQ8sYMO2xFER3pvtL04zUuijxTMl3xA5+igu3NmAqZy4aZZhqG4SDY0T3BxZn4/VLJuGy8OjlNYcPxLHhKS17m3xq41VsW+hCGB1I0C8YJWgaF67ohqCmhmn4oqYoCv7z4/F4c+EkPH7uQVEjvFYlOmPXLOMyntpqohZqv6LQ4hsi+Kt5H4Ni8oBx0E3QLvpkV2vGnDeh0vD1oenacgA58p3k9zIKBArieNrf7FNvbLXFWZhYXWjYqS302NWbtNGUfNEAEN9TBE0+WKddwVb8ZrECDzKr7rvNHt4bz184AaeaTHGSyb95rIVejGoJyp9rtO+MjiM1YBojq65eEzDVdwLkbTKpYRrnyLE+aCvflGJlxxrJ0y1YBISCTHIMItupD5ia/wYnDu8NqwK1tqd8PYsnUCMXZi/OduLm4wdjXLW2QyQaUa3+gEFmU9vTjdUMU5PjJt8dqTVqVvs1FrEPDq8rxv2njTJsXAHaQS/9MXd0OENuT5MXD324ISprSf+9D7T61Smicp1Fqy7DVG5k6juNd8wZjisODTUOFUXBfadEOmuRv48cS/kmDVbALMNU+5gccBXHaHWhBy9dPBEPnTHK9L1j0Qcq5QEfwXTRJ+l4GKXLYjKakq/vvB0rBeTkjDaZLxDA0s+2YG+TF6U5Ts3qwG3VMBWnpPwd9Vmd4jl9Jz7LYVPvw/GUxgi9l/F1cnRFnrp4oH7RrfYIBqHJFNefC2Yd3637W/B/L67EY59qFwgS5QjkoFiO04o+eS5MqMrXvFYebOoXbr/JgwJGwUpxfmVLdcjFwLQ+2CvI+0x/35OnwYrOnXxtMPv+YpEuobYky7QNJNO3MY2Ou/oWnzod325VNG24E4b3xi+PqtNMyddfj8Q9qU94EGHLvmZ1AaCqQuMpmkD0vaoy34ULJ1WpfyfawOK8MWtrFOnafPrFN80yTGNN4xXkjF+7NRQYiFUvsb9BB1m+PhrVm9UvviPoA9uR1ye28rDI0jxldB/DRW+OqCuGwxZdLsLIA6eNxBPnj8OQ3jmGx1JtSTYsiqKZXjtZyrDV/4n8HsPKtNdhUU5HJHVYFO176cltI3kmnfw7yq/JMxgErCpwR+0juf2tnzkjEgeqCtyYJtVcFNfKUnmR23BgyYjTZtFkhA7olQUlXGdVv1+s0vXFZbdGZTWb3fPMFnUpynKYXtsVRcFp4UUNG1sj5U1C5cSMzwN5YOqXR9Ui323HVYf1V889EbQSv+vI8jy8ddlkTZ1WQBtAK3Db0SvLYRigbItZhqnYnnrNlHxtdnr/8ExCsaiS02bRBMGN1vwwGjQxI1+rxHmtTsm3WgzrP9utCgaVZGP+pKo2J0/L1ziP3Rq1SJC41p8xtsIwWAhE+oeANss/y2GDzWrBG5dOwksXH9zm9SNWhqnTZtEs0hyr3yH7YvM+/OTJr9R/i/a+PsO0PM+F4mynJhs6321Xr1M2qwULD6nBRF3/A4guEQOE+lJygogcnxDlG4CODSwbydEkD4XeW+7ayPtAn2GqeR/puBC/gRhAae+U/Eum1GBYWQ5+fXQdeuc4DRO2urL4euyEkhxnVEO2v1TPVByjcqTfrE6b3Cg3GtETqxrKz7WVYSp3TkeV57UZxNJ3ZsU2bZdWghQnVP9eWYZBKqMgI6BtXBgFTbQ1TM0DzHNH9VHrsPiD8V94ik1WKZQNLs3BSxdP1GS1rtatihi6iGinE+W6bFH1kmRyFlK+244bjh2En07vbzo6piiKpsFo0XSuIu/lcVijgihCRb4bNouCvU1efB9ekS7WdNMCafVVNcPUG6mV2+y1qNsPAIVierEUVZtR20tteMQbMNUuNGNVMyvjoQ2YmndWjKZhyJ9rVrPK7PNiZZiuD3cInYYdDe0UFsMapnFmfukz2+Rrgfy+R9QV463VO9XGrRGjDFN9w8RutWiudbEyiH9xZC1+ftiASCdMujbJHQOrYpy9H8+xI7al1ReAfveaTfs3yjCVB7PsVkUdGLJbLfjJof1R3+wzDXbGEus7WJRQ5+DscRWaTlh0/UgLLp/WD1c+/TUe+WhTmyO8G8NBF31mqH5Kvhwg6pPnUqeVGm1ztUGJEfm6FStz2Wg/RJWS0E0NM/qMREUFTG0Wg4Cp8fvLx8MxQ0o1BfqNpuTr31cue2M2JX9vkw9Llm0EAFw4qUq3kF/sDFPBruugyMR+NBp86ZXtwIY9Te2akp/ttKpBwdtPjNT3uvDgKuw60IoThvWO+vt4BYGoDEXNdtjk72uNyop6+KONmn+LxfOMgmL6Nlihx4Ebjx2EbfUtGB6ujSgHxPQ1TAGgssCFL7fuVztHfk2GadvHrtWiaDrA+6TFMEWbRlv73vyek+Wwqvdso1k/RvTBdKO2wf5mH9aF72d5LrthW0v8lvsMFvPMUgOmoevnln3N6vvppxrL9Ofm8UN7a9qBvziyFudO6KtmDprd+/WBV7sui0xfw/SOOcNw//vr8cujoks6Gb23CHIXehxQTBZIE4yurfJxZRQIMAuAyvejQwcU4aut9dh5oNU0wGpm+sBeuGhSleZ+IK4ZTpsF06SFE9visltjDizWSgstid9tSr9CvB2uZ6vvjIvjUUFoGucrUk1VEfw4Y2wFemU54LRZTBMZgMhMLgCYKgVWs5025Lls2Nfs01wTjMon1BR5ombFuXUrs8vXpRm1xTh5pB0HVeYbZm3JCx76AsE2+zEPnj4Ke5u8MQcabLp+pttugYLIIJtZWQ+zPkRbC3SK87vJ61cTJ0aW50XVHh7fNx8b9jThcKke78DibLx88UQoioJHP9msySyUf2ej/rJ8LxhYHAogx3s/07yPvKq9QRZjo9cvlVmJ7K9BpdmR6cpiwTabtn38g8HCwkUxjlE9u1VR912zL4AsR1Btm9osCmoKPVgwpRrLNuzFx+FZfuI6JL6P2fVDgfbaI/rbctBQPh4LdO090QaQA+3ysSeOi1iJJTLxWza0+qIyTHOcNs12xerDyj7eGOmj1xZnYWSf0KCD3Fc4sq5YXVh4ZHkuPgn/zT1zh8c1SFRssD/z3Xb0znXi8/D4rXz8njamHP9avlmdxeuMsY+E+KfkR9cwlfehpr8bI2AqX6v0a520Y0wCQKhdLy9C/vsTh+LjjXsxtG9B+94wwzDD1MTo8lwcOqBIratnVPy/KMuB4WU56FvgVhf0kGMLZjVM5SwRo4CpmFUtj1Jpa5hG3zDkjICxcawkrycufKIunnxTGNAry7ARGFeGaRsd6VjBr6sPjy7wHI94MkyB0E1HXFyM6pEaNTqev3CC6SIiQPSUfEuMacyCfGHXZCjLHccYNySPw4ox4ZHpt8KrPccK4hR6HOp7i4a3WsPUblX3SSTDVPs7/PX0UVg0M7LqsdlUZz2rwY02XnEHTI0yTKWHnDZLzFqMgsg8iNUQWB/uDBgtVKIvwm04JT/ODFOjqZiCHMz87XGD8PKCg02zUgDjGqZGHXT5M9u6d8oNULkDIgd3zDIR4gmYqlPy/QaLPpkcR/KURdHpkjN/5AaC1RLKoPhxOLMpUbEWMLpjzjAsmFKNiydXa44lo59jar9CnDq6D4JA1LQhPXHslee5NA2k0IIWkX/LwVS50VffEl+Hu8BjV+8np40xzwQ3ymLUH7elOZHfPFkjz/rT3WkzmJJvUpZA1Hd0WBUcrluwRx44lN9bZrR4od7flm1U6zjq69YZDUTI57aYui3vz+gMU0X9Lnozh5aiX5FHvTe0xS5lK8jXajmIm++x45YThmByjDro8bhkag0sCtQOhXY7Ir+lXHvwpJFlOMagTl2OKzrDVJ4dI7jCU9+PHlyCc6Sps/K9xSjDdHA4u7Ei3AZs8UVW+I01iCDI1ziHVcHFU6pRmuOMWvhlRm0vlOU6Ma1/L/1bqOS6+YfVmr9Opg8KmdUS/zQ8YGA2Q0Ecm/uaowcTROBDXFc/WL8Hm8JtSaOBGEHfttAPGLjs1qjp7Eb05UL0dQrltlS2w4ZJNYV46IzRapZxLPLvIa6ncgCmT54Lf5g1FKU5Ttx98nDD9xBTjwHjNorZby7XzhtWlouF4UVbY5UOu+m4QThzrPa8ctks6JXt1HSsxbkxtV+haRmIthjdp2rDazKIc9eiaBdn1detFtea4mxH1DVWnj1x5KASTQanEfGdqgrcUX02UUqrreST6kJPVABBf1zK11uXzYKTR/VBdZFH098Rn2NRFPz66NC5fmyMZAtheJ9cTDWp+StoA6YWKIqiCT4WmgTs5ICp3I+MlRgQ+ozQex9o9at1raf0K9Qcyw6rgrtOHo6nLxgfdTyJ405/n2qrHyDfy8Wsw8sOqUFVgds0G9KIJllDl50LhAagfYEgrBZFUwZjUGlO1CCavk9gVItzQnVBm0FoQVEU9Xp15sOfYOETX6rP2cOroJ83oS+OlILQcrsu1uBNgcduGBzV15w1ei95hqs8wKmfkp8Isf/3NEYW0RNyXLao8ibxqivJxhuXTsI/zh6rbpOcaCKynAHgxmMH4fQx5fj3eQdFrR9jptigvZrntmsGtOTryqVTa/DnU0ZgQbiUhtmijO0hn7fiXq7NMI38t9GUfEE+F3sncftkQ3rn4JzxlTh6WFnbL+4CmGFqYkZtMU4dU45fPL8C3+04YLgYiKIo+Ovpo+APBNWAgNwg6WiGqTxKlciUfKOFftoiCvQu2xBa0TLLaVMbs0N756iLVwgeu9V0pFduXBgFhbQ1TJObrn5I/6KYAQwzRiv76W/ui2YObnMkTdwkFbQdEBM1YyxSSEo/fU9wO6ywxFhsbnK/QizbsFctsG82OjeqPBenjO6j3jCNpuT3ynJg9c4DahkG+Th02iwYrFsATN//khvHMvmiXhvnjUqQO0TBGMVejAIX+t8i32NH4z5/+PWh58ZW5qmjjkCkk9lWQxKIXqEViF7UxyioHM/ql4A2EGDVBcT0C4a11fHJM8owNTimNQHTBE7RHM2UfG1WRnQIyjjDQ0/sU68/ENVBM/u+cqdenMeTqgtw7eEDMLA4Gw98sF7NfomzBG+U8yf2xTtrdmHWCPNsu4nVheoUH3eMDFMgdO8I1Q204f73N8T8bFFPqzzfrTnWbFbtYiLyueuwWVDosWN3oxfD46wvVOix40+zh2HL/uaYwQWjDFN9JpVm8YEkTO0GomcdyJ0PwSyoVZQVyjbM99ij6oTpsxqB6ICpvrMoE5nF34QXL7l4SnXUeWecYRr5DHGoi/cAIjUiBbNFnwDg3Al9ce6EvlixrT7qOSPq9H6XXZMNkex7dDAYxLCyXLx+6STDwSu5Q9snz4VV4ZWfK/Pd8DiseHHFds3rxXdvK8PUbJqqZmEzgw7gSSPLUFPkQe8cJ57/eps608KiALkx6voK2trdFgzpnYPnLpwQ9brfHT8YgWDszJrTx1bglZU70L9XFkabLPSkp7/P6O9NwuvhhaSMpikDkXadUe05sR9Fm8HrD0IBcMKw3jEzAvWBQk+M7FogdlkVmXw+5rps6sKFQPw1fYUCTcA09F3ka8zhtcWY2r8oZpBLPq7kYMVtJw7F899sw48PNh6sK8xywGFV0OoPhqZAx3EuFmU5UFuivVYbtYuOGVKKtbsace6EtusgmgV/AgYBDtH/EMfC0N65MZMMxHWwIt+tydpfMKU64YVDJlUXYGJ1AWYNj74n98l14Zsf6jX7wqjtM7QsJ+q6pP93jtOm1uGUAzNm7ffjhpZiWFlO0gIn+gxTQJuNX2hyz2uUAl9Dy3Lwv+/3RG2rEfGbbd7XjNXh6/HkmkK8tiqSDZznDmWmx2pP5UhtQovSdvtaLnUjkpd657rwb6lmf6Lkc1d8vigfUprj1AQNB5dmRwUk4wmEHjawFw5ZOBmzHlimHiexiPvt7kYvPly/V33cbPFXuV0Xq0SZx2GNKp8CmAdM5f0xpiIPexpb8enm/Zg5NHI+ybP44kk+kYk+wdc/1MMfCCLbaVWngec42x8wPXtcRfSCmBYFL8yfgEBQe54XZzvx0zjWzZAZxVzy3TbNfpCvK06bRa19CwC3nDAEv3z+W8yPkZQRq18rkz9TrGNg0fUDhVhT8jXPOayajOJ4sm57IgZMTYgTZECvLLyEHWqat56iaBfZkNszZoE7j9QpMswwDZ84cmaMfNE+cXhvPPrJZs0IkHxB0I+OxmNKv0K1Mw2Ebr4LJtdgXN8CTB9QhBd0nZSqQrfp1BI5G7IjGaah55WoRR5iuX3W0LZfZMAow1S+ANcUejSrsJoRN5xcl63NKQVniCwA6WWaVfd0UxNjvduUfkX445tr1X8bNaydNgvuP20UAOC7HQ0AQsXXvf6AZkr+ldP7Y9X2BjWoImeBDO+TG7XP5FWLX5g/IWb5gcj7JNYQlveF2bQiwLjxoL/498pyYMu+ZhxcXYCfh6dp3DN3BI685311Iacsg7p4RvJcNuMMU92/DWuYxj0lX86OCZUy6JXlwM4DrZppZ/HId9ujtsWo0Srvq0RunXmaKfnSSKjJuRDPSttihc4WXyCqoWf290btLkVRMGdkKEsyNE1/T8xta8vFk6ujam7Foq1havwaRVFw4aRqDO2diyuf/ipmJhEQyuKRzweropgGTO0WBU/9aDwaWnxx16Is8DjgslvbzMQyyjDVnztyXbNkBUyNxLvoEwDNyrp3nzwcj326BXsavfhy6/6o1+o7SrYYGaZ5rkjjsyzXaTjl1biGafQ14dghpViybCNOGlkWNQgn6rnlxDiP4s10EUGmHGf0NPhkEoe02WCH3KEtl1apr8h3GbYX1ICppoZp6LF4sq7kYJvRcWy3WjChqgA7dVnHYgZJW+TAeKxsoLaCDUBo8Prv88agKMvR5tReQX/MmH3Gut2hASSz9qPoHBt1asVU6L4FblwypRr1LX7MHtG7zUX0ojJM2+iAG5XcAaIH3uTfWT9ImOjsFnnGktheOUh2RF3bmb7yMS1f06cNKIo5Hd6iKOidG5ra7nFYYh4/gt1qiaqhqL8mAqGAV6wFw4BQ/2RbfQummmSU+3WdfHmF6eOGlmLFtnqcPLKP5vePnpIf+v/KfDeqCz14+oJxKHA7Es5eA0IL3Cw+yTjLV8w00R9jR9QV4+sf6nHfKSOwt8mLfkVZUe0MfYapfL01ayPKmdFA7Fq+iZIH4MVAkfx7mU3JlzMFQ9m7cQZMw7/Zd+FgaV1JNkpynFGlL9qSowvgtHUNk4/3RJMsZPLHyIMpYt+JUmZ9crWLrw4qzY4KeIr28kkjy/DE51tNZ3DYpFIs7WW2poU8yBdr3zmsFsP2vdl2ydep0RV5mDWiDFv3N6NaOnY119IEB59EWRtR/mhgryx1ZkNuBzJMy01m1SWj5jpgHEDMcdm1Wdox7l0DemXhn+eMTfq2HFBXtZdiUNL+ibV9opQREDpOemU71DYr46XGGDA1ITLazh5fiRm1xW2u/ibIJ7xZ46/VF3lNL4MTWozayjcguYZIv6IsvHTxRE0NRJ/UCmtPerXNasGxQ0rx9483AQg1KvM9dhwRngagD8DFqmGkyTA16MhpA6axz8y/nH0QLljyEX46vX3T8+NVme+G1aJoGkpyMEbfMDRTVeCBx25V66PFQ74A6ut8CqHf3/y36lvgRt8CdyRrzqAzHpC+g3jvQBC45dXVauDeZbOiNMepCeQXSnXaxhhktcgjmPHeoKYkGOiTf5fGGPXv9Cs9BxGdUXPZITX437o9+NGEvmoDwaIoyHLa1ICpaCC2VXj8qEElhp14fWM/um6opc3BAkE+DkTn7a+nj8K7a3fFrCV479wRePKLrfhuR4PaGc5z26My29qsP5TA3dOshqlZZkw8nX510SdfAF5dgyrRzq9QLl3PzaaoJptLEzCN/ZmT+xXisNpiTT03I+V5LqyRai/brIqmQyB3nO3W0JTkWB3RfLddU5fMLCtPzyj4dXhtMf76wQa1RIQcvC3JSV0xeLnRn+O0xVygRTa+qgDjqwrwyca9uOjxL6Ke15+v+oXdZHlum9r4nFFbbLi/jc4J+d4pLtc/mtgXU/sXGWYFj6rIw+db9kctliLT1k01n86rZqu67IaLXnUW+fiVz9NeWQ7Da5W45sjPicc0AVOTwLk8GBcrQ18/SyDW4noybYZpx6818U4jFPQlnIzaBgdXFyDXZcOAXlk4wSA7D4hv4UkAcWUsqtumzzBt43puPiVf+z7Riz5Jg88JdvLltr84hpw2CxZMqUaz1x93LVnBLLvLzNDeOdiwpwl9CzyahSbN2K0KPLrBAbvJonRtuXfuCLy4YhtOHW1cF11uFl80uQqjpPZhvtuO3x43OPw68+8sjkdxrsuDJMk0uaYQ//5sC8ZLWawAcPPxgxEIBtXgtLxNQpHuPqifki+7Z+5wfLutoc26sAN6ZWH1zgPtGrCVB73FzBW5fqtZDc1fHFWLS//9JS6dWqM5DvV18vX056VYUEs+H80y02Xy7xZPQFycx3argmqTEnCJMpoFKgJPZbkuFHhCAyzF2Q70yXWpGZCCuM/8ZFo/DC/LxaQa4xl1QGidgl26qeexlOY4saOhRR3w15ZzkQOm2nadUJLt0Cw45LRZcNLIPli2YS+mS8ej2TVIXMdzXTYMKA4tKlitC/TLfeFE298iEUX8Jv2lgGm20xY1TT9eZTHKkKWKzaKgjzS42NGZOEN65+Ddtbvjuh7IyW2Abkq+9Dp55pQ+G1jOMLVbFRRlOdQBETLGgKkJ0ZCzKIph/VIz8lQ2s5HHURW5KHDbUV3kMTzJRIdGPnH0GR/6xr/cyY23ALPeicN6awKmscQaLdXWMI3uVGgCpm3Uv5xeV4K3Lpvc5g29o+xWC/rmu9WpGYA2AzDedPl8jx0vXDQh7hX+AO10c02GqW4q9smjyvDl1v2mI5qTagqxYc/mqPcR5HukvA+e+eoHTKwqiPpMIddtU6eYjjb47NYEVmx95KzRaPT627UauXAgRv1Fuy6zZG+TNyogNrI8DyMNAr/yq0TH2anLWtLXKzKrSXXYwF6YXFOIkeHVafX7I97p+IBuKk74vO+T58IpJp0Y4aC++Tiobz4WLP1CDZjmu2yaAKjdalxfVZZIMyDXpEHckcaEyI5paPVHnYdtlSAw+1S5U2Y2RTXZ3LrzuS1VcZwj5fkudXEVQNQwlRrW0iyFeII1fz19FK5+9ht1Abx4ajQCxp2fAcVZeOpH49TOW18p2yzeYFN7aFbWjTPgKxtTkYd+RR6s3aWtgaI/hhXdeSQLNVRD55xR/SvA+BjwaErvRAKBI0wG4H58cBXOHV8Zc/BFvndWFrjVa4GeGCSrzHdh5fYG0/dLNXn/leY4MaR3DhpafKgtyTZcOCEyJV/KxDeYkm92LOhL35jRZ+bHe2yZdXo7i77chFHw/k6TrDxZrOmX7ckGBEK/Tb7Hjr3SytsxXx/nlHx95lvvXBcuO6QGWc7oGt5tqZDuFUXS9fS8BALDskQDn4ZapQAA2/FJREFUptcdMRDnTeiLmiIPPly/J+r5YWU5GFSSjX9/vhWAyDA1z7hNRGWBGxdOqjZ9Xg6e/Gii+XTTWAOjYrbBkNLEZh0lanRFHl6/dJLh8W/0mAg+TR/YK2r75YFhfYbfuL4FGBfHIie/P3EI7n7ne5wt1VOOl9GU/HgyTMdU5OOthZNht1rw8reRWYPxTskXphoFTOPIMNUETOPop/bvlYWDKvMwtCzXtA5+R+j7lWV5LuS67Hhk3hh1Npe+Lyx+K5fdiuOGauuS6910/CD8+sWVuHhKdVzb0zvHif3NXrVEmnzcyfdFebaEfG6X5jg1AVNHeJBcn3V9UN98fL+7MWqW24DiLFwwsS8GleaYDuprYhxxDkYL+mnzA6XygblOG7btb07o/YR426nJNrI8F6ePKU9KwPYXR9bioQ83YNaItut93nbiUFz42OdqKReLSYZpji4oKpP3hd2qXUyPU/KNMWBqor0dOvliYnbQZTlsePbH4007OQGD4FysOlAAolYrbI/qIg9G9MnFF1v2a0YrAe1IMhC7M+/WZHpE3xTlC3ysFbgFl90a9fmpUFPk0QRMsx02HD+0FM99vQ0XmNSYMpJoAf1clx1/PmUE7FaLpgEi/7dVUXDM4BIMLM5CVYFxsHpKTSH+tTwUMDUKRAc1Gaba5z8IN8SNsiUsioIThvXGpr1Nhh33tlb/kw3qQKNY1GgaX5Vv+hq79JsVeEIB03j7R3JpAdFxlgPf+e5IwDTLEVqMYohJjS2b1YI/zRkW+bfFvMHdFjm4Gu+CZjKrJgvArhl80Qf2jTqTidw6c6RGs3zKmpWIiEdVoQdOm8VwpL6tjrpZYFrOGkp2jUYz8m8dT4NkcBz12yry3DEXffLYrXDZLGj2BeLqcPQtcONnh/XHRY9/EfrbODo1HrvVNAAsT8nNcdnw5Pnj1IUMUsVmUdRMSn3gIB6KouDkUX1w62urNY/HCnI7dLMmNAuzmQwKGr2f22HFDcfW4eVvd+DscfF1pNvKVJc7NRX55gHTcX3zcc/c4agtzsZL38bObO6Itu7lmunULhsePH0UgogeDBBiTsmX9oPZsSDuebmu2ME0/XPxdtI0GaYJdjCTId4apm2JVZ6mvQFTINT5FwHT9maY6he0kO8tos7svDjPJz35XtGeFbr1jNr3sbjsVrVklD7wGTpnR+CRjzaqj9ktSngmSST5It57baKMapgm6uczBuCkkWWmg0LJlEgg4PZZQ/HStztwwcHRgXE50NTe37Yi343fSYunJkI+D8Q9Wi67IM8K0xPXUHnBmra+g2bgyW1X2yY2TYJC2+eG/LvpB3LMtvXeU0a2+br20l/TRMagXE5Bf84nEiQcWJyNf5wd/1Ts4X1ysW53oxowNftc+V6mH2D8cmukZrnZjL9LplajLNeJ6QO15UQsioL5bZSZkvtJibbj9L/lAGm2RLbLhj75bjXjNBGpbE8KfztzNO5+53ss27AXh4azdRVFSbgeqpmiLAd+dlh8M2mH98nFWwsna2ZICvItUj7f9HWk5XaBw6poYkyMlxpjwNREPKNlRkZX5CHPZYtKY9eL1RGVR6D/NHsY3ly9E3NHma9QDADHDCnB51v2x72Yh5kzx5bjiy37MUi3sI/cyKsudGN8jBFUuUarcQ3T5GSeJVttSRZe/26n+u9spw2/OLIW50/om1CWcXvIBaIFfV0SRVFiTscbU5mHUeW52NHQipNGRY9SyW1b/U2/fy8PfjKtn7pAjd71R9aafm6rP/6AaUcsPe8gfLutIeYKzXZdo+L7XfHXqJRr5YrjUm5Q5bsd2Bhe+ff5+RNgVeIP/OiDJrkJdLzkBcnaGjgx/uzQNmY5rOGgvHkWvFEmSiI3T7nhLQcUjIJ18TY8s502TB/YC//V1VEG2s6EN+vgyjWPOms0NZ4apjL94mp6FiVUF1QfMNWv3p7rsqG5oVUzmBCLGCwsy4uvvIZ+FdlYUnkdlb+dw2ZBkzcQc7GRWGYOLcXXP9TjQIsPb67e1ebr5d88y6GtX61fEEownJJvt+KYwaU4ZnDszJVEuO1W9M5xIhAMYkJVgbrKsZ5FUeLKjEo1zXRqtz3q+n1wdQGWbdiLArcdOw+0qoNP8vklrgvxTsl/ZcHBbWZg6/dXe6bkmx0LqZTIbIZYLIoCt92idubFQAzQ/tIoQKijuDY8FdDdxswc+T4yujwXgWAoK0p/H5bPx3gCM21tn9DRmoQdfQ/9MSoOLbm/YrdaYFEUFHgc6hTXeMv/JCqRll9ZrhNb97dgcG/tfS3baTOc8ZNug0pzTAf4szUB09TOfjOizTAN7dsWqR0ez2J0cum2to4P+fwWU7X12xFXDVPpd4snwJosB4X7V/rAsL4NatTm0V/b2juDM5bbThyK17/bgQsnVeGlb6PbuYAuw9QjZ5hG9kGJrhzf5dNqDN8ry2Fr9wBSvCWOjOjXHOjfy4NZw3vjrdW7cOroPlAQGoQ50aQsjJELJrYv0z9RQ3rn4O65I9DQ4uvQAGGyyPtBbprI/QGrRcGrCw6GPxiMOva12afaDNPMicpkFgZMDTisStSIdbzcditevGhiu+rSiBqUh0irbU7uVxgzQCTMGl6Ginx3wqtK6h1WW4znL8yNqoEjD4ovPS/2CoVydqpRJp1TqqeUqoZce5wyqhyb9zbjP19vAxC6uFstiZVkSJV4jie7NbKoU1tcdiuOH1qKA61+nD+hEgOLs9u9+E0S+hBxKc52tlkjVZthGjqG4w2IeQOxR3Xl0gmJZhFHZ5jG34mrkRbcaU8ASJQkEGVGjKZznTamHP9avhmXH9ov6u/7mmQ0m1k0czC27m9BrZStfMbYcix6dTUm1xTi44170eILGA4SmDlhWKkmYPrLo2qR57K32Xg1Cxa47FYUZzuwo6G13cd9ouTgdDzHZHF2aDrytvoWVBe68cnGfZrnS8OLLsiXUJtF0Qwa2K0Kcl12bG9ojfta279XFm44tq7NhZ6E7ATPhVTRBqZCgZ14a7DquexW/ProOmzd3xxXwFT+bcV9Q90Wkw6GUcA0FcF7q0VRFxxw2a3YXt+CCVXpC4wGEfuGIf+W+QbXyT/MHobGVh9eWbkDz3+9Xb2OyBnconMQzzRVIL7gp/46Ee+UfP1x2dk6GjCUue1WNWDqtkdWYO5IB7JMWoCvrZkX+vv7LScYZ+jZdVnKHaEoCg6vLcZHG/bgsAFtL/DUlkSn5Mv0M4cs6r1dmy0EhAK9ImCaqgzTeEtVAcCT549Dqz/YoeB6pkhGhmlHyAMHog3X4m17dqPMbIVvI/JglBxo1dYwjSdgGnmfZA3kxKOq0IN/n3dQm4vM9TFY8M5utWgWH07F/pYXfzO7R2gWfcrSDpAI8mKaNx03SJNFnCyH1xbjtZU7MVZXCzgecluxT54LWQ4brj+yFtccHlTvkzccO6jN9xGL3l52SE27A7/tlYxZBskmtzH0zUqz8zJXPyU/W84wZcjUSObt+QyQ57Z36IBpbxDwvlNG4O01u3B0O7JLrBYlaZ0g/SgVkNg0Io/B1DiZPCKbjEUQFKCNLlh8clw2nDqmXAqYpu70SPRr21JwAfu/o+uS8j4XT67G8k37cMaY2DU1O4N87omGXbydOa/BaihyQK499RAFfemJRKbky9mQ3nZk84opmCIoIDe2RSPsikP74bQxfTS1Pe+dOwLvrN2F0xPcrzNqi6MemzOiDEN756BfURbW72nEM1/+gAti1DzTG1uZjz65TmzZH1qx9OhBJXGNdBtdf4QBvbKwo6FVEwhPJYuiwGmzoMUXiHta7F9PHwWfP4B73l0XFTAVx4U2w9SiuVbYLRb1WEvkWptIhmMiGaapJDcaxXFtllUYr7JcFx46Y1SbQSf5t81yaKd2m7UHOitQD2jvZZdNix4U6UxtTskP7zsFxtdJmyU0CHDSyD44aWRk5o12McnQf8sdYrOFUOIlVrEXt4n8GNNe9dur367OlMzAhMdhVRebcNstCK8xGVc9QjM/O7IObkto4c229pF8H431vcSgvMduTcpvfvPxg+ALBJPyXolOyZfp2xHiGiJn94n7uzxYlKrjLpHgr81qQRqSMVMi7QFTowxTn3ltfyNWi4J75g5HfbOvzZlLchtaPkcTHZjIll6TzIGceBitu6FfJ8JoEWYgdB/x+kPluBKt25kos3at2aJP6uCg3arJ4EzVOW+3WnDbrKHt+lu5HTJQKnuQaFvokXlj8PnmfZiWhAGs7kAzGzXOWEGOJmCqMMM0DgyYGkjlghSx9Mp2Ys7I2FPv0yWRRp68cIJRwEJT762NRZ/iYbcqaDVb+jdR0tukIu2+KMuBXQdaMSaB7DoAmmy9TFNZ4MaL8ydkxKiUHLiY3K8QOS5b3AMJPoNgpMtmwajyXDR7AxhcmoNnv9rWru2KyjBNIBhvtSjIc9mwr9mnTi1KhMgwFYFBuc8lsrIsihK1Oq1YNCoZFEVRp7cNLM6Ou1aPYFEUHD+0N/7y/npYlPiDf6NMFkgDQotpfP1DPcZU5Ce0LR3htlvR4gvEPWBisyiwWayGWe4i21oOvlqtiqa1Y7cpammEZDX060qyNYsCZUrGkM0gq1POxGivWCvQCxYltHiaLxDKoNLUrcygsjPpJBZRaWv1aFGLL8dlS6gjJb9WLkMidDR4DoSCPv5wVmW82cvyooMdmc7YXkVJ+N6CHIB22CzqYpCJzriQ9S3y4KoZA+KqUy+XtokVMBW/c7KCxYqidHhwX/xWlfntn7Gkzz4TWyRnEYnXiMCWVUld6avOml2UaeTAX6zavqliVMM0kbUEhHhLsMjBNzlgmniGqRwwTX/4QZ6VUJrjND1PXDYLRGXNVEzJlzlNAp25LhusSujzc3XTqYFQW0feT+mYzdAWOaDbvzi+GUxGemU5DBMzeir51hRvm6kkxwmP3Yo8tw0WRdGc12yyGkv/FSsDdXQKT3eUSINYzjYwnpKf2KJPbbn5+MH42TPf4KrDOl58eWBJFibVFKAs15WS6ZH3nzoST3/5A84YG1/W3t/PGoNvt9fjkP5tl2VIp0wIlgKIajAkspKtUcxdURT85dSRCCJUW2fNzgPtyuTWN8QSqWEKAP86Zyw27W3G8HYsjCB+EpGFokjBnXQ09tvr+GGlePijjeid62zzeHvkrNF4d+3umAvn9M51pWTKUiwiaJnotaWvQSdbNJrlS6jNomhGh+0WC846qAIehzVpo/F3nTQc736/C59t3o9nvvwhY6YoGU19bm8N0/ZwWC3wBfzRAVOTIJl8vZlYVYDpA2MHEjvT5dP64Y631uJXR5nXrk7UE+ePw67G1qiBGT0R7GpPO2xyTSHW7jqAceGBHjnA1pEZAoLNoqAlwfeTj4XsNAwu5Hvs+L+ja/Gb/67SPF5bnIVVOw5gTIxBJT39YmY5ztBAXn4Sftt4yPfRWBlqQ0pzMLoiD5NrMqfd9NAZo/HXDzZg4VTjuoLx0AdtxbEljkWLop2SD6Q2SH/UoBK8snIHqgvTX7aqM8nncfprmIY+/4RhvbH4ne8xqjy1i2cNk9bJ0NYwTWzRp0zoZ8vt37Jc83JfcpA01RnFZudrrsuOW04YgiyHTdN+FOd7gduhm82QGX0ymVmGKXWMfDzE27XwOKx49Jwx6vVLk2WeIf35TJP+K1YGSleGaSabUVeMN1fvwug4GteeNjJM5RG0ZEwbmDagF969fEpSbmQWRcEdc4Z3+H3MVBa4sfCQ+BvMdaXZqGtj8ReKkLO5kjXlVVFCQSiLVcHVhw9s13voF30qTnC1+17ZTtPpQm1/tnZKvnjMFwhG1XDKZGW5Ljx69ti46kvHWrAhncTvneihObxPLmqLs9DiC2B9eB6saHzqF32S2zo2q4LhfXLbFWg3k++x4/ihvfFdeKGWTMkwlTP5pvYvQkOLr1NWXhbsVgXwAllO7ZR8s4V+NIs7zhnWqVP023LWQRU4YVhpUqdNuuzWNoOlQOT3iidjSe+Ps4fCH5SvecnNaJL3a7ztRHl2TkcyMTviuCGlUQHTP80Zhv98tS2hBTbk2UN2q4LfHjcIuxu97VqMsD2sFkXN1IxV1sZlt+Ivp6Zude32GNI7B7e3cyqroF880aJE7u0Lp9bAYbOor1EDpinMNDukfyEeOWt0RtT570xyAC3VU7SNyIkmoj10xkEV6F+chZEpuuc9cNpIbN3foplxIQfl4rkeajNM09/Pltu/ZTEGz+V9nK4p+QAMB70jGeVdIcM0sv8HMGCaNHKb35pAsFNuj7nsVuQ4bahv8THD1AQDpgb680SOYrMo+N3MwXG9VpNhaljDVJ6Sn5wzMx11hCjzyI2NTApAWKWG5azhvXHU4JJO+2yxCJscpLVZFcCXnsZ+R3T1jpnIaEg0w9TjsOIfZ4/FsvV7cMm/vwQQubYaTUUWUpElLzjVaa/p7/gA2t/h0qk1uGRKdcoy340uLeLak+WwalbCNis7E5Bek0nXKiFd+1V09OJZdVlPURRIa0qiV7YTf5w9FNm6rJz2kvdTvBmmzdJUWXeaBheMzoPibCfOT3CFYXlwxGZRMLG68zM4bZZQCaZEZ2l0B/r2svzPs8drZ1MUhY/PVGaYyqV2ehI5UJieGqbRiz7ZLEpKM6pHludhpG5inLwdcU3Jl0sZZEDbU94GowWfBDmwmq4p+WbU8kMeh+a4TEf5l7bYLArOHleBfc0+VPWwrPRUktslHWnz9spyoL7FxxqmJnpeiyMOyczG6YlEp8BuVQwbE/KFPFOmclP3YEtBhmkyyNt15tiKTs3sPGNsOfJcNswcFskkCjV0/V1qSn53oGaYtvPYlDNL1ICpLsO0NKdzygycMKw39jf7cNLIsk75vLboz/dU3luMgm8iuyPbaUNDiy/yuOmU/B5aALANYoZKMqbQA8CUfskrdSCu4wriD+jKtQVTVUuys8j3LX22Y2exWy1o9fszogZiZ9MHQWINAohVjzMhMNXdyHVo03FOG03JTwfRHlEQX11+efZhe9tAySQHP8vyYk3Jz4wMUyOl4cVtqwrdmsHZTMwwBYCFh6R30cnuSL4PdKQSQ1G2A9/vbmRcxkTPa3HEYWjvnjdimkyV+W4MLM5C/15ZhieefMMJstOYcuP75mPZhr2Y2i9z6nmlitxIsCV40RcLKxWkoCSH3MBNRt3eRFTkuzF/crXh9nSlKfndQZ88F5Zv2oeSBEsyCHJwVCw8oZmCb7Ggb4ETvzt+cNICTmYq8t24pp0lKpLJZbOg2RdIqBZjRxkNxoip5FkOK5q9/qjH9XrqgiltOXpwCTbtbcbJozIjEC8TQcLcBBakko+FjNCBNpe2hml6OlXic3tiwDQqwzTGPhjRJw+zhveOq4wWJSbbacN/fjweDpslLcGFjAmYSudiPNdDObBTlQGzheS+aKwp+e5OrGF66IAivPHdzrhrvJ4xtgJDeudgdHkevty6X308E2uYUmrIp15HM0wBMMPURM9rcbThxwf3zZhFLLoqu9WCf8wbY3riyiNoia/rSIn63czBeOO7nThsYPdfVVCu4ZNohundc0fgvvfWYcGU9i/KYEYTMM2AkXWxDelYsKAnu3J6f5w4rDdGtHNhBjmrKye84qgc/xDH/OF13f9cFx6ZNwYvrdiOM8ZWdNpnGp3C4tqT5bBhb5M36nE9PyOmhoqznbj2iPQH4o2I62YigxHtWb06U7kd6Q+YnjyqD1Zsq++RNfD0bZpYu8BmUXD9kclbtI20OnvBSJlRDdP0bEe4fEoCSQbPLZyCNZv3oqrQk6rNipvNakGWw4oDrX5N1rCeHCRN9ZT8YwaXINdlw6CS+NaucNos6kK0mtrpzCzvMbQZpkkImKa/i5qRGBnUOfOgio4MwFNYrFEO+eQOsNOYcrkuO04cnnnZOqkgNyQTDZjWlWTjD7OHJXuTAGgDXRkRMA3/TpyS37mynTaM6kDGj3zsiClw8hW0s7OXM0F1oScqgzrVjKbCOtQp+da4VqsNsKHR5URWJY8/Q7y5GwVM5QzTZCzY2R4XdfK5nkkURYHDGqrhCqS2RjVlLvn+kuoAXjzbkciK98PK89DbacmYfvaNxw7C/mYfSnLMp+TLM9dSPSVfUZR2l5GxJXlBZeoa5L6uScn8uEQCpryvGOEZRWnFTiMlk6MDGaapJMdMMqF2E6fkd03yMS1mQsjX0EwIxvcERoGKivxQxlFVgVuzn8xqiTHDtOtRM0wTyKjqTvs5EzJMezo5EMJd0DMZLfqUDmLQtle2ebAx003tX4TjhpbGfI18u8/kBYbly4FZKSDqfuQ9zSn5qcMMU0qrbtSXoAxg70CGaU8iGtxcEKJrkY9oNcPUYEo+pZbRz/yLI2tx3oS+6N8rC2+v3a0+zin53Ud7puR3Jx67nMHEa006yIFqZpj2TOIYcFiVtN7zpw0owoIp1Th0QK+0bUNny+SAqdyiyNRFnyj55GtAR6bk9w+XuRGBU9JiwJTSihmmlEzyjSPRRZ9SKcdlh4JQgyY3ztWVU0k0uDklv2uRF5DJMsgwZQe6cxh1Ul12q9rglEvNmAWWBpZkA19vS80GUkqIgab8FCwM2BW4M2DRp56uI3XaqXsQpXfSPUPIbbfivAl907oNnUGRhqozerqy1BZkDdOeQ273d+TwHFCchb+ePgqV+emrz5zJGDCltBrSOyfdm0DdiKb4dQZ1JmwWBW9dNhnBYGZ0NNUaplz0qUupLgotlOCxR+pkcsip87XVaZKTR80yPeaOLIPXF1AXbKDMJ66biUzJ7048min57JCngzzVNv0tCUqHyIA322+dooucaFnSgtUcPO855F3dkQxTABjRp30L0vYEDJhSWjxx/jis3XmAnUVKKjkWmUkBUyD92QCywwb2wt4mL0a2c7V2So8shw2vLjhYMy0syCz9TtfWbORgHHVlbVYLzh5fmczNohQTgdKKGCsqd2eaDFNOyU8LGzNMezwxWOHmDKFO0VXOsupCDy6ZUo3CBBYlpK5PDpJyHDN1GDCltOhb4Ebfgp7Z6aDUydQM00wzb1wl5o1jsKYrytNlt7EUZudrK3vDLwVMM3oKHyXkp9P7Y0ZtL0ysjn+gd1zffHy0YS9OHN47hVvWOTxc9Cnt5BIfzCLrmfoWuGFRgAHhEjCUWl3pNDu3B5RIIC0L61p3CgZMiajb0ARMed+gHoAZpp2vrVgRd0n3VJrjxJGDShL6m1tmDsH763bjkP5FKdqqziNnmJotZkapJZf4YN+4Z6oscOP5+RN7bC3lzsbyI5TJ5PYoA6apw4ApEXUbfQvcGFSSjWynlRmm1CMwNtf5LG1cW7iYIQk5LlvCQdZM5eGiT2knB286Wq+Oui6uZN15zh1fiTe+24kTusEsAep+5CApb8upw4ApEXUbVouCh88aDYBTYaln4JT8ztfWKD7jpdQdcUp++mmm5HMfEKVcSY4TL8yfwD4FZSRNDVMeoynTaQFTr9eL119/Ha+//jq++OIL/PDDDwCAAQMGYPbs2Tj11FNhtca/KMphhx2GzZs3Gz43fvx4PPLII0nZbiLqWtiooZ6EU/I7X1uZXX7uE8pQHTkyXZySn3baGqZp3BCiHoT9CspYmin56duM7q7TAqYbNmzAZZddBo/Hg4MPPhiHHXYY6uvr8cYbb+A3v/kN3n77bdx7770JXZRycnJwzjnnRD1eXl6ezE0nIiLKSMww7XxtlTRjvJS6I5tFgdNmQYsvwAzTNJED1cwmIiLq2eS7AO8JqdNpAdPs7Gz86le/wuzZs+HxeNTHGxsbMW/ePLzxxhv473//i2OOOSbu98zNzcXChQtTsblEREQZj8G5zmdvI2LKGqbUXbnt1lDAlKsqpoUcMGUNUyIiErh2R+p02pya0tJSnHnmmZpgKQB4PB6cd955AICPPvqoszaHiIioyztpZBkAYPrAXmneku7v2sMHoNBjx6+Oro35OrnWI1F34rGHug1cOTo97FKHmPFSIiISeE9InYxY9MlmC21GIjVMAaC1tRVPPvkktm/fjuzsbAwfPhwjR45MxSYSERFlnMoCN96+bDJcNgYwUm3OyD6YPaKszdJB543viy+31OP4od1jdXQiwR0eDGCGaXrYpes8s4mIiEjgrIPUyYiA6RNPPAEAmDJlSkJ/t2PHDlx77bWax4YPH44//OEP6Nu3b7u2hcda5hD7gvuk5+G+75m439unO2Q0dpV9H0+d9YIsOx46c1TqN6Yb6Cr7vTvpyG/tCS/85LAqHd5n3PeJc+gWfepqvx33ec/Ffd8zcb+nlvy7Wiwdvy8nUyZtS0elPWD62GOP4e2338bEiRMxbdq0uP9uzpw5GDt2LGpra+HxeLBu3To89NBDeOaZZ3Duuefi2WefRXZ2dsLbU1SUk/DfUGpxn/Rc3Pc9E/d7z8V93zNxv3eOUVWF6NWr/b91WYEHX26tR0VJbofeR8Z9H7+cLGfkv7NdSdsHnY37vOfivu+ZuN9Tw+OJ3BNKinM0da4peRIOmC5atAitra1xv/7ss89GdXW14XNvvPEGbrzxRpSXl+P3v/99Qttx6aWXav49ePBg3HrrrQCAZ555BkuXLlVroyZi1656LqKRIRQldIHlPul5uO97Ju73nov7vmfifu8c/zh7DF5ftRPnjOmDnTvr2/0+Cyb1xdjyHAwrcnXofQDu+/bwtfrU/25qau3wPuhs3Oc9F/d9z8T9nlqNjS3qf+/e1ZBRpVrEvu8OEg6YPvbYY2hsbIz79UcddZRhwPStt97CZZddhqKiIvztb39DSUlyan2deuqpeOaZZ7B8+fJ2BUyDQa46nGm4T3ou7vueifu95+K+75m431OrtjgbtcWhWVcd+Z3L89yYM8Ld4feRcd/HT84eUtB1fzfu856L+75n4n5PDfk37cr3hEyXcMD0008/7fCHvvnmm1i4cCEKCgrw8MMPo7KyssPvKRQUFABAQkFdIiIiIiKiTGWXsoe4wAcREQnx1Nin9un0QgciWJqXl4eHH34YVVVVSX3/L774AgBQXl6e1PclIiIiIiJKBznD1JJBUy+JiKjzMUbaOTo1YPrWW29pgqVmtU0Fr9eLNWvWYMOGDZrH16xZg6ampqjXr1mzBrfddhsAYObMmUnbbiIiIiIionSxWyO9Y8ZLiYiIUi/hKfnttWbNGlx66aVobW3F+PHj8fzzz0e9pry8HHPmzFH/vW3bNhx77LEoLy/H66+/rj7+wgsv4KGHHsK4cePQp08fuN1urFu3Dm+//Ta8Xi/mz5+PcePGdcr3IiIiIiIiSiVNhilTi4iIiFKu0wKmO3fuRGtrKwAYBksBYPz48ZqAqZkJEyZgzZo1WLFiBT7++GM0NzejoKAAhxxyCM444wxMmTIlqdtORERERESULnKGKWuYEhERpV6nBUwnTJiAlStXJvQ3FRUVhn8zfvx4jB8/PlmbRkRERERElLHslkiGKeOlREREqdfpiz4RERERERFR/Ow2KcOURUyJiIhSjgFTIiIiIiKiDCZnmLKGKRERUeoxYEpERERERJTB5BqmTDAlIiJKPQZMiYiIiIiIMpjdygxTIiKizsSAKRERERERUQbTZJgyxZSIqEdTwPtAZ2DAlIiIiIiIKINpapimcTuIiCj9ggimexN6BN5viYiIiIiIMhgzTImIiDoXA6ZEREREREQZTFvDNI0bQkREaccp+Z2DAVMiIiIiIqIMxkWfiIiIOhcDpkRERERERBlMnpJvZcCUiKhHO35oKQDgkP5Fad6S7s2W7g0gIiIiIiIiczZpHj7jpUREPVtJjhPvXDYZThtzIFOJAVMiIiIiIqIM5pA6xVYWMSUi6vFcdmu6N6HbYziaiIiIiIgog9ktrGFKRETUmRgwJSIiIiIiymByDVMmmBIREaUeA6ZEREREREQZzG5lhikREVFnYsCUiIiIiIgog1ktippZamGKKRERUcoxYEpERERERJThRJYp46VERESpx4ApERERERFRhrOFI6Wckk9ERJR6DJgSERERERFlOJfdCkC7ABQRERGlhi3dG0BERERERESx/fjgvvh2WwOqCz3p3hQiIqJujwFTIiIiIiKiDHfSyD7p3gQiIqIeg1PyiYiIiIiIiIiIiMIYMCUiIiIiIiIiIiIKY8CUiIiIiIiIiIiIKIwBUyIiIiIiIiIiIqIwBkyJiIiIiIiIiIiIwhgwJSIiIiIiIiIiIgqzpXsDMo2ipHsLSBD7gvuk5+G+75m433su7vueifu95+K+73m4z3su7vueifu95+pO+1wJBoPBdG8EERERERERERERUSbglHwiIiIiIiIiIiKiMAZMiYiIiIiIiIiIiMIYMCUiIiIiIiIiIiIKY8CUiIiIiIiIiIiIKIwBUyIiIiIiIiIiIqIwBkyJiIiIiIiIiIiIwhgwJSIiIiIiIiIiIgpjwJSIiIiIiIiIiIgojAFTIiIiIiIiIiIiojAGTImIiIiIiIiIiIjCGDAlIiIiIiIiIiIiCmPAlIiIiIiIiIiIiCiMAVMiIiIiIiIiIiKiMAZMiYiIiIiIiIiIiMIYMCUiIiIiIiIiIiIKY8CUiIiIepTDDjsMdXV1+PDDDzWPf/jhh6irq8Nhhx2Wpi2Lz7x581BXV4cnn3wy3ZuSdrH2WUd/p7q6OtTV1WHTpk0d3cy06irf45prrkFdXR0WL16cMZ9r9ts9+eSTqKurw7x58zprM4mIiKiTMWBKRETUDd15551qZ/+ee+5J9+ZQhlmxYgVuvvlmzJo1CxMmTMDQoUMxfvx4nHzyyVi0aBG++eabdG9iymzatAmLFy/GkiVL0r0pGScYDGLGjBmoq6vDbbfdFtff7N+/HyNGjEBdXR2effbZFG9hYpYsWYLFixd3arA4HZ9JREREyceAKRERUTcTCATw9NNPq/9+6qmnEAwG07dBXYTb7UZNTQ0qKyvTvSkp09TUhJ///OeYPXs2/va3v2HlypXIz8/HkCFDUFBQgBUrVuChhx7C7NmzcfXVV6d7czukrKwMNTU1yMnJ0Ty+efNm3HXXXXj44Ydj/n1NTQ1qampgt9tTuZkZRVEUzJ49GwDwzDPPwO/3t/k3zz33HFpaWpCTk4Mjjzwy1ZuYkIcffhh33XUXNm/ebPqa4uJi1NTUoKCgIO73zcnJQU1NDcrKytr1mURERJT5bOneACIiIkqu999/H5s3b4bb7YbP58OGDRuwbNkyTJgwId2bltFGjBiB//73v+nejJRpaWnBueeei88++wzZ2dlYuHAhZs+ejby8PPU19fX1eOWVV3Dffffho48+SuPWdtytt97aob/vzsdCLHPmzMHdd9+N7du347333sMhhxwS8/Wi5MFxxx0Hl8vVGZuYVFdeeSWuvPLKhP7miCOOwBFHHJGiLSIiIqJMwAxTIiKibuaJJ54AEOrUi9qO4jHquW655RZ89tlnyMnJwaOPPopzzz1XEywFQplzc+bMwX/+8x+cdtppadpSSqc+ffrg4IMPBoA2679+9913+PLLLwEAJ510Usq3jYiIiKizMGBKRETUjezbtw+vvvoqAGD27NmYNWsWAODll19GQ0OD6d/JCyFt2bIF1113HaZOnYphw4bhsMMOw6JFi0z/Xl4YZeXKlfjJT36CSZMmYdiwYTjqqKNw1113obW1Nerv4llkyWxBlmAwiLfeegs33HADZs2ahYkTJ2LYsGGYOnUqLrvsMnz88cdt/VRxb4/Yhrb+ZxRcWr9+PX7961/jqKOOwsiRIzF69GicdNJJWLJkieFvInz77be45JJLMGHCBIwcORIzZ87EX//617imSBvZsmULHn/8cQDA1Vdfjbq6upivdzgcuPDCC6Me93q9ePTRR3H66adj3LhxGD58OGbMmIFf/vKXWL9+veF7LV68GHV1dbjmmmvg9/uxZMkSzJw5EyNHjsS4ceMwf/58fPXVV6bb4vV6cf/99+O4447D8OHDMWnSJFx22WVYuXJlzO9gtOjTvHnzcPbZZwMITc3X70N5IbC2Fkt6++23cdFFF6nH+uTJk7FgwQK8//77hq/XH1+vvfYa5s2bh4MOOgijRo3C3Llz8fzzzxv+bWtrK15++WVce+21mDlzJsaPH4/hw4fjsMMOw89//nN8++23MX+LRIng52uvvYZ9+/aZvk78trW1tRgxYoT6+J49e/CHP/wBxx9/PEaNGoXRo0dj5syZuPPOO1FfX5/w9nz88ce45ZZbcPLJJ2PKlCkYNmwYJk2ahAsvvBCvv/664XbV1dWp0+LPPvtszX6+5ppr1Ne2Z7Epo0Wf4vnM1tZWTJgwAXV1dXjvvfdM33/Dhg0YNGgQhg4diu3bt8e9XURERJQ8nJJPRETUjYh6gr1798bEiRMRCARQVFSEXbt24fnnn8epp54a8+9XrlyJSy+9FM3NzRg4cCDsdju2bNmChx56CJ9++in+8Y9/wGYzbj689957uOmmm2C1WlFTUwOr1Yp169Zh8eLFWLVqFe68886kfc/GxkZceOGFUBQFBQUFKCkpQWlpKbZu3YqXXnoJL7/8Mv7v//4Pp59+eoc/q7q6GmPGjDF8zuv1qhl2es8++yyuv/56tLa2wuVyoW/fvmhqasI333yDr776Cv/973/xwAMPIDs7W/N3b731Fi655BJ4vV643W70798fe/fuxa233orPPvusXd/hhRdegNfrRX5+Pk488cR2vUdDQwPmz5+vBqMrKytRWVmJtWvX4vHHH8ezzz6LP/7xj6YBcJ/PhwsvvBDvvvsuqqqqUF1djbVr1+LNN9/EBx98gEceeUQTdANCgcL58+fjf//7HwCgoqICeXl5ePPNN9XfKRG1tbXYu3cvVq1aBYfDgWHDhmme19c7NXPTTTepNVCLioowaNAgbNq0Ca+99hpee+01XHzxxfjJT35i+vd33XUXFi9ejF69eqFv377YuHEjvvjiC/z0pz/Fnj17cNZZZ2lev27dOixcuBAWiwVFRUUoLy+H1+vF5s2b8cwzz+CFF17AnXfeGXPwIRFHHHEE8vLysG/fPjz33HM488wzo17j8/nURZ7k7NLVq1fj/PPPx7Zt22C1WjFgwAAEg0GsXr0aq1atwtNPP42HHnoIVVVVcW/PJZdcgr179yI/Px/FxcUoKSnB1q1b8dZbb+Gtt97ChRdeqJlWX1RUhDFjxuCrr75Ca2sramtrNedZdXV1O36V2OL5TIfDgVmzZmHJkiVYunQpJk+ebPheS5cuRTAYxLRp01BSUpL0bSUiIqI4BImIiKjbmD17drC2tjZ4++23q4/dfPPNwdra2uDcuXNN/2769OnB2tra4NChQ4NXXXVVcP/+/epz//vf/4IjR44M1tbWBv/9739H/W1tba36t7feemuwublZfe7ZZ58N1tXVBWtra4Pvv/++5u8++OCDYG1tbXD69Omm23X11VcHa2trg3feeafm8ZaWluC//vWv4A8//KB53OfzBZ9//vngyJEjg0OHDg1u2bLF9Lt+8MEHCW+P2fbNmDEjuGvXLvXxjz/+ODhkyJDg0KFDg0uWLAm2tLSoz61duzZ40kknBWtra4PXXnut5v127doVHD9+fLC2tjZ42WWXBevr69XnXn311eCIESOCQ4cODdbW1gafeOKJuLfzoosuCtbW1gbnz58f99/oXXPNNcHa2trgxIkTgx999JH6eH19ffCnP/1psLa2Njhq1Kjgxo0bNX935513qsfH9OnTg19++aXm+5566qnB2tra4Jlnnhn1mX/84x+DtbW1wdGjRwffeecd9fG9e/cG58+fr/4WRvvsrLPOMvyd4t3P4rjWf58nn3wyWFtbGxw8eHDw0UcfDfr9/mAwGDr2HnzwQfV4f/HFFw0/d+jQocERI0YEn332WfU5r9cb/PWvf63+hvJ+DwaDwR07dgSffvrp4J49ezSPt7S0BP/+978HBw8eHBw/fnywsbEx7u/Rlt/85jfB2tra4EknnWT4/Kuvvqp+H3Hst7S0BI866ij1erN582b19evXrw+ecMIJwdra2uAJJ5wQ9Pl8mvczO9eDwWDw8ccfD27YsCHq8ffeey948MEHB2tra4Offvpp1PNm53q8n2v22z3xxBPB2tra4FlnnZXwZ65evVr93Xbv3h31vNfrDU6ePDlYW1sbfOONN0y3m4iIiFKLU/KJiIi6iW+//RZff/01AKhT8QGoq15//vnnWL16dcz3qKysxE033aTJtDv44INx8sknAwDeeOMN07896KCDcNVVV8HpdKqPzZw5E4ceemibf5soh8OBU089FaWlpZrHrVYrjj32WJxzzjnwer34z3/+k7TP1Lv33nvx1FNPIS8vD3/5y19QWFioPnfbbbfB5/PhZz/7Gc455xw4HA71uZqaGixevBgejwdPP/00tm3bpj73z3/+E3v37kVxcTFuvfVWTYbajBkzcPHFF8Pr9Sa8reIzKisr2/NVsWnTJjz99NMAgF/96lc46KCD1Oeys7Nxyy23oKKiAo2NjXjooYcM38Pr9eLWW2/VZHUWFhbil7/8JYDQtGt5unZjYyMeeeQRAMDll1+OKVOmqM/l5eXh9ttvh8fjadf36Yh77rkHAHDqqafi9NNPh8USak5brVacd955mDlzJgDg7rvvNvx7r9eL+fPnq68DAJvNhmuuuQaFhYVobGzUlAYAgF69euHEE09Efn6+5nGHw4EzzzwTxx57LPbu3ZvUc0xkjX755ZeG1w0xHX/69Onqsf/CCy/g+++/h91ux5133ok+ffqor+/bty/+9Kc/wWq14ttvv1VLh8Rj7ty5hsfupEmTcMUVVwAAnnrqqfi/XBr1798f48ePh9frxTPPPBP1/JtvvokdO3agd+/emDp1ahq2kIiIiADWMCUiIuo2xMJOI0eORL9+/dTHBw0ahMGDB2teY+bUU0+F3W6PenzUqFEAYFqnEoDhtF0AGD16dJt/215ffPEFbr/9dixYsADz5s3D6aefjtNPP11d4XzFihVJ/0wgFBi64447YLfbsXjxYs3vvW3bNixfvhw2m00NNOuVlZVh2LBh8Pv9mtXo3377bQDAKaecogk8C2eeeaZpSYRYRP3Z9gYY33nnHQQCAfTp0wdHHXVU1PM2mw3nnHMOgFDAx0hdXZ0m0CoMGTIEDocDwWAQGzZsUB//5JNP0NDQAJfLhblz50b9XVZWlunvmypr1qxRt/G8884zfM2PfvQjAMCqVauwZcsWw9ecccYZUY85nU4MGTIEADS/g+z999/HokWLcNFFF+Gss85Sj3dRJuGbb75J7AvFMHToUAwaNAhA9HVj9+7deOuttwBop+OLx4455hj07t076j1ramrUsgFmx4mZ1atX46677sLChQs157oojZCqcz0VRGmUf//731HPicfmzJkDq9XaqdtFREREEaxhSkRE1A20traq2ZRydqkwe/ZsrFixAs8++yyuvPJK06CbWW2/oqIiAMCBAwdMt6Ejf5son8+H6667zjBDS7Z3796kfabw2Wef4ZprrkEwGMSNN96ICRMmaJ4XC/BYLBb8+Mc/Nn2fdevWAQC2bt2qPrZ27VoAwIABAwz/JicnB6WlperCMvESmaqNjY0J/Z3w/fffAwhlx4mMSr3a2loAoWzU1tZWTVYtYH58KIqCoqIibN26VXOMiN+ivLzcNNA7cODAhL5HR4nfQdSkNTJgwABYrVb4/X6sXbtWk2UJAAUFBVGZooLZuXLgwAEsXLgw5kJBQPKP9zlz5uDmm2/Gf/7zH/zsZz9TA3jPPvssvF4viouLNVmQ4veJtV9qa2vxyiuvqK+Nx2233YYHHngAwWDQ9DWpONdT5cgjj0RBQQG+++47fPbZZ+qA1LZt2/D222/DYrF0+mAAERERaTHDlIiIqBt4/fXXsWfPHtjtdhx33HFRz8+cORN2ux07d+6MmdnldrsNHzcLkiXrbxP14IMP4plnnoHT6cS1116LF154AZ9++im+/fZbrFy5EjfddBOAUGA1mTZt2oQFCxagpaUFF198sVruQCZWFW9tbcXy5ctN/7d7924AQHNzs/q3IlDWq1cv022I9ZwZUbpg48aNCf+tvF3FxcWmr5GfMwqOx8puFceIHBAT7yGCiEZiPZcK8WyTzWZDQUGB5vWyRH8HALjlllvw3nvvoaCgAL/97W/xyiuv4PPPP8fKlSuxcuVKLFiwAEDyj/cTTjgBdrsdO3bsULOfgUjG6axZszRZkIkcJ/EOoDz//PO4//77oSgKLr30UjzzzDP45JNPsGLFCqxcuRJLliwBkPzvnkoOh0O9dixdulR9/IknnoDf78ekSZNQXl6ers0jIiIiMMOUiIioWxABDK/Xi/Hjx7f52sMPP7wzNismRVEARAeHZGYZkaJ+4tVXX21YCiAV2Wb19fW46KKLsGvXLhx//PG4/PLLDV8nAmJ9+vRJuKZkVlYW9u/fj507d5q+JtZzZsaOHYvXX38dn376qWH2ZzzbBQA7duwwfY38nHh9R4j32LVrl+lrYj2XCvFsk8/nw549ezSv7wifz6dmjy9atEitCSxLVXZlQUEBDjvsMLz00kt46qmnMH36dHz11VdYtWoVAO10fCCx4yTe30ac6+eeey4WLlwY9XxXyiyVnXrqqXjooYfwwgsv4LrrroPH41Gv46ecckqat46IiIiYYUpERNTFbdu2TZ2qW1BQgF69ehn+T2S9vf322+0KuiWbyEiNFXwS09b1Nm3aBACGNTGB0AJXyeTz+XD55Zfju+++w5gxY/C73/1ODfjq1dXVAQB++OGHhIM5ohbqmjVrDJ+vr6/XLBIVr2OPPRY2mw179+5ts4xBW9sVCAQMXyOCaJWVlQkHZGN95ubNm9HU1GT4mu+++y7h9zXbb4lsU3Nzs2md0dWrV8Pv9wMIlTDoqN27d6sDB511vMtEUFRksYsA5pgxY1BTU6N5rfh9Yu0XcZzIdX9jEef6uHHjDJ9P5XdPperqakyYMAGNjY144YUX8P7772PTpk0oKipS67wSERFR+jBgSkRE1MU99dRT8Pv9KCgowDvvvIP33nvP8H/vvvsuioqK4PP51BXP06mqqgqKoqClpcVwsZpPPvkEK1euNPxbEWw1ymRbs2ZNUlcLB4AbbrgB7733Hvr27Yu77747ZkCwsrISQ4cORSAQMF0x3oyoB/nYY4+htbU16vlHH320XVOP+/Tpoy6cdMstt6hBKzOtra24//77NdtlsViwZcsWvPTSS1Gv9/l86uI7RhmQ7TF27FhkZWWhubnZcHGcAwcOtLmImRGXywUApkHYWPr164eqqioAMN234vHa2lqUlZUl/Bl6cqkLo+P9/fffx9dff93hzzEzdepUlJaWwuv14sknn8Rzzz0HIDq7FACmTZsGAHjxxRfxww8/RD2/fv16vP766wDiP05ineu7d+/GU0891ebfymUvUi2RzxSLPy1dulSdmj9r1izDhfeIiIioczFgSkRE1MWJgIGoU2rGZrPhhBNOABCZ5ppOubm5asbcTTfdpE5jBkKrfV999dWm30dkm/3hD3/A9u3b1ce//fZbXHzxxUmtm/rggw/iscceQ15eHu677z4UFha2+TfXXHMNbDYb7rvvPvzxj3/E/v37Nc+3tLTgrbfewmWXXaZ5/PTTT0dubi527NiBa665Rl3dHgDeeOMN3Hvvve0OplxzzTUYPnw46uvrccYZZ2DJkiVR29XQ0IBnnnkGJ5xwAv75z3+qj5eXl6uLid14443qquzib6699lps3LgRHo8H5557bru2T8/j8WDevHkAgDvuuAP/+9//1Of279+Pq666ql0LifXt2xeKomD37t3qAl2JuPjiiwGEgtr/+te/1JISgUAAf/vb39QM3ksuuSTh9zaSk5OjrlZ/8803a/bZhx9+iJ/+9KdwOp1J+SwjFotFrbd55513Yt++ffB4PDjmmGOiXnvMMcegpqYGXq8Xl19+ObZs2aI+t3HjRvzkJz+B3+/HoEGDMGPGjLg+X5zr9913n2ahqI0bN2L+/PkxA99iYa73338/rs9KhkQ+84gjjkBRURE+//xzvPzyywCgDmwQERFRerGGKRERURf20UcfqdPWjTK+9E466SQ89NBDWLNmDT799FOMHj06xVsY289//nOcddZZ+PjjjzFt2jTU1NSgubkZ69atw5QpUzBq1Ci1fqPs8ssvVzPrZsyYgZqaGrS2tuL7779HWVkZLrnkEvzhD39Iyjb+/e9/BwA4nU5cf/31pq+76KKL1Ay78ePH4/e//z2uv/56/PnPf8YDDzyAmpoaZGVlYd++fdi0aRO8Xm/UexQVFeHWW2/FwoUL8fzzz+P1119H//79sWfPHmzevBlHHHEE9u3bh2XLliX8PVwuFx5++GH88pe/xHPPPYff/e53uPXWW1FZWYm8vDzs379fs1364+n666/H+vXr8cknn+DMM89EVVUVcnJysGbNGjQ1NcHlcuH2229HRUVFwttmZsGCBfj000/x4Ycf4rzzzlO3dfXq1QCAyy67DLfffntC75mfn49DDz0Ub7zxBk4++WQMHDgQ2dnZAIDrrrsOgwcPjvn3s2fPxjfffIOHH34Y//d//4fFixejrKwMmzdvVhfyuuiii3D00Ue34xsbu+qqq3DhhRfi7bffxrRp01BdXa3ur8GDB+Pggw/Ggw8+mLTP05szZw7+/Oc/q1mTRx99tGENUofDgcWLF+P888/HZ599hsMPPxwDBgxAMBjE6tWrEQgEUF5ejjvvvFOzWFQsF1xwAV588UVs3rwZxx9/PKqrq2GxWLB69WpkZ2fj6quvxg033GD4t7NmzcLrr7+Ohx56CK+++ipKS0thsVgwdepUXHjhhe3/QWJI5DPtdjvmzJmD+++/Hz6fD+PHj48qc0BERETpwQxTIiKiLkxkig4dOlTNQotl4MCBGDFiBAC0azpzso0YMQKPPvooDj30UDidTnz//few2+246qqrcN9998FmMx7braurw7/+9S/MmDEDLpcL33//PXw+H+bNm4ennnoq5ird7bV9+/aYq97ra7Eee+yxePHFF3HBBRdgwIAB2LJlC7788kvs2bMHw4YNw6WXXmpYGmH69Ol4/PHHMWPGDDgcDnz33XfweDz4+c9/jjvuuKND38Hj8eD222/HU089hXnz5mHgwIHYs2cPvvrqK+zatQuDBg3C+eefj2eeeQY333yz5m+zs7Pxt7/9Db/61a8wevRo7Nq1CytXrkRhYSHmzp2LZ555Jum1F51OJx544AFceeWV6NevH7Zt24YtW7bgkEMOweOPP46RI0e2631vueUWnHXWWejduze+++47LFu2DMuWLYvKuDVz/fXX4y9/+QumT5+OQCCAFStWQFEUzJgxA0uWLMEVV1zRru0yM2XKFDz88MOYNGkSAGDt2rVwOBy4+OKL8c9//lNdaCxVqqqqNDVEYw3ODBw4EM8++yzmz5+PmpoarF+/Hhs3bsSAAQOwYMECPP3002pZg3iUlpbisccew8yZM5Gbm4v169ejvr4es2bNwlNPPYUBAwaY/u1RRx2Fm2++GSNHjsTu3bvxySefYNmyZVi7dm3cn5+oRD9TXuCJ2aVERESZQwnGWpqWiIiIiIiIUmL58uU4/fTTkZeXh3feeSel5RWIiIgofswwJSIiIiIiSoPHHnsMAHDCCScwWEpERJRBGDAlIiIiIiLqZF988QWef/55WK1WnHXWWeneHCIiIpJw0SciIiIiIqJOMm/ePDQ1NWHFihXw+Xw488wzUV1dne7NIiIiIglrmBIREREREXWSuro6KIqC3r17Y+bMmVi4cCEcDke6N4uIiIgkDJgSERERERERERERhbGGKREREREREREREVEYA6ZEREREREREREREYQyYEhEREREREREREYXZ0r0BmWbXrnqwqmtmUBSgqCiH+6QH4r7vmbjfey7u+56J+73n4r7vebjPey7u+56J+73nEvu+O2DAVCcYBE/oDMN90nNx3/dM3O89F/d9z8T93nNx3/c83Oc9F/d9z8T9Tl0Zp+QTERERERERERERhTFgSkRERERERERERBTGgCkRERERERERERFRGAOmRERERERERERERGEMmBIRERERERERERGFMWBKRERERERERF2SPxDEtvqWdG8GEXUzDJgSERERERERUZd05dNf4/i/fIgP1u1O96YQUTfCgCkRERERERERdUnvfR8KlP5r+ZY0bwkRdScMmBIRERERERFRl+YPBtO9CUTUjTBgSkRERERERERdWpABUyJKIgZMiYiIiIiIiKhL8zNeSkRJxIApEREREREREXVpgQAjpkSUPAyYEhEREREREVGXFuCUfCJKIgZMiYiIiIiIiKhLY4IpESUTA6ZERERERERE1KVx0SciSiYGTImIiIiIiIioS+OiT0SUTAyYEhEREREREVGXxgxTIkomBkyJiIiIiIiIqEvzs4gpESURA6ZERERERERE1KUxXkpEycSAKRERERERERF1aX5OySeiJGLAlIiIiIiIiIi6NNYwJaJkYsCUiIiIiIiIiLo0TsknomRiwJSIiIiIiIiIujQu+kREycSAKRERERERERF1aZyST0TJxIApEREREREREXVpTDAlomRiwJSIiIiIiIiIurQAM0yJKIkYMCUiIiIiIiKiLs3PeCkRJREDpkRERERERETUpbGGKRElEwOmRERERERERNSl+VnElIiSiAFTIiIiIiIiIurSGC8lomRiwJSIiIiIiIiIujQu+kREycSAKRERERERERF1aQyYElEyMWBKRERERERERF0ap+QTUTIxYEpEREREREREXRozTIkomRgwJSIiIiIiIqIujRmmRJRMDJgSERERERERUZcWYMSUiJKIAVMiIiIiIiIi6tIYLiWiZGLAlIiIiIiIiIiIiCiMAVMiIiIiIiIiIiKiMAZMiYiIiIiIiIiIiMIYMCUiIiIiIiIiIiIKY8CUiIiIiIiIiIiIKIwBUyIiIiIiIiIiIqIwBkyJiIiIiIiIiIiIwhgwJSIiIiIiIiIiIgpjwJSIiIiIiIiIiIgojAFTIiIiIiIiIiIiojAGTImIiIiIiIiIiIjCGDAlIiIiIiIiIiIiCmPAlIiIiIiIiIiIiCiMAVMiIiIiIiIiIiKiMAZMiYiIiIiIiKjLCwaD6d4EIuomGDAlIiIiIiIioi7Pz3gpESUJA6ZEREQmvP4Anv96G7bXt6R7U4iIiIjIgCL9dyDAiCkRJYct3RtARESUqR7+aCP+/N565LlsePWSSeneHCIiIiLSsVgU+MOBUl8gCEeat4eIugdmmBIREZl4b+0eAMC+Zl+at4SIiIiIjNgskRxTPzNMiShJUpZhum3bNrz44ot4++23sXbtWuzcuRN5eXkYM2YMLrjgAowcOTLqbxoaGrB48WK8/PLL2LFjB0pKSnDUUUfh0ksvRVZWVtTrA4EA/vGPf+Dxxx/H+vXr4fF4MGnSJFxxxRWorKxM1VcjIqIewqK0/RoiIiIiSh+5veYLBNK3IUTUraQsw/SRRx7B7373O2zcuBGTJ0/Geeedh7Fjx+K1117DaaedhhdeeEHz+sbGRpx11llYsmQJ+vXrh3PPPRc1NTV48MEHcc4556ClJbp+3K9+9Sv89re/RTAYxLx58zB16lS8/PLLOPnkk7Fu3bpUfTUiIuohGDAlIiIi6jp8zDAloiRJWYbpiBEj8Mgjj2D8+PGaxz/++GOce+65+PWvf43DDz8cDkeowsgDDzyAFStW4Mc//jF+9rOfqa+/7bbbcP/992PJkiWYP3+++vgHH3yApUuXYty4cXjwwQfV9zn++ONx4YUX4sYbb8Rf//rXVH09IiLqARSFEVMiIiKiTBaUYqQMmBJRsqQsw/TII4+MCpYCwEEHHYQJEyZg3759WLlyJQAgGAxi6dKl8Hg8WLBggeb1CxYsgMfjwdKlSzWPi39ffvnlarAUAKZNm4bx48fj3XffxZYtW5L9tYiIqAdhvJSIiIgos8khUp+fAVMiSo60LPpks9k0/79u3Tps374dY8aMgcfj0bzW4/FgzJgx2LhxI7Zu3ao+/uGHH6rP6U2dOhUAsGzZslR9BSIi6gGYYUpERESU2YJSiikzTIkoWTo9YLplyxb873//Q3FxMWprawEA69evBwBUV1cb/o14XNQlbWxsxI4dO1BRUQGr1Rr1+qqqKs37EhERtUdaRhWJiIiIKG6aDFMu+kRESZKyGqZGvF4vfv7zn6O1tRU/+9nP1GBnfX09ACA7O9vw78TjDQ0NCb1evC4RTCbKHGJfcJ/0PNz3PVMm7neLtOpTJm1Xd5OJ+55Sj/u95+K+73m4z3uuztj3cg1TfyDI4ywD8JzvubrTPu+0gGkgEMA111yDjz76CKeccgpmzZrVWR+dkKKinHRvAulwn/Rc3Pc9Uybtd6cjcpvs1Stztqu7yqR9T52H+73n4r7vebjPe67O2vdZuW622TIIz3nqyjolYBoIBHDdddfhueeewwknnIDf/OY3mudzckInkcgg1ROPi8zReF8vXpeIXbvqNSNUlD6KErrAcp/0PNz3PVMm7nef16/+986dic9aoPhk4r6n1ON+77m473se7vOeqzP2vVzDdNfuA9jp6dSJtGSA53zPJfZ9d5DyK0kgEMC1116Lp59+GscffzwWLVoEi0VbFU7UHBU1SvXE46KWqcfjQXFxMTZt2gS/3x9Vx1TULhXvm4hgEDyhMwz3Sc/Ffd8zZdJ+l6eUZMo2dWeZtO+p83C/91zc9z0P93nPlcp9r6lh6g/yGMsgPOepK0vpehZysPTYY4/FrbfearhIU3V1NUpKSrB8+XI0NjZqnmtsbMTy5ctRUVGBsrIy9fHx48erz+m98847AIBx48Yl+RsREVFP0o1K8BARERF1S3JAjos+EVGypCxgKqbhP/300zj66KPx+9//3jBYCgCKomDu3LlobGzEPffco3nunnvuQWNjI0455RTN4+Lfd9xxB1pbW9XH33rrLSxbtgxTpkxBeXl5kr8VERH1JEp3qlpORERE1A1pMkwDTGckouRI2ZT8u+++G0899RQ8Hg+qq6tx7733Rr3m8MMPx+DBgwEAF1xwAV577TXcf//9WLFiBYYMGYJvvvkG7777LoYPH45zzjlH87cTJ07E3LlzsXTpUsyZMwfTpk3Djh078MILLyA/Px+/+MUvUvXViIioh7AwXkpERESUsYK6+d4+PwOmRJQcKQuYbt68GUBoSv2f//xnw9eUl5erAVOPx4O///3vWLx4MV5++WV8+OGHKC4uxvnnn49LLrkELpcr6u9vuOEG1NbW4vHHH8fDDz8Mj8eDI444AldccQX69u2bqq9GREQ9BDNMiYiIiDKXPjzKDFMiSpaUBUwXLVqERYsWJfQ3OTk5uO6663DdddfF9XqLxYKzzz4bZ599dns2kYiIKCaLZtGnIAOoRERERBlEv6AQA6ZElCwpXfSJiIioK1OkZZ/8bIATERERZZToDFMu+kREycGAKRERkQk5w5QZC0REREQZhjVMiShFGDAlIiIyoTBgSkRERJSxWMOUiFKFAVMiIiITcs1SZiwQERERZRbWMCWiVGHAlIiIyERQaoWzJhYRERFRZtGHR1lznoiShQFTIiIiE34pRsqMBSIiIqLMEtTXMGV7jYiShAFTIiIiEwFNhikb4ERERESZjDOCiChZGDAlIiIywYApERERUeaKWvSJNeeJKEkYMCUiIjIh18FiwJSIiIgoswQ4JZ+IUoQBUyIiIhNym9vPjAUiIiKijKKLlzJgSkRJw4ApERGRCb9mSj5rYhERERFlMgZMiShZGDAlIiIyEeCUfCIiIqKMFZVh6ucANxElBwOmREREJrjoExEREVHmCoI1TIkoNRgwJSIiMqFZ9Ik1TImIiIgyCmuYElGqMGBKRERkQo6RsgFORERElFn0rTM/22tElCQMmBIREZnglHwiIiKiDMYMUyJKEQZMiYiITMhZCv4AFxEgIiIiyiSsYUpEqcKAKRERkYkAp+QTERERZSx988zn5wA3ESUHA6ZEREQmOCWfiIiIKHPpW2dsrxFRsjBgSkREZEKeku/zswFORERElFGC2vZZi48ZpkSUHAyYEhERmZAzTL2sYUpERESUUfTD2Zv3NadlO4io+2HAlIiIyIScYeplhikRERFRRtElmGJbfQuavP70bAwRdSsMmBIREZmQY6ReLiJARERElFFEU81mUZDnsgEANu5pSt8GEVG3wYApERGRiQAzTImIiIgyVjCcYqooQN8CNwBgPQOmRJQEDJgSERGZkGuYtjLDlIiIiCgjKQD65LkAAD/sZx1TIuo4BkyJiIhMaKfkM8OUiIiIKJOI1pmiKCjwOAAAe5t86dsgIuo2GDAlIiIyoZ2SzwxTIiIiokwiZgMpAArcdgDAviZvGreIiLoLBkyJiIhMyFPyGTAlIiIiyiyiqaYoQL47tOjTHgZMiSgJGDAlIiIy4Q9y0SciIiKiTKdAQX44w3QvA6ZElAQMmBIREZnwy1PyA8wwJSIiIsokcoZpHgOmRJREDJgSERGZCHDRJyIiIqKMJbfO8lnDlIiSiAFTIiIiE34u+kRERESUsYJi0SclEjDd3+yDL8CBbiLqGAZMiYiITARYw5SIiIgoY4nWmQIFeS6b+lh9M7NMiahjGDAlIiIyEAwGNVPyW5lhSkRERJRZpBqmNqsFueGg6d4mXxo3ioi6AwZMiYiIDOhncvkYMCUiIiLKKJEM05B8LvxEREnCgCkREZEBeTo+ALRySj4RERFRRgmoNUxDIdM8VyhguocBUyLqIAZMiYiIDPh1KaZc9ImIiIgos0RnmIop+QyYElHHMGBKRERkwB/UB0yZYUpERESUUaQapkBkSv4+BkyJqIMYMCUiIjIQ0CWUevUPEBEREVFaBaEd0GYNUyJKFgZMiYiIDOgzTFt9DJgSERERZZKgmmEaSjFlwJSIkoUBUyIiIgM+XQ1T/b+JiIiIKL2iaph6GDAlouRgwJSIiMhAs9ev+TdrmBIRERFlGJMapnubfGnaICLqLhgwJSIiMtCsm4Lf6ueUfCIiIqJMImqYqhmmnJJPREnCgCkREZGBlnDA1GYJNcF9DJgSERERZRT9/B81YNrIgCkRdQwDpkRERAbElPwcpw0A4A8CftYxJSIiIsoYomlmURd9CrXbGr1+dfCbiKg9GDAlIiIyIKbk57hs6mNeZpkSERERZY5geEp+eE5+ttMGa/i/93FaPhF1AAOmREREBlp0GaYAF34iIiIiyiSiZSZqmFoUBXmsY0pEScCAKRERkQGRYZrttKqPceEnoo5paPGhsdWf7s0gIqJuIqhGTBX1MQZMiSgZGDAlIiIyIAKmbrsVTlvodsmAKVH7tfgCmH7X/zD9rvcQDDJbm4iIOk6fYQoABQyYElESMGBKRERkQCz65LRZ1IBpi5cBU6L2+mF/M4DQAh1ciIOIiJIhqKthCgD5asDUl45NIqJuggFTIiIiAyKg47JZ4bCGA6bMMCVKCgZMiYgomeQM0zx3qP48F30ioo5gwJSIiMiAyDB12aUMUwZ5iNrNL03DZ3kLIiJKBnFrUaQUU7c9VH++2cea2UTUfgyYEhERGRA1TJ02KxxqwJQNb6L2apUGHDj4QEREyWBUEZsD3USUDAyYEhERGWgO1yt12S1wiUWffFyohqi95I5rMzuxRESUBIFwiqlFmpPPgCkRJQMDpkRERAZENqnLZonUMGWGKVG7yUHSVnZiiYgoiRSpimmk3cZ7DRG1HwOmREREBuQp+WqmAusuErUbp+QTEVGyqeWxNRmmoRqmvNcQUUcwYEpERGRAnpKv1jD1suFN1F4tmoAps7WJiKjjguEqplK8VC2lxIApEXWELZVv/swzz+CTTz7BV199hVWrVsHr9eJ3v/sd5syZY/j6hoYGLF68GC+//DJ27NiBkpISHHXUUbj00kuRlZUV9fpAIIB//OMfePzxx7F+/Xp4PB5MmjQJV1xxBSorK1P51YiIqJtrlqbkqzVMmWFK1G7y+dPCesBERJQEIsNUMaxhysE5Imq/lAZM77jjDmzevBkFBQUoKSnB5s2bTV/b2NiIs846CytWrMCUKVNw3HHHYcWKFXjwwQfx0Ucf4R//+AecTqfmb371q19h6dKlGDhwIObNm4ft27fjxRdfxHvvvYfHHnsM1dXVqfx6RETUjUUyTK2RDFNmKhC1WzMzTImIKMkiM/IjEVMu+kREyZDSgOlvf/tbVFVVoby8HH/5y19w++23m772gQcewIoVK/DjH/8YP/vZz9THb7vtNtx///1YsmQJ5s+frz7+wQcfYOnSpRg3bhwefPBBOBwOAMDxxx+PCy+8EDfeeCP++te/pu7LERFRtyYa2S6bhQ1voiRgDVMiIko6owxTO9ttRNRxKa1hOmnSJJSXl7f5umAwiKVLl8Lj8WDBggWa5xYsWACPx4OlS5dqHhf/vvzyy9VgKQBMmzYN48ePx7vvvostW7Yk4VsQEVFPpE7Jt1u52ipREsjnD8tbEBFRMgQRXeKF7TaizrHzQCsuevxzvPzt9nRvSkpkxKJP69atw/bt2zFmzBh4PB7Ncx6PB2PGjMHGjRuxdetW9fEPP/xQfU5v6tSpAIBly5aldsOJiKjbElPynTaLutoqgzxE7ccMUyIiSrbIlPwILvpE1Dnueud7fLJxH65//tt0b0pKZETAdP369QBgWnNUPL5u3ToAoXqnO3bsQEVFBaxWa9Trq6qqNO9LRESUKHnRJ6ct1Axnw5uo/ZoZMCUioiQLhCOmFkWuYRqKEfBeQ5Raexpb070JKZXSGqbxqq+vBwBkZ2cbPi8eb2hoSOj14nWJkGufUHqJfcF90vNw3/dMmbbfRYap22GNZJj6Ahmzfd1Jpu17So1Wf2ShJ/lc4n7vebjvex7u856rs/a9okQ+wyXVMOUxlx485419u60eP+xvwaEDe6V7U5KuO+7zjAiYZpKiopx0bwLpcJ/0XNz3PVMm7HefPwBfOGWhvDQXhZtDA3BBqwW9eqV/+7qrTNj3lDqKLdLstDhs6v7mfu+5uO97Hu7znitV+z53ZyMAwG63qm00n90OAGjxB9huSzOe81pn3fY2AOC5hVMwrDwvzVvTcQ5HpG3XHc+1jAiY5uSEfliRQaonHheZo/G+XrwuEbt21SMYXTea0kBRQhdY7pOeh/u+Z8qk/X6g1Rf57/1N8LV4AQD1ja3YuTPx2QsUWybte0qdfQ3N6n/v2d+MXbvqud97KJ7zPQ/3ec+V6n2/b18TAMDnC6httANNoXabPxDED9v3w2bpRilvXQTP+dg+WLkNvZ0ZUSGzQ1qlPpM4/8S+7w4yImAqao6KGqV64nFRy9Tj8aC4uBibNm2C3++PqmMqapeK901EMAie0BmG+6Tn4r7vmTJhvze1hqbjKwDsFiWy2qo3kPZt684yYd9T6siLPrX6IucS93vPxX3f83Cf91yp2veihqmCyPuLdhsANHv9yHJkRNijR+I5b6yhxd/tfpfu9n2ADFn0qbq6GiUlJVi+fDkaGxs1zzU2NmL58uWoqKhAWVmZ+vj48ePV5/TeeecdAMC4ceNSu+FERNQtiQWfnDYLFEWBU11t1R/rz4goBnnRp2aeS0RElAQiSCPXTXTYImEOLvxEmUiezUaZKyMCpoqiYO7cuWhsbMQ999yjee6ee+5BY2MjTjnlFM3j4t933HEHWlsjK3O99dZbWLZsGaZMmYLy8vLUbzwREXU7YsEnlz00gyHfHaqFtavRm7ZtIurq5AxTdmCJiCg5QhFTedK9RVHgsIYe4f2GMtGB1u43cOzzd79zLaW56UuXLsUnn3wCAFi1apX62LJlywAAY8eOxdy5cwEAF1xwAV577TXcf//9WLFiBYYMGYJvvvkG7777LoYPH45zzjlH894TJ07E3LlzsXTpUsyZMwfTpk3Djh078MILLyA/Px+/+MUvUvnViIioGxOZcK5whkJFvgsAsHlfM3yBIGthEbVDCwOmRESUZJFpwNq2mdNmRavfhxYv7zeUebpLwFSeht/sCyDbmhE5mUmT0oDpJ598gqeeekrz2PLlyzXT6EXA1OPx4O9//zsWL16Ml19+GR9++CGKi4tx/vnn45JLLoHL5Yp6/xtuuAG1tbV4/PHH8fDDD8Pj8eCII47AFVdcgb59+6byqxERUTfW7A01Ylz20E2/JMcJp82CFl8AP+xvRkW+O52bR9QltUqZB+IcIyIi6ggRr1F0Y9lOmwX1LRygo8x0oKV7tIP8gUjEtMnrR7aze9ULTum3WbRoERYtWhT363NycnDdddfhuuuui+v1FosFZ599Ns4+++z2biIREVGUFjXDNDQl36IoKM9zYe2uRmzc28SAKVE7yDVMG5nxQ0RESRAMp7jpJ/94HFbgQCiIQ5QJglI6ZnepYSoPSDR3w7Zd98qXJSIiSgIR2HFKiwZUhoOkG/c0p2WbiLo6uVHNDiwRESWDmmGqe9wdrkPfyPsNZQi/NH29u0zJ7+4LejJgSkREpKOfkg9AzSrdtLcpLdtE1NW1MmBKRERJFjSZk+8Ot+F4v6FMEZCmrzd2l4CpdH4xw5SIiKgHaNZNyQeAvgWhWtobGTAlahdmmBIRUbK1lWHK+w1lioA0Jb+hpXtMyWeGKRERUQ8TK8N04x4GTInaQ170qanVr6nlRURE1B7iXqJf9MnjCE/Jb+1+WW/UNfmlds/OA63doh3EDFMiIqIeRr/oEwBUFoQCppv3NWtWhCSitgWDQU2GqT8IeP08j4iIKDmYYUqZTu4/+AJB7G70pnFrkkObYcqAKRERUbdntOhTSbYTdqsCXyCI/3z1Ax78YINmag0RmWs1CI6yE0tERB1lVsPU04mLPnWHTEFKvYAuntjVZ60FdIPh3bFdx4ApERGRjtGUfKtFQXleqI7pTa98h3vfW4f31+1Jy/YRdTUtUl0r0aXtjg1rIiLqXGY1TF0iwzTFi+u8uGIbZtz9Pj7esDeln0Ndn18XWN/QxQOmLbqM0uZu2K5jwJSIiEjHaEo+AFSG65gKDc3do2B7snXHBhN1TGv4nLIoQI7LBgBo6oa1roiIqHMFwyFTfcDU4wiFOlKdYbps/V7Ut/jwwjfbUvo51PXpZ6at7+IBU317vzHFgxPpwIApERGRjlGGKRCpYypYLPrmOf1r+WZMvfM9vL9ud7o3hTKIXOZC1JXrjg1rIiLqXCIGZdFNyRf3mlQP4vrCdSk/27wvpZ9DXZ9+DYQNexrTtCXJoR/4PtAN23UMmBIREelEgjvaDNMKXYbpgRZmmOrd/sYaAMCvXliZ5i2hTCKyth1Wi1pXjlPyiYioo4Imc/I9nTQ45wvX6N64txk7D7Sm9LOoa9NPyd+4t4tnmPq05xYDpkRERD2AOiVfl2HaV5dh2hkLCXRVTL4lWas/kmEqzisGTImIqKPMp+R3zuCcT1rJ53NmmVIMRos+deUFZJujMky7XyIJA6ZEREQ66pR8m/Y2eVBlPk4bU67+uzuOpCaLojBiShEt3ugp+QyYEhFRR4l4k77ZIRZ9akxxvWyfNM36000MmJI5kWGa5bDCZlHQ6g9iW31L1OsaWnw4+cGPcMdbazt7ExMSlWHa0v3adQyYEnVxDS0+rNvVteufEGUasyn5VouCK6f3x7yDKgB0z4ZBsjDDlGQt/sg5JbJ+mKFNREQdFZmRr214qOVfUj0lXwqYfr55f0o/i7o2UcPUZlFQke8CAGzYHT0t/4VvtmH9nib8/eNNnbp9iWKGKRFlrMZWP85/9FNMv+t/mLvkY6zecSDdm0TUbYgGgH5KvhAJ+HS/hkGy6BdfSERDiw9XPfM1Xlm5I4lbROnUIi36lBU+fzjgQEREHWaSYerupME5eSGfVTsa0MD69mRCTL+3WhT0LfAAADYY1DHtKrO0RIKJ0B1n3jFgStRFPf3lVny5tV7997trd6Vxa4g6Jphh9XvEFBP9lHzBw4CPIXk/dqSp98G6PXhz9S785r8rsamLF8SnkFax6JPNgmynDQC6TafyX8s3Y/5jn3fLzAqirioQDGLV9gZN9h91T0EY7+PscFst1fca+RgLBIEvtzLLlIyJGqahgGloXYQNe6LbuU6p/5HJNU5FCTOxvQyYElHG6C4dTSJfIIhz/vEprn72m3Rviiqy6JPV8PlsRyjgwynFWvJIc0em5IuAdYsvgFtfW51xAXVKnDbDVARMu8f5c/sba7B80z48/umWdG8KUZt6yvX0yc+34sxHluOfn2T2lFbqOLWGqe5xMTjX2OpPadDJ5w+9d64r9HlfcFo+mRA1TC2Kgko1YBpdWs9pjYTpGjM4CCkCpkUeO4DuGZ9gwJSoi+KIOXUXq7Y3YMW2Bry2aicCGXJcN0sL1BhRM0wzuBGTDvXNkYaSvwO70iv98fvr9uDVVTs7slmUAdQaplYLsp2dk/XT2eTjnygTbd3fjKPu/QD3vvt9ujcl5bbsawYAfLRhb3o3hFJOrWGqm8YsAqZBpHZGkC+cNliZHwqA7WpsTdlnUdemTslXgKoYGabyoZzJbSWRKFGU5QSQ2cHd9mLAlKiL8umiEZkRZiJKnEMKStZnSKMg/in5mbG9mWK/9Ht0pNHkDQfXrOEGY6YXvae2yRmm6pT8bjaFPUPGe4hMPbxsI/Y0efHghxvTvSkpJxILVm5vSPOWUKoF1aw97eNOmwWOcEMilfcbcawVhLPs6pu7X9CIkkPUu7VYFJTlhhZ92l7fEvU6OTGqIYODkCLBpCgrdOw3+wLdLqmLAVOiLko/FbiJU4Opi5IbuPubvOnbkDBfIKhmOJpNyc9K4kICW/c3Z/TocSIapAy7A62+dk/9bA3//mK60s6G6MYkZZZgMIgrnvoK1/5nheF+bwmfK6EapiLDtOvft+TFPszq6BFlCofJIGB3JDrtuxu92HmAGX/dWawrrxigS+UMAHGs5bu777RkSg6/mmGqqCUcWv1BdWq7IBIHgMxOzhAJJkVZDvWxxm42GN5z7ppE3cw+XWCJUwGpq5Kzpfc3pz9g2uKLNFrMMkyznMmpwfjD/maccP8yHHvfBx16n0whZwgHgtGrZ8ZLNBRFA0yfedzY6seKbfU9phZfV7CjoRXvrt2NV1ftMCxV0eqXMkwd3WfRJ3mw0t/Nsiqo+3GbDAJ2R2KaNMAs0+5OrWFqsLJ4Z8xoENf+gnDAVN9mCQaDbK8QgMiiTxaLgiyHVZ1JtV/Xj5dLU2Xy4LLIMM122uAJ3192NHSvASoGTIm6qL26gGkmp+sTxSJP3dAPBKSDuPkD5jVMc0XGQkv7sygB4IstoYUBmrztCyxmGn0nob2j4iJgWugJBUybvAH4pNH2m19ZhbP//ilr02UQeUENo9Ia8pT8HGf3CZjK36GlnQMERJ3FIwVMu3uAXx6MXcWAabem1jA1eC5HzTBNZQ1T3ZR86b4QDAaxYOkXOOuR5d1uqjIlTs4wVRQFOa7QMbNf1x7yylPyO6mt5A8EceNLK/HE5/EvYCkGjV02CwYWZwEAvt3Wva63DJgSdVF7m7QXz67Q8dzb5MU2gzot1LPJDcj9Tek/jkXQw2WzGGYrAJGVUP2BYIem5cvZPvL0m65Kn+ne3lFxMSW/MNz5ACKNyWAwiGXr9wIA1hsUyqf0kLOJ9ZkSgHEN0/oMzpqIl5xNm8lZIEQA4HZE7jldod3YEf4gA6Y9RSTDNPo5UQLmQCprmIbbb0ZT8n2BID7euA+rdhzA97sOpGwbqGtQa5iGj1XRn9DPsJOTBDqr3vsH6/bg2a+2YdGrq+P+G9H2c9mtGNI7BwDwzQ/1Kdm+dGHAlKgLWrm9AZv3aQMFXaHhe8Q97+P4v3yYEVmElDnkaXOZMCVf1OMxyy4FQg0D8bxRcChe8md05H0yhT6zcF8796dXnr4d7uyI32fr/hbsCV9DMuF4oZAWX+zzWBswFTVMO5ahnQnkxc027W3SZNoSZbJMWWQxVeQMUzElPxgMYhfrmXZDoX0dO8O0Exd9ku5t8r2xuZvMJqL2E20EazhiKgKm+uMzHVPy21NiSNReddstkYDpNgZMiSiN3lmzCz/+12dRU3gzreEbDAax+O21WPpZKK1fvvCu3skRVorQ1DA1CKZ3dgDC6wt9XluLY4hGzr4mL77euh+3vb464ZXhNdm13TJg2t4p+aHfxW61RHV2vtq6X31dd/jNugu5U2jUMRU1TB3WSIapLxDs8tPY5aylb7c34OFl3X/1ceq65LZYprUbk02+v27cG1pc8e531+HoP3+AZev3pHHLKNlEm8FqiQ6ZZqWhhqnXH7m3ybMvkrFQKHVtInFUHzDVt5flWWed1daV+z1tDTAsW78Hlz/5JT4MX0tdtkiG6artDd1i1pzAgClRFxIMBvGb/65EkzeACVX5mucybSrghj1NePijTbj1tdVo8QXUrD2Add5ISzslXxsw/fV/V+LE+5d1agZ1ixTYiUU0crbVt+KqZ7/BY59uwWurdiT0WdoGUdfPltQ3sNr7neTgmgiYnv/Pz3DXO9/ja2mqD7PVM4e8WJpRoDySYWqFx2FVp6MZDZJ0JfoFru5+d116NoQoDt1tkC4Wfb3I1TsOYPWO0ID9W6t3pWOTKEXErBMxJV7WmTVMc1x29d4mBiRa2yhXQz2LSAKxKCJgGs5K1gdMA3KGfOdkbMp9Ev1aKXp3vfM9/vf9HjR5A/DYrf/f3nmHR1Vmf/w7fTLplZIGAUKH0C2gUhQLWLB3ZWF1V9a2uur+dNV1XXvXXRYLUgTp0qQrCEIKJBBSSALpvSeT6TP3/v64Ze6dlpkQyCR5P8+zz+LMzcy98973vec953vOwagBQYgPUyNYJYfZRuNCHxJHEYcpgdCLMNtofiP6zoIxuGpoOP9eY4fJZ3XbpURoqJY06USKWCOJsBIEeNrA7c6tQ63WhL359ZftfCxeO0wZI+fb1DK+I6SvqX7ClJuuqjH9CSeFaRdr0nJjoJBJeMc0AKxKr0BujcBh2gd+s75CZwpTu8NUAqlEws+fZn3vTo/V+VmwkkDwhKguXh9XmDqmlBbUd/AB0dNVbT1xSoRLRKuece5wjSKFuKsR2Z1wdqxcKrE7aNn51Vm5mp7GbKVwqqJVtDZ0lcpWA05XkrnlCW5dknE1TFWu70+h8/JMVftlUWwKU/I7K6nFCRb+MT8Ze568AsOiAiGRSDB6QBAAIK+279SNJg5TQr+ioL4DNy1P5dPEextCBU+AQop/3jQKL88bjkClDDYaOFvd7uGvLy9CA6GwQSdykhInB0GI0GHqTjF4OVMHuXu3s5T8UNYIzxd0g3RsxtYZ5j6mMOU24JzKo+s1TAUp+WqxYuScoHkHUWv4D8I1vzOFKQCEBTDzp7mX1xPUOQQABwareuhMCITOETZC6uvrJ1cffXCoGgBQ1KiDiQ3en2/U9XmHcX+CC7yFaZwVppw90nIJsxlEDlPWNmxhnbjCvZs/zrl3DxbhyY3Z3ZIdcce3GVi64QyKSXMrt/AKU4eUfMd7Q1iuzGilLksjJaG4qTOFqZ49dszAYGgEzQT7YuMn4jAl9CtOV7ahUWfG5l7qMOXSOqQSpvZJaIACd04cjOuGRwIAMv0oYi5ymNZ3iAqdX+402mPFTagg3bT9FmFUu8WN2swx7fVSYleYumofYEeofORo9dFB2BM1ii4l3DXEhalF/+0rwjHgou8c/q7W6K+IFKYm53ExOwQi+E2srnePoY51uiSGBwDoepDgUtDbG2oRuh/hJryvNz/inFjx7POoSWfmg5QULa6HTejdcM6dCBcp+Vxd0c4cQF2FpmleNSiXSTA8KhAA+GwY4bPxUp3DxbAztw4AsPZkZbd9ZnYVmVvu4IJWMjYlP9TN/WmhxIrSUxWXfo8vFDe1GiwuVcdGiw1NOjP0bE3gQIGzFCAOUwLBrzlc1IiT5a0ej+EMpeImPRp7oaEorK0okdidOSmxoQCALD9KgxBGVPeda0CLwf57e6Mwrdca+VpTF0NWZRue25aLRd9lXPRnES4NQoVpU4f9PhFu9i9rDVMrlw7uXUo+ANwwMhqAPS3MW4Qp+X3BYcqNUyyr6OlqcMRss49BaICzYzoykEm7K202iGqaEnoOYyd12uwKU7HDtNen5LPBnJQ45jlssFA9XnbGaLHhru8y8PLO/B49D4L/IXze9vW0dM45HB3EqL6b9RZRPcnTxKnTZ2hmba9wFwpT7rUWH+0zbxGWfpBLJfY9GTu/TP2whilpbuUezg/KKUwj2PuzpFmPN/ac430Z3P5gAJu1cqqi9ZKfmzAlf3VGJWZ+/ruTI/2ZrTm4cXkqzOz5BShcO0wvNOpg8KNSgRcDcZgS+gRaoxUv7czDs9tyPNbxFDonMsp7X4dMM9u9W+WQKjyJ3ajl1rT7TUMlo4Osf2OWXdXrjRPlkW/T8eCaTFS2XpwyNL+OOFP8HbHD1MT/22zrGYcpt050lpI/KIRxCs5IDMMNo2IA+K4eEKbk94UGRlq2nmNsGKO2a+3i5sAsSMl3VZPsyiH2+s0fHDrv02f/UtiAooa+U1vJX3BUJjjCO0xljgrT3u4wZe7xAUEqyNkNUE+riMqaDShrMeCXokaSGkkQIXaYtosciH0N7lqjg5hnSKvezAsPAOCMH5WxIlwc3Job7sJeCPNRYeqrMt8qcphK+eBZjguFaV+w89whdBxfzqyw3oa9hiljL3AO/QuNeuzOq8fnvxUDsGdaXcHau2eqL30dU+HevbzFABtFI61M7C/JdBBnOSpMY4KUiAxUwkYDBXV9Y40lDlNCn6DDbAVFMw+lkx4iMELnREaZ++P8FU616egwTQgPQIRGAbON9hsJvKPj9rCgI6k3RktJow42isax4uaLOg+NIPLVlzcGvRmhsdmoM/PGqnC8LqfxZfKy6dOt4wbgtfnJeGfBGL4eo6+OEmEQ51LW17ocUDTNO7bjLlJhahWk5LtSjMxMiuD/7YvCNLdWi5d25uOB1ZldOi+Ce4RrfrOLNHtuXjkpTHu5w5QL0gaqZHYlUw/PZZ3FHqi4nA3zCP6P0KlhslI424fT0rlrjQp0rTDNqW7vlkY3hJ7FZKV4GzHcVUo+uy7rzDaP+wCjxYZF36bj6S05XjtNGztMfEo7wChMufIsrQYLDBabODDuhwrTAEX3uIOE1+lPTYj9DS4ln42vIjxA7OTPr+tAs97M742So4MQFqCA6TLUMRUqTLlAkycbTSmTQO6wV5JIJEiOZspSFPQR0RJxmBL6BMIHYGqpe+WoMDKTUd7a6+p7uWtGI5FIMDnOv9LyuXOdmhDGpxtwdGYwmKwU//cnSi/OYSpMq86t1eLV3fk410cW8L6CcANntlJ8Ko+ph+p7WqzeOUzVChluHTcQwWq5XS3nc0q+/Rp7ez05nckGbiQT2A1DV1PghArTSBeKkZTYULx6wwgAQJBK5vS+O4S1jC9Hx9H+hNBh2uSQZr/ieCmq24wAnB2m7uoW9xa4jXqQsuvrQHdjMNvHYl9+fa+zdQiXDqtD5/iMTkpZ9WYcFaZGKyWqMWy0UijohtJPhJ6Fe4bIpRKX9kCwSs53JPcU1C5rMaCi1YjUshaUedn34MmN2XhfkOUik0oQpJLzqru6dpM4mMie66eHi3HPypPQ+oEDNUjlXPaoKwivs6LFwDc3IojhfheZVKwwFZJa2mLPdpNJMCWe2eN7EoVx5Na040KjeF2rbTfiP8dK0CjI4nOFgR3DB6bE4qPbxwKwl7sAnNXXGqXre2dEdBAAoKCmb+y3icOU0CcQRrVSPTjYhCm+tVoTKluNl/S8uhuzg0JHyCQ/c5hy9exCVHLcNzlW9F5nNeu0AoM2tbQFj6zNxBt7C7p0HsJaqq/vOYd95xqw7lRVlz6LcGlw3MBxzgZhIKSzh3x3ws0zpdxz0ychnMGjt9jwBZtO4w19yWGqZdWlKrmUL1fQojeLHOLeYhGofB2NyUEhKkQGKjFnBFM3tsNk87pmpLCRV5328t1T/QHHOm3Ce/vrE+X8v7mAHzeuvV1hyjV90ihlvHO/p+uyCuvHVbebkE1SjwksXOf4QSGM6rJvO0xZO1Qt59d+zgmRFKkBAJwlc6PX02Kw1y8V9nfgkEgkfGMdT+p/4bp9vMQ7sYbQsSqB3QkWw9adrNOKHaaNHWZQNI1t2TUoadb7xdocJHB6XYyTU3idRy40YcfZ2os6r74KZxpx90pogAKOd+2J0hZRLX+uLm5eredyUu1GCx5bdxr3rTolGst/7ivEyrQKvLgjz+Pfc7Z0bKiat2da9Gb+sxyzRzVu1Mm8wtRPsl4vFuIwJfQJhI7Qilaj27qXjoqi3lbH1ORB+cY5TLOr2/1CTSIsHzCWLQDN0dBh9niOQjUhRTPpCbtz61Db7ruD2yCox1LTbmK/nzhK/AnHlDgumikMhFS3my66nq23mL1MyRcijND7UkbCLOpY3HOqNJqmL1oNwCklglRyRAQqIQFgo30vU9AqSJuUyySICBQrTLn1JEgl4zfBzV4q+joEaWL3fH8Sb+0r8Iv1si/gaEi7GxM16zDlGoNllbfydUB7Ix2ClPwoVsnW0NGzDlPHRgskLZ/AwQWwrhzClDXJrdX26vnnCS4YK5dKnGpbjhrAKKBqumBXEvwLLsjuKh2fg2v8leuhBIWwlExXyoHJBQHZgW4cpkYrhaJ6HR/UqvaD+08jqEHZbuj6WuBY7uDTI96LB/oT9pR85n6RS+0OfY7U0hb+91TIJBjBOiAdlaOOtArGT2h7c4GxnE4Un1xKvloh47NDbbS9vJbRYYy5xlWOjIhhzrewnjhMCQS/weIwgd2l5XOLD5cqkVHuH2pMb+HO35XCdEiEBhIwypKebjgBiDsic4aK8L0Ok3tFmNZNg5/fvYz4CjG4UJ7V9/BmliDGUWFazyr/HI0vYR3cS4nZy5R8IVKJBKsfmgSAqcPqLcIgjt5i65G6T+cbdLh/9Snc9/2pLqep2ygae88xTpkQlZzdoDLGVqPODIuN4sfVE+llLbj+vyd41YZSJuXTnDkenhYPgFGNRAgUfRcadfjtgud7RNg8zGKjsSOnzi8UHn0BoZofcK+Y5hSm4weHIDEiAB0mK3bn9l6HHl/DVCnnU38be/gZw23GuQ3PwcJGEhggALA/b+PC1IgNVcNG0X6TmdTd2AQOU8fSUHGhTNmYei2xB3s7vMPURWozx81jmMacq9Ir3NatFSpMsyrbfA4kyAXOI66zea3W6BRMPC7IhOyKEKS7ET4Zmg1dnw+O1xnPNv8kiKEocQ1TwPnebTVYkMM69+UyKa+Ir2ozutzXcggzuhoEa1ukQHjguOcSwomMAhQyyGVShKoZMUgTO8ccs7nclZRICNdAKZOIygP1ZojDlNAnMNu8dJiyx3EKpbIW/aU9sW7GUzMahUzKK7H8wQDkHpxqhYzfRArx5FRytwB3xah3jIYBPb+ZJYhxfHivTq+AjaKd5vWR842X5Xw41adjreDO4BRz7Uar1yniwqZPwOVP5d2XX4/H1mXhQqMeJc36Lpcp+elsDdaerARgV9tyBlqjzoy//pSLhV+n4Xwn0fEvj5aK/lspk4o2Ia/NT8YYgWKd+44mnRn3rTqFv/6U61FBonMRqFmfSUp0dAfOClPX9zIX8JNKJLgnZTAAYGNWVa916Ol4h6mMby7T0MNlBrgN1YxEprtuq8Hil81GCJcfK/vMkcukmBofBgA4XdU3g0Z2halU5DAAgNgw5nlNMo56J5WtBnxy+ALqtSZBSr7zXoNj0YRBiNAoUN1uws95zgG6UxWt+Py3Ev6/rRSNdB+bA8uldptxIFvyorBe5xT8F6b7V7f1/P0nDJR7E9h2h8nBZjdaL78AoDfgWMMUACIFDlMuc4qv5c8q5LmgT7EHO1p4r9UL1rYggYq4pMn933N7F64RGOdX4EonGS3iMXYncJJLJRgWFej2e3obxGFK6BNwTgduI3ayotVlBJE7Lp5tSFLUoOtU3n65qGw1YN2pSo8dHD0pTAEghnVM1vmBAShUmAYqnYuwezJS29kFmEuZ4qht9/26hI6r28YPBMCob/pqClpvhNvUzE2OQrBKjry6Dmw/W8PfQ9yD+0xV+2VxKNoVpt7XMAWYxgLc3PQ2JdfRKXw565gaLDa8tb9Q5Oiq1XbNYboxq5r/d7Came9RrKG1/WwtTpS2gKKZrsSeCHJYK7gUt4emxiE5OhDXj4wWvc8ZkMJ6pJ5SjjpczPtfixr5hkSEruPoMOUCU441bFWCgN8tYwcgUClDabMB6b20liL3LAlUygQK0559BnNO3LAABZ+mSmr2EgB7OqhcKsFQVrVU00fXP845LJNJeMUfwNSa5AKcPR3cIHSNf/xcgHWnqvDsthy+6ZOnlHy1QsZnp3yXVu60R/zo1wv8vzk77ncf0/KFwd1rhkUCAI4WNzkFioUBiq7aXN2JxaH8VVdxzDLp6dI0/gpnE8kE9XYHsHX/AeCqoRGi4xWsHcyt154akgmd1MK1TZh5mu+hDqqBd5gytrhQ+OD4+QBTNs8dyTFB7t/sZRCHKaFPwEW1kqMDEaCQQme2uVRKcc4JYZrAfatO9XiDBgBY/nspPjlcjDUnK9weY+rUYcoYhBcTIewuhOfqqgi7J4UpV8M0NlSN64ZH8q/Xstdlo2isTCv3qCTjsCttwvDKvBG885Y8yP0HblMzIFiF529IBgB8dayU3+DHhwVg9IAg0ACOdpJy3R3Ymz759oiUSCR80KLeS4eJYwr85XRqNOvNMFkpqORSXDmEUaLVeWksN3SY8NVRe8dNoeM3mFWYcg7TX4vsyuDaTq4v0KFbK6emf+baJPzwyBTeiOMYPYBRm+7IqeNfs3iw4DpcRMMpGtiQRVSmF4vR4fnE3cuOjlS5wGEapJLjrilxAIBNAqd7b8FspfhArDAlv6efL1wN0wCljHcUEYcpARA4EaUSXgXX2brcWxHWMBU6TJWCUlGNHaZeq27vz5xl7f+iBp1XKfkAcOfEQQgPUKCqzYg9DnWdhcKOWUnMvuP3kuZO7w2hSlC41RkRHYSrh0aAosU2kCP+EKwV1tK/mOAJJzaIY9XbOjMRp3D8WtTIl4zia5hKnWveAvaeJBwK1mYayDpVPT3LhfZWA3uc1UaJMkzy6tyLCriUfDVrazvaD44KU08MjdB4fay/QxymhD6BRbBRSwxnJmhps3O6PWcoOj5Uzzf0vMqU22DtzKlz23yFb/rkzmHKLmz7z9UjraxnG1pxkUa1m3P11MCHS8kPUcvx9i2jsfGxqQAYJ42VovFrUSP+c6wUj6073WmjGm7xvzopEjKpRLCh7ZsbhN4I18lWLpXg4SsSkRwdiHajFZ8cZgrGq+RSXDc8CsDlqWPalaZPHNwmzJ3DpMNkxT/3FuAEW8PKMSW/5iKi+74inGeDvDDEhGzMqsb36RX4kFVlCI00zrhLcpGO01m9rgCHjpuKTlS+N7F1yQrq7RFzTyU3HGsnc3Whtp+tJYb9RcI56TgjmWum4qg6ceThK4cAYJQ4/rB59AVhzWGNUmaf/zqz2zp5lwOuhqlGQRymBDFCJyK3SfeHOoqXApsbh6lKLuUDemYbjbaLaHRD6Bk4tR0ANHmhMAUY1dzD05gA3fLfS0X3Pac4BoCrhoZDAkbY0VlDSWFWTLtD2ZPHpjOKVk+7lGa9xesSTpcK4bPqYpqgcXZgeICCiFME6MxW/H1XPl74KRclTXqwWx6RwlToME2OFiszOZt6EHuMpzEyuUjJd+xr4qk5tGNK/mA2qMbZZgYHe+6hqXFuz2VQqMrte70N4jDtIdJKW1DU4F4STfANoSIsMYJRj7pymHLHKRwcId6qwS4lXB2QqjYjMitc1+rszJHDqduyqtqxbPNZtHrZObq7ya3VYjdbI4hTG80fxaTSTk8IAwDsya936+zkfotglZwfU7lUAopm1ACOhdk9wS3+nON2AP/A6fkxJzDwmxqZBHKZFC/NGw7A/pBXyqV8elNGeetFd3PvDLOVrRvUBYcpd3+5W9/XZ1ZhZ24dnt6Sw3wXO6e5tJfL1bHXStF8xDlYJRc0KPBuXnAK8cPnm1CvNYkMMs6JtGDsAP61kWxqTmef7xi97kzlGxcW4BSNrxOkuFE0jbTSFn4tdFSYXj8yGonhAdCZbdgpUKkSfIfr9suNB5faJ6wjfefEQU5/NzwmCDMSw0DRwK7c2stwpt0HV+IhQCGFTCpBVCBTZ8xG0Z02ILuUcJkVGqIwJTggciKygbLGHnbwXyqEwViRwlQmhVIuJeUqejGDBSnMZ6sZxZynGqYcd6UMRkyQEvUdZnwm6OIufE7dNGYAf79UdRLEE9bgd6zHnxIXiomDQ+znLHDKxoepoWFVfD2t8BYpTC8qJd++F+d+v9xOurL3B7RGK6wUDRrAqvRyu8JUoAcYEGJfn+LC1KJMUk44wAkbPI2R0GG6/1wDKlsNfMMmgFkLixp02HbWta3lmJLPqVq5knicjT5+UDB+eHgyls0a6vZchHO0t0Mcpj1AWmkLlm05i+e25V7U52w/W4P/25WPI+ebPHY86w9YBI7EIRHua3zYjxOrlvxB1SJsdLQjx3kho2kaZ9i6N+5S8gc6LE7lHlScl5J/7y/k/82d6xs3jsSmx6fiw9vHIlApQ2WrEeluVLBC5RvANAeJEWz6DAKnynsHz6Oqzf11Gvg6mMziH8eWY/CkcCVcXqwO9XwmxoYiMdxeNkMpk2JIpAYyqQQmq3fd1i8GSxdT8gHguhGMEnZHTp1TKjLgXKOUU71z13s51iKjxYY7v03Hss1nAQCharnPqZmcksJG0VhxokyklOWUmmEBCrx/6xg8Oj0ez1zLGFWd1SF2VHmq5c71jx0ROmYB8TUcKmzEsi1n8YcfTwMAOszi6LiVonE323jIU9rc5aRVb8ErO/OQUd6zWQK+YLDYeCXJNDYoxql3OAM7VC3Hy/NGuPx7TkGe56G2lj9ib/jEPKtkUglfK3vTmRqn4w0WG7Zm11zyGqdc0IIoTAmOCJ2IERoFFDImGN0Xa3l6SskH7KnDlR5sSIJ/IixnxIksOkvJB5i9wItzmKD8OUFmCrdm/mN+MuRSCe/c9LS/AJzr0DvyKKsyBewZLQATRObtrh5WeAt/y6KGDpeli7xBWIqNqze/6XTvK7XT3Qid8Xvz6/n9p7Ccg1AdHaSSi5yNCimXkt/5/SK8H41WCr9daOJr/A6L0uDPM4cAAD7+9QJKmsTCMqbZLrNmBrC292DeScvac1a7QzU5Jkh0DY4MCiUOU0IXsdoofHSYSWGs05ouSgH4zYly7C9owAvbc3Hndxk93mSgJ+E7yQkdpi4VpvbjXpwzjH/dLxymggfU3vx6Jwn9r0WNOFHKbKDdOUwTBE4moOecgsIFW8UuunJ2bAIUMt7Bsfm084YSsDd9Clbb6xkOCrFHe9sEv01Jsx5LfzwjcjgLcUwvsDtMe37MCQzCrr0cQwS1b7hO6VzKVMUlvq9NbgIr3nDNsEgMDFah1WDBhswqp3IfoYJ7urRZz88VThl/ORSmRQ06UWH/YLWCN+Rza9pdOnodaTfa5+B2h0j12EF2RcXsEVFYNmsoXze6TmvyGOATpsw/c20SNC4axjkyNzlKVPqjpEnPz/sj5xknaDkbQHPcCAyLCsQ4VgFS7qGQ/uXkYGEDDhY24o09BU41bv2VKnY9DVHL+WZ9dVoTrDaKT8l399wCgOQYpnxDYS/LvOEc/ML7dNGEQZBKgJPlrU4bkm9Ty/HOgSI8sTH7kp1TVZuBtxUClDLEsXaBK5uI0P8QOhGlEntavj/Ywd0JRdN8QxK5VCpKeeVsSM4e9Je1n+A9Rhd2SoQXDlMAGDOQqX1e02bkldVGB2UdZ296mhcUTTuVVXJkZlIEhrPliUYLGtmOiA7inbIX02ipO+DsjEClDAYLhW3ZrvdmncErTGVS3DFhEBQyCXJqtDhT0dpdp+oVNE3j8yPF+PjXC35Rn1hYcsFGA/vONQAQO0y5PU+wSg6NUsYHtgB7IECoMHWbUu8wL8pbDHxZiQiNEg9OjcOMxDCYrBTe2FsgOtYgOE+1wsFJq2W+0+hQ49QToWo5ApR9w9XYN67Cj6BpGh/9egGb3URUtpypERnQZS1dN2A5h5pKLkV1mxFpZa1d/qzejrCrNbfolDYbnBYUYUr7PZNi8eZNIwF0nnJxqbFRNK9UiQlSggawYEWaaHO/WaBWcZeS7+gwreghIzBO0FTLsUMyACxi0zKPFje5jJTxClOV3fgR1qZtY50114+MRkyQEg0dZnz+W7HT5wB2dROnVIvnFAVEYeo3CBUvHEMEkXhODcI53So6cXZrjVacqWrrsqFkuYgapnKphFcsfnG0BA+tOSVyVAidhXevPMmvPdy6VdlqdFKhdjeOv0qwWo6RMUGICVLCYKFw0otu5a5qviVFMtHrxwSKCo6YYBVUcimsFO1xA8I5oJbfM8FjbSQhgUo55rJqBuYzbDhYyBikXKkDgFFuciq7N24ciednD8Pc5Che3duoM3dZWdGdcA0s6jvM2JNX38nR/gEXxIgLC0BkoBKBShkoGthf0OCVgT08OhASMPXO/KEJo7foeYWp/doGhqj5piFbzohtweMlTO1ibxw0nxy+gP/9XurzOX19opz/t1ou5YMhJU36S17OhOD/8B2aWdVSYoTdtupLWAWOLK7cDwdnb8eHe5dxRNM0mT9+hqu6n2Gd1DDliA5SQiWXwkbbnZVc5hrvMGX3ClUe7E2zF8FliUSCD24bg7/MGooHpthtmgHBKr+oIUzTdlUhp4b9MbPKp2AtRdNo1Jn5PbZKLkVkoJJXma46Xtq9J90JNe0mrDlZifWZVXypoJ6Eu7fkDmpMqaCGqVohw6GnrsSOpdMhlUhw0xhGWLR4Rjxvxw4MUfGZdu4yRhwFD2KHqQJSiQSv38j4PvJqtSJxFjenJLAHuAcGi7/T4CBC8oREIkFSVFCnx/UGiMO0mymo78CPmVV479B5p3pAHSYrVpwoA2C/EV2ljXuDyUrxUYTZbApocVPPNy7qKYS1SePDAyABo9h0LNZt4Y9jFiku6lfUoHPp2LtcCDfpz1ybBIAZY2F9zlC13RCQupHABzl0mL7USjx3CB8Krn7XpMhATIkPZWvWOdcN5NJ9hQrTIYJNXyvrrJkaH4q3bhkFAPjpbK3LFFZucVc7KEzz6zoumZFyvlHHK9s8YbTYcLCgwS+cNJ2RXd2Ov+3I81mFUlDHrImeNhpCxQuHsLsi7zBlNzedBQL++lMOlvx4pssp1mZr1x2mAPiUXICJJp8SzGOtw1hzRk9iuAZjBwbDStH4Pr2iS9/rLY5p76FqOSQSCa5OigAAnPJCDcAFLYT1Q68dHonHZyQ4dbIHGMOQcwo7qu7E5+bsgPKGF2YPwyvXj+CdrCdKmLVAeE+tzqiAyUohLkyNm8fE4P7JsZBKJAhSyXlliuOa2RPqBKEBuyqjokefTd7CORziw9SQSiT8xuvz30r4hhyeFKaBSjk/v4vqe48to2MV0YEOz14uaLIrt060seeaDgLMJtnd2DbpzFh3qgrfpJb73KCwUJBmGhmoRFxYABQyCYxWqs+pCAm+4/i89WZd7o3YBGu3u7RRb4Ow605V4Z7vT+Ibdg9H6HkclXRKmcRru0EikdjHnrUnnfYKoawy34O96U02DsDsOx6ZHo8glRzjBwUjSCXDzKQIxPqBwlkYxF84biAiNArUd5hxoKDB68/477FS3Lw8FXvzxb0r7pkUCwDYlV1zyYUAQs5Wt/P//uDQBa8c25cS7t5KitTw6mZA7DAFgBC1gt/HPz4jAZsen4o/zbTXCFXIpEhg75kSNwEuE/tdnICqvMWAZva3j2Br/EYHqfisTaHvyCgoYSdhz00u+M7iJj1/z3tTLgsA/nX7OK+O83eIw7SbETasKHVYAPPrtGg3WjEgWMWnJJc1+75I0jTNp0NKJcB4Nv2xuI8ZO74gVISp5FI+zcExYs4tmsIuzhqFDDqzrceMRauNwtINZwAwapAbRsXw90dOjX3RF/puvK3hmFra4pTafzkQdg6+YVS0y2PmJjOvnxVcI4fWxJxzsGATOiSCSys08OlUYQEKTI4Lw12sYvXt/UWilALAuYB1YngAn2rz2ZESH6/MO+5fdQovbM9DZmWrx+O+TS3HK7vy8eruc5fkPLqTp7ecxa9FjXh5Z55Pf/fQ2kx89OsF7HBTYBxw7TBNihKm5DOvcwZAZ3M1i631uz6zyqdz5eCi7V2pYQoAoQEKLJpgb25TLljntUbX3VAVMgn+dPUQAIwq7VIqDhw7xXPzbNQAxpAravTssGKeQYzT9c/sOQPONZQd4eZwiYfgHhc8cAz+dEaQSo5FEwZhanwYAPs1CB3Ua05WAgBuHjOANwY5EsPt6wvHh7+cx43LUy97uZs2QbmD8hYDfvGT2qqeECpMAeDBKXGID1OjSWfGV0eZdVbdyXxKju59aflc8CHIYaM+LTEMkYFK6Mw2FAgcmMLA58Kv092u/cJuy6ernJ+RnuDsnHnJ0UiODoRcag9WXGjsv3YigcHxeTu0jzpMRQpT9lqFdUwBe03JvFqt27JOAPAp2xxIqN4m9CyOCtOwAIXTc90TvD3J7hMd9wrD2OfRhUad24C/sPzYvZMG48u7xnf6vcvvmYidS2cgNECB4aydW9Tg2eay2KhLZhMKSwoEKWW4bzLj5Fx/ynv7OadWCxqMEAWwl2IbOzAYYwYEwWyj+OyKy4FwX/l7STN+OFV52b7bFcJyD3cK9gaeNBnC57aQoZGe12vOocnZU3VaE2+DCGv8JkUy7xcLbALHoAF/LHufFgvKXTke444RA4I7P6gXQBymDrR1UlPUYqNw5HyT2wdrh0C549glmVPFDQ5RCZSNvm8MXtqZj5v/lwaA2egOE9zI/RVhbVLAfcScezBwyjG5VIIxg5jJ3Jlz61JxoLCBP08uujOeramXLegu2CK4Nz0pEt9ZMJqPYLYZrfjvsdJLcNae0bML6ge3jnGbhsl1zS50oSbinEohAoXpUL6Zlx7nWWdIKJt+89SsoRgQrEJVmxFrM+wPRpq2lzoIVNlrqb44lyn4nl3NKP82ZFbhw1/Od0u6lVCR9tt5zwYC19zr98toSDTpzCKHtrdwvyNnEPnK0WL312ivYWo3doexD3PAfu9zBoCwUL8nGrsY0b5YhSkAPHddEqbEM+rLCwIHJBcMWHJFAu6dNJh/XSmTYnpiGCbHhcJio/Ft6qXbmOkc1g9uno0QKO49YbBQ/KZ75IAgvHnTSMxKisANI10HRzh4A83Ns8pG0Xzqkq8KU47h7D1S3sxEwl05qG8aHeN8buy1C9V5G7Kq0ay3YGXapVX8OsIFubgN3XsHi/y+hAin0OJUO0q5FH+dPVz0nqqTmlfJ7DOhwMv57Q9w66JjrV2pRIKxrJIkt9b+HHcM6B0sbHCZjSCsEfzbhSavz4eiaT4F8elrh/IOhBHsvCioJx2L+zu8w5R93nLZO31tDyGsAcg5TO+YwGR/cAGyEdGBGBalgclKYe859+VPogSlXXorFhuFXwobRD0AejPeqjvdwdXNPlfHrIlCdR0ADAlnlPk6s82tMt/E/40UL8wZjhmJ4Z1+r1Iu5QPCI6KZZ15Fi8Hp2SDkHz+fw8Kv05HrQmDSGefqtB6FM0Knr0ImxR3jGYfeufoOrwU3jveU0HbmspC6unfoCtnV4t/pl8LuDzobLDav64ILyz1cNTTc6XVf4BymxW6Cn9x4xgSrMIH1JaSzZbYiNfZ1LMnFum9wUz5pGO9c1fHBXJWXCtO+AnGYOrB0wxmPKXhbztTghe25eGG76w73QlWAY1oZt6CEBih4SXZerdanlD8bRYvSTJkIFXMjV7cZe0RN6A9Y+LopjFHEPQjzasWbA7NDSj4AXDWEWby+PlHeZQfLxfBzrrORNimWecCcrmzjG4MJywv84YpEt583b2Q0vntgEj66fSwAYFt2DfLrLu8mSe9mEylkBFuzrlFnFqVqWG0U73AVpuQPDFHjptExoGj7JpVzmAap5PjjVcxvcrzU7pgzWCjeoAkPsD8oJsWGQiphagRWtxnx4a8XsCGrGmllF9+Vuk2wBtRqPUeEhdHDy5GW32qw4I5v03HbN+m8kegtwri9t2uWMA3GU11Cu+LF/kgSqjs5pXhyTBCkEsbp6y5NVVgKpbMUoHqtCY/9kIU9+eKyEC3sOip02PuKWiHDU2wqTWFDB2iahpWieWfk6IHBWHplIt8EKiJQCYlEwnew3JlbJ3KadCc6B4c5l644LIqZk006z3UkOQWkUiaBWi7FzWMG4OM7xnWqCnW1LuvNNhwrbsJru8/hoW/S+Ne5ruO+EhOkRKhaDhsN5Na2O82rSbEhohrLHGMcnFvCe/xypsvRNM2v9U9clYghEQFoM1qx1UXHdVf87/dSLF53+rKX6OHSGrmu0wBwdVIEHxgDvFGYug+i+Sv2EhLO9yvvMBUEPh1LcgDAB79ccApiOTaB3JXrXqEvpKHDDIuNhkwqQUyQXU03lrc5e48zuj/SYbLihZ9y8f6h810KbHoDX8NUYi9NJZMwtpi7uniXivSyFry1r+CS2N5W/jrBBw4enZ6AN24cia/ungCAef3m0UxGV1qpe/uPa3wCoNfuszadrsZLO/Px3DbX+9fehmNKfn2Hb/fQaFb5do515DnWZpTLpHyQ110Q2XwR9e4BpmRKhEYBGuLAuiMHWYffNz4G0gvqO/Dw2iw8sjbT7THc/lkqYWzBMI2CDyg47qHd0eIgNlMJ1IdcE8hzl8lharDYRIFvQCxm6y5e2pGHu1ae9MqJLVRuRgmey/le/r5COOFInps9nD1lXoq/zR0OYTUSocKUExfkuQjoOtYn5dTWGeWtfBk97jz6C8Rh6kB9h9ljlPXnPOZGyRTUpBMi3Jw5Rji4TWaoWoER0YFQyiRoM1p96tbd5rCBDlHLERqg4FMdz/iYutVXMDmk2nNRFccx4FP3BRu3eyfFYkR0IFoNFry9v/Cy16xz1RV7aKQGo2KCYKVo7GOj3pwD44eHJ/MLnScmx4Vh/qho0GDqyF1OXDXCcCRAIeM7g+e42VAGOzhgXr9xJJ8uIgEQJYiWcWq+c3UdfMpAi8FeO0/4ANAoZbyz8n+CYuTd8UAXGv6dPQyFNf3WXaKUkeo2I74+XoYmnRkXGnUwWCi0Giz433HfanEJU9lq3WyqGnVmbD5dzc+zBp39uKpWo9u5ZXORkg/YO8pzQSHmnrGn0LlCGFgwWCiP6dTfpJYht1aLf/xs7xRptNj4DdGgTlLMOyM5JghKmQTNegsqWo14bXc+f34hKmbt/ua+FHxyx1heTTgxNhQJ4QGwUbTTen64qBHvHChy2ezAF7g04gCFFNMSwvjC/BqljI9ee2r81M5mS4SofUuBG8eWjylrMaBVb0FVmwE3Lj+B57blYk9+PU4UM0q6uDB1l8shSCQSXuXx9v4ip6DFfBfqUsDuMD1X1yFSugLotGmAlaKdapZ3lQ9/ucBvzgaHqvHINKYW6Jlq757tm05X42xNO/6w/rRXzbu6A6PFxjtaHBsPpsSG8P/urOYVt7Eqbdb3GqeEp5q7nB1yorSF34g4lsNQyCSo05rwbap4PW53yGJ69+B5PrPCFW0GCw4WNOCDQ+cBMB2ehXUbhQEBf+gaTHDN+lNVOHKhCZtOV+PhtZmXRG3NrVWcwlSjlGE4G6w46+U60xV+L27G7d+k4+ez9uDPN6nl2JFTh5d25PnUZMYb7M2t7PNALpXglrEDRPZMCquAy65u79RGAcQBkN4EV1/ybI376/QXjhU34cblqW5FDBRNX7TClOtYX95iwPlGHZ+BKKzBzj2THPeSHJwowFN97s7g1mZ33yEcK08NqFyRwdoANe0mt6IMi0OGpvCccr1w6NE0zT+vuUzACEHzLc4xnVurxdcnyi46k6+z8gR5tVrYaKZe+KeLmPqZla1Gr9Wg3nKCDbB403PAsdxDDFvLfOwg39PVObvifIPO5ZhygQSlXIqRMUH8nhlghBkck+O4PbOW/xyjw3lyTGS/s1ZrgtFKYXJcKK53U26vr0Icpi44VeHaGQqIu39XtTmrToTOnpxarShCzHUVDg1QQCGT8gvSqgzv0/2adc4OUwBI4RSJVe7PvS/j2NVauDHnHI3CToDCB4NSLsU/bx4FpUyCY8XNLpsQXUqEDh5uUQKAG9mN/W8XmmCj7A+kCB9Sg/7MKtwyK1ovWRpOfp0W93x/UlQvklOIajpRiU1h6w0KmzXxDZ9UcqdC/TKpBH+dPQxv3zIKr984EmGCaNngEDWiApWwUjT/kOfKYHBNbYTMTWaapf0s6ELdHQEHoUKjut3ksUGRcFP89Ylyr2oV1rYbcd+qk9iWzWw6mvVmPLI2E58cvuDy+P8cK8GKE2V4anO2yCGcWdEGq41CZavBq9plQuM03cGI5R6yr/18Du8dOo9PDzP1vuq1dudxi8HidsPPpc4JU/IB4PsHJ+GhqXF4fvYw/jXOAZPhxhnU5KCMzHBoYFTVZsDvxc2i9QAAr1jlnMGBShmCVBeXcqKSSzGWXYv+e6yEVwkAQBC7dg+J1GAm21Gbg0thcgzMvbgjD1uza7DCR2e3I5yT555JsfjP3RMQIqirOJNt/OQpDZhzIPqaphgmCO6tyqjArpw6kWPy43sm4su7xmH1g5N9+lxHXpwzHDFBSpS3GFAqqEk6OESF+aNcO0yHRmgQqJRBb7HhXJ2WD7YATEOjNoMFerMNWZVtog1Mh8mKhSvS8OfNZ7tlE7rxtL2reliAgr8X8mq1nTrKTVaKV7h3mGz406ZsLFyRhiPnvU/p7grc/RColDl1KZ4geKapOql5FRmoxNBIDWgwz6zeAFfewpXDdFJcKGJD1dCarNjHOiscNzh/Zde27WdrRU4ZruzT7BFRuCIxHCYrhVd25rlUHdooGg+uycQru/JxhJ23j8+IFx0zIjoIKrkUrQaL16ohwuVnm8CZWN5iwOJ1WdgsWBO6A1c1w7l5ein3ELvz6lDZasSzP57m5zeX6pxd3Y6PfnVtw3QVV5krrhjFBjZbDBavxDDpLpqL9ga43g4AUOXnzd/e2FOAJp0Zyzafdfm+MHvprZtHQS6V4B/zk336jnCNElPjQ0ED+NPGbP51obOIq4nuLvho6gaHqX0f5Po7hBlBVW0Gn5pAKgRz3N3+xlXmJbeH9qYBqM5s4+faF3eNx0tzh2PBuAH8+4kRAbhp3EDYKBorjpdhZ4532RLu+OxIMW79Ot2tIpxzPE8YHIKrh0ZgKiumef6nXLeBWL3Zhue25eC/v5d6FbgR2nre7J+MgpR8gNnbPHddEpZ4yBZ1R1SQCoND1aABbD1T42R32u9J5rv+eFUi4sLUCFTK+OZNAJO1GRuqho22r/vuUvIjA5V8MFwll+LVG5KdGlb1dYjD1AXfppahwI3STFhj5O+7zjlNPqEDxEbRIkcQpw4NC2A2yk+yzTK2n60VRUtomna78XKXYsotuMeKm/w+cuiO2nZjl+u08Y5Q9qEVFqDgo4efHi7Gq7vzsUlgdCodHDPDowKx5Epm4dqa7V3qY3dgtlK8k/3VG0bg/dvG8O9xjovMyjacq+8ARTMGruOG1BODQ9VIitSAotEt6eaOmKwUHvshCyVNenz86wVYKVpUN9RR1u/IdFYJdrS4mTeAuN8jxMN13jAqBreMHSB6TSKRYGpCGAC7s4crZxCucXbsLLkyEUuuSBC9drKild+QthoseO3nc8hyY0C7oqRJj2e35ohee2F7Ll7cnutSucGlXHP36us/n8OPmVVo8ZAO/cOpKlxo1OPfB4pgo2h8c6Ic+XUdWHfK+e9omuaNsAuNer5xAcA4tddnVuGxH5h0HU9KTJqm0S7YMGw/W4tS1kg4XdmG6748jhe35/JGJef0cWxOdrzEfg+eKG3Gom/T8erufD4F3lFhGhcWgGeuTUK0IIXlyiER7Ge5ronquEamOhhVf991Ds9uy8Hb+4tEtai54+ramXMeEKzyST3pjimsw+ugQw0lxyYxor9hDbzjJc38ei5c1w9dZBOgDg9OnmuGMc7bfeca8MNJZ9Xz+UYd/raDafzljdLdkdvY+lhrT1Y6BQsXTY7DFUMiRKU4ukKYRoF3F44R3U/rHpmMLYunuS0bIJNKcAVbnuW34mZRihlFA//cV4i7V2bgjxvOYHeePaiWX6dFo86MrMq2Tmu/dobjHAxVKxAbqkZMEBMISitr9fj3nNNfJZfy6oFarQkvbM8V1WBbk1GBl3bk8fZMamkz/rm387RYo8Xmss5aRYu9fqnjnJmeYK/X5Zgm54rp7Bq+KqOyx1Sm9VoTbvlfKt5n1ZqesNcwdb6vZFIJbh/P1EzkOg5zcy8pUoMFYwfgtvGDEKKWo81oFT0juOdgqFqOf948EjFBSpQ2G7D2pHOAvbRZjzqtCUqZBIsmDMInd4zFgrEDRceo5FLMHsEECVdlVIo6I/cV6rQmLFyRhv/+XtrpsRYbhbf2FXhd6sBXKlsN2JFT2+nvbLTY8NGvF3Cmqg3NejMaOsyQANi5dDpmJkXAbKPx3qHzOOpDHdvOcOVIFNpOl2IPYbZSfBNTs43CC9vzcKFRJyqts+VMjct6vl3FVW10VyjlUlwzjJkbf/0p12WKrXBvt/9cg1eOJH9DJ1C3v7Iz36Od2dMIM0xcBYmEDZavHxmN356+GgvHDXQ6rjPeXjAag0NUomeN0HHIOdsK6jvQpDPDaqNwqqIVZ6vbUdlq4J1TXc2IEX5HRnkrUkud7VqhTWu20W6VqK5oFlyXO5vZUXAEgK+zebqyrdPSUNxvF6CQYkCwCnelDBaVqJFIJPjPg5PxyLQ4AM62sK9syKoGDUag4YqzAocpAPzrltEYFKJCeYsBL27PdZkNlF7WgmPFzfgutRx/WH8aFS0GNOrM+OTwBZe/m3A9qGg18Flb7jBaxY2SooNUeGBKXJdtXS4r7IujJfjD+jPYfLqaX7cdnfiBSjnWPDQZP/1hutP3cdlYXFCdV8K6uJ+5vcFTs4YiPty5rFVfhzhMXdCst+CJjWdw2oWjRKgGzKvV4o8bzogeOo7qgY1Z1fzk5BWmrJpnSnwYrx5579B5bMuuQWmTHjf8N9XtQuConuLS+WcmRUApk6C02YDNZ2q6JT3QYLEhs7K1W9JkKJrGxqwqtwu2laLxh/Wncdd3GR6l9jaKRofJitd+Pod7Vp7E72wjGfuCb3/Q3cAqifbk12PfuQZ88Is9eu2q3szCcQMhAZMe3l0NNiw2SmQQ/l7SjAdWn+INR079KpdKsHDcQEQIHHsJ4QFIDA+AlaLx5IYzAJhmUI5Opc7gHK9fHi3hHYjdxb5z9RDuB06Wt8Bso/kIaGd1CKcnhCFULUd1mxEf/HIeNE2jka1DFOqDY5hjDrshPFTYCBtF8yqxcBefJZVI8MTVQ/DerWNw3fBIyKQSmKwU72z9LrUce/Pr8ccNZ7xOIXGl8ixu0uPw+Sa8e7DIaSPCqcH+MX8kJsWFwmil8NGvF/Dyzny33yGMLh8+34jdAkX0IQdDpKRZj2a9BSq5VFSPk7uDPv+tBG1GK4xWCnvy3Tc8MFgo0feerdHi7u9P4sNfzuNYSTNsFI3DDiq2ihYDshzUKjtzavG3HXm49es0vLm3EBWtRuw718B3bu5MCQIA0xLCoJRJUNFqdLmx4YxLDRsh/bWokTdmrBTNO22259SKVJS78+pA0zSfwn2x6fgcc1glsxCpBKK57sjMoZFQyiQobtKjkHXCCWvjVrcZ+XXSaqPwbWpZpxu4M1VteGpTNooaOjzWXZwYG4qHpzKG7adHivGdQ82sf+61ly/gSiX4wkNT4/DGjSMRoJCKurM+NWuIz5/lifGDQ/DAFHsqUohaAXkndcY4g/CHk5X43qHR028XmvgaaVvP2J0sNW329X0/6xTrKgUODtcglQwSiQTzWON4v4emJFqjlW+qEBOkxJ0TB4ne/5HNAKBoGp//VoJfihrx5t4CfHm0BH/ZkoOduXV4dG0mSpr0MFsppJY2i+a8xUbh3lWncP+qU05OU+556ao2bJhGwTcDu3nMAKf3HVk0cRCCVXLk1WqxZP1pt802OsNosWFrdk2XMit259WhvsOMTaerXZbMEcJt5t2p0bmxO1XRiiadGR3s8Z8tGofXbxwJuVTCP6N/FtRS5gOHajnCNUo8c20SAGBbdq2TTcbVKB87MBivXD/CSbHOcRd7T/xa1Ihnt569ZDWSO+NYcRPOVLXBYqNwsrQZBwsafFJNuWNvfj1qtSZ8l1reqbP96IUm7Mipw5t7Cz3+DkfON3Zaw9hspZBV2SayE945UIS39hXii9+KPfwlU0Ljx8wqLPnxDJ9+Hx8egIEhanx8+1je4b4t21lJ1FVcpapfNSQcarkUNe0mfPTrBY81rLvC4+uyUMMGI5OiA9FutOKB1adA0YyDilurN2Z1n5q2gl2XvLGZX5ufjElxodCZbXhq81lRZhBN0/zeTsrWen1yYzbec2HTXUpq2424Z+XJLpdvEgbEztV34I8bznRa572nEAY2XSkSOQeUUiaBTCoRZQ36QoRGiU/YtG0OYdAvKkiFsQODQYMpybc6oxJPbszG4vWn8Yf1p3m77GIahCbHBOHKIUwWwbPbcp2COI6BzBUnyrx2dguPO1DQ4HKtc5WSHxcWgKGRGthozw2TKJrGH9n9qWMJNSESiQS3sWtZRlkLNmYxDr6vT5RhyfrTXq83Qh9Li8Hi5HOhabtDeTyrko0MZFLzg1QynK5qd+mwFSqu8+s6cN+qk7hpeSrWnarCM1tz8NKOPNE4NDsE1A8XeQ5oGRwUphfLn64egqevGQqFTIKzNe1479B5XhTlqkxEkEouysjk4PYnhwob0aI3O5UOEPLnmUOw6fGpuF+Q4t+fIA5TB3YunY7J7EPzH3vOOdVIaWYnzD/mJyMmSImSJj2+OlbKK6m4qMMDU2IhAdOZ7KG1mUgrbeE7YYcG2BeVW8YwTr1jxc3494Ei3P39SbQaLNh3rgE/59U5OWocH25PsQ1CglRy3kH4/qHzWPRdhtNDpllv9qmxzGdHivHEhmzc8/1J/FrUeFGGwcECxmH5zNYcPL8tx6m4dWWLAfUdZthoRkHnChtF46E1mZj95XHsza9HSbOeVYoV8psW4YK/YMwAjBsUjImDQ0SpKI7HcUQFKjE9MQwAo34CmE1oZmVrl2uuvL2/EAtXpPHqgP/blY+iBh2eYJuLNbGLboRG4SRvl0gkeOuWUYgJUvI1STj1jS/cykZda9pNeHCN82a3q9A0zW/COVZnVEIviLQFdNLpOkglxz9vHgUJgJ/O1uJPm7LxzsEiAMBkdix84coh4QhVy1GnNeHz34px9AIz51w9KDjmjIjCB7eNxWI2hXF1RgUqWw0obLCrobjP8URRQwdf0wYArhseia/vnYi3bxkFlVyKwgadSOVro2he4RiuUeAPM+xq18zKNnx2pNhlqofwof3yzny+/AHA1EHl1CP1WhNeYR2vKbEheP3Gkfxxr984En9wUNduOl3ttiYUZ2TJpRJ8c99EXDMsEhIwkV536YJ3rczAFrZRzWs3JEMll6KsxYBfixpR025yaah7s7EJUsl5J8Sb+wr59HqAMRS40hC3jR+IhPAAGCwUlv54BmmlLahpM8JK0ZBLJU4pVKcq2vDkxmze6SyscXYxjIgO4hvfvDR3OLYvmY7Nj0/zqEgIVssxi3XecWuRYzOO1/cUoKCuA9vO1mL572V4cmM2mnRm1GtNSCttcVqv3z5QxDyP1mTyQQl3Tp6/XDOUbz61/PdSnBCoHoSdTpOiNI5/6hW3jB2ANQ9NxvhBIYgPU+OTO8bi4Wnxnf+hjwhVdqFeRPKvHxmNq4dGwGSl+NTmsQODcXfKYDw4JQ6L2TlztqadN8orBaV5tp6p8UpF6Q6uEduwKA22LJ7Gb9y48iyHihpdNvCzUTQeX5eFv+9i5nt0kAqzhkXyjRsAJnPi6xNlomfvocJGrBLU36rvMOP+1adw9WfH8JctOfhCUPu6qEGH6jYjqtqMTg2oOMdEfLjrIMNbt4zC/+6dgLtTBnf6GyRFBuKb+ydiYLAKZS0GvLIr3+3zl7vHdWark22zJqMS7xwowl+2eC6VUNqsx578Ot6JtDq9Av85Vsq/v3jdaezKrXV7Dlwwxl297riwAIwbFAwbzdhU3PcIHQLcRvLnvHp+fReWpgGY1PzIQCUadWbscLDtuEZOowd6roc2MTYU7906Bmq5FGllrfjTxuxusQcoduN70IuAQXmLAc9vy8WSH8/gyk+O4a7lJ/DyznynGq5dQegk3dRJGrtwg/zkxmxsOVPtdA8dL2nGC9vzsGT9aY8NmL5PL8cfN5zBZ2wGB03TfFfidaeq+GwMx3NdnV6BXwUbbW7+coEoiUSCB6Ywwaujxc2Y/vFRbD1Tja3ZNdiRU9tlm9xVSr5aIePn54asatz+TTr+50V6qtFi6/Q8mnRmPvAHABv+eCViQ9V8sD0qUIl7JjHfnV7eelFrKAdN03j+J6a5UbMXQgGNUoZP7xiH8YNCoDPbRLXt9RYbuNje9iXTcefEQZBKgM1navhxvhxsO1uLkmY9Pjlc7LbppStOVbTivYNFfEmkdxaMxoBgFUqbDfjPscvb36CiWe/V+Aqv75Mjxfjwl/Oi+c3tiRxTh7tCUmQgrhvuOsgEAAvYbLYvj5aI1OvNegtWpjEB5YtJyZdKJPjo9rG4cXQMbBSNt/YV8sGT8w06PMmWC5BJGefwyfJW3LfqFLZl13SqYBeW8WsxWHDXdyfxMysO4LC4SMkHgIXsda85WSnKxhJS3KRHA2tPdtZ0KzFCg/mjomGjgQ9+OY9ntuZgxfEynKlux1v7Cr2qzX/eIajsWD+0vMWANqMVSplE1HAyKTIQD7Jr6cq0cqfr4QKzt4yJweS4UFG5LgD4pagRL/yUy9sBjg7eH05VispEOOLJEdkVZFIJHp4Wj9UPTebFISvTKkDTNExsMEHlhRN/SnwY4sKYskH3r87Eetb/4uo8FTKpqFFxf4M4TB0IVsvx6aJxiAlSoqbdhA9/Oc8buDRN86mjU+LD8O8FowEw6am3rEjDv/YV8hHUsQOD8dbNoxCqluNCox7LttjrsIQK6sUtGDsQb98yCg9OiXNucLOnAHevPCnarHIL0gNTYnHsmZmYk2wvuvvS3OF45tokRGgUqGk34Z/7CnmjcfvZGixYkYY7v8twcsK0Gy0uI+icerOy1Yi/7cjD7C+P81EhXzla3Cz6932rTuGroyX8ZwlrG27KqsKnh4uRXd0OSvAwKGroEB2nkEl4RxtntAijfGEaBVY+MAnf3J+C/7t+hOh8HGtjcjzOOq22nKnBCz/lYtF3GXhiQzbePVjkswJCa7Rid149bDRTO+WlHXm8sstsY2q5cI4Ld2qz0QOCsfbhybhiSDg0ChnmCcbbWxIjNLxKob7DjPtWncJTm7LxxIYzLgt6V7YasDOnFvl1Wnx5tASfHyl2+SDLZFNQ1XIp1j40GXKpBBnlrfiYrV+pkku9coBdNTQCL84dDqVMglMVbbxxe/cU3x0oaoWM32CsO1XF1wT1lP7Mcc+kWAQqZShq0OGObzOQKahl/ObeArx3sAinHeoXAsCu3Fo89kMW3tpXCIBJtZw/KhrPXpeElLhQ3DAqhv/939pXyKuxOkxWcJ8UopZjWmIYH0ABGEfZPd+fxB/WnxaprmtcKK5em5+MsAAFKlqN+PTwBVhtFN7YW8A3sLtmWCRmsR2r5VIJJsaG4Mmrh+DtW0ZhZlIEZFIJatpNeH3POeTUtDs9+IVqp4mxofjo9rG4lb0m7p5+86aRmD8qmlcnctNl6ZUJuHX8QDw6PR5BKhlvfHL8/MQMzEuOwuAQFV9gvzMenR6PYJWcKYGwLQcv78yH2Urhf8dLcaFRjwiNAo/PiMdfZw9DsEqOogYdlm05i8fWZQFgmqr9be5w/vP+dPUQqORSZFa28U7vlLgQl9/dFd67dTQ+vG0M7pw4CIND1V6lszw2nbn/9+bXY/nvpSJ1vkwqQWZlGx5amylKG75xeSpuWZGGZVvOYsXxMlE6P7fuU7S9gZA7BbhEIsHjMxJwy9gBoAE8vSUHL/yUi1+KGnl1cnSQkq+d3RUSIzT47oEUbFk8DTOTIn1WznvD0EgN/nXzKLx9yyivNlYKmRQf3zEWy2YN5V9TsZ1Gn70uCX+6egi/iXhzbwGOlzSL0uO0JiseXJOJP244g23ZNU5Otp05tXhiwxl8+Mt5/JxXh0OFDSIF42m2xtgd4weJmieNHhCMK4aEw0bReGRtFv60KRt5tVpsP1uDB1afwt935aNM8AwPUcsRoJBh8+JpSH1uFqYnhMFkpbDieBkeWG3vljsyJkjUuf6aYZGi59wPpyr5FMFzAkft/46X4usTZXwDIU6N5UphCjAbwslxYV5vKpMiA/H1fRMRqJQhr1aLb1PLndbd9w+dx1WfHsMDq0/hui+O49av00XPsw1ZjOGfX9eBL4+WoLRJ7/QZNE3j5Z15+MfPBbyt98VRsQOhUWfGm3sL8fqeAlS3OTeu6/Cg1ubg7ApOxS8B46DhmBQbijEDg2GyUnh4TSZe3J7L11LnMgMUMim/Jnx1tJS3hSiaxjG2WZqwBro75oyIwrf3pyA8QIHCBh3e3FvgcaOaWdmK9ZlVeGNvAeZ8eRyv7MzHL0WNonv7REkLVhwvwyu78vHi9lyPdbtPlDRD+AtyKYKrMypdZnUJ2XqmGg+tycS6U5Uulb/C712VXoGf8+rcKoyF86WoQYd3D57HghVpoqDmT2cZx3SLwYLX95zD+UYdP/5WG8UHGLmgw7pTVdhyptqpUdO9q07i6S1n+SyANoMFf96UjS+OluCsIEuCawg2XjCOQyM1mMUqkAHgnYPnefXqd2nO88IbeIepg4PkL9cMxWeLxmH0gCAYLBS+SS3HM1tz3DrVz1a3Y+5Xx/H8T7ke76EiQfB5anwoooNV+PxOu6qvw2RDbGgAb9++ubfgotXP3F7MFzRKGf42l6kpvO9cA74+wTxDOeeKXCrBgGAVXp43gncuv3OgyGM5I2+p05o6VdML339jTwG0RivO1Wnx3LYcl6UM6rQmPLctB09uzMZmQZBrSnwo3mH3rztz6vBjZpVo/FoNFnx9ogxfHS3BqYrWbslY5AQWcz86ggdXZ3rMljBYbPxcmJEYBhtFY0NWNR5cfQplzcw6buJqLV6Eo1LIy/NGYGiEBve4COrdOm4gbhgZDVdbQK4Mz8U4TAFmfX/zppGYPSIKFM3sFc5UteGpzfbaqkmRGqx6YBKGRWnQrLfg3weKcNvXadiXX+82oMftpx6dHo+hERp2LSvAmoxKvvwftxY6ColuHz8IoWo5ylsMWPh1Gt7aV4Cihg4cK27i57SwHvaVQ8LRGW/dPAovzB4GpUwiEpgcK27G3K+OY+5Xx/Hcthzc/L9UPLQmEyvTyvn9T4fJypcV48zFtRkV+PAXRl1J0TS/no4aEOwkSlg0kbme4iY9/rwpW+SA5wJoE2JD8dmicbzI6o0bR2Ltw5MRqJQht1aLb08way4n+EiK1CBIxewbb/5fKv61vxDrM6twvkEnGhN7M6XudbsNjwrEj49N4fcvC1ak8WXk1F58l1wqwSe3j0NcmBpNOjPfw6GflSf1CgndWwteXiIaG7WgaSZd568/5YIGkBjOSNMDFDLe4D369NVQK2R4e38hb1QJ+WzROFw1NILvvC5MV92xdLrLdM+9+fV8Kv60hDCcq+uA1mSFBIxTiUmRZybCs9cm4UHWMeGI0WLD8t/L8AObthGkkok6syplEswaFokhERrIJBJsPlONZr0FMxLDkBiuQYhaDpVciq+OlUImAe6bHMd/FtjfY1CoGsEqOYJUMvb/5fz/C6NUEjDO0K9PMFG4V+YNx9HiZhxjHagjY4IwakAQylsMLmtFxgSrcNWQcMQEqVDUqMOvgtp9n94xDnqLjY/KA8B7t47h07IdoWkam05XI1yj5Ot/uOKfewuw00Xjp2FRGsxIDEeQUg6NUoYApQyBChkUMgloANxMotnvOny+ia9b1hlXDQ3HZ4vGezyGU8Z1lYzyFry4PU9UQFwmleDaYZEYGKJCsEqOELUca09WOhmbg0JUuHJIBILYMQ9UynCwoAFZVe24c+IgvDxvBL5PK8dXAmVOhEaBfX+60uvzq2034n/Hy5Bbo8XCcQPw/M1j+PnoCzaKxubT1Ugra+Ed9X+ZNRSPTO/cAXuyvBXfppYhq7INNpr5feJC1aLN1diBwZiRGIaoIBWUMgk+PVLMzy8JgPWPTsEwh1RlvdmGR3/IRGmzAXKpBEMjNWjRW9CoM0Mtl+LoMzNF5/9rUSN+zqvD8dIW2CgagUoZZiZFYFhUIFalV0BntmFecjQOFjZgaIQG6x+dgl+KGvm5IJdKRJHnnUunY2CIGjqzFe1Gq9P6c+R8I17cnsdvZBUyCUbFBGHcoBDEhqpRqzVh7clKJIYHYPPiaQCYTd9Tm8/ym8Nfl13Fq6beOVCErdk1uHXcALx6Q7JTXcM1GRX4/LcSTBgcgm/vT+Ffl0iAqKhgr8a9VW/BqowK/JhZBStFI0Qt51VZH942BtcOZ9aBVoMF35wow6bT1bzBOzc5Cv9eMBpf/FaCELUcj89IQE27Ef8+UITU0haEquXY/cQVF20EXywf/XrBScV93fBILJs1FF+fKMO+c/b1ZWCwijd2hK+ZbZRIYRMfpkYFW8rl+wcnYawHVZrObMVHv1zA7rw60WYhKlCJPU9ecTGX5oQvY385+Pp4GVacKMPf5g4XKSMbdUyTtQYHNcVz1yUhp0aLXwobeDXSiOhATI0PQ4BSBrVcipVp5aImVwCz0Ro/KBjBagVOlDTDaKXww8OTkRwjDh7Ua014/9B5HC1ucrlxE/Lo9HiR05eiaRwsaMBXx0r5TffNY2Lw5k2jUKc14T/HSnDnxMGYMDgEx0uasSajAicFASOuhmpnSq1VD07im1l6i6dx33KmGu8eZAICowcEISkqEJEaBULUCvz391KnIKacrUMrl0qcSoQAzH07KS4UwSo5ooOUTA1oQckJhUzCpydGBynx46NTsC27Fv89VsKPaVyYGkMiNIgKVEIll2IDm0K85qFJGDXA/bXvyKnF6vQKlLUYMDhEhe1LZ4jeL6zvwOPrspyULe8sGM0r6i02Cn/ccAY5bJfu+DA11ApmsxakkmHPE1d4rbjKqmzDnzZlw0bRiNAoMGZgMCI0CoRrlIjQKPjA/rsHi3g1l5AhEQGI0CjZ0jfOQffwAAWGRmqY/0VoeMfot6nlKG8xYOLgELxy/QhMHzkAj36Tyj+rB4UwDS0Gh6gxKFSN6EAl5GwJFsfyILGhaqTEhWJEVCCCVXKsOFGGOq0JMqlEdG+EquUYPzgEKbGhCFLJoJBJsfZkJUqa9Hhh9jBYKRpbzlSjotXI29vRQUrszKmFw3AgQqPAyJggZFe3w2CxISE8AGXNBriakkEqGUYPCObriEsAXDk0HA0dZqd6x9/dnwKd2YpmvQVzRkSJxrGm3Yj/25WPszVayKWMvcld3/hBIRgzMAgqObPGqBVShKoVkMskkEoYUYFEwgTBpBLmHP6+Kx82Gtj9xxmIcZFJQdM0DhY24l/7CqG32KBRyJAQHoCBISoMDFFjUIgKbQYL9hc08GXBBgSrMCkuFJEaJQwWG6rbjBgRHYioICVyarQ4UNCAgcEqrHpoEkYmRqKxUYt9+fX4x54CPHFVIh6fkYA6rQkPrclEq4EpI5QQHoDYUDUGh6oxKESNsAAFpBK74OFCow46sw2BShkClXIEKmXQKGWQSyXIrdVidQazb3n6mqE+ZTCsTCvnVeZxYWqEqBXIq9WKbNo2gwWP/JCF6jYjglQyTBwcigiNAhGBSnYeKaCQSpFf14Hs6jYMjwrEkAgNIySQSSCXSvmxLG/RY2VaBSw2ClcMCcfYgcEIUMigksvQpDcjRCVHsFqOb0+UodqDI/iKxHAMjdQgNEAOs412mi8cac/PglQi4e00gPlNuTJgR843ie7nQKUMU+LDMGFwCILZ+VPbbkJhQwcSIzSI0CiglkvRbrSixWDBwBA11HIpZBJGESmVMnVfjwmEMwDTFC8+TA21XIZz9R2IClRieHQgjBYKqzMqEKCQ4shfrkZ6eSvePVjE32sRGgUSwwOQVdWO+DA1tv5hutdjezGcqmjFmoxKTE8MwwNT4nDkfBNWppUjt1aLu1MGi4LwXaW23YhH1mbx4iwh/3f9CNw+YRDMVgprT1bif8dLeVuAmy+J4RoMiQhAbJgaCqkUnx4pRqPOjG/um4gxA4Ox4ngZr8ocMzAYOpOV399Mig3BivtSRN+ZW6vFq7vz+d9eyOBQNaw2CvUdZoSo5Vj90CTEhjoHTV09439jfSz8Z4Wo3N7bKrkUVw2NQH6tFrVaE0LVcqy4byJWplVgr6CU2JCIAL7J50NT4/gyNkKKGjrw1KazaDFYEKCQYlhUIBIjNHxpsy/vHI8ZQ8JR225EWbMBM1gn8NbsGrxzgMl+HD0gCFIJs8bMTY7CTaNj8N6h80424cBgFZKiNBgYrMbJilaUtxjw2vxkPuuzO1mfWYWPBU3zxg8Kxsd3jPO654nFRmHLmRq+8d4frkjg++xcDNzY9wWIw9QB4YTek1+Hdw+cF6W8AowhuP/PzEPTYqOwK7cOVW1G5NZqkVvTDqVMio2PT+VVg2YrhR05tZBKJQgRpJO6Yv85JiXr/smxMFgofP5bMZ/WCjBRlTkjovDS3BEe04wpmlEwfp9WDhvNGEn3TBqMogad2y6QrhgVE4Q1D0/Gvvx6bDlTjZxarajunC8MjwrEmocmQS6T4qdsZmI6GuJ/nT0MMUFK/FLUiGPFzSLnHsez1ybhzomDeINyZ04t3tpXCBrA6ocmYbSHTYs3mNk6kseKm/Dg1DiEqhV4/5DzfeAtS65gnDHhGkaRdcWQcGw+XY3/HS/lN8+OG9xLhcFiw9ELTThe0oyDhY1uU7CDVXJQguZNntj02FQMidSApmkcKGjAkfNNyK5ux/Ujo/G0iweWN3SX8+RcnRa7cuvw2IwEnzp6txksSC9vRXyYGsOiApFR3oqDBQ3YX9Dg8jcLUcuhN9twx4RBbo2meq0JL+/MFylKACZosPZh113Bq9oM+Puuc05djaUS4NBTVyGzsg0jogN5B+hP2TX47De7AzcmSIk3bxrFN3TwxOGiRmzPqUVOjdZt/beU2BB8LTCo9GYbPjl8AQOCVXzTNIDZcJW3GJAQ7twEhnv/VEUb4sMDRKnvXRn39LIW/IPtqAoAt44bgNfmj3Q67kxVG745UY7Usha8Mm84Fk10VhJYbRQ2nq7G6AHBfH3pnsRKMUGeYxeacKqiFTYaWHxFAv7EGjK/FjVib349ll6ViGGRGuTVapFe3ooOkxXrM6uc1uqrhobj0zvG4XRVO5r1ZswZEeVVY6uSJj3WZ1bidGU7Spr1uG9yLN/du7vwN4cpwDRhigxUOv1GZc16fJtajjNVbahuN2FYlAYr7p2IELUCte1G7Mqtw+qMCifnKMAoEqYnhiOnph0lTXqnNTZULce+P13pNguitt2ITw4X8+r5EdGBGDswGJVtRtw9cRDSylrx6PR4pzI0ABNkeGV3Pk6Wt+LNm0Z6rClqtlL498EiUY1kAHj/1jFoM1hwvLQFx0ua+fXwhpHR+Ncto3xulNbZuK87VYkvj5a4tDtGDwjCrKRIFDZ0oE5rEpWLAID5o6IxYXAIDhU2IrdW6/Z5BzBOAW4srhseifdvHcNfy+GiRnxy+ILbDV1SpAY/PDKl04AmTdM4W6NFZKDC5cbyVEUrvkstZzayZhua9Wa8ekOyKH2/RW/GP34uQKpDE8fHZ8TjzzN9syGOnG/Cx7+e9+iEETI9IQwjY4KwNbvGpW3w4JQ4nK5qc5m5IkQmleDHR6dgaKQGUVHBqKhuxRt7C5xqcLsiUCnD0EgN8mu1Ts5Mjg2PTcGmLCZoWuFioy9k7UOTMXJAEHO/HyjE7jyx8m1echRuGjMAm7KqkVXV5vYeSggPwHXDI3G8pIVX/y6aMAivXD8CFS0GrEqvwHZBKYXwAAVev3EkVmVUIFApw8e3j+107lgpGhRFQyGTYO3JSrajc9cXywN/vtLjhvpMVRv+b/c5p3IwjkgAlw5jR568OhFLrkwUzXe92SZSWxfWd+CVXfmd1o31lq4+qzZkVuHz34pFAYwxA4Ox6sFJ/H9XtBjw0s68i2725yv/unkUvjpW4pWKNlApw3cPpEAtl+Gt/YUYHhXI/x5WG4U1Jyvx09laJ3VriFqOaQlhyKxoc+m86wpKmQQv3zQaBVWtfKDJE0MjNdj42FQATIr+Kzvz+ewYDseg++WGs3VjwwK6LUumsL4D/9pfiPy6DgyN0GD5vRMQrJI7KUCPlzRjR04tjhU3e3y2AcDWxdMQHx4AmqaxKr0Cy4+X8UEXlVyKeclReGxGgsuUa4qmcbqqDetPVbkMRALAR7eP5WvAO+LuGb8nvw4/nKzCc9clISU2FCcrmH3WgYIGGK0UllyRgBOlLaIsniCVDP+9ewJGDQiGjaJx9EITjhU342Bhg+iZ9OFtY3GtmzILTHPesy6fe+4EbYBrW+TBKXF49rokWCkaqaXN+I09H0fnKYcnUdfFkl7WguImPa4cEo7ELqbOn6vTYmt2DR6bnuDShvQV4jDtwzhO6HajBWllrdAaLegw2aAzWzE9MZzvSu+IlaIhgfuU765Q0qTH7yXNsNoo3DAqxqebuLrNiKMXmjA8OhBT4sNA0zTO1Xfg2IVmtBgsMFhsSIrUYFpCGLKq2tGqN6PNyKjQDBYb7p00GFcMsacDNevNOFutRYfJCi37vw72f1qTDVqTFTY2fYNTXYYFKBAWoMATVyeKul236M04WtyMeq0JzXoLpBLgyauH8JsDi41CUbsZh85WQ2e2wWSloFHI8OdZQ5zS39oMjFrPUdnXXbQZLPi1qBFlLQYYLDbozDYYzDboLDb+esFG89l/QgJg1rBI3D851qUhbKNoVLcZ0aQzY/TA4B5Rs+XWanGqvBVtRgva2XE32ygsnpGAsYOCYbRQkEqYaGBxkx56MzMHdGbmN5ieEHZJag/6o/MEYGpxHSpsRFFDB/9b0TQTzZwYyzTk8rTpoWkapc0GVLcZ0WIwQymTYszAYLeprABjrGRVtiGjvBWNHUxR7inxoS4dftzxNe1G1LabMHpAsGgz4g00TaOqzYizNe3IqdaiWW+BlWLu8btTBmN6YudpN12lq+NusNjwe3EzlHIpZiZFONUD5qBpGm1Gq9dRV39Cb7bhbHU7UuJCvVormLpxHYjQKKE329BmsGB6YrjP94MjFhvV5QYLnvDXOd8ZRovNpaqv1WDBL0WNqGxhOukarTZQNFNOZ0Q0ox7lGhSUtxhgsFBoM1gwLSEMKV446/PrtMiqbMOcEVEY6EODMpqm0Wawegy4CuHStBo7TJDLpLhqSDi/xrUaLKhuM0JrtGJKfGinTbVc4c24N3aYcKK0BU06M1oMFjTrLTCYbXj8igReIU3TNPJqtciv60CLwYJIjQK3jhvIn5PFRiG9rBV5tVoYrRTajRYYrRRomsbjMxKQGKFBTZsRtVojRg8IFjkpObRGK05XtaFBZ0ZThxmNOjOMVhuWXpnocQ2/FLToGZXi+UYdBoeoce3wSJ+d1QD7u5S3okFr4n/bFtYOtFE0lDIpnpo1BMEqOQYEqyCRSNBqsOB4STPkbE29xg4zIgLtmTsGiw1lzXoUN+lR0qRHabMeBgtz/6vkUjwwJRbTEsKdxr6hw4SqViOq242oaTeips2EJr0ZNooGRdNIigzEM9cmQSaVoMPEjEVujRalzQYYrTZYKRrjBgbjCYEyps1gQVWbEWllLex5UDBbKZhtFJIiNfjr7GGi3+18gw6nKhibyEYDD06JRQirtjVbKeTWanGuvgOjYoIQH6ZGYYMOpc16TE8M52uPthstOFujxcTBIaL7qKRJjxOlzahoMeDuSYORFHlx9mq91oTD55vQqDPBZGVKBOjNNrQbrbBSFCjanvFE0+z/gynLkhIbgr9c03lA22KjUNqsR027CbXtJtRpjahpNyFAwdguM5MiEaqW40RpCypbDWjSWaAzM1ks9R0mdJisMFooqORSPHddEqKDVZ3OdytFo7xFj+o2I6rbmFT16nYjOkxWUDQNigYoikZYgAIJ4QHQsza5zsTYphR7vQFKGV6cM1xU3sQXmnRmnG/QocXA2MczEsOcHBE2ikZmZSuqWo1oMVjQpDOjWW9Bq8ECG0VDKpVgRFQgJBImRd5spWClaOZ/NgqQSBCskiM+LACzR0Qiu7odFWz3daOFQjAbjNebbbBSFCbHhWHxFQmgaUbxX95iQGJEADpMNhwrbkKTzoI2gwUWikKoWoFrh0e63a9y0DSN+g4zLjTqcKGRqVW9aOIgjIgOAkXTOFfXgYzyVpxv1MFgtsHM7nlGRAdCZ7ahw2SFycrYwgNDVGjSmWGx0bDRzHXaKBrBKjmWXpWAK0cPQmOjFqVNepypakeDzoSGDjPCAhSQSSWo15r4v7llzABe4cfRojcjr7YDNe1GtButuGZYJIZHX5p9X0/C2eFRgcpOswasFI2aNiNKm/UoazGgtFmPunYTrDQTYBkZE4TnrksSrXN1WhOOnG+EWi7DnOQol887VxTUdcBgsSEuTI3KViNqtEbIJBJcPzLa7fPHV9vOaGHW8iCVHDRNI6dGi9TSFjTrzbhjwiCn7BuAWXOPl7Qgr1aLgSEq3Dsp1qMfxkoxpYQuNDLrN0XTiAsL6LQxZWOHCcdLW9Cit8Bio3D7hEEuxThaoxV5tVrUsutlrdYEhVSCv84e1i11d3sLxGHah+ltm7W+TG/dQBMuHjL2/RMy7v0XMvb9EzLu/Rcy9v0PMub9FzL2/RMy7v2XvuQw7RNNn7Kzs7F06VJMnToVKSkpuOeee/Dzzz/39GkRCAQCgUAgEAgEAoFAIBAIhF6GdxpsPyY1NRVLliyBUqnELbfcgsDAQOzfvx/PPfccamtrsXjx4p4+RQKBQCAQCAQCgUAgEAgEAoHQS+jVDlOr1YrXXnsNEokEP/zwA0aPHg0AeOqpp3DXXXfh448/xvz58xEbG9vDZ0ogEAgEAoFAIBAIBAKBQCAQegO9OiU/NTUV5eXlWLBgAe8sBYDg4GA8+eSTsFgs2LZtWw+eIYFAIBAIBAKBQCAQCAQCgUDoTfRqh2l6ejoAYObMmU7vca9lZGRc1nMiEAgEAoFAIBAIBAKBQCAQCL2XXp2SX1paCgBITEx0ei86OhoajQZlZWU+faZE0h1nRugOuLEgY9L/IGPfPyHj3n8hY98/IePefyFj3/8gY95/IWPfPyHj3n/pS2Peqx2mHR0dAJgUfFcEBQVBq9X69JmRka4/i9BzkDHpv5Cx75+Qce+/kLHvn5Bx77+Qse9/kDHvv5Cx75+QcSf0Znp1Sj6BQCAQCAQCgUAgEAgEAoFAIHQnvdphGhQUBABuVaQdHR1u1acEAoFAIBAIBAKBQCAQCAQCgeBIr3aYDhkyBABc1iltaGiAXq93Wd+UQCAQCAQCgUAgEAgEAoFAIBBc0asdptOmTQMAHDt2zOk97jXuGAKBQCAQCAQCgUAgEAgEAoFA6Ixe7TC98sorER8fj127diE/P59/XavVYvny5VAoFLj99tt77gQJBAKBQCAQCAQCgUAgEAgEQq9CQtM03dMncTGkpqZiyZIlUCqVuOWWWxAYGIj9+/ejqqoKL730EhYvXtzTp0ggEAgEAoFAIBAIBAKBQCAQegm93mEKANnZ2fj888+RlZUFq9WK5ORkPP7447j55pt7+tQIBAKBQCAQCAQCgUAgEAgEQi+iTzhMCQQCgUAgEAgEAoFAIBAIBAKhO+jVNUwJBAKBQCAQCAQCgUAgEAgEAqE7IQ5TAoFAIBAIBAKBQCAQCAQCgUBgIQ5TAoFAIBAIBAKBQCAQCAQCgUBgkff0CRB6nrq6OuzZswe//fYbiouL0djYiNDQUEyePBlLlizBxIkTnf6mo6MDX3zxBfbv34+GhgbExMRg/vz5WLZsGQIDA0XHNjU1YfPmzcjNzUVOTg6qqqoAAAUFBV6f44oVK/DRRx8BADZs2ICUlBSfrtFsNmPFihXYsWMHampqEBoaitmzZ+PZZ59FZGSk6NiXX34Z27Zt8/h5Tz/9NJ566imfzsEf8cexnzNnDn+cI9OnT8eaNWt8ukaKovDDDz9g48aNKCsrg0ajwVVXXYXnnnsO8fHxTsevWbMGeXl5yMnJwYULF2Cz2bB69WrMmDHDp+/1V8iYi8f8iy++wJdffunx8+688078+9//9ukc/BV/HH+OAwcOYN26dcjLy4Ner0d0dDRSUlLw4osvYtCgQV5foy/rvcFgwPr165Gbm4vc3FyUlpaCpmkcOnQIcXFxXn+nv0PGnTzn/WXst27dildeecXjOV9xxRVYtWqV19dI5rwYMuZkvvvL2AMATdM4cOAA1qxZg5KSEmi1WgwcOBAzZszA0qVLXdriniDzXQwZ8/473wH/HH+KorBu3Tps2bIFxcXFkMlkGD16NBYvXoy5c+f6fI09PedJ0ycCPvzwQ3z99ddISEjA9OnTERERgbKyMhw8eBA0TeOjjz7CzTffzB+v1+vxwAMPID8/HzNnzsTo0aORn5+PY8eOYfz48fjhhx+gUqn449PS0vDII49AIpEgMTERdXV1MBgMXjtMCwsLceedd0Iul0Ov1/vsMKUoCkuXLsWxY8eQkpKCadOmoaysDAcOHEBcXBw2btyIiIgI/viDBw8iPz/f5Wd999130Ov12LRpEyZMmOD1Ofgr/jj2c+bMQXt7Ox599FGn92JjY7Fo0SKfrvHVV1/Fpk2bMGLECFx77bWor6/Hnj17EBgYiA0bNmDIkCGi40eOHAkAiI6OBgA0NDT0KYcpGXPxmKelpSE9Pd3l52zatAl1dXX45JNPRL9Jb8Yfx5+mabz++uvYsGEDEhISMHPmTAQGBqK+vh4ZGRn44IMPMHXqVK+uz9f1vrKykjfeYmNjodPp0Nra2qc2UwAZd/Kc95+xz8/Px8GDB12+t2/fPhQVFeGFF17A0qVLvbo+MuedIWNO5ru/jD0AvPvuu1i5ciWio6Mxd+5cBAUF4dy5c/j999+h0Wjw448/Ijk52avrI/PdGTLm/Xe+A/43/jRN45lnnsG+ffuQkJCAa665BmazGYcOHUJTUxNee+01PPTQQ15fn1/MeZrQ79m3bx+dlpbm9HpGRgY9duxYetq0abTJZOJf/+yzz+jk5GT6gw8+EB3/wQcf0MnJyfTy5ctFrzc0NNDp6em0VqulaZqm58+fTycnJ3t1bmazmb7jjjvou+++m37hhRfo5ORkOisry6fr27x5M52cnEw///zzNEVR/Ovr1q2jk5OT6ddee82rzzl79iydnJxML1iwwKfv92f8cexnz55Nz549u6uXJOLEiRN0cnIy/eCDD4qu4/Dhw3RycjK9ePFip7/59ddf6fr6epqmafq1116jk5OT6dTU1G45H3+AjLnzmLuioaGBHjNmDD19+nTR5/R2/HH8v//+ezo5OZl+4403aKvV6vS+xWLx+vp8Xe87OjroY8eO0S0tLTRN0/TixYvp5ORkuqKiwuvv7A2QcSfPeUf8wcYTYjKZ6OnTp9NjxoyhGxoavP47MuedIWNO5rsjPTX29fX19KhRo+jZs2fT7e3tovdWrlxJJycn0y+//LLX10fmuzNkzPvvfKdp/xv/PXv20MnJyfR9991HGwwG/vWmpiZ69uzZ9Lhx43yaf/4w54nDlOAR7ibLzs6maZqmKYqiZ86cSaekpNA6nU50rE6no1NSUui5c+d6/ExfDKvPP/+cHjduHF1UVES/9NJLXXKY3nvvvXRycjJdWVkpep2iKHru3Ll0SkqKaEK74/XXX6eTk5Pp77//3qfv76301Nh3p/Ps+eefp5OTk+n09HSn9x566CE6OTmZrqqqcvv3fdFh6gky5nZWrFhBJycn02+//Xa3nFdvoCfG32Aw0NOmTaPnzp3rk4PMHRe73vfFzVRnkHG3Q57zl9fGE7J79246OTmZ/vOf/+zT35E57xtkzO2Q+X7pxz4rK4t3djhSUlJCJycn00888YTX10Dmu2+QMbfT3+Y7TffM+HMCt8OHDzu9xwXKP/vsM6+vwR/mPGn6RPCIXC4X/X9paSnq6+sxefJkaDQa0bEajQaTJ09GRUUFampqLvq7c3NzsXz5cixbtgzDhw/v0meYTCacOXMGQ4cORWxsrOg9iUSCq666Cnq9Hjk5OR4/x2g0YteuXVAqlbjtttu6dC69jZ4ce7PZjK1bt2L58uVYu3Ytzpw506XPSUtL48/NkVmzZgGA23Ts/ggZczubN28GANx9991dOo/eSE+M/7Fjx9DW1oZ58+aBoijs378fK1aswPr161FWVubTZ3XXet/fIOPOQJ7zl3fNd6Qray6Z875DxpyBzPfLM/aJiYlQKBTIyspCR0eH6L3Dhw8DYOrXegOZ775DxpyhP853oGfGv7GxEQBcpr9zr6Wmpnr1Wf4y50nTJ4Jbqqurcfz4cURHR/N1RriNjGPdR44hQ4bg2LFjKC0t9alZgyNmsxkvvfQSRo0ahSVLlnT5c8rLy0FRlMfzBZgFxFOttL1790Kr1eKWW25BWFhYl8+nt9CTYw8wdUMdGwSMHz8eH3/8MRISErz6DL1ej4aGBiQnJ0Mmkzm9n5iYCAA+b877KmTM7Zw8eRKlpaVISUnBiBEjvLyC3k1PjX9ubi4AQCqVYuHChSgtLeXfk0qleOyxx/DSSy959Vndtd73J8i42yHP+cu75gupqqrCiRMnMHDgQD6w5Q1kzvsGGXM7ZL5fnrEPDw/HCy+8gHfffRc33nijqJ5lWloaHnjgAa/rGZL57htkzO30t/kO9Oz4A0wt0WHDhoneq6ysBACRzecJf5nzxGFKcInFYsHf/vY3mM1mvPDCC7zzQavVAgCCgoJc/h33umNEyVc+++wzlJaWYuvWrS4dH97SXee7ZcsWAP1DbdbTY79o0SJMmTIFycnJ0Gg0KC0txcqVK7F9+3Y89thj2LFjh9tzEOLt+XLH9WfImIvhVC933XWXL5fRa+nJ8W9qagIAfP/99xgzZgw2bdqEYcOGIT8/H6+99hq+++47xMfH44EHHuj0sy7X/dpXIOMuhjzne24Obd26FRRF4Y477vDJ5iNz3nvImIsh8/3yjf1jjz2GmJgYvPrqq/jxxx/516dMmYIFCxbw6rfOIPPde8iYi+lP8x3o2fG/5pprsHv3bqxYsQJXXHEF30CqpaUFq1atAgC0t7d79Vn+MueJw5TgBEVRePnll5GRkYF77rkHt99++2X9/qysLHz33XdYtmyZVx30XHXDmz59erd1NS8rK0NGRgbi4uK8TiHorfT02APAsmXLRP89evRovP/++wCA7du3Y9OmTXj88ccBuO5wPnr0aMybN+/ynGwfgIy5mI6ODuzduxcajUbUVbKv0tPjT9M0AEChUOCrr77CgAEDAABTp07FZ599httuuw0rV67kHWeXer3vL5BxF0Oe8z17Plu3boVEIsGdd97p9D6Z8xcPGXMxZL5fXr788kssX74cTz/9NG699VYEBwcjPz8f77zzDh555BF8/vnnfFdrMt8vHjLmYvrTfAd6fvwXLFiArVu3Ii0tDQsXLsSsWbNgsVhw6NAhREZGAmAyiTh6w5wnDlOCCIqi8Pe//x27du3CrbfeijfffFP0fnBwMAD3nnzudW/UYK6wWq14+eWXMXLkSPzxj3/06m8OHjyIbdu2iV5btmwZZsyY0S3nu2XLFtA0jTvvvBMSicSrc+qN9PTYd8a9996L7du3IzMzk3eepaen48svvxQdd8cdd2DevHleny93XH+EjLkzu3fvhsFgwF133YXAwMDuuAy/xR/Gn/vbcePG8U4zjuTkZMTHx6OsrAzt7e0ICQm55Ot9f4CMuzPkOc/QE3Po+PHjqK6uxpVXXon4+Hin98mcvzjImDtD5jvD5Rj748eP44svvsBjjz0m2tdNnToVy5cvx7x58/Dee++JnGdkvncdMubO9Jf5DvjH+MvlcnzzzTdYsWIFdu7ciQ0bNiA4OBjXX389Fi9ejPnz5/OOU6B3zHniMCXwUBSFV155BT/99BMWLFiAd999VxQBAOw1AN3VnuBed1drojP0ej3/GePGjXN5zL333gsA+OqrrzBv3jy8++67ePfdd10eGx8fD6lU2uXztdls2LZtG2QymcsoeF/BH8a+M7iaKHq9nn/tL3/5C/7yl7+4PF6j0SA6OhqVlZWw2WxOKV9cHRfuuvobZMxds2nTJgB9P23HX8Y/KSkJgHsnNve60WhESEjIJV3v+wNk3J0hz3k7PbHmd7bmkjnfdciYO0Pmu53LMfa//fYbALhUi0VHRyMpKQl5eXnQ6XQIDAwk8/0iIGPuTH+Z74D/jD8AKJVKLFu2zCmDMC0tDYDYx9Mb5ry080MI/QHhJLv55pvx/vvvu6wpNGTIEMTExCAzM1PkxAAYp0ZmZibi4uK6XChYqVTirrvucvk/bjLMmTMHd911l1O3NFeo1WpMmDABJSUlqKqqEr1H0zSOHz8OjUbj1jl75MgR1NfXY9asWU4qmL6Cv4x9Z2RnZwOAV+POMX36dP7cHDl69CgAYNq0ad1zgr0IMuaux7ygoABnz57FiBEjkJKS4vsJ9xL8afw5g7q4uNjpPYvFgvLycmg0GkRERHT6WRe73vd1yLiT57w/jL2QlpYWHDp0CGFhYbj++ut9/nsy591DxpzMd38Ye4vFAgBobm52+X5zczOkUikUCkWnn0Xmu3vImPff+Q741/h7YufOnQDgdckzf5nzxGFK4OXbP/30E2688UZ88MEHbguwSyQS3H333dDr9fjPf/4jeu8///kP9Ho97rnnni6fi1qtxttvv+3yf5MmTQIAPPHEE3j77bcxevRorz6TO5+PP/6Yr5sGAD/++CMqKiqwcOFCqNVql3/b15u/+NPYA8CFCxdgMBhcvv7hhx8CABYuXOj153Hn89lnn8FsNvOvHzlyBOnp6Zg5c6ZPzri+ABlz92Pe1+c74H/jn5CQgJkzZ6KsrIxXHXGsWLEC7e3tmDdvntcNAi5mve/LkHEnz3l/GXsh27dvh8ViwcKFC6FUKrv0GWTOO0PGnMx3fxn7yZMnA2Aa/Dk23Fy/fj1qa2uRkpLi9b1A5rszZMz773wH/G/8Adcp9Hv37sWWLVswfvx43HDDDV5/lj/MeQkt/GZCv+SLL77Al19+CY1Gg0ceecTlBmXevHm8g1Kv1+P+++/HuXPnMHPmTIwZMwZ5eXk4duwYxo8fj7Vr1zrduC+//DL/7wMHDqCjowN33HEH/9rSpUsxbNgwj+f58ssvY9u2bdiwYYNP6i+KorB06VIcO3YMKSkpmDZtGsrLy7F//37ExsZi06ZNLlUsjY2NuPbaaxEWFoYjR454vXHrTfjb2H/xxRdYuXIlpk2bhsGDByMgIAClpaX47bffYLFY8MQTT+D555/36RpfffVVbNq0CSNGjMC1116LhoYG/PzzzwgMDMSPP/6IoUOHio5fsWIFr3rKyspCaWkpZs6ciejoaP736M1NpciYO485AJjNZsyaNQs6nQ5Hjx7lywH0Nfxt/AGgvLwc9913H5qamnDdddfx6VqpqamIjY3Fhg0b+PnXGV1Z79977z20tLQAAH7//XfU19dj/vz50Gg0ABhDe+rUqV59v79Cxp085/1p7DkWLlyIwsJC7NixAyNHjuzS9ZE57wwZczLf/WXsbTYbHn30UWRkZCAyMhJz5sxBcHAwv9ar1WqsWbMGEyZM8Or6yHx3hox5/53vgP+NPwDcdNNNGDRoEJKSkqBSqZCdnY309HTEx8dj1apVPomV/GHOE4cpgXdEeuKdd97BokWL+P/WarX44osvsH//fjQ2NiI6Oho33ngjnnrqKZeFdzszilavXt1pN7SuOkwBxhmyYsUKbN++HTU1NQgLC8N1112HZ599FlFRUS7/5uuvv8aHH36IJUuW4MUXX/Tp+3oL/jb26enpWLduHfLz89HY2Aij0Yjw8HBMmDABDzzwAGbOnOnzNVIUhbVr12Ljxo0oKyuDRqPBVVddheeeew4JCQlOxz/88MNOXdiFLFu2zG0Nzd4AGXPnMQeAn3/+Gc899xxuuukmfPrppz5/Z2/B38afo6amBp9//jmOHj2K1tZWREVFYc6cOXjqqadExeG9wdf1fs6cOU6pPkIcf4/eCBl38pz3RE+MfXZ2Nu6++25MmDDBSWXsK2TOiyFjTua7Jy732JvNZnz//ffYs2cPSkpKYLFYEBkZiRkzZuDJJ5/sVDDjCJnvYsiY99/5Dvjn+HOfXVVVBavViri4ONxwww1YsmRJlxo09fScJw5TAoFAIBAIBAKBQCAQCAQCgUBgITVMCQQCgUAgEAgEAoFAIBAIBAKBhThMCQQCgUAgEAgEAoFAIBAIBAKBhThMCQQCgUAgEAgEAoFAIBAIBAKBhThMCQQCgUAgEAgEAoFAIBAIBAKBhThMCQQCgUAgEAgEAoFAIBAIBAKBhThMCQQCgUAgEAgEAoFAIBAIBAKBhThMCQQCgUAgEAgEAoFAIBAIBAKBhThMCQQCgUAgEAgEAoFAIBAIBAKBhThMCQQCgUAgEAgEAoFAIBAIBAKBhThMCQQCgUAgEAgEAoFAIBAIBAKBhThMCQQCgUAgEAgEAoFAIBAIBAKBhThMCQQCgUAgEAgEAoFAIBAIBAKBhThMCQQCgUAgEAgEAoFAIBAIBAKB5f8BWlnnuN7iXesAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 1600x600 with 2 Axes>"
       ]
@@ -268,10 +268,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-05-26T17:06:09.974641Z",
-     "iopub.status.busy": "2023-05-26T17:06:09.974290Z",
-     "iopub.status.idle": "2023-05-26T17:06:10.038691Z",
-     "shell.execute_reply": "2023-05-26T17:06:10.037789Z"
+     "iopub.execute_input": "2023-09-26T14:48:06.646772Z",
+     "iopub.status.busy": "2023-09-26T14:48:06.645579Z",
+     "iopub.status.idle": "2023-09-26T14:48:06.671400Z",
+     "shell.execute_reply": "2023-09-26T14:48:06.670840Z"
     }
    },
    "outputs": [
@@ -279,16 +279,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Iteration:      1,   Func. Count:      7,   Neg. LLF: 255806977215.44\n",
-      "Iteration:      2,   Func. Count:     19,   Neg. LLF: 930291.4462204215\n",
-      "Iteration:      3,   Func. Count:     28,   Neg. LLF: 3486.7135083370713\n",
-      "Iteration:      4,   Func. Count:     36,   Neg. LLF: 2885.692473212838\n",
-      "Iteration:      5,   Func. Count:     44,   Neg. LLF: 65536430.69379793\n",
-      "Iteration:      6,   Func. Count:     53,   Neg. LLF: 1935.9527543235638\n",
-      "Iteration:      7,   Func. Count:     59,   Neg. LLF: 1935.9470521680555\n",
-      "Iteration:      8,   Func. Count:     65,   Neg. LLF: 1935.9470515573437\n",
+      "Iteration:      1,   Func. Count:      7,   Neg. LLF: 255813245019.088\n",
+      "Iteration:      2,   Func. Count:     19,   Neg. LLF: 930291.9264107295\n",
+      "Iteration:      3,   Func. Count:     28,   Neg. LLF: 3486.6798195388146\n",
+      "Iteration:      4,   Func. Count:     36,   Neg. LLF: 2885.7035662797234\n",
+      "Iteration:      5,   Func. Count:     44,   Neg. LLF: 65535916.590182975\n",
+      "Iteration:      6,   Func. Count:     53,   Neg. LLF: 1935.9527546214572\n",
+      "Iteration:      7,   Func. Count:     59,   Neg. LLF: 1935.9470520474433\n",
+      "Iteration:      8,   Func. Count:     65,   Neg. LLF: 1935.9470514368204\n",
       "Optimization terminated successfully    (Exit mode 0)\n",
-      "            Current function value: 1935.9470515573437\n",
+      "            Current function value: 1935.9470514368204\n",
       "            Iterations: 8\n",
       "            Function evaluations: 65\n",
       "            Gradient evaluations: 8\n",
@@ -300,8 +300,8 @@
       "Distribution:                  Normal   AIC:                           3881.89\n",
       "Method:            Maximum Likelihood   BIC:                           3907.50\n",
       "                                        No. Observations:                 1237\n",
-      "Date:                Fri, May 26 2023   Df Residuals:                     1233\n",
-      "Time:                        17:06:09   Df Model:                            4\n",
+      "Date:                Tue, Sep 26 2023   Df Residuals:                     1233\n",
+      "Time:                        14:48:06   Df Model:                            4\n",
       "                                    Mean Model                                    \n",
       "==================================================================================\n",
       "                      coef    std err          t      P>|t|       95.0% Conf. Int.\n",
@@ -344,10 +344,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-05-26T17:06:10.043031Z",
-     "iopub.status.busy": "2023-05-26T17:06:10.042487Z",
-     "iopub.status.idle": "2023-05-26T17:06:10.372971Z",
-     "shell.execute_reply": "2023-05-26T17:06:10.372037Z"
+     "iopub.execute_input": "2023-09-26T14:48:06.676451Z",
+     "iopub.status.busy": "2023-09-26T14:48:06.674914Z",
+     "iopub.status.idle": "2023-09-26T14:48:06.887404Z",
+     "shell.execute_reply": "2023-09-26T14:48:06.886824Z"
     }
    },
    "outputs": [
@@ -356,22 +356,16 @@
      "output_type": "stream",
      "text": [
       "0\n",
-      "1\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2\n"
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "3\n",
-      "4\n",
       "                    HAR - Fixed Variance (Unit Scale) Model Results                    \n",
       "=======================================================================================\n",
       "Dep. Variable:                       VIX Index   R-squared:                       0.876\n",
@@ -380,16 +374,16 @@
       "Distribution:                           Normal   AIC:                           3879.48\n",
       "Method:                     Maximum Likelihood   BIC:                           3899.96\n",
       "                                                 No. Observations:                 1237\n",
-      "Date:                         Fri, May 26 2023   Df Residuals:                     1233\n",
-      "Time:                                 17:06:10   Df Model:                            4\n",
+      "Date:                         Tue, Sep 26 2023   Df Residuals:                     1233\n",
+      "Time:                                 14:48:06   Df Model:                            4\n",
       "                                    Mean Model                                   \n",
       "=================================================================================\n",
       "                      coef    std err          t      P>|t|      95.0% Conf. Int.\n",
       "---------------------------------------------------------------------------------\n",
       "Const               0.5602      0.152      3.681  2.323e-04     [  0.262,  0.858]\n",
-      "VIX Index[0:1]      0.9381  3.616e-02     25.940 2.388e-148     [  0.867,  1.009]\n",
+      "VIX Index[0:1]      0.9381  3.616e-02     25.939 2.389e-148     [  0.867,  1.009]\n",
       "VIX Index[0:5]     -0.0262  3.774e-02     -0.693      0.488   [ -0.100,4.781e-02]\n",
-      "VIX Index[0:22]     0.0499  2.099e-02      2.380  1.733e-02 [8.810e-03,9.109e-02]\n",
+      "VIX Index[0:22]     0.0499  2.099e-02      2.380  1.733e-02 [8.809e-03,9.109e-02]\n",
       "=================================================================================\n",
       "\n",
       "Covariance estimator: robust\n"
@@ -422,10 +416,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-05-26T17:06:10.377144Z",
-     "iopub.status.busy": "2023-05-26T17:06:10.376772Z",
-     "iopub.status.idle": "2023-05-26T17:06:10.538566Z",
-     "shell.execute_reply": "2023-05-26T17:06:10.537601Z"
+     "iopub.execute_input": "2023-09-26T14:48:06.892584Z",
+     "iopub.status.busy": "2023-09-26T14:48:06.891040Z",
+     "iopub.status.idle": "2023-09-26T14:48:07.003671Z",
+     "shell.execute_reply": "2023-09-26T14:48:07.003086Z"
     },
     "tags": []
    },
@@ -442,8 +436,8 @@
       "Distribution:                  Normal   AIC:                           3881.23\n",
       "Method:            Maximum Likelihood   BIC:                           3922.19\n",
       "                                        No. Observations:                 1237\n",
-      "Date:                Fri, May 26 2023   Df Residuals:                     1233\n",
-      "Time:                        17:06:10   Df Model:                            4\n",
+      "Date:                Tue, Sep 26 2023   Df Residuals:                     1233\n",
+      "Time:                        14:48:06   Df Model:                            4\n",
       "                                   Mean Model                                   \n",
       "================================================================================\n",
       "                      coef    std err          t      P>|t|     95.0% Conf. Int.\n",
@@ -489,7 +483,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.11"
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,
diff --git a/univariate/univariate_volatility_forecasting.html b/univariate/univariate_volatility_forecasting.html
index c74c2865d2..2b5cde364f 100644
--- a/univariate/univariate_volatility_forecasting.html
+++ b/univariate/univariate_volatility_forecasting.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Volatility Forecasting - arch 6.1.0</title>
+        <title>Volatility Forecasting - arch 6.2.0</title>
       
     
 
@@ -47,9 +47,9 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css" />
-        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css?v=2aa19091" />
+        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -75,7 +75,7 @@
     <div data-md-component="skip">
       
         
-        <a href="#Future-Forecast-Shape-Changes" class="md-skip">
+        <a href="#Data" class="md-skip">
           Skip to content
         </a>
       
@@ -93,7 +93,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -103,7 +103,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -190,10 +190,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -328,13 +328,6 @@
     </label>
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
-        <li class="md-nav__item">
-  <a href="#Future-Forecast-Shape-Changes" class="md-nav__link">
-    <span title="/univariate/univariate_volatility_forecasting.ipynb#future-forecast-shape-changes (reference label)" class="md-ellipsis">Future Forecast Shape Changes</span>
-  </a>
-  
-</li>
-      
         <li class="md-nav__item">
   <a href="#Data" class="md-nav__link">
     <span title="/univariate/univariate_volatility_forecasting.ipynb#data (reference label)" class="md-ellipsis">Data</span>
@@ -525,13 +518,6 @@
     </label>
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
-        <li class="md-nav__item">
-  <a href="#Future-Forecast-Shape-Changes" class="md-nav__link">
-    <span title="/univariate/univariate_volatility_forecasting.ipynb#future-forecast-shape-changes (reference label)" class="md-ellipsis">Future Forecast Shape Changes</span>
-  </a>
-  
-</li>
-      
         <li class="md-nav__item">
   <a href="#Data" class="md-nav__link">
     <span title="/univariate/univariate_volatility_forecasting.ipynb#data (reference label)" class="md-ellipsis">Data</span>
@@ -902,13 +888,6 @@
     </label>
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
-        <li class="md-nav__item">
-  <a href="#Future-Forecast-Shape-Changes" class="md-nav__link">
-    <span title="/univariate/univariate_volatility_forecasting.ipynb#future-forecast-shape-changes (reference label)" class="md-ellipsis">Future Forecast Shape Changes</span>
-  </a>
-  
-</li>
-      
         <li class="md-nav__item">
   <a href="#Data" class="md-nav__link">
     <span title="/univariate/univariate_volatility_forecasting.ipynb#data (reference label)" class="md-ellipsis">Data</span>
@@ -1005,7 +984,7 @@
                   
 
 
-<h1 id="Volatility-Forecasting">Volatility Forecasting<a class="headerlink" href="#Volatility-Forecasting" title="Permalink to this heading">¶</a></h1>
+<h1 id="Volatility-Forecasting">Volatility Forecasting<a class="headerlink" href="#Volatility-Forecasting" title="Link to this heading">¶</a></h1>
 <p><em>This setup code is required to run in an IPython notebook</em></p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[1]:
@@ -1023,19 +1002,7 @@ <h1 id="Volatility-Forecasting">Volatility Forecasting<a class="headerlink" href
 </code></pre></div>
 </div>
 </div>
-<h2 id="Future-Forecast-Shape-Changes">Future Forecast Shape Changes<a class="headerlink" href="#Future-Forecast-Shape-Changes" title="Permalink to this heading">¶</a></h2>
-<div class="admonition warning">
-<div class="admonition-title"><h3></div><p>WARNING</p>
-</h3><p>The future behavior of <code class="docutils literal notranslate"><span class="pre">forecast</span></code> is changing.</p>
-</div>
-<p>Versions of <code class="docutils literal notranslate"><span class="pre">arch</span></code> before 4.19 defaulted to returning forecast values with the same shape as the data used to fit the model. While this is convenient it is also computationally wasteful. This is especially true when using <code class="docutils literal notranslate"><span class="pre">method</span></code> is <code class="docutils literal notranslate"><span class="pre">&quot;simulation&quot;</span></code> or <code class="docutils literal notranslate"><span class="pre">&quot;bootstrap&quot;</span></code>. In future version of <code class="docutils literal notranslate"><span class="pre">arch</span></code>, the default behavior will change to only returning the minimal <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> that is needed to contain the forecast results. In the current version of <code class="docutils literal notranslate"><span class="pre">arch</span></code>, calling <code class="docutils literal notranslate"><span class="pre">forecast()</span></code> without
-the <code class="docutils literal notranslate"><span class="pre">reindex</span></code> keyword argument produces a <code class="docutils literal notranslate"><span class="pre">FutureWarning</span></code>. You can silence the future warning by:</p>
-<ul class="simple">
-<li><p>Using <code class="docutils literal notranslate"><span class="pre">reindex=False</span></code> which uses the future behavior.</p></li>
-<li><p>Using <code class="docutils literal notranslate"><span class="pre">reindex=True</span></code> which uses the legacy behavior.</p></li>
-<li><p>Importing <code class="docutils literal notranslate"><span class="pre">from</span> <span class="pre">arch.__future__</span> <span class="pre">import</span> <span class="pre">reindexing</span></code> which will set the default to <code class="docutils literal notranslate"><span class="pre">False</span></code> and silence the warning.</p></li>
-</ul>
-<h2 id="Data">Data<a class="headerlink" href="#Data" title="Permalink to this heading">¶</a></h2>
+<h2 id="Data">Data<a class="headerlink" href="#Data" title="Link to this heading">¶</a></h2>
 <p>These examples make use of S&amp;P 500 data from Yahoo! that is available from <code class="docutils literal notranslate"><span class="pre">arch.data.sp500</span></code>.</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[2]:
@@ -1055,7 +1022,7 @@ <h2 id="Data">Data<a class="headerlink" href="#Data" title="Permalink to this he
 </code></pre></div>
 </div>
 </div>
-<h2 id="Basic-Forecasting">Basic Forecasting<a class="headerlink" href="#Basic-Forecasting" title="Permalink to this heading">¶</a></h2>
+<h2 id="Basic-Forecasting">Basic Forecasting<a class="headerlink" href="#Basic-Forecasting" title="Link to this heading">¶</a></h2>
 <p>Forecasts can be generated for standard GARCH(p,q) processes using any of the three forecast generation methods:</p>
 <ul class="simple">
 <li><p>Analytical</p></li>
@@ -1078,10 +1045,10 @@ <h2 id="Basic-Forecasting">Basic Forecasting<a class="headerlink" href="#Basic-F
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-Iteration:      5,   Func. Count:     35,   Neg. LLF: 6970.282172935112
-Iteration:     10,   Func. Count:     63,   Neg. LLF: 6936.718477483884
+Iteration:      5,   Func. Count:     35,   Neg. LLF: 6970.2779565773335
+Iteration:     10,   Func. Count:     63,   Neg. LLF: 6936.718477482076
 Optimization terminated successfully    (Exit mode 0)
-            Current function value: 6936.718476988963
+            Current function value: 6936.718476988951
             Iterations: 11
             Function evaluations: 68
             Gradient evaluations: 11
@@ -1091,7 +1058,7 @@ <h2 id="Basic-Forecasting">Basic Forecasting<a class="headerlink" href="#Basic-F
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[4]:
 </code></pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><code><span class="n">forecasts</span> <span class="o">=</span> <span class="n">res</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">reindex</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><code><span class="n">forecasts</span> <span class="o">=</span> <span class="n">res</span><span class="o">.</span><span class="n">forecast</span><span class="p">()</span>
 </code></pre></div>
 </div>
 </div>
@@ -1135,53 +1102,8 @@ <h2 id="Basic-Forecasting">Basic Forecasting<a class="headerlink" href="#Basic-F
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[6]:
 </code></pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><code><span class="n">forecasts</span> <span class="o">=</span> <span class="n">res</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">horizon</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">reindex</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">forecasts</span><span class="o">.</span><span class="n">residual_variance</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="o">-</span><span class="mi">3</span><span class="p">:])</span>
-</code></pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-                h.1       h.2       h.3       h.4     h.5
-Date
-2018-12-31  3.59647  3.568502  3.540887  3.513621  3.4867
-</pre></div></div>
-</div>
-<p>If you fail to set <code class="docutils literal notranslate"><span class="pre">reindex</span></code> you will see a warning.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[7]:
-</code></pre></div>
-</div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><code><span class="n">forecasts</span> <span class="o">=</span> <span class="n">res</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">horizon</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
-</code></pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area stderr docutils container">
-<div class="highlight"><pre>
-c:\git\arch\arch\__future__\_utility.py:11: FutureWarning:
-The default for reindex is True. After September 2021 this will change to
-False. Set reindex to True or False to silence this message. Alternatively,
-you can use the import comment
-
-from arch.__future__ import reindexing
-
-to globally set reindex to True and silence this warning.
-
-  warnings.warn(
-</pre></div></div>
-</div>
-<p>When not specified, or if <code class="docutils literal notranslate"><span class="pre">reindex</span></code> is <code class="docutils literal notranslate"><span class="pre">True</span></code>, then values that are not computed are <code class="docutils literal notranslate"><span class="pre">nan</span></code>-filled.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[8]:
-</code></pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><code><span class="nb">print</span><span class="p">(</span><span class="n">forecasts</span><span class="o">.</span><span class="n">residual_variance</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="o">-</span><span class="mi">3</span><span class="p">:])</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">forecasts</span><span class="o">.</span><span class="n">residual_variance</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="o">-</span><span class="mi">3</span><span class="p">:])</span>
 </code></pre></div>
 </div>
 </div>
@@ -1190,23 +1112,21 @@ <h2 id="Basic-Forecasting">Basic Forecasting<a class="headerlink" href="#Basic-F
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-                h.1       h.2       h.3       h.4     h.5
+                h.1       h.2       h.3       h.4       h.5
 Date
-2018-12-27      NaN       NaN       NaN       NaN     NaN
-2018-12-28      NaN       NaN       NaN       NaN     NaN
-2018-12-31  3.59647  3.568502  3.540887  3.513621  3.4867
+2018-12-31  3.59647  3.568502  3.540887  3.513621  3.486701
 </pre></div></div>
 </div>
-<h2 id="Alternative-Forecast-Generation-Schemes">Alternative Forecast Generation Schemes<a class="headerlink" href="#Alternative-Forecast-Generation-Schemes" title="Permalink to this heading">¶</a></h2>
-<h3 id="Fixed-Window-Forecasting">Fixed Window Forecasting<a class="headerlink" href="#Fixed-Window-Forecasting" title="Permalink to this heading">¶</a></h3>
+<h2 id="Alternative-Forecast-Generation-Schemes">Alternative Forecast Generation Schemes<a class="headerlink" href="#Alternative-Forecast-Generation-Schemes" title="Link to this heading">¶</a></h2>
+<h3 id="Fixed-Window-Forecasting">Fixed Window Forecasting<a class="headerlink" href="#Fixed-Window-Forecasting" title="Link to this heading">¶</a></h3>
 <p>Fixed-windows forecasting uses data up to a specified date to generate all forecasts after that date. This can be implemented by passing the entire data in when initializing the model and then using <code class="docutils literal notranslate"><span class="pre">last_obs</span></code> when calling <code class="docutils literal notranslate"><span class="pre">fit</span></code>. <code class="docutils literal notranslate"><span class="pre">forecast()</span></code> will, by default, produce forecasts after this final date.</p>
 <p><strong>Note</strong> <code class="docutils literal notranslate"><span class="pre">last_obs</span></code> follow Python sequence rules so that the actual date in <code class="docutils literal notranslate"><span class="pre">last_obs</span></code> is not in the sample.</p>
 <div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[9]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[7]:
 </code></pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><code><span class="n">res</span> <span class="o">=</span> <span class="n">am</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">last_obs</span><span class="o">=</span><span class="s2">&quot;2011-1-1&quot;</span><span class="p">,</span> <span class="n">update_freq</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
-<span class="n">forecasts</span> <span class="o">=</span> <span class="n">res</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">horizon</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">reindex</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="n">forecasts</span> <span class="o">=</span> <span class="n">res</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">horizon</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
 <span class="nb">print</span><span class="p">(</span><span class="n">forecasts</span><span class="o">.</span><span class="n">variance</span><span class="o">.</span><span class="n">dropna</span><span class="p">()</span><span class="o">.</span><span class="n">head</span><span class="p">())</span>
 </code></pre></div>
 </div>
@@ -1232,10 +1152,10 @@ <h3 id="Fixed-Window-Forecasting">Fixed Window Forecasting<a class="headerlink"
 2011-01-06  0.402483  0.411486  0.420425  0.429301  0.438115
 </pre></div></div>
 </div>
-<h3 id="Rolling-Window-Forecasting">Rolling Window Forecasting<a class="headerlink" href="#Rolling-Window-Forecasting" title="Permalink to this heading">¶</a></h3>
+<h3 id="Rolling-Window-Forecasting">Rolling Window Forecasting<a class="headerlink" href="#Rolling-Window-Forecasting" title="Link to this heading">¶</a></h3>
 <p>Rolling window forecasts use a fixed sample length and then produce one-step from the final observation. These can be implemented using <code class="docutils literal notranslate"><span class="pre">first_obs</span></code> and <code class="docutils literal notranslate"><span class="pre">last_obs</span></code>.</p>
 <div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[10]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[8]:
 </code></pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><code><span class="n">index</span> <span class="o">=</span> <span class="n">returns</span><span class="o">.</span><span class="n">index</span>
@@ -1246,7 +1166,7 @@ <h3 id="Rolling-Window-Forecasting">Rolling Window Forecasting<a class="headerli
     <span class="n">sys</span><span class="o">.</span><span class="n">stdout</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">)</span>
     <span class="n">sys</span><span class="o">.</span><span class="n">stdout</span><span class="o">.</span><span class="n">flush</span><span class="p">()</span>
     <span class="n">res</span> <span class="o">=</span> <span class="n">am</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">first_obs</span><span class="o">=</span><span class="n">i</span><span class="p">,</span> <span class="n">last_obs</span><span class="o">=</span><span class="n">i</span> <span class="o">+</span> <span class="n">end_loc</span><span class="p">,</span> <span class="n">disp</span><span class="o">=</span><span class="s2">&quot;off&quot;</span><span class="p">)</span>
-    <span class="n">temp</span> <span class="o">=</span> <span class="n">res</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">horizon</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">reindex</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">variance</span>
+    <span class="n">temp</span> <span class="o">=</span> <span class="n">res</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">horizon</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span><span class="o">.</span><span class="n">variance</span>
     <span class="n">fcast</span> <span class="o">=</span> <span class="n">temp</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
     <span class="n">forecasts</span><span class="p">[</span><span class="n">fcast</span><span class="o">.</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">fcast</span>
 <span class="nb">print</span><span class="p">()</span>
@@ -1283,10 +1203,10 @@ <h3 id="Rolling-Window-Forecasting">Rolling Window Forecasting<a class="headerli
 2010-01-29  1.085786  1.089594  1.093378
 </pre></div></div>
 </div>
-<h3 id="Recursive-Forecast-Generation">Recursive Forecast Generation<a class="headerlink" href="#Recursive-Forecast-Generation" title="Permalink to this heading">¶</a></h3>
+<h3 id="Recursive-Forecast-Generation">Recursive Forecast Generation<a class="headerlink" href="#Recursive-Forecast-Generation" title="Link to this heading">¶</a></h3>
 <p>Recursive is similar to rolling except that the initial observation does not change. This can be easily implemented by dropping the <code class="docutils literal notranslate"><span class="pre">first_obs</span></code> input.</p>
 <div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[11]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[9]:
 </code></pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><code><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
@@ -1300,7 +1220,7 @@ <h3 id="Recursive-Forecast-Generation">Recursive Forecast Generation<a class="he
     <span class="n">sys</span><span class="o">.</span><span class="n">stdout</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">)</span>
     <span class="n">sys</span><span class="o">.</span><span class="n">stdout</span><span class="o">.</span><span class="n">flush</span><span class="p">()</span>
     <span class="n">res</span> <span class="o">=</span> <span class="n">am</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">last_obs</span><span class="o">=</span><span class="n">i</span> <span class="o">+</span> <span class="n">end_loc</span><span class="p">,</span> <span class="n">disp</span><span class="o">=</span><span class="s2">&quot;off&quot;</span><span class="p">)</span>
-    <span class="n">temp</span> <span class="o">=</span> <span class="n">res</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">horizon</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">reindex</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">variance</span>
+    <span class="n">temp</span> <span class="o">=</span> <span class="n">res</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">horizon</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span><span class="o">.</span><span class="n">variance</span>
     <span class="n">fcast</span> <span class="o">=</span> <span class="n">temp</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
     <span class="n">forecasts</span><span class="p">[</span><span class="n">fcast</span><span class="o">.</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">fcast</span>
 <span class="nb">print</span><span class="p">()</span>
@@ -1337,17 +1257,17 @@ <h3 id="Recursive-Forecast-Generation">Recursive Forecast Generation<a class="he
 2010-01-29  1.085003  1.088783  1.092541
 </pre></div></div>
 </div>
-<h2 id="TARCH">TARCH<a class="headerlink" href="#TARCH" title="Permalink to this heading">¶</a></h2>
-<h3 id="Analytical-Forecasts">Analytical Forecasts<a class="headerlink" href="#Analytical-Forecasts" title="Permalink to this heading">¶</a></h3>
+<h2 id="TARCH">TARCH<a class="headerlink" href="#TARCH" title="Link to this heading">¶</a></h2>
+<h3 id="Analytical-Forecasts">Analytical Forecasts<a class="headerlink" href="#Analytical-Forecasts" title="Link to this heading">¶</a></h3>
 <p>All ARCH-type models have one-step analytical forecasts. Longer horizons only have closed forms for specific models. TARCH models do not have closed-form (analytical) forecasts for horizons larger than 1, and so simulation or bootstrapping is required. Attempting to produce forecasts for horizons larger than 1 using <code class="docutils literal notranslate"><span class="pre">method='analytical'</span></code> results in a <code class="docutils literal notranslate"><span class="pre">ValueError</span></code>.</p>
 <div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[12]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[10]:
 </code></pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><code><span class="c1"># TARCH specification</span>
 <span class="n">am</span> <span class="o">=</span> <span class="n">arch_model</span><span class="p">(</span><span class="n">returns</span><span class="p">,</span> <span class="n">vol</span><span class="o">=</span><span class="s2">&quot;GARCH&quot;</span><span class="p">,</span> <span class="n">power</span><span class="o">=</span><span class="mf">2.0</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">o</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">q</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
 <span class="n">res</span> <span class="o">=</span> <span class="n">am</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">update_freq</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
-<span class="n">forecasts</span> <span class="o">=</span> <span class="n">res</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">reindex</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="n">forecasts</span> <span class="o">=</span> <span class="n">res</span><span class="o">.</span><span class="n">forecast</span><span class="p">()</span>
 <span class="nb">print</span><span class="p">(</span><span class="n">forecasts</span><span class="o">.</span><span class="n">variance</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
 </code></pre></div>
 </div>
@@ -1357,10 +1277,10 @@ <h3 id="Analytical-Forecasts">Analytical Forecasts<a class="headerlink" href="#A
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-Iteration:      5,   Func. Count:     40,   Neg. LLF: 6846.496665348549
-Iteration:     10,   Func. Count:     75,   Neg. LLF: 6822.883179474602
+Iteration:      5,   Func. Count:     40,   Neg. LLF: 6846.48928243015
+Iteration:     10,   Func. Count:     75,   Neg. LLF: 6822.883188650879
 Optimization terminated successfully    (Exit mode 0)
-            Current function value: 6822.882823372691
+            Current function value: 6822.882823441358
             Iterations: 13
             Function evaluations: 93
             Gradient evaluations: 13
@@ -1368,10 +1288,10 @@ <h3 id="Analytical-Forecasts">Analytical Forecasts<a class="headerlink" href="#A
 Name: 2018-12-31 00:00:00, dtype: float64
 </pre></div></div>
 </div>
-<h3 id="Simulation-Forecasts">Simulation Forecasts<a class="headerlink" href="#Simulation-Forecasts" title="Permalink to this heading">¶</a></h3>
+<h3 id="Simulation-Forecasts">Simulation Forecasts<a class="headerlink" href="#Simulation-Forecasts" title="Link to this heading">¶</a></h3>
 <p>When using simulation- or bootstrap-based forecasts, an additional attribute of an <code class="docutils literal notranslate"><span class="pre">ARCHModelForecast</span></code> object is meaningful – <code class="docutils literal notranslate"><span class="pre">simulation</span></code>.</p>
 <div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[13]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[11]:
 </code></pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><code><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
@@ -1384,7 +1304,7 @@ <h3 id="Simulation-Forecasts">Simulation Forecasts<a class="headerlink" href="#S
 </div>
 </div>
 <div class="nboutput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[13]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[11]:
 </code></pre></div>
 </div>
 <div class="output_area docutils container">
@@ -1396,14 +1316,14 @@ <h3 id="Simulation-Forecasts">Simulation Forecasts<a class="headerlink" href="#S
 <div class="prompt empty docutils container">
 </div>
 <div class="output_area docutils container">
-<img alt="../_images/univariate_univariate_volatility_forecasting_28_1.png" src="../_images/univariate_univariate_volatility_forecasting_28_1.png" />
+<img alt="../_images/univariate_univariate_volatility_forecasting_23_1.png" src="../_images/univariate_univariate_volatility_forecasting_23_1.png" />
 </div>
 </div>
 <div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[14]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[12]:
 </code></pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><code><span class="n">forecasts</span> <span class="o">=</span> <span class="n">res</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">horizon</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">&quot;simulation&quot;</span><span class="p">,</span> <span class="n">reindex</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><code><span class="n">forecasts</span> <span class="o">=</span> <span class="n">res</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">horizon</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">&quot;simulation&quot;</span><span class="p">)</span>
 <span class="n">sims</span> <span class="o">=</span> <span class="n">forecasts</span><span class="o">.</span><span class="n">simulations</span>
 
 <span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">6</span><span class="p">)</span>
@@ -1421,11 +1341,11 @@ <h3 id="Simulation-Forecasts">Simulation Forecasts<a class="headerlink" href="#S
 <div class="prompt empty docutils container">
 </div>
 <div class="output_area docutils container">
-<img alt="../_images/univariate_univariate_volatility_forecasting_29_0.png" src="../_images/univariate_univariate_volatility_forecasting_29_0.png" />
+<img alt="../_images/univariate_univariate_volatility_forecasting_24_0.png" src="../_images/univariate_univariate_volatility_forecasting_24_0.png" />
 </div>
 </div>
 <div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[15]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[13]:
 </code></pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><code><span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
@@ -1435,28 +1355,28 @@ <h3 id="Simulation-Forecasts">Simulation Forecasts<a class="headerlink" href="#S
 </div>
 </div>
 <div class="nboutput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[15]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[13]:
 </code></pre></div>
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-&lt;AxesSubplot:&gt;
+&lt;Axes: &gt;
 </pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
 <div class="prompt empty docutils container">
 </div>
 <div class="output_area docutils container">
-<img alt="../_images/univariate_univariate_volatility_forecasting_30_1.png" src="../_images/univariate_univariate_volatility_forecasting_30_1.png" />
+<img alt="../_images/univariate_univariate_volatility_forecasting_25_1.png" src="../_images/univariate_univariate_volatility_forecasting_25_1.png" />
 </div>
 </div>
-<h3 id="Bootstrap-Forecasts">Bootstrap Forecasts<a class="headerlink" href="#Bootstrap-Forecasts" title="Permalink to this heading">¶</a></h3>
+<h3 id="Bootstrap-Forecasts">Bootstrap Forecasts<a class="headerlink" href="#Bootstrap-Forecasts" title="Link to this heading">¶</a></h3>
 <p>Bootstrap-based forecasts are nearly identical to simulation-based forecasts except that the values used to simulate the process are computed from historical data rather than using the assumed distribution of the residuals. Forecasts produced using this method also return an <code class="docutils literal notranslate"><span class="pre">ARCHModelForecastSimulation</span></code> containing information about the simulated paths.</p>
 <div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[16]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[14]:
 </code></pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><code><span class="n">forecasts</span> <span class="o">=</span> <span class="n">res</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">horizon</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">&quot;bootstrap&quot;</span><span class="p">,</span> <span class="n">reindex</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><code><span class="n">forecasts</span> <span class="o">=</span> <span class="n">res</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">horizon</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">&quot;bootstrap&quot;</span><span class="p">)</span>
 <span class="n">sims</span> <span class="o">=</span> <span class="n">forecasts</span><span class="o">.</span><span class="n">simulations</span>
 
 <span class="n">lines</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">sims</span><span class="o">.</span><span class="n">residual_variances</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="p">::</span><span class="mi">5</span><span class="p">]</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;#9cb2d6&quot;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
@@ -1473,17 +1393,17 @@ <h3 id="Bootstrap-Forecasts">Bootstrap Forecasts<a class="headerlink" href="#Boo
 <div class="prompt empty docutils container">
 </div>
 <div class="output_area docutils container">
-<img alt="../_images/univariate_univariate_volatility_forecasting_32_0.png" src="../_images/univariate_univariate_volatility_forecasting_32_0.png" />
+<img alt="../_images/univariate_univariate_volatility_forecasting_27_0.png" src="../_images/univariate_univariate_volatility_forecasting_27_0.png" />
 </div>
 </div>
-<h1 id="Value-at-Risk-Forecasting">Value-at-Risk Forecasting<a class="headerlink" href="#Value-at-Risk-Forecasting" title="Permalink to this heading">¶</a></h1>
+<h1 id="Value-at-Risk-Forecasting">Value-at-Risk Forecasting<a class="headerlink" href="#Value-at-Risk-Forecasting" title="Link to this heading">¶</a></h1>
 <p>Value-at-Risk (VaR) forecasts from GARCH models depend on the conditional mean, the conditional volatility and the quantile of the standardized residuals,</p>
 <div class="math notranslate nohighlight">
 \[VaR_{t+1|t} = -\mu_{t+1|t} - \sigma_{t+1|t} q_{\alpha}\]</div>
 <p>where <span class="math notranslate nohighlight">\(q_{\alpha}\)</span> is the <span class="math notranslate nohighlight">\(\alpha\)</span> quantile of the standardized residuals, e.g., 5%.</p>
 <p>The quantile can be either computed from the estimated model density or computed using the empirical distribution of the standardized residuals. The example below shows both methods.</p>
 <div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[17]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[15]:
 </code></pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><code><span class="n">am</span> <span class="o">=</span> <span class="n">arch_model</span><span class="p">(</span><span class="n">returns</span><span class="p">,</span> <span class="n">vol</span><span class="o">=</span><span class="s2">&quot;Garch&quot;</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">o</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">q</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">dist</span><span class="o">=</span><span class="s2">&quot;skewt&quot;</span><span class="p">)</span>
@@ -1491,15 +1411,16 @@ <h1 id="Value-at-Risk-Forecasting">Value-at-Risk Forecasting<a class="headerlink
 </code></pre></div>
 </div>
 </div>
-<h2 id="Parametric-VaR">Parametric VaR<a class="headerlink" href="#Parametric-VaR" title="Permalink to this heading">¶</a></h2>
+<h2 id="Parametric-VaR">Parametric VaR<a class="headerlink" href="#Parametric-VaR" title="Link to this heading">¶</a></h2>
 <p>First, we use the model to estimate the VaR. The quantiles can be computed using the <code class="docutils literal notranslate"><span class="pre">ppf</span></code> method of the distribution attached to the model. The quantiles are printed below.</p>
+<p><strong>Note</strong>: <code class="docutils literal notranslate"><span class="pre">forecast</span></code> is called with <code class="docutils literal notranslate"><span class="pre">align=&quot;target&quot;</span></code> so that the forecasts are already aligned with the target and so do not need further shifting.</p>
 <div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[18]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[16]:
 </code></pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><code><span class="n">forecasts</span> <span class="o">=</span> <span class="n">res</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">start</span><span class="o">=</span><span class="s2">&quot;2018-1-1&quot;</span><span class="p">,</span> <span class="n">reindex</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-<span class="n">cond_mean</span> <span class="o">=</span> <span class="n">forecasts</span><span class="o">.</span><span class="n">mean</span><span class="p">[</span><span class="s2">&quot;2018&quot;</span><span class="p">:]</span>
-<span class="n">cond_var</span> <span class="o">=</span> <span class="n">forecasts</span><span class="o">.</span><span class="n">variance</span><span class="p">[</span><span class="s2">&quot;2018&quot;</span><span class="p">:]</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><code><span class="n">forecasts</span> <span class="o">=</span> <span class="n">res</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">align</span><span class="o">=</span><span class="s2">&quot;target&quot;</span><span class="p">)</span>
+<span class="n">cond_mean</span> <span class="o">=</span> <span class="n">forecasts</span><span class="o">.</span><span class="n">mean</span><span class="p">[</span><span class="s2">&quot;2018&quot;</span><span class="p">:]</span><span class="o">.</span><span class="n">dropna</span><span class="p">()</span>
+<span class="n">cond_var</span> <span class="o">=</span> <span class="n">forecasts</span><span class="o">.</span><span class="n">variance</span><span class="p">[</span><span class="s2">&quot;2018&quot;</span><span class="p">:]</span><span class="o">.</span><span class="n">dropna</span><span class="p">()</span>
 <span class="n">q</span> <span class="o">=</span> <span class="n">am</span><span class="o">.</span><span class="n">distribution</span><span class="o">.</span><span class="n">ppf</span><span class="p">([</span><span class="mf">0.01</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">],</span> <span class="n">res</span><span class="o">.</span><span class="n">params</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">:])</span>
 <span class="nb">print</span><span class="p">(</span><span class="n">q</span><span class="p">)</span>
 </code></pre></div>
@@ -1515,7 +1436,7 @@ <h2 id="Parametric-VaR">Parametric VaR<a class="headerlink" href="#Parametric-Va
 </div>
 <p>Next, we plot the two VaRs along with the returns. The returns that violate the VaR forecasts are highlighted.</p>
 <div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[19]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[17]:
 </code></pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><code><span class="n">value_at_risk</span> <span class="o">=</span> <span class="o">-</span><span class="n">cond_mean</span><span class="o">.</span><span class="n">values</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">cond_var</span><span class="p">)</span><span class="o">.</span><span class="n">values</span> <span class="o">*</span> <span class="n">q</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span>
@@ -1557,13 +1478,13 @@ <h2 id="Parametric-VaR">Parametric VaR<a class="headerlink" href="#Parametric-Va
 <div class="prompt empty docutils container">
 </div>
 <div class="output_area docutils container">
-<img alt="../_images/univariate_univariate_volatility_forecasting_38_0.png" src="../_images/univariate_univariate_volatility_forecasting_38_0.png" />
+<img alt="../_images/univariate_univariate_volatility_forecasting_33_0.png" src="../_images/univariate_univariate_volatility_forecasting_33_0.png" />
 </div>
 </div>
-<h2 id="Filtered-Historical-Simulation">Filtered Historical Simulation<a class="headerlink" href="#Filtered-Historical-Simulation" title="Permalink to this heading">¶</a></h2>
+<h2 id="Filtered-Historical-Simulation">Filtered Historical Simulation<a class="headerlink" href="#Filtered-Historical-Simulation" title="Link to this heading">¶</a></h2>
 <p>Next, we use the empirical distribution of the standardized residuals to estimate the quantiles. These values are very similar to those estimated using the assumed distribution. The plot below is identical except for the slightly different quantiles.</p>
 <div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[20]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[18]:
 </code></pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><code><span class="n">std_rets</span> <span class="o">=</span> <span class="p">(</span><span class="n">returns</span><span class="p">[:</span><span class="s2">&quot;2017&quot;</span><span class="p">]</span> <span class="o">-</span> <span class="n">res</span><span class="o">.</span><span class="n">params</span><span class="p">[</span><span class="s2">&quot;mu&quot;</span><span class="p">])</span> <span class="o">/</span> <span class="n">res</span><span class="o">.</span><span class="n">conditional_volatility</span>
@@ -1584,7 +1505,7 @@ <h2 id="Filtered-Historical-Simulation">Filtered Historical Simulation<a class="
 </pre></div></div>
 </div>
 <div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[21]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[19]:
 </code></pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><code><span class="n">value_at_risk</span> <span class="o">=</span> <span class="o">-</span><span class="n">cond_mean</span><span class="o">.</span><span class="n">values</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">cond_var</span><span class="p">)</span><span class="o">.</span><span class="n">values</span> <span class="o">*</span> <span class="n">q</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span>
@@ -1620,7 +1541,7 @@ <h2 id="Filtered-Historical-Simulation">Filtered Historical Simulation<a class="
 <div class="prompt empty docutils container">
 </div>
 <div class="output_area docutils container">
-<img alt="../_images/univariate_univariate_volatility_forecasting_41_0.png" src="../_images/univariate_univariate_volatility_forecasting_41_0.png" />
+<img alt="../_images/univariate_univariate_volatility_forecasting_36_0.png" src="../_images/univariate_univariate_volatility_forecasting_36_0.png" />
 </div>
 </div>
 
@@ -1690,7 +1611,7 @@ <h2 id="Filtered-Historical-Simulation">Filtered Historical Simulation<a class="
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1737,10 +1658,10 @@ <h2 id="Filtered-Historical-Simulation">Filtered Historical Simulation<a class="
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-        <script src="../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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+        <script id="MathJax-script" src="../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
     
   </body>
 </html>
\ No newline at end of file
diff --git a/univariate/univariate_volatility_forecasting.ipynb b/univariate/univariate_volatility_forecasting.ipynb
index 626d5e99ca..f215beac3c 100644
--- a/univariate/univariate_volatility_forecasting.ipynb
+++ b/univariate/univariate_volatility_forecasting.ipynb
@@ -31,24 +31,6 @@
     "plt.rc(\"font\", size=14)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Future Forecast Shape Changes\n",
-    "\n",
-    "<div class=\"alert alert-warning\">\n",
-    "    <h3><b>WARNING</b></h3>\n",
-    "    The future behavior of `forecast` is changing.\n",
-    "</div>\n",
-    "\n",
-    "Versions of `arch` before 4.19 defaulted to returning forecast values with the same shape as the data used to fit the model.  While this is convenient it is also computationally wasteful. This is especially true when using `method` is `\"simulation\"` or `\"bootstrap\"`. In future version of `arch`, the default behavior will change to only returning the minimal `DataFrame` that is needed to contain the forecast results. In the current version of `arch`, calling `forecast()` without the `reindex` keyword argument produces a `FutureWarning`.  You can silence the future warning by:\n",
-    "\n",
-    "* Using `reindex=False` which uses the future behavior. \n",
-    "* Using `reindex=True` which uses the legacy behavior.\n",
-    "* Importing `from arch.__future__ import reindexing` which will set the default to `False` and silence the warning."
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -101,10 +83,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Iteration:      5,   Func. Count:     35,   Neg. LLF: 6970.282172935112\n",
-      "Iteration:     10,   Func. Count:     63,   Neg. LLF: 6936.718477483884\n",
+      "Iteration:      5,   Func. Count:     35,   Neg. LLF: 6970.2779565773335\n",
+      "Iteration:     10,   Func. Count:     63,   Neg. LLF: 6936.718477482076\n",
       "Optimization terminated successfully    (Exit mode 0)\n",
-      "            Current function value: 6936.718476988963\n",
+      "            Current function value: 6936.718476988951\n",
       "            Iterations: 11\n",
       "            Function evaluations: 68\n",
       "            Gradient evaluations: 11\n"
@@ -122,7 +104,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "forecasts = res.forecast(reindex=False)"
+    "forecasts = res.forecast()"
    ]
   },
   {
@@ -184,75 +166,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "                h.1       h.2       h.3       h.4     h.5\n",
-      "Date                                                     \n",
-      "2018-12-31  3.59647  3.568502  3.540887  3.513621  3.4867\n"
-     ]
-    }
-   ],
-   "source": [
-    "forecasts = res.forecast(horizon=5, reindex=False)\n",
-    "print(forecasts.residual_variance.iloc[-3:])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you fail to set `reindex` you will see a warning."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\git\\arch\\arch\\__future__\\_utility.py:11: FutureWarning: \n",
-      "The default for reindex is True. After September 2021 this will change to\n",
-      "False. Set reindex to True or False to silence this message. Alternatively,\n",
-      "you can use the import comment\n",
-      "\n",
-      "from arch.__future__ import reindexing\n",
-      "\n",
-      "to globally set reindex to True and silence this warning.\n",
-      "\n",
-      "  warnings.warn(\n"
-     ]
-    }
-   ],
-   "source": [
-    "forecasts = res.forecast(horizon=5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "When not specified, or if `reindex` is `True`, then values that are not computed are ``nan``-filled."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                h.1       h.2       h.3       h.4     h.5\n",
-      "Date                                                     \n",
-      "2018-12-27      NaN       NaN       NaN       NaN     NaN\n",
-      "2018-12-28      NaN       NaN       NaN       NaN     NaN\n",
-      "2018-12-31  3.59647  3.568502  3.540887  3.513621  3.4867\n"
+      "                h.1       h.2       h.3       h.4       h.5\n",
+      "Date                                                       \n",
+      "2018-12-31  3.59647  3.568502  3.540887  3.513621  3.486701\n"
      ]
     }
    ],
    "source": [
+    "forecasts = res.forecast(horizon=5)\n",
     "print(forecasts.residual_variance.iloc[-3:])"
    ]
   },
@@ -276,7 +197,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -302,7 +223,7 @@
    ],
    "source": [
     "res = am.fit(last_obs=\"2011-1-1\", update_freq=5)\n",
-    "forecasts = res.forecast(horizon=5, reindex=False)\n",
+    "forecasts = res.forecast(horizon=5)\n",
     "print(forecasts.variance.dropna().head())"
    ]
   },
@@ -317,7 +238,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -358,7 +279,7 @@
     "    sys.stdout.write(\".\")\n",
     "    sys.stdout.flush()\n",
     "    res = am.fit(first_obs=i, last_obs=i + end_loc, disp=\"off\")\n",
-    "    temp = res.forecast(horizon=3, reindex=False).variance\n",
+    "    temp = res.forecast(horizon=3).variance\n",
     "    fcast = temp.iloc[0]\n",
     "    forecasts[fcast.name] = fcast\n",
     "print()\n",
@@ -376,7 +297,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -420,7 +341,7 @@
     "    sys.stdout.write(\".\")\n",
     "    sys.stdout.flush()\n",
     "    res = am.fit(last_obs=i + end_loc, disp=\"off\")\n",
-    "    temp = res.forecast(horizon=3, reindex=False).variance\n",
+    "    temp = res.forecast(horizon=3).variance\n",
     "    fcast = temp.iloc[0]\n",
     "    forecasts[fcast.name] = fcast\n",
     "print()\n",
@@ -445,17 +366,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Iteration:      5,   Func. Count:     40,   Neg. LLF: 6846.496665348549\n",
-      "Iteration:     10,   Func. Count:     75,   Neg. LLF: 6822.883179474602\n",
+      "Iteration:      5,   Func. Count:     40,   Neg. LLF: 6846.48928243015\n",
+      "Iteration:     10,   Func. Count:     75,   Neg. LLF: 6822.883188650879\n",
       "Optimization terminated successfully    (Exit mode 0)\n",
-      "            Current function value: 6822.882823372691\n",
+      "            Current function value: 6822.882823441358\n",
       "            Iterations: 13\n",
       "            Function evaluations: 93\n",
       "            Gradient evaluations: 13\n",
@@ -468,7 +389,7 @@
     "# TARCH specification\n",
     "am = arch_model(returns, vol=\"GARCH\", power=2.0, p=1, o=1, q=1)\n",
     "res = am.fit(update_freq=5)\n",
-    "forecasts = res.forecast(reindex=False)\n",
+    "forecasts = res.forecast()\n",
     "print(forecasts.variance.iloc[-1])"
    ]
   },
@@ -483,7 +404,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -492,15 +413,15 @@
        "(16804.0, 17165.0)"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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TTTfppptuGshb286ytQf17u5q3X3uDCW7eq4I31oWHKDSW/9DScpOsQKIXOUHAADRUx3OQEyVRH9mAACARBaxKcyJaumHB/T69kqt2V/b6+O2WRmIhX3MQKQHIgAAiJJmr1+tvoCkYAaiRHsVAACAREYAcZCsw3VvjcUDpqntfZjALLX3QCQDEQAARIt1DklyGhqTlSJJamzzq8Xrj+ayAAAAECUEEAepzQog9tIX6GBti5q8fiU5DY3PTe319fLSghmI2ysaVVbfGrmFAgAA9JFVCZGd4lZ6slNuZ7BPNVmIAAAAiYkA4iC1+YMBxIpeAojWAJVJeWlyOXofFDO7KFNTC9JU1+LTt57bJG/o9QEAAIZLTShQmJOaJMMwlBuqkKAPIgAAQGIigDhIVgCxtwzEbX0coCJJbqdDPzt/pjKSXVpfUqf/W7EjMgsFAADoo+rm4LkmJzTczerRXNlIBiIAAEAiIoA4CKZpyus3JUmVvZT0WANUph5hgIplTHaKfnjONEnSk2tL9PzHhwa5UgAAgL6zeiBmhwKHeWlkIAIAACQyAoiDYAUPpSNkIJYHMxCL+5CBaFk0KU9fPGGcJOknr2xTaV3LAFcJAADQP1YA8fAMRAKIAAAAiYkA4iC0dehPWNVDALGh1aeDdcFhKH0pYe7oCyeM15T8NLX6AtpQUj/whQIAAPSDNUQlJ9UKIIYyEClhBgAASEgEEAehYwCxtsUXnsjckVW+PCIjWZked79e32EYGp+bIqn3DEcAAIBIqukwhVmScilhBgAASGgEEAfh8IBhd4dqq3y5v9mHlvzQgb23Kc8AAACRdHgJc541RKWXns8AAACIXwQQB6GtQw9EqftD9dZQBmJ/+h92lEcAEQAADLOeS5g5jwAAACQiAoiD0LGEWeq+zDg8gbmgbxOYD2cFEClhBgAAw6XGykAMBQ5z06whKmQgAgAAJCICiINweAnz4UE+f8DUjgorgEgJMwAAsL9WX0BNXr+kjlOYg+eR+lafvP6uPZ8BAAAQ3wggDsLhB+jDA4j7qpvV6gvI43JoTHbKgN6DDEQAADCcqkM9nV0OQ+nJTklSpsclp8OQRBYiAABAIiKAOAhHKmHeGhqgMqUgLXzo7i8rA7G6yStfwDzCowEAAAan4wRmwwieXxyGodxUq4yZi5oAAACJhgDiILT5eh+i0t7/cGDly1Lw8O4wJFNSDQd2AAAwxA4foGJpH6RCBiIAAECiIYA4CIdnIFY0dA7wDXaAiiQ5HUb4wE4fRAAAMNSqm9ozEDuyMhAruaAJAACQcAggDoLVAzHZFfwYDz9QbwuVMBcPIgNRau+DSAARAAAMNauEOefwAGKalYHIeQQAACDREEAchNbQFOaRGcmSgj0QTTNY1lzT5FVZKCNxyiADiPkMUgEAAMPEykA8vIQ5L9wDkRJmAACAREMAcRCsDMSiTI+kYECxsc0vqX2Ayugsj9KSXIN6n7y04IGdDEQAADDUqpt7KmEOZSBSwgwAAJBwCCAOQps/mG2Y4XEpLckpqT1LMBIDVCztGYhc8QcAAEOrpocMxNw0qwci5xEAAIBEQwBxENpCJcxJLke4T6HVB7G9/+HAB6hY6IEIAACGS3VPPRBT6YEIAACQqAggDoI1hTnJaYT7AllZgluHJAORAzsAABha1hCV7MN7IHIeAQAASFgEEAehPYDo6HSo9voD2lXZJEkqLiQDEQAAxI7wEJWUpE63F4TOI7UtvnAVBgAAABIDAcRBaPMFeyAeHkDcXdUkX8BUerJTRZnJg36fjq9tTXkGAACINJ8/oPpWn6SuJcyZHpeSnIYkLmoCAAAkGgKIg2BNYXa7OgcQwwNU8tNkGMag38cqYe445RkAACDSalt84f/O8Lg63WcYRvhMUt7QOqzrAgAAQHQRQByE1k49ENuHqGwts/ofDr58WZI8bmd4ynNFA1f8AQDA0GjxBS9UJrsccjq6XgTNTw9WVtAHEQAAILEQQBwEb7c9EL3hCcyRGKBiyT9syjMAAECktYZ6G3pc3R8RC9KtDETOIwAAAImEAOIgdOyBmN9h0Em4hDkCA1Qs4UEqHNgBAMAQsQKIyT0EEMMlzGQgAgAAJBTXkR+CnoSnMLscyksLNhq3SnochjQ5LzVi70UGIgAAGGqt3r4FECvogQgAAJBQyEAchLYOJczZqUnq2CloXE6KPG5nxN6LDEQAADDU2jMQuz/DFIR6IDKFGQAAILEQQByEttAh2+005HIYykl1h++L1AAVS8cSaQAAgKHQ0tcSZi5oAgAAJBQCiINgDVGxDtlWlqAU2QEqHV+bqYcAAGCotHaYwtyd/HQuaAIAACQiAoiD0OYPDlFxO0MBxNT2AGIxGYgAACDGHGmIijWFua7FF34sAAAA4h8BxEGwSpiTrABiWscSZjIQAQBAbDlSADEj2aUkZ7Drc0Ujg1QAAAASBQHEQeg4hVlqD/JleVzhK/SRYmUg1rb4wqXTAAAAkXSkAKJhGMq3BqnQBxEAACBhEEAcBG94CnPwSrwVQJxamC7DMHp83kBkprjkdARfkyxEAAAwFKwAoqeHKcySVMAgFQAAgIRDAHEQrEO2lYF46tR8LZqUq88eMybi7+UwDOWFpjwTQAQAAEPhSENUpPY+iPRlBgAASByuaC8glnlDQ1SsHogjMz361WdmDdn75acnq6yhjQM7AAAYEi1HKGGW2isuyEAEAABIHGQgDoLVA9GawjzUyEAEAABD6Ug9ECWpwOqByBAVAACAhEEAcYACpilfIJiBmDxMAcR8SoYAAMAQ6lsAMXQeIQMRAAAgYRBAHKA2X/skZLcrsgNTepKXGjywVzZ6h+X9AABAYgkHEN09D1EJlzBzQRMAACBhEEAcIKt8WWrvgTjUrAzEQ/WUDAEAgMgjAxEAAADdIYA4QG2hASqGJJdjeDIQp4/IkCR9tL9GLV7/sLwnAABIHNYUZk9vAcS0YA/E+lYf5xEAAIAEQQBxgLyhDMQkl0OGMTwBxJkj0jUiI1nN3oDe31M9LO8JAAASR18yENOTneH76csMAACQGAggDpB1wHY7hyd4KEmGYWjx1HxJ0mvbKvr8vIZWn97eWSlfh7JrAACAw/UlgGgYhvLTKGMGAABIJAMKIG7ZskVf+cpXdPzxx2vWrFlavHix7r77btXV1R3xucuXL9e0adO6/d+3vvWtgSwnKsIZiMPU/9ByaiiA+NaOqvAajuRP7+zR15Z/rOc3HhrKpQEAgBjX0ocAotTeB5FBKgAAAInB1d8n7Ny5U5dffrlcLpeuvPJKFRUVac2aNVq6dKnee+89PfbYY0pLS+vx+Zs3b5Yk3XPPPXK73Z3uGzduXH+XEzVWD8ThDiDOGZWp3FS3qpq8+mBfjU6YkHvE52yraJQk7axsGurlAQCAGNaegdjzFGZJyg/1QaSEGQAAIDH0O4B49913y+v16sknn9TkyZMlSZdffrlmzpype+65R3//+9/1pS99qcfnb968WYWFhbr44osHvmobaPO190AcTk6HoVOm5GvZuhKt2FbRpwBiSW2LJKmsnkM+AADoWV9KmCUpPzyJuXXI1wQAAIDo61f0q7W1VatWrdLChQvDwUPLhRdeKElauXJlr6+xZcsWFRcX92+VNtQWpRJmSVo8NU+S9Mb2SvkDZq+PDZimDtUHD/dlHPIBAEAvrCnMRyxhDvVALKcHIgAAQELoVwai2+3WCy+8oECga++9iorgUA+ns+eSl0OHDqm6ulpTp06VJLW1BQ+dSUlJ/VmGLVj9B4dziIpl4dhsZXpcqmryas2BWh09NrvHx1Y0tMkXCjKW1RNABAAAPbMyED19zECkByIAAEBi6FcA0eFwaOzYsd3e98ADD0iSjjvuuB6fb/U/3L9/v5YsWaLNmzcrEAho1qxZuv322/WJT3yiP8uRJBnDH7+T1J6BmOxyDPsa3C6HPjk5T//6+JBe316hheOye3xsaX1L+L/LG9sUME05HYNfsPU1R+vzR2fsR2xgn+yLvYkd7NXQ8QdMeUM9nj3u3s831hCVyoa2LnvC3tgb+xQ72Cv7Ym9iA/tkX+yNvfR1H/rdA7E7y5Yt07Jly1RUVKTLLrusx8dt2bJFkvTRRx/pc5/7nG655Rbt2rVLDz74oD7/+c/r3nvv1RlnnNGv987LyxjU2gfKsz84cTotxa38/OFfwwVHj9W/Pj6kN3ZU6ccXp8vRQ1CwYX/7ZGx/wJThSVJ+pidi64jW54/usR+xgX2yL/YmdrBXkdfU5gv/96gRWUpL7vmYWBwqRqloautyDmJvYgP7FDvYK/tib2ID+2Rf7E1sGXQA8cknn9T3v/99paam6t5771V6enqPj503b55uvPFGXXzxxZ0yGT/1qU/p05/+tO666y6deuqpvZZBH66ysl5m720Ah0RFdWiisd9URUX9sL//zFyPUtwOldS26M2PD2pWUWa3j9t6oKbTnzftqZKjaPDfpIYR/GaP1uePztiP2MA+2Rd7EzvYq6FT0+QN/3dDbZOae6lYcHmDwcb6Fp/2l9TI43ayNzGCfYod7JV9sTexgX2yL/bGXqz9OJJBBRDvvfde/e53v1N6erruv/9+zZkzp9fHH3vssTr22GO73D569GidccYZeuaZZ7R161bNmDGjz2swTUXlL1zHKczReP8kp0MnTszTK1vL9drWCh01svsAYklt576Hh+pbNXNk5KL80fr80T32IzawT/bF3sQO9irymr3BASouhyGHYfT6+aa6nUp2OdTqC6i8oU1jslPC97E3sYF9ih3slX2xN7GBfbIv9ia2DGiEsNfr1Z133qnf/e53Kiws1NKlS7Vw4cJBLSQvLzhZuLGxcVCvM1zapzBHr2j/1OJ8SdKKbRUye/iuO1gX7IForZJBKgAAoDvWAJUjTWCWJMMwwn0QmcQMAAAQ//odQPT7/br99tv19NNPq7i4WI8//rimT5/ep+feeOONOuuss8LTlzvasWOHJGncuHH9XVJUWBmIbueAYrAR8YmJOUpyGtpX06LtFd0HXktDAcRJ+amSpDIO+QAAoBv9CSBKUkF6siQuTgIAACSCfke/fv3rX+ull17SnDlztHTpUhUVFfX5ufn5+dq9e7eeeOKJTre///77evPNN7Vo0SIVFhb2d0lRYU0p7OsheyikJbl0/IRcScEsxMOZpqmSuuChfu6oLElSWQOHfAAA0JUVQPT08WxTGMpA5GwBAAAQ//rVA3Hfvn168MEHZRiGzjjjDK1YsaLLY/Ly8rRo0SJt3rxZW7Zs0bRp08IZirfeeqveeust3XPPPdq0aZNmzZql7du369FHH1VhYaHuuuuuyHxVw8AqYY5mBqIknTo1X2/uqNRr2yp0wycmdLqvptmrVl9AhqTZozK0bF0JWQIAAKBb7RmIfRtmNyLDIynYXxkAAADxrV8BxLfeeks+X3Dq3i9/+ctuH7NgwQItWrRIL7/8sn7729/qlltuCQcQR4wYoSeffFL33XefVqxYoeXLlysvL09LlizRLbfcMizZh81ev5KcDjl7mSzYF+09EKMbQDxpcq6cDkM7Kpq0p6pJ43NTw/cdDGUf5qcnaXRWsLk5WQIAAKA7/S1hHpERLGEmgAgAABD/+hVAvPLKK3XllVf26bG33nqrbr311i63FxQU6Ic//GF/3jZiVu2t1s1PrFdaslPzRmdpwZgsLRibrWmF6XL1M6AYnsIcxSEqkpTpceuYsdl6b0+1Vmyr0HXHtfeQtPofjszwqDAjVGZU3yrTNGUY0V03AACwl1ZfcAozAUQAAAAcrl8BxFi3oaRepqSGVr/e3lmlt3dWSZJS3U7NGZ0ZDCiOydLMkRlHLE0OZyBGsQeiZfHUPL23p1qvHRZAtPofFmUmqyAteMhv85uqbfYpO9UdlbUCAAB7aul3BmLw4iQBRAAAgPiXUAFEK2vwpEm5WjA2Wx/tq9GaA3Wqb/Xpvd3Vem93taTgwXnOqExdMm+UFk/N7/61QkNUol3CLEknT8nXT1/Zrk2HGlRa16KRmcGeRCW1wQzEoiyPklwO5aa6VdXk1aGGVgKIAACgk4GWMFc1edXmCyjZHf0zEQAAAIZGYgUQQ1mD43JSdfXCMbp64Rj5A6a2VzTqo/21+mhfjVbvr1Vti0+r9tZoS1mDTpmS1225r9cmQ1QkKS8tSfNGZ2r1gTq9tq1CVx49RpJUEiphLsoMHvAL05NV1eRVWX2rphWmR229AADAfvo7RCU7xa1kl0OtvoDKGlo1NidlKJcHAACAKIp+9GsYWQfjJFd7QNDpMDStMF1XLBitn19wlF768glaes0CSVJdi08Nrf7eXyvKPRAti4sLJEmvb6sI31YaKimyMhILQ5kCDFIBAACHCwcQ+5hJaBiGCtMpYwYAAEgECRVA7MvkZIdhqLgwXVmeYHJmTwdir416IErS4il5kqQ1B+pU0dgmqbsMxPZBKrGktK5Fv3p9R/jrAQAAkWcNUfH042zDIBUAAIDEYI/o1zBp60dvHytrr7S++6CVnXogSsH1zhyZIVPSm9srVN8he7LosAzEQw1t0VrmgPz9g/36x4cH9OhHB6K9FAAA4lZ/eyBKBBABAAAShT2iX8Ok1df3oN/IIxyI23z2ykCUpFNDA19e21YRztbLTnErxR3sZVSYHvyaymPskL+1vFGSdLCWDEQAAIbKQAKIhQQQAQAAEoJ9ol/DoK0fZcfWFfXSuh4CiH579UCUFJ4Y/cG+Wm0pa5DUXr4sSYUZoRLmGOqBaJqmtocCiPxyAgDA0GkZRAZirLVHAQAAQP8kVgCxXyXMvV9Rt9MUZsu4nBRNyU+TP2DqiTUHJbWXYkvtGYiH6ltlmmZU1thfh+pbVd/qk9RzMBcAAAxef6cwS5QwAwAAJAr7RL+GQWsfhqhYwhmIRyhh7s9V+uFglTFvOtRdBmLwv5u9ATW2dT9d2m62VzSG/7u62asWb2ysGwCAWEMPRAAAAPTEXtGvIdafvoXhA3EPk3+tISp2ykCU2suYLUUdMhBT3E5lHmG6tN1Y5cuWngK6AABgcKwpzAMJINa2+LjIBwAAEMfsFf0aYlbfwuR+ZCCWNbTJH+ha7tvWj2zG4TQ5P1XjclLCf+6YgSi1lzHHSh/EjhmIknSIMmYAAIaElYHo6UcAMSPZpRR38PGxcnESAAAA/Wev6NcQa+1HBmJ+erKchuQLmKpqautyf3s2o32GqEiSYRg6ZUp7FmLHHohSh0EqMXLI3xbKQLSyIUrrmcQMAMBQGEgPRMMwKGMGAABIAAkVQPT2Ywqzy2GoIL3nA7HXphmIknRqcXsA8fAMROtrKqvvGhS1mzZfQHuqmiRJC8dmS2KQCgAAQ6XF2/8MRKlDdQMBRAAAgLhlv+jXEApfWe9j0C88SOWwoJUvYCrUAtF2PRAlaeaIdF159Gh9/vhxyvS4O903wgqKxkAJ866qJvnNYHnU7FEZkuiBCADAUBlID0TpyIPnAAAAEPtc0V7AcGrrRwaiJI3MTNbag10PxFb2oWS/KcxSsJzoa6dM7va+WCph3hHqfzilIC08DIZfTgAAGBrhC63ugQUQKWEGAACIX4kVQLT6Fjr71rewpwOx9TqSPTMQe1MY+prWl9Tp7pe2akJuqo4amaF5ozNlGPbq52j1P5yan3bEqdgAAGBw2nsgEkAEAABAZwkTQDRNU22huuO+ZiCOyAhlvR0WtLIyGZ1GsFdiLJmSnyaXw1BDq1/PrC8N337/ZXO0YEx29BbWje3lXTMQD9W3KmCactgs2AkAQKxrGWgAMZMAIgAAQLxLmACiFTyU+j74pMcMxFAAMdayD6XgEJWnPneM1h+s0+6qJv17c5n217Roa1mj7QKI26wS5vw0FaYnyVBwH6ubvMpLS4ru4gAAiCOmaQ5oCrPUfl6KhQFtAAAAGJjECSD6+t+3cGQPV9TbfP3LZLSbUVkejcoKZvQ1ef36x4cHVGKz0uDqpjZVNgZ/EZmcnyaX06GC9CSVNbSptL6VACIAABHU8ULrQKcw17f61Njqi+i6AAAAYA+xGQEbgNZQ1qChvpcdjwxdUa9q8oavyksdhrHEYAbi4cLDSersVXa0PZR9OCbbo9SkYCaEVVJOH0QAACLLmsAs9b+EOT3ZpbTQz+qS2uaIrgsAAAD2EPsRsD4KD1BxOfo8LCTT4wpfhe84tdjr798wFjuzAoh2y0C0BqhMyU8L32ZlhDKJGQCAyLIulDoG2N/ZKmM+WGOv8wQAAAAiI+ECiP25qm4YRoegVfuB2Dpkx2IPxMMVhb6+ErtlIFoTmAs6BBBDv5zYLVsSAIBY13ECc18vtHZkBRDJQAQAAIhPsR8B66PWAZYddzdIJZyBGKM9EDuyMhBrmr1q9vqP8Ojhs72CDEQAAIZLywAHqFjIQAQAAIhvsR8B66OOJcz9MTKja49Aq9F4PPRAzPC4lJ4c6ltkkzJmf8DUzsomSdKUgvTw7SPCe2GPdQIAEC9aB1Cp0ZEVQCyt5Wc0AABAPIr9CFgfWYNPkiOQgRgORsZBD0SpYx9Ee2T27atpVqsvII/LodGhadFSz1OxAQDA4FhDVAYbQNxYUifTNI/waAAAAMSahAsg9jcDcUQ3ZbMDfS27au8taI+sAav/4eT8NDk7NHLvOBW7xUbl1gAAxLrBZiCeMCFHyS6H1h+o1ft7qiO5NAAAANhAfETA+qA9a7C/Jcw990CMhyEqkjQqlOV3sNYemX3d9T+UglOxU93BcmuyEAEAiJxWb/Bs4xlgADE/PVlL5hZJku7/7x6yEAEAAOJMfETA+qD9ynr/yo7DJcx1reHDcKvPDL1WfHx8IzPt1VvQykCcUtA5gGgYRrcZoQAAYHAGm4EoSZ89dqw8bofWl9Trnd1kIQIAAMST+IiA9cGAS5hDAcQmr1/1rT5J8ZeBWBQKytmlB+K2UAbi1MMCiFKHjFCbrBUAgHjQOsgpzJKUn5akq48bL0n60ztkIQIAAMST+IiA9cFAS5g9bqeyU9yS2stmw8HIuBuiEpkMxMH8wtDQ6tPB0ATHyfndBBDDGYj2yJYEACAetEQgA1GSvnTyZCW7HNpYWq//7qqKxNIAAABgAwkTQGz1B4Na/Q0gSh2HjIQCiAMMRtqVlYFY0dgW/toGqs0X0BUPf6jvPr9pQM/fEco+LEhPCgduOxqZYZVbk4EIAECkDHYKs6UgI1mXzh8liSxEAACAeBIfEbA+CAf9BnAwHnlYiW+bFYyMkx6I2Snu8C8Mgx1Osre6WTsqmvTylnL5/P0PRu7oYYCKZSQ9EAEAiLhI9EC0XHvMGKW4Hdp0qEFv7SQLEQAAIB7ERwSsD9oGcTBun1IcLJtti7MeiIZhaFSojPngIMuYG0J9IgPmwIJ828p77n8odRhqQwARAICIiWQAMSc1SZfMGy2JLEQAAIB4ER8RsD5o9Q+87Pjw4Jo1RCU5TgKIUofMvkEGEBvb/OH/tgKu/bG9ovsJzJaO6wzwCwkAABERiSEqHV2zcIxS3U5tKWvQG9srI/KaAAAAiJ74iYAdwWBKmK0MxAM1zZ1eyx0nQ1SkjoNUBpfZ19jmC/93fwOIpmmGA4hT89O7fcyI9GQ5jWAZeWVj28AXCgAAwqwAoidC7VmyU93tvRDf3cNFPwAAgBiXOAHEQWQNhkuY6zqXMMdLD0SpfZDKYCcxWyXMUv/LoQ/Vt6qh1S+nw9D43JRuH+NyOsJlzAPJcAQAAF1FaohKR1ctHKO0JKe2lTfqdbIQAQAAYlr8RMCOYDAZiKNDAcSGVr/qWrztQ1TiqIQ5chmI7SXMB2r6F+Cz+h9OzE3ttb/kqOxgcPEAAUQAACIikj0QLdkpbl22INgL8YF3yEIEAACIZfETATuCwWQNetxO5aa6JQWz3sLByDgKIEaqB+JgMhCP1P/QMjoU7CSACABAZLQMQQBRkq46erTSkpzaXtGoFdsqIvraAAAAGD7xEwE7gvCV9QH2LRzdYRJzPJYwW2XaZfWt8gUGniEwmCEq4QnM+UcIIGYTQAQAIJLC5yR3ZM82mR63rljQPpGZLEQAAIDYFD8RsCMYbNAvPEiltiU8hTkpjoao5KUlye005Del8oaBlzE3dAggVjV51ez19/LozqwMxMlHyEAMT8UmgAgAQEREegpzR1cePUbpyU7trGzSK1vKI/76AAAAGHqJE0AcZNlxxwBia3gKc/x8fA7DCA8nGcwglcYOJcxS34N8rb6A9lY1SepHBmJoKjYAABicoeiBaMnwuHTlgjGSpD+/u1f+QVQ6AAAAIDriJwJ2BK2+4GF1oAfjjllvXmuIShyVMEvSSGuQSm1kMhClvgcQd1c2yW9KWR6XCtKTen2sFcwtb2gLB4YBAMDAWVOYPUN0trni6NHKSHZpVxVZiAAAALEoviJgvbBKmAeaNTiqux6IcZSBKEmjMiOXgWj9AtLXAOK2igZJwQEqhtF7aXhOilspbofMQa4VAAAEDWUGoiSlJ7t01cLQROZ395CFCAAAEGPiKwLWi7ZBHoytstmSuvYS5njqgSi1ZyCW1g08A9EaomJNUu7rJObt5cHy5SlHKF+WJMMwOpWUAwCAwRnqAKIkXTZ/tLI8Lu2pbtZ/NpcN2fsAAAAg8hImgNg6yKzBERkeOQ2pzW+qIjRkJN5KmK0y7d2hXoQD0RDKQCwuSJckHajpYwDRykDsQwBRkkZnpUhikAoAAJEwlENULMEsxGAvxL+8t1c+shABAABiRnxFwHoRHqIywKCfy9E+ZCTUAjGuhqhI0rwxmTIkrT1Yp33VAxtQYmUgFhf2LwNxW3lwAvPUI0xgtpCBCABA5LQMQwaiJF06f5SyPC7trW7WfzaRhQgAABArBnRK3LJli77yla/o+OOP16xZs7R48WLdfffdqqurO+Jz/X6/HnroIZ199tmaM2eOTj31VP3qV79SS8vQBoKsvoXJgwj6WUEry1Afsofb6KwUHT8hR5K0bF1Jv5/v9QfCGQxTQxmIB2tbZJq9ZxhUNbWpqskrQ9KkPmcgtvekBAAAA+cLmOGehEN9tklLcumaY8ZKkv7y3h6yEAEAAGJEv0+JO3fu1OWXX653331Xl112mb773e/quOOO09KlS3XllVeqsbGx1+ffdddd+slPfqKpU6fq29/+tk488UTdf//9uvnmm48YaBqM1kFmIEpdA4jxloEoSRfPGyVJem5Dafgz66vG1vYJzFYpcmObX7Utvl6ftz2UfTgm26MUd99Kp7rLQKxp8urSv36gn76yrV/rBgAgkVkTmKWhm8Lc0SXzRik7xa19NS16ceOhIX8/AAAADJ6rv0+4++675fV69eSTT2ry5MmSpMsvv1wzZ87UPffco7///e/60pe+1O1z165dq8cee0yXXnqpfvSjH4VvLyoq0m9+8xu9+OKLOueccwb4pfTMNM1BlzBLXQOI8TZERZJOnJirkRnJKq1v1atby3XOzBF9fm5DW/sE5tQkp/LTklTR2KaDtS3KTnH3+LztFcEA4pRQ1mJfdJeB+MrWcu2qatKuqiadXlygheOy+/x6AAAkqo4XDIejv3NqklPXHjNG9765S395b6/OnlEoVxxelAUAAIgn/Tqttba2atWqVVq4cGE4eGi58MILJUkrV67s8fnLly+XJF133XWdbr/uuuvkdru1bNmy/iynz/wBU1ZuYyRLmAc6kMXOnA5Dn5lTJEl6cs3Bfj3X6n+YnhyMS4/qY5lxuP9hH8uXO752fatPdS1eSdJ/d1WF7//NGzsVGMKMVgAA4kW4SsNpyGEMz8XRi+eNUm6qWwdqW/Q8WYgAAAC2168ImNvt1gsvvKC77rqry30VFRWSJKez5xLUtWvXKiMjo0vwMTU1VVOnTtW6dev6s5w+syYwS4O7sm5N/o3Ea9nZ+bNHyuUwtL6kXlvKGvr8PGsCc1pS8O9AXwOIVgnzlD4OUJGkFLdTuanu8Ou3+gJatbdGkuR2Gtpc1qB/05wdAIAjavUO/QTmw6W4neFeiA++t1def//apgAAAGB49auE2eFwaOzYsd3e98ADD0iSjjvuuB6fX1paqqKiom7vGzFihDZu3Kj6+nplZGT0eU19uVDe8VCa7DL69JzujM5uz0B0Ogw5HfFXwixJBelJWjw1Xy9vKddTaw/qf84s7vIY6zPs+Fk2edszEA2jQ5lxXUuPn7kvYGpnZTCAWFyY1q+9GZ3lUVWTVwdqW1Td7FWrL6DC9CRdOn+UfvvWbv3+7V06rThfnj72VYxl3e0H7Id9si/2JnawV5FnXWhNdjkG9bn2d28umVekv6/ap4N1rXppc5k+PWvkwN8cfcb3UOxgr+yLvYkN7JN9sTf20td96HcPxO4sW7ZMy5YtU1FRkS677LIeH1dfX69x48Z1e19KSjC7r6mpqV8BxLy8Iz+2raZZUjBjsKAgs8+v3fW9THncDrV4A/K4HMrP7/s6Y83nT56sl7eU69+bynXXkjnK9HTfw7Dj52/sq5Uk5aQnKz8/Q9PGZEvaq7Imb4+f1fayBrX5TaUmOTVnUoEc/QjKThqRofUl9ar1mdpcGcyUPG3mCN1y5nQtX39IB2qa9czmCt28eEqfXzPW9eX7AdHHPtkXexM72KvISWkMtgJJTXZF5GzTn725+oQJ+s2r27TyQJ2uO2XqoN8bfcf3UOxgr+yLvYkN7JN9sTexZdABxCeffFLf//73lZqaqnvvvVfp6X0fhNGRNYHZ4ehfWXBlZb2O1OqutDoUQHQaqqioH9D6LEUZHu2qapIrAq9lZ5Mz3JqUl6qdlU165M0dumzB6E73G0bwm73j518aGobiNqSKinplhrZyT0Vjj5/Vqq3lkqRJeamqqup7ubQk5XmCmYVbDtbq/d3VkqSjR2WoobZJN35inL73whb9fsV2nTEpR7lpSf167VjT3X7Aftgn+2JvYgd7FXmHKoI/f12hn98DNZC9mV2YKkn67/YKlZXXDVsPxkTG91DsYK/si72JDeyTfbE39mLtx5EMKoB477336ne/+53S09N1//33a86cOb0+Pi0tTc3Nzd3e19IS7JOXmdm/DEHT1BH/wlm9fZKcjkH/5RyVFQwgRuK17M3QRXOL9PPXdujJtSW6ZN4oGd0c6jt+/lYPxPQkp0yzvQdiSV2L/AGz218KtpUHf2mZkp/W789zdGYwa/W93dU6WNsit9PQMWNzZJrSmdML9Y8PD2jToQbd/84efev0xMhq6Mv3A6KPfbIv9iZ2sFeR0+JtL2GOxGfan705akSGUt1O1Tb7tPVQo6aNGNiFaPQf30Oxg72yL/YmNrBP9sXexJYBTQHxer2688479bvf/U6FhYVaunSpFi5ceMTnjR49WocOdT9pr7S0VDk5OUpOTh7IknrVsbfPYFlBsXicwHy4c2aOkMfl0K7KJn20v/aIj28ITWFOC01hLkxPltNhyOs3Vd7Q1u1zwhOY+zFAxXL4kJYFY7KUGhrg4jAM3XbKJEnS0+tKtKuyqd+vDwBAIrCmMHuiMBzO5XRo/pgsSdLKvdXD/v4AAADom36fFP1+v26//XY9/fTTKi4u1uOPP67p06f36blz585VTU2N9u7d2+n2xsZGbd++XfPnz+/vcvqkLXQwdkcg6JdIAcT0ZJc+NaNQkvTU2pIjPr6xQwaiFBw0MzIjGBDuaRLzjlDZ8+T8/gcQOw61kaRPTMzt9OcFY7J1ypQ8+U3p3jd39vv1AQBIBFYAcTinMHd0zLhsSdKqvTVReX8AAAAcWb+jYL/+9a/10ksvac6cOVq6dGmPU5W7c95550mS/vznP3e6/aGHHpLX69WSJUv6u5w+afNFLgNxTCiA6HHHfwBRki6eN0qStGJbhSobu88itByegSi1B1wP1HYtXW9o9elgXaukYAlzf1kZjpYTDwsgStItJ02U02Ho7Z1VWkVmAwAAXbT6gj+/I3FOGggrgLh6f628oaoRAAAA2Eu/eiDu27dPDz74oAzD0BlnnKEVK1Z0eUxeXp4WLVqkzZs3a8uWLZo2bVo4Q3HBggVasmSJHnvsMdXW1mrRokVat26dnnjiCS1evFinn356ZL6qw1glzJHIGjxhYq4umD1SJ03KG/RrxYJphemaXRScdvzshlJdf1z3U7Sl9gzEtKT2DIax2SlatbdG+6q7BhCt7MPC9CRlpXQ/5bk3Toehosxk7a9p0dhsj8bnpnZ5zPjcVF00p0iPrzmo37yxSw9fnU2DdgAAOmiJ4IXWgZhSkKbsFLdqmr3aUFIfLmkGAACAffQrgPjWW2/J5wsGiX75y192+5gFCxZo0aJFevnll/Xb3/5Wt9xyS6cS5x/96EcaN26cnnrqKb366qsaOXKkbrrpJn3pS1/qdkhHJFgZiEkROBgnuxz67pnFg36dWHLR3FFaX7JFy9aW6NpjxnbK+uuoMZSBmN4hA3FcTnDQyd7qriXM7f0PB94wfVSmR/trWrqUL3f0xRPG6/mNh7SlrEEvbizTuUeNGPD7AQAQb1qjHEB0GIYWjs3WK1vLtWpvNQFEAAAAG+pXAPHKK6/UlVde2afH3nrrrbr11lu7vqHLpZtuukk33XRTf956UNqsISoJ0LdwKJw+rUC/en2HSutb9d9dVfrk5O6zLxu6y0AMBRD31XTNQNw+iP6HlovnjVJjm1+XzR/d42OyU9363HHjdN9bu/T7t3fptOJ8edzR6fMEAIDdRDuAKEnHjrcCiDW64RNRWwYAAAB6kBARtUhmICaiZJdD580aKUl6au3BHh/XbQZidiiAWN0s87D57NsHMYHZsnhqvh66an44UNmTyxaMVlFmssoa2vSPDw8M+P0AAIg30R6iIrX3QVxfUq+m0HkCAAAA9pEQEbW2CPZATFRL5gSH5by7q1r7u8kmlLrPQByd7ZHDkJq8flU2ecO3m6YZzkCcMogAYl8luxy6edFESdLfVu474kAYAAASRTiAGMUBcWOyUzQqM1n+gKnVB2qjtg4AAAB0LyEiau1X1hmeMVBjc1J0/PgcmZKWryvpcr9pmuEMxI4BRLfToZGZwUnMe6ubwreX1LWqsc0vl8PQhCNkD0bKGdMLNHNkhpq8fj3w7p5heU8AAOzOmsLsiXKlxjHjciRJq/bURHUdAAAA6CohAojhDERKmAfl4nnBLMRnNxwKl4Vb2vymfIFgiXLHEmapcxmzxco+nJiXKtcwZYY6DEO3nTxJUjAIurOycVjeFwAAO7NDD0SpvYx51d7qqK4DAAAAXSVERC3cA5ES5kE5cVKeCtOTVNPs1atbyzvdZ5UvG5JSkzr3UBrbzSRmq//hlEEMUBmI+WOydMqUPAVM6b43dw3rewMAYEd2CSDOHZ0pSdpZ2RS+KAkAAAB7SIiIWqsveAiN9sE41rkchj4T6oX4xJrOZcxW+XJqklMOo3Op+LhuJjFvi8AAlYG65aSJcjoMvb2zSiv3kOUAAEhsLTYJIBZmJCvZ5ZAvYKqktuXITwAAAMCwSYiIGkNUIufC2SPldBhad7BOm0rqwrd3N0DFYmUgdi5hbpA0PANUDjc+N1UXzw0GQn/9xk75yXIAACQwO0xhloKtRqyLjns69E0GAABA9CVERC1cwkwG4qDlpydr8ZQ8SdLf32sfRNLYFgogHtb/UOrQA7GmWQHTVKsvoL2hYOLUYS5htnzh+PFKT3ZqW3mjXtx0KCprAADADuxSwixJ48NtT5qP8EgAAAAMp+ifFIdBKxmIEXXR3FGSpOWrD4R/6WhsDZYwpyd1DSAWZXnkdBhq9QVUVt+qXZWNCphSlselvLSk4Vt4B9mpbn3uuHGSpD+8vVstXn9U1gEAQLRZU5jtEEAMZyBWEUAEAACwk+ifFIcBGYiRdfTYLGUku9TU5g+XJTeEMxC7lj+5HIZGZ3kkBbMQO/Y/NA7rlzicLp0/WqMyk1XW0KalH+6P2joAAIgm62KgxwbnpPG5qZKkvZQwAwAA2Er0T4rDwOqBmEwGYkQYhqEJucEMgd1VwQN+bxmIkjQ2u70P4vaK0ATmgvShXmqvkl0O3XzSREnS31buU0VjW1TXAwBANNiphLm9ByIZiAAAAHYS/ZPiMGglAzHirAwBK4DYWwai1P4Lwd7qlvYMxCj1P+zojGkFOmpkhpq9AT320YFoLwcAgGFnlyEqUvt5obyhTU1ttBcBAACwi4SIqHn9BBAjzcpAtHoUWRmI3U1hljpMYq5p1o5QBuLkKExgPpxhGLpg9khJ0uayhiivBgCA4WenDMRMj1s5KW5JCrdJAQAAQPRF/6Q4DMIHY0qYI8bKQNxjlTCHsgTSu5nCLLVPYl5/sE5VTV4ZkibnpQ79QvtgwmFfCwAAicI0TVsFECVpvHWRkj6IAAAAtmGPk+IQawtnIEZvYEe8mRAuYW6WaZpqaA2VMB8hA7G62Rv+s8cd/VIpqT2bsqSulWnMAICEYgUPJfsEEOmDCAAAYD/2OCkOsfAUZjIQI2ZMtkdOh6Emr18VjW3tGYg9DFEZkZEst7M9gDvVBuXLluwUt7I8wXXv5ZcVAEAC6RhAtMMUZkkan0NlAAAAgN3Y46Q4xOxWmhMP3E6HxnUYpGJlIKb3METF6TA0Jisl/OcpNhigYjEMo8tQGAAAEoF1RnIakssmF1rbB69xUQ8AAMAu7HFSHGJtflMSQ1QibVIoCLinqjmcgZjWQwai1P4LgWSvAKLUdSgMAACJwE4TmC3jctsDiKZpRnk1AAAAkBIlgEgJ85CYFCpD3l3VpMa23jMQpfY+iJI0xUYlzFLHno5kIAIAEocdqzTGZKXIYQQHtFU2eaO9HAAAAChBAoitfvsdjuPB5IJ0ScEm5w2tR85AtAKIqW6nRmV5hn6B/UAJMwAgEbX6gj+/7XRGSnI5VJQZPCfQBxEAAMAe7HNaHCK+gCl/IFTCTAZiRE0KBRB3V7ZnIKb1koE4a2SGJGnemEw5DHtNxLYyEPdUNytAuRQAIEG02DADUZLG59IHEQAAwE56TheLE15/+3RBux2OY93kUBlyaX1r+Lb05J7/ShUXpuufnz1aIzOSh3xt/TUqyyOXw1CrL6BD9a3hzAcAAOKZHUuYJWlcTqre2VVNABEAAMAm7HVaHALWwVgKTg5G5OSmJSnT0x4wdBqS5wi/gEzJT+s1yBgtLocRLrGmjBkAkCjsOERFksbnWMPN+JkMAABgB3EfUbMGqDgdhpwOe5XNxjrDMMKlv5KUluySYbPS5P5oH6RCtgMAIDGEA4huex0Jx+VQwgwAAGAn9jotDoE2a4AK2YdDYnyHycppSfbKXuivCblkOwAAEos1ROVIFQTDzRputr+2Rb4O7WgAAAAQHfY6LQ4BK4CYZLODcbwY3zEDsZcJzLEgPEiFACIAIEHYtQdiQXqSPC6H/AFTB+taj/wEAAAADCl7nRaHgFXCnOSM3dJaO7Oy9iQpvZcJzLFgfLgHIuVSAIDEYNcAosMwwmXMXNgDAACIPnudFoeAXQ/G8WJCHGUgWtmUFY1tamj1RXk1AAAMvRYbn5OsM8auSgKIAAAA0Wa/02KEUcI8tEZne2Qld8Z6BmJ6skv5aUmSyHYAACQGu05hlqTJ+WmSpB2VjVFeCQAAAOI+qtbmMyVJSQxRGRJup0Ojs4MlRrGegSi1l2RTxgwASAR2rtSYnB/MQNxZwUU9AACAaLPfaTHCWv32PRjHC6t3YKxPYZbay5h3k4EIAEgA1hRmO56TJuUFMxB3VTXJHzCjvBoAAIDEZr/TYoS1D1GJ+y81ao4dnyNJmj4iPcorGbwJBBABAAnEykD02DCAOCrLo2SXQ62+gA7UtkR7OQAAAAkt9mtOjyAcQLThwTheXL5gtD41vVDZqe5oL2XQrBLmPZQwAwASgJ1LmJ0OQxNzU7W5rEE7KxrDU5kBAAAw/Ox3WowwSpiHRzwED6X2DMR9Nc3yhf7uAAAQr+wcQJTa+yAySAUAACC67HlajCBKmNEfhRnJSnU75QuY2ltDFiIAIL612HgKs9TeB5FBKgAAANEV91G1Nj8BRPSdwzDC2Q7by8l2AADEt1av3TMQgwFEMhABAACiy56nxQhqpQci+mlS+JcVsh0AAPHNzlOYpfYS5j1VtBYBAACIJnueFiPISwYi+mmKFUAkAxEAEOfs3gNxREay0pJoLQIAABBt9jwtRlD7wdiI8koQK6wA4vYKAogAgPhmnZM8Ng0gGoahSXmhQSr0QQQAAIgae54WIyjcA9GmB2PYj1UudaC2Rc1ef5RXAwDA0AlfaHXbc4iK1HGQChf2AAAAoiXuo2pMYUZ/5aQmKTfVLYlfVgAA8c3uGYiSNCl0YY/exAAAANFj39NihLT6TEn27e0De6KMGQCQCOw+REWSJocyEHfwMxkAACBq7HtajJA2hqhgACZbg1TotwQAiFM+f0D+4HVWewcQQxmI+2uawxmTAAAAGF72PS1GSLiE2cYHY9gPGYgAgHjX0iEYl+yybw/EvLQkZXlcCpjS7iou7AEAAERD3EfVWv3WFOa4/1IRQZMLKJcCAMQ3K5vPkJTkNKK7mF50nMS8s5KfywAAANEQ91E1hqhgICblpcqQVNXkVVVTW7SXAwBAxLV2qNIwDPsGECVpEq1FAAAAoiruo2rhHohkIKIfUtxOjc72SCILEQAQn2JhArNlUmiQypayhiivBAAAIDEN6sS4du1azZw5U++8806fHr9y5UpNmzat2/9dc801g1lKj6zDcTIZiOin9j6IZDsAAOJPLExgtiwYkyVJen93tTYfqo/yagAAABKPa6BP3L17t26++Wb5/f4+P2fz5s2SpG984xsqLCzsdF9+fv5Al9Irq4TZHQOHY9jL5Pw0vb69kgxEAEBcCl9kjYEz0pSCNJ01vUD/2VyuX7+xU3+4ZI7ty66BRPPipkN6ck2JfvLpGSrMSI72cgAAETagAOLLL7+s//mf/1FtbW2/nrd582Y5nU5de+21Sk4e+h8q/oCpylD/uuyUAcdKkaAm5zNIBQAQv1rCAUT7TmDu6JaTJur17ZX6cF+t3theqVOmDs3FZwAD89SaEq07WKdXtpbryqPHRHs5AIAI6/cl5xtuuEG33HKLCgoK9OlPf7pfz92yZYvGjh07LMFDSSpvaJXXb8rlMDQiwzMs74n4MaVDADFgmlFeDQAAkRVLGYiSNDLToyuPHi1JuvfNnfKG+lwDsAcrcWMrvUoBIC71+8S4c+dO3X777Vq+fLkmTJjQ5+f5/X5t27ZNxcXF4T83Nzf39+37ZX9NiyRpVJZHLgdlLuifsTkpcjsNNXsDOljbEu3lAAAQUbEWQJSkzx47VnlpSdpX06In1hyM9nIAdFDV6JUkbSmjegcA4lG/63pfeOEFJSUl9fuNdu/erdbWVjU0NOiqq67S2rVr5fV6NXnyZN1yyy0655xz+v2aktRb+5v9tcEA5ZhsT6+PQ/9Zn2c8f65up6GJuanaWt6onZVNGpuTEu0l9SgR9iMesE/2xd7EDvYqctpCQ1Q8LkdEPs/h2Jv0ZJduWjRed/9nm/787l6de9QIZae4h+4N4xDfQ7Ejlvaqqc2vJm/w35RdlY1q8wdi6uJEf8XS3iQy9sm+2Bt76es+9DuAOJDgoRQsX5akNWvW6Prrr9fnP/95lZSU6KGHHtLXvvY1VVRU6Nprr+336+blZfR4X0XrfklScVGW8vN7fhwGrrfPPx7MGpOtreWN2l3Xqoti4O9QvO9HvGCf7Iu9iR3s1eC5PFWSpIy0pIiek4Z6b64/OV1PrTukTSV1+vvqEv3g/KOG9P3iFd9DsSMW9mpPZXvWod+UKrym5o60/7oHKxb2BuyTnbE3sWXYJotMmDBBN998sxYvXqzZs2eHb7/gggt07rnn6pe//KXOO+885eTk9Ot1Kyvr1VN7um0H6yRJ+R6nKirqB7x2dGUYwW/23j7/eDB7RJqWSXp14yF9dsGoaC+nR4myH7GOfbIv9iZ2sFeRU1nTJEkyAoGInJOGc2++ctJ43fT4ej3y7m59elq+JuSlDu0bxhG+h2JHLO3VtgOdh2u+v7VMo1NiY0DTQMTS3iQy9sm+2Bt7sfbjSIYtgDhz5kzNnDmzy+3p6elasmSJfv/73+vDDz/U6aef3q/XNU1p1Z4ardhWoVs/OVEed/sPqn01wRLm0Vke/lIOEdNUXH+2x48PBrQ3ldarutGr7FR7l0nF+37EC/bJvtib2MFeDV64B6LTGdHPcjj2ZuHYHH1ycp7e3FGpX7+xU7/6zKyhfcM4xPdQ7IiFvapsaOv05y1lDbZfcyTEwt6AfbIz9ia22KIxRX5+viSpoWFgE7v++N/denzNQb2ytTx8m2ma2l9j9UC0b+862Ft+erKmFqTJlPT+nupoLwcAgIhpicEhKh195ZMT5XQYentnld7fzc9oIJoqm4IDVNzOYCOtLUxiBoC4M2wnxu9///tavHixSktLu9y3fft2SdL48eMH9NoVjcErXptK239QVTZ51ewNyGFIozI9A3pdQJJOmJArSXp3d1WUVwIAQOS0emM7gDg+N1WXzAu2F/n1GzvlD5DCAERLZej3sXmjsyRJ28ob5eN7EgDiyrCdGIuKinTw4EH97W9/63T7jh07tGzZMk2ePFnz5s0b0GvXNAeveG061B5APBDKPhyRkaykGD0Ywx4+MTFYxvzu7moFyK8GAMSJ1tAU5lgNIErSF44fp0yPS9srGvXMhq4XqQEMj/YAYqZS3A61+gLaU9UU5VUBACJpSE6M+/bt0zPPPKPVq1eHb/vsZz+radOm6cEHH9Rtt92mf/7zn/rFL36hSy+9VG63Wz/72c9kDGCGd5svoMa24AF4a3lD+ErXPsqXESFzRmUq1e1UVZNX28oaj/wEAABiQKyXMEtSVopbXzwhWMFy/393q6HVF+UVAYnJCiDmpyeruCBdEmXMABBvhuTEuGrVKt1xxx167LHHwrelpqbqH//4hz73uc9p3bp1uvvuu/XUU0/plFNO0ZNPPqlZswbW/Lqmuf2g2OoLaHfoSte+mhZJ0lgCiBgkt9OhheOyJUnvUMYMAIgT4SEq7tielHrx3CKNy0lRVZNXD63cF+3lAAnJ6oGYl5qkaYUEEAEgHg1qCvOtt96qW2+9tcvtS5Ys0ZIlS7rcnp6erjvvvFN33nnnYN62k5rmzhO/Nh+q15T8NO2vtjIQ6X+IwTthQo7e3FGpd3dX6/rjxkV7OQAADFprHGQgSpLL6dBXT56krz/9sf754X4tmVOkUVmc/4DhVGVlIKa5wwHErQQQASCuxPaJUe39Dy2bQ30Q99cGMxApYUYkHD8h2Adx3cE6yqMAAHHB6oHoifEAoiSdNClXx4zLVpvf1G/f2hXt5QAJxTRNVTYFA4h5aR0zEBtl0j8cAOJGzJ8YDw8gWoNU9od6IFLCjEgYk52icTkp8gdMfbC3JtrLAQBg0OIlA1GSDMPQbSdPkiHp5S3lWnugNtpLAhJGfatPXn8wUJiTmqRJ+alyOQzVt/p0sK4lyqsDAERKzJ8Ya0M9EKcWpEkKpspXN7WpriV4+2hKmBEhJ0xon8YMAECsi6cAoiQVF6br/NkjJUm/en0nmU/AMKlsDCZ0ZCS7lOxyyO10aHJ+8HezLQwgBIC4EfMnRisDcd7oLKW4HWrxBfT2zuCgi/y0JKXEeGNw2McJE3MlSSu2Vai+hTJmAEBsi4cpzIe78cQJSnIa+ri0XntC/bABDC1rAnNemjt827RCK4BIH0QAiBcxf2KsDgUQ89LcKi4I9tt4eUu5JGks2YeIoGPHZWtcToqqm730VwIAxLz2DMT4udian5ak6SMyJEkbS+ujvBogMbQHEJPCt4X7IB4igAgA8SLmA4g1oRLmnBS3ZowMHhhXhnrUMUAFkeR2OvSdM6ZKkpatK9Hq/fRXAgDErngrYbYcNZIAIjCcwgNUUtsDiFYgf9OhetoJAECciPkTo1XCnJ2apBkjgle6/IHgDykCiIi0o8dm64JQf6Ufv7xVbaFfvgAAiDXWFOZ4DSB+TAARGBZWD8TcDhmIxQVpcjoMVTV5VVLXGq2lAQAiKOZPjLXNwSteOSluTQ8FEC1jKGHGEPjKJycqN9Wt3VXNemjl3mgvBwCAAbEyED3xFkAsCgYQt5Q1yOvnQh8QSfuqm3XfmztVFco6lDpmILb3QPS4nSoODbkkmA8A8SHmT4w1LcErXjkpbo3PSVWKu/1LGptDBiIiL9Pj1jdOnSJJ+uv7+7SzkulyAIDY4g+Y8vqDFRvxloE4OsujLI9LXr+pbeX8jAYiaemH+/Xwqv169KMD4du664EotWcDbyipG74FAgCGTMyfGBtbg1eWs1PdcjqM8CAVSRqTRQARQ+P04nwtmpQrX8DUj1/apgC9XQAAMaStQ2ZePA1RkSTDMMJZiBtKyHwCIqmiIRgsXH+wPSjYYwAx9H34Md+HABAXYj6AKElOQ8r0uCQpXMac5XEpI3QbEGmGYejO06Yo1e3U2oN1Wr6upM/P/e1bu/SL17bTUBoAEDWt3o4BxLg4DnbSPkiFzCcgkmpD1V8fl9aH+85XNQVvOzyAOGtkpiRpc1mDfLQTAICYFxcnxqwUtxyGIUmaGTowjs9NjeaSkABGZnp006IJkqT73tylsvojN4iuafbqbyv36bHVB7W7qnmIVwgAQPdaQgNUXA5DTocR5dVE3lGhwAW914DIsgZYNnsD2lnZKH/AVHU3PRAlaVxuitKTnWr1BbSjomnY1woAiKy4CCDmdPhhdXpxgT577Fh99eRJUVwREsUl80ZpVlGGGtv8+sWKHUd8/N7q9qBhx9IPAACGkzVAJR6zDyVp5shgRcruqmY1tPqivBogftQ2t38/rS+pV02zVwFTMiRlp3bOQHQYhmaOCLUTIBsYAGJeXJwac1LaA4hJLoduOWmi5ozKjOKKkCicDkPfOWOqnA5DK7ZV6PVtFb0+fm91+9XXdTSUBgBESbwHEHNSkzQqyyNJ2kgWIhARpmmqLlTCLEkbDtaF+x/mpLrl6iabeRZ9EAEgbsTFqTE7JenIDwKGyNSCdF2zcIwk6Wevbe8104EMRACAHVgBRE+cBhCl9j6IlDEDkdHQ6pe/QwvvDSX1qmzqfoCK5aiiYFLHBr4PASDmxcWpMeewfhvAcPv88eM0Ntuj8oY2/e6tXT0+bl+HAOLOyibVt1BWBQAYfu0ZiPE1gbmj9kEqBC6ASLD6H1p9U3dVNWlPqKd3XmoPAcTQ9+HuyibaCQBAjCOACESAx+3Ut8+YKkl6am2J1h6o7fZxe6o7D06hHwwAIBrivYRZIgMRiDRrAnN+WpLGZAdbBLy5o1KSlJvW/e9jeWlJKspMlilp0yG+FwEglsXFqbFjD0QgWo4Zl6PzjhohU9I9L2+T1x/odH/ANMMZiNYvNZQxAwCioTU0hTmeA4jTRqTLaUjlDW0qq2+N9nKAmGcNUMnyuDQrVJr80b4aST1nIErtU9E30AcRAGJaXJwayUCEXXz15EnKSXFrV2WT/rZyX6f7yhva1OILyOkwdNaMQknS+oMcpAAAw68lATIQU9xOTcpPk0QWIhAJVgZidopbs0PDUayeiD31QJSkoxikAgBxIS5OjdlkIMImslLc+vriyZKkB9/fq/KG9owHawLz6CyPFozOkhQsYQ6YZtcXAgBgCCVCCbPUnvG/oYSMf2CwrB6IWSnucAaipbcA4izr+7C0XibnXgCIWXFxaiQDEXZy5vQCTS9Ml9dvauWemvDt1gTmcTkpmlyQphS3Qw2tfu2qbIrSSgEAiSoRhqhI0pxRwSDHmgMEEIHBqrUCiB6XphakdboAkddDD0RJmh5qJ1DZ2KadnHsBIGbFRwCRDETYiGEYWjguW5K0usMwlY4BRJfD0Ez6IAIAoiQcQHTHxVGwR/PHBDP+N5bWq8Xrj/JqgNhW2xLqgZjiltvp0PTC9PB9ub30QPS4nVowNluSdPvTH+sQPUkBICbF/KnRYUiZHgKIsJd5oRLltT0EECVpdqj0Yz1lVQCAYWYNUfHEeQnz6CyP8tKS5AuY2sgEWGBQrAxEq31UxzLm3kqYJemHZ0/T2GyPDta26OYn1qmqqW3oFgoAGBIxf2rMTHbJ6TCivQygk7mjgweq3VXNqg4dkLoEEENlVQxSAQAMt0TpgWgYhuaHfiav2c8FO2AwasIZiC5J0qzQcBSXw1Cmx9Xrc/PTk/W7S+ZoREay9lQ365Yn16suNJQFABAbYv7UmEX/Q9hQdopbk/JSJQX7Lvn8AR2obZEkjcsJ3m5Nr9tV1cQBCgAwrBIlgChJc0NVAWs6VAUA6L/2HojB37+OHpultCSnjhqZIYdx5ISOokyPfn/JHOWmurWtvFFfXbZBjW2+IV0zACByYv7UmE35MmxqXodfWA7WtcofMOVxOVSQHizxyElN0thsjyRpQwlZiACA4dOSQAHE+aGfx+sO1skfYAIsMFC1HaYwS8Gz7HNfPE6/v2ROn19jXE6KfnfJHGV5XNpQUq/bl39Mf1IAiBExf2rMJgMRNjVvTPvkx73VwYlzY3NSOl2htcqY1zJIBQAwjBJlCrMkTSlIU1qSU41tfm0vb4z2coCYZQ1RyU5pL1fO8LiU1M8LEVPy03TvRbOVluTUR/trdcezG9UW+jcJAGBfMR9AzDpCvw0gWqyMhy2H6rX5UIMkaXyo/+Hhj1m9n7IqAMDwSaQSZqfDCF+wo4wZGJgWrz/870ZWBCrAZo7M0K8/M0vJLofe3V2t776wWT4yhAHA1mL+1JhDBiJsamSmRyMykuU3pRc3lUlqH6BiOXpstiRpQ0kd5RsAgGFjTWFOhACi1H7BjgAiMDA1ofJlp8NQWlJkMpfnjcnSLy84Sm6noRXbKvTDf29RwCSICAB2FfOnxixPUrSXAPRoXmjyY/sE5tRO94/J9qggPUlev0kfRADAsLEyiTwJEkCcO7q9rYhJgALot9rm0ARmj0tGHwam9NVxE3L0k0/PlNMIXnD/2avb+R4FAJuK+VNjdiolzLCv+WOyOv358AxEwzC0IPSYD/fVDNeyAAAJLpFKmCXpqJEZcjkMVTS26UBtS7SXA8ScmpZgBmJ2SuSrv06ekqcfnjNdhqSn1pboN2/sIogIADYU86fGHKYww8asScyWwwOIUnsZ84f0QQQADJNECyB63E7NGJEhib7DwEAcPoE50s6cXqj/OXOqJGnph/v1wsayIXkfAMDAxfypMYseiLCxiXmpygwN+snyuLo9dNEHEQAw3BJpCrNl/phgGfPaA3VRXgkQe6wJzEM5wPKC2UX67LFjJUkvbykfsvcBAAxMzAcQs8lAhI05DENzQ5MfD+9/aBlLH0QAwDCzLlglSgaiJM0NVQWsZpAK0G81Q5yBaDljWoGkYKawzx8Y0vcCAPRPzJ8aM1PogQh7O35CriRpxoj0bu+nDyIAYLglWgmzFBxsZig42KysvjXaywFiSriEeYiTN6YWpCnL41KT16+PS7mwDgB2EvOnRpcjclPAgKFw0dwi/e/5M3XjiRN6fAx9EAEAwykRA4iZHremhy7mrdpbE93FADHGKmHOHuLkDYdhaOG4bEl8nwKA3STOqRGIEqfD0KlT85XRS88YK4D4cUld+Jc6AACGgmma4Z81ngQKIErSseNzJEkr91ZHeSVAbBnqISodHUMAEQBsKbFOjYBNWX0Q2/ymNpTQ3B0AMHS8flNm6L8TaYiKJB0bCkys3FMj0zR7fzCAsPYhKsMRQAwG+tczYBAAbIUAImAD9EEEAAyXjpnuiVTCLAUHqSS7HKpobNPuquZoLweIGdYQlaEuYZaCF9YLQwMGmZoOAPaRWKdGwMasMuYPKNcAAAyhVl8wo8eQ5HYmVi/pZJdDc0dlSpJW7qGMGeir4SxhNgxDx4TbDdQM+fsBAPqGACJgE1a/l9UH6vT46gPRXQwAIG61dBigYhiJFUCUOvZBrInuQoAY4fMH1NgWvPCQPQwlzFJ7u4FV9CsFANsggAjYxJjsFH3pE+MlSb94bYde3lIe5RUBAOJRIk5g7ujY8dmSgi1DfAH6IAJHYvU/NKRehwJG0sJQZc7mQw2qa/EOy3sCAHqXmCdHwKY+f/w4XTJvlExJ339hs96nvAoAEGGJHkAsLkhXlselxja/NpbWR3s5gO1Z/Q8zPC45HcOTtVyYkazxOSkyJX20r3ZY3hMA0LvEPDkCNmUYhr6+eLJOL86XL2Dqjmc28ssNACCirACix51YE5gtToehheFpzFyoA46ktsUaoDI85cuWY8JlzDXD+r4AgO4RQARsxukwdNfZ03XMuGw1ef26bdkG7a1mUiQAIDKsISqJmoEotfdXow8icGS1zcES5qxhKl+2WINUCCACgD0k7skRsLEkl0M/v2Cmphemq7rZq1ufXKfyhtZoLwsAEAcSvYRZah+ksv5gnZq9/iivBrC34ZzA3NHCsVlyGNKuqiaV1rUM63sDALpK3JMjYHNpSS795qJZGpvt0cG6Vn3lqQ2qDzWx7mhrWYM+/881emF9SRRWCQCINQQQpdFZHo3KTJYvYOqj/fRXA3pjDVEZ7gzETI9bs4syJUn/3VU1rO8NAOhqUCfHtWvXaubMmXrnnXf6/Jzly5frwgsv1Lx587Ro0SLdddddqq3l4AZ0Jzc1SfdeNFt5aUnaXtGorz+9QS0dMiXqW3y649mNWnugTve+ui2KKwUAxIoWAogyDCOchfgugQmgVzVRykCUpBMn5UqS3t7J9ykARNuAT467d+/WzTffLL+/72Uf999/v771rW8pOztb3/zmN3XeeefpiSee0LXXXquWFtLSge6MyU7RvUtmKS3JqdUH6vS9FzbLFzBlmqZ++J8tOlAb/N7ZXFqvklq+jwAAvWvPQEzMISqWRZPyJElv7aySaZpRXg1gX1YJ83APUZGkRaEA4qq9NZ0uogMAht+AAogvv/yyLr30UpWXl/f5OSUlJbrvvvt00kkn6cEHH9RVV12lO++8U/fcc482b96sRx55ZCBLARJCcWG6fnnhUUpyGnp9e6V++so2/fOjA3p9e6XcTkOjszySgr8EAQDQG0qYg44dn60kp6GDtS3aVdUU7eUAthWtEmZJmpKfpsL0JLX6AvqQdgMAEFX9PjnecMMNuuWWW1RQUKBPf/rTfX7ev/71L3m9Xl177bVyONrf9vzzz9eIESO0bNmy/i4FSChHj83W3efOkMOQnllfql+9vlOSdNvJk7VkbpEk6e0dldFcIgAgBjCFOSjF7dTC0DTmt3dwAQ7oSbSGqEjBdgNWGfM7XCgHgKjq98lx586duv3227V8+XJNmDChz89bu3atJGnevHmdbjcMQ3PmzNHOnTtVX1/f3+UACWXx1Hx96/Sp4T+fXlygS+YV6SSrvGNfjZraKO8AAPTMykD0JHgAUWovY357JxfggJ6EeyB6hj+AKEknTgx9n+6i3QAARFO/89BfeOEFJSUl9fuNSktLlZqaqszMzC73jRw5UpJ04MABTZ8+vV+vaxj9XgoiwPrc+fyH35K5RUpyGvq4tF43nzRRDoehSfmpGpebqr1VTVq1r1qnTMmP9jLRDb5v7Iu9iR3s1eB1LGGO5OcYi3tz0qRc/exVae3BOtW1eKOSYTXcYnGfEpVd9soqYc5JdUdlLcd1aDewp7pZE/NSh38Rh7HL3qB37JN9sTf20td96HcAcSDBQ0mqr69XWlpat/d5PMH+bU1N/e8/k5eXMaD1IDL4/KPjulO6fu6nTi/UQ+/s1gcH6nXx8ROjsCr0Fd839sXexA72auCM0PCUnMwU5edH/nOMpb3Jz8/Q9JEZ2lxarw2VzbpgXm60lzRsYmmfEl009yoQMFXXEsxAnDg6W/mZnqis4/jJ+Xpza7lWH2rQMdNGRGUN3eH7KDawT/bF3sSW4e+E2w0rFb1jb8S+qqysF5nsw88wgt/sfP72YBjSaTOCAcSXNx7S7Z+sk4PLObbD9419sTexg70avNqGVkmSv82niorItY+J1b05YXy2NpfW64U1B3TimK6VMvEmVvcpEdlhr+pavAqE3tvf1KqKNm9U1nHsmEy9ubVc/1lfoiUzC6Oyho7ssDc4MvbJvtgbe7H240iGLYCYlpamioqKbu9raWmRJGVk9D/6bJriL1wU8fnbx7ETc5XqdqqysU2bShs0cyRXc+yK7xv7Ym9iB3s1cC3e9hLmofgMY21vFk3K01/f36d3dlXL6zflciTGBbhY26dEFs29qmkKli+nuB1yO4fm34y+OHFirn6hHVpzoE71LT6lJ9siD4bvoxjBPtkXexNbhq179pgxY9TQ0KCGhoYu95WWlsrhcGjECPukowOxJtnl1HETciTRDB4A0DOmMHd21MgMZae4Vd/q09oDtdFeDmAr1gCV7Cj3Bx2TnaLxOSnyB0y9t7s6qmsBgEQ1bCfHOXPmSJLWr1/f6XbTNLVu3TpNnTpV6enpw7UcIC5Z05jf3lkV5ZUAAOyq4xAVSE6HoRMnWhfg+PkJdFTbEt0JzB19cnJwGvOKbd1XtQEAhtawnRzPPvtsud1u/eUvfwn3PJSkZ555RmVlZVqyZMlwLQWIWyeGAoibDjXoUH1rlFcDALAjK4DoIYAYtmhSMDCxfF2J1uwnCxGw1DYHS5izUqJfMnxacb6kYKDf+ncMADB8huTkuG/fPj3zzDNavXp1+LbRo0frxhtv1FtvvaXPfe5zevzxx/W///u/+u53v6vZs2fr8ssvH4qlAAklLy1Jc0cFG8C/xtVZAEA32jMQnVFeiX2cMiVPC8dlq7HNr68sW68P99VEe0mALdgpA3HmyAyNyEhWk9dPGTMARMGQBBBXrVqlO+64Q4899lin22+55Rb94Ac/UFlZmX74wx/qxRdf1GWXXaa//OUv8ng8Q7EUIOGcNq1AkvTqlvIorwQAYEeUMHflcjr0qwuP0vETctTsDeiryzbofQIUgG16IEqSYRg6dWowC/G1bZxzAWC4DerkeOutt2rLli36xCc+0en2JUuWaMuWLfrpT3/a5TlXXHGFnn/+eW3YsEGvv/66vve97ykrK2swywDQgXWwWnuwjjJmAEAXDFHpnsft1C8uOEqLJuWq1RfQ7U9v0H/piYgEZ6cSZqm9jPnNHZVqo4wZAIYVJ0cgzozISKaMGQDQoxYyEHuU7HLoZ+fP1ClT8tTmN/WNZz7W6/wsRQKzUwmzJM0elan8tCQ1tPq1am9NtJcDAAmFkyMQhyhjBgD0hBLm3rmdDv3k0zN0xrQC+QKmvvWvTXqFn6dIULWhEuYsG5QwS5LDMLQ4VG3z6la+LwFgOHFyBOIQZcwAgJ4QQDwyl9OhH54zXefMLJQ/YOp/nt+kFzcdivaygGFXY7MSZqlzGbPPTxkzAAwXTo5AHKKMGQDQHX/AlC9gSpI8TGHulcth6PtnTdP5s0YoYEr/74UtenZDabSXBQwrq4TZDkNULPNGZyknxa3aFp8+3Fcb7eUAQMIggAjEKcqYAQCHa+0wdMDj5hh4JE6Hof85s1gXzS2SKelH/9mqZWsPRntZwLAwTbO9hNkmPRCl4PdluIyZacwAMGw4OQJxijJmAMDhrAnMkpRECXOfOAxDd542RZfNHyVJ+skr2/XYRweivCpg6LX4AmrzBzOW7VTCLEmnhsqYX9taIe8Ay5h9AVNff/pjfff5TTJNM5LLA4C4xMkRiFMdy5hpMg0AkNozEJOchhyGEeXVxA7DMPT1xZN1zcIxkqRfrtih3VVNUV4VMLSs7EOXw1Cq214tD44em638tCTVtvj0zq6qAb3GO7uq9OaOSv1nc7k2lNRHeIUAEH8IIAJx7IxQGfOLG8si9ppVTW3aV90csdcDAAyflvAAFXsFA2KBYRi69ZMTdcKEHJmSnv+YoSqIbzXN7f0PDZtdcHA5DH1qRqEk6fkBnnP/1eF7+PmNfD8DwJEQQATi2FnTC+VyGNpc1qDt5Y0Rec2bn1ivKx7+kCAiAMQgJjAPjmEYOn/WSEnSCxsPyR+g7BHxq9aGE5g7OnfmCEnSWzsqw9mSfVXT5NVbOyrDf35pc3mnHrEAgK44PQJxLDvVrUWTciV1vso6UA2tPm2vaFSrL8AkSgCIQQQQB++kyXnK9LhU1tCmD/bWRHs5wJCxJjDbaYBKR1MK0lRckCZfwNTL/Rwa+OLmMvkCpqYVpmtERrLqW316e2dlj4+vaGjVf3dVKUCvRAAJjNMjEOc+fVQwU+LFTYfkG2SmxN4OWYfPk3kBADHHGqJCAHHgkl2OcIuQf1H2iDhWE85AtGcAUZLOPSqYhfhCP78XnwtdCD9/1kidHSqF7u1i+/de3KLblm3Qd/61SS1ef4+PA4B4xukRiHMnTsxRTopbVU1evbd7YE2mLR0DiOUNbXpvT/VglwcAGEZkIEbGp0NBixXbKtTQ6ovyaoChURvugWjPEmZJOnN6oZyGtL6kXnv6ONhoy6EGbStvlNtp6KzpBeEg5Lu7qlTZ2Nbl8Q2tPq3eVyNJenVrhW58fF23jwOAeMfpEYhzLqcj3GR6sGXM1sHMaqP9L8qYASCmWAFEDwHEQTlqZIbG56So1RfQq1v7VzoJxAq7lzBLUn5ako6fEGzX8+Kmvg1Tee7j4Pn15Ml5ykpxa0JuqmYVZchvSv/Z3PU1Vu+vld+UclPdyvK49HFpva5bulpbDzG5GUBi4fQIJAArU+LNHZXhiXoDYWUgWgHJ17dXqqZp4K8HABherUxhjgjDMMI/W5nGjHhlnRntXMIsSefMDJ5LX9h4SB/tr9GmQ/XaXdmk0roW1TZ71eYLyAz1LvT6A/p3KND46dBApOBr9Pz9vCrU6/TkKXl68Mr5GpeTopK6Vl30+3cGXd0DALHEvvnoACKmuDBdxQVp2lreqJc2l+vS+aMG9DpWAPG04gLtqmzS5rIG/XtzmS5fMDqSywUADJEWSpgj5uyZI/T7t3dr9YE67a9p1pjslGgvCYio2pZQD0SPvX9l/OTkPKUlOVVS16ovPbau28c4DcnjdsrtdKi2xaeC9CQdPz4nfP+Z0wr0fyt2aGt5o7aVN2hqQXr4vpV7gy17jh2Xo3E5KfrLFfN057Mb9dH+Wn31qQ2647QpWjJ3YGdrAIglnB6BBGH1d1m27uCA+jWZphkOII7PSdF5oau2z1HGDAAxgx6IkTMiI1nHjs+WxM9CxKf2Hoj2zkD0uJ26/ZTJmjEiXeNzUlSYnqRMj0tupxF+jN+UGtv84azKi+YWyelovz8rxa1PTs6TJD29rv37uaKxTTsqgi18Fo7NlhT8PH578WwtmT9aflP6ySvb9avXd0R9uGBDq0//3lQW/nceACLN3peTAETM2TMKdf9/92hHRZOu/8dq/fyCozQhN7XPz69obFOT1y+nIY3O9uis1AL9+o3gldothxo0bUT6kV8EABBVrV6mMEfShbOL9P6eGj21tkTXHzdOHjel4YgftTFSwixJ588eqfNnj+xyu88fUIsvoGavX83e4P/LlKYUpHV57JI5RXptW4We33hIN580UalJTn0QKl+eVpiu7NT2zyHJ5dAvL52rwlSX/vjfPfrHhwd0oKZFPzp3ulKi9O/APz7crwfe3atL543SN0+bEpU1AIhvnB6BBJGTmqTfXzJbhelJ2l3VrOuWrtYb2yv7/Hwr+3BUlkdup0NZKW6dMiVfkrR8fcmQrBkAEFlkIEbWKVPzNSrLo9oWn57dQC9ExJdYKWHujcvpUHqySwXpyRqXk6JphemaNiK9U/ah5Zjx2RqXk6LGNr/+HRqmsipUvnzMuOwujzcMQ184YbzuPme6kpyG3thRqRseXavyhtYh/Zp6sqsymCn53MelTIcHMCQ4PQIJ5KiiTD189QLNH52pxja/vvHMx7r/v7sVMI9ccrEnFEAcl9OetfiZOcErvS9sPMRBBQBiAENUIsvlMHTV0cE+wP/4cH/USxiBSPH6A2psC2Ysx0IGYiQ4DEMXzS2SJD255qBM09TKPTWSFG5X0J2zZhTq95fMUXaKW5vLGnTd0tXaUdE4DCvu7FB9mySp2RtguBOAIUEAEUgweWlJ+v0lc3RZaJDKn9/bq68//bHqW3oPAO6tsgKI7U3iF47N1qS8VDV7A3qOgwoA2F44gOjmCBgp580aqSyPSwdqW/T69opoLweICCv70JCUkRy7GYj99emjRijZ5dC28kY9v/GQSutb5XIYmjc6q9fnzR2dpb9eOU8TclNU1tCmb/9r07D3IizrkPn4+JqDfUoQAID+4PQIJCCX06FvnDpFP/jUNCW7HHp7Z5Wu+0fvV0v3VAfLIjoGEA3DCAcin1h9gIMKANic9QuthxLmiElxO3XxvODPwkdW7ZfJz0LEAWvYSKbH1W25b7zK9Lh11vQCSdIvXtshSZozKrNPfQ3HZKfogcvmKTfVrV2VTfrzu3uGdK0d+QOmKkIBRJfD0N7qZq0KZU8CQKRwegQS2LlHjdADl8/VyIxk7a1u1vX/WK3XtpZ3+9jwBObclE63nz1zhDKSXdpX06J3d1UP+ZoBAAPX4mOIylC4ZN4oJTkNfVxar9UHaqO9HGDQYmmASqRdErogYJVwd9f/sCfZqW59+/SpkqSHV+3Tx6X1EV9fd6qa2uQ3JachXRAaJvPEmoPD8t4AEgenRyDBzRiRoYevnq+F47LV7A3ozuc26Xdv7erUx8nnD+hAbYukzj0QpWDmxfmzggeVR1cfGL6FAwD6jSEqQyMvLUnnHjVCUjALEYh17QNUEi+AOH1EhmYVZYT/fOz4nH49/5Sp+TpreoECpvTDf29R2zCUMpfVB7MP89KSdPn8YF/Wt3ZW6mDo/A4AkcDpEYByUpN030WzddXRYyRJD63cp9uWbwj/onmwrlX+gCmPy6GC9KQuz794XpEMSe/trtbuqqbhXDoAoB8IIA6dq44eI0PS2zurwtNQgVjVnoGYOP0PO7p4bjALMS3JqZkjM47w6K6+sXiKclPd2lnZpN+9vUs+/9AGEQ81BAeojMjwaEJeqo4dl62AKT21tmRI3xdAYuH0CEBSsF/KbadM0j3nTleyy6H3dlfrqbXB0oc9oaDg2JwUOYyufXDGZKdo0aRcSdITqymXAOzKFzD17u6qYcmGgD0xhXnojM9N1clT8iRJSz8gCxGxzQogZidgCbMknTW9QNcsHKPvnDFVrgH0gMxOdevOUCnzPz48oHP/9L7ue3Nn+EwdaVYG4oiM4IX+S0M9ypetOxi+DwAGiwAigE7OnF6ory+eLCn4C5DXH2jvf3hY+XJHly0IlkssX1+ipR/sZ6BKFPz+7V26/h+rtZb+W+jBU2sO6itPbdBPX9kW7aUgSshAHFpXLwxm8r+w6VB4oAEQi2qaE7eEWQoOHPzKyZN05vTCAb/GqVPz9ZVPTlRuqltVTV49vGq/LvnrB3ptW+SntVtBwsKMZEnSokl5mjEiXQ2tfv3g31s4lwOICE6PALo4d+YI5aclqayhTS9uKgsHEMcdNkClo2PHZev04gJ5/aZ+/cZO3fzEOpXW0XdlOD21tkQbSup1w2Nr9fu3d8k7xOUyiD3v7QkOOnph4yEdqG2O8moQDQQQh9bc0VmaXZQpr9/UY2TkI4bVtiR2CXOkXHPMWD1/w3H62fkzdfTYLJlSl17jkXDICiCmBwOIToehH54zXR6XQ6v21uifH9KnHMDgcXoE0EWSy6Erjw5mFD68cp92hcotxuf0HEA0DEM//vR0feeMqUpxO/TBvlpd/rcP9cLGQzK56jnkGtt8qgs1PA+Y0l/f36fr/7FGOyoao7wy2IVpmlp/sE6S5DcZ9JCoWkNTmD0EEIfMNccEsxCfWluixjZflFcDDEwiT2GONJfTocVT8/V/F85SlselvdXNenVreUTfo6yhcwaiJE3ITdXXTpkkSfrd27u0tawhou8JIPFwegTQrSVzi5SR7NKe6mat3h8siR3XSwBRCgYRPzOnSEuvOVqzizLU2ObX/3txi77zr02qCR1EMTSsK88ZyS7973kzlOVxaUtZg679+0eUlEOStLe6WbUtPlmdnJ7bUEqJZQIiA3HofXJynsblpKi+1adnNxyK9nKAAbGmMCdqD8ShkJrkDLf8+ev7+yJ6NguXMB827PAzc4r0ycl58vpNffeFzWpq80fsPQEkHk6PALqVluTSJaEGzJYjBRAtY3NS9KfL5+mmEyfI6TD0ytYKXfG3D/Xu7qqhWCokldYFD44jM5N1anGBHr1uoU6cmKu2UEn5l59YpxJKyhPa+pJg9uHsUZmaOypTbX5TSylpSjgMURl6Toehq0JZ/P/8cL98ES5VBIaDdeE3y0MJcyRdNn+U0pKc2l7RqLd2ROZcHDBNlYWnMCd3us8wDH33zKnKS0vSrsomXfHwh1q1tzoi7wsg8RBABNCjy+ePCmep5KS4ldmPRtouh6HPHT9Of71ynibkpqiisU1feWqDfvbqdrV4ufoZaaXh6XvBg2N+WpJ+9Zmj9O1QSfmH+2p1xd8+1L8+LqWkPEGtC5UvzxmVqeuPGydJemrtQbKDE0wLGYjD4pyZI5ST4lZJXaseXrkv2ssB+o0S5qGR6XHrornBC/R/fX9vRM5k1U1e+QKmDAXPf4fLSU3Sz8+fqZEZyTpY26IvP7FeP355qxpaabEAoH84PQLoUU5qki6YNVKSNL6XASq9mTEiQ49cvUCXhbIZn1hzUFc98pE+Lq2P2DrRXsI8ssOVZ8MwtGROkf5x7dGaMypTjW1+3fXvrbrj2Y2qbmqL1lIRJR0DiJ+YmKPigjQ1ewN6fDVZiInCNE1KmIeJx+3UF04IBur/8N/d+uv7e6O8IqDvAqap+lBwKZsMxIi7auFoJbsc+ri0Xiv31gz69az+h3lpSXI5u/+3ffaoTD163dG6eG6RJGn5ulJd9tAH+u9OqoMA9B2nRwC9+uIJ43XuUSP0xRPGD/g1PG6nvnHqFN130SwVpCdpb3WzPv/PNVoT6q2IwTsUKk8+vHRFksZkp+hPl83VlxdNkMth6PXtlbr8bx8SxE0gDa0+7awIDkOaPSpThmGEsxCXfnBAH+6rieLqMFza/O2ZLgQQh96l80frS58I/uz8/du79cA7e8gAR0yob/HJqrwnAzHyclOTdOHs4AX6B98bfBZiuP9hN2fAjtKSXLrz9Kn646VzNCbbo7KGNt22fIN+8OLmcMYpAPSG0yOAXmWnuvWDT03TseNzBv1ax0/I1T+vPVonTsyVP2DqZ69tl5/eUBFhlTCPzPR0e7/TEQwYPXTVfE3KS1VVk1d3PPOxqshEjEtbDjV0apS+oaROpqRRWZ5wedPiqflaMCZLTV6/bnlyvV7cxLCHeGdNYJaYwjxcvnDCeN1y0kRJ0p/e3aNfrtihZtp4wOasASppSU65e8how+Bcc8xYuZ2GPtpfq/f2DK4n4aH67vsf9uTosdn657VH68qjR8uQ9PzGMl360Adasa1iUOsAEP/4iQBgWGWluPX/PlWsjGSXtpU36pkNpdFeUlwID1E5wuFxWmG6HrxynsbnpKisoU3/8/xmgrhx5m8r9+nqv3+kO579OJzVsP5gMNt0zqjM8OOcDkO/WTJLp07Nly9g6vsvbIlIJgTsyypfdhrqscwNkffZY8fqtpMnSZIeW31Ql/41+Is632uwKwaoDL0RGcm6ZF6wvc99b+4a1ERmq4T58AnMvfG4nfraKZP1lyvmaWJu6MLysxv17ec2cXEZQI84PQIYdjmpSfpiqKzrD2/vVn0LTZwHI2Ca7T0QM4989TktyaWfXTBTKW6HPthbo/vf2T3EK8RweWVLuX771i5J0vt7avTfXcHeRlb/w9lFmZ0e73E79ZPzZujqhWMkBXu1ffnJ9dpR0TiMq8ZwYQJz9Fy1cIx+eeFRKspMVml9q+54dqNuW75B+2uao700oAsGqAyP648bp7Qkp7aVN+o/m8sG/Dplhw3S64/ZozL192sW6HPHjZXTkF7ZWq5L/0pvRADdI4AIICoumVukibmpqmn26oF390R7OTGtqrFNvoAphyHlp/ft8DgpL03fPbNYkvTX9/fpzR2VQ7lEDIP1B+v0g39vkSSNCgWS731jl7z+gNaXBAOIc0dldnmewzD01ZMn6c7TpijJaeiDvTW66uEP9X8rdjChMc4wgTm6Pjk5T49ft1DXHzdWLoehd3ZV67KHPtAD7+wJB3cBO6htsTIQCSAOpewUtz577FhJ0h/f3q22Af47YF1ELuzjGfBwSS6Hblo0UX+7aoGmFqSptsWnbz77sVYOsrQaQPzhBAkgKlxOh25fHCzpenzNQe2qbIryimKXdXAsSE+Wy2H0+XlnTi8MT8f+4b+30EA7hh2sbdE3nvlYrb6AFk3K1cNXL1CWx6VdVU369es71djmV4rbockFaT2+xsXzRunx6xfqlCl58pvSPz86oIseXKVnN5QOqrQK9sEE5ujzuJ368qKJ+udnj9ax47LV5jf1p3f36PK/fRDOGAairbY5ePEoK4US5qF2xYLRyk9L0sG6Vj21rmRArxEuYR5ABmJH00ak6+Gr5mvx1Hx5/aa+8czH+jh0ARIAJAKIAKLo+Am5+uTkPPkDpv731W0EKQaodBClK189eZKm5AevNt//Dpmgsaih1afblm9QVZNXxQVpuufcGcpKcYcnpz++5qAk6aiRGUcMMI/OStHPLzhK9140S+NyUlTV5NWP/rNVn//nGm1kanfMs4aoEECMvgm5qfrtxbN1z7nTVZCepP01Lbpt2Qbd8exGlda1RHt5SHBWD8RsSpiHnMftDLf1+cu7e7RyT7W8/r5nIpqm2WEKc997IPbE5XTo7nOm69hx2Wr2BvTVZRtoawIgjBMkgKj62imT5HE59OG+Wi39YH+0lxOT+jpApTtup0PfOHWyJOmptQe1vZxDYl/VNHv1vRc26/7/7o7aGnz+gL713EbtqmxSQXqS/u8zs5SaFOxvt2RukcblpIQfO6eb8uWenDAhV49+9mh95ZMTlep2akNJva5bulp3v7RV1TRXj1lkINqLYRg6c3qhnrh+oa48erSchrRiW4Uu+esHemTVPoasIGooYR5e588aqfE5Kapt8enmJ9frjN+/qzue3ahnN5SqorH3n7m1zT61+YP/VhSkDS4D0ZLkcujnFxylWUUZqm3x6dan1oeDygASGydIAFE1JjtFty8OBrB+//ZubSlriPKKYk9pPwaodOfosdk6rThfAVP6xYrt/NLaB4fqW3XDo2v1701l+vN7e/XG9ooheZ9HVu3T46sPdnufaZr62Wvb9f6eGqW4HfrVhbM6ZaG6nQ7dctLE8J9n9yOAaD3/mmPG6snPLdTZMwplSnpmfakuevADPfbRAfmY3h1zWr0MUbGjtCSXvnbKZP39mqM1b3SmWnwB3fvmLv3kFTLzER2UMA8vl8PQr5fM0nlHjVBuqluNbX6t2FahH/1nq87+43u69u8f6U/v7NbG0vou/yYcCpUv56a6lRTBi0OpSU79+jOzND4nReUNbfpdaEAbgMRGABFA1F04e6ROmZInX8DU957frBavP9pLiilWuduIDM+AX+OrJ09ScigT9LVtQxMMixd7q5v1xUfXaFdVk5yhkuCfv7ZDTW2R/Xu7r7pZ9765Sz9/bbve6aY32t8/2K/l60rlMKS7z52haSPSuzzmlCl5Omt6gWaMSNfRY7MHtI6C9GT98Jzp+vPlc1VckKb6Vp9+sWKHvv70BvkJIsaUcAaim+OfHU0pSNOfLpurO06bIochLV9Xqh+8uIVgPYadlYFICfPwGZOdou9/appevPF4PXTVfH3xhHGaEfq5vulQgx54d68+u3S1zv7je/rRf7aoJHT2KxvkAJXeZKW4wwP3nl5fqvUH6YcIJDpOkACizjAM/c8ZxcpPS9Kuqib95o2d0V5STDk0yAxESSrK9OjaY8ZIkn79+k6CuD3YXdWkLz66RiV1rRqXk6J/Xnu0RmUm61B9q/4c4Wnimztk4/7vq9s77cmKbRW6781gNsBtp0zWJyfndfsahmHo7nNn6OGrFyjFPbiss7mjs/Tw1Qv0rdOnKNnl0Du7qvX7t8lIiCVWANFDCbNtGYahS+aN0o/OmS6nw9CLm8r07ec2Dng6KzAQ4QxESpiHncMwdNTIDN3wiQl6+OoFevFLx+l7ZxZr8dR8pbqdqmry6tkNh3Tbsg3y+gMRG6DSk3ljsnTeUSMkST99ZRsXNIAExwkSgC1kp7r1/z4VvMr55NoSvbebaZR9FQ4gDvLweO0xYzUyI1ml9a164N29kVha3Hlo5b7wsJIHLp+riXmp+sapUyRJ//joQER7SHYs5z9Y26IHQgHKj0vr9b0XNsuUdPHcIl0emqQ9HJwOQxfNHaXvnxX8Xn141X69tLls2N4fg9PCEJWYceb0Qv3veTPldhp6fXulfvba9mgvCf+/vfuMjqpc/z7+nZLee0JCSAgwtEDoHQSlClLEAhaaChb02Hk8NlT+ejwqiuhBVBRFUYo0adJEeieEQEJNI4X03iaznxdJRiIESEgyM+H6rMUCpuXe88ves+fad7mNVM53J0OYTc/T0YZ7Qn358J62bHmqF/PvDcXNzooL6QUsPhh/RQ/EW19ApToz+wfjbKvlTGo+K45fe1oVIcTtQc4ghRBmo2eQOw9UFEPm/HGW/BK9iVtk/opKy8goKD/Rr80qzFeytdIYi2E/HY6XVXev4Wh8FlB+Mu1uX36y3i/EgztalK8m/kEdzllWWUDs19wdgJ8OJ/DX+XReXB1Jsd5A72A3XhzUApXq+isr14chrb2NPVbf2XyGMzJ3qUWQRVQsy4AWHvx3dDugfP7RAzGZJm6RuB0oivL3IioyhNmsWGvV9AhyMy5+t+hAHIfisoFbPwe8Hjd7a56umFN5wZ4YUit6PQohbj9yBimEMCtP9wumiYstybnFzP9LhkfeyOW88tX57KzUONveek+BARVz5pUp8O7mM5SWybC5Skk5RSTlFKNRQYcmLlXue3FgCHZWasITc3jkx6NsP5N6S4VERVGITikvyk3tGciglp6UKfDi6kjS80to4enAnLvboFU3fPGw0lN9g+nZzI1ivYGX1kSSUyQrNJq7IikgWpw+we7cH1ZxYW3LGbmwJupdYamB0opVfWUORPM0WOdF3+bulJYpRCSVz0tYX0OYK40J9aWdrxP5JWV8uE0W3BPidiVnkEIIs2JnpeHfg1sC5UOZj1T0+BLXVrmAiq+TbZ31RHtxYAiudlacS8vn+4PxdfKajcHR+PKr/G18nbC3rjqfoK+zLf8e3Ap7Kw1nUvN5dd1pHlx8hE2nL9dqvqDUvBIyC0tRq6CFpwMvDQrBoeJnejhYM3dsOxxtTDu0TKNWMWdkawJcbUnKKebDbTLE8lr0BoWMghJTNwO4sgeirMJsSZ7uF0wTZxuScuTCmqh/lb0PrTUqmS/VTKlUKl69swX2V8xtXJ89EKF8bsZ/D2mJVl0+rcKGUzJ9iRC3o1p9KmRmZvLuu+8ycOBAOnTowD333MOKFStu6rmrVq1Cp9Nd88+sWbNq0xwhRCPTvZkbYzv4AvDeH2dkQY/rSK6Y+8bnFhZQ+Sc3e2terhwesz+Oc2l1N6+fJTuakAVA5wCXa94/tI03ax7vzmM9A3G00XAxvYA3NkRx/3eHWHsyGX0NenNWDl9u5m6PrZUGL0cb3hjaio5NnPl0bDt8nWu/4nZdcra1Kl/sQQWbo1JlPsRr+GDLWYYv2M+6k8mmbooMYbZQ9tYa/j3k7zmC5cKaqE9/z39oZZIpMsTN8XW25el+Qcb/e9XDKsz/1NLLkSd6NwPgv9vPGS9iCyFuHzXuvlBQUMDUqVM5e/YsEydOpHnz5mzcuJF///vfpKWlMWPGjOs+PyoqCoA5c+ZgZVW1W3xgYGBNmyOEaKSe7d+cPRcySMgqYv6ui8a5+URVKTl1s4DKPw3WefFHVCo7z6cze2M0304Iw/o2LzocTSjvgdg5wLXax7jaWTG9TxAPdQ1g+fFEfjqcQHxWEe9uPsM3+2KZ1L0po9r53vC9rCwg6rwdjbfd2cqLO1t53fqG1LH2fs5M7hHIt/vj+M+2c4T5u9T7UCpLUVpmYEt0KgYF5mw5i5ejNT2D3E3WnmJZRMViVV5YW3UimXc3n+HHhzvjVAfTVgjxT9mVBURZgdns3duxCaeScynSGwhwbZgLi490a8qu8+lEJOXy7uYzfD4+FLUUmoW4bdT4DHLJkiWcOnWK999/n9dee40HH3yQ7777jj59+jB//nySkpKu+/yoqCi8vb0ZP348o0ePrvKnU6dOtd4QIUTj4mijNfa4+PVYIjvPpZu4ReapcgXmuh66olKpmHVXC1xstURdzuOL3bf3sLnLucUkZBWhVkFHf+cbPt7RRsuUHoGsfbwHzw1ojru9FUk5xXyw9Rxjvj3I0qOXjL3BruVaBURz9ljPQNr4OJJTpOfdP87I3EgVTiTmUFDRg7rMoPDq2tNVVtduaJW/czIs0TI92785fs42XMou4t/rT1NWi+kRhLiR7MLyeTZlBWbzp1GreHt4az4Y1bbBinhatYq3humw0ao5GJfFiuPX/+4vhGhcanwGuXr1ary8vBg5cuTfL6JWM23aNEpLS1m3bt11nx8dHU2rVq1q3lIhxG2nd7A7E7v4A/Du5mgZKgFsOJXCU8tPkJBVCEBybsUciHU4hLmSp6MNbw3TAfDzkUvsOn/7FnGPVfQ+1Hk71mjuQXtrDQ93DWDNY915eVAI3o7WpOaV8MmO87y0JrLaAkDlqsatLaSAqNWomT28NTZaNftjMll2LNHUTTILey9mAOU9ers2daGgtIx//XbSZMcyGcJs2RxttPz3nnbYaNXsi8nki12394UdUT8q50CUBVREdZq52zOzYlXmeX9d4ERijolbJIRoKDU6g8zNzeXChQt06NDhqjkxOnbsCMCJEyeqfX5KSgqZmZm0bFm+QEJJSQklJeYxsbgQwjw90y+YNj6OZBfpeXNDVK0WpGhMlhxO4FBcFq+sPUVRaRnJxiHM9TN0pV+IBxM6lxdxZ2+K5nJFj8fbTeXw5U7VzH94I7ZWGu7v5M+qad35f4NbGgtt3+6PveqxOUWlJFbk2srbofaNbmDBHvY8U/GF4tOdF4hMki8Uey9mAjAgxIMP72lHiKc9afklPL0igtS8ht+XpIBo+XQ+jrw5tPxC/I+HE9h4OsXELRKNjbEHogxhFtdxX6cm9GvuTrHewIurI4nPLDR1k2pNURSScooIv5Qt864LcQM16puekpKCoij4+flddZ+joyMODg4kJCRU+/zK+Q8TEhIYN24cUVFRGAwG2rdvzwsvvEDv3r1r2HyQKRdMo/J9l/ffPDTmPKy1av5vZBse/vEoxy7l8OXuizzdNwitxvK+AN9qTgZFIa7iBO1saj7vbz1rXETFz8Wm3vKf2T+YownZRF/O4/UNp5k9vDVNXMxjEY+6UlJm4EhsJnuikolIzOViegGTugcwvK0PcMUCKk1dbul9trFSc29HP2y1at7aGM03++Lo0MSZXsF/z4t3NrV80Zomzja4WFgPkAc7N+FoQjY7zqbx6rrT/PRIZ1zt63YbLOV4l5JbzLm0fNQq6BnshrOdls/GtefxX8KJyyxkxrITLHygA54NMPF9JeMQZitNvbx/lpKNpRvaxptzafl8dyCeOX+cJcjdnra+Tjf9fMnJcpgiq6yiykVUtPI7ch23+36kUan4v5FteOLXcE6n5PHcbxF8N7FTnX/m36rr5bQ2IpnVEcmcT8snv6S8cGirVdMvxIMhrb3oHewuF9zq0e2+D5mbm82hRgXE3NxcAOzt7a95v52dHYWF1V99iI6OBuDo0aNMnTqVZ555hosXL7Jo0SKmTZvGvHnzGDx4cE2ahIfHzZ8wibon7795aax5eHo68X/jQnnul+P8eCiBTVGpPNC1KQ92b0qA27WPR+astjklZBZQrDegUZcf4Tec+nvF2zZBHthoNXXSvmtZ8GhXRs7bxbGEHO75+iC9mnswvksAw0N9sbe2rHmSFEUhLqOAY3FZHI/P4lhcJqeScigtq9q79Z3NZ2gd6E6QhwMxGYWoVHBXB39c7a1vuQ2TBjgRnVHIzwfieHNjNL8/2w9/VzsAEk6nAhDa1BVPT8vbpz97qDOj5+/hYlo+s7ec5fsp3Y2/s3XJ3I93Wy+U9z7s2NSVFk3LC8Senk4sm9GbBxfuJy6zkKdWnuSXx3vi3UCralf2q/B0c6jX3y1zz6YxeGN0KHHZxWyLuswra0+zdmYfvGvYE11yshwNmVVRxfS8/p6OFvkZ1NBu9/1o8WM9GPvFXuKzCnnl99MsfLRrg14Yu1n/zOnkpWze2XzG+H+tWoWLnRXp+SVsiU5lS3QqjjZaBrf14e5QP/q18qzX8+zb2e2+D1maGn3ru9Gk6IqiXDW0+UphYWHMmDGD8ePH07RpU+Ptw4YNY+TIkcyePZtBgwah0dz8zpmenovM1d7wVKrynV3ef/NwO+TRJ8CZFweG8P2BOFJzi5m/4xxf7DhH72B3xnX0o09zd7T1UKSoS7ea07GK+dSaudkxsp0P8/4qn//Kw8Ga3KwCcuuysf/gBHw2rj0L98VyKDaLfRfS2XchnTdWn+QunSej2vsS5u983c8AUwu/lM33B+KJSMolq2KVySt5OlrTzteJ9n5OnEzKZee5dJ5YfJhJPco/r1p4OqAvKCatoG6Gnj7dK5BjMRmcTslj+uJDfPVAR2y0ao5cLJ9rMtjVlrS0+ky1/rx/d2sm/3SMXWfT+L+1J3myb1CdvbalHO82R5RPLN8twKVKjnbAl+PbM/3XE1xIzee+BXv55sGOuNVBYfpG8ip6FpUUFtfL75alZNNYvDG4Becv5xKTUchj3x1iwf0dbrjCO0hOlsQUWV3OLu8MojUYLPYzqCHIflRODXw6th1Tfz7O0bgsevzfNgaEeDCmgy89mrnVywXEmqgup/+sPwVA/xAPnu4XRKCbHVq1ilPJecYCYkpuMauOXWLVsUs42mgY2MKTwa296B7oapEjocyN7EPmpTKPG6lRAdHBoXwupup6GRYWFhIQEFDt87t370737t2vut3f35/BgwezZs0azpw5Q5s2bW66TYqC/MKZkLz/5qWx5/FgZ3/u7ejHX+fTWRmexKG4LPZczGDPxQy8Ha0ZE+rH6FBfvOt4ReK6VtucLmaUH3uD3O15uGsAEUm57DibRhNn2wbJvaO/C1+M70ByThHrT6Xwe2QKCVlFrD2ZwtqTKfi72DKynQ93t/PBr4F6VNXEp39eICKp/MuQlUaFztuR9n7OhPqVFw07hHiRnp6HokBhaRmPLT3OmdR8Pv3zAgCdA1zq9H221qj5YFRbHllylJNJuczeGM27d7cmOqV8AZVWXo4Wuz+HeDrw2pCWvLkhmm/3xxHkbs+wNt51+jPM+XinLzNwMLa8B2LvILer2tnExY7/3d+BGctOEJtRyIurT/HlfR3qfahUcWl51yJrjbpe3ztzzqYxcbDW8vGY9kz+6RgnEnP4YOtZXh/S6qYv5EhOlqMhs8quuMDmYquV34+bIPtR+XnpvHvb89H280Qm57L9bBrbz6bh62TDPe19GdXeB18TnxdemdPJpBx2XchAo4Jn+wfTzP3v0UxtfZ1o6+vEzP7BRCTmsPVMGtvOpJKaV8K6yBTWRabgYqtlas9AJnapvu4hbp7sQ5alRgVEf39/VCoVKSlXT9icm5tLQUEBvr6+tWqIh4cHAPn5+bV6vhDi9mClUXNnKy/ubOVFXGYhq04kse5kMpfzSli4L5Zv98fSt7kH94U1oUeQ2w1fLyajAA97a5xszX8YbmxGAQBB7naoVCreGtaKZm529Lli/ryG4Otsy7SezZjaI5DwSzn8HpnCluhULmUX8dXeWL7aG0vXQFdeHhRCcw/zWAREb1A4UzG34Mdj2tGzmVuVnjoqFVW+dNtZafhoTDsmLTlGZsWXqc61XEDlepq42PKfUW15ZmUEf0Sn4ulobcxZZyErMFdneBsfolLy+PnIJd7ZHI2XozVdmrqaulkN4kRSDvklZbjaWdGmmrnpAlzt+PzeUKYuLS/+zN4UzXt3t0Zdj714jXMgypxOjUagmx1zRrbmX7+dZO3JFDwcrJnYJaDWK+huP5NKdpGeMaG+Zt2jXNSfLGMB0bzmshPmrb2fM98/1ImzqXmsiUhmw6nLJOcWs3BfLF/vi6VXsBujQ/3o39zd5L33FuyJAWBEW58qxcMrqVUqOvq70NHfhefvaM7xS9lsiUpl+9k0MgpKmfvnBWy1asZ1bNKALRfC9Gq09zo6OhISEkJERMRV94WHhwPQuXPnap8/Y8YMhg4des2Vl8+fPw9AYGBgTZokhLiNBbrZ8dyA5qyf3pN3R7SmU4ALZQrsPJ/OMysj+Hrv1SvcXmnz6cvc991h7vpyL1N+Psb/dl/kSHwWJRVfss1NTEVhqfJkx8Fay9P9ggmrh8LWzVCpVIQFuPD60FZserIns4fr6BroCsDhuCyeWh7BpWzzWJUvrmL+SDsrNX2bu9/UMD8/Z1v+c09bNGoV1hpVrVdgvpGuga78e3BLAH4+cokyBdzsrPByrP8hrfXtuQHNGdTSk9IyhZfXnOJC+u1xkbBy9eWeQW7XLQgGe9jzYcXv2JboVL6q+FJTX/5ehVnmcWpMegW5G1dA/+5APMMX7OflNZFsP5tWo8+zC+n5zFp3mv/bcpZv9sXVV3OFmTOuwmxhi3gJ89DSy5GXBrVgw/QevDuiNV2auqBQ/rn46tpT3L3wAJ//dYG0vLqZDqamjiVkcyA2C41axbReN1d3UKtUdA5w5dW7WrJhek+m9ix/3n+2neOv8+n12VwhzE6Nu9zcc889fPLJJ/z++++MHDkSAIPBwKJFi7C2tubuu++u9rmenp7s2LGD5cuX89BDDxlvP3DgAH/99Rd9+/bF27tuhzgJIRo/G62aYW28GdbGmwvp+Sw7lsjK8CQW7ovFxU7L/Z38r3qOvszAlxVf1g0KnEzK5WRSLosOxGOjVdPJ34XuzVzpHuhGS2+Heu0VdLNiKoYwV3e11JTsrDSMaOvDiLY+XMou5KXVpziXls/MFRF8/WAYHg6mLYZFXy4fFtzSy7FGWXYKcGHRhDD0BqVe56gb1d6XhOwiFu0v/9Ku83ZsFL1/1CoVs4frSMsv4URiDs+tPMlLg1rQpakLjjbm3+u3tvZWzFfaO/jGvaC7Bbrx2uCWvLv5DIsOxHMoLhsPByvc7a1xt7fCzfi3FR721rjZW+Fsq63V78ffBUTpgdjYPNw1AAcbLSuPJ3ImNZ8/z6Xz57l0nG21DNZ5MbyNNx2aXH+e2oV7Y6kcRbZwXyyu9lbcFya9a24npWUGCkrLl1tytWu8x2hR/2ytNMZz87jMQtZEJPN7ZDIZBaX8cCiBLdGpLHygY4MObVYUhf9VnPuPCfXF38Wuxq+hUauY0bsZaXnFrD2Zwmu/n+ar+zvQzs+5jlsrhHmq8SfDpEmTWLt2LbNmzSIyMpLg4GA2bNjAvn37eOWVV/Dy8gIgKiqK6OhodDodrVu3BmDmzJns2rWLOXPmcPr0adq3b8+5c+f45Zdf8Pb2Zvbs2XW7dUKI205zDwdm3dUSD3trFu6L5b/bz+Nsa3XV/Gu/R6aQmF2Eu70VCx/oSERSDgdjszgYl0V6fgn7YzPZH5sJXMTFVku3QLfygmIz11qdcNyqvGI96fnlvbebuTX8z68Jfxc75t3bnseWHic+q4jnfjvJgvs7mLRgFJ1S3vOtNsOC21YzBLWuzejdjEtZhWyOSjX25GwMbK00fDy6HdN+OU5cZiEvrYlErYLWPk50bepKt8DyIUJ2Vo2jV9yl7ELOpuajAno1u7npBe5p70tidhHf7o8jIinnho/XqFUMCPFg9nAdtjV434r15YUBKSA2PiqVinEd/BjXwY9zaflsPJXCxtOXSc0rYWV4EivDkwhwtWVEGx9GtPO+anXd6JQ8tp1JQwXc3c6H3yNT+O+2c7jaWTFY52WajRINrnL+Q7WKRn2RRzSsQDc7ZvYP5sk+zdh9IYN5f10gPquIp1dE8PWDHXFvgEXEAA7HZ3EsIRtrjYopPWo/6lGlUvH/7mpJal4J+2Iy+deqSL5+oCNBHuZ3gV+IulbjTwZbW1t+/PFHPvnkE9asWUN+fj7BwcH85z//YcyYMcbHbdmyhfnz5/PMM88YC4g+Pj6sWLGCzz//nB07drBq1So8PDwYN24czzzzjPQ+FELUmcd6BZJVWMqy44m8vSkaRxsNfZuXz7VaWmbg24qeXpN7BNLM3Z5m7vaMbOeLoihcSC/gYFwWB2MzORqfTXaRnq1nUtl6JhUAfxdbegW58UTvZg2ycir8Pf+hl6O1RZzUezna8Pn4Djz+y3GiL+fx4upIPh3X3mRFoujU8h6IOm/zmJPxWlQqFe+MaM1DXQNo6Wm+7awNV3srvryvA98diONQXBZxmYWcSs7lVHIuPxyKR6tWEernRJemrnQNdCXUz/mmhpmbo//tjgGgezNXXO1vfgjgjD5BDGzpSUJWIRkFpWQWlJBRUFrl35kFpeQW6ykzKGw/m0ZOsZ65Y9rdVBFRX2agrKJ7mRQQG7cWng7M7N+cp/oGczg+i42nUth+No2ErCIW7otl4b5YejX34LU7Q4y9fxbsjQFgSGsv3hzaClutmhXhSby5IQoHaw29G3iuXWEaWRXDl51trcxi5IVoXLQaNXe09KS1jyOP/xJOXGYhz6yIYMH9HXC2tcKgKBSVGrC3rp9zxV+PJgLlF+18bnHBRW3FQngzloVzOiWPGctPsOD+DgSZ4SghIeqSSlEse82btDRZ9tsUVCrw9HSS999MSB7XZlAU3twQxeaoVDRqFW8ObcWItj6sDE/kg63n8HK05rep3a775VtfZiAyObeid2ImEUm5lBnK3+Rgd3u+vC8UT8ebOwm5lZzWR6bw9qZouga68r/7OtTsySYUlZLLjGUnyC8po5O/M3PHtcfBumELoIqicNeX+8gp0vPjw51o7XN1j0LZhxpWSm4xR+KzOBRX/iclt+pcSDZaNR2bONM10JVuga609nFCqy7/MmvOWUUk5jB16XFUwI+PdK6XhXBK9AbCE7N5afUpCkrL6B7oysc3UUTMK9YzcP5eAHY926dGPRdvljlnc7srLC1jx9k0Np66zMG4TAwKuNtb8d/R7QCYtvQ4GhUsm9KNQDc7ygwKr6+PYuuZVKw0Kj4Y1Zb+IR4m3orbT0PvU0fis5ix7ATN3OxYMbVb/f9ACybHu1sTl1nI478cJ6OgFA8Ha9QqyMgvoUyBO1t5MuuulrVeDOpKlTlFXkxj1MIDGBRYNrkrwXXUWzCroJSnVpzgbGo+ng7WLLi/g1lONWSOZB8yL5V53IhcghZCNFpqlYq3h+kY2tqLMoPCWxujWbQ/zjjP3OTugTf8Eq3VqOno78LjvZvx9YNhbHu6Fx+PaYe3ozUXMwp44tdwknOK6n1bKhdQCTLz4cv/1NrHic/vDcXBWsOxSznMXHGSvGL9NR+blFPE1uhUMgquXmjrViTnFpNTpEejVpnNqtC3Ox8nG0a09eGtYTrWPd6dVdO68drglgzReeFub0Wx3sDBuCy+3B3DlJ+Pc/dX+zkYm2nqZl+XoijM/bN8QbhR7X3qbRVta62aboFuzLu3PXZWag7GZfHSmkiKKuYtq07xFYtpSA/E20/lPLWfjw9l1bRutPFzJqOglBnLwpm9KRqAke18Caz4jNGoVbwzQmdcBOmVtafYfjbNlJsgGkDlEOa6KNwIcT2BbnZ8cV8HXGy1pOeXkJpXYuwlv+1MGhN/OFKnn/urTyRhUKBzgEudFQ+hYoTF+A608HQgLb+EJ5efMI4aEqIxkjNIIUSjptWoy4eFdgkA4H97YricV4K3ozVjQn1r/HoO1lr6h3iw8MGONHG2IT6riOm/htf7asPGAqIFXtUMbeLMl/d1wMlGS0RSDk+viDB+SamUmF3ElJ+P8/9+P82w/+1n6s/HWLQ/jrOpedxqR/kzFQuoNPewt9hhsY2ZSqUiwNWOsR38mDOyDZtm9OTXyV14eVAId7TwwNlWS0ZBKc+ujGD58URTN7daW6JTiUjKxc5KzYw+QfX+8zr6u/DZuFDsrNQciM3imWvsV1e6cgGVxrBAj6g9f1c7VszoxYAWHpSWKcRlFmKlUfHYP1YktdKomTOyDUN05RfhXlt3il+OXiKroPrfM2HZsopkBWbRcFp4OrBiSjc+HduexQ91YsP0Hnz/UCeaudmRmlfC0ysieHtTNBtOpRCXWVjr88HSMgOrTiQDcG9Hv7rcBKBympZQQjztSc0r4YlfwzmdklvnP0cIc2D+E2kJIcQtUqtU/OuO5ng7WTP3zwsATOsZeEvFJH8XO756oCNPLT9BfFYRjy0NZ+7YdtccHlsXYo0rMFtWD8RKbX2d+N99HXh6xQlOJefy2C/H+XRce/xd7MgqLC8OpeeX4GCtIb+kjIikXCKScvnfnhh8nWzo29ydviEedG3qWuPeU5UrMNdXjzBRt1Sq8p6izT0cuL+TP8V6A3P+OMPG05f5cNs5zqfl88H9YaZuZhXFegPzd10E4NFuTfG6yWkNblWngPIi4gurTxKemMPjv4Yzb1z7a65qKSswiys52Gj57+i2zP8rhp8OxzOpW9Nr/t5o1eVzs1ppVKw/dZmPd5znkx3nae/nRO9gd/o2d6eVd81Wtxfmq/IihIutfEUUDcPV3oo+zf+eY9XL0YYfH+nMZzsvsDI8ifWRKayPTAHA0UaDi60V9tYa7Kw02FtpsLPWYG+lLv9/5e3WGvqHeBDgWn7OvO10Cmn5JbjbWzGwpWe9bIebvTVf3teBZ1eeJPpyHjN+PcGH97SlR5Bbvfw8IUxFPh2EELeNiV0CCPF04GJ6AaNDb/0KpK+zLQsf6MgzKyM4n1Y+nHnO3W3oV8fzROkNCvFZ5QVES+yBWEnn48iCBzry3MoIYjIKmfrzcd4f1Yb5f8UQm1mIj5MNiyaEoVLB7gsZ7DqfzsG4LJJzi1kRnsSK8CTsrNT0aOZG3+buDNZ539RE29GXa78CszA9G62a2cN1hHg68MWui6wMT+JSbjHvDdfhYmsevWSWHkkgKacYb0drHu4a0KA/u1OAC18/EMazv0VwMb2AaUuP89m4UFp4VR2uLyswi39Sq1TM7B/M472uP52HRq3izWE6mns4sCnqMmdT840Xeb7aG4uHgzW9g9zo09ydHs3cLGKhL3FtWZUFROmBKEzIzkrDrLtaclcrL/46n87JpFyiL+eSV1xGXvH1p+uo9NWeWN69uzUDWnjw04HyqYvuae+Llab+PgPd7cvnQHxl7SkOxWXx3KqTvDWsFcPb+NTbzxSitnacTePrfbFM6xnIna28bvp5soiKqBWZ9NS8SB6mlVesZ9a6UxyIzUKtghfuCOGBzv5XPa62OcVlFnLvokPYatXsfLaPxff0SM0r5l+/neRMar7xNicbLd9M6HjVHIVFpWUcjs9i1/kMdl9I53Le3/Mjuttb8VivZowN9UV7nRPCkQsPkJJbzFcPdKBzgOs1HyP7kGX463w6b6yPoqC0DH8XWz4e044QE69YnZ5fwr2LDpFfUsbs4TpGtDXNF4WknCJmroggNrMQrVrFg539mdYz0FjMCb+UzWO/hBPgasuqad3rpQ2yH1mGW83pcm4xey9msOdiBgdjsyi4Yv5NjVpFmL8zA1p4Mr6jX71+Wb8dNPQ+9famaNZHpvB03yAm9wi88RNuY3K8a1ilZQbiMgvJLymjsKSMgtIyCkvLKCj5++/K26JS8jidkocKmNDFn5+PXEIFrH6sO01cru5pXR9tnb0pms1RqQBM6dGU6b2D0Kgt+/y9rsk+ZFpPLgvncHw2UP47OqNPED7ezjd8nlwiFEKIW+Roo+XTse35YNs51kQk89GO80RfzuOVO1vUyUqnlfMfNnO3t/jiIZQPT1n4YEf+37rT7IvJxFqj4uMx7a65wImtlYa+zT3o29wDRWnBmcv5/HUhnQ2nUkjIKuLDbedYeiSBZ/s3545rDEvJKiw1rvDbykt6IFq6/iEeLJoYxsvrThGfUci0pcd5d0TrOu/1WxNf7Y0hv6SMNj6ODGvjbbJ2+Dnb8s2EMGZvimb3hQyWHE5gw6kUZvYP5u62PhTJEGZRR7ydbBjTwY8xHfwo0Rs4fimbPRcz2HMhg9jMQo7EZ3MkPpvfTybzzojWJi/yi5sni6gIc2WlUd/0sURfZuC/28/z24kkfj5yCYDewe4NUjyE8ra+M6I13o42/Hg4ge8OxBN9OY/3RrTBSaYHEGZAb1CITP57ns7vDsSTkFXE11NufIFZziKFEKIOaDVq/j24JTP7BaNWwbrIFKYuPU585q0vrhJrXEDFMuc/vBYHay2fjG3Pa4Nb8tUDHekU4HLD56hUKnQ+jjzeqxnLJ3fllTtb4G5vRXxWES+vPcX/bTlz1Uq0lfMfBrjayrC6RqKFlwNrnu5L5wAX8kvKeGF1JPN3XURfZrjxk+vYudR81kSUT8z+/B0hJi/wu9pZMXdse+aObUegmx0ZBaXM3nSGV9aeIj2/vPeujfbWL2oIUclaq6Z7MzeevyOEFVO7sWpaN56/ozkutlrOpObz6JKjLD16CYN0L7EI2YWyiIqwfFqNmll3teDlQSFoKj6Wx4fV/eIp16NWqXh2QHPeHdEaG62avRczmfzzMZYevcSR+CxyimQxKmE651PzKSw14GCtYfZwHTZaNftibm7Vc/k2JYQQdUSlUvFo96a09nHk9fVRnE3N59GfjvJ4r2YMa+ONh4N1rV73yh6IjYlWrWJsh9qd0Gk1au4La8KItt4s2h/Pj4fiWXUimYjEXN4f2YYgj/L36owsoNIouTtY88V9oXy8/TwrwpNYfDCeYwnZzLm79TUXgqgPiqLw6c7zGBS4s5XnTRXBG0rf5h70aObGT4cT+GpvLH+eS2f3hQxAeiCK+hXgasfELgEM0XnxzuYz7IvJ5JMd59lzIZ03h+rwdmqYBYZE7WQXVc6BKF8RhWVTqVTc38mftr5OpJUY6NvMNJ/Rw9p4E+Rux8trThGXWcgnO84b7/N1sqGllwMtvR3ReTnQ0ssRf1dbk1+MFDdPURRUFpjXiaQcANr7OTGirQ/NPez5dOeFm3qufDoIIUQd697MjSWPdOb//X6aE4k5zP3zAvN2XqBXsDtjuzQlxMWaJs62N/2BE1O5ArNb4+mBWFccrLXM7B9M90BX3twYxbm0fB5ZcpR/3dGccR38ZAXmRsxKo+bVu1rSpakr7/1xhhOJOUz44QjNPRxQq8qv/qvVKtSAWq1Co1KhUoGm4vYezVwZE+pX6zmJ9l7M5EBsFlYaFc/0C67bjasDVho1k3sE0r2ZG6/9fppL2UWAFBBFw/B0tOGzce1ZEZ7EZzsvcCA2iwk/HGHWXS0ZrLv5ydpFw/p7FWbpgSgah9AmziafZ6+1jxM/PtyZFeGJRKXkcSY1j6ScYpJzy//sqrjAB2BvpSHE04G7dJ7c38kfrcybaLa+OxDHD4fimdojkIldAixqjsuIxPICYocm5XMetvZx4qsHOt7Uc6WAKIQQ9cDbyYav7u/Aqohk1kemEJmcy+4LGcZeQK52VrTzdaKdnxPt/Zxo5+uEczUn7H8PYW5cPRDrUo8gN356tAtvbIjicFwWH2w9x9YzaVyqWL26lRQQG627dF609nHk3+ujOJWcy4mKk6Ib2XE2jQ2nLvPGkFbGHqs3S19m4NOd5b0IHujkT4Cr+Rb32/o6seSRzsz54yxbz6TKhQjRYFQqFfeFNaFb0/ILPKdT8njt99OsDE/E1c4KjUqFnbWG0e19CW1y44nbb8ayY5eISsljas9As94vzVGZQSGnqHwIs8yBKETdcq1Y+K9SbpGes2l5nL2cz9nUfM6k5nE+LZ+C0jIiknKISMph0+nLvD6klZzDmqm1J5PJKy5j3l8X2XE2nTeHtbKY72qV58q1+eyVVZhFrciqSeZF8jB/MRkFbDp9maOJOZy8lE1p2dVBNXOzKy8m+jkT5G5HfFYRZy/nsSI8CRXw17N96mRRlsaszKCw7HgiX+y6SLH+7znxNs7oied1hpDLPmQ5qstKX2bgSEI2BSVlKIpCmQIGg4IBBYMByhTFeHtGfglLDieQX1KGtUbFE72DeKiL/3VX877S8uOJfLjtHK52Vvw2tZtFTIquKAoxGYU0dbW96e2sKdmPLIMpctKXGfh6fxzfH4jD8I+fqQLGdfTj6b7Bt7QvVa40DuU9bR/rGcjDXQPq7fe9ITRkVlmFpQz+ch8A+/7V16Lft4YgxzvLYEk56Q0KcZkFHI7LYsGeWHKL9WjUKiZ1b8qU7k0b3XcAS8rmn9LySxi+YD8qwN5aQ35JGTZaNf2ae9DExQYfJxt8nGzxdbbB18kGZ1ut2Qx1Ts8vYVhF27c93dv4uVuZx42Y/xmvEEI0AkHu9jzZNwhPTycSk7M5czmPk0m5nEzO5WRSDglZRcRmFhKbWcj6U5even4LL4dGd+JQHzRqFRM6+9M32J13N0dz7FIOfs421y0eisZBq1HTo5nbTT9+ZDsf5mw5y/6YTObvusj6yBReHBhCj6Drv0ZukZ6v9sQA8ETvZhZRPITy3mDBNexpKURd0WrUPNkniLtaeRKRmEOZUv5l+XRyLhtPX2ZleBJ/nkvnka4B6Lwdae5pj5ud1U1/4dIbFD7cdg4o7z2XVVjKF7tj2BR1mVfubEHnANd63LrGoXL4soO1RoqHQpiAVq2iuYcDzT0cGNTKiw+3nWPH2TQW7Y9j06kU/nVHCHe08DCbQtTt7MSlbABCPB2YO7Ydc/44y/7YTLaeSb3m4+2s1Pg62eJTUVBs6+vEyHY+WJngWFs5fDnYw75W57CWcdYrhBCNiLVWTTs/Z9r5OfNAxW1ZBaVEJucSkZRDZFIu8VmFNHW1I8TTgZZeDvQOvvnCiICmbnYseKAjf55LJ8ClYRbVEJbF19mWeePas/5UCvN2XuRiRgHPrIxgQIgHzw1oTtNqhvouOhBHdpGeYHf7Wi8CJMTtqqWXIy29qg7HGx3qy/tbzhKbWVhlEncXWy3NPewJ9nAg2MOeYA97mnvY4+lgfdUX6N/CEzmTmo+TjZZfJ3dhf0wmc/+8wPm0Aqb/eoI7Wngws39zAutwCP+huEz+37rTtPBy4J72vgxq6WnRF/qyKuc/lOHLQpicp4M1H97Tlu1nUvl4x3kSc4p5Ze0pugW6Mq1nIGH+LhY1515jE15RhOvo71x+Pnlvew7EZnIhvYDkyvktc4pIyS0mo6CUwlIDFzMKuFgxLdXqiGR+PBTPM/2bM7CBi8IRSVXnP6wpKSAKIYQZcLW3ok9zd/o0dzd1UxoNtUrFoJaepm6GMGMqlYqR7XwZEOLJN/tj+fXoJXaeT2f3hXRGtfdlWs/AKqs6J2QV8uuxSwA8N6C5TG4uRB3o0tSVnx/twvLjiRxNyOZCej6XsorILtJz7FIOxy5VndfU0UZDa29HJnQJoF9zdzIKSvlfRa/gp/oG4W5vzYi2PvQOdmfBnhhWnSjv3bjrQgYDW3jQxMUOHydrvB1t8HYq/+Nub1WjlU/zS/TM3nSG7CI9R+KzORKfzYfbztEzyA0fp/Je756O1uV/O9jg5WiNg7XGrHsOZcv8h0KYnUGtvOgV7M73B+NZciieQ3FZHIrLws3OigEtPBis86JboKtZH1sao/BLVYtwKpWKnkHu9Ay6+ntcUWkZl/NKSM4pIjm3mEvZRayJSCY+q4hX154izN+Zp/oG0ymgYVYKj7iF+Q9BCohCCCGEuM052Wp5/o4QRof68tnOC+y9mMnqiGQ2nEphZDtfujdzpWMTZz7/6yKlZQo9m7lJr2Ah6pC1Vs1DXQN4qGsAUP6FKzazkIvpBVxMz+dCegEX0gtIyCokr7iMw/HZHI7Ppo2PI862WvKKy2jj41ilV7CrnRWz7mrJ/Z2aMG/nRfZczGDrmbRr/nyNWoW3499Fxaautgxv61PthPj/2x1DSm4xTVxsGdnOh99PJpOYU8y2al4fyudl9KooKjZxsWV0qC+d/F3M5ov/3yswy9dDIcyJnZWGJ/sEMaqdD4v2x7HzfDqZhaWsjkhmdUQybXwcmdojkP4tPGp0IaQhlOgNnE3LR+ftWO1F1zKDwtboVNafSsHOSkOQu33FHzuaudtjZ2Y9u4tKy4i6nAdAmP+Ni362VhoC3eyq9ICf1K0pPxyKZ8nhBI5fyuGJX8Pp2tSFx3s3q9cpN0rLDJxKKW97B7/aFRBlERVRK5Y86WljJHlYBsnJfEk2lqMhsjqekM2Xuy9e1fMJQK2Cnx7pQgsvh/r54RZM9iPLYMk5legNxGaWL0q27FgiRRWLZamA7yaG0e46X4iOJWQTkZjD5bxiLueVcDm3mMt5xaTllVDd29CzmRv3dWpCn2B343DBiMQcpi09jgLMvzeUHkFuGBSFYwnZRKXkkZZfUv4nr9j477zismu+fqifE5O6N6Vvc49rDkdsyKyWHE7gs50XGNbGm3dHtK7fH9YIWPJ+dDtpjDlVLhq3/UwaG06lGI+DIZ72BLtXPTe5sp545RGmpZcDY0L9cLWv3x7HL6+J5M9z6TRxseWhLv6Mau9rLAjml+g5mJjH/O1nicssrPY1fJ1sCHK3p5m7HUHu5dNZ6LwdcbQxzcWOI/FZzFh2Ai9Ha9Y/0eOWLgKl5Bbz7f5Y1p1MQV+xwljlRRy9QUGrVhHoZk+Ipz0hng74u9iW95x3tMa1BvMEV4pMzmXyT8dwsdXyx1O9qhScZREVIYQQQohaCAtw4asHOnIwNosd59I4kZjDudR8FGBcBz8pHgphItZatXEexYe7BvDjoQTWnkxmXEe/6xYPAToFuFxziJi+zEBafkmVouLhuCx2X8hgf2wm+2MzaeJiy/iOfoxo68N7f5xBAe5u621cdEmtUtGlqStdmrpe82cXlZZVFBXLC4qH4rL4PTKZiKRcXlpzCo1ahY9T+eT6rnZWlBkUSsoMGBSFvq28GRzihoeDza2+fdeVJT0QhbAIlYvG9Wjmxow+zfj5yCWWH0/kfFoB59MKbuo1/ohO5Zv9cQxr7c2Dnf3r5bzmr/Pp/HkuHYDE7CL+u/08C/fG4u1kQ1JOUZULK862Wu4La4KTjZaYjAJiMwq4mFFIVmFp+XyCucXsj800Pt5Gq2ZYa2/uC2uCzsfxqp9dn05Uzn/YxPmWe5D7ONnw2uBWTO0RyPcH41kTkWycTqJSRFKOcd7CK1lpVHg5WOPtZIOXY/lUGT4V//Z2tDbeduVCLZVtb+/nXOveqtIDUdRKY7yaY8kkD8sgOZkvycZymCqrvGI9cZmFtLrOMJzbnexHlkFyujmXsgtZeTyJNSeTyan4QqdWgUEBNzsrlk3pekvzBabll/Dr0UusCE+stodiJY1aRf8QD+5q5YmPkw3u9tZ4OFhjb113Q/vm/HGG1RHJPNG7GY/3alZnr9tYyX5kGW6XnLILS9l5Lp0iffmx5J/beuV/S8sMbIlO5XTFUFaAUD9nxoT6cpfOq06OK0WlZTyw+AiJ2UU82NmfQDc7fjqcwKXsoiqP83e1Y3xHX8Z28MPB+uqLF1kFpcRmFhCTUcDF9EJiMws4n5ZPUk7xVW2/U+d5zdeoa//67SR7LmbwwsAQJnT2r9PXziooJa2gBK1KhVqtolhfxsX0As6nF3AhLZ/knPKLXBkFpTf9mh4O1vRt7s6odj78cjSRrWdSebJPEFN7BlZ53M32QJQCoqiV2+VgbCkkD8sgOZkvycZySFbmS7KxDJJTzRSVlrE56jK/HkvkbGo+AO+NaM3QNt518vp6g0JaXjEpucUk5xSTXVSKVqPGWqOiWG9g69l0Dl/R8+ZKtlo17g7WeNhb4eFgjbu9Ne72VuW3Vdzubm+Nn4vtDS98vLL2FDvOpvHKnS24L6xJnWxbYyb7kWWQnK5NURROJOaw9Ogl/jybRlnFe2NvpaFTgItxyGyIpwPB7vZYa9XXf8F/WLg3hq/3xeHtaM3yKd2wt9ZQZlA4HJ9FmUHBz9kWP2cbmjZxrXE2lW1fdiyRbWfTKKsY+murVTOolScj2vjQJdC1Xi72GhSFu77YR26xnsUPdaKt740LbvWhtLLnfG75lBypecVczi3hcl5x+b8rbistu/Yb++V9oXQLrDqXtwxhFkIIIYQQQohbYGulYXSoH/e09yUiKZe8Yj29g69eabO2tGoVvs62+Drb0vEfnVlUKphxl479p5NZdSKJU8l5ZBSUkFFQQmGpgSK9gcTsIhL/0avnn1xstfQP8WBQK0+6B7pdsxggi6gIcftQqVR09Heho78LafklrI9MYe3JZOIyC9lzMYM9FzOMj9WooKmbHSGeDvg52xoXg/J2tMGzYqiszRXHlISsQhYfjAfg+TtCjD0aNWoVPZq5XdGGW2/783nFrItMYX1kCrGZhWw4dZkNpy7jamfFwJYe3NnKi84BLlWG8d6Ki+kF5BbrsdWqaWXC6WysNOqKIqxttY9RFIWswlLOpeWz/tRltkWnUqQ3YKNV0863dguogBQQhRBCCCGEEOK6VCoVHZrU/kvXrWjh5cBLg1pUua2gpIyMghLS80vIKCit8u/0/BLS88tvS8svIbtIz7rIFNZFpuBgraFboCu9gt3p0cwVFSqSc4tIzikvQrrcwrBsIYTl8XSwZlL3pjzaLYBTybmcTsnjfFo+59PyOZdWXjCLySgkJqP6hU5cbLV4VRQUL+cWU1Km0D3QlTtbedZv2x1tmNIjkMndm3IyKZf1p1LYdiaNrMJSVp1IZtWJZBysNfQKcqNfiAchng6421vhZmeFthZFxfBL2QC093Oq1fMbkkqlws3emm6B1nQLdOPlQSHsOp+Br5PNLQ1TlwKiEEIIIYQQQlgQe2sN9tZ2BLjaXfdxeoNC+KXyFVu3n00jLb+EP8/9vbjBP3k4WNdHc4UQZk6lUtHOz7nKglSKopCaV8L59HwupBVUDJEtX2X+csWiUMV6A9lFerKL9JxLK5/mQatW8fKgFre8yEhN2h7axJnQJs68NKgFR+Kz2HYmlZ3n0skoKGXrmTS2nkmr8hwXW235tA8OVn9P/1Dxt5ejDX4uNvg52xpXjQYIr1iEpIP/1QtymTsHay3D6mDqDSkgCiGEEEIIIUQjpFX/vUL0i4NCiL6cx96LGey7mElEUs7fK0A729KxiTMhHvambrIQwkyoVCq8nWzwdrKhV9DVUzcoikJOkZ7U/CuKinkltPN1IshExxJtxVDpHs3cmHWXwunkXP66kMGBmEySc4vJKiihTMFY9LxitPY1udtb0cbHidAmThyOywLKV2C+XUkBUQghhBBCCCEaObVKRRsfJ9r4ODGtZzNK9Aa0GhXqBuolJIRoXFQqFS52VrjYWdHC03RzAlZHfUWvyif7BAHlC6HkFOpJr5hPNiO/lIzCUjLyK/5fUMrl3GKScorJLdaTUVBaZV5IFZhsOgtzIAVEIYQQQgghhLjN1HRlVSGEsHRqlQpXeytc7a0I4fpFz9wiPXGZBUQk5XIiMYfTKbn0CnLH0eb2LaPdvlsuhBBCCCGEEEIIIcQ/ONlqjT0YH+zsb+rmmAW57CSEEEIIIYQQQgghhKiWFBCFEEIIIYQQQgghhBDVkgKiEEIIIYQQQgghhBCiWlJAFEIIIYQQQgghhBBCVEsKiEIIIYQQQgghhBBCiGpJAVEIIYQQQgghhBBCCFEtKSAKIYQQQgghhBBCCCGqJQVEIYQQQgghhBBCCCFEtaSAKIQQQgghhBBCCCGEqJYUEIUQQgghhBBCCCGEENWSAqIQQgghhBBCCCGEEKJaUkAUQgghhBBCCCGEEEJUSwqIQgghhBBCCCGEEEKIakkBUQghhBBCCCGEEEIIUS0pIAohhBBCCCGEEEIIIaqlNXUDbpVKZeoW3J4q33d5/82D5GEZJCfzJdlYDsnKfEk2lkFyshySlfmSbCyD5GS+JBvzcrM5qBRFUeq3KUIIIYQQQgghhBBCCEslQ5iFEEIIIYQQQgghhBDVkgKiEEIIIYQQQgghhBCiWlJAFEIIIYQQQgghhBBCVEsKiEIIIYQQQgghhBBCiGpJAVEIIYQQQgghhBBCCFEtKSAKIYQQQgghhBBCCCGqJQVEIYQQQgghhBBCCCFEtaSAKIQQQgghhBBCCCGEqJYUEIUQQgghhBBCCCGEENWSAqIQQgghhBBCCCGEEKJaUkAUQgjRaCmKYuomCCGEEEIIIYTFkwKiEELUASlUmZ+0tDTi4uIAMBgMJm6NEJYpIyOD3377jfT0dFM3RQiLJucJQgghLJ0UEAUAkZGRxMXFkZuba+qm3PYyMjKk2GEhlixZwp49ewBQqVQmbo240scff0zfvn354YcfAFCr5ePOHB0+fJjo6Giys7NN3RRxDfPmzaN37968+eabcowzYwkJCRQUFJi6GeIG8vLyTN0EUY34+Hjy8/NN3QxxAzt27DCedwvzc/jwYU6fPk1SUpKpmyLqkdbUDRCmtW7dOubPn09WVhYFBQXodDqmTp3KiBEjTN2028769etZvHgxhYWFFBYW8vDDD3PXXXcREBCAoijy5c3M/PXXX7z33nuMHTuWVq1a4eXlJTmZgdWrV/Of//wHgOeee47hw4ebuEXiWjZs2MDcuXPJysqiqKgIf39/nnrqKe655x5TN00Aa9as4YMPPkCr1eLs7IyTk5OpmySuYd26dSxcuJDc3FyKi4sZN24co0aNonXr1hgMBrlwYiZ+//13li5dSklJCcXFxYwaNYpBgwYREhIi5w0mtmHDBr744gvy8vIoKSlh1KhR3HPPPbRv316yMUMvv/wyXbt2JTAwkKZNm5q6OaLCunXr+Oyzz8jOzqaoqAhPT0+mTZvGhAkT0Gg0pm6eqGNSQLxN5eXlMXfuXJYvX87w4cNp3rw5hYWFLFmyhFmzZuHi4kKfPn1M3czbQmFhIZ988gnLly/nzjvvxNbWlgsXLvDBBx8QGRnJ+++/j1Yru6q5iYmJAeDYsWPs3r2bsWPHyommCUVHR/P6669z+vRpxowZw/jx42ndujW2trambpr4h2XLlvHxxx8zaNAgQkNDycvL45dffmHnzp307dsXd3d3UzfxtnXy5EneeustoqKiGDVqFA888AAff/wxOTk5kouZUBSFgoIC5s6dyy+//MLw4cNxcXEhJiaGb7/9lvDwcL777jusrKxM3dTbmqIo5Ofn8/HHH7Ns2TJGjBiBk5MTaWlpfPzxxyxfvpzFixfj5+dn6qbelhRFYeHChSxcuJAhQ4bg6+tLYmIiP/zwA5s2beLbb7+lZcuWpm6muEJiYiJ5eXkcO3aM/fv34+vrK8c5EysqKmLevHn89NNPDB8+nLZt22IwGFi6dClz5szBysqKsWPHYm1tbeqmijokVYnb1MmTJ/n999+ZMGEC06ZNw9vbG4AWLVrwzjvv8Ouvv9KnTx+5gt0Azp49y8aNG41ZeHp6oigKEydOZPPmzdx777307NnT1M0UFSr3idzcXJycnLh06RK///47nTp1IigoSK5Ym8j3339PREQE7733HkOGDMHZ2fmqx8jxzPTy8vL4+eefadmyJc888wz+/v4A3HnnnVhbW+Pu7i77kAmUlJTw7rvvsnz5cnr16sX8+fPp2rUrTk5OFBUV4ejoSGlpKRqNRvYhE1OpVERHR7Nu3TpGjx7N888/j6enJwBPPfUU27dvZ8uWLTKSxMRUKhWRkZH8/vvv3H333bz44ov4+PgA5Z9Xn3zyCW+88QZvv/02AQEBJm7t7Sc1NZWVK1fSq1cvnnvuOXx9fQHo1KkTc+fO5Y033uD111+nffv2Jm6pqDx3O3ToECqVioKCAlasWEFoaCitW7c2dfNua+fOnWP16tWMHDmSZ5991niMa9u2LW+++Sbr1q1j1KhRUkBsZOQs8Da1adMmSktLeeSRR/D29jbOudezZ09cXFzYsWMHGRkZ8kWhAWzdupWMjAweeOABPD09KSsrQ6VSMW7cOEpKSq5ZCBGmU1nYOHv2LO3ateOxxx5jz549bN++XQofJnTXXXdhbW2NlZUVzs7O6PV6430pKSnA3/MgykT2pnPp0iWioqJ48MEH8ff3p6ysDICQkBBjb1HZhxpeSkoKiqIwe/Zs3n//fQYOHIiTkxO5ubkUFhai1+uxsrKScwIzsX37drKzs5kwYQKenp6UlpYCMHnyZAD5smYmfvvtN3Jzc5kyZQo+Pj7Gc+2xY8cyZswYdu/ezYYNG2RuRBOIi4sjLi6OCRMm4OvrazxnuOeee3j66ac5ceIEy5Ytk8WjzEDl586qVavw8fHh0Ucf5cSJE2zdulXmrTSxffv2kZGRcdUxrnXr1ri5uXH48GHS0tJM3EpR1+RM8DZT+cXZYDCg1+tRFMV4ZaesrAxPT0/c3d3x8vKiuLjYxK1t3CqzKCgoQKVSkZeXh8FgQKPRoNfrOXz4MC4uLjg6Osrk6GZEpVKh1+spLS3F19eXkSNHEhAQwOrVqzlx4gQgBSpTaNasGQEBAfz0008AaLVa1q5dy7hx43jsscd49NFH+eKLLygpKZEClQnFxsYC5VM3AGg0GtasWcPIkSN5+OGHefjhh/nyyy+rFIBF/WvatCkvv/wy9957r7EnDoCTkxNlZWVYWVmRl5cnxzYTq3z/K+eUioqKAjAO4zt+/Dienp7odDpZjM2EKnNSFAVra2s0Go3xXFtRFFxcXOjatStQPj9iZY6i4VReWExISAAwThVkb2/PiBEjGDx4MOvXr2f//v0ma+PtpqioiOnTp7Np0yYA4wVGgBUrVrB//36effZZHnnkETp16sRvv/1GZGSkqZor+HthqMzMTABjPcHZ2RkXFxccHBykyNsIyRDmRuzixYv8+uuv2NraEhAQQO/evWnSpAl6vR4vLy/c3NwoKSkxXtnRaDTk5eVx7tw54/2iblSXBUC/fv3IycnBx8fHmMUff/zBn3/+iV6v57nnnsPe3p5p06YxaNAgU27GbeGfWfXp0wc/Pz9j70JFUdBqtSQmJtKtWzdCQkJ4+OGH+eCDD9i4cSNt27bFysqKkpIS6QVSx6rLBsp7sHXs2JENGzawdetWdu3axfLly+nZsydWVlZERkZy8OBBEhMTeeKJJ2jWrJn0GK0n18upRYsWWFlZkZqaSklJCe+99x6//fYb/fr1w9bWlqNHjzJv3jxSU1OZNm2aDO2rB9V9Hjk7O1fZHwwGA0VFRXh5eZGamoqjo6MJW337ud5+NGjQIBYvXsyPP/6Ih4cHHTt25Ny5cyxdupSCggJeeOEF/Pz8GDt2LAMGDJCeo/Wouv2ppKQEDw8PSkpKSEpKolWrVsaCiEajITg4GCsrK86cOcMff/xBhw4d5JyhjhUVFfHcc88xduxYhg0bRllZGWq1GpVKRbt27bCxseHixYsUFxdjY2NjPCfw9PTkoYceYu/evWzevJlu3boZp3oS9ScpKYmdO3eSlJTEsGHDqhTei4uLCQsLY8CAAbi4uHD//ffzxhtvsHbtWkJCQvDw8DB18xuta+1HlRexmjdvTkBAQJX9p7IjzLlz59BqtcapakTjIQXERshgMPDVV1/x1Vdf4ePjQ05ODpmZmXh7e/PBBx/Qu3dvJkyYwP3333/VB2JERAT5+fkMGzYMW1vbKgcJUXPXy+I///kPvXr1onfv3gwYMMD4nOTkZN577z3atGlD27ZtuXz5Mps3b+bll1/mww8/ZNCgQVL0qAc3yqpyHsrK+VcyMzONK8ANGTKErVu3smHDBlq2bEl4eDg9e/Zk+PDhklUduFE23bp1Q6vVMmjQIFatWsWiRYvQarUsWLCAjh07GhcYWLhwIWvWrKFFixZMmjRJvlTXsZs53jk5OdGyZUu2bNnC0KFDOXr0KG+//TZDhw7FycmJmJgY/ve//7F8+XKaN2/OQw89JDnVkZs9xlV+CVCr1djb26NSqcjJySEmJoagoCDTbsRt4Gb2o44dO/LSSy+xaNEiZs6cib+/P7GxsfTt25cWLVqQkZHBjh072LFjB6+//jrjx4+Xc7k6djPn2t26dWPx4sXMmzePLl26VCnCr127lr59++Ll5cWaNWuYPn26FEHq2LUKUpW9Q+3t7QkLC+OPP/7g0UcfpUmTJlXO19q2bcvdd9/N2rVriYmJkQJiA6jsxXbmzBm+//57Jk+ejF6vx9ramokTJ3L33Xfj6uoKQK9evRg8eDAbNmygb9++DBs2zIQtb9yuV9gdMGAA/fr1u2qRtaioKNLS0ujZsyfOzs5ST2hk5Ky8EYqIiOCHH37g/vvv58svv2T37t188MEH2NraMmvWLLZv346npyc+Pj7GD8vKoS4HDx4EyicRFrfuelm8+uqrbNu2zTj0qHLInru7O/PmzeM///kPr7zyCh999BEffvghDg4OrFq1yjj0XNStm9lvKpWVlVFcXGwc8tKkSRMeeeQR0tLSeOutt/jrr79o2rSpDCGrIzfK5s8//wRg8ODBNG3alKNHjxIWFkb//v1xcXEBIDAwkEmTJuHn58fu3bspLS2V/aiO3eh49+eff+Ll5UWvXr04ffo006dPx9vbm/Hjx+Pk5ASUD0WfPHkyvr6+7Ny5U453dehmj3GV5wWVvaVCQ0ONQ85F/bvRfrR161YAHn74YVasWMErr7xCXl4eU6ZM4f3332fWrFl8+OGHfP755wQGBrJy5UrjcE1Rd26U086dOxk0aBDjx48nMjKSF198keXLl3PkyBE++ugjfvnlF+6880569OhBdnY227ZtA2QKlLr0z4IU/H1c8/b2ZsCAASQmJrJq1aqrnuvo6Mgdd9xBSUkJe/bsAZBzunpS+f1nxYoVAHh6evLpp5+Sk5ODtbW1cW54V1dXY34+Pj6MGzcOW1tbVqxYQVxcnMna39hdaz+q3BdcXV2rFA8r8zl8+DBFRUV06dIFkONaYyMFxEZo5cqVlJaWMm3aNEJCQtBoNIwaNYrXX38dvV7P//73P+NcbZUHgMoeHhs3bsTDw4MePXoAf8+zIx+atXOjLBYsWGDMojIDa2trOnfubFzJCqB79+60aNGC3bt3U1xcLL3a6sH1siotLWXBggWEh4cD5Vfj8vLyaNeuHVA+h9GcOXPQarWoVCoeffRRQkND5WpbHakum3//+9+Ulpby1Vdfcfz4cQAee+wxRo0axX333QeUn7QoioJarcbPz4+goCCOHj1qPCEVdedGx7t58+Zx4cIFxowZQ2BgIAkJCcaFU6D8c0alUuHv70+rVq04ePAgpaWlklMduZlj3JXnBpXHryZNmlBYWEhERITxPlF/brQfXXm8s7OzY+PGjZSWljJhwgS8vLyMrxMaGkqXLl04ceIEJSUlJtqaxut6OZWVlfHZZ59x4cIF/vWvfzFt2jR27tzJG2+8wUMPPcTatWt56623GD9+PO3bt8fOzo6kpCSZVqOOVBakli9fDpQXpD777DNycnLQarXGRYf69+9P+/btWbx4MefPnzc+v/IY17JlS/z8/Dhy5Ihx+LOoe1qtlri4OLZv38748eMZN24cRUVFfPTRR0DVhdWu7EXaoUMHxowZw969e9m1a5cc5+rYjfaja50LVJ43bNq0CWtra/r06QP8PcdoWVmZFBMbATkSNiKVC6Kkp6fj5uZmLEDp9Xo0Gg19+vRhxowZREREsHr1aoqLi1Gr1cYDwOHDh4mJiaFfv37GoZkGg4Hz58+zceNGkpOTTbZtlqa2WVR++FWepFRm4+HhgUqlwtvbWxYXqGO1yUqj0eDi4sLhw4eZMmUKs2bN4o477uD//u//sLe3588//+TChQuAfNm+FTfKpm/fvsZs1q5dS0lJCWPGjOG5554zHsOuPPF0dnamuLgYX19fyaUO3ew+dOrUKZYsWULLli0ZPnw4UD63zj8/WypXnnd3d5cFpOpAbT+PKnsS6HQ6AI4cOWIctiTqXk1yWrNmDUVFRdja2pKdnU3z5s3x9/entLTUeGxzcHCgrKwMW1tb6UFah2p6vHNwcODll19mxYoVfPTRR3zyySesWLGCsWPHAuXHORsbG2xtbaV4WEcqC1I7duwwFqQKCwuNBanKY1iLFi0YO3YshYWFfP7558aeVpX8/f2xs7PDyclJLgjXs59//pmsrCxmzpzJk08+Sbt27Vi2bBlRUVFVPo/g7/M6Z2dnhg8fTnBwMMuWLePcuXOman6jdKP9qDrR0dEcP36cbt260bZtW+DvesKaNWuMFyOF5ZKzwEakcs4ig8FATk6OsSdBZdVfo9Ewfvx4OnXqxMaNG40ri1UeiHfv3g3AwIEDgfK5+NatW8e//vUvXnzxRZKSkhp6kyxWbbOonEA7NTUVKD/J0ev1HDx4kMOHD9O/f3/j/B+ibtQ0q+PHj5Ofn09aWhr//e9/UalUfPPNN7z++usMGTKERx55hIMHD7J69Wr0er182b4FNc1m37592NjYGBfeSEhIMPZq0+v1HDhwgOPHj9O3b19ZEKIO1SSn9evXc/jwYSZNmkSvXr3Yu3cvGzZsIDMz03i8O3nyJMePH6dXr15VelSJ2qnt51Hlsatr166EhIRw9uxZ42eTqHs1yWnTpk3GIWKenp4cP36chIQErKysUKvVlJaWEhERwV9//UW/fv1o3bq1KTetUanp/rR3714A2rdvz8iRIxkxYgTe3t7GgtTmzZvJysoiJCTENBvUSF2vIKXRaIwX7IcNG8a9997Lpk2b+Pnnn8nIyDDme/DgQWJiYmjTpo2Jt6bxu+OOO5g+fTqurq7Y2dkxceJEAObMmQNQbQG3VatWPPjgg5w/f57ffvuNnJycBmvz7aAmhd1K+/btA+DOO+8EykdtVdYTXnvttasK9cLyyDfbRqTyqvOwYcPIzs4mMjLSuGNfOWnw9OnTyczMZOfOneTl5aFSqSgrK+Po0aO4uLgQHBzMvn37ePvtt3n11Vdp2rQpf/75p8yLWAO1zSIjI4NHH32UBx54gOXLlxMdHc22bdv48ssv8fb25t577zXZNjVWNckqKyuLzZs3Y2dnx6RJk5gzZw4fffQRPXv2xNraGmtra4YPH46Pj4/0FK0Dt7IfPfHEE0yaNIklS5Zw6tQptm7datyPxo0bZ7JtaoxqklN2dja///477u7uPPXUU4SFhfHhhx/y1ltv8ccff7Bq1So+/fRT1Gq1sYeOuDW3cm5Q+fy77rqLU6dOGXtWi7pX05y2bt2KnZ0dI0eOBOD5559ny5YtnD59mh07dvDpp59iMBgYP348KpVKho3VkZrmtGvXLvLy8gDYv38/7777Ljt37iQ+Pp4dO3awevVqBg0aZBzqJ+rGjQpS1tbWKIqCh4cHU6dOZciQIXz++ee8/fbb7Ny5k/Xr1/PNN9/g6+tr7DEv6k/Xrl15/vnnsbGxAWDUqFEMGjSIQ4cOsXnzZoBrFqtsbGzo27cvLVu2JDY2Vi7a17GaFHYrj3/h4eHY2dnRvHlz9u/ff1U94cqFQ4WFUkSjc/r0aWXIkCHKQw89pCQlJV11f25urjJ16lRlyJAhxvuLi4uVfv36Kb1791ZeeOEFJSwsTBk+fLiyf//+hm5+o3KzWQwePFhJTU1VioqKlMWLFyv9+/dXdDqd0qNHD6VDhw7KqFGjlPDwcBNswe3jZrMaOnSocunSJaWgoEDR6/XG+w0Gg6IoilJWVqakp6c3WLtvB7XZj77//nulb9++ik6nU7p27aqEhobKflTPbjanO++8U0lNTVUMBoOSkpKizJgxQ+nQoYPSpk0bpWvXrsqYMWOUo0ePmmALGrfanBtU+vPPP5XWrVsrkydPNh7rRP2oyX6UkZGh5OfnKwsXLlR69+6t6HQ6pXv37kpYWJgycuRI2Y/qUU32p+TkZMVgMCgHDx5UwsLCFJ1OpwwePFjp2rWrcu+99yrR0dEm2ILGrbS0tMr/i4uLlSeffFLR6XTKpk2bFEVRlJKSkir3//vf/1a6dOmitGnTRgkLC1PuueceOWeohZycHOX06dNKcnKyUlZWpiiKctOfG2VlZcbn7NmzR+nUqZMycOBA4/3Xeh2DwaAkJCTUQcvFP93MfnTldyFFUZQRI0Yo7dq1U2bOnKl06tRJ6gmNkBQQLUBND8T5+fnKJ598ouh0OmXp0qXGD8jK5+j1euWHH35QdDqdcvDgQUVRFCU+Pl4ZNGiQ8eRzyZIl9bxVlqk+s7jy4HrmzBll7dq1yvfff69s27atHreo8WqI/UbUTkPtR9HR0cqqVauU7777Ttm+fXs9blHjVJ85HThwwPi8oqIiJTY2Vjl16pQUPGqgIY5xlV8e0tPTlddee0357bff6nOTGqWGPN6tXbtWWbJkibJjx47626BGqqGOd1FRUcovv/yifPbZZ8Yv4OL66rsgVVkA0ev1Snx8vHL06NEqmYmb9/nnnyt9+/ZVunfvrnTq1El54YUXlEuXLtX69WbPnq3odDrliy++UBTl6mKVuHn1vR8ZDAYlJydHmThxorETjNQTGietqXtAiuubP38+v/76KyUlJZSWljJw4EBefPFFmjRpUu1z7O3tGThwINu3b+f777+nQ4cOtG3b1jhUWaPR4O/vD0BiYiIAAQEBODs7M2TIEF566SWZLPga6juLlJQU4/NatmxJy5Yt632bGqv6zkrmA629htyPWrVqRatWrep9mxqj+s7pyoVTbGxsCAwMrPdtakwa6hin1WpRFAV3d3fjkCVx8+R4Zxka8nin0+mMCxOJG6tNNle6ckhr7969GTNmDD///DNffvklTz31VJUV5zUaDQEBAcZ5lMXNKyoq4q233mLXrl3ce++9uLm5ceLECXbt2kXHjh159NFHa/R6lQt2TZw4kb/++ouvvvqK+++/H09PT1nMqxYaaj9ycnLC1dWVJ554gueee07qCY2U7H1mqqioiFdffZWff/6ZMWPGMH36dPr378+uXbvYunVrtc+rnJslNDSUiRMnkpiYyKJFi4iLiwP+nqugqKgIKC8cKhVzFixdupRXX31VdvZ/aKgsKk80Re1JVuZLsrEMkpN5M0U+sjJszcl+ZBkkJ/NV22yqU5nZxIkTCQgI4KuvviItLQ2NRmO8T9TeyZMn2bx5MxMmTOCJJ55g6tSpzJ07l4ULFzJ06FCAq+ZhvfL/8fHxVe67cqXsCRMmUFxczL///e8q94kba8j9qHJhorlz5/LCCy9IPaExM20HSFGdQ4cOKR07dlTmzZun5OTkKIpS3j342LFjSnJysvH/V7ry/8nJyUp2drby4YcfKjqdTnnhhReU3NxcJTc3Vzl16pQyYcIE5ZFHHlEKCgoabqMslGRhOSQr8yXZWAbJybxJPpZBcrIMkpP5utVs4uLiqn3tb775RtHpdMoTTzxRDy2/Pc2fP1/R6XRKbm5uldv1er1SXFxc5bbKobCKoigXL15Upk6dqnTr1k25fPnyNR+Xnp6uTJkyRVm8eHE9tb7xkv1I1AcZwmymDhw4QFFREVOmTMHR0REo7wUQGhpqXIXqyhUS1Wo1KpWKmJgY3nnnHU6ePMn69et5+eWXSU1NZe3atWzfvp2goCDy8vIoLCzknXfewc7OzmTbaCkkC8shWZkvycYySE7mTfKxDJKTZZCczNetZPPuu+8SERHB+vXr8fLyMr5m5ePGjh3Lnj17ZOXrW/DPYcRnz57FxcWFzMxMY17z589n/fr1ODk50bJlS4YPH07fvn1Rq9Xk5eXxxRdfsGTJEtq0acPbb7+Ni4tLlZ+hVqsxGAy4u7vzzTffSM/DWpD9SNQHKSCaibo+EL/11ls4ODgAMHv2bB544AF+//13cnJy8PPz46mnnsLe3t4k22ruJAvLIVmZL8nGMkhO5k3ysQySk2WQnMyXFKTM38mTJ/npp594/fXXcXBwQClfjBW1Wk2bNm3YsWMHBoOBlJQUpk+fTnJyMmFhYeTk5LBy5Up+++03Pv/8c/r168ecOXNYu3Yts2bNYtiwYVUKVFeqzEiyujmyH4kGYeoukLeziIgIZdasWUpeXp6iKOVdhiu7ay9YsEDp0KGDEhMToyQnJyujR49WevTooUyfPl2ZMGGCotPplNatWytbtmxRioqKlFmzZilt27ZVfvjhh6u6gF/pn8uxi3KSheWQrMyXZGMZJCfzJvlYBsnJMkhO5ssU2Yjae++99xSdTmdcWffKoa7r1q1TdDqd8vnnnyvbtm1Txo4dq4SHhxuzXbNmjXLXXXcpI0eOVM6dO6ckJiYq58+fl1WV64DsR6KhSQHRhORAbD4kC8shWZkvycYySE7mTfKxDJKTZZCczJdkYxkqc5k5c6ai0+mU0aNHK/Hx8YqiKEpJSYmiKIqSmJioDBs2TOnUqZPSq1cv5csvv6zy3IKCAuXbb79VdDqd8uuvvyqKUnUuRFF7sh+JhiZ9Tk1AqVh1KiUlBYDly5eTkJCASqWitLQUgC5duhAcHMyiRYt4/fXXGTx4MB06dDAOhRg8eDATJkzg7NmzHDlyBD8/P4KCgmTFoxqSLCyHZGW+JBvLIDmZN8nHMkhOlkFyMl+SjeWJj4/nyJEjODs7ExUVxdKlSwGwsrLCYDDg5+fH3XffTXFxMYWFhTRt2hQon19PURTs7OwICwvDxsaG3bt3AzIs+VbJfiRMRfZcE5EDsfmQLCyHZGW+JBvLIDmZN8nHMkhOlkFyMl+SjeVQqVRs3LiR3Nxc3n33XZydnVmxYgXHjx8HQK/XAzBp0iSCg4MpLCwkIyPD+PzKxToqF+7w9fVt8G1orGQ/EqYgvyEmIAdi8yFZWA7JynxJNpZBcjJvko9lkJwsg+RkviQb85OVlUVMTEyV99lgMACQkJDAkiVL6NGjB0OHDmXWrFlkZ2fz448/AmBtbU1paSlOTk489dRT2Nra8v333xMZGQmAVqulpKSE3377Db1eT1hYWINvX2Mk+5EwFSkg1hM5EJsPycJySFbmS7KxDJKTeZN8LIPkZBkkJ/Ml2ViOuXPnMnLkSCZOnMjdd9/N+++/T3p6epWeaAMHDuThhx8GYNy4cbRu3Zr169ezadMmoLyYBTBixAgee+wxsrOzefrpp/n222/ZuXMnq1ev5vvvv6d79+706tWr4TfSQsl+JMyRSqkcQC/qzNy5c1m5ciUGgwFFUbjnnnt44okn8PDwAMp3+K+//ppBgwYxYMAAAMaMGUNUVBSffvopw4YNQ6/Xo9VqgfLl1r/77jucnJx45JFHaNGiBSkpKXz33Xd4eXnx2Wef4ebmZrLtNWeSheWQrMyXZGMZJCfzJvlYBsnJMkhO5kuysQyFhYW8/vrrHDx4kPHjx+Pk5MSxY8fYsmULYWFhvPPOO7Rq1QqA3NxcnJycUBQFlUrFoUOHeOSRR+jatSsLFizA0dGR0tJSrKysyM/PZ8+ePXz00UfExcVhZ2cHQO/evXnnnXeMvwfi+mQ/EuZKCoh1SA7E5kOysBySlfmSbCyD5GTeJB/LIDlZBsnJfEk2luXo0aNMnjyZxx57jKlTp+Lo6EhZWRk//fQTH330Ef369eOFF14gJCTEmFNl2UClUvHss8/yxx9/8OqrrzJlyhTjYyqlpKSQnJxMdnY2Pj4+6HQ6U22qRZH9SJi9Ol/X+TZ25MgRJTQ0VPnss8+U3NxcRVEURa/XK4sXL1ZCQ0OVp556Sjl37pyiKH8vsW4wGIz/njlzpqLT6ZRFixZVeUyl5ORk5fjx48rOnTuVqKiohtosiyRZWA7JynxJNpZBcjJvko9lkJwsg+RkviQby7JkyRJFp9Mp2dnZiqIoSmlpqaIoipKbm6t8/PHHik6nUz766CNFr9crivJ3HpX/T05OVnQ6nTJkyBDl4sWLVe4TtSf7kTB3UkCsQ3IgNh+SheWQrMyXZGMZJCfzJvlYBsnJMkhO5kuyMV8bNmxQVq1apWzfvt34ns6dO1fR6XTKnj17FEVRlLKyMuPj09PTlVGjRin9+vVTdu/erShK1UJU5Wt8+umnik6nU95///2G2pRGT/YjYe5kEZVa2rhxI6tXr2bHjh3GVYxSU1MBOHnyJPD3MuiOjo5MnjyZVq1asWbNGvbv31/ltTQaDWVlZfj4+PDkk08SGxvLL7/8YrxPXJ9kYTkkK/Ml2VgGycm8ST6WQXKyDJKT+ZJsLMPWrVsZNGgQb775Jm+++SZPPvkkU6ZM4fjx4/Tt2xeA6Oho9Ho9arXaOETZ3d2dp556isuXL7Nt2zby8/OrDE+uzPa5557D19eX77//nr179zb8Blo42Y+ERTJ1BdPSbNmyRRk4cKDStWtXJTQ0VNHpdMojjzyiHDt2TDl06JCxy3Dl1YIrr9Zs3LhR0el0yuzZs5W8vLwqr3vl4wYMGFDlipC4NsnCckhW5kuysQySk3mTfCyD5GQZJCfzJdlYjuXLlyvdunVTnn/+eWXz5s3K3r17la+//lrR6XTKuHHjlMOHDytjxoxRHn74YSU5Ofmq5xcUFCiTJ09WBg0apERGRl51f2XGy5YtU/r06aOcPn263repsZD9SFgy6YFYAytWrOC1114jLCyMOXPm8NVXX/HSSy9x8OBB3n33XVQqFW3atGH79u2kp6cDVLlaM2DAAHr16sXOnTuJjY2t8toqlQq9Xg/A008/jaenJ+7u7g23cRZGsrAckpX5kmwsg+Rk3iQfyyA5WQbJyXxJNpYjLy+PJUuW4O/vz8yZMxkyZAi9evXiscce4/777ycyMpKIiAjuuOMODh8+zP79+zEYDADGXoi2trbcc889XLp0ifj4eADjYwDj6r733Xcfu3fvpnXr1g28lZZJ9iNh8UxdwbQUubm5yujRo5UxY8YoFy5cqHLfG2+8oeh0OuW7775TPv30U6V169bK6tWrjXNJXDnB6W+//abodDpl06ZNiqJUnW9C3BzJwnJIVuZLsrEMkpN5k3wsg+RkGSQn8yXZWJbExERFp9MpS5YsURSl/H2u7M22atUqRafTKcuWLVNOnDihDBw4UBk7dqxxvrwrHT16VAkLC1Nmz55d7c/65yIdonqyH4nGQHog3qTc3FyioqIYP348wcHBGAwGY4W/c+fOADg4ODBo0CD8/PxYvHgxcXFxwN9XDVQqFUFBQdjZ2XHgwAHg73kNrqRUXPkR1yZZWA7JynxJNpZBcjJvko9lkJwsg+RkviQby5KYmAhUnUevch68/Px8ALKzs2nbti33338/p0+fZvny5WRkZABQWloKQJs2bSgtLcXT07Pan3Vl7zhxfbIficZACog3SQ7E5kOysBySlfmSbCyD5GTeJB/LIDlZBsnJfEk2liUsLIwOHToQFhZGcXEx8Pf7mpaWBkDbtm3RaDQMHz6coUOH8uOPP7Jq1SoArKysKCkpYe3atej1enQ6nWk2pJGR/Ug0BlpTN8BS/PNAbGNjc90DcVRUFD/++CPu7u5MmzZNDsR1SLKwHJKV+ZJsLIPkZN4kH8sgOVkGycl8STaWRaPR8MMPP2Bra1vl9pKSEg4dOoSdnR0tWrQAoFmzZsyaNYuYmBj++9//EhsbS+fOncnPz2fJkiX07NmTrl27mmIzGh3Zj0RjIAXEmyQHYvMhWVgOycp8STaWQXIyb5KPZZCcLIPkZL4kG8tzZVYGgwG1Ws3p06cJDw+nd+/eeHh4YDAYUKlU+Pr6MnfuXBYvXszy5ctZu3YtGo2GAQMG8NZbb+Hi4mLCLWk8ZD8SjYFKkQHytVJ5IA4PD+fhhx+md+/efPnll6hUKuOfixcvGg/EVlZWciCuJ5KF5ZCszJdkYxkkJ/Mm+VgGyckySE7mS7KxTAsXLuSTTz7hjTfe4KGHHjLeXlxcjJWVFWq1mtjYWHJzc3FwcCA4ONiErW38ZD8Slkh6INZS5WSlBw4coLS0lP79+xvnMIDyA3GzZs14++23mTJlihyI65FkYTkkK/Ml2VgGycm8ST6WQXKyDJKT+ZJsLE9JSQmbNm1Cq9UyfPhwoLyAFR8fz7Zt28jKyuKFF16gWbNmJm7p7UP2I2GJpIB4C+RAbD4kC8shWZkvycYySE7mTfKxDJKTZZCczJdkY1mioqKIiYlhwIABuLu7k5KSwqFDh1i0aBGnTp3imWeeAcpX75UFOBqO7EfC0kgB8RbIgdh8SBaWQ7IyX5KNZZCczJvkYxkkJ8sgOZkvycYyVL7/sbGxFBQUEBAQwKFDh/j+++/Ztm0bffv2ZevWrQQEBACyem9Dk/1IWBopINaCHIjNh2RhOSQr8yXZWAbJybxJPpZBcrIMkpP5kmwsS+X7f+7cOQAiIiJYuXIl7u7ufPvtt/Tp08eUzbttyX4kLJUUEGtBDsTmQ7KwHJKV+ZJsLIPkZN4kH8sgOVkGycl8STaWydvbG4Dz588zc+ZMJk2aZOIW3d5kPxKWSgqIt0AOxOZDsrAckpX5kmwsg+Rk3iQfyyA5WQbJyXxJNpYlJCSE559/nilTpmBtbW3q5ogKsh8JS6NSFEUxdSMs1f79+wkPD5cDsRmQLCyHZGW+JBvLIDmZN8nHMkhOlkFyMl+SjWWROfTMk+xHwtJIAfEWyIHYfEgWlkOyMl+SjWWQnMyb5GMZJCfLIDmZL8lGiFsn+5GwNFJAFEIIIYQQQgghhBBCVEtt6gYIIYQQQgghhBBCCCHMlxQQhRBCCCGEEEIIIYQQ1ZICohBCCCGEEEIIIYQQolpSQBRCCCGEEEIIIYQQQlRLCohCCCGEEEIIIYQQQohqSQFRCCGEEEIIIYQQQghRLSkgCiGEEEIIIYQQQgghqqU1dQOEEEIIIYR5+fzzz5k/f36V29RqNba2tvj7+3PHHXcwbdo03Nzcav0zcnJy0Ov1uLu732pzhRBCCCFEPZMCohBCCCGEuKYHHniALl26AGAwGMjJySE8PJxvv/2W1atXs2TJEoKCgmr8ujt37uSVV15h3rx59OjRo45bLYQQQggh6poUEIUQQgghxDWFhYUxevToq24fO3Ys06dPZ/r06axfvx6ttmanlEePHiUrK6uOWimEEEIIIeqbzIEohBBCCCFqpF+/fkyePJmYmBjWrl1r6uYIIYQQQoh6JgVEIYQQQghRY+PHjwdg27Ztxtv27dvHk08+Se/evWnXrh3dunVj0qRJ7N271/iYRx55hAULFgDw6KOPMmjQION9xcXFfPHFFwwbNoz27dvTo0cPnn32Wc6cOdNAWyWEEEIIIa5FhjALIYQQQogaCw4OxtbWlsjISAA2b97Mc889R9u2bXniiSdwcHDg7NmzLF++nMcff5zVq1fTsmVLZsyYgZOTE9u2bWPGjBmEhoYCUFJSwtSpUzl+/DijR49m8uTJpKSk8Msvv3D//fezaNEiOnfubMpNFkIIIYS4bUkBUQghhBBC1JhKpcLFxYWMjAwAFixYgKenJ0uWLMHe3t74uGbNmvHOO+/w119/0bJlS/r06cPBgwfZtm0bvXv3Ni6isnjxYg4fPsynn37K8OHDjc+fOHEio0aN4o033mD9+vUNu5FCCCGEEAKQAqIQQgghhKil0tJSVCoVAMuXLycnJ6dK8bCkpAS1unzGnLy8vOu+1vr163F2dqZHjx7GoiSARqOhf//+rFmzhvPnzxMSElIPWyKEEEIIIa5HCohCCCGEEKLG9Ho9ubm5eHt7A6DVaklMTOTLL7/k/PnzXLp0iYSEBAwGA4Dx7+pcvHiRoqIievXqVe1jLl26JAVEIYQQQggTkAKiEEIIIYSosdOnT1NaWkr79u0B+Pjjj1m4cCEBAQF07dqVXr16odPp0Ov1PPXUUzd8PYPBQEBAAO+++261j2ndunWdtV8IIYQQQtw8KSAKIYQQQogaW7t2LQBDhw4lMTGRr7/+ms6dO7N48WKsra2vetyNBAQEkJKSQrdu3bCysqpy39GjRyksLMTW1rbuNkAIIYQQQtw0takbIIQQQgghLMvBgwdZunQpLVq0YOjQoWRnZ6MoCsHBwVWKh4WFhfz4448AlJWVGW/XaDRA1WHNQ4cOJT8/n6+//rrKz0pJSeHJJ5/kxRdfNM6nKIQQQgghGpb0QBRCCCGEENd0/PhxY7FPURSys7M5fvw4f/zxB+7u7nz++edotVpatGhBs2bNWL16Nfb29uh0Oi5fvsyqVatITU0FICcnx/i6Hh4eACxdupTLly8zevRoHn/8cXbs2MFnn33G6dOn6dmzJzk5Ofzyyy/k5OTw0UcfSQ9EIYQQQggTUSmKopi6EUIIIYQQwnx8/vnnzJ8/v8ptKpUKe3t7goKC6N+/P5MmTcLNzc14f1xcHB999BGHDx8mPz8fb29vunTpwtNPP81DDz2ElZUVW7duRaVSkZuby/PPP8/BgwexsbFh586d2Nvbk5+fz8KFC9m0aROXLl3C2dmZNm3a8Pjjj9OzZ8+GfhuEEEIIIUQFKSAKIYQQQgghhBBCCCGqJRPJCCGEEEIIIYQQQgghqiUFRCGEEEIIIYQQQgghRLWkgCiEEEIIIYQQQgghhKiWFBCFEEIIIYQQQgghhBDVkgKiEEIIIYQQQgghhBCiWlJAFEIIIYQQQgghhBBCVEsKiEIIIYQQQgghhBBCiGpJAVEIIYQQQgghhBBCCFEtKSAKIYQQQgghhBBCCCGqJQVEIYQQQgghhBBCCCFEtaSAKIQQQgghhBBCCCGEqJYUEIUQQgghhBBCCCGEENX6/3VL6Gd7jKXVAAAAAElFTkSuQmCC",
       "text/plain": [
-       "<Figure size 1152x432 with 1 Axes>"
+       "<Figure size 1600x600 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -518,14 +439,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 1152x432 with 1 Axes>"
+       "<Figure size 1600x600 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -533,7 +454,7 @@
     }
    ],
    "source": [
-    "forecasts = res.forecast(horizon=5, method=\"simulation\", reindex=False)\n",
+    "forecasts = res.forecast(horizon=5, method=\"simulation\")\n",
     "sims = forecasts.simulations\n",
     "\n",
     "x = np.arange(1, 6)\n",
@@ -548,24 +469,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<AxesSubplot:>"
+       "<Axes: >"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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IIYckeX6vQwAAAAB6n5XDbM/AXf0LbrnllgwaNGib88ceeyxJ0tS09fLVsWPHJklaWlp29WUBAAAAKJMpU460apiSdrks3F5RmCRr165Nkuy+++5bzYYMGZIkefbZZ3f1ZWOPTQAAAADYdbvSq+1yWbgjxWJxh7MXc7rOqFHDX3QmAAAAdt0vfvGLXHzxxfmXf/mXvO51r6t2HAB6QdnLwqFDhyZJ1q9fv9Vs04rCESNG7PL3feKJtdlODwkAAEAZdXSsz5e+9OWsXv1EvvSlL2effQ6wrxlAnSoUdn4hXtnLwvHjxydJ/vznP28127Sf4aa9C3dFsRhlIQAAQC+56aYFWbNmdZJkzZrVuemmBXn3u0+qcioAKm2XT0PekX333TfDhw/PAw88sNVs0ynIkydPLvfLAgAAUCbt7W1ZtGjB5q2kisViFi1akPb2tionA8pl8eJ7cuqpJ2fx4nuqHYUaU/aycODAgXnrW9+aJUuWZMmSJZuvr1+/Ptdee21Gjx6dN77xjeV+WQAAAMqgWCxm/vxvbLUf/bauA/Wno2N95s2bm5UrH8+8eXPT0bH1VnL0X2UvC5Pk//7f/5vRo0dn1qxZueyyy3LddddlxowZ+f3vf5/zzz8/jY2NlXhZAAAAeqi1dUWWLl2Srq6uLa53dXVl6dIlaW1dUaVkQLksXLjlNgMLFy6ociJqSUXKwjFjxuQ//uM/8oY3vCHf+ta38qUvfSmDBg3KFVdckbe+9a2VeEkAAADKoLl5QiZNmpyGhi1/XWxoaMikSYeluXlClZIB5WCbAXakUKyTNeSrVjkNGQAAoDe0t7flrLNOTWdn5+ZrAwYMyKWXXpGxY5uqmAzoiWKxmAsu+Lc88MDSLVYPNzQ05JBDJuXjH/9MCoVCFRNSKYVCMnr0zp2GXJGVhQAAANSvpqZxmTp1+ubSoFAoZOrU6YpCqHO2GWBnKAsBAADYyrRp07PHHiOTJCNHjsq0adOrnAjoKdsMsDOUhQAAAGylsXFwTjlldsaM2TOzZp2exsbB1Y4E9FChUMjMmadt9ahxoVDIrFlbX6d/smchAAAAQD/yH//x7dx00w0pFospFAp55zv/Ke9+90nVjkUF2bMQAAAAgJJsM8D2KAsBAAAA+hHbDLA9HkMGAAAAgD7MY8gAAAAAwC5TFgIAAAAASZSFAAAAAEA3ZSEAAAAAkERZCAAAAAB0UxYCAAAAAEmUhQAAAABAN2UhAAAAAJBEWQgAAAAAdFMWAgAAAABJlIUAAAAAQDdlIQAAAEA/s3jxPTn11JOzePE91Y5CjVEWAgAAAPQjHR3rM2/e3Kxc+XjmzZubjo711Y5EDVEWAgAAAPQjCxcuyJo1q5Mka9aszsKFC6qciFqiLAQAAADoJ9rb27Jo0YIUi8UkSbFYzKJFC9Le3lblZNQKZSEAAABAP1AsFjN//jc2F4U7uk7/pCwEAAAA6AdaW1dk6dIl6erq2uJ6V1dXli5dktbWFVVKRi1RFgIAAAD0A83NEzJp0uQ0NGxZBzU0NGTSpMPS3DyhSsmoJcpCAAAAgH6gUChk5szTUigUtro+a9bW1+mflIUAAAAA/URT07hMnTp9czFYKBQyder0jB3bVOVk1AplIQAAAEA/Mm3a9Oyxx8gkyciRozJt2vQqJ6KWKAsBAAAA+pHGxsE55ZTZGTNmz8yadXoaGwdXOxI1pFCsk3OxV61am/pICgAAAAC1o1BIRo8evlNfa2UhAAAAAJBEWQgAAAAAdFMWAgAAAABJlIUAAAAAQDdlIQAAAACQRFkIAAAAAHRTFgIAAAAASZSFAAAAAEA3ZSEAAAAAkERZCAAAAAB0UxYCAAAAAEmUhQAAAABAN2UhAAAAAJBEWQgAAAAAdFMWAgAAAABJlIUAAAAAQDdlIQAAAACQRFkIAAAAAHRTFgIAAAAASXqhLPzjH/+Ys846K0ceeWQOPvjg/OM//mO++c1vpqurq9IvDQAAAADsgoqWhS0tLTnxxBPzk5/8JNOmTct5552XsWPH5nOf+1w+9alPVfKlAQDoJYsX35NTTz05ixffU+0oQJm5vwH6n4qWhVdffXX+8pe/5MILL8w555yTGTNm5Oqrr84RRxyRG264IQ8//HAlXx4AgArr6FifefPmZuXKxzNv3tx0dKyvdiSgTNzfAP1TRcvCP/3pT0mSo446avO1QqGQo48+Oknyu9/9rpIvDwBAhS1cuCBr1qxOkqxZszoLFy6ociKgXNzfAP1TRcvCfffdN0ny+9//fovry5cvT5LstddelXx5AAAqqL29LYsWLUixWEySFIvFLFq0IO3tbVVOBvSU+xug/yoUN/3XvwJWrlyZ973vfenq6sonP/nJTJgwIT/96U/zuc99LkcccUSuvvrqFAqFnfpeTzyxNpVLCgDArigWi/nsZ/8tDzywdIuD6xoaGnLIIZPyiU98Zqff5wG1xf0N/cPixfdk/vzLM3PmqZky5chqx6HCCoVk1KjhO/e1lSwLk+S2227Leeedl6effnrztcmTJ2f+/PkZOnRoJV8aAIAKWb58ef75n/95m/Nvfetb2WeffXovEFA27m/o+9avX58ZM2Zk1apVGT16dK677roMHjy42rGoEQMr+c3nzZuXr3zlKxk/fnzmzJmTvfbaKw888ED+3//7fznxxBNz1VVXZc8999yp72VlIUB988kl9C1Dh47MpEmTS648OvTQV2Xo0JFZtWptFRMCL5b7G/q+6677dlateiJJsmrVE7nyyqvz7nefVOVUVFJNrCx8+umn87rXvS7Dhg3LzTffnJEjR26e3XXXXfnABz6Qt771rbnooot26vutWqUsBKhXHR3rc8YZp2T16icycuSoXHbZvDQ2+uQS6l17e1vOOuvUdHZ2br42YMCAXHrpFRk7tqmKyYCecn9D37Wt+/uSSy5PU9O4KiajkgqFZPTonSsLK3bAyR//+MesX78+Rx999BZFYZK89rWvzd57753/+q//qtTLA1BDnKYIfVNT07hMnTp9895lhUIhU6dOVyRAH+D+hr6pWCxm/vxvbLFqOEm6uroyf/43UuGd6qgTFSsLGxsbk2SrfwE36ezs3OYMgL7DaYrQt02bNj177PH8B8MjR47KtGnTq5wIKBf3N/Q9ra0rsnTpkq1KwWKxmKVLl6S1dUWVklFLKlYW7rfffmlubs5tt92W1tbWLWY//vGP09LSkte//vWVenkAasCmTy5LvRnxySX0DY2Ng3PKKbMzZsyemTXrdFsMQB/i/oa+Z9y48Rk+vPSjqMOHj8i4ceN7ORG1qKKnIf/yl7/MKaeckmHDhuWf/umf0tTUlAcffDA33XRTRo4cmRtuuCHjxu3c8/D2LASoPy0tj+ass07b5vySS76R8eP37sVEAADQf3l/3n/VxJ6FSfKa17wmCxYsyJQpU3L99dfnM5/5TH72s5/lXe96VxYuXLjTRSEA9am5eUImTZqchoYtf9w0NDRk0qTD0tw8oUrJAACg/9n0/rwU78/ZpKIrC8vJykKA+uQ0Rej7Fi++J1dddXk++MFTM2XKkdWOA5SR+xv6nvb2tpx55oe2OEeioaEhX/vaPO/P+7CaWVkIAE1N43LccVO3uHbccVO9EYE+oqNjfebNm5uVKx/PvHlz09GxvtqRgDJxf0Pf1NQ0LtOmnbDFtWnTTvD+nM2UhQD0ukKh2gmAclm4cEHWrFmdJFmzZnUWLlxQ5URAubi/oe+aNm16Ro4clSQZNWq0087ZgrIQgIpqb2/LzTcv2uLaD36wKO3tbVVKBJRLe3tbFi1asPlk82KxmEWLFri/oQ9wf0Pf1tg4OC972cuTJAcccKDTztmCshCAiikWi5k//xv56+1xt3UdqB/ub+i73N/Q9z311JO5++5fJEnuvvsXeeqpJ6uciFqiLASgYlpbV2Tp0iVbbJ6cJF1dXVm6dElaW1dUKRnQU+5v6Lvc39D3feELF2yxcviLX7ywyomoJcpCACqmuXlCJk2anIaGLX/cNDQ0ZNKkw9LcPKFKyYCecn9D3+X+hr7tgQeW5ne/++0W15YtezAPPLC0OoGoOcpCACqmUChk5szTUvirE00KhUJmzdr6OlA/3N/Qd7m/oe/q6urKRRd9vuTsoos+v9WKYvonZSEAFdXUNC5Tp07f/ItFoVDI1KnTM3ZsU5WTAT3l/oa+q6lpXI47buoW1447bqr7G+rckiWLs3bt2pKztWvXZsmSxb2ciFqkLASg4qZNm5499hiZJBk5clSmTZte5URAubi/of+woBDq3+TJUzJ8+PCSs+HDR2Ty5Cm9nIhapCwEoOIaGwfnlFNmZ8yYPTNr1ulpbBxc7UhAmbi/oW9qb2/LzTcv2uLaD36wKO3tbVVKBJRDQ0ND5sw5t+Ts7LPP3WqvUvon/xYAANAjU6YcmcsvvyZTphxZ7ShAGRSLxcyf/410dnZucb2zszPz539j8wmqQH0aM2bPktdHjx7Ty0moVcpCACquo2N95s2bm5UrH8+8eXPT0bG+2pEAgG1obV2RpUuXlJwtXbokra0rejkRUC6bPgwoddq5DwPYRFkIQMUtXLgga9asTpKsWbM6CxcuqHIiAGBbmpsnZOLE/UrOJk7cL83NE3o5EVAumz4M+OtTj7u6unwYwGbKQgAqqr29LYsWLdj8KWWxWMyiRQvseQQANapYLObPf24vOfvzn9utPII61tw8IZMmTS65snDSpMN8GEASZSEAFbTpMYe//qViW9cBgOpbsmRx1q1bV3K2bt26LFmyuJcTAeVSKBQyc+ZpKfzV8eaFQiGzZm19nf5JWQhAxXjMAQDqz+TJUzJ8+PCSs+HDR2Ty5Cm9nAgop6amcTnuuKlbXHv726dm7NimKiWi1igLAagYjzkAQP1paGjI+98/q+Ts/e+fudXPdaD+eeCHF/JfeQAqxmMOAFB/isVifv7zn5ac/fznP7WNCNS59va23Hzzoi2u3XzzInuKs5myEICKamoal6lTp28uBguFQqZOne4xB+hDFi++J6eeenIWL76n2lGAMti0jUgpthGB+mZPcXaGshCAips2bXr22GNkkmTkyFGZNm16lRMB5dLRsT4XX/ylrFz5eC6++Evp6Fhf7UhAD23aRqTUkwG2EYH6Zk9xdoayEICKa2wcnFNOmZ0xY/bMrFmnp7FxcLUjAWVy3XXfzvr1zyZJ1q9/Ntdd9+0qJwJ6atM2IqXYRgTqmz3F2RnKQgB6xZQpR+byy6/JlClHVjsKUCbt7W354Q+/t8W1H/7we/Y8gj7MI4pQ3+wpzs5QFgIAsMuKxWK++tUvlJx99atfUChAHdu0d1mpMsGeZlD/7CnOjigLAQDYZStW/CkPP/yHkrOHH/5DVqz4Uy8nAsrFnmbQ99lTnO1RFgIAsMsee+zPPZoDtcueZtD3NTYOzlFHHZOGhob8n/9ztD3F2YKyEACAXbbXXtt/VGlHc6B22dMM+r6OjvW5884fpaurK3fe+aN0dKyvdiRqiLIQAIBdNmHC3pk4cb+Ss/322z8TJuzdy4mAcmpqGpeJE/ff4tp++x1gTzPoIxYuXJDVq59Ikqxe/UQWLlxQ5UTUEmUhAAC7rFAo5L3vPbnk7D3veb+VR1Dn2tvb8j//87strj300DKnnUMf0N7eloULv7vFtYULv+v+ZjNlIQAAu6xYLOa7372u5Oy7373OaalQx4rFYubO/WrJ2dy5X3V/Qx3bdNp5qQOMnHbOJspCAAB2WUvLiixb9mDJ2bJlD6alxWmpUK9aWh7NsmW/LTlbtuy3aWl5tJcTAeWy6bTzUpx2zibKQgAAXoQdrTywMgHq1462EbDNANSrcePGZ/jw4SVnw4ePyLhx43s5EbVIWQgAwC4bP37vvPzlB5WcvfzlB2f8eAecQL1qbh6foUOHlpwNHToszc3KBKhXbW0tWbt2bcnZ2rVPpa2tpZcTUYuUhQAA7LJCoZDZsz+81UEmhUIhZ5zxLw44gTrW1taSdevWlZytW/e0MgHqmJWF7AxlIQAAL0pT07i84x3v3OLaO97xzowd21SlREA5KBOg77KykJ2hLAQA4EU74YR3Z/DgIUmSIUOG5IQT3l3lREBPKROg72punpC99/6bkrO9994nzc0TejkRtUhZCABAjwwcODBJMmDAwConAcqhuXlCJk2aXHI2adJhygSoY11dXWlpKX3icUvLo+nq6urlRNQiZSEAvWLx4nty6qknZ/Hie6odBSijhQsX5Omnn06SPP3001m4cEGVEwE9VSgUttpiYJN3vGOaPUmhjt1xx63bLAS7urpyxx239nIiapGyEICK6+hYn3nz5mblysczb97cdHSsr3YkoAza29uyaNGCJMXuK8UsWrQg7e1t1YwF9FCxWMz3v39Tydn3v39TisViyRlQ+4455i09mtM/KAsBqLiFCxdkzZrVSZI1a1ZbeQR9QLFYzPz539hqdUJXV1fmz/+GMgHqWGvriixduqTkbOnSJWltLf0II1D7/vzn7X+gt6M5/YOyEICK2rTyaFNxUCxaeQR9waYy4a9LwWKxqEyAOtfcPCEvf/lBJWcvf/kr7FkIdcyepOwMZSEAFbNp5VGpMsHKI6hv48aNz/Dhw0vOhg8fkXHjxvdyIqCcNmzYsI3rHb2cBCgne5KyM5SFAFTMppVHpR5TtPII6ltbW0vWrl1bcrZ27VNpa2vp5URAubS0rMjDD/+h5Ozhh/+wzZNUgdpXLBbz3e9eV3L23e9e58N8kigLAaggjzlA3+X+hr5sR2WBMgHqVUvLiixb9mDJ2bJlD/owgCTKQgAqqFAoZObM00rOZs06zWMOUMc23d8NDVu+nWxoaHB/Q50bP37v7exZeHDGj9+7lxMB5ePDAHZMWQhARa1c+XjJ648//lgvJwHKralpXKZNO2GLa9OmnZCxY5uqlAgoh0KhkNmzP1xydsYZ/+LDAKhj48fvnb33Ll3477333/gwgCTKQgAqqKurK1/+8r+XnH35y/++1V6GQP1529vevrk4KBQKedvb3l7lREA5+LAP+qZisZiVK1eWnK1c+bg9C0miLASggu67b3HWrVtXcrZu3brcd9/iXk4ElNv3v79w8y8WxWIxP/jBwionAnrKh33Qd91337159tlnS86effbZ3Hffvb2ciFqkLASgYvbaa68ezYHa1t7elu9//6Ytrn3vezelvb2tSomAcrjvvnt38GGfMgHq1V57bX+rkB3N6R8qXhZ2dXXl2muvzdvf/vYccsgh+bu/+7v867/+ax57zPJ1gL5uwoS/ycSJ+5WcTZy4fyZM+JteTgSUS7FYzNy5F2/1uNK2rgP1Q5kAfdeECXtv8/35fvvtnwkT7FlIL5SF5557bj772c9m/PjxOe+88/KWt7wlN998c0466aQ89dRTlX55AKqoUCjkwx8+p+RszpxzbJAOdaylZUWWLXuw5GzZsgfT0rKilxMB5aJMgL5re+/PP/xh7895XkXLwjvuuCPf//73M2PGjHz961/PiSeemH/913/NZz/72fzpT3/K9ddfX8mXB6AGNDWNy7HHHr/FtWOPPd5pqVD3drRy0MpCqFfKBOifPBXAJhUtC//jP/4jQ4cOzdlnn73F9be97W055ZRTss8++1Ty5QGoETNmnJTBg4ckSYYMGZIZM06qciKgp8aP3/bKo4kT98/48VYeQT1rahqX/fY7YItr++//Mh/2QZ0rFouZP/8bJWfz539DYUiSCpaFnZ2d+dWvfpXDDjssw4YNS5KsX78+GzZsyKBBg3L22WfnTW96U6VeHoAa0tg4OK961WFJkkmTDktj4+AqJwLKYdCgxm1cH9TLSYBya29vy8MP/36La3/4w/84wAjqXGvriixduqTkbOnSJWlttY0IycBKfeOWlpZ0dHRk/Pjxuf322/O1r30t//M//5MBAwbkNa95Tc4///zsu+++O/39rHQHqF9PPfVk7r77F0mSu+/+RdaufTIjRrykyqmAnmht3f6ehW1tK6wuhDq1aeVRqQOM5s//Rj7xic94FBnq1LhxzRkwYEA6Ozu3mg0YMCDjxjXrX/qoXfn/tWJl4ZNPPpkk+eUvf5mbbropJ598cs4666z87ne/y/z58/Pud787N954YyZMmLBT32/UqOGVigpAhX3yk+du/oWjWCzmoos+n8suu6zKqYCeGDXqoEyZMiX33Xdfurq6Nl9vaGjI4YcfnkMPPUiZAHVq+fLl2115tG7daltKQZ36xS9+UbIoTJ5/QvQPf3gwr3vd63o5FbWmYmXhhg0bkiR//OMfM3fu3BxzzDFJkmOOOSaveMUrcuqpp+aSSy7Jl7/85Z36fk88sTYenQeoP/ffvzT//d//vcW1Bx54ID/+8c9z6KGTqhMKKIv3v/+ULF48a4trXV1def/7T8kTTzxdpVRATw0Z8tLtrjwaMuSlWbVqbRWSAT21//4HZ+jQoVm3bt1Ws6FDh2b//Q92f/dRhcLOL8SrWFm4++67J0n22muvzUXhJkcddVT23HPP3HXXXTv9/YrFKAsB6kxXV1cuuujzJWcXXfT5XH31dWloqOhZW0AFbeu9WVdX0fs2qGNLlvxquyuPliz5VQ4//MheTgWURyFjxzbl4Yf/sNVk7NhxSQp+hlO5A06amp4/JWv06NEl56NHj87atdpqgL5syZLF2/xv/dq1a7NkyeJeTgSUS7FYzNy5Xy05mzv3q05ThDo2efKUDB9eevXJ8OEjMnnylF5OBJRLa+uKkkVhkjz88O8dcEKSCpaFe+yxR/bee+8sX748HR0dW8w6OzvT0tKS8ePHV+rlAagBkydPydChQ0vOhg4d6pcNqGMtLY9m2bLflpwtW/bbtLQ82suJgHJpaGjInDnnlpydffa5ngqAOrbXXk09mtM/VPS/8u985zuzbt26zJ8/f4vr119/fZ566qkce+yxlXx5AKqsUChk1KhRJWejRo12+AHUtR3dv+5vqGeHHDIpI0du+TN81KjReeUrD61SIqAcbrzx+h7N6R8qtmdhknzgAx/IT3/601x66aV55JFHcsQRR+TBBx/MggULcuCBB+aDH/xgJV8egCpraVmRRx8tvbro0Uf/lJaWFZkwYe9eTgWUQ3Pz+AwZMiTPPvvsVrMhQ4akudkTJFDP2tvbsnr1E1tce+KJVWlvb0tT07gqpQJ6avr0d2+3EJw+/d29mIZaVdGVhYMGDco111yT2bNn54EHHshnP/vZ/OxnP8v73ve+fOc738ngwYMr+fIAVN2O9iyzpxnUq9bWFSWLwiR59tln7XkEdez5PUkvLjmbO/die5JCHWtoaMigQYNKzgYNarTNAEkqvLIwef6T5TPPPDNnnnlmpV8KgBozfvzeefnLDyq5r9nLX35wxo+3qhDq1Y66Al0C1K+WlhVZtuzBkrNlyx70ZADUsSVLFmfDhg0lZxs2dGTJksVOO6eyKwsB6N8KhUKOOeYtJWfHHPMmexZCHdvR7ev2hnrmyQDoq5x2zs5QFgJQMV1dXfnmN68sOfvmN69MV1dXLycCymX8+L0zceJ+JWcTJ+5v5TDUMfc39F1OO2dn+LcAgIpZsmRx1q5dW3K2du3aLFmyuJcTAeX0zDPrtnH96V5OApTbtvc0K30dqB+HHDIpAwduuSvdwIEDnXbOZspCACrmVa86fJufTjY0NORVrzq8lxMB5fLoo8vT3t5ectbe3p5HH13eu4GAsmltXVFyv+Hk+T0LHWAE9e2nP/1xNm7cuMW1jRs35qc//XGVElFrlIUAVExbW+s2HzXu6upKW1trLycCyuW3vy19+MHOzoHaNW7c+AwdOrTkbOjQoRk3bnwvJwLKpbOzM1//+iUlZ1//+iXp7Ozs5UTUImUhABVT3MFxqDuaA7XrTW/6x+2sHB6QN73pH3s5EVAura0tWbeu9DYD69atS2trSy8nAsrljjtu3WYh2NnZmTvuuLWXE1GLlIUAVJDTFKGvGjBgQGbP/peSszPO+JcMGDCgdwMBZdPVtf2VRTuaA7Xr6KPf3KM5/YOyEICKKRS2/2NmR3Ogtr3sZS8vef2AAw7s5SRAOT3++GM9mgO1a+nS+3o0p3/wWxoAFTN+/IRMnLhfydl+++2f8eMn9HIioFyKxWLmzv1qydncuV+1zQDUsT333KtHc6B27bVXU4/m9A/KQgAqatCgxpLXd9ttUC8nAcqppeXR7ZyW+tu0tDzay4mAcmlo2P42AjuaA7VrwoS9t/lh/sSJ+2fChL17ORG1SFkIQMW0tq7IsmWlT0RdtuzBtLau6OVEQPkUejgHalVz8/ZPQ25udhoy1KtCoZD3vvfkkrP3vvf9KRT8/EZZCEAFjRs3PsOHDy85Gz58RMaN88sG1KvtlwnDlAlQx9ratn8aclub05ChXhWLxXz3u9eVnH33u9fZRoQkykIAKqitrSVr164tOVu79im/bEAd236Z8LT7G+pYU1PzNk80HzBgQJqamns5EVAuLS3bf/KnpcWTPygLAaig5uYJmTRpcsnZpEmHpbnZASdQr9zf0Hf9+te/SmdnZ8lZZ2dnfv3rX/VyIqB8drRy0MpClIUAVFChUMjMmaeloWHLHzcNDQ2ZNes0e6JAHSsUCnnHO95ZcvaOd0xzf0MdO/TQ0h8E7OwcqF3jx2//gJPx4x1wgrIQgAprahqXPfYYucW1kSNHZezYpiolAsqhWCzm2muvKTm79tpr7HkEdezHP76tR3OgXvnZzfOUhQBU1AMPLM0TT6za4tqqVSvzwANLqxMIKIsVK/6Uhx/+Q8nZww//IStW/KmXEwHlcswxb+nRHKhdLS0rtvvz256FJMpCACqoq6srF130+ZKziy76fLq6uno5EVAujz32WI/mQO3685/bejQHalexuP333zua0z8oCwGomCVLFm/nNOS1WbJkcS8nAspl8uTDt9qPdJOGhoZMnnx4LycCymXcuPEZPnx4ydnw4SMybtz4Xk4ElMuOdgmxiwiJshCACpo8ecp2f9mYPHlKLycCyqWtrXWbq4O7urrS1tbay4mAcmlra9nOh31Ppa2tpZcTAeXy+ON/7tGc/kFZCEDFNDQ0ZM6cc0vOzj773G2uSgJqn8eYoO8aN258GhsbS84aGwdbWQh17LDDjsjgwYNLzgYPHpzDDjuilxNRi/yWBkBFHXLIpBxwwIFbXHvZy16eV77y0ColAsrBY0zQd61Y8ad0dHSUnHV0rHeAEdS5QqGwS9fpf5SFAFTcfvvtv8WfT5y4/za+EqgXHmOCvuu3v/1Nj+ZA7VqyZHGeffbZkrNnn33WnuIkURYCUGHt7W259dYfbnHt1ltvTnu7kxShnk2adFiP5kDtOuigg3s0B2rXoYdO7tGc/kFZCEDFFIvFzJ17cYp/9Tzitq4D9eNHP/r/ejQHateO9iS0ZyHULz+/2RnKQgAqpqVlRZYte7DkbNmyB9PSsqKXEwHl8vKXb39l0Y7mQO264Ybv9GgO1K6XveygHs3pH5SFAFTQjlYOWlkI9WrAgAE9mgO1a0dnHDgDAerXtj7I39k5/YOyEICKGTdu/HZPW/MYE9SvpqZxPZoDteu1r31Dj+ZALfNhPjumLASgYpYsWbzNfQmLxaLT1qCO3XbbLT2aA7VrwICBPZoDtevggw/t0Zz+QVkIQMXs6AATB5xA/fKYIvRd23oqYGfnQO0aP35Cj+b0Dz4SAqBiDjvsiBQKhZKlYKHQkMMOO6IKqYByeMUrDunRHKhd48Y1p6GhIV1dXVvNGhoaMm5ccxVSQW0pFovp6Oiodoxddt9923+y5+6778phh03ppTTl09jY6IOMMlIWAlAxbW2t23kMuSttba2ZMGHvXk4FlIOVCdB3LVmyuGRRmCRdXV1ZsmRxpkx5dS+ngtpRLBZz/vkfzUMPLat2lLK76KLPVzvCi3LggQflggu+qDAsE48hA1AxHkOGvuu+++7t0RyoXXvt1dSjOfQHSin6MisLAaggp61B37WjX5L8EgX1akePGXsMmf6uUCjkggu+WJePISfJb37zQD73uU9vdf288z6VV7zilVVI1HMeQy4vZSEAFVMobH8B+47mQO0aPXp0j+ZA7frRj/6/Hc7f8pZjeykN1KZCoZDBgwdXO8aLcvjhR2TffffLI4/8YfO1/fd/WV3uVUhlKAsBqJjtb5A+wMoEqGN33/3LHc733Xe/XkoDtaleD0B4wxuOytVXz0tnZ+dWswEDBuQNbzgq69evr0KynrHyCP7XRz96fk477eTNf37eeZ+sYhpqjbIQgIr59a9/tZ0N0jvz61//KocffmQvpwLK4V3v+qfcdNP1251Df9ZXD0Do7OzMP//zCdWO8aI4AAH+14gRIzb/8fHHvzMjRrykimmoNZ7/AqBiJk+ekuHDh5ecDR8+IpMne9QB6tX99y/p0Rz6A6UUUA+mT59R7QjUGCsLAaiYhoaGzJlzbj796fO3mp199rlpaPCZFdSrMWP26tEc+rp6PwAhSVpbW/Kxj521+c8vueTyjB49poqJesZjyAA7R1kIQEWtXv1EyetPPLGql5MA5bRy5eM7nO+zz9/2UhqoTfV8AEKSNDeP3/zHRx75mowfP6GKaQDoLZZ0AFAxnZ2dmTv34pKzuXMvLrlxOlAfXvWqw3o0B+rLmWd+pNoRAOglykIAKua2227ZzgEnXbnttlt6ORFQLvfdd2+P5gAA1CZlIQAVs8ceI3s0B2rXtrYY2Nk5AAC1SVkIQMWsXr39fQl3NAdq19/93dE9mgMAUJuUhQBUzKhR2z8xcUdzoHZ9+cuf69EcAIDapCwEoGIKhUKP5kDtOvTQST2aAwBQm5SFAFRMU1NTj+ZA7Ro7dlyP5gAA1CZlIQAVM27c+B7Ngdq155579WgOAEBtUhYCUDF33HFrj+ZA7Vq27MEezQEAqE29VhZ2dnZmxowZednLXpaNGzf21ssCUEUveclLezQHatfIkSN7NAcAoDYN7K0Xuvzyy3Pffff11ssBUAOefPIvPZpDf1AsFtPR0VHtGLtsjz1G7XC+fv36XkpTPo2NjQ5fAgD6tV4pCx944IF8/etfz6BBg7Jhw4beeEkAasDf//2bMn/+5dudQ39WLBZz/vkfzUMPLat2lLI777yzqx3hRTnwwINywQVfVBgCAP1WxR9DXrduXT7ykY/kDW94QyZNmlTplwOghtx003d7NIf+QCkFAEAtqfjKwgsvvDBr167NBRdckA9/+MOVfjkAashrX/u63HTT9dudQ39WKBRywQVfrMvHkJPkZz/7aa644mtbXT/ttDPz+tf/XRUS9ZzHkAGA/q6iZeHtt9+em266KXPnzs3o0aN79L28ZwOoP+PHT9jh3H/f6e8KhUKGDBlc7Rgvypvf/JZ897vfyZo1qzdfGzVqdP7hH95cxVRAubzwZ3Sh4Hcy6Evc3/3Prvx/XLGy8LHHHssnPvGJvOtd78oxxxzT4+83atTwMqQCoDfNnz9/u/P//M+FmTlzZi+lASrh0ksvyUknnbT5zy+//BsZPdr7NugLnn32f39dHDVqWIYMGVLFNEA5ub/ZnoqUhcViMeecc06GDx+e8847ryzf84kn1qZYLMu3AqCXvPKVhyX51nbnq1at7b1AQNkNGLD75j8+8sjXZMCA3d3X0Ee88ETzJ554OoMHb6xiGqCc3N/9T6Gw8wvxKlIWXnPNNbn77rszd+7cdHR0bN6H57nnnkuS/OUvf8luu+2Wl7zkJTv9PYvFKAsB6sxvf/ubHc733Xe/XkoDVMIL35+deeZHvF+DPuSF97Pfx6BvcX+zPRUpC++8884Ui8WcfvrpJeeve93r0tzcnJ/85CeVeHkAasTy5X/s0RwAAIDeVZGy8JxzzslTTz211fXPf/7zeeihh3LVVVd5Hh6gH3jpS1/aozkAAAC9qyJl4cEHH1zy+qbHjl/96ldn4MCKHsQMQA2YOPGAHs0BAADoXQ3VDgBA39XU1NSjOQAAAL1LWQhAxbS1tfVoDgAAQO/q1bLw29/+dh566CGPIAP0E7/4xU97NAcAAKB3WVkIQMUMHrx7j+YAAAD0LmUhABVz+OFH9GgOAABA71IWAgAAAABJlIUAVNCqVat6NAcAAKB3KQsBqJhHH/1jj+YAAAD0LmUhABUzZMjQHs0BAADoXcpCACrmv//7/h7NAQAA6F3KQgAq5jOf+XyP5gAAAPQuZSEAFfODHyzs0RwAAIDepSwEoGI2btzYozkAAAC9S1kIQMXsscfIHs0BAADoXcpCACrmwQcf6NEcAACA3jWw2gEA2LFisZiOjo5qx9hlK1eu2uF8/fr1vZSmfBobG1MoFKodAwAAoOyUhQA1rlgs5vzzP5qHHlpW7Shl96c/PZL3vOed1Y6xyw488KBccMEXFYYAAECfoywEqANKKQD6i3pdTd8XvXD1fz0+CdCXecoBqCRlIUCNKxQKueCCL9blL05dXV35wAdm5Lnnnttqtttug3L11d9JQ0P9bZ/rDTpA5XR0dNTlqvO+7oMffE+1I/AC3/nOTRk8eHC1Y+wyHwbUDh8G1K5a+F1DWQhQBwqFQl2+IUyS8877VD796fO3un7++Z/K7rvvXoVEAABUgw8DapMPA2pLLXwYoCwEoKIOOWRS/uZv9smf/rR887V99tk3r3zlodULRd2zMqF2WJlQu2phZUJPnZNkULVD9HPF7v+t73+T+oYNSb5Q7RBAv6AsBKDizj33kznttJM3//knP3lBFdPQF1iZUJusTKgttbAyoacGJRmkpoJuxR1/SR15+6TTM7Bht2rH6NeKxef/nar3D5b6go1dz+UHS79e7RibKQsBqLgRI0Zs/uPjj39nRox4SRXTAABQbQMbdsvAAdYOQy1SFub5Nv3JJ5+sdowe6+rqytNPr612DLoNGza8Lg9u+GsveclLfNJEWU2fPqPaEehjOo/r9I6m2jynWDs2JgNuHlDtFABAHev3b62LxWLOP/+jeeihZdWOAjXpwAMPygUXfFFhCNSugfGOBgAAyqT+lz2VgRIEAAAAAHwOn0KhkAsu+KLHkCk7jyFXn9NSa4fTUmtXXzgtFQAAKJ9+XxYmzxeGL33pS6sdoyxGjhxZ7QhQM5yWWpucllpb+sJpqQAAQPnU/7InAAAAAKAsrCwE+oW5b/xLGgcUd/yFVEyx+x+/J16rr6OzkNk/e2m1YwAAADVIWQj0C40Dihk8oNopoFYozgEAgNI8hgwAAAAAJFEWAgAAAADdPIYM9AtPbSiko473LCwWkw1d1U7BJoMa6nvvxY7OOg4PAABUlLIQ6LOKxf8tB+f84qXVCwI17IX3CQAAgMeQAQAAAIAkVhYCfdjgwYNz7bU3pqOjo9pReqxYLGbDhvr/++grBg1qTKGen0Pu1tjYmMGDB1c7BgAAUEOUhUCfVSgUMmTIkAwZMqTaUYBK2ljtAFBD3A8AQA8pCwGAuvPCvRYH3DygikmgdvWFPUk3JEnq/+8DymFDtQMA/YayEAAAqBkvLDm/UMUcUMv6wocBQO1SFgIAdeeFe0Z2HtfpHQ1ssvF/V9v2hb1Vgb5rY+dz1Y4ANaPW7gdvrQGA+jYw3tFAH/LCkvOcJIOqFwVqyob872rbev0w4IUrIn9w/9ermARqVy2sHPbWGgCob/V+oEMxSWe1Q7DZgCT1+Tv48+r9fvgrg5IMquv/Q6Ccql8gAP2DshAAqGsOOAGA+vDCFZFvP/T0DBywWxXTQO3Y2Pnc5tW2tbByWFkIAAAA9KqBA3bLwAE2GoBapCwEAOpOY2NjvvOdm6odoyyKxWI6OjqqHYNujY2NNfGJfjk0NjZWOwIAUIeUhQBA3SkUChk8eHC1Y5TNkCFDqh0BAACSJA3VDgAAAAAA1AYrCwEAgJq0IYkTYKtr0z/9vvFwfn3bUO0AQL+hLAQAAGrSF6odAAD6IY8hAwAAAABJemFl4UMPPZS5c+fm3nvvzdNPP50xY8bk6KOPzplnnpkRI0ZU+uUBAIA60ldOOy8Wi/nMZz6e//mf31U7Ct0OOODA/Nu/XdAnTjx32jlQSRUtCx955JGceOKJGThwYGbMmJGmpqYsXbo03/nOd3L33XfnhhtuyNChQysZAQAAqCN96bTzf//3L6ejo6PaMXqkWHx+18K+UrD1hb+PvmJj13PVjtDv9aX7u97V2v1Q0bLwggsuyHPPPZcbb7wxEydOTJKceOKJOeigg3LhhRfm2muvzYc+9KFKRgAAAKiKvlR8Qrn9YOnXqx0B2IaK7VnY0dGRxYsX5/DDD99cFG5y/PHHJ0nuvffeSr08AAAAALCLKraycLfddsstt9ySrq6urWarVq1KkgwYMKBSLw8AAADUkL6yJ2lfsH79+nzwg+9Jklx11Xesgq4htbAnacXKwoaGhkyYMKHk7Morr0ySHHnkkTv9/TxCDwAAAPWrUChkyJC+UUoVi8W63pP0hR1LoVD/nYs9SXdsV/7xFIqbdrTsJQsXLsy//uu/pqmpKT/84Q8zbNiw3nx5AAAAgBetWCxm9uzZ+c1vflPtKHR75Stfmcsuu0xhWCYVPeDkr9144435t3/7t+y+++659NJLd6kofOKJtendWhMAAABgS8ViMRs3br3lGtXz3HOdWbVqrbJwOwqFZNSo4Tv3tb21svDSSy/N3LlzM2zYsFxxxRU5/PDDd+mvX7VKWQgAAABUX70/hpw8//eQpE8UbB5D3rFCIRk9eufKwoqvLHzuuefy8Y9/PN/73vey55575sorr8yBBx5Y6ZcFAAAAqIhCoeBQEPqsipaFnZ2dmTNnTm6//fYccMABmTdvXpqamir5kgAAAADAi1TRsvDiiy/O7bffnkMOOSRXXXVVRowYUcmXAwAAAAB6oGJl4YoVK3L11VenUCjkH/7hH3LnnXdu9TWjRo3K61//+kpFAAAAAAB2QcXKwp///OfZuHFjkuQrX/lKya+ZPHmyshAAAAAAakSvnYbcU05DBgAAAIBdtyunITdUOAsAAAAAUCeUhQAAAABAEmUhAAAAANBNWQgAAAAAJFEWAgAAAADdlIUAAAAAQBJlIQAAAADQTVkIAAAAACRRFgIAAAAA3ZSFAAAAAEASZSEAAAAA0E1ZCAAAAAAkSQZWO8DOKhSqnQAAAAAA6s+u9GqFYrFYrFwUAAAAAKBeeAwZAAAAAEiiLAQAAAAAuikLAQAAAIAkykIAAAAAoJuyEAAAAABIoiwEAAAAALopCwEAAACAJMpCAAAAAKCbshAAAAAASKIsBAAAAAC6KQupGWvWrMlnP/vZHHXUUTnkkEPy9re/PTfeeGO1YwFldP/99+eggw7KXXfdVe0oQJk89NBDOfPMM/PqV786Bx98cI466qhccMEFeeqpp6odDeihRx99NHPmzMkb3vCGTJo0KdOnT8/NN99c7VhAGXV2dmbGjBl52ctelo0bN1Y7DjViYLUDQJI888wz+cAHPpDf//73mTFjRvbdd9/ceuutOf/887Nq1aqceuqp1Y4I9NDy5csze/bsdHZ2VjsKUCaPPPJITjzxxAwcODAzZsxIU1NTli5dmu985zu5++67c8MNN2To0KHVjgm8CK2trTnhhBPS2dmZk046KaNGjcott9ySj3zkI2ltbfX+HPqIyy+/PPfdd1+1Y1BjCsVisVjtEDBv3rx85StfyZe//OUcd9xxSZKurq7MnDkz9957b+644440NTVVOSXwYt1xxx05//zz8+STTyZJrrnmmrz2ta+tciqgpz7wgQ/k3nvvzfe///1MnDhx8/VvfetbufDCCzNnzpx86EMfqmJC4MU6++yz85//+Z+54YYbcuihhyZ5fgXSu971rjz88MP5+c9/npe85CVVTgn0xAMPPJB3v/vdaWhoyIYNG/Lggw9m4EBryvAYMjXie9/7XsaMGZNjjz1287WGhoZ88IMfzHPPPedxB6hjp5xySs4444yt7nGgvnV0dGTx4sU5/PDDtygKk+T4449Pktx7771VSAaUQ0NDQ4466qjNRWGSDBgwIK9+9avT0dGRhx9+uIrpgJ5at25dPvKRj2zeZgBeSGVM1a1duzaPPPJI/v7v/z6FQmGL2aY3Jw888EA1ogFl8Mgjj2TOnDk5+eSTc8UVV1Q7DlAmu+22W2655ZZ0dXVtNVu1alWS54sFoD596UtfKnn9t7/9bRoaGjJu3LheTgSU04UXXpi1a9fmggsuyIc//OFqx6HGKAupusceeyzFYrHkY8bDhg3L0KFD09LSUoVkQDnccsstGTRoULVjAGXW0NCQCRMmlJxdeeWVSZIjjzyyNyMBFbJ27dosX7481157be6+++68973vzdixY6sdC3iRbr/99tx0002ZO3duRo8eXe041CBlIVW3du3aJMnuu+9ecj5kyJA8++yzvRkJKCNFIfQvCxcuzMKFC9PU1JR/+qd/qnYcoAw++tGP5s4770zy/JM/s2fPrnIi4MV67LHH8olPfCLvete7cswxx1Q7DjXKnoVU3Y7O2CkWi1s9ngwA1J4bb7wxH//4x7P77rvn0ksvzbBhw6odCSiD6dOnZ+7cuTn11FPz0EMP5R3veEceffTRascCdlGxWMw555yT4cOH57zzzqt2HGqYlYVU3dChQ5Nkm6sHn3322YwfP743IwEAu+jSSy/N3LlzM2zYsFxxxRU55JBDqh0JKJOjjz46SXLMMcfkla98ZWbPnp2vfe1r29zXEKhN11xzTe6+++7MnTs3HR0d6ejoSJI899xzSZK//OUv2W233Zx0jrKQ6mtubk6hUMhjjz221Wzt2rV55pln7IkCADXqueeey8c//vF873vfy5577pkrr7wyBx54YLVjARVy9NFHZ9iwYfnNb35T7SjALrrzzjtTLBZz+umnl5y/7nWvS3Nzc37yk5/0cjJqjbKQqhs2bFgmTpyY//7v/95qdv/99ydJJk+e3NuxAIAd6OzszJw5c3L77bfngAMOyLx580oeWAbUl1WrVuU973lPDj744HzlK1/ZYvbcc8+lo6MjQ4YMqVI64MU655xz8tRTT211/fOf/3weeuihXHXVVe5tkigLqRFvf/vbc9FFF+WHP/xhjj322CRJV1dXrr766gwaNChve9vbqpwQAPhrF198cW6//fYccsghueqqqzJixIhqRwLKYPTo0SkUCrnjjjvyhz/8Ifvtt9/m2dVXX53nnnvOwQhQhw4++OCS1zc9dvzqV786AweqiVAWUiPe97735Qc/+EHOPffcPPjgg/nbv/3b3HLLLfnlL3+Zj33sYxkzZky1IwIAL7BixYpcffXVKRQK+Yd/+IfNJ6W+0KhRo/L617++CumAnvr0pz+dmTNn5qSTTsp73vOe7LHHHrnnnnty2223ZfLkyfngBz9Y7YgAVIiykJowePDgfPvb385FF12U73//+1m3bl3+9m//Nl/4whdy/PHHVzseAPBXfv7zn2fjxo1JstVjiptMnjxZWQh16sgjj8z111+fyy67LN/85jezfv36TJgwIWeddVZmzpyZQYMGVTsiABVSKBaLxWqHAAAAAACqr6HaAQAAAACA2qAsBAAAAACSKAsBAAAAgG7KQgAAAAAgibIQAAAAAOimLAQAAAAAkigLAQAAAIBuykIAAAAAIImyEAAAAADopiwEAAAAAJIoCwEAAACAbspCAAAAACBJ8v8DWmQxUfGJzl0AAAAASUVORK5CYII=",
       "text/plain": [
-       "<Figure size 1152x432 with 1 Axes>"
+       "<Figure size 1600x600 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -589,14 +510,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 1152x432 with 1 Axes>"
+       "<Figure size 1600x600 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -604,7 +525,7 @@
     }
    ],
    "source": [
-    "forecasts = res.forecast(horizon=5, method=\"bootstrap\", reindex=False)\n",
+    "forecasts = res.forecast(horizon=5, method=\"bootstrap\")\n",
     "sims = forecasts.simulations\n",
     "\n",
     "lines = plt.plot(x, sims.residual_variances[-1, ::5].T, color=\"#9cb2d6\", alpha=0.5)\n",
@@ -632,7 +553,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -645,12 +566,14 @@
    "metadata": {},
    "source": [
     "## Parametric VaR\n",
-    "First, we use the model to estimate the VaR.  The quantiles can be computed using the `ppf` method of the distribution attached to the model. The quantiles are printed below."
+    "First, we use the model to estimate the VaR.  The quantiles can be computed using the `ppf` method of the distribution attached to the model. The quantiles are printed below.\n",
+    "\n",
+    "**Note**: `forecast` is called with `align=\"target\"` so that the forecasts are already aligned with the target and so do not need further shifting."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -662,9 +585,9 @@
     }
    ],
    "source": [
-    "forecasts = res.forecast(start=\"2018-1-1\", reindex=False)\n",
-    "cond_mean = forecasts.mean[\"2018\":]\n",
-    "cond_var = forecasts.variance[\"2018\":]\n",
+    "forecasts = res.forecast(align=\"target\")\n",
+    "cond_mean = forecasts.mean[\"2018\":].dropna()\n",
+    "cond_var = forecasts.variance[\"2018\":].dropna()\n",
     "q = am.distribution.ppf([0.01, 0.05], res.params[-2:])\n",
     "print(q)"
    ]
@@ -678,14 +601,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 1152x432 with 1 Axes>"
+       "<Figure size 1600x600 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -738,7 +661,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -760,14 +683,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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lqY+5devWZRrHarXan9dBgwZddlyr1V4WSH333XcvOy8rK4uIiAj7a+XfNf4qonnz5nz//ffEx8ezZcsW9u7dy4EDB0hOTiY3N5dVq1axZs0aXnrpJR566KEyXdPJyemyLafe3t72f4eFhV12H3d3d3JzczEYDNf3gEoxduxYxo4dW+K2wsJCYmNjOX78uH2bbEWfzz179mC1Wmnfvv1l2adgCzzfddddHDp0iN27d18WLLy0dmAxX19fFEXBarWSn59fpmAh2JqWLFy4kBUrVvDiiy/as5PNZrP9vXXplw0DBw68rNaq2Wzm/PnznDx50h6QvtJzc9NNN9nX1mg0cujQIZKSktDpdDz33HOMGDGizHMXQgghbmQSLBRCCCHqgFdffbVM2WmV5cKFC4AtSFRaoOhSFy9eLBEsdHd3v+o1/52p+G8mk4mkpCRcXV3twZpLt/ZeqmnTphXeilxZ9Ho9s2fP5oUXXiAiIoKZM2cyc+ZMXF1d6d27N0OHDmXgwIFXbdwB2JtOFNcvLE1xEDYpKemyY2Wt35iRkWF/Xhs3blym+wCcOnWKhQsXcvToUWJjY8nKygKu3pCkovz9/bn33nu59957AdvW9u3bt/Pzzz8THR3NjBkzaNOmzRWDqpfy8PC4bI6X/ve/s0H/fbwqZGZmsmjRIrZv305UVBSJiYn2bePFY1ut1gpdu/h1dLVu6MXHSmt0Utr7V6VSoVKpMJvNV93e/m9t27YlLCyM48ePs23bNvr16wfYtsEnJibSuXPnywK5+fn5LFu2jI0bN3Lu3DkSEhIwmUzAtZ+bxx9/nB49etj/u7CwkE8++YS5c+fy5Zdf0rp16zK9ZoQQQogbnQQLhRBCCHEZs9kM2LIASwumXOrfNc+utP21+JpXy5Is5uDgUKaATUVq2FWFTp06sXbtWnbt2sXWrVvZu3cv4eHhrFmzhjVr1nDzzTfzzTfflNqwozyKgyRarfayY1fbdnyp4sBLeXz88cd8++23ADRp0oSePXvSokULWrduTbNmzRg1alS5r3mptLQ0YmNjcXR0LLXxRPPmzWnevDmjR49m0qRJHDp0iKVLl5Yp8FMdr5Hi13ZZHD58mEceeYSsrCycnZ3t3a5btmxJ586def/999m9e3eF51KWIGNxwK+011FlB0rHjx/PtGnTWLJkiT1YWNyJ+t8lDKKjo5k4cSIJCQnodDratm1L7969CQoKolOnTixYsOCyzuFX4+DgwNSpU0lISGD16tU88cQTLFq06LItzkIIIYQoqXb8hS2EEEKIWqVhw4ZERUVxzz33XLYt8HquGRcXx1NPPVWi/uDV6PV6CgoKOH/+fKkZdwkJCZUyt8rg4ODALbfcwi233AJAeno6q1atYsaMGWzbto0NGzaUuu23WIMGDQCIjY294jkxMTGAbQt2RXl4eODg4EBhYSEXL14sNWN106ZN5Obm0q1bNy5cuMC3336LRqPhs88+u+wxnD59usJzKbZu3Tr++9//0qlTJ3777bcrnqfX6xk+fDiHDh0iPT39usctj+IgWmmBwczMzDJfZ+rUqWRlZXHnnXfy7rvvotPpShwvztisqOL6i1erIVn8Grue11FZDRs2jBkzZrBx40YyMzMxmUxs3rwZT09Pbr/99hLnvvPOOyQkJNCrVy9mzpx5WZbjnDlzKjSH6dOnc/jwYS5evMiUKVNYvHhxqYFSIYQQQtiU7StoIYQQQtRLV8oiKt7Kt379+lKPR0VFMXDgQB544AF7jbVrudY1d+/ezaBBg3j88cft2W/F9Q1Xr1592fk5OTns3bu3TGNXpVWrVjF48GD+85//lLjd09OTe++9lz59+gD/bMO+knbt2uHs7ExycrK9puC/FdcS7NWrV4Xn6+DgYK9Lt3HjxsuOWywW3nnnHV544QWio6PZt28fAKGhoaUGO7du3VrivhXRpUsXFEXh0KFD1+xCfe7cOft8qlNxbdDk5OTLjh08eLBM10hJSSEqKgqAxx577LJAYWJiIuHh4UDJ57I82X7du3dHpVLZt4v/m9VqZeXKlcD1vY7KytHRkeHDh2M0Gvn7779Zs2YNJpOJUaNGXRawK36tTZo06bJAYW5uLgcOHADK/zpzcXGxl1MIDw/nm2++qejDEUIIIW4IEiwUQgghbmDFwYp/ZzONHz8eNzc3li1bxpw5c0pkU6WlpfHSSy8RGxuLXq8vc628SZMm4eDgwJw5cy7bShgXF8e0adOIiYnBz8/PvnV08uTJqFQqvvrqqxK1CQsKCpg6dSo5OTkVediVKiQkhOjoaJYuXXpZkC8mJsYeSLq0s61erwdKPu86nY77778fsNWoLA6KgS3A89NPP7Fq1Sr0en2pHajL48EHHwRg1qxZHDlyxH67xWLhs88+4/z587Rs2ZJu3brZt6GfO3fOHugqtmbNGmbPnm3/74o2BGnVqhV33XUXYAuirVix4rIMvsLCQn7++WcWLFiAi4sLEyZMqNBYFVWcDbto0aISWY2xsbF8+OGHZbqGi4uLPUBW3OW5WHx8PE899ZQ9UH7pc1n8ejGbzdd8zfv5+TF06FDMZjNTpkwpUZfQZDLx4YcfcujQIby8vOzPeVUbN24cYAusr1y5EkVRGD9+/GXnFb/WNmzYUGI7dVpaGlOmTCEtLQ2o2Ousd+/e9u3y33zzDREREeW+hhBCCHGjkG3IQgghxA0sMDAQgBMnTjBp0iSCg4N57bXX8PLy4osvvuCpp57ik08+Yd68ebRu3ZrCwkIOHDhAfn4+QUFBvPfee2Ueq1WrVrz33nu89tprvPrqq3z11VcEBweTnZ3NwYMHKSwspGvXrrz00kv2+3Tp0oUXX3yRjz76iAcffJAuXbrg7e3NwYMHyczMtDdPqElBQUE888wzzJo1iwceeIB27drRuHFjMjIy7I9r3LhxdO7c2X6fwMBAtm7dyhdffMHevXsZPnw4AwYM4Omnn+bs2bNs2LCBu+66i86dO+Pl5cXp06eJjo7GycmJ999//7prrvXv35/HHnuMb775hvHjx5cYJzY2Fg8PDz755BNUKhVDhgzhq6++Ij4+nuHDh9OtWzccHR0JDw+3B3ezsrLIzc0lNTW1wo14pk+fTmFhIatWreLFF1/knXfeISwsDDc3N7Kzszl27BiZmZl4e3sza9Ys+3bb6jJu3Djmz59PbGwsgwcPplu3buTk5LB//37atGlDcHCwPSvwSvR6Pffddx8//PADM2fOZN26dTRr1oyUlBR7RmVAQADR0dH2wBjYgmgeHh5kZGRwzz33EBAQwAcffICzs3Op47z55pvExcVx+PBhBg0aRNeuXXF2dubIkSMkJCTg6enJF198UaIrdFUKDg6mc+fO7N69G4vFQu/evUstK/DII4/w9ttvs3DhQvbt20dwcDCZmZkcPHgQo9FIy5YtiYiIKPHclMcrr7zCli1bSE1NZdq0acyfP7/MtT6FEEKIG4n8dhRCCCFuYKGhoUydOhU/Pz/279/Pxo0b7Rk9vXr1Yvny5dx3333odDp27NjBsWPHCAwM5MUXX2ThwoXlrnl211138eeffzJq1ChMJhNbtmzh7NmzhIWF8dZbbzF37lz7ds9ikydPZu7cufTu3ZuIiAi2bdtGUFAQv/zyC23btq205+J6PP3003zyySf07NmTuLg41q9fz5kzZ+jRoweff/45b7/9donzH3/8cQYNGoTZbGbr1q0cO3YMsG0Rnj17NjNmzKBz586cPn2ajRs3YrFYuO+++1i6dOlldd4q6vnnn+fbb7+lT58+REREsHHjRoxGI+PGjWPZsmX2bb7Ozs4sXLiQ+++/n0aNGrF371527NiBo6MjTz/9NCtWrKB79+6ArdZhRWm1Wj777DN+/fVXxo0bR4MGDThx4gR///03J06cIDAwkBdeeIG1a9fSpUuXSnkOyqNhw4b88ccfjBgxAq1Wy5YtW7hw4QKPPvoov/zyyzWb9hR76aWXeO+992jXrh3nz59n48aNXLx4kSFDhrBw4UKef/55oORzqSgKn376KcHBwURFRbF3796r1iR0cXHhl19+4Y033iA0NJSDBw+yZcsWnJycePTRR1mxYgXdunW7vieknMaPH4/ZbMZqtV4xM/bee+9l9uzZdO3alYyMDDZu3EhkZCS33HILP/74IzNmzABg+/btGI3Gcs/Bw8ODadOmAXDo0CHmzZtX8QckhBBC1GOKtSwt04QQQgghhBBCCCGEEPWeZBYKIYQQQgghhBBCCCEACRYKIYQQQgghhBBCCCGKSLBQCCGEEEIIIYQQQggBSLBQCCGEEEIIIYQQQghRRIKFQgghhBBCCCGEEEIIQIKFQgghhBBCCCGEEEKIIhIsFEIIIYQQQgghhBBCAKCp6QmUR2pqNlZrTc9CFFMU8PZ2lXWpIfL81w2yTrWTrEvdIOtUe8na1A2yTnWDrFPtJOtSN8g61U6yLrVX8dpcS50KFlqtyAutFpJ1qVny/NcNsk61k6xL3SDrVHvJ2tQNsk51g6xT7STrUjfIOtVOsi51l2xDFkIIIYQQQgghhBBCABIsFEIIIYQQQgghhBBCFJFgoRBCCCGEEEIIIYQQAqiGmoUWi4X58+ezcOFCoqOj8fT05KabbmLKlCk0bNiwqocXQgghhBBCCCGEEEKUUZUHC6dOncqyZcu47bbbmDBhAlFRUcybN48DBw6waNEi3NzcqnoKQgghhBBCCCGEEEKIMqjSYOHff//NsmXLmDBhAv/973/tt4eGhjJ16lR+++03Hn300aqcghBCCCGEEEIIIYQQooyqNFi4YMECnJ2deeGFF0rcPnToUCIjIwkICKjK4YUQQgghhBBCCCGEEOVQZcFCs9nM/v376dGjBy4uLgAUFBSgUqnQarWXBRCFEEIIIYQQQgghhBA1q8qChfHx8RgMBvz9/Vm3bh2zZs0iPDwctVpNr169eP311wkMDCzXNRWliiYrKqR4PWRdaoY8/3WDrFPtJOtSN8g61V6yNnWDrFPdIOtUO8m61A2yTrWTrEvtVdY1UaxWq7UqJnD06FHuvvtuWrRowYULF3jwwQdp164dp0+f5rvvvkOn07Fo0SKaNm1aFcMLIYQQQgghhBBCCCHKqcoyC41GIwBRUVHMnj2bAQMGADBgwADatm3L448/zsyZM/n444/LfM3U1GyqJrQpKkJRwNvbVdalhsjzXzfIOtVOsi51g6xT7SVrUzfIOtUNsk61k6xL3SDrVDvJutRexWtzLVUWLHRycgKgYcOG9kBhsVtvvZUGDRqwc+fOcl3TakVeaLWQrEvNkue/bpB1qp1kXeoGWafaS9ambpB1qhtknWonWZe6QdapdpJ1qbtUVXVhPz8/AHx8fEo97uPjQ3Z2dlUNL4QQQgghhBBCCCGEKKcqCxZ6enrSrFkzoqOjMRgMJY6ZzWbi4+Px9/evquGFEEIIIYQQQgghhBDlVGXBQoDRo0eTm5vLd999V+L23377jaysLIYNG1aVwwshhBBCCCGEEEIIUe9UUb9ioAprFgI89NBDbN68mS+++ILIyEi6d+/OiRMn+OOPPwgNDWXy5MlVObwQQgghhBBCCCGEEPXKj3tiWXDwPF+OaUcrX5dKv36VZhZqtVrmzp3LU089xdGjR3nnnXfYunUrEydOZN68eej1+qocXgghhBBCCCGEEEKIemXruTTS8gr5eV98lVy/SjMLARwdHXn22Wd59tlnq3ooIYQQQgghhBBCCCHqtcyCQgA2hCfzQr8gPJwcKvX6VZpZKIQQQgghhBBCCCGEqDxZBSYACs1WVpxIqPTrS7BQCCGEEEIIIYQQQog6wGK1klWUWQjw59GLWCq52YkEC4UQQgghhBBCCCGEqANyDWYsRbFBZ62a+IwC9sVkVOoYEiwUQgghhBBCCCGEEKIOKK5X6OigYmibhgAsPnqxUseQYKEQQgghhBBCCCGEEHVAZlG9Qje9A6M6+AGwNSKF5BxDpY0hwUIhhBBCCCGEEEIIIeqAzHxbZqGbXkOQjzOdmrhhtsKiI5WXXSjBQiGEEEIIIYQQQggh6oDiTsjujg4AjOnYGIC5u2P57eD5ShlDgoVCCCGEEEIIIYQQQtQBxZmF7noNAANCfBnXqTFW4JNN55i1NQrrdXZHlmChEEIIIYQQQgghhBB1gD2zUG/LLFQpCi/cGsSTfQIA+HlfHG+tOYPJbKnwGBIsFEIIIYQQQgghhBCiDijuhuxWlFkIoCgKD/Zoxn8GB6NWYOXJJJ5feoI8o7lCY0iwUAghhBBCCCGEEEKIOiDzXzULL3VnWCM+HtEWnUbFruh0nvjjKOl5xnKPIcFCIYQQQgghhBBCCCHqgKxSMgsv1SfQm6/Htsddr+FkQjYP/3aE85n55RpDgoVCCCGEEEIIIYQQQtQBmfklaxaWJszPje/u6Yifm47Y9HwmLzjCmcScMo8hwUIhhBBCCCGEEEIIIeqA4sxC9ytkFhYL8HLi+3s60tLHmdRcI48tPMLBuIwyjSHBQiGEEEIIIYQQQggh6oCr1Sz8N18XHXPGdaCzvzu5RjMvLz9VpjEkWCiEEEIIIYQQQgghRC1ntljJLgoWXqlm4b+56jV8MbodtwX7YLJYy3Sfsl1ZCCGEEEIIIYQQQghRY7INJorDfWUNFgLoNCreHdqapccSynS+ZBYKIYQQQgghhBBCCFHLZRVlFTpr1TioyxfSU6sUxnT0K9O5EiwUQgghhBBCCCGEEKKWK25uUp6swoqQYKEQQgghhBBCCCGEELVcZn5RcxP9tZubXA8JFgohhBBCCCGEEEIIUctlSmahEEIIIYQQQgghhBACILOoZqG7o2QWCiGEEEIIIYQQQghxQ8vKl8xCIYQQQgghhBBCCCEEklkohBBCCCGEEEIIIYQoUtwN2V0yC4UQQgghhBBCCCGEuLFJN2QhhBBCCCGEEEIIIQQg3ZCFEEIIIYQQQgghhBBFpGahEEIIIYQQQgghhBAC+KdmoWQWClHkfGY+SdmGmp6GEEIIIYQQQgghRLUyWazkGMxA1Tc4qdqrC1FJotPyGP/jfsxWCPZ1pk+QNzcHetGmkSsqRanp6QkhhBBCCCGEEEJUmeyirEIA1ypucCLBQlEnnE7MwWy1/Ts8OZfw5Fx+2B2Ll5MDvVp4cXOgFz2ae+Kik5e0EEIIIYQQQggh6pfieoUuOjUaVdUmTUlkRdQJyTm27cc3B3pxW7Av2yPT2BWdRlpeIStPJLLyRCJqlUInf3duDvSifysfGrnpa3jWQgghhBBCCCGEENcvM9+WWehexVmFIMFCUUckFtUqbOHtxNC2DRnatiEms4XD57PYFpnK9sg0YtPz2R+bwf7YDL7eEc0Xo9rR0d+9hmcuhBBCCCGEEEIIcX2yijILq7q5CUiwUNQRyTlGAHxddPbbNGoVXZt50LWZB8/1CyI2PZ/tkamsOpnEmaQc/u/P48wa0472jd1qatpCCCGEEEIIIYQQ1y2zqGahu2PVZxZKN2RRJyQVbUNu4Kq74jnNPB2Z0MWf78Z3oGszD/IKzTy7+BgnLmZV1zSFEEIIIYQQQgghKl1xZmFVd0IGCRaKOiKpaBtyAxftNc/VO6j5dERbOvu7k2s08/TiY5xLya3qKQohhBBCCCGEEEJUieqsWSjBQlHrmS1WUnNt25AbuFw5s/BSjg5qPhsZRofGbuQYzHy9I7oKZyiEEEIIIYQQQghRdTKrsWahBAtFrZeWZ8RsBZUCXs7Xziws5qRV8/qgYAC2RKQSl55fVVMUQgghhBBCCCGEqDL2bchSs1AISCpqbuLjrEWjUsp13xbeTvRu4YUV+O3g+SqYnRBCCCGEEEIIIUTVKt6GLJmFQvBPvULfMm5B/rcJXZoAsPx4gv3NJYQQQgghhBBCCFFXSGahEJdILkMn5Kvp1syDVr7OFJgsLDl6sTKnJoQQQgghhBBCCFHlMguKG5xIZqEQJGYXNzcpe73CSymKwr1d/AFYePgChWZLpc1NCCGEEEIIIYQQoqpl2RucSGahEP9kFlZwGzLAoFBffF20JOcY+ftMcmVNTQghhBBCCCGEEKJKFZot5BrNgGQWCgH8Eyz0da1YZiGAg1rF2I6NAfhmZwznM6UzshBCCCGEEEIIIWq/4qxCBXDRSbBQCHs35OvJLAQY1cGPRq46LmQW8ND8w5y4mFUZ0xNCCCGEEEIIIYSoMkcv2OIX3s5a1CqlyseTYKGo1axWq70b8vUGC930Dnx/T0eCfZ1JyyvksYVH2Xw2pTKmKYQQQgghhBBCCFHprFYrP+6NA+DOsIbVMqYEC0Wtlm0wUWCyNSTxrWCDk0s1cNUxZ3wHbmrhicFk4eXlJ1l9KvG6ryuEEEIIIYQQQghR2fbFZnAyIRudRsX4zk2qZUwJFoparXgLsrteg95BXSnXdNZq+GREGMPbNcIKvLX6jGQYCiGEEEIIIYQQotYpziocHtYIL6frT6IqCwkWilrNvgXZ9fq2IP+bRqXw2sBWDG3bELMVXlt5ij3R6ZU6hhBCCCGEEEIIIURFnUjIZl9sBmqVwn3d/KttXAkWilrN3gm5ErYg/5tKUZg2KJj+rXwoNFt5YdkJtp5LxWq1VvpYQgghhBBCCCGEEOXx455YAG5v3QA/N321jSvBQlEtLAUFFTpWWZ2Qr0SjUpg+NJReAbYahi8sPcFjC49yMD6jSsYTQgghhBBCCCGEuJbI1Fw2R6SiABO7Na3WsSVYKKqcMTaGMzd3J+23eZcdyztyiFPdO5C9dXOp962sTshX46BW8eFdbZjQpQlatcKh+Ewe+/0oTy86yvGLWVU2rhBCCCGEEEIIIURpfi6qVdi3pTctvJ2qdWwJFooql/bbPIwx0cT935MlAoZ5Rw4ROWY4poSLJH3xWanbf5OLMwtdq7aIp95BzXP9glgyuTujO/ihUSnsicngwfmHeW7Jcc4k5lTp+EIIIYQQQgghhLg+F7MK2BGVVtPTuG4XswpYczoZgEk9mlX7+NUaLDSbzUyYMIGQkBBMJlN1Di1qUMOXXsV70mSwWu0Bw+JAoTkzA6duPQj48VcURbnsvkn2moVVl1l4qQauOqYOaMWih7pyZ9uGqBXYHpnGfb8eZOqKk0Sm5lbLPIQQQgghhBBCCFF2ZxJzuP+Xg0z58zgbwpNrejrX5dd98ZgtVro186BtI9dqH79ag4Vff/01Bw4cqM4hRS2gKApNZnz6T8Dw2Sc4O7CvPVAY+PufqF1sL/7o1Dz71mOonm3IpWni7sh/bg/h90ldGRzqiwJsCE9h/I8HeGPVaWLT86t1PkIIIYQQQgghhCjdqcRsnlx0lMwCW2LanJ0xWOpo89K0PCPLjicA8GCP6q1VWKzagoVHjx7lf//7H1pt1W4nFbVTccDQqUvXErcH/v4nVicX/j6TzOQFh7n7x/3c+8tB0vOMFBSa7W/0qt6GfCXNvZyYPrQ18yd24dZWPliBNaeSGDt3H9PXhnMx68rNWYS4GqvVyorjCeyJSa/pqQghhBBCCCFEnXUiIZun/jhGVoGJMD9XXHRqIlPz2BieUtNTq5DfDp7HYLLQtpErXZt61MgcqiVYmJuby4svvsjNN99Mx44dq2NIUQvlHz1MwdmzJW47+OdqRny3l9f+OsXRC7ZmIhn5hXyzM4aUXFu9Qp1GhatOU+3zvVRLH2c+vKsNv9zXiT6BXpitsOx4AqO+38eHGyIoKDTX6PxE3bMjKo2314bz9KJjzFh/FoPJUtNTEkIIIYQQQohaITI1l4fmH2LnNeoPHruQxVN/HCXbYKJjEze+HNOOezo3AeDbXXUvuzDHYGLhoQsATOretNRybdWhWoKF7777LtnZ2UyfPr06hhO1UHGNQktWJk5du+N593gAVqzYRmK2AXe9hod7NuP9Ya0BWHL0IjsibT8UGrrqauwN8m+hDV35bGQY39/TkW7NPDBZrPxx+AJfbI2q6amJKrYlIpWPN0aQmV9YKddbcTzR/u9FRy7y4PxDRKfmXdc198dm8NWOaHKNUhNWCCGEEELUPVarlfCkHBYeukBEitSLv5EtPnyRYxezeXdd+BUTK46cz+SZxcfINZrp5O/OzFHtcNZquKezf53NLlx0+AK5RjMtvJ24paV3jc2jytO11q1bx+LFi5k9ezY+Pj7Xda1aEi8SRYrX41rrcmkzE+duPQhc+CcqZxdULs7knbfVIrzTIZXH+9wEwMazKfx9JplZ22wBOF8Xba1b+w5N3PhqbHs2hCfzyvJTLDp8gSFtGtC+sVu1zaGsz7+4PmaLla93RDN3j61tvdFs4fVBwWW+f2nrlJFfyLbIVACm9Avkpz1xnE3O5f5fD/Lena25Jaj8vxT+OHSBjzZGYLFCvtHMC/2Dyn2NG4m8f+oGWafaS9ambpB1qhtknWonWZfqk5ZrZE9MOruj09kdk0Fq0Q4zbycHfn+wKx6ODle8r6xT7VQZ63L4QiYASTlG/jx6gQld/Esej8/k2cXHySs007WpO5+NDMNRqwbAzVHDPZ2b8O2uWL7dFcNtIT6oavmLxGq1svJEov1z58TuTVGrKn/OZX0aqjRYmJiYyBtvvMGYMWMYMGDAdV/P27v6O8CIa7vWusSePII5MwP3m26i05o1aFxt5/t8/y3Ki98CoIs5i7e3C4qi8ObIdmz9eLP924NmPi74+NTOtR/n48re+GwWH4xnxoZzrHimD1pNtfYNkvdFFcrML+TF3w6x+cw/nbSWHUvgyduCadWwfM/7peu0alc0hWYrbfzcmHJ7aybc1IIpvx9m57lUXlx6greHh3Ffz+Zluq7ZYuX9Vaf4bvs/2a2Lj1zkyQHBNPVyKtccb0Ty/qkbZJ1qL1mbukHWqW6QdaqdZF0qn9Fk4WBsOlvDk9l6Npnj57NKHHd0UOOoVZOaa2TWjhhmju90zWvKOtVOFV2XrIJCIpL/ySz9aW88k/u1wrmoPNnuyFSe/dMWKOzT0odvH+hqDxQWe3pQKL8dukBkah57L+YwrH3jij+QKpacbeC1Jcf4+6Rt91nPQC/u7ROIg7p6YwuXqrJgodVq5ZVXXsHV1ZXXXnutUq6ZmppNHdtuXq8piu3Nf611cbpnEs21TrgNHkKGATBk248VBodBVDqNx44jNTUHAB3wQDd/vt0VC4Cbg4qUlOxSrlw7PNGzKRtOJXImMZvP15zioZ7NqmXcsj7/omIKzRbu++Ug51Ly0GlUvDE4mPVnktkckco7y0/w6ci2ZbpOaev02+4YAIaE+pKSko0K+PSu1rz391mWH09k2tLjnLuYyZN9Aq66Bb+g0Mwbq06z6awtS/GpmwPYG5PBvtgMPvjrBG/dEXpdz0F9Ju+fukHWqfaStakbZJ3qBlmn2knWpfJFp+Xx5dYo9sZkkPevmu/Bvs70DPCkVwtPOjR252xyLg/OP8Sywxe4ubkH/VqVvktR1ql2ut512RWVhsUKfm46NCqFuIwCZv99hod6NmNfbDpT/jyBwWShZ4AnHwwNITcrj9I2rd/TuQlzdsbwyZozdGvkUiWZetdrQ3gy7/8dQUZ+IRqVwuO9m3N/t6ZkplfNNvzitbmWKgsWzp07l927dzN79mwMBgMGgwGAwkJbva+MjAwcHBxwd3cv8zWtVuQHQC1UlnXxGDXWfu6lcgy2XxIujtoSxx7o1pTlxxNJzDbQyFVXq9fd3dGB528N5D+rzvDdrhj6t/KheTVmdMn7omocv5DNuZQ8XHUavrq7PSENXQjxdWHbuVS2nkvlUFwmHf3L//MrIiWXk4k5qFUKg0N97WunVqmYNiiYRq565uyKYe6eOIwmK1P6BZZ6vdRcIy8sPcGJhGy0aoX/3h7CoNAGdGvmyaR5h1h1Mon7ujalpa9zZTwd9Za8f+oGWafaS9ambpB1qhtknWonWZfKEZOWx2MLj9q3GHs6OtAjwJOezT3pEeCJj7O2xPltGrlyf7em/LQ3jvf+PkvHJu64X2U7sqxT7VTRdTlUlG3ayd+dXgFevLHqNL/si6eZpyP/XX0Gg8lCrwBPPhreFp1GdcUx7unchN8PnicqLY9VJxMZ1rbRdTyaypVVUMhHG8+x5lQSAK18nXlrSAitfF2Amn89V1mwcNOmTVitVp588slSj/fu3ZsmTZqwcePGqpqCqANyihoxFKcTGyILMOfYAojT2wSy9mIqfS0u5B39J6qudlGjC9RX/2Sv4vbQBqw6kcTumHTeX3+Wr+5uX2uasoiKOZloy2bt7O9OSEPbD+wAbyfuateIJUcT+GJrJN/f07Hc67zyhC21/OZALzydSv5RpCgKj9zUnAauWqavO8u8A/H0a+l9WVAyMjWX5/48zoUsW3OgT0a0pUMT2zltG7lyW7APG8JTmL09is9GhnEqMZt5++PJNZp5e0gorvqa7S4uhBBCCCFuHPEZ+Tz5hy1Q2MrXmTcGBxPSwOWaNeQe6dWcrRGpRKXlMW3laZp7ORKTnk9GXiGjO/gxor1fNT0CUd2OnrfVK+zQxJ1Bob78uDeWcyl5TF1xCoA+gV7MuLPNNUuAueg0TOzelC+2RjFnZwyDQhpUatmwhKwC/rc9mnu7+Ns/M5bFrug03lkbTnKOEZVi63r8cK/mNbrt+N+q7BPjK6+8QlZW1mW3f/DBB5w5c4bvv/8eR0fHqhpe1BHZBbZgoYtOgyGygNM9j9iPKcDtwAUyL7tf6O4OtSpgqCgKrwxoyfifDnAgLpMVJxK5K6z2fGshyu9kgi1Y2KZRyRTtR3s1Z/XJJI5dzObbXTG0buiKm16Du94Bd0cNrnoHNFdIbzdZrKwqqkMxrG3DK449vJ0fRy9ksfx4Iu+sC2fe/Z3RO9hqcOyLTefl5SfJMZhp5unIZyPDaOZZ8mfpE70D2Hw2he2RaUxecJijF/75WfzF1shyNWgRQgghhBCioi5mFfDEwqMk5Rhp4e3E7DHtLvvC/Ep0GhX/uT2YyQsOszsmnd0x6fZj7/59lqQcA4/eVLY636LuMJktHLto+yzWsYkbKkXh8ZsCeGn5SQD6Bnnz3rDWZQ763d2xMfMPnOdiloGlxy4ytlOTSpvrl9uiWHs6mdNJOSx4oMs1tznnGc3M3BLJn0cvAtDM05G3hoQQ5ld9jVLLqsqChWFhYaXeXrztuGfPnmg0kt1yo8s1Fm1D1qoxJ5mvcfY/irMPaxN/D0ceu6k5X2yNYuaWSPoEeuFVxl+EovYpDha2blTyGyIfFx33dvXn+92x9rqa/+asVePu6IC7XoObXkMDDyf0ihWDyUJaXiGejg70buF11fGn9A1iZ1Q6sen5fLsrlmduacFfJxKYvu4sZouVjk3c+Gh421K7wzX3+icD8uiFLNQK9A70Zuu5VJYeS2BwaAO6NvOo2BMjhBBCCCFEGU1dcYqEbAPNPB35393tyxwoLBbm58arA1qxIyqNJu6ONPdyJD4jn5/3xfPtrlgy8gv5YOy1G6CIuuNMUg4GkwV3vYaAovJefVt6M6FLE8wWK//Xt3yNP/QOah7u1YwP1kfw/e5Y7gxrhKOD+tp3vIaUHAPrw1MAiErNY+XJqycMHY7P5M01ZzifWQDAuE6NefrmFvakkNpGonWixpgt1n+ChToNUFizE6oE93TxZ82pJMKTc/l00zmmD21d01MSFZBVUEhchu2HeOtSuh5P7N6UzPxC4jMKyCwoJLPARFZBob0GZ67RTK7RzIXipNiYjBL3H9y6AZpr/IJz1WuYOqAVLy47wbz9cWTkG1l+3JaVODjUlzcGh6C7yrdpj/cOICXHiJ+bngldm9DE3ZEP1p9l8ZGLvPt3OAse6FJrfzEJIYQQQoi6z2Cy2L+Anzkq7LK6hGU1ov3lW44buen5aEMEfxy+SJ4FXu8fVKu2cIqKO1xUr7BdYzf7VnVFUXiuX1CFr3lXWCN+2RfP+cwCfjt4ngd7XH9T0j+PXsRssaLTqDCYLMzZGcPg0AaXfUZLyzMye1uU/bNcI1cd/709pNYnb0iwUNSYPOM/2YEuOg3mehAs1KgUpg0OZtK8Q6w9ncyQNg2vmUEmap9TibbO3E3c9aVm7jk6qHllQKvLbjdZrGQXBQ8z8wvJKjCRZTBhUqm4mJpLZn4hVuChHk3LNI++Lb0ZFOLLujPJ9l8uD/VoymO9A65Z48XLScunI0tmeD99cwu2nUslPqOAb3bG8H99S2+eIoQQQgghxPVKzLY1OdVrVDRxr9wSUnd3bIy7XsN/V59h5dGLJGfm8+FdbXDWSoijrjtSVEKpY5OyN5O8Fge1ikdvas5/V5/h531xjGjXqNxZrpcqNFv482gCAK/c1pKvd0STmG3gj8MXuK+rP2DbTv3HkYvM2RltTyoZHtaIKf0Ci5Klardqn+Evv/xS3UOKWqq4uYmDWkGnUZFXw/OpLK0bujK+cxPmHzjPjPVn+X1S10pJcxbV50r1Cq9Fo1LwdNKW+MWjKODj40pKSnaFOlq92D+IA/GZZOQX8trAVtdVC9NFp+HVga14bskJ5h+IZ0CwD21rYX0MIYQQQghR913Msu3U8XPTV0nzx0GhDXB3dODl5SfZG5PBk38c4/ORba8rCCRqltVq5UhRc5OOTSr3c8rg0AbM2x9PeHIun2+J5K0hoRW+1obwFFJzjfg4axnSugFW4J214fy4J5bhYY3YF5fB7G1RxKbnA9C6oQsv9m9J+8aV95jUKSfRxmwkv/1kcKj8fiCSpytqTI7BFix0rQNR9fJ67KYA/Nx0XMwy8Ov++Jqejigne73CcnS0qiqeTlp+e6ALyx/uXilNc/oEejM41BeLFV796xQZeXU/o1cIIYQQQtQ+iVm2zMKGbroqG6NngCcLHumJu6OGkwnZPPLbEQ7FZ2KtyLf0osbFZRSQlleIVq2UWg7qeqhVCq8NbIUCrDqZxJ5LGuaU18JD5wEY3cEPjVrF0DYNaeHtRGaBiTFz9/HK8pPEpufj4ejAqwNbMXdCp0oNFAK47HwXl90f4LLr3Uq9bjEJFooaU5yKWxdScMvLSavmmVtsWzx/2RdHSo6hhmckyqOimYVVxcPJgQaulfdH1su3tcTfQ8/FLAOv/nUSk9lSadcWQgghhBACLs0srLpgIUCHph58P74jDV11xKTn8+jvR5g47xDrTifJ37l1zOGirMLWDV3L3O24PNr6uXF3x8YAfLD+LAWF5W+cejIhm2MXs3FQK4wsqqWpVik81ScAgLS8QvQaFQ/1bMaSyd0Y1d7vml2SK0KdGQ2A/thPaBIOVvr1JVgoakxxZqGztn5u0R0Q7EM7PzfyCy18vSOmpqcjyigl10hSjhEFCK0FmYVVwU3vwMfD2+LkoGZ/XCafb4ms6SkJIYQQQoh65mJRzUI/t8qtV1iaAG8n5k7oyMj2jdBpVJxKzOH1lacZ8f0+ftkXZ//sKWq3o0XNTTpUYr3Cf3uiTwANXLTEZxQwd09sue5rsljt9xkQ7Iv3JU17bgny5tFezbm3iz9LJnfjid4BVZcYZbWiyrXVTFSw4rr5ZTBX7o4xCRaKGlNcs7A+ZhaCrWPTlH627MLlxxM4m5xzxXNNFisRKbmsOpnIrK2RrDiegMkiqfM14VRRVmGAt1O9LpAc5OPMW0NCAPj90AWWHbtYwzMSQgghhBD1SWJRZmHDStwhczW+LjpeGxjMike689hNzfFyciAx28AXW6MY+s0ePt10jvOZ+dUyF1Exh6uoXuGlXHQaXuzfEoCf9sVzLiW3TPeLTs1j8oLDbI5IBWBc5yYljiuKwiM3NWdKv0B8XKr2Na8UpKGYbcF4i94TTeppHI/MqdQx6u8nYVHr/Xsbstql7BmG5Tm3JrVv7MaAYF/Whyczc0sks0a3w2i2BQbPJOVwJjGHM0k5RKTkYjCVTJH/cW8cT/UJ4NZWPlVSEFiUzr4FuZ5mFV6qXysfHunVjG93xTJ93Vm2nkvjqZsDCPR2rumpCSGEEEKIOu5iVvVlFl7K00nLw72ac3+3pqw9lcS8A/FEpuax4OB5fj90nv6tfJjQxZ92lVxD7krS8oxYrZTIQhOXS88zElPUEKSy6/v9W7+W3vQN8mbLuVQ+3hjB/+5uf8XP3Barld8Onud/26MxmCy46NS8clsr2tZgySp1zgXb3Bx9yen9Om7rp+C891MMQUOxuAdUyhgSLBQ1pjgV3KVoG7IuUE/o7g6Yc65eN0DtokYXWL2/cK7H07cEsOVcCntiMrh77n7iM/Ixl5I06OSgJriBM809ndgckUJsej6vrDhFgJcjPi469BoVeo2KLk09uL11A1z18vatCicTa1e9wqr2cK/m5BjMLDx0nq3nUtkemcqwtg15pFdzGlXzH3ZCCCGEEKJ+sFitJNq3IVdPZuG/6TQq7mrXiDvDGrI7Jp35+8+zOyad9eEprA9PYUKXJjzXL+iK97dYrczeFo2Ho4b7uzWt0BxyDCbu+ekAZouV3yZ2qfKMs7rsSNEW5EBvJ9wdHap0LEVReP7WIHbHpLM/LpNNEan0b+Vz2XnxGfm8vTacQ/G2jMeeAZ5MGxRcbdmyV6LKtgULza6NMQSPxnh6Edr47Xgsu4ecPv/B2OJ2uM6EI4k2iBpTWoOTuhQELKsm7o6M69SEX/fH278p8XB0ILSBC8ENXAhp4ExIAxeaejqiKnpDT+kXyLz98cw7EE90Wj7Raf+ky68PT+GLrZHc3roBfUIacjw2jciUPFJyjfRt6c3YTo3r9fbZqmS1WjmZYNsufqMEC1VFvyhHtffjfzui2XQ2heXHE1l7OplxnRozsXtT3PRV+8taCCGEEELUL6m5RkwWK2qFGg+QKYpCrwAvegV4EZGSy/z98aw4kcj8A+fp3tyT3i28Sr3fvtgMft4XB0BLX2d6BZR+3tWsPpVEWp6tltzMrVG8c0doxR9IPXfkQnG9wurJ+Gzsrue+rv58vzuWmZvP0buFF7qipipWq5UlRy/y+ZZI8gstODqomNIviJHtGtWKXX+qXFsJKYuLHygK2f1m4LH0btTZcbivfgSjfx9y+ryJ2bvirzeJKIgaY88s1NWNLcXX48k+AQR4OeLppCWkgQsNXLRX/SHjotPwWO8AxnZqzJHzWRSYLBhMZtLyCll1MpHotHyWHE1gydGEEvc7kZDNvP3xTOjiz9hOjettPciqkpBtICO/ELVKoZVv/d+GfKkAbyc+vKsNxy5kMWtbFIfiM/l5XzxLjibwYI+m3N2xMXqH+v9eFUIIIYSoz4wmC19ui6JLU3f6trw8k6qyFG9B9nXRoamCTrAV1dLHmf/cHoKLTsOCg+eZvjacBRO74FFKJttfJxLt/56xPoLfJnYp19/DVquVRYcv2P97zakkRrZvRGd/j+t6DLVFSo6Bt9eGM6K9X6lZeeV1xF6vsOqam/zbxO5NWXE8gQtZBuYfiOfBHs1IzDYwfW04u2PSAejk785/Bgfj7+FYbfO6luJtyGZnWzdmi3tz0u7ZhNPB2Tgd/gZt/HY8fx9MQdj95HZ/Aaves9xjSIMTUWP+CRbW/4CWg1rF8HZ+3BLkTUNXXZm/jfB00tKvlQ+3t27A8HZ+PNijGQsndeWbce0Z0roBPVp4MaqDHy/cGsTUAS1p5ulIZoGJr3ZEM/y7vXy3K0Y6f5VDcb3Clj7O9m+VbjTtGrvxzdj2fD4yjCAfJ7INJr7YGsXoH/ax/FgCZmm8I4QQQghRZ605lcSCg+d5feVpLhY1IKkKCUXXrqktyNdSnMyRkmvkww0Rlx3PMZjYdDYFAGetmvOZ5e+ce/h8FpGpeeg0KgaH+gLw4YYITGbLNe5ZcTFpefy0N46M/MrtjFuaJUcT2BWdzlurz9jXu6IKCs2cSrTt8KquzEIARwc1z9xia0r6w+5YFhw8z/if9rM7Jh2dRsVz/QL5emz7WhUoBFDlFGcWNv7nRq0zeT1fJm3CJgyBQ1CsZhyP/YjXvFvQH/8ZLOWLC9yYn4ZFrWDvhixbZstFURQ6+3vwztBQfn+sF68NbMX4zk0Y3aExCyd15e07Qmju6UhWgYlvdsZw17d7+XZnDNkFEjS8GoPJwsJDtm9o2jS6sbIK/01RFHoHejHv/i68eXsIjVx1JOUYeWddOPf+cqBK/7AUQgghhBBV5+8zyYDtb99PN52rsnGKMwtraw1svYOaN4eEolZsz8m600kljm8IT8ZgstDCy4n/3B4CwM/74olKzSvzGIuP2D5b3N66AS/1b4m7XsO5lDwWXpJtWJlMFivPLz3Bl9uimPjrQc4k5VTJOMWKM+/yCs18cp2vpRMJ2ZgsVnxdtDSu5tfM4FBfOjR2o6DoPZFjMNO2kSu/3teZCV387aXCahNVcYMTF7/LjlncmpE15Fsy7voNk1cIqoJ0XLe8hufCITic31n2MSpttkKU0z81C2VrY2VRqxSGtG7I75O6Mv2OUFp42TLD5uyK4a7v9jBnZzRZBVX/LVNdY7JYmbbyFAfjM3FyUDO2Y5OanlKtoFYpDG3bkEUPdWNK30D7HzhP/XGU1FxjTU9PCCGEEKLeMOfmVuhYeaTlGdkXawvwqBXYHJHKzqg0+3Gr1Upkai6FlZD5Vpxp1qiWZhYCtG3kykM9mwEwY0NEiey44i3Iw9o25NaW3vQJ9MJksfLB+rMYTdd+flJzjWwIt2Umjungh7ujA0/d3AKAOTtjSM4xVPbDYfXJRGKLauRfyDIwecFh1pxKusa9Kia7wMSJi7Yag8WvpS0RKRW+XnFzkw6N3aq9JqCiKLzQPwi1SkGjUniyTwDf3dORAG+nap1HeahzbOXIzJdmFv5LYdM+pI9bS/bN72DRuaNJPYXH0rG4rp9SpjEkWChqTPH2WOcbYBtydVOrFAa3bsCCiV14d2gogd5O5BjMfLsrlru+3cvXO6LJrIbU9LrAarXywd9n2RyRioNa4eMRbWjp61zT06pVdBoV93b1Z/4DXWjspiMuo4BnFh+TwLMQQgghRCXIXLua093ak3f44GXHUr6fQ3jfnhhjY657nI3hKZit0LqhC+M6274c/3hjBEaThYSsAv7vz+OM+/EAY37Yx6qTiVisFS8/k5BduzMLiz3UoxltGrmSVWBi6opTGE0W4tLzOXw+C5UCQ9o0QFEUXr6tJXqNioPxmfT7cgcPzT/EZ5vPsf5Msr3r86WWH0/AZLES5udKaENb48Th7RrRppEruUYzTy86RkolfvluMlv4brdtm/SDPZpyUwtPDCYLb6w6zWebz2Gq5FJC++MyMFuhuacj9xV1iv5wQwS5xortZjtywVavsEM11iu8VOuGrsy7vzOLH+rGgz2a1ao6m5exWkrfhlwalYaC9g+Sdt928sMmYlVU6KLWlmkYCRaKGlMcLHSVYGGVUasUBoXagobvD2tNkI8TuUYz3++OZfh3e/lqe1S11LOozb7cFs2y4wmoFJg+tDXdmpW/+OuNooGrji/HtMfbWcvZ5Fym/HmC/EJzTU9LCCGEEKLOslqtpMz5H6aUZM6NGV4iYJjy/RzOv/oixtgYMpb+ed1jFW9BHhjiyyO9muPtrCUuo4BXVpxk3I8H2BVtyzq8kGXgv6vPcN8vB9kZlYa1AkHDi7W8ZmExjVrF+8Na46bXcCIhm5lbIvnrpC2rsGeAJ75FnZz93PS8MTgYLycHCs1Wjl3MZv6B87z61ymGzdnD0G928+qKk8w/EM+xC1ksOWoL5ozu8M82UZWi8PaQEHxdtESm5vHY70dKDTRWxPITiVzILMDLyYGHejTj0xFhPNTDFsSbf+A8zyw6Snpe5QUn9xRtQe4Z4MnDPZvR2F1PUo6RL7dGXTMwaTWZyN23x/7fZouVo0WdkNt5asg/drTS5lkeQT7ONHav3cFtACU/FcVixIqCxblhme5j1XuS0/dd0seuwRAwoEz3kWChqDG5xuJtyBIsrGoqRWFAiC/zH+jCjDtb08rXmVyjmR/2xDHmh33EpJW99kZ98su+OH7eFwfAawNbVUoXr/quqacjX45uh5tew7GLWUz4+QDrTidd1zfPQgghhBA3KkVRCPhpPk7de2LJyrQHDIsDhQC+T0/B95kpV7yGyWzhfGY+B+IyWHkike93x/DuunB7zTyA5BwDh+Jt2VsDQ3xx0WmY0tfW2GF7ZBp5hWbaN3bj1/s682SfAJy1as4m5/J/fx7niT+O2recllVCUc1CP9faH3xp7K7nrSG2uoQLD19gwYF4AIa2KRmIGRTagDWP92TJ5G68NSSEMR38CGnggkqBpBwj68NT+GxzJA8tOMzFLAPueg0Dgn1LXKO5lxNzxnWgkauO2PR8Hv39CBcyr68euNFk4YeirMJJPZqhd1CjVik80acFM+5qg5ODmv1xmdz/6yFOJWZf11jFdhcFlns090TvoOaV21oCsOjIRYZ8vZt314WzNTz5smYuVpOJmMcnEzF8CBl/LQcgMjWXHIMZJwcV6mfuJ2LEHeTu31sp86yP1MVZhU4NQH15F++rMfu0IXvgrDKdK1EaUSPMFuslwUKpWVhdVIpC/2Bf+rXyYUtEKv/bHkV0Wj7T14XzzbgOtbJ4a1VZfjyBL7ZGAfD0zS0Y3u7y4rCidC19nZk5KowXlp4gPqOA11ee5pd98UzpF0iXph41PT0hhBBCiDpF7eJK4G+LiRw/mry9uzk7qJ/9mO/TU/B74y0URcFitbL2dBLnUvJIyCrgYpaBhKwCUnKNlJrMdQwsVri7Y2PWh6dgBdo3drNvDR4c6suG8GT2xWbweO8A7u7YGLVKIaShCyPb+zF3Tyx/HL7AgbhMJs0/zG3BPjzRO4DmXlev5ZZdYLJ/1mtYyzMLi/UJ9OahHk35YU8c+YUWXHRq+ra8PJFAURT8PRzx93DkjqJgYp7RzMmEbI5dzOLohSyOXcgis8DEPV2aoHe4/LOuv4cjc8Z34Mk/jhKfUcA9Px3g3q5NmNDFv0KJNEuPXSQx20ADFy2j2pf8TNO/lQ8BXo68tOwksen5TPz1EF2aeXB7qC+3tvLBTX/1YJPVauXQ+UyauDvS0NW2lvEZ+ZzPLECjUux/+9/Uwounb27BL/tsnZiXHE1gydEE3PUa+rb05rZgX7o180CjKCgaDZhMxDw6Ceb8yGH/rgAEJZ7FsHc3ancP2zmiVP80N2lUpePICogakWf8Z+uidEOufipF4dZWPoQ0cGH8T/s5fD6LPw5dsNcuqe82n03h3XXhANzX1Z8HuvnX8IzqnjA/N5ZM7s78A/H8uj+e00k5PPXHUebe24nWRXVZhBBCCCFE2RQHDI8H/vP3uMvNfe2BQoAN4Sn8Z9WZUu/voFZo5KqjkZueRq46jGYLa08n8/HGCPzcdPxd1O13YMg/mW6KojDjrjYAlyUNeDg68Fy/IMZ3bsI3O2NYdSKRDeEpbD6bwvB2fjzSqxk+LqUHAou3IHs4OuBYSrCstnr0pgCOXcxmX2wGt4c2QKcp20ZMJ62ars086NrMA7AF2DILTLjrr/w5189NzzdjO/Dy8pOcSMjm212xLDx0gQd7NGN0B79Sg4ylyS80M3ePbafUgz2alTrnQG9nfrq3E++sDWfj2RT2x2awPzaDGRsi6N3Ci8GhDegT6HXZmIVmCzM2RLDsWAJeTg78PrErHk4O9qzC9o3dcNL+c5+J3Ztyb1d/DsRlsPFsClsiUknNNbL8eCLLjyfiqrMFDvu/+AF+iorcxb8T8+gk9k2bBzjT4qQtUBj4x1KcOnYu0+O/EZW5XuF1kiiNqBE5RYVPtWoFbRl/CIvK19hdzzO3BPLhhgi+3BZFnyAvmrg71vS0qtSBuAxeX3kKixXubNuQZ29pUe0dt+oLJ62ah3s1Z3QHP95aE86OqDSmrw3np3s7oVHL+1oIIYQQojzSf19Q4r/zjhwm/8ghe+CkuNtsZ393+gR60chNj5+bLUDo5eRQIuBntVrRaVQsP57IqytOUWCyoAADgktmy11rZ5Gfm543bw/hvi7+zN4exfbINP48epFVJxN58uYW3FNKskFxc5PaXq/w39QqhY+Gt2FDeMp1lSdSFAUPx2tvD23gqmPuhI5sPJvCV9ujiUnP5/Mtkfy6P56J3Zsysr3fNQOW3++OJSXXSGM3HcPbXTnTzEWnYcZdbTifmc+608n2DNXNEalsjkjFyUFNv1beDA5tQPdmHuQYzLyy4iQHi7aup+UVMmNDBO/f2bpEvcJ/06gUejT3pGeAJx+O7cj6I+f5+0wym86mkJZXyF8nEvnrRCIubSbRx6cbPRfP4khCLrg7E5IeK4HCMlAXZRZerRNyZZBgoagRxc1NpF5hzRvdwY/1Z5I5GJ/J9HVn+d+YdvU2eHY6MZsXlp7AaLbSN8ib1wYF19vHWp08nbT85/Zgxs7dT3hyLvMOnGdi96Y1PS0hhBBCiDrj0hqF3pMmk3/yBHl7d3NuzHCCFi1D176TvQHJE70D6Oh/9a6xiqLw6oBWJGQZ2BubAUCXpu5XzAa8lpa+znw2MoyD8Rl8uTWKYxez+XTTORq56ujX0htTUiIODW3BqoSizMKGrjosRiOWrCw0PnWjNrizVsNdYVW7vfNSiqJwW7AvfVv6sOpEIt/tjuFiloFPNp3jl31xTOrRjOFhjUpNsIlOzWPeflt9xedvbYlDGb6sb+LuyIM9mvFgj2ZEJOey9nQSa08ncTHLwKqTSaw6mYSnowMOaoWkHCPOWjWTezZj9rYo1ocn0/eUN/uKXk89ml+9MaRGraJrMw+6NPXgpf4tOXIhkw1nUth4NoWUXCNrPEJZM3k2ACqLma6D+kigsAz+ySys2jJakvohakSOQZqb1BYqRWHaoGB0GhX7YzNYciyhpqdUJWLS8nh28XFyjWY6+7vz7rDWaFQSKKwsXk5anusXBMC3u2KITc+v4RkJIYQQQtQN/25m0mTGpwT+trhE05MDW/eTVWDCTa8hrLFbma6rUauYcVcbgnxsNQaHtClb59Sr6ezvwff3dGR8UUbhW2vOsO/N6YT370PBmdMAXCxqbtLIxYHYRx8kYvjtFCYmXvfY9ZlGpXBXu0Ysfqgbrw5oSUNXHUk5Rj7cEMGoH/ax+MgFCi9pFmK1WvlwYwQmi5U+gV7cEuRV7jFb+jrz1M0tWPZwd74b34G7OzbGw9GB9PxCknKMNHbX8/09Hbm/W1Mm9WgGwNtrz5BrNOOu1xDSwKXMY6lVCp39PXjptpasfKwHM4cG0T3pJGqzLYmoxYUzZH/5ib3pibiy6tqGLMFCUSOyizILnbV1p4ZFfdbU05En+wQA8MWWSPu3gfVFUraBZxYfIz2/kNAGLnwyom2Za5CIsrujTQN6NPfAYLLw3t/hWKVDshBCCCHEVVmtVnL37ARKNjMprmFYHDDcdtoWIOjZ3LNcX3i76DR8N74jn48K48621x8sBFs23P/d0oKOTdzINZp5nxByMzI5N2oYBWdO2z9L6Ff9SeaqFRhjYygIP10pY9d3DmoVozo05s+HuvHybS3xddGSmG3gg/URjPp+H0uOXsRktrA+PIV9sRlo1Qov3Bp0XbulFEWhQxN3Xr6tJasf68HMUWE82SeAHyd0JMjHGYDJPZvRyteZQrPt7/vuzT1RVzDxwpqbg+8Lk3h07st88uuzvBLiwHOm0/amJxIwvLp/tiFLZqGohyqyDVkbsxFt5FpU2edBghCVblynJrTzs/3Cf+/vs/Uq0DNjQwQXsww083Rk5ugwyWitIoqi8OrAVug1Kg7EZTLvwPmanpIQQgghRK2mKArN/vcdzb7+vkQzE/in6UnA3HkcdLJl8vUOLH8GmYtOQ+8WXpVafkejVvH+sNZ4OTkQ79aI+WOnUZicxLlRw7iQaNsu7bR7E4pOR8BP83G9uW+ljX0j0GpU3N2xMUsmd+fFW4PwdtaSkG3gvb/PMvqHfXy8MQKASd2b4e9ReTXnNWoVN7Xw4sEezfB00tpvd1CrePP2EHugukdzjwpd32IwEDluFHn79qB296DTzz8zZlgvun/+CR6j7rYHDLPWra6Mh1P/WC2ocm07ASWzUNRL5d2GrEk4iPtfD+C+ejLeP/fA+/t2uC8dh/OOd9Cd+RN1WjhYzNe+kLgitUrhP4OD0aoVdkWns/Jk+bcKRKXm8eGGCA4XFcKtLY5fzALgP4OD8brkl56ofE3cHXnq5hYAzNwSyfJ6uq1dCCGEEKKyKBoNnqPuLjWYp3ZxxXDLIMKTc1GAXqU0lagpPi463r+zNWoFdvh3Ys2I/8OUnMT588kAeBdkEPDTfNz6D6zhmdZdOo2KcZ2bsHRyN57rF4iXkwMXsgyk5RXSxF3P/d38q20uwQ1ceGtICEPbNGBwaIMKXUPRanG5+ZbLuh4rajXNZs/BY9TdaBs3QR/apjKnXm8oeSkolkKsigqLU8XWoKwkvUbUiNyibsguZdyG7JB4CACrxgksRlSGDLTnd6A9v8N+jlWjx+TdGpNPGMZmfTG2GAzSvKJcArydePSmAL7cFsWnmyLp0dwT3zIUQS4oNPP97lh+3R+PyWJlzakkFkzsQkPXmu+AlmMwkZZXCGBPoxdVa1ynxlzMKmD+gfNMXxeO3kHFoAr+QSGEEEIIcaPbGZUGQFs/1xLZXrVBZ38PnusXxMebzrE4ZCCq7rFkutiyH9s8PEkChZVE76BmQhd/RrX3Y9GRi2yPTOXJPi3QO1RvWa9BoQ2u6+96RVFo9Mo0fCZOxsGvZGZcccDQlJKCQ8PK2TJf36hzi+oVOvmC+todt6+HZBaKGlHebcjq9LMA5Ld/iJRHz5A+djXZt35EfruJFDbqilXjhGIqwCHxEI4nfsF99cO4rZyIqmg/vyi7e7v607qhC9kGEx+sj7jmduRt51IZ++N+ftwbh8lixVmrJttg4q01Z7DUgq3MxY02vJwcZPtxNVEUhSl9AxnV3g8r8J/VZ1h46AIRybkYTZZr3l8IIYQQQvxjR1Gw8KYW5d+CXB3GdW7Cw91t26T/uHUyAFpjAQVffmJveiIqh95BzX1d/fl6bAfal7HRTW2jKMplgUL7MbVaAoVXURzfqOotyCCZhaKGFG9Ddi1rsDDNFiw0eQWDWofJtx0m33b/nGAxo86MRpNyHE3CARyP/4ouZiMOC24jt/d/KGg9XrIMy0ijUvjP4BDu//UgW8+lMu/Aee7renl6e0JWAZ9sOsfmiFQAGrrqeKl/EAFeTtz3y0H2xWbw28HzTOhSfanxpSkOFjb3rLxaHuLaFEXhlQEtySs0s+ZUEh8V1VVRK+Dv4UigjzOB3k62//k409zTEQe1fH8lhBBCCHEpo8nC3pgMAHrX0mChxWhk0Pf/IdrQiPVdhwPgY8jCXFTDMOjPv9CHhNbwLIWo+9TZxcHCqm1uAhIsFDWkOLPQWVeGtGmrFU3aGQDMXsGln6NSY/YMwuwZhKHVcAra3ofrxhdwSDyE66aXUOUlk9f12cqafr3X0teZ5/oF8tHGc8zaGkkrX2d6NLfVRzGZLcw/cJ5vd8VQYLKgVinc26UJD/dqjmNRGvyUfoF8sD6CL7dF0b2ZJy19a277b1xRsLCZp1ONzeFGpVIU/nt7CP7uevbGZhCZmkuOwUxMej4x6flsOvvPuWrF1pW7Z4AXz9zcAq10qxZCCCGE4PD5TPIKzXg5ORDa0KWmp3MZi9FI7KMPkrVqBeN0etSDhrE2TU1ou5bow9pTcPyoBAyFqCSqom3IZsksFPVVjrHs25CV/BRUhgysKJg8g8p0fbNXMBmjluJ08Euc93yE075PMTbrh6lB++ua943k7o6NOZWYw18nEnn9r1P8fF9nErMNfLD+LJGpeQB0auLGywNa0fJftQBHtfdje2Qa2yPTmLbqFF+P7YCHY9XWVLiSmHTbXJtKZmGN0KgUHusdwGO9wWq1kpxjJDI1l8jUPNv/UvKITM0l12gmOi2f6LTzqBWFKf0CS1zHZLZgBck+FEIIIcQN5dItyKrauFPKYsFiKEDR6Wjx0zze6tePQVFptG3kivutyzg3ZjiF5+OwGo01PVMh6jxVTlHNQmfJLBT1VHm6IWvSwgGwuDUDTTkCPio1eV2eRZ1yCv25v3Bd/yzpY1eX7xo3MEVRmDqgFedScjmVmMOD8w/ZG4V4ODrw7C0tGNa2Yald2xRFYdqgYCb8fIBzKXk8NP8Qn40Mo7lX9Wf3xWUUANBMgoU1TlEUGrjqaOCqo2fAP9torFYrSTlGdkSm8v76COYdiKdXgCc9irr9nU7M5rklJ3DSqvlufIdaV9hbCCGEEKKqnLiYDUD35h41O5ErUOn1BMydR/6JYzh36QbALUHetoPO3gQtWkZhQgKObdrW4CyFqB/UxcHCasgslBQNUSPsDU7K0A1ZXRQsNHmFlH8gRSGn3/uYnRqiSY/Aedf75b/GDUynUfHhXW3wdHSwBwpHtGvEHw925c6wRqUGCot5O2v5amx7GrvpiMsoYPKCwxyMz6immdtYrVZiJbOw1lMUhYauOkZ1aMzoDrZvyd5cc4b0PCOH4jN5fOFRUnKNxKbn89aa8Gs23RFCCCGEqC+yij43+TjX3i9LVXq9PVD4bxovbwkUClFJihucmF0lWCjqqfJ0Qy7OLDR7tarQWFa9J9n9PwbA6egPOMRtq9B1blSN3PTMHB3G8HaN+P6ejrw+KLjMW4oDvZ35YUInwvxcySww8dQfx1h1MrGKZ/yP9PxCcgxmFMDfXV9t44qKm9I3kBZeTqTkGnluyQmeWXyMXKOZMD9XtGqFHVFpzD9wvqanKYQQQghRLXLL8blJCFGPWS2ochOA6tmGLMFCUSPKsw1ZnV6cWXiF5iZlUNj8VvLDHgDAfeUknHd9gGLMrvD1bjStG7oybVAw7Ru7lfu+3s5avrq7PbcF+2CyWPnv6jN8syO6WrLDipubNHLToXcoQzMdUeP0DmqmDw3FQa1wIiEbg8lCn0Avvrq7Pc/faqtZ+uW2KE4kyPtXCCGEEPWf/XOTVoKFQtRXhsgC8o7mXvF/hsgCVFlxKBYTVkWFxblBlc9JfuKIame2WMkrLA4WXiOAY7VekllY8WAhQM5N01BnRqON24rTwS/Rn/qN3O4vUtB2AigSN69Kegc17w1rzf+2R/PT3ji+2x1LXEY+bwwOQVeFXW9jioKFTT1kC3JdEtzAhef7BfHRxghub92ANwYFo1GrGNXej70xGWw8m8Jrf51ico9mNHTT0chVRxMPRzSqWlj0WwghhBCigi793OR8rc9NQog6yRBZwOmeR65xloU+s2YCYGrQAVRVH8qTYKGodrlFnZDh2t+QKfmpqArSbZ2QPVpe38AOTmTeOQ9t9N8475yOJiMS1y1T0Z1dQvZtn9kaqIgqo1IUnr65BU099Ly/PoK1p5NJzTXyxeh2VdbhtjizUJqb1D1jOjZmSJsGOF/yM6K4cc6pxGwuZBbwzrpw+7EGLlru7tiYEe39aqzzthBCCCFEZSrP5yYhRN1kzjFf85zmNy1Hn7Idq0ZP9q0fV8OsZBuyqAHFqfRatYL2GlllmrQzQFEnZIdKCPgoCsYWg0gfv4GcPm9i1TihvbAHrwUD0J/4FaRxQpUb3s6PL0aF4axVsz8uk5lbIqtsrNjizEIJFtZJzqX8Ueyq1zB7THtGd/CjZ4AnLbyd0GtUJOUYmb09mmFz9jB9XTgRybk1MGMhhBBCiMqTayz75yYhRP3k1iScoP7zAcjp8yZm7wo0fq0A+XpCVLvyNDdRp58Frq9eYekXdiC/w8MYAgbiuuF5tBf34Lp5Ko5HvsMQNBRDyzsxe4XAVbr9iorr3tyTt4aE8uKyE/x+6AJhfm7c3rry6y4UBwubezpV+rVFzWnq6cjUAf80PDKaLPx9JpnfDp7ndFIOy44lsOxYAl2bujOuUxNuDvJGLVuUhRBCCFHHlOdzkxCi/lHrcgkb/RkqtZlcn9spaHNvtY0tX0+IapdjrEgn5EoOFhaxuDcnc+Qf5PT+L1aNHk16BM77Z+L12wA8F9yK056PUKeekozDKtC3pTcP9WgKwPR14ZxNzinX/QsKzUyad4gHfj1IVkHhZcctVitxGbIN+Uag1agY2rYhP9/XiW/HdWBAsA9qBfbHZfLS8pOM+mEf8/bHk11guvbFhBBCCCFqifI0hRRC1DdWWg/7BifPJPLTG5Da6p1qTWaSYKGoduXqhJxW3Am51TXOvA6KivyOj5A66SBZAz7HEDAQq0p7SeBwIJ7z+9kChyknJXBYiR69KYCezT0xmCy8vPwkJ8vR4XbunlhOJGRzKjGHaStPY7aUXJekbAMGkwW1SsHPXV/ZUxe1kKIodPR35/0727D04e480K0p7noNFzIL+HxLJEPn7OadtWf4+0wyGXmXB5iFEEIIIWqT4sxCZ600NxGiLFQZUbivuA+vn3rYPrvXYY07baBR2A4sFhXHFj+HVeNWrePLVxSi2tnT6S/9pVeYj/b8Thzit2P2aEFB2APApZmFVb8v36pzwxAyBkPIGBRDFtrov9FFrEQbuxlNxjk0+2fivH8mJo9ADEHDMLQchtm7tWxVvg5qlcI7Q0N54NeDxGcUMHHeIUIbuDCygx+3hzbA6Qp/GEWn5vHzvnj7NXZFp/O/zWeZELcdrwceRFEUe1ZhE3c9pKWSvmk9nnePr7bHVhZKQQZWjR40EsysbI3c9DxzSwse6dWM1aeS+P3Qec6l5LH8eCLLjycC0K6JOy/2C6RNI9canq0QQgghxOXKsyNLiBuauRDHI3Nw3vspitkAgPvqR0gfuwqrzr2GJ1d+zj5xhAz5HoBzGyeQdT6YhtU8B/mpI6rdpZmFqswYXLe+hsP53fY3NYBSmE9B6BhUBWmV0wm5nGyBw9EYQkajGLPRRv2N7lxx4DASzYEvcD7whS1w2PIu8jo/CQ5SF68iPBwd+N/d7flmZwwbwpM5nZTD+3+f5YstkdzeugGj2vsR3MDFfr7VamXGhrOYLFb6BHpxe2gDpq06zc+HEtAvW8wd4adpPH2GvV6hv7Oac6PvpODUCSwFBXjfP6mGHmlJ6pSTeC4egVWtJb/DZPLbTcKq96zpadU7egc1I9v7MaJdIw7GZ7L1XCp7YzKISMnl2PlM/u/P48yd0BF/D9mqLoQQQojapfhzk2QWihuRKvs8zrveI7/dg5j8ul7xPE3iYVw3vYwm1ZZJaPTvgzozGnVWDK4bnidryLeg1J1NtSqNgbAxn6J2MJJ6rgMxO4bXyDwkWCiqXa79GzI1jicXoI3dAoDZ1R+TVzC6mI247HwHdWYUUImdkCvIqnXFEDIKQ8goW+Awej26iL/+CRzu/xyHi3vIHPpzjc6zLvP3cOSdO0J5oV8Qf51MZMnRi8Sm57P4yEUWH7lImJ8ro9r7MTDEl00RKeyPy0SnUfFi/yCauDtyJimHX/bH8+Md/0fDea8AEDvwMQDctqym4NQJNA0a4tyrd4Xmp049g/O+T9AkHSNrwExMjbtf3wO2WnHZ/l8UUx6KKQ/nvZ/geOhrCsLuJ6/zUxUKGqqyL+C850M0SUfJvu1TTA07Xt8c6xlFUejS1IMuTT0ASM0z8tLyUxw7n8mUP4/zw4SOuOkdanaSQgghhBCXyL1RGpxYrSh5yWhST6FJPY0m9RTq9LMYAwaQ13WK7OS6QTnt/xz92WVoYzeTfvcqLO7NS55gzMV570c4Hv0BxWrBovMgp8+bGEJGo0k+hsfiEeii1uJ46CvyOz9VMw+inLQu6QQPmotrw1gMOe6cWPIMNVU9sJ7/1BG10aVdvVR5tu2AuV2eJa/HSwA473gLpyPf4XjiV6CK6xWWk1XriiF4JIbgkbbAYeRaXLZOQ3t+F+6rHiJz6A+gkYBhRXk4OXBfV3/u7dKE/XEZ/HkkgU0RKRy/mM3xi9l8tjnSfu7kns1o4m57rp+6uQVnU3LZHZ3OzDFv8uqvL3JG2wFcmuITcRRNg4YELV2FvmX5XkuqzBic936CLnwJCraaiO6rJ5M+ZgUW94AKP07tuZVoz+/CqtaR0+dNHI//gib1JE6HvkZ/8jdyu79AQdv7QH3t4JVizMbpwGwcj3xrz851WzWZjLv/wuLiV+E51nc+zlq+m9iVu2ZtJyY9n5eXn2TW6HY4qOvOt45CCCGEqN9yjPWwwYkpH03aWdSpp0oEB1X5qZed6pB0BKUgg9w+b0rA8EZjKkAXsRIAlSHT9hls1DLQOgOgjdmIy5bXUGfbSlMVBI8kp8+bWB29bXdv0J6cW97BdfMrOO+egalBRwr9K5Y4UtXULmpcGkbRrOdfNGq3HZXaFi85seRZjLmeJc6rTvXop46oK+zbkLUaVKnJAJjdm9t/AeT2/g+qvGT0Z5fZjlVRJ+TrZdW6Yggdg9m9OR7L70Ubvw331Y+QOeQ7qUF3nRRFoVszT7o18yQ118iK4wksOZbAhcwCAAK8HLmvq7/9fLVK4f1hrXn09yOcBT4f+zZGixaAxtb8CgUKlfw0PBcNQ1WQDoAh6A5UWfE4JB/FfeUkMkYvq1j9C1M+LjunA5DX6QkKwu6noO19aGM24rzrPTRpZ3Dd9gaOx38iu/8nmBp1Kf06FhP6kwtw3vsJqvwUAIyNe6LKT0OTHo7b6ofJGLFIsl2voqGbns9HteXhBUc4EGfLMBzXuQm9AjwlaCiEEEKIGldqrfe6ypSP856PbVlglssbzVkVFWb3Fpi8W2P2DgWLCef9n+N09HsUs5Gcvu/Wqa2k4vpoo9ejMmZhdm4IViua1NO4bXye7Fum47L9zX9iBa7+ZPd9n8Lmt152jYI2E3BIOID+9ELc1j5uy050a1rdD+XKLGa00etxP/It/o/vtt9c4NaZzKaP4z2nL95Ft6ld1OgCqzfGIMFCUe2yi7t66dQoebZgodXR558TFBXZt32KqiAdbdxWjE1uqolplpnJrxuZd/6M+4r70cZuxm3NY2QNmQNqXU1PrV7wdtYyqUczHujelD0x6eyKSmdke7/LgjkuOg0zR4Xx0K8HSaCJ/fbmjdzRBZW/5qUuag2qgnTMbs3JGvwVpgbtUeUm4vHHUDTpEbitfYLMYT+Dqnw/Rp0Oz0GdHY/ZxY+84nR4RcEYcBvGZn3Rn/zNtp04PQKPZePJHPYThZe+B6xWW2Bx53Q06WcBMHkEktvrdYwtBqHKisVz0TAcko7guvEFcvv8B230BrTR61EshWT3/wSLc3WXx629Wvm68P6drXnuz+Psjc1gb2wGbnoNA0N8efrmFmX+Jj8hq4AtEan4umjpH+xbxbMWQgghxI0gp55sQ9Zc3I/rxufRZNh2CVn0Xpi8W2PyaW0PDpo8gy/7ktvs1hTXjS/ieOIX1NmxWLTuqPISUYw5GAOHkNfpMflivJ7Sn/kTwNaANGAgHkvvtvUQiF6PYjZgVVTkt3+Y3B4vXrl3gKKQ3fdd1KmnbQkfqx4iffSySu01oM6IxGn/TPLb3Fv2UlXGXPSnf8fpyPeos2IAsKo0GIKGkt/hYUwNO6ECarojQt3+qSPqpJLbkG0ZURanf324VuvIHPYLqux4W83CWq6wcU8yh/6I+8qJ6GI24Lb2SbIGf12mbaSibFSKQq8AL3oFeF3xHM+CbJ5f/h5v93qUHCd3HAoNOKxdzoVpr9B4+gyUcmxf0J5bDUBB63GYGrQHwOLckKyhP+Lx50i0cVvx/rEbFr07VgdnrA5ORf/vjFXjaP83Wifw8EZnVGNVNDgd+BKA3JumXf7HjUpDQdh9GFrdidu6J9HGbsH9rwfIHPoThf69USefwGXnO2jjt9vmo/ckt9vzJbYsW9ybk3X7HNyX34M+Yjn6iOUlhnBfOpbMEQslYHiJXgFe/Hp/F1acSGDd6WRSco0sPnKR8KRcZo0Jw1lb+q/K5BwD68NTWH8mmaMXsuy3P3+rkXs6Nyn1PkIIIYQQZZVr34ZcdzMLHQ99g/PO6ShYMTs1JKffBxgDBpRpW7Gh9ThQaXDd8Jy9zn0xh5QT6E8vJLfPf8F7dFVNX9QAJT8NbexGAApCRmP2Cia1xzR8dv0XxWzA4NWavNs+xtSgw7UvpnEk647v8Fw4FE3qKdw2PGf7nF4Z29rNhbiufQKHlBNoo9aRMXo55quUUFNlxeN4bC76kwtQGW2fHSw6dwra3kt+u0lYXBpf/5wqkQQLRbX7Zxuyyr598rJgIYBKfXkR01qs0L83mXfMxX3lJHRRa3H7+ymyBs6WgGE1MaWkcG70nXidOsGUfCNfjJxGe50JFVZSvv0aoMwBQ8WQhTZ+BwCGwCElx/ENI2vgLNzWPYUqPxlVfnKZ5ud6yb8L/bpjaHnXFc+16tzJHPI9bmseRRezEfe/HsAYMADtuVUoWLGqtOR3eIi8Ls+UuhW6sEkvcm55F9fNtmYvhQ07YWzeH/3JBWgyzknAsBQtfZ15rl8Qz94SyO6YdN5YeZpjF7N4fskJZo4KQ+9g+yM9JdfIxvAU1p9J4vD5rKJKlqAAgT5OnEvJ49NN51ArCmM71a5f+EIIIYSoW4qTLK70xWWtZzbivHsGClYKQsbYasrpPcp1CUPIaCyuTdDGbMbi6I3FuQFKYR5Oez9BnRWL26rJcGY+6p7/weQRVDWPQ1QrXcQKFIuJQp8wchyb8u60V/j55x8ZF2JCq4bfzhzjvgcW8tproeh0197NZ3FpTOaQOXgsHYvu3Eqc9s8kr9uU656n08HZOKScAEBlzMZ95STS7/6rZLNKYy66yNXozyzCIX6HvQ6+ySOQ/PaTKQi9u1IzHStTHf2pI+qynKJuyF5ko1htgUPLpduQ67DCpjeTOeQ73FdNRnduFa7K/5E9cBao6u63gXWB1WIh8t4x9q7Hg7/9kiEBQTioFdI8ZhH//DOkfPs1Do0a0+CZKde8njZmI4rFiMmzZanfDhkDbyd14l7U2fEohbkohXlX+H/bv/UqA8bcLDDmgaKQ0/f9a3+bpdGTNeRb3NY8hi56PbpztgK/Ba2Gk9tz6jXrbRS0vZfCRl2w6L2wOjew3RY8Co+lY4sChneTOfx3aYLyL2qVQu8WXswa046n/jjKwfhMXlh6gv7BPvx9JpmDcZn2ACFA+8ZuDAzxpX8rH3xdtMzeHs1Pe+P4aGMEahWM7iABQyGEEEJUTF3PLFSnR6BYjFi0bmTf9lmFs7kKG/eksHHPErcVtLwL5wOzcDw8B+XcBjyitpLf4WHyuv4fVq1LZUxf1BB9uG0Lcl7LEdx7793s3Lkdi8XCT0eKzyjkm29mc/r0SebPX4Rafe33h8mvGzl938V108s47/0Yi5MvBW3vrfAc1amncNo/E8DWsPLId6izYnBb8ziZw37C4eJe9KcXoYtcjWLKt9/P6N+H/A6PYGx+a62vwSnBQlHtcou+IfOwZgBg0XnUq+y7wua3kjVkDm6rH0EfsRyLkw+5N79d09Oq1xSVikavTOP8y8/T4rfFJZqZeN83EYCUb7/Gc3zZfiHoIm1bkI0tbr/iOVZHb0yO3lc8bp+bAnofV7JSsrFar3l6SWodWbd/g8vWN1DlJpDXdQqmRp3LfHezd2iJ/7a4Nydj5B94LLkbTUYkHn8MI2vInCs3UbmBtW3kysxRYTyz+Ji9lmGxMD9Xe4CwkVvJQsNP9QnAbLHy6/54PlgfgVat4s6wRtU8eyGEEELUB3W9ZqEm5SQAJp/Wld/NWOtMbq+pFLQZi9ee6Shn1+F06Ct0Z/7E0Go4xub9KWzcTerI1zGqjCgcEg5gVVQsO6dh+/atpZ5nsVjYtGkDq1at4M47R5Tp2gVtJthqDB76GpfNU7GqtRhC7y7/JC0mXDe8gGIpxBAwiPz2kzH698Zj8Qi053fg8317FFOe/XSTewCGkDEUhIyqEyXWitXNnzqizopJyyMpx4gCNFAV7dN3alCzk6oCxoABZA2chfvax3E6+gMWt+bkd5hc09Oq19z6D8Bl535UWu1lx7zvm4jn2HtKPXYZUz7amE0AGIKGXOPkssnKyuLrr//HwoULSElJoXnz5tx//4OMGDEaB4drBMrVOnJu/bBS5gFgcWtGxshFuK+chCbtDB5LxpBzy/Tr+matvurQxJ3PRoYxbeVpfF20DAzx5bZgXxq7X7kTmaIoPHtLC8wWKwsOnmf6unBcdBpubVU/sqeFEEIIUX3+Kd9UNz+2a4q2aJp82lbZGBaPQLj3DzL3LcFl239RZ8XgdGQOTkfmYHFwptC/D8bmt2Js1h+Lq+z4qO304UsA2469r7/7E5VKhcViKfVctVrNL7/8VOZgIUBur9dRTAU4HvsR140vgFqLodXwsk+wqKu3Q/JRLDp3cvrZdoyZvVuTPXAWbqsmo5jysOjcMbQaTkHIaEwNO1d+sLwa1M2fOqLOWnTkIgC9A73wtIQDYHGqnx+ijS2HkZP1Gi673sN5+5uYXf0xBg62HbRaUfJTUWfHoc6KR5Udhzo7HlVWHOqcC5jdmmJoeSfGFoMljb4crhYMLFOgENDGbUMx5WF2aYzJt/11z+nChfOMHDmUqKgorEWphYmJCezevYt5835m/vxFODlduU5FZGQEW7duwWw2061bd9q373jdc7K4NSVj9DJcNz5v2y6/+RUc4ndQEHYfhX49ZNv8Jbo09WD14z2vfeIlFEXhuX6B5BpNLD+eyOsrT/H5yDC6N7fVLzGYLMRl5JOYbSAx20B6npE2jVzp1tQDjbp2b0cQQgghRPWwWq3kGoszC+vm32bFmYVm7zZVPlZhiwGk+fdBG70ebcwmtLGbUOcloYtaiy5qLQAm71CMzftjbHYrhY261qvdbfWC1Yr+zGLAVj4pNvaNKwYKAcxmM9HRUeUbQ1HIufltMBtxPDkf17+fxarW//M5/QpUuYnoj/2E44lfUBWkA7btx5fWgDe2GETmiIUoxmyMzfrW+axWCRaKapNnNLPieAIAYzs1RpWxE7hCc5N6Ir/TE6izYnE88Stufz9FYeOeqLLjbbXuTAVXvJ8m7Qy66PVY1ToKm/TE4uAKagesai1m79YY/XtjuXSLqdUChfmgda6GR1W/FW9BNgTeXinfAD366IPExsbaA4WA/Zfe7t07mT79v7z33keX3S8jI52nn36MdevW2JuyWK1WOnfuwjffzKV584DrmpdV60LW4G9wOvAlTns+tHdONjs3xNDyLgythts6jNXBb8FqA0VReG1gMDkGMxvPpvDishMMad2QU4nZnE3OxWS5fE+6u15D/2AfBob40tnfA7VKnnshhBDiRpVXaKb4z4U6uQ3Zav0ns9C36jILS9DoMbYchrHlMLBa0KScQBuzEW3MRjSJh9CknkaTehqng//DonUjr+v/kd/x0Sv/vWu14njkW1uWWOjYiv1dbMrHfeVDYCkk6/ZvsJahjNGNSpN4EHVWDFaNE4bAIXh7f86FC+dLfI66lKIo+PpWIPFIUZHT7wMUSyH603/gtu5JMob/hsmv2+VzSj6G4+Fvi5quFAJgdvUnr/OTGELGXHZ+YZNe5Z9PLVUHf+qI2uRKb9ziY5d2nl1zKpFco5lmno70aO6J6oKti2x9DhaiKOTcMh11djza2M1oYzfbD1lRsLg0wuLqj9nVH7NrUyxu/licG6FJPITu7DI0GZFoY7eUemmLow94B+KZeQFVbiKKpRCTd2sMgbdjCByC2bsKaoPUd+ZCtFF/A2AMvP4tyMePH2PPnt1XPG6xWPj115959dU3cHV1s99eWFjI3XeP4Pjxo0DJ99mRI4e5667b2bRpB15e1/nHhqKQ1/UZjP43oT+5AF3katS5iTgd+RanI9/a6mu0Go6h1XDMXsHXN9YNSK1SeOeOUHKXHmdPTAZ/Hr1oP+aq09DITUdDVx3OWjX7YjNIyytkydEElhxNwMvJgduCfRkY4kuHJm6o5L0shBBC3FCKtyCrFdBpKnfngebifszuzbFW4ecwVc5FVIYMrIoak+flDQOrnKLC5NsOk2878rr+H0pBOtrYLbbgYexmVAVpuOx8B1V+Crm9Xiv1c5MufAkuO2y153NzLlaog67zvplo47cB4P7XA2QO/73+7ByzWtBFrMTYuIe9oeL10J+xNTYxBN4ODk6MGzeBY8eOXnl4q5WxYydUbDBFRfatH6EUZKCL/hv3lZPIGLXE9pnHYkYbtRbHI9+jvbjHfpdCv+7kdZiMscVgUNX/UFr9f4SiyuSfOM7+qc/T5OsfcGjsX+JY9qYNJM36jICf5qN2dcNqtbLw8AUAxnRsjEpRUOUVBQvrSSfkK1JpyLx9Do4n52N1cMbs2hSzmz8Wl8agLn1rrLF5f/K6PY8m5TiapCNgLkSxFKIU5uKQsB+HC3tQ5adAfAqXbkrQpJ5Ck3oK532fYXZrjiFoCIbAIZgadqr13ZZqA4cLu1EZMrA4elPo1/26r7dnz04URblqUL2gIJ/jx4/Rq1dv+20rVy7nyJFDpZ5vNptJTEzgxx+/5/nnX77uOQKYGnUhp1EXcvq+izZmM7qzy9BFr0OTGY1m/0yc98/E5N2aglbDMbQagcXN/9oXFQBoNSo+vKstX+2IRqXYGqeE+bnh56Yr8WWKyWLlQFwGf59JZvPZFNLyCvnj8AX+OHyBBi5abgv2ZVCoL20buZa4nxBCCCHqp3+2IGsq9Xe/Nno97isnYXZuRMaoJVjcmlbatS+lSS3aguzZEjRXrvdcXax6TwzBIzAEjwCLGccj3+KyczpOh75CMWaTc8u7JUvxGHNx3vWu/T+d936MVeNIfqfHyjymOuUkjoe/to2vccIh6Qhuqx4ic9jPVfacaBIPo4tYQUHoWMzeIVUyRjH9iV9x3fIaZpcmZIxcfH2fEcxGdBHLASgIGQ3APffcx7fffk1cXCxms7nE6Wq1moCAFowZM67iY6o0ZA36Hx7Lx+OQcAD3FfeR324ijsd/RZ0dB4BVpcHQ8k7yOzxs23V1A5FgoagQq9VK3AvPkndgP/kjhhK0ZCXaJrYfDtmbNhA18R6sBQUkzfocv9f+w8H4TM6l5KHXqBjWxravX5V/A2QWFnNwIr/Dw+W7j6LYvw27jNmAQ+IhPBzyyDC7Y3ZuhFWtQxu7GV3karSxm23FfQ99jdOhrzE7N8QYeDuGwDsobNzjhvgmxBBZgDnHfMXjahc1usB/fklrz63CdZMt+GZoMaiS6vaV7Q+7f/8B+Mcfv6NSqbFYSp+/xWJhwYJ5lRYstFPrMAYOxhg4mGxjLrrodejOLkMbuxlN6ilcUk/hvOcj8ro+S16XZ6XOSxk5adW8cGvQVc/RqBR6NPekR3NPpt7Wkj2x/wQOk3KMLDh4ngUHz9OxiRufjgjDVV//38NCCCHEjaw4s9C5MrcgW8w473ofAHVuAu7L7yFj1JKSGYamfFDrr3uH0j/NTaq+XmG5qdTkd3ocq84Nl02v4HjiVxRjNtm3fWqvM+d0cDbq3ETMbs0oCB6F8/7Pcdn5DlaNnoKwB679/FjMuG56CcViwhB4O3ldnsV96d1oz+/Ebd1TZN3+TaV/JlNlRuO+4l5UhkwcD8/B0Go4ed2fx+wRWKnjAEVbtL8HQJ1zHo9l48gYuQiLi1+FLmfL9kzH7NSAQn9bEoWLiyvLlq3mscceZPfuXSVKM/XseRNff/0Dzs7XWYbLwZHMoT/isXgEmoxzuBS9Pyw6D/LD7qeg3UQszo3sp589G8769esoLDTSrl0H+va9FZWqfiblyKcNUSGKohDw3U9Ejb6T/MhIzo20BQwN4WfsgUK32++g4YtTAfijKKvwjjYN7R9ylbwUgCpNf6+31DpMTXqCjyumlGyKE9cMoWMwhI4BYy7a2E22wGH0BtS5iTge+wnHYz9h0XtiCBiEMWgIRv8+teKbvspmiCzgdM8j1zwvdHcH9P6FuGz7L/rTCwEo9Akjr9vzlTKP3r1vvmpWIYCTkxNhYSUbqSQnJ10xUFgsPT31uud3VVpnDMEjMQSPRClIR3duFbrwP9Fe2IPzvs/Qxmwke8BM27fFolJp1Cp6t/CidwsvDANasTs6jb/PJLMlIpXD57N4atFRZo1uh7ujBGuFEEKI+sgQWUBqdA4AThaFvKO5JY7/+0vvstKdXYIm7QwWnTtWrSuazGjcV9xH5vDfcLiwB8djP6KN347ZrXnRjpLhFc5OK25uUpWdkK9XQZsJWB1ccF3/LPqzy1DlJJA15FuUwhycDn8DQE7vNzAGDkGxmHA6+CWuW1/Hedd7mN0DMLu3wOzRosS/rY4+oCg4HpuLQ9IRLFpXcm6ZjsW5EVl3/ID7Xw+gi1qLxx9Dyek3A1PDjpXzYArzcF/9MCpDJhZHH1T5KejPLi3KMhxDXtcplZpF6hC/DU3GOSwOLlgdvVFnxeC+bDwZIxdV6PO9rngLcqsRJYKofn6NWb58LcePH2P37h0oikKvXn1o06byXldWvSeZd87D/a/7QFGT3/5BCoJHg4Oj/ZysrEyefPIR1q1bg0qlQlEUzGYzzZsH8P33P1+1CeXu3buYO3cOhw8fwtHRkTvuuJOJEx+iYcNGV7xPbaBYr/VJ9jqdOXOG2bNns3fvXnJycvD19eW2227j2Wefxc3N7doXuETKJUERUfMUBVzy0tnbty/G6OgSx9xuv4Pm3/2MSqslMdvA8G/3YLbCgge60NLXFv33mtsZdV4S6WNXl549J65KUcDHx/Xa7wuzAW3cdrSRq9BFrbN3bwKwODhjDBpKzs1vYdW6Vv2kq0ne0VzODjh+zfPCFhfgGzUVdXYcVhTyOz9Fbvfnr7g9vCJGjx7Grl07MZlMlx1TqVQ89thTvPXWuyVuf+SRifz11/LL0u2LKYpC69Zt2Lx5V6XNs6x0Z5fhsuU1VIZMrGod+W3vszVDadipTtXILPP7pxYJT8rhqUXHyMgvJKSBC1+OaYdHPQ8Y1sV1ulHI2tQNsk51g6xT7VRT61L8pffeUDNzhpsIjlV4ecHlf5uG7u7wT8DQbEBVkI5SkI4qP832/wUZmHzaYGrU2X6O17x+qLPjyOk5FUPQUDz/HIUqPxmrSotiMZY6H5N3KAWtRmBodRcWt2Zlfhyev/ZBkxlNxl0LKGx6c7mfh7KqjHVyiN2C29rHURmzMbs1w+zaFO35HRib9CZz+G+2QaxWnPZ8aNu2bLn87/piFgcXzO4BaDLOoZjyye77PgVh99uPa6M34Lr+Wdvf0igUtJtIbo+XsOrcKzZ5AKsV17+fRn92GRZHX9LHrkKVn4LTno/RxWywnaJyoKDNPeR1edpWDus6ua18CF30OvLaPUh+x8fwWDIKdc4FzC5+GJvegqlhJ1xDepOibopVuXqOmmLIxHtuZxSzgfSxazD5hl33/CqTxWLhzjsHc/Dg/lK3Qzs5ObFhw3YCAlqUOGa1WnnvvbeZOfMT1GoNZrPtdaNSqXBycmLhwqV07Xr9pa/Kq/g9c83zqjJYGBkZyejRo9H8P3vnHR5F1fbhe2a2pfeQQkLovVdBARUUlSLSRRTF/uqr8uqn2HuvWBAUFBELivSOFOm9dwiQkJBeN9k6M98fmyzE9JCygb259tqw057Zs3PmzO88RaPh7rvvJjw8nP3797No0SKaNm3K77//Xim3UfcN1LUo/JEl7j/K0c6XlH2PDp1otnwtos5xU5u25RyztsfRpaEf08cUxPmrCsHTGiOoMukTdxdx7XVTMap0Y1TsaBN3oItdWVDMwlGd2tagC9lDfkbVV07Ad1XKEwsF0UaT/vOIuWEhAgqyT0NyB3zhCNGuZlJTUxgxYjDHjx9HFEUURUGSJGRZ5qabBjB79q/o9foi22zYsI7Ro+8s3X5B4L33PuaBBx6qdnsrgmi8iM+6Z9HFXyq+I/tGY2p7D6bOj9aL/Jj19cHsdFoej887SKbJRqMAD7pF++Oj1+DnoaV/syAa+nuUv5N6RFntpKoq8Vlmtp/LYNu5TE6n5tG3aRBP9G2Mh7Y60gi4KYv6eg1da7jbqX7gbifXpK7apXAcu7GjzJxBdjqdEnniL8fkoKQz0WzAz3j4J+PT2YZGyHYIgzZj6fvr/Bh5Pf8Pw5E5+Gx6FdmzARn3bAatB1LaUfwXjES05qAYAjC3uRtzq1Fo0o6gP7kIXdx6ZwVYcDwzWJoPw9xsSJkFLQSrkeDvWgGQ9sCBGq0AXF3tJGWcwm/ZRKSc8wCogkjmmNXIQa2KrihbkXLikbLPImWdLfIu5iYgcMkIW3gPsob/WWxsLOSn4b3lTQwnHd50qqTHGjMAc7MhWBvdXMSjrSJ47J+B95Y3UUUNWcPmYY+4JEBpkvbgteNjZ5EVAHtAM2xhXbGHdccW3s0RplzK+F3MS8ZwaDb2kLZYm97h+CwnjsA5fRBQybh7I3JAU8Sss/gvHOV8vixE1XhgC+mAvUEnbA06Y2/Q2SFWXuZkYDj6Kz7rn8Me2JLMsWtdzgHh779XM25c8crHhUiSxH33PcD7739S5PPFixfw4IP3lbiNKIr4+vqxb9/RKw+lriQVFQtrNAz57bffxmaz8eeff9K0qSNf09ixY2nTpg3vvPMOP//8M488UvEEoW5cE/Opk0X+b09Pw56agi6yIVa7wsKCCqCjO1+awRDMmQiqQ5VXDO7y8bWGqMHWsA+2hn3Iu+ENtBe24rvqEbTJe/FbPI7sIXNRDf51bWWN4hl0gXZ3TcU34gwA5lajMN7wZo15VoaGhrJnzx6mT5/F77//QmpqCjExjZkw4X4GDbodSSouavTrdyODBw9j2bLFxcKYJUmiXbsOjBt3T43YWxEU73Cyh/yM7vw69Cf/Qn92NVJOHN7b3kXKicPY7916IRjWR5oFe/HtmA48Nu8g5zNNnM80OZdN33KOZ29qytB2YVdtEZQ8q53dcVlsO5fJtnOZJGabiyyftz+RrecyeH1QSzpGXsEMvRs3bty4cVPHmPSOMaDBcumzRtctJqr7Ssd//qUPqoKIaghAMQSiGAJB1KBL2ILnvmloL+5EynaIYPndn3aKUXJwG7JGLUVKP4610U3O9ERyQDMszYc5KsXGrkB/ahHahK1ok/eiTd6L15Y3sEVch6nTw1hjbi5mu5R+3LEfrwY1KhRWJ3JgczJHLsF3xUPoLu7A1OGB4kIhgKRDDmiKHFA8H7VqM7Hn7z/Zu/pX1JxE9m23M1D9kyFD7kSnu+QdqnoGkztwKuZWo/De/DqajBPozyxDf2YZqsYTS8zNWJre4WgTrWeZdutiV+C19W0AjH1eKyIUgqOQYfawX9EmbMNz58foEnegyTyNJvM0HPsdcOTnc4iH3bCFd8UW2glBseOxbxqeB75DsDvGW8brX8fU8UE8Dv+EgIo1qp/ze1D8G5N593q0F7aiSdmPNnkfutSDCJYcdBd3FKkqrHiEYAvrgi3yOqyRvdGf+BMAc4vhLicUAvz1159OZ4+SkGWZP/74vZhY+M03XzqdRf6NoihkZWWyYMGf3HNPyYJiXVNjnoUWi4Vu3brRtWtXfvzxxyLLcnJy6N69O9dffz0zZ86s8D7ds22uhSCAsHcb+4cNQzWb8WjfEXt6GrbEBHQxjWm6YBl/52h5dfkJQr11LHqwBxrJISBI6ccI/G0giiGA9EmH6vhM6ifVNouWegT/xWMRzZnYQtqTPfQXVENA9RlaB5TsWajSsPtKmg/8CUlrxZrvTXaXd+DmETVqS1XbyWaz8eGH7zJz5nSMRsdoUKvVMXr0WN588118fFzIC9SWj+HYb3hveg0BFVO7ex0V5VzwZl9IfffiSMm1sP5UGlkmG7kWO0eTjBy6mAPAgBYhvDiweb0tgmJXVHLNNmTV4T0oa7WsPHCBrWczOZiYg6xcajCNKNAp0pdeMYGE++qZ+s9ZknMtiALc2z2KR/vEIImu+zusz9T3a+hawd1O9QN3O7kmde1ZuPAGO0t7y9y4R2T8Wi2ixsL1Tz+KziuHs5uH4/XwLWhbRzgFQlXvW2yyVndmGT7rnkO0OsYIsm8jMu7eUKUidUJeCobTSxzCYfJe5+d5Pf5Hfreni3qKHZqNzz8vYYm+kZwhc6r0PVTYrupuJ9mGJuM49uB2lRrL2mw2Hn74fpYtW+wUlgqFonbtOvDnn4sIDCxBOFVVhyfnqUXoTy9Byr1waZHGgDW6v0M4jBlQzLlBG78Jv6X3IShWTG3GYez/Ybk2C6YMtEl70CbtRpO0G23yfgTZUmQdVZBQNQZEmyNXpuzbyOlxmdd9Mh4HZyFassi+/QesjQeWfBwBggO9yDy9HylpH9qU/WiS96FJP1ZqGHf6vTtRfK48RLq6GT9+NGvWrCxzHUEQSErKck7Y22w2IiPLFsolSeLOO0cwbdr31WZrRahzz0KtVsvy5ctLVFHT0hyFLUryqHFTf8hZ/zfn7r1UzKTR9z9hT0nmzPA7sJ47y5nhdzDv8RkADO8Q7hQKAcSC4iaKZ+nu625qBzmkLVl3zsN/0Vi0qYfwXziGrGG/oXoE1rVp1YbOK4s2w74iuPk+ANLPdOTIwidotLAvZc/V1R1arZaXXnqNZ555joMH92O322nbth0BAS7YLlpPzB0eQNX54vP3M3gc/gkQMPZ926UFw/pMqI+eMV0inf9XVJU5uy4wbcs51p5M5WhSDp8Mb0ez4NoNa7hScs127vl5bzGPwcuJ8jfQKyaQXjEBdIvyx1N3aSzRu3EgH68/w7Ijyfy4M54LWSZev60Veo3b09WNGzdu3NQv8gsc0TwsjrFUeIeN6LxyMGWFELtuHM2mdEQIK/s+b216B5kh7fFd9Ria1EMY+7xcJaEQQPUKxdRxEqaOkxBz4vDcNx2Pw7Px2vkJUsYpcm/+BDQOj8XC4iayCxc3KRVJW6V8+h9++C7Lly8BcHqgFWohx44d4T//eZhff51ffENBwB7SDntIO/KuexFNyoECL8PlSDnn0ceuRB+7ElXUYY3uWyAcDkTKOoPf8kkIihVL0zsw9nu/QuNu1SMQa+OBl0Q+2Yom7WiBeLgH7cVdSHlJCLY87AHNyev1AtbGt+C56zO8dn2K165PHZv5RDk8H8tCFJEDm2MPaI6l9WjHZ3YTmtQjaC/uRJewFW3iDgS7CUvMgDoTCrOyMvntt7ns27cHSdJw4403M2TInRgMDk/bmJiYIjkHSyIiIrLSkT1qwcS4q1JjYqEoikRFlVxt57vvvgOgZ8/K5QdzP3O6DqqqkvLFpyhmM36DbqfRTEcxEykqimYLl3F6+B2cNIkcTregEQWGdwwv0n6SKRUAxTPY3a5VpPB7q47vTwluTfbwP/BbOAZN+lH8F40me9hvqJ7BldyRHU3KAexBrct1ma9J/v2VFAqFsl3L6TUTiN95GyAiUPP9ypW2k5eXJ9dd17v6DKpBrK1HYkTF++/JeByejZh3EWP/98vMaVNXVOf14wpIgsDEnlF0i/bjpaXHScg28+Cv+/lgaGt6xbigwFwKs3bEFREKJVHASyfRuaEfvRoFcF3jgDLzMvoYNLxxW0t6NfLnjZUnWXsyjUzTIT65sy3eeg3JuRb2J2TTONCTFqHetXFKVy1X2zV0teJup/qBu51ck7pql8LDFYYhe1gAFKKvcwhR8TvuQFWlCo9jVb9oskctRTBnonoEFhsnVwXVL5q8/u9gD22H94YpGE4vRso5T+6gaSi+0WjSjwBgD2nj8mPt6iA/P5+ZM6eXKvzIsszff6/hzJlTNGvWvPQdCQJyWCfywzqR3/tFpLSj6M8sQ3d6GZqsM+jPrUV/bi2qqAFRi2A3YY3qR+4tUxGq6oil0SGHdUIO6wQ8CICYm4iYdxF7aEcQNQiAqedk0HrgtdVRmNHc/r4yj1lqu2g9kCO6IUd0w9z1cUcOyMzTyL6N6qQNV69eyYMP3ofZbEYQBARB4M8/f+ett17jjz8W0qpVa8aPv5fvvvu21H2IosjEiZOK2K/TaenUqQsHD+4v0YEOQFFk+vS5vvb7mAoer8arIf+bv/76iylTphAeHs7SpUvx9nYP1usr9uxszn34IU1ee81ZzKQQc1wcT8/YwEp7EMM6RfDF2M5FN976Jax+GdqNhJEVD0V3U8OknoTZQ8CYBCGt4N7F4NOg/O2y4mDvHNj3M+QmOradsAB862Z2KGdvDju77gRAkGz0f2ECksbGrpnvkn2hpXO9Hnt64NvFhcJ5rxb2zYUlT4FiA49AGPwZtL2zrq26ZsjKt/LwnD3sPJuBJAq8Oawt43s2qmuzyuVsWh63fLYRm6zyw/3dubHllYnMW06n8cicPRgtdhoFOSYvzqfnA6CTRN69qz0juza8YrvduHHjxo2b6qRwHPvVcBv7WyhMWKlhRP4+Ot39HnazJ5s+m45s9XSdcey5zfD7BDBlgM4bbnkbVk4Buwme2A3BZYhjVwmbN2/mhhvKrvgsCAJfffUVjz/+eOUPoKqQehyOLoajiyDFIcYS1dPxzKWrxUiSA7/BuU0w6APQ128t5/Dhw3Tp0gW73V5invjg4GBOnTqFj48PkydP5rPPPiu2D0mSaNOmDVu3bi2mbf3222+MGzeuxGM7Cpz4Eh8f77KaWK0mNPrzzz959dVX8fT0ZOrUqZX+UtLT3Xk8XAlBEGn2zjsF7VI0z0GW4M06NRhQGdYmlLS03CLLPVMv4AmYpADy/rXMTcUQBAgK8qne60IIR7xzHn4LxyClHkf+/lZyBv9YYgJfZBu6c2swHJmLNu6fIpW/SD2O/N1Asof+ghLQpJqMqzgW26Xfo294LJLGhjXPl+wLLYqsl2uzYK3h31+NtJOrEzUUaXQzfNY+7QhD+eM+7BvaIftEoXiHO15eYcjOvxs4E2rXFld7u3w+rA1vrz7J8qMpvLTgMNM3nKF9uA/tI3zpEOFL02Avl8vl98bCI9hkld4xAbQP8iAtLfeK2qmlv55vR3fgqfmHnCKhKEC4r4GEbDPP/nGAQ+cz+M8NMYhud55Kc7VfQ1cL7naqH7jbyTWpq3bJz3Tcs5yehVaIvm4xABf2DkC2OibAsjLzsaa5wP3LuyPiqGX4rH0abeIOWPo04KiCm66EwDUw1k5Pr9g5ZmfnFXsurjBiQ2j3OLR7HDEzFm3KfiwxA9m3cRe7du1Eo9HQr19/mjatYXE28g7HK1eF3NLPxRXapTzef/9DVFUt0SNUlmWSk5OZPn0mEydOYsqU1wkODmPq1M9ITnZUfNbr9YwdO55XXnkds1nFbC76fdx88+088cRTfPXVF0XCmEVRwsPDwNy5f5S4XU1T2DblUWti4dSpU/n666/x9vZm+vTpdOjQodL7cMR014Bxbq6Iktpl0cEkrLJKq1Bv2oX5FFtemLNQ9gx2t+kVUt3XhezXhKw7/8R/0Rik7LP4/TGY3IFfOSudiVln8Tj2K4ZjfyAWhJMDWBvegLnN3dhD2uK7bCKarFj8/xpO9pCfq5T340rQNTHQantHZKOMz4WdcBbs0d1pvvaSHZK3hK6JodZ+f9da/2UPakPmyKV47v4Czz1foUk9jCb130VnLqEYAlG8wrA16ERe75cdSbprgau1XbSSyOuDWhId4MF3W88Tl2kiLtPEsqMpAHhqJdqE+9Ah3Id24b60D/fF37NqOYyqg91xWWw4nY4kwFP9mxRrk6q2U8tQb34c35kVx1JoEeJNx0hfPHUS07eeZ9b2OGbvjOdMWh53tg+jQ4QvAZ668nfqpghX6zV0teFup/qBu51ck9puF9HbEdpp0jv+38grlsDGR1BkifgddxRZz1V+L7JPFFnD5uFx4Du8tn+IoFixB7VCFSS4Bsbabdu2Q6fTYbVaS11HVVW6d+9ZLTbK/k04laHy8O23c/DgAQRBBByi18CBt/LVV9NdJs+5K/dry5YtxW4vPQ+hIAisWLGM++6bBAg89NBj3H//Qxw/fgyr1UKzZs3x9fUDSjtHgVdffYubb76FWbNmcODAfgwGA4MHD+W++yYRHh7hst8N1IJYaLPZePnll1m4cCGhoaF89913tGpVQglyN1cNsqLy54FEAEZ1jigx0aeYX5Cz0COkVm1zUzEUv0ZkjlyC38pH0F7cie+yiZg63I8m/QS6hC3O9WTPUCytRmNqMxbFL8b5edZdC/BbMgFt6kH8/xpOfseHMXV5HFVXey7W+iYOTzWvhIOOc2rRE88O9avYQ71H0pHf8znMrcegSTuKaLyIlHcR0ZiEmHex4P9JCHYzojkD0ZyBJv0o2uT9ZA+Z4/A4BARzJp57vkLVepLf7SkQ62eV39pGEAQm9WrEyI4RHE7K5VBiDocScziSlEueVWZ3XBa747Kc60cHeOCt12Cxy1jsCn4GLff3jKJv06BKJ2yuDLKi8umGMwDc1TGCJkHVe52G+Rq4v2d0kc8e6xND40BP3lp1gs2xGWyOzQCgUYAHHSN96RjhR4dIXxoFeNToubtx48aNGzf/Rl846b3yAJgs9B2/GbLAFHYb0X/1BxyT3oVjXZdBlDB1fhRrdH8893yFudWourao1vDz82fs2PH8/PNPKIpcbLkkaejYsSMdO3YuYevKk5aWxtChg0hPdzjgqOqlnHjr1q1l1Kg7WbHib7TaupsIrg/YbKWLu+AQeE0mU5HPNBoN7dpVzhGmT58b6NOn7DB1V6RGn7hkWWby5MmsXr2aFi1aMGPGDMLDw2vykG5cgM2xGVzMseBn0HBLy5LFQKdY6OkWC10V1TOErGG/4b3pVTyO/IznwVmOzxGwRvfH3PZurI0GlFhRTfUIIvvO3/Fd9Si6uI147ZmKx9FfyOvxP8xtxtWe2KOqaJL2AGAL61apTQ8e3M/u3buQJIl+/W4kJqZxTVh4TaD4RmP1jS55oaoiWLIcwmH2WXw2vuwosjP/TrKH/Iw2cTte295DtGQBoEk5QM4t02o3N0s9x89DS5/GgfRp7JhhlhWVs+n5HLyY4xQQzxd4Hl7OBcw8u+go3aL8eLpfU1o2qBmxf/nRZE6l5uGj1/DwdbWXW3FQ61BiAj2Yf+AiBxJzOJuez/lME+czTSw+nAxAgIeWDhG+dIz0pVOkH+3CfdziYT3BaLGz6ngKkiDQLdq/zMI4bty4ceNq6JsYyMMhAAWqpwCw9xiFZ4zrj3/koFbk3vJVsc/PnTvLH3/8RlLSRUJDGzBq1BiaNGlWBxbWDK+//jaHDh1k3769CMKlKreiKBIaGsqMGT9W27FmzZpBWlpqiYUzZFnm4MH9rFy5jCFD7qy2Y16NtG3bjt27d5co8IIjH2F1Cbz1kRp9Yv/8889ZvXo1HTp0YObMmfj6ukACVjdXxL4L2Rgtdq5vEljqA9Of+x1ehUPbhWHQllwhSTAVzIK4xULXRtJh7P8+9pD2GI7+gjX6Rsytx6L4ll8UQNX5kD34Z3RnV+G19R002Wfx2TgFj4MzybvuJawxA2q8bJmYG4+Un4IqarGHVmwG6Pz5czzyyP3s3bsHQRBQVRVBEBg06HamTp2Gn59/jdp8zSEIqIYAZEMAcnAbMoPb4bdkPJrscwT8eiNCwUypPaA5Um48+vPr8F84kuw7ZrtkleX6gCQKNAvxolmIF3d1cEzgZZtsHEvOxSqr6DUiBo3IlrMZzN19gd3x2Uz4eS93dgjjiRsa42uovllqWVH5cWc8APf3jKr1UOhWDXx46RZHzpZsk42DiTkcSMzhYEI2R5JyyTTZ2HgmnY1n0gHo0ziQN25riZ/H1TlTb1dUDiXmoNOINAnyxKOUe3hdsisuk8WHk+nXNIibWwQXG4vkmG38tjeB3/Ymkmu5FFoU4aunW7S/4xXlT4i3vrZNd+PGjZsKo6oqxoI+TGdx3IMKIy7qG4qi8PrrLzN9+teIougcX3/yyQc88MBDvPPOh0hVreTrQnh7+7Bo0Qp+//0XZs+eRXx8HIGBgYwbdw/33ns/gYFB1Xas33//pdQKu+DIiTdv3m9usbAcHnzwUXbuvL/U5YqicN99pS+/2qmxasjx8fEMGjTI6V3YoEHxzi0oKIjrr7++wvtMS3Pd5JjXAucz8hkzew+yonJrqxBeuqUF0RH+RdrlXEY+o37YjQAseLA7kX4lzOQrMsHfNkZQFdIn7kbxCqvV87haEAQIDvapH9eFbMNw5Ge8dn2GaHaE+1kjepF3/evYQ9rV2GH1J/7Cd+1/sTXoTNbIJeWun56ezo039iY1NQVZLjrDVDiztHTpajSais+z1Kt2chGE/DT8lt6LNvUgitaL/B7PYmo/EU3qIfyW349oSkf2DscW0QvFEIBqCMAe1Bpbwz6ouvKT9YK7XSrKxRwzX286y6rjDm/wQE8tk/s35ZZWIdXiYbfmRCovLj2Gn0HD4od64qkr+rBQl+1ktSscTzFyICGbAwk5bDuXgVVWifDV8/7QNrRuULHfmquRb5V5Y+UJWoZ6c2/3hmgkEYA8q50Xlx5j69lM57oRfgZahnrTtaEfXaP8aRLs6SwGU9ttczotjy//iS1iX89G/jx7UzOiAzw4mJDDyuMprDyWQp7V0X83DvTEz0PDoYu5yEpRIxsHejrFw64N/a5aAdjd19UP3O3kmtRlu5htMjdM3YKIwhmPe+v1c9Nnn33Ee++9VeIyQRB45plneeGFV6q8/2vx+omJCSc/P6/Mdbp1687y5X/XkkXFqQ/toigK//3vY8yb9yuiKDoFWEmSkGWZt99+n4cfrkL1ahensG3Ko8Y8Czdt2uRMFvnJJ5+UuE6XLl0qJRa6qVu+3XLeOdhedTyVkyl5zLivGwGXPdsVehVe3ySwZKEQR/4xQVVQEVAM1TfD4saFkbSYO9yPpeUIPPd+jceB79Elbkf751By+7+PpfXoGjmsNmk3ALaw7hVaf9asGaSkJJfq0r93725WrlzO4MFDq9VON0VRPYPJuvMP9GdXYWvY2zkwtod1IXPEIvyWTECTfRbp5IKi24kabA26YovujzW6n0OIFsS6OIWrhnBfA2/f0Zq7Oobz3ppTnMsw8fLy4yw5ksQLA5pfUWinqqr8sCMOgDFdIosJhXWNTiPSoaB69ITucCLFyPOLj5KQbWbSr/t59samDO8QXu/CkteeTGXdqTTWnUpj45l03rytJQatxDMLDnMqNQ+9RsRLJ5GRbyMx20xitpn1pxzRAH4GDV2i/OkW5RAPAwNrJjQ912wnNj2PM+n5xKblcSYtj70XslFUh2fsDU0C2Xo2gx3nsxg3ew9BXjqScy3O7ZuHePFAz2huahGMKAjkW2X2J2Q78nTGZ3E82cjZjHzOZuTzx/5EBKBzQz9eGNCcxkGeNXJObty4cVMZjAWTHoHkOqMs6uNzU35+Pl9++Xmpy1VV5ZtvvuKJJ57G27t+TsLVJtu3b+O1114sVyiUJA2NGrlTKJWHKIpMnTqN667rw/Tp33D8+FEEQaB37+v5z3+e4qabBtS1iXVKjXkW1gSurEpf7RxLzuXen/chAFMGNue7bedJNVrx0Eo8e1NThrRtQL5N5o7pO8izynw5oh29YkquwCSlHyPwt4EohkDSJx2s3RO5iqgPszWlIeYm4P3PK+jPrQYgv+PD5PV+CcTKiQXahK2gKNiiSp50CPjtFjTpR8keNB1r0ztKXOdyunZtR3x8XKnLJUnilltuY/bsXypsY31uJ1dFsOSgO7cGMT8V0ZyJkJ+G9uJONNlni6yneARhjeqLNbof1qh+RdIeuNul8ljtCnN2xzNre5wzXHlSr2ju6dYQrVR5UXbL2Qye/uswnlqJxQ/1KNGzy9XaKdds542VJ5xhyf2aBvHSLc3rVRXlF5ceY82JS5XsDRoRzwJxMNBTy6fD29E2zIesfBtn0vM4lJjDnvhs9idkY7YXnUgJ9NLRKcKXrlF+9GgUQExg5YQ2s03mTFoeZ9LyOZOeR2yBOJhiLDnh+M0tgnn8+sZEB3gQn2ni4/WnnZ6GXjqJ/s2Dua1VKN0b+Ts9IEsi22Rj7wWHeLgrLouzGfkA6DUiT9zQmNGdI8rcvj7hateQm5Jxt5NrUpftUhit1VmfwALhORRDAOmTDtWuEdXAunVrGDt2RLnr/fDDXO64Y0iVjnGtXD9btmxi1KhhKIpSZghyIX/9tZTrr+9bC5aVTH1sF6vViiRJV0VYfFnUuWehm6uLr/5xPITf1iaU4R3C6dcsiFeWHWdnXBZvrTrJP6fTadnAmzyrTHSABz0aBZS6L3dxEzeKTyQ5t3+P585P8dr9OZ4HZqDJOIGp4ySs4T0rVLxCf+x3fNf9D4D8DpPI6/1ykWIrgjUXKeM4APawrhWyKyMjo8zlsiyTmppcoX25qTlUvS+WlsUHnmL2eXTxG9HFbUR7YTOiKR3DyQUYCjwQbcHtsEX2xh7UEiWoJfh2qW3T6zU6jcikXo0Y2DKUD9aeYmdcFt9sPsfyo8n0bBRApL8HDf0MRPobiPA1lJqztpAfC7wK7+oYXm9CQH0MGj4a1oZf9iTw9eazbDyTzqHZObx6a0t6Nw5weS9DWVHZed4hrr1zRysWHkpiV1wWZrtCkyBPPr+rHeG+juqa/p5aunr60zXKn4k9wS4rHE02sic+iz3xWRxIyCEjz+r0UgRH0Zgnb2hMqE/Z+QAVVWXxoSS+3HSWHLO9xHVCvXU0DfaiSZAXTYI9aRfuU6RSdlSAB58Pb8feglzKPRsFlPubK8TPQ8uNzYO5sXkwAInZZt5bc4rt5zP5ZP0Z/jmTzrM3Na32ytxu3LhxU1EK0ylEanJBBsWjfj435eWV7QFXSHmectc6qqry7LNPVUgoFASBoUOH18vqu3WNTld9k7+qqnLy5AkyMzNo2DCKhg2jqm3ftYVbLHRTLjvOZ7IzLgutJPBI7xgAAj11fDmyPQuOpfLxqhNFEsCP7FT2jLxbLHQDgCCS3/NZ7EGt8P37aYfIE7/RUYykQWesDftga3g9tgadQSracetOL8Vn/XPO/3senIkm/Sg5t36L6uEI0dAk70dQFWSfqArnd4mMjOTUqZOU5nAtSRqio2Oqdr5uahzFrxFmv3sxt7sXZCvapD3o4jagjduINu2w8+VE1OLZ5XHyuk+utFfrtUx0gAdfjWzPyuMpfL4hlnMZJs5lmIqtF+KtI9LP4Hj5ezj/bujvQVymif0JOWglgbu7RtbBWVQdQRAY360h3aP9eWX5cWLT83l6wWEi/AxcFxNAr0YB9IqpuHBVmxxPMZJttuOlk7ipeTADWoaw4OBF4jJNPHRdI7z1pQ8LNdKlsOz7e0ZjVxQSTDJ/H77oCO+Ny2LlsRQ2nk7j/p7RXN8kEINGQq8RHUVztBI6SeBsRj7vrTnF/oQcwFFxulmIV4Ew6EmTIE+aBnuVaUshgiDQNcr/ir+XCD8DU0e0488DF/liYyy74rIY8+MebmgSyL3do+gY6evyQrAbN26uLgqLm0QUioWewXVsUdVo2bJ1hdZr1api612r7N69kzNnTpe7nqenJ48++gTPPvuC+75Vh6xZs5I333yNEyeOOT+74YZ+vPXW+7Rp07YOLascbrHQTZkoqsrXmxxehSM6RhDhZ3Auk0SBR/s1pUOIJ68sP86ZtHw8tCKD25RdqUvMd3ggKB7186bnpnqxNhtMpn8TPA7ORHdhC1LuBbQXd6K9uBN2fYaq8cQW0QNrw+uxNbwe0XgR3zVPIKgKpjbjsEbfiM/fz6BL2EbAvNvJuXUa9rAul+UrrJhXIcC9997PK69MKXW5LNsZP/7eKz5nN7WApMMWeR22yOvguikI+ano4jeiST6AJuMkmowTiKY0PHd/gebiTnIHflVvqwzWBYIgcFvrBvRpHMjfJ9O4kGXiQpaZhGwzF7JM5FllUo1WUo1WpyhUZPuC9yFtw+ptVdoWod7MHt+Zbzaf488DiSRmm5l/4CLzD1wk1FvH0/2bMqCEar11yY5zDq/C7tH+zsImIzpGVGlfWkmkW4wfMd5aHugZzdGkXD5Zf4aDiTl8s/kc32w+V2wbASicivHQijzaJ4bRnSPRiHX/HQmCwKhOEfSI9uerTWfZeDqdTbEZbIrNoEtDP/7v5mY0DXZ7Grpx46Z2yCsQCxuIjntofXWyaNGiJT169GLPnl3FigeCI8VP27btaN++Yx1YV38oK03S5bz//ieMHTu+hq1xUxKbN//D9Olfs3Hjesxmc7HlW7du5vbbb2bZsrW0bVtzBT6rE7dY6KZEVFVl74VsZu+M51iyEU+txP09S3addTwwdWHpkSQaB3niYyj7ZyXmpwCgeIZWu91u6idycBuMN30CqoqYcx7dhc1oL2xFl7AF0ZSOLm4DurgNRbYxNx+Gsd/7IEpkBTTDd/kkNNln8f9rOPk9nkWbuBMAW3i3Cttxzz0T+fXXuRw/frTYgEYQBAYPHlanuT/cVB3VMwRLy5FYWo4ECnJ1XFyFsvi/DqH591sxXvcitoZ9ULwjHCu4KRdfg5bhHcKLfKaqKtlmOwnZZhKyTAXvZhKyHX8n51pQVEeuvAndG9aR5dWDQSsx+camPHZ9DHvis9h2NpMNp9NIMVp5cekxFkT7M7l/U5oGe7qEaLi9IAS5V0zpqUKqSpswH74f25FVx1P5aVc86XlWLHYFs11xFkcrFAqvbxLI/93czBny7Eo0CvTko2FtOZeRz9zdF1h2NJm9F7IZP2cv93RryIO9ol3Sa9SNGzdXF0aLYxwaWigW1mMni08//ZI77hiI0ZhbZHwtSRKenp588cW0OrSufhAQUHItgH/ToEH9q5Z9NfDll5/z1luvFqmo/G9kWcZisfDKKy/w119La9nCquEucOKmGDvPZ/L15nMcTcoFQBTguZuaMbJTUe+DqiYt9Vn7FIYT8zFe9yKmLldfKfLaoj4mja00qoKUfhxdwla0FzajTdiOaDNiaXwrObd+WzRHoSUH741TMJxaVGQXmaNXOqriVpDs7CxeeWUK8+fPw2azAeDt7c2DDz7Kc89NQautXG61a6Kd6iGF7ZJx6gC+Kx9Bk34pTED2CsMW0Yu8Hv9D8S+nkpyqIubGo03aiyblAIpPQ0ztJhQLnXdzCZuskJhtxksnEVyOV2F9vH7MNpk5uy7w405HIRhwhNq2auBd8PKhVag34b76WhUQjRY7A77ZhqyoLJjU/YoqWUPl2sYuO0RDS0GBlCCv+nN9XMwx88m6M85UK6HeOq6LCaRtuA/twn1oHORV556RiqoSl2niaFIuR5NySTVaaRnqTcdIX9qF+9Aw3L9eXUMVITHbzPbzmQR76WgU4EGkv0edt8OVUB/7umuBumyXX/Zc4LMNsfwS9AO989Zg7PUCpq5P1K4R1ci5c2f57LOPmD9/HlarFa1Wy513juB///s/mjRpdkX7vhauH6vVSocOLcnISC91ncDAIA4dOlnpZ5Wa4lpoF4Bdu3Zwxx0DK7XNnj2HiYqKriGLysdd4MRNpVFVldk74/lm8zlUHFUBB7dtwPiuDYkKuLKHistxhiHXU3d6N7WIICIHt8EU3AZTxwdBtiHlnEf2bwJC0eqrqt6X3IFfYY2+Ee9/XkK05aFqPLEHtarUIf38/Jk6dRqvv/42hw8fQqPR0KlTFzw9K1fl0039QAloQubIxXju+Rpd3Ho0aUeQ8pKQTi1Ef3Yleb1ewNThAefvTbAa0aQccIiDyXvRJu9DNKUV2af++DxyB3yOHOTOv1MSWkmkUSWr5tYnDFqJh3o34rY2oXyxMZZNZ9LJNNnYdi6TbQVhwAB+Bg0tQy8JiK0beF+xgFcWe+KzkRWVKH9DjR6nJDSSiLckUh8jzsN9DXx8Z1s2nk7n43WnScq1sOhwEosOJwGOsVKzYC9ahnrTMtTx3jTYq0reh6fT8th3IZvejQOI9CveRrKicjHHTGx6PufS84nNyOdswd/5tqLe8IVFZyRR4LZ2YTzUoyERJeyzvpFvlflxZxxzd19wivEAGlGgob+BRgGeNAr0oFGgJ40CHO/+9aSAkhs3l1OYszCAbADUGvIsPHPmFKtXr8JiMdOuXXtuvHFAjVSCjYlpzBdffMMHH3xKdnYWvr5+eHjU/z6pttDpdLz00mv873//LXWdl156zWWEwmuJmTNnoNFosNtLLthWEhcuxNepWFhR3GKhGwBMNpk3V55g7UnH4HJYuzAevyGGQM/qn/2/FIbsFgvdVAxZllm2bDFz5szm/PlzhISEMHr0OEaOHIOX12U5pAQBS6uR2MK64r3tXWwRvUCsWjcXGBhE3779q+cE3Lg2Gg/yez5Lfs9nwWZCm7IPz11foEvYgvfm19GfWY49oBna5L1I6ScQKDo9qopa7MFtsYe0Q39mGdq0IwTMu5387pPJ7/JYlX+Dbuo3Df09+GhYW8w2mdNpeRxPNnI8xcjxZCNn0vLINtvZGZfFzrgs5zadI30Z3y2KG5oGllkorCpsP+eo9t6zUfWHIF8L9GsWRM9G/mw/l8mRpFwOJ+VyLCmXPKvMkaRcjhREYwBIAsQEeRYIiI5X6wY+eOpKfwA/kWLkkd8POCugtg3z4cbmwdgVhbPpDlHwfKbJ6Z35b/QakZah3rQN8yHEW8fRpFwOJOaQarSy9OBFVh9JYlzXhkzsEVWh4jGuyOrjKXy2IZa0PCvgCH23ywpxmSbMduVSsaUzRbfz99Bya6sQJvaIKteb2Y0bV6GwL/CTHZNM1f3cZDTm8sQTj7B8+VJEUUQQRGTZTmRkJDNm/Ej37j2r9XiFGAwGDAZ3qGxVmDBhIlarlbfeepX8/HwkSUKWZTw9PXnllTeYMGFiXZt4TbJz5/ZKCYVQ8bDyusYdhlzHnMvIJ8xHX+X8NzlmG/P2JdIy1JvrmwRWKaQpIdvEc4uOcio1D40o8NxNTbmrAgnPq+JaLFhzCZzdA9GaS8boVcgh9acakKtxrbh2m81mJkwYy8aN65w3RUEQUFWVZs2as3DhCkJDXTf/5bXSTvWNcttFVTAcmYvX1rcRbXlFFsk+DbE16II9rAu2Bp2xB7cFjSP3mpCXgs/GKejPrgLAFtqJ3Js/Qw5sXtOndFVytV4/VrtCbHoex5KNnCgQEI+nGJ25/RoFeHBj82DCffWE+RpoFOhRoqeZxa6Qa7YR5KUrdv+3xJqRjZe8ze7efJgEk4V3OzalT6g/AJK3hL5J1fIGXq1tUxkUVeVClpkTKcZLr2QjmSZbsXV99Bru7xnF6M6R6DVFPePjM008+Nt+MvJtBHvpyMi3opTyneokgUaBjqrRjYM8aRzoSeMgL6L8Dc6iNYWoqsrJ1DymbTvPltOO0DWDRiQmsMD7LsCTAE8tPnoNPgYNvgYN3nrHu49eg/Zf+6tLftgR5yyaE+ln4Jn+TejbNAhBEFBUlZRcC+czTJzPzOdchonzGQ5xNTnX4tyHXiMyulME9/aIcjlvQ/f15JrUZbu8teoEiw8nc8jnv/jY0sgctQx7aPUUAVFVlZEjh7F166ZiObpFUUKv17FmzT+0aNGyWo5X01xr109eXh4rViwlOTmZBg0acNttg4s6T7gI10q79OjRkXPnzlZoXUEQaN68JZs27ajTXNYVDUN2i4V1SGEuigAPLeO6RjKyY0S5xUEu51hyLi8sOUZitqPaTtswHx7rE0OPRv4V/vHtPJ/Ji0uPkW22E+ip5YMhbejU0K9C21alA/DeMAWPI3OQfRuRcfeGIjnn3FSOa6UDfuWVKXz33bQSk8VKksR11/Vx6SSx10o71QSqqtbYjbSi7SLmxONx4HvQ6B0CYYPO5VdNVlX0J+fj/c+riNYcVElPXs//w9L4FqSc80jZ55GMiQjmLERLFoI1F9mvMbawztgbdEH2a+wusFLAtXT9JOdamLcvgb8OXnQmtr+cAS2Ceez6xkQHeGC2yfyxP5HZO+PJNtvx0WtoGuxJs2AvmoV4EW3VYh9+Bk+r43eU6qcy5VErkgyfT9XhYb30+2q1vWOVBMNrqW0qg6qqpBqtRQTEo0m5pBgd3nBhPnoe7t2IbtH+hHrryci38uCv+0nMsdA8xIsZYzpitiusP5XGtrMZ+Bo0NA7yonGQQyAM9zUgVSI3nyBAUJA3f+04zxcbY4nLNFV4W4NGdAiHBg2tG/gwqFUo3aL9K3X8K0VVVb7efI7ZO+MBmNCtIY/2iUGnqZiQabLJ7E/I5rut5zl00eH96aWTuLtrJHd3begyXpbu68k1qct2eX7xUdafSuG0x31Iqkz6vTtRfKpWvf7fbNu2hWHDbit1uSRJjBo1jqlTv6mW49U07uvHNblW2uWll/6PWbO+K7Ha9+UUPtP89NNv3Hpr6ddfbeAWC12cwxdzePC3A04vAnAMXvo2DXLO8v57htfXoMWn4O+lR5L4ZP0ZbLJKqLeOHLMdc0FoSueGfjzWJ4bOZYh+qqry694EvtgYi6I6Qjk+HNqGBj4VD8+obAegTdiK/8LRAGQN+x1bwz4VPpabSyQlXeSnn35g7dpVqKpC1649mDjxQVq1uvryoxmNubRt2wyTqeyHm3/+2eGy53+t3Ciri6ysTKZP/4aff/6R5ORk/P39GTNmPI8//iTh4dUzSIbaaRfReBGf9c8Vq+RdHooh4DLPxS7YG3RC1ZV/Q78auRavnzyrnZXHUjiTlk9SjpmkXAunU/NQcYS3DmwVyt74LKf4VBaB2dAw1SGqHGym0Dxe4PlfiqYXab62HZ4dKu+RcC22TVWRFZUVx5KZtvlckXbTSgJ6jYjRIhPlb+C7sZ2qvfjL5e1kl1Xis0wOD7yMfOIyTWSbbeRa7OSY7RgL3gvDH0siyEvHra1CuKtDeI3nHrXaFb7YGMu8/YkA/LdvYyZ0j6rSvlRVZcvZDKZtPsfJVIe3uK9Bw4RuDRnTJRKPaqhwbbLJzNoehyDA3V0bVsp70X09uSZ12S7/+eMgJ+Pi2W94BIDUR8+AVD1h9M899wxz584uM3RSp9MTF5eMKLqOh3FpuK8f1+RaaZczZ07Rt28v7HY7ZUlrfn7+fPjhpwwfPrIWrSsZt1jowuSa7dwzZw+JORZubhFM/2bBzN4Zz+m0vPI3/hd9mwbx2qAW2GSVH3fGM/9AIraChM+9YgJ4tE8MbcOK/hDMNpl315xixTFH7sA72jZgyoDmxcJiyqNSHYDNROBvA5ByzmNqMx7jjR9U6lhuHGze/A/jx4/CYrE4Pe0kSUJRFN5//xPuv//BOraweilv5hMcszTvvfcxDzzwUC1ZVTmulRtldZCSksLgwQOJiztfxJNUkiT8/PxZunQ1zZpVTzhvrbWLqmI49iteW99FkM3Ivo0cL59IVI9AFEMAqsYDTfoJtMl70aQeQpAtRXeBgBzYAltYFyxNB2OL7leDBrsW7uvHwenUPL7efJbNsRnOzxoUeKjd0jKE+CwTp9PyOJ2ax+m0PE4lGkm1FA+FvfMficHbinpSucXC2sNsk/ltbwLLjiZzIcuMvWDCONhLx/fjOpYYan6lVKWd7IpKnsVObsErI8/Gpth01p5IJdt8SVzo2cifUZ0iuL5JUJW9DXPMNnbFZWGxKxg0InqNRFKumS2xGeyKy8JsVxCAFwY0q1CKnPJQVJX1p9KYvuU8ZzPyAQj01HJ/z2iGdwiv9Fi4kCNJuby6/LjTc9NbL3Ff9yjGdolEK4mkGi0k5pjJMjlE2cLiFWG+BiJ9DUT6G2jcMMB9PbkYddnP3Td3H5bk4/ytfw5F70f6g0eqbd8PPzyRxYsXlhi1cznnzyfXiyIk7vuRa3IttcuqVSuYNOle7Hab87oSBAFJkhgz5m5uumkAt9xyG3q9a+TNdYuFpZBvlTmVaiTFaKVHtD9+tZyzRFVVpiw9xt8n04jw1fPzhK74GDSoqsr285mcSDYWm+HNvew912xHxTEb/VifGO7p1rBImF5SjpkfdsSz6HCS02uxb9MgHu3TiOYh3iTlmHlu0VGOpxiRBHi6f1PGdI6oUqhfZToAry1v4bl/OrJXGJnj1qHqfSt9vGsJWZZZu3Y1S5cuIi8vjxYtWnD77UMYOnQQZrO5xJu7IAgsWbKaHj1qJiFxXbB162buvPP2MtcRBIF33/2ISZMeriWrKse1dKO8Uh54YAIrViwt0Y1fkiTatm3H2rWbquVYtd4uasE1K5TzICpb0aQdRZu0x1ltWcqJK7KKudkQjNe/gerlurk6qwv39VOUPfFZ/Lk/kfYRvozoGFGqsJF/MI/9dxwiIUQlIUTlQoiCRQtj/9bgbS56v3eLhXWDrKikGC0kZpuJCfSsdo/CQqqznWyywtazmSw8dJEtsRnOUk+NAjx48LpGDGwZUiHRMM1oYeOZdNafSmN3QZXu0gjx1vF0vybc0qp6+ztZUVl1PIUZW8+TUJDOJ9Rbx73do7itTSi+hrKfD2RFJdNkI81oYdOZDGZuP4+sOvbh56HlVIH3opdOwmxXyjzHQvw8tIT76onwNRDhd+kV6Wsg3M9QZSHTTdWpy35uxKxdRGTv4Tfd29j9m5I5fmO17fudd97gq68+LzNsMjAwiGPHYus0r1pFcd+PXJNrrV0Ko/+2bt2MIAj07duf8ePvc8nc+m6xEMjML8wZk+fMGxOfaXIObjy0IsPahzO+ayRhvlVL8F1IntXOxRwLF7PNXMwxO/7OMZOYbSY514JWEgny0uGhFdkTn40kCswc25G24ZUTzRRVJc8io5GEMkMmLmSZ+H57HCuOJjuTZN/YPJj9F7LJNNnwM2h4f0gbukX7V/mcK9oBaJL34T9/GIKqkH3HbKwxN1f5mNcCKSkpjB59J0ePHkaSNCiKjCiKRQp7lIRGo+G22+5g5sw5tWxxzZGbm0Pbts0wm81lrrdhwzbatHHNYjnX2o2yqiQnJ9GxY6tyZ7lXr95Ap05drvh49aldhPxUtEl70cVvxHBkLoIqo+j9yO8+GXtAM1SNB6rOGzmgGUg1IzjUFeW2k2JHyj6HJu0oYk4ctoie2MO6XfM5H/MP5nFqwOEKresWC69uaqqdErJN/HXgIgsPJZFT4G3YKMCD+3tGM6BlSDFh60KWiQ2nHQLhocScIjXlmwR5EuKtw2JXMNsUPHUSvWIC6NM4kOYhXjUqVthlhcVHkpm57bwzRFwnCdzY3BH5k2+VSc2zkGq0kma0kppnJc1oIT3Pivyv73NAixBeGNAMH4OGlcdSmLb5HEkFBVY0okCYr54ADx3eegkfvQYVuJhjJiHLXGJRnH8T4q0jwtdA+whf7mjTgGYhrlfQ4GqjLvu5Qd9u5zrTRr7SfYk1oifZw+dX275jY8/Qq1fnUpeLosjTT/+PF154pdqOWZO470euibtdXJerXiw8n5HPnwcusuJoMqIg0DTYkyZBXnjrJU6m5nEyxVhqPp8Qbx0eWskZKiCJAre2CmFCt6gyb7xZJhsHEnIKxEBzEXHw8rCMinAleVcqw7n0fGZsO8+aE6nOz1qEePHRsLZE+F2ZQFqhDkC2EDDvdjQZJzC3uIvcgVOv6JhXO6qqcuut/Tl06GC5SVJLwtfXl9OnL9SAZXXHSy/9HzNnzii1wEmPHr1YtGhFHVhWMdw3yoqxbt1axo69q9z1Pvroc+6774ErPl59bRdN6mG81z+HNvVQsWWyVwNMHR/G3HY8qs67Dqyrfi5vJ/Iz0KQfQ5N+DCnN8a7JOFEsbNsW2hFTp4exNLn9mi2i5RYL3RRS0+1ktNj5Y38ic3dfcI6FffQabmsdSt+mQRy8mMP6U2lOT7tC2oX7cGOzYPo3DyY6oO7DHC12hcWHk1hw8GIxW0tDAAK9dDTw0TO2SwSDWoUWETYtBVXPAzy0hHjry/S6NNlkTKLEkfPpJGSZSch2OBwUvvJtxceELUO9GdQ6lPbhPjQP8cZTd+V5F10Fs00mI99GA5+yv7eapqauH1lRScw2E5uej69BQ6dI32Ki+PVfbGacupzXtT9hbjqY3EHfVp8BwPvvv82nn35Y7HNJkmjcuAkrVvyNn59/tR6zpnDfj1wTd7u4LhUVC12jBFglOJOWx2cbzrDjfFaRz3fHZ7M7PrvY+tEBHrQI8aZlqBctG3jTMtSbQE8dqqqy43wms3ddYHdcFsuPprD8aAp9GgcyoXtDujT0c3baqqqy/GgKH68/XWKFwkJ8DRrCfQ2E++od734GInz1hPkYsCkK6XlW0vKseGglbmtdO+6oMUGevDu4NRN7RPHTrnh8DVr+27cxhmpI5FwRPHd/iSbjBIpHMMYb3qiVY9Zntm3bwv79+6q8vVKBMJf6xssvv8HRo0fYsmUToiiiKIrz2oyObsT06bPq2EI31YFOVzGPOFfJ9VFX2EPakTVyCR6HfkR/ZjmC1Qj2fERTBlJeMt5b38Jzz1RM7Sdi6vxo/SuMoip4HJyFJmkvqAoCCqgmApKOIuUnl7yJxgN7UCsUz1B0cRvQphxAu/o/yL7RGHu/jLXJbde8p6EbNzWFt17D/T2jGdUpgj/2J/LXgYsk5VqYtz/RWZgEHMV5Okf5OwTCZkGEVqKgXm2g14iM6hTByI7hHEs2svhwEkeTcgnw1BLipSfYW0ewl44Qbx3B3npCvHQEeunQlCFk6TUirRtUrA/21ElEB/sQpKHYQ7WqqmSb7CTkmInLzGfdyTQ2x2Y4o6bAIVw29Dfga9AiCgKSCAaNRICnlgBPLYGeOgIL3720BHg4/q5oVena5EBCNs8sOEKuxY5WEmjo50F0gOMVFXDp72AvncuHyF4SBfOITc93vNLyOJ9pwmK/NAneNNiTu7s05NbWoeg1InZZwWJXCNY4nm1Vz+Bqt+35518iIiKSzz77iIQEh6OBTqdj5MgxvPrqm/VGKHTjxk3NUa88C0+cz+CeOXtINVoRgD5NAhnZKYIADy2x6XmcScvHaLHTLNiLlqHeNA/1wktXvh56JCmXObviWXcyzRkWER3gweC2DbguJoAZW8+zqSCpeJS/geYh3pdEQT8DEb4Gwnz1eOvrnfZ6RZQ3WyClHSXgj9sRFDvZt36Ltdng2jeynvHaay/x3XfTyqxOVhqSJHHjjTfzyy9/1oBldYvNZmPx4gX89NOPxMWdIzg4mLFjxzNmzN14e7u2GOKeVasYJpOJtm2bYTTmlrqOKIrs33+MsLDwKz7eVdcusgXDiQV47PsGTVYsAIpHEHndJ2Nuc7dLeNhZYs3IxtIn3CRvicDkz/Dc+3Wp68i+jbAHt8YedOml+DVy5oIU8tPwODIHj0OzEU1pAFgj+5B33RRARco4hSYrFntgcyzN7wTx6vHEuRy3Z6GbQmq7nWRFZVdcJosOJbP3QhZtwny4sXkwfZsE4e9Z9/2Qq1LZdsrKt7H6RApbz2Y6c7FXBW+9RKi3npuaBzOkXdgVRx1VhhyzDUWhyO/iQEI2T/11mDyrjACU9VV4aiWiAjwI9dYhiQKSKKARBTpG+nFzi2ACPa88LUdVrh+LXWH6lnPsOJ9ZTBS8HL1GJDrAgwtZJkw2xzqBnloGtgyhZ6MAJi88wvuaGYzVbCCv53Pkd3vqis+nJBRF4fjxY1gsZpo2bYavr1+NHKcmcd+PXBN3u7guV2UY8thvt7L9XCaNAz357K621V41Lj7TxM+7L7D8aDLmf3XsGlHg4d6NmNA9qsxZxGuJMjsAxY7/n0PRph7E0mQQOYO+c3t2VIApU55l9uwfsNvLz11TEvPmLaR//5uq2So3V4L7RllxPvroPT7++P0S83KKosjIkWP46qvp1XKsq7ZdFBld7Aq8dnyEJusMAHb/Jlijb0T2b4zsF4Ps1xjFJxLE2pvgssSaOd7rQJnrRHZbSes7vgMgv/NjyN4RCKKIt58fWboobAGtQFdBUcuWj+fer/Hc922xMOVC7AEtyLtuCtaYAVfd/cktFropxN1O9YMrbafMfCun0/Iw2RQURUVRVfJtMpn5NjLybWTmW0nPt5FZ8HdGvs1ZjdtpA9A92p9eMQFE+nsQ6Wcgyt+j2sKbrXaFpUeT2Xchm6NJucRlmhAF6N04kJEdI/DSSTy9wCEUdovy4+M725JtshOXmU9cpsOrMj7LRFymicRsM2UF04iC41y6NPRHpxHRio5c7+0jfIkJ9KiwR2JV2mXm9vN8u+W88/96jUijAA+aBHvRJMiz4OVFhJ8BSRTINdtZeOgiv+9LJDm36P3qB/3H3CjsJbf/B5jbjq+YAdcg7n7ONXG3i+tyVYYhH76Yi7de4uM7q18oBIgK8GDKwOb8t19j1p1MY+mRZPZeyKZVqDevDWp5zSQSLs/7AxweIIampc8+euyfgTb1IIreD2Pfd666B7Gaol27DhUSCi8vdCJJGmTZzvPPv+QWCt3UayZP/j8uXIjn119/RqPRYLfbne/9+9/Ehx9+Vtcmuj6ihLXZYKyNb8Vw9Be8dn2KJivW6W1YiCpqkX2jkP0aFxERVY0HgmID2Yqq88HeoHO1eN+Vd08JbrGLVrfNBCCvx7Pkd38acNw6vIN9sKfllu1i8m+0nuT3fA5z67F4b30LXexKFI8Q5IBmyL4N0ceuRJN5Er/l92ML7YgtrCuyfxNk/6bI/k1QvMPLr1ztwkjeFW+zyqzrxo0b1yTAU0f36Ip70qmqSq7FTka+jeMFYde74rLYWfAqRCsJ3NQ8mOEdwunS0A9ZhRPJuey9kE2eVS5SuTnUR1+qQ8XBxBzeXnWSsxn5RT5XVNgcm8HmgggugG5Rfnw2vB0GrYSXTkOEn4FeMUX3Z5MVErLNxGWaSM+zoqgqigq5Zjsbz6RzNCmXHeeziqWtAkfF6p6NAmgb7kOEn6EgWqx6qk2nGS3M3hkPwBM3NOam5sFOUbA0fAwaJnSPYlyXSLady2TtyVQ2nk53fL9SLiigeIZcsW1u3LhxU1nqlWdh+9dW8dbtrejTJLDWjpltsuFj0CBeI2JXRbw/Cmm9vSORPUOKzRZIWbEE/DYQQbaQc9OnWFqPriFrrz7y8vLo0KEFRqOx1KrHcEksDAgIoE+fG3jwwUfp3fv6WrTUTUVxz6pVnv379/LLL3NISEggJCSEUaPG0rv39dWam+haaRfBmov+9FKkzNNI2eeQss4i5Zwv1dvu38heYVha3oW5xQjkoJZVtqMsTzef8DN0u/9lJK2V3LBRmO/61DnBVG3tpMhFRE/Bko3n3m/wOPB9id+FKukdIqp/U+z+TZxCoj20Y70JXa7oxJ++SdXCDl39GhLMmejiN6EKInJgS2S/GJcIx69tXL2d3DhwhXZKyDax+ngqZ9LySMw2c+FfVZojfPVkmewlFlsBR8HIMB+9UzyMLHgdTMxh3r5EVBxhtiM6htMu3Je2YT5kmmwsOHiRpUeSyTHbiwiFV8KFLBNrTqQSn2nCrqjYFZXMfCsHE3Ow/ruMdQGF1abDC+33NRDhr6d1dBCC1YqHRip3HPL26pMsOpRE+3AfZo7rVOVxi8WucDAxmwHrbkWXl0jmiMXYw7pUaV/XAq5w/bgpjrtdXJerMgz5sxXHuKdbw7o246qm7NAlhaY3/obOO5u4bYOJ/O02om8OK9oBqAr+C0aivbgTa3Q/sgf/7PYqrCR//72ae+8dh6qqZeYuFAQBHx8fDh06iYeHZy1a6KYyuG+Ursk13S6qgmi86BAOs88hZZ91iojIVpB0qKIWKTce0XKpcJgttBPmtvdgbj4UtJXrc0q7t0j6PHo+/ByegcmknepM3sQ5eHbydy6v6XYS85LQnV+HlBWLlBmLlB2LlH3e4V1ZAvaA5uR3fxpL08H1RjQsCSnjJIohAPVf3iqalAMYjv6KKulQvMJRvMMdHqgBzVH1vkXWrfVrSFXRJO9Ff3IhusRt2EI6Ym4zDntYV4cxqoqUeRptwhb0sSvRJmxDUC+JGqqoRfZvgj2wBXJgi4L3AhGxFkPya5truq+rR7hqOx1PzuWvgxdZdSzVKRL6GjR0jvQjyEtHYo6jWvPFHDO2UkS4Qga3bcDT/Zrg51FctDfbZI4m59I+3BetVHNe3WabzP6EbHaez+JsRj4XC+wvzBlYFnqNSIi3ju7R/gxoEUKXKP8inpQnU4zcM2cvKjBzXCc6RPiWvrOKoKoET2+GIFtIn7ANxTfqyvZ3FeOq18+1jrtdXJerUixMTc3BkVHDTU1RlljYuN88mvb/HQBVFcgPGYTXqFdJk6KcHYDh0I/4/PMyitaLzLF/o/i6xd2qcOjQQT7//GOWLFlY7rpfffUto0ffXfNGuakS7hula+JulwpgN6M7/zeGE3+hO7/OKaApOh8sLe/C1PYe5KDWFdpVyfcWlXYjPiOs3RZMmaHsmP4xjZf1LJI/r07aSbEj5sSjyYp1iIgFL03qQUSrowCPPaA5+V2fxNJsSL3zVtMmbsdvwSgQNVhaDCe/08OoWm+8dnyA4eSCUreTPRsgBzZHDmiGPbAFSkAz/Jp1Ic2kR62usZmqIliyHGK28SJi3kVE40VEYxK6xO0OQftf2ANaIPtFo724G9GSVXRZUCtUyYCUeQrRllfyIUUdckATbA26YGo/ETm4TfWci4vg7uvqB67eTnlWO7vjsgn31dMsxKtYxJWiqqQarSRkO/IJJmabSSh4B5jUK5peMbUXGVYZVFUly2QjMcfiED6zzSTmOOy/mGMm1Wgl31rcmzLAQ8uNzYO5uUUwXaL8eWr+IXbGZTGgRQjvDanYvbEsBEsOwd87+qPUR06BpvpTcF0tuPr1c63ibhfX5aoUC90/tJqnNLEwtM1WOoz6BIDM860JaHTMucwaM4D8Tg8j+0QR+OvNCPZ8cvu+jbn9xNoy+6rk5MkTXH999zLX0Wq1PPbYk7z88uu1Y5SbSuO+Ubom7napHEJ+Gobjv+Nx5Jcigo0trCvm1uOwBzZH8Qh25FUqweuwpHtLROe1tBk6DUWW2P3D2+QktChWbMOV2kmw5OBxcBYeB75zelzK3pGYOj6Iuc04VJ133RpYQXyXPYD+3Ooin6mi1ikGm5sPQ/GOQMxLQjQmOrxP85JL3Z+i93d46gW3wdx8GPawbhWKKBDyUzGcmI8m/ViBIHgRKe8igt1c6jaqxhNLk1uxRt+I7sJm9KcXF1lflfTYGnTGGjMAS5NBKH4xBQsUxNxENBknkDJOosk8hZRxAk3GKQR70Rxq1oY3YOr0ENaofvXae7QQV7qG3JSOu51ck8J2iU/MIi3PSlymifWn0lh/Ko1s86XoH1+DhhyzHa0k8Mf93aolt76UFUvg3L4oWm/SHz5+xfu7mnFfP66Ju11cl6uywImbusEn/Axt7/wSgPPbhnBq9US8Qs/T4ZXVeKWtQnduLbpza1G0Xgj2fKzhPTG3u7eOra7/eHmVX1BHUZQKrefGjRs3V4LqGYypy38wdX4M7YXNeBz5GV3sKrRJe9Am7Sm6rsbDIRx6BKF4BqN4BKMz+hHV04o1zw9bni+ICi0LCpqcWXc3OQkt6uK0KoWq9yW/+9OYOjyAx6Ef8Tg4C8mYgPeWN/Da8RG2kPbIwa2wB7XGGtUXxTe6rk0uhpgTj+7cGgByBkxFf3YlutiVCIoNa0Qv8vq8ij20Q7HtBEsOUuYppMzTBULbKTSZp5Fy4hAtWYgXd6K9uBOPQz9i94vB0moU9oBmIOpQJR1IWlTR8S6YMzEc+x197AoEpeRUG4ohENnbEQZdGA5t92+CtdFNTjHa0vIujNe/jv7MMgRbHrYGXbCHtAOphCIPgoji2xCrb0OIufnS56qCmJuAJv04+pN/oT+zDN2FTegubEIxBGBtdBPWRgOwNuqPqit/UO3GjZurEw+dREOtBw39PejdOJDnb27Gnvhs1pxMZcNlwuHYzpHVVoRTzE8FQPEMrpb9uXHjxk1lcXsWuinC5d4fGkMuQc3203zgTxh8M0g71Zn9v04B1THT3mJtO6I7GjFt+ALD8XkIdjOqpCdz7Bpk/yZ1eRpXDTfffANHjhxCUUrPpbJ1626aNXP9B+1rFfesmmvibpcrR8xLxnDsd3Tn1iDmpyLmp1a4cEoh6ac7sm/uy4AjR5UrexYWw27GcGI+HvtnoMk6U2yxNbIP5tZjsDS9zWXCx7y2vo3nvm+xRvUle+gvAIg5FxCsOY6Q8krkGBYECPbTkHn6AFLGSbTxmzGcXlLMU68sbA26YGl8C4p3hCM/onc4ilcYaKpWdOVKEXPi8Tg4C8PxeUXydaoaD8zNh2JuM95RIbwe5WJ26WvIjRN3O7kmFWkXu6ywOz6LuEwzd7YPQ1cNVZUBdKeX4rfqUWzh3cm6q/QUEW5q7/o5fPgQixcvICsrkyZNmjJq1DiCgoJq7oD1HHe/5rq4w5DdVIn8/VnkPf814R034B91AkF0iFTG1IbsmvkusuXSQ1yLte2cBU7Iz0B/aiFyUCtskb3ryvyrjlWrVjBhwpgSl4miyF133cW0abPc14UL475RuibudqkBVBVs+YimNMcrv+DdlI6SlEze0tPovHLQeWeh88rGYvRn35zXsOb5O3fRanvHItV560U7qQpS+nE06cccr+T9aBN3IOAwWBW1yD6RKL7RyD4NkX2jUXwaOgqH+EajegTXjvhkMxE0uxuiJZvs23/A2njgFe2uxLax5qGPXYH+zDKH2CZbQbEhyDZQrI53wBpzsyPnpavmB1TsaJN2OyInzq5GkxXrXGQPaIY9qA2yXyMU30bIftHIPlEo3uG1WyhFVRFM6UjGRMSCl+PviwhWI4pvFHb/pigBTfBr05c0o+C611BlkW3o4jagTdqFovdD8WqA4tmg4D0UVe9XrwRdqCd93TVIXbZLYR54S5PbyLntu9o9eD2jptvJZDLx+OMPsWzZYiRJgygKyLKMJEm8+eZ7TJr0cPUf9CrA3a+5Lu4wZDeVQ1XQn1qE/56P0Q69lI/KmBxN2qkuxG0fXEQoLLa5RyDmDg/UhqXXFLfeehufffYVzz//P2w2KxqNxlkledCg25k9ezb5+cWTLrtx4yokJydx6NABJElD9+498PZ2h/JdtQgC6LxQdF4ofo2KLba3MWMxFu2vGo289LfkLRURCusNgogc3AY5uA2FfpVizgUMx+dhOD4PKfcCmuxzkH2uxM1VjQHZJwrZrxG2yN5YG93s8M6vZrHDcGoBoiUb2TfaEc4LHDiwj1WrVmC1Wmnbth133DEUna6EMN6KovPC0mokllYjy1/XlRE12CJ6YYvoRd51L6FJ2oPH0bnoTy1Gk3kaTebpYpuogojiFY7s2xDFJ8ohEPtEOURhn0gUn4ZliomCOQuvLW+hS9yOPbAltrAu2EM7IsiWgpyOiU5hUMpNRMxLqrgn73IDPo1vxdxyBNaovvW2+rOUcQrD0V8xnPwL0ZRW6nqqpHcKh4pXKLJXGLaIXlgb3egyXr5u3JTHpTDkkHLWdFPTPP30f1ixYikAsmxHLhjKKIrClCnPEhoaypAhd9adgW7c1BBuz8K6xJqH4cQfyH6NsUX2Aklf6V1oEndiOPEHilcYlqaDkYNaVm4Hqoru7Gq8dnyIJuNEgVm+nN86jOQjvTFnh5a66eWehVdVu7ggWVmZ/Pnn78TGnsHHx4ehQ++iXbt27tmaesC1OquWnp7O889PZunSRc4weoPBgwceeIgXX3z1ygSJKqCqKpmZGciyQnBwMKIoXJPtUt+o99dPQWENKTcOMecCUk4cUu4FxJx4x2fGJKcH4uXIvo2wh7RDMQSieASieIdjjRmI4tXgspWs6C5sRjReRPEKQ/aJQPGORNX7lmCHSsDvt6BJP4ax98skxozmwQfvY9OmjUiShCiK2Gw2AgMD+e672dxwQ79yT63et00VEMxZaBN3OIq+5JxHyj5X0JYJCIq1zG0VvR+WpoOxtByOLbwHCJdCFbUXtuDz99NIxouVskdFQPEMceR29I5A9o5A8Y5A1Xo57MuKRZN+vEhRIlXjURDuXeCFp/VG1Xo6XhoP0Hhc9n/HZ6rWA9mvMapH3VSzFay5eO74GI9DPyCojvuJ4hGMpfFABNmKmJeMmJeCmJ9cJHz83yhaL0fxm2aDsUb3dynh8Fq8nuoDddku3uufx+PoXPK6Tya/x+TaPXg9oybbKTb2DL16dS7j2ALNm7dk06YdCPXMo7mmcfdrrovbs9DFEfOS8V02EW3qIcAxgLFF98cW1vXSAO2ywZuq8QSth3OZJu0Inrs+Q5ew1blPr12fYQ9ogaXZHeULh6qK9sImvLZ/iDZlv8MGnS+5jR5k7/1dkG3lD6Ak7/pfJbC+4O8fwIMPPlrXZrhxUyFyc3MYOvRWYmPPFMm3aTabmDbtS86fP8esWXNqZVClqirz5v3KV199wYkTjiruUVHRPProf3j++f/V+PHdXOMUFNZQfBtCZAnLZStiboLD+zD9GLrz69EmbncIPZcJPACq8CK2iOuwNBmEJv0Y+jPLES1ZxXapaL0dOQB9wp3iEQho0o+hagzktxzN+JGj2bfPUZhGlmXkAjeJrKwsxo0bwerVG2nTpm01fxn1H9Xgj7XJrSUsUBx5O3MvIOXEO95zLyDlxjtE4twLiJZsPI7OxePoXGTPBih+jVA8g1ERHUVaULH7xZDX6wUk40U0yXvRpB5G1XoVbU+vcBSfAmHQK6zkgi6XIaASbD2NacdP6E8uQjRnOMKqLwutrih2/ybYwrpji+iBreENKD4Rld5H5Q5oQh+7Cq8tbyHlOypyW2IGYG4z3iH2SdoStxHzUx3iYV4SYn4KUvY59GdXI+VewHBqEYZTiy4TDocUCIfV4NUsW9DHrgZVxhpzs7sozlWOJdaMbCw7uudKPOYLvWfdnoV1y/LlSxFFsdT88aqqcvLkcc6dO0vjxu6c/W6uLtyehXWAlHESvyUTkIwJKIYAVFHnHARVFlXUYmkxHMGcgS5uI4Jicy4rTTjUXNyF1/YP0CVud+xD44GpwyTyOz+KavCv8M3P0NTgni2oQ9yzNfWDa7Gdvvzyc9555/UyC/P89ddSrr++b43b8uabr/LVV58jCAKFt7tCkXLMmDF88cW3CEL1JCN3U/1ci9ePYDWiTdiGmBuPaM50iDsph9Am7y22ruwZij2kHWJeMlJuQoni4eWY2tzNEm5lzJjhpa4jSRLDh4/gm2++L9vOa7Btqowio03Yhv7kAvSxyxGtucVWMbUZj7HPq6ArPeVLVSjSTnabo4J1fqEnXiqCzYhgy0ewm8BmQrDnF/w/H8FmQrCbEKy5SMbEYvu2+zfFFnUDlpgB2CL7lCzelYZsQZN2DE3KATRpRxAUG6qoLaiYnYUm7RhS1mmnJ6HdLwZj33ewRZfv9VoiqoomZT/600vRn16KZExwLnIIhwMLPA77VdrjUDBnYTjyMx4Hf3CO51VJ7xAjYwYiKDYEUzqiORPBanR8p3YToKIYglA8glA9g/AObUi23QvZEIjqEYRiCKzcd+qm2imtn7PEmjne60CF9vHvXLwVxf/PoWiT95J923dYm9xW6e2vJWryfvTBB+/wxRefYLfby1xv3bottGvXvnoPXs9xjxNcl2vGs1Cw5qIKEmg9iy6wmxw5XQpndnMTkHLiHbO7uRcQzZnYwro6BgcxA1D8G1fNAFVFsOUVDAIyEE0ZCOYMxIL/Oz7PAlFC0fmi6rwxHJuHaM3B7t+E7MFzUHyj0KQeQnd2DVL2OccgwjlQc7y4/DNVQZX0mFuPJb/L4yg+DncFwZKN7uwa9GeWoovbiCbzJJpdJy95HDa9DU3qIfTn1zlMF3WY2k0gv+sTqJfNWtXLnFFu3LhxGebM+bFMoVCSNPzyy5waFwv37NnFV199DsDl82KFf//2228MHHi7O8+MG5dC1XmXWHxEzIlDf2oxuviNyH4xWJrfiS2iF4iXefnb8pEK89vlJlxW+OIiKHbyu/6XJa9/gEajKfXBR5ZlFi1awFdfzUAU3UJ6tSBK2KKuxxZ1PcZ+b6NNOYhQUEFcNGdgC++BLeqGmrdD0iIHNEUOaFrpTQVzJtqkvWgv7kKbuA1N8j40WWfQZJ3B49CPKHo/x5i68S3YIq9DNQRc2liRkTJPok0+4BAHUw+iSTtaZIK7NJKNCt/utXMiuDn/6RFGi0pbXngCAvYGnbE36Exe75fRJO9zCIdnliIZEzGcWojh1EJUSY+tQWdskddhD27nGJNbshEt2QgFr6J/5yDmpzjzR8peYahaLzRZZ9CfWYb+zLJKmen3r/8rej8Uj2CHeFjwkv2bYovoiT24Tb3NP1nfKc+poqrrXo7Ts9DD7VlYl7Rs2apcoVCr1REdHV3uvpKTk/nuu2n8/vtcMjMziYiIZMKE+7n//knunN5uXJL651koy2gTt6OL34g2fhOa1MMIqAXV0MJQNR6OgXF+SqX2bfdvijVmANaYAdjCuxe7+QqWHAzH56FJ3odocoiBQqE4WE6empKwhXUj+44fig6mKoKqgmxx5LopI/Tk38Lh5QMyVZAwtx5NfrennUJjVXDPFtQt7u+/fnAttlN0dAPMZlOZ6/Tq1ZvFi1fWqB1PPvkof/45D1kueZAnSRKdO3flxhtvZt++PWg0Wm66aQAjR452D9pchGvx+qlpHnzwviK5REsjPj4Vvb70XMqu0DYpKSns2bMLVVXp2rU7DRo0KH+ja4yaaifBko02YSu68xvQn11VpOCIikCmLpIENZQGUg7B9gREe/F7gmIIwB7aEVtoR1StN4JiIyczle9nz2HHOSN7E21cNDqMliQJvV7PwoXL6dSpS/WdiKoUCIfLnMJhVbAHtSK/06NYmg8FUYsm7Qj6kwvQJu9F0fk4PQVVnY8zDySqWuBckI5oykBvz8Kek4JoSkMwZzq9KktD0Xpjb9DpUoV1n0hUrScggiCiSlrH8XQ+qDpvVJ2vY7mre9PLVgxHf0XKjXcUqfEMdeTmLChYo+p8a63adWnXT/7BPE4NOFyhfTRf2w7PdvqC0PiLiMaLSHlJjsJFeUmoWi+s0f2wNbyhSM7Z4OnNEewm0u/ZjOIXU81ndnVRk/cji8VChw4tyMrKoiTZRJIkRo0ax9Sp35S5n9jY0wwefGtB/uxLArIgiLRo0ZIlS1bi719JXcDFcYVxgpuSqahnYb0SC9PjzuGz8mF0iTsqtL6i9Sq4eTryBcneDZF9o1B8GqJqPNBd2ITu3Fq0idsRlEsPk4rO15GPJbwn9pB26M6uxnB8HqItr9RjqRqPgkTkQageAQVhBYGohiAUQwCodkRLLoI1G8UQhKnDxFpLrOwUDs+tRtH7Yer8mKPSYikYjUYsFgsBAQFlehW4O4C6xf391w+uxXbq3LkNCQkXSl0uSRJ33DGU77+fXaN23HzzDRw6VH6YUGEumsJQ5aCgIObNW0j79h1r1L6qkpycxIoVyzAajTRr1pwBA25Bo6n/3iUWi4Vlyxaza9cORFHk+uv7ccsttxIWFnBNXT81zXvvvcnUqZ8VeVj5N2FhYRw8eLLM/dRl35abm8OUKc8xf/4853k4wqdH8v77H+Pr+2//rGuXWmknRUabtBvdmeWYjywhUC4+YW8V9BDeGXtoR+yhnbA16IjiE1VM9Ln77lGsX7+2xN+nKEo0adKELVt210zOW1VFyj6LNmEb2oRtSFmxDpFN74ei90XV+xf87Yeq90PV+zqcFQyBDjHnCmwq1k6K7PBcNKU5HBRM6Q5hMT8VTeohtBd3IVpzKn+KCJeJhw4hUTEEYAvr6vCmDOlQK6HPgiUbKfscIBTxkNQk78dn/bNo0o+Xfg6S/pJ46BmCYvAHQQIEEDXYA5phD+uKPaj1FZ9LVcRCQbLRqPcifMPPoPfNwKtxNpItvVzxVxU1jnYI74E9uC1+qxy5ylMfOlHt6QmuNmq6n1u7dhUTJowFKNI3SZJEREQkK1asIzS09KKgqqoycGBfjhw5XGLfJkkSd901iq+/nlH9xtch1+IzUH3hqhQL5andkDJOOXKLNBmENaov1oY3gNbDMTtjTESwm5wCoar3r9CNW7DkoI3/B/25NejOr0M0Z5a4nj2gBeaWdzmqznkEoRaIg4ohELSuU1GtqmzcuJ7PP/+YLVs2ARASEsqkSQ/z2GNP4uFR/PzcHUDd4v7+6wfXYjt9+OG7fPrph2V6Ls2dO4+BAwfVqB3Dht3G9u1bS5wJLgtJkvDz82PXroP4+JRQWbaOsNlsvPzyC/z00ywURUEURWRZJiQklC+//JabbhpQ1yZWmX379nDPPaNJTU1Fo9ECKna7nejoRqxevYqgoIhr5vqpaeLiztO9e4dSrwtRFPm//3uRyZP/r8z91FXfZrVaGTp0EAcO7Cv20CVJEu3adWDp0tVlekW6OiaTiWXLFnP2bCx+fn4MHjyMiIiqRYLUZjv9/vsvPPnko4R6CfRrJNGhgcSpDIVdCTIn0hWmfTuL4cNHlrr9hQvxdO3artw+e/HilfTq1bu6za9TKt1OioyUcQJN6qGCYjoJiIVVuVUFVAXBbnHkpLTmOl5K2aGUUFAt268ximcwikcwsk8ktoiejgrehSmfZKszbZLiEYziEVi+A4TNhOHUQvTH/0STcaJIflVF54MtsjeKIQDD8XkIqoJiCMTSfCiCORMxP+VS0ZpKCKSqxgPZNxpV1DjESEmP3b8xclBr7IGtkH0bohZ4e5b2vFh5sVCh7fCphHfYVNweUYPiFeaoXu8d7ihW5B2GaExEd36do/BQCeeQ9sipCp/z1YLNZmPNmlUcP34UDw9PBg26vcziIbXRz+3atYNPPvmQ9evXoqoqnp6ejBt3D5MnP09ISNmh4vv27eHWW28scx2NRsOhQ6cICgqqTrOLEBt7mjVrVmGxWOnQoSN9+/av0XQj1+IzUH3hqhQLea8hdo8Qcm6fhRzQrGaOocgFM3Y70SbuQJN6CHtQa0wdJ2FreEOtub3XNr/8ModnnnnC+fBZiCiKdO/ekz/+WITBUDSXobsDqFtc+ftPTEzgp59msXbtamw2Oz179uL++x+ides2dW1arePK7VRTpKenc9NNfUhJSS72MC+KIr169Wb+/CVIUs1WVP/++2956aXnKy0WgqMIyrvvfsSkSQ/XgGVVY/LkJ5k796di5yMIApIksWjRCrp371lH1lWdxMQEbrihB3l5+ShKcfEnNDSUzZt34uNzbXuL7d+/lzlzfuTkyRP4+/szdOhwhg4dXiVRbOrUz3j77deKFP4Bx/fdpk07Fi9eiZdX2Z4sddW3zZv3K0888UiZ60ydOo2xY8fXkkXVy+LFC3jmmSfJzc1Bo9E4J13uvfcB3nnnA7TaynlK1VY72Ww2OnZsRVpaaqnrhIeHs3fv0VL7/nXr1jJ27F1lHkcQBD744FMmTpx0Rfa6GjXeTqoKshnBkotYKCBachFsuUi5CWgTd6BN3F6qw4QqarGHtHd6BApq0b5a0XoVyavoiLQKQvEIRjRedERoWbKLbuMRArKlmABobjEc4/VvoHoEFjfEbkLMTysQEAtERHM2oBYIpGY0aYfQJO8vdrxSvxpRg6oPQDEEoHgEoBoCHCHjhgBUj0C8gsPJtnsWLA9C8Yki/7C5RLGw6Y2/0LjvfBRZ4sz6ceSnRRL6fg+0nZugegSVGQIuZp9Hd2ETmqR9aJP3ock8iaXxreTcPrNC53G1sHnzPzzyyAOkpqY4+0BVVbnzzhF89tlXeHp6Ftum8PpZs2YDf/zxO+npaURGRjFu3D00a9a8Wu3Lzc3BaDQSGBhU4fvvrFnfMWXKs+WOR+fPX8INN1SxiFMZ5Obm8MQTj7BixTJEUUQQBGRZJjq6Ed9/P7t6UztcxrX4DFRfuCrFQsvP48nt/2GRfA5urpyUlBQ6dSo9easoirz44qv897+Ti3zu7gAcbuWKotS46FESrvr9b9y4ngkTxmCz2YqEhymKwvvvf8L99z9YxxbWLq7aTjVNfHwcjz/+EDt2bHN+Jooiw4eP5KOPPsfb27vGbcjNzaF3726kpaWWGXJZEoIgcNNNA/j11/k1ZF3lOHs2ll69Opc60JQkieuv78sffyyqZcuunLfffp2vv/6i1DYSBIG3336fhx56rJYtcw1UVeW1117i22+/QpI0yLLdGTrfrFlz/vprKWFh4ZXe7/z58/j00484deoEAF5eXtxzz0Sef/7FCuXsrKu+bdiw29mxY2upnsuOSc4eLFmyuvaMqibWr//bKZaVNClw770P8NFHn1Vqn7XVTuvX/11mle1CFixYRp8+JRdz2b59K0OHlu9x/uWX3zJmzN2VttGVcYmxgqogZZ5GzE1whDyb0tBknER7YUuR6tFQIA7qfCqVu132bYSp3QSsUX2RfRs5QmsLnTQubEaTdQZL08FYY26upnM5g5iXBKqMoMgItjykjJNoMo4jpR9HyksuqEpdORStNxbPdiQtjSAnoTnZF5pjzQsgovNa2gydBsCRRf/h4v6bgIKchR2qEEZsN4Okr7KjitFo5Pjxo85JoPrgbX3o0AEGDboZWbYX6+NFUWTgwEHMmfNbse0sFjNPPfUof/31V4HAqCIIjpDhRx75D2+++W7NpC6oID/99APPPvtUuevVhNe0qqoMH34HO3ZsK2ECX8LT04O//95cpudmVXGJfs1NiVyV1ZBzb/0GFRdPylsP+e23n8sMF1QUhZkzp/Pkk8/UaUfrSsTGnuarr75g/vw/MJnyCQsLZ+LESTz88GPXdGGElJQU7r13LBaLpciDTuHN6fnnJ7N37x6efnoyTZtW70yfG9fg1KmT/PHHb6SkJNOnz/X897/PkJqaiiRJ9O3bn/DwiFqzxcfHlwULljF27F3ExZ135vUrr6odOAZXVmvli1fVFAsXzi/m+X05siyzceN60tPTazSEpSZYuHB+uWLu4sULr1mx8KeffuDbb78CcBbrKbxnnz0by733jmXVqg2Vvj+PGDGau+4axYUL8VgsFiIjG5aYcsTVSEy8UO6YJSEhodTlrsx7772FIAglnp+qqvz00yyeemoyDRtGAY7KmnPnzmbPnl1Ikob+/W9i1KgxdZI+oSyPwoqu16VLNwIDg8jISC91HY1Gw80331Jp+9xUAEFEDmyBHPivmtOqiphzHm3yPkc15oBmKF7hjqdNVXV4KRbmVCwQGUVTBoIpDdWYjCwr2NuMQm58c3HPOlHC3qAT9gadauBcmiMHljPWtJsQzZkI5ixHAUtzpqOA5WXvBjkHW67jnMT8FESbEY/s7TS+TPM2ZQWj980AIHbjSKdQeEVoDOWvUwL5+fm8++4bzJkzG5MpHwA/P38eeuhRJk/+P5fOcexIYSOX2AcqisKqVcvZv39vMU+4KVOeY+HChUDxMd706V8TGtqAJ598uqbMLpd+/W4s5sn/b3x8fOnYsXO1H/uffzawdevmEpcpiozJZOKbb76s9ESUm2sD1+0tSkIQwa1KVzvHjh0t9yHj4sWL5OfnlxuWdC2wZ88uRowYgsVicT7gJiVd5P333+bTTz+kRYuW3HnnCMaPv6/ePbRfKXPnzi4mFP6befN+4fff5/LAAw/xzjsf1olX5rXEiRPHmTFjGitWLMVms9GpU2cmTXqEW2+9rVrFf1mWef75yfz00w9IkuQcFMmyzEMPPcZbb71Xo3lRSqNZs+Zs376P1atXsmnTBmRZpkePXvz440z27NlVqkhVWCnZVcjMzCxTLCwkOzuz3vU7+fn5ZS5XVZXc3Nxassa1UBSFqVM/LfUhQ5Zl9u/fx86dO+jZs1el9y8IAlFR0dVhaq0RHh5OfHxcmZ6FYWFhtWzVlRMfH8f+/XvLXEcQBJYsWcRjjz1R8D4Ju93uLNC0cuUyPvjgHebNW1AjD51lUdGcimWtp9PpePrp//Hqqy+WuNzhXXk/wcHBVbLRTRURBBS/GCwlVeQVBFS9r6PYi39j58e7d+/k8+kfs2bNKlRVJSBgFffdN4knn3zapXIBo/FA8fYA7whKursKAhiCfci+rPCMlHEC4chO8n9aj1/Dk3iFXMDD31EN/OKBvsRuGFtkHwkZ8TSnVc2fC46crmPGDGfXrh1F+sjs7Cw++eQDTp06yYwZP1Tr+C85OZnZs2eycOF8jEYjrVq14f77H2TQoNsrdRyTycSKFcvKnAzSaDQsWDC/iFiYnJzEr7+W7fjy1Vef8cgjj6PT6SpsT3XSqFEMgwcPZfnypSWO4wRB4JFHHq+RCbsFC/5Eo9GUOlEuyzJ//PGrWyx0UyL1Syx0UyN4enqW25kLglDjHayiKKSkJCMIIqGhoS7pxSjLMg8+eC9ms6VYbi1w3KQPHz7E0aNH+PrrL5g/fynt2rWvA0vrho0b15d5s4ZLoVU//PA9/v4BvPDCy7Vh2jXJqlUreOCBe1AUxTk42bz5HzZuXM8DDzzMe+99VG3X2XvvvcWcOT8CFBsIfffdNAIDA/nf/56vlmNVFo1Gw+23D+b22wc7P9Pr9UyadG+Z29177/01bVqFiY6OLtcjUqvVEhraoJYsqj5atWrNtm0ZpQqhGo2Gtm3b1rJVrkFc3Hni4+PKXEej0bB+/doqiYX1kXHjJrB9+7ZSlyuKwt13l31tuyLZ2eXnVxNFiezsLI4cOczDD0905vKCS/fWnJxsRo26k5079+PvH1CjNl/Oddf1ISIikosXE0sUtgVBICamMd269ShzP4888h/S09OZOvVTZ24tcHgLjRgxmrfeer9G7HdTfaxatYKJEx1h4oW/hczMTL788jNWrVrOkiWr6m/FclFCDm4D/dqgRo0l3SiTYTeizTnAsj+/55PFx1CEJ1EKcjpaRAspE1L45Zc/ayQX3b/544/fiqR/uRxVVVm06C/uvnsCN95YDaHeOMKG77prCLm5Oc7xf2pqChs2/M3IkWP48stvK+wUkJeXV+4zBDj6uMtZt67k6umXk5mZyd69u+u0MNLnn39NSkoKO3ZsQ5IkZFl2phYZMWJ0jY2RMzMzy/1+8vPzC+xxO3C4KYo7ptcNt98+uMyHUEmSuOWWQZVOql1RZFnmm2++pEuXNnTo0JL27ZvTu3fXEhP51zXr1q0hISGhRKHwchRFIScnh3HjRrhUKGNNU5n2UlWVadO+wmi8Nj2Gapr09HQefPBe7HZ7kUFC4d+zZs1g4cLqyceXk5PNjBnflNn+X389tVwPstpkyJBhPProo4DjAbwQSdIgCAKffvol0dGN6sq8YowYMbrMPliSJIYPH1kv0yDcf/9DZQ5k7XY7EydeW7lOCykMOy5/vcrl5KwJbDYbc+f+xMCBfWnSJJKOHVvx5puvkphYvSHBw4ePpH37jiU+1EiSRNu27RgxYnS1HrM2iIyMLPdBzW63ERPTmOnTvwFK9zbNzs7i999/qSFLS0YURT788FOAYpNQgiAgCALvvfdxhSanX3rpNXbuPMDkyf/H6NHjeOyxJ9m4cTvffPNdjY1F3VQP+fn5PP74g0UmKQuRZZmTJ0/w0UdXh+Crb2LAs4MXHl0aMOdsHA/NW8lx5Qwn1ROcLvgXr8Rjs9mYOPFujEZjjds0e/bMMqM4JEni559nV8uxbDYb48ePxmjMLSLyFbb7n3/+zsyZ0yu8P39//3LzWSuKQqNGMUU+M5lMFZr4NpvNFbalJvDx8WXhwuX8/PPvDBlyJ3363MDo0WNZsmQ1X389o8aEuujoRkXGuSURFhbmFgrdlIhbLHTDjTcOoG3b9iV2EoWhT08+ObmELa8cRVF45JEHeOONl0lMTHR+Hht7hmeeeYJXXplSI8etKocOHaxwrg9ZlklOTmLZssU1bJXrcN11fSp1szGZ8tm06Z8atOja5Zdf5mCz2UoV8ERRZMaMadVyrI0bN5Q7CDMac9m+fUu1HK86EASBb775hhkzfqBLl65otVoMBg9uu+12lixZzbhx99S1iUUICAjkjTfeAYo/iEuSREBA/fXSveOOIdx11yinoFBI4d+TJ0+mR4/6V+W5OoiKaoS/v3+Z69jt9joPmbdYLIwbN4JnnnmCQ4cOYjTmcvFiItOmfUnfvr04dOhgtR3LYDAwf/5ibr99SLHfy6BBd/DXX0vrRe7FfxMQEMiQIcPKvId6eXkxZMidrFq1vEwhWVVVVq9eWRNmlsktt9zGL7/8QZMmTYt83qxZC3777S9uumlAhffVqFEMzz03hc8//5pXXnmD1q3bVLe5tYrZbCY29jSJiQkuNxFenSxevIDc3NxSz1GWZebOnV3nwk11oqoq06d/XapgpSgKubm5/PXXHzVuy/nz58r0zpNlmbNnY6vlWCtXLiMp6WKZk1Xffvt1hbwFweElP378feU+R/y7uFHbtu3LvaYEQaBly9oJBS8LhwPObcyY8QMLFizjiy++oWfPXjUaTXfPPfeVeb8QRZF7732gxo7vpn7jFgvdIIoiv/32F23btgMcnbVGoykIPdYzY8YPNfagtnTpIhYvXlCsky/8/4wZ37Bz544aOXZV0On0KErFB3kajYbNmzfVoEWuxb333l8kbKgiFCZfdlO97N69s8zBk6Io7Nu3p1oeWirahvn5la86WJMIgsDw4SNYvnwtCQnpxMUlM2vWzy4rTE2a9AjTpn1fxONRFEVuuWUQK1eudxY9qG+IosjXX8/g7bffL3IOzZq14IsvvuHjjz+uQ+vqFp1OxwMPPFSqp4gkSYSHR3DLLeVXkK1JPvvsQzZvdkz8/NvDJC/PyH33jatW70d//wBmzvyJvXuP8O23M5k27Xv27DnMDz/8TEBAYLUdp7Z55ZU38fcPKPawXHhf/eijz/Hy8sJms5W7r7qKarj55lvYunUPa9Zs5Oeff2ft2n/YvHkn/ftXQ8GHeojRmMurr75ImzZN6NWrC506taZ//+tYtOivujatRjh69Ei53p9Go5GkpIu1ZFHNYzTmEht7pszxlCRJ7Nmzq8ZtKS/1gCiK1ZLzMynpIt9882W54/0LF+JJSUmu8H6ffvpZIiMblioYvvTS68UK5fXo0ZMWLVqWuo0kSQwadHutFthzJVq2bMVjjz1Z4jJJkmjWrDmPPPJ4LVvlpr7gzlnoBoAGDRqwevVGNm3ayMqVy7BYLLRp05ZRo8bi5+dfY8edNes7RFEqNaxXo9Ewe/ZMl3l4HzDgFt5885VKbnX1ziD/m4iISKZP/8GZS6kis4ktW7auBcuuPS4vMlIa1VVwpKIeH64wq1vfKaxge/ToEYxGIzExjWnQoP7lKfw3kiTx0EOPMWnSI6SlpSGKIkFBQYii4JL5a8Ehxixbtpi//vqTzMx0mjRpzj333Ef37j2qbPPZs7H89NMP7Nu3B51Ozy233MqkSQ+za9dONm3aiCiKzn5VkiQ8PT2ZPfuXOq1uabVamTXru1L7e1mWuXAhnr//Xs0tt9xWrceOjGzIXXeNqtZ9lsaFC/EsXbqI7OxsGjduwuDBw/D09KzWY0RFRbNq1XreeOMVli9f4hRYW7duy4svvsLAgQ5RuHPnLmzdurnMAk1dunSrVtsqgyAIFSqwcv78OebO/YkTJ47j5eXF4MHDuOWWQS5drbUyGI1Ghg27naNHDxdpq+PHj/HQQxNJSEjg8cdLfoivr3h6elRoUt3Do3qvnbqkvBBPcFwTtfG7HjPmbj788N1S+2NFURg5cswVHeOPP37jv/99vMJpMipzPwwKCmLFinW88cbLLFjwp3NiJCamMc8++wKjR48rcf/Tp89i2LDbyMvLK3KtSZJEWFg477//SYVtuBp5/fW3iYqKYurUT0lKSgIck5EjR47htdfecq2iQ25cCkGtR77waYWVqOoZVqsVjUZTJ5VAaxJBgOBgnytql1atGpORkV7mOm3btmf9etcJX7z77pGsX/93hb0kvvpqeok3tyulOr7/muLkyRPMnDmduXN/KtW7QZIkOnToxKpV62vZutqlrtrpp59+4Nlnnyp1uSRJ9Ot3I7/9Vj3eDQMH9uPw4YMlXheSJNG9e08WL679sLjScOXrx80lXLWdUlNTGTlyKMeOHXEKeIXVBu+55z4+/viLSt/z58z5kWeffcpZ9brwASsgIIBffpnP4cMH+fHH74mNPYOXlzcjRozmoYcerbNqxoVts23bHnr3LluY0mg0PPnk00yZ8motWVd92Gw2pkx5jjlzHBVERVHCbrfh4+PDJ59M5c47R9TIcdPT00lIiMfHx5fGjZsUWbZixTLuu6/0cYUgCGzbtocmTZq57DU0bdpXvP76S87rp/B337Jla/78cxENGtS/itb/5pNPPuCjj94rVbgRBIG9e48QGdnwitrJbrezZs0qTp06iZeXF7ffPrjOvKgOHtzPgAF9S10uiiLt23dkzZqN1XZMVVVrbFKpou0yaNBN7N+/t8xJ8u+/n83QocNrwMpLpKen07//daSlpRYbj0mSRPPmLVi9eiMGg6FK+9+1aweDB99SoaiUwqJG27fvq1L7ZGVlcv78OTw8PGnevEWZ+xAEyM5O4c0332HevF8xm834+PgyYcJE/vOfpwgJCan08a9GZFnm6NEjWK0WmjVrXqMOQeC6Yzg3l9qmPK4u9cqFsNlsfPfdNHr27ETDhsFERgZx333j2LXLdUJqXYHyEtkKgoCPj2sl7P/225nOan6CUPolVOgZU9MDA1ekRYuWfPDBp6xfvxU/P/9ioQGSJOHl5c0XX3xTRxZe/dx11ygCAwNLnfGWZZnHH/9vtR1v6tRpeHp6ldjWvr6+fPbZl9V2LDdu6pqHHrqPkyePA5fCbgsLhf3882ymTfuqUvvbtm0Lzz77FKqqOh/wVFVFVVWys7MZP34Ud901inXrtnDuXBJHjpzmzTffrTOh8HIqKopKUv30Fpsy5VnmzPkBVVVRFAW73eHpYjQaeeSRB1i3bm2NHDcoKIgOHToVEwoBBg26nUmTHgGKfv+FHuUfffQ5TZo0qxG7qoOlSxfz2msvOn/vl//uT58+yfjxo+p9Xj9VVfnhh+/LFI9EUWTu3J+u6DgbNqyjY8dW3HffON57701efPE5Onduw//+9986CUXv0KET/frdWGpIqKIoTJ78f1d8nAMH9vHII/cTFRVCWJg/N9zQk9mzZzn7YYvFwsGD+zlwYB8mU82nQHniiadLbWtJkmjYMIrbbhtc43YEBQWxePFKWrduCzh+Y4V9RK9evZk/f2mVhUKAadO+rHCfr6oqjz/+3yoLuf7+AXTs2JkWLVpWaB9Nmzblk0++4Ny5JGJjEzl1Ko7XX3/bLRRehiRJtG/fga5du9e4UOjm6sAtFtYAhdWhXn75Bc6dOws4HsxXr17JkCG3XrV5SqrC8OEjy01kW1Oz9lXF19ePRYtWMG/eQsaMGed8WLv8PBximBdz5/5xRTfl+k7z5i1Ys2Yjo0ePQ6fTAw6391GjxrFmzUZatXKHINcU3t7e/P77Anx9fYsMsgqr/b7zzgf07du/2o7XunUb1q79h1GjxqHT6QDQ6/XcffcE1q7dRNOmzavtWG5qDqvVysKF83n00Qe47767+eCDd0hIuFDXZrkUhw4dKDMEFOCbb6Y6H1orwjffTC31AUyWZdLT01iw4M9K21obNG7chMjIhmWuY7fb6dev/uWsi4s7z5w5P5YoXBV6M73//tvVcqz8/HzWrVvDkiWLOHnyRJnrCoLAu+9+yPffz6Zbtx5oNBr0ej0DB97KwoXLuffe+6vFppri888/LvP3fvCg4xqrz5jN5nJztakqnDlzqsrH2LNnF3ffPZK0tFQAp/CqKApz5/7E//5XfROCleH772fTs+d1wKU86KIoIkkaPvjgU2677Y4r2v/y5UsZNOgmlixZhMViQVVVTp48zv/93zPO+1b79s0ZMKAvAwf2o23bprz55qs1WlRl8OChTJniSFNUODFSOPYKDg7h998X1Fol78aNm/D335tYtmwNr7zyJq+//jbr129lwYJlVyycrV69qtzIqsLzvv/+B+ukLxJFEW9v76suos+Nm7rAHYZcAxSGVpT01QqCgFar4+DB4wQGBtWBddVHdbgWJyYmcMMNPcnPzyvRXT40tAFbtuzC29u1vAsvR1VV1q1bw6xZ33PkyCE8PT0ZOvRO7rtvUo2GgdQ3126LxUJOTg6+vr7o9fq6NqfCJCYmMHPmDP7883dyc3Np0qQJEyc+WCCA6srdvq7bKSsrk99+m8vKlcuxWMx07tyViRMfpEWLlpw6dZIffviOf/7ZiCAI9O9/I/ff/+AVe6RYrVZnW1fkO6oL6rpdXJG4uPOMGjWMs2djkSTJGRoI8MEHn9bJoN8V2+nLLz/nnXfeKDXXbiHr1291Fg4rC1VViYoKKdMLSBAEbr99CD/88HOl7a0pLm+b77+fwZQpz5a4XqEnw6pVG1w2/2RpfPPNl7z55ivl5t/ds+dwlb08FUXhiy8+4csvP8dozHV+3qNHLz777CuaN29Rpf0W4mrXUHp6Oq1bNy5zHY1Gw0MPPeasAF8fkWWZhg1DyszrptFoGDduAp988kWl2+nUqZPcc89ozp07W6YX5tate2jWrPYn61RVZefOHSxe/BdGo5GmTZszdux4QkNDr2i/WVmZdOjQ0ikSVhRRFLn++r78+uv8Sol2lW2Xo0ePMHv2TA4fPoiHhyeDBw9j5MjRLv0cU1FUVSU8PKDc/rBBgzC+/XYmvXtfX2t9vqv1c24cuNvFdaloGHL9jAlxYVRV5fvvvy31BqaqKjabld9+++WqS2pcFSIiIpk/fzH33DOGlJRkZ/Jfu91OdHQjfv31T5e/wQqCwM0338LNN99S16a4NHq9vt6FAhw+fIjhw+/AaMx1itmHDx9i8uQnmT//D3799U+X9xz19w/g0Uef4NFHnyjy+R9//MaTTz6KIAhFwr++/3463347k2HD7qryMXU6XbVU23NTdXJzbkZO3gAA/AdJREFUc/jxx1nMmfMjSUkXCQoKYty4e5g06RGCgopPVNntdsaMGU5c3HkA52+i8P3ZZ58iOrpRiRVNLxcVrwVk2U5Fnn8qmvzdsW7ZwqOqqpXyVKxtHnjgIU6dOsmsWTOQJA2ybHfmoouJaczs2b/WO6EQICcnyymcl71eTpWP8frrL/Ptt8XD1vfs2cXttw9gzZqNxMSULa7VJ6xWSwXWEiq4nusiSRJDhgxlyZJFpV7fdrudYcMql6rGZDLx3/8+yqJFCypkw4IFf/Lcc1MqdYzqQBAEevbsRc+evap1v7///kulhUJw3Kf++WcDCxfOZ9SosdVq0+W0adOWDz74tMb2X5cIgkDbtu04cuRwqX2iKIrcd98D9OlzQy1bVzPIsszff69mz55diKJEv3430bNnr3p5P3PjpipcO6P7WiI/P5/4+Lgy1xFFkcOHD9aSRa5Pp05d2Lv3CN999yMPPPAQkyY9wty589i6dY9L59xxc3UjyzL33TeuiFAIl/KTbdu2mU8++aCuzLsijh8/xpNPPoqiKEXOTZZlZFnm0UcnXVFoVH1FVVU2bdrIc889w2OPPcjHH79PYmJCXZtVadLS0rj11ht5553XOXcuFrPZRELCBT799ENuuqlPifeoNWtWcebM6TKrq3755efO/yclXeTVV1+kefNowsL8adOmKe+992a5BauuBrp06VauuOfp6Vnh0HtBEGjduk25OXC7dOlaKTtrE0c47scsX76WUaPG0q1bd/r3v5kvv/yW9eu31lmxhSslJqaJsxpnaUiSRGRkZJX2f+7cWaZP/7rEZbIsYzTm8tlnH1Vp365KSEgoQUFlTybZ7TY6dOhUOwbVIE8+ORlRlEoUFiRJolu3HtxwQ79K7vMRlixZVKF1VVXlwIH9FS7IVx84ePBAlSenRFFk9uyZ1WzRtcVDDz1W5uSJIIiMH39vLVpUcxw+fIgePTpyzz1j+PLLz/nii08YOvRWbrmlX70cG9YEhSL87NmzWLhwPrm5VZ84c+OauMXCakar1ZY72yAIgst7I9U2Op2OYcPu4u23P+Ctt95j4MBB5eYydOOmEFVVOXXqJNu3bytXrK8oGzb8TXx8XKmDbEVR+PHH77FY6p/3w8yZM8oUJgB++OH7WrLGNcjKymTIkFsZMWIIc+fOZsGC+XzyyQd06dK20sUq6prnn5/M2bOxxQb0iqKQkpLMf/7zULFt1q5d5fTsLglZltm0aQP/93/P0LVrOzp1as306V+TnZ0FQFpaKlOnfsbAgX1JSrpYjWfjetxwQz+aNm1W6j1KFEUmTJiIl5dXufvatm0Lt98+gMOHD6GqpVdM1Wg03H236z+AdevWg6lTv2H58r/57bf5jBlzd70e7wwZcmeZ7ejwHhuGv39Alfb/xx+/lSl8yLLM/Pnz6uV9pjQ0Gg0PPPBQqectiiI+Pj5X5N3uKrRv34G5c+fh6+sHOJ4RCvPZ9erVm7lz51XKQ+nkyRMsXrywXE/XQhRFYfXqFXTr1o59+/ZU/gRcEJ1OV2WvLkVROHv2bDVbdG0xevQ4RowYDfy7uJIjH/YXX3xdbyeHLufixUSGD7/DKQra7Xand//hw4e4667BtVI4x5XZtGkj3bq1Z+TIoTz33NM8/PD9tG3bnE8++aDeF6hycwm3WFjN6HQ6+vYtvQoYODqcm24ayNKli/nmmy/59defyczMqEUr3bi5eli3bi033tiHPn26MXTorXTt2o677hrM4cOHrmi/u3fvKlM8AcjOzubs2dgrOk5dsGHD32WGSMqyzIYN62vRorpn0qR72bNnF+DooxXF4WWpKAqvvfYiixeXH/LlCiQnJ7Fs2eJSRW5Zltm+fRvHjx8r8rnZbK7Q4G7OnB+Jj49DUZRi68uyTGJiIs8993SV7a8PCILArFk/4+vrW+ReLwgCgiDQtWt3pkx5tdz9rF//N3fdNZi9e3eXuo4kOQoDTJs2kwYNGlSL/W4qjpeXFx999DmCIBQTtyRJIiAgkFdeebPK+09Kuliu8GG1WsnKyqryMVyRJ598hp49r3NeM4VoNBokScN3383G09Oz1uxJTk7i008/5J57RjNx4nhmz56F0Wisln33738Thw6d5JtvvuPhhx/nqacms3r1BhYsWEZAQGCF92M2m3n//beqJJQlJSUxYsQQZ9HF+syAAbdeUUqGgICqCftuHIiiyNdfz2Dq1Gm0bdsOURTR6XQMGnQbS5asZvTocXVtYrUwc+aMYpFFhciyTGzsmWu6YOnOnTsYM2Z4MQ9Ls9nEBx+8w/vvv1Vtx8rLyyMu7jw5OdnVtk83FcctFtYATz01udRZP0mSCA8P53//+y8PPHAPb731Kk899Tjt2rXg/fffqvBsoRs3rsj58+fYsGEde/furpXf8vLlSxk3bgTHjh0p8vm2bVu4446BHDpU9XB/SZIqJJ6UJyi6IhU5r2tpVvDAgX1s2rSxVIFNEIR6M1N66NCBCl17/xaoOnToWKHtygtnk2WZ1atXXvUVlFu3bsPGjdt54omnCQ+PwMvLi1at2vD++58wf/6ScoUOWZZ5+un/oChKmeOFkSNHs3r1RoYMGVYTp+GmAowcOYa5c+fRtm1752eFHoWrVq2vcmETcITkltevaLVa/Pz8qnyM2iA29gyvvDKF3r270qNHJ5566nEOHtxf6voGg4F58xby5pvv0qhRDODIa3znnSNYtWo9N900oHYMBxYvXkCXLm358MN3Wb16JStWLOW5556me/f2VzSGuByDwcDIkWN47bW3eOGFl+nUqUultt+2bQsdO7Zi6dLFVboPybKMyWQuNeS9PnHrrbfRuHGTKkUfiaLokmKW3W5n6dLF3HvvOAYNuomHHrqPdevWuuwzoSiKjB07nr//3szFi5lcuJDGDz/MpUePnnVtWrUxf/68Msc7oijy119/VGhff/+9mlGjhhEZGURkZBAjRgxhzZqV1WVqnfDOO6+XOX6ZOvUzUlNTr+gYcXHn+c9/HqZFi2i6dWtP8+bRTJgwptr65auRvLw8Tp8+xcWLidW2T3c15AJsNhsrVy5jyZKF5Obm0qxZc+65ZyItW7aq0v5+/fVnJk/+rzOsqLCIQHh4hDNEq6Sv/plnnq2QR4Ir4K5wVLe40vd/8uQJXnjhf2ze/I/zs8jIhkyZ8kqNDcxsNhsdO7YiPT2txGupMB/QkiWrqrT/3bt3cvvtZT+wRERE8v/s3XVcE/8fB/DX3Y0UTAxsEezu7lYURcVAUUAxMbC7xe5uBUVRERCxAP3a3QliYIASCkjudr8/+G06t90GrNDP8/Hwod5ud+/tdvW+z+f9uXfvKe9Fqz5tJ7FJk8bBx+ewwtaFDCOAk9NweHqu0XJk2vP7dvH0XIr161crTYQ9ePAcpUqV1lKEORMWFgIHB+UF8zdt2g4Hh0GS/3//noCaNSshIyNDLUlRL6+j6NSpa66Xo4/7j6ri4+Nw+XIY0tLSUKNGTdSsWVvy2qVLoejf307pMnQ1kqkq8vK2+d27d2/x33+XwLIs6tdvwFsr7/37d0hMTETp0qVhbGyCL18+wdQ0H0qUsMzRuiMjI9CkieLEEcMw6NOnH7Zs2ZntZQuFQoSGXsDr169QrFhhtGjRDiVLqv/4FRwcBFfXoVI1cBmGAcuyWL58FVxc3JQuQyQSybQy1IYnTx6hU6c2cltKMwyDAgUK4M6dxzA3z6/xWBTtT2/ehKNt2+bIyMjIdfKoQIECCA+PymWkuvf+/TvY29viw4f3koGUlGEYBkWLFsPlyzey1aJT08e5pKREDBhgjzt3bkn2G/HfnTt3w549B2FoaKj+Ff9l1L2drK1LKx24qm7d+jh3jr8Xztq1K+HpuUSyTYFfx8fp02fDw2N67oPVsi9fPqN2bf78CEVRWLZsJVxd3XK0XSIj36Bbt/b48eOH1LU5wzAQCATw9Q1AkyZNc/oR/jqxsbHw9FyCY8cOIy0tDQBQu3ZdTJ06Q+G1uKqjIZOWhQBiYmLQvn1LuLgMRUCAP0JCLmD37h1o2bIRVqxYmqMbp4EDHfHw4XNMnz4btrZ26NvXAfv2eaNw4axRKBUtc/PmDYiLyxsF4pOTkxEVFYWUlBRdh0Lo0Js34ejatT1u3LgmNf3Tp48YN84Ne/bskJqekZEBP7/jmDNnOubNm4XQ0As5ugAODb2I2NhvCvcllmVx69YNREa+yfayAaB+/YaoV68+byJw7Fj3PFlb08XFDSKR4sQYx4ng7DxSixHpVlpamkoF09PT07QQTc59/vwJV65cUnrDTdM0WrduKzWtYMFC2LZtD2ialulamxOGhkY5ep+mvX0biYAAPwQHB+H79wSNrCMjIwMzZ05BzZqV4ObmjAkTxqB9+5bo2LEVXr9+BQAqdwd8/z7vdxvUV4mJPzBs2CA0blwHU6ZMwIwZHujQoRW6dGmL9+/fyX1PuXLlUaZMGaxatRxVqlRAkyb1UKtWZXTs2BrnzgVnOwYrK2sMH+6qcAAMU1NTeHhMy/Zyr179D/XqVYejowOWLl2I8ePHo169Gpg4caxa6x9++vQRI0Y4QSgUygyWBQAzZ07FnTu3lC6HpmmdjC6aVY+WknsdwbIsEhIScOyYj9bj+t327VuRmZmpllZm6uparWvlypXHtWt3sW3bbjRr1uL/vx35vx/x76pKlWrw9w/OVqJQGyZPHi9p6S/eb8R/nz9/FsuW5bzMAZFzVlYVea8LBQIBbGwq8S7j7t3b8PRcAgByj48rVizF7dvKj4/6JjY2Vuk8DCPIVcvCmTOnyCQKgazvLjMzE+PGjdTblrfaFhcXh65d28Hb+4AkUQhkPQxzdHTAkSNeuVr+P58s5DgOTk4DEB6edQEvvoEW/zjXrFmBY8eO5GjZxYuXwKRJU7Fr135s2rQdVatWxbNnT3iTj1lN0VUb5ex3MTHRWLVqOTp2bI3WrZtiypSJua7ZpsiLF8/h4jIE1tZlULZsWVhbl4W7+2iFF9eE/ouPj8Pu3dsxf/5sbNiwJlvbcvHiBUhJ+amwVdaCBXMkgyDcu3cHdetWhZubM/bu3Y3du7djwAB7tGzZKNu/nw8f3ikdpANAjgc8oSgK+/cfRoUKVgB+FXIWdzseNswFrq6jcrRsXatRoybWrNkIiqKkEkMMw4CmaWzYsBWVKlXWYYTaVb16DaUjnpqZmWmkVY66XLt2BU2b1se2bZt5zzE0TaNPn35yW0L16NETZ85cRPfuPSUtGSpWtEHJktkrVp4vXz40bKhf3ZE+f/6E/v17o3HjOnB1dYKT00DUqGGD2bOnIyMjQ63rGjfODXv37pb5TT19+gQ9enRCVNQHFCxYUKVliQdGINRLKBSif//eOHcuWLK/iP9+9OghbG07y31w+/17Arp374h9+3YjNfXXg9InTx5hyBAHHDy4L9uxLFu2ChMnesDY2ERqevXqNRAQcA5WVtbZWt7jxw/h4NAHX7/GAMi6nuU4DiKRCD4+3pgwYUy2Y1Tk4MG9EApZhccchhFg586talufup09G8RbvxeAzrsL+vufVGk0Y1UGVyxbtpy6wtIooVCId+/e4sOH9woTAkZGRrC374+UlJT/f3ZFv0EGR4+eRGjoVcn1XE5FRr7B9etXER7+OlfLEQsPf42AgFMKty/HibBv3y4kJyepZX2qSklJwYcP7yXX7v+i4cNH8CajhEIhhg515l3G3r27JAMaySMQCLBv364cx6grxYuXUHq8YVlhjnviREV9wKVLobwDTH748B7Xrl1ReZnp6en48uWz1vclbVi9ejk+foyS+b7Ev9+pUyfl6uG4xpOFCQkJWLx4Mdq2bYtatWqhZ8+eOH78uKZXq7Lbt2/h/v17vLWqNmxYo5ZuWfHxygcxYRgm24Od3LhxDY0b18WaNSvw6NEDvHjxDN7eB9CuXXO1X6Q9eHAPXbq0xZkzpyXfWWZmBnx9fdCxY2u8eROu1vURmrdz51bUrFkZs2fPwO7d2+HpuQSNGtXG1KmTlBaRjo+P+//FtuIL2YyMDJw6dRJRUR/Qt29PyQ2YUJgpWX5k5Bv07t0dP3/+VDnuggULKRw99HeFC+f8KXKJEpYIDb2GLVt2om3bDmjQoCH69h2AoKALWLlynU5aQqiLo6MTLl68AgeHQShTpizKli2HQYOGIjT0mlT31H9Bjx5Zo5kqeorMMAwGD3ZSaVTXtLQ0+Ph4o2/fnmjbtjlcXIYgNPSiRusdJiTEw9GxP9LT0xTui+LP1qRJM6xcuU7hsurWrY/duw8gKuoboqO/4/r1u7Cz66tyC1qKojBy5GiVRgLWlri4OHTv3hFXrlySmp6RkYHdu3fAzc1Zbdvn4cP7OHXqhNxjE8uySEpKxObN69GhQyeZ5NCfSpYshXr1GqglLkLa2bNncP/+XYXF679+jZF7E7d+/RpERr5ReFE+c+aUbPcOYRgGM2fOw/PnEdi3zxtbtuxESMgVXLx4BdWr18jWsgBg9WpPiESs3BtdkUiEkyd9ZQY4yi6O43Dt2hUcOLCPt5U6ywpx5cp/Cl/XlatX/0P79i2VtrTjOE7nI1H/npRWhKIodO/eU+l8w4a5qiMkjREKhdiwYQ1q166CRo1qo0GDmqhfvwZ2794u9/f89m2k0hrZQqEQMTExubpeu3nzJrp27YAmTerCzq4bmjdvgPbtW+LKlcs5Wl5GRgaWLl2Idu1aKD33pKam4t49xYNg5cTLly8wc+YU9OzZBQMG2OPQof34+fMnPn/+hIkTx0pqxFWqVA6DBvXDw4f31br+vKBvXwe0bt1O4XXhsGEuSms03rt3h/dhhFAoxN27t3MVpy4UK1YMHTp04r0uNDQ0RK9eykviyPPmTYTS/YKiKElPDT6xsbGYOXMKKlUqh9q1q6BixdIYNKivZEDDjx+jsGHDGsyePQ0bN66TGbBF36WlpeHwYS/e+/DMzAycOKFafU15NFqZPyUlBc7OzggPD8egQYNgZWWF4OBgzJ49G7GxsRg1SvetckJDz0MgEChMinAch4iIcHz69BGlS5fJ1bpKlSoNipLf3UFMKBRmq1h2QkI8Bg/uh7S0VKmTpfhHM2fODFSrVgMtWrTKeeD/x3Ecxo4difT0dJkTs/gmaNq0yThxIjDX6yK04+jRw5gzZ4bk/5mZv7brwYN7YWJigkWLlil8f0xMjNJm4AKBAB8/RmHXru1IS0uTOz/Lsvj4MQp+fsfh6OikUuydO3eFsbGxVJPr31EUhfLlK6BGjVoqLU8RY2Nj9Os3AP36DcjVcvRRzZq1sH697guep6WlISDADyEh55GRkYnatetg4MAhWhv91djYGDt27IWjY39JfVkxmqZRpUo1TJs2U+lyvn79ij59euD165eSOkovXz5HYKA/bG3tsGPHXo0MiHPkiDdSUlJ4zy0WFhZYv34L2rbtoFLi7/f6YcOGuWDnzq285y9xDZ7+/Qdi2rTZOfsgGrJr1zZ8+fJFblKD40QICgrArVs31VL/xtfXh/eagmVZ+PgchqfnGkyePJW3i9ns2fPzZJmDvMDX10eqhtSfRCIRjhzxwpQpv58fM3Ho0H7ei3KWZXHs2BGMHj0u2zGZmZmje3fbbL/vd8nJSVKtJeURCATw8/PNcX1skUiEiRPHwsfHO6dh6lRYWAgGDeqr0gMChmE0krD/9u0bQkLOIzU1FVWrVkfjxk0UJrOsrW3w/PkzhfHSNI0hQ4Zh1ar1WLBgDrZu3SgzD8MwqFmzNpyc+FtC6RLLsnBxGYqzZ4OkPuunTx8xa9Y0vHz5QjIyuZgq3RwZRiBpZQtkbf/du3fg/v27MDQ0QOfO3eDqOkphb4rbt2+hd+/uMsf0Z8+eoH9/O3h7H0O7dh1V/pwikQguLkNx/vxZlR54A1Da+jU7NmxYg6VLF0qOfxRFITT0AlasWAqhUIgfP75LjnEcxyEs7CIuXw6Fj89JtGzZWm1x6DsDAwN4eR3FunWrsHfvTsmI9CVLlsK4cRNUqsWqSq3JvFqPcu7cRbh+/SrS0uQ/pJ4/f3GOe0ao8rCZ4ziYmZnxzhMTE4OuXdvhy5fPf/ymQxAWFoKuXbsjKCgQFEWDYWiwLItlyxbC3X0yZs6cq/IDhi9fPsPX1wefPn1EkSIW6Nu3f7Z7BOTU168xSh8oCQQCREZG5HgdGm1Z6OXlhefPn2P58uWYNWsWBgwYgH379qF58+bYvHkzvnz5osnVqyQjI1OlH4M6uilZWpZE27bteS/+zc3N0a2b6heLPj6H8fPnT97RFLdv35ztWOW5ceMaIiLCFa6LZVlcuXIZb99GqmV92hIXF4ft2zdj6tRJWLRoHh49eqDrkLRCJBJhxYqlCl/nOA67d2/nrU2hSqs9lmVRpEgRnDp1gvcmi6Io+PufVLo8sfz5C2DSpKkKX+c4DnPnLsrTrf/+BRER4WjSpC7GjXODv/8pnDkTCE/PJahXr5rKI82pQ9u27XH2bCh69Ogp6TZStGgxeHhMR2DgWZUK3Lu5DZe0rhYfJ8W/+dOn/bFmzQqNxH7t2n9Kb3x//PiBDh065yj5VL58BezYsVemnqH4iXv16jUwdOhwnD9/CZs2bde7BJe39wHe1k8CgQBHj6on8fHt21elD1BSU1OQmpqKCRM8MGXKDBgYGICiqP8nkimYmJhi5cp1f+UDCn3x7VuM0q6d8fFxf/w/HklJ/AXvaZpBRITuelgkJSWpkASjEB+f8y5JW7duUjlRyDACtGyZ+4fV6iISiTBlygTeUTx/x3EchgwZptKyL18Ow6BBfVGhQklYWZXCsGGDZGo5Z2RkYNq0yahVqxLc3UdjxgwP9OzZGc2bN8SjRw/lLtfZeSTvNhWJftUYnj9/MVasWCvV/S9fvnxwdXXDyZOnlY7SLpY1qn0wJk0ah1GjXLBx41p8/foVAHD//l24uQ1H5crlYGNTFkOHDpAa3C6nTp/2R3DwaYWf9eDBfbh+/arUtBIlSqjwWYSSshuLF8+Hg0NvhIZeQFxcLL58+YJDh/ajbdtmcmuOchwn6WXz5+9F/Bvy8JiQrdpp58+fxblzZ1ROFGYleuuovHw+QUGBWLp0IQBIJU+ArKRDfHyc3BpxLMti7NiRKnWH/5sYGRlhxow5ePo0Ateu3cXNmw9w//4zuLqOUuneokuX7rzXQwzDoGvXHuoMWWuqVKmK06cvoH79hlLTLS0tsWHD1lyVaapbtz6KFeNvLGBgYICOHTvzzrN48TypRKEYy2a1vBePLi8SsZK6sCKRCOvXr8bmzRuUxslxHFav9kTdutWwbNlieHkdwLp1q9CkST14eLgr7Z2nDubmygcoEYk4leZTRKPJwlOnTqFo0aLo0ePXjkDTNFxcXJCZmYnAQN23QKtTp67SWlUFCxbMdatCsYULl8HExETm4CE+6Hh6roGJCX/XpN9duXKJ9yKCZVlcvHgeJ04cy3HCk2VZbNiwBoMH91dpfnH9x7zg0KH9qFWrEhYsmANv74PYvn0zOnZsjYED+/41haAVefLkET5+5B8VTygU4vx5xUXbixcvgebNW4Km+UYDpmBnZ4+UFP4uxhzHISkpe7UkJk6cgunTZ0uezIn3K3Pz/Ni8eQd69FDeLYdQD1Vvvn6XmpoKe3tbxMRE/38Zv2prZWZmYsyYEZKuAtpQs2Zt7Np1AFFRXxEZ+RlPn4Zj6tSZMDNTfpJ99uwprl27wlN7iJO0rlU3VVrI5Labra2tHa5duwMXl5Gwtq4EK6uKcHQchkuXbiAs7DpWrFiLOnUUj+qqTU+fPsGOHVuwfftmPHhwT2nrE6FQiC9fPqtl3SVKlFQ6WI65uTlMTExAURSmTZuFx49fY+XKdfDwmI6NG7fi2bNwDBvmopZ4ciM5OQnXr1/FtWtXkJj4Q9fhqFW5chV4a0lRFCVTbylfPlMVbhBzd1GeW4ULF1F6DSkSsTmuXScUCrFt2yaV52dZIUaOVF+NxNy6efM6oqI+KD0eMgwDiqKwZs1GlC9fQely169fjX79eiEsLAQ/fyZLWnj26tUVu3Ztk8w3fvwoHDiwVyZR8/btG/Tq1RWvXslePw8YMBjt2nWU+e2JjzPTps1C1arVAGT9bocPd8W9e09x7dpdhIVdx7Nnb7B4safSVjhi0dFf0LZtczg6OuDo0cM4deokli1bhDp1qsDdfTS6dm2PwEB/JCQk4MeP77hw4Rz69OmBDRvWqLR8Rfbu3c17LckwAhw4sEdqWtmy5dCkSTPe95mamqJbN1sEBwdh06asEhx/DjYhFArh4jJU5lzx9OkTPH/+VOG1Dcdx+PTpI3x9VR8E59Ch/So/UGMYBnZ29ihatKjKy+ezceNalQZz+5NIJEJ09BeEhV1USxx5jaGhIWxsKikd9ORPw4a5wNDQUO57aJqGgYGBXrf2VaZ69Ro4ffo8rl+/h8OHfREYeB737z/HwIGOuVquQCDA1KmKe/NQFAVXVzfJoLHy/PjxHX5+x3mvyfls2LBG6fX63r07sXLlsv/f/2QlHMXr8/I6gMWLc9Z6PzsKFSqMFi1a8R4DWVYIW9ucdQkHNJgsTEpKQmRkJGrVqiVzgqtduzYA4PHjx5pavcq6du0BCwsLhTs/TdMYPtxVbc2EK1eugqCgi2jevKXUdGtrG+zffzjbLQlUuQEUiUQYPdoVPXp0ynaxWo7jMGbMCCxbtgg/f6qWPMuXT7ULEl07e/YMPDzcJU8Tfq+hd+lSCEaPzvnN2tOnT7B27UosX74IAQF+ai+grw6qJENpmlbakmLmzHmgaUrhPjRy5BiUKGEJG5vKvCdZhmEkF7yqoigKHh7T8fRpONav34JZs+Zj5859ePo0HP37D8zWsoicCQ29iL59e6JkycKwtCyETp3a4ORJX5WOTf7+J+U+9ROjKBpbtyq/MU1LS1PrEzyBQAAzM7NstUq9evWy0ovIxMQfeP78abbjefjwPtzdx6Bp03po3bopli1bhE+fPkpeb9KkudJ9q0mTZtle75+srKyxZMkKXL+e9YR99er1qFateq6Xqy4xMTHo1asL2rVrjvnzZ2PBgjno3Lmt0u3CMAK5A75ILzsajx8/VFrPZuBAR97forj+5e+/rSJFisDJyRkeHtMxYMBglZLTmpSWloa5c2eiWjVr2Nl1Q+/e3VGjhg1mzpyClBTl9dP0RVpaGiIiwvH+/TuZ49GgQUOUdu378ybOzMxcae8QoVCY4zpN6mBkZAQHh0G8MVIUlePzY0REOL59+6p0PvHgY8uXr9KrwY5UHfCsZs3a8Pc/i8GDhyqd99atm5JyAvJGPJ09ezqePn2CJ08ew8/vuMJ6pmlpaVi2TLbsi4GBAQ4ePIJZs+ZJtbapXLkqtm/fI9VVXoymadjYVEL16jVUbk0ojsPBobfkoX9Wizr2/9fIQvj4eIPjOKljnPhzLl26EDdvXld5XX+KiHittP6lvGTqwoVLIRAwCo/z8+cvgZmZGXbs2KLwhjrrM2XC2/uA1HRVfy/jx4/CwIH2Mq2R5Xn3LlLlAWsqVaqM5ctXqRSDMsnJSXjw4F6OR5BlGAavX6tnYJd/RalSpeHt7QtjYxNJaRfxH2NjE3h5HctW6TF9ZW1tgw4dOqNx4yZq61kydOhwzJo1TzLwooGBgWTZQ4YMx9y5/KOEf/jwXmljMD6JiT9w/briAVQyMzOxZs1Kha9n9c7bqdIxIbeyzgGc3HsWmqbRpUv3HNU/FtNYzcKYmBhwHAdLS9kLcDMzM+TLlw8fP36U807FNNGbMDIyAoMGDcW2bZukalWJv/BGjZpg0qSpal13tWrVcOJEAKKiPuDDhw8oVKgQqlatlqPuko0bN0VYWIhKB/8nTx5h8uTx2Lv3kMrLDwsLgZ+f6gPSFC5cBI0aNdbItlK3VauWS+qK/YllWZw7F4yXL59nK4H148d3uLoOw6VLof9/Mk1DKMyEhYUFdu8+IJMkzg3xd5zT79rKykppDU2RSISKFa1519G4cWMcOXIc7u5j8OXLZ8kyDQ0NMWaMO2bOnAOKAlxcRmDMGMWtxFiWxbBhzjn6PIUKFcLgwUOy/0YtyO120mdbt27C/PmzwTCMZD96/PghRo1ywZ07t7B8+Sre49qFC+cU7oNA1s3B+fPBcr+7zMxM7NmzE7t378D79+9AURTat+8Id/fJaNpUeWJM3dtF1QtwjhNla51btmzEggVzwDACSXLj9euX2L59M7y9j6FVqzZwdByKNWs8kZaWJnd/ZlkWo0ePy5O/QVW3U0pKCnr37iYpgyGvhq8iLCuEg8Mguet48uQxFi2ah0uXQiXTmjVrgblzF6BBg0Yy81erVg1OTs44eHCfzLZgGAYWFkUxbtwEvd0WLMti6NAB+O+/S1LfYVpaGvbt243nz5/hxImA/3edznpN3z7Lz58/sWrVchw4sE8y8qG1tQ0mTZoiSZK1bNkKtrZ2OH3aX+52qlq1GgYPHirz2SZPnobLl8PknjsZhkGLFq1Qr159zX04FXh4TMe5c8H4+lV+V+s5cxagRImc1YPlS+b8rkaNGli3bjNq166To/VoiqoDni1YsBhNm6pWw3Tv3h28dUoZRoD9+3fD3NxcaT3TI0eOwNNzLQwMpBsoGBkZYuJED4wfPxHfvn2FQGCAIkWKqL3MSljYRbx48TxH72UYBrt2bVPp/MtxHK5fv4bQ0IvIzMxA3br1kC9fPvA1AqcoCgUK5JfZJ+vVq49Tp4IwdepkPHv2RDK9ePHimDVrPgYNymrldPfubd7fr0gkwu3bN6WWn50B8i5dCkXfvj1x9mwojIyMFM5nYVEUERHhvNfehoaGmDt3IRwdnVRuEaqMqvuu4veLYGaWT++O92L6ej5q2bIVHj58hqNHD+Pataxu9M2atcCAAYNQqFDOB2DMK3K6XSiKwqRJUzBo0BAcP34UHz9GwcLCAn369FNpVPN8+VR/SKLIz58/Fcb94ME9xMby91rJzMxASMh5jTdead68BXbvPgB399FITk6GgYEBRCIRWJZF167dsWXLTrmfQ+VtwmnI/fv3uUqVKnGrV6+W+3qzZs24Tp06aWr1vOLi4riQkBCuSZMmHADJH4qiOAMDA45hGK5y5crchg0buLS0NJ3EqKro6GjO2NiYoyhK6rMo+kNRFPf+/XuVl9+7d2+OYRiVlg2AW7duneY+rBpFRUUp/SwMw3CLFi1SeZksy3LNmzeX+33RNM0ZGxtzT5480eCnyr5u3bop3L40TXMlS5bkhEKhSssSCoVccHAwt2nTJu7gwYNcfHy8zOu9evWS+a2K/z9lyhSV446KiuLmzp3LNW/enGvevDk3d+5cLioqKlufncidx48fK92HAgMDeZdha2urdBk0TXMikUjqfRkZGVynTp04iqKkfk8Mw3AURXGHDh3S5EeX68aNG0o/i6mpKZeUlCTz3j8/n9jFixd5j+Wmpqbct2/fOI7juLNnz3JGRkZS+7NAIOAAcAsXLtToZ9cHu3btUum3JG9ar1695G6DO3fucCYmJjLHSIZhOAMDA+7y5ctyYxEKhdyCBQs4c3Nzqe3VpUsX7t27d5r+KnLF19dX6ffo5eWl6zAV+vnzJ9eoUSOZbSY+Tvx+Ts/IyOBmzZoltZ0MDAy4YcOGcQkJCQrX4efnJ3mPgYGBZD/r1q0bl5iYqIVPqdzHjx+5/v37S30P5cuX5/bt25er5aampnL58+dX+hu5cOGCej6ImqWlpXGFChXijd3S0pLLzMxUeZllypRR+n1Uq1aNc3JykvxW+P7ExcVp8Bvg5+bmplKMiv6UKFFC6To+ffrE1atXT3KOMjAwkJwf5R2jf9+Ht2zZwrvsBw8ecCdOnOAuXboksw2NjY15Y6coirO1tZV6j1Ao5EqWLJmt70DZ8XHnzp1Kz1OK7p1zQyQScVZWVirfL/75h2EY7vPnz2qPiyA0QSQScVWqVMnx7x0A7z17cHCw0vdTFMVt3bpVa5/558+f3L59+7hp06Zxixcv5p4/f66W5WqsZSGnpAsax8lvLsknLi4JuSm79OTJYyxfvhgXLpxTGBPLsmjRohV8fU/9vwtmBpKS9K8LqRjDmGLPnoMYNmywpGAnH47jcPr0WZWz3M+ePVepuTxN05g0aQoGD3ZGbGz26s7pwsePyrvR0DSNb9/iVf48ly+H4dq1a3Jfy6rBJsTChYuxbdvubMWqCEUBRYqY52q/mD9/KW7evIkfP35IbWdxvZ6NG7chIUH1bmcNGjRHgwbNAQAsC5nvbvv2fahdezN27tyK6OisOnXm5uZIS0vD6tWrcfjwETg7j4Crq5vC0bDOnQuGs/MQSdFlALh58yZWrFiBPXsOokuXbtn6DjRNHdtJH61bt5F3NFGGYbBmzTo0adJa4TIqV66GoKAghcctmqZRrVp1HDlyHDdv3gBFUWjRoiVevnyBCxcuyJxnxLE4Ozujfv1mvHV+1L1dKlashho1auHFi2dyvxOapuHo6IS0NA5paUlITk7Gnj07sX//Hnz69BFmZubo188BY8aMR7ly5QEAnp4rFX7HHMchLS0Nmzdvx7hxE1C/fjNcvXob+/btRnBwEDIyMtCwYSO4uLihSZOmeeK4LI+q22nv3v1KW0obG5tI1U41MDDE0KHDsHDhUsTFyZZlcHFxRXp6hkyLDPGxp3379nB0dIKrqxsqV64iNc/YsZMxfPgo3Lp1A+np6ahatZpku6q6LcLDX2Pbtk04dcoPqakpsLKyhovLCDg6OsHQ0BCvX79CWFgIWJZF3br10aRJ01y3NNqyZRvvfk3TNLZs2YrOnXvq5bFt06b1uHv3rswxRfy7mDdvHjp16oGKFbNGKpw0aQbc3Nzx4MF9sKwQNWrUROHCRSAUKt5OLVq0x9On4fD398Pr1y9hYmKCbt1sUaNGTaSnA+npv94nEokQFhaCGzeyumc2bdoMbdu2z1HdsOwwMsqPLVt2Y8GC5Xj3LhKlShWDpWU5UBSd62PBsGGu2Lx5vdzjNsMwKF++AmrXbqS3x5xZs+Zh6tRJCl+fPXs+vn9PVXl5fLWixCiKhoVFcaX3Rvny5UNmJqW17y4zMxPXr19FQkICypUrh4SExFzVt6Vp/t9XRkYG2rZthzdvskbm/L2VZWpqKjiOA00zMsdchmFQokQJdOtmx7v80qUronTpigAgsw1btWqDkJALvPc0jRs3l1n+3LkLMXr0CIXv+R1N09i9ey86d1ZcL7tz556wtl6Ft29luyMzDANLS0vY2fXXyG/A1XUUZs+eLvc1vvMnRVEYOnQ4DAzM9Ha/1sfzEaGb7fL9ewKOHPFGRkZmjo5nDMOgdu26KFGinMLfe7FipeVO/x3HcShZsrxW95kePezx21AhvOsWbxtlKC43ZwUer169Qs+ePTFkyBDMmTNH5vW6devCxsYGx44dU3mZsbE5/6HdvXsbffr0kCo+ycfH5yTateuQs5VpCcdxuHPnNt68CUdychJCQi4gNFR58dmNG7dhwIDBKq3D1rYzbt++ybuzWVhY4OLFKyhZspTKsetaSkoKqlSpgLQ0/gvC7HxXHh7uOHLEi7delYGBAaKivqnlRoGiAAsL81ztFwDw/v07eHougb//SUnsrVu3w/Tps+R2s1MH8Uh7bm4u/98nf31nWQmiGvD3PyMzAu3bt5Fo0aIhhEKhzG9SPJrolSu3YWVVUSNx54S6tpO+6dSpDR4+vM87T6FChfDq1XuFr3/+/An169fgPSYXKVIEcXFx/x8pNuvmQiAQgGVZhcclmqYxa9Z8uLsrviHUxHZ5+zYSPXt2kRoRV9zNukmTZvDxOQlTU1P8+PEdvXp1w8uXz6VuuBlGAFNTE/j5BaFWrTooV644UlP5j1Ht2nWAj4/qo4jnNapup+bNGyodXKt48eI4deoMHj16CAMDQzRv3kJhgexnz56ibVvl3elomgFFAdu370GvXn2Uzq+qa9euYMCAPhAKWcnxUZwIbNy4GYyNjXDpUuj/zyUURCIWlSpVwd69h1CpUmXeZX/8GIXXr1/B1NQU9eo1kKrJ3LhxXbx9+4b3/SVLlsLDhy/08thWr1513oG7GIbB6NHjMW8ef70jsUePHuDu3dugaQatW7fN1rklIiIcjo79ERn5Rur4ZWVVEV5ex2BtbaPysnJD3dspLS0Ngwf3w5Url6XKSNA0g4IFC8LfP1gmea4P4uLicPjwIVy6FIqPHz/g48coZGZmSj6Dubk5FixYqvLox2KzZ0/D3r27eBLsDCZOnAwHh8Fo0qSuwvMWwzAYM2YM5s1bqpX9ycvrAJYuXYi4uFjJtKJFiyE29lsOb7AFGDRoCNasUTyKqL//SYwYMUzh6zTNwMTEBD9/JkvtMzY2leHtfUylwWYUuXbtCnr37q5gvTTMzMxw795TFChQUOo1igJOnTqKyZMnqzQIX7VqNXDpEn/txpiYGIwcOQw3blyT3A+IRCLUq1cfu3Yd0FgdO5ZlMXLkcAQGnpLadxmGgUAgwIABg+HtfRAikUhSXoZlWQwYMBhr1myEgYGBRuJSB308H+UEx3F49eolUlJ+onz5CryDeOQF2t4uL1++QJ8+3REXFydzHKNpGjRNQygUokWLVrh//y7S09NlGsuYmpoiMPC80prc/frZ4erVywobCJQrVx43bz5Qe7kIdRFvG2U01rKwVKlSoCgKMTExMq8lJSUhJSVFpSHv1YHjOLi7j0ZGRoZKdaUYhoGvr49eJwvv3r0Nd/cxiIj4VWzWyMhYacsKAGjYUPUEkL19f9y6dUPh6zRNY+TIMXkqUQhkjY42YMAgHDq0X+5OTlEUzMzM0LOn6oXKExMTlf6+MjMzkZGRAWNj42zHrCnlypXHtm27sXLlWnz9GoOCBQujSBHNnpw4jsP06R7IyEiX+c5EIhFevHgGT88lWLpUunjsvn27IRKJ5P7Guf+Portv324sXrxco/ETUOk3bGiouG4PkJV0WLduMyZMGAOapiX7ovgi1tDQEN+/fwcg3QJBlcFMnj9/wvt6WloaoqN/gmUFSuNUVYUKVrh8+QYOHtyHo0cPIyEhHuXKlYeTkwv69nWQJGWWLFmIV69eyPz2WVaIlJQUODsPwe3bj1S6sNLQ8748x8rKCpGREbwt4sqXr4CKFW1QsaLyJI249qEy4hYwo0a5ok6dekhOTsaLF89gYmKKVq1ayzzwUEVaWhqGD3eUDL4lJt7WN29ek1x8/v76mzfh6NmzMy5fvonixWWvr6KiPmD6dA+EhJyXLKtw4SKYONEDbm5jQVEUihYtinfvInlbmFhYWGT7M2kDy7K8iULxPOJWTXzevXuLkSOH4eHDXxf6HMehU6cu2Lx5BwoWLMT7/oSEePTq1VVS3Pz3Y9b79+/Qq1dXXL16O0/WrDI2NoaPz0kcP34UBw7sQWRkJPLnz49+/QZg2DBXFC+es3qImvDz508EBp7C1auXcerUSWRm/mppIr5x7NvXAR06dELnzt2UjiQtj7PzCOzfv0futQlFUTA0NMDQoc4oWbIUxo6dgM2b18ssQ1zPdOZMxSOAqtOePTswc+ZUmemqJQopZPWy+20KlTVtxIhRvO8MCJBOUv1JJGLBMDS8vI7i1q2boGkaLVq0QqtWbXJ9w928eUssW7YSs2dPl7neMDU1xeHDJ2QShWKurq7o1MkWLVs2xocPih+AMgyDcuXKKY2lePHi8PcPxrNnT3Ht2n/gOA6NGzdFnTr1cvTZVMUwDHbu3IeTJ7th796dePnyBYyNjWFra4eRI8fA2toG06bNxvHjRxEV9R6FChVGnz59VTpnErl37NgRrFq1HO/fvwOQtb169uyNRYuWyT2nE9IyMzMxYEAfJCQkyD2OiUQidOjQGTNmzEGNGjXx6tVLrFy5FGfOnAbLsv//vu0wbdoslX7zK1asQbdu7eX2zjMwMMDmzTv0NlGYHRpLFpqZmaFixYp48kT2hu3Ro0cAgHr1NHtQFLtz5zYiIsJVnp9lWcTERGswotx5+vQJ+vTpITPCbnp61hDfihKGDMOgZcs22Tro9+3rgM2b1+PTp49ym8sXLVoUQ4cOz8Gn0Kzk5CT4+HjjyBEvfPv2DWXKlIGj4zD06dNPUnh4xow5+O+/S3j//p3MTg4AmzfvzNYocuIBQ/gUK1act/CxtiQkxMPH5zCCg08jNTUVderUw7BhLrkaLSk7Llw4h+joLwpfZ1kWXl4HMWvWfKnuyCEh53lbobEsi5CQ8yRZqAWdO3fDrVs3eFpJCNC9u63S5QwYMBjly1th69YNuHDhPFhWiMqVq6J8+Qo4d+5Mjkbuo2kaRkbyk5lv3oRjzZoVOHUqqyWtoaER7O37wcNjOsqWVX6Rr0xW8mUKJk6cIvf1pKRE+Ph4KfwdsyyLDx/e4/LlMDRt2gz//XeJNwGmzkGT8rIhQ4bh3Llgha+LRCIMGaL6uapAgQLZWj/HidC9e0d8/frrAamxsTHc3MZixow52RohMCDAD9+/JyhZn+x+x7Isfvz4gV27tmPOnAVSr0VHf0HXru0RFxcr9d74+DjMmzcLcXFxmD17Pvr3H4jbt2/xrlvV1vbaRtM0TExMeFvjMgwDc3P+J+lxcXGwte0sKV7++/cVEnIB/frZ4cyZi7ytbLy8DipMvLAsi9jYb/D2PoRx4yYo+1h6JyYmGj9//oSdnT0GDnTUdTgK+fr6YNq0Sfj586fc10UiESiKwokTxzBmjHuOEoUAULGiDfbu9YKLy1AIhZlSrcqNjIxw8KCP5IH63LkLUbRoMWzYsBrx8fEAIBmca8WKNbC0tNR4l7Xk5CQsWjRf7mviElEcx8mUI6AoCtWq1cCbNxFSD3rFZWu2b9+DKlWq8q47KUn5Q/XU1FR06tQVnTp1zeYnU0woFOLq1f9QsGAhrF+/BQ8fZrUYNjIyROfO3TBo0FDesiVAViODCRM84OHhrnAelmXh6OikclzVq9fQ2nW3GMMw6NdvAPr1GyD39aJFi2L06HFajYnIGjBwwYLZUtNYlkVAgB9u376Jc+cuoVixYjqKLm84ezYInz9/Uvg6TdP4+DEKNWrUBABUrlwFe/YcQlJSIuLj41GkSBGYmSlvaSdmZVURFy78h9WrPXH8+DFkZmaApml07dodU6bMVNoyMa/QaNGUnj174suXLzh9+rRkmkgkwt69e2FoaIju3eU3B1e3yEjlT5F/xzAClC5dRkPR5N6KFUtkWhz8TnxxKk5ciYdpL1u2HDZt2p6tdZmZmcHfP1iyYzEMI7npqVjRBpcvX9Z4K7TsiomJQYcOrTB79nQ8ffoE0dFfcO/eXUyYMAZ2dt0koyMWLlwEwcEhGDFitOTgQFEUWrdui1OngtG1a/Z+n4MGDeW9CKJpGsOHu+r8KcOTJ4/QpEldLFgwGzdvXsejRw/g5XUAbds2w6ZN67UWg7iLiSKpqSl49+6t1DRVWpSpMg+Re4MGOaJAgYJy6zVRFAWapuHqyt/KQKxJk6Y4eNAHnz/H4cuXBFy+fANv3oTnKFEIZP0GOneWrV359OkTdOzYGn5+JyS/k4yMdBw7dgQdOrTCmzeqP1TKqYiIcKSnp/POwzAMHj9+CDe3sQoThVktVgwxcKB+jgKubR06dEaXLt3lHl/FSdXevfvKvJaY+APR0V+QmZkpNb1Jk2awsOC/efydSCSSShQCWS0EN25ci2nTFHeHl+fRowcQCHLW3YtlWRw9elhm+rp1qxAXF6vw97Rx41p8+PAeffs6wMbGRm5yM6vVTHmZZGF6ejquXLmMc+eCs329pU4URaFXL3vexCzLsujVi7/HwL59u/Dt21e53xXLsnj06AHOng3iXYafny9vCy2O4+Dn58u7DH1z9uwZdOjQCjVrVkKTJnVRtWoFzJ49DT9+fNd1aDLOnw/GuHFuChOFYuJttGvXtlytr3Pnrrh37wmmTJmBli1bo1WrNpg5cy7u3n2K1q3bSuajKAqjR4/D48evERBwFkeP+uH+/Wfw8jqmtfuOoKBApKYqrkUt/k6aNm0Oisq6TSxVqjTmzVuMs2dDcefOY0ybNgtNmzZH48ZNMX78JNy69VClnjiVKlUGwyi+9qMoChUqqLeMjK+vD2rXroL+/e0wZswITJgwBhcunMX06bNw5kwIJkzwUJooFOvXbwDq1Wug4LqHRqdOXdC+fSe1xk/8/WJiYrB48Ty5r7Esi+joL1i3bqXc1+VJTk5GYOApeHkdwPXrV/+ZHihXrlzmvbcUiUR4/vypzDnL3Dw/ypUrn61EoViZMmWxYcNWRERE4cGD54iI+Ii9e73+mkQhoOFkoZOTE6ytrTFjxgysWLECx44dg7OzM65du4aJEyeqfHDOrXz5sjfsPMsK9fZp6ffvCTh//ixv6yqKotC1aw/UrFkLRYpYoEqVqli0aBkuXvwvR91DSpUqjfPnLyMo6AKmTZuFKVNmwM8vCFev3kKlSpVy83E0YuzYEXj//h04jpMcIMVJhwcP7mPu3F/dPAoVKoxFi5bh5cu3ePz4FSIiouDjcxKNGzfJ9nrLlSuPWbOyDvZ/3rAyDIMqVarBzW1sTj+WWqSkpMDBoTcSE6WLWItrYi1ePA8XLpzVeByGhoYqnbz+bIXZpEkz3gtNhhGgSZPmuY4vL0pOTsbp0wE4csQLN28qbvGnLoUKFcbx4/4oVKgggF9duiiKgrGxMQ4dOpLtmlwURUlu9FWpDSQPwzCwtrZBp05dpKZzHIfx40chNTVV5vjJsiySkhIxebLiFgPqYmBgqHQejuNgYGCIdu06YObMuQAgdQGU1cXBEPv3e2v8PPrq1UvMnTsTTk6DMHHiWFy+HKaXF540TWPPnoOYMMFD6oLP1NQUI0aMxuHDx6Vagl2/fhX29rawti6DWrUqo0qVCliwYI7kItLAwAAzZsjWW84ujuNw6NB+vH7NX0/xd1m/kZx/x39eCGdmZsLH5zDvdQNN0/Dx8YapqSn8/M5IWqyKHzYCQKNGTRAQcFby/XIch7Vr16JmzUqwt7fFkCEOaNKkHuzsumkl8S7PuHETYGBgILcuMMMwqFWrDtq168i7DB8fbyUP/hgcO+bDu4zExESlsaoyj744cGAvhg4dgKdPH0umpaSkYO/eXejevRMSE39oNZ7k5CScOHEMu3dvx7lzwVLJfo7jsGzZYpWXxbKswoEPs6N48RKYMmUGTpwIxPHjAbxJKENDQzRpkjXYTalSygvlq1NMTIxKLZ2nT5+Djx+/4d27aNy//wxjx7rDyMgIxYsXx+TJ0+DvH4zAwHOYNWueyjX2HB2HSdWolsfZWbXBRFRx9OhhjB07Et++SQ9q+OnTRwwZMgAhIeeztTxjY2McPx4AR8ehUrVeTU1NMWbMeOzd66XxwYuIv8+xY0d4r6tYlsXhw14yPQr/xHEc1q1bherVreHiMhSTJ4+HnV03NGpUG9evX81xfAkJ8di+fTPGjXODh4c7zp49o9L4D9omEql23ZTThgh8TExMUKpUaZiZZS/nlBdobIATsfj4eKxduxahoaH4+fMnKlSogGHDhsHOzi7by8ppcczk5CRUr27D+yRNjKIo9OjRC7t3H9B5CzB5IiPfoEmTurzzGBgYwNl5pMa7YupjMdnw8Ndo3rwB7zwGBoZ4+vS1xmoF+fr6YO3alZK6SKamphg8eChmzJiTo/pViuTk+z9yxAsTJoxR+DrDMGjcuClOnTqjpijle/r0Cdq1U5zUoygKZcqUxe3bj6QuvB4/fogOHVrxLvvChcuoXZt/H9EmTe8nHMdhw4Y1WL9+NVJSfh3jKla0xvr1W3OU+M6Onz9/ws/vOP77LwxCIYtGjRrDwWFQrvevfv164erV/3hb1v36OyuhwbJCWFvb4NixUzKtNB4+vI9OndooXe/16/eyneQMDb2IHTu24ObN66AoCi1btsbo0ePRrFkLmXmFQiHq1q2mtNTF1at3JANV3L59C3v27MDdu7dhYGCALl26Y9gwl1wVe1eG4zgsXDgXW7duBMMI/l9LioFQKESzZi1w6JCPWo9niuRk/0lNTcWzZ0/AcRyqVq0uc/EWEOCHESOGSdWtArKOfxUr2iAo6LykdtX27ZuxdOlCpa1B+QgEAowZ4y7TNViRK1cuw95eeRd+eSiKQsWKNrh+/a5kWlxcHKpW5f+tCAQC9Os3ABs2bJVMe/XqpaRVQpMmzWSelHt6LsbatatklpXV1Tc/Lly4LBkBWh3S0tJw7NgRHDy4F1FRH1C4cBE4OAyCk5Oz1PHm5s3rcHZ2RGxsLAwMDMBxHIRCIZo2bY69e72U9oawsiol6YGgSL169XH2bJjC1wcN6oewsIu8o8W3bdsBhw9rvnVhbs5B0dFfsHPnNmzZskHJwBzumDt3oRqi5cdxHDZtWofVq1cgLS0VFEWD40SwsCiKlSvXoUePnnj7NhKNG9fJ1nILFy6Cly/fKp9Rg7R1Te3j4w1399FK57t16yEqVLBS+/pXrFiKNWtWyJRMomkaTZu2wNGjJ6UScTmVkZGBWrUqSbp7/4miKFhZVcT16/cU3u/9+PEdt2/fgLGxABUqVEbp0r+Sot+/J+Dp0yegaRq1atX5K5MEeYk+3pOqasqUiTh8+KDSnlGPH79CiRKWCl9XdE6maRoMwyAw8Bzq1eO/R/5TYKA/xoxxRUZGhqRBQNagQ5Vw9Kif0hbR2twuR48exvjxins0ZbVctsKNG/f1MsejbaoOcKLxZKE6yfuhietrKLN6tSdWrlzGO4+JiSmcnUdg1qx5ejviU2LiD1SuXF5pC4H585dovOaEPh6YvbwOYPLk8UrnO3rUD23bttdYHBzH4f37d0hPT0eZMmWzVftQVTn5/keNcoG//0mlLVM/f47PVo2tnFCWDFq7dpPc2i+7d2/HrFnTwDACyRNq8b+XLl2BESOUXwRrk6b3k+XLF2HdutUy02mahkAgQFDQBb1KnqoqKCgQw4fz10bbtWs/3r9/j+fPn8DIyBidO3dDp05d5HZDUPUGad8+b5VqLYqJzy2/13cS/1vR73HXrm2YPXu63OVlJRHa4/Dh4yrHoAni/UwehmHQqVMXHDhwRONxqHv/ET88TEtLVVjb19V1lNTDth8/vmPLlo1Yv152P1OFQCCAvX1/lcuAcByHdu1a4OXL59l+ek9RFBYvXo6RI389FEpPT0eFCpa8NyICgQCjRo1TeZTgz58/oW7darwJpH79BmDjxtx17xRLTk5Gv369cP9+VhL090EqSpQoAX//s1KJyYyMDJw9G4THjx/B0NAQHTt2Rt269VVaV7Nm9XnrXDOMAL169cb27XsUznP+fDAcHR141+PldVStddkUyek+5OPjjUmTxkMkUjzyvFiBAgXx4kWk0vIiubVhwxosXSqblBTfBxw+7AsLi6Lo2LG1ystkGAYdOnTCoUNH1RZnTmjrmjopKRE1atjw1vakKBq9e9tjzJjxqFWrjtpj8PX1wfr1ayQj2BcuXBjOziPh7j5ZbQMAnjsXjCFD+PdBADh//pLMwCIZGRlYuHAuDh7cK3lQRFEUOnTojLVrN5LBJvSQPt6TqmrJkgXYsmUjb6tbiqLw5s0nhUnpr1+/onbtyrwPqJo1a4ETJwJVjuv+/bvo3r2j3MGbGIZB+fIV8N9/t3hzJtrcLmlpaahTpyq+f/8uGXzuTytXrsOwYS6aDSSPUDVZmCfbSrMsi0OH9qN166YoUaIgypYtBje34Xj06IHC90yePA3jxk2UdJUTd1MRj4Tm43MCz56FY/78xXqbKASA/PkLoEePnryJHIqi0KdPPy1GpT9UfVKg6ScKFEWhfPkKqFy5ikYShTmlykW/eFRhTdu1a7/kAk38exb/PXnyVAwePFTu+1xdRyEw8Dy6d7dFoUKFUahQIXTr1gOBgef1LlGoaTExMdi4cZ3c10QiEViWxfLlqnfH0iddu3ZHz569Fe6rAwc6omfP3nB3n4Tt2/diw4at6Nath8Kb1d8HyuGTnf315s3rkodQv1+gif8trpv6J1fXUZKSBOJ4xb/9unXrY9u23SqtXyQSITDQH3369ECNGjZo2rQeVqxYipiYGOVv5sGyLDZsWMv7enBwkE7r0+XUyZPHFSYKgazP5u19QKolYYECBTFr1jysW7dZqqs8AJW6nHEcx9sa4E8UReHwYV9Jqx7xOsTrrVWrttz9gmEY1KxZG46Ow6SmGxkZwc6uD+91g1AoRN++ym+sxXx9fXg/O8uyOHHimFRr59xYuHAOHj68L1VeBMjaB2JivsLNTXrwGkNDQ/Ts2Rtz5izAtGmzVEoUPnnyCP372ykdEI9lhQrPT2IdO3aBvX1/uduJoijY2/dHx45d5LxTP9y8eR0TJowBywpVKjvw48d3ycj1mpKY+ANr1qyQ+5o4xoUL56FUqTLZetjJsqzK9XX/Bubm+SUlcxThOBECAvzQuXNbBAfz1+fMiX79BuDq1dt49Ogl7tx5jCdPwjFt2iy1JQoByNSQVeTP8yXHcXB1dcLu3dulzgMcxyE09AK6d++odAAqgsgOOzt73kQhwzDo3Lkrb+vVgICTSrsyX7lyOVsDuG7alHV/oWigrjdvInD2rGZ7omWHsbExvLyOwtTUROocIP73gAGD9XJQVn2n2UeAGiAUCuHiMhTBwaclTdjT0tIQEOCPgIBT2LXrAHr06CnzPpqmMW/eIowYMQonTvji27evsLS0hL29g9ZqJyoSExMDb+8DuHw5DCKRCE2bNsfQocMVNu2dMWMOQkMvIiUlRe4ThClTZuSoNuHfQF63vz8ZGRmhXj3VWhj8bRo0aAR/fz+Fr9M0jWrVauD+/Xu4cSOr+5m4iLW6E6wFCxZCUNAFhIVdxKlTJ5GYmIgKFawwePBQ2Njw18Js3LiJxrvX5gX+/ieUXhyEhYXg27dvOj/OZRdN09ixYy9q166LnTu3Si5wSpYshdGjx2HEiNHZ+k22bt0WxsbGSEtLUziPuXl+NG2qes3LXbu2S7Vw/RPDMNi3bzfWrNkgNV3c+mvAgMHw9j6Ad+/eoVChQujTpy/atu2gUgJKKBRi1CgXBAT4SVoyfv0ag3XrVmHPnp04efK0ZGCq7Hr+/JnSC0qapnHhwjm4uVnnaB3qlJGRgZCQC/jw4R0KFiyELl26SboR/+n165cQCAQyA5r8Ljk5GTEx0TKjYw8ePBT16zfEvn27cO3aVTAMg3btOuDz508ICPDjHeHawWFQtj6TpWVJXLp0A2fOBCIw0B/JyUmoVKkKhg4dDiuriti8eT22bt2EhISsLnbGxsYYMGAw5s1bJDfhPWnSNJw5cxppaekyT9zFyavsFOT+/PmTTDfuP2VmZiIhIT7XD8wSE3/Ax8eb5/sV4v79e3j8+GGOW0Hdu3cHdnbdlHYDE5eqadGCvxwGRVHYvHkHatasje3bNyM6+gsAoEQJS4waNQ5ubmP0uhvUpk3rlG7f31EUpfEHo1m/X8XHb47j8PLlc8TFxaJbtx44c+Y0b/zi46aHx3SpQUj+BW5uY2FoaARPz8VISJCf+BIKhaAoCm5uw/H48SsULFhIrTFQFAVLy5JqXebvVL0PKlFCupXgtWtXFA5gxLIsPn6Mwt69uzB5svyW9wSRXTVq1IStrR2CggJkGmtkNWxi4OEhvzeK2LdvX8EwjNLGHrGxsSq1jOU4DufOBSs9hp49GwRb215Kl6ctDRo0wpUrt7Fv3274+59EamoKqlatAWfnEejSpZten3f1VZ5LFh44sEdyEP9zgIasUcZc0Lz5K4X1siwtS2LcuAlaiVUVly+HYejQAUhPT5fs4Hfv3sbmzeuxffseuaOLZdVUuoipUyfi1q0bkumFCxeGh8f0f+oJ6Z8qVLBCx45dEBp6Qe4BjqZpODo6IX/+AjqITvccHAZh2bLFSEtLlXtCEYlE+P49Aba2nSRPYliWRbVqNbB/v7faa6TRNI327TuR0eNy6OtX5RcHHMchNjbvJQuBrAuR8eMnYsyY8YiK+iCpZZmTAuL58xeAm9tYbNy4VmGC1d19UrZaNty9e5v3aTDLsrhz56bC16tXr4Fly2Try6hi+/YtCAw8JVmPmEgkQlJSIoYMccCdO49z1C0wI0N5fT6KopCezl9sWxuCggLh4eGO+Pg40DQNkUgEIyMjTJjgAQ+P6TIXhqam+VRqKWVqKr8lapUqVbFihXSry48fo3D5cih+/Pgh97zj7Dwi23UwgazWcXZ29rCzs5d5bcIED4wePR7Pnj2BUChElSpVeWtI2thUwqlTZzBmzAhERIRLHrYKBAIMHTocixd7Zis2C4uiSm9KGIZRmLTNjmfPniqtGUlRFG7dupGjZCHHcZg8eTwyMzN5P5OZmTlGjHDD1KmzVLrhyKrlNx5ubmMQFfUBAFCkiAUuXQqFl9cBlClTBq1atVV7112WZZGWlgZTU1O5cWZkZODkSV8cOrQfUVEfYGFhgQEDBmPgQEeYm+eHSCRCSMgFlXsYiEsnaDpZGB8fL1XuQZG4uFjMn78E169fxffv3+XOb2RkjObNW2DkyNFKB7z5Ww0f7opBg4agQYOaCh8OcRyH9PR0HD16WOcD9GVXmzbtUbhwEcTHx8l9Pau+q7XMMcPHx5v3dyYSif5f8ogkCwn12bJlJ0xMTODr6wOKokDTNIRCIYoUscC2bbuVlhOytCyl0sMuVbvQsyyrdHkikYj3AY6ulCpVGnPmLFC5TjTBL091Q+Y4Drt2Ka75w3EcMjIy4ONzWItR5dyXL58xZIgD0tLSpC7KWJYFy7Jwc3PGy5cv5L63SpWqCAw8h2vX7uLAgSM4efI0Hj9+ne3WNn+jTZu2oXLlKgBku281a9YC8+blzW6ZimRkZOD8+WAcPLgPZ8+e4b2pKlCgIPbt84JAIJDbRDtfPjN8+fIZwK/fIQC8evUCdnZdZUbZJHTL0tISQiH/jRNFUShWLG+3NBbXRilXrnyuRhqcMWMOnJycJcsU7wdZD5rGw919craWp8pNviqjH2cXy7LYuXMLb1faT58+5niET2trG6VxsyyLmjVr5Wj56hIaegHOzo6S1nXi82h6ejpWrlyGVatkB/nq3t2W9wKYpmk0bNgYFhYWKsdRunQZBAVdQMOGjaWm58uXD1OnzsxxQlgZQ0ND1K1bHw0bNlZpsJk6derh2rW7CAg4ixUr1mLTpu14/Pg1PD3XZLv8ir19f6UtDnr06KmWov80rVqXUlXn+9OjRw/w4sVzpckxb+9jmDlzXraTewzDoFy58jh/Phi1a1eBi8sQTJ06EQMG2KNu3Wpq6+b5/PkzuLk5o0yZoqhQwRLVq1tj5cplSE5OQnh4OGbOnIpatSqjfPkScHcfjbt3byM6+guePn2CuXNnon37loiO/gKWZVVOFIqvNydNmqqWz8CndOnSKrV0LFWqNMqWLYdz5y7JlO2pW7c+jh71Q1TUV/j4nPxnE4ViQqFQpVbkjx491E5AamRoaKhwoEfxKO9LlqyQuWf6/PmT0t9Zbkt9EMSfjI2NsXnzDty58xiLFi3DtGmzcODAETx69BKtWrVR+n47uz6853GGYdCxY2eVr20EAgGsrCry5hQoikLVqtVUWh6Rd+WpAU6ior6hbNlivPPQNAM7uz68haf1hafnEqxfv1rhRRnDMBg82AmrV6/XbmAq0udismlpaTh16gR8fLwRExODcuXKYfBgJ3Tt2l3jBbi1haKAc+cC4O7uLjXaW8GCBbFoUVYXR0XevAnHnj07ERQUiPT0NNSsWQdlypSFt/cBhQkIiqIwf/4SjBmjfAAZ4hdN7idxcXGoVauSwi6VWYXbO+PQIR/1rjiPe/MmHMePH0ViYgIKFbJAv34DczRq66xZU7Fv326FNxY0zWDSpCmYPn12LiOW9vFjFOrV4+8yKhAYYMSIUVi4cGmO1jFx4lgcPXpY7mdjGAalSpWWGa1cExTtPxzHoW3bZnj58oXCc6iBgSGePQuX6T7Xv39vXLlySe5noygKR46cQLt2HXIU7+vXr/Dq1QsYGxujWbOWKtfKzItmzPDAvn27Zc4ZNM3AyMgI586FoUqVqrleT0pKCmrUsEZycjLvfDkZyRwA/PyOw83NWel827fvyXE96B07tmDu3Jky038fmCM3LeyvXv0PAwb0AcuKpFo70zSNUqVKIyYmWuoBoDwMw6Bx46Y4deoMmjVrgDdvwpW2wjUzM8OWLbvQtWv3HMeuqrS0NNSsWUnhQ0uaptG4cVP4+wdLTU9IiMfnz59RoEABpSN36pIurqmzBuHj73UgEAjQuXM3lC9fAe/fv0PBggXRu3dftGjRKluNEziOw/37dxEe/hpmZuZo06adVkYQ9vM7jvnzZyE6+ldStFy58vD0XC13nxszZgT8/I7z7iuWliXx6NFLjcRL5Iw+35Nqy7ZtmzF//iyZ6QyTdU4ODg7NVnJv9+7tmD17Ou9AZvfvP+MtJ6CP2yUzMxPh4a/BsiysrW1gYmKi65B04q8cDfnLl3iUKmXBe/HCMAL07z8AGzZs1WJkOdOxYyulT+tKliyFhw/lty7UNX08APxLTp06gZEjFRdq3bRpe7bqZHXp0hYPHtzn3b9q1KiJ0NBr2YrzX6fp/WTjxnVYsmS+zPSsiwNjnD0bqpYb9r+NOrbLmzfhaNWqCYRC2UEAaJqGkZERbt58oPa6TJ8+fUTduvwXfAKBAG5uYzF/fs5aUickxMPWtgsiIl5LJeMYhoGJiQn8/IK0Msq2ou0UERGOZs2U155dt26zzGAUSUmJcHYegsuXw/7/8IgCywphYGCI1avX8z5oIX4RiVisX78C69atk2rRbmNTGVu27JAZYTQ3li5dqLCEAMMwaN26LXx8TuZo2aGhFzBggGxX7z8dPuyLDh06Z3v5ycnJqFHDWuFgLxRFwcamMq5cuZWjniEZGRmoU6cK4uPj1TI42eXLN3Hz5nXMmOGh8HqApmksWrQcjo5OWh3E7fjxoxgzZoSkG/3v8RgaGuL06fMaGb03JziOQ0REOBISElC6dGmULFmKd35dXVPb29vi+vWrSlvTiUueMAwDoVCI5s1b4tAhH5iZKb/ZfPDgHtzdx+DVq1/3MyYmphg/fiImT56m8YdOLMvixo1riI39BkvLUmjUqLHCfe3SpVD072+ncFkMw2DSpKmYNk02KUPoTnb3n4SEeBw/fhRv3kQgf/4C6NWrD6pXr6H5QDXs0KH98PRcgm/fvkqm1a/fECtXrst2b5DMzEwMGeKAsLAQqeOtuJv+2rWb4OjoxLsMfcoViEQibNmyEdu2bUJs7DcAWeVFnJycMX36bLUOsJQX/JWjIRsYGKBly9a8o5yxrDBHF3O6oKz7IADeeljEv4tlWblPj363cOFc3iL+f4qPT1DaikDTox0S2Td+/ER4eq5B4cJFpKbXqlUHgYHnSKJQgypWtMG+fV4wMDCUutmhKArGxibw9vbVSAF3S8uSKFu2HG9iQSgUomVL/kEY+BQqVBjBwRcxZcoMSQF4U1NTODoOQ0jIVa0kCvnExcmvQ/U7hmGkWl2LmZvnh6+vP86dC4Ob21g4Og7FkiWeePr0NUkUZgPDMFi+fDmePQvHzp37sG7dZgQFXcDVq7fVmigEgGnTZqFr1x6S9QK/yoxUqVIVW7bsyvGymzdvpbS2Yv78BdCiRescLf/8+WDeUaE5jsPr1y/x4sXzHC3/7NkgxMbGqiVRKK79OHTocHTp0l3SXVOMYQSgaRpbt+7CyJGjtZooBIC+fR2wb59s/eT69RsiMPCc3iQKQ0LOo02bZmjevAF69OiIunWrwcGhN1690r/WaOPGTVSYKPx927MsC47jJGUcbt68jrFjRypd/vPnz2Bn1w3h4a+lpqempmDlymVYtIh/ZGZ1YBgGLVq0gp2dPRo3bsJ77mzVqg3atesoN4HJMAxKlCgBFxc3TYZLaNihQ/tRs2YlzJkzE4cO7cfmzevRtm0zODkN5D1W5wVDhgzDo0cv4e8fjEOHjuLq1TsIDg7JUdkYAwMDHDp0FAsWLJVqld28eUv4+vorTRTqE47jMHHiWCxZMl+SKASA5OQkbNu2CQMH2iMjQ/d1uPVRnmpZGBubhEuXwtCvn/xRdxiGQenSZXD9+r1s19/RhZkzp2D//r08I2kK0K1bD+zZc1DLkalGn54W/GuuX78KO7tuSufLqsmjWne6gQP74tKlEIUXjQzDoHnzVjh+3D9bsf7rtLWfZGZm4ubN60hKSoKVVUWSJFRCndtFPKL9jRvXQdMUWrRojUGDhqBIkSLK35xDe/fuwowZHnJfE9d4vHbtrtpabLAsy/ugTlMUbSdVWlcCwM6d++QOEELIiomJga+vD6Ki3qNQocKwt+/POzK9tq8BRCIRLl0KlYwgbmFhgf79B6JHj14wMjLK1bJ37tyKOXNmKHx90aJlGDVqXI6XPW/eLKXJvOPHA1SqTfWn5csXYdOmDRAKVX84yGfVqvVwcnIGy7Lw8jqAXbu2S0YR79SpK8aOdZepz6ltHMfhyZNHiI+PR9myZWFlpd5R2d+9e4ugoEAkJf2AlZU1evTopXJiNCDADyNGDJPEKSZulX3mTIjc87Mur6n37duNmTOngKIosCwrGSxKlQFlbt68z/v9OzkNxPnzZ3kTkg8ePFfa8lKbUlNTMWfOdBw54iVV47ZFi1bYtGk7SpUqrcPoCHlU3X+Cg4Pg5DRQwTIoNGrUGLt3H1R5IJB/Bcdx+PkzGQYGhtk63+pLrkCV++b167dg0KAhWopI9/7KbsgbN26Dt/cBREZGIjb2m+RkJv67TJmyOH48ABUqWOk6VJW8fv0KLVs24m3NFRBwFk2aNNNiVKrTlwPAvyggwA+ursqf6GzevAP9+8s/Kf7p7NkzGDp0AO88e/Ycgq2t/GQ9IR/ZT/RTXt8uHMdh2rRJOHBgLxhGAJYVSlpLFC9eAqdOBan9BloX+LZT3749ce3aFYU3oebm+fHsWcQ/17UkJzZuXIflyxeB4zhJd0OWZdG//0CsXbsJhoayA97k9X3odxzHYePGtVi5chmEQiEEAoHk7ylTZmDixCk5Hjzu9OkAODs7Kp3vxo17qFgx+zUX161bhZUrl6k0+Icq5NV+FCeQNDmAXlTUB+zfvwfBwaeRkZGBevXqw9l5pFavgdPT0+Hh4S4ZkZRhGGRmZsLc3Bzr12+Bra2d0vfXrFkJ378nyH1d3MLN11f2oauu96cPH97Dy+sAnj17AmNjE9SsWQvLli3ifQ9N01i4cKnCkZJ//PiOypXL8ybKaZrGrFnz4e4+KVfxa0JsbCyuXfsPJiYCVKxYNUf7J6Edqu4/7dq1wPPnT5X+Jnv06IWVK9fK9NohskfXxzWx0aNdcerUSYUNtGiaRq1atXH+/GUtR6Y7qiYL89RID3PmTEdycjI4jpMkCIsWLYrGjZuhW7cesLW1y/XTZXlYlsXFi+cREnIeQqEQtWvXhb19P5XqdPCpVKkyVq/egClTJoCmGckPWPwkb/bs+XqbKCR0S9Wujdl5+tmpUxd0794TZ84EyiSwKYpCp05d0K1bj2zFSeRMTEw03r9/j4IFC8LGptI/P8I5IYuiKKxcuQ52dvY4cGAPXrx4gfz586N3b3v07z8Q+fMX0HWIGrdo0XJ069Ye6enpUokScT0zT8/VJFGoAm/vg1J1T3+/ifL19YGJiSlWrVqni9C0hqIoTJjggSFDhuHUqZP4+jUaxYqVgJ1dn1zfLHbs2BkFCxZUWMaDpmnUqVMvx4mITp26YvnynNUm/R3DMGjVqo3cQWI03ar46tX/MGhQX2RmZkr25c+fP+HUqZPw8Jiu9kGiFPHwcMfx40fBcRw4jpPsC8nJyXB1dYKvrz9v68/z54MVJgqBrPuJy5fD8OnTR71rnVa2bDnMmvWrS/D9+3eVJgspipKqV/onVepoMgyDb9/0c3RhCwsL2Nn10YtkB5F7UVEf8PTpY6XziUQiBAUF4OXL5zh7NjTX9/uE7kVEhPOWdhOJRHj79q0WI8o78lTNQuBXk37xyScuLg4VK1qjcOHCmD17OsaOHYnNmzcgNjY2V+uJiYnG+vWrMWzYIFSuXA5DhjjAy+sAfHy8MXXqRNSsWRmhoRdy/XmGDBmGM2cuomfPXihUqBAKFCiIjh0748SJQEyYIL+LGUE0aNAI5ctXUJhEoigKpUqVRtOmzVVeJk3T2LlzH6ZOnYlChX6NHlqwYEFMnjwNe/d66aQb4r8kMjICQ4Y4oFatyujRoyNatGiIFi0aISgoUNehEXqIoig0b94SO3fux5UrtxAUdAGurqP+iUQhAFSrVh1BQRfRuHFTqelWVhWxb583+vXjbylNZCUvVq1arvB1juPg5bUfMTHRCuf5mxQuXATOziMwY8ZcODuPUEurEiMjIyxdulLuazRNg2EEWLxY8TZQpnr1GmjXrgPv+ZmvHIH4OsLa2iZXtR9z6vv3BAwZ4oCMjAyppL+4++eaNStw9uwZjccRGRmBY8eOyE1ucRz3/wc0y3iX8f79e5Wuk6KiPuQ4Tm2xtraR26L4dyzLokaNmgpft7CwUPp9sCyLEiXUX9uXIP708+dPledlWRbh4eE4eHC/5gIitKZw4cJKy/IUKPBvXDtnV57qhpw/f34kJSXJTBe3MhQIBJKngQzDYN26zSp3wfzdnj07MWfOdIhEIoVdhCmKgkBggIsX/8vWMOR/E31pWvyvunQpBAMH9pV6+g38uvA/dMgHnTp1zdGyMzIyEB7+GhzHwcamkkZa7P4rVN1PIiPfoEuXdkhKSpTbSmrjxm1k8AU1IsevXziOw61bN/H8+VOYmJigXbuOKF68uK7DAqD6dnr//h0+fMiqtVe9eg3SGldFjx49QMeO/AN3UBSFVavWY+jQ4X9MJ/tQdgQE+GHhwrlSiaJatWpj2bLVaNQodzUAExN/YMiQAbhx4xoEAgFEIg4UlfVgffhwV5w86YvExETJtYL4urlIEQtUqlQZDg6D0Lt3X5iYmOQqjpzYsWML5s2bpfB6m2EYNG7cFKdOaTZhuGHDGnh6LlHanfvJk9cK65l5ex/EpEnKa1v+998tmbqF+rg/TZw4FkePHpb7ndA0g5IlS+Lu3Se8N+Gurk4ICgrgrYf98OELva0Rp4/bhZClynZKTk5C1apWvK1h/2RlVRE3bz5QU5T/Hn3Zf3x8vOHuPlrh6zTNYNKkKVprxa4P/spuyIqIL35+L0IrEokwfvwolCpVGs2bt1R5WcHBQZg5c4rS+cQJmm3bNmPjxq3ZD5ogcqlt2/YIDg7GuHHj8fr1K8l0K6uKWLLEE+3bd8rxsg0NDVG9eg11hEmoaMmS+TKJQuBXa+oZMzxga2uHfPny6SI84i/15MkjuLm5ICLitSQxzTAMBg0agmXLVuWZBwXlypVHuXLldR1GnqPKyI8URWWrRQYhX8+evdGjRy88eHAPcXGxKFOmnNoeNufPXwCnTp3B9etXcerUSSQl/UD58lYYNGgIypUrhxUrlmHjxq0IDj6NtLR01KvXAMOGufC2CtOWGzeu877Osixu3bohad2nKT9+/ABN00qThYmJiZLEVlpaGi5cOItPnz6iSBELtGrVGgYGhsjMlD+qJkVRsLGpjMqVq6g9fk2YP38x7t69jYiIcKmH0gwjgJGRIXbvPqC0tc6MGXMQFnYRKSkpcr/biROn6G2ikPi7mJmZo29fB/j4eKtc4zU6+ouGoyK0wc7OHps2rUNk5BuZbc8wDAoWLIjhw0foKDr99lckCxWhaRobNqzJVrJwzZoVkieuyrCsEKdPn9J6sjA1NRVpaanIn78A6Rb6j+vYsSOuXr2Nx48fITr6C4oVK47ateuSVjV5TFxcHM6cOc173ElJSUVg4CnSupBQm8jICPTq1RWpqakAfiWmWZaFt/dBfP/+HXv2HNRliISGWVlVVHrNIxKJUKmS4lGRCdXRNI369RtqZNnisgTyrnktLCzg7j4J48fr3yASqlyvcByHwMBTuHXrBiiKQrNmLdGpUxcIBOq7jbGyqijV6EAeAwNDWFpaAgCOHTuCWbOmITHxh2QfMjExQdOmzfDff5cULmPu3IV55hqtUKHCOHPmInbs2Ir9+/fg27evMDY2Rt++Dhg71l2lOpvW1jY4ffoCPDzccffubcn0ggULwsNjOoYNc8XRo4fh7X0AHz9+RLFixTBw4BD06zdA5RGoCUJVs2bNx5Url/Hp00eVEoYWFkW1EBWhacbGxjh58jScnYfgzp1bkvwJy7KoUMEK+/cfRrFixXQcpX76K7oh86EoCu/fx6hU5DwmJgY1a2avwLRAIMDnz/HZek9O3b17G+vXr8aFC+fAcRwKFSoEJycXjB8/Eebm+bUSw+/0pWnxv4p8/3mDKtvpyZNHaN+e/6GGQGCACRMm57qJ/J8tIbp06ab0+BEZ+Qa7d2+Hv78f0tJSUbVqNTg7j4Sdnb3SVgX6iuw/gLv7GPj6HuG9YD5//hLq1Kmnxaikke2keU5OA3H+/FkFXQ1plChhiXv3nso8nCTbJm/Q9+20a9c2zJkzQ2E3ZJqmIRAIkJGRIUkOCoVClC5dBocPH5fpzptTyclJqF7dWvLw5E8Mw6Bfv4HYuHErAgNPwcVlqMJltWrVBjduXINQKJS0VsyfvwBWrlyLPn36yX2Pvm8nIKtEjYGBQY6Tna9evURERDjMzMzQpEkzZGZmon9/O9y9e1uScM1q4Q5UrlwFp06dQZEiuh2NNi9sFyJ72yk2NhZr1nji0KH9yMiQ3woYyDr2TJs2C5MnT1NztH+nxMQf8PX1QVhYKFhWiIYNG8PR0QnVq1vr1f7z8OF9XL4cBpZl0bBhY7Ro0SrPPMBRJ1W7If/1yUIAiIiIUqng+/v379CwYS2Vl0tRFKpWrY5Ll/i7UKjD2bNnMHx4Voui3y/oGYZBpUpVEBh4VutF7ckJVLfI9583qLKdoqI+oH59/m7fNE1j0aJlGDlyTI5jOXHiGGbMmIIfP75LLsyNjU0wdepMjBs3Qe7J8vr1qxg40F6q+Lz4vb169cH27XvyZAvnf33/EQqFKF/eEhkZimv3CAQCuLiMxOLFnlqMTFput5NQKMS1a1cQG/sNpUqVRqNGTfJsgltTPn6MQufObREfHydzfcEwDI4dO4VmzVrIvC8n2yYuLg6xsd9gZGSEsLAQyRP+Nm3aoUePXnmm23teou/Huh8/vqNu3WpISUlR2MJVXuvXrK5jhXD9+l0UKlRYLbEcPXoY48ePklkfwzAoWrQYzp0LQ/HiJdCwYS3eQUry5cuH//67hUuXQpGQEI+yZcuhS5fuvA0X9H07acLEiWPh43MYIpHsgwqGYdCuXUd4ex/TQWS//IvbJS/KyXZKSUlBnz498PDhfbnHlxIlLBESckUtA1397e7du4MBA+yRmPgDQFZrcPGDniNHjqBVq45k/9GAnz9/4vLlMCQnJ6FiRWvUq9dAaeIzPT0dgYGn8PDhA+zYsUXpOvLsFbOqF/slSpRQudVdiRKW2Wqhx3EcXFxGqjx/Tv38+RNjxrhCJBLJPPlnWRavX7/EqlW6u5kjCCJ3ypQpi3r1Gig9rvXs2TvH6zh9OgCjR7vix4/vAH7Vek1LS8XixfOwdesmmfekpKTAyWkg0tPTpY494vcGBPhh//7dOY6J0J3U1BTeRCGQdY6LjY3VUkTqd/z4UdSpUxX9+vXC6NGu6NmzCxo2rIWLF8/pOjS9Urp0GVy8+B8GDRoiSdZRFIVOnbogODhEbqIwu54/fwZHx/6oVs0KLVs2QqNGtTF9+mScPOmLEyeOYfRoVzRoUBMvXjyXeW9k5Bvs3LkVmzatR1hYiNyEkkgkQkjIeYwa5YJ+/Xph8uTxuHPnlsLWaoT+KFCgILy9fWFkZCz14EncipCiKLnbnGVZJCTEw9v7kNpicXAYBC+vo1K1HA0MDNC7d1+cOxcGS8uSePDgntLRjH/+/ImHD+9jyJBhcHefDDs7e5V6OP1L4uLi/j/6tPyW7SzL4uLFc3j7NlLLkRH/ClNTUxw/HoA+ffrJXH83atQUgYHnSKJQBfHxcXBw6IOkpETJQLNA1nk5MzMTDg4OeP78mY6j/LtwHIcNG9agRg1rDBs2COPGuaFr1/Zo2bIx7t27o/B9Dx7cQ9261TBmzAj4+h5RaV15NlloY1MJK1eug6GhocJ5aJqGs/NIlZuWGhkZYejQ4Sq1kqEoCh06dMbAgY4qx5xTAQF+SE5OVnjRm1Vf6gDS0tI0HgtBEJoxc+ZchQXcKYqCs/NIlChhmaNlcxyHhQvn8B4LV61ahuTkZKlpp06dwI8fP3jrmW3fvoXckOdB+fKZwcxMefeD0qXLaCEa9Tt69DDGjBmBr19jpKZ//BgFR0cHhIZe1FFk+snSsiTWrNmI168/4MGD53jz5iMOHDiCmjVr53rZjx49QNeu7RESckHmWCESiSR14mJjv8HevoekZUJychKcnR3RpEldzJ07C8uWLYSDQ280alQbDx/elywjOTkJffr0wMCBfeHvfxKXL4fhyBFvdO/eEePHj1K5kL22CYVCnDlzGitXLsOGDWvw7NlTXYekM02bNsfNm/cxYYIHqlathgoVKqJ3774oU6Ys7/lFJBLB3/+EWmPp1KkrLl68grt3nyAs7DqeP3+DrVt3wdKyJAAgIUG10kNxcXFqjetv8+DBXaU1IjmOw61bN7QUEfEvMjMzw9atu/DgwXNs3rwDGzZsxZUrt+HvfybPXv9o25Ej3khKSpR7ryA+fu/atU3bYf3Vli9fjKVLF8oMPhcR8Rq9e3fH06dPZN7z5ctn9O3bE/HxWecwZcdfsTyZLGQYBmXKlMWwYS7YsGErKIqSSfDRNI1GjZpg1Khx2Vq2h8c0VKtWg7eFT6lSpTF//hIcOHA4W8WVo6O/wMvrAHbv3o7r16+qfIP94sVzGBgY8M6TnJxMRmwiiDysdeu22LvXCwULFgSQNdqg+Ng2cuQYLF68PMfLfvToAd6/f8d7zElJSZFpcXXv3h3eYxzHcXj//h2+f0/IcWz/kqwbn5tYuHAuZszwwMGD+5CcnP3SGupA0zQGDx7C+3CMZVkMHJj3BtTJyMjAvHkz5b4mfurNVyPtX2ZiYoJSpUqrlEhW1eTJ7jKtk+VhWVbS2ojjODg5DcKZM0EAAI771bPi48eP6N27OyIjIwAAkyaNkyQUxPOwbNZFsK+vD9asWaG2z6Iut2/fQt261TBs2CCsX78anp5L0LZtMzg49P5nj6eWliUxY8YcXL58E7duPcCWLTtVGmwwKSlZ6Tw5UbZsOVSvXgMFChSUml66dFkV36/afP+qf7FGGKG/LC1Lon//gRg40DHPjFauLy5cOMt7PSUUCnH27BktRvR3i47+gk2b1sl9Tdya09Nzscxr+/fvxs+fKQpbcyuSJ0dDZlkW79+/AwDY2/dHyZKlsHHjWoSGXgTHcbC0tISLixtGjhyT7Wb/Zmbm8PcPxrZtm7Bv327Exn4DTdPo0qUbXF1HoVq16ihUqHC2TnLp6emYMWMKfHy8wLLs/4v3cqhY0Rrbtu1WWjze1NQEIpHymxoTEzJqGEHkZd2726Jjx844d+4M3r6NRP78BdCtm22uR+gSP0XiQ1GUTEsImlatFqE6R6T822RmZuLcuWDcvXsbgYF+iIqK+v/3RUEozMS8ebOwZctOdO9uq/XYxo+fjMBAf8TERMtN5IwePR5WVtZajyu3wsJCkJCgOOHCcRwiIl7j8eOHqF27rhYj+/c8efIYT548ytZ7goICUaVKNVy5clnu6yIRi7S0NGzZshETJnggIOCUwhsVjuOwY8dWjB8/CSYmJtmOXxPCw1+jX7+eSE/PKgPw+9P9//67hMGD+yMw8ByprQmgSpVqiI7+ojDRzDACVK9eXcsxVUXt2nXx5MkjuclMiqJQtGgxtGrVVqtx5TV169aHgYEBMjMzFc5DURSaNGmmxagIgsguZSVtAPDu50T2+Pmd4E3OsiyLCxfOIT4+TqobfUDAqWwnCoE82rKQoiipocybNm2OI0dO4MOHr4iM/ISHD1/C3X1yjuuDmJmZYerUmXj2LAJv3nzEhw9fsX//YbRo0QqFCxfJ9tOw0aNdceTIIcnFjngDv3v3Fr17d8ebN+G87+/atYfkKbk8NE2jdu26KF68eLbiIghC/xgaGsLW1g7u7pMxbJhLrhOFQFZNRGU4jpNpCdGmTTveZuo0TaNWrTo6GY09L7h+/Srq1KkKZ2dHbN26EVFRUQCykgNCYdaFU2pqClxdh+LOnVtaj69YsWI4c+YiOnXqInVeK1y4MBYsWIoFC5ZoPSZ1iImJVut8RM6JW/+piuM4pKamwM/vOO9DCJZl4evrg0uXQpW2EE1KSpTqtqxrmzevR0ZGhsI6fHfu3MLly2E6iEz/DB/uwtsilWWFcHJy0WJEWTw9V0MgMJBJ6Ir/v3r1BvIQTYnChYvAwWGQwqQ4wzDo1KkrypevoOXICILIjvr1G/L2UmEYBvXqNdBiRH+3r19jlJbM4zhOpgHIn12WVZUnk4Ucx8HBYZDMdCMjI5iZmautaTtFUTA3z89bF1GZhw/v4/Rpf4UXhWlpadiwYS3vMmrXrotWrdoo/GGIRCJ4eEzPcYwEQfzdbGwq8Q6gIm4J0bZtB6npnTt3Rdmy5XiPPe7uk9Qe79/g+fNncHDojbg4/gFCxImODRvWaCMsGSVLlsKBA0fw8OELHDt2CgEB5/D48WuMGTM+z3YTK168hErz5bQGKKG67D5IYBgBatWqg4SEBKXdltPS0pCenq7S7zQjIyNbcWgKx3Hw8zvO+9kYRoBTp9Rbhy+v6tixi+R6//ftLP63k5MzWrZsrfW46tdviICAYNSv31BqeqVKVXDkyAl06dJN6zHlRYsXe6Jhw8YAfiVaxdu2cuUq2LBB+UidBEHolpOTC2/JCJZl4erqpsWI/m4lSpRQen1E0zQsLCykplWrVkOlcTlklpXtd+gYwzCoVKkyevfuq+tQVHLixDGlT8dPnvRVWmRyz56DaNSoKYCsLn8CgUAyJPnKlevIhQlBELyWL18FAwMDma7F4gvzlSvXyRyrBAIBjh49iWLFikvNKz7ZjBw5Gi1atNJ06BKxsbFYvdoTjRrVho1NWbRv3wIHDuzVy8GdNm5cC6GQVanmlrjLgLhboi5YWpZEmzbt0KRJ01w9INMHbdu2R6FChRS+TlEUbGwqq2XwDoJf8+YtZWq+8RG3FCtbtpzSMghFixZDgwYNlbYsFAgEqF69Ju882iJ+SMxHJGKRmJiopYj0G0VR2LBhK5YvXyU12ED58hWwatV6rFy5TmcPNerVa4CgoAu4efM+Tp48jf/+u4XLl2+gXbsOyt9MAADy5cuHkydPY/v2PWjatAXKlauAhg0bYd26zQgODiUj0RJKcRyHO3duYcOGtVi+fDlu3bqZ5+oRv3r1Er6+PggI8FN5ACV9Ym1tgxUrsho+/Z6MEv970qRJ6Nixs05i+xv17t2P97zHMAy6dOmGQoUKS013dnbN0YBvFJeH9qj8+fOjbt362LZtD4oWLar8DXpgzJgRSp8iA0BERBTy5y/AO4+4OH5gYNboyNbWlTBgwGCdfRcUBVhYmCM2Ngl551f09yDff96gT9vp4cP7mDNnBm7fvimZVrlyVSxYsBjt23dS+L7k5GScOHEMp0/7IyEhAampqXj37i0yM7Na67Rs2RpTp85CkyZNNRb727eRsLXtjNjYb5IEnPhkWb9+Qxw7dgpmZmYqL0+T24VlWZQpU1TlkcbEwsM/ZCux8reLiYlBUJA/MjNTUbSoJbp2tVW57tzRo4cxfvwomekURYGiKBw+fJzc1OeSqvvQjh1bMHeu/AFnxGiagUjEYvbs+ZgwwQPh4a/RvLnibks0zWDSpCmYNm0WOnRoiefPn8m9zmIYBr1722Pr1t0qfy5Nq1WrMu+AdAzDYNSocZg/X7ZAeU7o0zkoNziO+//o5hSKFSuWZ1s+K/K3bKe/Ddku+iclJQVnzgTi2bOn8Pc/iY8foySJKZZlUb16Tezf741y5crrNlAl3r6NhLv7aKkRvw0MDDF06HAsXLg0zz24vXHjGrZt24RLl0LBsiI0aNAQI0eOhpPTIMTFJcvsP58/f0JiYiJKlSpFyhll05o1K7BixVKZ6QzDwMjIGGfPhqJKlapSr3Ech8mTx8Pb+yAoioKZmZlKDybzVLLwxo17sLaupOswsmXZskXYtGkdb7LQ3Nwc4eFRea6YNTmB6lZ2vn+O43DpUij27duNZ8+ewNTUFD179sbQoc6k1qWG6eN+Ehn5Bp8/f0KRIhaoUqWqyjdd0dFf0LlzW3z9GiN1TBNfpB04cBidOnVVe7wcx6F9+5Z48UJ+QoCmGQwb5gxPT9W78mpyuyQnJ8PKqmS23mNhURRPn4bnufOAJrAsi4UL52LXrm0QiURgGAZCoRDm5uZYtWo9+vTpp9JyTpw4hgUL5kjVJixXrjw8PVfzJscJ1ai6D3Ech3XrVmH1ak+IRCLQNA2WZaVafzRq1ARjx05A167dJdMWLZqHzZvXyyyPYRhYWVVEcHAI8ucvgDdvwtGjR2d8/y7ddZmmaVhb2yAg4KxetVBat24VVqxYytvq+MaNe6hY0UYt69PHcxAhi2wn/US2i345duwIZszwQHKy4lHQGUaA4sWL4fLlm3r7ADYmJhpt2zZHQkK8zHUtRdHo3t0We/YczPMPReTtP2FhIfD0XIIHD+4BAAwMDNCnTz/MmjUPlpbZu3b+V3Ech127tmH1ak98//5dMr1OnXpYs2YjatasJfd9IpEIhw7tx/btm/H1a8zflyzMiwfqyMg3aNJE8WiLDMPA1XUUFi9ersWo1IOcQHUrOzdq06dPxv79e8AwjOSkRNM0zMzMcfJkIGrVqqOdoP9B2dlO4sSIvhozxhV+fifkJuwoikLBggXx+PFrGBkZqXW9d+/eRrdu/K3AjI2N8exZhMpPJzV5/OI4DlWrWiE+Pk75zMjaFz08pmPqVP7WV/+KefNmYceOLXK7ElEUhUOHfFROSguFQty4cQ2xsd9QsmRpNGrUOM9ffOuL7O5DsbGx8PPzRXR0NIoXLw47u74wNzcHwzByW1BwHIe9e3di/fo1koSvgYEh7O37Y8GCxVIJwJiYaOzcuQ0+Pt5ISIiHpWVJODk5Y/hwV71rsZCcnAxb2854+fK51LGUoihwHAcPj+mYPn222tZHrtXyBrKd9BPZLvrj9OkAODs7qjQvRVFYsGApRo8ep+GocmbevFnYtWsbb2OiM2cuokGDRlqMSv3+3H/8/I5j1CgXUBQl9cCMYRgULVoM586FkYRhNqSnp+PGjWtISkpCxYrWqFatukrv4zgOiYnfYW2tfABMkizUAr6n48WLl8D585fVMuIpH47j8PNnMgAqW131+JATqPpFRITj8eOHMDAwRIsWLWXqDfxO1e/f2/sgJk2Sf7KkaQZFihTB/fvP1J7gIbIo206PHz/Eli0bEBQUiIyMDFSoYAUXl5EYNsxVr7ogfP+egGrVKirtWrtr13706tVHrevevn0zFiyYo7T+X2DgeTRu3ESlZWr6+OXpuRjr16+FSKS8CHHNmrVx6tQZ5MuXT/2B5DExMTGoU6eKwgtoiqJQpUo1XLp0nST9dExb1wAsy+L582dIT0+DtbUNChZUXI8yr0hKSsTy5Yvh7X0IqakpALJavU6aNBUDBzqq9bdNrtXyBrKd9BPZLvqB4zg0bVoPb99GqlyTsEaNmggNvabhyHLGxqYsfvz4rvB1gUCAwYOdsGrVOu0FpQG/7z/JyT9Ro4Y1UlJS5G5DhmFgb98fmzfv0EGk/x7xtlGG9HfSgrlzF2Lx4uUoUuTXqDQ0TaNz524IDg7RaKKQ4zgcOrQfLVo0hJVVKVhZlUSHDq3g53dcY+vMC4RCIW7duonQ0Iv48OG9rsPBhw/vYWfXDc2a1ceoUS5wcRmCGjUqYdasabkaxZHjOGzZslHhjYdIxOLbt68ICgrI8TqInDt/PhhdurRDQIC/ZDu/e/cWc+fOxMCB9jod8OJPHz68V5ooFAgECA9/rfZ1Mwyj0kU6w+jPKW3MGHfY2NjwthQtWLAgxo+fBD+/IJIo/L+goADepDDHcXjx4hkiIyO0GBWhSwzDoGbNWmjQoNFfkSgEskaJXrZsFZ4/f4OwsOu4fv0ebt16iEGDhpAkOEEQxB+ePXuKyMg32Rq8JCEhQYMR5RzLsryJQiDrPjWrRuvfIzDwFH7+/KlwG7IsCz+/40hM/KHlyAg+iofpJdSGoii4uY2Fs/NIPHx4H2lpaahUqTKKFy+h0fX+WchS7OnTx3Bzc8bLl88xc+Y8jcagbziOw/79e7B6tSe+ffsqmd66dTusWLEGVlYVtR7T169f0b17R8TGfpOanpmZgT17duDz50/Yt88rRzcQ378nICKCP3kjEAhw/fo1leuAEeqRlJSIkSOdZWp3if997doVbNu2CRMnTtFViFJUaZEsEonU1nL5dy1btgHH8bcqLFCgAGrUkF+jQxfy5y+AwMBzWLp0EY4e9ZaMgFqhghXc3Maia9fusLAoCgMDAx1Hql9+/PguqVHIPx+5mCTyvnz58qF69Rq6DoMgCEKvKUuu/Smrtq21ZoLJJYZhUKhQId5kpkAggKWlpRaj0rw3byJgYGCAzMxMhfNkZmbi8+fPSgd9JbRHf5ph/AMMDAzQsGFjtGzZWuOJQgA4f/4svL0PAoBUMkLcamPdutW4d++OxuPQJ+vWrcL06ZOlEoUAcPXqZXTt2k4nrQx37NiC2NhvcrvdcRyHM2cCcfv2rRwtW9UEI2nJoH0bNqxBSoriJ2wikQh79uxU2vVWWypUqIhKlarw/lY4jkO3brZqX3eVKlXRunVbha30KIrCiBGjYWxsrPZ150bBgoWwatU6PH8eibCw67hx4x5u3nwAZ+cRsLQsSRKF//fz509ERX1AcnISypevoDRRSFEUSpUqo6XoCIIgCILQpbJly2VrfpZl4eQ0XEPR5N7gwU68PU+EQiEGDBisxYg0z9w8v0r3NObmyrvGEtpDkoV/sT17dvIeiBhGgH37dmsxIt2KiYnGqlXyB5JhWRaJiYlYvdpTy1Fl1RTkK3ArEAhw7NjhHC27QIGCsLGpzJvgEQqFaNaseY6WT2Tf69ev0L59C2zcqLwOSUxMNOLj47UQlXIURWH69NkKk5s0TaNfvwHZvqBT1fbte1G5chXJuoBfozD37NkbkydPU/s6X758gTlzpqNfPzs4Ozvi5EnfHJUFMDMzQ/XqNVCxog1JzP/m7dtIjBnjChubMqhfvwZsbMrC3/8kzMzMFH5PDMOgU6cuZBR3giAIgvhHlClTFi1btlZpEECaptGhQ2d0795TC5HlzOjR41GsWHG5n4eiKPTt64A6derpIDLNsbXtxZsspGkadevWR6lSpbUYFaEMSRb+xZ49e8KbhGJZIZ4+faLFiHTL1/cob60LlmVx4oQvfv78qbWYOI5TOmKqUChEdHR0jpZPURTGjnVX+LnFg+zo8wn1b/LxYxR69OiE58+fqfweIyP9GeTE1rYXVq1aD0NDQ1AUBQMDA8mFjp2dPdas2aixdRcpUgTnzl3Ctm270aZNO9SqVQe2tr3g6+uPnTv3QSBQb1WNVauWo1WrxtizZycuXw7FmTOnMWqUC9q0aYovXz6rdV3/otevX6Fjx9bw8zshaUnIsizOnQtGenoGOI6TJIXFGIaBmZk5Fi5cqouQCYIgCILQkSVLVsDY2Jg3YVigQAFMnOiB/fu9VUos6krRokVx5sxFtGzZRmq6sbEJxo2biI0bt+kmMA2qUMEK/foNkLm2E+M4DtOnz9JyVIQypGbhX8zExETpPKamplqIRPe+fs0axENZYdzMzAzEx8dpbbABiqJQpIgF4uJiFc4jEAhQokTO61YMHOiIJ08eY8+eHWAYAVg268acpmmYm+fH4cPH9WrU3b/Zli0bkJSUyJvEF6NpGvXrN4S5eX4tRKY6Jydn9Oxph5MnffHu3Vvkz18AvXr1QaVKlTW+biMjI9jb94e9fX+NrufEiWOSVsjibSV+Gvr2bSQGD+6PkJArpJVgLkyZMgE/fybL7AtZNTyBUqVKo1Chwnj69DGArGNlx46dsWDBEr2tQ0QQBEEQhGZUrVoNwcGhWLBgNsLCQiT3dDVr1saQIU7o0KENihYtDSMj/SpJo0ipUqVx7Jgf3r17i6dPn8DIyBBNmzaHmdnf2w1X3Kjg2LEjoGkGDEMjM1MIU1NTrFmzAe3addRxhMSfKC47wwrpGBm2PnsWLpyL7ds3K0xMUBSF+fOXYMyY8Tla/u/DoevzdvHzO45x49wgFAqVJgtpmkZ4+AetJmiWLl2IzZvX8yaQgoIuoGHDxlLTsvP9cxyHq1f/w759u/Hs2RPky5cPPXv2xuDBTihatKg6PsY/g+M4XL9+Fa9evYSpqSk6dOgMCwsLhfOLt9O3b4mwsiqF5ORkldfl5XUUnTp1VUfYxB8U7T8cx6Fly8YID3/Fe7w4efI0WrRopYVI/z4REeFo1qy+0vnOnLmIIkWKAMiAmVkRFC1aTPPBESrLK9cA/zqynfIGsp30E9ku+ik6+gs+foxC4cJFYGVVkWwnPaVou0RGvsHp0/5ISkqClVVF2NraaWSAREIx8bZRhrQs/Is5O4/Avn27kJaWJlMjgGEYFCxYEIMGOeooOvW7d+8Odu7chsuXQ8FxHJo3b4k2bdpj2rRJKhVUZRgGnTt31XpLLje3sfD19UFMTLRMwpCiKNja2qFBg0a5WgdFUWjZsjVatmydq+X86+7du4PRo13x7t1bUBQFjuMgEAgwfPgILFiwhHfAiszMTBUThRRomsLSpStIolAHvn79itevX/LOIxAIEBJygSQLcyg8nH+EdrHXr19h8OAh5AaAIAiCIAiJEiUsc9XritAtK6uKcHefrOswCBWQmoV/sTJlyuLo0VOS5JdAIJDU9SpatBhOnDiNggUL6TJEtTlwYC+6deuAwMBTiI+PR0JCAoKDz2DKlAkqvZ+maQgEAnh4zNBwpLIsLCwQFHQBLVpIJ/KMjIzg5jYW27btJt0d9cCLF8/Ru3d3yYjZ4lZnQqEQu3dvV/pbMzQ0RKFC/PsbRVGoV68e7t59AhcXN/UETmRLZqYqA5hQKs5HyKNqmQfylJkgCIIgsgiFQhw7dgTdurVHpUplUa9edSxZsoDUUSYIQmNIN+R/QEpKCk6dOoHbt2+Cpmm0aNEKPXr0ynWdOn1p8v3ixXO0adNUaRdjPuXKlceWLbvQqFFj5TNrUGTkGzx9+hiGhkZo1qw58ucvoHBeffn+/xUuLkNx5kwgb3fxq1fvyNTu+307LVo0H1u3buRdRljYdVSvXkNtcRPyKdp/hEIhatasxFtHFAC2bt2Fvn0dNBzl3yk9PR21alVCQkKCwnmMjY3x7FkE8ufPT45zeoqcg/IGsp3yBrKd9JO+bJeMjAw4OQ1CSMh50DQt6TElHvjr5MnTqFmzlu4C1DF92U6ENLJd9Jeq3ZBJy8J/gKmpKQYNGoL167dg7dpN6NOnX54a0ILjONy9exuHDu3H8eNHERcnPXrwvn27FI6spIr69Rvi9u1HOk8UAlnNsnv27I0uXbrxJgoJ7fr586fSRCHDMDh58hjvckaPHo8SJSzljtBGURSGDBlGEoU6JhAI4OIyUuExhaZpFC5cGLa2dtoN7C9iZGSESZOmKnydoiiMHj1O7wb3IQiCIAhd2LhxLUJDLwCAVGkllmWRnJyEoUMHqDR4HkEQRHaQZCGh154+fYLWrZuiW7cO8PBwx5gxI1CrViXMmjUVmZmZAIAbN67n+ARJ0zTatGlHuvkSvFQZwZiiKMTGxvHOY2FhgTNnLqJDh85Svzlz8/yYOnUmVq1ar45wiVwaP34SmjdvBYqipLYTwwhgaGiEffu8YWRkpMMI8z43t7GYPHkaaJoGTdMwMDAAwzCgKArOziMxbdpsXYdIEARBEDqXmZmJ3bt3KOxBxbIsPn36iAsXzmk5MoIg/nZkgBNCb0VGRqBnz85ITU2Vmp6ZmYk9e3bi+/fv2Lp1l9xWWqqiKAqOjk65DZX4yxUsWAhGRkZIT09XOA/HcShdurTSZVlalsShQz749OkjXrx4BiMjYzRo0AgmJibqDJnIBSMjIxw5chyHDx/C3r078eZNBExMTNC7dz+MGjUGFSva6DrEPI+iKMyYMQdOTs7w9T2KL18+oWjRYrC3749y5crrOjyCIAiC0AtRUR8QH8//MFogMMCdO7fQpUs3LUVF/M0yMzNx/fpVxMfHoXTpMmjQoBFpWPOPIslCQm+tX78Gqampclt0cRyH48ePYuzYCejQoRNevXqhtOXXnzU+RCIR1q3bjFKllCd4iH+bsbEx+vUbgCNHvBT+zjiOQ//+A1VeZqlSpclvT48ZGhpi2DAXDBvmoutQ/mqWliXh7j5J12EQBEEQhF5SrVEEl6vGE/rsw4f32Lt3F4KCApCWloY6derC2Xkk6RmmIUeOeGHx4vmIjf0mmWZlVRErV65Dq1ZtdBcYoROkGzKhlzIzM3HypC9vAlAgEMDX1wdOTs5gGIHcEwZFUaBpGjNmzEWVKtUAZHUl7NChM/z9gzFgwGCNfQaxd+/ewtNzCcaMGYG5c2fgwYN7Gl8noX4eHtNRqFBhhRdjkydPQ8mSpbQcFUEQBEEQBPG3KlOmLEqXLsM7j1AoROvWbbUUkfZcu3YFLVs2wo4dW/D+/TvExETj4sXzcHDojblzZ+ZqcEtC1qFD+zFhwhipRCGQdS/r4NAb169f1VFkhK6QZCGhl5KTk5CRkcE7D8dxiI39hjJlyuLAAW8YGhpKDUrAMAwYhsGuXQcwefJUXLp0HV++JODz5zgcOuSDJk2aafQzcByH5csXoXHjOtiwYQ38/I5jz56d6Ny5LYYOHSjTvZrQb6VKlUZwcIjMk8yiRYth+fLVmDp1pg6jIwiCIAiCIP42NE1j3LiJCl9nGAbVqtVAs2YttBeUFiQlJWLIEAekp6dLNR4R/3vnzq3w8zuuq/D+OikpKViwQH69aJFIBI7jMG/eLC1HRega6YZM6CVz8/wwMTFFamoK73ylSmW15GrfvhPu3HmMgwf34b//LoHjRGjevBWGDBmGMmXKSubXZhP9vXt3Yd261QAg00Ly/PlgTJ06EZs379BaPETulStXHkeOnMDHj1EID38NU9N8qF+/AQQC/TuUxsbG4u3bNzA1zYeqVavlasRwgiAIgiAIQjeGD3fF69cvsXfvLggEAgiFQlAUBY7jUKpUaXh5Hf3ruuQeO+aDnz9/Kmw9SNM0tm/fgj59+mk5sr/ThQtnkZSUpPB1kUiEx48f4tWrl6hcuYoWIyN0Sf/ucAkCWV2MBw4cjAMH9irsisyyLBwcfnUjLlHCEtOmzcK0abp/6sGyLNavX63wdZFIBF9fH8ycOZfUrcuDSpcuo7RLiK58+fIZ8+bNxOnTAZJ9p2zZcpg6dSYcHAbpODqCIAiCIAgiOyiKgqfnGvTp0x8HDuzB69evUKBAAfTu3Re9e/eFqamprkNUu9u3b0gSovKIRCI8fHgfmZmZMDQ00HJ0f5+YmGip+v5885Fk4b+DJAsJvTVx4hQEBvojPj5ObsJw1KhxsLKqqIPIlHvy5BFiYqKVznfhwjkygAKhNjEx0ejSpR2+fo2R2mc+fHiP8eNHIS4uDmPGjNdhhARBEARBEERONGrUGI0aNdZ1GFqR1VJSeWvJv61Fpa4UL15CaaJQPB/x7yD90gidEIlECAk5D1dXJ3Tp0g7Dhg1CcHCQVIKjRAlLBAeHoHXrtlIngoIFC2Lu3EVYuHCpLkJXiSr1CCmKQlqaftctjI7+goUL56JqVStYWhZC3brVsG7dKiQm/tB1aIQcq1evkEkU/m7x4nn4+vWrlqMiCIIgCIIgCNW1aNEaIpHigS5pmkHjxk31shRQXtSxYxeYmZkrfJ2madSsWZu0KvzHkL2L0Lr09HQMHz4YFy+eB8MwYFkWDMPgzJnTaNq0Oby9fWFmZgYgq/ukj89JfPjwHq9fv4SxsQkaNmwMIyMjHX8KftbWlSSfTRGRSISqVatrMarsiYgIh61tJ3z//l3yOT59+ogVK5bC19cHgYHnUaRIER1HSYilpaXh6FFv3t8cx3E4duwIxo2boMXICIIgCIIgCEJ1vXv3xeLF8/Hjxw+5SUORiMXo0aS3jLqYmppi/vzFmDp1osxrNE2DoigsWrRM+4EROkVaFhJat2DBHISGXgTwa+AP8d+3b9/E9OmTZd5Ttmw5dOjQGS1atNL7RCEAFC1aFLa2vRQOqELTDMqWLYeWLVtrOTLVcBwHN7fhUolCMZFIhLdvIzF79jQdRUfIEx8fh7S0NN55aJrBhw/vtBMQQRAEQRAEQeRAvnz54ONzAmZmZlKD9DFMVlunqVNnolu3HroK76/k5OSM9eu3oEgRC6np5cqVh4/PSTRv3lJHkRG6QpKFhFb9+PEdhw7tU1gTgWVZnDzpi+joL1qOTP0WL/aEpWVJmYQhwwhgZGSEHTv26u0ItQ8e3MOTJ495B5fx9z+Jb9++aTkyQpH8+fMrrdvCcSIUKlRISxERBEEQBEEQRM7UrVsfN27cx8yZc1GnTl1UrlwV/foNwLlzYZg6daauw/srDRo0BI8fv8LRo37Ytm03AgPP4+bNB2jduq2uQyN0gHRDJrTq9u2byMjI4J2HZVlcu3YF9vb9tRSVZhQvXgLnz1/Gpk3r4OV1AElJiTAwMICdnT0mTPBApUqVdR2iQg8fPuAdgQzI2k7Pnz8lJw89YWZmjo4dOyMk5AJvkrd3735ajowgCIIgCIL416SkpMDf/yTCwi5CKGRRt259DBzoCAsLC+Vv/r+iRYtiwgQPTJjgocFIid8ZGBigbdv2ug6D0AMkWUhoFcsqH2UJAIRCoYYj0Q4LCwssXLgU8+YtQnJyEkxN88HAwEDXYSllaGjImyj8fT5Cf0yZMgNhYSHgOE6m9S5N07Czs0eVKlV1FB1BEARBEATxL3j+/Bn69euFb9++gqZpcByHM2cCsWLFUuzatR9du3bXdYgEQSihn30gib9W7dp1VOp6W79+Qy1Eoz0Mw6BAgYJ5IlEIAG3atFPapdXcPD/q1q2vpYgIVdSpUw8+PidRtGgxAFm/O4qiQNM0BgxwxIYNW3UcIUEQBEEQBPE3S0pKRN++toiPjwOQVe9c/CA7MzMDzs6OePbsqY6jJAhCGdKykNAqS8uS6N69J86cCZTbVZJhGDRr1hLW1jY6iI4QK126DOzs7OHvf1JBfUkKo0aNhbGxsdZjI/i1bNkaDx48R0jIBbx69RL58uVD167dUbJkKV2HRhAEQRAEQfzljh3zQVxcnNxeSlnTKOzYsRUbN5KH2AShzzSWLExNTcX27dtx9uxZfPr0CSYmJqhduzbGjh2LunXramq1RB6wcuU6vHr1AuHhrwFAciKhaRolS5bC5s3bdRke8X9r1mzEt29fcfXqf2AYBizLgmEEYFkhHBwGYvJkMhqyvhIIBOjcuSs6d+6q61AIgiAIgiCIf8iFC2d5X2dZIYKDTwMgyUKC0GcaSRZyHIexY8fi2rVr6NatG5ycnBAfH48jR47A0dERO3bsQIsWLTSxaiIPKFKkCM6eDYWX1wEcPLgf0dFfYGFRFI6OQ+Hk5IwCBQrqOkQCgJmZGY4fD8Dly2E4fvwoYmO/oXTpshg0yBH16jVQ2k2ZIAiCIAiCIIh/S1pamtLa55mZ/ANeEgShexpJFp4+fRrXrl3D6NGjMXHiRMl0e3t72NraYvHixTh37pwmVk3kEWZm5hg1ahxGjRqn61AIHjRNo23b9mRELIIgCIIgCIIglKpduy5u3boht+QUANA0g1q1ams5KoIgsksjA5xcvXoVADBgwACp6ZaWlmjUqBHevXuHb9++aWLVBEEQBEEQBEEQBEHogJPTcAU1z7OIRCxcXUdpMSKCIHJCI8nC6dOn4+TJkyhevLjMa3FxWaMiMQyjiVUTBEEQBEEQBEEQBKEDVlbW8PRcA0D6np+ms1IPgwcPha2tnS5CIwgiGzTSDblw4cIoXLiwzPQ7d+7g4cOHsLGxkfu6MqREmn4Rbw+yXXSDfP95A9lO+olsl7yBbCf9RbZN3kC2U95AtpN+Itsl55ydXWFjY4MtWzbi0qVQiEQi1KhRE25uY9Cv3wC11j5X13ZiWRYXLpzD4cNe+PTpI0qWLImBAx3RuXNX0tApB8j+o79U3SYUp6z66G82b96MHz9+8M7Tu3dvVKtWTWb6ly9fMHDgQHz58gVbtmxBhw4dVF0tQRAEQRAEQRAEQRDZkJqaCh8fH3h7eyM2NhaVK1fGyJEj0a5dO60NVshxHDiOk7Qs1EcpKSmwtbVFaGgoGIYBy7KSv1u1aoUzZ84gX758ug6TILQqW8nCdu3a4dOnT7zzrF27Ft27d5eaFhUVheHDhyMqKgouLi6YNm1ajoKNi0uC6tESmkZRQJEi5mS76Aj5/vMGsp30E9kueQPZTvqLbJu8gWynvIFsJ/2U17dLdHQ07Oy64c2bCNA0DZFIJEmA9e3bH5s37/grWsypYztNmjQehw8fkltrkaZp9O8/EJs2bctlpP+WvL7//M3E20aZbHVDDg0NzXYgjx49wujRoxEXF4fhw4fnOFEIABwH8kPTQ2S76Bb5/vMGsp30E9kueQPZTvqLbJu8gWynvIFsJ/2UV7eLs7Mj3r17CwCSJJh4hOITJ3xRuXJVTJjgobP41C2n2ykuLg4+Pt4KB2URiUTw9fXBnDkLUaxYsVxG+e/Jq/sPoaEBTsQuXryIoUOHIj4+HlOmTMGMGTM0uTqCIAiCIAiCIAiC+Kc9fHgfd+7cliQH/8RxHLZv34LMzEwtR6Z/bt68DqFQyDsPy7K4efOaliIiCP2gsWTh2bNn4e7uDpFIhLVr12LEiBGaWhVBEARBEARBEARBEAD++++y0i7GcXGxePXqpZYi0l8ikfyE6p8UJV4J4m+lkdGQX758iWnTpkEgEGDHjh1o2rSpJlZDEARBEARBEARBEMRvVE2AcZz8rrf/knr1GoCiKPAN5UBRFOrVa6DFqPRPZmYmgoNP48yZQKSkpKBy5apwdHRCuXLldR0aoSEaSRZ6enoiPT0dbdq0wdevX+Hv7y8zT4cOHciIQgRBEARBEARBEAShRg0bNlbaEs7MzBzW1pW0FJH+KlWqNLp06Y7z54PlfmcMw6Bdu47/dFLs8+dP6Nu3JyIiwiWD5Fy4cA4bN67FokXL4OY2VtchEhqg9mRhamoqbt68CQC4dOkSLl26JHe+8+fPk2QhQRAEQRAEQRAEQahRs2YtYGNTGZGREXITYDRNY9gwF5iYmOggOv2zZs1G9OzZGW/eRACApJUhRdEoV6481q3brMvwdEokEmHgwL54+zYSwK/u2OK/586diXLlKqBLl246i5HQDLUnC01MTPDyJal9QBAEQRAEQRAEQRDaRlEUDhw4jJ49uyA+Pk4y0i9N0xCJRGjWrAWmTZul4yj1h4WFBc6fvwRv74Pw8jqA6OhoFC9eHIMHO8HRcSjMzfPrOkSduXQpFC9ePFP4Ok3T2LBhDUkW/oU00g2ZIAiCIAiCIAiCIAjdsLa2weXLN3Hw4F4cO3YEP358R4UKVnBycoG9fX8YGBjoOkS9YmZmDje3saRL7R8uXjwHgUCgcMRokUiEe/fu4MeP7yhQoKB2gyM0iiQLCYIgCIIgCIIgCOIvU7RoUXh4TIeHx3Rdh0LkURkZmWqdj8g7aF0HQBAEQRAEQRAEQRAEQeiXWrVqK2xVKFa8eAkUKVJESxER2kKShQRBEARBEARBEARBEISUPn36IV8+M1AUJfd1mqYxYsQo0DRJLf1tyBYlCIIgCIIgCIIgCIIgpJiZmWH37v0QCARgmF9V7CiKAkVRaNmyNUaNGqfDCAlNIclCgiAIgiAIgiAIgiAIQkb79p1w7twl9O5tD2NjY1AUhYoVbbBs2Sp4e/vC0NBQ1yESGkAGOCEIgiAIgiAIgiAIgiDkqlGjJrZu3YWtW3eB4ziF3ZKJvwdpWUgQBEEQBEEQBEEQBEEoRRKF/waSLCQIgiAIgiAIgiAIgiAIAgBJFhIEQRAEQRAEQRAEQRAE8X+kZiFBEAQAjuPw/PkzxMREo3jxEqhWrTppYk8QBEEQBEEQBEH8c0iykCCIf97ly2GYO3cmXr58LplWtWo1LF7siVat2uguMIIgCIIgCIIgCILQMtINmSCIf1po6EUMGNAHr169kJr+6tVLODj0RlhYiI4iIwiCIAiCIAiCIAjtI8lCgiD+WSKRCNOmTYJIJALHcTKviUQiTJ8+WeY1giAIgiAIgiAIgvhbkWQhQRD/rNu3b+HDh/cKk4Ecx+Hdu7e4ffuWliMjCIIgCIIgCIIgCN0gyUKCIP5Znz9/VGm+T5+iNBwJQRAEQRAEQRAEQegHkiwkCOKfVaSIhUrzWVgU1XAkBEEQBEEQBEEQBKEfSLKQIIh/VvPmLZUmAosVK45mzVpoKSKCIAiCIAiCIAiC0C2SLCQI4p8lEAgwf/5i3nnmzl0IgUCgpYgIgiAIgiAIgiAIQrdIspAgiH+ag8MgrFu3GfnzFwAAUBQFAChQoCDWr98CB4dBugyPIAiCIAiCIAiCILSKNJchCOKfN3jwUNjb98fFi+cRExON4sVLoEOHTjA2NtZ1aARBEARBEARBEAShVSRZSBAEAcDY2Bg9evTUdRgEQRAEQRAEQRAEoVOkGzJBEARBEARBEARBEARBEABIspAgCIIgCIIgCIIgCIIgiP8jyUKCIAiCIAiCIAiCIAiCIACQZCFBEARBEARBEARBEARBEP9HkoUEQRAEQRAEQRAEQRAEQQAgyUKCIAiCIAiCIAiCIAiCIP6PJAsJgiAIgiAIgiAIgiAIggBAkoUEQRAEQRAEQRAEQRAEQfwfSRYSBEEQBEEQBEEQBEEQBAGAJAsJgiAIgiAIgiAIgiAIgvg/kiwkCIIgCIIgCIIgCIIgCAL/a+++w5sq+zCOf9O0pYwWKGWWvcpUpuy9kT1lb5GhiKCigiigIqJsZEvZL2XL3kuEggiykb1HWW2BjrTn/aM0EtnYkqTcn+vywuacJM9z7pyT5JfnPAdULBQREREREREREZEHVCwUERERERERERERQMVCEREREREREREReUDFQhEREREREREREQFULBQREREREREREZEHVCwUERERERERERERQMVCEREREREREREReUDFQhEREREREREREQFULBQREREREREREZEHVCwUERERERERERERQMVCEREREREREREReUDFQhEREREREREREQFULBQREREREREREZEHVCwUERERERERERERQMVCEREREREREREReUDFQhEREREREREREQHA1d4NkPhz6NBBunfvxPDhoylevIT19sjISH76aRgbNqwladKk1K5dlw4duuDqavtyGDCgH6GhIYwYMe5VN12eYurUifzyy2RKlCjFjBnTH7vO7t276N27Bx06dKFTp65x8ryXL1+iadN6z1wvWbJkrF69OU6e0966devEgQP72b59j72bIk7o22+/ZuXKXx+77PPPB1K7dl0AQkJC+P77wezcuYOUKVPRrFkLmjZ955H7dOnSjpw5c/Ppp188dxvKli32XOutWrUJT0/P535cRzVo0ADWrl1FQMAy0qfPYO/miIiIiIg4JRULE6jz58/x+ed9iYqKemTZnDkzWLlyGa1bt8cwDGbNmk7SpMlo2bKNdZ3Dhw+yZctGpkyZ8SqbLS9g167fCQgIoFKlmq/0ebNkyUrbth2fuNzV1e0VtkbEcZ04cZwMGXwfW7AvUOAN6/+PHz+KXbt20r59Z65du8qoUcPx8fGhUqWq1nU2blzP6dMn+e674S/cjhQpUvD++x89dZ3EiRO/8OOKiIiIiEjCpGJhArRlyyaGDh1MSEjwY5evWrWC8uUr0aVLNyCmsLhixVKbYuH48aOpXLkauXPneSVtlpczdOhQ8uUrRJo06V7Zc6ZM6U2NGrVf2fOJOCOLxcKZM6epUKHyU/eX6Oho1qxZRZMmzWjduj0AR44cYvnyZdZiocViYdKkcTRt2gIfn9Qv3BYPj8TaZ0VERERE5LlpzsIE5uOPe/HFFx+TKpUPVavWeOw6165dJWPGTNa/M2bMxJUrl61/79ixnUOHDvDuu93jvb2OyDAMtm3bQs+eXWncuC49e3Zl27YtGIZh76bZqFSpCqGhoQwdOsTeTRF55cJPhXHvr7tP/C/8VJhd23fu3FkiIiLInj3HU9e7ffsWERHh+Pr+c0z29c3E1av/HJOXLl1ESEgwrVq1i7f2ioiIiIiIxHptRhYahkGYJdrezXgiD1cXTCbTf36cs2fP0LVrD5o3b8XMmb88dh1vb2+bUYe3b98mZUpvIGaUy4QJY6hfvzEZMvj+5/Y4m/DwcDp1asvataswm81ERUVhNpuZP38uNWrUYsqUGSRKlMjezQSgfv1GhIffZ8eOHSxbtph69Ro+8z7Xrl3ll18ms3PnDm7duknKlN6ULFmaDh26kCZN2jhtX0REBF26tOPkyb8ZMWKczbyZn33Wl23bNvPVV99Yi9q3bt1k+vQp/PbbNm7evImPjw8VK1ambdtOJEuWzHrfc+fOMHXqJP74Yzd374aSNm06qlSpTtu2HUiUyMOmDYcPH2T69KkcOLCf8PAwfH0zUrt2PZo1a4HZbLauFxUVxZw5M1ix4leuXbuCr29G2rXr9Nh+RUdHs2jRfJYvX8a5c2dxd3cjf/43aNeuI2+8Uci6XuzckjNn/o/Roxexbt16QkNDyJQpCy1atKZmzbdtHjc++p9QhZ8K42jJ/c9cL8/ON0mU3T7b5MSJvwHIli2mWBgWFoabm5vN6w7Ayys5ZrPZ5ph8585tUqRICcC9e/eYPn0Kbdt2tHkdxAfDMOjT5wMCA3/n00/7U7duA+uyUaN+JCBgLl279qRNm/bWts2c+QubNm3g2rWrpEyZkhIlStGxY1d8fHys971+/RrTpk1m587fuHXrJqlS+VCuXAU6dOhC8uQpbNrwuNd33bp1aNq0Fe7utlkuXbqIRYvmc/78eXx8fGjWrMUT+7Z27WoWLJjHqVMnMJlM5M6dhxYt2lC2bHnrOitX/sq3337NqFE/s2vXDtavX8utWzdJnz4D9eo15J13Wts8Znz0X0RERETEEbyyYuG8efMYOHAgQ4YMoWnTpq/qaYGYL0Cd5+3nr0uPPy3XEbyZwYvJ77z5nwuGs2YF4O7u/tR1ihcvwaZN662npW3dutF6utuqVcu5cuUK7dt3/k/tcFYDB37O+vVrAKzzPcb+u27dGgYO/JyhQ3+0W/v+bciQIdSpU5dx40by1lulSJfuyacjnz17hh49OnP37l3q1m1A9uw5OHHiBMuXL2Hbti2MHz+FzJmzPPM5o6Is3L59+4nLEydOTKJEiXB3d2fgwCF07tyWYcO+wd9/HkmSJGHx4gVs27aZevUaWguFN24E0alTG27dukndug3ImTM3J078zf/+N4cDB/5izJiJuLq6cvjwQT78sAdJkyalUaOmpEzpzcGDfzFjxjT27Alk9OgJ1mLu9u1b6N//UzJk8KVly7YkSZKY3bt3MW7cSA4c2M833wyz7m9ff92fjRvXUapUGZo2fYfz58/xzTdfPXb+xa+//oING9ZRsWIV6tVrSHDwHVau/JX33+/KV199YzPPHMAnn/QmXbq0tG3bgYiICObPn8uQIQNJlcrHWkCNj/4nZFGhj87F+l/Wiw8nThwHYOfO3xgxYhhXr17Bzc2NkiVL07Nnb3x9MwLg6upK4cJFWb58KSVLluH69av8+ecfdO78HgBz584kUaJENGz48u+b0dHRT91nEyVKROLEiTGZTHzxxUDatWvBuHEjKVmyNKlTp+H337ezYME83nqrFK1bx4xuDAsL4733OnDq1EmqV6/FO++05NKliyxcOJ8///yDSZP88fT05NKli3Tr1pGIiEjq129EunTpOXHib5YsWcjOnTuYMOEXUqRIAfDY1/ehQ38xYcIEtm7dbvP6Hj9+NHPmzKBgwTfo1u19bt68waRJPz+2f7HrFi9egq5dexIREc769Wvo1+8jPvjgI5o1a2mz/tChg/Hw8KBp0xa4urqyeHEAY8eOJGnSZNYCanz0X0RERETEUbySYuGpU6cYOnToq3iqJ/rvY/acw7MKhQCdO7/H8ePH6NYtZuRUwYJv0qVLN8LDw5k6dSItW7Z5Lb+83Lx5g5kz/YmOfvwI1OjoaGbO9OfTT7+wjsS0N19fX3r27MWwYd/y/feDn3rl6p9++p7bt28zatTPFC1a3Hp72bLl6dv3A3744VvGjJn4zOc8cOAv6tSp+sTlD3/5zp49Bz16fMCIET8wYcIYGjZsytixI8iRIye9evW13mfSpPEEBV1n+PDRlCxZ2np7qlSpmDp1Ir//vp2yZSvw3XeDSJYsGdOnz8HLKzkADRs2oXDhIgwdOoQFC+bRqlU7wsLCGDp0MNmz52TChGnW/aJx4+ZMnvwz/v5T2bhxPVWqVGPv3j1s3LiO2rXr8vnnA63P/dZbJfnkkw9t+rZhwzo2bFhH9+4f0LJlW+vtzZq1pEuXtvz441BKlSqLh8c/I6AyZcrMjBnTuXEjFMOAvHnz8/77XVm58ldrsTCu+y/2d/LkCQAOHjxAhw6d8fRMzsGDf7FgwTwOHNjPxInTrQXD3r0/4ZNPPqR9+5iRceXKVaRZs5bcuBHEvHmz6NOn33Md25/k2rWrT91nmzZtQa9efQBIlcqHzz77kk8/7c0PP3xLv34D+PbbQaRK5cOAAYOsBfZ582Zx6tRJ+vbtR4MGTayPlS1bDr755itWrVpOs2Yt+Omn7wkLC2PatNnW/gJUqFCJ3r17MGXKBPr27YdhGI99fTdq1ISyZUvTv39/6+v7woXz/O9/sylevATDh4+2jtasXLkqXbrYvv4PHz7InDkzaNiwKX36fGq9vXnzVnz0UU9+/nkMFStWsRlZnThxYqZMmWnd5mXLlqdp03qsXLnMWiyM6/6LiIiIiDiSeC8WRkZG0rfv46/K+6qYTCYmv/Pma3Ea8vNIlcqHSZOmc/bsaVxczGTJkhWTycTMmdOJirLQvHkrLBYLEyeOY+PGdZjNZmrWfJu2bTvi6ppwz1z//fcdREZGPHWdyMgIduz4jbffrvuKWvVs9es3YtOmDezevYslSxbSoEHjR9a5ffs2e/fuoXDhojaFQoCSJUtTqFAR/vzzD+upyU+TI0cuevb88InL/z06sXHj5uza9TtLlixk167fcXFxYdCgodYRQoZhsGXLJrJnz2FTKANo3rwlZcqUI3PmrJw48TenT5+iQYPGREcbNiOlSpcuh7t7IrZs2USrVu3YvXsnt2/fpnnzVty7d4979+5Z161SpRr+/lPZunUjVapUY+vWzQA0bfqOzXOXLl2WbNmyc/r0KettsaNOK1So/MhIrQoVKjNjxjT27dtr04/q1Wva7Nt58uQDYorT8dV/sb9q1WqQL19+2rTpYC06VahQiQIFCvLFF58wceI4Bg36Doi5wvjs2Qs4deoESZMmsxaVpk2bhK9vJqpXr8W9e/cYNWo4u3b9/mDkXTMaN272XG3x9k7FgAGDnrg8bVrbEcllypSjYcOmLF4cQPfuXQgOvsPIkeNJmTKldZ1NmzaQLJkndevaTn9QrVpNsmTJSubMWQkODiYwcCclS5YmadJkNq/ZXLn8yJDBl61bN9G3b78nvr5NJqhUqZLN6/u337YSFRVFw4ZNbU7rzpXLj7feKsmOHdutt8Xus1WqVHtkn61cOebHgh07ttkU/CpVqmpTnE2fPgMpUqTgxo0b8dZ/ERERERFHEu+Vn1GjRnHmzBm6dOnCuHFPHvUU30wmE4ndzM9e8TVhNpvJnj2n9e87d24ze/Z03nuvJ4kTJ2batEksW7aIAQMGER1tMGTIQNzc3GjTpoMdWx2/nlUofNH1XqVPPx1A27bNGT9+9CMFJ4BLly5gGAbZsmV/7P2zZ8/Bvn17uXTp0jOLhZ6enjbzDz6Pzz77khYtGnPx4gV69epLlixZrcuCg+8QGhpCsWJvPXK/JEmSWq/Ife7cWQCWLFnIkiULH/s8sRfqiV134sRxTJz4+OPO5csx6166dAGAjBkzP7JO1qy2xcLz52Met3nzBk/s65Url2z+9vb2sfnbzS3m1ObYH1Dio/9if/+ekzJWhQqVSZMmLYGBv9vc7urqanP1+XPnzrB8+VK+/34ELi4ujBz5A3v2BDJgwCCuXr3C0KGD8fLyolq1ms9si7u7+wvvsz179mLXrh1cuHCO5s1bUrhwUZvlly5dJEuWrI/Mwejq6kq+fAWAmFF90dHR7Nix/akjG8PDw1/o9X3xYuw+m+mRdbJmzWZTLIx93J49333i88ceC2J5e6d6ZB03N3ebUedx3f/XYfoAEREREXEe8VosDAwMZOrUqQwZMuSVjZqTlzNjxjRSpvSmTp0GAKxevYKKFatQtmwFIOb0rpUrf03QxcI33ywcp+u9SunSpaNnzw8ZNuwbvvtuMK1bt7VZ/qwLOcd+CXZ3f3SOvrhw9OgRQkNDANi4cS2NGv0zIshisQA88xhhGDFtbNCgMRUqVH7sOrEjX6OjYzrcoUMXm4uOPCxJkqQ2f0dEhJM4ceLHPmes6OhoPDw8+O67J89b+e+RlS4uT+9XfPRfHJu3dypu3Ah66joTJozjzTcLU7JkaaKioli7dhXt23e2jgxes2YVq1Ytf65i4cs4d+4c169fA2D79q106vQeSZIksS6PirLwrLf12P2wXLmKNGr05DkXzWbzE1/fJhMkT56EO3fuYTbbvr4jIh794Sb2Of/99zff/GDT/of9e2Sli4vLE9saK677LyIiIiLiSOLtm2VwcDCffPIJVapUoXHjxixatOg/P6bqjS8mdnuZTE/fdpcvX2Lx4gV8+eVg3NxiXhJBQUF4e6ey3i9FipRcu3bV5nEefvyEIHv27FSqVIWtWzc/9rR5s9lM+fIVyZ798aPzXpV/b/fYf+vXb8jmzRsIDNyJp2cy6zKTKWZuQ4DTp089Nq+Y202kSZP2iXk+7+vp327dusl33w0iTZq01KpVB3//qcyYMZWOHWNG+qRMmZLEiZNw/vzZRx739u3b/PDDt1SqVMV6dW7DMHjrLdtRUtHR0WzevJEMGXwxmSBDhgxAzIUb/r3u3bt3CQz8HR8fH0ymf0YnnTt35pHC4oUL5236niGDL+fOnSVHjhykSmU7YvD48WPcuBFEkiSJbbbR0/41meKn/wnd83bRxJNfq/F5/Lp58wa9enUnS5asDBnyvc0yi8XChQvnnprVwYN/sW3bZiZP9sdkguDg21gsFry9va33SZkyBSdO/P3c7X+RfoaHh/H11/1xc3Ona9cejB07kpEjf+CLL/6Z0zN9+gxcuHABw4i2Ka5FRUXx1Vf9efPNQlSuHDOaLiIi/JHXLMC2bZvx9EyOm5vrE1/fMftIUhYuXEr69DHbLFOmmFHA586dIW/evDaPeeHCOev9Hj4W+Pj4UKBAwX+te56zZ8+QJEmSR7bPs143cd1/Z5TQPgMkVMrJOSgnx6RcnINyckzKxXE9byYv9Al17Nix3Llz56nrNGzYkHz58jFw4EAsFguDBj15nqQXlSqVZ5w91usgSZKY05qSJ0+Cj8+Tt93QoZPJmzcvTZs2sN6WPn06bty4ar1fUNAV0qdP/9jHSUi5zJzpT5kyZbhw4YJNwdBsNpMxY0ZmzvR/6rZ8FWJz9fKKGSXz8PYfNmwoderUYfPmjdZ1fXw88fHx5K233iIwMJC//z5IqVKlrPfZvn07+/f/SYkSJciZ89HT+mKFhcWMxHNzc32hbfDFF325efMGU6dOpVSpUvz55278/adRvXoVChUqBECVKpVZvnz5I21btGgumzdvpH79upQt+xa+vr6sWbOSbt3eJVu2bNb15s6dy1dffUXLli0pW/YtateuxrBh37Bw4f9o164V3t7/nFo9ffpEJk+ezMcff8xbbxWiUaP6zJ8/l//9bxYVK5axfvHfvn07J078DWDtb506tdm5cwczZ8aMmI4VGhrK119/wcWLF9mwYQM+Pp5PzCl2JOHD2zGu+5/QBad8xlDZB1KkTILXM16r8XH8SpUqGdHRUWzfvpXr1y/YFLTGjh1LaGgoXbt2feJ+NHHiWGrWrEm5cjEFJm/vpLi7u3Pr1nXrfa5evYyvb4bn2hfNZpcX2me//vonzpw5xaBBg2jevDl//32ElSt/pUaNqtSsGTOSsWbNGkycOJFt29bTuPE/86SuXLmSjRvXUaTIm+TOnZWiRYsSGLiT06ePUrz4P/OlbtmyhX79+lKhQgWqVi3/Qq/vBg3qMH78aAIC5tC0aQPr/IInTpxg584dQEyB0cfHkwYN6rJkyUJmzpzKxIkTraNvIyMj+eCDIfzxxx8EBATg45MdT8+YCxN5eno8sr1cXEw22zGu++/MEtJngIRMOTkH5eSYlItzUE6OSbk4rxcqFi5atIiLFy8+dZ1ChQpx/PhxVq5cyaRJk2y+pP9XN26EPPN0SvnHvXvhANy5c4+goJDHrnP8+FGWL1/OmDETbNapUqU6/v7T+O67HwBYt24dnTp1tVnHZIrZ+RNSLokSebF27WamTp3EzJn+BAVdx8cnNW3atKNTp3dJlMjridvyVYnNNTg45qIdD29/N7dk9OjxId9/P8S6bmx7P/igL926daZLly7Uq9eQbNmyc+rUSZYtW4yXV3J69fr4qX27desuAFevXmP27P89tY2FCxclTZq0LFw4n82bN1OvXkPy5HmTW7fu8emnA+jQoRW9e3/E9OlzSJo0KR07vsfvv++kS5cuNGjQmGzZsnPs2BGWL19GyZKlKVq0NLdu3ePjjz/nk09607hxYxo0aEzGjJk4cuQwK1Ysw9c3I++80/ZBH0z07v0x3347iDp16lC/fiN8fFLzxx+72bBhHfny5adGjXoEBYWQKVNOGjVqyqJFAbRs2YqKFatw9eoVFi6cT8qU3ty6ddO6XcqXr0aJEisICAjg1KkzlCtXAYvFwq+/LuHs2TN07/4BZnMSgoJCnphTbLEwMtJifdy473/CFh4Z/lzrhUSGE/GE7RHfx6/evT+hb99etGnThkaNmuLjk5o9ewLZsmUTRYoUo27dJo/Natu2zfz111/Mnh3wyDF51qzZuLl5cO3aNQ4cOMDAgUOeK++7d+8+c5/NkycfWbJkZceO7cyZM4e33ipJlSq1CQoK4f33+7Br1y769x9A5sw5SZMmLY0bt2DNmnX079+fHTt2kjdvfs6fP8fixQvIlSs3NWvWJygohF69PqZHjy506NCBevUakiNHTs6ePcOSJQtJnjw5777b09qH5319u7t78u673Rk3bhSNGjWmVq06hIaGsGDBfDw9vbh16ya3bt3FwyOEXLkKUKdOfZYvX0qjRk2oWrU6bm5urF27isOHD9GoUVMyZMhGUFAIISFhAISEhD2yXaOjDaKioq23x0f/nU1C/AyQECkn56CcHJNycQ7KyTEpF8cVm80z1zOMuI3uwoUL1K9fn4oVK/LFF19Yb1+5ciWDBw/ms88+o169enh6elon+n9eQUF6ob2IqVMn8ssvkxkxYtwTJ7fv3bsHrq6u/PDDKJvbIyIimDBhDOvWrcHFxYXatevSqVNXmznRTKaYEVfK5dWKzXXkyHHUqlX1sdv/o496Ehi4kw4dutCpU1fr7ZcvX+KXXyaza9fv3Llzm1SpfChVqizt2nUkdeo0T33ey5cv0bRpvedq47ffDidTpsx07tyGlCm9mTFjns0cgbNn+/Pzz2OoWfNt+vf/GoDr168xbdokduzYTkhIMOnTZ6BatZq0aNGaRIk8rPf9++9j+PvHXHX47t1QUqdOQ+nSZWnTpsMjpwbv3buHOXNmcPDgASIiIkiXLh2VKlWlRYs2JEuWzGbdxYsXsGjRfC5evEDq1Glo2bIt+/f/ydq1q9i+fY91PYvFwvz5c1izZiXnz5/Hw8ODrFmz0bx5S5u51p6Uk8VioWLFkhQqVISxYydZ14+P/idk4afCiAp9dLqAWOZkZhJl93ji8ldx/Dp69DC//DKZ/fv3ER4eRoYMvlSvXosWLdrYXG03VlRUFO3avUPhwsXo0+dTm2WhoaGMHPkDv/22jaRJk9K06Ts0b97qmW0oW7bYc7X1gw8+omrVGrRr14LIyAj8/efZzOW3YcM6Bg78jCJFijFy5HhcXFwICQlh+vTJbNmyiRs3gkidOg3lylWkXbtOeHl5We978eIF/P2n2hx3ChcuSvv2nR+5SMnjXt9VqlSmadNWj1wsaMOGdcydO5PTp0+SPHkK6tVrSHh4ODNn/kJAwDLSp485BdkwDJYvX8qyZYs4ffoUZrOZTJmy0KBBI95+u751vtCVK3/l22+/5tNP+1O3bgOb52rYsDZms5kFC3613hYf/Xcm+gzgHJSTc1BOjkm5OAfl5JiUi+OKzeaZ68V1sXDRokV89tlnz1xvxowZlCjxYldn1AvNsegAYF/a/s5BOTkm5eIclJPjUjbOQTk5B+XkmJSLc1BOjkm5OK7nLRbG+azaZcuW5Zdffnnk9u3btzN16lQ6dOhA+fLlyZMnT1w/tYiIiIiIiIiIiPwHcV4sTJMmDWnSPHo645UrVwDIkSMHpUuXjuunFRERERERERERkf/Ixd4NEBEREREREREREcegYqGIiIiIiIiIiIgA8XAa8pM0atSIRo0avaqnExERERERERERkRekkYUiIiIiIiIiIiICqFgoIiIiIiIiIiIiD6hYKCIiIiIiIiIiIoCKhSIiIiIiIiIiIvKAioUiIiIiIiIiIiICqFgoIiIiIiIiIiIiD6hYKCIiIiIiIiIiIoCKhSIiIiIiIiIiIvKAq70bIHHv22+/ZuXKXx+77PPPB1K7dl1CQkL4/vvB7Ny5g5QpU9GsWQuaNn3nkfW7dGlHzpy5+fTTL+K72fKSDh06SLdunRg+fDTFi5ewWRYZGclPPw1jw4a1JE2alNq169KhQxdcXW13/QED+hEaGsKIEeOe+3mbNKnLlSuXn7neL7/MJlcuv+d+XEc1adJ4ZsyYxujREyhSpJi9myMiIiIiIiISL1QsTIBOnDhOhgy+dOrU9ZFlBQq8AcD48aPYtWsn7dt35tq1q4waNRwfHx8qVapqXXfjxvWcPn2S774b/sraLi/mzJkzfPZZX6Kioh67fM6cGaxcuYzWrdtjGAazZk0nadJktGzZxrrO4cMH2bJlI1OmzHipNgwYMOipy9OmTf9SjysiIiIiIiIir56KhQmMxWLhzJnTVKhQmRo1aj92nejoaNasWUWTJs1o3bo9AEeOHGL58mXWYqHFYmHSpHE0bdoCH5/Ur6r5DsEwDEwm03Pfbi9btmzi+++HcOfOnSeus2rVCsqXr0SXLt0AOH/+HCtWLLUpFo4fP5rKlauRO3eel2rHk15nIiIiIiIiIuJ8NGdhAnPu3FkiIiLInj3HE9e5ffsWERHh+Ppmst7m65uJq1f/OaV06dJFhIQE06pVu3htr6O58sN3nO/VHeNfI/WMqCjOf9CNKz98Z6eW2fr44158/vnHpE6dmmrVajxxvWvXrpIx4z85Z8yYyebU4R07tnPo0AHefbd7vLZXRERERERERJzD6zOy0DDAct/erXgy18QQB6PWTpz4G4Bs2WKKhWFhYbi5uWE2m63reHklx2w2ExISbL3tzp3bpEiREoB79+4xffoU2rbtSLJkyf5zm5xF2LGjXP1pGDwoFGYaMRaT2RxTKPywB7f+NwfMZlLUa4iH38uNwosrZ8+eoWvXHvTs+R4jRox+4nre3t42Od++fZuUKb2BmBGmEyaMoX79xmTI4BvvbR427BuWLVtM27YdbYqT8+fPYfTon6hfvxEff/w5EDOydd68Waxdu4qLFy/g6enFm28WplOnrmTOnMV635CQEPz9p7JlyyauX79K8uTJKVGiNB07diVdunQ2z3/9+jWmTZvMzp2/cevWTVKl8qFcuQp06NCF5MlT2Ky7efMGZs+ewenTJ/H09KJOnfpPPNV7167fmT3bn6NHjxAVZSFr1uw0btyM2rXrPrTOLtq2bcuXXw7m6tUrLF++lGvXrpIqlQ/Vq9eiY8d3beaRjI/+i4iIiIiIiDyP16NYaBikWNQQtyt77N2SJ4pMX5zbDRf954LhiRPHAdi58zdGjBjG1atXcHNzo2TJ0vTs2Rtf34y4urpSuHBRli9fSsmSZbh+/Sp//vkHnTu/B8DcuTNJlCgRDRs2/c/9ciYefnnIMmEqZ9/rxK15swHI9ONozn/0vrVQmGXiNLsXCgFmzQogUSJ33N3dn7pe8eIl2LRpvfVU4a1bN1pPNV+1ajlXrlyhffvO/6ktt2/ffuIyd3c3kiRJCsAHH/Rh3769zJ7tT8WKlcmdOw9//32MCRPGkiNHLj74oA8QU8Ts2/cD9uwJpHTpctSv35g7d26zYMH/2Lt3D5Mn+5MuXXqCg4Pp1q0jV65cpm7dBmTNmp2LFy+wZMlCduzYxoQJv1hHVV66dJFu3ToSERFJ/fqNSJcuPSdO/M2SJQvZuXMHEyb8QooUKQBYvHgBP/44lOzZc9C583uEhYWxaFEAYWFhj/Rv0aIARowYRp48+ejQoQtmswtbt27m22+/5u+/j9OrVx+b9SdP/pno6Gjq12+El5cXK1b8yowZ0wCsBdT46L+IiIiIiIjI83o9ioUQJ6P2nMHJkycAOHjwAB06dMbTMzkHD/7FggXzOHBgPxMnTsfXNyO9e3/CJ598SPv2LQAoV64izZq15MaNIObNm0WfPv2eWYhKiFLUbwRgLRjGFg1jC4Up6jW0Y+v+8bzZdO78HsePH6Nbt04AFCz4Jl26dCM8PJypUyfSsmUba5HsZdWpU/WJy8qVq8B33/0IgIeHBwMHfsN773Xg228HMW7cJL766gvMZlcGD/6ORIkSAbB27Sr27AmkVat2dOv2vvWxihQpRs+e7zJ//hw++KAPkyaN58KF84wbN9l64R6AWrXq0LlzG0aO/IHhw2NGXf700/eEhYUxbdpsfH0zWtetUKESvXv3YMqUCfTt24979+4yfvxosmXLzqRJ/nh4eFgfs337ljZ9u3btKmPG/ESpUmX5/vufrPNZNm3agiFDBhIQMJdq1WqQP38B633CwsKYPXsBXl5eAFSvXpv69WuycuWv1mJhfPRfRERERERE5Hm9HsVCkylm1N5rcBpytWo1yJcvP23adLAWlCpUqESBAgX54otPmDhxHIMGfUeWLFmZPXsBp06dIGnSZNYCyrRpk/D1zUT16rW4d+8eo0YNZ9eu30maNCmNGjWjceNm/7mNji5F/UYY0dGc69rRelvmn6c4TKHwRaRK5cOkSdM5e/Y0Li5msmTJislkYubM6URFWWjevBUWi4WJE8exceM6zGYzNWu+Tdu2HW1Oi32aESPGPXFZ7CnPsfz88tClSzfGjx9Np05tuXDhHAMGDCJz5qzWdTZv3gBAixZtbO5bqFARJk/2J0MGXwzDYOPGdWTNmo2MGTPbjG709k5F/vwF2b17F/fu3cNisRAYuJOSJUuTNGkym3Vz5fIjQwZftm7dRN++/di9O5D79+9Rt24Da6EQIG3adFSrVoNFiwIeaudGIiMjqVKl2iMXmalatQZr1qxky5ZNNsXCUqXKWAuFAEmSJCFLlqwcP3403vqfJEkSRERERERERJ7X61EshJhCnFvC/9Jcs+bbj729QoXKpEmTlsDA3623ubq62lwB99y5MyxfvpTvvx+Bi4sLI0f+wJ49gQwYMIirV68wdOhgvLy8qFatZrz3w56MqChCNq63uS1k43pS1G2A6aG5H52F2Wwme/ac1r/v3LnN7NnTee+9niROnJhp0yaxbNkiBgwYRHS0wZAhA3Fzc6NNmw7P9fjFi5d4ofa0aNGGbdu2cODAfsqXr/TI1ZQvXbpIihQpHjviMW/e/ADcunWT4OA7BAffeerIxuvXr3H3bijR0dHs2LH9qeuGh4dz8eIFADJmzPzI8qxZs9v8fe7cWQAGD/7yiY955colm79TpfJ5ZB03Nzeio6Otf8d1/7NkyfrE5SIiIiIiIiL/9voUCwVv71TcuBH0xOUTJozjzTcLU7JkaaKioli7dhXt23emaNHiAKxZs4pVq5Yn6GLhvy9mkvztetxZseyfOQwfXPTEmc2YMY2UKb2pU6cBAKtXr6BixSqULVsBgMqVq7Jy5a/PXSx8UUFB1zl79gwA+/bt5fr1a6ROnca63GKxAE8fZRtbXCtQ4A06der6xPVSp05DSEgIEHOqfaNGT56H8+GLAEVEhD+y3DCiH/t33779bK4s/rB/j6w0Pcfo4bjuv4iIiIiIiMiLULEwAbl58wYfftidzJmzMmTI9zbLLBYLFy6ce+JVbw8e/Itt2zYzebI/EDP6zGKx2BQ7UqRIwcmTf8dX8+3u34XC2DkKby9dZHvREycuGF6+fInFixfw5ZeDracZBwUF4e2dyrpOihQpuXbtarw8v2EYDBnyFXfvhvLhh30ZPfonhgz5ipEjx1kLaenT+3L+/DmCg+/g5ZXc5v4//PAt3t6paN++M4kTJyE4+M5jRzbu3r0TFxcz7u7uZMiQAYgZOfi4dbdt24yXV3JcXV3JlCmm6BdbzHzY+fPnbf5Onz7mcZMl83zkcYOCgjh8+CAZMiR+ru1i+7hx238RERERERGRF+Fi7wZI3EmZ0puIiEi2b9/C338fs1k2c+YvhIaGUrt2vcfed9y4UVSqVJU8efIBMQUjd3d3Ll26aF3n0qULCXqkUvipk9xZvuyRi5mkqN+ILBOmgtnMnV+XEn7qpJ1b+vImT/6ZnDlzU7FiFettadKk4dKlC9a/L168QOrUaePl+efOnckffwTSsmVbmjR5h2bNWvLHH4HMnTvTuk758hUxDIOAgHk29z148ABLly7i1q1bmM1mypevwLlzZ1mzZqXNeidO/M3HH3/IyJE/4Orqird3Kt54oxC7d+9k3769Nuv+/vtvfPZZX2bNmg5A8eIl8fT0YtGiAOuIRIg57Xf16hU2961QoTIuLi7MnPnLI1dKHjPmJz7/vC/Hjh3lRcV1/0VERERERMQ5GYbB6tUrady4HtmyZSBXrkx069b5ke+2cU3fJBMQk8lE3779+PjjXrz//ns0atQUH5/U/PFHIFu2bKJIkWK8806rR+63bdtmjhw5xBdffGW9zcXFhSpVqrNoUQBeXl5cv36NI0cO8+WXQ15Zf141j1y5yT5vEZHXrpCibgObZSnqNwKTCbe06fHIlds+DfyPjh8/yrp1qxk9eoLN7dWq1WTGjGlMmRJz+9atm556auu//btY9W9ZsmQjT568HD9+lMmTfyZ79hx07PguAF26dGPHjm1MnvwzxYq9Re7ceXj77Xps2LCWX36ZzOnTJyla9C1u3Ahi4cL5pEmTlg4dOgPQrdsH/PnnXr755iv++GM3+fIV4Nq1qyxZshCz2UyfPv2sbejTpx89enShd+8e1KvXkOzZc3Lu3BmWLFlI8uTJ6dHjQyDmis19+/bj66/706lTa+rXb0R0tMHixQG4ubnZ9CtTpsx07PguU6ZMoH37ltSuXQdPTy+2bdtMYOBOypQpR4UKlZ57O8aKj/6LiIiIiIiIczEMgy+//JyJE8dhNpuJiooCYOnSRSxevICxYyfSpEnzeHluk2EYRrw8cjwICgrBeVprP0ePHuaXXyazf/8+wsPDyJDBl+rVa9GiRZtHTkuMioqiXbt3KFy4GH36fGqzLDQ0lJEjf+C337aRNGlSmjZ9h+bN/yk2mkzg4+OpXOwkdvt///1wpk2bzIgR4556sZHevXvg6urKDz+Msrk9IiKCCRPGsG7dGlxcXKhduy6dOnV95qi0Jk3qcuXK5We2s2nTFrz3Xg86dmzNhQvnmTTJHz+/fy6sc/DgX3Tv3plMmTIzbdosEiXyICIiglmzprN27WquXr2Mt3cqihV7i86d37MZ3Xrr1k38/afy22/bCAq6TvLkKcifvwDt2nWyuXgPxIyY9Pefyq5dv3Pnzm1SpfKhcOGitG/fmYwZbecc3LMnkOnTp3Ds2BESJfKgevWa+PpmYsSIYYwePYEiRYpZ192yZSMBAfM4fvwY0dFRZMjgS82adWjSpDnu7u6YTHDy5GHatm1L27Ydeffd7jbP1a1bJw4c2M/27XtsMonr/ostHb+cg3JyXMrGOSgn56CcHJNycQ7KyTEpl7ixatUK2rVr8cTlZrMru3fvf+T77NPEZvPM9VQslJelA4B9afs7B+XkmJSLc1BOjkvZOAfl5ByUk2NSLs5BOTkm5RI3Gjasw86dv1lHFP6bi4uZXr1689lnXz73Yz5vsVBzFoqIiIiIiIiIiDiQvXt3P7FQCBAdHcWePXueuPy/ULFQRERERERERETEgZjNz77MiJtb/FyKRMVCERERERERERERB1K1arWnFgxNJhOVK1eNl+dWsVBERERERERERMSBvPdeT6KjnzRfoQuenl40b94yXp5bxUIREREREREREREHUqRIMUaNGo/ZbMZsNltvN5lMJEvmyfz5i0mePEW8PHf8nNwsIiIiIiIiIiIiL+2dd1pRunRZZs6czu7du3Bzc6dq1Wo0b96SFClSxtvzqlgoIiIiIiIiIiLigDJnzsIXXwx8pc+p05BFREREREREREQEULFQREREREREREREHlCxUERERERERERERAAVC0VEREREREREROQBFQtFREREREREREQEULFQREREREREREREHlCxUERERERERERERAAVC0VEREREREREROQBFQtFREREREREREQEULFQREREREREREREHlCxUERERERERERERABwtXcDXoTJZO8WyMNi81Au9qHt7xyUk2NSLs5BOTkuZeMclJNzUE6OSbk4B+XkmJSL43reTEyGYRjx2xQRERERERERERFxBjoNWURERERERERERAAVC0VEREREREREROQBFQtFREREREREREQEULFQREREREREREREHlCxUERERERERERERAAVC0VEREREREREROQBFQtFREREREREREQEULFQREREREREREREHlCxUERERERERERERAAVC0VEREREREREROQBFQtFRCTBMAzD3k0QERERERFxaioWioi8JBWmHEtQUBDnzp0DIDo62s6tEXE+N2/eZNGiRdy4ccPeTRFxWvpsICIiCYGKhWJ16NAhzp07R0hIiL2b8lq6efOmChxOYNasWfz2228AmEwmO7dGYv3444+ULVuWGTNmAODiorc3R7Rnzx6OHTvGnTt37N0U+ZfRo0dTunRpvvzySx3bHNSFCxe4d++evZshzxAaGmrvJsgTnD9/nrt379q7GfIUmzZtsn7OFseyZ88ejhw5wuXLl+3dFHlFXO3dALG/X3/9lbFjx3L79m3u3buHn58fHTt2pHbt2vZu2mthxYoV+Pv7c//+fe7fv0/r1q2pWrUqGTNmxDAMfWlzIFu3bmXIkCE0bNiQ3Llzkzp1amVkZ0uWLOH7778HoFevXtSqVcvOLZLHWblyJSNGjOD27duEhYXh6+tL9+7dqVevnr2b9tpbunQpQ4cOxdXVFS8vLzw9Pe3dJPmXX3/9lUmTJhESEkJ4eDiNGjWibt265MmTh+joaP044iCWL1/O3LlziYiIIDw8nLp161K5cmVy5Mihzwp2tnLlSsaNG0doaCgRERHUrVuXevXqUaBAAWXjYD7++GOKFStG5syZyZQpk72bI8S8B40aNYo7d+4QFhaGj48PnTp1okWLFpjNZns3T+KRioWvsdDQUEaMGEFAQAC1atUie/bs3L9/n1mzZtGvXz+SJ09OmTJl7N3MBOv+/fv89NNPBAQEUKVKFTw8PDh16hRDhw7l0KFDfPfdd7i6ahd1JGfOnAHgzz//ZPv27TRs2FAfMO3k2LFj9O/fnyNHjtCgQQOaNGlCnjx58PDwsHfT5F/mz5/Pjz/+SOXKlSlYsCChoaHMmzePLVu2ULZsWby9ve3dxNfSwYMHGThwIEePHqVu3bo0b96cH3/8keDgYGXiAAzD4N69e4wYMYJ58+ZRq1YtkidPzpkzZ5g6dSr79+/nl19+wc3Nzd5Nfa0ZhsHdu3f58ccfmT9/PrVr18bT05OgoCB+/PFHAgIC8Pf3J3369PZu6mvJMAwmTZrEpEmTqF69OunSpePSpUvMmDGD1atXM3XqVHLlymXvZsoDly5dIjQ0lD///JOdO3eSLl06HePsKCwsjNGjRzN79mxq1apFvnz5iI6OZu7cuXzzzTe4ubnRsGFD3N3d7d1UiSeqRLzGDh48yPLly2nRogWdOnUiTZo0AOTMmZNBgwbxv//9jzJlyuhX63jy999/s2rVKuv29/HxwTAMWrZsyZo1a2jcuDElS5a0dzMFrPtASEgInp6eXLx4keXLl1O4cGGyZs2qX6XtYPr06Rw4cIAhQ4ZQvXp1vLy8HllHxy77Cw0NZc6cOeTKlYuePXvi6+sLQJUqVXB3d8fb21v7zysWERHB4MGDCQgIoFSpUowdO5ZixYrh6elJWFgYyZIlIzIyErPZrP3HjkwmE8eOHePXX3+lfv369O7dGx8fHwC6d+/Oxo0bWbdunc4CsTOTycShQ4dYvnw5b7/9Nn369CFt2rRAzPvUTz/9xIABA/jqq6/ImDGjnVv7+rl+/ToLFy6kVKlS9OrVi3Tp0gFQuHBhRowYwYABA+jfvz8FChSwc0tfb7Gf13bv3o3JZOLevXssWLCAggULkidPHns377V14sQJlixZQp06dfjggw+sx7Z8+fLx5Zdf8uuvv1K3bl0VCxMwfQp8ja1evZrIyEjatGlDmjRprPPllSxZkuTJk7Np0yZu3rypLwvxZP369dy8eZPmzZvj4+NDVFQUJpOJRo0aERER8djih9hHbCHj77//Jn/+/HTu3JnffvuNjRs3qtBhJ1WrVsXd3R03Nze8vLywWCzWZVevXgX+mbdQk83bz8WLFzl69CjvvPMOvr6+REVFAZAjRw7rKFDtP6/W1atXMQyDr7/+mu+++45KlSrh6elJSEgI9+/fx2Kx4Obmpvd+B7Bx40bu3LlDixYt8PHxITIyEoD27dsD6Auag1i0aBEhISF06NCBtGnTWj9PN2zYkAYNGrB9+3ZWrlypuQzt4Ny5c5w7d44WLVqQLl0662eFevXq0aNHD/766y/mz5+vizrZWez7zeLFi0mbNi1t27blr7/+Yv369Zpj0o5+//13bt68+cixLU+ePKRMmZI9e/YQFBRk51ZKfNInwddQ7Bfn6OhoLBYLhmFYf9GJiorCx8cHb29vUqdOTXh4uJ1bm/DEbv979+5hMpkIDQ0lOjoas9mMxWJhz549JE+enGTJkmkicwdhMpmwWCxERkaSLl066tSpQ8aMGVmyZAl//fUXoILUq5YlSxYyZszI7NmzAXB1dWXZsmU0atSIzp0707ZtW8aNG0dERISKUXZ09uxZIGbaBQCz2czSpUupU6cOrVu3pnXr1owfP96m2CvxK1OmTHz88cc0btzYOsoGwNPTk6ioKNzc3AgNDdUxzY5it33sXFBHjx4FsJ6Ot2/fPnx8fPDz89OF0ewoNifDMHB3d8dsNls/TxuGQfLkySlWrBgQM59hbI7y6sT+eHjhwgUA6/Q+SZIkoXbt2lSrVo0VK1awc+dOu7XxdRIWFkbXrl1ZvXo1gPUHRIAFCxawc+dOPvjgA9q0aUPhwoVZtGgRhw4dsldzX3uxP3DcunULwFor8PLyInny5CRNmlTF3AROpyEncKdPn+Z///sfHh4eZMyYkdKlS5MhQwYsFgupU6cmZcqUREREWH/RMZvNhIaGcuLECetyeXlP2v4A5cqVIzg4mLRp01q3/9q1a9m8eTMWi4VevXqRJEkSOnXqROXKle3ZjQTv3zmVKVOG9OnTW0cNGoaBq6srly5donjx4uTIkYPWrVszdOhQVq1aRb58+XBzcyMiIkIjPeLQk3KBmJFpb775JitXrmT9+vVs27aNgIAASpYsiZubG4cOHSIwMJBLly7x7rvvkiVLFo0CjSdPyylnzpy4ublx/fp1IiIiGDJkCIsWLaJcuXJ4eHiwd+9eRo8ezfXr1+nUqZNO04tjT3oP8vLystkXoqOjCQsLI3Xq1Fy/fp1kyZLZsdWvl6ftP5UrV8bf35+ZM2eSKlUq3nzzTU6cOMHcuXO5d+8eH330EenTp6dhw4ZUqFBBo0Hj0ZP2pYiICFKlSkVERASXL18md+7c1gKI2WwmW7ZsuLm5cfz4cdauXcsbb7yhzwlxLCwsjF69etGwYUNq1qxJVFQULi4umEwm8ufPT6JEiTh9+jTh4eEkSpTI+lnAx8eHVq1asWPHDtasWUPx4sWtUzJJ/Lh8+TJbtmzh8uXL1KxZ06bAHh4eTqFChahQoQLJkyenWbNmDBgwgGXLlpEjRw5SpUpl7+YnSI/bf2J/qMqePTsZM2a02W9iB7ecOHECV1dX6/QykjCpWJhARUdHM3HiRCZOnEjatGkJDg7m1q1bpEmThqFDh1K6dGlatGhBs2bNHnljPHDgAHfv3qVmzZp4eHjYHDTk+Txt+3///feUKlWK0qVLU6FCBet9rly5wpAhQ8ibNy/58uXj2rVrrFmzho8//phhw4ZRuXJlFTri2LNyip0zMnb+lFu3blmvzFa9enXWr1/PypUryZUrF/v376dkyZLUqlVLOf1Hz8qlePHiuLq6UrlyZRYvXsy0adNwdXVlwoQJvPnmm9aLAEyaNImlS5eSM2dO2rVrpy/Scex5jnOenp7kypWLdevWUaNGDfbu3ctXX31FjRo18PT05MyZM/z8888EBASQPXt2WrVqpZziwPMe22I//Lu4uJAkSRJMJhPBwcGcOXOGrFmz2rcTCdzz7D9vvvkmffv2Zdq0abz//vv4+vpy9uxZypYtS86cObl58yabNm1i06ZN9O/fnyZNmujzWhx7ns/TxYsXx9/fn9GjR1O0aFGbYvuyZcsoW7YsqVOnZunSpXTt2lVFjzj2uAJU7KjPJEmSUKhQIdauXUvbtm3JkCGDzWe0fPny8fbbb7Ns2TLOnDmjYmE8ix2hdvz4caZPn0779u2xWCy4u7vTsmVL3n77bVKkSAFAqVKlqFatGitXrqRs2bLUrFnTji1PuJ5WwK1QoQLlypV75KJnR48eJSgoiJIlS+Ll5aVaQQKmT+QJ1IEDB5gxYwbNmjVj/PjxbN++naFDh+Lh4UG/fv3YuHEjPj4+pE2b1vqmGXsqS2BgIBAz+a+8nKdt/08//ZQNGzZYTyeKPf3O29ub0aNH8/333/PJJ58wfPhwhg0bRtKkSVm8eLH1lHGJO8+zn8SKiooiPDzcegpLhgwZaNOmDUFBQQwcOJCtW7eSKVMmnRIWB56Vy+bNmwGoVq0amTJlYu/evRQqVIjy5cuTPHlyADJnzky7du1Inz4927dvJzIyUvtPHHvWcW7z5s2kTp2aUqVKceTIEbp27UqaNGlo0qQJnp6eQMzp5O3btyddunRs2bJFx7k48rzHttj3/9iRUAULFrSeMi7x61n7z/r16wFo3bo1CxYs4JNPPiE0NJQOHTrw3Xff0a9fP4YNG8aYMWPInDkzCxcutJ5yKXHnWTlt2bKFypUr06RJEw4dOkSfPn0ICAjgjz/+YPjw4cybN48qVapQokQJ7ty5w4YNGwBNXRKX/l2Agn+OaWnSpKFChQpcunSJxYsXP3LfZMmSUbFiRSIiIvjtt98A9DkuHsR+11mwYAEAPj4+jBw5kuDgYNzd3a3ztqdIkcKaXdq0aWnUqBEeHh4sWLCAc+fO2a39Cdnj9p/YfSBFihQ2hcLYbPbs2UNYWBhFixYFdDxLyFQsTKAWLlxIZGQknTp1IkeOHJjNZurWrUv//v2xWCz8/PPP1rnWYg8IsaM5Vq1aRapUqShRogTwz5w5evN8fs/a/hMmTLBu/9jt7u7uTpEiRaxXmgJ46623yJkzJ9u3byc8PFwj1uLY03KKjIxkwoQJ7N+/H4j55S00NJT8+fMDMfMPffPNN7i6umIymWjbti0FCxbUL2tx4Em5fPHFF0RGRjJx4kT27dsHQOfOnalbty5NmzYFYj6wGIaBi4sL6dOnJ2vWrOzdu9f6QVTizrOOc6NHj+bUqVM0aNCAzJkzc+HCBetFTSDmPcVkMuHr60vu3LkJDAwkMjJSOcWB5zm2PfwZIPa4lSFDBu7fv8+BAwesyyR+PGv/efg4lzhxYlatWkVkZCQtWrQgderU1scpWLAgRYsW5a+//iIiIsJOvUm4npZTVFQUo0aN4tSpU3z44Yd06tSJLVu2MGDAAFq1asWyZcsYOHAgTZo0oUCBAiROnJjLly9rSow4EluACggIAGIKUKNGjSI4OBhXV1frBYHKly9PgQIF8Pf35+TJk9b7xx7fcuXKRfr06fnjjz+spzBL3HJ1deXcuXNs3LiRJk2a0KhRI8LCwhg+fDhge6Gzh0eGvvHGGzRo0IAdO3awbds2HePi0LP2n8e9/8d+Vli9ejXu7u6UKVMG+Gcu0KioKBUOExgdDROY2IuV3Lhxg5QpU1oLTxaLBbPZTJkyZXjvvfc4cOAAS5YsITw8HBcXF+sBYc+ePZw5c4Zy5cpZT7eMjo7m5MmTrFq1iitXrtitb87gZbd/7Jtf7AeU2DxSpUqFyWQiTZo0ugBAHHqZnMxmM8mTJ2fPnj106NCBfv36UbFiRb799luSJEnC5s2bOXXqFKAv2C/rWbmULVvWmsuyZcuIiIigQYMG9OrVy3q8evgDp5eXF+Hh4aRLl06ZxKHn3X8OHz7MrFmzyJUrF7Vq1QJi5sb59/tI7JXfvb29dVGn/+hl34NiRwv4+fkB8Mcff1hPQ5K49SIZLV26lLCwMDw8PLhz5w7Zs2fH19eXyMhI6zEtadKkREVF4eHhoVGhcehFj3NJkybl448/ZsGCBQwfPpyffvqJBQsW0LBhQyDm+JYoUSI8PDxUKIwjsQWoTZs2WQtQ9+/ftxagYo9fOXPmpGHDhty/f58xY8ZYR1LF8vX1JXHixHh6euoH33g0Z84cbt++zfvvv0+3bt3Inz8/8+fP5+jRozbvQ/DPZzkvLy9q1apFtmzZmD9/PidOnLBX8xOcZ+0/T3Ls2DH27dtH8eLFyZcvH/BPrWDp0qXWHxslYdCnwAQmdu6h6OhogoODrSMHYiv+ZrOZJk2aULhwYVatWmW9+lfsQXn79u0AVKpUCYiZR+/XX3/lww8/pE+fPly+fPlVd8mpvOz2j53s+vr160DMBxyLxUJgYCB79uyhfPny1jk85L970Zz27dvH3bt3CQoK4ocffsBkMjFlyhT69+9P9erVadOmDYGBgSxZsgSLxaIv2C/pRXP5/fffSZQokfWiGBcuXLCOVrNYLOzatYt9+/ZRtmxZXbAhDr1ITitWrGDPnj20a9eOUqVKsWPHDlauXMmtW7esx7mDBw+yb98+SpUqZTNiSl7cy74HxR6zihUrRo4cOfj777+t70cSt14ko9WrV1tP9/Lx8WHfvn1cuHABNzc3XFxciIyM5MCBA2zdupVy5cqRJ08ee3YtQXnRfWnHjh0AFChQgDp16lC7dm3SpEljLT6tWbOG27dvkyNHDvt0KIF6WgHKbDZbf4yvWbMmjRs3ZvXq1cyZM4ebN29a8w0MDOTMmTPkzZvXzr1J2CpWrEjXrl1JkSIFiRMnpmXLlgB88803AE8s1ObOnZt33nmHkydPsmjRIoKDg19ZmxO6Fyngxvr9998BqFKlChBz5lVsreDzzz9/pBgvzk3faBOY2F+aa9asyZ07dzh06JB1R394st+uXbty69YttmzZQmhoKCaTiaioKPbu3Uvy5MnJli0bv//+O1999RWffvopmTJlYvPmzZrH8BledvvfvHmTtm3b0rx5cwICAjh27BgbNmxg/PjxpEmThsaNG9utTwnRi+R0+/Zt1qxZQ+LEiWnXrh3ffPMNw4cPp2TJkri7u+Pu7k6tWrVImzatRn/+R/9l/3n33Xdp164ds2bN4vDhw6xfv966/zRq1MhufUqIXiSnO3fusHz5cry9venevTuFChVi2LBhDBw4kLVr17J48WJGjhyJi4uLdQSOvLz/8hkg9v5Vq1bl8OHD1pHSErdeNKP169eTOHFi6tSpA0Dv3r1Zt24dR44cYdOmTYwcOZLo6GiaNGmCyWTSKWBx5EVz2rZtG6GhoQDs3LmTwYMHs2XLFs6fP8+mTZtYsmQJlStXtp62J3HjWQUod3d3DMMgVapUdOzYkerVqzNmzBi++uortmzZwooVK5gyZQrp0qWzjoCX5/Oix5pixYrRu3dvEiVKBEDdunWpXLkyu3fvZs2aNQCPLU4lSpSIsmXLkitXLs6ePasf5OPQixRwY/Pev38/iRMnJnv27OzcufORWsHDF++UBMAQpxEdHf3c6x45csSoXr260apVK+Py5cuPLA8JCTE6duxoVK9e3bo8PDzcKFeunFG6dGnjo48+MgoVKmTUqlXL2LlzZ5z1wZnFx/avVq2acf36dSMsLMzw9/c3ypcvb/j5+RklSpQw3njjDaNu3brG/v3747IbCV585FSjRg3j4sWLxr179wyLxfLIc0VFRRk3btz4741PwOJ7/5k+fbpRtmxZw8/PzyhWrJhRsGBB7T8vIT5yqlKlinH9+nUjOjrauHr1qvHee+8Zb7zxhpE3b16jWLFiRoMGDYy9e/fGZTcSpPj+DBBr8+bNRp48eYz27du/0HNK/O0/N2/eNO7evWtMmjTJKF26tOHn52e89dZbRqFChYw6depo/3lB8bUvXblyxYiOjjYCAwONQoUKGX5+fka1atWMYsWKGY0bNzaOHTsWl91IkF70mBMZGWnzd3h4uNGtWzfDz8/PWL16tWEYhhEREWGz/IsvvjCKFi1q5M2b1yhUqJBRr149fVZ4QXfv3jWCg4Nttu3zioqKMqKiogzDMIzffvvNKFy4sFGpUiXr8se9BqKjo40LFy68fINfE/Gx/zz8vccwDKN27dpG/vz5jffff98oXLiwagUJnIqFTuJFD8p37941fvrpJ8PPz8+YO3eu9X6xBxGLxWLMmDHD8PPzMwIDAw3DMIzz588blStXtn4InTVrVvx0xgnF5/Z/+AB7/PhxY9myZcb06dONDRs2xH1HErhXsZ/Ii3tV+8+xY8eMxYsXG7/88ouxcePGuO9IAhefOe3atct6v7CwMOPs2bPG4cOHVeR4Tq/i2Bb7peHGjRvG559/bixatCgeepJwvcrj3LJly4xZs2YZmzZtivN+JHSv6jh39OhRY968ecaoUaOsX7rl6eK7ABVb9LBYLMb58+eNvXv32mQmz2fMmDFG7dq1jRo1ahh169Y11q5da4SEhBiGYVgzeBFff/214efnZ4wbN84wjEeLU/J84nv/iY6ONoKDg42WLVtaB7aoVpDwudp7ZKM829ixY1m1ahVRUVG4u7vz/vvvU6pUKZIlS/bECciTJElCpUqV2LhxI9OnT+eNN94gX7581tONzWYzvr6+AFy6dAmAjBkz4uXlRfXq1enbt68m+X0gvrf/1atXrffLlSsXuXLlemV9S0jiOyfN1/lyXuX+kzt3bnLnzv3K+paQxHdOD1/UJFGiRGTOnPmV9c3Zvapjm6urK4Zh4O3tbT0FSZ6PjnPO4VUe5/z8/KwXDJJne5lsHvbw8tKlS9OgQQPmzJnD+PHj6d69u81V381mMxkzZrTOdyzP59SpU3z++eecPn2aqlWrEhISwqFDh+jTpw89e/bk3XfffaFThGNzbdmyJVu3bmXixIk0a9YMHx8fXWDrBb2q/cfT05MUKVLw7rvv0qtXL9UKXgPaCx3YqVOneOedd5g5cyaFChUid+7c3L17lz59+jBnzhyAx+78sfOsFCxYkJYtW3Lp0iWmTZvGuXPngH/mHwgLCwNiioTGg3kI5s6dy6effqqdn1e3/WM/ZMrLUU6OSbk4B+XkuOyRja7S+mK0/zgH5eS4XjabJ4nNrGXLlmTMmJGJEycSFBSE2Wy2LpOXs2rVKo4fP86nn37K559/zujRo5kyZQpms5lff/31sT+qGw/Na3j+/HmbZQ9frbpFixaEh4fzxRdf2CyTp3uV+0/sxYJGjBjBRx99pFrB68LOIxvlKcaOHWsULlzYWLhwoREaGmoYhmGcOnXKOkfNpUuXHrnPw3MVXLlyxbhz544xbNgww8/Pz/joo4+MkJAQIyQkxDh8+LDRokULo02bNsa9e/deWZ+ciba/c1BOjkm5OAfl5LiUjeNTRs5BOTmu/5rNuXPnnvjYU6ZMMfz8/Ix333037hv+mrlx44ZRvXp1o3r16tbbYqeu6Nevn1GiRAkjODjYuuzhjE6fPm107NjRKF68uHHt2jWbx4099fXGjRtGhw4dDH9///jsRoKj/Ufim05DdlA3b95k2bJlpE6d2nolT4vFQrZs2ahZsyabNm0iWbJk1vUNw8BkMmEymThz5gyDBg3i4MGDrFixgo8//pjr16+zbNkyNm7cSNasWQkNDeX+/fsMGjSIxIkT26ubDkvb3zkoJ8ekXJyDcnJcysbxKSPnoJwc13/NZvDgwRw4cIAVK1aQOnVq63qxp102bNiQ3377TVegfkkPn76aIkUK3N3dsVgsnDx5khw5cuDi4sL9+/c5c+YMxYsXt3n9m0wmQkNDGTduHLNmzSJv3rx89dVXJE+e3OY5XFxciI6OxtvbmylTpmhE4QvQ/iOvgoqFDiSuD8oDBw4kadKkAHz99dc0b96c5cuXExwcTPr06enevTtJkiSxS18dkba/c1BOjkm5OAfl5LiUjeNTRs5BOTkuFaAc28GDB5k9ezb9+/cnadKk1rwsFguVKlVi0qRJLFiwgCZNmpAuXTp+/fVX9u3bR86cOencuTNFihShefPmpE2blsGDB7N8+XL69etHzZo1bQpSD4vNRzk9m/YfeeXsNaRRYhw4cMDo16+fdehw7HDs8PBw48cffzT8/PyMoUOHGidOnDBCQ0ONuXPnGnny5DHq1KljtGvXzhg1apRx5coVwzAM45NPPjHy5ctnzJgx45Fh3g/792XSX2fa/s5BOTkm5eIclJPjUjaOTxk5B+XkuOyRjbycIUOGGH5+ftar3D58deNLly4ZPXv2NAoVKmSUKVPGqFy5slGgQAHjo48+Mj777DOjWbNmhp+fn9G8eXPj5MmTxpUrV4yTJ0/q6sb/kfYfsSeTYTw086i8ct988w0zZ85kwIABtGrVyuYXg8uXL/Ptt9+yfft2kiZNSqJEibh27RrVq1cnUaJEnDx5kv3791OoUCG+/fZbkiZNyt27d8mSJYsmHX1O2v7OQTk5JuXiHJST41I2jk8ZOQfl5LiUjeMzHpyi+sEHH7B27Vry5MnD2LFjyZgxIxaLBVfXmJMRIyMjOXHiBBMmTGDv3r189tlnlC1bFi8vL6Kiovj555+ZMGEC3bp1o0ePHtbHlZen/Ufsyr61ytdX7OSi77//vuHn52fUr1/fOH/+vGEYtr9URkREGIcPHzY++OADo2zZssaKFSuMO3fuGIZhGBaLxRgzZoyRP39+Y+zYsTaPK0+n7e8clJNjUi7OQTk5LmXj+JSRc1BOjkvZOI/o6Gjj3LlzRunSpY3ixYsbfn5+xrBhwx5ZxzAM48SJE4afn5/Ro0ePR0YNHjlyxChTpozRrFmzV9b2hEr7jzgCnYRuR+fPn+ePP/7Ay8uLo0ePMnfuXADrrzeGYeDm5oa7uztr1qzhzTffpEaNGnh5eQFgNpupWrUqKVKkYOvWrQD69eYFaPs7B+XkmJSLc1BOjkvZOD5l5ByUk+NSNs7BZDKxatUqQkJCGDx4MF5eXixYsIB9+/YBEBUVZd3u165dA6Bo0aKYzWbCwsKIjo4GIE+ePFgsFlKmTEl4eLhd+pKQaP8Re1Ox0E50ULYvbX/noJwck3JxDsrJcSkbx6eMnINyclzKxrHcvn2bM2fOcPPmTettsdv4woULzJo1ixIlSlCjRg369evHnTt3mDlzJhBTdIpd12KxYDKZWL16NQAeHh64uLgQHh5OQEAAt2/fpkKFCiRKlOgV9zBh0f4jjkDFwnikg7J9afs7B+XkmJSLc1BOjkvZOD5l5ByUk+NSNs5hxIgR1KlTh5YtW/L222/z3XffcePGDZsr3VaqVInWrVsD0KhRI/LkycOKFSusmcRmVa5cOcqVK8f+/fsZNmwYx48f58iRI6xYsYIpU6ZQrFgxKlWq9Oo76YS0/4jDs9f5zwndTz/9ZJQpU8YoVaqUUbJkSePbb781goKCrMvPnz9vfPnll8bmzZutt9WvX9/w8/MzVq1aZRiG7XwEXbp0Mfz8/Izvv//eOHbsmHH48GFj4cKFRvXq1Y1WrVoZly9ffnWdcwLa/s5BOTkm5eIclJPjUjaOTxk5B+XkuJSN47t3757x0UcfGWXLljVGjhxpTJ061ejZs6f1qsXHjh2zrhscHGwYxj9z2gUGBhp+fn5Gq1atjJCQEMMwYq7AaxiGcfjwYaNr166Gn5+fkS9fPqNChQpG/vz5jW7dutm8BuTJtP+IM9DVkOPY/fv36d+/P4GBgTRp0gRPT0/+/PNP1q1bR6FChRg0aBC5c+cGICQkBE9PT+uVonbv3k2bNm0oVqwYEyZMIFmyZERERODu7s6RI0cYNWoUmzdvxmw2kzp1aoKCgihfvjyDBw8mVapUdu65Y9D2dw7KyTEpF+egnByXsnF8ysg5KCfHpWycx969e2nfvj2dO3emY8eOJEuWjKioKGbPns3w4cMpV64cH330ETly5LBmFFsaePjqyJ9++ikdOnSwubpxZGQkGzZsICgoiNDQUEqXLs0bb7xhz+46Be0/4lTsUKBM0P744w+jYMGCxqhRo6y/wlgsFsPf398oWLCg0b17d+PEiROGYfzzy010dPQjVzyaNm2azTqGEXO1o1WrVhkzZ840fv75Z2P//v2vsmtOQdvfOSgnx6RcnINyclzKxvEpI+egnByXsnEes2bNMvz8/KxXx40diRYSEmL8+OOPhp+fnzF8+HDrVY1js4j9+8qVK4afn59RvXp14/Tp09ZluqLuy9P+I85ExcI4poOyfWn7Owfl5JiUi3NQTo5L2Tg+ZeQclJPjUjaOaeXKlcbixYuNjRs3Wrf1iBEjDD8/P+O3334zDMMwoqKirOvfuHHDqFu3rlGuXDlj+/bthmHYFp5iH2PkyJGGn5+f8d133z32eZXbi9H+I85EFzj5D1atWsWSJUvYtGkTUVFRAFy/fh2AgwcPAlgnjk2WLBnt27cnd+7cLF26lJ07d9o8ltlsJioqirRp09KtWzfOnj3LvHnzrMsevsy5oTPHAW1/Z6GcHJNycQ7KyXEpG8enjJyDcnJcysbxrV+/nsqVK/Pll1/y5Zdf0q1bNzp06MC+ffsoW7YsAMeOHcNiseDi4mLdtt7e3nTv3p1r166xYcMG7t69a5NBbK69evUiXbp0TJ8+nR07djzy/A/fR2xp/xGnZ6cipVNbt26dUalSJaNYsWJGwYIFDT8/P6NNmzbGn3/+aezevds6NDj2l4KHK/2rVq0y/Pz8jK+//toIDQ21edyH16tQoYLNL0HyD21/56CcHJNycQ7KyXEpG8enjJyDcnJcysY5BAQEGMWLFzd69+5trFmzxtixY4cxefJkw8/Pz2jUqJGxZ88eo0GDBkbr1q2NK1euPHL/e/fuGe3btzcqV65sHDp06JHlsfnOnz/fKFOmjHHkyJF471NCoP1HEgqNLHxBCxYs4PPPP6dQoUJ88803TJw4kb59+xIYGMjgwYMxmUzkzZuXjRs3cuPGDcD2F5cKFSpQqlQptmzZwtmzZ20e22QyYbFYAOjRowc+Pj54e3u/us45AW1/56CcHJNycQ7KyXEpG8enjJyDcnJcysY5hIaGMmvWLHx9fXn//fepXr06pUqVonPnzjRr1oxDhw5x4MABKlasyJ49e9i5cyfR0dHAPyPPPDw8qFevHhcvXuT8+fMA1nUAXF1dAWjatCnbt28nT548r7iXzkf7jyQo9q5WOpOQkBCjfv36RoMGDYxTp07ZLBswYIDh5+dn/PLLL8bIkSONPHnyGEuWLLHODfHwBKWLFi0y/Pz8jNWrVxuGYTt/hDyZtr9zUE6OSbk4B+XkuJSN41NGzkE5OS5l4zwuXbpk+Pn5GbNmzTIMI2Ybx45UW7x4seHn52fMnz/f+Ouvv4xKlSoZDRs2tM5x97C9e/cahQoVMr7++usnPpfmw3s+2n8kodHIwhcQEhLC0aNHadKkCdmyZSM6Otpa3S9SpAgASZMmpXLlyqRPnx5/f3/OnTsH/POLgclkImvWrCROnJhdu3YB/8xV8DBDcw08QtvfOSgnx6RcnINyclzKxvEpI+egnByXsnEely5dAmznvjObzQDcvXsXgDt37pAvXz6aNWvGkSNHCAgI4ObNmwBERkYCkDdvXiIjI/Hx8Xnic2lewuej/UcSGhULX4AOyval7e8clJNjUi7OQTk5LmXj+JSRc1BOjkvZOI9ChQrxxhtvUKhQIcLDw4F/tmlQUBAA+fLlw2w2U6tWLWrUqMHMmTNZvHgxAG5ubkRERLBs2TIsFgt+fn726UgCov1HEhpXezfAmfz7oJwoUaKnHpSPHj3KzJkz8fb2plOnTjoo/0fa/s5BOTkm5eIclJPjUjaOTxk5B+XkuJSN8zCbzcyYMQMPDw+b2yMiIti9ezeJEycmZ86cAGTJkoV+/fpx5swZfvjhB86ePUuRIkW4e/cus2bNomTJkhQrVswe3UhQtP9IQqNi4QvQQdm+tP2dg3JyTMrFOSgnx6VsHJ8ycg7KyXEpG+fycE7R0dG4uLhw5MgR9u/fT+nSpUmVKhXR0dGYTCbSpUvHiBEj8Pf3JyAggGXLlmE2m6lQoQIDBw4kefLkduxJwqD9RxIak6ET3l9a7EF5//79tG7dmtKlSzN+/HhMJpP1v9OnT1sPym5ubjooxyFtf+egnByTcnEOyslxKRvHp4ycg3JyXMrG+UyaNImffvqJAQMG0KpVK+vt4eHhuLm54eLiwtmzZwkJCSFp0qRky5bNjq1N2LT/iLPTyML/IHay0V27dhEZGUn58uWt8xJAzEE5S5YsfPXVV3To0EEH5Tim7e8clJNjUi7OQTk5LmXj+JSRc1BOjkvZOJeIiAhWr16Nq6srtWrVAmIKVufPn2fDhg3cvn2bjz76iCxZsti5pa8H7T/i7FQs/I90ULYvbX/noJwck3JxDsrJcSkbx6eMnINyclzKxnkcPXqUM2fOUKFCBby9vbl69Sq7d+9m2rRpHD58mJ49ewIxV9LVBTJeDe0/4sxULPyPdFC2L21/56CcHJNycQ7KyXEpG8enjJyDcnJcysbxxW77s2fPcu/ePTJmzMju3buZPn06GzZsoGzZsqxfv56MGTMCupLuq6T9R5yZioUvSQdl+9L2dw7KyTEpF+egnByXsnF8ysg5KCfHpWycR+y2P3HiBAAHDhxg4cKFeHt7M3XqVMqUKWPP5r2WtP9IQqBi4UvSQdm+tP2dg3JyTMrFOSgnx6VsHJ8ycg7KyXEpG+eTJk0aAE6ePMn7779Pu3bt7Nyi15f2H0kIVCz8j3RQti9tf+egnByTcnEOyslxKRvHp4ycg3JyXMrGeeTIkYPevXvToUMH3N3d7d0cQfuPODeTYRiGvRvhzHbu3Mn+/ft1ULYTbX/noJwck3JxDsrJcSkbx6eMnINyclzKxnlo3jvHo/1HnJmKhf+RDsr2pe3vHJSTY1IuzkE5OS5l4/iUkXNQTo5L2Yi8PO0/4sxULBQREREREREREREAXOzdABEREREREREREXEMKhaKiIiIiIiIiIgIoGKhiIiIiIiIiIiIPKBioYiIiIiIiIiIiAAqFoqIiIiIiIiIiMgDKhaKiIiIiIiIiIgIoGKhiIiIiIiIiIiIPOBq7waIiIiIiP2MGTOGsWPH2tzm4uKCh4cHvr6+VKxYkU6dOpEyZcqXfo7g4GAsFgve3t7/tbkiIiIiEs9ULBQRERERmjdvTtGiRQGIjo4mODiY/fv3M3XqVJYsWcKsWbPImjXrCz/uli1b+OSTTxg9ejQlSpSI41aLiIiISFxTsVBEREREKFSoEPXr13/k9oYNG9K1a1e6du3KihUrcHV9sY+Pe/fu5fbt23HUShERERGJb5qzUERERESeqFy5crRv354zZ86wbNkyezdHREREROKZioUiIiIi8lRNmjQBYMOGDdbbfv/9d7p160bp0qXJnz8/xYsXp127duzYscO6Tps2bZgwYQIAbdu2pXLlytZl4eHhjBs3jpo1a1KgQAFKlCjBBx98wPHjx19Rr0RERETkcXQasoiIiIg8VbZs2fDw8ODQoUMArFmzhl69epEvXz7effddkiZNyt9//01AQABdunRhyZIl5MqVi/feew9PT082bNjAe++9R8GCBQGIiIigY8eO7Nu3j/r169O+fXuuXr3KvHnzaNasGdOmTaNIkSL27LKIiIjIa0vFQhERERF5KpPJRPLkybl58yYAEyZMwMfHh1mzZpEkSRLrelmyZGHQoEFs3bqVXLlyUaZMGQIDA9mwYQOlS5e2XuDE39+fPXv2MHLkSGrVqmW9f8uWLalbty4DBgxgxYoVr7aTIiIiIgKoWCgiIiIizyEyMhKTyQRAQEAAwcHBNoXCiIgIXFxiZrgJDQ196mOtWLECLy8vSpQoYS1AApjNZsqXL8/SpUs5efIkOXLkiIeeiIiIiMjTqFgoIiIiIk9lsVgICQkhTZo0ALi6unLp0iXGjx/PyZMnuXjxIhcuXCA6OhrA+u+TnD59mrCwMEqVKvXEdS5evKhioYiIiIgdqFgoIiIiIk915MgRIiMjKVCgAAA//vgjkyZNImPGjBQrVoxSpUrh5+eHxWKhe/fuz3y86OhoMmbMyODBg5+4Tp48eeKs/SIiIiLy/FQsFBEREZGnWrZsGQA1atTg0qVLTJ48mSJFiuDv74+7u/sj6z1LxowZuXr1KsWLF8fNzc1m2d69e7l//z4eHh5x1wEREREReW4u9m6AiIiIiDiuwMBA5s6dS86cOalRowZ37tzBMAyyZctmUyi8f/8+M2fOBCAqKsp6u9lsBmxPTa5RowZ3795l8uTJNs919epVunXrRp8+fazzH4qIiIjIq6WRhSIiIiLCvn37rIU9wzC4c+cO+/btY+3atXh7ezNmzBhcXV3JmTMnWbJkYcmSJSRJkgQ/Pz+uXbvG4sWLuX79OgDBwcHWx02VKhUAc+fO5dq1a9SvX58uXbqwadMmRo0axZEjRyhZsiTBwcHMmzeP4OBghg8frpGFIiIiInZiMgzDsHcjRERERMQ+xowZw9ixY21uM5lMJEmShKxZs1K+fHnatWtHypQprcvPnTvH8OHD2bNnD3fv3iVNmjQULVqUHj160KpVK9zc3Fi/fj0mk4mQkBB69+5NYGAgiRIlYsuWLSRJkoS7d+8yadIkVq9ezcWLF/Hy8iJv3rx06dKFkiVLvurNICIiIiIPqFgoIiIiIiIiIiIigOYsFBERERERERERkQdULBQRERERERERERFAxUIRERERERERERF5QMVCERERERERERERAVQsFBERERERERERkQdULBQRERERERERERFAxUIRERERERERERF5QMVCERERERERERERAVQsFBERERERERERkQdULBQRERERERERERFAxUIRERERERERERF5QMVCERERERERERERAeD/cj5Vu8St05AAAAAASUVORK5CYII=",
       "text/plain": [
-       "<Figure size 1152x432 with 1 Axes>"
+       "<Figure size 1600x600 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -807,7 +730,7 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -821,7 +744,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.8"
+   "version": "3.10.11"
   }
  },
  "nbformat": 4,
diff --git a/univariate/univariate_volatility_modeling.html b/univariate/univariate_volatility_modeling.html
index fdf3919a4c..7b708aeb6f 100644
--- a/univariate/univariate_volatility_modeling.html
+++ b/univariate/univariate_volatility_modeling.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>ARCH Modeling - arch 6.1.0</title>
+        <title>ARCH Modeling - arch 6.2.0</title>
       
     
 
@@ -47,9 +47,9 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css" />
-        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css?v=2aa19091" />
+        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -93,7 +93,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -103,7 +103,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -190,10 +190,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -851,7 +851,7 @@
                   
 
 
-<h1 id="ARCH-Modeling">ARCH Modeling<a class="headerlink" href="#ARCH-Modeling" title="Permalink to this heading">¶</a></h1>
+<h1 id="ARCH-Modeling">ARCH Modeling<a class="headerlink" href="#ARCH-Modeling" title="Link to this heading">¶</a></h1>
 <p><em>This setup code is required to run in an IPython notebook</em></p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[1]:
@@ -869,7 +869,7 @@ <h1 id="ARCH-Modeling">ARCH Modeling<a class="headerlink" href="#ARCH-Modeling"
 </code></pre></div>
 </div>
 </div>
-<h2 id="Setup">Setup<a class="headerlink" href="#Setup" title="Permalink to this heading">¶</a></h2>
+<h2 id="Setup">Setup<a class="headerlink" href="#Setup" title="Link to this heading">¶</a></h2>
 <p>These examples will all make use of financial data from Yahoo! Finance. This data set can be loaded from <code class="docutils literal notranslate"><span class="pre">arch.data.sp500</span></code>.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[2]:
@@ -896,7 +896,7 @@ <h2 id="Setup">Setup<a class="headerlink" href="#Setup" title="Permalink to this
 <img alt="../_images/univariate_univariate_volatility_modeling_5_0.png" src="../_images/univariate_univariate_volatility_modeling_5_0.png" />
 </div>
 </div>
-<h2 id="Specifying-Common-Models">Specifying Common Models<a class="headerlink" href="#Specifying-Common-Models" title="Permalink to this heading">¶</a></h2>
+<h2 id="Specifying-Common-Models">Specifying Common Models<a class="headerlink" href="#Specifying-Common-Models" title="Link to this heading">¶</a></h2>
 <p>The simplest way to specify a model is to use the model constructor <code class="docutils literal notranslate"><span class="pre">arch.arch_model</span></code> which can specify most common models. The simplest invocation of <code class="docutils literal notranslate"><span class="pre">arch</span></code> will return a model with a constant mean, GARCH(1,1) volatility process and normally distributed errors.</p>
 <div class="math notranslate nohighlight">
 \[r_t  =  \mu + \epsilon_t\]</div>
@@ -905,7 +905,7 @@ <h2 id="Specifying-Common-Models">Specifying Common Models<a class="headerlink"
 <div class="math notranslate nohighlight">
 \[\epsilon_t  =  \sigma_t e_t,\,\,\, e_t  \sim  N(0,1)\]</div>
 <p>The model is estimated by calling <code class="docutils literal notranslate"><span class="pre">fit</span></code>. The optional inputs <code class="docutils literal notranslate"><span class="pre">iter</span></code> controls the frequency of output form the optimizer, and <code class="docutils literal notranslate"><span class="pre">disp</span></code> controls whether convergence information is returned. The results class returned offers direct access to the estimated parameters and related quantities, as well as a <code class="docutils literal notranslate"><span class="pre">summary</span></code> of the estimation results.</p>
-<h3 id="GARCH-(with-a-Constant-Mean)">GARCH (with a Constant Mean)<a class="headerlink" href="#GARCH-(with-a-Constant-Mean)" title="Permalink to this heading">¶</a></h3>
+<h3 id="GARCH-(with-a-Constant-Mean)">GARCH (with a Constant Mean)<a class="headerlink" href="#GARCH-(with-a-Constant-Mean)" title="Link to this heading">¶</a></h3>
 <p>The default set of options produces a model with a constant mean, GARCH(1,1) conditional variance and normal errors.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[3]:
@@ -924,10 +924,10 @@ <h3 id="GARCH-(with-a-Constant-Mean)">GARCH (with a Constant Mean)<a class="head
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-Iteration:      5,   Func. Count:     35,   Neg. LLF: 6970.2765831170655
-Iteration:     10,   Func. Count:     63,   Neg. LLF: 6936.718477482658
+Iteration:      5,   Func. Count:     35,   Neg. LLF: 6970.2779565773335
+Iteration:     10,   Func. Count:     63,   Neg. LLF: 6936.718477482076
 Optimization terminated successfully    (Exit mode 0)
-            Current function value: 6936.718476988985
+            Current function value: 6936.718476988951
             Iterations: 11
             Function evaluations: 68
             Gradient evaluations: 11
@@ -939,8 +939,8 @@ <h3 id="GARCH-(with-a-Constant-Mean)">GARCH (with a Constant Mean)<a class="head
 Distribution:                  Normal   AIC:                           13881.4
 Method:            Maximum Likelihood   BIC:                           13907.5
                                         No. Observations:                 5030
-Date:                Wed, Apr 12 2023   Df Residuals:                     5029
-Time:                        15:58:55   Df Model:                            1
+Date:                Tue, May 30 2023   Df Residuals:                     5029
+Time:                        10:55:28   Df Model:                            1
                                  Mean Model
 ============================================================================
                  coef    std err          t      P&gt;|t|      95.0% Conf. Int.
@@ -974,7 +974,7 @@ <h3 id="GARCH-(with-a-Constant-Mean)">GARCH (with a Constant Mean)<a class="head
 <img alt="../_images/univariate_univariate_volatility_modeling_12_0.png" src="../_images/univariate_univariate_volatility_modeling_12_0.png" />
 </div>
 </div>
-<h3 id="GJR-GARCH">GJR-GARCH<a class="headerlink" href="#GJR-GARCH" title="Permalink to this heading">¶</a></h3>
+<h3 id="GJR-GARCH">GJR-GARCH<a class="headerlink" href="#GJR-GARCH" title="Link to this heading">¶</a></h3>
 <p>Additional inputs can be used to construct other models. This example sets <code class="docutils literal notranslate"><span class="pre">o</span></code> to 1, which includes one lag of an asymmetric shock which transforms a GARCH model into a GJR-GARCH model with variance dynamics given by</p>
 <div class="math notranslate nohighlight">
 \[\sigma^2_t   =  \omega + \alpha \epsilon_{t-1}^2 + \gamma \epsilon_{t-1}^2 I_{[\epsilon_{t-1}&lt;0]}+ \beta \sigma_{t-1}^2\]</div>
@@ -1003,8 +1003,8 @@ <h3 id="GJR-GARCH">GJR-GARCH<a class="headerlink" href="#GJR-GARCH" title="Perma
 Distribution:                  Normal   AIC:                           13655.8
 Method:            Maximum Likelihood   BIC:                           13688.4
                                         No. Observations:                 5030
-Date:                Wed, Apr 12 2023   Df Residuals:                     5029
-Time:                        15:58:56   Df Model:                            1
+Date:                Tue, May 30 2023   Df Residuals:                     5029
+Time:                        10:55:29   Df Model:                            1
                                   Mean Model
 =============================================================================
                  coef    std err          t      P&gt;|t|       95.0% Conf. Int.
@@ -1015,7 +1015,7 @@ <h3 id="GJR-GARCH">GJR-GARCH<a class="headerlink" href="#GJR-GARCH" title="Perma
                  coef    std err          t      P&gt;|t|       95.0% Conf. Int.
 -----------------------------------------------------------------------------
 omega          0.0196  4.051e-03      4.830  1.362e-06  [1.163e-02,2.751e-02]
-alpha[1]   2.2928e-10  1.026e-02  2.234e-08      1.000 [-2.011e-02,2.011e-02]
+alpha[1]   2.4027e-10  1.026e-02  2.341e-08      1.000 [-2.011e-02,2.011e-02]
 gamma[1]       0.1831  2.266e-02      8.079  6.543e-16      [  0.139,  0.227]
 beta[1]        0.8922  1.458e-02     61.200      0.000      [  0.864,  0.921]
 =============================================================================
@@ -1023,7 +1023,7 @@ <h3 id="GJR-GARCH">GJR-GARCH<a class="headerlink" href="#GJR-GARCH" title="Perma
 Covariance estimator: robust
 </pre></div></div>
 </div>
-<h3 id="TARCH/ZARCH">TARCH/ZARCH<a class="headerlink" href="#TARCH/ZARCH" title="Permalink to this heading">¶</a></h3>
+<h3 id="TARCH/ZARCH">TARCH/ZARCH<a class="headerlink" href="#TARCH/ZARCH" title="Link to this heading">¶</a></h3>
 <p>TARCH (also known as ZARCH) model the <em>volatility</em> using absolute values. This model is specified using <code class="docutils literal notranslate"><span class="pre">power=1.0</span></code> since the default power, 2, corresponds to variance processes that evolve in squares.</p>
 <p>The volatility process in a TARCH model is given by</p>
 <div class="math notranslate nohighlight">
@@ -1048,12 +1048,12 @@ <h3 id="TARCH/ZARCH">TARCH/ZARCH<a class="headerlink" href="#TARCH/ZARCH" title=
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-Iteration:      5,   Func. Count:     45,   Neg. LLF: 6829.203002130782
-Iteration:     10,   Func. Count:     79,   Neg. LLF: 6799.178617397159
+Iteration:      5,   Func. Count:     45,   Neg. LLF: 6829.197763035331
+Iteration:     10,   Func. Count:     79,   Neg. LLF: 6799.178613053096
 Optimization terminated successfully    (Exit mode 0)
-            Current function value: 6799.178521515934
-            Iterations: 13
-            Function evaluations: 96
+            Current function value: 6799.178523172141
+            Iterations: 14
+            Function evaluations: 103
             Gradient evaluations: 13
                   Constant Mean - TARCH/ZARCH Model Results
 ==============================================================================
@@ -1063,27 +1063,27 @@ <h3 id="TARCH/ZARCH">TARCH/ZARCH<a class="headerlink" href="#TARCH/ZARCH" title=
 Distribution:                  Normal   AIC:                           13608.4
 Method:            Maximum Likelihood   BIC:                           13641.0
                                         No. Observations:                 5030
-Date:                Wed, Apr 12 2023   Df Residuals:                     5029
-Time:                        15:58:56   Df Model:                            1
+Date:                Tue, May 30 2023   Df Residuals:                     5029
+Time:                        10:55:29   Df Model:                            1
                                   Mean Model
 =============================================================================
                  coef    std err          t      P&gt;|t|       95.0% Conf. Int.
 -----------------------------------------------------------------------------
-mu             0.0143  1.091e-02      1.311      0.190 [-7.087e-03,3.570e-02]
+mu             0.0143  1.091e-02      1.311      0.190 [-7.086e-03,3.570e-02]
                                Volatility Model
 =============================================================================
                  coef    std err          t      P&gt;|t|       95.0% Conf. Int.
 -----------------------------------------------------------------------------
-omega          0.0258  4.100e-03      6.299  2.990e-10  [1.779e-02,3.386e-02]
-alpha[1]       0.0000  9.156e-03      0.000      1.000 [-1.794e-02,1.794e-02]
-gamma[1]       0.1707  1.601e-02     10.664  1.505e-26      [  0.139,  0.202]
-beta[1]        0.9098  9.672e-03     94.066      0.000      [  0.891,  0.929]
+omega          0.0258  4.100e-03      6.299  2.987e-10  [1.779e-02,3.386e-02]
+alpha[1]   5.9528e-09  9.156e-03  6.502e-07      1.000 [-1.794e-02,1.794e-02]
+gamma[1]       0.1707  1.601e-02     10.664  1.501e-26      [  0.139,  0.202]
+beta[1]        0.9098  9.671e-03     94.067      0.000      [  0.891,  0.929]
 =============================================================================
 
 Covariance estimator: robust
 </pre></div></div>
 </div>
-<h3 id="Student's-T-Errors">Student’s T Errors<a class="headerlink" href="#Student's-T-Errors" title="Permalink to this heading">¶</a></h3>
+<h3 id="Student's-T-Errors">Student’s T Errors<a class="headerlink" href="#Student's-T-Errors" title="Link to this heading">¶</a></h3>
 <p>Financial returns are often heavy tailed, and a Student’s T distribution is a simple method to capture this feature. The call to <code class="docutils literal notranslate"><span class="pre">arch</span></code> changes the distribution from a Normal to a Students’s T.</p>
 <p>The standardized residuals appear to be heavy tailed with an estimated degree of freedom near 10. The log-likelihood also shows a large increase.</p>
 <div class="nbinput docutils container">
@@ -1102,12 +1102,12 @@ <h3 id="Student's-T-Errors">Student’s T Errors<a class="headerlink" href="#Stu
 <div class="output_area docutils container">
 <div class="highlight"><pre>
 Iteration:      5,   Func. Count:     50,   Neg. LLF: 6728.995165345449
-Iteration:     10,   Func. Count:     90,   Neg. LLF: 6722.151188120595
+Iteration:     10,   Func. Count:     90,   Neg. LLF: 6722.15117736426
 Optimization terminated successfully    (Exit mode 0)
-            Current function value: 6722.151187386988
-            Iterations: 11
-            Function evaluations: 97
-            Gradient evaluations: 11
+            Current function value: 6722.1511833987915
+            Iterations: 13
+            Function evaluations: 110
+            Gradient evaluations: 12
                      Constant Mean - TARCH/ZARCH Model Results
 ====================================================================================
 Dep. Variable:                    Adj Close   R-squared:                       0.000
@@ -1116,32 +1116,32 @@ <h3 id="Student's-T-Errors">Student’s T Errors<a class="headerlink" href="#Stu
 Distribution:      Standardized Student&#39;s t   AIC:                           13456.3
 Method:                  Maximum Likelihood   BIC:                           13495.4
                                               No. Observations:                 5030
-Date:                      Wed, Apr 12 2023   Df Residuals:                     5029
-Time:                              15:58:56   Df Model:                            1
+Date:                      Tue, May 30 2023   Df Residuals:                     5029
+Time:                              10:55:29   Df Model:                            1
                                  Mean Model
 ============================================================================
                  coef    std err          t      P&gt;|t|      95.0% Conf. Int.
 ----------------------------------------------------------------------------
-mu             0.0323  2.364e-03     13.650  2.020e-42 [2.763e-02,3.690e-02]
+mu             0.0323  2.292e-03     14.077  5.292e-45 [2.777e-02,3.676e-02]
                                Volatility Model
 =============================================================================
                  coef    std err          t      P&gt;|t|       95.0% Conf. Int.
 -----------------------------------------------------------------------------
-omega          0.0201  3.493e-03      5.744  9.249e-09  [1.322e-02,2.691e-02]
-alpha[1]   1.4150e-08  8.223e-03  1.721e-06      1.000 [-1.612e-02,1.612e-02]
-gamma[1]       0.1721  1.512e-02     11.385  4.978e-30      [  0.143,  0.202]
-beta[1]        0.9139  9.578e-03     95.415      0.000      [  0.895,  0.933]
+omega          0.0201  3.492e-03      5.745  9.179e-09  [1.322e-02,2.691e-02]
+alpha[1]       0.0000  8.223e-03      0.000      1.000 [-1.612e-02,1.612e-02]
+gamma[1]       0.1721  1.512e-02     11.386  4.925e-30      [  0.143,  0.202]
+beta[1]        0.9139  9.578e-03     95.414      0.000      [  0.895,  0.933]
                               Distribution
 ========================================================================
                  coef    std err          t      P&gt;|t|  95.0% Conf. Int.
 ------------------------------------------------------------------------
-nu             7.9553      0.881      9.034  1.648e-19 [  6.229,  9.681]
+nu             7.9549      0.880      9.036  1.620e-19 [  6.229,  9.680]
 ========================================================================
 
 Covariance estimator: robust
 </pre></div></div>
 </div>
-<h2 id="Fixing-Parameters">Fixing Parameters<a class="headerlink" href="#Fixing-Parameters" title="Permalink to this heading">¶</a></h2>
+<h2 id="Fixing-Parameters">Fixing Parameters<a class="headerlink" href="#Fixing-Parameters" title="Link to this heading">¶</a></h2>
 <p>In some circumstances, fixed rather than estimated parameters might be of interest. A model-result-like class can be generated using the <code class="docutils literal notranslate"><span class="pre">fix()</span></code> method. The class returned is identical to the usual model result class except that information about inference (standard errors, t-stats, etc) is not available.</p>
 <p>In the example, I fix the parameters to a symmetric version of the previously estimated model.</p>
 <div class="nbinput docutils container">
@@ -1166,8 +1166,8 @@ <h2 id="Fixing-Parameters">Fixing Parameters<a class="headerlink" href="#Fixing-
 Distribution:       Standardized Student&#39;s t   AIC:                           13829.9
 Method:            User-specified Parameters   BIC:                           13869.0
                                                No. Observations:                 5030
-Date:                       Wed, Apr 12 2023
-Time:                               15:58:56
+Date:                       Tue, May 30 2023
+Time:                               10:55:29
       Mean Model
 =====================
                  coef
@@ -1221,7 +1221,7 @@ <h2 id="Fixing-Parameters">Fixing Parameters<a class="headerlink" href="#Fixing-
 <img alt="../_images/univariate_univariate_volatility_modeling_24_1.png" src="../_images/univariate_univariate_volatility_modeling_24_1.png" />
 </div>
 </div>
-<h2 id="Building-a-Model-From-Components">Building a Model From Components<a class="headerlink" href="#Building-a-Model-From-Components" title="Permalink to this heading">¶</a></h2>
+<h2 id="Building-a-Model-From-Components">Building a Model From Components<a class="headerlink" href="#Building-a-Model-From-Components" title="Link to this heading">¶</a></h2>
 <p>Models can also be systematically assembled from the three model components:</p>
 <ul class="simple">
 <li><p>A mean model (<code class="docutils literal notranslate"><span class="pre">arch.mean</span></code>)</p>
@@ -1257,7 +1257,7 @@ <h2 id="Building-a-Model-From-Components">Building a Model From Components<a cla
 </ul>
 </li>
 </ul>
-<h3 id="Mean-Models">Mean Models<a class="headerlink" href="#Mean-Models" title="Permalink to this heading">¶</a></h3>
+<h3 id="Mean-Models">Mean Models<a class="headerlink" href="#Mean-Models" title="Link to this heading">¶</a></h3>
 <p>The first choice is the mean model. For many liquid financial assets, a constant mean (or even zero) is adequate. For other series, such as inflation, a more complicated model may be required. These examples make use of Core CPI downloaded from the <a class="reference external" href="https://fred.stlouisfed.org/">Federal Reserve Economic Data</a> site.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[10]:
@@ -1303,8 +1303,8 @@ <h3 id="Mean-Models">Mean Models<a class="headerlink" href="#Mean-Models" title=
 Distribution:                  Normal   AIC:                           6609.68
 Method:            Maximum Likelihood   BIC:                           6632.57
                                         No. Observations:                  719
-Date:                Wed, Apr 12 2023   Df Residuals:                      715
-Time:                        15:58:57   Df Model:                            4
+Date:                Tue, May 30 2023   Df Residuals:                      715
+Time:                        10:55:30   Df Model:                            4
                                    Mean Model
 ===============================================================================
                    coef    std err          t      P&gt;|t|       95.0% Conf. Int.
@@ -1323,7 +1323,7 @@ <h3 id="Mean-Models">Mean Models<a class="headerlink" href="#Mean-Models" title=
 Covariance estimator: White&#39;s Heteroskedasticity Consistent Estimator
 </pre></div></div>
 </div>
-<h3 id="Volatility-Processes">Volatility Processes<a class="headerlink" href="#Volatility-Processes" title="Permalink to this heading">¶</a></h3>
+<h3 id="Volatility-Processes">Volatility Processes<a class="headerlink" href="#Volatility-Processes" title="Link to this heading">¶</a></h3>
 <p>Volatility processes can be added a a mean model using the <code class="docutils literal notranslate"><span class="pre">volatility</span></code> property. This example adds an ARCH(5) process to model volatility. The arguments <code class="docutils literal notranslate"><span class="pre">iter</span></code> and <code class="docutils literal notranslate"><span class="pre">disp</span></code> are used in <code class="docutils literal notranslate"><span class="pre">fit()</span></code> to suppress estimation output.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[12]:
@@ -1350,24 +1350,24 @@ <h3 id="Volatility-Processes">Volatility Processes<a class="headerlink" href="#V
 Distribution:                  Normal   AIC:                           6369.19
 Method:            Maximum Likelihood   BIC:                           6414.97
                                         No. Observations:                  719
-Date:                Wed, Apr 12 2023   Df Residuals:                      715
-Time:                        15:58:57   Df Model:                            4
+Date:                Tue, May 30 2023   Df Residuals:                      715
+Time:                        10:55:30   Df Model:                            4
                                    Mean Model
 ===============================================================================
                    coef    std err          t      P&gt;|t|       95.0% Conf. Int.
 -------------------------------------------------------------------------------
 Const            2.8500      1.883      1.513      0.130      [ -0.841,  6.541]
-CPILFESL[1]      1.0859  3.534e-02     30.726 2.594e-207      [  1.017,  1.155]
+CPILFESL[1]      1.0859  3.534e-02     30.726 2.596e-207      [  1.017,  1.155]
 CPILFESL[3]     -0.0788  3.855e-02     -2.045  4.085e-02   [ -0.154,-3.282e-03]
 CPILFESL[12]    -0.0189  1.157e-02     -1.630      0.103 [-4.154e-02,3.820e-03]
                              Volatility Model
 ==========================================================================
                  coef    std err          t      P&gt;|t|    95.0% Conf. Int.
 --------------------------------------------------------------------------
-omega         76.8684     16.016      4.799  1.592e-06 [ 45.477,1.083e+02]
+omega         76.8696     16.017      4.799  1.592e-06 [ 45.478,1.083e+02]
 alpha[1]       0.1345  4.003e-02      3.359  7.827e-04 [5.600e-02,  0.213]
-alpha[2]       0.2280  6.284e-02      3.628  2.860e-04   [  0.105,  0.351]
-alpha[3]       0.1838  6.802e-02      2.702  6.894e-03 [5.047e-02,  0.317]
+alpha[2]       0.2280  6.284e-02      3.628  2.861e-04   [  0.105,  0.351]
+alpha[3]       0.1838  6.802e-02      2.702  6.894e-03 [5.046e-02,  0.317]
 alpha[4]       0.2538  7.826e-02      3.242  1.185e-03   [  0.100,  0.407]
 alpha[5]       0.1954  7.091e-02      2.756  5.856e-03 [5.643e-02,  0.334]
 ==========================================================================
@@ -1391,7 +1391,7 @@ <h3 id="Volatility-Processes">Volatility Processes<a class="headerlink" href="#V
 <img alt="../_images/univariate_univariate_volatility_modeling_36_0.png" src="../_images/univariate_univariate_volatility_modeling_36_0.png" />
 </div>
 </div>
-<h3 id="Distributions">Distributions<a class="headerlink" href="#Distributions" title="Permalink to this heading">¶</a></h3>
+<h3 id="Distributions">Distributions<a class="headerlink" href="#Distributions" title="Link to this heading">¶</a></h3>
 <p>Finally the distribution can be changed from the default normal to a standardized Student’s T using the <code class="docutils literal notranslate"><span class="pre">distribution</span></code> property of a mean model.</p>
 <p>The Student’s t distribution improves the model, and the degree of freedom is estimated to be near 8.</p>
 <div class="nbinput docutils container">
@@ -1419,37 +1419,37 @@ <h3 id="Distributions">Distributions<a class="headerlink" href="#Distributions"
 Distribution:      Standardized Student&#39;s t   AIC:                           6358.51
 Method:                  Maximum Likelihood   BIC:                           6408.86
                                               No. Observations:                  719
-Date:                      Wed, Apr 12 2023   Df Residuals:                      715
-Time:                              15:58:58   Df Model:                            4
+Date:                      Tue, May 30 2023   Df Residuals:                      715
+Time:                              10:55:31   Df Model:                            4
                                    Mean Model
 ===============================================================================
                    coef    std err          t      P&gt;|t|       95.0% Conf. Int.
 -------------------------------------------------------------------------------
-Const            3.1221      1.861      1.678  9.342e-02      [ -0.525,  6.770]
-CPILFESL[1]      1.0843  3.525e-02     30.762 8.338e-208      [  1.015,  1.153]
-CPILFESL[3]     -0.0730  3.873e-02     -1.885  5.947e-02    [ -0.149,2.916e-03]
-CPILFESL[12]    -0.0236  1.316e-02     -1.791  7.331e-02 [-4.935e-02,2.224e-03]
+Const            3.1220      1.861      1.678  9.343e-02      [ -0.526,  6.770]
+CPILFESL[1]      1.0843  3.525e-02     30.762 8.350e-208      [  1.015,  1.153]
+CPILFESL[3]     -0.0730  3.873e-02     -1.885  5.948e-02    [ -0.149,2.919e-03]
+CPILFESL[12]    -0.0236  1.316e-02     -1.791  7.329e-02 [-4.935e-02,2.223e-03]
                              Volatility Model
 ==========================================================================
                  coef    std err          t      P&gt;|t|    95.0% Conf. Int.
 --------------------------------------------------------------------------
-omega         87.3616     20.628      4.235  2.285e-05 [ 46.931,1.278e+02]
-alpha[1]       0.1715  5.064e-02      3.386  7.090e-04 [7.221e-02,  0.271]
-alpha[2]       0.2202  6.394e-02      3.443  5.743e-04 [9.485e-02,  0.345]
-alpha[3]       0.1547  6.327e-02      2.445  1.447e-02 [3.071e-02,  0.279]
+omega         87.3503     20.625      4.235  2.283e-05 [ 46.927,1.278e+02]
+alpha[1]       0.1715  5.064e-02      3.386  7.087e-04 [7.222e-02,  0.271]
+alpha[2]       0.2202  6.394e-02      3.444  5.741e-04 [9.486e-02,  0.345]
+alpha[3]       0.1547  6.327e-02      2.446  1.447e-02 [3.072e-02,  0.279]
 alpha[4]       0.2117  7.287e-02      2.905  3.677e-03 [6.884e-02,  0.354]
-alpha[5]       0.1959  7.854e-02      2.494  1.261e-02 [4.198e-02,  0.350]
+alpha[5]       0.1959  7.853e-02      2.495  1.261e-02 [4.199e-02,  0.350]
                               Distribution
 ========================================================================
                  coef    std err          t      P&gt;|t|  95.0% Conf. Int.
 ------------------------------------------------------------------------
-nu             9.0448      3.366      2.687  7.208e-03 [  2.447, 15.642]
+nu             9.0451      3.366      2.687  7.205e-03 [  2.448, 15.642]
 ========================================================================
 
 Covariance estimator: robust
 </pre></div></div>
 </div>
-<h2 id="WTI-Crude">WTI Crude<a class="headerlink" href="#WTI-Crude" title="Permalink to this heading">¶</a></h2>
+<h2 id="WTI-Crude">WTI Crude<a class="headerlink" href="#WTI-Crude" title="Link to this heading">¶</a></h2>
 <p>The next example uses West Texas Intermediate Crude data from FRED. Three models are fit using alternative distributional assumptions. The results are printed, where we can see that the normal has a much lower log-likelihood than either the Standard Student’s T or the Standardized Skew Student’s T – however, these two are fairly close. The closeness of the T and the Skew T indicate that returns are not heavily skewed.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[15]:
@@ -1593,7 +1593,7 @@ <h2 id="WTI-Crude">WTI Crude<a class="headerlink" href="#WTI-Crude" title="Perma
 <img alt="../_images/univariate_univariate_volatility_modeling_44_0.png" src="../_images/univariate_univariate_volatility_modeling_44_0.png" />
 </div>
 </div>
-<h2 id="Simulation">Simulation<a class="headerlink" href="#Simulation" title="Permalink to this heading">¶</a></h2>
+<h2 id="Simulation">Simulation<a class="headerlink" href="#Simulation" title="Link to this heading">¶</a></h2>
 <p>All mean models expose a method to simulate returns from assuming the model is correctly specified. There are two required parameters, <code class="docutils literal notranslate"><span class="pre">params</span></code> which are the model parameters, and <code class="docutils literal notranslate"><span class="pre">nobs</span></code>, the number of observations to produce.</p>
 <p>Below we simulate from a GJR-GARCH(1,1) with Skew-t errors using parameters estimated on the WTI series. The simulation returns a <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> with 3 columns:</p>
 <ul class="simple">
@@ -1711,33 +1711,33 @@ <h2 id="Simulation">Simulation<a class="headerlink" href="#Simulation" title="Pe
   <tbody>
     <tr>
       <th>0</th>
-      <td>2.729532</td>
-      <td>2.293291</td>
-      <td>2.700167</td>
+      <td>2.242949</td>
+      <td>2.325649</td>
+      <td>2.213584</td>
     </tr>
     <tr>
       <th>1</th>
-      <td>1.310209</td>
-      <td>2.294658</td>
-      <td>1.280844</td>
+      <td>0.404109</td>
+      <td>2.301873</td>
+      <td>0.374743</td>
     </tr>
     <tr>
       <th>2</th>
-      <td>-3.711848</td>
-      <td>2.240700</td>
-      <td>-3.741213</td>
+      <td>-0.106860</td>
+      <td>2.232734</td>
+      <td>-0.136226</td>
     </tr>
     <tr>
       <th>3</th>
-      <td>2.212867</td>
-      <td>2.417869</td>
-      <td>2.183502</td>
+      <td>-1.678962</td>
+      <td>2.165269</td>
+      <td>-1.708327</td>
     </tr>
     <tr>
       <th>4</th>
-      <td>2.987078</td>
-      <td>2.387496</td>
-      <td>2.957713</td>
+      <td>-1.388209</td>
+      <td>2.155309</td>
+      <td>-1.417574</td>
     </tr>
   </tbody>
 </table>
@@ -1968,7 +1968,7 @@ <h2 id="Simulation">Simulation<a class="headerlink" href="#Simulation" title="Pe
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -2015,10 +2015,10 @@ <h2 id="Simulation">Simulation<a class="headerlink" href="#Simulation" title="Pe
     <script id="__config" type="application/json">{"base": "..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
         <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+        <script id="MathJax-script" src="../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
     
   </body>
 </html>
\ No newline at end of file
diff --git a/univariate/univariate_volatility_modeling.ipynb b/univariate/univariate_volatility_modeling.ipynb
index 9c8fc4c53a..f1165dca2e 100644
--- a/univariate/univariate_volatility_modeling.ipynb
+++ b/univariate/univariate_volatility_modeling.ipynb
@@ -134,10 +134,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Iteration:      5,   Func. Count:     35,   Neg. LLF: 6970.2765831170655\n",
-      "Iteration:     10,   Func. Count:     63,   Neg. LLF: 6936.718477482658\n",
+      "Iteration:      5,   Func. Count:     35,   Neg. LLF: 6970.2779565773335\n",
+      "Iteration:     10,   Func. Count:     63,   Neg. LLF: 6936.718477482076\n",
       "Optimization terminated successfully    (Exit mode 0)\n",
-      "            Current function value: 6936.718476988985\n",
+      "            Current function value: 6936.718476988951\n",
       "            Iterations: 11\n",
       "            Function evaluations: 68\n",
       "            Gradient evaluations: 11\n",
@@ -149,8 +149,8 @@
       "Distribution:                  Normal   AIC:                           13881.4\n",
       "Method:            Maximum Likelihood   BIC:                           13907.5\n",
       "                                        No. Observations:                 5030\n",
-      "Date:                Wed, Apr 12 2023   Df Residuals:                     5029\n",
-      "Time:                        15:58:55   Df Model:                            1\n",
+      "Date:                Tue, May 30 2023   Df Residuals:                     5029\n",
+      "Time:                        10:55:28   Df Model:                            1\n",
       "                                 Mean Model                                 \n",
       "============================================================================\n",
       "                 coef    std err          t      P>|t|      95.0% Conf. Int.\n",
@@ -251,8 +251,8 @@
       "Distribution:                  Normal   AIC:                           13655.8\n",
       "Method:            Maximum Likelihood   BIC:                           13688.4\n",
       "                                        No. Observations:                 5030\n",
-      "Date:                Wed, Apr 12 2023   Df Residuals:                     5029\n",
-      "Time:                        15:58:56   Df Model:                            1\n",
+      "Date:                Tue, May 30 2023   Df Residuals:                     5029\n",
+      "Time:                        10:55:29   Df Model:                            1\n",
       "                                  Mean Model                                 \n",
       "=============================================================================\n",
       "                 coef    std err          t      P>|t|       95.0% Conf. Int.\n",
@@ -263,7 +263,7 @@
       "                 coef    std err          t      P>|t|       95.0% Conf. Int.\n",
       "-----------------------------------------------------------------------------\n",
       "omega          0.0196  4.051e-03      4.830  1.362e-06  [1.163e-02,2.751e-02]\n",
-      "alpha[1]   2.2928e-10  1.026e-02  2.234e-08      1.000 [-2.011e-02,2.011e-02]\n",
+      "alpha[1]   2.4027e-10  1.026e-02  2.341e-08      1.000 [-2.011e-02,2.011e-02]\n",
       "gamma[1]       0.1831  2.266e-02      8.079  6.543e-16      [  0.139,  0.227]\n",
       "beta[1]        0.8922  1.458e-02     61.200      0.000      [  0.864,  0.921]\n",
       "=============================================================================\n",
@@ -321,12 +321,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Iteration:      5,   Func. Count:     45,   Neg. LLF: 6829.203002130782\n",
-      "Iteration:     10,   Func. Count:     79,   Neg. LLF: 6799.178617397159\n",
+      "Iteration:      5,   Func. Count:     45,   Neg. LLF: 6829.197763035331\n",
+      "Iteration:     10,   Func. Count:     79,   Neg. LLF: 6799.178613053096\n",
       "Optimization terminated successfully    (Exit mode 0)\n",
-      "            Current function value: 6799.178521515934\n",
-      "            Iterations: 13\n",
-      "            Function evaluations: 96\n",
+      "            Current function value: 6799.178523172141\n",
+      "            Iterations: 14\n",
+      "            Function evaluations: 103\n",
       "            Gradient evaluations: 13\n",
       "                  Constant Mean - TARCH/ZARCH Model Results                   \n",
       "==============================================================================\n",
@@ -336,21 +336,21 @@
       "Distribution:                  Normal   AIC:                           13608.4\n",
       "Method:            Maximum Likelihood   BIC:                           13641.0\n",
       "                                        No. Observations:                 5030\n",
-      "Date:                Wed, Apr 12 2023   Df Residuals:                     5029\n",
-      "Time:                        15:58:56   Df Model:                            1\n",
+      "Date:                Tue, May 30 2023   Df Residuals:                     5029\n",
+      "Time:                        10:55:29   Df Model:                            1\n",
       "                                  Mean Model                                 \n",
       "=============================================================================\n",
       "                 coef    std err          t      P>|t|       95.0% Conf. Int.\n",
       "-----------------------------------------------------------------------------\n",
-      "mu             0.0143  1.091e-02      1.311      0.190 [-7.087e-03,3.570e-02]\n",
+      "mu             0.0143  1.091e-02      1.311      0.190 [-7.086e-03,3.570e-02]\n",
       "                               Volatility Model                              \n",
       "=============================================================================\n",
       "                 coef    std err          t      P>|t|       95.0% Conf. Int.\n",
       "-----------------------------------------------------------------------------\n",
-      "omega          0.0258  4.100e-03      6.299  2.990e-10  [1.779e-02,3.386e-02]\n",
-      "alpha[1]       0.0000  9.156e-03      0.000      1.000 [-1.794e-02,1.794e-02]\n",
-      "gamma[1]       0.1707  1.601e-02     10.664  1.505e-26      [  0.139,  0.202]\n",
-      "beta[1]        0.9098  9.672e-03     94.066      0.000      [  0.891,  0.929]\n",
+      "omega          0.0258  4.100e-03      6.299  2.987e-10  [1.779e-02,3.386e-02]\n",
+      "alpha[1]   5.9528e-09  9.156e-03  6.502e-07      1.000 [-1.794e-02,1.794e-02]\n",
+      "gamma[1]       0.1707  1.601e-02     10.664  1.501e-26      [  0.139,  0.202]\n",
+      "beta[1]        0.9098  9.671e-03     94.067      0.000      [  0.891,  0.929]\n",
       "=============================================================================\n",
       "\n",
       "Covariance estimator: robust\n"
@@ -393,12 +393,12 @@
      "output_type": "stream",
      "text": [
       "Iteration:      5,   Func. Count:     50,   Neg. LLF: 6728.995165345449\n",
-      "Iteration:     10,   Func. Count:     90,   Neg. LLF: 6722.151188120595\n",
+      "Iteration:     10,   Func. Count:     90,   Neg. LLF: 6722.15117736426\n",
       "Optimization terminated successfully    (Exit mode 0)\n",
-      "            Current function value: 6722.151187386988\n",
-      "            Iterations: 11\n",
-      "            Function evaluations: 97\n",
-      "            Gradient evaluations: 11\n",
+      "            Current function value: 6722.1511833987915\n",
+      "            Iterations: 13\n",
+      "            Function evaluations: 110\n",
+      "            Gradient evaluations: 12\n",
       "                     Constant Mean - TARCH/ZARCH Model Results                      \n",
       "====================================================================================\n",
       "Dep. Variable:                    Adj Close   R-squared:                       0.000\n",
@@ -407,26 +407,26 @@
       "Distribution:      Standardized Student's t   AIC:                           13456.3\n",
       "Method:                  Maximum Likelihood   BIC:                           13495.4\n",
       "                                              No. Observations:                 5030\n",
-      "Date:                      Wed, Apr 12 2023   Df Residuals:                     5029\n",
-      "Time:                              15:58:56   Df Model:                            1\n",
+      "Date:                      Tue, May 30 2023   Df Residuals:                     5029\n",
+      "Time:                              10:55:29   Df Model:                            1\n",
       "                                 Mean Model                                 \n",
       "============================================================================\n",
       "                 coef    std err          t      P>|t|      95.0% Conf. Int.\n",
       "----------------------------------------------------------------------------\n",
-      "mu             0.0323  2.364e-03     13.650  2.020e-42 [2.763e-02,3.690e-02]\n",
+      "mu             0.0323  2.292e-03     14.077  5.292e-45 [2.777e-02,3.676e-02]\n",
       "                               Volatility Model                              \n",
       "=============================================================================\n",
       "                 coef    std err          t      P>|t|       95.0% Conf. Int.\n",
       "-----------------------------------------------------------------------------\n",
-      "omega          0.0201  3.493e-03      5.744  9.249e-09  [1.322e-02,2.691e-02]\n",
-      "alpha[1]   1.4150e-08  8.223e-03  1.721e-06      1.000 [-1.612e-02,1.612e-02]\n",
-      "gamma[1]       0.1721  1.512e-02     11.385  4.978e-30      [  0.143,  0.202]\n",
-      "beta[1]        0.9139  9.578e-03     95.415      0.000      [  0.895,  0.933]\n",
+      "omega          0.0201  3.492e-03      5.745  9.179e-09  [1.322e-02,2.691e-02]\n",
+      "alpha[1]       0.0000  8.223e-03      0.000      1.000 [-1.612e-02,1.612e-02]\n",
+      "gamma[1]       0.1721  1.512e-02     11.386  4.925e-30      [  0.143,  0.202]\n",
+      "beta[1]        0.9139  9.578e-03     95.414      0.000      [  0.895,  0.933]\n",
       "                              Distribution                              \n",
       "========================================================================\n",
       "                 coef    std err          t      P>|t|  95.0% Conf. Int.\n",
       "------------------------------------------------------------------------\n",
-      "nu             7.9553      0.881      9.034  1.648e-19 [  6.229,  9.681]\n",
+      "nu             7.9549      0.880      9.036  1.620e-19 [  6.229,  9.680]\n",
       "========================================================================\n",
       "\n",
       "Covariance estimator: robust\n"
@@ -470,8 +470,8 @@
       "Distribution:       Standardized Student's t   AIC:                           13829.9\n",
       "Method:            User-specified Parameters   BIC:                           13869.0\n",
       "                                               No. Observations:                 5030\n",
-      "Date:                       Wed, Apr 12 2023                                         \n",
-      "Time:                               15:58:56                                         \n",
+      "Date:                       Tue, May 30 2023                                         \n",
+      "Time:                               10:55:29                                         \n",
       "      Mean Model     \n",
       "=====================\n",
       "                 coef\n",
@@ -523,7 +523,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1600x600 with 1 Axes>"
       ]
@@ -646,8 +646,8 @@
       "Distribution:                  Normal   AIC:                           6609.68\n",
       "Method:            Maximum Likelihood   BIC:                           6632.57\n",
       "                                        No. Observations:                  719\n",
-      "Date:                Wed, Apr 12 2023   Df Residuals:                      715\n",
-      "Time:                        15:58:57   Df Model:                            4\n",
+      "Date:                Tue, May 30 2023   Df Residuals:                      715\n",
+      "Time:                        10:55:30   Df Model:                            4\n",
       "                                   Mean Model                                  \n",
       "===============================================================================\n",
       "                   coef    std err          t      P>|t|       95.0% Conf. Int.\n",
@@ -709,24 +709,24 @@
       "Distribution:                  Normal   AIC:                           6369.19\n",
       "Method:            Maximum Likelihood   BIC:                           6414.97\n",
       "                                        No. Observations:                  719\n",
-      "Date:                Wed, Apr 12 2023   Df Residuals:                      715\n",
-      "Time:                        15:58:57   Df Model:                            4\n",
+      "Date:                Tue, May 30 2023   Df Residuals:                      715\n",
+      "Time:                        10:55:30   Df Model:                            4\n",
       "                                   Mean Model                                  \n",
       "===============================================================================\n",
       "                   coef    std err          t      P>|t|       95.0% Conf. Int.\n",
       "-------------------------------------------------------------------------------\n",
       "Const            2.8500      1.883      1.513      0.130      [ -0.841,  6.541]\n",
-      "CPILFESL[1]      1.0859  3.534e-02     30.726 2.594e-207      [  1.017,  1.155]\n",
+      "CPILFESL[1]      1.0859  3.534e-02     30.726 2.596e-207      [  1.017,  1.155]\n",
       "CPILFESL[3]     -0.0788  3.855e-02     -2.045  4.085e-02   [ -0.154,-3.282e-03]\n",
       "CPILFESL[12]    -0.0189  1.157e-02     -1.630      0.103 [-4.154e-02,3.820e-03]\n",
       "                             Volatility Model                             \n",
       "==========================================================================\n",
       "                 coef    std err          t      P>|t|    95.0% Conf. Int.\n",
       "--------------------------------------------------------------------------\n",
-      "omega         76.8684     16.016      4.799  1.592e-06 [ 45.477,1.083e+02]\n",
+      "omega         76.8696     16.017      4.799  1.592e-06 [ 45.478,1.083e+02]\n",
       "alpha[1]       0.1345  4.003e-02      3.359  7.827e-04 [5.600e-02,  0.213]\n",
-      "alpha[2]       0.2280  6.284e-02      3.628  2.860e-04   [  0.105,  0.351]\n",
-      "alpha[3]       0.1838  6.802e-02      2.702  6.894e-03 [5.047e-02,  0.317]\n",
+      "alpha[2]       0.2280  6.284e-02      3.628  2.861e-04   [  0.105,  0.351]\n",
+      "alpha[3]       0.1838  6.802e-02      2.702  6.894e-03 [5.046e-02,  0.317]\n",
       "alpha[4]       0.2538  7.826e-02      3.242  1.185e-03   [  0.100,  0.407]\n",
       "alpha[5]       0.1954  7.091e-02      2.756  5.856e-03 [5.643e-02,  0.334]\n",
       "==========================================================================\n",
@@ -761,7 +761,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 1600x600 with 2 Axes>"
       ]
@@ -811,31 +811,31 @@
       "Distribution:      Standardized Student's t   AIC:                           6358.51\n",
       "Method:                  Maximum Likelihood   BIC:                           6408.86\n",
       "                                              No. Observations:                  719\n",
-      "Date:                      Wed, Apr 12 2023   Df Residuals:                      715\n",
-      "Time:                              15:58:58   Df Model:                            4\n",
+      "Date:                      Tue, May 30 2023   Df Residuals:                      715\n",
+      "Time:                              10:55:31   Df Model:                            4\n",
       "                                   Mean Model                                  \n",
       "===============================================================================\n",
       "                   coef    std err          t      P>|t|       95.0% Conf. Int.\n",
       "-------------------------------------------------------------------------------\n",
-      "Const            3.1221      1.861      1.678  9.342e-02      [ -0.525,  6.770]\n",
-      "CPILFESL[1]      1.0843  3.525e-02     30.762 8.338e-208      [  1.015,  1.153]\n",
-      "CPILFESL[3]     -0.0730  3.873e-02     -1.885  5.947e-02    [ -0.149,2.916e-03]\n",
-      "CPILFESL[12]    -0.0236  1.316e-02     -1.791  7.331e-02 [-4.935e-02,2.224e-03]\n",
+      "Const            3.1220      1.861      1.678  9.343e-02      [ -0.526,  6.770]\n",
+      "CPILFESL[1]      1.0843  3.525e-02     30.762 8.350e-208      [  1.015,  1.153]\n",
+      "CPILFESL[3]     -0.0730  3.873e-02     -1.885  5.948e-02    [ -0.149,2.919e-03]\n",
+      "CPILFESL[12]    -0.0236  1.316e-02     -1.791  7.329e-02 [-4.935e-02,2.223e-03]\n",
       "                             Volatility Model                             \n",
       "==========================================================================\n",
       "                 coef    std err          t      P>|t|    95.0% Conf. Int.\n",
       "--------------------------------------------------------------------------\n",
-      "omega         87.3616     20.628      4.235  2.285e-05 [ 46.931,1.278e+02]\n",
-      "alpha[1]       0.1715  5.064e-02      3.386  7.090e-04 [7.221e-02,  0.271]\n",
-      "alpha[2]       0.2202  6.394e-02      3.443  5.743e-04 [9.485e-02,  0.345]\n",
-      "alpha[3]       0.1547  6.327e-02      2.445  1.447e-02 [3.071e-02,  0.279]\n",
+      "omega         87.3503     20.625      4.235  2.283e-05 [ 46.927,1.278e+02]\n",
+      "alpha[1]       0.1715  5.064e-02      3.386  7.087e-04 [7.222e-02,  0.271]\n",
+      "alpha[2]       0.2202  6.394e-02      3.444  5.741e-04 [9.486e-02,  0.345]\n",
+      "alpha[3]       0.1547  6.327e-02      2.446  1.447e-02 [3.072e-02,  0.279]\n",
       "alpha[4]       0.2117  7.287e-02      2.905  3.677e-03 [6.884e-02,  0.354]\n",
-      "alpha[5]       0.1959  7.854e-02      2.494  1.261e-02 [4.198e-02,  0.350]\n",
+      "alpha[5]       0.1959  7.853e-02      2.495  1.261e-02 [4.199e-02,  0.350]\n",
       "                              Distribution                              \n",
       "========================================================================\n",
       "                 coef    std err          t      P>|t|  95.0% Conf. Int.\n",
       "------------------------------------------------------------------------\n",
-      "nu             9.0448      3.366      2.687  7.208e-03 [  2.447, 15.642]\n",
+      "nu             9.0451      3.366      2.687  7.205e-03 [  2.448, 15.642]\n",
       "========================================================================\n",
       "\n",
       "Covariance estimator: robust\n"
@@ -1191,33 +1191,33 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>2.729532</td>\n",
-       "      <td>2.293291</td>\n",
-       "      <td>2.700167</td>\n",
+       "      <td>2.242949</td>\n",
+       "      <td>2.325649</td>\n",
+       "      <td>2.213584</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>1.310209</td>\n",
-       "      <td>2.294658</td>\n",
-       "      <td>1.280844</td>\n",
+       "      <td>0.404109</td>\n",
+       "      <td>2.301873</td>\n",
+       "      <td>0.374743</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>-3.711848</td>\n",
-       "      <td>2.240700</td>\n",
-       "      <td>-3.741213</td>\n",
+       "      <td>-0.106860</td>\n",
+       "      <td>2.232734</td>\n",
+       "      <td>-0.136226</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>2.212867</td>\n",
-       "      <td>2.417869</td>\n",
-       "      <td>2.183502</td>\n",
+       "      <td>-1.678962</td>\n",
+       "      <td>2.165269</td>\n",
+       "      <td>-1.708327</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>2.987078</td>\n",
-       "      <td>2.387496</td>\n",
-       "      <td>2.957713</td>\n",
+       "      <td>-1.388209</td>\n",
+       "      <td>2.155309</td>\n",
+       "      <td>-1.417574</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1225,11 +1225,11 @@
       ],
       "text/plain": [
        "       data  volatility    errors\n",
-       "0  2.729532    2.293291  2.700167\n",
-       "1  1.310209    2.294658  1.280844\n",
-       "2 -3.711848    2.240700 -3.741213\n",
-       "3  2.212867    2.417869  2.183502\n",
-       "4  2.987078    2.387496  2.957713"
+       "0  2.242949    2.325649  2.213584\n",
+       "1  0.404109    2.301873  0.374743\n",
+       "2 -0.106860    2.232734 -0.136226\n",
+       "3 -1.678962    2.165269 -1.708327\n",
+       "4 -1.388209    2.155309 -1.417574"
       ]
      },
      "execution_count": 18,
@@ -1483,7 +1483,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.10.11"
   },
   "pycharm": {
    "stem_cell": {
diff --git a/univariate/univariate_volatility_scenarios.html b/univariate/univariate_volatility_scenarios.html
index 4d57fc5af3..3c69078922 100644
--- a/univariate/univariate_volatility_scenarios.html
+++ b/univariate/univariate_volatility_scenarios.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Volatility Scenarios - arch 6.1.0</title>
+        <title>Volatility Scenarios - arch 6.2.0</title>
       
     
 
@@ -47,9 +47,9 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css" />
-        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css?v=2aa19091" />
+        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -93,7 +93,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -103,7 +103,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -190,10 +190,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -715,7 +715,7 @@
                   
 
 
-<h1 id="Volatility-Scenarios">Volatility Scenarios<a class="headerlink" href="#Volatility-Scenarios" title="Permalink to this heading">¶</a></h1>
+<h1 id="Volatility-Scenarios">Volatility Scenarios<a class="headerlink" href="#Volatility-Scenarios" title="Link to this heading">¶</a></h1>
 <p>Custom random-number generators can be used to implement scenarios where shock follow a particular pattern. For example, suppose you wanted to find out what would happen if there were 5 days of shocks that were larger than average. In most circumstances, the shocks in a GARCH model have unit variance. This could be changed so that the first 5 shocks have variance 4, or twice the standard deviation.</p>
 <p>Another scenario would be to over sample a specific period for the shocks. When using the standard bootstrap method (filtered historical simulation) the shocks are drawn using iid sampling from the history. While this approach is standard and well-grounded, it might be desirable to sample from a specific period. This can be implemented using a custom random number generator. This strategy is precisely how the filtered historical simulation is implemented internally, only where the draws are
 uniformly sampled from the entire history.</p>
@@ -772,7 +772,7 @@ <h1 id="Volatility-Scenarios">Volatility Scenarios<a class="headerlink" href="#V
 </div>
 <p>The one advantage of constructing the model using the components is that the NumPy <code class="docutils literal notranslate"><span class="pre">RandomState</span></code> that is used to simulate from the model can be externally set. This allows the generator seed to be easily set and for the state to reset, if needed.</p>
 <p><strong>NOTE</strong>: It is always a good idea to scale return by 100 before estimating ARCH-type models. This helps the optimizer converse since the scale of the volatility intercept is much closer to the scale of the other parameters in the model.</p>
-<div class="nbinput docutils container">
+<div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[3]:
 </code></pre></div>
 </div>
@@ -780,22 +780,13 @@ <h1 id="Volatility-Scenarios">Volatility Scenarios<a class="headerlink" href="#V
 
 <span class="c1"># Build components to set the state for the distribution</span>
 <span class="n">random_state</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
-<span class="n">dist</span> <span class="o">=</span> <span class="n">Normal</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span><span class="p">)</span>
+<span class="n">dist</span> <span class="o">=</span> <span class="n">Normal</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="n">random_state</span><span class="p">)</span>
 <span class="n">volatility</span> <span class="o">=</span> <span class="n">GARCH</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
 
 <span class="n">mod</span> <span class="o">=</span> <span class="n">ConstantMean</span><span class="p">(</span><span class="n">rets</span><span class="p">,</span> <span class="n">volatility</span><span class="o">=</span><span class="n">volatility</span><span class="p">,</span> <span class="n">distribution</span><span class="o">=</span><span class="n">dist</span><span class="p">)</span>
 </code></pre></div>
 </div>
 </div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area stderr docutils container">
-<div class="highlight"><pre>
-C:\git\arch\arch\univariate\distribution.py:63: FutureWarning: random_state is deprecated and will be removed in a future update. Use seed instead.
-  warnings.warn(
-</pre></div></div>
-</div>
 <p>Fitting the model is standard.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[4]:
@@ -820,8 +811,8 @@ <h1 id="Volatility-Scenarios">Volatility Scenarios<a class="headerlink" href="#V
 Distribution:                  Normal   AIC:                           16403.5
 Method:            Maximum Likelihood   BIC:                           16436.1
                                         No. Observations:                 5030
-Date:                Wed, Apr 12 2023   Df Residuals:                     5029
-Time:                        15:59:51   Df Model:                            1
+Date:                Tue, May 30 2023   Df Residuals:                     5029
+Time:                        10:55:35   Df Model:                            1
                                  Mean Model
 ============================================================================
                  coef    std err          t      P&gt;|t|      95.0% Conf. Int.
@@ -838,16 +829,18 @@ <h1 id="Volatility-Scenarios">Volatility Scenarios<a class="headerlink" href="#V
 =============================================================================
 
 Covariance estimator: robust
-ARCHModelResult, id: 0x1548c0a8850
+ARCHModelResult, id: 0x202821906a0
 </pre></div></div>
 </div>
 <p>GJR-GARCH models support analytical forecasts, which is the default. The forecasts are produced for all of 2017 using the estimated model parameters.</p>
+<p>All GARCH specification are complete models in the sense that they specify a distribution. This allows simulations to be produced using the assumptions in the model. The <code class="docutils literal notranslate"><span class="pre">forecast</span></code> function can be made to produce simulations using the assumed distribution by setting <code class="docutils literal notranslate"><span class="pre">method='simulation'</span></code>.</p>
+<p>These forecasts are similar to the analytical forecasts above. As the number of simulation increases towards <span class="math notranslate nohighlight">\(\infty\)</span>, the simulation-based forecasts will converge to the analytical values above.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[5]:
 </code></pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><code><span class="n">forecasts</span> <span class="o">=</span> <span class="n">res</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">start</span><span class="o">=</span><span class="s2">&quot;1-1-2017&quot;</span><span class="p">,</span> <span class="n">horizon</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">reindex</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">forecasts</span><span class="o">.</span><span class="n">residual_variance</span><span class="o">.</span><span class="n">dropna</span><span class="p">()</span><span class="o">.</span><span class="n">head</span><span class="p">())</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><code><span class="n">sim_forecasts</span> <span class="o">=</span> <span class="n">res</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">start</span><span class="o">=</span><span class="s2">&quot;1-1-2017&quot;</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">&quot;simulation&quot;</span><span class="p">,</span> <span class="n">horizon</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">sim_forecasts</span><span class="o">.</span><span class="n">residual_variance</span><span class="o">.</span><span class="n">dropna</span><span class="p">()</span><span class="o">.</span><span class="n">head</span><span class="p">())</span>
 </code></pre></div>
 </div>
 </div>
@@ -858,31 +851,30 @@ <h1 id="Volatility-Scenarios">Volatility Scenarios<a class="headerlink" href="#V
 <div class="highlight"><pre>
                 h.01      h.02      h.03      h.04      h.05      h.06
 Date
-2017-01-03  0.623295  0.637504  0.651549  0.665431  0.679154  0.692717  \
-2017-01-04  0.599455  0.613940  0.628257  0.642408  0.656397  0.670223
-2017-01-05  0.567297  0.582153  0.596837  0.611352  0.625699  0.639880
-2017-01-06  0.542506  0.557649  0.572616  0.587410  0.602034  0.616488
-2017-01-09  0.515452  0.530906  0.546183  0.561282  0.576208  0.590961
+2017-01-03  0.623295  0.637251  0.647817  0.663746  0.673404  0.687952  \
+2017-01-04  0.599455  0.617539  0.635838  0.649695  0.659733  0.667267
+2017-01-05  0.567297  0.583415  0.597571  0.613065  0.621790  0.636180
+2017-01-06  0.542506  0.555688  0.570280  0.585426  0.595551  0.608487
+2017-01-09  0.515452  0.528771  0.542658  0.559684  0.580434  0.594855
 
                 h.07      h.08      h.09      h.10
 Date
-2017-01-03  0.706124  0.719376  0.732475  0.745423
-2017-01-04  0.683890  0.697399  0.710752  0.723950
-2017-01-05  0.653897  0.667753  0.681448  0.694985
-2017-01-06  0.630776  0.644899  0.658858  0.672656
-2017-01-09  0.605543  0.619957  0.634205  0.648288
+2017-01-03  0.697221  0.707707  0.717701  0.729465
+2017-01-04  0.686503  0.699708  0.707203  0.718560
+2017-01-05  0.650287  0.663344  0.679835  0.692300
+2017-01-06  0.619195  0.638180  0.653185  0.661366
+2017-01-09  0.605136  0.621835  0.634091  0.653222
 </pre></div></div>
 </div>
-<p>All GARCH specification are complete models in the sense that they specify a distribution. This allows simulations to be produced using the assumptions in the model. The <code class="docutils literal notranslate"><span class="pre">forecast</span></code> function can be made to produce simulations using the assumed distribution by setting <code class="docutils literal notranslate"><span class="pre">method='simulation'</span></code>.</p>
-<p>These forecasts are similar to the analytical forecasts above. As the number of simulation increases towards <span class="math notranslate nohighlight">\(\infty\)</span>, the simulation-based forecasts will converge to the analytical values above.</p>
+<h2 id="Custom-Random-Generators">Custom Random Generators<a class="headerlink" href="#Custom-Random-Generators" title="Link to this heading">¶</a></h2>
+<p><code class="docutils literal notranslate"><span class="pre">forecast</span></code> supports replacing the generator based on the assumed distribution of residuals in the model with any other generator. A shock generator should usually produce unit variance shocks. However, in this example the first 5 shocks generated have variance 2, and the remainder are standard normal. This scenario consists of a period of consistently surprising volatility where the volatility has shifted for some reason.</p>
+<p>The forecast variances are much larger and grow faster than those from either method previously illustrated. This reflects the increase in volatility in the first 5 days.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[6]:
 </code></pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><code><span class="n">sim_forecasts</span> <span class="o">=</span> <span class="n">res</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span>
-    <span class="n">start</span><span class="o">=</span><span class="s2">&quot;1-1-2017&quot;</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">&quot;simulation&quot;</span><span class="p">,</span> <span class="n">horizon</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">reindex</span><span class="o">=</span><span class="kc">False</span>
-<span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">sim_forecasts</span><span class="o">.</span><span class="n">residual_variance</span><span class="o">.</span><span class="n">dropna</span><span class="p">()</span><span class="o">.</span><span class="n">head</span><span class="p">())</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><code><span class="n">forecasts</span> <span class="o">=</span> <span class="n">res</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">start</span><span class="o">=</span><span class="s2">&quot;1-1-2017&quot;</span><span class="p">,</span> <span class="n">horizon</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">forecasts</span><span class="o">.</span><span class="n">residual_variance</span><span class="o">.</span><span class="n">dropna</span><span class="p">()</span><span class="o">.</span><span class="n">head</span><span class="p">())</span>
 </code></pre></div>
 </div>
 </div>
@@ -893,24 +885,21 @@ <h1 id="Volatility-Scenarios">Volatility Scenarios<a class="headerlink" href="#V
 <div class="highlight"><pre>
                 h.01      h.02      h.03      h.04      h.05      h.06
 Date
-2017-01-03  0.623295  0.637251  0.647817  0.663746  0.673404  0.687952  \
-2017-01-04  0.599455  0.617539  0.635838  0.649695  0.659733  0.667267
-2017-01-05  0.567297  0.583415  0.597571  0.613065  0.621790  0.636180
-2017-01-06  0.542506  0.555688  0.570280  0.585426  0.595551  0.608487
-2017-01-09  0.515452  0.528771  0.542658  0.559684  0.580434  0.594855
+2017-01-03  0.623295  0.637504  0.651549  0.665431  0.679154  0.692717  \
+2017-01-04  0.599455  0.613940  0.628257  0.642408  0.656397  0.670223
+2017-01-05  0.567297  0.582153  0.596837  0.611352  0.625699  0.639880
+2017-01-06  0.542506  0.557649  0.572616  0.587410  0.602034  0.616488
+2017-01-09  0.515452  0.530906  0.546183  0.561282  0.576208  0.590961
 
                 h.07      h.08      h.09      h.10
 Date
-2017-01-03  0.697221  0.707707  0.717701  0.729465
-2017-01-04  0.686503  0.699708  0.707203  0.718560
-2017-01-05  0.650287  0.663344  0.679835  0.692300
-2017-01-06  0.619195  0.638180  0.653185  0.661366
-2017-01-09  0.605136  0.621835  0.634091  0.653222
+2017-01-03  0.706124  0.719376  0.732475  0.745423
+2017-01-04  0.683890  0.697399  0.710752  0.723950
+2017-01-05  0.653897  0.667753  0.681448  0.694985
+2017-01-06  0.630776  0.644899  0.658858  0.672656
+2017-01-09  0.605543  0.619957  0.634205  0.648288
 </pre></div></div>
 </div>
-<h2 id="Custom-Random-Generators">Custom Random Generators<a class="headerlink" href="#Custom-Random-Generators" title="Permalink to this heading">¶</a></h2>
-<p><code class="docutils literal notranslate"><span class="pre">forecast</span></code> supports replacing the generator based on the assumed distribution of residuals in the model with any other generator. A shock generator should usually produce unit variance shocks. However, in this example the first 5 shocks generated have variance 2, and the remainder are standard normal. This scenario consists of a period of consistently surprising volatility where the volatility has shifted for some reason.</p>
-<p>The forecast variances are much larger and grow faster than those from either method previously illustrated. This reflects the increase in volatility in the first 5 days.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[7]:
 </code></pre></div>
@@ -927,7 +916,7 @@ <h2 id="Custom-Random-Generators">Custom Random Generators<a class="headerlink"
 
 
 <span class="n">scenario_forecasts</span> <span class="o">=</span> <span class="n">res</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span>
-    <span class="n">start</span><span class="o">=</span><span class="s2">&quot;1-1-2017&quot;</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">&quot;simulation&quot;</span><span class="p">,</span> <span class="n">horizon</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">rng</span><span class="o">=</span><span class="n">scenario_rng</span><span class="p">,</span> <span class="n">reindex</span><span class="o">=</span><span class="kc">False</span>
+    <span class="n">start</span><span class="o">=</span><span class="s2">&quot;1-1-2017&quot;</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">&quot;simulation&quot;</span><span class="p">,</span> <span class="n">horizon</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">rng</span><span class="o">=</span><span class="n">scenario_rng</span>
 <span class="p">)</span>
 <span class="nb">print</span><span class="p">(</span><span class="n">scenario_forecasts</span><span class="o">.</span><span class="n">residual_variance</span><span class="o">.</span><span class="n">dropna</span><span class="p">()</span><span class="o">.</span><span class="n">head</span><span class="p">())</span>
 </code></pre></div>
@@ -951,11 +940,11 @@ <h2 id="Custom-Random-Generators">Custom Random Generators<a class="headerlink"
 2017-01-03  0.970796  0.977504  0.982202  0.992547
 2017-01-04  0.955295  0.965540  0.966432  0.974248
 2017-01-05  0.905952  0.915208  0.930777  0.938636
-2017-01-06  0.856175  0.873866  0.886221  0.890002
+2017-01-06  0.856175  0.873865  0.886221  0.890002
 2017-01-09  0.851104  0.864591  0.874696  0.894397
 </pre></div></div>
 </div>
-<h2 id="Bootstrap-Scenarios">Bootstrap Scenarios<a class="headerlink" href="#Bootstrap-Scenarios" title="Permalink to this heading">¶</a></h2>
+<h2 id="Bootstrap-Scenarios">Bootstrap Scenarios<a class="headerlink" href="#Bootstrap-Scenarios" title="Link to this heading">¶</a></h2>
 <p><code class="docutils literal notranslate"><span class="pre">forecast</span></code> supports Filtered Historical Simulation (FHS) using <code class="docutils literal notranslate"><span class="pre">method='bootstrap'</span></code>. This is effectively a simulation method where the simulated shocks are generated using iid sampling from the history of the demeaned and standardized return data. Custom bootstraps are another application of <code class="docutils literal notranslate"><span class="pre">rng</span></code>. Here an object is used to hold the shocks. This object exposes a method (<code class="docutils literal notranslate"><span class="pre">rng</span></code>) the acts like a random number generator, except that it only returns values that were provided in the <code class="docutils literal notranslate"><span class="pre">shocks</span></code>
 parameter.</p>
 <p>The internal implementation of the FHS uses a method almost identical to this where <code class="docutils literal notranslate"><span class="pre">shocks</span></code> contain the entire history.</p>
@@ -979,11 +968,7 @@ <h2 id="Bootstrap-Scenarios">Bootstrap Scenarios<a class="headerlink" href="#Boo
 <span class="n">shocks</span> <span class="o">=</span> <span class="n">std_shocks</span><span class="p">[</span><span class="s2">&quot;2008-08-01&quot;</span><span class="p">:</span><span class="s2">&quot;2008-11-10&quot;</span><span class="p">]</span>
 <span class="n">scenario_bootstrap</span> <span class="o">=</span> <span class="n">ScenarioBootstrapRNG</span><span class="p">(</span><span class="n">shocks</span><span class="p">,</span> <span class="n">random_state</span><span class="p">)</span>
 <span class="n">bs_forecasts</span> <span class="o">=</span> <span class="n">res</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span>
-    <span class="n">start</span><span class="o">=</span><span class="s2">&quot;1-1-2017&quot;</span><span class="p">,</span>
-    <span class="n">method</span><span class="o">=</span><span class="s2">&quot;simulation&quot;</span><span class="p">,</span>
-    <span class="n">horizon</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
-    <span class="n">rng</span><span class="o">=</span><span class="n">scenario_bootstrap</span><span class="o">.</span><span class="n">rng</span><span class="p">,</span>
-    <span class="n">reindex</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">start</span><span class="o">=</span><span class="s2">&quot;1-1-2017&quot;</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">&quot;simulation&quot;</span><span class="p">,</span> <span class="n">horizon</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">rng</span><span class="o">=</span><span class="n">scenario_bootstrap</span><span class="o">.</span><span class="n">rng</span>
 <span class="p">)</span>
 <span class="nb">print</span><span class="p">(</span><span class="n">bs_forecasts</span><span class="o">.</span><span class="n">residual_variance</span><span class="o">.</span><span class="n">dropna</span><span class="p">()</span><span class="o">.</span><span class="n">head</span><span class="p">())</span>
 </code></pre></div>
@@ -1006,12 +991,12 @@ <h2 id="Bootstrap-Scenarios">Bootstrap Scenarios<a class="headerlink" href="#Boo
 Date
 2017-01-03  0.958215  1.043704  1.124684  1.203893
 2017-01-04  0.945120  1.013400  1.084042  1.158148
-2017-01-05  0.889032  0.961424  1.022413  1.097192
+2017-01-05  0.889032  0.961424  1.022412  1.097192
 2017-01-06  0.840667  0.895559  0.957266  1.019497
 2017-01-09  0.820788  0.887791  0.938708  1.028614
 </pre></div></div>
 </div>
-<h2 id="Visualizing-the-differences">Visualizing the differences<a class="headerlink" href="#Visualizing-the-differences" title="Permalink to this heading">¶</a></h2>
+<h2 id="Visualizing-the-differences">Visualizing the differences<a class="headerlink" href="#Visualizing-the-differences" title="Link to this heading">¶</a></h2>
 <p>The final forecast values are used to illustrate how these are different. The analytical and standard simulation are virtually identical. The simulated scenario grows rapidly for the first 5 periods and then more slowly. The bootstrap scenario grows quickly and consistently due to the magnitude of the shocks in the financial crisis.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[9]:
@@ -1050,7 +1035,7 @@ <h2 id="Visualizing-the-differences">Visualizing the differences<a class="header
 </code></pre></div>
 </div>
 </div>
-<h2 id="Comparing-the-paths">Comparing the paths<a class="headerlink" href="#Comparing-the-paths" title="Permalink to this heading">¶</a></h2>
+<h2 id="Comparing-the-paths">Comparing the paths<a class="headerlink" href="#Comparing-the-paths" title="Link to this heading">¶</a></h2>
 <p>The paths are available on the attribute <code class="docutils literal notranslate"><span class="pre">simulations</span></code>. Plotting the paths shows important differences between the two scenarios beyond the average differences plotted above. Both start at the same point.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[11]:
@@ -1091,7 +1076,7 @@ <h2 id="Comparing-the-paths">Comparing the paths<a class="headerlink" href="#Com
 <img alt="../_images/univariate_univariate_volatility_scenarios_21_0.png" src="../_images/univariate_univariate_volatility_scenarios_21_0.png" />
 </div>
 </div>
-<h2 id="Comparing-across-the-year">Comparing across the year<a class="headerlink" href="#Comparing-across-the-year" title="Permalink to this heading">¶</a></h2>
+<h2 id="Comparing-across-the-year">Comparing across the year<a class="headerlink" href="#Comparing-across-the-year" title="Link to this heading">¶</a></h2>
 <p>A hedgehog plot is useful for showing the differences between the two forecasting methods across the year, instead of a single day.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span><code>[12]:
@@ -1193,7 +1178,7 @@ <h2 id="Comparing-across-the-year">Comparing across the year<a class="headerlink
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1240,10 +1225,10 @@ <h2 id="Comparing-across-the-year">Comparing across the year<a class="headerlink
     <script id="__config" type="application/json">{"base": "..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
         <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
-        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+        <script id="MathJax-script" src="../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
     
   </body>
 </html>
\ No newline at end of file
diff --git a/univariate/univariate_volatility_scenarios.ipynb b/univariate/univariate_volatility_scenarios.ipynb
index 8fe8387b47..8204c7e6e5 100644
--- a/univariate/univariate_volatility_scenarios.ipynb
+++ b/univariate/univariate_volatility_scenarios.ipynb
@@ -91,22 +91,13 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\git\\arch\\arch\\univariate\\distribution.py:63: FutureWarning: random_state is deprecated and will be removed in a future update. Use seed instead.\n",
-      "  warnings.warn(\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "rets = 100 * nasdaq.pct_change().dropna()\n",
     "\n",
     "# Build components to set the state for the distribution\n",
     "random_state = np.random.RandomState(1)\n",
-    "dist = Normal(random_state=random_state)\n",
+    "dist = Normal(seed=random_state)\n",
     "volatility = GARCH(1, 1, 1)\n",
     "\n",
     "mod = ConstantMean(rets, volatility=volatility, distribution=dist)"
@@ -135,8 +126,8 @@
        "Distribution:                  Normal   AIC:                           16403.5\n",
        "Method:            Maximum Likelihood   BIC:                           16436.1\n",
        "                                        No. Observations:                 5030\n",
-       "Date:                Wed, Apr 12 2023   Df Residuals:                     5029\n",
-       "Time:                        15:59:51   Df Model:                            1\n",
+       "Date:                Tue, May 30 2023   Df Residuals:                     5029\n",
+       "Time:                        10:55:35   Df Model:                            1\n",
        "                                 Mean Model                                 \n",
        "============================================================================\n",
        "                 coef    std err          t      P>|t|      95.0% Conf. Int.\n",
@@ -153,7 +144,7 @@
        "=============================================================================\n",
        "\n",
        "Covariance estimator: robust\n",
-       "ARCHModelResult, id: 0x1548c0a8850"
+       "ARCHModelResult, id: 0x202821906a0"
       ]
      },
      "execution_count": 4,
@@ -173,50 +164,18 @@
     "GJR-GARCH models support analytical forecasts, which is the default.  The forecasts are produced for all of 2017 using the estimated model parameters."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                h.01      h.02      h.03      h.04      h.05      h.06   \n",
-      "Date                                                                     \n",
-      "2017-01-03  0.623295  0.637504  0.651549  0.665431  0.679154  0.692717  \\\n",
-      "2017-01-04  0.599455  0.613940  0.628257  0.642408  0.656397  0.670223   \n",
-      "2017-01-05  0.567297  0.582153  0.596837  0.611352  0.625699  0.639880   \n",
-      "2017-01-06  0.542506  0.557649  0.572616  0.587410  0.602034  0.616488   \n",
-      "2017-01-09  0.515452  0.530906  0.546183  0.561282  0.576208  0.590961   \n",
-      "\n",
-      "                h.07      h.08      h.09      h.10  \n",
-      "Date                                                \n",
-      "2017-01-03  0.706124  0.719376  0.732475  0.745423  \n",
-      "2017-01-04  0.683890  0.697399  0.710752  0.723950  \n",
-      "2017-01-05  0.653897  0.667753  0.681448  0.694985  \n",
-      "2017-01-06  0.630776  0.644899  0.658858  0.672656  \n",
-      "2017-01-09  0.605543  0.619957  0.634205  0.648288  \n"
-     ]
-    }
-   ],
-   "source": [
-    "forecasts = res.forecast(start=\"1-1-2017\", horizon=10, reindex=False)\n",
-    "print(forecasts.residual_variance.dropna().head())"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "All GARCH specification are complete models in the sense that they specify a distribution. This allows simulations to be produced using the assumptions in the model.  The `forecast` function can be made to produce simulations using the assumed distribution by setting `method='simulation'`.  \n",
     "\n",
-    "These forecasts are similar to the analytical forecasts above.  As the number of simulation increases towards $\\infty$, the simulation-based forecasts will converge to the analytical values above. "
+    "These forecasts are similar to the analytical forecasts above.  As the number of simulation increases towards $\\infty$, the simulation-based forecasts will converge to the analytical values above."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -242,9 +201,7 @@
     }
    ],
    "source": [
-    "sim_forecasts = res.forecast(\n",
-    "    start=\"1-1-2017\", method=\"simulation\", horizon=10, reindex=False\n",
-    ")\n",
+    "sim_forecasts = res.forecast(start=\"1-1-2017\", method=\"simulation\", horizon=10)\n",
     "print(sim_forecasts.residual_variance.dropna().head())"
    ]
   },
@@ -258,6 +215,43 @@
     "The forecast variances are much larger and grow faster than those from either method previously illustrated. This reflects the increase in volatility in the first 5 days."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                h.01      h.02      h.03      h.04      h.05      h.06   \n",
+      "Date                                                                     \n",
+      "2017-01-03  0.623295  0.637504  0.651549  0.665431  0.679154  0.692717  \\\n",
+      "2017-01-04  0.599455  0.613940  0.628257  0.642408  0.656397  0.670223   \n",
+      "2017-01-05  0.567297  0.582153  0.596837  0.611352  0.625699  0.639880   \n",
+      "2017-01-06  0.542506  0.557649  0.572616  0.587410  0.602034  0.616488   \n",
+      "2017-01-09  0.515452  0.530906  0.546183  0.561282  0.576208  0.590961   \n",
+      "\n",
+      "                h.07      h.08      h.09      h.10  \n",
+      "Date                                                \n",
+      "2017-01-03  0.706124  0.719376  0.732475  0.745423  \n",
+      "2017-01-04  0.683890  0.697399  0.710752  0.723950  \n",
+      "2017-01-05  0.653897  0.667753  0.681448  0.694985  \n",
+      "2017-01-06  0.630776  0.644899  0.658858  0.672656  \n",
+      "2017-01-09  0.605543  0.619957  0.634205  0.648288  \n"
+     ]
+    }
+   ],
+   "source": [
+    "forecasts = res.forecast(start=\"1-1-2017\", horizon=10)\n",
+    "print(forecasts.residual_variance.dropna().head())"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 7,
@@ -280,7 +274,7 @@
       "2017-01-03  0.970796  0.977504  0.982202  0.992547  \n",
       "2017-01-04  0.955295  0.965540  0.966432  0.974248  \n",
       "2017-01-05  0.905952  0.915208  0.930777  0.938636  \n",
-      "2017-01-06  0.856175  0.873866  0.886221  0.890002  \n",
+      "2017-01-06  0.856175  0.873865  0.886221  0.890002  \n",
       "2017-01-09  0.851104  0.864591  0.874696  0.894397  \n"
      ]
     }
@@ -298,7 +292,7 @@
     "\n",
     "\n",
     "scenario_forecasts = res.forecast(\n",
-    "    start=\"1-1-2017\", method=\"simulation\", horizon=10, rng=scenario_rng, reindex=False\n",
+    "    start=\"1-1-2017\", method=\"simulation\", horizon=10, rng=scenario_rng\n",
     ")\n",
     "print(scenario_forecasts.residual_variance.dropna().head())"
    ]
@@ -335,7 +329,7 @@
       "Date                                                \n",
       "2017-01-03  0.958215  1.043704  1.124684  1.203893  \n",
       "2017-01-04  0.945120  1.013400  1.084042  1.158148  \n",
-      "2017-01-05  0.889032  0.961424  1.022413  1.097192  \n",
+      "2017-01-05  0.889032  0.961424  1.022412  1.097192  \n",
       "2017-01-06  0.840667  0.895559  0.957266  1.019497  \n",
       "2017-01-09  0.820788  0.887791  0.938708  1.028614  \n"
      ]
@@ -358,11 +352,7 @@
     "shocks = std_shocks[\"2008-08-01\":\"2008-11-10\"]\n",
     "scenario_bootstrap = ScenarioBootstrapRNG(shocks, random_state)\n",
     "bs_forecasts = res.forecast(\n",
-    "    start=\"1-1-2017\",\n",
-    "    method=\"simulation\",\n",
-    "    horizon=10,\n",
-    "    rng=scenario_bootstrap.rng,\n",
-    "    reindex=False,\n",
+    "    start=\"1-1-2017\", method=\"simulation\", horizon=10, rng=scenario_bootstrap.rng\n",
     ")\n",
     "print(bs_forecasts.residual_variance.dropna().head())"
    ]
@@ -382,7 +372,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 1600x600 with 1 Axes>"
       ]
@@ -434,7 +424,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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       "text/plain": [
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@@ -540,7 +530,7 @@
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diff --git a/univariate/utility.html b/univariate/utility.html
index 2e33537044..f9fa0d8e82 100644
--- a/univariate/utility.html
+++ b/univariate/utility.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -848,50 +848,50 @@
                   
 
 
-<h1 id="utilities">Utilities<a class="headerlink" href="#utilities" title="Permalink to this heading">¶</a></h1>
+<h1 id="utilities">Utilities<a class="headerlink" href="#utilities" title="Link to this heading">¶</a></h1>
 <p>Utilities that do not fit well on other pages.</p>
-<span class="target" id="module-arch.utility.testing"></span><h2 id="test-results">Test Results<a class="headerlink" href="#test-results" title="Permalink to this heading">¶</a></h2>
+<h2 id="test-results"><span id="module-arch.utility.testing"></span>Test Results<a class="headerlink" href="#test-results" title="Link to this heading">¶</a></h2>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.utility.testing.WaldTestStatistic">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.utility.testing.</span></span><span class="sig-name descname"><span class="pre">WaldTestStatistic</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.utility.testing.WaldTestStatistic.__init__.stat" title="arch.utility.testing.WaldTestStatistic.__init__.stat (Python parameter) — The test statistic"><span class="n"><span class="pre">stat</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.utility.testing.WaldTestStatistic.__init__.df" title="arch.utility.testing.WaldTestStatistic.__init__.df (Python parameter) — Degree of freedom."><span class="n"><span class="pre">df</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.utility.testing.WaldTestStatistic.__init__.null" title="arch.utility.testing.WaldTestStatistic.__init__.null (Python parameter) — A statement of the test's null hypothesis"><span class="n"><span class="pre">null</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.utility.testing.WaldTestStatistic.__init__.alternative" title="arch.utility.testing.WaldTestStatistic.__init__.alternative (Python parameter) — A statement of the test's alternative hypothesis"><span class="n"><span class="pre">alternative</span></span></a></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.utility.testing.WaldTestStatistic.__init__.name" title="arch.utility.testing.WaldTestStatistic.__init__.name (Python parameter) — Name of test"><span class="n"><span class="pre">name</span></span></a><span class="o"><span class="pre">=</span></span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;&#39;</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/arch/utility/testing.html#WaldTestStatistic"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.utility.testing.WaldTestStatistic" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.utility.testing.</span></span><span class="sig-name descname"><span class="pre">WaldTestStatistic</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.utility.testing.WaldTestStatistic.__init__.stat" title="arch.utility.testing.WaldTestStatistic.__init__.stat (Python parameter) — The test statistic"><span class="n"><span class="pre">stat</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.utility.testing.WaldTestStatistic.__init__.df" title="arch.utility.testing.WaldTestStatistic.__init__.df (Python parameter) — Degree of freedom."><span class="n"><span class="pre">df</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.utility.testing.WaldTestStatistic.__init__.null" title="arch.utility.testing.WaldTestStatistic.__init__.null (Python parameter) — A statement of the test's null hypothesis"><span class="n"><span class="pre">null</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.utility.testing.WaldTestStatistic.__init__.alternative" title="arch.utility.testing.WaldTestStatistic.__init__.alternative (Python parameter) — A statement of the test's alternative hypothesis"><span class="n"><span class="pre">alternative</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.utility.testing.WaldTestStatistic.__init__.name" title="arch.utility.testing.WaldTestStatistic.__init__.name (Python parameter) — Name of test"><span class="n"><span class="pre">name</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;&#39;</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/arch/utility/testing.html#WaldTestStatistic"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.utility.testing.WaldTestStatistic" title="Link to this definition">¶</a></dt>
 <dd><p>Test statistic holder for Wald-type tests</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.utility.testing.WaldTestStatistic-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.utility.testing.WaldTestStatistic-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.utility.testing.WaldTestStatistic.__init__.stat"><span class="sig-name descname">stat</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><a class="headerlink" href="#arch.utility.testing.WaldTestStatistic.__init__.stat" title="Permalink to this definition">¶</a></dt><dd><p>The test statistic</p>
+<dt id="arch.utility.testing.WaldTestStatistic.__init__.stat"><span class="n sig-name">stat</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)">float</a></span></span><a class="headerlink" href="#arch.utility.testing.WaldTestStatistic.__init__.stat" title="Permalink to this definition">¶</a></dt><dd><p>The test statistic</p>
 </dd>
-<dt id="arch.utility.testing.WaldTestStatistic.__init__.df"><span class="sig-name descname">df</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.utility.testing.WaldTestStatistic.__init__.df" title="Permalink to this definition">¶</a></dt><dd><p>Degree of freedom.</p>
+<dt id="arch.utility.testing.WaldTestStatistic.__init__.df"><span class="n sig-name">df</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)">int</a></span></span><a class="headerlink" href="#arch.utility.testing.WaldTestStatistic.__init__.df" title="Permalink to this definition">¶</a></dt><dd><p>Degree of freedom.</p>
 </dd>
-<dt id="arch.utility.testing.WaldTestStatistic.__init__.null"><span class="sig-name descname">null</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a></span></span><a class="headerlink" href="#arch.utility.testing.WaldTestStatistic.__init__.null" title="Permalink to this definition">¶</a></dt><dd><p>A statement of the test’s null hypothesis</p>
+<dt id="arch.utility.testing.WaldTestStatistic.__init__.null"><span class="n sig-name">null</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a></span></span><a class="headerlink" href="#arch.utility.testing.WaldTestStatistic.__init__.null" title="Permalink to this definition">¶</a></dt><dd><p>A statement of the test’s null hypothesis</p>
 </dd>
-<dt id="arch.utility.testing.WaldTestStatistic.__init__.alternative"><span class="sig-name descname">alternative</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a></span></span><a class="headerlink" href="#arch.utility.testing.WaldTestStatistic.__init__.alternative" title="Permalink to this definition">¶</a></dt><dd><p>A statement of the test’s alternative hypothesis</p>
+<dt id="arch.utility.testing.WaldTestStatistic.__init__.alternative"><span class="n sig-name">alternative</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a></span></span><a class="headerlink" href="#arch.utility.testing.WaldTestStatistic.__init__.alternative" title="Permalink to this definition">¶</a></dt><dd><p>A statement of the test’s alternative hypothesis</p>
 </dd>
-<dt id="arch.utility.testing.WaldTestStatistic.__init__.name"><span class="sig-name descname">name</span><span class="p"> : </span><span class="desctype"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a>, default &quot;&quot; (empty)</span></span><a class="headerlink" href="#arch.utility.testing.WaldTestStatistic.__init__.name" title="Permalink to this definition">¶</a></dt><dd><p>Name of test</p>
+<dt id="arch.utility.testing.WaldTestStatistic.__init__.name"><span class="n sig-name">name</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)">str</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;&#39;</span></code><a class="headerlink" href="#arch.utility.testing.WaldTestStatistic.__init__.name" title="Permalink to this definition">¶</a></dt><dd><p>Name of test</p>
 </dd>
 </dl>
 </dd>
 </dl>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.utility.testing.WaldTestStatistic.critical_values">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">critical_values</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#arch.utility.testing.WaldTestStatistic.critical_values" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">critical_values</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.11)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span></span><a class="headerlink" href="#arch.utility.testing.WaldTestStatistic.critical_values" title="Link to this definition">¶</a></dt>
 <dd><p>Critical values test for common test sizes</p>
 </dd></dl>
 
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.utility.testing.WaldTestStatistic.null">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">null</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.utility.testing.WaldTestStatistic.null" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">null</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><a class="headerlink" href="#arch.utility.testing.WaldTestStatistic.null" title="Link to this definition">¶</a></dt>
 <dd><p>Null hypothesis</p>
 </dd></dl>
 
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.utility.testing.WaldTestStatistic.pval">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">pval</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.utility.testing.WaldTestStatistic.pval" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">pval</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.utility.testing.WaldTestStatistic.pval" title="Link to this definition">¶</a></dt>
 <dd><p>P-value of test statistic</p>
 </dd></dl>
 
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.utility.testing.WaldTestStatistic.stat">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">stat</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.utility.testing.WaldTestStatistic.stat" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">stat</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><a class="headerlink" href="#arch.utility.testing.WaldTestStatistic.stat" title="Link to this definition">¶</a></dt>
 <dd><p>Test statistic</p>
 </dd></dl>
 
@@ -964,7 +964,7 @@ <h1 id="utilities">Utilities<a class="headerlink" href="#utilities" title="Perma
   
     Created using
     <a href="https://www.sphinx-doc.org/" target="_blank" rel="noopener">Sphinx</a>
-    7.0.1.
+    7.2.6.
      and
     <a href="https://github.com/jbms/sphinx-immaterial/" target="_blank" rel="noopener">Sphinx-Immaterial</a>
   
@@ -1011,7 +1011,7 @@ <h1 id="utilities">Utilities<a class="headerlink" href="#utilities" title="Perma
     <script id="__config" type="application/json">{"base": "..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../_static/sphinx_immaterial_theme.cea6a0c1d5b0618cf.min.js"></script>
+        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/univariate/volatility.html b/univariate/volatility.html
index 4438e6084e..b6bbd45884 100644
--- a/univariate/volatility.html
+++ b/univariate/volatility.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Volatility Processes - arch 6.1.0</title>
+        <title>Volatility Processes - arch 6.2.0</title>
       
     
 
@@ -47,8 +47,8 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.b223ac77c5eb52f42.min.css" />
-        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch 6.1.0" class="md-header__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-header__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch 6.1.0
+            arch 6.2.0
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch 6.1.0" class="md-nav__button md-logo" aria-label="arch 6.1.0" data-md-component="logo">
+    <a href="../index.html" title="arch 6.2.0" class="md-nav__button md-logo" aria-label="arch 6.2.0" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch 6.1.0
+    arch 6.2.0
   </label>
   
     <div class="md-nav__source">
@@ -1134,7 +1134,7 @@
                   
 
 
-<h1 id="volatility-processes"><span id="id1"></span>Volatility Processes<a class="headerlink" href="#volatility-processes" title="Permalink to this heading">¶</a></h1>
+<h1 id="volatility-processes"><span id="id1"></span>Volatility Processes<a class="headerlink" href="#volatility-processes" title="Link to this heading">¶</a></h1>
 <p>A volatility process is added to a mean model to capture time-varying
 volatility.</p>
 <table class="autosummary longtable docutils data align-default">
@@ -1165,7 +1165,7 @@ <h1 id="volatility-processes"><span id="id1"></span>Volatility Processes<a class
 </tr>
 </tbody>
 </table>
-<h2 id="parameterless-variance-processes">Parameterless Variance Processes<a class="headerlink" href="#parameterless-variance-processes" title="Permalink to this heading">¶</a></h2>
+<h2 id="parameterless-variance-processes">Parameterless Variance Processes<a class="headerlink" href="#parameterless-variance-processes" title="Link to this heading">¶</a></h2>
 <p>Some volatility processes use fixed parameters and so have no parameters that
 are estimable.</p>
 <table class="autosummary longtable docutils data align-default">
@@ -1178,7 +1178,7 @@ <h2 id="parameterless-variance-processes">Parameterless Variance Processes<a cla
 </tr>
 </tbody>
 </table>
-<h2 id="fixedvariance">FixedVariance<a class="headerlink" href="#fixedvariance" title="Permalink to this heading">¶</a></h2>
+<h2 id="fixedvariance">FixedVariance<a class="headerlink" href="#fixedvariance" title="Link to this heading">¶</a></h2>
 <p>The <code class="docutils literal notranslate"><span class="pre">FixedVariance</span></code> class is a special-purpose volatility process that allows
 the so-called zig-zag algorithm to be used.  See the example for usage.</p>
 <table class="autosummary longtable docutils data align-default">
@@ -1188,7 +1188,7 @@ <h2 id="fixedvariance">FixedVariance<a class="headerlink" href="#fixedvariance"
 </tr>
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 </table>
-<h2 id="writing-new-volatility-processes">Writing New Volatility Processes<a class="headerlink" href="#writing-new-volatility-processes" title="Permalink to this heading">¶</a></h2>
+<h2 id="writing-new-volatility-processes">Writing New Volatility Processes<a class="headerlink" href="#writing-new-volatility-processes" title="Link to this heading">¶</a></h2>
 <p>All volatility processes must inherit from <a class="reference internal" href="generated/arch.univariate.volatility.VolatilityProcess.html#arch.univariate.volatility.VolatilityProcess" title="arch.univariate.volatility.VolatilityProcess (Python class) — Abstract base class for ARCH models.  Allows the conditional mean model to be specified separately from the conditional variance, even though parameters are estimated jointly."><code class="xref py py-class docutils literal notranslate"><span class="pre">VolatilityProcess</span></code></a> and provide
 all public methods.</p>
 <table class="autosummary longtable docutils data align-default">
@@ -1275,7 +1275,7 @@ <h2 id="writing-new-volatility-processes">Writing New Volatility Processes<a cla
   
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